Identifying the Benefits and Risks Associated with Flash Sale Websites and Their Potential Implications for Return Customers

Identifying the Benefits and Risks Associated With Flash Sale Websites and Their Potential Implications for Return Customers

James Brian Aday, B.B.A., M.S.

A Dissertation

HOSPITALITY ADMINISTRATION

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

DOCTOR OF PHILOSOPHY

Approved

Dr. Kelly Virginia Phelan Chair of Committee

Dr. Shane Blum

Dr. Alan Reifman

Dominic Casadonte Interim Dean of the Graduate School

May, 2013

Texas Tech University, James Brian Aday, May 2013

ACKNOWLEDGEMENTS

The dissertation contained within the following pages is the result of countless hours of work and effort from me, and numerous individuals. It would be a disservice not to acknowledge the individuals that contributed to the completion of this study and aided, guided, motivated, and stood by me in my pursuit of the doctoral degree.

There are numerous individuals who have contributed to my success as a graduate student, and I thank all of you. I would like to specifically thank my fellow graduate students and those in our Ph.D. group. Thank you for your motivation and encouragement during this whole process.

I would like to personally thank Dr. Shane Blum for his contributions, especially during the formulation of interview and survey items. Your expertise guided the creation of measurement instruments which provided the findings presented within this study. To Dr. Alan Reifman, I thank you for your insight and wisdom related to all things SEM. Your input and proficiency were instrumental to the success of this dissertation and the time you willingly provided to help me during statistical analysis was incredibly generous.

I am a very lucky and incredibly fortunate graduate student. When I was working on my master’s thesis my path crossed that of Dr. Kelly Virginia Phelan, who eventually became my mentor and so much more. Dr. Phelan, words cannot express the gratitude, respect, and admiration I have for you. You have served numerous roles in my studies including chair, mentor, boss, advisor, and friend. This study would have never come to fruition without your countless hours of work, constant pushing,

Texas Tech University, James Brian Aday, May 2013

“Mighty Red Pen”, “Eyes of Shame”, and consistent encouragement. You pushed me to demand more from myself than I wanted to give, but it made this whole process and my entire graduate studies something I will forever cherish. I have the utmost respect for you, and thank you with all that I have for everything you have done for me.

I could not have accomplished this research or my doctoral degree without the support of my loving wife. Your understanding, insight, editing, and tolerance of me during this whole process are something I will never be able to repay. Thank you for being my rock on the bad days as well as the good. The countless nights and days I was away from home typing away in my office were not easy, and you never once complained. I am forever grateful to you.

Finally, to my sons Jeremy and Jace Aday, sometimes life throws you a major curveball and you are forced to adapt. This study is for you two, and symbolizes the dedication your mother and I have to you both, and the quality of life we desire to provide.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... ii

ABSTRACT ...... xi

LIST OF TABLES ...... xiii

LIST OF FIGURES ...... xiv

I. INTRODUCTION ...... 1

1.1 Statement of the Problem ...... 4

1.2 Purpose of Study ...... 4

1.3 Study Objectives ...... 4

1.4 Limitations ...... 6

1.5 Assumptions ...... 7

1.6 Significance of the Problem ...... 8

II. REVIEW OF LITERATURE ...... 11 2.1 Electronic Marketing ...... 11 2.1.1 History...... 12 2.1.2 Growth...... 13 2.1.3 Importance...... 15 2.1.4 Financial...... 17

2.2 Types of Electronic Advertising Mediums ...... 18 2.2.1 Mobile platforms...... 18 2.2.2 Social media...... 20 iv

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2.2.3 Direct e-marketing...... 23 2.2.4 Email and listserv...... 24 2.2.5 Databases...... 25 2.2.6 Viral...... 27 2.2.7 Pop-up ads...... 28 2.2.8 Banner ads...... 28

2.3 Online retailers ...... 29 2.3.1 Amazon.com...... 30 2.3.2 eBAY.com...... 31

2.4 Social Exchange Theory ...... 32 2.4.1 Purpose of SET...... 35 2.4.2 Justice and social exchange...... 37 2.4.3 SET in the service industry...... 40 2.4.4 SET and social status...... 41 2.4.5 Applications of SET...... 42 2.4.6 SET and customer loyalty...... 43 2.4.7 SET in the marketing environment...... 45 2.4.8 SET in tourism research...... 46 2.4.9 Problems associated with SET...... 47 2.4.10 Previous models...... 49

2.5 Purchase Motivation ...... 52

2.6 Group Purchase Decisions ...... 53

2.7 Theory of Planned Behavior ...... 55

2.8 Product Driven Consumers ...... 58

2.9 Approach-Avoidance ...... 59 2.9.1 Online environment...... 60

2.10 Behavior Theory...... 62

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2.10.1 Price shoppers...... 62 2.10.2 Discount shoppers...... 63 2.10.3 Pricing strategy...... 65

2.11 Promotional Sales ...... 67 2.11.1 Deals...... 67 2.11.2 Coupons...... 68 2.11.3 Online pricing ...... 69

2.12 Purchase Behavior ...... 70 2.12.1 Expectation-confirmation theory...... 70 2.12.2 Online purchase behavior...... 72

2.13 Product and Brand Driven Consumers ...... 73 2.13.1 Brand loyalty...... 73 2.13.2 Brand preference...... 74

2.14 Substitute Products ...... 75

2.15 Purchase Frequency ...... 76

2.16 Business Location ...... 77

2.17 Qualitative Content Analysis ...... 78

2.18 Gaps in the Literature ...... 79

2.20 Summary of the Review of Literature...... 82

III. METHODOLOGY ...... 88 3.1 Note on Human Subjects ...... 89

3.2 Content Analysis ...... 90 3.2.1 Nationwide analysis...... 90

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3.2.2 Statistical analysis...... 94

3.3 Consumer Study ...... 95 3.3.1 Model development...... 95 3.3.2 Survey design...... 98 3.3.3 Pilot test...... 100 3.3.4 Full study...... 101

3.4 Business Interviews ...... 105 3.4.1 Firm selection...... 106 3.4.2 Instrument development...... 108 3.4.3 Data collection...... 109 3.4.4 Data analysis...... 109

IV. IDENTIFCIATION AND CLASSIFICATION OF DAILY-DEAL OFFERINGS ON GROUPON AND LIVINGSOCIAL THROUGH CONTENT ANALYSIS ...... 111 4.1 Introduction ...... 111 4.1.1 Study purpose...... 112

4.2 Review of Literature ...... 113 4.2.1 Qualitative content analysis...... 113

4.3 Methodology ...... 114 4.3.1 Nationwide analysis...... 115 4.3.2 Statistical analysis...... 118

4.4 Results ...... 119 4.4.1 Nationwide analysis – Groupon...... 119 4.4.2 Nationwide analysis – LivingSocial...... 128

4.5 Conclusion and Implications ...... 139 4.5.1 Limitations and future studies...... 147

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V. DAILY-DEAL OR LONG-TERM FAILURE: UTILIZING FLASH-SALES AS A SERVICE EXPOSURE TECHNIQUE TO GENERATE LONG-TERM CUSTOMERS ...... 150 5.1 Introduction ...... 150 5.1.1 Problem statement...... 153

5.2 Review of Literature ...... 153 5.2.1 Electronic marketing...... 153 5.2.2 Types of electronic advertising mediums...... 160 5.2.3 Online retailers...... 172 5.2.4 Social exchange theory...... 175 5.2.5 Purchase motivation...... 195 5.2.6 Group purchase decisions...... 196 5.2.7 Theory of planned behavior...... 198 5.2.8 Product driven consumers...... 201 5.2.9 Approach-avoidance...... 202 5.2.10 Behavior theory ...... 205 5.2.11 Promotional sales...... 210 5.2.12 Purchase behavior...... 213 5.2.13 Product and brand driven consumers...... 216 5.2.14 Substitute products...... 218 5.2.15 Purchase frequency...... 219 5.2.16 Business location...... 220

5.3 Methodology ...... 221 5.3.1 Model development...... 221 5.3.2 Survey design...... 224 5.3.3 Pilot test...... 226 5.3.4 Full study...... 227

5.4. Results ...... 234 5.4.1 Pilot test...... 234 5.4.2 Full study...... 243

5.5 Conclusion and Discussion ...... 256 5.5.1 Limitations and future research...... 262

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VI. LET’S MAKE A DEAL, OR NOT: GAINING INSIGHT INTO THE RATIONALE AND MOTIVATIONS EMPLOYED BY BUSINESS OPERATORS WHEN CHOOSING TO OFFER DAILY-DEAL VOUCHERS ...... 264 6.1 Introduction ...... 264 6.1.1 Problem statement...... 268

6.2 Review of Literature ...... 269 6.2.1 Grounded theory...... 269

6.3 Methodology ...... 270 6.3.1 Business interviews...... 270

6.4 Results and Discussion ...... 276 6.4.1 Businesses which HAVE used flash-sales...... 276 6.4.2 Businesses which have NOT used flash-sales...... 290

6.5 Conclusion ...... 297 6.5.1 Limitations and future research...... 301

VII. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ...... 303 7.1 Summary of the Study ...... 303

7.2 Summary of Results ...... 305

7.3 Discussion ...... 308

7.4 Limitations ...... 311

7.5 Recommendations for Future Research ...... 314

7.6 Conclusion ...... 315

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APPENDICES A. IRB APPROVAL LETTER ...... 371 B. IRB PROPOSAL ...... 373 C. QUALITATIVE INTERVIEW QUESTIONS FOR BUSINESSES WHICH HAVE UTILIZED A FLASH SALE ...... 409 D. QUALITATIVE INTERVIEW QUESTIONS FOR BUSINESSES WHICH HAVE NOT UTILIZED A FLASH SALE ...... 411

Texas Tech University, James Brian Aday, May 2013

ABSTRACT

This dissertation is focused on the emergent concept of daily-deal sites such as

Groupon and LivingSocial. Flash-sales have become a relevant marketing medium for firms, offering thousands of daily-deals each day. This research aimed to (1) identify the availability of content on sites such as Groupon and LivingSocial relevant to the hospitality industry, (2) examine consumer perceptions, attitudes, and motivations when purchasing a daily-deal voucher, and (3) measure the motivations and justifications business operators employ when choosing whether to offer a flash-sale.

Three separate studies were conducted as part of this research. The first component was a content analysis of the daily-deal offerings for randomly selected cities on the Groupon and LivingSocial websites. A detailed measurement rubric based off of six Standard Industrial Classification (SIC) Codes was employed to monitor and classify the frequency of vouchers available for purchase. Upon completion of the content analysis, a consumer survey was administered online to end- users of flash-sale sites. The survey contained inquiries related to factors which influence customers to purchase daily-deal offers, and components which affect the likelihood of revisitation. A structural model was employed to measure predictive relationships related to purchase motivation. The final component of the research relied upon qualitative interviews with businesses which have employed flash-sales and those which have relied upon alternate marketing mediums. Interview questions were derived to measure justification for adoption and avoidance of flash-sales by operators.

Texas Tech University, James Brian Aday, May 2013

The content analysis identified an under-representation of hospitality and tourism firms on the flash-sale sites with the exception of restaurants. Findings established tourism providers were likely to target their marketing efforts towards individuals outside their city of operation and would not benefit from offering a deal to locals. Results from the consumer survey highlight customers were highly influenced to purchase based on their attitude towards the product offered, price of the voucher, and proximity of the firm to their residence. Consumers demonstrated the possibility of establishing a long-term relationship with firms encountered via flash- sales, but were perceived as being easily influenced to switch brands based on a high- discount offering from a competing firm. The qualitative interviews demonstrated businesses which have employed flash-sales are weary of the actual likelihood of success and feel as though the deals brought in already established customers. Firms which have not utilized daily-deals indicated a major hesitation towards adoption related to low profit margins per item in their operation, and the percentage split of the sale price between the firm and flash-sale provider.

This study is unique as no academically published research currently exists related to the flash-sale concept. Measuring flash-sales in terms of product availability, consumer motivations, and business attitudes provides findings which may provide a foundation for future research studies. Furthermore, findings may prove beneficial for consumers which purchase flash-sales, businesses considering implementation of the medium and sites which provide daily-deals.

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LIST OF TABLES

1 Breakdown of Social Networks by Members, Revenue, and Year ...... 22

2 Population Breakdown of CitiesAanalyzed by Groupon Offerings ...... 121

3 Total Average Daily Savings by City ...... 122

4 Average Weekly Hotel and Daily Motion Picture/Sporting Event

Category Offers ...... 124

5 Average Daily Listings per Category by City ...... 126

6 Total Listings per Category and Average Daily Listings by City ...... 128

7 Population Analysis Based on City and Geographic Region ...... 130

8 Total Average Daily Savings by City ...... 132

9 Average Weekly Hotel and Daily Motion

Picture/Sporting Event Category Offers ...... 134

10 Average Daily Listings per Category by City ...... 137

11 Total Listings per Category and Average Daily Listings by City ...... 139

12 Breakdown of Social Networks by Members, Revenue, and Year ...... 165

13 Item Codes, Descriptions, and Associated Factors ...... 229

14 Pilot Study Respondents' Demographic Information ...... 236

15 Standardized Factor Loadings for Pilot Structural Model ...... 242

16 Respondents' Demographic Information (N=280) ...... 244

17 Standardized Factor Loadings for Structural Model ...... 252

18 Correlations between manifest indicators for Positive Attitude, Purchase

Frequency, Product Awareness and Purchase Frequency ...... 254

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LIST OF FIGURES

1 Social Exchange Theory Model………………………………………………49

2 Social Exchange Theory Model Adapted for Tourism Research……………………………………………………….……51

3 Proposed Hypothesized Daily-Deal Interaction Model…………………………………………………………………………85

4 Proposed Hypothesized Daily-Deal Interaction Model…………………………………………………………………………99

5 Modified Consumer Daily-Deal Interaction Model………………………………………………………………………..104

6 Social Exchange Theory Model…………………………………………….192

7 Social Exchange Theory Model Adapted for Tourism Research…………………………………………………………...194

8 Proposed Hypothesized Daily-Deal Interaction Model………………………………………………………………………..225

9 Modified Consumer Daily-Deal Interaction Model………………………………………………………………………..233

10 Hypothesized Model after EFA…………………………………………….238

11 Model after Completion of CFA……………………………………….……240

12 Final Mediated Structural Model…………………………………………...241

13 Tested CFA Model………………………………………………………….247

14 Model after Completion of CFA……………………………………………249

15 Final Structural Consumer Daily-Deal Interaction Model…………………………………………………………..250

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CHAPTER 1

INTRODUCTION

The role of marketing in an organization is a critical function which establishes a connection between the firm and consumer, and focuses on generating sales while building upon this relationship (Moorman & Rust, 1999). Traditionally, marketing messages have been conveyed to consumers via mediums such as radio, TV, and print advertisements (Dutton, Campbell, Elliott, & McClaren, 2012). However, over the last two decades marketing has focused on Internet based advertisements (Taylor &

Strutton, 2010), and more budding uses of technology such as delivering ads via smartphone and tablet devices (Persaud & Azhar, 2012).

A relatively new phenomenon in Internet commerce revolves around daily-deal or flash-sale websites. The terms daily-deal and flash-sale are synonymous and classified as heavily discounted deals which are distributed to consumers via the

Internet. Upon completion of a purchase, consumers are emailed a voucher containing a barcode which is then redeemed at the business. The term voucher in this research refers directly to the offer which was purchased. Both voucher and offer are used interchangeably throughout this paper. It should be noted that daily-deal vouchers may be redeemed by purchasers for a variety of goods and services. Flash-sales may have a dollar amount associated with it, for instance $20 worth of food at a particular restaurant. Additionally, there may be a product such as a Pilate’s machine, or an experience, like a NASCAR driving activity, which customers may acquire through these sites.

Texas Tech University, James Brian Aday, May 2013

The two most recognizable daily-deal sites are Goupon and LivingSocial which offer limited quantity vacation and hotel packages in addition to numerous other discounts for both products and services, directly to consumers (Yu, 2011).

However, there are also hundreds of local organizations which offer similar discounted sales. Flash-sale mediums are still in their infancy in the online environment, as Groupon was founded in 2008 (O’Dell, 2011) and LivingSocial the following year (LivingSocial, 2012). These new marketing channels attained seemingly immediate success, with each firm generating significant net worth within their first two years of operations (LivingSocial, 2012; O’Dell, 2011). While Groupon recognized higher earnings within this two year period, establishing itself as a financially significant firm with a value of $1 billion (O’Dell, 2011), LivingSocial followed closely behind reporting a net worth of $600 million (LivingSocial, 2012).

Both firms have solidified their market position with LivingSocial providing offers to over 400 geographic areas, and Groupon serving slightly fewer than 600 cities.

Additionally, each firm has continued to increase revenue with Groupon reporting over $760 million (Hickins, 2011) in sales and LivingSocial generating $500 million

(Rusli, 2011) for 2010.

The rapid growth of flash-sale websites, the vast number of offers and predictions which indicate flash-sales will reach more than $4.1 billion in the next two years (Patel, 2012) appear to signify the business model will continue for the foreseeable future. However, recent reports indicate growing consumer distaste for flash-sales as email inboxes have become inundated with daily-deals, and the low

Texas Tech University, James Brian Aday, May 2013 quality of products offered (Patel, 2012). Reports identify consumers took a binge approach to daily-deals when they initially began appearing, however the novelty has seemingly worn off and some individuals have indicated they are hesitant to purchase because they feel as though the deals are restrictive, and obligate them patronize firms they normally wouldn’t visit (Tuttle, 2011).

In addition to the question of whether consumers are beginning to tire of flash- sale opportunities, is the debate over whether this medium is truly beneficial to businesses. Recently, a massage parlor in Washington, D.C., made headlines when their daily-deal offer sold over 4,000 massages at a deeply discounted rate (Noguchi,

2012). The instant demand from customers and the requirement to honor all vouchers resulted in a loss of $50 per massage, and required management to borrow $150,000 to cover increased expenses (Noguchi, 2012). Findings of a recent report identified only

38% of the surveyed businesses which had employed a daily-deal earned a profit

(Liddle, 2012). However, the remaining 62% indicated losing money or simply breaking-even on their offer (Liddle, 2012). Further demonstrating the potential risk firms face when utilizing flash-sales, recent statistics exhibit only 3% of businesses gain repeat customers as a result of this type of promotion (Klein, 2012). These accounts suggest flash-sales may be perceived as offering high-risk and a low probability of success. Thus, sites such as Groupon and LivingSocial may be reaching a level of market saturation and may soon become extinct. This study is designed to examine this phenomenon from the perspectives of businesses and customers, in

Texas Tech University, James Brian Aday, May 2013 addition to a comprehensive content analysis of the specific offerings of flash-sale sites on a daily basis.

1.1 Statement of the Problem

Currently, no published scholarly research exists pertaining to flash-sale sites.

More specifically, the only credible publications regarding flash-sales are from news sources. Thus, based on the lack of research in this area, coupled with the increased financial implications and market reach of daily-deal sites, this research aimed to (1) identify the availability of content on sites such as Groupon and LivingSocial relevant to the hospitality industry, (2) examine consumer perceptions, attitudes, and motivations when purchasing a daily-deal voucher, and (3) measure the motivations and justifications business operators employ when choosing whether to offer a flash- sale.

1.2 Purpose of Study

This study was exploratory in nature and aimed to establish a foundation of academic research on flash-sale operations. This research relied upon a review of extant literature related to marketing, consumer behavior, and online interaction theories in an effort to establish a research premise which was reliable. Employing prior literature enabled the creation of a survey instrument, formulated interview prompts, and provided a rubric for assessing content of daily-deal websites.

1.3 Study Objectives

This research scrutinized the relatively new marketing medium of daily-deals, particularly those offered through Groupon and LivingSocial, to ascertain a more

Texas Tech University, James Brian Aday, May 2013 complete picture regarding the types and frequency of offerings, consumer behavior related to purchasing such vouchers, and business operator attitudes towards flash- sales. This research was a three-part study, encompassing a content analysis, consumer survey, and qualitative interviews, which were aimed at measuring various aspects of flash-sale operations. The specific details regarding the various data collection and analysis measures are discussed in this section. The goals associated with each study were:

Step 1 – Content Analysis

 Objective 1: Identify the frequency of hospitality and service offerings

belonging to the six Standard Industrial Classification (SIC) categories.

 Objective 2: Measure the average daily savings for LivingSocial and Groupon

in an effort to ascertain which site provides the greatest savings for consumers.

 Objective 3: Establish which site offers the highest number of daily-deal

offerings on a daily basis.

 Objective 4: Assess which factors influence increased offers for specific

geographic locations.

Step 2 – Consumer Study

 Objective 1: Determine the role price plays in a consumer’s likelihood of

purchasing a flash-sale.

 Objective 2: Estimate the probability of repeat visitation from high frequency

discount users compared to those who use such discounts sparingly.

Texas Tech University, James Brian Aday, May 2013

 Objective 3: Establish the influence of customer experience on return intention

after utilizing a daily-deal voucher.

 Objective 4: Articulate the factors which are most likely to influence and

predict flash-sale purchases.

Step 3 – Business Interviews

 Objective 1: Ascertain what motivates businesses to employ flash-sales

methods.

 Objective 2: Establish the level of satisfaction felt by businesses after daily

deal coupons have been offered.

 Objective 3: Reveal factors influencing companies to avoid utilizing one-off

sales.

 Objective 4: Evaluate whether the perceived benefits for businesses outweigh

potential risks when choosing to offer flash-sale vouchers.

1.4 Limitations

This research relied upon an author generated survey, interview questions, and content analysis rubric. The consumer survey was limited in its approach as it relied upon online participants, which limited the availability to those individuals with

Internet access. The size of the sample prevents the findings from being generalizable to an entire population. Filter questions were employed in an effort to identify flash- sale purchasers, thus limiting responses to a specific segment of the targeted population and potentially underrepresenting certain population segments.

Furthermore, the online component prevented the ensuring of truthful responses and

Texas Tech University, James Brian Aday, May 2013 could be perceived as an unreliable measure (Shadish, Cook, & Campbell, 2002). The businesses selected for the interview process were selected from the city’s Convention and Visitors Bureau (CVB) website. Thus, firms which do not pay to advertise with the CVB were potentially overlooked for analysis and may have provided findings significantly different than those of the respondents. The content analysis rubric for measuring frequency relied upon generalized content headings attained from the U.S.

Department of Labor Standard Industrial Classification (SIC) Codes and did not accurately specify how offerings would be classified within the codes. Rather, the author relied upon his personal judgment in segmenting the daily-deals which could present selection bias.

1.5 Assumptions

Employment of a survey and interview instrument relies upon assumptions of the sample, more specifically:

1. It was assumed that the terminology employed in the measurement instruments

were accurately interpreted by the respondents.

2. It was assumed the respondents were candid in their answer choices and were

not influenced by external sources.

3. It was assumed the respondents only took the survey once and did not fill out

multiple surveys in an effort to influence their personal beliefs into the findings

in a manner which would skew the data.

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1.6 Significance of the Problem

Flash-sale sites such as Groupon and LivingSocial are reliant upon two separate groups for success, consumers who purchase and businesses which offer the daily-deals. Presently, no research based data exists in scholarly publications which measure the motivations towards these sites from either perspective. While numerous news articles have focused on the flash-sale concept over the last 4 years, coverage has focused on the success of the flash-sale sites, horror stories on behalf of firms which employed the sites, and the monitoring of consumer usage.

Millions of daily-deal vouchers have been purchased since the inception of such sites with no focus on whether businesses which utilize such a method are reaching their target audience through a sustainable medium. More specifically, data is not existent in relation to the effectiveness of daily-deals as a customer generation strategy for firms. This study aims to provide insight into the overall perceived effectiveness from the business operator stand point on the efficiency and reliability of daily-deal offers through interviews with firms which have used such a medium.

Conversely, understanding why firms prefer to use other marketing mediums through interviews may provide keen insight into the potential shortcomings of daily-deal offers.

This study aims to address the representativeness of hospitality and tourism industry offerings and the availability of vouchers specific to such firms on daily-deal sites. Through content analysis, findings may identify whether operators in these industries chose to rely upon marketing messages conveyed through a different source,

Texas Tech University, James Brian Aday, May 2013 which may be indicative of risk aversion or an unwillingness to keep up with changes in the marketing environment. Furthermore, the research may ascertain a preponderance of deals for firms operating within these segments, identifying the businesses which rely upon the reach of flash-sale providers to generate customers.

Consumers play an integral role in the daily-deal process as their purchases drive the success of daily-deal providers. However, recent reports indicate the novelty of purchasing vouchers may be waning, resulting in declines of purchases as consumers search for other advertising messages to influence their purchase decisions.

A second aspect of the consumer involvement in flash-sales is the physical redemption, in most cases, of the flash-sale voucher at the firm. Measuring consumers’ experiences with businesses which have provided daily-deals may provide profound insight as to what characteristics of a firm encourage the customer to revisit in the future.

This research is important as it aims to address specific issues related to a growing marketing opportunity which is under-researched. Findings will be valuable to academics, managers, and flash-sale providers. Academics will benefit from the findings and be able to base future research on this study. The justification for employment or avoidance of implementing flash-sales into daily operations from managers will provide reasoning to hospitality operators and educate them in the potential success and risk associated with these offers. Flash-sale providers may find the results beneficial as they will demonstrate current preferences from their two main

Texas Tech University, James Brian Aday, May 2013 clientele, consumers and businesses, and may potentially highlight areas of improvement which could be implemented to generate future daily-deal offerings.

Texas Tech University, James Brian Aday, May 2013

CHAPTER 2

REVIEW OF LITERATURE

2.1 Electronic Marketing

Electronic and viral marketing are inexpensive marketing methods in which companies create online advertisements that are disseminated to consumers. After the initial distribution, a domino effect is achieved when the original recipients forward the material to other individuals through social networks or online communities

(Howard, 2005; Xiong & Hu, 2010). Viral marketing is synonymous with traditional word-of-mouth advertising (Howard, 2005). Most daily-deal advertisements are based loosely upon the premise of viral marketing. Within their email solicitations, companies include options such as links entitled “share with friends,” which enables recipients to enter multiple email addresses of contacts with whom they wish to distribute the deal. The proven success of viral marketing is a considerable motivational force behind companies’ decisions to adopt this type of advertising, since referrals from friends have shown viral ads are “able to bring in customers who are not likely to join though traditional advertising and promotions by the company” (Kumar,

2008, p. 87). Thus, viral and electronic marketing have led to the assimilation and extensive grouping of demographically variant consumers linked together through an assortment of personal relationships (Kauffman & Wang, 2001; Krishna, 1994; Winer,

1985).

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2.1.1 History.

Electronic marketing (e-marketing) is commonly defined as delivering advertisements and offers, as well as building relationships between a firm and potential customers via the Internet (Brodie, Winklhoffer, Coviello, & Johnston, 2007;

Shaltoni & West, 2010). However, more recent definitions include delivering messages electronically through the Internet and mobile communication channels

(Pride & Ferrell, 2012) including social media, digital outdoor marketing, and Quick

Response Code (QR Code) (Cartman & Ting, 2009; Pride & Ferrell, 2012). E- marketing was first introduced two decades ago, as the Internet became readily available in everyday households, and technological innovations improved individuals’ ability to utilize and navigate the World Wide Web (Taylor & Strutton,

2010). Around the same time, e-marketing research emerged, prompting studies involving electronic-commerce to appear in major academic journals worldwide

(Wang & Chen, 2010). Furthermore, in the last 15 years, scholarly journals such as the

Journal of Interactive Marketing, International Journal of Electronic Commerce, and

Electronic Commerce Research, have emerged as publications dedicated to the study of online consumer and business activities (Taylor & Strutton, 2010).

E-marketing was first developed in the early 1990s as the expansion of the

Internet to the public sector was followed by the creation of online businesses such as

Amazon.com and Ebay.com (Mowery, & Simcoe, 2002). While these businesses were not the only business-to-consumer websites (B2C) created at the time, they are two of the most widely known, and exemplify the early leaders in electronic commerce (e-

Texas Tech University, James Brian Aday, May 2013 commerce). The first form of online advertising was a banner advertisement which appeared on the Hotwired website, an online version of the print magazine Wired, in

1994 (Hollis, 2005). In 1994, search engines such as Yahoo! began to surface and relied upon funding from advertisers in the form of cost-per-click ads (Evans, 2009).

Soon after, Hotwire, an industry leader in e-marketing, partnered with the world- renowned research firm Millward Brown International, and produced one of the first studies measuring the effectiveness and influence of online advertising (Elliot, 1996;

Hollis, 2005). Between 1996 and 2001, online marketing expanded each year, with revenue generated from the sale of Internet ads producing over $7 billion in 2001

(Evans, 2008). The “dot-com bust” (Evans, 2008) of the early 2000s reduced the demand for online advertising; however, between 2004 and 2007 online advertising sales rebounded with estimated revenues of over $21 billion in 2007 (Evans, 2008).

2.1.2 Growth.

While scholars disagree on when and where the advent of the Internet occurred, most researchers concur that the foundations for the Internet were established during the Cold War Era (1960s) with programs such as the Advanced

Research Projects Agency (ARAPNET) (Rosenzweig, 1998). In the late 1970s and early 1980s ARAPNET began to appear on college campuses as a way to network research (Rosenzweig, 1998). The following decade the Internet became more widely available to the public sector, and in 1995 the Internet as it is known today was established (Frischmann, 2001). The increase in the number of Internet users worldwide since its creation has been incredible. In 1995 there were 16 million Internet

Texas Tech University, James Brian Aday, May 2013 users. Five years later that figure increased nearly twenty-fold, amounting to 304 million in 2000. At the end of 2005 there were 1.1 billion Internet users, and as of

March, 2012, nearly one in three global citizens, 2.2 billion people, were connected through online access (InternetWorldStats, 2012).

The growth of the Internet over the past decade has created the modern availability and demand for online retailing and websites which supplement traditional brick and mortar stores (Yani-de-Soriano & Slater, 2009). This shift in shopping methods, the availability to order goods and services from the Internet without having to leave the house, has resulted in higher control of consumer choice and empowered consumers like never before (Yani-de-Soriano & Slater, 2009). AdSense is a division of Google.com, and a market share leader in the world of online advertising with their marketing messages appearing on numerous websites (Golldfarb & Tucker, 2011).

While no concrete data exists regarding the number of online advertisements produced annually, studies have identified approximately 76% of Internet users in the U.S. have been exposed to some form of Internet marketing from AdSense (Goldfarb & Tucker,

2011). Recent data suggests there are over 230 million Internet users in the U.S. alone

(Islam & Snow, 2011; World Bank, 2011), which equates to approximately 174 million people who have been exposed to AdSense e-advertisements. Internet advertising has continued to increase its reach through various websites, and begun to focus on social networks.

As the prevalence of online usage has grown, so too has the frequency of social networking. Facebook is one of the largest social networking sites in the world with

Texas Tech University, James Brian Aday, May 2013 over 901 million active users in March 2012. The term “active user” describes any individual who logs in to the site at least once a month (Associated Press, 2012). With the abundant number of Facebook users, e-marketing efforts offer impressive potential with extraordinary reach. The most recent data identified that in the first three months of 2010, Facebook “flashed more than 176 billion banner ads at users” (Fletcher,

2010). In July 2010 (Associated Press, 2012), there were over 500 million active

Facebook users, meaning consumers were exposed to an average of 117 e-ads each month.

Search engines have further extended the viability of online marketing as findings from the Pew Research Center’s Internet and American Life Project (Pew,

2012) established 73% of all Americans utilized a search engine in February of 2012.

Search engine sites rely upon advertising dollars for financial sustainability, displaying ads every time a user conducts a search (Ghose & Yang, 2009). Based on data available from the U.S. Census Bureau (2012), there were over 311 million Americans as of December 31, 2011. The findings of the Pew (2012) study, coupled with U.S.

Census Bureau (2012) data equates to 227 million Americans being exposed to e- marketing through search engine usage.

2.1.3 Importance.

Texas Tech University, James Brian Aday, May 2013 record TV shows and skip the commercials, in spreading their firms’ marketing messages and building customer relationships (Trainor, Rapp, Beitelspacher, &

Schillewaert, 2011). Businesses are increasingly relying upon integrating e-marketing into their promotional mix in an effort to garner greater market reach, share, and influence (Trainor et al., 2011). The rapid influx of the Internet and the number of businesses relying on websites to garner sales has led to Internet marketing serving as a crucial platform for firms in the modern business environment (Liang, Lin, & Chen,

2004). The use of electronic advertisements has enabled companies to market to previously financially challenging markets, such as foreign destinations (Dou, Nielsen,

& Tan, 2002; Chrysostome & Rosson, 2009), and aids in eliminating geographic location as a barrier for customers and firms (Sheth & Sharma, 2005). Online advertising allows firms to reach their target audience round the clock and in any physical location where the Internet is available (Ha, 2003). Furthermore, web-based marketing media serves as a major component of firms’ customer relationship management (CRM) programs (Kimiloglu & Zarali, 2009).

E-marketing campaigns can be carried out in real-time; the use of this technology allows for better management of firms’ production and supply, while immediate feedback promotes constant adjustment (Richey, Tokman, & Dalela, 2010).

Texas Tech University, James Brian Aday, May 2013 allows firms to potentially achieve a competitive advantage through fiscal savings compared to traditional mediums (Kalaignanam, Kushwaha, & Varadarajan, 2008).

2.1.4 Financial.

The web-based advertising market is one of the fastest growing media segments, with global revenues of over $31 billion recorded for 2011 (Business Wire,

2012). During the first quarter of 2012 e-marketing revenues set an all-time record, bringing in approximately $8.4 billion (MarketWatch, 2012). Online advertising is mutually beneficial to firms and the site hosting the advertisements (Yao & Mela,

2011). Sites such as search engines are compensated by companies who wish to advertise based on keyword searches (Yao & Mela, 2011). While the cost can vary, and the terms of each contract between the host and third-party are kept confidential, recent findings exemplify the growing e-marketing budgets of firms. For example, in

2009, data shows the top 50 online advertising companies spent roughly $276 million on such ads (TNS Media Intelligence, 2009).

A 2012 study broke down e-marketing into ten categories: (1) finance and insurance, (2) retailers and general merchandise, (3) travel & tourism, (4) jobs and education, (5) home and garden, (6) computers and consumer electronics, (7) vehicles,

(8) Internet and telecom, (9) business & industrial, and (10) occasions and gifts (Abell,

Texas Tech University, James Brian Aday, May 2013 the University of Phoenix, which spent $59.1 million, $55.2 million, and $46.9 million, respectively (Abell, 2012). While these earning results are impressive, e- marketing consists of a much broader spectrum than just simple advertisements on websites.

2.2 Types of Electronic Advertising Mediums

Electronic commerce provides a unique medium for delivering content specific and mass distribution advertisements to a wide assortment of consumers (Huang,

Chen, & Wu, 2009). Electronic advertisements are delivered through a multitude of mediums (Kim & McMillan, 2008), including mobile platforms (Gao, Sultan, &

Rohm, 2010), social media (Chu, 2011), direct e-marketing (Wright & Cawston,

2012), email and listserv (Illum, Ivanavo, & Liang, 2010; Pavlov, Meville, & Plice,

2008), databases (Pels, Coviello, & Brodie, 2000), viral campaigns (van der Lans, van

Bruggen, Eliashber, & Wierenga, 2010), banner ads (Wu, Wei, & Chen, 2008), and pop-up advertisements (Chatterjee, 2008). While these e-marketing platforms are not a complete list, they are identified in the literature as the most widely used forms of web-based advertising.

2.2.1 Mobile platforms.

Mobile based marketing is a relatively new concept in the realm of e- marketing and only became a focus of research in the latter-half of the 2000s

(Okazaki, 2009). Scholarly based research publications on mobile advertising are rare, and studies have primarily focused on content delivery and ease of use (Okazaki,

2009). As smart-phones become the standard norm in the mobile industry, companies

Texas Tech University, James Brian Aday, May 2013 have continuously pursued innovative opportunities for delivering messages via web searches, text messages, and games which are downloaded via an application (app) store (Laszlo, 2009). The unique portability of mobile devices allows firms the capability to communicate with consumers on the go, as they do not have to rely on the mobile user to sit down at a computer to receive the marketing message (Laszlo,

2009).

Marketers have employed multiple tactics to garner a large database of phone numbers of potential customers who are willing to receive updates from specified companies (Friedrich, Grone, Holbling, & Peterson, 2009). Recent data implies that mobile penetration, or the number of individuals within a given population who have access to a mobile device, is nearing 100% in most Western countries (Leek &

Christodoulides, 2009). Research demonstrates that businesses are recognizing the limitless potential as they have the ability to capture the attention of a large target market via mobile media (Friedrich, et al., 2009). Furthermore, the utilization of QR codes have been integrated into traditional forms of media as well, allowing mobile customers to simply scan the QR code, and then they are automatically redirected to a specific mobile advertisement (Okzaki, Li, & Hirose, 2011). Other firms are relying upon location or spot-specific advertising, sending messages to a mobile device via

Bluetooth technology with personalized ads whenever a consumer is in proximity to a particular business (Leek & Christodoulides, 2009). A recent phenomenon is the availability of apps from businesses which are downloaded by consumers and allow instant access to the firm (Xu, Liao, & Li, 2008).

Texas Tech University, James Brian Aday, May 2013

The mobile applications market has continued to expand, as confirmed by the

25 billionth app recently being downloaded from the Apple App Store (Brian, 2012).

Based on statistics from Apple, there are over 315 million users of this service, and data exhibits the store maintains over 550,000 apps, with the typical consumer downloading an average of 80 apps to their device (Brian, 2012). While there are numerous free apps available from the Apple App Store, since 2008, Apple has generated $5.71 billion in revenue from the store alone (Faletski, 2012). Phones operating on the Android system, which is owned by Apple’s competitor Google, have access to a similar app store with approximately 400,000 apps offered (De Vere,

2012). In 2011, Google realized $1 billion in net revenue from its Play Store (Farber,

2012).

As the costs of traditional forms of media have continued to rise, firms have increased their focus and spending on smart phone ads (Uzor, 2012). Mobile advertising has increased steadily over the past ten years, and experts predict the mobile based delivery market will generate revenues of close to $2.61 billion in 2012

(Cohan, 2012). As more consumers worldwide gain access to mobile technology, the mobile market is expected to continue to grow in relevance with a valuation of $10.6 billion in four years (Frederickson, 2012). In 2011, mobile advertising in the U.S. generated revenues of $1.45 billion (Frederickson, 2012).

2.2.2 Social media.

Social media sites such as Facebook, MySpace, Twitter, YouTube, and blogs gained prominence in the multimedia realm beginning in 2005 (Weinberg & Berger,

Texas Tech University, James Brian Aday, May 2013

2011). Sites such as these allow users to log in and instantly connect with friends and family via preference lists (Kaplan & Haenlein, 2010). As soon as they log in, users are presented with a multitude of advertisements bordering their screen. These advertisements come in various forms and may be displayed as a result of computer matching which recognizes identified preferences of the user from their profile with a specific company, or they may be mass generated propaganda (Wright, Khanfar,

Harrington, & Kizer, 2010). Users of social media can also follow (Twitter) or like

(MySpace and Facebook) certain companies and information from these firms will automatically appear in their news feeds (Kietzmann, Hermkens, McCarthy, &

Silvestre, 2011). Additionally, companies’ followers can post status updates tagging the particular firm, which are visible to everyone in their friend list, passing on an advertisement or announcing a recent purchase from the firm (Weinberg & Berger,

2011).

There are numerous social media sites and countless blogs on the Internet.

Some of the most well-known social media maintain millions of active users, defined as those logging in at least once per month (Goldman, 2012). For example, based on the most recent data available, Facebook maintains over 900 million active users

(Goldman, 2012), Twitter has 140 million (Arthur, 2012), and 100 million people are

Texas Tech University, James Brian Aday, May 2013 number of users per site is an important facet of e-marketing, as social media sites are dependent upon advertising revenues to fund the platform, and the greater the number of users, the more attractive the particular site is to advertisers (Clemons, 2009;

Enders, Hungenber, Denker, & Mauch, 2008). Additionally, the number of visitors to these sites makes them more attractive to firms wishing to advertise, and increased active user numbers allow for higher advertising rates to be charged (Fletcher, 2010).

The broad reach and market potential of social media sites is highlighted in their annual advertising revenues. In 2011, LinkedIn established revenues upwards of

$522 million (LinkedIn, 2012). Facebook, the largest social media site in terms of active users, realized $3.1 billion in revenue (Raice, 2012) while Twitter achieved

$139 million in 2011 (Bercovici, 2012). YouTube, which is owned by Google, does not directly report ad revenues, though analysts estimate the video engine earned approximately $1.3 billion in 2011 (Schonfeld, 2011). The predicted annual revenue growth for these sites from advertising is indicative of the increasing reliance upon e- marketing. For clarity, Table 1 exhibits the top five social networks along with their number of active members, annual revenue, and the year they were established.

Table 1 Breakdown of Social Networks by Members, Revenue, and Year Social Network Active Members* Revenue* Year Established Facebook 900 $3,100 2003 MySpace 95 $109 2003 LinkedIn 100 $522 2003 Twitter 140 $139 2006 YouTube 840** $1,300 2005 * In millions ** Monthly visitors

Texas Tech University, James Brian Aday, May 2013

2.2.3 Direct e-marketing.

Direct marketing was established as a means of communicating personalized messages to consumers and overcoming the high-costs associated with the traditional forms of marketing (Palmer & Koenig-Lewis, 2009). The global recession of 2008 forced marketing managers to slash their budgets and identify new, cost-effective measures, to reach potential target audiences (Palmer et al., 2009). Direct e-marketing involves sending or displaying advertisements which are highly-specified to consumers’ preferences based on previous purchase behavior or identified information from websites, including social media (Seret, Berbraken, Versailles, & Baesens,

2012).

Direct marketing relies upon the firm consistently adjusting their advertising strategy until sales are achieved (Bose & Chen, 2009). For example, a firm may email or display an advertisement for a potential customer which is highly customized, and depending on whether or not the ad generates a sale, the firm may be required to go back and change the direct marketing message to more effectively influence the customer (Bose & Chen, 2009). Research studies have identified consumers who are influenced by direct marketing are likely to pass company information along to their social circles (Phelps, Lewis, Mobilio, Perry, & Raman, 2004). Direct marketing can be incorporated into a firm’s customer relationship management strategy (CRM) and the positive relationship built by the messages between the business and customer may lead to higher profit margins (Morgan, Slotegraaf, & Vorhies, 2009).

Texas Tech University, James Brian Aday, May 2013

According to a study identified in Kozinets, Calck, Wojnicki, and Wilner

(2010), spending on direct advertising through social media, viral campaigns, and guerilla marketing is expected to surpass $3 billion in 2013. Further research has established that direct marketing campaigns, on average, generate a return on investment (ROI) of $6.50 higher per dollar spent than non-direct marketing (DMA,

2012). Further broken down, direct marketing generates a return on investment of more than $11.70 for each dollar spent on this medium (DMA, 2012). Based on figures from the Direct Marketing Association (2012), if a firm spent $10,000 on direct marketing the expected return on investment could potentially reach $117,000 solely from the e-advertising campaign.

2.2.4 Email and listserv.

Listserv was created in the late 1980s and is a program that manages email distribution lists for mass e-mailings (Grier & Campbell, 2000). Hotmail.com and

Juno.com were two of the first entrants into the email market, providing free web- based email addresses for users in 1996 (Hugo & Garnsey, 2005). While Hotmail struggled in its new environment initially, the firm quickly became a pioneer of email marketing through simple techniques (Wilson, 2005). One of the earliest utilizations of e-marketing from the firm came in the tagline of each email message that was sent from Hotmail accounts, and read “Get your private, free email at http://www.hotmail.com” (Wilson, 2005).

Firm-generated emails to large masses of individuals are extremely inexpensive, and often times the marginal cost of this medium is $0 (Umit Kucuk &

Texas Tech University, James Brian Aday, May 2013

Krishnamurthy, 2007). Email marketing is a direct, inexpensive, and asynchronous channel to target end-users in a non-obtrusive manner (Murphy & Gomes, 2003). This type of marketing is continuous and provides interactive feedback to the firm, allowing them to garner responses related to effectiveness in a timely manner (Miller,

2010). The expanded reach of email as a marketing medium has resulted in a majority of firms investing in this tool, as over 85% of online consumers have stated using email at least once per month (VanBoskirk, 2007).

A recent study by the Direct Marketing Association (DMA) identified email marketing as an efficient tool for businesses. The ROI for each dollar spent on email ads realized an average return of $39.40 and is expected to bring in $40.56 under the same conditions in 2012 (Magill, 2011). Market analysis identified email advertising was responsible for $63 billion in sales during 2011, and is predicted to account for

$67 billion in transactions in 2012 (EmailStatCenter, 2012; Magill, 2011). While ascertaining precise figures related to email spending is effectively impossible, recent survey results from marketing managers indicated over 60% of the responding firms identified they will increase their expenses for this media in 2012, a percentage which is higher than both social media and mobile advertising spending by firms (Palmer,

2012).

2.2.5 Databases.

As companies’ marketing efforts have taken advantage of the digital platforms now available to convey their advertising message, database marketing has transformed how firms collect and manage customer information (Zwick & Dholakia,

Texas Tech University, James Brian Aday, May 2013

2004). Utilizing software such as cookies and click-through monitoring, businesses are able to record every detail of a user’s visit to their site, down to minute details

(Miyazaki, 2008). This type of data collection, or customer database, allows the firm to analyze the results of site visitation through specially designed software which identifies trends, purchase behavior, and creates a digital identity for each customer

(Zwick & Dholakia, 2004). The employment of database marketing in the online context allows the firm to present customers with specialized products, based on previous visits, and aids in establishing a relationship marketing experience

(Schoenbachler & Gordon, 2002).

Database marketing has been incorporated into the CRM policies of firms, as they strive to establish a bond between the business and customer that will potentially garner future sales (O’Leary, Rao, & Perry, 2004). Numerous studies have examined the key inputs associated with effective CRM or relationship marketing strategies, and the common denominator in the research findings revolves around trust; more specifically, the trust the consumer has for the firm (Luo, 2002; Zwick & Dholakia,

2004). Businesses relying upon database usage to generate sales campaigns must account for the user’s perceived trust with the firm, and reduce any negative concerns for the relationship in order to be mutually beneficial (Schoenbachler & Gordon,

2002). Establishing trust levels and building relationships through database marketing can lead to a firm’s competitive advantage in the industry and potentially yield long- term financial rewards (Heffernan, O’Neill, Travaglione, & Droulers, 2008; Kanagal,

2009).

Texas Tech University, James Brian Aday, May 2013

2.2.6 Viral.

Recent articles in the New York Times and the Wall Street Journal implied the traditional form of TV advertising, the 30-second commercial, will be non-existent in the near future due to the influence of e-marketing (Manly, 2005; Steinberg, 2004).

Viral marketing campaigns influence electronic word-of-mouth (EWOM) as they rely upon users sharing information about a particular company within their online social groups (Porter & Golan, 2006). Helm (2000) defined viral marketing as both “a communication and distribution concept that relies upon customers to transmit digital products via electronic” mediums to their peers (p. 159). Viral marketing has caught on with online users today and established itself as the central advertising trend of the past decade (Ferguson, 2008).

While viral advertising was initially hosted on a particular firm’s website, the advent of social media has created an inexpensive and highly visible medium for businesses to create and promote viral marketing campaigns (Xiong & Hu, 2010).

Viral marketing also includes commercials which appear online. In 2011, several viral marketing campaigns garnered millions of views on the Internet. According to

Advertising Age, a leader in marketing research, the 10 most viewed viral campaigns collectively generated over 281 million views (Advertising Age, 2011). Many of the advertisements featured are from well-known brands, such as Volkswagen, Apple, and

Fiat, and originally aired early in 2011 (Advertising Age, 2011). However, through viral marketing, the brands and the products they promoted have remained at the forefront of consumer attention. One of the longest-running and most memorable viral

Texas Tech University, James Brian Aday, May 2013 campaigns was launched by Burger King in 2004 at www.subservientchicken.com

(Ferguson, 2008). The site was an instant hit, and has recorded over 450 million views since its inception (Inc., 2010). Viral marketing has established itself as an inexpensive medium which has the potential to effectively reach millions of viewers

(Mills, 2012). Recent figures demonstrate companies spent $1.97 billion on online video advertising in the U.S. in 2011 (Nield, 2011).

2.2.7 Pop-up ads.

Pop-up advertisements are a form of marketing which appear to users on websites which allow this type of medium (Chatterjee, 2008). This nature of advertising is designed to capture the user’s attention and prevent them from proceeding to the actual website until the advertisement has been closed (Edwards, Li,

& Lee, 2002). Numerous academic-based studies have identified consumers’ distaste for pop-ups, with respondents repeatedly protesting the intrusive nature of these ads

(McCoy, Everard, & Loiacono, 2009). A study by Smith (2012) found only 4% of

Millennials, represented as those individuals born after 1980, prefer pop-up ads as a form of marketing they are likely to interact with. A similar study in 2010 demonstrated respondents not only find pop-up advertisements invasive, but the appearance of a company in such an ad can lead to negative feelings of the consumer towards the brand (Madhavaram & Appan, 2010).

2.2.8 Banner ads.

The first commercial application of banner ads was on Hotwired.com in 1994

(Chatterjee, 2008). Banner ads appear on various website locations and when clicked,

Texas Tech University, James Brian Aday, May 2013 visitors are taken to an external site for the stated firm (Hofacker & Murphy, 2000).

While multiple forms of marketing delivery, including text links and pop-up ads, may populate a webpage, banner ads are the most prevalent (Cho, 2003). Research identified respondents who react positively to banner ads are more likely to carry out a search for the product or brand featured (Rutz & Bucklin, 2012). A study by Hsieh &

Chen (2011) demonstrated banner ads are particularly effective on sites which display videos or picture-graphics. Studies have further reported banner ads as being most effective when targeted towards users of a specific site (Charters, 2002).

The cost of banner advertising is expensive, as prime location on major websites can exceed over $500,000 daily (Bloomberg Businessweek, 2006). Spending on banner ads in the U.S. was over $6 billion in 2010 and accounted for approximately

24% of all Internet advertising spending (PricewaterhouseCoopers, 2010). In 2011, banner ads represented 21.5% of all Internet marketing revenues and earned $6.8 billion (PricewaterhouseCoopers, 2011). Recent figures estimate overall Internet marketing revenues will exceed $21 billion in 2014 (eMarketer, 2012) with banner ads accounting for approximately $10.9 billion of those advertising receipts (Interactive

Advertising Bureau, 2011).

2.3 Online retailers

The Internet has grown at an unprecedented rate over the past 20 years (Khosla

& Acharya, 2012) with over 215 million domains registered worldwide from its advent through 2010 (Rueter, 2011). With the Internet serving as an integral business medium (Ahrholdt, 2011), electronic retailing or e-tailing has become a significant

Texas Tech University, James Brian Aday, May 2013 force (Cheng, Wang, Lin, & Vivek, 2009), accounting for over $198 billion in sales in

2011 (Briggs, 2012). In 2011, 500 online retail companies accounted for approximately $152 billion, or 77% of total sales (Briggs, 2012), and analysts predict e-tailing sales to surpass $250 billion annually by 2014 (Schonfeld, 2010). While a multitude of profitable web-based businesses exist today, two of the first movers, and most financially successful of the all-online retail movement were Amazon.com and eBay (Phan & Vogel, 2010).

2.3.1 Amazon.com.

Amazon.com (Amazon) was a first-mover (Mellahi & Johnson, 2000;

Vradarajan, Yadav, & Shankar, 2008) in terms of a firm hosted entirely online, when it established its website in 1995 (Chevalier & Goolsbee, 2003). Amazon’s early pioneering of the web, growth, and their decision to abandon the traditional brick-and- mortar approach to book selling, led to the Amazon.com name becoming synonymous with e-commerce (The Economist, 2000). By eliminating the overhead associated with traditional stores and relying solely on a web-based platform, Amazon was able to garner a substantial share of the book selling market (Wunker, 2011). Utilizing justin-time inventory, an expansive distribution network, and the ability of users to purchase multiple products from different vendors and have them packaged together helped to further Amazon’s competitive advantage (Patsuris, 2001).

Texas Tech University, James Brian Aday, May 2013 start, Amazon has excelled, and recent data exhibits Amazon currently accounts for more than 10% of online sales in North America (Watson, 2011). Amazon has continued to diversify its product offerings and established itself as a major international retailer as well (Galante, 2009). Amazon recorded sales of $48 billion dollars in 2011, an increase of more than $34 billion dollars from 4 years earlier

(Amazon, 2012).

2.3.2 eBAY.com.

Based on an auction system, eBay.com (eBay) was established in 1995

(Standifird, 2001), and relied on sellers to list an item, and buyers (bidders) to then place incremental bids on the item until the specific auction closed and the highest bidder won (Gilkeson & Reynolds, 2003). In the late 1990s Internet usage led to privacy concerns and eBay addressed bidder and seller apprehension through the implementation of a feedback system (Dellarocas, 2003). Establishing online trust was critical, as data identified 89% of transactions between buyers and sellers were one- time occurrences. But allowing users to obtain community feedback provided a unique framework in which purchasers felt more comfortable buying from an unknown third- party (Dellarocas, 2003). Research by Gilkenson and Reynolds (2003) confirmed buyers are more likely to purchase an item, and may be willing to pay a higher price based on positive feedback about the seller from previous auction interactions.

In the late 1990s eBay grew at a record pace, increasing more than fourfold, from 2.2 million users to over 10 million registered users in 1999 alone (Whitman,

2000). The firm focused on building trust on the site between its bidders and sellers.

Texas Tech University, James Brian Aday, May 2013

The success eBay achieved as a result of the feedback system allowed it to capture more than 85% of the online auction market in 2003 (Boyd, 2002; Evans, 2003). In

1995 Amazon.com entered into the online marketing environment, and it remains the single largest competitor to eBay online (Haruvy et al., 2008; Lucking-Reiley, 2000).

In 2008, the global recession, coupled with increased competition from sites such as

Quibids.com and ubid.com, negatively impacted eBay, causing the firm’s market share to shrink to only 17% (Maltby, 2008). The latest member numbers from eBay indicate there are 94.4 million active users (Pepitone, 2011) with 2011 revenues in excess of $11.6 billion (eBay, 2012).

2.4 Social Exchange Theory

Social Exchange Theory (SET) became a leading topic in the field of psychological research in the late 1960s and early 1970s (Cropanzano & Mitchell,

2005). SET is derived from and maintains both psychological and sociological perspectives (Anderson & Narus, 1984; Emerson, 1976; Luo, 2002), specifically

“utilitarianism” and “behaviorism” (Cook & Rice, 2003, p. 53). Utilitarianism is a widely accepted belief that acting in a manner which is moral and ethical will produce the greatest possible positive outcome during an interaction (Posner, 1979).

Behaviorism involves the belief that human behavior is an interactive process which is heavily influenced by exchanges (Riedel, Heiby, & Kopetskie, 2001). These two definitions establish the framework of SET and determine that interactions or exchanges, and individuals’ responses to these relationships will influence the overall outcome of such communications.

Texas Tech University, James Brian Aday, May 2013

Several scholars claim SET finds its origins in the early 1920s (Cropanzano &

Mitchell, 2005); however, the overall consensus is that SET was the product of

Homans (1958), Thibaut and Kelley (1959), and Blau (1964) (Anderson & Narus,

1984; Cook & Rice, 2003; Cropanzano & Mitchell, 2005; Emerson, 1976). Each of these studies focused on varying aspects of the behavior of individuals in group settings (Emerson, 1967).

The research of Homans’ (1958) brought economics and social psychology together through the study of small group exchanges in the workplace. The study provided insight into how people react during interactions, and how each individual responds to the actions of the other (Homans, 1958). Homans found that individuals are more likely to exert positive emotion and communication when presented with reinforcing communication (Homans, 1958). For example, if a person exhibits behavior during a conversation which is distasteful to the other party, the person he is speaking to is more likely to respond negatively or cut off communication with the speaker.

Thibaut and Kelley (1959) furthered Homans research and introduced a

Texas Tech University, James Brian Aday, May 2013 documented each time. If the reaction is favorable and a reward is given, such as acknowledgement of a job well done, the researchers argue this will be the case the majority of the time (Thibuat & Kelley, 1959). Negative outcomes or costs are measured as well. The researchers also included external factors, personal values and needs in their analysis to determine if different external variables influenced the interaction between subjects A and B (Thibaut & Kelley, 1959).

Blau (1964) aimed to identify, from a psychological perspective, how interactions between employees and their co-workers influence the group as a whole, and how each person reacts when obligations are placed upon them (Cropanzano &

Mitchell, 2005). Blau (1964) tested the general belief of reciprocity, expressing gratitude for assistance or a person volunteering to support those who have helped him in the past (Blau, 1964). Findings demonstrated most individuals expect some form of gratitude, however obscure it may be, and reciprocity shown to the helper by the individual helped build strong, long-lasting bonds amongst group members (Blau,

1964).

Emerson (1967) is credited with amalgamating the varying ideas of these researchers and formulatting the SET model which is still utilized today (Dur &

Texas Tech University, James Brian Aday, May 2013 individual takes into account the garnered rewards or exhibited costs and these variables may have bearing on future interactions. Furthermore, Emerson (1967) contends group interactions should not be measured by each occurrence; rather, they should be calculated long-term as people will act differently from day-to-day (Thibaut

& Kelley) and reciprocity for actions might not occur for an extended period of time

(Emerson, 1967).

Thus, Emerson’s (1967) conclusions shaped SET in its original form, which states individuals are more likely to participate openly in an exchange if they believe the rewards will outweigh the costs, the parties believe reciprocity will occur, and their status will be reinforced (Yoon, Gursoy, & Chen, 2001). The initial version of SET was widely adopted by those in both social psychology and business (Konovsky &

Pugh, 1994). However, a major limitation of the original model was that interactions only included dyadic exchanges which were significant from the business perspective: employee to co-worker, and employee to supervisor (Euwenma, Wendi, & Emmerik,

2007; Kovnosky & Pugh, 1994; O’Reilly & Chatman, 1986). While the original SET baseline was ideal for manufacturing firms, it was not until the mid to late 1980s when

SET began to significantly include a third interaction type, employee to customer

(Solomon, Suprenant, Czepiel, & Gutman, 1985).

2.4.1 Purpose of SET.

Texas Tech University, James Brian Aday, May 2013 theory aimed to identify the chief inputs, specific needs, and expected outcome of each interaction in an attempt to create a work-force content with their jobs, with the expectation that a pleasant social environment within the work setting would yield higher output (Konovsky, 2000; Konovsky & Cropanzano, 1991). However, SET was originally orchestrated to measure dyadic relationships of employees and their transactions with coworkers and supervisors. It was postulated by Emerson (1967) that social exchange theory implied an obscure form of contract in which the parties, often referred to as actors in the literature, would essentially enter into a binding agreement through exchanges without the formalities of signing a written document (Konovsky

& Pugh, 1994). Further research on SET identified the employee-supervisor relationship as being directly correlated with how the employee feels towards the firm, as the supervisor is often viewed by the employee as the business itself (Konovsky &

Pugh, 1994). For example, an employee working at McDonald’s will treat exchanges with his supervisor as an interaction with the firm. While there is no one person who fills the role of McDonald’s, the supervisor is assigned that role by the worker.

Further studies conducted on SET identified that while exchanges amongst individuals were the primary source of data, the theory was actually better suited to measure perceived organizational support by employees (Settoon, Bennett, & Liden,

Texas Tech University, James Brian Aday, May 2013 example, if employees believed they had a supportive working environment from coworkers, then negative exchanges with supervisors were less likely to lead to decreased work performance and job dissatisfaction (Settoon, Bennett, & Liden,

1996). This fresh approach to understanding SET argued that all actors (employees and supervisors) in a small-group setting are self-interested and seek to accomplish goals jointly for mutual reciprocity (Lawler & Thye, 1999). Additionally, in the late

1980s and early 1990s, research on SET began to focus heavily upon emotions and the role specific emotions play during exchanges (Lawler & Thye, 1999). Researchers acknowledge it is seemingly impossible to accurately measure feelings, and that emotional responses will vary between individuals (Lawler & They, 1999). With the inclusion of the variable of short-lived sentiment prior to and after exchanges, research on SET identified that reciprocity can be negated based on short-term emotional responses (Lawler & Thye, 1999). For instance, if an employee is subjected to a meaningless task, such as taking out the garbage, the supervisor might thank the employee upon his or her return from the dumpster. While reciprocity is demonstrated, the employee might be incensed from having been assigned trash duty and go on a tirade against the manager. This illustration exemplifies the belief that short-lived emotions are relevant in SET.

2.4.2 Justice and social exchange.

During the 1990s, SET was again adapted and became a central component of research relating to procedural, interactive, and distributive justice in the workplace

(Masterson, Lewis, Goldman, & Taylor, 2000). Procedural justice, though definitions

Texas Tech University, James Brian Aday, May 2013 vary slightly, is widely accepted as the fairness or perceived fairness of the requirements placed upon employees to garner success (Folger & Konovsky, 1989).

The majority of procedural justice research examines the perceived extent to which employees believe they must go to earn a pay raise or promotion (Skarlicki & Folger,

1997). While researchers acknowledge employees likely have concerns with other bureaucratic areas or requirements of the organization, they are most likely to exhibit a response based on those requirements which directly affect their pay, tenure, and promotion within the firm (Kim & Mauborgne, 1998).

Interactive justice is rooted in exchanges the employee encounters and the attitudes or feelings garnered by employees after such interactions (Chen & Tjosvold,

2002). The ideals of interactive justice hold true when actors believe they are treated fairly or justly and in a manner which is self-affirming when decisions are made regarding their employment (Chen & Tjosvold, 2002). For example, if a process related concern were to arise in the work environment between individuals working in a specific department, interactive justice would be prevalent if all employees left the conflict feeling as though their voices had been heard and their opinions on how to fix the problem were weighed equally with others’ opinions. Furthermore, employees who apply for a promotion and are unsuccessful may believe that interactive justice has occurred if they are re-assured of their positive job performance and given reasons as to why another individual received the promotion.

Distributive justice is highly related to employee pay but is less concerned with the procedures required to attain a pay raise or promotion (McFarlin & Sweeney,

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1992). Distributive justice, as argued by researchers, is the perceived fairness of employee compensation (McFarlin & Sweeney, 1992). Little insight is given to the means in which higher pay is earned; rather, the primary focus of distributive justice research focuses on employees with similar job titles and responsibilities and the effect wage discrepancies amongst these workers has on their perceived fairness of the organization. For example, let us assume there are three assembly line workers who all perform the same tasks and maintain identical time-in-service. However, one of the employees is paid $4 an hour more than his cohorts. This situation will likely lead to the lower paid workers acting out against the higher paid individual, or making this person feel isolated and not part of the team. Furthermore, the lower paid individuals are more likely to reduce their output and become antagonists to the assembly process.

This example highlights how similar employees perceived the distributive justice of the company, and how their feelings and actions created resounding effects for their coworkers and the firm (McFarlin & Sweeney, 1992).

As highlighted in the discussions above, the overall theme amongst interactive, procedural, and distributive justice balances upon the employee’s perceptions of fairness in relation to their exchanges with the firm (Zapata-Phelan, Colquitt, Scott, &

Livingston, 2009). While these exchanges were not always linked directly to exchanges with individuals, the generalizable attitude exhibited in each form of justice relies upon reciprocity, which is a key foundation of SET (Aryee, Budhwar, & Chen,

2002). Utilizing SET as a means to identify and measure perceived justice and its predictive capability on job satisfaction and performance has allowed researchers to

Texas Tech University, James Brian Aday, May 2013 broaden the capability of SET, and provided the necessary theoretical framework for justice related research in the business environment (Rupp & Cropanzano, 2002).

2.4.3 SET in the service industry.

While the original testing of SET was carried out in manufacturing firms where employees have limited contact with customers, over the past 20 years SET research has begun to focus on the third component of exchange, customer to employee and employee to customer (Anderson & Narus, 1990; Briggs & Girsaffe,

2009). The service industry has recently emerged as an acceptable and viable area in which to apply SET (Anderson & Narus, 1990; Briggs & Grisaffe, 2009; Garbarino &

Johnson, 1999). SET has been further adapted in service research to study the interactions and exchanges in business-to-business (B2B) and business-to-supplier

(B2S) relationships, and the effects such exchanges exert on a particular firm (Briggs

& Grisaffe, 2009). While the examination of B2B and B2S relationships might appear out of context for SET, researchers contend reciprocity maintains high levels of significance in such exchanges and, thus, SET is an accurate means to measure such interactions (Dur & Roelfsema, 2010).

The expansion of the SET framework to include measuring employee and customer interactions has provided researchers with new models which quantify the effects of these relations and the level of perceived reciprocity needed for positive results to emerge (Pamatier, Jarvis, Bechkoff, & Kardes, 2009). Measuring the interaction and perceived costs and benefits of each exchange has provided insight into the appropriate levels of communication necessary to stimulate consumer

Texas Tech University, James Brian Aday, May 2013 satisfaction, and transform one-time purchasers into long-term, loyal customers

(Narasimhan, Nair, Griffith, Arlbjorn, & Bendoly, 2009). Furthermore, SET applied to employee to consumer interactions in the service setting has provided strong data which indicates businesses rely on a consumer to business relationship (Palmatier,

2008). In this affiliation, the customer’s positive experience generates reciprocity, which in this case would be positive feedback, and allows the firm to use such inputs to further improve their operations with a more consumer-needs driven focus

(Palmatier, 2008).

2.4.4 SET and social status.

Current studies have begun to focus on SET and its implications for improved social status (Begley, Khatri, & Tsang, 2010). Studies which utilize SET and improve social status focus not necessarily on status as a measurement of wealth; rather, the research centers on improved feelings of self-worth or reputation (Wischniewski,

Windmann, Juckel, & Brune, 2009). The ideal of social status reduces the role reciprocity plays in understanding SET and conveys individuals’ willingness to help others out of generosity while expecting little in return (Wischniewski et al., 2009).

Additionally, research has concluded that being identified by one’s peers as a person who will go out of their way to help others and expect little to nothing in return is in itself a form of reciprocity (Neuberg, Kenrick, & Schaller, 2010). Essentially, the tradeoff experienced by the individual who creates the positive exchange comes in the form of recognition by others as being an exemplary person, resulting in improved social status, or morale, for the individual (Neuberg et al., 2010). The linking of SET

Texas Tech University, James Brian Aday, May 2013 to social status is a relatively young research area and, while there are multiple publications indicated as being in-press on the topic, little previously published research is currently available.

2.4.5 Applications of SET.

While SET research is predominantly focused in the business field, the theory has been adopted by a broad range of other disciplines. During the 1990s, SET proved to be a research gateway into the creation of leader-member exchange theory (LMX)

(Graen & Uhl-Bien, 1995). The use of LMX essentially took the approach of SET and exchanges, yet focused primarily on the interactions between employees and supervisors (Hooper & Martin, 2008). The utilization of SET to form LMX provided a new dimension for SET and allowed for specific research on firms’ organizational structures and the varying effects decision making from top managers has on lower- level managers (Hooper & Martin, 2008).

SET has also been adopted by sports researchers who have established a foundation upon which to measure interactions amongst teammates as well as between players and coaches (Johns, Lindner, & Wolko, 1990). The application of SET has provided insight into perceived fairness of athletes from coaches and management, as well as perceived fairness athletes believe exist between players (Breukelen, Leedn,

Wesselius, & Hoes, 2012). Through academic insight, the coach-player exchange

(CPX) and player-player exchange (PPX) have been measured to assess the impacts of these exchanges on the team’s overall attitude and the performance as a unit

(Breukelen et al., 2012).

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Further research has demonstrated a direct link between SET knowledge sharing amongst employees and the fears of job security, which has served as an integral role in Job Support Theory (JST) (Bartol, Liu, Zeng, & Wu, 2009).

Researchers of JST contend employees might be unwilling to share their knowledge or expertise with coworkers out of their concern for job security (Bartol, et al., 2009).

That is, employees may withhold information to avoid having a coworker or manager suggest their ideas as their own (Bartol, et al. 2009). Research on JST suggests employees who have previously been wronged or suffered severe losses from knowledge sharing in past exchanges are less likely to provide their knowledge in the work setting, which may lead to negative exchanges and perceptions about the employee (Lin & Huang, 2010). Thus, in the above contexts, the costs associated with the interaction have outweighed the benefits gained and little to no reciprocity was felt by the employee who provided the knowledge.

2.4.6 SET and customer loyalty.

Researchers contend the primary foundations of SET and the addition of the employee to customer interaction have the propensity to affect customer loyalty.

Texas Tech University, James Brian Aday, May 2013 fails to hold up their portion of the agreement, the customer can become displaced and angry as their trust and expectancy of reciprocity has been betrayed (Sierra &

McQuitty, 2005). For example, if a person has made arrangements for a car to pick them up from the airport and the driver fails to show, the traveler is now stranded and must begin to identify an alternate means of transportation. Under the same scenario, if we assume the car arrived but the driver was obnoxious and made the guest uncomfortable, the passenger is unlikely to utilize this service in the future, which eliminates the potential long-term viability of that customer.

The initial experience between the customer and provider is the only opportunity the firm has to establish trust with the consumer (Luo, 2002). However, the exchange of trust is mutual in this scenario as the customer is trusting the business will deliver its promised service, and the business is relying upon the customer to pay for services rendered (Luo, 2002). Furthermore, the relationship between the parties can play a significant role in the event of service failure (Mattila, 2001). The initial trust established in the business to customer relationship can establish a strong bond between the two parties, enabling firms to more easily recover from service failures

(Mattila, 2001). The loyalty established from the onset can influence a consumer to remain steadfast to a firm during service recovery so long as appropriate actions, in the view of the customer, have taken place to correct the problem (Buttle & Burton,

2006).

Texas Tech University, James Brian Aday, May 2013

2.4.7 SET in the marketing environment.

Scholars have utilized the concepts of SET in their marketing strategies, more specifically in approaches which rely upon relationship marketing and personal selling

(Morgan & Hunt, 1994). Morgan and Hunt (1994) identify that while SET is suited for use in external marketing campaigns, the theory can also be highly effective when employed for internal marketing purposes. The authors maintain that strong employee bonds, emphasized through SET, can assist firms in promoting internal agendas more easily than in organizations where there is little employee interaction (Morgan &

Hunt, 1994). Additionally, research has proven the use of SET principles can create extremely effective cause-related marketing campaigns (Ross, Stutts, & Patterson,

1991). Cause-related marketing can include non-profit organizations such as the Red

Cross which elicits donations via advertisements and relies heavily upon its relationship with donors as a primary means of financial support (Ross et al., 1991).

In the service industry, marketing campaigns generally rely upon some form of relationship marketing which is geared towards establishing trust and long-term customers (Zboja & Hartline, 2010). Utilizing previous interactions, businesses are able to gear advertisements towards specific groups, in this case established clientele, and rely on positive past experiences to generate demand (Zboja & Hartline, 2010).

This customer base, through relationship marketing, can also serve as a target for cross-selling or upselling (Zboja & Hartline, 2010). For example, customers who have experienced positive dealings with service providers might be influenced to buy other services from the firm based on their past encounters (Archol, 1996).

Texas Tech University, James Brian Aday, May 2013

Similar to relationship marketing, personal selling relies heavily upon the connections established between the customer and seller (Solomon et al., 1985).

Personal selling is argued to be the most expensive form of marketing, and firms demand results from employees in the field or job security becomes a critical issue

(Albers, Mantrala, & Sridhar, 2010). The relationship established between the two parties is essential for future sales, and each exchange is highly critical to the long- term viability of the client (Albers et al., 2010). The initial interaction allows the firm and consumer to establish trust and the highly competitive environment necessitates that future meetings between the two parties must continue to produce feelings of goodwill or the relationship will deteriorate rapidly (Pelham, 2009). Furthermore, the high level of trust recognized between the two individuals necessitates mutual reciprocity and understanding of each side’s needs and limitations (Pelham, 2009).

Thus, SET principles are highlighted as the two sides become inter-dependent parts; the salesman trusts the business owner not to switch to a competitor while the business owner relies upon the salesperson to deliver the promised goods.

2.4.8 SET in tourism research.

Texas Tech University, James Brian Aday, May 2013 be able to gain benefit from the encounter (Yoon et al., 2001). The benefits gained can certainly be financial, but may serve a personal motivation, such as attempting to understand cultural differences of foreign visitors (Yoon et al., 2001).

To date, the vast majority of tourism related studies which rely on SET have concentrated primarily on employees and their exchanges with other employees and customers (Ma & Qu, 2011). The findings in the tourism studies which focused on these relationships produced similar results to the studies related to businesses and service providers. The interactions between tourists and native residents have subsequently dominated a large portion of the available research as well (Ap, 1992;

Jamal & Getz, 1995; Moyle, Croy, & Weiler, 2010; Trauer & Ryan, 2005). A vast review of extant literature identified that residents of tourist destinations largely view visitors as a revenue stream and interactions are primarily motivated by the ultimate goal of making a sale (Ap, 1992; Moyle, Croy,& Weiler, 2012).

2.4.9 Problems associated with SET.

The fact that SET has been widely adopted across a multitude of disciplines does not mean the theory is without flaws. Dinesch and Liden (1986) contend that most methodological approaches to testing SET are inconsistent. The authors argue

SET has been subjected to measurement based on numerous scales with some containing as few as two items and others employing as many as 24 (Dinesch &

Liden, 1986). The varying constructs on which SET is measured weakens the findings and prohibits researchers from drawing conclusions which can be applied unilaterally

(Dinesch & Liden, 1986). On the same premise, Giddens (1979) believed very early

Texas Tech University, James Brian Aday, May 2013 on that SET became oversimplified and fails to take into account the differences in individuals and workplace settings. For example, SET fails to recognize that an employee may be affected at work by troubles or concerns at home, such as a divorce or financial worry, which affects how they may react on the job. Recent scholars agree prior findings have failed to take into account that SET studies in a specific business environment are not universally applicable to other business models (Cropanzano &

Mitchell, 2005).

The study of SET has been limited to applications involving controlled situations, and relatively few studies have focused on private enterprises as a source for data collection (Aryee et al., 2002). Furthermore, the majority of previous SET studies have been conducted in the United States where laws and regulations place limits and constraints on employees and managers (Aryee et al., 2002). Thus, the findings of previous studies might represent a cultural obscurity and may not necessarily be applicable internationally (Aryee et al., 2002). Furthermore, researchers argue uncontrolled variables, such as emotion or personal needs, can influence exchanges and these external variables are difficult to measure (Cropanzano et al.,

2002). For instance, if an employee is going through personal duress from financial problems or difficulties at home, these variables will exert impact in the interaction and affect the results. Yet, such outside factors are unmeasured and can lead to false results (Cropanzano, et al. 2002). Other factors, such as previous exchanges and personal feelings towards supervisors and other coworkers can lead to biased

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interactions and provide results which might be different under other circumstances

(Settoon et al., 1996).

2.4.10 Previous models.

A major limitation of SET is the non-existence of a standard structural model

which allows for theory testing (Dinesch & Lident, 1986). Examination of current

literature identified a litany of models employed to test and measure SET, yet little

congruency was maintained amongst variations. This section will examine the two

models which are most applicable to this current study. The first model comes directly

from Wayne, Shore, Bommer, and Tetrick (2002) and is exhibited in Figure 1.

Figure 1. Social Exchange Theory Model. Taken directly from Wayne, S. J., Shore, L. M., Bommer, W. H., & Tetrick, L. E. (2002). The role of fair treatment and rewards in perceptions of organizational support and leader-member exchange. Journal of Applied Psychology, 87(3), 590- 598.

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As observed in Figure 1, this model has been adapted to include the components of work related justice and leader-member exchange. While these additions vary from other models, it is demonstrated on the left-hand side of this figure that employees maintain certain needs from their exchanges at work. Ideas such as inclusion, rewards, dyad tenure, recognition, procedural and distributive justice, and organizational tenure are all addressed. These factors all influence perceived organizational support (POS) and the LMX. This is in-line with traditional SET research which argues that employees weigh multiple variables when making decisions and determining their perceived job satisfaction. The factors of POS and

LMX serve as mediating variables for the outcome variables of commitment, organizational citizenship behavior, and employee performance ratings. The authors of this study contend each predictor variable is an accurate representation of variables which should be analyzed during SET research, and each moderating variable exhibits some form of influence over the outcome variables.

While the SET model displayed in Figure 1 provides a summation of findings from multiple disciplines, it is necessary to examine a model used in tourism research to establish commonalities and contrasts between the fields. Figure 2 is taken directly from Ap (1992) and exhibits the SET relationship from a tourism perspective. This model was chosen based on information from Google Scholar and the ScienceDirect database which identified Ap (1992) as being the most highly cited article on SET in the tourism industry. Furthermore, other models assessed in tourism literature

Texas Tech University, James Brian Aday, May 2013 appeared too limited and failed to take into account the multiple aspects of SET when examining the interactions of tourists and locals.

Figure 2. Social Exchange Theory Model adapted for tourism research. Taken directly from Ap, J. Residents’ perceptions on tourism impacts. Annals of Tourism Research, 19(4), 665-690.

As demonstrated in Figure 2, the primary motivating factor in exchanges between tourists and locals at a destination is need satisfaction. This is also true in

SET as employees and customers are brought together by needs; the consumer requiring a service and the employee needing to provide the service in order to garner income. The second and third stages, antecedents and form of exchange relation, are grouped together under a general heading of exchange relation. This, based on previous studies, is the area most important to locals who are selling goods and

Texas Tech University, James Brian Aday, May 2013 services (Ap, 1992; Jamal & Getz, 1995; Moyle, Croy, & Weiler, 2010; Trauer &

Ryan, 2005). The providers strive to entice travelers to purchase their wares and rely upon a positive initial interaction to help secure the sale. The consequences of exchange stage, in this setting, are geared more towards the traveler and identify areas of concern which weigh on the purchase decision. As demonstrated in the model, if the perceived reciprocity is high, the transaction will occur and no long-term relationships will be formed. However, if the perceived costs outweigh the benefits or if the initial interaction does not alleviate the concerns of the traveler, the sale will fall through.

2.5 Purchase Motivation

Previous studies have identified and focused on a plethora of purchase motivations such as: goal-directed (Lawson, 1997), social obligation (Goodwin,

Smith, & Spiggle, 1990), internal decision making characteristics (Wells & Prensky,

1996), external influences (Armstrong & Kotler, 200), and behavioral processes (Wu,

2003). While these studies and concepts established key characteristics of purchase motivation in a conventional setting, current research has shifted focus towards studying online shopping motivations (Rohm & Swaminathan, 2004). Online or e- commerce platforms are unique in that they maintain characteristics of a marketing channel and point of sale with one-click (Hausman & Siekpe, 2009). Motivations such as the perceived ease of use (Chen, Clifford, & Wells, 2002) and information availability are two new areas of inquiry (Montoya-Weiss, Voss, & Grewal, 2003).

Furthermore, motivations such as time-savings (Rohm & Swaminathan, 2004) and

Texas Tech University, James Brian Aday, May 2013 variety seeking have proven to influence online purchase decision making (Menon &

Kahn, 1995).

The previous examination of online shopping and traditional purchase motivations provides a unique aspect for this study. Flash-sale purchases are made online, and as such incorporate the motivations identified for online shoppers; however, while the initial purchase occurs online, the redemption of the coupon takes place in a physical location. The experience of the consumer while at the business is motivated to attend based on redemption, but may include additional purchases of full- price products or services during the visit.

2.6 Group Purchase Decisions

Consumers can be propelled to purchase a service based on their desire for social acceptance into a group of their peers or those with similar social standing

(Bearden & Etzel, 1982). Individuals considering a purchase may also rely on the group for information and input from members may significantly influence purchase decisions (Bearden & Etzel, 1982). While the term group may appear plural, research into group influence has been broken down into sizes as small as two members

(Corfman & Lehmann, 1987). Peer influence is established through like-minded groups of individuals who aid in establishing identity and often lead to a desire for conformity (Chen-Yu & Seock, 2002; Lee, 2009). For example, members of a social group may purchase specific articles of clothing or frequent a specific location, and in an effort to conform, each individual within the group may purchase identifiable clothing or spend time in a preferred location based on the group-think mentality.

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Consumers are extremely receptive to input from their peers and may be motivated to purchase a service based solely on a desire to conform, regardless of the product quality, or lack of tangible attributes (Hsu, Kang, & Lam, 2006). Reference groups are also predominant in the realm of online shopping (Park & Stoel, 2005).

Individuals may rely on their group and the group decision making process to weigh and evaluate potential solutions with other members’ input (Herrera, Martinez, &

Sanchez, 2005). Group thinking provides advantages and disadvantages to marketers as factions can persuade members to make a purchase (Sheth & Parvatiyar, 1995).

Conversely, peers may dissuade a consumer from buying an item they would have purchased had the group not given their opinion, or individuals may be reluctant to purchase a certain product because they fear buying the item will associate them with a specific crowd (Escalas & Bettman, 2005).

The influence of peer groups in the online environment is somewhat limited, as studies have primarily focused on online reference groups, such as message boards and social media (Pempek, Yermolayeva, & Calvert, 2009), online word-of-mouth

(eWOM) (Fong & Burton, 2006), and online auctions (Stern & Stafford, 2006).

However, in these studies it has been ascertained that consumers value the opinions of their peers in these settings, and may rely on their input regarding pricing and purchase decisions (Stern & Stafford, 2006). Smith, Menon, and Sivakumar (2005) contend that businesses can build trust with new users by establishing positive relationships with other members of the peer group. Additionally, the authors

Texas Tech University, James Brian Aday, May 2013 identified peer influence in the electronic environment as being significant and having the power to affect product choice (Smith, et al., 2005).

2.7 Theory of Planned Behavior

The Theory of Planned Behavior (TPB) is an augmentation of the Theory of

Reasoned Action, created by Ajzen & Fishbein and has been influential in psychological research (Ajzen, 1991; Ajzen & Fishbein, 1980; Fishbein & Ajzen,

1975). In TPB, a critical factor is measuring intention of an individual to perform a specific behavior (Ajzen, 1991). Intent to perform the specific behavior is influenced by internal forces (attitudes) and how the accomplishment of the goal or behavior will be in-line with external expectations (subjective norms) (Ajzen, 1985; George, 2004).

When examining peripheral influences, the individual may be influenced in their decision based upon how their choices will be viewed by their peers, and whether the group will approve (Ajzen, 1991; Quintal, Lee, & Soutar, 2010). TPB is reliant upon self-efficacy, or the confidence individuals exert when faced with a decision (Ajzen,

1991). Individuals’ self-efficacy, a factor of Social-Cognitive Theory, can effect which activities the person engages in, as well as the amount of effort put forward to participate in the activity (Bandura, 1982, 1991). As stated in the literature, TPB originated in psychology; however, numerous studies have adapted TPB to fit into consumer behavior research.

TPB is a model frequently utilized to predict consumer buying behavior (De

Canniere, De Pelsmacker, & Geuens, 2009; Ouelette & Wood, 1998). Research on

TPB has demonstrated that focusing on affective (internal) attitudes by marketers aids

Texas Tech University, James Brian Aday, May 2013 in changing perceptions about a product or brand (Arvola, et al. (2008). Essentially, affective attitudes are feelings or emotions towards a particular product or service, and forming a favorable impression is crucial (Arvola et al. 2008). A recent study identified TPB can predict behavior and frequency of purchase, and suggests marketers should rely on influencing intention to purchase, then shift that focus to building repeat business through a business-to-consumer relationship (De Canniere et al., 2009). Thus, based on the social constructs of TPB, and the need for acceptance of choice by peers and the consumer research perspective of influencing attitudes, TPB will be extended in this study. Focusing on the influence of peers and the impact of the decision to purchase a daily-deal offering and revisit intention/frequency will directly contribute to TPB constructs.

Marketers recognize that dividing segments based upon group dynamics creates a significant target market because groups of individuals with common interests have shown a propensity to make like-minded purchases (Bearden & Etzel,

1982; Makgosa & Mohube, 2007; Valck, Bruggen, & Wierenga, 2009). These similarities allow advertisers to understand how their targeted consumers weigh the risk of purchases in an analogous manner (Tversky & Kahneman, 1992; Wilson &

Woodside, 1994). Clusters of individuals maintain comparable price thresholds, waiting to make a major purchase decision until the current price makes the offer appear both affordable and beneficial (Krishna, 1994). These tendencies make it imperative for online retail firms utilizing viral marketing to determine the role and amount of variability associated with target markets’ group dynamics (Forsyth, 2010).

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Individuals receiving electronic marketing messages from a daily-deal site may forward the information along to friends who they hope will be interested, with the intent of securing a potential travel companion.

Group buying behavior has also been examined from the perspective of influence on motivation to purchase goods and services (Mangleburg, Doney, &

Bristol, 2004). Previous research concluded peer influence in buying behavior elicits spontaneous purchases based on the dynamic of the individual seeking to be socially accepted by friends (Borges, Chebat, & Babin, 2010; Mangleburg, et al., 2004). When making a decision regarding whether to purchase an unfamiliar product, group influence can aid in reducing the perceived risk of trying the new service, and influence customers to buy (Kiecker & Hartman, 1994). Studies also identified that while consumers might rely upon peers for purchase approval, they are also motivated to encourage their counter-parts to make like-minded purchases as a form of emotional support (Burnett, 2000; Palmer & Koenig-Lewis, 2009; Wellman & Gulia, 1999). The reliance upon peers for information when making a purchase and the influence individuals maintain amongst their friends is directly applicable to offerings on

Groupon and LivingSocial. The daily-deal offerings may originate from a business which the consumer has no knowledge of, and the buyer may seek the input of peers before making a purchase decision. Furthermore, the consumer might try to influence a friend or family member to purchase the coupon as well so they can visit the provider with a support network.

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2.8 Product Driven Consumers

Consumers can be motivated to purchase a product based on the cost, attributes, and personal benefits of the merchandise (Chang & Wildt, 1994). Research has identified product features effect the perceived quality of products by consumers

(Chang & Wildt, 1994). Attributes are wide-ranging, and can include physical descriptors such as shape, color, size, capabilities, and stylishness (Lehdonvirta,

2009). Essentially, product features are the characteristics which enable them to be employed to achieve a desired purpose for the end-user (Lehdonvirta, 2009).

Product benefits have become a leading tool in marketing communication messages (Kleef, Trijp, & Luning, 2005). These tangible qualities, if conveyed effectively by the firm, maintain the potential to persuade consumers to make a purchase (Kleef, Trijp, & Luning, 2005). The perceived desirability, coupled with item features, can aid in overcoming consumer hesitation pre-purchase and reducing internal feelings of risk on behalf of the customer (Honkanen, Verplanken, & Olsen,

2006; Pickett-Baker & Ozaki, 2008). Research demonstrates companies can rely upon

“add-on” products and features, such as extra services or ancillary products, which complement the original purchase as a means of further demonstrating the wide- ranging attributes and capabilities of the product (Bertini, Ofek, & Ariely, 2009, p.

17). Furthermore, add-ons can also overcome pre-purchase objections and provide a sense of increased perceived value for the end-user (Bertini et al., 2009).

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2.9 Approach-Avoidance

Consumer needs and values influence receptiveness to available product features, and the extent to which marketers appeal to an individual’s expectations influence the potential positive reception of the marketing message and increased likelihood of making a sale (Pickett-Baker & Ozaki, 2008). Human emotions greatly influence purchase behavior (Penz, & Hogg, 2011; Watson & Spence, 2007). Thus, the matching of attributes to consumers’ internal purchase emotions, such as perceived risk or potential benefit, leads to a major challenge faced by firms trying to sell their products (Penz & Hogg, 2011). Commonly referred to in the literature as approach- avoidance conflict, theory based on this topic has identified that “in approach motivation, behavior is instigated or directed by a positive/desirable event or possibility” while avoidance “behavior is instigated or directed by a negative/undesirable event or possibility” (Elliot & Thrash, 2002, p. 804).

Approach-avoidance theory was first established by Mehrabian and Russell

(1974) and further expanded upon by Mehrabian (1976). These initial studies were founded in environmental psychology and focused on atmospherics, more specifically lighting in a room serving as an influencer of increased stimuli (Mattila & Wirtz,

2001). While books and scholarly research articles related to the work of Mehrabian and Russell exist, the summation of their findings determined physical environment, in this instance an illuminated room, influences emotion and behavior (Summers &

Hebert, 2001). Donovan and Rossiter (1982) were among the first researchers to apply

Texas Tech University, James Brian Aday, May 2013 the findings of Mehrabian and Russell (1974) to retail shopping (Hui & Bateson,

1991; Summers & Herbert, 2001).

Research by Donovan and Rossiter (1982) established that consumers are influenced to spend more time in a store which provides pleasant atmospherics, and these factors, such as lighting and sound, are key motivators of arousal. The increased arousal aids in overcoming discomfort on behalf of the consumer and has the potential to increase sales (Donovan & Rossiter, 1982). Since the early 1980s, consumer research on atmospherics and their effect on approach-avoidance conflict has become an increasingly relevant topic (Mattila & Wirtz, 2001; Turley & Milliman, 2000).

Recent studies have examined color, spacing, music (Turley & Milliman, 2000), the exterior and interior features of stores as well as shelf layout (Berman & Evans, 1995), and more recently the effect of scents and smells (Mattila & Wirtz, 2001). Each of these studies have exhibited that consumers are influenced to approach and spend more time in a store with inviting characteristics which in turn increases purchase likelihood.

2.9.1 Online environment.

Atmospherics and approach-avoidance conflict research is not solely limited to traditional brick and mortar retail stores as studies have begun to focus on these issues in the online shopping environment as well (Eroglu, Machleit, & Davis, 2001). Studies based on online shopping contend that a consumer’s willingness to visit and stay on a website (approach) or navigate away from the page (avoidance) are determined by the atmospherics and visual representations of the site, especially during exploratory

Texas Tech University, James Brian Aday, May 2013 shopping (Huang, 2000). Research on web-based approach-avoidance identified websites which are inviting, easy to navigate, and include pleasurable visual cues result in higher amounts of time spent on the sites (Eroglu, Machleit, & Davis, 2001).

The higher visitation times increase stimuli and user-website interaction (Balbanis &

Reynolds 2001) and the propensity for sales (Kim, Fiore, & Lee, 2007; Kwak, 2002)

2.9.1.2 Approach-avoidance in marketing.

A comprehensive examination of extant literature identified that previous research focused on approach-avoidance related to marketing primarily through increasing stimuli (arousal), which is accomplished with methods nearly identical to consumer behavior studies of the same topic (Mowen & Mowen, 1991; Spangenberg,

Crowley, & Henderson, 1996; Wirtz & Bateson, 1999). Commonly studied variables which act as a marketing mechanism include music, colors (Wirtz & Bateson, 1999), exposure (Mowen & Mowen, 1991), temperature, and cleanliness (Spangenber, et al.,

1996). While marketing research examines these variables as a motivator to attract customers, the findings are nearly identical to consumer behavior findings; that is, advertising through atmospherics has the propensity to increase visitation which should lead to a higher likelihood of sales.

Research which examined approach-avoidance in the online environment have been more broad in their approach, and while stimuli such as website design are measured, studies also focused on the effectiveness of generating visits through price transparency, product descriptions, and sale terms (Eroglu, Machleit, & Davis, 2003;

Mollen & Wilson, 2010). However, each of the measured variables was based on the

Texas Tech University, James Brian Aday, May 2013 same construct, how to increase and maintain engagement between the user and the website (Mollen & Wilson, 2010). An exhaustive literature review of previous studies identified a gap in the literature; studies in approach-avoidance are entirely centered around measuring time spent on websites and the influence of the site to generate sales.

Currently, there are no academic publications which examine approach- avoidance based upon broadcast advertising via the web, or its influence on a consumer to visit a physical store location. For example, daily-deal advertisements are distributed via their website and broadcast email, and require an online purchase, but the consumer must also visit the business to redeem the offer in most cases. Thus, this study aims to identify whether product offerings via advertisements lead to visitation of the firm.

2.10 Behavior Theory

2.10.1 Price shoppers.

Recent studies have determined certain consumer segments are heavily motivated in their purchase decisions based on the price of the product or service being offered (Pan & Zinkham, 2006). Previous studies also showed lower prices seemingly indicate perceived value for specific customers (Zeithaml, 1988). Kerns and

Hair (2008) identified this type of shopper as one who consistently searches for the best price with highest benefit and termed such individuals as “price-shoppers” (p.

23). Retailers have been aware of this segment for decades and low-cost leading chains design their business models with these consumers serving as their primary

Texas Tech University, James Brian Aday, May 2013 target market (Ganesh, Reynolds, Luckett, & Pomirleanu, 2010). Further research by

Ganesh et al. (2010) concluded a major shared characteristic amongst shoppers is the tendency to search for bargains and the lowest prices during their product search. The relevance and significance of price-shoppers was further demonstrated in a study which identified some patrons hesitate to purchase unless they are confident they are getting the best price (Zinn & Liu, 2008). Price shoppers are presumed to serve as an important group in the sales of daily-deals as these consumers may be motivated to purchase a voucher solely based on its cost and perceived benefit without intention to revisit the property in the future unless additional discounts were offered.

2.10.2 Discount shoppers.

Upon examination of shoppers who are motivated by price or potential savings, extant literature states it is more appropriate to classify some consumers as

“discount shoppers” (Carpenter & Moore, 2009, p. 68). While customers labeled as discount shoppers share some of the sensitivities related to the cost of items that price shoppers exhibit, they are typically more concerned with promotion and perceived markdowns (Carpenter, 2009; Fox, Montgomery, & Lodish, 2004). In addition to apparent savings, the availability of differing product assortments has been recognized as a key influence in purchase decisions for this type of buyer (Fox, et al., 2004).

Previous studies have found discount shoppers who make purchases online are considered young, but maintain high levels of education, and a more price-sensitive budget (Carpenter & Balija, 2010). Recent studies also indicate discount shoppers are primarily attracted to leisure and socially motivated offerings (Jin & Kim, 2003). The

Texas Tech University, James Brian Aday, May 2013 identification of shoppers who are motivated by perceived savings and their attraction to leisure and socially motivated offerings relate closely to the targeted respondents of this study.

A study by Dickson and Sawyer (1990) exhibited product pricing influenced the consumer purchase decision, but after a brief period of time few people remembered the price they paid. However, research by Wakefield and Inman (1993) coined the term “price vigilantes” or those who self-identified as being price conscious when shopping (p. 217). Based on results of their study, the price vigilantes were individuals in lower income brackets. Though gender played no role in price sensitivity, these shoppers were able to correctly identify the price or sales price of

55% of the items in their shopping cart after checkout (Wakefield & Inman, 1993).

Price sensitive shoppers typically rely upon reference pricing, either from previous purchases or price exposure, when making a purchase decision (Puccinelli et al., 2009). Further evidence demonstrates price sensitive shoppers utilize reference points and price cues to identify when a particular item is a considerable savings

(Puccinelli et al., 2009). Consumers will typically seek out the lowest price for an item; however, as a deadline approaches, such as a time limit to buy, the consumers may become less price sensitive (Lemieux & Peterson, 2011). Consumers under time deadlines face price uncertainty and may be convinced to make a purchase they are not completely comfortable with as they are unable to explore all pricing options and do not want to miss out on the potential savings being presented (Lemieux & Peters,

2011).

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2.10.3 Pricing strategy.

There are numerous pricing strategies used by every industry in an effort to attract new business. Among the most common pricing strategies are: premium pricing

(Berends, 2004), penetration pricing (Kotler & Armstrong, 2009), economy pricing,

(Sullivan & Adcock, 2002), price skimming (Liu, 2010), and psychological pricing

(Aerts, Van Campenhout, & Van Caneghem, 2008). Premium pricing relies upon a difficult to duplicate (luxury) item or service which is able to command a higher price in the market (Berends, 2004), whereas penetration pricing may be ideal for a new firm or new product that is trying to gain market share (Kotler & Armstrong, 2009).

Firms which offer lower quality items at a lesser cost could be defined as pursuing economy pricing (Sullivan & Adcock, 2002), while firms with a new or innovative service may rely on price skimming to garner the highest possible price until a competitor begins to saturate the market (Liu, 2010). The last pricing strategy relies upon psychological pricing, such as offering an item for $5.99 instead of $6 to make the price appear as a savings (Aerts et al., 2008). While each pricing strategy is complex and important, penetration pricing and psychological pricing will be the primary areas of focus for this study as they relate most directly to the concept of daily-deal advertising.

Numerous businesses rely upon psychological pricing, ending the total price of an item in an odd number such as $9.99 instead of $10.00 (Holdershaw, Gendall, &

Garland, 1997). While a multitude of research contends the psychological aspect of this approach relies upon individuals seeing the .99 as being less than the next whole

Texas Tech University, James Brian Aday, May 2013 dollar, thus implied savings, recent studies contend the rationale, claiming consumers look at the first number and identify this as the price (Holdershaw et al., 1997).

Referring to the $9.99 instead of $10.00 example, these studies argue people will see the first 9 and associate the cost of the product as being only $9 when it is actually much closer to $10.

Marketing and sales research also suggests using whole or round numbers can serve a psychological purpose (Sehity, Hoelzl, & Kirchler, 2005). Data exhibits that consumers are slightly more likely to purchase a product that has an even number, such as $10 than an item for $9.99, when they are buying for personal usage (Manning

& Sprott, 2009). Additionally, utilizing round or whole number pricing can make an item appear as though it has more value because it does not end in a round number

(Manning & Sprott, 2009). The concept of round pricing is heavily utilized by daily- deal sites, as content analysis has identified every offer being priced as a whole number. This could potentially indicate that daily-deal sites avoid odd number pricing to avoid being identified as inferior. Psychological pricing is not the sole means of gaining consumer attention; numerous firms rely upon market penetration pricing to garner sales and raise awareness (Liu & Chintagunta, 2009).

Introducing a product or service to new markets, whether it is a new product or not, may be effectively achieved by introducing the service at a low price in an effort to penetrate at an effective cost (Liu, 2010). Entering a market in this manner will likely result in decreased profitability in the short-term, but the focus is on garnering new customers with the low cost and convert them into long-term, profitable

Texas Tech University, James Brian Aday, May 2013 consumers (Liu, 2010). Penetration pricing can be extremely successful in undifferentiated markets, such as those in the hospitality and service industries where products and services are fairly identical between firms (Indounas & Avlonitis, 2011).

Furthermore, penetration pricing is effective for firms targeting customers which exhibit high price sensitivity (Indounas & Avlonitis, 2011).

Penetration pricing is an ideal pricing strategy when targeting large homogenous markets but is not necessarily the best option for niche markets (Shaw,

2012). Utilizing penetration provides the firm with the potential to capture a large share of the market in a relatively short time period; this is specifically true in markets with numerous service providers (Guo, 2012). Thus, firms which choose to employ daily-deal advertisements with significantly lower prices than other competitors in the market maintain the potential to garner significant market share. As these flash-sale offerings are distributed to the masses, penetration pricing, through discounts, is potentially the best pricing strategy for such firms.

2.11 Promotional Sales

2.11.1 Deals.

Pricing strategies vary in nature and are geared towards generating sales through lower or competitive pricing (Bolton, Shankar, & Montoya, 2010). The retail sector has begun to move more towards customized pricing, offering price levels based on location, rather than corporate mandate, to attract business in various geographic locations (Bolton, Shankar, & Montoya, 2010). Over the past 50 years, retailers have relied heavily upon short-term discount pricing or deals to increase sales

Texas Tech University, James Brian Aday, May 2013 in an effort to steal customers away from their competition (Gamliel & Herstein,

2011). Traditional research has identified customers are primarily influenced by two types of discounts; monetary (a dollar value amount off) and non-monetary (i.e. buy one get one free) (Gamliel & Herstein, 2011).

While traditional promotional advertising has focused on perceived savings through their deals, Gamliel and Herstein (2011) suggest a more effective approach may rely upon highlighting the perceived loss by not making the purchase. For example, through their research, the authors found that when an ad was framed in a negative manner, such as failure to buy a product at X savings will actually result in the loss of money; it was more persuasive in influencing purchases (Glamiel &

Herstein, 2011). This type of advertising is similar to the methods employed by

LivingSocial and Groupon, as the daily-deal offering presents the user with the overall retail value, the discount percentage, and the discount price in the same message.

Consumers may be affected by the perceived loss of savings if they fail to purchase the offer.

2.11.2 Coupons.

Coupons have become a commonplace occurrence in the United States over the past several decades, with hundreds of billions of coupons printed and distributed in the 2000s (McKenzie & Tullock, 2012). The likelihood of consumers’ coupon redemption is referred to in the literature as coupon proneness (Pillai & Kumar, 2012).

Lichtenstein, Netemeyer, and Burton (1990) labeled coupon prone consumers as those who, through a coupon, demonstrate an increased potential to respond to the

Texas Tech University, James Brian Aday, May 2013 advertisement and possibly make a purchase. The authors also establish that a distinguishable difference exists between individuals who are coupon prone and shoppers who are determined to be value conscious (Lichenstein et al., 1990). The authors, as well as more recent studies, contend coupons can influence purchases in consumers who are not necessarily value conscious, but are able to identify the savings presented through the offer (Lichenstein et al., 1990; Pillai & Kumar, 2012)

Current research indicates coupons are effective when a higher perceived savings exists (Barat & Paswan, 2005). In 2009, the most recent data year available,

U.S. consumers redeemed over 3.3 billion coupons at retailers, a 26% increase over

2007 (Tilley, 2010). In the same year, over 367 billion coupons were distributed, with

Internet based coupon redemption up 263% from 2006 to 2008 (Nielsen, 2010).

Findings demonstrated that coupon redemption is not solely limited to lower socio- economic classes, as 41% of self-identified frequent coupon users reported earning over $70,000 annually, and the largest redemption growth segment in 2009 was individuals with annual income over $100,000 (Nielsen, 2010).

2.11.3 Online pricing

For numerous firms, offering products and services for purchase in the online environment provides reduced marginal costs and as a result the prices on their website may generally be lower (Yan, 2008). Companies which utilize dual sales channels, online and traditional brick-and-mortar store formats, gain access to two markets. The pricing strategies of both methods focus on the overall benefit to the firm

(Fruchter & Tapiero, 2005). Online sales channels present firms with lower

Texas Tech University, James Brian Aday, May 2013 advertising costs, real time sales data, easier price adjustment options, and up-to-the minute inventory (Oh & Lucas, 2006). Thus, firms utilizing online sales channels are able to quickly adapt to market changes and price fluctuation; as competitors lower their prices so does the firm, and vice-a-versa, providing price transparency for consumers (Oh & Lucas, 2006). Research demonstrated firms which utilize their online presence in a coordinated manner with their physical location are able to increase overall revenue as their online and in-store pricing are complimentary to one another (Ling, Guo, & Liang, 2011).

As demonstrated in the literature, offering goods and services online can be a less costly alternative for firms, and allows for greater control. In essence, flash-sales are built upon this principle as firms establish sales limits and price points which are significantly lower than if a consumer were to visit the location and pay for identical services. Utilizing an online sales strategy through daily-deal offerings may potentially equate to long-term customers, integration of multiple channels, and allow the company to reach a market that was previously inaccessible or unaware of the firms’ presence.

2.12 Purchase Behavior

2.12.1 Expectation-confirmation theory.

Expectation-Confirmation Theory (ECT) was created by Oliver (1980) and originally centered on consumer expectations and the probability of them coming to fruition (Oliver, 1980). ECT was also concerned with overall satisfaction post- purchase or post-occurrence, and whether expectations of the consumer were met

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(Oliver, 1980). Over the past two decades, ECT has become a main theory of study when measuring consumer behavior, more specifically satisfaction and post-purchase feelings (Bhattacherjee, 2001). Research pertaining to ECT has also become predominant in the study of Internet related consumer behavior, and the willingness of individuals to purchase goods and services online as well as the overall customer experience (Cheung, Chan, & Limayem, 2009).

Expectations in ECT are broken down into three types: “the ‘will expectation’, the ‘should expectation’ and the ‘ideal expectation’” (Liao, Chen, & Yen, 2007, p.

2807). The will expectation is concerned with pre-purchase behavior, and aims to predict consumer attitudes prior to buying and their expected emotions after the sale

(Liao, et al., 2007). When examining the should expectation, researchers aim to measure what the customer believes will occur during future transactions with the firm

(Liao, et al., 2007). Finally, the ideal expectation is intended to measure if the product or service will balance with post-purchase emotions and form the idyllic buying experience (Liao, et al., 2007).

Previous research related to ECT established that consumer satisfaction relating to a purchase, whether in a traditional store or online, will influence repeat buying (Thong, Hong, & Tam, 2006). Through measurement of pre and post purchase expectations and satisfaction levels, any discrepancies between initial beliefs allows researchers and marketers to gain keen insight into the factors which may encourage or dissuade future business (Thong, et al., 2006). Prior studies have identified that perceived usefulness of a product is the greatest influencer of adoption, and that

Texas Tech University, James Brian Aday, May 2013 increased user expectations generally result in amplified satisfaction levels (Lee,

2010).

This study is concerned with consumer behavior related to online purchase behavior as well as consumer satisfaction and its influence on revisit intention.

Employing measurements aimed at identifying post-purchase revisit intention will aid in furthering current ECT research by evaluating behavior on two levels, online and in-person. Research will establish overall experience related to ECT based on the online purchase and the redemption in addition to revisit likelihood. Findings will provide researchers and industry professionals alike with keen insight into the benefits or disadvantages of utilizing a flash-sale site on customer satisfaction.

2.12.2 Online purchase behavior.

In the online shopping environment, consumers gather greater confidence for making online transactions, and formulate purchase decisions based on a picture and description rather than physically observing the product they are purchasing (Sheth &

Parvatiyar, 1995). As consumers continue to make online purchases, especially multiple transactions with the same company, the buyer experiences decreased risk, and is more likely to continue purchasing from the firm (Park & Kim, 2003; Ravald &

Gronroos, 1996). Bakos, (1991, 1997) argues that the reduced effort necessary to search for multiple products, and the available information provided to consumers in the online environment entices them to become Internet purchasers. Research by Park and Kim (2003) examined online purchase behavior, and determined customers are highly influenced to purchase based on the information provided about the product. A

Texas Tech University, James Brian Aday, May 2013 recent review of the available discounts on Groupon and LivingSocial identified businesses are allotted 300 word statements regarding their products. These statements, as evidenced in the literature, potentially play a vital role in a consumer’s decision when purchasing a daily-deal.

2.13 Product and Brand Driven Consumers

2.13.1 Brand loyalty.

Brand loyalty is a construct which aims to identify the key elements in consumer purchase decision making through examination of attitudes and behavioral intentions of the purchaser (Kim, Morris, & Swait, 2008). Extant studies have established that measuring brand loyalty consists of biased purchases of products or services from one brand when there are equally interchangeable products available from other sets of brands (Jacoby & Chestnut, 1978; Kim, Morris, & Swait, 2008;

Odin, Odin, & Balette-Florence, 2001). Loyalty to a particular brand is generated primarily from the perspective of consumers and their commitment to the particular firm (Knox & Walker, 2001). However, previous studies have determined brands must operate in a manner which delivers consistently reliable products which meet or exceed customer satisfaction and establish brand credibility (Erdem, Swait, &

Louviere, 2002). When companies are viewed as credible by consumers, and previous purchases have proven to exceed expectations, purchasers build exclusive preference for products from the brand and are highly unlikely to transact with other firms in the industry (Dick & Basu, 1994).

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Further research has indicated that marketing mediums and elements of the marketing mix (price, place, product, and promotion) are influential antecedents in establishing brand loyalty, and might serve as the primary step in establishing loyalty to firms which consumers are unfamiliar with (Hong-Youl, John, Janda, & Muthaly,

2011; Morothy & Zhao, 2000; Olsen, 2002; Yoo, Donthu, & Lee, 2000). Thus, recipients of flash-sale email messages might encounter a featured business for the first time through this medium, and this advertisement and subsequent coupon being offered will determine the likelihood of purchase by the consumer. Well established brands with a loyal customer following might not benefit from utilizing a daily-deal offering as they already have an established customer base. Those consumers who are loyal to a particular firm may prove to be unwilling to purchase services via email solicitations from competitors of the desired brand, resulting in wasted advertising resources for the firm offering the coupon.

2.13.2 Brand preference.

Buyers are influenced by brands which they have previous experience with and prior interactions, if positive, can establish purchasers who develop a preference for a particular firm’s products or services (Chang & Liu, 2009). The image a brand conveys, based on the consumer’s perspective, relies upon the established equity of the brand (Chang & Liu, 2009; Chaudhuri, 1995; Faircloth, Capella, & Alford, 2001;

Keller, 1993). However, recent literature has identified that consumers who have developed a brand preference but not loyalty towards the firm are willing to switch brands to experience new and innovative products (Lam, Ahearne, Hu, &

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Schillweaert, 2010), or if the relationship between the firm and the consumer has relied solely upon previous purchase experiences and a personal relationship between the two parties is weak or non-existent (Lam, et al., 2010; Bhattacharya & Sen, 2003).

Furthermore, when brand preference is present, dynamic pricing, which includes offering lower prices or reduced financing rates, can serve as motivation for consumers to switch from their preferred brand if it is perceived higher personal benefits will occur through changing (Chen & Pearcy, 2010). Utilizing vouchers which possess face value and exhibit significant savings as a form of marketing can influence buyers to switch from preferred brands based on the positive perceptions offered by the discount (Bonnici, Campbell, Fredenberger, & Hunnicutt, 1997;

Krishna & Shoemaker, 1992). Groupon and LivingSocial offerings maintain face value after purchase as the original value can be redeemed at the business after the promotional discount has expired. For example, a voucher which costs $15 and is good for $30 worth of a particular service must be used within a specified time-frame; however, once the expiration date for savings has passed, the purchased discount can still be redeemed for its $15 face value at the business. Furthermore, the perceived savings associated with the daily-deal offerings, and the maintained face value may convince consumers who have a preferred brand but are not necessarily loyal to switching firms.

2.14 Substitute Products

Consumers can be influenced to exhibit brand preference or loyalty for products which maintain few substitutes (Lattin & McAlister, 1985). However, in

Texas Tech University, James Brian Aday, May 2013 mature markets, where the products and services offered are not unique and little product differentiation exists, consumers may switch between similar brands based on price (Liu & Yang, 2009). Services and products which maintain little risk might be perceived as easier to substitute as minimal effort is required of the buyer to begin using the substituted product (Shukla, 2009). The hospitality and service industries are typically highly saturated and consist of multiple substitute products available on the open market (Agarwal, Erramilli, & Dev, 2003). Research has determined the price of products and the perceived fairness of the price can influence customer preference

(Ranaweera & Neely, 2003). However, substitute products which demonstrate a perceived higher customer value may lead to product switching (Meng & Elliott,

2008). Based on this research and the service offerings provided through daily-deal websites, consumers may elect to switch between service providers based on the discount offered and perceived value of the substitute product to the purchaser.

2.15 Purchase Frequency

Purchase frequency is designated as the number of times a consumer makes a purchase from a particular business (Leenheer & Bijmolt, 2008). Purchase frequency is considered a major predictor of future sales and typically consumers who exhibit higher purchase frequencies spend more per purchase than non-frequent customers

Texas Tech University, James Brian Aday, May 2013 conducted by Fagerstrom and Ghinea (2011) identified that out of 252 participants, on average, respondents purchased three or more items online within the past 6 months.

To better understand the potential impacts on businesses which employ a flash-sale strategy, customer purchase frequency will be assessed to determine if long-term customer relationships are established between the user and the business, as well as between the purchaser and Groupon or LivingSocial.

2.16 Business Location

Businesses and academic researchers consistently emphasize the key to success for any firm is location, location, location (Grewal, Levy, & Kumar, 2009). Henderson

(2009) contends the physical location of a business is critical in the service industry.

Research has identified the location of a business can detract from sales in the retail environment, as isolated properties which are relatively distant from housing and are difficult to access are viewed as less desirable by consumers (Ozsoy, 2010). Studies have further revealed two key variables which are assessed by consumers when deciding to visit a business, relative proximity to the purchaser’s home and the proximity of the business to other stores which they might want to visit (Fox, Postrel, and McLaughlin, 2007). Extant literature demonstrated location is not only important to customers, but also plays a key role in the overall business model as it influences prices, advertising requirements, and product offerings (Grewal, Levy, & Kumar,

2009). Examining location as a constraint consumers encounter when deciding whether or not to purchase a flash-sale may provide keen insight to businesses, and the areas where they choose to offer their daily-deal coupons. If results demonstrate a

Texas Tech University, James Brian Aday, May 2013 majority of consumers are influenced by location, the finding may indicate that businesses need to focus their advertisements on areas immediately surrounding their property.

2.17 Qualitative Content Analysis

Researchers identify content analysis as a widely adaptable method for examining qualitative data (Cavanagh, 1997). The primary objectives of content analysis vary across disciplines, yet most are directed towards establishing impressions of a particular field (Weber, 1990), and focus on either theoretical or functional interests of the researcher towards the problem being addressed (Rosengren,

1981). Employing content analysis requires the researcher to focus on language, visual characteristics, and content applicable to the research goals (Tesch, 1990). While the presented text is the primary focus, researchers should focus on dividing this text into categories with sufficient levels of representation which accurately address the message being conveyed (Hsieh & Shannon, 2005). For example, breaking the content of a website down into measurable categories based on similarities. There are three primary types of content analysis utilized across disciplines in modern research (Hsieh

& Shannon, 2005). Conventional content analysis is a type which involves studying a phenomenon within a particular setting (Kondracki & Wellman, 2002). Directed analysis is dependent upon a theoretical base, is typically more structured, and identifies key concepts and their theoretical applications (Potter & Levine-

Donnerstein, 1999). The third approach to content analysis is summative, and focuses on the recurrence of specific words or themes which are present in a particular domain

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(Morgan, 1993). The content analysis of this study was summative in nature, as the data was examined for recurring themes or businesses.

2.18 Gaps in the Literature

Currently, academically based research related to daily-deal sites is virtually non-existent, specifically from the hospitality and service marketing standpoint. While a plethora of extant studies have focused on purchase motivation, purchase frequency, marketing, consumer behavior, and peer group influence in both the conventional and online shopping environments, a gap exists between these studies and the current research. Flash-sale sites are a relatively new concept in the e-commerce realm and maintain a unique attribute where the vouchers must be purchased from an online website and, in most instances, be redeemed at the physical business location.

Recent literature suggests individuals are influenced by their peers in the online and physical shopping environments (Chen-Yu & Seock, 2002; Lee, 2009;

Stern & Stafford, 2006). However, data does not exist in this area relating to flash- sales and the influence of peers on both purchase and redemption. This study aims to address whether reference groups influence which daily-deals are purchased and if these factions affect redemption. Social Exchange Theory (SET) has been heavily researched in numerous domains including marketing (Morgan & Hunt, 1994), customer loyalty (Sierra & McQuitty, 2005), social status (Begley, Khatri, & Tsang,

2010), the service industry (Anderson & Narus, 1990; Briggs & Girsaffe, 2009) and tourism (Ap, 1992). However, this research is unique in that it combines elements from each area and measures them in the online and physical interaction aspects.

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Prior research related to the Theory of Planned Behavior (TPB) has concentrated on consumer intention to perform in a certain manner (Ajzen, 1991), and employing the idea that individuals segmented by key characteristics will make similar purchases (Bearden & Etzel, 1982; Makgosa & Mohube, 2007; Valck, Bruggen, &

Wierenga, 2009). However, these studies focused on specific segments and products where studying flash-sale sites measures a large and highly diverse demographic sample presented with identical advertisements. For example, Groupon and

LivingSocial offerings are distributed to an entire population, such as a city or multiple counties, and not specifically targeted to a certain demographic subset of the geographic area. Thus, findings may establish groups represented by similar characteristics may not be a primary indication of purchase frequency.

Expectation-Confirmation Theory (ECT) ascertains individuals will exhibit varying levels of product or service expectations pre-purchase and satisfaction post- purchase (Thong, et al., 2006). Findings from the current research maintain the potential to influence existing ECT, as flash-sale users may identify as having low expectations of daily-deal offerings simply based on unfamiliarity or the price of the offering; thus, a key aspect relies upon their experience and satisfaction upon redemption. Online purchase motivations have received significant attention in extant literature with heavy emphasis placed on ease of use (Chen, Clifford, & Wells, 2002), available information (Montoya-Weiss, Voss, & Grewal, 2003), and variety (Menon &

Kahn, 1995). There exists a gap in the area of flash-sales as they are heavily dependent upon savings as motivation. However, the amount of information provided in the

Texas Tech University, James Brian Aday, May 2013 daily-deal description or the variety of offerings may not prove to be key purchase influencers.

Research of daily-deal offerings maintains the potential to establish key links between previously established constructs and this emerging online medium.

Traditional research related Theory of Planned Behavior, Expectation-Confirmation

Theory, group purchase influence, online purchase motivations, and Social-Exchange

Theory, may be significantly influenced by current findings. Bridging the gaps of previous studies through this research maintains the potential to provide key academic and statistically significant findings to this field and managers in the industry.

Focusing on such a varied array of research areas will allow for unique insight into the habits and expectations of the daily-deal consumer, and establish key motivations which must be present at purchase. Additionally, establishing which physical interactions or requirements daily-deal users require will provide keen insight for the firms relying on flash-sales to recognize their ultimate goal of building a long-term customer base.

2.19 Grounded Theory

When performing qualitative research, the goal is to obtain a description which is trustworthy and accurate (Glaser & Holton, 2007). Literature has identified grounded theory as an appropriate technique of developing theory which “is grounded in data systematically gathered and analyzed” (Strauss & Corbin, 1994, p. 273). The systematic collection of data and subsequent analysis of the phenomenon allows for theory creation (Strauss & Corbin, 1990). Grounded theory has been employed as a

Texas Tech University, James Brian Aday, May 2013 means of generating a “general methodology” (Strauss & Corbin, 1994, p. 275) which allows for gaining an understanding through qualitative data measurement (Strauss &

Corbin, 1994).

Grounded theory research has traditionally involved the analysis of text data, and involved dissecting the data into smaller units based on themes or identifying similarities in the text (CPRJournal, 2012). Extant literature has demonstrated data is examined line-by-line as key phrases or concepts are identified for analysis (Chesler,

1987). Charmaz (2006) indicated line-by-line coding of rich text sources of data provided assistance in establishing relationships, and aids in preventing the interjection of the researcher’s feelings and attitudes into the data. Upon completion of line-by-line coding, focused coding is applied, as specific themes or topics identified in the line-by-line analysis are combined to formulate overarching themes representative of larger segments of the data (Charmaz, 2006). Due to the exploratory nature of this research, and the fact there were no established research upon which to build, grounded theory was utilized as a means for developing a theoretical framework based upon themes which emerged during data collection and analysis.

2.20 Summary of the Review of Literature.

This research is focused on flash-sale sites, their offerings, consumer motivations to purchase, and business rational for usage of this medium as a way to generate long-term customers. The literature presented was designed to encapsulate these topics while providing an overview of the content areas as well as the basis for commencement of the following study. As identified by the current gaps in the

Texas Tech University, James Brian Aday, May 2013 literature, this current study was developed to accurately measure, through a three pronged approach, an overall understanding of the types of daily-deals offered in addition to their perceived savings, consumer motivations for purchase as well as revisit intentions and provocations based on their experience, and develop a deeper understanding of managerial justification or avoidance related to the adoption of flash- sales as a means of creating a new customer base.

A key component of measuring consumer behavior related directly to structural equation modeling (SEM) and a hypothesized model was developed for this study. The proposed Consumer Daily-Deal Interaction model is presented in Figure 3.

The model was developed from numerous consumer behavior theories as well as extant literature about branding, consumer motivations, purchase behavior, purchase frequency, as well as incentive based advertising. The first order constructs, Product

Driven, Price Driven, Group Driven, and Purchase Motivations were derived from the literature and intended to measure items or variables which may influence consumer purchase decision. As described in the preceding literature review, consumers may be motivated to purchase based simply on product offerings (Product Driven) and the associated costs, attributes, and perceived personal benefits associated with the item

(Chang & Wildt, 1994). Consumer influence to purchase based primarily on price or perceived savings (Price Driven) is indicative of shoppers labeled as “price shoppers”

(Kerns & Hair, 2008, p. 23) and those which are “discount shoppers” (Carpenter &

Moore, 2009, p. 68). Each of these consumer types rely on savings as they make purchase decisions. Group persuasion (Group Driven) maintains a central component

Texas Tech University, James Brian Aday, May 2013 of scholarly research related to consumer motivation; thus, a factor was created geared towards measuring the influence of social groups (Borges, Chebat, & Babin, 2010;

Mangleburg, Doney, & Bristol, 2004). Previous studies exhibited numerous characteristics related to consumer purchase motivation outside those described in the three previously discussed factors. Based on these prior studies another first order factor, Purchase Motivations, was established to measure the constructs of social obligation (Goodwin, Smith, & Spiggle, 1990), internal decision making characteristics (Wells & Prensky, 1996), external influences (Armstrong & Kotler,

200), and behavioral processes (Wu, 2003).

The second-order, or mediating factors, were based in extant literature, and the central components of each were ascertained as serving more of a supportive role in the decision making process. Business Location, Purchase Behavior, and Purchase

Frequency are the identified mediators for this study. Research on business location in the hospitality and service fields is abundant, with similar findings throughout the majority of studies, relative proximity to the consumers’ home and the location of the firm in relation to other stores which may be of interest (Fox, Postrel, & McLaughling,

2007; Ozsoy, 2010). Due to the unique nature of flash-sales, purchasing online with physical redemption at a store, ECT and TPB components (Purchase Behavior) were included in this study. The justification for utilizing these theories relies upon the importance of the initial experience of the consumer and their post purchase behavior

(Bhattacherjee, 2001), and the fact that daily-deal offerings are broadcast to a large

Texas Tech University, James Brian Aday, May 2013

Product Driven Revisit usiness Intentions Location

Price Driven

Purchase ehavior

Group Driven Discount ased Revisit Purchase Frequency

Purchase Motivations

Figure 3. Proposed hypothesized Consumer Daily-Deal Interaction model.

Texas Tech University, James Brian Aday, May 2013 demographically diverse population which, based on purchase patterns, may identify groups of likeminded individuals consuming these vouchers (Ajzen, 1991; Quintal,

Lee, & Soutar, 2010). Measuring how frequently individuals purchase (Purchase

Frequency) was measured in an effort to better understand the type of consumer that is most likely to utilize flash-sale offerings (Leenheer & Bijmolt, 2008).

The second-order, or mediating, factors were based in extant literature, and the central components of each were ascertained as serving more of a supportive role in the decision making process. Business Location, Purchase Behavior, and Purchase

Frequency are the identified mediators for this study. Research on business location in the hospitality and service fields is abundant, with similar findings throughout the majority of studies, relative proximity to the consumer’s home and the location of the firm in relation to other stores which may be of interest (Fox, Postrel, & McLaughling,

(Bhattacherjee, 2001), and the fact that daily-deal offerings are broadcast to a large demographically diverse population which, based on purchase patterns, may identify groups of likeminded individuals consuming these vouchers (Ajzen, 1991; Quintal,

Lee, & Soutar, 2010). Measuring how frequently individuals purchase (Purchase

Frequency) was measured in an effort to better understand the type of consumer that is most likely to utilize flash-sale offerings (Leenheer & Bijmolt, 2008).

Texas Tech University, James Brian Aday, May 2013

The dependent factors in the model relied upon Revisit Intentions and Discount

Based Revisit motivations. The primary goal for businesses which employ daily-deals as a customer generation technique is a long-term, profitable customer base. However, as this study is the first of its kind, no data or measurement currently exist which measure the motivations for a consumer to revisit a property after voucher redemption.

Thus, this study aims to identify if the customer experience and interactions, SET, are influential in Revisit Intentions (Anderson & Narus, 1990; Briggs & Girsaffe, 2009).

Furthermore, this model aims to identify if the consumers will only be motivated to revisit the firm if a discount is offered for the future visit as well (Discount Based

Revisit).

Texas Tech University, James Brian Aday, May 2013

CHAPTER 3

METHODOLOGY

This study scrutinized the relatively new marketing medium of daily-deals, particularly those offered through Groupon and LivingSocial, to ascertain a more complete picture regarding the types and frequency of offerings, consumer behavior related to purchasing such vouchers, and business operator attitudes towards flash- sales. This research was a three-part study, encompassing a content analysis, consumer survey, and qualitative interviews, which were aimed at measuring various aspects of flash-sale operations. The specific details regarding the various data collection and analysis measures are discussed in this section. The goals associated with each study were:

Step 1 – Content Analysis

 Objective 1: Identify the frequency of hospitality and service offerings

belonging to the six Standard Industrial Classification (SIC) Code categories.

 Objective 2: Measure the average daily savings for LivingSocial and Groupon

in an effort to ascertain which site provides the greatest savings for consumers.

 Objective 3: Establish which site offers the highest number of daily-deal

offerings on a daily basis.

 Objective 4: Assess which factors influence increased offers for specific

geographic locations.

Texas Tech University, James Brian Aday, May 2013

Step 2 – Consumer Study

 Objective 1: Determine the role price plays in a consumer’s likelihood of

purchasing a flash-sale.

 Objective 2: Estimate the probability of repeat visitation from high frequency

discount users compared to those who use such discounts sparingly.

 Objective 3: Establish the influence of customer experience on return intention

after utilizing a daily-deal voucher.

 Objective 4: Articulate the factors which are most likely to influence and

predict flash-sale purchases.

Step 3 – Business Interviews

 Objective 1: Ascertain what motivates businesses to employ flash-sales

methods.

 Objective 2: Establish the level of satisfaction felt by businesses after daily

deal coupons have been offered.

 Objective 3: Examine factors influencing companies to avoid utilizing one-off

sales.

 Objective 4: Evaluate whether the perceived benefits for businesses outweigh

potential risks when choosing to offer flash-sale vouchers.

3.1 Note on Human Subjects

This study was conducted in three phases with distinct, separate samples. Two components, the consumer survey and business interviews, required preapproval from the Texas Tech University Protection of Human Subjects Committee (IRB) to ensure

Texas Tech University, James Brian Aday, May 2013 respondents were protected. IRB approval for the study was received on May 24, 2012 and allowed for data collection from up to 1,000 consumer respondents, and 20 businesses. A copy of the human subjects’ approval is located in Appendix A.

3.2 Content Analysis

This study aimed to identify the frequency of offerings, average advertised savings, and the percentage of hospitality and service related offers available on

Groupon and LivingSocial. The areas of content measured included hotels, transportation offerings, restaurants, and a variety of other service related businesses.

In an effort to ascertain qualitative data which is representative of the daily-deal offerings of the two websites, two separate analyses were conducted.

3.2.1 Nationwide analysis.

At the onset of data collection, Groupon was represented in 169 cities in the

United States. LivingSocial demonstrated a considerably larger presence, serving 268

U.S. cities. In order to achieve the research objectives related to the content analysis, it was necessary to divide the number of cities on each site into a manageable yet representative size. After considerable deliberation, the researcher elected to examine

5% of the cities represented on each website. In total 24 cities, 14 from LivingSocial and 10 from Groupon were selected. To assure validity and reliability, the cities were assigned numerical identifiers based off the alphabetic listing of their state. For example, Birmingham, AL, is the first alphabetic listing for Groupon based on the state of AL and the first letter of B in the city name. Birmingham was then assigned

Texas Tech University, James Brian Aday, May 2013 the number 1 with other cities in Alabama and then municipalities in outside states following sequentially.

Upon completion of number assignment, RAT-STATS was employed to generate random numbers for each list. For the Groupon listing, the lowest value was identified as 1 with the highest integer of 169. RAT-STATS then produced 10 random numbers within this range, and the corresponding cities were selected from the list.

For example, RAT-STATS generated the number 115 which identified Cincinnati,

OH, which was then marked for inclusion in the Groupon analysis. The same process was then carried out for the LivingSocial list.

Upon completion of random number generation, the following cities were identified as those which would be monitored for Groupon: Cincinnati, OH; Las

Vegas, NV; Syracuse, NY; Los Angeles, CA; Gainesville, FL; Seattle, WA;

Richmond, VA; Santa Cruz/Monterey, CA; Greenville, SC; and Green Bay, WI.

The list of LivingSocial cities included: Atlanta, GA; Mercer/Middlesex

Counties, NJ; Piedmont Triad Region, NC; Indianapolis, IN; San Jose, CA; Northern

Kentucky, KY; Fort Myers/Cape Coral, FL; Fresno, CA; Savannah/Hilton Head,

GA/SC; Phoenix, AZ; Weber/Davis Counties, UT; Las Vegas Valley-Wide, NV; El

Paso, TX; and Greater Boston, MA.

Due to the vast reach of the daily-deal websites, the cities identified above are, in numerous instances, represented by both Groupon and LivingSocial. However, cities were only monitored based on which daily-deal site they were selected under.

Texas Tech University, James Brian Aday, May 2013

For example, Atlanta, GA, is represented by both sites but this study only monitored the LivingSocial offerings for this location.

An in-depth content analysis rubric was generated based loosely upon classifications obtained from the U.S. Securities and Exchange Commission’s listing of Standard Industrial Classification (SIC) Code List under the heading of Service

Categories. The seven variables employed in the analysis are closely related to the hospitality and service industries and include: hotels; motion pictures or sporting events; amusement and recreation activities; museums, art galleries, and zoos; restaurants; and transportation. It is important to note that not all service related businesses were chosen from the SIC Code List. Offerings for businesses which are service related under SIC but not specifically listed above were blended into the identified categories or identified as other listings. For example, a number of concerts were offered via the sites. While a specific concert category did not exist, concerts were absorbed into the amusement and recreation activities heading. Live performances of a play at a local theatre were considered art, and included in the museums, art galleries, and zoos category. Furthermore, sporting events was added and included with motion pictures as both were perceived as a spectator commitment lasting approximately the same time. Live stage shows and concerts were included in this category as well. Deals identified as online deal or nationwide deal were not included in the analysis as they were not offered solely to the identified city. The online deals offered on both sites are valid for redemption at online businesses and are presented to every city represented by the sites. Additionally, nationwide deals are not

Texas Tech University, James Brian Aday, May 2013 location specific and were not included in this analysis as they did not aid in the identification and analysis of region specific offerings. An example of a nationwide deal would be a magazine subscription offered at a significantly discounted rate. The consumer would purchase the subscription code and then go to the magazine’s website to redeem the offer.

In addition to the seven categories listed above, details regarding average savings, and total number of listings were also compiled. While the calculation of average savings and total number of listings are described in-depth in the Statistical

Analysis section of the article (Chapter 4), the inclusion of this data was vital to measure typical savings thresholds and the number of offerings available for each geographic region.

The content analysis began in early August, 2012 and ran continuously for 6 weeks. All offerings from the 10 Groupon and 14 LivingSocial cities were evaluated each weekday during the six week data collection period. Weekends were excluded as the sites did not typically offer new deals on these days. Six of the offering categories were measured on a daily basis, except for hotels, which was measured weekly. The reasoning behind this decision was the extended length of time hotel deals are posted on the sites. Counting the same hotel every day for multiple days would skew the data, making it appear as though more hotels were represented when only one property was available. Each day the total savings of all deals within each specific city was also calculated. While some offerings include the percentage off in their tagline, numerous

Texas Tech University, James Brian Aday, May 2013 percentages were calculated by hand. Daily monitoring and analysis concluded in

September, 2012. The content analysis included 30 days of actual data per city.

3.2.2 Statistical analysis.

Statistical analysis relied upon daily counts for each offering type. For example, the total number of offers for each city was calculated on a daily basis. The offerings were divided into the previously described categories with deals not meeting the requirements of one of the 6 hospitality and service related categories being grouped into the other category. Additionally, the average daily savings per site were calculated and percentage of savings derived. For some larger cities with more than 30 offerings daily this process was incredibly intricate, and involved numerous hand calculations. Some of the offerings included the percentage in their tagline where other offers listed the purchase price and the regular price, which required taking 1 – purchase price divided by the actual reported value. For instance, an offering identifying a purchase price of $30 for $70 worth of merchandise was calculated as 1 –

(30/70) = .5714 or a 57.14% discount.

At the conclusion of the 6 weeks of data collection, the daily analyses were compiled into a comprehensive analysis for each city. The daily total counts for each category were then totaled and average number of listings identified. For example, if a city had 60 restaurant offerings over the 30 day period, the 60 offerings were divided by 30 days resulting in an average of 2 deals per day. After each city’s comprehensive analysis was completed, the total or grand averages for each category were compiled into a listing which incorporated all of the cities.

Texas Tech University, James Brian Aday, May 2013

3.3 Consumer Study

The purpose of the consumer study was to identify (1) the role price plays in determining the willingness of a potential customer to purchase a flash-sale, (2) the probability of repeat visitation from high frequency discount users compared to those who use such discounts sparingly, (3) the likelihood of repeat visitation after utilizing a coupon based on the customer experience, and (4) which factors are most likely to influence and predict flash-sale purchases. In an attempt to formulate a methodology to garner data relevant to these objectives, an extensive literature review was first conducted to develop the proposed Consumer Daily-Deal Interaction Model. The hypothesized model was then tested through a pilot study. Pilot testing was rigorous and employed to identify a structural model, through analysis, which would be the best fit for the full scale survey. Based upon findings from the pilot test, alterations were made to the model and corresponding survey. The full study was geared towards measuring the stated objectives, and considered to be a more precise measurement technique. In the following section, the pilot and full-scale studies are discussed.

3.3.1 Model development.

The Consumer Daily-Deal Interaction Model was developed from numerous consumer behavior theories as well as extant literature about branding, consumer motivations, purchase behavior, purchase frequency, and incentive based advertising.

The first-order constructs, Product Driven, Price Driven, Group Driven, and Purchase

Motivations were derived from the literature and intended to measure items or variables which may influence consumer purchase decision. As described in the

Texas Tech University, James Brian Aday, May 2013 preceding literature review, consumers may be motivated to purchase based simply on product offerings (Product Driven) and the associated costs, attributes, and perceived personal benefits associated with the item (Chang & Wildt, 1994). Consumer influence to purchase based primarily on price or perceived savings (Price Driven) is indicative of the shoppers labeled as “price shoppers” (Kerns & Hair, 2008, p. 23) and those which are “discount shoppers” (Carpenter & Moore, 2009, p. 68). Each of these consumer types rely on savings as they make purchase decisions. Group persuasion

(Group Driven) maintains a central component of scholarly research related to consumer motivation; thus, a factor was created which was geared towards measuring the influence of social groups (Borges, Chebat, & Babin, 2010; Mangleburg, Doney,

& Bristol, 2004). Previous studies exhibited numerous characteristics related to consumer purchase motivation outside those described in the three previously discussed factors. Based on these prior studies another first-order factor, Purchase

Motivations, was established to measure the constructs of social obligation (Goodwin,

Smith, & Spiggle, 1990), internal decision making characteristics (Wells & Prensky,

1996), external influences (Armstrong & Kotler, 200), and behavioral processes (Wu,

2003).

The second order, or mediating factors, were based on extant literature, and the central components of each were ascertained as serving more of a supportive role in the decision making process. Business Location, Purchase Behavior, and Purchase

Frequency were the identified mediators for this study. Research on business location in the hospitality and service fields is abundant, with similar findings throughout the

Texas Tech University, James Brian Aday, May 2013 majority of studies, relative proximity to the consumer’s home and the location of the firm in relation to other stores which may be of interest (Fox, Postrel, & McLaughling,

Lee, & Soutar, 2010). Measuring how frequently individuals purchase (Purchase

Frequency) was conducted in an effort to better understand the type of consumer that is most likely to utilize flash-sale offerings (Leenheer & Bijmolt, 2008).

The dependent factors in the model relied upon Revisit Intentions and Discount

Thus, this study aims to identify if the customer experience and interactions, SET, are influential in Revisit Intentions (Anderson & Narus, 1990; Briggs & Girsaffe, 2009).

Furthermore, this model aims to identify if the consumers will only be motivated to revisit the firm if a discount is offered for the future visit as well (Discount Based

Texas Tech University, James Brian Aday, May 2013

Revisit). The hypothesized model, identified as the Consumer Daily-Deal interaction

Model, is exhibited in Figure 4.

3.3.2 Survey design.

Data for this study was collected through utilization of a survey, which was created based on extant scholarly literature related to branding, online purchase frequency, reference groups, purchase motivation, price conscious consumers, product awareness, and related theories derived from or studied in the examined works. The survey instrument relied upon categorical measurements based on a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree) to record respondents’ answers.

Demographic questions were categorical and dichotomous in nature. The original survey instrument contained 77 questions, with five serving as demographic questions.

Questions were derived to address the nine constructs pertaining to consumer behavior and online purchases related to flash-sales. Each of the nine subject areas were represented by seven and nine questions apiece. For example, the product awareness category contained nine questions which evoked information from respondents related to numerous product awareness constructs. Each construct was utilized as a factor in the hypothesized model, as items for each factor were established to measure the predictability of the relationships.

Texas Tech University, James Brian Aday, May 2013

Product Driven Revisit usiness Intentions Location

Price Driven

Purchase ehavior

Group Driven Discount ased Revisit Purchase Frequency

Purchase Motivations

Figure 4. Proposed hypothesized Consumer Daily-Deal Interaction

Texas Tech University, James Brian Aday, May 2013

3.3.3 Pilot test.

3.3.3.1 Data collection.

This study relied upon purposive sampling techniques, specifically convenience sampling, as participants in the study were selected because they were easily accessible (Gelo, Braakmann, & Benetka, 2008), and in a population of interest

(Guarte & Barrios, 2006). The utilization of purposive convenience sampling was selected with the fore-knowledge that findings from adopting this technique are not generally applicable to an entire population. However, this type of sampling technique was determined as appropriate as this research is an exploratory study. As the research aimed to identify the purchase behaviors and motivations of flash-sale consumers, as well as their intention to revisit a property after their initial experience with the daily- deal, recent users of flash-sale purchases were identified as demonstrative of the intended population.

The survey for this study was hosted by Qualtrics Labs, and a link to the study was distributed to undergraduate Hospitality Management students at a large, southwestern university. The sample was identified as appropriate for the pilot study as the researchers were interested in end-users of flash-sale coupons. Current college students, as a generation, have been identified as technologically savvy (Oblinger &

Oblinger, 2005) and rely upon web-based promotions to identify discounts (Seock &

Baily, 2008). After obtaining IRB approval, the survey link was distributed to nine (9) undergraduate hospitality courses. Data collection began in May, 2012 and concluded in August, 2012. The survey contained two filter questions in an effort to ensure the

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Texas Tech University, James Brian Aday, May 2013 sample met the requirement of being a daily-deal user. The first filter question elicited information as to whether the participants had purchased a flash-sale coupon within the previous 12 months. Positive respondents were directed to a second filter question which verified participants were at least 18 years of age. The survey was distributed to approximately 350 students, with 249 responding, generating a response rate of

71.1%.

3.3.3.2 Data screening.

The data were screened for univariate outliers; however, no responses were identified as outlying. The measurement scale for items was a 7-point Likert scale, thus preventing the presence of univariate outliers. The data were tested for reliability and validity, which were measured utilizing Cronbach’s alpha. Reliability testing alpha coefficients ranged from .840 to .953, indicating internally consistent and reliable variables (Santos, 1999). Analysis identified one multivariate outlier which was subsequently deleted. Deleting the multivariate outlier was justified because the values were very extreme and would have skewed the exploratory factor analysis

(EFA). No pairs of items were identified as having > .70 correlation; thus, multicollinearity was not an issue.

3.3.4 Full study.

3.3.4.1 Model and survey revision.

Based upon the findings of the pilot study, which are discussed in the Results section of the article (Chapter 5), the Consumer Daily-Deal Interaction Model was revised, becoming a four factor mediated solution. Five factors from the original

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Texas Tech University, James Brian Aday, May 2013 hypothesized model indicated non-significance in pilot testing; thus, the factors and items which loaded upon them were dropped from the full study. Factor names were changed as numerous items which were employed in the pilot testing were a better statistical fit for different factors. Thus, based on overall themes amongst the items which loaded on specific factors, the names were modified to clarify their representation. The model for the full study, identified as the Modified Consumer

Daily-Deal Interaction Model, is exhibited in Figure 5.

The initial survey instrument employed during the pilot study phase of data collection relied upon 72 items which measured the nine previously mentioned factors.

For the full study, 35 items were utilized representing four factors: positive attitude, purchase frequency, product awareness, and purchase motivations.

3.3.4.2 Data collection.

The full study relied upon convenience sampling techniques (Cohen & Arieli,

2011) specifically snowball, commonly referred to as chain-referral, sampling

(Handcock & Gile, 2011; Heckathorn, 2011). Snowball sampling has been recognized as an effective means of reaching target populations by employing members of the stated population to enlist similar members (Biernacki & Waldorf, 1981; Erickson,

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Texas Tech University, James Brian Aday, May 2013 regionally comprehensive sample. This was geared towards ascertaining the purchase behaviors and motivations of daily-deal users. Additionally, revisit intention upon redemption of the initial flash-sale voucher based on their experience with the firm was a central component. Thus, individuals who self-identified as having purchased a flash-sale voucher within the previous 12 months were identified as demonstrative of the intended population.

The survey for this study was hosted by Qualtrics Labs, and a link to the study was distributed via social media to 350 Facebook friends of the researcher. These virtual cohorts were encouraged to take the survey in addition to posting the link on their Facebook walls, and to share via any other means. As a means of increasing participation rates, a 16 GB Wi-Fi enabled Apple iPad was offered as incentive and one random participant was selected as a winner. Data collection commenced on

September 15, 2012 and concluded on November 22, 2012. The sample was identified as appropriate for the study as the researchers were interested in end-users of flash- sale coupons. Facebook users were determined to be a good target for this sample as research had demonstrated individuals rely upon such mediums when purchasing online (Park & Cho, 2012). Identical to the pilot test, the survey contained two filter questions to ensure participants had purchased a flash-sale coupon within the previous

12 months, and were at least 18 years old. While the total number of shares, views, and posts of the link is impossible to calculate, the survey link received 635 responses.

However, filter questions reduced the number of eligible participants to 305 respondents, indicating a valid response rate of 48.03%.

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Positive Attitude

Product Purchase Awareness Motivations

Purchase Frequency

Figure 5. Modified Consumer Daily-Deal Interaction model.

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Texas Tech University, James Brian Aday, May 2013

3.3.4.3 Data screening.

The data were tested for reliability and validity, which was measured utilizing

Cronbach’s alpha. Reliability testing alpha coefficients ranged from .801 to .967, indicating internally consistent and reliable variables (Santos, 1999). Analysis identified 25 multivariate outliers which were subsequently deleted as indicated by

Cooks and Mahalanobis distance and box plots. Further examination indicated these cases either selected the same answer for each question in the survey, such as selecting

7 = Strongly Agree on all 33 questions, or they were missing more than 5% of their data. After subsequent analysis, the number of useable surveys was established as

280. The useable cases were established using the same data screening procedures the pilot study employed. Furthermore, none of the valid cases contained outliers or more than 2% of missing data for the Likert-scale questions; thus, they were deemed satisfactory for analysis.

3.4 Business Interviews

The purpose of this study was to identify (1) business motivations for using flash-sales, (2) the level of satisfaction felt by businesses after daily deal coupons have been offered, (3) the factors influencing companies to avoid utilizing one-off sales, and (4) whether the perceived benefits for businesses outweigh potential risks when choosing to offer flash-sale vouchers. Flash-sales are an emergent concept in marketing and this research was geared towards establishing baseline data which measured restaurant operators’ attitudes towards the daily-deal phenomenon. In an effort to ascertain comprehensive qualitative data, five business owners/operators

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Texas Tech University, James Brian Aday, May 2013 which have employed flash-sales, and five which have not utilized the medium were interviewed. As this research was geared towards establishing an understanding of business operator decisions pertaining to flash-sales, an examination of current daily- deal offerings was first conducted.

3.4.1 Firm selection.

As daily-deal sites rely upon businesses offering vouchers to succeed, it was necessary for this study to incorporate a qualitative component, interviews. Utilizing interviews provided in-depth feedback and a rich stream of data for analysis which would have been difficult to achieve through a categorical response type of quantitative survey. This method was employed to measure the attitudes and behaviors of managers and business owners towards the usage and success of utilizing e-channel flash-sales. Thus, interviews were conducted with local businesses.

Since this study was exploratory in nature, the researcher decided to designate one city for qualitative data collection. Several U.S. cities were considered. Close proximity to the researcher’s academic and home location, the availability of Groupon,

LivingSocial and local online daily-deal outlets in those municipalities, and the frequency of flash-sales were evaluated when determining the city in which the interviews should be conducted. Ultimately, one southwestern city was selected.

After the location of the interviews was determined, businesses were considered for inclusion based on the examination of the city’s Convention and Visitors ureau

(CVB) webpage. The CVB categorized businesses by primary function (i.e. restaurants, hotels, transportation, etc.) which enabled the researcher to compile a

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Texas Tech University, James Brian Aday, May 2013 comprehensive list based on the type of establishment. The compiled list revealed 241 restaurants registered with the CVB. Restaurants were selected for analysis based on evidence which demonstrated no hotels in the geographic region offered a flash-sale voucher in the previous 12 months. Furthermore, an examination of current daily-deal offerings identified restaurants as the most highly represented segment of the hospitality industry on flash-sale sites. Each restaurant was assigned a numerical identifier based on their alphabetic listing on the CVB website. For example, the first restaurant began with a number in its name and was labeled as 1.

Once the restaurants were assigned numerical identifiers, a comprehensive review of Groupon, LivingSocial, and local daily deal sites was completed. The purpose of the analysis was to identify restaurants which had employed a flash-sale in the previous 12 months. The review indicated 17 firms met the specified criteria.

These establishments were then compiled into a separate list and given new numerical identifiers, ranging from 1 to 17. The justification for parsing out these firms relied upon the need to interview flash-sale users and non-users. Upon the completion of the two lists, users and non-users, RAT-STATS was employed to select random numbers from each list. For example, the non-user list had been reduced to 224 firms, and

RAT-STATS randomly selected five numbers between 1 and 224. The same process was carried out for the list of businesses with previous daily-deal utilization.

Anonymity was guaranteed to participating businesses to encourage honest and accurate responses. Thus, while the names of the firms are proprietary information,

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Texas Tech University, James Brian Aday, May 2013 they consisted of bars, specialty restaurants, fine dining establishments, a wine bar, and casual service operations.

3.4.2 Instrument development.

A comprehensive review of extant literature pertaining to marketing and firm motivations for employing various mediums provided the foundation for the interview questions. However, as previously stated, the lack of scholarly publications regarding flash-sales required the researcher to establish questions, which were evaluated by numerous academics, and geared towards accurately measuring restaurant operator motivations for employing or avoiding daily-deal offerings.

Two separate interview forms were developed with 11 questions each. The interviews varied in their question types with slight overlap as one was designed to gauge the measured effectiveness of flash-sales from businesses which have offered a service on Groupon, LivingSocial, or through a local provider of daily-deals. The second interview guide was used to question managers of businesses which have not utilized flash sale sites and aided in further developing an understanding of their objections to the use of such mediums. Appendix C exhibits the questions asked to firms which have employed flash-sales while Appendix D demonstrates the inquiries presented to operators which have elected not to employ flash-sales.

The aim of the research was to formulate an understanding of motivations which influence operators in their reasoning of employing or avoiding the usage of daily-deal sites. Based on this goal, questions were derived which inquired the reasoning behind each operators decision regarding flash-sales. For businesses which

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Texas Tech University, James Brian Aday, May 2013 have offered flash-sale vouchers, questions were formulated to address the reason for choosing such a medium, how success was measured, whether the deal was an effective means of advertising, and their overall attitude towards the success or failure of their offer. Firms which have not used a daily-deal site were presented with inquiries related to justification for non-usage, loss of sales and potential new customers, and justification for employing alternative marketing approaches.

3.4.3 Data collection.

The business interviews took place over a two week time period in December,

2012. In total, five businesses which have utilized daily-deals were interviewed as were five which have not employed these types of flash-sales. Firms were contacted via telephone to arrange face-to-face interview times convenient for both the researcher and the businesses representative. Interviews were recorded utilizing a digital voice recorder and transcripts were then typed into a Microsoft Word document and stored for data analysis.

3.4.4 Data analysis.

Data analysis relied upon multiple coding and analytic procedures. Open- coding was employed as each transcript was scrutinized line-by-line in an attempt to classify potential concepts and categories (Chenail, 2012). Meticulous examination of each interview allowed the data to be grouped into categories (DeCuir-Gunby,

Marshall, & McCulloch, 2011).

Axial coding was employed to identify the interaction between each theme and how the overall components of specific areas affect the other sub-topics through the

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Texas Tech University, James Brian Aday, May 2013 use of nodes. For example, common words associated with decisions related to flash- sales, such as demand generation, awareness, and efficient reach were all identified as representing motivations for employment of daily-deals. Axial coding was beneficial as it aided the researcher in establishing criteria used in the decision making process and identifying the similarity in decision making amongst operators.

The third component of data analysis relied upon QSR-NVivo v. 10 (NVivo) software from QSR International. NVivo is designed to manage and arrange multiple data types, which makes this software an extremely beneficial tool for qualitative researchers (Hutchison, Johnston, & Breckon, 2010). NVivo allowed for the creation of nodes, the ability to examine commonly occurring ideas, and aided in identifying frequent word usage. Identified themes were analyzed via tag clouds, tree maps, and cluster analysis to garner a visual image of what the findings represent. Identifying themes through nodes were specified in the program to extract words which were classified by NVivo as exact, including stemmed words, and including synonyms, which provided keen insight into word association amongst themes.

Each of the analysis methods is appropriate for this research as they aid in creating a valid and reliable analysis of the data and are suitable measures of grounded theory. Line-by-line coding requires the researcher to “verify and saturate categories”

(Glaser, 1978, p. 58) and reduces the probability of neglecting an important concept or theme (Walker & Myrick, 2006). Axial coding and line-by-line analysis are founded in grounded theory, and provide enhanced interpretation of the phenomena being studied (Corbin & Strauss, 1990).

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CHAPTER 4

IDENTIFCIATION AND CLASSIFICATION OF DAILY-DEAL OFFERINGS

ON GROUPON AND LIVINGSOCIAL THROUGH CONTENT ANALYSIS

4.1 Introduction

The vast reach of Groupon and LivingSocial and the wide variety of products and businesses featured through these listings creates a challenge for firms choosing to offer a flash-sale, forcing them to question which e-channel will garner the highest revenue and lead to the generation of a new long-term customer base. There is a wide assortment of daily-deal or flash-sale offerings available to consumers, ranging from local newspapers and television stations to various websites promoting a similar approach. However, the market is heavily dominated by Groupon and LivingSocial.

Regardless of the number of subscribers or geographical reach of the outlet, the premise of each site is remarkably similar; customers purchase a deeply discounted voucher online and, in most scenarios, redeem the voucher at a physical storefront for the purchased value. For example, a restaurant may offer a voucher for a sale price of

$10 with a redeemable value of $20. Thus, the consumer is paying half price.

Groupon and LivingSocial are relatively new mediums which allow for broadcast messaging to consumers in specified geographic markets. Groupon was launched in November, 2008, and the popularity of the site grew so rapidly, the site was valued at over $1 billion less than 18 months later (O’Dell, 2011). LivingSocial,

Groupon’s primary competitor, launched their first daily-deal offering in July of 2009 and ascertained a net worth of over $600 million within its first two years of operation

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(LivingSocial, 2012). Since its founding, Groupon has offered deals for over 35,000 companies in 565 cities (Hickins, 2011). In addition to the cities where Groupon maintains an online presence, they also serve 51 million email subscribers while attaining revenues of $760 million in 2010 (Hickins, 2011). LivingSocial currently serves 400 cities, with revenues of $500 million in 2010 (Rusli, 2011). These sites are both represented in numerous markets, creating a difficult decision for potential firms.

Ultimately any business condsidering a daily-deal must evaluate their goals and select a provider.

4.1.1 Study purpose.

In an effort to garner a comprehensive review of the Groupon and LivingSocial daily-deal phenomenon, this paper utilized a dual content analysis approach. The analysis focused on examining offerings in 5% of the total markets served by Groupon and LivingSocial. Specifically, the daily-deal offerings of 10 Groupon cities and 14 cities serviced by LivingSocial were monitored. The areas of content examined included hotels, transportation offerings, restaurants, and a variety of other service related businesses. While this list is by no means comprehensive, it aids in identifying the service categories for evaluation. This analysis aimed to determine frequency of offerings, average advertised savings, representation of hospitality and service offerings, and average listings relative to market size. The information obtained through such measurement aided in building a more complete picture of the depth and scope of flash-sales relative to varying markets and established the frequency of hospitality and service related firms presented on the sites. In order to obtain measured

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Texas Tech University, James Brian Aday, May 2013 categories, hospitality and service specific categories were identified and measured based upon the U.S. Securities and Exchange Commission’s listing of Standard

Industrial Classification (SIC) Codes. Employing the SIC codes allowed the researcher to efficiently categorize flash-sale offerings within these content areas.

While the analysis of each site was intended to be as comprehensive as possible, there were four main objectives of this research:

 Objective 1: Identify the frequency of hospitality and service offerings

belonging to the six Standard Industrial Classification (SIC) Code categories.

 Objective 2: Measure the average daily savings for LivingSocial and Groupon

in an effort to ascertain which site provides the greatest savings for consumers.

 Objective 3: Establish which site offers the highest number of daily-deal

offerings on a daily basis.

 Objective 4: Assess which factors influence increased offers for specific

geographic locations.

4.2 Review of Literature

4.2.1 Qualitative content analysis.

1981). Employing content analysis requires the researcher to focus on language, visual

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Texas Tech University, James Brian Aday, May 2013 characteristics, and content applicable to the research goals (Tesch, 1990). While the presented text is the primary focus, researchers should focus on dividing this text into categories with sufficient levels of representation which accurately address the message being conveyed (Hsieh & Shannon, 2005). There are three primary types of content analysis utilized across disciplines in modern research (Hsieh & Shannon,

2005). Conventional content analysis is a type which involves studying a phenomenon within a particular setting (Kondracki & Wellman, 2002). Directed analysis is dependent upon a theoretical base, is typically more structured, and identifies key concepts and their theoretical applications (Potter & Levine-Donnerstein, 1999). The third approach to content analysis is summative, and focuses on the recurrence of specific words or themes which are present in a particular domain (Morgan, 1993).

The content analysis of this study was summative in nature, as the data was examined for recurring themes or businesses.

4.3 Methodology

This study aimed to identify the frequency of offerings, average advertised savings, and the percentage of hospitality and service related offers available on

Groupon and LivingSocial. The areas of content measured included hotels, transportation offerings, restaurants, and a variety of other service related businesses.

In an effort to ascertain qualitative data which is representative of the daily-deal offerings of the two websites, two separate analyses were conducted.

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4.3.1 Nationwide analysis.

At the onset of data collection, Groupon was represented in 169 cities in the

United States. LivingSocial demonstrated a considerably larger presence, serving 268

For example, RAT-STATS generated the number 115 which identified Cincinnati,

OH, which was then marked for inclusion in the Groupon analysis. The same process was then carried out for the LivingSocial list.

Upon completion of random number generation, the following cities were identified as those which would be monitored for Groupon: Cincinnati, OH; Las

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Vegas, NV; Syracuse, NY; Los Angeles, CA; Gainesville, FL; Seattle, WA;

Richmond, VA; Santa Cruz/Monterey, CA; Greenville, SC; and Green Bay, WI.

The list of LivingSocial cities included: Atlanta, GA; Mercer/Middlesex

Counties, NJ; Piedmont Triad Region, NC; Indianapolis, IN; San Jose, CA; Northern

Kentucky, KY; Fort Myers/Cape Coral, FL; Fresno, CA; Savannah/Hilton Head,

GA/SC; Phoenix, AZ; Weber/Davis Counties, UT; Las Vegas Valley-Wide, NV; El

Paso, TX; and Greater Boston, MA.

For example, Atlanta, GA, is represented by both sites but this study only monitored the LivingSocial offerings for this location.

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Texas Tech University, James Brian Aday, May 2013 were offered via the sites. While a specific concert category did not exist, concerts were absorbed into the amusement and recreation activities heading. Live performances of a play at a local theatre were considered art, and included in the museums, art galleries, and zoos category. Furthermore, sporting events was added and included with motion pictures as both were perceived as a spectator commitment lasting approximately the same time. Additionally, live stage shows and concerts were included in this category. Deals identified as online deal or nationwide deal were not included in the analysis as they were not offered solely to the identified city. The online deals offered on both sites are valid for redemption at online businesses and are offered to every city represented by the sites. Additionally, nationwide deals are not location specific and were not included in this analysis as they did not aid in the identification and analysis of region specific offerings. An example of a nationwide deal would be a magazine subscription offered at a significantly discounted rate. The consumer would purchase the subscription code and then go to the magazine’s website to redeem the offer.

Analysis section of the methodology, the inclusion of this data was vital to measure typical savings thresholds and the number of offerings available for each geographic region.

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The content analysis began in early August, 2012 and ran continuously for 6 weeks. Each of the 10 Groupon and 14 LivingSocial offerings were evaluated each weekday during the six week data collection period. Weekends were excluded as the sites did not typically offer new deals during these days. Six of the offering categories were measured on a daily basis, except for hotels, which was measured weekly. The reasoning behind this decision was the extended length of time hotel deals are posted on the sites, and counting the same hotel every day for multiple days would skew the data, making it appear as though more hotels were represented when only one property was available. Each day the total savings of all deals within each specific city was also calculated. While some offerings include the percentage off in their tagline, numerous percentages were calculated by hand. Daily monitoring and analysis concluded in

September, 2012. The content analysis included 30 days of actual data per city.

4.3.2 Statistical analysis.

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Texas Tech University, James Brian Aday, May 2013 purchase price divided by the actual reported value. For instance, an offering identifying a purchase price of $30 for $70 worth of merchandise was calculated as 1 –

(30/70) = .5714 or a 57.14% discount.

At the conclusion of the 6 weeks of data collection, the daily analyses were compiled into a comprehensive analysis for each city. The daily total counts for each category were then totaled and average number of listings identified. For example, if a city had 60 restaurant offerings over the 30 day period, the 60 offerings were divided by 30 days resulting in an average of 2 restaurant offers per day. After each city’s comprehensive analysis was completed, the total or grand averages for each category were compiled into a listing which incorporated all of the cities.

4.4 Results

The dual content analysis approach generated keen insight into the daily offerings of daily-deal websites and the types of coupons which are most prevalent.

More specifically, analysis provided specific data which pertains directly to the hospitality and service sectors. In this section, the results from each analysis will be presented and a clearer picture will emerge of the dynamics behind flash-sale vouchers. This section will first examine the nationwide analysis of Groupon cities, followed by the analysis of LivingSocial cities.

4.4.1 Nationwide analysis – Groupon.

The analysis of Groupon comprised the daily monitoring of 10 cities nationwide which provide flash-sale offerings from the site. These cities included

Cincinnati, OH; Las Vegas, NV; Syracuse, NY; Los Angeles, CA; Gainesville, FL;

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Seattle, WA; Richmond, VA; Santa Cruz/Monterey, CA; Greenville, SC; and Green

Bay, WI. The identified cities varied in population with Greenville, SC, representing the lowest number of inhabitants with 60,379 residents, and Los Angeles, CA, maintaining the highest population with over 3.8 million residents (U.S. Census

Bureau, 2012). However, the average population for all cities was approximately

605,681. In an attempt to further understand the distribution of Groupon offers, the population of each county, and in some instances counties, for each of the identified cities was also calculated. For example, Groupon lists Santa Cruz/Monterey, CA, as one listing; however, each city is located in a separate county which are geographically adjacent. Analysis identified the highest populous as Los Angeles

County, CA, with 9,889,056 residents, and the smallest county as Alachua, FL, which includes Gainesville, with a population of 249,365. The listing of each city’s population and their respective county populations is exhibited in Table 2. On average, each geographic county possessed 1,860,483. This calculation was determined as being skewed, considering Los Angeles County possessed approximately 8 million more residents than the closest competitor. Upon eliminating Los Angeles County from the analysis due to it being an outlier, a more representative average of 856,911 residents per county was derived.

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Table 2 Population Breakdown of CitiesAanalyzed by Groupon Offerings City County City State Population Population County/Counties Cincinnati OH 296,223 800,362 Hamilton Green Bay WI 105,809 251,412 Brown Las Vegas NV 589,317 1,969,975 Clark Syracuse NY 145,151 466,960 Onondaga Gainesville FL 125,326 249,365 Alachua Seattle WA 620,778 1,969,722 King Richmond VA 205,533 * * Santa CA 88,588 686,196 Santa Cruz & Cruz/Monterey Monterey Greenville SC 60,379 461,299 Greenville Los Angeles CA 3,819,702 9,889,056 Los Angeles Notes: * indicates a city which is independent and does not reside in a county. Information retrieved from U.S. Census Bureau, 2012.

Analysis of daily savings indicated Las Vegas, NV maintained the highest average with a typical discount of 60.72%. Cincinnati, OH, maintained the lowest perceived discounts with an average of 58.18%. The mean savings for all cities included in the Groupon analysis was identified as 59.56%. Table 3 provides the total average daily savings per city. It is imperative to note that not all offerings possessed such significant savings, as typically the savings were closer to the range of between

50% and 55%; however, each city possessed offerings which were much more substantial, thus inflating the average savings. For example, some daily-deals offered different purchase options where the increase in quantity purchased further exemplified the savings. Additionally, some vouchers were of minimal cost with high savings, such as $50 for $500 worth of microdermabrasion skin care, a savings of

90%.

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Table 3 Total Average Daily Savings by City City State Avg. Daily Savings Cincinnati OH 58.18% Gainesville FL 58.49% Green Bay WI 60.15% Greenville SC 59.60% Las Vegas NV 60.72% Los Angeles CA 59.02% Richmond VA 60.32% Santa Cruz/Monterey CA 59.51% Seattle WA 59.87% Syracuse NY 59.78%

As previously mentioned, hotels was measured on a weekly average, as the same property would often appear for several days and including that as a new offering each day would skew results. The data exhibited that on average, each city received 0.72 hotel offers per week. Los Angeles demonstrated the highest frequency with 1.67 per week and Syracuse, Santa Cruz/Monterey, and Greenville received the fewest hotel discounts with an average of 0.17 per week. The hotels segment was problematic to assess, as over 95% of the listings were for hotels located in geographic proximity to the city, but were not within the city or county limits. For example, a hotel property in Mount Shasta, CA, was offered as a deal for Santa Cruz/Monterey.

While this property is in the geographic region of these cities, it is approximately 350 miles away; thus, the property does not directly affect these cities. Additionally, the specific pages for each city possessed a Groupon Getaways section, which included a vast array of hotel properties around the continental U.S. and in international locations. These properties were not included in this analysis as the Groupon

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Getaways are not city specific and would be generalized as more of a regional or national offering.

On average, motion pictures or sporting events, which also included live stage shows and concerts, were the least represented of all categories with a total average comprising only 0.06 offerings between the 10 cities on a daily basis. Las Vegas maintained the highest average with 0.27 offerings daily, and five cities, Cincinnati,

Gainesville, Green Bay, Greenville, and Syracuse offered no motion picture or sporting event deals during the data collection. Las Vegas advertised eight total offers over the 30 day monitoring period, and was heavily influenced by a repeated advertisement for burlesque show tickets. It is important to identify that sporting events, in this context, refer to spectator opportunities while active sporting pursuits were categorized under the amusement and recreation activities category. Table 4 exhibits the total findings related to average weekly hotels, and average daily motion pictures or sporting events offers.

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Table 4 Average Weekly Hotel and Daily Motion Picture/Sporting Event Category Offers City State Avg. Hotels1 Avg. Motion Pictures/Sporting Events2 Cincinnati OH 1.50 0.00 Gainesville FL 0.50 0.00 Green Bay WI 0.33 0.00 Greenville SC 0.17 0.00 Las Vegas NV 0.33 0.27 Los Angeles CA 1.67 0.13 Richmond VA 1.17 0.07 Santa Cruz/Monterey CA 0.17 0.03 Seattle WA 1.17 0.13 Syracuse NY 0.17 0.00 Notes: 1Indicates offerings were measured on a weekly average; 2 Represents a daily average.

Amusement and recreation activities were consistently distributed via Groupon with a total daily average of 2.06 offerings per city. Green Bay exhibited the fewest deals in this category with an average daily representation of 0.50 while Los Angeles was the leader with 5.93 offers daily. Seattle was a close second to Los Angeles, with an average of 5.33 offerings. The types of offerings in the amusement and recreation activities section were highly varied, ranging from a Halloween themed 5K in Las

Vegas or horseback trail rides outside of Los Angeles, to flying lessons in Seattle and a golf outing in Cincinnati. While it is recognized some of these flash-sales may be a better perceived fit for other categories, it was established the overall theme of amusement and recreation were prevalent in each offer; thus, they were included in the amusement and recreation activities category.

Further analysis of flash-sale offerings identified deals which were best categorized into the grouping of museums, art galleries, and zoos. These topic areas

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Texas Tech University, James Brian Aday, May 2013 were incredibly broad and included art shows, as well as small musical productions at local community theaters. Los Angeles was once again the best represented city, with an average of 0.90 offerings daily, and Syracuse and Gainesville were the least represented with no average listings. Several cities, Green Bay, Greenville, Richmond, and Santa Cruz/Monterey possessed 0.07 offerings per day on average. The total daily median for all cities was 0.19. Las Vegas maintained the second highest average number of listings with 0.23 per day.

Offerings which were categorized for restaurants demonstrated a total daily average between the cities of 2.06 offerings. Again, Los Angeles demonstrated the highest propensity of deals with 5.40, followed by Cincinnati at 2.43, Las Vegas

(2.30), and Richmond (2.10). The lowest representation for a city was Gainesville, which maintained an average of 0.77 restaurant listings per day. The majority of restaurant offers (95%) were identified as fast casual or quick service, and the remaining 5% included deserts, carry out, or fine dining. A vast majority of the offerings were for local establishments, with rare deals for national chains.

The final category employed for this analysis related specifically to transportation offerings via flash-sales. The majority of offers in this category were directly related to limousine or car service for a specified time period, and were only represented in the higher population areas. There were a minimal number of exceptions in this category which qualified as transportation. For example, exotic car rentals were available in Las Vegas. Las Vegas and Seattle represented the highest number of offers with a daily average of 0.33, closely followed by Los Angeles at 0.20

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Texas Tech University, James Brian Aday, May 2013 offers daily. Green Bay and Syracuse were identified as offering an average of 0.07 flash-sales in this category daily. The total daily mean average for all cities was 0.11.

Table 5 provides city specific findings related to amusement and recreation activities, museums, art galleries, and zoos, restaurants, and transportation.

Table 5 Average Daily Listings per Category by City Museums, Amusement Art Recreation Galleries, City State Activities Zoos Restaurants Transportation Cincinnati OH 1.43 0.13 2.43 0.00 Gainesville FL 0.57 0.00 0.77 0.00 Green Bay WI 0.50 0.07 0.90 0.07 Greenville SC 0.57 0.07 1.00 0.10 Las Vegas NV 1.73 0.23 2.30 0.33 Los Angeles CA 5.93 0.90 5.40 0.20 Richmond VA 2.77 0.07 2.10 0.00 Santa CA 0.67 0.07 1.40 0.00 Cruz/Monterey Seattle WA 5.33 0.37 3.37 0.33 Syracuse NY 1.07 0.00 0.90 0.07

While the categories identified by the U.S. Securities and Exchange

Commission’s SIC Code List under the heading of Service Categories were selected based on their representativeness of the hospitality and service industries, there were an abundance of offers which did not fit into these categories. For such listings, they were still utilized in the analysis and labeled as belonging to the other category. These types of offerings were wide ranging and included weight loss programs in Las Vegas, air duct cleaning in Seattle, Zumba classes in Cincinnati, and speed dating and Botox treatments in Los Angeles. As demonstrated by these brief examples, the task of

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Texas Tech University, James Brian Aday, May 2013 categorizing each offer was realistically unachievable and would not serve the intended purpose of measuring hospitality and service representation in daily-deals.

Daily monitoring and summative analysis measured the total number of offers per category, hotels; motion pictures or sporting events; amusement and recreation activities; museums, art galleries, and zoos; restaurants; transportation; and other.

Data highlighted that all cities contained more offers in the other category than the other six categories combined. Las Vegas attained 154 total offers over six weeks from the six SIC categories and 255 daily-deals which were grouped as other. Seattle exhibited 286 SIC category offers and 398 which were categorized as other. On average, each city possessed 42.99% more offers that related to the other category than the six SIC categories.

Analysis identified city and county population as primary determinants of the number of total offers, those in the six SIC code categories and the offers categorized as other, for each location. Los Angeles represented the highest population and also maintained the greatest number of total offerings with 833. Seattle and Las Vegas, including their counties, were similar in population, approaching 2 million, and possessed 684 and 409 total offers, respectively. Gainesville was the lowest in regards to population and also exhibited the fewest number of total offers with 115 over six weeks. The number of total offerings was influenced by the number of daily listings per city. While some offers ran for multiple days, larger metropolitan areas demonstrated the highest average number of daily-deal listings. Cincinnati with a regional population of over 800,000 residents maintained an average daily listing of

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13.43 where Green Bay, with a population of slightly over 251,000 attained a mean of

3.90 flash-sale offerings. Table 6 exhibits the total number of SIC category listings, offers identified as other, total daily-deal offers, and average number of flash-sale listings per city on a daily basis.

Table 6 Total Listings per Category and Average Daily Listings by City Total SIC Total Total Avg. Number of City State Offers1 Other2 Offers3 Daily-deals4 Cincinnati OH 120 283 403 13.43 Gainesville FL 40 75 115 3.83 Green Bay WI 46 71 117 3.90 Greenville SC 52 72 124 4.13 Las Vegas NV 154 255 409 13.63 Los Angeles CA 377 456 833 27.77 Richmond VA 150 251 401 13.37 Santa Cruz/Monterey CA 65 84 149 4.97 Seattle WA 286 398 684 22.80 Syracuse NY 61 88 149 4.97 Notes: 1Total number of offers based on the SIC categories of hotels; motion pictures or sporting events; amusement and recreation activities; museums, art galleries, and zoos; restaurants; and transportation. 2Total number of offers which did not fall into the SIC categories. 3The total number of offers over a six week period. 4Average number of daily-deal offerings per day.

4.4.2 Nationwide analysis – LivingSocial.

LivingSocial maintained a presence in 268 U.S. cities at the time of data collection. Analysis involved the monitoring of 14 cities and their daily flash-sale offerings on the LivingSocial site. Cities used for examination included: Atlanta, GA;

El Paso, TX; Fort Myer, FL; Greater Boston, MA; Indianapolis, IN; Las Vegas Valley

Wide, NV; Mercer/Middlesex Counties, NJ; Northern Kentucky, KY; Phoenix, AZ;

Piedmont Triad Region, NC; San Jose, CA; Savannah/Hilton Head, GA/SC; and

Weber/Davis Counties, UT. The geographic make-up of the randomly selected cities

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Texas Tech University, James Brian Aday, May 2013 varied significantly from the Groupon listings in terms of size and how they were comprised. For example, LivingSocial offers deals to areas such as Greater Boston, which incorporates seven counties in two states, Massachusetts and New Hampshire.

Furthermore, Weber/Davis Counties are non-city specific and rely upon geo-proximity targeting of residents living within these counties. Another example is the inclusion of areas such as Northern Kentucky and the Piedmont Triad Region, and the numerous counties which form these areas.

Total population was calculated for each of the LivingSocial cities/regions in an effort to ascertain whether city size influences the number of flash-sales offered. As stated previously, numerous listings are region, not city, specific. Thus, when achievable the total population for the city was attained in addition to county figures, and in other cases a regional tally of counties and their respective populations were formulated. The region comprising Greater Boston demonstrated the highest average population (4,590,842), with Phoenix and its associated county exhibiting a population of 3,880,244 inhabitants. Overall, the total population, including counties, was relatively similar in terms of population. The Savannah/Hilton Head region demonstrated the lowest populous with 304,175 and was closely followed by the six counties which comprise Northern Kentucky, and their 421,209 total residents. Table

7 exhibits the population for each city and/or counties. The median population for all

14 cities was 1,486,745; however, when the two outliers of Greater Boston and

Phoenix were eliminated from the calculation, the average was 943,034, which was a more accurate representation of the 12 remaining regions.

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Table 7 Population Analysis Based on City and Geographic Region City County City State Population Population County/Counties Atlanta GA 432,427 1,649,492 Fulton & DeKalb Mercer/Middlesex NJ **** 1,181,280 Mercer & Middlesex Counties Piedmont Triad NC **** 1,155,286 Guilford, Forsyth, Region Davidson, & Randolph Indianapolis IN 827,609 911,296 Marion San Jose CA 967,487 1,809,378 Santa Clara Northern Kentucky KY **** 421,209 Boone, Kenton, Campbell, Gallatin, Grant, & Pendleton Fort Myers/Cape FL 220988 631,330 Lee Coral Fresno CA 501,362 942,904 Fresno Savannah/Hilton GA/SC 309,219 304,175 Chatham, Beaufort Head Phoenix AZ 1,469,471 3,880,244 Maricopa Weber/Davis UT **** 546,231 Weber & Davis Counties Las Vegas - Valley NV 589,317 1,969,975 Clark Wide El Paso TX 665,568 820,790 El Paso Greater Boston MA ***** 4,590,842 Norfolk, Plymouth, Suffolk, Middlesex, Essex, Rockingham, and Strafford Notes:**** Indicates no specific city was measured as the listing was a regionally comprehensive location.

Similar to the Groupon analysis, LivingSocial cities were examined according to the number of hotels on a weekly basis, average potential savings of all offers, total number of offers on a daily basis, and the types of deals presented which could fit into the six SIC categories. As a whole, savings amongst the 14 cities averaged 59.63% daily. El Paso and the Savannah/Hilton Head regions exhibited the highest daily

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Texas Tech University, James Brian Aday, May 2013 savings average at 61.82% while Atlanta demonstrated the lowest with 58.32%.

Again, nationwide and online deals were not taken into account in this study as they would skew the data for specific regions. It is important to note that some offers greatly influenced the perceived savings and generated results which may exhibit inflated savings. For example, an offer for paintball in Boston was available for purchase at $25 with a stated retail value of $360, a savings of 93.05%. However, the majority of offers were categorized as saving between 48% and 55%. Additionally, some discounts increased with the quantity purchased, which resulted in the advertised savings varying between 50% and 70% for the offer. Table 8 provides the total average daily savings per city.

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Table 8 Total Average Daily Savings by City Avg. Daily City State Savings Atlanta GA 58.32% El Paso TX 61.82% Fort Myers/Cape Coral FL 58.84% Fresno CA 59.88% Greater Boston MA 59.97% Indianapolis IN 58.38% Las Vegas Valley-Wide NV 58.96% Mercer/Middlesex NJ 59.37% Counties Northern Kentucky KY 59.44% Phoenix AZ 58.73% Piedmont Triad Region NC 60.21% San Jose CA 58.60% Savannah/Hilton Head GA 61.82% Weber/Davis Counties UT 60.51%

As previously stated, the frequency of hotels offered was computed on a weekly basis. San Jose maintained the largest average with 4.50 hotels offered each week. The regions of Atlanta and Las Vegas Valley-Wide possessed the second highest amount of average weekly hotel listings with 3.00. The lowest number of offerings was in Weber/Davis Counties with 0.83 offers weekly.

LivingSocial provided hotel listings in a unique manner, offering properties in their daily-deal section and also through their LivingSocial Escapes, a section located on the bottom of each region’s page. The Escapes were not included in the analysis as they were for properties throughout the U.S. and some foreign destinations, and did not directly relate to the specific region. Similar to the Groupon findings, hotel offers

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Texas Tech University, James Brian Aday, May 2013 were not specifically for properties in the listed cities but were located in relative geographic proximity.

The total daily average listings for the motion pictures or sporting events category, which also included concerts and live stage shows was not a significantly represented area with a mean of 0.12 listings amongst the 14 cities examined. Table 9 exhibits the total findings related to weekly hotel frequency and the daily motion pictures or sporting events for each geographic location. The regions of El Paso, Fort

Myers/Cape Coral, Fresno, Indianapolis, Northern Kentucky, Savannah/Hilton Head, and Weber/Davis Counties maintained 0.00 daily listings on average.

Mercer/Middlesex Counties possessed the highest frequency with 0.50 offers daily and were closely followed by San Jose with 0.37. Mercer/Middlesex maintained a higher frequency of listings for this category based on a Buckcherry and Puddle of Mudd concert which repeatedly appeared where a majority of the postings for San Jose were related to an international film festival.

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Table 9 Average Weekly Hotel and Daily Motion Picture/Sporting Event Category Offers City State Avg. Hotels1 Avg. Motion Pictures/Sporting Events2 Atlanta GA 3.00 0.27 El Paso TX 1.00 0.00 Fort Myers/Cape Coral FL 2.00 0.00 Fresno CA 1.50 0.00 Greater Boston MA 2.83 0.30 Indianapolis IN 1.83 0.00 Las Vegas Valley-Wide NV 3.00 0.00 Mercer/Middlesex Counties NJ 3.17 0.50 Northern Kentucky KY 1.33 0.00 Phoenix AZ 2.33 0.07 Piedmont Triad Region NC 1.83 0.13 San Jose CA 4.50 0.37 Savannah/Hilton Head GA 1.00 0.00 Weber/Davis Counties UT 0.83 0.00 Notes: 1Indicates offerings were measured on a weekly average; 2Represents daily average. A key category pertaining to hospitality and service related to amusement and recreation activities. The usage of the terms amusement and recreation maintain a wide variety of applicability, and some of the listings included in this category may potentially fit into other categories as well. However, the overall premise of amusement and recreation was prevalent in these daily-deals which qualified them for inclusion in amusement and recreation activities. These types of offerings were the second highest generated form of flash-sale amongst the 14 cities, with a grand daily mean of 3.07 offers. Once again, San Jose was the leader of this type of flash-sale and was also an outlier exhibiting 11.60 on a daily average. The Greater Boston area maintained the second highest propensity with 5.23, followed by Phoenix and Fort

Myers/Cape Coral with 4.90 listings daily each. El Paso was the least represented location, boasting only 0.57 as a daily average. The offers solicited in the amusement

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Texas Tech University, James Brian Aday, May 2013 and recreation activities segment was highly diverse. A full-day tour of Hoover Dam was offered in Las Vegas Valley-Wide, a winery crush party was available in San

Jose, Fort Myers/Cape Coral offered a create your own dock workshop, and Greater

Boston featured a fall foliage sunset boat cruise.

Another component of LivingSocial offerings related directly to museums, art galleries, and zoos. The museum and art gallery titles are wide ranging and allowed for the inclusion of local theater productions and art shows. Listings in this category were not as prevalent, and accounted for a total daily average of only 0.21 amongst the cities. Regions such as Fresno, El Paso, and Savannah/Hilton Head did not exhibit any offerings, where San Jose was represented with a daily average of 0.63. Examples of listings in this category were the option to purchase performing arts classes in Atlanta and zoo admission in Greater Boston.

Restaurants represented the highest daily average listing of the six SIC categories in the identified regions, with a daily average of 3.65. San Jose was the leader in this category with 14.57 offers on average. However, the Greater Boston and

Mercer/Middlesex Counties regions were well represented with 6.47 and 5.20 daily offerings on average, respectively. El Paso maintained the lowest number of offerings daily with 1.12. Numerous other locations, including Fresno, Indianapolis, Las Vegas-

Valley Wide, and Savannah/Hilton Head also possessed daily averages below 2. The vast majority of restaurant offerings were quick service, accounting for approximately

96% and fine dining and carry out representing the remaining 4%. Nationwide chains

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Texas Tech University, James Brian Aday, May 2013 were present in the offers; however, the overwhelming majority was for local establishments.

The analysis of LivingSocial cities related to the six SIC categories concluded with measuring transportation offerings. This category focused on pre-paid time usage for cars and limousines and was not well represented, with a total daily average between the cities of 0.25 listings. San Jose was the dominant location for transportation averaging 1.00 daily. However, this figure is slightly skewed, as a majority of the offerings occurred concurrently over a very short time span. A unique limousine offer distributed to San Jose was a six hour wine country tour. El Paso,

Northern Kentucky, Savannah/Hilton Head, and Weber/Davis Counties did not possess any transportation offerings during the six week monitoring period. Table 10 provides city specific findings related to hotels, motion pictures or sporting events, amusement and recreation activities, museums, art galleries, and zoos, restaurants, and transportation.

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Table 10 Average Daily Listings per Category by City Amusement Recreation Museums, Art City State Activities Galleries, Zoos Restaurants Transportation Atlanta GA 2.37 0.07 3.07 0.63 El Paso TX 0.57 0.00 0.40 0.00 Fort Myers/Cape Coral FL 4.90 0.30 2.77 0.33 Fresno CA 1.60 0.00 1.67 0.00 Greater Boston MA 5.23 0.43 6.47 0.27 Indianapolis IN 1.13 0.00 1.73 0.20 Las Vegas Valley-Wide NV 1.57 0.13 1.40 0.27 Mercer/Middlesex Counties NJ 3.87 0.63 5.20 0.30 Northern Kentucky KY 0.97 0.13 2.13 0.00 Phoenix AZ 4.90 0.27 4.00 0.30 Piedmont Triad Region NC 2.43 0.27 3.90 0.27 San Jose CA 11.60 0.63 14.57 1.00 Savannah/Hilton Head GA 1.17 0.00 1.53 0.00 Weber/Davis Counties UT 0.67 0.07 2.23 0.00

As with the Groupon results, the vast array of products and services offered through LivingSocial required the creation of a catchall category labeled other. For example, tattoo credits sold in Las Vegas Valley-Wide, two hours of home organizing in San Jose, Phoenix termite treatment offers, and an offer for picture framing in

Mercer/Middlesex Counties would each require separate categories.

The major focus of this content analysis was to measure how well the hospitality and service sectors are represented via LivingSocial; however, summative analysis was utilized to measure not only the number of offers per SIC category, but also the number of daily-deals in the other category. Data exhibited a vast difference between the total number of listings in the SIC categories and the other offers. On average, each city contained 52.45% more listings from the other category than the

SIC designations on a daily basis. During the six week analysis, data identified Atlanta 137

Texas Tech University, James Brian Aday, May 2013 received 192 offers which fell into the SIC categories while receiving 526 daily-deals for the other category. Las Vegas Valley-Wide attained 101 SIC related flash-sales while being exposed to 419 other offers.

As was the case in the Groupon analysis, LivingSocial posting frequency was highly influenced by regional population in both the six SIC code and the other categories, with one exception. San Jose maintained the highest total number of offers with 1806 and a regional population of 1.8 million people. Greater Boston was the largest market in terms of populous (4.5 million) and exhibited the second highest number of flash-sales with 835. The smaller market regions, such as El Paso and

Fresno, contained between 820,000 and 942,000 residents and maintained the lowest frequency of offerings with 82 and 248, respectively. Consequently, the number of advertisements per day was heavily influenced by population. The Phoenix region, comprised of 3.8 million people received an average of 26.37 offers daily. Less populated areas, such as Weber/Davis Counties were only exposed to a daily average of 9.80 flash-sale offers. Table 11 provides the total number of SIC category listings, offers identified as other, total daily-deal offers, and average number of flash-sale listings per city on a daily basis.

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Table 11 Total Listings per Category and Average Daily Listings by City Total SIC Total Total Avg. Number of City State Offers1 Other2 Offers3 Daily-deals4 Atlanta GA 192 526 718 23.93 El Paso TX 29 53 82 2.73 Fort Myers/Cape Coral FL 249 306 555 18.50 Fresno CA 98 150 248 7.97 Greater Boston MA 381 454 835 27.83 Indianapolis IN 92 293 385 12.83 Las Vegas Valley-Wide NV 101 419 520 17.33 Mercer/Middlesex NJ 315 426 741 24.70 Counties Northern Kentucky KY 97 177 274 9.13 Phoenix AZ 286 505 791 26.37 Piedmont Triad Region NC 210 278 488 16.27 San Jose CA 845 961 1806 60.20 Savannah/Hilton Head GA 81 92 173 5.77 Weber/Davis Counties UT 89 205 294 9.80 Notes: 1Total number of offers based on the SIC categories of hotels; motion pictures or sporting events; amusement and recreation activities; museums, art galleries, and zoos; restaurants; and transportation. 2Total number of offers which did not fall into the SIC categories. 3The total number of offers over a six week period. 4Average number of daily-deal offerings per day.

4.5 Conclusion and Implications

The six week qualitative content analysis provided keen insight into the types of offerings on flash-sale sites. Six service related categories based on SIC codes were monitored and non-service related offers were grouped into a separate category.

Additionally, offer frequency, total number of offers, average daily listings, and perceived savings were measured. This section will examine each of the six SIC categories, which included hotels, motion pictures or sporting events, amusement and recreation activities, museums, art galleries, and zoos, restaurants, and transportation. The focus will then shift to the other category and finish with an

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Texas Tech University, James Brian Aday, May 2013 overall examination of average listings, savings, total listings, and which factors influenced the frequency of offerings.

Hotels were measured on a weekly average and results indicated hotel listings were not a frequent occurrence on either site. While Groupon offered significantly fewer hotels in the daily-deal section of each city’s website, they did include offers via

Groupon Getaways, their travel partnership with Expedia. LivingSocial also utilized their travel program, LivingSocial Escapes, at the end of their daily-deal section for each location. However, LivingSocial offered more hotels on average in their daily- deal content. As was discussed in the results section, hotel offerings were generally located within a contiguous geographic area.

Generally, hotels listed on each city’s page were within 8 hours driving distance. The properties were best classified as resort type hotels. The lack of local properties, those within the region offering the flash-sale, may be a wise selection, as nearby areas aim to increase their tourism product from closer proximity locales.

Furthermore, the likelihood of residents in one of the monitored cities purchasing a hotel stay in their hometown appears impractical, and would likely be a waste of advertising dollars for the firm. The higher representation of properties on

LivingSocial may be indicative of a growing trend of trying to influence daily-deal purchasers. While consumers can sign up for weekly email offers for both Getaways and Escapes, consumers are required to manually elect to receive these offers. A hesitation on behalf of the message recipient to avoid intrusive emails may lead them to avoid signing up for such offers. Thus, including a limited number in the daily-deal

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Texas Tech University, James Brian Aday, May 2013 section may increase the likelihood of contact with potential guests and theoretically derive new sales.

The categorization of offerings into the motion pictures or sporting events, which included concerts and live stage performances, allowed for unique insight into these types of flash-sale opportunities. Such daily-deal promotions are closely related to the entertainment industry, a component of hospitality, and may potentially influence consumers to purchase. The majority of offerings in this segment were concerts at venues typically within 30 miles of the geographic region receiving the flash-sale offering. A unique aspect in this category was strict redemption dates for the vouchers. For instance, in many instances the concert was going to be held on a particular date and time, offering no flexibility in redemption for purchasers. This approach was not the same in other categories where vouchers were valid for a wide- range of dates, typically lasting several months.

LivingSocial was the dominant provider of offers in this category which may be explained by total population and market size. The LivingSocial regions contained higher population densities than the Groupon cities, and the inclusion of four more

LivingSocial cities than Groupon provided the potential for higher representation.

Stage shows were included in this category but were not present outside of Las Vegas.

Spectator sporting events were minimal, but were offered in Los Angeles. Overall, it seems as though marketers and organizers of concerts, sporting events, motion pictures, and live stage shows do not prefer the daily-deal medium. Justification for not using these avenues may rely upon steady demand streams currently in-place,

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Texas Tech University, James Brian Aday, May 2013 which eliminates the need to sell at a discount. For example, Buckcherry and Puddle of Mudd tickets were offered via LivingSocial in Mercer/Middlesex Counties. These bands are not household names and utilizing this medium may have increased demand.

However, a current touring singer with high demand, such as Justin Beiber, sells out concerts in a matter of minutes at full face value, thus eliminating any need for the flash-sale medium.

Amusement and recreation activities offerings were well represented amongst the geographic locations of both Groupon and LivingSocial. While the total average daily offerings for LivingSocial were slightly higher than Groupon, cities of relative population size offered similar amounts of these listings. The Groupon cities of Los

Angeles and Seattle averaged 5.93 and 5.33 listings per day, respectively. Similar in size, the LivingSocial locations of Greater Boston and Phoenix each offered an average of 4.90 per day. Amusement and recreation are closely related to the hospitality industry as guests coming to visit a location are often looking for local sources of entertainment. Thus offering such listings may increase the likelihood of sales for these businesses.

Amusement and recreation activities encompassed a wide range of pleasure seeking alternatives, from daily tours, sight-seeing flights, 5k runs, Segway tours, and paintballing to more athletic type activities including archery and soccer participation.

Businesses choosing to utilize flash-sales in this category were region or city specific, and allowed for multiple redemption dates. Advertising these types of businesses through a daily-deal offering may be a fruitful endeavor, as these firms utilize a

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Texas Tech University, James Brian Aday, May 2013 medium with broad reach to highlight and potentially sell their business. A medium such as Groupon or LivingSocial may generate awareness to a previously inaccessible target market and new customers. Additionally, the content analysis took place in the fall season, and as winter approaches businesses which rely upon outdoor pursuits may not focus as heavily on advertising, electing to offer more flash-sales in the spring months with summer redemption dates when participation would likely be higher.

A category which appeared lackluster and maintained minimal representation among both cites was museums, art galleries, and zoos. Local theater productions were also included in this category as they are part of the overall art theme. While there were minimal offers, San Jose and Mercer/Middlesex Counties were the leaders for LivingSocial with a daily average of 0.63 each, and Los Angeles exhibited the highest propensity of offers for Groupon with 0.90. For the most part these offers were not as hospitality or service specific. A wine crush party was offered on LivingSocial, but outside of this single offering, most were for local art galleries, zoos, or museums.

Generally, with the exception of the New England Zoo in Greater Boston, the museums and art galleries featured in these flash-sales were not well-known or easily identified properties. Numerous museums and zoos are non-profit organizations typically funded through tax payer dollars which may be the reason they do not employ flash-sales. These properties could be operating at the lowest price possible simply covering expenses, and not be able to afford to sell entrance into the venue at a steep discount.

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Restaurants were well represented on both sites. While the types of restaurants featured were primarily quick service, over 95% for each site, some fine dining and carryout options did exist. Generally, locations with the highest population maintained the highest propensity of restaurant offerings. The vast majority of postings were for local establishments, within the city or county of the offer, provided a discount of at least 50%, and did not allow the purchaser to redeem alcohol with the voucher.

Flash-sales for local restaurants is not a novel concept and, as was potentially the case for amusement and recreation activities providers, may use such sites as a wide reaching means of generating awareness with the intention of immediate and future sales. High failure rates in the restaurant industry may increase the perceived pressure on operators of local establishments to generate awareness, and employing flash-sale coupons could be the best avenue of approach. A common observation was the close relation of restaurant types. For example, the cities with higher frequencies of flash-sales demonstrated an increased number of similar food concepts. Pizza parlors were abundant on both sites and often competitors list deals simultanaeously within the same city. For example, in one week three different restaurants, all with pizza as their primary dish, offered flash-sale vouchers in Mercer/Middlesex Counties.

While this is only one example, it was a recurring theme. This type of increased competition on the daily-deal sites may prove to be damaging to the firms, as price or discount wars may ensue, forcing the operator to function on a slimmer margin with each sale. Hypothetically, a company may want to appeal to more consumers so they will offer their pizza at a 10% higher discount than their competitor, resulting in a new

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Texas Tech University, James Brian Aday, May 2013 competitor entering the flash-sale arena offering a savings of 10% above that. The third operator is realistically offering his product at a 70% cost discount (i.e., $3 for

$10 worth of product), causing them to receive only $1.50 of the $3 selling price, as the other half goes to the flash-sale site. This type of undercutting in sales will likely strain the finances of the lowest cost provider and may increase the propensity for failure.

The sixth and final SIC category was transportation, and this segment was not prevalent in the number of average daily listings. Generally, only large metropolitan areas experienced flash-sales in this category, and the most common voucher was for limousine service. Essentially, consumers would purchase a specific dollar amount worth of credit with a limousine company, at a significant discount, and this credit could be redeemed at a future date. The transportation providers maintained strict policies on redemption and virtually ensured the vouchers could not be redeemed during peak times. Another type of offer was for exotic car rental. While rentals were generally only for a short time period, such as 4 hours, these vouchers allowed the purchaser to get behind the wheel of a dream car. Transportation companies utilizing flash-sales may lose money on flash-sale sites and choose not to use this venue. For instance, a limousine company could sell hundreds of discounted vouchers, and realistically lose money on each sale. Additionally, limousine service is not a high- demand item for leisure pursuits, and the likelihood of repeat customer generation could decrease significantly.

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Offers described in the results section of this paper which did not fit into one of the six SIC categories were generalized as other. While the high variability of offer types has been discussed, this segment was the most highly represented in all cities.

The high volume of offers in this segment, 398 in Seattle, 526 in Atlanta, and 454 in

Greater Boston emphasize the significant amount of offers designated as other. While the focus of this analysis pertains specifically to hospitality and service related content, the high volume of vouchers presented which fall outside of these areas may indicate an unwillingness to employ flash-sales as a major marketing medium in these industries. Furthermore, the vast number of other postings may serve as a deterrent to service providers, as the region or city specific pages are inundated with flash-sales which may appear more lucrative to potential customers, or cause frustration through information overload on visitors to the site. The wide assortment of offers may also overshadow the service related daily-deals, as the perceived discount for other category business may far outweigh the discount offered by the service firm.

The amount of savings was relatively constant on both sites and throughout the municipalities. These findings indicate that both providers, Groupon and

LivingSocial, provide nearly identical advantages to consumers. As discussed in the results, the average calculated daily savings percentages are somewhat skewed with the influence from large discount ads, those offering savings of up to 95% off. The results indicated LivingSocial provides more flash-sale offerings on a daily average than Groupon. However, it is important to note that Groupon primarily serves actual

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Texas Tech University, James Brian Aday, May 2013 cities while LivingSocial has taken on both cities and geographic regions, i.e.

Mercer/Middlesex Counties as a region and San Jose as a city.

Overall, both sites maintained similar representation for all categories when populations were comparable. However, a unique aspect of LivingSocial and their regional approach is that they are able to offer more flash-sales for surrounding areas and specific cities. Covering multiple counties and states with high population regional areas such as Greater Boston provides the site with the ability to focus on numerous smaller municipalities in that region in addition to the city of Boston.

Findings demonstrated the impact of population upon the number of offerings. Higher populous areas maintained increased offerings overall and on a daily average. These results are not surprising, as the increased population likely equates to a higher number of businesses operating in the area which may employ a flash-sale site, and the large number of individuals likely increases the probability of demand for the flash- sale offerings. Finally, LivingSocial offers more regional listings, even on city specific pages. For example, San Jose possessed offerings from San Francisco, CA, and

Oakland, CA, as well as deals from numerous other cities in the region on a consistent basis.

4.5.1 Limitations and future studies.

This research was not without limitations, and several factors limited the results. Geographic location maintained a crucial role in the analysis, and cities in regions with less favorable weather exhibited fewer offerings related to motion pictures or sporting events, amusement and recreation activities, and museums, art

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Texas Tech University, James Brian Aday, May 2013 galleries and zoos. Additionally, cities such as San Jose, Seattle, and Los Angeles provided more options related to amusement and recreation than other metropolitan areas. The setting and outdoor space requirements businesses need to offer services in these categories may not be available in markets such as Greater Boston, where unincorporated land is virtually non-existent. Furthermore, large regional areas allow for more coverage than city specific listings, which could influence the type and frequency of postings. Likely geographic location, culminated with the season in which this analysis occurred, fall of 2012, influenced the types of flash-sales presented. Service providers who rely upon a welcoming climate may not post daily- deal offerings in the fall months with the winter approaching. Rather, they may elect to offer their services in the spring months with summer redemption dates to increase the propensity of potential sales.

Groupon and LivingSocial cover far more cities and regions than those analyzed, as the overall monitoring focused on only 5% of the total available locations. This limited the amount of data collected for analysis and may potentially present findings which are not indicative of all locations served by these firms.

Another limitation pertained to city and region population size. Population was not equal in city or region size which may limit the applicability of results as numerous cities of roughly the same population were not compared, and the data may be skewed for the less populated areas.

While this analysis relied upon the observation and monitoring of the specific websites for each geographic area, the daily emails and featured deals were not

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Texas Tech University, James Brian Aday, May 2013 evaluated. Featured deals may provide statistically significant results which differ from the analysis provided by the websites. Finally, while this research was geared towards measuring hospitality and service related offers, Groupon Getaways and

LivingSocial Escapes were not included in the analysis. These areas are dominated with hotel and tourism offers, and may have provided more relevant data about offerings for these sectors than the generalized content analysis.

Future studies should expand upon the current research. Studies similar in nature to this research do not currently exist. Thus, findings from this analysis should serve as baseline measures in future studies. Furthermore, focusing on areas which maintain similar populations or those within closer geographic location may generate significantly different findings than the current study and provide data which is more applicable. Including more categories than the six SIC groupings used in this study is recommended and may potentially identify previously unnoticed segments with viable hospitality and service applications.

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CHAPTER 5

DAILY-DEAL OR LONG-TERM FAILURE: UTILIZING FLASH-SALES AS

A SERVICE EXPOSURE TECHNIQUE TO GENERATE LONG-TERM

CUSTOMERS

5.1 Introduction

Electronic commerce is continuously revolutionizing traditional business models, allowing consumers to make seamless and instantaneous purchase decisions for a plethora of products, including hospitality and tourism related services (Breukel

& Go, 2009; Watson, erthon, Pitt, & Zinkhan, 2000). United States’ Internet sales witnessed an average annual growth rate over 18% between 2002 and 2009, resulting in business to consumer e-commerce sales reaching $145 billion in 2009 (U.S. Census

Bureau, 2011). Online air travel purchases accounted for over $19.2 billion and travel arrangement services which include travel agencies, car rentals, and a variety of hospitality services, hovered around $7.4 billion (U.S. Census Bureau, 2011). Travel websites such as Travelocity, Expedia, and Priceline have grown significantly in their proportion of sales over the past decade, accounting for 30 percent of domestic travel bookings (Tedeschi, 2007).

A Nielsen Global Online Shopping Survey conducted in late 2007 determined more than 90% of Internet users over 15 years of age in North America identified themselves as having purchased a good or service via the Internet (Nielsen, 2008).

Furthermore, recent studies indicate over 75% of U.S. adults research products and services online and 71% of users acknowledged using the Internet to make a purchase

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(PewResearchCenter, 2011). Previous studies identified these online shoppers seek out price and bargain hunting (Ganesh, Reynolds, Luckett, & Pomirleanu, 2010). The

Internet is considered the fastest growing sales medium and marketers and retailers see the channel as an effective and cost-efficient way to meet consumer demand with decreased labor requirements (Brashear, Kashyap, Musante, & Donthu, 2009).

A relatively new phenomenon in Internet commerce revolves around daily-deal or flash-sale websites such as Groupon, LivingSocial, and Yuupon. These sites offer limited quantity vacation and hotel packages in addition to numerous other discounts for both products and services, directly to consumers (Yu, 2011). Recent reviews of

Groupon and LivingSocial for cities within southwestern states indicated that larger markets, those containing over 1.5 million people, typically have 15 to 20 flash-sales occurring at one time. Midsized markets, populations between 150,000 and 500,000, may contain up to 20 sales but on average maintained between 8 and 14 offerings daily (Groupon, 2012; LivingSocial, 2012). The specific businesses offering services through these sites varied in name; however, overall the available goods were on average 90% service related. Furthermore, some markets possessed higher numbers of food service and restaurant vouchers available (Groupon, 2012; LivingSocial, 2012).

Flash-sale mediums are somewhat young in the online environment, as

Groupon was founded in 2008 (O’Dell, 2011) and LivingSocial the following year

(LivingSocial, 2012). These new marketing channels attained seemingly immediate success, with each firm generating significant net worth within their first two years of operations (LivingSocial, 2012; O’Dell, 2011). While Groupon recognized higher

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Texas Tech University, James Brian Aday, May 2013 earnings within this two year period, establishing itself as a financially significant firm with a value of $1 billion (O’Dell, 2011), LivingSocial followed closely behind reporting a net worth of $600 million (LivingSocial, 2012). Both firms have solidified their market position with LivingSocial providing offers to over 400 geographic areas, and Groupon serving slightly fewer than 600 cities. Additionally, each firm has continued to increase revenue with Groupon reporting over $760 million (Hickins,

2011) in sales and LivingSocial generating $500 million (Rusli, 2011) for 2010.

In addition to their traditional product and service offerings, flash-sale sites have ventured into supplementary markets through new product lines. LivingSocial created LivingSocial Escapes. Dealing solely in hotel and vacation travel, this component of their business sold over 200,000 hotel nights valued at $100 million in less than one year of operations (Rusli, 2011). In June 2011, Groupon announced its firm would begin offering travel deals in partnership with Expedia through Groupon

Getaways. Within the first three days of operation, Groupon Getaways sold 15,000 travel deals (Duryee, 2011). With the increased growth of daily-deal websites and their current offerings of hospitality and tourism related products and services, a need exists for researchers to identify the factors which are most prevalent in consumer purchase decisions in order to ascertain why daily-deals are a growing trend and how businesses can use them most profitably. Understanding customer reasoning will allow hospitality firms an opportunity to capture more of the flash-sale market; thus, increasing sales revenue and ultimately garnering repeat customers.

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5.1.1 Problem statement.

The large financial volume generated through flash-sale sites justifies their position in the market as a viable distribution channel. However, businesses that utilize these sites are often selling at a loss with the expectation of garnering future patronage (Rusli, 2011). Businesses trying to generate awareness or increase visibility may turn to a flash-sale instead of a more traditional advertising medium as a way to get customers in the door with the discount, hoping consumers will become loyal patrons based on their experience. This study is multi-faceted as it aims to gain perspective from consumers and identify:

1. The role price plays in determining the likelihood of purchasing a flash-sale;

2. The probability of repeat visitation from high frequency discount users

compared to those who use such discounts sparingly;

3. The influence of customer experience on return intention after utilizing a daily-

deal voucher; and

4. The factors which are most likely to influence and predict flash-sale purchases.

5.2 Review of Literature

5.2.1 Electronic marketing.

(Howard, 2005; Xiong & Hu, 2010). Viral marketing is synonymous with traditional

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Texas Tech University, James Brian Aday, May 2013 word-of-mouth advertising (Howard, 2005). Most daily-deal advertisements are based loosely upon the premise of viral marketing. Within their email solicitations, companies include options such as links entitled “share with friends,” which enables recipients to enter multiple email addresses of contacts with whom they wish to distribute the deal. The proven success of viral marketing is a considerable motivational force behind companies’ decisions to adopt this type of advertising, since referrals from friends have shown viral ads are “able to bring in customers who are not likely to join though traditional advertising and promotions by the company” (Kumar,

1985).

5.2.1.1 History.

Shaltoni & West, 2010). However, more recent definitions include delivering messages electronically through the Internet and mobile communications channels

(Pride & Ferrell, 2012) including social media, digital outdoor marketing, and Quick

Response (QR) Code (Cartman & Ting, 2009; Pride & Ferrell, 2012). E-marketing was first introduced two decades ago, as the Internet became readily available in everyday households, and technological innovations improved individuals’ abilities to

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Texas Tech University, James Brian Aday, May 2013 utilize and navigate the World Wide Web (Taylor & Strutton, 2010). Around the same time, e-marketing research emerged, prompting studies involving electronic- commerce to appear in major academic journals worldwide (Wang & Chen, 2010).

Furthermore, in the last 15 years, scholarly journals such as the Journal of Interactive

Marketing, International Journal of Electronic Commerce, and Electronic Commerce

Research, have emerged as publications dedicated to the study of online consumer and business activities (Taylor & Strutton, 2010).

E-marketing was first developed in the early 1990s as the expansion of the

Internet to the public sector was followed by the creation of online businesses such as

Amazon.com and Ebay.com (Mowery, & Simcoe, 2002). While these businesses are not the only business-to-consumer websites (B2C) created at the time, they are two of the most widely known, and exemplify the early leaders in electronic commerce (e- commerce). The first form of online advertising was a banner advertisement which appeared on the Hotwired website, an online version of the print magazine Wired, in

1994 (Hollis, 2005). In 1994, search engines such as Yahoo! began to surface and relied upon funding from advertisers in the form of cost-per-click ads (Evans, 2009).

In 1996, Hotwire, an industry leader in e-marketing, partnered with the world- renowned research firm Millward Brown International, and produced one of the first studies measuring the effectiveness and influence of online advertising (Elliot, 1996;

Hollis, 2005). Online marketing expanded each year, with revenue generated from the sale of Internet ads producing over $7 billion in 2001 (Evans, 2008). The “dot-com bust” (Evans, 2008) in the early 2000s reduced the demand for online advertising;

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Texas Tech University, James Brian Aday, May 2013 however, by 2007 online advertising sales rebounded with estimated revenues of over

$21 billion that year (Evans, 2008).

5.2.1.2 Growth.

While scholars disagree on when and where the advent of the Internet occurred, most researchers collectively agree the foundations for the Internet were established during the Cold War Era (1960s) with programs such as the Advanced

Research Projects Agency (ARAPNET) (Rosenzweig, 1998). In the late 1970s and early 1980s ARAPNET began to appear on college campuses as a way to network among researchers (Rosenzweig, 1998). The Internet became more widely available to the public sector, and in 1995 the Internet as it is known in the modern era was established (Frischmann, 2001). The rise in the number of Internet users world-wide since its creation has been incredible. In 1995 there were 16 million Internet users.

Five years later that figure increased nearly twenty-fold, amounting to 304 million in

2000. At the end of 2005 there were 1.1 billion Internet users, and as of March, 2012, nearly one in three global citizens, 2.2 billion people, were connected through online access (InternetWorldStats, 2012).

The growth of the Internet over the past decade has created the modern availability and demand for online retailing and websites which supplement traditional brick and mortar stores (Yani-de-Soriano & Slater, 2009). This shift in shopping methods, the availability to order goods and services online without having to leave the house, has resulted in higher control of consumer choice and empowered consumers like never before (Yani-de-Soriano & Slater, 2009). AdSense is a division

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Texas Tech University, James Brian Aday, May 2013 of Google.com, and a market share leader in the world of online advertising with their marketing messages appearing on numerous websites (Golldfarb & Tucker, 2011).

2011). Recent data suggests there are over 230 million Internet users in the U.S.

(Islam & Snow, 2011; World Bank, 2011), which equates to approximately 174 million people who have been exposed to AdSense e-advertisements..

As the prevalence of online usage has grown, so too has the frequency of social networking. Facebook is one of the largest social networking sites in the world with over 901 million active users in March 2012. The term “active user” describes any individual who logs into the site at least once a month (Associated Press, 2012). With the abundant number of Facebook users, e-marketing efforts offer impressive potential with extraordinary reach. The most recent data identified that in the first three months of 2010, Facebook “flashed more than 176 billion banner ads at users” (Fletcher,

2010). In July 2010 (Associated Press, 2012), there were over 500 million active

Facebook users, meaning consumers were exposed to an average of 117 e-ads each month.

Search engines have further extended the viability of online marketing as findings from the Pew Research Center’s Internet and American Life Project (Pew,

2012) established 73% of all Americans utilized a search engine in February of 2012.

Search engine sites rely upon advertising dollars for financial sustainability, displaying

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Texas Tech University, James Brian Aday, May 2013 ads every time a user conducts a search (Ghose & Yang, 2009). Based on data available from the U.S. Census Bureau (2012), there were over 311 million Americans as of December 31, 2011. The findings of the Pew (2012) study, coupled with U.S.

Census Bureau (2012) data equates to 227 million Americans being exposed to e- marketing through search engine usage.

5.2.1.3 Importance.

Over the last decade, firms have transitioned their marketing objectives and e- marketing has begun to usurp traditional mediums, as the preferred message delivery method for businesses (Varadarajan & Yadav, 2009). In recent years, marketing managers have faced increased difficulty, such as software that allows consumers to record TV shows and skip commercials, in spreading their firms’ marketing message and building customer relationships (Trainor, Rapp, Beitelspacher, & Schillewaert,

2011). Businesses are increasingly relying upon integrating e-marketing into their promotional mix in an effort to garner greater market reach, share, and influence

(Trainor et al., 2011). The rapid influx of the Internet and the number of businesses relying on websites to garner sales has led to Internet marketing serving as a crucial platform for firms in the modern business environment (Liang, Lin, & Chen, 2004).

The use of electronic advertisements has enabled companies to market to previously financially challenging markets, such as foreign destinations (Dou, Nielsen, & Tan,

2002; Chrysostome & Rosson, 2009), and aids in eliminating geographic location as a barrier for customers and firms (Sheth & Sharma, 2005). Online advertising allows firms to reach their target audience round the clock and in any physical location where

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Texas Tech University, James Brian Aday, May 2013 the Internet is available (Ha, 2003). Furthermore, web-based marketing media serves as a major component of firms’ customer relationship management (CRM) programs

(Kimiloglu & Zarali, 2009).

Employing online advertisements encourages unique customization and reach, and may improve the customer-to-firm relationship and perceived quality of the product or service in the minds of consumers (Richards & Jones, 2008). Marketing plans which incorporate web-based components not only encourages differentiation, but also allows firms to potentially achieve a competitive advantage through fiscal savings compared to traditional mediums (Kalaignanam, Kushwaha, & Varadarajan, 2008).

5.2.1.4 Financial.

The web-based advertising market is one of the fastest growing media segments, with global revenues of over $31 billion recorded for 2011 (Business Wire,

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2009, data shows the top 50 online advertising companies spent roughly $276 million on such ads (TNS Media Intelligence, 2009).

(8) Internet and telecom, (9) business & industrial, and (10) occasions and gifts (Abell,

2012). Each of the ten categories contained the top five spending companies for online advertisements in 2011. Essentially, 50 companies from 10 industries were noted and data identified these companies as spending over $30 billion on web-based ads (Abell, 2012). The top three online advertisers were Lowe’s, Amazon.com, and the University of Phoenix, which spent $59.1 million, $55.2 million, and $46.9 million, respectively (Abell, 2012). While these earning results are impressive, e- marketing consists of a much broader spectrum than just simple advertisements on websites.

5.2.2 Types of electronic advertising mediums.

Electronic commerce provides a unique medium for delivering content specific and mass distribution advertisements to a wide assortment of consumers (Huang,

Chen, & Wu, 2009). Electronic advertisements are delivered through a multitude of mediums (Kim & McMillan, 2008), including mobile platforms (Gao, Sultan, &

Rohm, 2010), social media (Chu, 2011), direct e-marketing (Wright & Cawston,

2012), email and listserv (Illum, Ivanavo, & Liang, 2010; Pavlov, Meville, & Plice,

2008), databases (Pels, Coviello, & Brodie, 2000), viral campaigns (van der Lans, van

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5.2.2.1 Mobile platforms.

Mobile based marketing is a relatively new concept in the realm of e- marketing and only became a focus of research in the latter-half of the 2000s

(Okazaki, 2009). Scholarly based research publications on mobile advertising are rare, and studies have primarily focused on content delivery and ease of use (Okazaki,

2009). As smart-phones become the standard norm in the mobile industry, companies have continuously pursued innovative opportunities for delivering messages via web searches, text messages, and games which are downloaded via an application (app) store (Laszlo, 2009). The unique portability of mobile devices allows firms the capability to communicate with consumers on the go, as they do not have to rely on the mobile user to sit down at a computer to receive the marketing message (Laszlo,

2009).

Christodoulides, 2009). Research demonstrates that businesses are recognizing the

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Texas Tech University, James Brian Aday, May 2013 limitless potential as they have the ability to capture the attention of a large target market via mobile media (Friedrich, et al., 2009). Furthermore, the utilization of QR codes have been integrated into traditional forms of media as well, allowing mobile customers to simply scan the QR code, and then they are automatically redirected to a specific mobile advertisement (Okzaki, Li, & Hirose, 2011). Other firms are relying upon location or spot-specific advertising, sending messages to a mobile device via

Bluetooth technology with personalized ads whenever a consumer is in proximity to a particular business (Leek & Christodoulides, 2009). A recent phenomena is the availability of apps available from businesses which are downloaded by consumers and allow instant access to the firm (Xu, Liao, & Li, 2008).

The mobile applications market has continued to expand, as confirmed by the

25 billionth app recently being downloaded from the Apple App Store (Brian, 2012).

2012). In 2011, Google realized $1 billion in net revenue from its Play Store (Farber,

2012).

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5.2.2.2 Social media.

Social media, sites such as Facebook, MySpace, Twitter, YouTube, and blogs gained prominence in the multimedia realm beginning in 2005 (Weinberg & Berger,

Harrington, & Kizer, 2010). Users of social media can also follow (Twitter) or like

(MySpace and Facebook) certain companies and information from these firms will automatically appear in their news feeds (Kietzmann, Hermkens, McCarthy, &

Silvestre, 2011). Additionally, companies’ followers can post status updates tagging the particular firm, which are visible to everyone in their friend list, passing on an

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Texas Tech University, James Brian Aday, May 2013 advertisement or announcing a recent purchase from the firm (Weinberg & Berger,

2011).

There are numerous social media sites and countless blogs on the Internet.

(Goldman, 2012), Twitter has 140 million (Arthur, 2012), and 100 million people are

LinkedIn (Reisinger, 2011). YouTube measures the number of visitors, those simply visiting its site each month, and in 2011 more than 800 million people visited the site monthly (YouTube, 2012). Though one of the older social networks and least utilized sites, MySpace still boasts a respectable 95 million active users (Barnett, 2011). The number of users per site is an important facet of e-marketing, as social media sites are dependent upon advertising revenues to fund the platform, and the greater the number of users, the more attractive the particular site is to advertisers (Clemons, 2009;

The broad reach and market potential of social media sites is highlighted in their annual advertising revenues. In 2011, LinkedIn established revenues upwards of

$522 million (LinkedIn, 2012). Facebook, the largest social media site in terms of active users, realized $3.1 billion in revenue (Raice, 2012) while Twitter achieved

$139 million in 2011 (Bercovici, 2012). YouTube, which is owned by Google, does

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Texas Tech University, James Brian Aday, May 2013 not directly report ad revenues, though analysts estimate the video engine earned approximately $1.3 billion in 2011 (Schonfeld, 2011). The predicted annual revenue growth for these sites from advertising is indicative of the increasing reliance upon e- marketing. For clarity, Table 12 exhibits the top five social networks along with their number of active members, annual revenue, and the year they were established.

Table 12 Breakdown of Social Networks by Members, Revenue, and Year Social Network Active Members* Revenue* Year Established Facebook 900 $3,100 2003 MySpace 95 $109 2003 LinkedIn 100 $522 2003 Twitter 140 $139 2006 YouTube 840** $1,300 2005 * In millions ** Monthly visitors

5.2.2.3 Direct e-marketing.

2012).

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Direct marketing relies upon the firm consistently adjusting their advertising strategy until sales are achieved (Bose & Chen, 2009). For example, a firm may email or display an advertisement for a potential customer which is highly customized, and depending on whether or not the ad generates a sale, the firm may be required to go back and change the direct marketing message to more effectively influence the customer (Bose & Chen, 2009). Research studies have identified consumers who are influenced by direct marketing are likely to pass company information along to their social circles (Phelps, Lewis, Mobilio, Perry, & Raman, 2004). Direct marketing can be incorporated into a firms’ customer relationship management strategy (CRM) and the positive relationship built by the messages between the business and customer may lead to higher profit margins (Morgan, Slotegraaf, & Vorhies, 2009).

According to a study identified in Kozinets, Calck, Wojnicki, and Wilner

2012). Further broken down, direct marketing generates a return on investment of more than $11.70 for each dollar spent on this medium (DMA, 2012). Based on the figures from the DMA (2012), if a firm spent $10,000 on direct marketing the expected return on investment could potentially reach $117,000 solely from the e- advertising campaign.

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5.2.2.4 Email and listserv.

Listserv was created in the late 1980s and is a program that manages email distribution lists for mass e-mailings (Grier & Campbell, 2000). Hotmail.com and

Firm-generated emails to large masses of individuals are extremely inexpensive, and often times the marginal cost of this medium is $0 (Umit Kucuk &

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Texas Tech University, James Brian Aday, May 2013 same conditions in 2012 (Magill, 2011). Market analysis identified email advertising was responsible for $63 billion in sales during 2011, and is predicted to account for

2012).

5.2.2.5 Databases.

2004). Utilizing software such as cookies and click-through monitoring, businesses are able to record every detail of a user’s visit to their site, down to minute details

(Schoenbachler & Gordon, 2002).

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2004). Businesses relying upon database usage to generate sales campaigns must account for the users’ perceived trust with the firm, and reduce any negative concerns for the relationship in order to be mutually beneficial (Schoenbachler & Gordon,

2009).

5.2.2.6 Viral.

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Texas Tech University, James Brian Aday, May 2013 on with online users today and established itself as the central advertising trend of the past decade (Ferguson, 2008).

Viral marketing also includes commercials which appear online. In 2011, several viral marketing campaigns garnered millions of views on the Internet. According to

(Mills, 2012). Recent figures demonstrate companies spent $1.97 billion on online video advertising in the U.S. in 2011 (Nield, 2011).

5.2.2.7 Pop-up ads.

Pop-up advertisements are a designed to capture the user’s attention and prevent them from proceeding to the actual website until the advertisement has been

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Texas Tech University, James Brian Aday, May 2013 closed (Edwards, Li, & Lee, 2002). Numerous academic-based studies have identified consumers’ distaste for pop-ups, with respondents repeatedly protesting the intrusive nature of these ads (McCoy, Everard, & Loiacono, 2009). A study by Smith (2012) found only 4% of Millennials, represented as those individuals born after 1980, prefer pop-up ads as a form of marketing with which they are likely to interact. A similar study in 2010 demonstrated respondents not only find pop-up advertisements invasive, but the appearance of a company in such an ad can lead to negative feelings towards the brand (Madhavaram & Appan, 2010).

5.2.2.8 Banner ads.

The first commercial application of banner ads was on Hotwired.com in 1994

(Chatterjee, 2008). Banner ads appear on various website locations and when clicked, visitors are taken to an external site for the stated firm (Hofacker & Murphy, 2000).

The cost of banner advertising is expensive, as a prime location on major websites can exceed over $500,000 daily (Bloomberg Businessweek, 2006). Spending on banner ads in the U.S. was over $6 billion in 2010 and accounted for approximately

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Advertising Bureau, 2011).

5.2.3 Online retailers.

The Internet has grown at an unprecedented rate over the past 20 years (Khosla

2011 (Briggs, 2012). In 2011, 500 online retail companies accounted for approximately $152 billion, or 77% of total sales (Briggs, 2012), and analysts predict e-tailing sales to surpass $250 billion annually by 2014 (Schonfeld, 2010). While a multitude of profitable web-based businesses exist today, two of the first movers, and most financially successful of the all-online store movement were Amazon.com and eBay (Phan & Vogel, 2010).

5.2.3.1 Amazon.com.

Amazon.com (Amazon) was a first-mover (Mellahi & Johnson, 2000;

Vradarajan, Yadav, & Shankar, 2008) in terms of a firm hosted entirely online, when it established its website in 1995 (Chevalier & Goolsbee, 2003). Amazon’s early

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Texas Tech University, James Brian Aday, May 2013 pioneering of the web, growth, and their decision to abandon the traditional brick-and- mortar approach to book selling, led to the Amazon.com name becoming synonymous with e-commerce (The Economist, 2000). By eliminating the overhead associated with traditional stores and relying solely on a web-based platform, Amazon was able to garner a substantial share of the book selling market (Wunker, 2011). Utilizing justin-time inventory, an expansive distribution network, and the ability of users to purchase multiple products from different vendors and have them packaged together helped to further Amazon’s competitive advantage (Patsuris, 2001).

Although Amazon was the first solely online bookseller to appear in 1995, and posed a significant threat to traditional stores, the company actually lost over $2.8 billion dollars before earning its first profit in 2001 (Hansell, 2002). Despite its rocky start, Amazon has excelled, and recent data exhibits Amazon currently accounts for more than 10% of online sales in North America (Watson, 2011). Amazon has continued to diversify its product offerings and established itself as a major international retailer as well (Galante, 2009). Amazon recorded sales of $48 billion dollars in 2011, an increase of more than $34 billion dollars from 4 years earlier

(Amazon, 2012).

5.2.3.2 eBAY.com.

Based on an auction system, eBay.com (eBay) was established in 1995

(Standifird, 2001), and relied on sellers to list an item, and buyers (bidders) to place incremental bids on the item until the specific auction closed (Gilkeson & Reynolds,

2003). In the late 1990s Internet usage led to privacy concerns and eBay addressed

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Texas Tech University, James Brian Aday, May 2013 bidder and seller apprehension through the implementation of a feedback system

(Dellarocas, 2003). Establishing online trust was critical, as data identified 89% of transactions between buyers and sellers were one-time occurrences. But allowing users to obtain community feedback provided a unique framework in which purchasers felt more comfortable buying from an unknown third-party (Dellarocas, 2003). Research by Gilkenson and Reynolds (2003) confirmed buyers are more likely to purchase an item, and may be willing to pay a higher price based on positive feedback about the seller from previous auction interactions.

In the late 1990s eBay grew at a phenomenal pace, increasing more than fourfold, from 2.2 million users to over 10 million registered users in 1999 alone

(Whitman, 2000). The firm focused on building trust on the site between its bidders and sellers. The success eBay achieved as a result of the feedback system allowed it to capture more than 85% of the online auction market in 2003 (Boyd, 2002; Evans,

2003). In 1995 Amazon.com entered into the online marketing environment, and they remain the single largest competitor to eBay online (Haruvy et al., 2008; Lucking-

Reiley, 2000). In 2008, the global recession, coupled with increased competition from sites such as Quibids.com and ubid.com, negatively impacted eBay, causing the firms’ market share to shrink to only 17% (Maltby, 2008). The latest member numbers from eBay indicate there are 94.4 million active users (Pepitone, 2011) with 2011 revenues in excess of $11.6 billion (eBay, 2012).

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5.2.4 Social exchange theory.

Social Exchange Theory (SET) became a leading topic in the field of psychological research in the late 1960s and early 1970s (Cropanzano & Mitchell,

2005). SET is derived from and maintains both psychological and sociological perspectives (Anderson & Narus, 1984; Emerson, 1976; Luo, 2002), specifically

Several scholars claim SET finds its origins in the early 1920s (Cropanzano &

Mitchell, 2005); however, the overall consensus is that SET was the product of

Homans (1958), Thibaut and Kelley (1959), and Blau (1964) (Anderson & Narus,

1984; Cook & Rice, 2003; Cropanzano & Mitchell, 2005; Emerson, 1976). Each of these studies focused on varying aspects of the behavior of individuals in group settings (Emerson, 1967).

The research of Homans (1958) brought economics and social psychology together through the study of small group exchanges in the workplace. The study provided insight into how people react during interactions, and how each individual

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Texas Tech University, James Brian Aday, May 2013 responds to the actions of the other (Homans, 1958). Homans found that individuals are more likely to exert positive emotion and communication when presented with reinforcing communication (Homans, 1958). For example, if a person exhibits behavior during a conversation which is distasteful to the other party, the person he is speaking to is more likely to respond negatively or cut off communication with the speaker.

Thibaut and Kelley (1959) furthered Homans research and introduced a

Matrix of Possible Interactions and Outcomes (Thibaut & Kelley, 1959). This matrix is represented by a large square containing 64 smaller squares which identify interactions and their possible outcomes (Thibaut & Kelley, 1959). This matrix sought to build a baseline reaction set based on several scenarios, as well as rewards gained and costs incurred. For example, person A and person B are presented with multiple scenarios and the communication between the pair as well as the final reaction is documented each time. If the reaction is favorable and a reward is given, such as acknowledgement of a job well done, the researchers argue this will be the case the majority of the time (Thibuat & Kelley, 1959). Negative outcomes or costs are measured as well. The researchers also included external factors, personal values and needs in their analysis to determine if different external variables influenced the interaction between subjects A and B (Thibaut & Kelley, 1959).

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1964).

Emerson (1967) is credited with amalgamating the varying ideas of these researchers and formulatting the SET model which is still utilized today (Dur &

Roelfsema, 2010). Emerson (1967) argued that while reinforcement and positive interactions are necessary (Homans), the greatest factor which influences group congruency is reciprocity (Blau). His research concluded that reciprocity can easily be compared to a monetary value, as each person provides unique attributes or resources to every exchange and they desire value for their services (Emerson, 1967). Thus, each individual takes into account the garnered rewards or exhibited costs and these variables may have bearing on future interactions. Furthermore, Emerson (1967) contends group interactions should not be measured by each occurrence; rather, they should be calculated long-term as people will act differently from day-to-day (Thibaut

& Kelley) and reciprocity for actions might not occur for an extended period of time

(Emerson, 1967).

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Texas Tech University, James Brian Aday, May 2013 status will be reinforced (Yoon, Gursoy, & Chen, 2001). The initial version of SET was widely adopted by those in both social psychology and business (Konovsky &

2007; Kovnosky & Pugh, 1994; O’Reilly & Chatman, 1986). While the original SET baseline was ideal for manufacturing firms, it was not until the mid to late 1980s when

SET began to significantly include a third interaction type, employee to customer

(Solomon, Suprenant, Czepiel, & Gutman, 1985).

5.2.4.1 Purpose of SET.

In its original form, SET was derived to measure and assess interactions between small clusters of employees, and how these exchanges affect each person individually and the group as a whole (Cropanzano, Prehar, & Chen, 2002). The theory aimed to identify the chief inputs, specific needs, and expected outcome of each interaction in an attempt to create a work-force content with their jobs, with the expectation that a pleasant social environment within the work setting would yield higher output (Konovsky, 2000; Konovsky & Cropanzano, 1991). However, SET was originally orchestrated to measure dyadic relationships of employees and their transactions with coworkers and supervisors. It was postulated by Emerson (1967) that social exchange theory implied an obscure form of contract in which the parties, often referred to as actors in the literature, would essentially enter into a binding agreement through exchanges without the formalities of signing a written document (Konovsky

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1996). The argument made by researchers regarding SET was that the evolving nature of business and employee relationships had created a scenario in which employees were searching more for reciprocity in the work environment, and were not wholly influenced in their attitudes by each exchange (Settoon, Bennett, & Liden, 1996). For example, if employees believed they had a supportive working environment from coworkers, then negative exchanges with supervisors were less likely to lead to decreased work performance and job dissatisfaction (Settoon, Bennett, & Liden,

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Texas Tech University, James Brian Aday, May 2013 emotional responses will vary between individuals (Lawler & They, 1999). With the inclusion of the variable of short-lived sentiment prior to and after exchanges, research on SET identified that reciprocity can be negated based on short-term emotional responses (Lawler & Thye, 1999). For instance, if an employee is subjected to a meaningless task, such as taking out the garbage, the supervisor might thank the employee upon his or her return from the dumpster. While reciprocity is demonstrated, the employee might be incensed from having been assigned trash duty and go on a tirade against the manager. This illustration exemplifies the belief that short-lived emotions are relevant in SET.

5.2.4.2 Justice and social exchange.

During the 1990s, SET was again adapted and became a central component of research relating to procedural, interactive, and distributive justice in the workplace

(Masterson, Lewis, Goldman, & Taylor, 2000). Procedural justice, though definitions vary slightly, is widely accepted as the fairness or perceived fairness of the requirements placed upon employees to garner success (Folger & Konovsky, 1989).

The majority of procedural justice research examines the perceived extent to which employees believe they must go to earn a pay raise or promotion (Skarlicki & Folger,

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Interactive justice is rooted in exchanges the employee encounters and the attitudes or feelings garnered by employees after such interactions (Chen & Tjosvold,

Distributive justice is highly related to employee pay but is less concerned with the procedures required to attain a pay raise or promotion (McFarlin & Sweeney,

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Texas Tech University, James Brian Aday, May 2013 the lower paid workers acting out against the higher paid individual, or making this person feel isolated and not part of the team. Furthermore, the lower paid individuals are more likely to reduce their output and become antagonists to the assembly process.

2002). Utilizing SET as a means to identify and measure perceived justice and its predictive capability on job satisfaction and performance has allowed researchers to broaden the capability of SET, and provided the necessary theoretical framework for justice related research in the business environment (Rupp & Cropanzano, 2002).

5.2.4.3 SET in the service industry.

2009). The service industry has recently emerged as an acceptable and viable area in

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Texas Tech University, James Brian Aday, May 2013 which to apply SET (Anderson & Narus, 1990; Briggs & Grisaffe, 2009; Garbarino &

Johnson, 1999). SET has been further adapted in service research to study the interactions and exchanges in business-to-business (B2B) and business-to-supplier

(B2S) relationships, and the effects such exchanges exert on a particular firm (Briggs

(Palmatier, 2008).

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5.2.4.4 SET and social status.

5.2.4.5 Applications of SET.

(Graen & Uhl-Bien, 1995). The use of LMX essentially took the approach of SET and

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Texas Tech University, James Brian Aday, May 2013 exchanges, yet focused primarily on the interactions between employees and supervisors (Hooper & Martin, 2008). The utilization of SET to form LMX provided a new dimension for SET and allowed for specific research on firms’ organizational structures and the varying effects decision making from top managers has on lower- level managers (Hooper & Martin, 2008).

Wesselius, & Hoes, 2012). Through academic insight, the coach-player exchange

(CPX) and player-player exchange (PPX) have been measured to assess the impacts of these exchanges on the team’s overall attitude and the performance as a unit

(Breukelen et al., 2012).

Researchers of JST contend employees might be unwilling to share their knowledge or expertise with coworkers out of their concern for job security (Bartol, et al., 2009).

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Texas Tech University, James Brian Aday, May 2013 knowledge sharing in past exchanges are less likely to provide their knowledge in the work setting, which may lead to negative exchanges and perceptions about the employee (Lin & Huang, 2010). Thus, in the above contexts, the costs associated with the interaction have outweighed the benefits gained and little to no reciprocity was felt by the employee who provided the knowledge.

5.2.4.6 SET and customer loyalty.

Researchers contend the primary foundations of SET and the addition of the employee to customer interaction have the propensity to affect customer loyalty.

Sierra and McQuitty (2005) make the argument that because the service environment is dependent upon the exchange between the firm and the customers, and the mutual interdependence of both parties on each other, high feelings of reciprocity will influence customer loyalty and long-term customer viability. Service is unique because providers rely upon customers, but customers also rely upon providers to ensure their needs are met (Sierra & McQuitty, 2005). In this relationship, if the service provider fails to hold up their portion of the agreement, the customer can become displaced and angry as their trust and expectancy of reciprocity has been betrayed (Sierra & McQuitty, 2005). For example, if a person has made arrangements for a car to pick them up from the airport and the driver fails to show, the traveler is now stranded and must begin to identify an alternate means of transportation. Under the same scenario, if we assume the car arrived but the driver was obnoxious and made the guest uncomfortable, the passenger is unlikely to utilize this service in the future, which eliminates the potential long-term viability of that customer.

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2006).

5.2.4.7 SET in the marketing environment.

Scholars have utilized the concepts of SET in their marketing strategies, more specifically in approaches which rely upon relationship marketing and personal selling

Hunt, 1994). Additionally, research has proven the use of SET principles can create extremely effective cause-related marketing campaigns (Ross, Stutts, & Patterson,

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1991). Cause-related marketing can include non-profit organizations such as the Red

Cross which elicits donations via advertisements and relies heavily upon its relationship with donors as a primary means of financial support (Ross et al., 1991).

Similar to relationship marketing, personal selling relies heavily upon the connections established between the customer and seller (Solomon et al., 1985).

Personal selling is argued to be the most expensive form of marketing, and firms demand results from employees in the field or job security becomes a critical issue

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Texas Tech University, James Brian Aday, May 2013 high level of trust recognized between the two individuals necessitates mutual reciprocity and understanding of each side’s needs and limitations (Pelham, 2009).

5.2.4.8 SET in tourism research.

While research regarding the relationship and practicality of SET in the service industry is abundant, the adaptation of the theory to the field of tourism is severely limited, though particularly insightful. Utilizing SET in the hospitality and tourism arena allows researchers to measure the perceptions which residents have of individual tourists (Ap, 1992). SET research in the tourism field has identified that community locals at a destination are likely to interact with tourists if the locals believe they will be able to gain benefit from the encounter (Yoon et al., 2001). The benefits gained can certainly be financial, but may serve a personal motivation, such as attempting to understand cultural differences of foreign visitors (Yoon et al., 2001).

Jamal & Getz, 1995; Moyle, Croy, & Weiler, 2010; Trauer & Ryan, 2005). A vast

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Texas Tech University, James Brian Aday, May 2013 review of extant literature identified that residents of tourist destinations largely view visitors as a revenue stream and interactions are primarily motivated by the ultimate goal of making a sale (Ap, 1992; Moyle, Croy, & Weiler, 2012).

5.2.4.9 Problems associated with SET.

SET has been subjected to measurement based on numerous scales with some containing as few as two items and others employing as many as 24 (Dinesch &

Liden, 1986). The varying constructs on which SET is measured weakens the findings and prohibits researchers from drawing conclusions which can be applied unilaterally

(Dinesch & Liden, 1986). On the same premise, Giddens (1979) believed very early on that SET became oversimplified and fails to take into account the differences in individuals and workplace settings. For example, SET fails to recognize that an employee may be affected at work by troubles or concerns at home, such as a divorce or financial worry, which affects how they may react on the job. Recent scholars agree prior findings have failed to take into account that SET studies in a specific business environment are not universally applicable to other business models (Cropanzano &

Mitchell, 2005).

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Texas Tech University, James Brian Aday, May 2013 studies have been conducted in the United States where laws and regulations place limits and constraints on employees and managers (Aryee et al., 2002). Thus, the findings of previous studies might represent a cultural obscurity and may not necessarily be applicable internationally (Aryee et al., 2002). Furthermore, researchers argue uncontrolled variables, such as emotion or personal needs, can influence exchanges and these external variables are difficult to measure (Cropanzano et al.,

(Settoon et al., 1996).

5.2.4.10 Previous models.

A major limitation of SET is the non-existence of a standard structural model which allows for theory testing (Dinesch & Lident, 1986). Examination of current literature identified a litany of models employed to test and measure SET, yet little congruency was maintained amongst variations. This section will examine the two models which are most applicable to this current study. The first model comes directly from Wayne, Shore, Bommer, and Tetrick (2002) and is exhibited in Figure 6.

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Figure 6. Social Exchange Theory Model. Taken directly from Wayne, S. J., Shore, L. M., Bommer, W. H., & Tetrick, L. E. (2002). The role of fair treatment and rewards in perceptions of organizational support and leader-member exchange. Journal of Applied Psychology, 87(3), 590- 598.

As observed in Figure 6, this model has been adapted to include the

components of work related justice and leader-member exchange. While these

additions vary from other models, it is demonstrated on the left-hand side of this figure

that employees maintain certain needs from their exchanges at work. Ideas such as

inclusion, rewards, dyad tenure, recognition, procedural and distributive justice, and

organizational tenure are all addressed. These factors all influence perceived

organizational support (POS) and the LMX. This is in-line with traditional SET

research which argues that employees weigh multiple variables when making

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Texas Tech University, James Brian Aday, May 2013 decisions and determining their perceived job satisfaction. The factors of POS and

While the SET model displayed in Figure 6 provides a summation of findings from multiple disciplines, it is necessary to examine a model used in tourism research to establish commonalities and contrasts between the fields. Figure 7 is taken directly from Ap (1992) and exhibits the SET relationship from a tourism perspective. This model was chosen based on information from Google Scholar and the ScienceDirect database which identified Ap (1992) as being the most highly cited article on SET in the tourism industry. Furthermore, other models assessed in tourism literature appeared too limited and failed to take into account the multiple aspects of SET when examining the interactions of tourists and locals.

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Figure 7. Social Exchange Theory Model adapted for tourism research. Taken directly from Ap, J. Residents’ perceptions on tourism impacts. Annals of Tourism Research, 19(4), 665-690.

As demonstrated in Figure 7, the primary motivating factor in exchanges between tourists and locals at a destination is need satisfaction. This is also true in

Ryan, 2005). The providers strive to entice travelers to purchase their wares and rely

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Texas Tech University, James Brian Aday, May 2013 upon a positive initial interaction to help secure the sale. The consequences of exchange stage, in this setting, are geared more towards the traveler and identify areas of concern which weigh on the purchase decision. As demonstrated in the model, if the perceived reciprocity is high, the transaction will occur and no long-term relationships will be formed. However, if the perceived costs outweigh the benefits or if the initial interaction does not alleviate the concerns of the traveler, the sale will fall through.

5.2.5 Purchase motivation.

Previous studies have identified and focused on a plethora of purchase motivations such as: goal-directed (Lawson, 1997), social obligation (Goodwin,

Smith, & Spiggle, 1990), internal decision making characteristics (Wells & Prensky,

1996), external influences (Armstrong & Kotler, 200), and behavioral processes (Wu,

Furthermore, motivations such as time-savings (Rohm & Swaminathan, 2004) and variety seeking have proven to influence online purchase decision making (Menon &

Kahn, 1995).

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5.2.6 Group purchase decisions.

Consumers can be propelled to purchase a service based on their desire for social acceptance into a group of their peers or those with similar social standing

(Bearden & Etzel, 1982). Individuals considering a purchase may also rely on the group for information and input from these group members may significantly influence purchase decisions (Bearden & Etzel, 1982). While the term group may appear plural, research into group influence has been broken down into sizes as small as two members (Corfman & Lehmann, 1987). Peer influence is established through like-minded groups of individuals who aid in establishing identity and often lead to a desire for conformity (Chen-Yu & Seock, 2002; Lee, 2009). For example, members of a social group may purchase specific articles of clothing or frequent a specific location, and in an effort to conform, each individual within the group may purchase identifiable clothing or spend time in a preferred location based on the group-think mentality.

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Individuals may rely on their group and the group decision making process to weigh and evaluate potential solutions with other members’ input (Herrera, Martinez, &

Sanchez, 2005). Group thinking provides advantages and disadvantages to marketers as factions can persuade members to make a purchase (Sheth & Parvatiyar, 1995).

(eWOM) (Fong & Burton, 2006), and online auctions (Stern & Stafford, 2006).

However, in these studies it has been ascertained that consumers value the opinions of their peers in these settings, and may rely on their input regarding pricing and purchase decisions (Stern & Stafford, 2006). Smith, Menon, and Sivakumar (2005) contend businesses can build trust with new users by establishing positive relationships with other members of the peer group. Additionally, the authors

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Texas Tech University, James Brian Aday, May 2013 identified peer influence in the electronic environment as being significant and having the power to affect product choice (Smith, et al., 2005).

5.2.7 Theory of planned behavior.

The Theory of Planned Behavior (TPB) is an augmentation of the Theory of

Reasoned Action, created by Ajzen & Fishbein and has been influential in psychological research (Ajzen, 1991; Ajzen & Fishbein, 1980; Fishbein & Ajzen,

TPB is a model frequently utilized to predict consumer buying behavior (De

Canniere, De Pelsmacker, & Geuens, 2009; Ouelette & Wood, 1998). Research on

TPB has demonstrated that focusing on affective (internal) attitudes by marketers aids

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Group buying behavior has also been examined from the perspective of influence on motivation to purchase goods and services (Mangleburg, Doney, &

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5.2.8 Product driven consumers.

(Chang & Wildt, 1994). Attributes are wide-ranging, and can include physical descriptors such as shape, color, size, capabilities, and stylishness (Lehdonvirta,

2009). Essentially, product features are the characteristics which enable them to be employed to achieve a desired purpose for the end-user (Lehdonvirta, 2009).

2006; Pickett-Baker & Ozaki, 2008). Research demonstrates companies can rely upon

17). Furthermore, add-ons can also overcome pre-purchase objections and provide a sense of increased perceived value for the end-user (Bertini et al., 2009).

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5.2.9 Approach-avoidance.

Consumer needs and values influence receptiveness to available product features, and the extent to which marketers appeal to an individual’s expectations influence the potential positive reception of the marketing message and increased likelihood of making a sale (Pickett-Baker & Ozaki, 2008). Human emotions greatly influence purchase behavior (Penz, & Hogg, 2011; Watson & Spence, 2007). Thus, the matching of attributes to consumers’ internal purchase emotions, such as perceived risk or potential benefit, leads to a major challenge faced by firms trying to sell their products (Penz & Hogg, 2011, p. 106). Commonly referred to in the literature as approach-avoidance conflict, theory based on this topic has identified that “in approach motivation, behavior is instigated or directed by a positive/desirable event or possibility” while avoidance “behavior is instigated or directed by a negative/undesirable event or possibility” (Elliot & Thrash, 2002, p. 804).

Approach-avoidance theory was first established by Mehrabian and Russell

Hebert, 2001). Donovan and Rossiter (1982) were among the first researchers to apply

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Texas Tech University, James Brian Aday, May 2013 the findings of Mehrabian and Russell (1974) to retail shopping (Hui & Bateson,

1991; Summers & Herbert, 2001).

5.2.9.1 Online environment.

Atmospherics and approach-avoidance conflict research is not solely limited to traditional brick and mortar retail stores as studies have begun to focus on these issues in the online shopping environment as well (Eroglu, Machleit, & Davis, 2001). Studies based on online shopping contend that a consumers’ willingness to visit and stay on a website (approach) or navigate away from the page (avoidance) are determined by the atmospherics and visual representations of the site, especially during exploratory

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The higher visitation times increase stimuli and user-website interaction (Balbanis &

Reynolds 2001) and the propensity for sales (Kim, Fiore, & Lee, 2007; Kwak, 2002)

5.2.9.2 Approach-avoidance in marketing.

Mollen & Wilson, 2010). However, each of the measured variables was based on the

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5.2.10 Behavior theory

5.2.10.1 Price shoppers.

Hair (2008) identified this type of shopper as one who consistently searches for the best price with highest benefit and termed such individuals as “price-shoppers” (p.

23). Retailers have been aware of this segment for decades and low-cost leading chains design their business models with these consumers serving as their primary

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Texas Tech University, James Brian Aday, May 2013 target market (Ganesh, Reynolds, Luckett, & Pomirleanu, 2010). Further research by

Ganesh et al. (2010) concluded a major theme amongst shoppers is the tendency to search for bargains and the lowest prices during their product search. The relevance and significance of price-shoppers was further demonstrated in a study which identified that some patrons hesitate to purchase unless they are confident they are getting the best price (Zinn & Liu, 2008). Price shoppers are presumed to serve as an important group in the sales of daily-deals as these consumers may be motivated to purchase a voucher solely based on its cost and perceived benefit without intention to revisit the property in the future without discount offering.

5.2.10.2 Discount shoppers.

Upon examination of shoppers who are motivated by price or potential savings, extant literature states it is more appropriate to classify some consumers as

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A study by Dickson and Sawyer (1990) exhibited product pricing influenced consumer purchase decision, but after a brief period of time few people remembered the price they paid. However, research by Wakefield and Inman (1993) coined the term “price vigilantes” or those who self-identified that they are price conscious when shopping (p. 217). Based on results of their study, the price vigilantes were individuals in lower income brackets. Though gender played no role in price sensitivity, these shoppers were able to correctly identify the price or sales price of 55% of the items in their shopping cart after checkout (Wakefield & Inman, 1993).

2011).

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5.2.10.3 Pricing strategy.

There are numerous pricing strategies used by every industry in an effort to attract new business. Among the most common pricing strategies are: premium pricing

(Berends, 2004), penetration pricing (Kotler & Armstrong, 2009), economy pricing,

(Sullivan & Adcock, 2002), price skimming (Liu, 2010), and psychological pricing

Firms which offer lower quality items at a lesser cost could be defined as pursuing economy pricing (Sullivan & Adcock, 2002), while firms with a new or innovative service may rely on price skimming to garner the highest possible price until a competitor begins to saturate the market (Liu, 2010). The last pricing strategy relies upon psychological pricing, such as offering an item for $5.99 instead of $6 to make the price seem less than $6 (Aerts et al., 2008). While each pricing strategy is complex and important, penetration pricing and psychological pricing will be the primary areas of focus for this study as they relate most directly with the concept of daily-deal advertising.

Numerous businesses rely upon psychological pricing, ending the total price of an item in an odd number such as $9.99 instead of $10.00 (Holdershaw, Gendall, &

Garland, 1997). While a multitude of research contends the psychological aspect of this approach relies upon individuals seeing the .99 as being less than the next whole

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Referring to the $9.99 instead of $10.00 example, these studies argue people will see the first 9 and associate the cost of the product as being only $9 when it is actually much closer to $10.

& Sprott, 2009). Additionally, utilizing round or whole number pricing can make an item appear as though it has more value because it does not end in a round number

(Manning & Sprott, 2009). The concept of round pricing is heavily utilized by daily- deal sites, as content analysis has identified every offer being priced as a whole number. This could potentially indicate that daily-deal sites avoid odd number pricing to avoid being identified as inferior. Psychological pricing is not the sole means of gaining consumer attention. Numerous firms rely upon market penetration pricing to garner sales and raise awareness (Liu & Chintagunta, 2009).

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Furthermore, penetration pricing is effective for firms targeting customers which exhibit high price sensitivity (Indounas & Avlonitis, 2011).

Penetration pricing is an ideal pricing strategy when targeting large homogenous markets but is not necessarily the best option for niche markets (Shaw,

5.2.11 Promotional sales.

5.2.11.1 Deals.

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Texas Tech University, James Brian Aday, May 2013 in an effort to steal customers away from their competition (Gamliel & Herstein,

While traditional promotional advertising has focused on perceived savings through their deals, Gamliel and Herstein (2011) suggest a more effective approach may rely upon highlighting the perceived loss by not making the purchase. For example, through their research, the authors found that when an ad was framed in a negative manner, such as failure to buy a product at X savings will actually result in the loss of money, was more persuasive in influencing purchases (Glamiel & Herstein,

2011). This type of advertising is similar to the methods employed by LivingSocial and Groupon, as the daily-deal offering presents the user with the overall retail value, the discount percentage, and the discount price in the same message.

5.2.11.2 Coupons.

Lichtenstein, Netemeyer, and Burton (1990) labeled coupon prone consumers as those who, through a coupon, demonstrate an increased potential to respond to the advertisement and possibly make a purchase. The authors also established that a distinguishable difference exists between individuals who are coupon prone and

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Texas Tech University, James Brian Aday, May 2013 shoppers who are determined to be value conscious (Lichenstein et al., 1990). The authors, as well as more recent studies, contend coupons can influence purchases in consumers who are not necessarily value conscious, but are able to identify the savings presented through the offer (Lichenstein et al., 1990; Pillai & Kumar, 2012)

Current research indicates coupons are effective when a higher perceived savings exists (Barat & Paswan, 2005). In 2009, the most recent data year available,

U.S. consumers redeemed over 3.3 billion coupons at retailers, a 26% increase over

2007 (Tilley, 2010). In the same year, over 367 billion coupons were distributed, with

Internet based coupon redemption up 263% from 2006 to 2008 (Nielsen, 2010).

5.2.11.3 Online pricing.

(Fruchter & Tapiero, 2005). Online sales channels present firms with lower advertising costs, real time sales data, easier price adjustment options, and up-to-the minute inventory (Oh & Lucas, 2006). Thus, firms utilizing online sales channels are

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Texas Tech University, James Brian Aday, May 2013 able to quickly adapt to market changes and price fluctuation; as competitors lower their prices so does the firm, and vice-a-versa, providing price transparency for consumers (Oh & Lucas, 2006). Research demonstrated firms which utilize their online presence in a coordinated manner with their physical location are able to increase overall revenue as their online and in-store pricing are complimentary of each other (Ling, Guo, & Liang, 2011).

5.2.12 Purchase behavior.

5.2.12.1 Expectation-confirmation theory.

Expectation-Confirmation Theory (ECT) was created by Oliver (1980) and originally centered on consumer expectations and the probability of them coming to fruition (Oliver, 1980). ECT was also concerned with the overall satisfaction, post- purchase or post-occurrence and whether expectations of the consumer were met

(Oliver, 1980). Over the past two decades, ECT has become a main theory of study when measuring consumer behavior, more specifically satisfaction and post-purchase

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Texas Tech University, James Brian Aday, May 2013 feelings (Bhattacherjee, 2001). Research pertaining to ECT has also become predominant in the study of Internet related consumer behavior, and the willingness of individuals to purchase goods and services online as well as the overall customer experience (Cheung, Chan, & Limayem, 2009).

Expectations in ECT are broken down into three types: “the ‘will expectation’, the ‘should expectation’ and the ‘ideal expectation’” (Liao, Chen, & Yen, 2007, p.

2807). The will expectation is concerned with pre-purchase behavior, and aims to predict consumer attitudes prior to buying and their expected emotions after the sale

(Liao, et al., 2007). When examining the should expectation, researchers aim to measure what the customer believes will occur during future transactions with the firm

2010).

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This study is concerned with consumer behavior related to online purchase behavior as well as consumer satisfaction and its influence on revisit intention.

5.2.12.2 Online purchase behavior.

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Texas Tech University, James Brian Aday, May 2013 statements, as evidenced in the literature, potentially play a vital role in a consumer’s decision when purchasing a daily-deal.

5.2.13 Product and brand driven consumers.

5.2.13.1 Brand loyalty.

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Texas Tech University, James Brian Aday, May 2013 to firms which consumers are unfamiliar with (Hong-Youl, John, Janda, & Muthaly,

5.2.13.2 Brand preference.

Buyers are influenced by brands which they have previous experience with and prior interactions, if positive, can establish purchasers who develop a preference for a particular firm’s products or services (Chang & Liu, 2009). The image a brand conveys, based on the consumers’ perspective, relies upon the established equity of the brand (Chang & Liu, 2009; Chaudhuri, 1995; Faircloth, Capella, & Alford, 2001;

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5.2.14 Substitute products.

Consumers can be influenced to exhibit brand preference or loyalty for products which maintain few substitutes (Lattin & McAlister, 1985). However, in mature markets, where the products and services offered are not unique and little product differentiation exists, consumers may switch between similar brands based on price (Liu & Yang, 2009). Services and products which maintain little risk might be

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Texas Tech University, James Brian Aday, May 2013 perceived as easier to substitute as minimal effort is required of the buyer to begin using the substituted product (Shukla, 2009). The hospitality and service industries are typically highly saturated and consist of multiple substitute products available on the open market (Agarwal, Erramilli, & Dev, 2003). Research has determined the price of products and the perceived fairness of the price can influence customer preference

(Ranaweera & Neely, 2003). However, substitute products which demonstrate a perceived higher customer value may lead to product switching (Meng & Elliott,

5.2.15 Purchase frequency.

(Liu, 2007; Bolton, Kannan, & Bramlett, 2000). A recent study measuring online purchase frequency identified approximately 40% of the 705 respondents indicated they had purchased between one and four items online with the same company within the prior 12 month period (Zhu, Lee, O’Neal, Lee, & Chen, 2009). Further research conducted by Fagerstrom and Ghinea (2011) identified that out of 252 participants, on average, respondents purchased three or more items online within the past 6 months.

To better understand the potential impacts on businesses which employ a flash-sale

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Texas Tech University, James Brian Aday, May 2013 strategy, customer purchase frequency will be assessed to determine if long-term customer relationships are established between the user and the business, as well as between the purchaser and Groupon or LivingSocial.

5.2.16 Business location.

Businesses and academic researchers consistently emphasize the key to success for any firm is location, location, location (Grewal, Levy, & Kumar, 2009). Henderson

(2009) contends the physical location of a business is critical in the service industry.

2009). Examining location as a constraint during a consumer’s encounter when deciding whether or not to purchase a flash-sale may provide keen insight to businesses and the areas where they choose to offer their daily-deal coupons. If results demonstrate a majority of consumers are influenced by location, the finding may indicate that businesses need to focus their advertisements on areas immediately surrounding their property.

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5.3 Methodology

The purpose of this study was to identify (1) the role price plays in determining the willingness of a potential customer to purchase a flash-sale, (2) the probability of repeat visitation from high frequency discount users compared to those who use such discounts sparingly, (3) the likelihood of repeat visitation after utilizing a coupon based on the customer experience, and (4) which factors are most likely to influence and predict flash-sale purchases. In an attempt to formulate a study methodology to garner data relevant to these objectives, an extensive literature review was first conducted to develop the proposed Consumer Daily-Deal Interaction Model. The hypothesized model was then tested through a pilot study. Pilot testing was rigorous and employed to identify a structural model, through analysis, which would be the best fit for the full scale survey. Based upon findings from the pilot test, alterations were made to the model and corresponding survey. The full study was geared towards measuring the stated objectives, and considered a more precise measurement technique.

5.3.1 Model development.

The first-order constructs, Product Driven, Price Driven, Group Driven, and Purchase

Motivations were derived from the literature and intended to measure items or variables which may influence consumer purchase decision. As described in the

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Motivations, was established to measure the constructs of social obligation (Goodwin,

Smith, & Spiggle, 1990), internal decision making characteristics (Wells & Prensky,

1996), external influences (Armstrong & Kotler, 200), and behavioral processes (Wu,

2003).

Frequency were the identified mediators for this study. Research on business location in the hospitality and service fields is abundant, with similar findings across majority

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Texas Tech University, James Brian Aday, May 2013 of studies, consumers evaluate the relative proximity of the business to their home, and the distance to other stores which may be of interest (Fox, Postrel, &

McLaughling, 2007; Ozsoy, 2010). Due to the unique nature of flash-sales, purchasing online with physical redemption at a store, ECT and TPB components (Purchase

Behavior) were included in this study. The justification for utilizing these theories relies upon the importance of the initial experience of the consumer and their post purchase behavior (Bhattacherjee, 2001), and the fact that daily-deal offerings are broadcast to a large demographically diverse population which, based on purchase patterns, may identify groups of likeminded individuals consuming these vouchers

(Ajzen, 1991; Quintal, Lee, & Soutar, 2010). Measuring how frequently individuals purchase (Purchase Frequency) was conducted in an effort to better understand the type of consumer that is most likely to utilize flash-sale offerings (Leenheer &

Bijmolt, 2008).

The dependent factors in the model relied upon Revisit Intentions and Discount

Thus, this study aims to identify if the customer experience and interactions, SET, are influential in Revisit Intentions (Anderson & Narus, 1990; Briggs & Girsaffe, 2009).

Furthermore, this model aims to identify if the consumers will only be motivated to revisit the firm if a discount is offered for the future visit as well (Discount Based

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Revisit). The hypothesized model, identified as the Consumer Daily-Deal interaction

Model, is exhibited in Figure 8.

5.3.2 Survey design.

Demographic questions were categorical and dichotomous in nature. The original survey instrument contained 77 questions, with five serving as demographic questions.

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Product Driven Revisit usiness Intentions Location

Price Driven

Purchase ehavior

Group Driven Discount ased Revisit Purchase Frequency

Purchase Motivations

Figure 8. Proposed hypothesized Consumer Daily-Deal Interaction

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5.3.3 Pilot test.

5.3.3.1 Data collection.

Oblinger, 2005) and rely upon web-based promotions to identify discounts (Seock &

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71.1%.

5.3.3.2 Data screening.

The data were screened for univariate outliers; however, no responses were identified as outlying. The measurement scale for items was a 7-point Likert scale, thus preventing the presence of univariate outliers. The data were tested for reliability and validity, which were measured utilizing Cronbach’s alpha. Reliability testing alpha coefficients ranged from .840 to .953, indicating internally consistent and reliable variables (Santos, 1999). Analysis identified one multivariate outlier which was subsequently deleted. The justification for deleting the multivariate outlier was because the values were very extreme and would have skewed the exploratory factor analysis (EFA). No pairs of items were identified as having > .70 correlation; thus, multicollinearity was not an issue.

5.3.4 Full study.

5.3.4.1 Model and survey revision.

Based upon the findings of the pilot study, which are discussed in the Results section, the Consumer Daily-Deal Interaction Model was revised, becoming a four factor mediated solution. Five factors from the original hypothesized model indicated

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Texas Tech University, James Brian Aday, May 2013 non-significance in pilot testing; thus, the factors and items which loaded upon them were dropped from the full study. Factor names were changed as numerous items which were employed in the pilot testing were a better statistical fit for different factors. Thus, based on overall themes amongst the items which loaded on specific factors, the names were modified to clarify their representation. The model for the full study, identified as the Modified Consumer Daily-Deal Interaction Model, is exhibited in Figure 9.

The initial survey instrument employed during the pilot study phase of data collection relied upon 72 items which measured the nine previously mentioned factors.

For the full study, the number of items utilized was 35 representing four factors, a result of 37 cases and five factors being dropped from the initial analysis. These remaining factors, positive attitude, purchase frequency, product awareness, and purchase motivations were represented by items which were found to be statistically significant from the pilot study. The items, their codes, and their respective factors which were used in the full study are displayed in Table 13.

Once the hypothesized model was modified, it was necessary to revise the survey instrument accordingly. Questions were derived to address four modified constructs pertaining to consumer behavior and online purchases related to flash-sales.

These factors are: positive attitude, purchase frequency, product awareness, and purchase motivations. Each category was represented by items which were found to be statistically significant in the pilot study. Each construct was utilized as a factor in the hypothesized model, as items for each factor were established to measure the

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Texas Tech University, James Brian Aday, May 2013 statistical strength and predictability of the relationships. The final survey included 35

Likert-scale and five demographic questions elicited from the preliminary study.

Table 13 Item Codes, Descriptions, and Associated Factors Factor Item Code Synopsized Item Description PosAt Q2_3* The greater the advertised discount, the more attractive the flash-sale offering is PosAt Q2_5** More inclined to purchase from Groupon or LivingSocial than local flash-sale source PurMo Q2_6 Check the geographic location of the business on a flash-sale site before purchasing PurMo Q2_7 More likely to purchase a flash-sale coupon if the service is offered at multiple locations PurFr Q2_9 I rely on flash-sale sites as a source of knowledge for products which I am unfamiliar with PosAt Q2_10** I trust flash-sale sites to provide me with service options which are safe and reliable PurFr Q2_12 I generally purchase more than 5 coupons from flash-sale sites annually PosAt Q3_1*** More likely to revisit a property which discovered through a flash-sale sent direct discount PosAt Q3_2** Information provided flash-sale advertisements enough information to influence purchase PurFr Q3_3 Convince someone to purchase a flash-sale before I am willing to make the purchase PosAt Q3_4* Positive experience through a flash-sale, continue to use without future discounts offered PosAt Q3_5* The price of a service offered through flash-sales is the main motivating factor in purchase PosAt Q3_7*** Product or service received redeeming flash-sale coupon was good, will revisit business PurMo Q3_11 Date-range the coupon must be used influences my decision when purchasing flash-sale PurFr Q4_2 Purchase at least one offering for business in city which do not reside at least once per year PosAt Q4_4* I would feel comfortable trying a new product or service offered through a flash-sale site PosAt Q4_5** Likely to use a flash-sale coupon I purchased even if I cannot find someone to go with me PosAt Q4_6* I feel comfortable spending between $50 to $100 for a service offered via a flash-sale

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Table 13 (cont.) PurMo Q4_9 I am more likely to purchase products or services I am familiar with through flash-sale ProdA Q4_11 Lower the price of a service being offered through a flash-sale, the lower the quality PosAt Q4_12*** I am generally satisfied with the services I receive when redeeming a flash-sale coupon PosAt Q5_1*** Likely to revisit property redeemed flash-sale coupon if discount is not enough to cover bill PurFr Q5_3 I typically purchase a coupon from a flash-sale site at least once every 3 months ProdA Q5_9 Attracted to services provide entertainment rather than a specific function from a flash-sale ProdA Q5_10 Likely purchase name-brand products through flash-sales sites than brands not familiar ProdA Q5_11 Purchased a flash-sale coupon, take people along to the business who not purchased coupon PosAt Q5_12** My experience using flash-sale sites has always been positive PosAt Q6_3* I feel comfortable spending between $10 to $49 for a service offered via a flash-sale PurMo Q6_5 Location of the business offering through the flash-sale influences my decision to purchase PosAt Q6_8*** If a coupon for a future visit is offered I am more likely to revisit the business PurFr Q6_9 A majority of my online purchases are made through flash-sale sites PosAt Q6_10*** I generally revisit a business I first encountered through a flash-sale offering PosAt Q1_1** Flash-sale sites provide an easy to use platform for purchasing goods and services online PurFr Q1_4 I have become a long-term customer of a business discovered through a flash-sale coupon PurMo Q3_12 Likely to purchase a service via a flash-sale for a company within 10 miles of my residence Notes: * indicates variables which were parceled into the variable QPRI ** indicates variables which were parceled into the variable QBEH *** indicates variables which were parceled into the variable QREV

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5.3.4.2 Data collection.

This study relied upon convenience sampling techniques (Cohen & Arieli,

2011) specifically snowball, commonly referred to as chain-referral, sampling

1979; Kendall, et al., 2008). Employing convenience sampling methods may influence selection bias, creating a sample which is not a representative measurement of an entire population (Cohen & Arieli, 2011). However, due to the varied size and scope of daily-deal offerings based on geographic location, the researcher elected to employ snowball sampling as a measure of generating responses from a wider and more regionally comprehensive sample. This was geared towards ascertaining the purchase behaviors and motivations of daily-deal users. Additionally, revisit intention upon redemption of the initial flash-sale voucher based on their experience with the firm was a central component. Thus, individuals who self-identified as having purchased a flash-sale voucher within the previous 12 months were identified as demonstrative of the intended population.

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Texas Tech University, James Brian Aday, May 2013 one random participant was selected as a winner. Data collection commenced on

12 months and were at least 18 years of age. While the total number of shares, views, and posts of the link is impossible to calculate, the survey link received 635 responses.

However, filter questions reduced the number of eligible participants to 305 respondents, indicating a valid response rate of 48.03%.

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Positive Attitude

Product Purchase Awareness Motivations

Purchase Frequency

Figure 9. Modified Consumer Daily-Deal Interaction model.

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5.3.4.3 Data screening.

The data were tested for reliability and validity, which was measured utilizing

Cooks and Mahalanobis distance and box plots. Further examination indicated these cases either selected the same answer for each question in the survey, such as selecting

7 = Strongly Agree on all 33 questions, or they were missing more than 5% of their data. After subsequent analysis, the number of useable surveys was established as

5.4. Results

5.4.1 Pilot test.

5.4.1.1 Sample.

A total of 249 usable surveys were collected between May 1st and August 27th

2012. As this was a pilot study, the small sample size was geared toward measuring the effectiveness of the current survey instrument and model. Table 14 provides demographic information relating to age, ethnicity, education, and income of the respondents. A majority of participants (66.3%) were between 21 and 25 years of age.

The next largest age grouping included those 18 to 20 years of age (24.5%). Most

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Texas Tech University, James Brian Aday, May 2013 participants (80.7%) were currently in college, but had not yet obtained a bachelor’s degree. The remaining respondents indicated possessing a bachelor’s degree (17.7%) or a master’s degree or higher (1.6%). The sample was identified as consisting primarily of Caucasian individuals (85.2%) and a majority of the sample (55.4%) earns less than $24,999 annually.

5.4.1.2 Exploratory factor analysis.

Initially, exploratory factor analysis (EFA) was performed utilizing principal axis factoring with Promax rotation. Promax rotation is an oblique tool which allows for correlation between factors. In order to meet the standard requirements of Gorsuch

(1997), only factor loadings of 0.40 or greater were considered. Upon analysis, results indicated the data set contained nine factors with 51 items; however, several items had to be removed from the analysis due to inadequate goodness-of-fit values which arose as items were dropped. Although six factors had been extracted using the Kaiser-

Guttman criterion, only four-factors were retained using Gorsuch’s (1997) criteria.

Further analysis revealed cross-loading between several items, which were eliminated from the analysis. After fourteen analyses, a four-factor solution was deemed appropriate based on Gorsuch’s (1997) criteria. The four identified factors are:

Positive Attitude, Purchase Frequency, Product Awareness, and Purchase

Motivations. Five of the original factors dropped off and two were renamed as they had multiple items loading on them from non-significant factors.

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Table 14 Pilot Study Respondents' Demographic Information (N=249) Variable Frequency Percent Age 18-20 61 24.5 21-25 165 66.3 26-29 15 6.0 30+ 8 3.2 Ethnic Group African/African American 8 3.2 Asian 5 2.0 Caucasian 212 85.2 Hispanic 15 6.0 Other 9 3.6 Education In College 201 80.7 Bachelor's Degree 44 17.7 Master's Degree or Beyond 4 1.6 Income Less than $24,999 138 55.4 $25,000-$49,999 39 15.7 $50,000-$74,999 11 4.4 $75,000-$99,999 12 4.8 $100,000-$124,999 8 3.2 Greater than $125,000 41 16.5

The hypothesized model derived from the EFA solution can be seen in Figure

10. This model utilized 35 (48.6%) of the original 72 items and was determined to be a well-fitting simple structure. However, based on eighteen (18) items loading on one of the factors, parceling was used to create a smaller and more manageable number of manifest indicators where different items were combined to reflect single latent constructs (Hall, Snell, & Foust, 1999). Essentially, the 18 items were broken down into three groups of 6 and the average score was taken for each group. For example,

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Texas Tech University, James Brian Aday, May 2013 the set titled QPRI is the average score of 6 items that loaded on the factor and were thematically selected. The four identified factors collectively explained 58.13% of the total variance.

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QPRI Q3_1 Q6_5 Q3_1 Q2_7 Q2_6 Q4_9 Positive QBE Attitude

QRE Q5_1

Q5_1 Product Purchase Awarenes Motivation Q5_3 Q5_9

Q2_1 Q4_1

Q6_9 Purchase Q1_4 Frequency

Q3_3

Q4_2

Q2_9

Figure 10. Hypothesized model after EFA. As identified in the model, five constructs were dropped, two new factors were identified, and the 18 items which loaded onto Positive Attitude were combined into three sets of 6 questions apiece.

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5.4.1.3 Confirmatory factor analysis.

After the identification of factors from the EFA, a confirmatory factor analysis

(CFA) was conducted on the EFA results using Mplus 6 (Muthén & Muthén, 1998-

2011). The model did not fit the data well, χ2 (164) = 350.55, p < .0001, CFI = .894,

RMSEA = .070 (90% CI .060-.080). The RMSEA in the model was considered mediocre at .070 with the identified confidence intervals (MacCallum, Browne, &

Sugawara, 1996). Thus, the model was respecified until a good model fit was achieved, χ2 (84) = 143.435, p < .0001, CFI = .956, RMSEA = .055 (90% CI .039-

.070). The final CFA Model (see Figure 11) retained 30 (41.7%) of the original 72 items. Factor determinacy scores indicated all four factors were well measured:

 Positive Attitude (3 sets) = .956

 Purchase Frequency (4 items) = .922

 Purchase Motivation (5 items) = .911

 Product Awareness (3 items) = .841

Following the CFA, a structural model was tested. The structural model was a good fit to the data χ2 (140) = 220.83, p < .0001, CFI = .941, RMSEA = .051 (90% CI

.038-.063). A mediated, created through the employment of an additional path, model was then tested and established as being a slightly better fit than the unmediated model

χ2 (139) = 218.77, p < .0001, CFI = .942, RMSEA = .051 (90% CI .037-.063). The final structural model is exhibited in Figure 12.

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QPRI Q3_12 Q6_5 Q3_11 Q2_7 Q2_6 Positive QBEH Attitude

QREV

Q5_11

Product Purchase Q5_10 Awareness Motivations

Q5_9

Q5_3

Q2_12 Purchase Frequency Q6_9

Q1_4

Figure 11. Model after completion of CFA. While 15 items remain visible, each set contains 6 items. Thus, the model is represented by four factors and 30 items. After CFA, two paths were eliminated connecting Purchase Frequency to Product Awareness and Purchase Motivations.

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Q3_1 Q6_5 Q3_1 Q2_7 Q2_6

QPRI Positive .44/.10 QBEH Attitude Purchase Motivations QREV -.18/.06 EDUC .51/.11 .35/.09

.59/.05 Q5_11 Product Q5_10 Q5_3 Awarenes Q5_9 Q2_12 Purchase Frequency Q6_9

Q1_4

Figure 12. Final mediated structural model. Represented by four factors and 30 items.

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Factor loadings in the mediated structural model for each indicator on its respective factor were all identified as being .40 and above in magnitude. Table 15 displays the standardized factor loadings for the associated manifest indicators and the factors upon which they load.

Table 15 Standardized Factor Loadings for Pilot Structural Model Indicator Loading

POSITIVE ATTITUDE QPRI .88 QBEH .87 QREV .86

PURCHASE FREQUENCY Q5_3 .83 Q2_12 .79 Q6_9 .62 Q1_4 .66

PRODUCT AWARENESS Q5_11 .73 Q5_10 .57 Q5_9 .48

PURCHASE MOTIVATIONS Q3_12 .75 Q6_5 .63 Q3_11 .74 Q2_7 .63 Q2_6 .51

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5.4.2 Full study.

5.4.2.1 Sample.

Data collection using the revised model and survey commenced on September

15, 2012 and concluded on November 22, 2012. In total, 280 useable surveys were received. While the pilot test yielded a largely homogenous sample, respondents to the full survey were more diverse. Table 16 provides demographic information related to education, age, ethnicity, gender, and income of the sample. The majority of the sample (29.6%) fell between 21 and 25 years of age, with the next largest age group including individuals 30 to 35 years of age (18.2%). As was the case for the pilot study, the higher age groups were condensed into a single category, in this case everyone over the age of 40 (16.1%), in an effort to measure groups of approximately equal size. Gender was dominated by female participants (59.3%). Caucasians were the most highly represented (71.8%) with African/African Americans demonstrating the lowest density amongst the sample (1.4%). Respondents indicating their academic standing as in-college were the largest group (26.4%); however, individuals which identified as having some college (18.9%) and bearing a bachelor’s degree (19.6%) were also well represented. Finally, the income segment was almost equally represented by those earning less than $24,999 (23.6%) and those with earnings between $25,000 and $49,999 (21.1%). The unequally distributed number of respondents earning greater than $150,000 annually necessitated combining those figures into a single variable which was labeled $150,000+.

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Table 16 Respondents' Demographic Information (N=280) Variable Frequency Percent Age 18-20 24 8.6 21-25 83 29.6 26-29 46 16.4 30-35 51 18.2 36-40 31 11.1 41+ 45 16.1 Gender Male 101 36.1 Female 179 63.9 Ethnicity African/African American 4 1.4 Asian 17 6.1 Caucasian 201 71.8 Hispanic 48 17.1 Other 10 3.6 Education High School Graduate 5 1.8 In College 74 26.4 Some College 53 18.9 achelor’s Degree 55 19.6 Some Master's Coursework 25 8.9 Master's Degree 39 13.9 Beyond Master's Degree 29 10.4 Income Less than $24,999 66 23.6 $25,000 - $49,999 59 21.1 $50,000 - $74,999 47 16.8 $75,000 - $99,999 30 10.7 $100,000 - $124,999 28 10.0 $125,000 - $149,999 22 7.9 $150,000 + 28 10.0

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5.4.2.2 Factor analysis.

Exploratory Factor Analysis (EFA) and strenuous data screening on the pilot study sample allowed for the data from the full study to begin with minimal data screening, checking for outliers, Cook’s and Mahalanobis Distance, and box plots.

Upon completion of the data screening, Confirmatory Factor Analysis (CFA) commenced. The same criteria employed during the initial data screening on the pilot study were maintained. The hypothesized CFA model is exhibited in Figure 13. As previously discussed, the configuration of constructs and manifest indicators for the

CFA model tested in the full study is based on the results of the pilot study, and only included the 35 variables listed in Table 13.

Analysis was conducted utilizing Mplus 6, and a CFA was conducted with the data from the full study. The creation of manifest indicators through parceling was adopted for this study, as well, to measure the single latent constructs (Hall, et al.,

1999). Initially, the model did not fit the data well, χ2 (164) = 497.89, p < .0001, CFI =

.780, RMSEA = .085 (90% CI .077-.094). The model was modified through additional iterations, eight in total1, until a good model fit was achieved, χ2 (84) = 153.87, p <

.0001, CFI = .933, RMSEA = .055 (90% CI .041-.068). The final CFA model (see

Figure 14) retained 30 (85.7%) of the original 35 items employed for this version of the study. Factor determinacy scores indicated all four factors were well measured:

 Positive Attitude (3 sets) = .941

 Purchase Frequency (5 items) = .911

1 The eight iterations involved changing the marker variable for three factors and dropping five factors from the analysis based on their loading scores. 245

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 Purchase Motivations (4 items) = .859

 Product Awareness (3 items) = .815

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QPRI Q3_1 Q6_5 Q3_1 Q2_7 Q2_6 Q4_9 Positive QBE Attitude

QRE Q5_1

Q5_1 Product Purchase Awarenes Motivation Q5_3 Q5_9

Q2_1 Q4_1

Q6_9 Purchase Q1_4 Frequency

Q3_3

Q4_2

Q2_9

Figure 13. Tested CFA Model. As identified in the model, five constructs were dropped, two new factors were identified, and the 18 items which loaded onto Positive Attitude were combined into three sets of 6 questions apiece.

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5.4.2.3 Structural Equation Modeling

Upon completion of the CFA, a structural model was tested. The structural model was a good fit to the data, χ2 (140) = 243.66, p < .0001, CFI = .901, RMSEA =

.056 (90% CI .045-.069). A mediated model was then tested and was determined to have a better fit, χ2 (139) = 241.13, p < .0001, CFI = .902, RMSEA = .054 (90% CI

.043-.066). The final structural model is exhibited in Figure 15.

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QREV Q6_5 Q3_12 Q3_11 Q2_6 Positive QPRI Attitude

QBEH

Q5_9

Product Purchase Q5_10 Awareness Motivations

Q5_11 Q2_12

Q5_3 Purchase Q2_9 Frequency

Q4_2

Q1_4

Figure 14. Model after completion of CFA. While 15 items remain visible, each set contains 6 items. Thus, the model is represented by four factors and 30 items. After CFA, two paths were eliminated connecting Purchase Frequency to Product Awareness and Purchase Motivations.

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Q6_5 Q3_1 Q3_1 Q2_6

QPRI Positive .66 QBE Attitude Purchase Motivation QRE

.32 .31

.46 Q5_9 Q2_1 Product Awarene Q5_1

Q5_3 Q5_1 Purchase Q2_9 Frequency

Q4_2

Q1_4 Figure 15. Final Structural Consumer Daily-Deal Interaction Model. The variables listed on each path are the standardized estimates.

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5.4.2.4 Full-structural model results.

The Final Structural Consumer Daily-Deal Interaction Model identified a direct positive relation from Positive Attitude to Purchase Motivation. There also appears to be an indirect effect linking Positive Attitude and Purchase Motivation, through Product Awareness. Purchase Frequency and Positive Attitude correlate, sharing error which is unexplained by the model and identified by the double headed arrow.

5.4.2.5 Manifest loadings.

Factor loadings in the mediated structural model for each indicator on its factor were all identified as being at the .40 and above level. Table 17 displays the standardized factor loadings for the associated manifest indicators and the factors upon which they load.

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Table 17 Standardized Factor Loadings for Structural Model Indicator Loading

POSITIVE ATTITUDE QREV .88 QPRI .82 QBEH .79

PURCHASE FREQUENCY Q2_12 .84 Q5_3 .79 Q2_9 .40 Q4_2 .42 Q1_4 .44

PRODUCT AWARENESS Q5_9 .70 Q5_10 .61 Q5_11 .50

PURCHASE MOTIVATIONS Q6_5 .72 Q3_12 .47 Q3_11 .57 Q2_6 .42

5.4.2.5 Correlations for manifest indicators.

Results from correlation analyses between manifest indicators revealed notable patterns of relations. The items which were employed to measure the effect of location on the motivation to purchase (Q2_6, Q3_12, and 6_5) indicated significant levels of correlation with each other. This pattern would be expected as the variables specifically utilize the word “location” and are geared towards the same latent construct (Purchase Motivations). The correlation matrix between manifest indicators

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Texas Tech University, James Brian Aday, May 2013 can be seen in Table 18. The items utilized to measure Product Awareness were also significantly correlated at the p<.01 level. While these items Q5_9 (Entertainment) and Q5_10 (Product Awareness) measure vastly different areas, entertainment and product awareness, they are similar in nature as they both stimulate what a consumer may be seeking in their flash-sale purchase.

The three parcels, QREV, QPRI, and QBEH, maintain significant correlations at the p<.01 level as well. This is indicative of the circumstances under which they were created. As the three subsets actually represent the scores of 18 items which significantly loaded on the Positive Attitude factor, it makes sense they display a relationship. Items Q4_2 and Q4_9 displayed significant correlation at a high level.

While Q4_9 fell off during the CFA, the relationship is unique as this variable pertains to familiarity and its partner is related to purchases outside the city of residence.

However, a likely explanation is that younger respondents may not consider this city home; thus, in their hometown they may be highly familiar with a business offering products and be inclined to purchase the flash-sale for future usage. All the correlations were positive and no statistically significant inverse relationships existed.

Overall, moderate levels of correlation existed amongst the variables, a potential indication they are closely related to the model and measuring similar constructs.

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Table 18

Correlations between manifest indicators for Positive Attitude, Purchase Frequency, Product Awareness and Purchase Frequency Q2_6 Q2_7 Q2_9 Q2_12 Q3_3 Q3_11 Q4_2 Q4_9 Q4_11 Q5_3 Q5_9 Q5_10

Q2_6 1.00

Q2_7 .840** 1.00

Q2_9 .496** .498** 1.00

Q2_12 .842** .632** .499** 1.00

Q3_3 .348** .349** .161** .352** 1.00

Q3_11 -0.01 -0.01 -0.02 -0.01 .634** 1.00

Q4_2 -0.01 -0.01 -0.03 -0.01 .505** .757** 1.00

Q4_9 .251** .252** 0.11 .252** .521** .673** .811** 1.00

Q4_11 .283** .284** .123* .285** .611** .757** .913** .740** 1.00

Q5_3 .243** .245** 0.10 .243** .697** .651** .620** .631** .759** 1.00

Q5_9 .243** .245** 0.10 .243** .697** .651** .620** .631** .759** .553** 1.00

Q5_10 .243** .245** 0.10 .243** .697** .651** .620** .624** .759** .553** .553** 1.00

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Table 18 (cont)

Q5_11 Q6_5 Q6_9 Q1_4 Q3_12 QPRI QBEH QREV

Q5_11 1.00

Q6_5 .734** 1.00

Q6_9 .734** .688** 1.00

Q1_4 .625** .674** .674** 1.00

Q3_12 .721** .691** .691** .774** 1.00

QPRI .739** .732** .832** .609** .632** 1.00

QBEH .786** .663** .663** .662** .663** .772** 1.00

QREV .786** .585** .663** .662** .663** .772** .868** 1.00 p<0.01** or 0.05* respectively

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5.5 Conclusion and Discussion

This study aimed to address whether flash-sale attributes and consumer preferences relate to re-visitation intentions of daily-deal users in a manner consistent with the specified model. More specifically, this study sought to identify:

1. The role price plays in determining the likelihood of purchasing a flash-sale;

2. The probability of repeat visitation from high frequency discount users

compared to those who use such discounts sparingly;

3. The influence of customer experience on return intention after utilizing a daily-

deal voucher; and

4. The factors which are most likely to influence and predict flash-sale purchases.

Based on the findings, online purchase frequency, positive attitude

(receptiveness), and product awareness predict their purchase motivations. It must be acknowledged that placements of the constructs and arrows in different directions than what was specified in the present model could also fit the data well (MacCallum,

Wegener, Uchino, & Fabrigar, 1993). This study establishes that consumers’ purchase frequency (exposure) of online daily-deal offerings exists in a mutually beneficial relationship with their attitude towards the products and services offered via flash-sale sites. According to the specified model, this attitude (perception) directly influences product awareness and directly affects purchase motivations. Essentially, a positive attitude may lead to increased product awareness which subsequently impacts purchase motivation. However, the positive attitude or receptiveness of the offering by

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Texas Tech University, James Brian Aday, May 2013 the consumer may be powerful enough to directly affect purchase motivations regardless of awareness.

While the covariates of education, age, gender, ethnicity, and income were measured, they did not significantly correlate with, nor significantly predict in SEM, the consumer constructs. These variables were broken down into categories, such as

18 to 20 for age, and compared individually to the model through almost 14 iterations with no combination resulting in a statistically significant loading. Thus, these variables bear minimal influence in relation to the four factors identified in the model.

The demographic make-up of the sample was somewhat homogenous, predominately

Caucasian, with an abundance of the sample falling into similar age, income and education brackets. Thus, the results indicate individuals 21 to 29 years of age with income less than $74,999, who are either currently in college or possess a bachelor’s degree are seeking different aspects from daily-deal offerings. Despite the fact that the sample was predominantly female, gender did not relate to a factor, indicating disconnect amongst this group in their flash-sale preferences.

The Purchase Frequency factor added two additional items (Q2_9 and Q4_2) in the final structural analysis and dropped an item (Q6_9) which was significant in the pilot structural model. However the two items which were added in the final structural model were indicated as loading significantly on this factor in the CFA. The items which loaded on purchase frequency varied in nature regarding their description.

For example, Q2_9 related to flash-sale offerings providing knowledge about products of which the consumer was previously unaware, and Q4_2 related to purchasing daily-

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Texas Tech University, James Brian Aday, May 2013 deal vouchers for cities outside of their current residence. Items Q5_3 and Q1_4 pertained to purchase frequency and establishing long-term relationships with vendors who utilize daily-deal offerings, respectively. The fifth item (Q2_12) also related to purchase frequency except it was on an annual basis.

The data exhibited that some of the participants have established a long-term relationship with a business they encountered on a daily-deal site, yet the strength of this relationship, while statistically significant, was relatively modest at .44.

Conversely, Q2_12 (purchase more than 5 daily-deals annually) maintained the greatest statistical relationship at .84, almost two times greater than the previously discussed item. Thus, these findings potentially identify that only a high volume user of these sites, who maintains familiarity with businesses providing the offerings, may demonstrate a willingness to become consistent patrons of a vendor. However, results indicate a majority of respondents purchase more than 5 flash-sale vouchers annually and at least one a month. These consumers may demonstrate price shopper characteristics, and are only interested in purchasing at a deep-discount, which defeats the purpose of the business trying to generate long-term customers through daily-deal exposure. This finding is further exemplified in the fact that purchase frequency does not predict purchase motivations or product awareness. Thus, due to the increased contact with flash-sale vendors through frequent purchases, there exists a possibility of garnering new clientele through exposure; yet, this is more likely attributed to chance on behalf of the business. Managers may find this information beneficial as the results exploit consumers’ willingness to establish a relationship with the firm. However,

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Texas Tech University, James Brian Aday, May 2013 findings indicate flash-sale purchasers are highly likely to acquire multiple daily-deal offers, perhaps from similar businesses which may be direct competitors, in the future.

Thus, loyalty may be influenced by competitor offers.

The positive attitude factor centered on a component of trust in a majority of the sets and examined consumer willingness to try new products and services offered through flash-sales and their price sensitivities (QPRI), their perceived trust with a new business they are experiencing for the first time (QBEH), and their revisit intentions (QREV). As the structural model demonstrates, positive attitude predicts awareness and motivation for purchase. Overall, data demonstrated consumer confidence in the description of the products and services offered, in addition to exhibiting ease of use regarding the sites. Items measured consumer trust in the flash- sale offerings and indicated a higher level of trust amongst the respondents for deals offered through Groupon or LivingSocial as compared to local newspaper offerings.

The combination of a preference for the major flash-sale websites and a high level of trust towards the offerings should be taken into account by service providers considering the adoption of daily-deals. If their goal is to reach a larger audience which identified willingness to purchase and an established comfort level with a particular medium, in this case the websites, they may want to consider providing their deal via one of these channels instead of a local source to garner sales.

Relating to price, consumers specified $49 as being the maximum purchase price they would consider, and the greater the identified savings and cheaper price, the more likely they are to try the new product. Furthermore, due to the decreased

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Texas Tech University, James Brian Aday, May 2013 financial investment required, respondents indicated a willingness to try new products and services they are unfamiliar with through a flash-sale. Thus, cheaper prices with higher savings lead to decreased risk and openness on behalf of the consumer to potentially purchase a daily-deal, if the offer is lucrative enough. These findings are beneficial to businesses which choose to use daily-deal offerings, as unique service experiences are more openly purchased via this online medium and could provide exposure to niche markets.

Re-visitation intentions were also measured in this factor, and the results demonstrated the potential exists for consumers to revisit based on a positive experience. However, the majority of questions in this set identify satisfactory service or experience in culmination with a future discount being offered as significant motivators for customer revisits. Respondents did indicate they are not opposed to businesses whose flash-sale vouchers do not cover the entirety of the bill. This finding suggests a level of open-mindedness on behalf of the customer, so long as the experience meets expectations. Managers may find this information useful as consumers come into their establishment to redeem their voucher. Individuals should be given some form of incentive to increase the likelihood of their patronizing the firm in the future. However, this is also risky as it may create a customer who expects a discounted rate every visit, which decreases profitability for the business long-term or failure to meet expectations from the customer perspective.

Product awareness was demonstrated to predict purchase motivation, which theoretically makes sense as higher awareness for products or services may lead to

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Texas Tech University, James Brian Aday, May 2013 decreased perceived risk for the customer. This factor provided keen insight into consumer awareness and interests in daily-deals. More specifically, Q5_9 (.70) which described entertainment offerings as serving a more desired role in flash-sales than other products or services, was the highest loading item. Another significant item exhibited a preference for brand name products (Q5_10, .61). The data indicate businesses that offer services which could be considered more along the lines of entertainment likely lead to higher sales rates, as do brand name products. While the entertainment offerings aspect may prove to be beneficial to managers, especially those offering leisure pursuits, brand name retailers or service providers may want to steer clear of flash-sales. The justification can relate to brand loyalty. Consumers who are already loyal to a brand may see the offering on a daily-deal site as tarnishing the brand’s reputation. Additionally, selling the brand at a discount may prove to be a losing strategy from the revenue perspective as loyal customers likely would have purchased the brand name product at full price.

Regarding the purchase motivations factor, consumers demonstrated the significance of location of the firm in relation to their home as a motivating factor.

Several items (Q6_5, Q3_12, and Q2_6) all assessed various aspects related to location and each loaded significantly. The final item, Q3_12, pertained to valid date ranges of the voucher and its effect on purchase motivation. The findings from this factor are beneficial to operators employing daily-deals in a specific geographic area, as is the case for most flash-sale offers. Each site, Groupon and LivingSocial, breaks up its offers based on a geographic location and offers region specific vouchers under

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Texas Tech University, James Brian Aday, May 2013 that locale’s page. Thus, firms should consider staying within the geographic confines established by the flash-sale sites and resist offering deals outside the specified area.

5.5.1 Limitations and future research.

In addition to the inability to demonstrate causation, this study was limited in its methodological approach by sample size, limits to measures, and sample representativeness. The sample size (n = 280) is appropriate for the analysis, but a larger sample size, based on the number of items, for the CFA and structural modeling could have influenced the results. The small sample size and sampling techniques employed generated results which are not generalizable of the entire population; thus, results from this study are not generalizable to all daily-deal users. Additionally, the study was limited by violating internal threats identified by Shadish, Cook, and

Campbell (2002), including assumptions of statistical tests, unreliability of measures, and heterogeneity of units. Since the survey was distributed online via Qualtrics, the inferences about covariation may be inaccurate because there is no way to guarantee the respondents are independent of one another.

Future studies should avoid committing these same violations of internal and external validity. Additionally, future researchers should focus on targeting a larger sample size which is more conducive to CFA and structural modeling. Focusing on a larger sample may provide more in-depth understandings and identify a different factor loading schematic than the end result of this study.

Future research should aim to expand upon the current findings and identify items or factors which would enhance the current model. The final structural model

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Texas Tech University, James Brian Aday, May 2013 was determined to be acceptable through two analyses with reasonable sample sizes, indicating it may be applicable as a suitable model in future studies. Authors should focus on specific geographic markets in an effort to ascertain the role geographic location plays in answer choice. Additionally, splitting the positive attitude factor in two and splitting the items instead of parceling may generate a new factor which better explains consumer choice than the current model.

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CHAPTER 6

LET’S MAKE A DEAL, OR NOT: GAINING INSIGHT INTO THE

RATIONALE AND MOTIVATIONS EMPLOYED BY BUSINESS

OPERATORS WHEN CHOOSING TO OFFER DAILY-DEAL VOUCHERS

6.1 Introduction

Recently, news publications have been inundated with stories related to the latest marketing phenomenon, flash-sales or daily-deals offered by online discount firms such as Groupon and LivingSocial (Bruder, 2011; Goltz, 2011; Rusli, 2011).

While flash-sales are dominated by businesses offering steeply discounted vouchers for purchase, their market offerings have expanded through local non-profit organizations including Groupon Grassroots and LivingSocial donations (Campbell,

2012). Another unique utilization of the flash-sale concept was employed by

President Obama’s inaugural committee in January of 2013, as a daily-deal email was circulated to donors offering specialty tickets to inauguration day festivities as a means of fund raising (Confessore, 2013). Groupon and LivingSocial have gained notoriety since their inception, with Groupon having sold over 170 million discount vouchers and achieving 150 million email subscribers since its founding in 2008

(Lunden, 2012). LivingSocial, a privately held firm, trails closely behind with 66.5 million subscribers, an increase of over 40% from the year prior (Bensinger, 2012).

The swift rise to market dominance Groupon and LivingSocial accomplished has prompted some concerns over the perception that consumers have become tired of daily-deal websites (Clifford and Miller, 2012; Geron, 2011; Greenfield, 2011), the

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Texas Tech University, James Brian Aday, May 2013 risk associated with operating flash-sale sites (Tauli, 2012), and tails of horror on behalf of businesses which have used these distributors (Bhasin, 2011; Clifford and

Miller, 2012; Kavoussi, 2012). Flash-sale vouchers are geared towards offering benefits to consumers and businesses alike (Tuttle, 2012). Consumers typically realize a 50% savings over the regular retail listing price when they purchase the daily-deal.

The offering firms and host website (i.e. Groupon), then equally share the revenue earned from the sale (Liddle, 2012). To put this in perspective, a hotel room which normally sells for $100 would be offered for $50 through LivingSocial. Of the $50 generated from each sale, the merchant would earn $25 and LivingSocial would receive the remaining $25. The discounted sale price is used to entice consumers and guarantee at least some revenue for firms. However, the daily-deal market has become inundated with competitors. One report indicates 30,000 daily-deals are offered per month from more than 650 existing flash-sale sites (Tuttle, 2012).

After witnessing a peak in 2011, reports suggest flash-sale sites are seeing a steady decrease in sales, with Groupon’s active customers, individuals who purchased a deal in the past 12 months, growing by only 3% in the second quarter of 2012

(Clifford & Miller, 2012). Analysts have suggested the slowdown in sales and active membership may be linked to an increased number of competitors, reduced novelty associated with purchasing a daily-deal voucher, and consumer irritation with the barrage of marketing emails (Wong, 2012). A lack of interest on behalf of consumers may be a potential detriment to businesses which are considering flash-sale offerings

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Texas Tech University, James Brian Aday, May 2013 as the expectation of garnering sales and guarantying customers is now carried out at considerably higher risk.

According to Tauli (2012), in the second half of 2011, over 790 daily-deal sites ceased operations. The statistics identified by Tauli (2012) included local flash-sale operators, and highlight the risk associated for businesses considering offering a flash- sale. The rapid closure of numerous daily-deal sites may serve as a red-flag as businesses may fear the flash-sale site they use could go out of businesses, causing uncertainty related to payment and whether they must honor the purchased vouchers.

Further exacerbating the risk for businesses, a recent report identified only 55% of firms which utilized flash-sales between 2009 and 2011 reported a profit (Smith,

2012). While no marketing medium is a guaranteed profit generator, a slightly higher than 50% chance of earning a profit may be seen as risky by firms and detract from the appeal of flash-sale sites on behalf of businesses. A mode of risk mitigation for the firms is established through redemption periods for operators, as they are able to set established time periods in which the offers must be claimed, in turn reducing uncertainty.

Daily-deal vouchers specify redemption periods during which time the discount must be used (Tuttle, 2011). For example, a voucher purchased for $10 with a redeemable value of $20 only maintains the $20 worth for a limited period, such as 6 months. After the promotional period expires the voucher is still worth the paid price

($10) for an additional, previously determined length of time (Tuttle, 2011).

Businesses have typically been able to retain the money earned from unclaimed

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Recent news articles have highlighted horror stories from operators related to their flash-sale experience. A bakery owner in England gained international media attention when her flash-sale, a 75% savings on cupcakes, sold 8,500 vouchers

(Bhasin, 2011). The incredible rush of unanticipated sales forced the owner to hire extra staff, and the expense of the orders and additional workforce ended up costing the firm over $19,000 in unforeseen expenses (BBC, 2011). Similar to the cupcake report from England, a café in Oregon claimed it lost over $10,000 because of poor revenue forecasting provided to them by a flash-sale site when they made the decision to utilize the company (Lee, 2011). Recently, a waffle restaurant in Washington, D.C., was forced to close its operation due to lack of payment by the daily-deal site despite customers redeeming vouchers (Kavoussi, 2012). Media coverage of incidents similar to these is widespread, and may cause owners to be hesitant when considering the possibility of offering a flash-sale.

Currently, no published scholarly research exists pertaining to flash-sale sites.

More specifically, the only credible publications regarding flash-sales are from news sources, and have thus far failed to examine the motivations and justifications businesses employ when choosing whether to offer a daily-deal. While flash-sales were originally a novel concept and garnered immediate sales and traction as a

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Texas Tech University, James Brian Aday, May 2013 marketing medium, the sales generated by the sites have decreased. Recent reports highlighting the slowdown in growth, high risk, and negative media coverage related to the sites may serve as a deterrent to businesses considering flash-sales. Despite such coverage, Groupon and LivingSocial continue to offer deals to a wide variety of geographic locations with fresh deals appearing daily in many larger metropolitan areas. Thus, this study is geared towards building an understanding of the reasoning operators employ when deciding between using a flash-sale site or other marketing medium.

6.1.1 Problem statement.

The lack of extant scholarly research related to Groupon and LivingSocial, as well as other local providers of daily-deals, has created a significant need for research into this phenomenon. Despite flash-sale sites generating millions of dollars in revenue in their brief history, no scholarly research has examined the rationale behind business decisions related to utilizing flash-sale sites. Thus, this article aims to fill this void and will examine, through qualitative interviews, the motivations behind business operators’ decisions to use or avoid sites such as Groupon and LivingSocial. More specifically, this study aims to:

1. Ascertain what motivates businesses to employ flash-sales methods;

2. Establish the level of satisfaction felt by businesses after daily deal coupons

have been offered;

3. Reveal factors influencing companies to avoid utilizing one-off sales; and,

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4. Evaluate whether the perceived benefits for businesses outweigh potential risks

when choosing to offer a flash-sale voucher.

6.2 Review of Literature

6.2.1 Grounded theory.

Corbin, 1994).

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Texas Tech University, James Brian Aday, May 2013 in the line-by-line analysis are combined to formulate overarching themes representative of larger segments of the data (Charmaz, 2006).

6.3 Methodology

Qualitative research frequently relies upon sample sizes which are significantly reduced compared to studies which are quantitative in nature (Mason, 2010). Glaser &

Strauss (1967), considered among academics as the pioneers of grounded theory, contend sample size is dependent upon the point at which the data becomes saturated, or no longer generates new insight. Employing interviews as a means of qualitative research relies upon smaller samples, but allows for the collection of rich, in-depth text, for analysis based on interaction (Potter & Hepburn, 2005). Due to the exploratory nature of this research and the desire to generate results which could provide a strong foundation for subsequent, future studies, qualitative interviews were deemed the most appropriate data collection method based upon the objectives stated.

6.3.1 Business interviews.

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6.3.1.1 Firm selection.

After the location of the interviews was determined, businesses were considered for inclusion based on the examination of the city’s Convention and Visitors ureau

(CVB) webpage. The CVB categorized businesses by primary function (i.e. restaurants, hotels, transportation, etc.) which enabled the researcher to compile a

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RAT-STATS randomly selected five numbers between 1 and 224. The same process was carried out for the list of businesses with previous daily-deal utilization.

Anonymity was guaranteed to participating businesses to encourage honest and accurate responses. Thus, while the names of the firms are proprietary information,

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Texas Tech University, James Brian Aday, May 2013 they consisted of bars, specialty restaurants, fine dining establishments, a wine bar, and casual service operations.

6.3.1.2 Instrument development.

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Texas Tech University, James Brian Aday, May 2013 have offered flash-sale vouchers, questions were formulated to address the reasoning for choosing such a medium, how success was measured, whether the deal was an effective means of advertising, and their overall attitude towards the success or failure of their offer. Firms which have not used a daily-deal site were presented with inquiries related to justification for non-usage, loss of sales and potential new customers, and justification for employing alternative marketing approaches.

6.3.1.3 Data collection.

The business interviews took place over a two week time period in December,

6.3.1.4 Data analysis.

Marshall, & McCulloch, 2011).

Axial coding was employed to identify the interaction between each theme and how the overall components of specific areas affect the other sub-topics through the

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6.4 Results and Discussion

Data for this study was generated based on two near-identical survey instruments. The first set of interview questions was developed for restaurants which have previously employed flash-sales in their operation. A mirrored survey instrument was created to inquire as to why other businesses elected not to utilize daily-deal offers. Each survey contained 11 questions which were geared towards gaining insight into the justification operators consider when choosing whether or not to advertise via flash-sale mediums. The interview questions for both users and non-users of the flash- sales are exhibited in Appendix C and Appendix D, respectively. The data collected was qualitative in nature as the business operators were interviewed at their establishments. All interviews were audio-recorded, transcribed and saved in a

Microsoft Word document, and all identifying characteristics removed in order to protect participants’ identities.

6.4.1 Businesses which HAVE used flash-sales.

The interviews for restaurant managers who had previously employed daily- deals as part of their business model consisted of 11 questions. These questions were geared towards facilitating an understanding of motivations for utilizing such a medium, and measuring the perceived success or failure of the venture. The five businesses which were interviewed were selected from the city’s CV listing after confirming they had offered a flash-sale within the previous 12 months. Telephone calls were made to these businesses soliciting participation, and upon agreement personal interviews were scheduled with willing individuals. Seventeen (17)

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Texas Tech University, James Brian Aday, May 2013 restaurants were identified as having participated in a flash-sale offering in the previous 12 month period. After cold calling each firm, it was established five of the firms on the list no were no longer in business. Thus, interviewing five of the 12 remaining firms represented a response rate of 41.67%. The firms interviewed included the following restaurant types: fast casual, casual, wine bar, full-service restaurant with wine bar, and a bar.

6.4.1.1 Justification for utilizing flash-sales.

As daily-deals are a relatively new marketing opportunity, interview respondents were first asked the primary reasoning for selecting this form of sales instead of traditional marketing mediums such as TV, radio, or print advertisements and coupons. Market reach was heavily discussed in the responses, as was customer value, incentive to try a new firm, inexpensive advertising method, and social media.

The operators all agreed that the use of flash-sales enabled them to reach previously untapped clientele and helped raise awareness about their operation. The minimal expense per contact provided access to target market segments which may have not been economically feasible through traditional marketing media. Email lists were discussed in-depth and a majority of the operators demonstrated an opportunity to engage the customer through interaction. A direct email message was determined to be convenient and non-intrusive, and allowed the customer to purchase the voucher via their smartphone or tablet. Respondents indicated difficulty exists in measuring the results of any marketing message, especially TV and radio ads. Operators signaled reliance upon customers divulging where they heard about the firm during their visit

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Texas Tech University, James Brian Aday, May 2013 and increased sales volumes as the primary means of gauging the effectiveness of the advertisements.

Providing a benefit to customers in the form of the steeply discounted voucher, combined with the ease of purchase and redemption, was the primary justification for commissioning the daily-deal sites as well. One manager stated, in 2012 marketing his firm is “not primarily driven by radio or television commercials” anymore with a majority of the younger generations relying upon podcasts, Hulu, and Netflix instead of the traditional AM/FM radio and cable TV channels. However, the same operator contend customers “can get email on their phone and it takes virtually no time to see what the daily-deal is” which will ideally lead to purchase. Social media and the presence of daily-deal sites on social media portals such as Facebook, and the ability of consumers to share their recent flash-sale purchases with those in their friends list, was viewed as a means of generating word-of-mouth marketing for the restaurants.

Based on the findings, it can be implied that social media plays a major role in the decision making characteristics of the firms and influenced their decision related to advertising medium selection. The vast reach of flash-sale sites coupled with their social media presence demonstrated the operators wanted end-users to have the ability to share their deal purchase and experience. Through sharing and word-of-mouth, the firms are hopeful they will generate sales and expose new guests to their brand. Ease of purchase and delivery method and the feature of daily deal emails with one-click purchasing influenced the restaurants in their selection of a daily-deal provider. The necessitation of these features signaled operators recognize the difficulty associated

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Texas Tech University, James Brian Aday, May 2013 with garnering attention through advertising, and desired a delivery method which was easy for the customer to use. Additionally, flash-sale vouchers may be redeemed at the business via smartphone, eliminating the need for printing and supplying an added convenience for businesses as they simply have to scan the barcode displayed on the screen. Thus, ease of use is twofold as operators prefer it for themselves and their guests, and were influenced in their decision based on ease of purchase and redemption methods.

Surprisingly, the managers did not discuss specific mobile applications available through Groupon and LivingSocial, which may demonstrate a need for daily- deal sites to emphasize the applications and its benefits, such as ease of use, to potential businesses. Customer value and incentive based purchases demonstrated the firms desired the customer to feel they are garnering significant savings, and the sale price offered to end-users needed to be considered. The interviewed restaurants are relying upon flash-sales as a means of customer generation, as demonstrated in the responses, and influencing the prospective buyer to purchase the voucher was the main step towards achieving customer generation. Thus, promotional reach, social media inter-connectedness, content delivery features, and flexibility in discount options offered to potential firms by flash-sale providers may aid in increasing the number of daily-deals.

6.4.1.2 Flash-sale site selection and justification.

A majority of the interviewed firms, three out of five, identified having employed a local daily-deal, such as those offered by the local newspaper, TV news

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Texas Tech University, James Brian Aday, May 2013 station, or radio station before utilizing either Groupon or LivingSocial. The managers indicated the relative success for their firms from the local daily-deal was a motivating factor when choosing a flash-sale provider. One operator mentioned his business had only used the local sources and never considered the possibility of a nationwide flash- sale marketer. The reasoning behind daily-deal site selection was unique to each business; however, overarching themes revolved around the persistence of the flash- sales distributors, market reach, and feedback options. One manager indicated the decision on which site to use was made at the corporate level. The remaining restaurants identified having been pursued by all providers, local and national, and the persistence of each influenced their decision.

A major factor discussed was the forecasting and feedback options provided by

Groupon and LivingSocial. One operator stated LivingSocial arranged the redemption component so the manager could monitor spending in relation to the voucher value, explaining if the customer “came in and spent $10 over the voucher amount, I could type in what they spent over it, and at the end of the report it had a line which stated you made this much over” the initial voucher value. According to another manager, he employed Groupon because the company provided “feedback and predictions as to what we should sell, and what we can expect as a profit” prior to offering the flash- sale. Market reach was mentioned by four of the five operators, and results indicated the flash-sale medium provided an inexpensive means of maximizing business exposure to large email member lists, and the daily-deal websites.

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Texas Tech University, James Brian Aday, May 2013

The findings from these interviews provide unique insight into the reasons managers cite for utilizing a flash-sale site. While the representation of sites used was seemingly equal, two businesses offered deals through Groupon, two via LivingSocial, and one through local sources, an overarching sentiment was the persistence of salespeople from the daily-deal sites. The operators indicated they were continually contacted by each site, a tactic which ultimately influenced their selection. This information could be beneficial to flash-sale providers as it is indicative that their persistence in contacting these firms led to sales and new daily-deal offerings. The reach of the flash-sale email distribution lists and websites was viewed as a cost- effective means for generating customers.

Some firms were willing to identify the inner workings of their daily-deal arrangement, and the findings demonstrated businesses would receive half the sale price of the voucher. For example, if a restaurant offered a voucher for $20 worth of product, according to most flash-sale guidelines, the sale price would be $10. Since the daily-deal site and the restaurant share the cost of the sale 50/50, the restaurant would receive only $5 despite being liable for $20 worth of food or drink. The firms justified this small return on investment (ROI) because they were able to easily identify the number of vouchers sold and redeemed, which enabled them to know how many people actually visited based solely on the flash-sale offering. Furthermore, the vast number of people subscribed to the email listings for each site was an incentive because of the potential new customers that could be targeted. This justification exhibits how the customer contact each site offers entices flash-sale providers.

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Texas Tech University, James Brian Aday, May 2013

Forecasting and feedback options, ease of use, and the ability to track redemption patterns were the three primary motivators which influenced firms’ selection of flash-sale providers. A key attribute difference between the national flash- sale providers, Groupon and LivingSocial, and their local counterparts was identified when the businesses indicated the local providers gave the email addresses of those who had purchased a daily-deal directly to the firm. The national providers did not provide this data, which prevented the firm from contacting purchasers in the future.

However, the feedback tools provided via the national flash-sale sites demonstrated firms desire a dashboard which provides real-time data and allows for managerial input to determine the effectiveness of the daily-deal.

6.4.1.3 Structure of the daily-deal offer.

Once it was established how businesses determined their desire to advertise a daily-deal and the method for selecting which site to use, information related to the specific structure of each offering was elicited. More specifically, the question was geared toward ascertaining whether the flash-sale offer was quantity or time limited, and the established cutoff points. Cutoff points refer to how long the deal was available for purchase, such as the deal being available in unlimited quantity for a 24 hour period or a specific number of vouchers available. Additionally, operators were questioned about the components and pricing strategy of their specific deal. Three of the restaurants explained their deal was set-up on a quantity limited basis, and two firms utilized a specific time period sale. The time-period offerings were available for either 48 or 72 hours. The quantity limited offerings varied amongst the firms as well

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Texas Tech University, James Brian Aday, May 2013 with the number of vouchers available for purchase ranging from 1,000 to just under

4,000.

One firm which employed the limited time period would not divulge the number of deals sold, and the other stated consumers had an opportunity to purchase their deal for 48 hours, and sold approximately 490 vouchers. For the firms which offered a limited supply of vouchers for purchase, the cutoff points varied greatly. One restaurant which utilized limited quantity refused to identify the number sold, whereas another manager conveyed his operation established a cap of 1,000. The remaining firm stated it “initially set it up to sell 500” and after selling out and talking to his flash-sale representative, they “added 1,000 which sold out in an hour.” After adding more vouchers throughout the day, management “finally had to cut it off at just under

4,000” in order to avoid overextending their resources or oversaturating the market.

When discussing the particular arrangement and specifications of their daily- deal offerings, each firm indicated their redeemable voucher was launched as a 50% discount for the user with the firm receiving half the sale price. While four of the surveyed businesses served alcohol, only two allowed the voucher to be used towards alcoholic beverage purchases. The operators divulged similar strategies in their pricing approaches with the average voucher not worth enough to cover a check for two people eating at the establishment. The overwhelming thought process behind this reasoning was to force the patron to spend a few extra dollars in an effort to increase revenue. The justification for prohibiting alcohol purchases was derived in similar thinking, with the restaurant trying to compel patrons to purchase high revenue items

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Texas Tech University, James Brian Aday, May 2013 via cash or card. A unique aspect to the responses came from two firms, which employed time-redemption constraints on the daily-deals. For example, one establishment wanted to increase dinner business and included the stipulation the voucher could only be used after 2 pm.

The inquiry related to quantity and time constrained offerings provided keen insight into the ramifications of each method. The data demonstrated businesses which set a maximum quantity witnessed higher sales than the firm which allowed their deal to be available for a specified 48 hour period. Potential reasoning behind the sales discrepancy may be a lack of urgency on behalf of the consumer viewing the deals. A deal which states limited quantity or only half remaining may encourage the customer to make a purchase based on fear of missing the deal; however, the 48 hours remaining statement may provide the consumer a chance to contemplate the purchase and decide against purchase or the user may simply forget to go back and make the purchase. Thus, it may be wise for businesses offering flash-sales to consider a quantity limited sale as a way to increase the probability of purchase.

Pricing considerations and redemption values, more specifically strategically establishing the voucher at a dollar amount which requires consumers to spend additional money, was perceived as a strong motivating factor for firms configuring their daily-deal offering. Allowing firms additional flexibility in pricing by may lead to more businesses choosing to employ flash-sales in the future. Furthermore, pricing flexibility provided a sense of control for operators, reducing perceived risk associated with the deal. The ability to combine a time-redemption period coupled with strategic

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Texas Tech University, James Brian Aday, May 2013 redemption values provides flexibility while allowing the firm to address specific shortcomings in their current operations, for example the dinner hour, and may be viewed as an incentive for implementing daily-deals by firms in the future.

6.4.1.4 Measuring satisfaction and information gathering.

Data from the interviews established the respondents utilized daily-deals as a means of garnering new customers with the intention of transitioning these individuals into long-term, repeat clients. Based on this stated desire, the initial interaction between firm and daily-deal purchaser occurs upon voucher redemption at the establishment. This logic implies inquiries were geared towards ascertaining if and how satisfaction of these first time guests was measured, and whether personal information was collected from the individuals in order to stimulate relationship building. Three out of the five businesses indicated they did not collect any information or measure satisfaction of guests when redeeming their flash-sale vouchers. One operator stated their business encouraged patrons to sign “up for our value card” which enabled the restaurant to collect “an email address and contact information.” Each time the value card was swiped, the restaurant had the ability to monitor the frequency of visits and average check from the client. However, the manager did acknowledge the firm failed to indicate in the value card database whether the person was redeeming a voucher; however, they were able to collect some form of contact information. The other firm which collected information also measured satisfaction as they “heavily pushed our comment cards to all guests redeeming” a voucher. Included on the comment card was a blank for email addresses,

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Texas Tech University, James Brian Aday, May 2013 and the establishment collected approximately 1,000 email addresses which have since been utilized in direct email marketing campaigns.

Firms justify the utilization of flash-sale sites with the potential for creating long-term customers from this method; however, the findings highlight a confounding realization that firms are not actively monitoring guest satisfaction or collecting information from daily-deal redemptions. This prevents the restaurant from being able to accurately measure the number of customers gained and potentially indicates what may be a troubling trend among businesses which employ daily-deals. Without measuring satisfaction, the restaurant has virtually no feedback on the guest experience and the business cannot establish the likelihood of repeat patronage from the consumer based on the initial experience. Furthermore, failing to collect information severely limits the firm as future correspondence cannot be sent to the guest. Restaurants should focus on collecting contact information as well as customer generated feedback from daily-deal customers in an effort to grow their potential marketing base and encourage repeat visitation.

6.4.1.5 Effectiveness of the daily-deal.

Businesses assume risk when employing advertising as the expense associated with the marketing may not generate enough revenue to produce a profit. Flash-sale offerings maintain the same risk, as companies offer their products at a significantly discounted rate to generate sales. Thus, the high expense and associated risk of the flash-sale vouchers necessitated the measurement of overall effectiveness of the deals for the firms. Furthermore, as the restaurants are reliant upon consumers redeeming

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Texas Tech University, James Brian Aday, May 2013 the vouchers, as this is where the interaction occurs and the customer to firm relationship is established, inquiries were formulated to measure redemption rates.

Analysis of the effectiveness of the flash-sales produced results which indicated the restaurants were seemingly split in their opinion of overall success with the daily-deals. Two firms produced responses insinuating the offering was not effective. Positive feelings towards flash-sales and the perception that they were successful were generated by two firms. One restaurant responded “it’s so hard for me to tell, I just don’t know.” The operator associated with the unsure response stated sales had increased, but a majority of individuals redeeming the vouchers were regular customers. This restaurant perceived they were losing profit on established customers using the vouchers, as these individuals likely would have purchased their meal at full value.

The two firms which demonstrated feelings of no overall benefit conceded their business likely gained exposure, but most daily-deal customers took a “one and done” approach. Additionally, these firms responded the demographically diverse email lists used by the flash-sale sites resulted in visits from customers which were not their target clientele. As these consumers were not likely to revisit based on factors such as income, the offer was deemed ineffective for long-term customer generation.

The firms which experienced positive effectiveness from the flash-sales provided varying justifications as to why they believed the effort was valuable. One firm recognized the expiring promotional value was beneficial as redemption was somewhat low, allowing the firm to make money off of the unclaimed vouchers.

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Texas Tech University, James Brian Aday, May 2013

Another restaurant indicated vouchers were effective as the establishment “had a lot of return on them” with first time visitors, who were brought to the businesses by the daily-deal, becoming regular customers.

Measuring the results of customer generation was extremely limited for the restaurants as four of the businesses provided responses highlighting the difficulty associated with measuring return. Operators stated an inability to identify customer return rates after their initial visit with the voucher. Managers explained it was seemingly impossible to track revisits, and this posed a challenge for restaurants as they were unsure of the true success of the daily-deal. Information elicited pertaining to the number of vouchers sold compared to the amount redeemed produced data which indicated the businesses did track each. However, only one business would state redemption rates, identifying “more than half” of the vouchers were redeemed. The remaining firms were unwilling to discuss their sales and redemption totals.

The varied responses related to effectiveness raise key concerns. Some firms indicated perceived success from the daily-deals; however, the majority of operators provided a response demonstrating they did not monitor or were unable to gauge the number of re-visits based on the initial flash-sale purchase. This finding, coupled with the number of regulars who purchased the voucher, leads to the perception that the restaurants were unable to establish gains in long-term customer volume. The high redemption rates of the vouchers highlight a potential deception for the operators as the sales figures may be inflated during the redemption period but not sustainable long-term. Visits from non-targeted customers could detract from the firm as a person

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Texas Tech University, James Brian Aday, May 2013 using a voucher at a higher priced establishment may not have the financial means to become a frequent guest, and likely took a one time only approach to the opportunity.

An inability to measure repeat visitation seemingly defeats the purpose of a flash-sale from the operator’s perspective, as there is no way to ensure the financial effort will be profitable. Thus, operators which choose to employ flash-sales may consider establishing protocol to measure overall re-visitation rates, thus enabling them to make more educated decisions regarding future daily-deal efforts.

6.4.1.6 Establishing overall success.

An interview question was formulated to measure whether the firms rated the overall experience with the flash-sales, from the offer through redemption, as a positive endeavor. Restaurants unanimously identified the entire flash-sale experience as pleasant or positive with one exception, the constant barrage from flash-sale companies trying to influence the company to extend their deal or run a subsequent sale. Results demonstrated upon completion of the flash-sale offer, the restaurants were contacted “seemingly non-stop by salespeople every month.” However, while satisfaction with the overall experience was deemed positive, only two out of the five restaurants provided a response expressing the daily-deal was worth the effort. The same two firms were the only respondents which stated they would consider offering a future deal as well. The remaining firms described how other businesses may be better suited to offer flash-sales, but the tight profit margins and inability to measure long- term customer gains made it difficult to justify the expense and effort. The firms

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Texas Tech University, James Brian Aday, May 2013 which did not claim success exhibited answers which demonstrated they would likely pursue advertising via social media or direct mail in the future.

Results demonstrated the overall attitudes of operators towards flash-sale sites which hosted their deal. As stated previously, a majority of the operators identified having utilized local daily-deal sites prior to their usage of a national site and their experience with the local flash-sale somewhat influenced their decision to offer a voucher on a national site. However, results demonstrate the firms’ aspiration for success from the larger sites did not come to fruition. Responses included frustration from the high volume of sales calls from the site operators as a deterrent of overall satisfaction; however, prior responses in the interview pertaining to the reasoning behind offering a daily-deal identified the high frequency of calls as a motivating factor for flash-sale adoption. Thus, even though the sales calls induced irritation amongst respondents, they contributed to previous daily-deal offers. Thus, this form of solicitation seems poised to remain a key tactic for flash-sale operators. The minimal profit margins per sale may be unique to restaurants, as stated in the interviews, and potentially demonstrated a need for flash-sale providers to focus on other industries, where decreased profit margins are not as detrimental to the firm.

6.4.2 Businesses which have NOT used flash-sales.

In an effort to gain a more comprehensive view of the motivations behind why businesses do or do not utilize daily-deals, interviews were also conducted with managers who had not previously used this type of marketing method. As previously mentioned, 224 restaurants were identified as meeting the criteria for inclusion in this

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Texas Tech University, James Brian Aday, May 2013 data collection. Using random number generation via RAT-STATS, 14 restaurants were selected and subsequently solicited for interviews. Five restaurants consented to participation, resulting in a response rate of 35.71%. The firms interviewed included the following restaurant types: fast casual, casual, fine dining, full-service restaurant, and a coffee shop with a limited food menu.

6.4.2.1 Justification for not implementing.

The interview process revealed a lack of control, margins, other advertising means, and ineffective customer generation as the primary justification for electing not to adopt flash-sales as part of their current marketing efforts. Further analysis identified three of the five restaurants cited “percentage split” “profit margin” and

“percentage of sales price” as the primary detractors when evaluating flash-sale implementation. Two firms focused their marketing efforts on word-of-mouth and experience based marketing, and thus saw no benefit to offer a flash-sale based upon their business models. Four firms stated their concerns over control. More specifically, apprehension about who was purchasing the vouchers, and the inability to “guarantee that my existing customers wouldn’t buy up all of the coupons.” The majority of restaurants, four out of five, claimed that while drastic discounts are attractive to the flash-sale consumer, such a reduction in potential profits coupled with their low existing profit margins would make it incredibly difficult to recoup their potential losses.

Findings derived from the data identified pricing and revenue split offered via the daily-deal providers as a significant concern. The split the operators were

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Texas Tech University, James Brian Aday, May 2013 referring to involved the division of the sale price of the voucher, half of the sale to the firm and the remainder to the flash sale site, and was labeled as insurmountable as the split would require an unrealistic number of revisits from the purchaser to make the deal feasible. Focusing on word-of-mouth and experience based marketing, through free meals and tastings, signaled the firm’s confidence in their product and ability to attract customers based on positive guest experiences. Advertising through free product translates into financial loss by the restaurant, as each meal given away would add to the marketing expense. However, the operators believed this approach would maintain long-term benefits as they were able to target specific market segments. Lack of control was notable and echoed sentiments similar to statements made during the interviews with managers who had previously utilized flash-sales, which indicated regular customers purchased a majority of the vouchers, not new targeted customers.

Control or lack thereof, must be addressed by daily-deal providers and could potentially be resolved through the creation of demographic specific email lists.

Pricing margins and the split of sales is something which is difficult to repair. Flash- sale providers would be required to take less of a percentage, which they may be unwilling to concede.

6.4.2.2 Solicitation by flash-sale providers.

While none of the businesses interviewed as part of this data collection had used daily-deals before that was not due to a lack of interest from flash-sale site administrators. Participants acknowledged frequent contact from the sales departments of Groupon, LivingSocial, and local media outlets. A majority of the operators, 4 out

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Texas Tech University, James Brian Aday, May 2013 of 5, reported Groupon “has called at least two dozen times” and “called and emailed relentlessly,” stating “Groupon has been the most persistent.” Operators provided information detailing contact from other sources; however, the resounding response highlighted a bombardment of sales pitches from Groupon representatives.

As mentioned in the interviews with restaurants which employed flash-sale sites, a motivating factor in their decision was the persistence of sales calls. However, the operators in these interviews maintained different attitudes and alluded to the bombardment of calls as an “annoyance” and “constant interruption.” Thus, relentless contact from flash-sale providers does occur, and it can be surmised that firms may find the consistent pressure from sites such as Groupon to be a nuisance, and avoid using them due to the disruption they create.

6.4.2.3 Affecting brand image.

Questions were formulated to address the perception of brand devaluation in the eyes of consumers if the firm were to appear on a flash-sale site. Respondents exhibited answers demonstrating they did not perceive offering their products on a daily-deal site as detrimental to brand image. Analysis identified the keyword of

“startup” occurring in a majority of responses. This terminology referred to new, small-scale restaurants which utilize flash-sales in an effort to raise awareness and create a customer base. Another recurring theme revolved around coupon or flash-sale shopper. Participants provided answers indicating daily-deal consumers pursue large savings and these individuals may be appreciative of the restaurants which offer their products. Four of the five operators signaled flash-sale discounts as similar in nature to

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Texas Tech University, James Brian Aday, May 2013 offering a discount or daily specials which the restaurants offered at various meal periods. For example, feedback such as “we run really good lunch and dinner specials” and “we do a lot of lunch specials” demonstrates how savings are offered directly to customers. Overall, businesses indicated savings or discounts are prevalent in most food service operations and the medium on which they are offered does not necessarily detract from brand image or devalue the product.

Based on the data, it can be implied that most operators acknowledge the difficulty of establishing a new restaurant, raising awareness of the firm, and attracting customers. Newer local restaurants could potentially benefit from utilizing flash-sales as a way to spread awareness, while providing customers with an affordable experience. As stated in the interviews, specials and discounts are delivered in a variety of ways, and relying upon a daily-deal site to convey savings does not indicate a positive or negative aspect of the business. Restaurants advertise specials through various mediums, and while the non-flash-sale restaurant savings may not be as high for the consumer, potential clients are able to take advantage of lower prices. In-house specials relates to control, which was discussed in a previous question. Limiting discounts or specials to in-house operations allows the firm to control pricing as well as maintain an interaction with customers.

6.4.2.4 Coupons in current operations.

Data analysis identified the surveyed operators do not offer coupons similar to those on daily-deal sites. However, respondents acknowledged providing lunch, dinner, and happy hour specials as an enticement to customers. One firm confirmed

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Texas Tech University, James Brian Aday, May 2013 they are restrictive in their coupons and instead rely upon a value card, where customers purchase a set number of drinks and get one free. Two participants indicated they utilize a similar rewards feature in addition to their lunch specials.

These two restaurants utilize a punch card specifically designed for their lunch menu which enables a free meal after a set number of purchases. A recurring theme amongst respondents for this question revolved around appetizers, and free appetizer cards exhibited the most frequent type of discount cards distributed at the establishments.

However, most of the coupons required the purchase of a full price entrée to qualify for the offer. One manager stated his operation distributed coupon cards to large groups which visit the firm, but stipulates the discount must be used during a future visit, thus encouraging repeat visitation. However, outside of the lunch, dinner, and happy hour specials, free appetizer cards, and rewards type cards, high-discount coupons were virtually non-existent in daily operations.

Findings are indicative of restaurants which maintain reliable and consistent customer bases, and do not rely upon enticement to generate sales. Utilizing specific reward cards allowed the businesses to monitor repeat visits while encouraging future patronage as the guest is rewarded for their loyalty. The lunch, dinner, and happy hour specials demonstrated restaurants are concerned about appealing to individuals seeking a discount and they meet the customers’ needs with the savings. However, the daily specials may indicate a desire of the firm to increase sales during specific meal periods. Offering specials, rewards cards, and free appetizer coupons could potentially detract customers from purchasing daily-deals as they are able to get a discount each

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Texas Tech University, James Brian Aday, May 2013 visit which, over time, could equate to more significant savings than those offered by flash-sales.

6.4.2.5 Gaining competitive advantage.

Increasing market reach and share has been identified as an objective of firms employing flash-sales. Thus, non-users were presented with an inquiry regarding whether site users had gained a competitive advantage over them. Consensus in responses exhibited by the non-users signaled they did not feel as though an advantage was gained via employment of flash-sales by their competitors, or their electing to avoid this method placed them at a disadvantage. An unexpected finding related to the inquiry was derived as the participants demonstrated an unawareness of which firms had employed flash-sales, and contended most of the users were start-ups. The context of start-ups generated results signifying the participants did not consider these types of restaurants as their competition. For example, several respondents specified national chains as their main competitors and contended that the chains did not rely on daily- deal mediums. One operator harped upon his establishment’s loyal customers stating

“we get them in here and they love it, they may try the other place, but they’ll come back.” Another responded, “we’ve been here for many years, and they know about us.

I don’t think these users steal customers.”

The resounding reply of “no” dominated the responses to this question, and specified the interviewed restaurants are confident in their product, establishment, and the positive experiences for their customers. Firms did not identify feeling threatened by restaurants which employ flash-sale sites and referred to the business model of

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Texas Tech University, James Brian Aday, May 2013 larger chain restaurants, and their non-usage of the sites as justification for not feeling threatened. The findings may potentially highlight pride in ownership of the operators, and demonstrate the close relationship between the operators and their customers.

6.4.2.6 Potential future incorporation of flash-sales.

While interview respondents had previously avoided using flash-sales, the researcher wanted to determine their openness towards the possibility of a flash-sale in the future. The entire sample, with the exception of one firm, indicated a response similar to “I would never say never, but not at this time.” This direct quote from one operator was easily generalized as the overall feeling exhibited by a majority of the firms. The respondents remained open to the potential of employing flash-sales, but clearly signified this type of marketing was not in their immediate plans.

Thus, it would appear as though these firms have not taken a staunch stance opposing daily-sites; rather, the business model currently utilized has yielded favorable results. Furthermore, the interviewees do not identify daily-deals as an effective means of increasing business for their particular restaurant. However, data analysis signaled worsening market conditions or increased consumer demand for flash-sale offers could potentially cause some of the firms to change their current mindset and reconsider daily-deals in the future.

6.5 Conclusion

This study was unique as it signifies an early approach towards building an understanding of the motivations which influence restaurant operators in their decisions towards flash-sale implementation. Interviews with businesses which have

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Texas Tech University, James Brian Aday, May 2013 utilized this medium and firms which rely upon other marketing methods aided in providing a complete picture from multiple angles. The interviewed operators can be classified under the heading of restaurant; however, the varied types of these businesses (i.e. fast casual, wine bar, etc…) established the diverse attitudes taken towards flash-sale sites within a specific industry.

Respondents demonstrated flash-sales are generally employed with the expectation of generating a new customer base through exposure. The usage of daily- deals is further implemented to raise awareness of the firm via the provider website and widely distributed email lists. Participants who had used the daily-deals identified increased sales during the voucher redemption period which indicated the flash-sales generated customers. However, the user group recognized a significant number of daily-deal purchasers were established customers. Firms which have not employed flash-sales indicated a major hesitation towards adoption of daily-deals was the concern that their loyal customers would purchase the vouchers. Thus, the lack of control pertaining to who purchased the vouchers was substantiated by the firms which have employed the deals. These results demonstrated what may be perceived as an unachievable need, controlling who purchases the daily-deals, in order to assure new, first-time customers, are being generated.

Firms electing methods other than daily-deals exhibited responses which identified the margins and percentage split of the sale price of the voucher between their restaurant and the flash-sale sites as a major drawback. These businesses identified their operations as reliant upon profit margins which would be in jeopardy if

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Texas Tech University, James Brian Aday, May 2013 they offered the steep discounts required in flash-sales. However, users of the daily- deal offers demonstrated strategic pricing of their vouchers enabled them to compel flash-sale customers to spend money out of pocket during their visit. Thus, the reluctance to employ deals solely based on percentage split appears to be negated if the firm established the pricing of their deal in a manner which guaranteed revenue in addition to the voucher purchase price. Daily-deal providers should focus on clarifying the importance of pricing strategy, and provide guidance for firms which exhibit resistance of adoption based on the fear of overcoming the margins and percentage split.

The generation of long-term customers through daily-deals was identified as a primary goal; however, firms which utilized the sites declared an inability to accurately measure the repeat visitation frequency of guests who were initially exposed to the establishment via daily-deals. Furthermore, three of the five establishments recognized their failure to collect data related to the customer experience or some form of future contact information. The lack of future communication means created an inability to try and persuade the customers to patronize the business again in the future. The failure to collect customer information made it difficult to identify the long-term benefits the flash-sale had on the firm, and is an area which should be addressed in the future by firms offering flash-sales.

Collecting an email address would allow for follow up with flash-sale guests and allow for direct marketing.

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The results demonstrated quantity specific offers sold more vouchers than time-period controlled deals. This finding demonstrates how by creating a sense of urgency, consumers may feel as though they should buy before the vouchers sell out, thus producing a more effective means of generating sales. Firms which have avoided flash-sales indicated lack of control as a major concern related to margins and the deals serving as a discount for established customers. However, the ability to control the number sold might potentially lessen the perceived risk for the firm. Limiting the number of vouchers sold would provide a small sample of flash-sale customers redeeming the vouchers. The firm could monitor whether the majority of customers are from their established base and if these consumers spend more than the redemption value during the visit. The monitoring of the small sample may confirm their expectations but could possibly identify new customers as purchasing a majority of the vouchers, potentially leading to a broader daily-deal offering from the firm.

Results measuring the perceived success of the flash-sale vouchers amongst users were split. Two firms identified success and two indicated no real benefit. The remaining firm exhibited responses of uncertainty about the outcome of their offer.

However, the non-users all demonstrated satisfaction and perceived success with their current marketing endeavors. The employment of daily lunch, dinner, and happy hour specials, customer loyalty cards, and experience based marketing efforts were identified as generating a sustainable customer base. These results highlight the risk associated with flash-sales as only two firms found the method successful compared to the five non-users unanimously stating success with other marketing mediums. Thus,

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Texas Tech University, James Brian Aday, May 2013 the overall perception based on the data is that flash-sales may not be an effective marketing medium for the restaurant industry and may be viewed as a high risk strategy by these firms.

6.5.1 Limitations and future research.

This research is not without limitations. Specifically, the sample size was limited and focused on a narrow sample of restaurant types. The geographic location of these restaurants, their time in operation, and their current business financial state may have influenced the results. Most of the firms which have not utilized flash-sales have been in operation for at least 7 years, with established, loyal customer bases.

Those which have employed such sites varied in their length of operation, but maintained a limited target market based on their geographic location. The respondents were constrained by questions formulated by the author, and questions which would have painted a broader picture or provided more insight may potentially have been overlooked. Furthermore, some questions were limiting in their approach, allowing the participant controlled freedom in their responses.

Future studies should aim to expand this area of research and focus on a wider selection of restaurant types as well as focus on more respondents. Focusing on multiple geographic regions or on a more populated geographic area may yield significantly different results and demonstrate findings which either support or disagree with the findings of this paper. Future studies should also seek out firms based on which daily-deal site they utilized as a more specific picture of each flash- sale operation could be obtained. This study was exploratory in nature and was

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Texas Tech University, James Brian Aday, May 2013 intended to serve as a baseline of knowledge for managers, academics, and future researchers. Future studies are planned which will examine push and pull motivations of flash-sale messages, return on investment requirements, and pricing strategies of flash-sales as a risk mitigation technique.

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CHAPTER 7

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

This study focused on multiple aspects of flash-sales in the hospitality and tourism industries. More specifically, three separate studies were conducted in an effort to (1) measure consumer motivations for purchases of flash-sales and the factors which facilitate re-visitation to businesses, (2) examine the motivations and justifications utilized by operators when considering the implementation of daily- deals, and (3) identify and categorize, based on SIC codes, the varying types of businesses featured on flash-sale sites with specific focus on hospitality and tourism offerings. The data collected during the research process allowed for the formulation of results which relate to the intended goals.

This chapter provides a summation of key findings from each study and discussion of potential implications for consumers, businesses, and flash sale providers. Furthermore, limitations will be presented and future research recommendations provided.

7.1 Summary of the Study

The introduction of this study highlighted the significant role flash-sale providers have encompassed as a relatively young marketing medium. Daily-deal sites have continued to expand the number of offerings, cities served, and the amount of members in their email distribution lists since their inception, creating a lucrative marketing opportunity for interested businesses. This section also highlighted the perceived risk associated with the daily-deal providers, the difficultires encountered by

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Texas Tech University, James Brian Aday, May 2013 businesses attempting to generate new customers, the lack of profits realized by firms, and consumer weariness as the novelty of such sits has seemingly begun to affect sales.

Presenting extant literature related to marketing, consumer behavior, and purchasing theory, in addition to relevant previous reserach pertaining to each of the studies identified key concepts and aided in formulating measurement instruments.

Marketing theories were discussed in-detail and specific concepts identified in relation to the consumer survey. Furthermore, consumer behavior theory, more specifically

Social Exchange Theory, was highlighted as the interaction between firms and consumers plays an integral role in customer willingness to revisit an establishment.

Each relevant topic presented in the literature review was incorporated into varying aspects of the study and provided justification for the employment of the measurement instruments utilized.

The methodology section provided keen insight into the overall design of the study, the measurement instruments, data collection procedures, and statistical analysis techniques. The research necessitated three steps which included: (1) a content analysis of the daily offerings from selected cities on both the Groupon and

LivingSocial websites, (2) a consumer survey measuring motivations for purchase regarding daily-deals and revisit likelihood based on their redemption experience, and

(3) qualitative interviews with restaurants which have employed flash-sales and firms which have chosen to rely upon other marketing methods. Upon completion of the

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Texas Tech University, James Brian Aday, May 2013 methodology section, the paper shifted to the three separate studies, presented as articles, and their results.

7.2 Summary of Results

The first measured component of the research relied upon a content analysis of randomly selected cities on the daily-deal websites of Groupon and LivingSocial. The study aimed to identify the various types of firms which maintained the highest frequency of offerings. In an effort to measure hospitality and tourism specific businesses, six Standard Industrial Classification Codes (SIC codes) were employed as segmentation variables. The utilization of the SIC codes allowed for the examination of specific segments, such as restaurants and entertainment, of the various flash-sale offers.

Based on the findings, results demonstrated the restaurant industry was well represented on both sites. Offerings which were classified as amusement and recreation activities maintained the second highest representation amongst the offers.

Listings for amusement and recreation activities were not solely tourism specific; however, these offers provided activities available within the host cities which tourists may potentially purchase for entertainment during their stay. The results identified the prevalence of restaurants available and demonstrated this segment of the hospitality industry as being well represented. However, other industry segments, such as hotels, were relatively sparse with some cities not offering deals for these firms. Thus, it was ascertained from the analysis that hospitality and tourism specific firms are not abundant on the flash-sale sites of the monitored cities. The justification for avoidance

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Texas Tech University, James Brian Aday, May 2013 of flash-sales on behalf of these firms may be the result of their efforts to attract customers from outside their current city. For example, it would seem illogical for a hotel in a specific city to offer a flash-sale for that municipality as most residents are unlikely to desire a hotel stay in their city of residence.

The second component of the research relied upon an online survey distributed to end-users of flash-sale sites. Respondents were limited to individuals who had purchased a daily-deal offer within the previous 12 months. Questions were derived from relevant literature and presented in a manner which aimed to identify the factors and components of daily-deal offerings which motivate consumers to purchase, and the dynamics behind their intention to revisit a firm where the voucher was redeemed based on their initial experience.

Results demonstrated consumers’ online purchase frequency, positive attitude

(receptiveness), and product awareness directly influences their purchase motivations in relation to flash-sale offers. Purchase frequency inquiries identified respondents generally maintain higher purchase patterns, purchasing more than 5 daily-deals annually, and sought out flash-sale offerings based on the perceived benefit of each offer. These respondents further exhibited responses which demonstrated willingness or openness to become long-term customers of firms where vouchers are redeemed based on their experience. The positive attitude factor generated from the data identified trust as a key component of influence related to daily-deal purchases and the likelihood of revisit from the customer. Product awareness influenced purchase motivations and demonstrated higher product awareness increases the potential for

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Texas Tech University, James Brian Aday, May 2013 purchase, regardless of the user’s familiarity with the firm offering the deal. Inquiries related to purchase motivation were specific to factors which influence the consumer when considering procurement of a flash-sale offering. The findings from the motivations questions identified flash-sale users are heavily persuaded by the perceived savings of the voucher and the relative location of the firm to their residence.

Based on the findings, it was established the majority of those surveyed purchased multiple daily-deal offers annually. They exhibited a willingness to become long-term customers; however, they also indicated the perceived savings was influential in their purchase decision. These findings highlight the potential risk for businesses, as consumers state the possibility of loyalty, but demonstrate they are easily persuaded to switch their loyalty when a high-savings voucher is presented.

Thus, daily-deal consumers are considered price-shoppers which consistently seek the lowest price with highest return offers, and represent a customer base which is not sustainable.

The final segment of this study incorporated qualitative interviews with restaurants which have adopted flash-sales, and operators that have employed various other marketing mediums. Two separate interview forms were generated, for users and non-users, in an effort to measure operator’s justifications for employment or avoidance. The interviews were conducted face-to-face at the specific establishments and were audio-recorded. The results identified businesses which have utilized flash- sale sites maintain mixed feelings in regards to effectiveness. Only two operators

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Texas Tech University, James Brian Aday, May 2013 expressed belief in the success of their flash-sale offering. The remaining respondents stated most flash-sale vouchers were purchased by their established customers, defeating the attempt of the firm to generate new clientele.

Restaurants which have not employed flash-sales in their operations maintained the mentality that their current marketing efforts were better suited for each operation. These managers exhibited responses which indicated they feel as though flash-sales are an effective means of garnering customers and generating awareness for new businesses, but are not effective for firms with proven customers.

Furthermore, these operators expressed apprehension in relation to the percentage splits offered via the flash-sale providers, and exhibited responses that the relatively low return on each voucher sold would make profit generation seemingly impossible.

Each measurement technique and its corresponding results were presented as an independent article within this study. The next section of this paper will focus on a comparison of results as well as managerial, consumer, and flash-sale provider implications from the articles.

7.3 Discussion

Results from this study demonstrate the wide ranging motivations and influences associated with flash-sales. Daily-deals serve as an intermediary and maintain a unique relationship with businesses and consumers. The daily-deal providers are reliant upon consumers and businesses for their business model to work; consumers depend on offerings from the providers and businesses, and businesses require the providers to host their offer and are dependent upon consumer purchases.

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The interdependency of each segment; providers, businesses, and consumers; on other sections within the daily-deal model is explained in the findings, and will be discussed in this section.

The content analysis identified an under-representation of hospitality and tourism specific firms outside of restaurants. The reasoning behind a lack of offerings for theses categories was explained in the independent article as operators focusing on other mediums to target markets outside of their city of operation. However, based on the findings from the consumer survey, individuals purchasing multiple offers for minimal financial commitment and low revisit likelihood, it could be ascertained tourism operators maintain a different justification for avoidance. Operators may be aware or have experience with customers of flash-sale offerings only visiting the establishment for redemption of the voucher and failing to return. Thus, rather than target single patronage customers, they are focused on establishing long-term customers through other mediums.

The consumer survey findings related to being influenced by the discount and low revisit likelihood seemingly echoed the concerns of businesses which have not adopted flash-sales, and the complaints of those operators which utilized the deals.

Non-users expressed resistance to adoption of daily-deals based on the perception they would not generate new customers as the users of such sites likely sought out deals rather than the firms. Firms which have utilized flash-sales stated they had no way of tracking repeat visits, with three operators identifying the deal as an ineffective customer generation technique. Thus, the consumers which purchased the vouchers

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Texas Tech University, James Brian Aday, May 2013 likely visited for redemption and never returned, resulting in a potential loss to the businesses.

The findings from this study may be beneficial to managers in the hospitality industry as they highlight consumer desires when considering a flash-sale purchase, and provides experiences of firms which have relied upon flash-sales. More specifically, managers need to realize the volatility associated with flash-sale purchases and the unlikelihood of these consumers becoming frequent guests.

However, as demonstrated in the results, strategic pricing strategies which seemingly obligated guests to spend money out of pocket during redemption enabled the firm to generate additional sales while exposing new clientele to the restaurant. Thus, the opportunity to influence the consumer in a positive manner provides the firm an opportunity to positively influence the guest with the hope of generating revisits from the experience.

Consumers identified a preference for high-discounts and close proximity from the firms offering the flash-sale. Thus, businesses considering offering a daily-deal should research the demographics of customers within close proximity to their establishment, and determine whether these individuals are likely to become long-term customers. Furthermore, businesses which operate on higher mark-ups may possess the ability to offer a steeply discounted voucher as it will not be as influential on their potential profits.

Finally, consumers and businesses highlighted important aspects they look for in flash-sale offers and the providers. The businesses which have used daily-deals

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Texas Tech University, James Brian Aday, May 2013 indicated the ease of purchase options for consumers and the interactive dashboards available for tracking redemption as key motivators for adoption. Flash-sale sites should emphasize these features to businesses they are targeting as a means of potential sales generation. Percentage split was an overarching theme amongst businesses which have avoided flash-sale sites as they stated their current profit margins and the return on investment from the daily-deal vouchers would make it incredibly difficult to generate a profit. Thus, while the percentage split may be non- negotiable based on the provider’s business model, sites such as Groupon and

LivingSocial should highlight the potential benefits of incorporating strategic pricing or time-specific redemption periods as a means of influencing targeted firms.

7.4 Limitations

This study was not without numerous limitations. The content analysis measurement rubric was developed by the author and relied upon SIC codes as a means of classifying offers available on each site. However, the creation of the rubric could be perceived as personal bias as the researcher was forced to use discretion when deciding how offers should be categorized. Analysis of the sites from another study may rely upon different classification criteria which would produce varying results. The flash-sale sites were examined on a daily basis for offers; however, firms which demonstrated high popularity may have potentially sold all of their vouchers prior to data collection. For example, an offer with limited number of vouchers may have sold out immediately, thus removing the firm from the site as no vouchers remained prior to the researcher collecting data from the site.

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The sample for the consumer survey was limited in sample size as an ideal sample would have contained more respondents. The survey link was distributed via

Facebook to individuals within the author’s friends list, thus limiting the opportunity to take the survey to these friends and individuals they may have shared the link with.

The vast amount of material posted on Facebook, which consequently shows up on an individual’s news feed, may have prevented them from observing the survey invitation posting. On the same premise, the posting of numerous links by Facebook users inundates news feeds and the invitation to participate may not have attracted attention based on the associated text seeking participants. The possibility of spam and the wording of spam messages may have influenced potential respondents to deem the link harmful, and prevented them from opening it as well.

Online surveys are limited to individuals that maintain Internet access and may have further limited the number of responses. Hosting online surveys prevents the researcher from ensuring participants are taking the survey in a manner which provides honest answers and not simply clicking answers as a means of finishing the questionnaire rapidly. This type of survey is also at risk of individuals taking the survey multiple times in an effort to influence the results if the topic is of high importance to them. Furthermore, as a chance to win an iPad was offered as incentive, respondents may have taken the survey multiple times in an effort to increase their odds of winning.

Another limitation of the study relates to the wordage used in the survey instrument. Flash-sales and daily-deals are relatively new concepts and participants

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Texas Tech University, James Brian Aday, May 2013 may have misinterpreted the inquiries and provided responses which may have been different with alternative wording.

Flash-sale sites are represented in multiple areas throughout the U.S., and while Groupon and LivingSocial maintain seemingly consistent operations in terms of deals offered, local flash-sale providers may operate on a different premise. Thus, while a filter question ensured the survey participants had purchased a flash-sale in the previous 12 months, it did not specify where the flash-sale was acquired from.

Consumers may have had a negative experience with one provider and a positive with another, thus leading to their discretion in answer choices regarding which firm they were rating. The respondents may have been influenced in their experiences based on each question as well, thinking of a certain experience for select questions and another experience for the remaining questions in the survey.

Social desirability may have influenced respondents as research indicates some respondents may be influenced to provide answers which they feel are socially acceptable instead of their true feelings (Fisher & Katz, 2000). Thus, the survey was limited in its assumption that responses were truthful and accurate.

During data collection several news reports were distributed highlighting horror stories associated with flash-sale sites. Groupon was also featured in several news articles in relation to their initial public offering. Media influence may have affected answer choices as individuals could have allowed the negative coverage to transition into their responses.

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7.5 Recommendations for Future Research

Future studies should aim to avoid or mitigate the effects of the limitations described. This study was unique as it is a first of its kind in relation to flash-sale sites.

Future research should expand upon the ideas and findings presented in an effort to provide a clearer picture of daily-deal operations from multiple perspectives. Content analyses should be carried out on a regional basis with strenuous categorization requirements for offers. Employing a specific set of standards as to how offers are categorized may produce findings which contrast this research, and present a more representative picture of the daily-deal offerings in relation to the hospitality and tourism industries.

Expanding the consumer survey based on the identified model should be a focus of future research articles. A foundation has been established, and focus on a more accurate measurement of the specific factors may provide findings which support this research while identifying variables which should have been included.

Focusing on users of specific sites, such as only Groupon purchasers or consumers which purchase industry specific offerings (i.e. restaurants) may potentially yield data which demonstrates variation from these findings. Furthermore, consumers segmented by specific site (i.e. LivingSocial) could demonstrate site specific variables which influence their purchase decision.

A potential research topic related to the current study may revolve around customer loyalty to flash-sale providers. Studying specific providers may provide insight into possible preferences for specific sites based on a multitude of facets,

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Texas Tech University, James Brian Aday, May 2013 including types of products offered, ease of purchase, or an established sense of trust between the consumer and daily-deal site. Examining consumer experiences with flash-sale purchases is another topic which could emerge from the current study.

Consumers purchase flash-sales from daily-deal vendors and are required to redeem their vouchers at a physical store location. Thus, if the consumer has a negative experience, where do they assert the blame? And what is the effect the experience will exert on future purchases? Future researchers may consider these questions when formulating their research objectives.

The qualitative interviews study should be expanded upon also. Focusing on a larger sample or a different industry may identify specific differences between restaurants and the studied business type. Concentrating on a specific restaurant type, such as fast-casual, may yield significant findings as well. Researchers should consider interviews with firms which have employed the same flash-sale provider in an effort to understand specific dynamics of each site, and whether variation exists related to satisfaction between providers. Furthermore, studies should incorporate a quantitative component to measure operators’ motivation for choosing or avoiding flash-sales.

7.6 Conclusion

Flash-sale sites, while relatively young as a marketing medium, have garnered significant interest from consumers and businesses alike. As a result, daily-deal providers are continuously attempting to expand their offerings and provide products and services consumers desire. The lack of available literature created an opportunity

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Texas Tech University, James Brian Aday, May 2013 for this study to establish a foundation of research related to flash-sales. The findings from the study are unique and should contribute to scholars, operators, and daily-deal providers. Based on the findings, it is apparent flash-sale sites are viewed as a favorable medium by businesses and are able to generate sales from consumers.

However, research in this area needs to be expanded to present a more representative assessment of the daily-deal industry.

This research created a structural model for future studies and identified factors in consumer behavior which may influence their purchase motivations. The findings provided keen insight into the purchase and implementation of daily-deals by consumers and businesses, respectively.

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REFERENCES

Abell, J.C. (2012, January 23). Who buys all those Google ads? An infographic

breakdown. Wired.com. Retrieved April 23, 2012 from

http://www.wired.com/business/2012/01/google-ad-buyers-infographic/

Advertising Age. (2011, December 12). What we shared: Top 10 viral advertising

campaigns of 2011. Advertising Age. Retrieved June 2, 2012 from

http://adage.com/article/special-report-book-of-tens-2011/top-10-viral-

advertising-campaigns-2011/231497/

Aerts, W., Van Campenhout, G., & Caneghem, T. V. (2008). Clustering in dividends:

Do managers rely on cognitive reference points?. Journal of Economic

Psychology, 29(3), 276-284. doi: 10.1016/j.joep.2007.10.003

Ahrholdt, D.C. (2011). Empirical identification of success-enhancing web site signals

in e-tailing: An analysis base d on known e-tailers and the theory of reasoned

action. Journal of Marketing Theory and Practice, 19(4), 441-458.

Amazon. (2012, April 13). Form 10-K (Annual Report). Amazon.com. Retrieved May

6, 2012 from http://phx.corporate-ir.net/phoenix.zhtml?c=97664&p=irol-

reportsannual

Agarwal, S., Erramilli, M. K., & Dev, C. S. (2003). Market orientation and

performance in service firms: Role of innovation. Journal of Services

Marketing, 17(1), 68-82.

Armstrong, G., & Kotler, P. (2000). Marketing: 5th Ed. Englewood Cliffs, NJ:

Prentice-Hall.

317

Texas Tech University, James Brian Aday, May 2013

Arthur, C. (2012, May 15). Twitter now has 10m users in UK. The Guardian.

Retrieved May 18, 2012 from

http://www.guardian.co.uk/technology/2012/may/15/twitter-uk-users-10m

Arvola, A., Vassallo, M., Dean., M., Lampila, P., Saba, A., Lahteenmaki, L., &

Shepherd, R. (2008). Predicting intentions to purchase organic food: The role

of affective and moral attitudes in the Theory of Planned Behavior. Appetite,

50(2/3), 443-454. doi: 10.1016/j.appet.2007.09.010.

Associated Press. (2012, May 18). Number of active users at Facebook over the years.

The Seattle Times. Retrieved June 10, 2012 from

http://seattletimes.nwsource.com/html/businesstechnology/2018236355_apuste

cfacebookipogrowth.html

Azjen, I. (1985). From intentions to actions: a Theory of Planned Behavior. In J. Kuhl

and J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11-

39). New York, NY: Springer.

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and the

Human Decision Processes, 50(1), 179-211.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social

behavior. Englewood Cliffs, NJ: Prentice-Hall.

Bakos, J. Y. (1997). Reducing buyer search costs: Implications for electronic

marketplaces. Management Science, 43(12), 1676-1692.

Bakos, J. Y. (1991). A strategic analysis of electronic marketplaces. Management

Information Systems Quarterly,15(3), 295-310.

318

Texas Tech University, James Brian Aday, May 2013

Balbanis, G., & Reynolds, N. L., (2001). Consumer attitude towards multi-channel

retailers’ web sites: The role of involvement, brand attitude, Internet

knowledge and visit duration. Journal of Business Strategies, 18(2), 105-129.

Bandura, A. (1982). Self-efficacy mechanism in human agency. American

Psychologist, 37, 122-147.

Bandura, A. (1991). Social-cognitive theory of self-regulation. Organizational

Behavior and Human Decision Processes, 50(2), 248-287.

Barat, S., & Paswan, A. K. (2005). Do higher face-value coupons cost more than they

are worth in increased sales?. Journal of Product & Brand Management, 14(6),

379-386. doi: 10.1108/10610420510624530.

Barnett, E. (2011, March 24). MySpace loses 10 million users in a month. The

Telegraph. Retrieved May 22, 2012 from

http://www.telegraph.co.uk/technology/myspace/8404510/MySpace-loses-10-

million-users-in-a-month.html

BBC. (2011, November 18). Groupon vouchers decision cost Berkshire business

thousands. BBC: News. Retrieved from: http://www.bbc.co.uk/news/uk-

england-berkshire-15791507

Bearden, W. O., &Etzel, M. J. (1982). Reference group influence on product and

brand purchase decisions. Journal of Consumer Research, 9(2), 183-194.

319

Texas Tech University, James Brian Aday, May 2013

Bensinger, G. (2012, June 6). LivingSocial steers clear of IPO. The Wall Street

Journal. Retrieved from

http://online.wsj.com/article/SB10001424052702304765304577478723102501

452.html

Bercovici, J. (2012, June 4). Why is Twitter soaring while Facebook is slowing?.

Forbes. Retrieved June 6, 2012 from

http://www.forbes.com/sites/jeffbercovici/2012/06/04/why-is-twitter-soaring-

while-facebooks-slowing/

Berends, W. R. (2004). Proven pricing strategies and when to use them. Price and

profit. Ontario, Canada: Berends & Associates.

Berman, B., & Evans, J. R. (1995). Retail management: A strategic approach (6th

Edition). Englewood Cliffs, NJ: Prentice-Hall Inc.

Bertini, M., Ofek, E., & Ariely, D. (2009). The impact of add-on features on consumer

product evaluations. Journal of Consumer Research, 36(1), 17-28.

doi: 10.1086/596717.

Bhasin, K. (2011, November 22). This baker had to make 102,000 cupcakes because

of a Groupon deal and lost a ton of money. Business Insider. Retrieved from

http://www.businessinsider.com/london-baker-makes-102000-cupcakes-

groupon-deal-2011-11

Bhattacharya, C. B., & Sen, S. (2003). Consumer-company identification: A

framework for understanding consumers’ relationships with companies.

Journal of Marketing, 67(2), 76-88.

320

Texas Tech University, James Brian Aday, May 2013

Bhattacherjee, A. (2001). Understanding information systems continuance: An

expectation-confirmation model. MIS Quarterly, 25(3), 351-370.

Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problem and techniques of

chain referral sampling. Sociological Methods and Research, 10(1), 141-163.

Bloomberg BusinessWeek. (2006, March 27). Wiser about the web. Bloomberg

BusinessWeek. Retrieved June 1, 2012 from

http://www.businessweek.com/magazine/content/06_13/b3977401.htm

Bolton, R. N., Shankar, V., & Montoya, D. Y. (2010). Recent trends and emerging

practices in retailer pricing. In M. Krafft and M.K. Mantrala (Eds.), Retailing

in the 21st century: Current and Future Trends (pp. 301-318). New York, NY:

Springer.

Bonnici, J., Campbell, D. P., Fredenberger, W. B., & Hunnicutt, K. H. (1997).

Consumer issues in coupon usage: An exploratory analysis. Journal of Applied

Business Research, 13(1), 31-40.

Bolton, R. N., Kannan, P., K., & Bramlett, M. D. (2000). Implications of loyalty

program membership and service experiences for customer retention and

value. Journal of the Academy of Marketing Science, 28(1), 95-108.

Borges, A., Chebat, J. C., & Babin, B. J. (2010). Does a companion always enhance

the shopping experience?. Journal of Retailing and Consumer Services, 17(4),

294-299.

Bose I., & Chen, X. (2009). Quantitative models for direct marketing: A review from

systems perspective. European Journal of Operational Research, 195(1), 1-16.

321

Texas Tech University, James Brian Aday, May 2013

Boyd, J. (2002). In community we trust: Online security communication at eBay.

Journal of Computer-Mediated Communication, 7(3), 0.

Brian, M. (2012, March 5). Apple his 25 billion App Store downloads. USAToday:

Tech. Retrieved May 19, 2012 from

http://www.usatoday.com/tech/news/story/2012-03-05/apple-app-

downloads/53372352/1

Briggs, B. (2012, June 7). IRCE 2012 report: Top 500 retailers continue outgaining

overall market. Internet Retailer. Retrieved May 9, 2012 from

http://www.Internetretailer.com/2012/06/07/top-500-retailers-continue-

outgaining-over

Brodie, R.J., Winklhoffer, H., Coviello, N.E., & Johnston, W.J. (2007). Is e-marketing

coming of age? An examination of the penetration of e-marketing and firm

performance. Journal of Interactive Marketing, 21(1), 2-21.

Breukel, A., & Go, F. M. (2009). Knowledge-based network participation in

destination and event marketing: A hospitality scenario analysis perspective.

Tourism Management, 30(2), 184-193.

Bruder, J. (2011, March 9). In Groupons $6 billion wake, a fleet of start-ups. The New

York Times: Business Day. Retrieved from

http://www.nytimes.com/2011/03/10/business/smallbusiness/10sbiz.html?_r=0

Buhalis, D., & Law, R. (2008). Progress in information technology and tourism

management: 20 years on and 10 years after the Internet-the state of etourism

research. Tourism Management, 29(4), 609-623.

322

Texas Tech University, James Brian Aday, May 2013

Burnett, G. (2000). Information exchange in virtual communities: A typology.

Information Research: An International Electronic Journal, 5(4). Retrieved

from www.shef.ac.uk/,is/publications/infres/ircont.html

BusinessWire. (2012, April 18). Internet ad revenues hit $31 billion in 2011, historic

high up 22% over 2010 record-breaking numbers. BusinessWire: A Berkshire

Hathaway Company. Retrieved May 31, 2012 from

http://www.businesswire.com/news/home/20120418006029/en/Internet-Ad-

Revenues-Hit-31-Billion-2011

Campbell, J. (2012, November 18). How Groupon and LivingSocial are raising

millions for charity using social media. SocialMedia Today. Retrieved from

http://socialmediatoday.com/jcsocialmarketing/1006991/how-groupon-and-

livingsocial-are-raising-millions-charity-using-social-med

Carpenter, J. M., & Balija, V. (2010). Retail format choice in the US consumer

electronics market. International Journal of Retail and Distribution

Management, 38(4), 258-274.

Carpenter, J. M. (2009). Extreme value shoppers in the United States. International

Journal of Retail and Distribution Management, 37(9), 804-813.

Carpenter, J. M., & Moore, M. (2009). Utilitarian and hedonic shopping value in the

US discount sector. Journal of Retailing and Consumer Services, 16(1), 68-74.

Cartman, J., & Ting, R. (2009). Designing for a progressive medium. In M.J. Nolan &

B. Winter (Eds.), Strategic mobile design: Creating engaging experiences

(pp. 41-60). Berkeley, CA: New Riders.

323

Texas Tech University, James Brian Aday, May 2013

Cavanagh, S. (1997). Content analysis: Concepts, methods and applications. Nurse

Researcher, 4(3), 5-16.

Century Market Research. (2012, Feb 15). Online advertising one of the fastest

growing advertising segments. Yahoo! Finance. Retrieved June 2, 2012 from

http://finance.yahoo.com/news/Online-Advertising-One-iw-991038561.html

Chang, H. H., & Liu, Y. M. (2009). The impact of brand equity and brand preference

and purchase intentions in the service industries. The Service Industries

Journal, 29(12), 1687-1706.

Chang, T. Z., & Wildt, A. R. (1994). Price, product information, and purchase

intention: An empirical study. Journal of the Academy of Marketing Sciences,

22(1), 16-27. doi: 10.1177/0092070394221002.

Charmaz, K. (2006). Constructing grounded theory: A practical guide through

qualitative analysis. Thousand Oaks, CA: Sage Publications.

Charters, D. (2002). Electronic marketing and privacy issues in business-marketing:

The ethics of doubleclick experience. Journal of Business Ethics, 35(4), 243-

254.

Chatterjee, P. (2008). Are unclicked ads wasted? Enduring effects of banner and pop-

up ad exposures on brand memory and attitudes. Journal of Electronic

Commerce Research, 9(1), 51-61.

Chaudhuri, A. (1995). Brand equity or double jeopardy?. Journal of Product and

Brand Management, 4(1), 26-32.

324

Texas Tech University, James Brian Aday, May 2013

Chen, Y., & Pearcy, J. (2010). Dynamic pricing: When to entice brand switching and

when to reward customer loyalty. RAND Journal of Economics, 41(4), 674-

685.

Chen, Q., Clifford, S. J., & Wells, W. D. (2002). Attitude toward the site II: New

information. Journal of Advertising Research, 42(2), 33-45.

Chenail, R.J. (2012). Conducting qualitative data analysis: Reading line-by-line, but

analyzing meaningful qualitative units. The Qualitative Report, 17(1), 266-

269.

Cheng, J.M.S., Wang, E.S.T., Lin, J.Y.C., & Vivek, S.D. (2009). Why do customers

utilize the Internet as a retailing platform? A views from consumer perceived

value. Asia Pacific Journal of Marketing and Logistics, 21(1), 144-160.

Chen-Yu, J. H., & Seock, Y. K. (2002). Adolescents’ clothing purchase motivations,

information sources, and store selection criteria: A comparison of male/female

and impulse/nonimpusle shoppers. Family and Consumer Sciences Research

Journal, 31(1), 50-77.

Chesler, M. A. (1987). Professionals’ views of the ‘dangers’ of self-help groups.

Unpublished manuscript, Department of Sociology, University of Michigan,

Ann Arbor, Michigan.

Cheung, C. M. K., Chan, G. W. W., & Limayem, M. (2009). A critical review of

online consumer behavior. In S. Bandyopadhyay (Ed.), Contemporary

research in e-branding (pp. 262-279). New York, NY: Information Science

Reference.

325

Texas Tech University, James Brian Aday, May 2013

Chevalier, J., & Goolsbee, A. (2003). Measuring prices and price competition online:

Amazon.com and BarnesandNoble.com. Quantitative Marketing and

Economics, 1(2), 203-222.

Cho, C.H. (2003). Factors influencing clicking of banner ads on the WWW.

CyberPsychology & Behavior, 6(2), 201-215.

Chrysostome, E., & Rosson, P. (2009). The Internet and SME internationalisation:

Promises and illusions. International Business and Entrepreneurship

Development, 4(1/2), 107-118.

Chu, S.C. (2011). Viral advertising in social media: participation in Facebook groups

and responses among college-aged users. Journal of Interactive Advertising,

12(1), 30-43.

Clemons, E.K. (2009). The complex problem of monetizing virtual electronic social

networks. Decision Support Systems, 48(1), 46-56.

Clifford, S., & Miller, C. C. (2012, August 17). Merchants and shoppers sour on daily

deal sites. The New York Times: Technology. Retrieved from

http://www.nytimes.com/2012/08/18/technology/merchants-and-shoppers-

sour-on-daily-deal-sites-like-groupon.html

Cohan, P. (2012, May 10). Can Facebook tap $11 billion mobile ad market to justify

P/W of 206?. Forbes. Retrieved May 29, 2012 from

http://www.forbes.com/sites/petercohan/2012/05/10/can-facebook-tap-11-

billion- mobile-ad-market-to-justify-pe-of-206/

326

Texas Tech University, James Brian Aday, May 2013

Cohen, N., & Arieli, T. (2011). Field research in conflict environments:

Methodological challenges and snowball sampling. Journal of Peace

Research, 48(1), 423.

doi: 10.1177/0022343311405698

Confessore, N. (2013, January 11). Fund-raising is lagging, so far, for inaugural plans.

The New York Times: Politics. Retrieved from

http://www.nytimes.com/2013/01/12/us/politics/fund-raising-lags-for-

inaugural-events.html

Corbin, J., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and

evaluative criteria. Qualitative Sociology, 13(1), 3-21.

Corfman, K. P., & Lehmann, D. R. (1987). Models of cooperative group decision-

making and relative influence: An experimental investigation of family

purchase decisions. Journal of Consumer Research, 14(1), 1-13.

CPRJournal. (2008). The grounded theory method of qualitative research. Counseling

& Psychotherapy Research Journal: Grounded Theory. Retrieved from

http://www.cprjournal.com/documents/groundedTheory.pdf

Crompton, J. L. (1979). Motivations for pleasure vacation.Annals of Tourism

Research, 6(4), 408-424.

Crompton, J. L., & Ankomah, P. K. (1993). Choice set propositions in destination

decisions. Annals of Tourism Research, 20(3), 461-476.

327

Texas Tech University, James Brian Aday, May 2013

DeCuir-Gunby, J.T., Marshall, P.L., & McCulloch, A.W. (2011). Developing and

using a codebook for the analysis of interview data: An example from a

professional development research project. Field Methods, 23(2), 136-155.

Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of

online feedback mechanisms. Management Science, 49(10), 1407-1424.

Dick, A. S., & Basu, K. (1994). Customer loyalty: toward an integrated conceptual

framework. Journal of the Academy of Marketing Science, 22(2), 99-113.

Dickson, P. R., & Sawyer, A. G. (1990). The price knowledge and search of

supermarket shoppers. Journal of Marketing, 54(3), 42-53.

De Canniere, M. H., De Pelsmacker, P., & Geuens, M. (2009). Relationship quality

and the theory of planned behavior models of behavioral intentions and

purchase behavior. Journal of Business Research, 62(1), 82-92.

doi: 10.1016/j.jbusres.2008.01.001.

De Vere, K. (2012, April 3). At $679,000 a day, Google Play makes and eighth of iOS

App Store’s $5.41M daily revenue. Inside Mobile Apps. Retrieved June 6,

2012 from http://www.insidemobileapps.com/2012/04/03/at-679000-a-day-

google-play-makes-an-eighth-of-ios-app-stores-5-41m-daily-revenue/

DMA. (2012). What is the direct marketing association?. Direct Marketing

Association. Retrieved June 1, 2012 from http://www.the-

dma.org/aboutdma/whatisthedma.shtml

Donovan, R. J., & Rossiter, J. R. (1982). Store atmosphere: an environmental

psychology approach. Journal of Retailing, 58(1), 34-57.

328

Texas Tech University, James Brian Aday, May 2013

Dou, W., Nielsen, U.O., & Tan, C.M. (2002). Using corporate websites for export

marketing. Journal of Advertising Research, 42(5), 105-115.

Duryee, T. (2011, July 28). Expedia and Groupon sell 15,000 travel deals in three

days. The Wall Street Journal.Retrieved July 29, 2011 from

http://allthingsd.com/20110728/expedia-and-groupon-sell-15000-travel-deals-

in-three-days/

Edwards, S.M., Li, H., & Lee, J.H. (2002). Forced exposure and psychological

reactance: Antecedents and consequences of the perceived intrusiveness of

pop-up ads. Journal of Advertising, 31(3), 83-95.

Elliot, A. J., & Thrash, T. M. (2002). Approach-avoidance motivation in personality:

Approach and avoidance temperaments and goals. Journal of Personality and

Social Psychology, 82(5), 804-818. doi: 10.1037/0022-3514.82.5.804.

Elliot, S. (1996, December 3). Banner ads on Internet attract users. The New York

Times: Technology. Retrieved June 12, 2012 from

http://www.nytimes.com/1996/12/03/business/banner-ads-on-Internet-attract-

users.html

EmailStatCenter. (2012). ROI. EmailStatCenter.com. Retrieved May 3, 2012 from

http://www.emailstatcenter.com/ROI.html eMarketer. (2012, February 22). Google edges closer to Facebook as US display

advertising becomes two-horse race. eMarketer: Digital Intelligence. Retrieved

June 12, 2012 from http://www.emarketer.com/PressRelease.aspx?R=1008856

329

Texas Tech University, James Brian Aday, May 2013

Enders, A., Hungenberg, H., Denker, H.P., & Mauch, S. (2008). The long tail of social

networking. Revenue models of social networking sites. European

Management Journal, 26(3), 199-211.

Erdem, T., Swait, J., & Louviere, J. (2002). The impact of credibility on consumer

price sensitivity. International Journal of Research in Marketing, 19(1), 1-19.

Eroglu S. A., Machleit, K. A. & Davis, L. M. (2001). Atmospheric qualities of online

retailing: A conceptual model and implications. Journal of Business Research,

54(2), 177-184. doi: 10.1016/S0148-2963(99)00087-9.

Ergolu, S. A., Machleit, K. A., & Davis, L. M. (2003). Empirical testing of a model of

online store atmospherics and shopper responses. Psychology & Marketing,

20(2), 139-150. doi: 10.1002/mar.10064

Erickson, B. H. (1979). Some problems of inference from chain data. In K. F.

Schuessler (ed.), Sociological Methodology (pp. 276-302). Cambridge, MA:

Blackwell Publishers.

Evans, D.S. (2009). The online advertising industry: Economics, evolution, and

privacy. Journal of Economic Perspectives, 23(3), 37-60.

Evans, D.S. (2008). The economics of the online advertising industry. Review of

Network Economics, 7(3), 359-391.

Evans, D.S. (2003). Some empirical aspects of multi-sided platform industries. Review

of Network Economics, 2(3), 191-209.

330

Texas Tech University, James Brian Aday, May 2013

Excalas, J. E., & Bettman, J. R. (2005). Self-construal, reference groups, and brand

meaning. Journal of Consumer Research, 32(3), 378-389.

doi: 10.1086/497549.

Faircloth, J. B., Capella, L. M., & Alford, B. L. (2001). The effect of brand attitude

and brand image on brand equity. Journal of Marketing Theory and Practice,

9(3), 61-75.

Faletski, I. (2012, March 8). Apple’s App Store: An economy for 1 percent of

developers. CNET: News. Retrieved May 22, 2012 from

http://news.cnet.com/8301-32973_3-57392575-296/apples-app-store-an-

economy-for-1-percent-of-developers/

Farber, D., (2012, May 31). Is Facebook building and iTunes/Google Play

competitor?. CNET: News. Retrieved June 5, 2012 from

http://news.cnet.com/8301-1023_3-57443891-93/is-facebook-building-an-

itunes-google-play-competitor/

Ferguson, R. (2008). Word of mouth and viral marketing: Taking the temperature of

the hottest trends in marketing. Journal of Consumer Marketing, 25(3), 179-

182.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An

introduction to theory and research. Reading, MA: Addison-Wesley.

Fletcher, D. (2010, May 20). How Facebook is redefining privacy. Time Magazine.

Retrieved June 13, 2012 from

http://www.time.com/time/printout/0,8816,1990798,00.html

331

Texas Tech University, James Brian Aday, May 2013

Fong, J., & Burton, S. (2006). Online word-of-mouth: A comparison of American and

Chinese discussion boards. Asia Pacific Journal of Marketing and Logistics,

18(2), 146-156. doi: 10.1108/13555850610658282.

Forsyth, D. R. (2010).Structure.In D. R. Forsyth (Ed.), Group dynamics, (pp. 143-

176). Belmont, CA: Wadsworth, Cengage Learning.

Fox, E. J., Postrel, S., & McLaughlin, A. (2007). The impact of retail location on

retailer revenues: An empirical investigation. Unpublished manuscript, Edwin

L. Cox School of Business, Southern Methodist University. Dallas, TX.

Fox, E., Montgomery, A., & Lodish, L. (2004). Consumer shopping and spending

across retail formats. Journal of Business, 77(2), 25-60.

Frederickson, C. (2012, January 25). New forecast: US mobile ad spending soars past

expectations. eMarketer. Retrieved June 9, 2012 from

http://www.emarketer.com/PressRelease.aspx?R=1008798

Friedrich, R., Grone, Fl, Holbling, K., & Peterson, M. (2009). The march of mobile

marketing: New chances for consumer companies, new opportunities for

mobile operators. Journal of Advertising Research, 49(1), 54-61.

Frischmann, B. (2001, June 8). Privatization and commercialization on the Internet

infrastructure: Rethinking market intervention into government and

government intervention into market. The Columbia Science and Technology

Law Review, 1. Retrieved from

http://www.stlr.org/html/volume2/frischmann.pdf

332

Texas Tech University, James Brian Aday, May 2013

Fruchter, G. E., & Tapiero, C. S. (2005). Dynamic online and offline channel pricing

for heterogeneous customers in virtual acceptance. International Game Theory

Review, 7(2), 137-150.

Gao, T., Sultan, F., & Rohm, A.J. (2010). Factors influencing Chinese your

consumers’ acceptance of mobile marketing. Journal of Consumer Marketing,

27(7), 574-583.

Galante, J. (2009, October 23). Amazon.com breaks dot-com record as profit beats.

Bloomberg. Retrieved May 7, 2012 from

http://www.bloomberg.com/apps/news?pid=newsarchive&sid=ay_0eQaPKW

Ganesh, J., Reynolds, K. E., Luckett, M., & Pomirleanu, N. (2010). Online shopper

motivations, and e-store attributes: An examination of online patronage

behavior and shopper typologies. Journal of Retailing, 86(1), 106-115.

George, J. F. (2004). Theory of planned behavior and Internet purchasing. Internet

Research, 14(3), 198-212. doi: 10.1108/10662240410542634.

Geron, T. (2011, August 30). First Facebook, now Yelp scaling back daily-deals:

Report. Forbes: Tech. Retrieved from

http://www.forbes.com/sites/tomiogeron/2011/08/30/first-facebook-now-yelp-

scaling-back-daily-deals-report/

Ghose, A., & Yang, S. (2009). An empirical analysis of search engine advertising:

Sponsored search in electronic markets. Management Science, 55(10), 1605-

1622.

333

Texas Tech University, James Brian Aday, May 2013

Gilkenson, J.H., & Reynolds, K. (2003). Determinants of Internet auction success and

closing price: An exploratory study. Psychology and Marketing, 20(6), 537-

566.

Glaser, B.G. (1978). Theoretical sensitivity: Advances in methodology of grounded

theory. Mill Valley, CA: Sociological Press.

Glaser, B. G., & Holton, J. (2007). Remodeling grounded theory. Historical Social

Research, 19, pp. 47-68. doi: 40981068

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies

for qualitative research. Chicago, IL: Aldine Publishing Co.

Goldfarb, A., & Tucker, C. (2011). Online display advertising: Targeting and

obtrusiveness. Marketing Science, 30(3), 389-404.

Goldman. D. (2012, April 23). Facebook tops 900 million users. CNN Money.

Retrieved May 19, 2012 from

http://money.cnn.com/2012/04/23/technology/facebook-q1/index.htm

Goltz, J. (2011, March 16). Is Groupon ruining retailing?. The New York Times:

Business day. Retrieved from http://boss.blogs.nytimes.com/2011/03/16/is-g

roupon-ruining-retailing/

Goodwin, T. (1999). Measuring the effectiveness of online marketing. Journal of the

Market Research Society, 41(4), 403-407.

Goodwin, C., Smith, K. L., & Spiggle, S. (1990). Gift giving: Motivation and the gift

purchase process. Advances in Consumer Research, 17(1), 690-698.

334

Texas Tech University, James Brian Aday, May 2013

Gorsuch, R. L., (1997). Exploratory factor analysis: Its role in item analysis. Journal

of Personality Assessment, 68(3), 532-560.

Greenfield, R. (2011, August 30). People are sick of daily deal sites. The Atlantic

Wire: Business. Retrieved from

http://www.theatlanticwire.com/business/2011/08/people-are-sick-daily-deals-

sites/41895/

Grewal, D., Levy, M., & Kumar, V. (2009). Customer experience management in

retailing: An organizing framework. Journal of Retailing, 85(1), 1-14.

Grier, D.A., & Campbell, M. (2000). A social history of Bitnet and Listserv, 1985-

1991. Annals of the History of Computing, 22(2), 32-41.

Groupon. (2012). All deals. Groupon. Retrieved January 14, 2012 from

www.groupon.com

Guarte, J. M., & Barrios, E. B. (2006). Sampling theory: Estimation under purposive

sampling. Journal of Communications in Statistics- Stimulation and

Computation, 35(2), 277-284.

Guo, Z. (2012). Optimal decision making for online referral marketing. Decision

Support Systems, 52(2), 373-383. doi: 10.1016/j.dss.2011.09.004.

Ha, L. (2003). Crossing offline and online media: A comparison of online advertising

on TPB web sites and online portals. Journal of Interactive Advertising, 3(2),

24-35.

335

Texas Tech University, James Brian Aday, May 2013

Handcock, M. S., & Gile, K. J. (2011). On the concept of snowball sampling.

Sociological Methodology, 41(1), p. 367-371.

doi: 10.1111/j.1467- 9531.2011.01243.x

Haruvy, E., T.L. P., Leszczyc, P., Carare, O., Cox, J.C., Greenleaf, E.A., Jank., W.,

Jap, S., Park, Y.H. & Rothkopf, M.H.. Competition between auctions.

Marketing Letters, 19(3/4), 431-448.

Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on

consumer online purchase intentions. Journal of Business Research, 62(1), 5-

13.

Heckathorn, D. D. (2011). Snowball versus respondent-driven sampling. Sociological

Methodology, 41(1), p. 355-366. doi: 10.1111/j.1467-9531.2011.01244.x

Heffernan, T., O’Neill, G., Travaglione, T., & Droulers, M. (2008). Relationship

marketing: The impact of emotional intelligence and trust on bank

performance. International Journal of Bank Marketing, 26(3), 183-199.

Helm, S. (2000). Viral marketing: Establishing customer relationships by ‘word-of-

mouse’. Electronic Markets, 10(3), 158-161.

Henderson, J. C. (2009). Food tourism reviewed. British Food Journal, 111(4), 317-

326.

Herrera, F., Martinez, L., & Sanchez, P. J. (2005). Managing non-homogeneous

information in group decision making. European Journal of Operational

Research, 166(1), 115-132. doi: 10.1016/j.ejor.2003.11.031.

336

Texas Tech University, James Brian Aday, May 2013

Hickins, M. (2011, February 26). Groupon revenue hit $760 million, CEO memo

shows. TheWall Street Journal. Retrieved July 10, 2011 from

http://online.wsj.com/article/SB10001424052748703408604576164641411042

376.html

Honaken, P., Verplanken, B., & Olsen, S. O. (2006). Ethical values and motives

driving organic food choice. Journal of Consumer Behavior, 5(5), 420-430.

doi: 10.1002/cb.190.

Hong-Youl, H., John, J., Janda, S., & Muthaly, S. (2011). The effects of advertising

spending on brand loyalty in services. European Journal of Marketing, 45(4),

673-691.

Howard, T. (2005, June 22). ‘Viral’ advertising spreads through marketing plans. USA

Today: Advertising and marketing. Retrieved July 12, 2011 from

http://www.usatoday.com/money/advertising/2005-06-22-viral-usat_x.htm

Hofacker, C.F., & Murphy, J. (2000). Clickable world wide web banner ads and

content sites. Journal of Interactive Marketing, 14(1), 49-59.

Holdershaw, J., Gendall, P., & Garland, R. (1997). The widespread use of odd pricing

in the retail sector. Marketing Bulletin, 8, 53-58.

Hsieh, Y.C., & Chen, K.H. (2011). How different information types affect viewer’s

attention on Internet advertising. Computers in Human Behavior, 27(2), 935-

945.

Hseih, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content

analysis. Qualitative Health Research, 15(9), 1277-1288.

337

Texas Tech University, James Brian Aday, May 2013

Hollis, N. (2005). Ten years of learning on how online advertising builds brands.

Journal of Advertising Research, 45(2), 255-268.

Hsu, C. H. C., Kang, S. K., & Lam, T. (2006). Reference group influences among

Chinese travelers. Journal of Travel Research, 44(4), 474-484.

doi: 10.1177/0047287505282951.

Huang, M. H. (2000). Information load: Its relationship to online exploratory and

shopping behavior. International Journal of Information Management, 20(5),

337-347. doi: 10.1016/S0268-4012(00)00027-X.

Huang, L., Chen, K.H., & Wu, Y.W. (2009). What kind of marketing distribution mix

can maximize the wholesaler travel agencies’ perspective?. Tourism

Management, 30(5), 733-739.

Hugo, O., & Garnsey, E. (2005). Problem-solving and competence creation in early

development of new firms. Managerial and Decision Economics, 26(2), 139-

148.

Hui, M. K., & Bateson, J. E. G. (1991). Perceived control and the effects of crowding

and consumer choice on the service experience. Journal of Consumer

Research, 18(2), 174-184.

Hutchison, A.J., Johnston, L.H., & Breckon, J.D. (2010). Using QSR-NVivo to

facilitate the development of a grounded theory project: an account of a

worked example. International Journal of Social Research Methodology,

12(4), 283-302.

338

Texas Tech University, James Brian Aday, May 2013

Illum, S.F., Ivanov, S.H., & Liang, Y. (2010). Using virtual communities in tourism

research. Tourism Management, 31(3), 335-340.

Inc. (2010). 7 Great viral marketing campaigns. Inc.com. Retrieved May 2, 2012 from

http://www.inc.com/ss/7-successful-viral-marketing-campaigns#2

Indounas, K., & Avlonitis, G. (2011). New industrial service pricing strategies and

their antecedents: Empirical evidence from two industrial sectors. Journal of

Business & Industrial Marketing, 26(1), 26-33.

doi: 10.1108/08858621111097184.

Interactive Advertising Bureau. (2011, August). August 2011: Search, display trends

push online ad spend past $31 billion. Interactive Advertising Bureau.

Retrieved May 15, 2012 from http://www.iab.net/

InternetWorldStats. (2012, April 30). Internet growth statistics. InternetWorldStats.

Retrieved from http://www.Internetworldstats.com/emarketing.htm

Islam, K.R., & Snow, C.M. (October, 2011). An architecture for delivery of distance

education in developing countries. Paper presented at the Special Interest

Group for Information Technology 2011 (SIGITE) conference, West Point, New

York, USA. Retrieved from

http://sigite2011.sigite.org/wpcontent/uploads/2011/10/session13-paper03.pdf

Jacoby, J., & Chestnut, R. W. (1978). Brand Loyalty: Measurement and Management.

New York, NY: Wiley

339

Texas Tech University, James Brian Aday, May 2013

Jin, B., & Kim, J. O. (2003). A typology of Korean discount shoppers: Shopping

motives, store attributes, and outcomes. International Journal of Service

Industry Management, 14(4), 396-419.

Jopson, B., & Makan, A. (2011, December 20). TripAdvisor to be valued at about

$4bn. Financial Times. Retrieved January 6, 2012 from

http://www.ft.com/intl/cms/s/0/c1e18ace-2b45-11e1-9fd0-00144feabdc0.html

Kanagal, N. (2009). Role of relationship marketing in competitive marketing strategy.

Journal of Management and Marketing Research, 2(1), 1-17.

Kaplan, A.M., & Haenlein, M. (2010). Users of the world, unite! The challenge and

opportunities of social media. Business Horizons, 53(1), 59-68.

Kauffman, R. J., & Wang, . (2001) New buyers’ arrival under dynamic pricing

market microstructure: The case of group-buying discounts on the Internet.

Proceedings of the 34th Annual Hawaii International Conference on System

Sciences, USA, 34(7), 1-10. doi:10.1109/HICSS.2001.927065

Kavoussi, B. (2012, July 23). ack Alley Waffles, D.C. restaurant blames Groupon’s

shocking business practices for its demise. Huffington Post. Retrieved from

http://www.huffingtonpost.com/2012/07/23/back-alley-waffles-washington-dc-

groupon_n_1695634.html

Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based

brand equity. Englewood Cliffs, NJ: Prentice Hall.

340

Texas Tech University, James Brian Aday, May 2013

Kendall, C., Kerr, L. R. F. S., Gondim, R. C., Werneck, G. L., Macena, R. H. M. M.,

Pontes, M. K., Johnston, L. G., Sabin, K., & McFarland, W. (2008). An

empirical comparison of respondent-driven sampling, time location sampling,

and snowball sampling for behavioral surveillance in men who have sex with

men, Fortaleza, Brazil. AIDS and Behavior, 12(1), 97-104.

Kerns, R., & Hair, N. (2008). Consumer perceptions of business-to-consumer

relationships in electronic retail environments: a means-end chain approach.

Journal of Relationship Marketing, 7(1), 5-27.

Khosla, S., & Acharya, H. (2012). Information availability in different languages and

various technological constraints related to multilinguism on the Internet.

International Journal of Computer Applications, 44(4), 10-14.

Kiecker, P., & Hartman, C. L. (1994). Predicting buyers’ selection of interpersonal

sources: The role of strong and weak ties. Advances in Consumer Research,

21(1), 464-469.

Kietzmann, J.H., Hermkens, K., McCarthy, I.P., & Silvestre, B.S. (2011). Social

media? Get serious! Understanding the functional building blocks of social

media. Business Horizons, 54(3), 241-251.

Kim, J., Fiore, A. M., & Lee, H. H. (2007). Influences of online store perception,

shopping enjoyment, and shopping involvement on consumer patronage

behavior towards an online retailer. Journal of Retailing and Consumer

Services, 14(2), 95-107. doi: 10.1016/j.jretconser.2006.05.001.

341

Texas Tech University, James Brian Aday, May 2013

Kim, J., & McMillan, S.J. (2008). Evaluation of Internet advertising research: A

bibliometric analysis of citations from key sources. Journal of Advertising,

37(1), 99-112.

Kim, J., Morris, J. D., & Swait, J. (2008). Antecedents of true brand loyalty. Journal of

Advertising, 37(2), 99-117.

Kimiloglu, J., & Zarali, H. (2009). What signifies success in e-CRM?. Marketing

Intelligence and Planning, 27(2), 246-267.

Kleef, E. V., Trijp, H. C. M., & Luning, P. (2005). Consumer research in the early

stages of new product development: A critical review of methods and

techniques. Food Quality and Preference, 16(3), 181-201.

doi: 10.1016/j.foodqual.2004.05.012.

Knox, S., & Walker, D. (2001). Measuring and managing brand loyalty. Journal of

Strategic Marketing, 9(2), 111-128.

Kondracki, N. L. & Wellman, N. S. (2002). Content analysis: A review of methods

and their applications in nutrition education. Journal of Nutrition Education

and Behavior, 34(3), 224-230.

Kotler, P., & Armstorng, G. (2009). Principles of Marketing. New York, NY: Pearson.

Kozinets, R.V., Valck, K., Wojnicki, A.C., & Wilner, S.J.S. (2010). Networked

narratives: Understanding the word-of-mouth marketing in online

communities. Journal of Marketing, 74(2), 71-89.

Krishna, A. (1994). The effect of deal knowledge on consumer purchase

behavior.Journal of Marketing Research, 31(1), 76-91.

342

Texas Tech University, James Brian Aday, May 2013

Kumar, V. (2008).Maximizing customer profitability. In Y. Wind (Ed.), Managing

customers for profit: Strategies to increase profits and build loyalty (pp. 75-

92). Upper Saddle River, NJ: Pearson.

Kwak, H., Fox, R. J., & Zinkhan, G. M. (2002). What products can be successfully

promoted and sold via the Internet?. Journal of Advertising Research, 42(1),

23-38.

Lam, S. K., Ahearne, M., Ye, H., & Schillewaert, N. (2010). Resistance to brand

switching when a radically new brand is introduced: A social identity theory

perspective. Journal of Marketing, 74(6), 128-146.

Landau, U., Prashker, J. N., and Alpern, B. (1982). Evaluation of activity constrained

choice sets to shopping destination choice modeling. Transportation Research

Part A: General, 16(3), 199-207.

Lattin, J. M., & McAlister, L. (1985). Using a variety-seeking model to identify

substitute and complementary relationships among competing products.

Journal of Marketing Research, 22(3), 330-339.

Lawson, R. (1997). Consumer decision making with a goal-driven framework.

Psychology and Marketing, 14(5), 427-449.

Lee, A. (2011, June 10). Using Groupon ‘worst decision I have ever made,’ says

merchant. Huffington Post. Retrieved from

http://www.huffingtonpost.com/2011/06/10/groupon-

merchants_n_874896.html

343

Texas Tech University, James Brian Aday, May 2013

Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-

learning: An extension of the expectation confirmation model. Computers &

Education, 54(2), 506-516. doi: 10.1016/j.compedu.2009.09.002.

Leenher, J., & Bijmolt, T. H. A. (2008). Which retailers adopt a loyalty program? An

empirical study. Journal of Retailing and Consumer Services, 15(6), 429-442.

Lehdonvirta, V. (2009). Virtual item sales as a revenue model: Identifying attributes

that drive purchase decisions. Electronic Commerce Research, 9(1/2), 97-113.

doi: 10.1007/s10660-009-9028-2.

Lemieux, J., & Peterson, R. A. (2011). Purchase deadline as a moderator of the effects

of price uncertainty on search duration. Journal of Economic Psychology,

32(1), 33-44. doi: 10.1016/j.joep.2010.10.005.

Leung, R., & Law, R. (2007). Analyzing research collaborations of information

technology publications in leading hospitality and tourism journals: 1986–

2005. In M. Sigala, L. Mich, & J. Murphy (Eds.), Information and

communication technologies in tourism, pp. 547–557.

Liang, T.P., Lin, C.Y., & Chen, D.N. (2004). Effects of electronic commerce models

and industrial characteristics on firm performance. Industrial Management and

Data Systems, 104(7), 538-545.

Liao, C., Chen, J. L., & Yen, D. C. (2007). Theory of planning behavior (TPB) and

customer satisfaction in the continued use of e-service: an integrated model.

Computers in Human Behavior, 23(6), 2804-2822.

doi: 10.1016/j.chb.2006.05.006.

344

Texas Tech University, James Brian Aday, May 2013

Liddle, A. J. (2012, December 12). Operators reveal pros and cons of daily-deals.

Nation’s Restaurant News. Retrieved from http://nrn.com/latest-

headlines/research-operators-reveal-pros-and-cons-daily-deals

LinkedIn. (2012, March 2). Form 10-K (Annual Report). LinkedIn Corp. Retrieved

May 10, 2012 from http://files.shareholder.com/downloads/ABEA-

69T44N/1940094231x0xS1193125-12-94556/1271024/filing.pdf

Liu, Y., & Yang, R. (2009). Competing loyalty programs: Impact of market saturation,

market share, and category expandability. Journal of Marketing, 73(1), 93-108.

Liu, Y. (2007). The long-term impact of loyalty programs on consumer purchase

behavior and loyalty. Journal of Marketing, 71(4), 19-35.

LivingSocial. (2012). 1-day deals. LivingSocial. Retrieved January 15, 2012 from

www.livingsocial.com

LivingSocial. (2012). Our history. LivingSocial – Corporate. Retrieved from

http://corporate.livingsocial.com/ourhistoryoverview/a-brief-company-

timeline/2009

Lucking-Reiley, D. (2000). Auctions on the Internet: What’s being auctioned and

how?. Journal of Industrial Economics, 48(3), 227-252.

Lunden, I. (2012, May 7). eyond the daily deal: Groupon’s 170M deals and 33M

users; aims to be ‘the OS for local commerce’. TechCrunch. Retrieved from

http://techcrunch.com/2012/05/07/beyond-the-daily-deal-groupon-has-170m-

deals-33m-users-aims-to-be-the-os-for-local-commerce/

345

Texas Tech University, James Brian Aday, May 2013

Leek, S., & Christodoulides, G., (2009). Next-generation mobile marketing: How

young consumers react to Bluetooth-enabled advertising. Journal of

Advertising Research, 49(1), 44-53.

Lee, K. (2009). Gender differences in Honk Kong adolescent consumers’ green

purchasing behavior. Journal of Consumer Marketing, 26(2), 87-96.

doi: 10.1108/07363760910940456.

Lichstein, D. R., Netemeyer, R. G., & Burton, S. (1990). Distinguishing coupon

proneness from value consciousness: An Acquisition-Transaction Utility

theory perspective. Journal of Marketing, 54(3), 54-67.

Ling, L., Guo, X., & Liang, L. (2011). Optimal pricing strategy of a small or medium-

sized hotel in cooperation with a web site. Journal of China Tourism Research,

7(1), 20-41. doi: 10.1080/19388160.2011.551044.

Liu, H., & Chintagunta, P. K. (2009). Pricing under network effects. In V. R. Rao

(Ed.), Handbook of pricing research in marketing (pp. 435-451).

Northhapmton, MA: Edward Elgar Publishing Inc.

Liu, H. (2010). Dynamics of pricing in the video game console market: Skimming or

penetration?. Journal of Marketing Research, 47(3), 428-443.

doi: 10.1509/jmkr.47.3.428.

Luo, X. (2002). Trust production and privacy concerns on the Internet: A framework

based on relationship marketing and social exchange theory. Industrial

Marketing Management, 31(2), 111-118.

346

Texas Tech University, James Brian Aday, May 2013

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and

determination of sample size for covariance structure modeling. Psychological

Methods, 1, 130-149.

MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R. (1993). The

problem of equivalent models in applications of covariance structure analysis.

Psychological Bulletin, 114(1), 185-199.

Madhavaram, S., & Appan, R. (2010). The potential implications of web-based

marketing communications for consumers’ implicit and explicit brand

attitudes: A call for research. Psychology and Marketing, 27(2),

Magill, K. (2011, October 4). Email remains ROI king; Net marketing set to overtake

DM, says DMA. The Magill Report. Retrieved May 1, 2012 from

http://www.magillreport.com/Email-Remains-ROI-King-Net-Marketing-Set-

to-Overtake-DM/

Makgosa, R., &Mohube, K. (2007). Peer influence on young adults’ products purchase

decisions. African Journal of Business Management, 1(3), 64-71.

Maltby, E. (2008, October 17). Has eBay hit its twilight?. CNNMoney. Retrieved May

2, 2012 from

http://money.cnn.com/2008/10/17/smallbusiness/ebay_twilight.smb/index.htm

Mangleburg, T. F., Doney, P. M., & Bristol, T. (2004). Shopping with friends and

teens’ susceptibility to peer influence. Journal of Retailing, 80(2), 101-116.

347

Texas Tech University, James Brian Aday, May 2013

Manly, L. (2005). The future of the 30-second spot. The New York Times. Retrieved

May 11, 2012 from

http://www.nytimes.com/2005/03/27/business/yourmoney/27dvr.html

Mangold, W.G., & Faulds, D.J. (2009). Social media: The new hybrid of the

promotion mix. Business Horizons, 52(4), 357-365.

Manning, K. C., & Sprott, D. E. (2009). Price endings, left-digit effects, and choice.

Journal of Consumer Research, 36(2), 328-335. doi: 10.1086/597215.

MarketWatch. (2012, June 11). Internet advertising revenues set first quarter record at

$8.4 billion. The Wall Street Journal: MarketWatch. Retrieved June 14, 2012

from http://www.marketwatch.com/story/Internet-advertising-revenues-set-

first-quarter-record-at-84-billion-2012-06-11

Marriott. (2011, March 31). Franchise disclosure documents – Item 19. Marriott.com.

Retrieved January 9, 2012 from

http://files.shareholder.com/downloads/PCLN/1621860135x0x475227/129348

1E-68D2-45D0-AB7B-DB0B781BEA37/2010_Annual_Report.pdf

Mason, M. (2010). Sample size and saturation in PhD studies using qualitative

interviews. Forum Qualitative Social Research, 11(3). Online journal.

doi: urn:nbn:de:0114-fqs100387

Mattila, A. S., & Wirtz, J. (2001). Congruency of scent and music as a driver of in-

store evaluations and behavior. Journal of Retailing, 77(2), 273-289.

doi: 10.1016/S0022-4359(01)00042-2.

348

Texas Tech University, James Brian Aday, May 2013

McCoy, S., Everard, A., & Loiacano, E.T. (2009). Online ads in familiar and

unfamiliar sites: effects on perceived website quality and intention to reuse.

Info Systems Journal, 19(4), 437-458.

Mehrabian, A. (1976). Public spaces and private spaces: The psychology of work,

play, and living environments. New York, NY: Basic Books.

Mellahi, K., & Johnson, M. (2000). Does it pay to be a first mover in e-commerce?

The case of Amazon.com. Management Decision, 38(7), 445-452.

doi: 10.1108/00251740010373458.

McKenzie, R. B., & Tullock, G. (2012). Why so many coupons. The new world of

economics. Upper Saddle River, NJ: McGraw Hill.

Mehrabian, A., & Russell, J. (1974). Approach to environmental psychology.

Cambridge, MA: MIT Press.

Meng, J. G., & Elliott, K. M. (2008). Predictors of relationship quality for luxury

restaurants. Journal of Retailing and Consumer Services, 15(6), 509-515.

Menon, S., & Kahn, B. E. (1995). The impact of context on variety in seeking product

choices. Journal of Consumer Research, 22(3), 285-295.

Mills, A.J. (2012). Virality in social media: the SPIN framework. Journal of Public

Affairs, 12(2), 162-169.

Miller, R. (2010). Online marketing. In J. M. Korman and H. J. Furnas (Eds.). The

business of plastic surgery: Navigating a successful career (pp. 149-168).

Tuck Link, Singapore: World Scientific Publishing Co.

349

Texas Tech University, James Brian Aday, May 2013

Miyazaki, A.D. (2008). Online privacy and the disclosure of cookie use: Effects on

consumer trust and anticipated patronage. Journal of Public Policy &

Marketing, 27(1), 19-33.

Mollen, A., & Wilson, H. (2010). Engagement, telepresence and interactivity in online

consumer experience: Reconciling scholastic and managerial perspectives.

Journal of Business Research, 63(9/10), 919-925.

doi: 10.1016/j.jbusres.2009.05.014.

Montoya-Weiss, M., Voss, G. B., & Grewal, D. (2003). Determinants of online

channel use and overall satisfaction with a relational, multichannel service

provider. Journal of Academic Marketing Science, 31(4), 448-458.

Moorman, C., & Rust, R. T. (1999). The role of marketing. Journal of Marketing, 63

(4-Special Issue), 180-197.

Morgan, D. L. (1993). Qualitative content analysis: A guide to paths not taken.

Qualitative Health Research, 3(2), 112-121.

Morgan, N.A., Slotegraaf, R.J., & Vorhies, D.W. (2009). Linking marketing

capabilities with profit growth. International Journal of Research in Marketing

Research, 26(4), 284-293.

Morothy, S., & Zhao, H. (2000). Advertising spending and perceived quality.

Marketing Letters, 11(3), 221-233.

Mowen, J. C., & Mowen, M. M. (1991). Time and outcome valuation: Implications

for marketing decision making. Journal of Marketing, 55(4), 54-62.

350

Texas Tech University, James Brian Aday, May 2013

Mowery, D.C., & Simcoe, T. (2002). Is the Internet a US invention? –an economic

and technological history of computer networking. Research Policy, 31(8/9),

1369-1387.

Murphy J., & Gomes, L. (2003). E-mail customer service by Australian educational

institutions. Australasian Marketing Journal, 11(2), 56-69.

Muthén, L. K., & Muthén, B. O. (1998-2011). Mplus User's Guide. Sixth Edition. Los

Angeles, CA: Muthén & Muthén.

Nield, B. (2011, June 20). How viral ads have exploded into the mainstream. CNN.

Retrieved May 4, 2012 from

http://edition.cnn.com/2011/BUSINESS/06/17/viral.marketing/index.html

Nielsen. (2008). Trends in online shopping: A global Nielsen consumer report.

Nielsen. Retrieved January 4, 2012 from

http://th.nielsen.com/site/documents/GlobalOnlineShoppingReportFeb08.pdf

Nielsen. (2010, April). Coupon redemptions grow 27%. Nielsen. Retrieved from

http://www.marketingcharts.com/print/coupon-redemptions-grow-27-

12586/nielsen-coupon-category-redemption-apr-2010jpg/

Noguchi, Y. (2012, July 6). For some businesses, daily-deals have a dark side. NPR:

Business. Retrieved from http://www.npr.org/2012/07/06/156333505/for-

some-businesses-daily-deals-have-a-dark-side

Oblinger, D. G., & Oblinger, J. L. (2005). Educating the net generation. Washington,

D.C.: Educause.

351

Texas Tech University, James Brian Aday, May 2013

O’Connor, P., & Murphy, J. (2004). Research on information technology in the

hospitality industry. International Journal of Hospitality Management, 23(5),

473–484.

Odin, Y., Odin, N. N., & Valette-Florence, P. (2001). Conceptual and operational

aspects of brand loyalty: An empirical investigation. Journal of Business

Research, 53(2), 75-84.

O’Dell, J. (2011, January 7). The history of Groupon. Forbes. Retrieved from

http://www.forbes.com/sites/mashable/2011/01/07/the-history-of-groupon/

Oh, W., & Lucas Jr., H. C. (2006). Information technology and pricing decision: Price

adjustments in online computer markets. MIS Quarterly, 30(3), 755-775.

Okazaki, S. (2009). The tactical use of mobile marketing: How adolescents’ social

networking can best shape brand extensions. Journal of Advertising Research,

49(1), 12-26.

Okazaki, S., Li, H., & Hirose, M. (2011). Benchmarking the use of QR code in mobile

promotion. Journal of Advertising Research, 52(1), 102-117.

O’Leary, C., Rao, S., & Perry, C. (2004). Improving customer relationship

management through database/Internet marketing: A theory-building action

research project. European Journal of Marketing, 38(3/4), 338-354.

Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of

satisfaction decisions. Journal of Marketing Research, 17(4), 460-469.

352

Texas Tech University, James Brian Aday, May 2013

Olsen, S. O. (2002). Comparative evaluation and the relationship between quality,

satisfaction, and repurchase loyalty. Journal of the Academy of Marketing

Science, 30(3), 240-249.

Ouelette, J. A., & wood, W. (1998). Habit and intention in everyday life: The multiple

processes by which past behavior predicts future behavior. Psychological

Bulletin, 124(1), 54-74.

Ozsoy, M. (2010). User preference on transformations of shopping centers into private

urban public places: the case of Izmir, Turkey. African Journal of Business

Management, 4(10), 1990-2005

Palmer, A. (2012, March 1). Marketers combat declining engagement. Direct

Marketing News. Retrieved May 18, 2012 from

http://www.dmnews.com/marketers-combat- declining-

engagement/article/228998/

Palmer, A., & Koenig-Lewis, N. (2009). An experiential, social network-based

approach to direct marketing. Direct Marketing: An International Journal,

3(3), 162-176.

Park, H., & Cho, H. (2012). Social network online communities: Information sources

for apparel shopping. Journal of Consumer Marketing, 29(6), 400-411. doi:

10.1108/07363761211259214.

Park, J., & Stoel, L. (2005). Effect of brand familiarity, experience and information on

online apparel purchase. International Journal of Retail & Distribution

Management, 33(2), 148-160. doi: 10.1108/09590550510581476.

353

Texas Tech University, James Brian Aday, May 2013

Patel, K. (2012, February 27). Daily-deals: Do consumers still care?. AdAge: Digital.

Retrieved from http://adage.com/article/digital/daily-deals-consumers-

care/233033/

Patsuris, P. (2001, June 7). Amazon’s about face. Forbes. Retrieved May 14, 2012

from http://www.forbes.com/2001/06/07/0607amazon.html

Pavlov, O.V., Melville, N., & Plice R.K. (2008). Toward a sustainable email

marketing infrastructure. Journal of Business Research, 61(11), 1191-1199.

Pels, J., Coviello, N.E., & Brodie, R.J. (2000). Integrating transactional and relational

marketing exchange: A pluralistic perspective. Journal of Marketing Theory

and Practice, 8(3), 11-20.

Pempek, T. A., Yermolayeva, Y. A., & Calvert, S. L. (2009). College students’ social

networking experiences on Facebook. Journal of Applied Developmental

Psychology, 30(3), 227-238. doi: 10.1016/j.appdev.2008.12.010.

Penz, E., & Hogg, M. K. (2011). The role of mixed emotions in consumer behavior:

Investigating ambivalence in consumers’ experiences of approach-avoidance

conflicts in online settings. European Journal of Marketing, 45(1), 104-132.

doi: 10.1108/03090561111095612.

Pepitone, J. (2011, January 19). EBay stock pops 5% on strong sales. CNNMoney.

Retrieved May 3, 2012 from

http://money.cnn.com/2011/01/19/technology/ebay_earnings/index.htm

354

Texas Tech University, James Brian Aday, May 2013

Persaud, A., & Azhar, I. (2012). Innovative mobile marketing via smartphones: Are

consumers ready?. Marketing Intelligence and Planning, 30(4), 418-443.

doi: 10.1108/02634501211231883

Peters, R. S. (2012, April 20). Daily-deals: The very real risks from unclaimed

property laws. Forbes: Tech. Retrieved from

http://www.forbes.com/sites/ciocentral/2012/04/20/daily-deals-the-real-risks-

from-unclaimed-property-laws/

Pew. (2012, March 9). Search engine use 2012. Pew Internet & American Life

Project: A Project of the PewResearchCenter. Retrieved June 16, 2012 from

http://pweinernet.org/Reports/2012/Search-Engine-Use-2012.aspx

PewResearchCenter. (2011, May). What Internet users do online. PewResearchCenter.

Retrieved January 11, 2012 from http://www.pewInternet.org/Trend-

Data/Online-Activites-Total.aspx

Phan, D.D., & Vogel, D.R. (2010). A model of customer relationship management and

business intelligence systems for catalogue and online retailers. Information &

Management, 47(2), 69-77.

Phelps, J.E., Lewis, R., Mobilio, L., Perry, D., & Raman, N. (2004). Viral marketing

or electronic word-of-mouth advertising: Examining consumer responses and

motivations to pass along email. Journal of Advertising Research, 44, 333-348.

Pickett-Baker, J., & Ozaki, R. (2008). Pro-environmental products: Marketing

influence on consumer purchase decision. Journal of Consumer Marketing,

25(5), 281-293. doi: 10.1108/07363760810890516.

355

Texas Tech University, James Brian Aday, May 2013

Pillai, K. G., & Kumar, V. (2012). Differential effects of value consciousness and

coupon proneness on consumers’ persuasion knowledge of pricing tactics.

Journal of Retailing, 88(1), 20-33. doi: 10.1016/j.jretai.2011.03.002.

Porter, L., & Golan, G.J. (2006). From subservient chickens to brawny men: A

comparison of viral advertising to television advertising. Journal of Interactive

Advertising, 6(2), 26-33.

Potter, J., & Hepburn, A. (2005). Qualitative interviews in psychology: Problems and

possibilities. Qualitative Research in Psychology, 2(4), 281-307.

doi: 10.1191/1478088705qp045oa

Potter, W. J., & Levine-Donnerstein, D. (1999). Rethinking validity and reliability in

content analysis. Journal of Applied Communication Research, 27(4), 258-284.

Priceline. (2011, April 29). 2010 Annual Report. The Priceline Group. Retrieve

January 7, 2012 from

http://files.shareholder.com/downloads/PCLN/1621860135x0x475227/129348

1E-68D2-45D0-AB7B-DB0B781BEA37/2010_Annual_Report.pdf

PricewaterhouseCoopers. (2010). The IA ’s Internet advertising revenue report.

Pricewaterhouse Cooper. Retrieved May 2, 2012 from

http://www.iab.net/media/file/IAB_Full_year_2010_0413_Final.pdf

PricewaterhouseCoopers. (2011). The IA ’s Internet advertising revenue report.

Pricewaterhouse Cooper. Retrieved May 7, 2012 from

http://www.iab.net/media/file/IAB_Internet_Advertising_Revenue_Report_FY

_2011.pdf

356

Texas Tech University, James Brian Aday, May 2013

Pride, W.M. & Ferrell, O.C. (2012). E-marketing, digital media, and social

networking. In J.W. Calhoun & M. Acuna (Eds.), Marketing: 2012 edition (pp.

280-316). Mason, OH: South-Western Cengage Learning.

Pucinelli, N. M., Goodstein, R. C., Grewal, D., Price, R. , Raghubir, P., & Stewart, D.

(2009). Customer experience management in retailing: Understanding the

buying process. Journal of Retailing, 85(1), 15-30. doi:

10.1016/j.jretai.2008.11.003.

Quintal, V. A., Lee, J. A., & Soutar, G. N. (2010). Risk, uncertainty and the theory of

planned behavior: A tourism example. Tourism Management, 31(6), 797-805.

doi: 10.1016/j.tourman.2009.08.006.

Raice, S. (2012, February 2). Facebook sets historic IPO. The Wall Street Journal:

Technology. Retrieved May 11, 2012 from

http://online.wsj.com/article/SB10001424052970204879004577110780078310

366.html

Ranaweera, C., & Neely, A. (2003). Some moderating effects on the service quality-

customer retention link. International Journal of Operations and Production

Management, 23(2), 230-248.

Rao, L. (2012, January 3). Eyeing an IPO, Kayak adds HomeAway founder and CEO

Brian Sharples to board. TechCrucn.com. Retrieved January 9, 2012 from

http://techcrunch.com/2012/01/03/eyeing-an-ipo-kayak-adds-homeaway-

founder-and-ceo-brian-sharples-to-board/

357

Texas Tech University, James Brian Aday, May 2013

Ravald, A., & Gronroos, C. (1996). The value concept and relationship marketing.

European Journal of Marketing, 30(2), 19-30.

Reisinger, D. (2011, March 22). LinkedIn hits 100 million users. CNET: News.

Retrieved May 21, 2012 from http://news.cnet.com/8301-13506_3-20045851-

17.html

Richards, K.A., & Jones, E. (2008). Customer relationship management: Finding value

drivers. Industrial Marketing Management, 37(2), 120-130.

Richey, R.G., Tokman, M., & Dalela, V. (2010). Examining collaborative supply

chain service through technologies: a study of intensity, relationships, and

resources. Journal of the Academy of Marketing Science, 38(1), 71-89.

Rohm, A. J., & Swaminathan, V. (2004). A typology of online shoppers based on

shopping motivations. Journal of Business Research, 57(7), 748-757.

Rosengren, K. E. (1981). Advances in Scandinavia content analysis: An introduction.

In K. E. Rosengren (Ed.), Advances in content analysis, (pp. 9-19). Beverly

Hills, CA: Sage

Rosenzweig, R. (1998). Wizards, bureaucrats, warriors, and hackers: Writing the

history of the Internet. The American Historical Review, 103(5), 1530-1552.

Rueter, T. (2011, March 21). The number of web users in Latin America increases

15%, comScore says. Internet Retailer. Retrieved May 8, 2012 from

http://www.Internetretailer.com/2011/03/21/number-web-users-latin-american-

increases-15

358

Texas Tech University, James Brian Aday, May 2013

Rusli, E. M. (2011, August 3). Daily deal web sites become players in their own deals.

The New York Times: Mergers & Acquisitions. Retrieved from

http://dealbook.nytimes.com/2011/08/03/daily-deal-web-sites-become-players-

in-own-deals/

Rusli, E. M. (2011, April 4). In race with Groupon, LivingSocial raises $400 million.

The New York Times. Retrieved July 11, 2011 from

http://dealbook.nytimes.com/2011/04/04/livingsocial-chief-races-to-escape-

shadow-of-groupon/

Rutz, O.J., & Bucklin, R.E. (2012). Does banner advertising affect browsing for

brands? Clickstream choice model says yes, for some. Quantitative Marketing

and Economics, 10(2), 231-257.

Sanchez-Franco, M. J., & Rondan-Cataluna, F. J. (2010). Virtual travel communities

and customer loyalty: Customer purchase involvement and web site design.

Electronic Commerce Research and Applications, 9(2), 171-182.

Schoenbachler, D.D., & Gordon, G.L. (2002). Trust and customer willingness to

provide information in database-driven relationship marketing. Journal of

Interactive Marketing, 16(3), 2-16.

Schonfeld, E. (2011, March 21). Citi: Google’s YouTube revenues will pass $1

billion. TechCrunch. Retrieved May 5, 2012 from

http://techcrunch.com/2011/03/21/citi-google-local-youtube-1-billion/

Schonfeld, E. (2010, March 8). Forrester forecast: Online retail sales will grow to

$250 billion by 2014. TechCrunch. Retrieved June 11, 2012 from

359

Texas Tech University, James Brian Aday, May 2013

http://techcrunch.com/2010/03/08/forrester-forecast-online-retail-sales-will-

grow-to-250-billion-by-2014/

Sehity, T. E., Hoelzl, E., & Kirchler, E. (2005). Price developments after a nominal

shock: enford’s Law and psychological pricing after the euro introduction.

International Journal of Research in Marketing, 22(4), 471-480.

doi: 10.1016/j.ijresmar.2005.09.002.

Seock, Y. K., & ailey, L. R. (2008). The influence of college students’ shopping

orientations and gender differences on online information searchers and

purchase behaviours. International Journal of Consumer Studies, 32(2), 113-

121. doi: 10.1111/j.1470-6431.2007.00647.x

Seret, A., Verbraken, T., Versailles, S., & Baesens, B. (2012). A new SOM-based

method for profile generation: Theory and an application in direct marketing.

European Journal of Operational Research, 220(1), 199-209.

Shaltoni, A.M., & West, D.C. (2010). The measurement of e-marketing orientation

(EMO) in business-to-business markets. Industrial Marketing Management,

39(6), 1097-1102.

Shaw, E. H. (2012). Marketing strategy: From the origin of the concept to the

development of a conceptual framework. Journal of Historical Research in

Marketing, 4(1), 30-55. doi: 10.1108/17557501211195055.

360

Texas Tech University, James Brian Aday, May 2013

Sherr, I. (2010, August 25). Online coupons get smarter: Groupon, rivals add

personalized bargains, staff as some merchants gripe. The Wall Street Journal.

Retrieved July 14, 2011 from

http://online.wsj.com/article/SB10001424052748703447004575449453225928

136.html

Sheth, J. N., & Parvatiyar, A. (1995). Relationship marketing in consumer markets:

Antecedents and consequences. Journal of the Academy of Marketing Science,

23(4), 255-271. doi: 10.1177/009207039502300405.

Sheth, J.N., & Sharma, A. (2005). International e-marketing: Opportunities and issues.

International Marketing Review, 22(6), 611-622.

Sheth, J. N. & Parvatiyar, A. (1995). Relationship marketing in consumer markets:

Antecedents and consequences. Journal of the Academy of Marketing Science,

23(4), 255-271.

Shukla, P. (2009). Impact of contextual factors, brand loyalty, and brand switching on

purchase decisions. Journal of Consumer Marketing, 26(5), 348-35

Smith, G. (2012, January 6). Fewer online daily-deals ahead?. MSN: Money. Retrieved

from http://money.msn.com/saving-money-tips/post.aspx?post=78cdbfd9-

2f5c-4aed-8995-20e12a160680

Smith, K.T. (2012). Longitudinal study of digital marketing strategies targeting

millenials. Journal of Consumer Marketing, 29(2), 86-92.

Smither, D., Menon, S., & Sivakumar, K. (2005). Online peer and editorial

361

Texas Tech University, James Brian Aday, May 2013

recommendations, trust, and choice in virtual markets. Journal of Interactive

Marketing, 19(3), 15-37. doi: 10.1002/dir.20041.

Spangenberg, E. R., Crowley, A. E., & Henderson, P. W. (1996). Improving the store

environment: Do olfactory cues affect evaluations and behaviors?. Journal of

Marketing, 60(2), 67-80.

Standifird, S.S. (2001). Reputation and e-commerce: eBay auctions and the

asymmetrical impact of positive and negative ratings. Journal of Management,

27(3), 279-295.

Steinberg, B. (2004, October 15). More networks are pulling the plugs. The Wall

Street Journal. Retrieved May 4, 2012 from

http://online.wsj.com/article/0,,SB109779276088945965,00.html

Stern, B. B., & Stafford, M. R. (2006). Individual and social determinants of winning

bids in online auctions. Journal of Consumer Behavior, 5(1), 43-55.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory

procedures and techniques. Thousand Oaks, CA: Sage Publications Inc.

Strauss, A., & Corbin, J. (1994). Grounded theory methodology. In N. K. Denzin & Y.

S. Lincoln (Eds.), Handbook of qualitative research (pp. 273-285). Thousand

Oaks, CA: Sage Publications.

Sullivan, M., & Adcock, D. (2002). Retail pricing. Retail pricing. London, UK:

Thomson.

362

Texas Tech University, James Brian Aday, May 2013

Summers, T. A., & Hebert, P. R. (2001). Shedding some light on store atmospherics:

Influence of illumination on consumer behavior. Journal of Business Research,

54(2), 145-150. doi: 10.1016/S0148-2963(99)00082-X.

Tauli, T. 798 Daily-deal sites die – in 6 months. InvestorPlace. Retrieved from

http://investorplace.com/ipo-playbook/798-daily-deal-sites-die-in-6-months/

Taylor, D.G., & Strutton, D. (2010). Has e-marketing come of age? Modeling

historical influence on post-adoption era Internet consumer behaviors. Journal

of Business Research, 63(9/10), 950-956.

Tedeschi, B. (2007, July 9). As domestic sales slow, travel sites go global. The New

York Times. Retrieved July 13, 2011 from

http://www.nytimes.com/2007/07/09/business/09ecom.html

Tesch, R. (1990). Qualitative research: Analysis types and software tools. Bristol, PA:

Falmer. Tversky, A., &Kahneman, D. (1992). Advances in prospect theory:

Cumulative representation of uncertainty. Journal of Risk and Uncertainty,

54(3), 430-455.

The Economist. (2000, February 24). Amazon’s amazing ambition. The Economist.

Retrieved April 22, 2012 from http://www.economist.com/node/285575

Thong, J. Y. L., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs

on the expectation-confirmation model for information technology

continuance. International Journal of Human-Computer Studies, 64(9), 799-

810. doi: 10.1016/j.ijhcs.2006.05.001.

363

Texas Tech University, James Brian Aday, May 2013

Tilley, J. (2010, January 21). 2009 Coupon trends review. Inmar. Retrieved from

http://www.pmalink.org/resource/resmgr/pdf/inmar_s_2009_coupon_trends_.p

df?hhSearchTerms=coupon+and+statistics

TNS Media Intelligence. (2009, December 28). Top 50 Internet advertisers by media

value, October, 2009. Clickz: Marketing News and Expert Advice. Retrieved

May 30, 2012 from http://www.clickz.com/clickz/news/1695024/top-Internet-

advertisers-media-value-october-2009

Trainor, K.J., Rapp, A., Beitelspacher, L.S., & Schillewaert, N. (2011). Integrating

information technology and marketing: An examination of the drivers and

outcomes of e-marketing capability. Industrial Marketing Management,40(1),

162-174.

Turley, L. W., & Milliman, R. E. (2000). Atmospheric effects on shopping behavior:

A Review of the experimental evidence. Journal of Business Research, 49(),

193-211. doi: 10.1016/S0148-2963(99)00010-7.

Tuttle, B. (2011, August 30). How daily-deals are losing their allure – For businesses

and consumers alike. Time: Business and Money. Retrieved from

http://business.time.com/2011/08/30/how-daily-deals-are-losing-their-allure-

for-businesses-and-consumers-alike/

Umit Kucuk, S., & Krishnamurthy, S. (2007). An analysis of consumer power on the

Internet. Technovation, 27(1/2), 47-56.

364

Texas Tech University, James Brian Aday, May 2013

U.S. Census Bureau (2012, January 17). State & county quickfacts. U.S. Census

Bureau. Retrieved June 14, 2012 from

http://quickfacts.census.gov/qfd/states/00000.html

U.S. Census Bureau. (2011, May 26). E-Stats.U.S. Census Bureau: E-Stats. Retrieved

July 24, 2011 from

http://www.census.gov/econ/estats/2009/2009reportfinal.pdf

U.S. Travel Association. (2011, May 20). U.S. travel forecasts. U.S. Travel

Association. Retrieved September 6, 2011 from

http://www.ustravel.org/sites/default/files/page/2009/09/ForecastSummary.pdf

Uzor, B., (2012, June 22). Expanding mobile advertising through telco participation.

BusinessDay. Retrieved June 24, 2012 from

http://www.businessdayonline.com/NG/index.php/analysis/features/40007-

expanding-mobile-advertising-through-telco-participation

Valck, K., Bruggen, G. H., &Wierenga, B. (2009). Virtual communities: A marketing

perspective. Decision Support Systems, 47(3), 185-203.

Varadarajan, R., & Yadav, M.S. (2009). Marketing strategy in an Internet-enabled

environment: A retrospective on the first ten years of JIM and a prospective

on the next ten years. Journal of Interactive Marketing, 23(1), 11-22.

Varadarajan, R., Yadav, M. S., & Shankar, V. (2008). First-mover advantage in an

Internet-enabled market environment: Conceptual framework and propositions.

Journal of the Academy of Marketing Science, 36(3), 293-308.

doi: 10.1007/s11747-007-0080-y.

365

Texas Tech University, James Brian Aday, May 2013

VanBoskirk, S. (2007). US Interactive marketing forecast, 2007 to 2012. Interactive

Marketing Professionals. Retrieved April 30, 2012 from

http://impaqt.com/downloads/WP_mediaMix_03-08.pdf van der Lans, R., van Bruggen, G., Eliashber, J., & Wierenga, B. (2010). A viral

branching model for predicting the spread of word of mouth. Marketing

Science, 29(2), 348-365.

Wakefield, K. L., & Inman, J. J. (1993). Who are the price vigilantes? An

investigation of differentiating characteristics influencing price information

processing. Journal of Retailing, 69(2), 216-234.

Walker, D., & Myrick, F., (2006). Grounded theory: An exploration of process and

procedure. Qualitative Health Research, 16(4), 547-559.

Wang, C.C., & Chen, C.C. (2010). Electronic commerce research in latest decade: A

literature review. International Journal of Electronic Commerce Studies, 1(1),

1-14.

Watson, A. (2011, February 4). Amazon takes over 10 percent of online market share.

Dealnews. Retrieved May 5, 2012 from http://dealnews.com/features/Amazon-

Takes-Over-10-Percent-of-Online-Market-Share/432456.html

Watson, L, & Spence, M. T. (2007). Causes and consequences of emotions on

consumer behavior; a review and integrative cognitive appraisal theory.

European Journal of Marketing, 41(5/6), 487-511.

doi: 10.1108/03090560710737570.

366

Texas Tech University, James Brian Aday, May 2013

Watson, R. T., P. Berthon, L. F. Pitt, & Zinkhan G. M. (2000).Electronic commerce:

The strategic perspective. Fort Worth, TX: Dryden.

Weber, R. P. (1990). Basic content analysis. Beverly Hills, CA: Sage.

Weinberg, B.D., & Berger, P.D. (2011). Connected customer lifetime value: The

impact of social media. Journal of Direct, Data and Digital Marketing

Practice, 12(4), 328-344.

Wells, W. D., & Prensky, D. (1996). Consumer behavior. New York, NY: John Wiley

& Sons.

Whitman, M. (2000, January). Quarterly letter from Meg. eBay.com. Retrieved May 1,

2012 from http://pages.ebay.com/community/news/letter-012600.html

Wilson, E. J., &Woodsisde, A. G. (1994). Influence in group purchasing decisions.

International Journal of Physical Distribution & Logistics Management, 24(5),

34-44.

Wilson, R.F. (2005, February 1). The six simple practices of viral marketing. Web

Marketing Today. Retrieved May 6, 2012 from

http://library.softgenx.com/Children/marketing/ViralMarketing.pdf

Winer, R. S. (1985). A price vector model of demand for consumer durables:

Preliminary developments. Marketing Science, 4(1), 74-90.

Wirtz, J., & Bateson, J. E. G. (1999). Consumer satisfaction with services: Integrating

the environment perspective in services marketing into the traditional

disconfirmation paradigm. Journal of Business Research, 44(1), 55-66.

doi: 10.1016/S0148-2963(97)00178-1.

367

Texas Tech University, James Brian Aday, May 2013

Wong, W. (2012, September 20). Groupon and its rivals adjust to new realities of

daily-deals landscape. Chicago Tribune: Business. Retrieved from

http://articles.chicagotribune.com/2012-09-20/business/ct-biz-0920-daily-

deals-20120920_1_groupon-online-deals-deals-market

Woodside, A. G., &Lysonski, S. (1989). A general model of traveler destination

choice. Journal of Travel Research, 27(4), 8-14.

Wright, E., Khanfar, N.M., Harrington, C., & Kizer, L.E. (2010). The lasting effects of

social media trends on advertising. Journal of Business and Economics

Research, 8(11), 73-80.

Wright, S. & Cawston, J.A. (2012). The shifting sands of university funding: A

values-driven approach to formulating employer liaison direct marketing

activity. International Journal of Marketing Principles and Practices, 2(1), 6-

19.

World Bank. (2011). Internet Users. The World Bank. Retrieved June 11, 2012 from

http://data.worldbank.org/indicator/IT.NET.USER

Wu, S.I., Wei, P.L., & Chen, J.H. (2008). Influential factors and relational structure of

Internet banner advertising in the tourism industry. Tourism Management,

29(2), 221-236.

Wu, S. I. (2003). The relationship between consumer characteristics and attitude

toward online shopping. Marketing Intelligence and Planning, 21(1), 37-44.

368

Texas Tech University, James Brian Aday, May 2013

Wunker, S. (2011, November 28). The Jeff Bezos formual for asymmetric

competition. Forbes. Retrieved May 13 from

http://www.forbes.com/sites/stephenwunker/2011/09/28/the-jeff-bezos-

formula-for-asymmetric-competition/

Xiong, L., & Hu, C. (2010). Harness the power of viral marketing in hotel industry: A

network discount strategy. Journal of Hospitality and Tourism Technology,

1(3), 234-244.

Xu, D.J., Liao, S.S., & Li, Q. (2008). Combining empirical experimentation and

modeling techniques: A design research approach for personalized mobile

advertising applications. Decision Support Systems, 44(3), 710-724.

Tilley, J. (2010, January 21). 2009 Coupon trends review. Inmar. Retrieved from

http://www.pmalink.org/resource/resmgr/pdf/inmar_s_2009_coupon_trends_.p

df?hhSearchTerms=coupon+and+statistics

Yan, R. (2008). Pricing strategy for companies with mixed online and traditional

retailing distribution markets. Journal of Product & Brand Management,

17(1), 48-56.

doi: 10.1108/10610420810856512.

Yani-de-Soriano, M., & Slater, S. (2009). Revisiting Drucker’s theory: Has

consumerism led to the overuse of marketing?. Journal of Management

History, 15(4), 452-466.

Yao, S., & Mela, C.F. (2011). A dynamic model of sponsored search advertising.

Marketing Science, 30(3), 447-468.

369

Texas Tech University, James Brian Aday, May 2013

Yoo, B., Donthu, N., & Lee, S. (2000). An examination of selected marketing mix

elements and brand equity. Journal of the Academy of Marketing Science,

28(2), 195-211.

YouTube. (2012). Statistics. YouTube.com. Retrieved May 20, 2012 from

http://www.youtube.com/t/press_statistics

Yu, R. (2011, July 18). Groupon getaways: Some deals, but shop around. USA Today:

Travel. Retrieved July 19, 2011 from

http://travel.usatoday.com/digitaltraveler/story/2011/07/Groupon-Getaways-

touts-great-deals/49482358/1

Zeithaml, V. A. (1988). Consumer perception of price, quality, and value: A means-

end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.

Zhu, D. S., O’Neal, G. S., Lee, Z., C., & Chen, Y. H. (2009). The effect of trust and

perceived risk on consumers’ online purchase intention. CSE ’09: International

Conference on Computational Science and Engineering. Vancouver: Canada.

Zinn, W., & Liu, P. C. (2008). A comparison of actual and intended consumer

behavior in response to retail stockouts. Journal of Business Logistics, 29(2),

141-157.

Zwick, D., & Dholakia, N. (2004). Whose identity is it anyway? Consumer

representation in the age of database marketing. Journal of Macromarketing,

24(1), 31-43.

370

Texas Tech University, James Brian Aday, May 2013

APPENDIX A

IRB APPROVAL LETTER

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APPENDIX B

IRB PROPOSAL

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PROPOSAL FORMAT FOR RESEARCH USING HUMAN SUBJECTS Primary Investigator: Kelly Virginia Phelan, Ph.D. Co-Investigator: James Brian Aday Proposal Title: Identifying the Benefits and Risks Associated With Flash Sale Websites and Their Potential Implications for Return Customers in the Hospitality and Service Industries

I. RATIONALE Electronic commerce is continuously revolutionizing traditional business models, allowing consumers to make seamless and instantaneous purchase decisions for a plethora of products, including hospitality and tourism related services (Breukel & Go, 2009; Watson, Berthon, Pitt, & Zinkhan, 2000). United States’ Internet sales witnessed an average annual growth rate over 18% between 2002 and 2009, resulting in business to consumer e-commerce sales reaching $145 billion in 2009 (U.S. Census Bureau, 2011). Online air travel purchases accounted for over $19.2 billion and travel arrangement services which include travel agencies, car rentals, and a variety of hospitality services, hovered around $7.4 billion (U.S. Census Bureau, 2011). Travel websites such as Travelocity, Expedia, and Priceline have grown significantly in their proportion of sales over the past decade, accounting for 30 percent of domestic travel bookings (Tedeschi, 2007). A relatively new phenomenon in Internet commerce revolves around daily deal or flash sale websites such as Groupon, LivingSocial, and Yuupon. These sites offer limited quantity vacation and hotel packages in addition to numerous other discounts for both products and services, directly to consumers (Yu, 2011). Sites, which allow customers to purchase these discount vouchers, have grown significantly, with Groupon having offered deals for over 35,000 companies in 565 cities (Hickins, 2011). In addition to the cities where Groupon maintains an online presence, the also serve 51 million email subscribers while attaining revenues of $760 million in 2010 (Hickins, 2011). LivingSocial, Groupon’s main competitor, currently serves 400 cities, with revenues of $500 million in 2010 (Rusli, 2011). Recent reviews of Groupon and LivingSocial for cities within Texas indicated that larger markets, containing over 1.5 million people, typically have 15 to 20 flash- sales occurring at one time. Midsized markets, populations between 150,000 and 500,000, may contain up to 20 sales but on average maintained between 8 and 14 offerings daily (Groupon, 2012; LivingSocial, 2012). The specific businesses offering services through these sites varied in name; however, overall the available goods were on average 90% service related. Furthermore, some markets possessed higher numbers of food service and restaurant redeemable vouchers available (Groupon, 2012; LivingSocial, 2012). In addition to their traditional product and service offerings, flash sale sites have ventured into additional markets through new product lines. LivingSocial created LivingSocial Escapes, dealing solely in hotel and vacation travel, this component of their business sold over 200,000 hotel nights valued at $100 million in less than one year of operations (Rusli, 2011). In June 2011, Groupon

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Texas Tech University, James Brian Aday, May 2013 announced its firm would be offering travel deals in partnership with Expedia through Groupon Getaways. Within the first three days of operation, Groupon Getaways sold 15,000 travel deals (Duryee, 2011). With the increased growth of daily deal websites and their current offerings of hospitality and tourism related products and services, a need exists for researchers to identify the factors which are most prevalent in consumer purchase decisions in order to ascertain why daily- deals are a growing trend and how businesses can use them most profitably. Understanding customer reasoning will allow hospitality fields an opportunity to capture more of the flash sale market; thus, increasing sales revenue and hopefully garnering repeat customers. Currently, there are not any peer-reviewed publications that have focused on flash-sales or the companies offering these services. Research in this area will provide a foundation for future daily deal research and bridge the gap between existing literature, which examined similar sales concepts, and the rapid growth of firms such as Groupon and LivingSocial. This study will employ a two-part mixed methods approach geared towards examining consumers and businesses which have previously used Groupon or LivingSocial The first portion of this study will focus on consumers and their interactions with and motivations to purchase vouchers through e-channels. Qualitative interviews will subsequently be conducted with business owners and managers to ascertain their attitudes toward such e-marketing. The large financial volume of sales generated through flash sale sites justifies their position in the market as a viable sales channel. However, businesses that sell through these sites are often selling their products at a loss with the intent of garnering future sales (Rusli, 2011). Business trying to generate awareness or increase visibility may turn to a flash sale instead of a more traditional advertising medium as a way to get customers in their doors with the discount and hope consumers will become loyal patrons based on their experience. This study is multi-faceted as it incorporates research objectives for consumers who have utilized Groupon or LivingSocial and for businesses administrators. This study aims to garner perspective from consumers and identify: 1. The role price plays in determining the willingness of a potential customer to purchase a flash sale; 2. Whether the company offering the daily deal increases propensity to purchase a daily deal; 3. The likelihood of repeat visitation after utilizing the coupon based on the customer experience; 4. Whether correlation exists between individuals who purchase a higher number of flash sale discounts and low brand loyalty; 5. The decreased likelihood of repeat visitation from high frequency discount users compared to those who use such discounts sparingly. Deals such as these provide consumers the opportunity to experience a new product or service with minimal cost and associated risk. Firms also benefit by generating foot traffic and being given a chance to make a favorable impression

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on a new customer base. However, businesses maintain the highest amount of risk when offering daily deal vouchers. The decision to rely upon a flash sale as a way to generate new customers is not the best alternative for all businesses, even those which offer similar services because of risks. These risks can include customers who only take advantage of the flash sale and never return, and the loss of potential revenue through offering such discounts. Thus, solely examining business owners and managers, this study aims to address: 1. Business motivations for using flash-sales; 2. Level of satisfaction felt by businesses after daily deal coupons have been used; 3. Factors influencing companies to avoid utilizing one-off sales; 4. Whether the perceived benefits outweigh potential risks when choosing to offer a flash vouchers.

II. SUBJECTS (a) Sample for Consumer Survey: This study is focused on respondents who have utilized flash sale sites in the previous 12 months. Since both Groupon and LivingSocial require purchasers of daily-deals to be at least 18 years of age all survey respondents will be required to be 18 or older to participate. Approximately 1000 individuals will be targeted for survey participation.

Recruitment Procedures Consumer Survey: The online portion of the survey will be announced by posting the URL for the survey and a brief description of the survey on Facebook.com pages, blog postings, printed flyers, business cards, and through email distribution lists. The paper copy of the survey will be distributed in and around Lubbock, TX. Additionally, survey distribution and collection will occur in the free speech areas on the Texas Tech University campus.

(b) Sample for Qualitative Interviews: Participating businesses will be targeted through the Lubbock Better Business Bureau (BBB) website. Utilizing the listings of service related businesses from the Lubbock BBB website, the researcher will create a spreadsheet which separates restaurants, hotels, and entertainment venues in Lubbock, TX. Each business on this list will be assigned a random number via a random number generator. After assigning random numbers, the businesses will be ordered sequentially based on their number. Approximately 20 businesses in total will be targeted for participation in the qualitative interview.

Recruitment Procedures Qualitative Interviews: Upon completion of sequentially ordering the businesses identified from the Lubbock BBB website, businesses will then be contacted via telephone to inquire whether they have offered their products or services on flash sale sites in the past, and whether or not they will be willing to participate in the study. The business representatives will then be interviewed and the data coded and analyzed.

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III. PROCEDURES: (a) Consumer Survey: Data will be collected by utilizing a self-administered, online and/or paper survey during a specified data collection period. Each survey will contain a total of 78 questions. The online version of the survey (Attachment C) will vary slightly from the printed version (Attachment E) with regards to the filter questions. The online survey will be administered by distributing a URL created from the COHS user account through Qualtrics. The paper surveys will be distributed in and around Lubbock, TX, and at the free speech areas of Texas Tech University. Participants will be sought out through random distribution of surveys both online and in-person. The printed survey will be an exact copy of the online survey with two exceptions. The two exceptions relate to the two filter questions. Online, if the participant selects “No” to the Survey Information Sheet or either filter question, they will be redirected to the Texas Tech University College of Human Sciences (COHS) webpage. Individuals filling out the paper version of the survey, if they disagree with the Survey Information Sheet or select “No” for an answer on the filter questions, will have text asking them to stop immediately and return their survey to the distributor. This survey is intended to gauge consumer perceptions and motivating factors in purchase decisions related to flash sale offerings. For the purpose of this study, it is vital that the participants have purchased a flash-sale offering within the past 12 months. Thus, the survey will contain a filter question which asks participants if they have purchased a flash sale voucher within the past 12 months. If the participant selects “No” as their answer choice they will be redirected to the COHS webpage (online) or will be asked to return the survey (in-person). Additionally, both Groupon and LivingSocial require purchasers of daily-deals to be at least 18 years of age. As prior purchase of a flash sale is a required criteria to participate, this survey will comprise of an additional filter question to ensure that participants are of the proper age. The online and paper survey’s will have a disclaimer appear asking them to verify they are at least 18 years of age before they are able to take the survey. If the participant is younger than 18, they will be thanked for their interest, but not allowed to fill out the survey. Online participants will be redirected back to the COHS webpage and those filling out the survey in person will be asked to hand-in their survey. One 16GB Wi-Fi Apple iPad will be offered as incentive for survey participation. Odds of winning the iPad are solely dependent upon the number of respondents. As this survey is targeting 1,000 respondents, the odds of winning the iPad are approximately 1 in 1,000. The iPad drawing will take place on December 1, 2012. Participants who wish to be included in the drawing for the iPad will be asked to provide their email address and contact information at the end of the survey. However, the email list will be compiled separately from the survey responses. There is not a possibility of matching participants email addresses to their responses. The software used to develop the online survey (Qualtrics) allows

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Texas Tech University, James Brian Aday, May 2013 separating certain parts of a survey. Participants who elect to be entered for the iPad drawing, will seamlessly be redirected to a secondary survey which will collect their personal information and ensure that personal information is not matched to their survey responses. The email list will be used strictly for the iPad drawing purposes. Proper procedures for awarding participation based incentives will be followed. Respondent Information Sheet for the consumer survey (Attachment B) will be located on the introductory webpage of the online survey and on the cover page of the printed survey. Particularly, the statement will specify that: (1) Participation is voluntary and participants can withdraw at any time, even in the middle of the survey, and still be included in the raffle. (2) The study is conducted strictly for research purposes. There is no selling, nor will the participants be contacted at a later date for any sales or solicitation. (3) All responses will be kept anonymous. No personal data will be asked and the information obtained will be recorded in such a manner that participants cannot be identified. (4) The data will be used solely for statistical and no other purposes and will be available only to the research group working on this project. Since there is no sensitive data being requested from participants, nor held by the off-site web server company, the requirement as stated in the Texas Tech University Institutional Review Board Electronic Data Policy Statement (October 2007) for providing the host providers evidence of security does not apply to this study.

(b) Qualitative Interviews: Data will be collected through in-person interviews at local businesses. The co- investigator will conduct the interviews at amicable times for the business operators. Two separate interview forms have been developed, incorporating 11 questions each. The first interview questions are designed to gauge the measured effectiveness of flash-sales from businesses who have offered their service on Groupon or LivingSocial. The second interview form is designed for businesses which have not utilized flash sale sites and will aid in further developing an understanding of their objections to the use of such mediums. The qualitative interview questions for businesses which have utilized a flash sale (Attachment H) will vary, with some slight overlap, from the interview questions presented to businesses which have not employed a flash sale (Attachment I).

Respondent Information Sheet for qualitative interviews (Attachment G) will be handed to the person or persons being interviewed at each business. The statement will not vary for business which have and those which have not offered flash-sales. Particularly, the statement will specify that: (1) Participation is voluntary and interviewees can withdraw at any time, even in the middle of the interview. (2) The study is conducted strictly for research purposes. There is no selling,

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nor will the participants be contacted at a later date for any sales or solicitation. (3) All responses will be kept anonymous. No personal data will be asked and the information obtained will be recorded in such a manner that participants cannot be identified. (4) The data will be used solely for statistical analysis and no other purposes and will be available only to the research group working on this project. Since there is no sensitive data being requested from participants, nor held by the off-site web server company, the requirement as stated in the Texas Tech University Institutional Review Board Electronic Data Policy Statement (October 2007) for providing the host providers evidence of security does not apply to this study.

(d) Benefits to participants: Participants will not be compensated for their participation in this research.

IV. ADVERSE EVENTS AND LIABILITY (a) The proposed research does not involve risks exceeding the ordinary risks of everyday life and no specific liability plan is offered.

V. CONSENT FORM (a) None

Attachments:

 Attachment A: Recruitment Email  Attachment B: Respondent Information Sheet for Consumer Survey  Attachment C: Online Survey  Attachment D: Consumer Survey In-Person Recruitment Statement  Attachment E: Paper Survey  Attachment F: Qualitative Interview Initial Phone Call Recruitment Statement  Attachment G: Respondent Information Sheet for Qualitative Interviews  Attachment H: Qualitative Interview Questions for Businesses Which HAVE Utilized a Flash Sale  Attachment I: Qualitative Interview Questions for Businesses Which Have NOT Utilized a Flash Sale

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REFERENCES Breukel, A., & Go, F. M. (2009). Knowledge-based network participation in destination and event marketing: A hospitality scenario analysis perspective. Tourism Management, 30(2), 184-193. Duryee, T. (2011, July 28). Expedia and Groupon sell 15,000 travel deals in three days. The Wall Street Journal.Retrieved July 29, 2011 from http://allthingsd.com/20110728/expedia-and-groupon-sell-15000-travel-deals- in-three-days/ Groupon. (2012). All deals. Groupon. Retrieved January 14, 2012 from www.groupon.com LivingSocial. (2012). 1-day deals. LivingSocial. Retrieved January 15, 2012 from http://www.livingsocial.com Hickins, M. (2011, February 26). Groupon revenue hit $760 million, CEO memo shows. TheWall Street Journal. Retrieved July 10, 2011 from http://online.wsj.com/article/SB10001424052748703408604576164641411042 376.html Rusli, E. M. (2011, April 4). In race with Groupon, LivingSocial raises $400 million. The New York Times. Retrieved July 11, 2011 from http://dealbook.nytimes.com/2011/04/04/livingsocial-chief-races-to-escape- shadow-of-groupon/ Tedeschi, B. (2007, July 9). As domestic sales slow, travel sites go global. The New York Times.Retrieved July 13, 2011 from http://www.nytimes.com/2007/07/09/business/09ecom.html U.S. Census Bureau. (2011, May 26). E-Stats.U.S. Census Bureau: E-Stats. Retrieved July 24, 2011 from http://www.census.gov/econ/estats/2009/2009reportfinal.pdf Watson, R. T., P. Berthon, L. F. Pitt, & Zinkhan G. M. (2000).Electronic commerce: The strategic perspective. Fort Worth, TX: Dryden. Yu, R. (2011, July 18). Groupon getaways: Some deals, but shop around. USA Today: Travel. Retrieved July 19, 2011 from http://travel.usatoday.com/digitaltraveler/story/2011/07/Groupon-Getaways- touts-great-deals/49482358/1

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ATTACHMENT A – Recruitment Email

Hello, My name is Brian Aday, and I am currently a doctoral candidate at Texas Tech University in Lubbock TX. I am conducting a study to obtain information on consumers’ feelings towards flash sale and daily deal offerings from sites such as Groupon and LivingSocial. The survey will take approximately 20 minutes to complete. The survey is not designed to sell you anything, or solicit money from you in any way. You will not be contacted at a later date for any sales of solicitations. Participation is voluntary and anonymous. All responses will be kept confidential and will be used only for statistical analysis by the research personnel. Through a random drawing, one participant will receive a 16 GB Wi-Fi iPad at the end of the data collection period. Odds of winning the iPad are approximately 1 in 1,000. If you would like to take part in the drawing, please provide your email address and contact information upon completion of the survey. Your personal information will not be associated with your responses and will be collected separately from the survey. You will be contacted via e-mail on how to claim your iPad should you be selected. Participants must be at least 18 years of age to participate in the survey. Please follow the link below to access the survey: (LINK) If you have any questions or if you would like to know the results of the study, please contact Dr. Kelly Phelan or Brian Aday at 806.742-3068 ext. 270 or email at [email protected] For questions about your rights as a subject, contact the Texas Tech University Institutional Review Board for the Protection of Human Subjects, Office of Research Services, Texas Tech University, Lubbock, Texas 79409 (806. 742-3884)

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ATTACHMENT B – Respondent Information for Consumer Survey

We are conducting a study to obtain information on consumers’ feelings towards flash sale and daily deal offerings from sites such as Groupon and LivingSocial. The survey is not designed to sell you anything, or solicit money from you in any way. You will not be contacted at a later date for any sales or solicitations. Participation is voluntary and anonymous. All responses will be kept confidential and will be used only for statistical analysis by the research personnel. No personal data will be asked and information obtained will be recorded in such a manner that you cannot be identified. Through a random drawing, one participant will receive a 16 GB Wi-Fi iPad at the end of the data collection period. If you would like to take part in the drawing, please provide your email address and contact information upon completion of the survey. Your personal information will not be associated with your responses and will be collected separately from the survey. You will be contacted via e-mail on how to claim your iPad should you be selected. You must agree with the terms and conditions to continue. You will be contacted via e-mail on how to claim your iPad should you be selected. Participants must be at least 18 years of age to participate in the survey. Please follow the link below to access the survey: [LINK] If you have any questions or if you would like to know the results of the study, please contact Dr. Kelly Phelan or Brian Aday at 806.742-3068 ext. 270 or email at [email protected]

For questions about your rights as a subject, contact the Texas Tech University Institutional Review Board for the Protection of Human Subjects, Office of Research Services, Texas Tech University, Lubbock, Texas 79409 (806. 742-3884)

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ATTACHMENT C – Online Survey

Survey Information Sheet

Through a random drawing, one participant will receive a 16 GB Wi-Fi iPad at the end of the data collection period. The drawing will be in early September and the odds of having your name drawn are about 1 in 1,000. If you would like to take part in the drawing, please provide your email address and contact information upon completion of the survey. Your personal information will not be associated with your responses and will be collected separately from the survey. You will be contacted via e-mail on how to claim your gift card should you be selected.

You will be contacted via e-mail on how to claim your iPad should you be selected. Participants must be at least 18 years of age to participate in the survey.

If you have any questions or if you would like to know the results of the study, please contact Dr. Kelly Phelan or Brian Aday at 806.742-3068 ext. 270 or email at [email protected]

 Yes, I agree with the terms and conditions  No, I do not agree with the terms and conditions

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Have you purchased a flash-sale coupon from a site such as Groupon, LivingSocial, or local newspaper within the past 12 months?

 Yes  No

Age Verification The survey elicits information pertaining to people’s purchase behavior via the Internet and flash sale sites; thus, participants must be 18 years of age in order to participate in the survey. Please indicate whether or not you are at least 18 years of age.

 Yes, I am at least 18 years of age  No, I am not at least 18 years of age

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CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree Flash sale sites provide an easy to use platform for purchasing goods        and services online I seek out travel related offerings        from flash sale sites I am more likely to try an unfamiliar service if the price is        low enough on a flash-sale offering I have become a long-term customer of a business which I        discovered through a flash-sale coupon The higher the price of a service being offered through a flash sale        site, indicates that the quality of the service is higher I typically confer with friends, family, or my significant other        before purchasing a service offered on a flash-sale site I am more likely to purchase a service rather than a product from        a flash-sale site I typically make online purchases from an online retailer where I        earn rewards instead of from flash sale sites I am more likely to purchase a flash-sale coupon for a business        which I visit regularly than a business I am unfamiliar with The service I receive when redeeming a flash-sale coupon at a business is better than the        service I have received when visiting without a flash-sale coupon If a business I visit through a flash-sale coupon offers a customer rewards program, I am        likely to sign up for the program and revisit the property I generally do most of my        shopping online

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CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I feel more comfortable purchasing services via        a flash-sale site because there is less risk. I feel safe purchasing services via the Internet        from a flash sale site The greater the advertised discount, the more        attractive the flash-sale offering is I will usually research a service I am considering purchasing through a flash-sale to        ensure I am actually getting a discount over the regular price I am typically more inclined to purchase a flash-sale coupon from a website such as Groupon or LivingSocial than I would be from        a local flash-sale source (For example, a local newspaper) I generally check the geographic location of the business offering the service on a flash-sale site        before purchasing I am more likely to purchase a flash-sale coupon if the service is offered at multiple        locations throughout the city The perceived amount of entertainment provided by the service being offered on the        flash-sale site is a motivating factor for me to purchase I rely on flash-sale sites as a source of knowledge for products which I am unfamiliar        with I trust flash-sale sites to provide me with        service options which are safe and reliable I am less likely to purchase a flash-sale offering for a business with multiple locations if I am        forced to select a single location at which to use the voucher I generally purchase more than 5 coupons from        flash-sale sites annually

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CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I would be more likely to revisit a property which I discovered through a        flash-sale offering if they sent me direct discount offers via mail or email The information provided in flash-sale advertisements provide enough significant information regarding the        product or service being offered to influence me to make a purchase I try to convince someone to purchase a flash-sale I am interested in before I am        willing to make the purchase myself If I have a positive experience with a product or service which I purchased through a flash-sale, I am likely to        continue to use this product without future discounts being offered The price of a service offered through flash-sales is the main motivating factor        in whether I make a purchase I typically only visit businesses which I have purchased a flash-sale coupon with        to redeem my coupon, and I never return in the future If the product or service I received when redeeming my flash-sale coupon was        good, I will revisit the business in the future without a coupon I typically purchase products I do not necessarily need from flash-sale        offerings I am more likely to purchase a flash-sale offering if someone I know has        purchased the same service I use all of the coupons I purchase via        flash-sale sites The date-range in which the coupon must be used influences my decision        when purchasing from a flash-sale site I am more likely to purchase a service via a flash-sale site for a company which        is within 10 miles of my residence

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CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I typically purchase items at a business which I am visiting because of a flash-sale coupon which are not covered by the        coupon (For example, if you buy a drink but the coupon only covers food) I usually purchase at least one flash-sale offering for a business in        a city which I do not reside at least once per year When utilizing a flash-sale coupon at a business, a manager or supervisor generally asks me        questions about the quality of my visit I would feel comfortable trying a new product or service offered        through a flash-sale site I am likely to use a flash-sale coupon I purchased even if I cannot        find someone to go with me I feel comfortable spending between $50 to $100 for a service        offered via a flash-sale Specialty products offered through flash-sales are less attractive than        widely available products offered throught the same sites When visiting a business to redeem my flash-sale coupon, I have already made up my mind that this        will be my only visit to this property I am more likely to purchase products or services I am familiar        with through flash-sale offerings I am likely to make a purchase through a flash-sale site so long as        it is inexpensive The lower the price of a service being offered through a flash-sale,        the lower the quality of the service I am generally satisfied with the services I receive when redeeming        a flash-sale coupon

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CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I am likely to revisit a property where I redeemed a flash-sale coupon if the discount is not enough to cover the entire bill (For example, if the        coupon was good for $15 worth of food and the bill ended up being $20) I am more likely to purchase an unfamilar product or service offered through a flash-sale        offering than I would be if I encountered the product on a store shelf. I typically purchase a coupon from a flash-sale        site at least once every 3 months I intend to purchase a coupon from a flash-sale        site in the next 6 months I am more likely to purchase services which are tailored to my specific needs from a flash-sale        site I am less likely to purchase a flash-sale offering if the business is located more than 10 miles        from my residence I will purchase multiple coupons for the same service offered through a flash sale so I can take        a friend, family member, or significant other with me I feel as though products and services offered        through flash-sale sites are typically inferior I am more attracted to services which provide entertainment rather than a specific function when purchasing from a flash-sale website (For        Example, I am more likely to purchase something like movie tickets than a service such as a massage) I am more likely to purchase name-brand products offered through flash-sales sites than        products from brands which I am not familiar with Generally, when I have purchased a flash-sale coupon, I take people along with me to the        business who have not purchased the same coupon My experience using flash-sale sites has always        been positive

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CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I am more likely to purchase a flash-sale for a service I am        unfamiliar with if someone I know is willing to go with me I typically spend more than $200        annually on online purchases I feel comfortable spending between $10 to $49 for a service        offered via a flash-sale I am more likely to purchase a flash-sale which is socially        acceptable to my friends The location of the business offering the service through the        flash-sale influences my decision to purchase I seek out flash-sales which offer services which a group would        find enjoyable I typically make my flash-sale        purchases from one specific site If a coupon for a future visit is offered to me at the time of my visit while redeeming my flash-        sale offering, I am more likely to revisit the business A majority of my online purchases are made through flash-        sale sites I generally revisit a business I first encountered through a flash-        sale offering I only revisit properties which I redeemed a flash-sale coupon at if        the business offers a discount again in the future I only purchase non-essential services through flash-sale        offerings

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To what age group would you be categorized?  18-20  21-25  26-29  30-35  36-40  41-45  46-50  51-55  56-60  61-65  66+

Average Annual Household Income  Less than $24,999  $25,000 - $49,999  $50,000 - $74,999  $75,000 - $99,999  $100,000 - $124,999  $125,000 - $149,999  $150,000 - $174,999  $175,000 - $199,999  Greater than $200,000

Gender  Male  Female

Which best represents you education level?  High School Graduate  In College  Some College  Bachelors Degree  Some Master's Coursework  Master's Degree  Beyond Master's Degree

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Ethnicity  African/African American  Asian  Caucasian  Hispanic  Other

What is your zipcode?

Would you like to be entered into the drawing for the iPad?  Yes  No (If NO is selected, the survey will end)

***NOTE: If “Yes” is selected above, the respondent will be automatically redirected to a second survey which elicits their personal information. There is no way to link respondents answer choices from the original survey to their personal information. If “Yes” is selected, the respondents will be presented with the following: iPad Raffle Information You have elected to be entered into the drawing for a 16 GB Wi-Fi iPad. Odds of winning the iPad are approximately 1 in 1,000. Please enter your personal information into the following question blocks. This information is confidential and cannot be used to identify a person to their responses from the original survey.

Please enter your first and last name

Please enter your email address

Please enter your phone number, including the area code

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ATTACHMENT D – Consumer Survey In-Person Recruitment Statement

Hello, My name is Brian Aday and I am currently a doctoral candidate at Texas Tech University. As part of the requirements of my dissertation, I am conducting research which measures characteristics of consumers’ usage of flash sale sites such as Groupon and LivingSocial. As part of the incentive, I am going to award a 16 GB Wi- Fi iPad away to one person at the end of the data collection period. The odds of winning the iPad are approximately 1 in 1,000. The survey will take approximately 20 minutes to complete, would you be willing to complete the survey today?

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ATTACHMENT E – Paper Survey

Survey Information Sheet

You will be contacted via e-mail on how to claim your iPad should you be selected. Participants must be at least 18 years of age to participate in the survey.

If you have any questions or if you would like to know the results of the study, please contact Dr. Kelly Phelan or Brian Aday at 806.742-3068 ext. 270 or email at [email protected]

 Yes, I agree with the terms and conditions  No, I do not agree with the terms and conditions (If selected, STOP HERE, Return survey to administrator)

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Have you purchased a flash-sale coupon from a site such as Groupon, LivingSocial, or local newspaper within the past 12 months?

 Yes  No (If selected, STOP HERE, Return survey to administrator)

 Yes, I am at least 18 years of age  No, I am not at least 18 years of age (If selected, STOP HERE, Return survey to administrator)

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Texas Tech University, James Brian Aday, May 2013

CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I feel more comfortable purchasing services via a flash-sale site because        there is less risk. I feel safe purchasing services via the        Internet from a flash sale site The greater the advertised discount, the        more attractive the flash-sale offering is I will usually research a service I am considering purchasing through a flash-        sale to ensure I am actually getting a discount over the regular price I am typically more inclined to purchase a flash-sale coupon from a website such as Groupon or LivingSocial than I would        be from a local flash-sale source (For example, a local newspaper) I generally check the geographic location of the business offering the service on a        flash-sale site before purchasing I am more likely to purchase a flash-sale coupon if the service is offered at        multiple locations throughout the city The perceived amount of entertainment provided by the service being offered on        the flash-sale site is a motivating factor for me to purchase I rely on flash-sale sites as a source of knowledge for products which I am        unfamiliar with I trust flash-sale sites to provide me with service options which are safe and        reliable I am less likely to purchase a flash-sale offering for a business with multiple        locations if I am forced to select a single location at which to use the voucher I generally purchase more than 5        coupons from flash-sale sites annually

397

Texas Tech University, James Brian Aday, May 2013

CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I would be more likely to revisit a property which I discovered through a flash-sale        offering if they sent me direct discount offers via mail or email The information provided in flash-sale advertisements provide enough significant information regarding the product or service        being offered to influence me to make a purchase I try to convince someone to purchase a flash- sale I am interested in before I am willing to        make the purchase myself If I have a positive experience with a product or service which I purchased through a flash-        sale, I am likely to continue to use this product without future discounts being offered The price of a service offered through flash- sales is the main motivating factor in whether I        make a purchase I typically only visit businesses which I have purchased a flash-sale coupon with to redeem        my coupon, and I never return in the future If the product or service I received when redeeming my flash-sale coupon was good, I        will revisit the business in the future without a coupon I typically purchase products I do not        necessarily need from flash-sale offerings I am more likely to purchase a flash-sale offering if someone I know has purchased the        same service I use all of the coupons I purchase via flash-        sale sites The date-range in which the coupon must be used influences my decision when purchasing        from a flash-sale site I am more likely to purchase a service via a flash-sale site for a company which is within        10 miles of my residence

398

Texas Tech University, James Brian Aday, May 2013

CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I typically purchase items at a business which I am visiting because of a flash- sale coupon which are not covered by the        coupon (For example, if you buy a drink but the coupon only covers food) I usually purchase at least one flash-sale offering for a business in a city which I        do not reside at least once per year When utilizing a flash-sale coupon at a business, a manager or supervisor        generally asks me questions about the quality of my visit I would feel comfortable trying a new product or service offered through a        flash-sale site I am likely to use a flash-sale coupon I purchased even if I cannot find someone        to go with me I feel comfortable spending between $50 to $100 for a service offered via a flash-        sale Specialty products offered through flash- sales are less attractive than widely        available products offered throught the same sites When visiting a business to redeem my flash-sale coupon, I have already made        up my mind that this will be my only visit to this property I am more likely to purchase products or services I am familiar with through flash-        sale offerings I am likely to make a purchase through a        flash-sale site so long as it is inexpensive The lower the price of a service being offered through a flash-sale, the lower        the quality of the service I am generally satisfied with the services I receive when redeeming a flash-sale        coupon

399

Texas Tech University, James Brian Aday, May 2013

CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I am likely to revisit a property where I redeemed a flash-sale coupon if the discount is not enough to cover the entire        bill (For example, if the coupon was good for $15 worth of food and the bill ended up being $20) I am more likely to purchase an unfamilar product or service offered through a flash-        sale offering than I would be if I encountered the product on a store shelf. I typically purchase a coupon from a flash-        sale site at least once every 3 months I intend to purchase a coupon from a flash-        sale site in the next 6 months I am more likely to purchase services which are tailored to my specific needs        from a flash-sale site I am less likely to purchase a flash-sale offering if the business is located more        than 10 miles from my residence I will purchase multiple coupons for the same service offered through a flash sale        so I can take a friend, family member, or significant other with me I feel as though products and services offered through flash-sale sites are        typically inferior I am more attracted to services which provide entertainment rather than a specific function when purchasing from a flash-sale website (For Example, I am        more likely to purchase something like movie tickets than a service such as a massage) I am more likely to purchase name-brand products offered through flash-sales sites        than products from brands which I am not familiar with Generally, when I have purchased a flash- sale coupon, I take people along with me        to the business who have not purchased the same coupon My experience using flash-sale sites has        always been positive

400

Texas Tech University, James Brian Aday, May 2013

CONSUMERS’ PERCEPTIONS AND MOTIVATIONS TOWARDS FLASH SALE SITES The purpose of this survey is to assess how consumers utilize flash sale offerings and which criteria most influences purchase decision. Flash-sales are vouchers which are purchased online from sites such as Groupon or LivingSocial and can be redeemed at the particular business on a future date. Participation in this survey is voluntary. We would appreciate complete responses as they will help with this research and build a more accurate profile of flash sale consumers. Please select the answer which best suits you. (Choose only one answer per question) Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree nor Agree Agree Disagree I am more likely to purchase a flash- sale for a service I am unfamiliar with        if someone I know is willing to go with me I typically spend more than $200        annually on online purchases I feel comfortable spending between $10 to $49 for a service offered via a        flash-sale I am more likely to purchase a flash- sale which is socially acceptable to my        friends The location of the business offering the service through the flash-sale        influences my decision to purchase I seek out flash-sales which offer services which a group would find        enjoyable I typically make my flash-sale        purchases from one specific site If a coupon for a future visit is offered to me at the time of my visit while        redeeming my flash-sale offering, I am more likely to revisit the business A majority of my online purchases are        made through flash-sale sites I generally revisit a business I first encountered through a flash-sale        offering I only revisit properties which I redeemed a flash-sale coupon at if the        business offers a discount again in the future I only purchase non-essential services        through flash-sale offerings

401

Texas Tech University, James Brian Aday, May 2013

To what age group would you be categorized?  18-20  21-25  26-29  30-35  36-40  41-45  46-50  51-55  56-60  61-65  66+

Gender  Male  Female

Which best represents you education level?  High School Graduate  In College  Some College  Bachelors Degree  Some Master's Coursework  Master's Degree  Beyond Master's Degree

402

Texas Tech University, James Brian Aday, May 2013

Ethnicity  African/African American  Asian  Caucasian  Hispanic  Other

What is your zipcode?

403

Texas Tech University, James Brian Aday, May 2013

iPad Raffle Information

As an incentive for participation, respondents are eligible to participate in a random drawing for a 16 GB Wi-Fi iPad. Odds of winning the iPad are approximately 1 in 1,000. If you would like to be entered into the drawing, please check “Yes” below and fill out your personal information. This information is confidential and cannot be used to identify a person to their responses from the original survey. In order to ensure confidentiality, please REMOVE THIS PAGE AND HAND IT TO THE RESEARCHER SEPARATELY FROM YOUR SURVEY.

Would you like to be entered into the drawing for the iPad?  Yes  No (If selected, STOP HERE, Return survey to administrator)

Please enter your first and last name

Please enter your email address

Please enter your phone number, including the area code

404

Texas Tech University, James Brian Aday, May 2013

ATTACHMENT F – Qualitative Interview Initial Phone Call Recruitment Statement

Hello, My name is Brian Aday and I am currently a doctoral candidate at Texas Tech University. May I please speak to a manager? My name is Brian Aday and I am currently a doctoral candidate at Texas Tech University. As part of the requirements for my dissertation, I am conducting research pertaining to business decisions regarding the utilization of flash sale sites Groupon and LivingSocail as part of a firm’s overall marketing strategy. I wanted, with your consent, to ask if your business has employed a flash-sale in the past? If Yes – Ok, great. I know you are busy, but thank you for agreeing to participate. When would be a good time for you that we could sit down and discuss the reasoning behind your decisions? I have 11 interview questions which will take approximately 30 minutes to answer. I will record what is said, but your confidentiality is guaranteed and protected. If No – Ok, thank you.

405

Texas Tech University, James Brian Aday, May 2013

ATTACHMENT G – Respondent Information for Qualitative Interviews

We are conducting a study to obtain information on business’ feelings towards flash sale and daily deal offerings from sites such as Groupon and LivingSocial. The survey is not designed to sell you anything, or solicit money from you in any way. You will not be contacted at a later date for any sales or solicitations. Participation is voluntary and anonymous. All responses will be kept confidential and will be used only for statistical analysis by the research personnel. No personal data will be asked and information obtained will be recorded in such a manner that you cannot be identified.

If you have any questions or if you would like to know the results of the study, please contact Dr. Kelly Phelan or Brian Aday at 806.742-3068 ext. 270 or email at [email protected]

406

Texas Tech University, James Brian Aday, May 2013

ATTACHMENT H – Qualitative Interview Questions for Businesses Which HAVE Utilized a Flash Sale

Which flash-sale site did you choose to use and why?

What was the primary reason your business chose to utilize a flash-sale website as a form of marketing?

How did you set-up your daily deal offering? Was the quantity limited? What was the discount?

Did you collect any information from individuals who cashed in their flash-sale coupon? Was some form of tool used to measure their overall satisfaction with their visit?

Has your business kept track of how many times customers who initially visited through the flash-sale have revisited?

Is your business considering offering another daily-deal in the future, if so, why or why not?

Was the flash-sale offering effective? How did you measure the results? Did you keep track of the number of vouchers redeemed? If so, were more purchased than redeemed?

Has your business offered similar coupons through in-house promotion campaigns?

What other methods of advertising does your business rely upon and how do you measure their effectiveness?

Were there any problems during the flash-sale process? If so, would you please elaborate?

Overall, how would you rate the whole experience, from offering the flash-sale through redemption? Was it worth the effort?

407

Texas Tech University, James Brian Aday, May 2013

ATTACHMENT I – Qualitative Interview Questions for Businesses Which Have NOT Utilized a Flash Sale

With the recent influx of flash-sales from sites such as Groupon, LivingSocial, and plethora of smaller sites offering similar services, what are the primary reasons your company has elected not to use such a marketing medium?

Has your company been solicited by one of the firms which offer flash-sales, and if so, what were your thoughts regarding the sales pitch?

Does your concern over the image of your business weigh into your decision to not utilize such services? Do you feel that offering your services at a significant discount via a mass distributed email detracts from the value of your brand?

Has your business offered similar coupons through in-house promotion campaigns?

Does your business offer a customer rewards program? If so, what are the benefits provided to customers who elect to enroll in such a program?

Do you see your business utilizing flash-sales as a form of promotion in the future?

Do you believe that competitors which have employed flash-sales have reached a wider target market than your business, and do you think these competitors have gained a competitive advantage?

What are the main methods of advertising you rely upon and how do you measure their effectiveness?

Does your business offer coupons, and if so, how is distribution carried out?

Does your business have a website?

What information is available on the website?

408

Texas Tech University, James Brian Aday, May 2013

APPENDIX C

QUALITATIVE INTERVIEW QUESTIONS FOR BUSINESSES WHICH

HAVE UTILIZED A FLASH SALE

409