Consumer Behavior of Hotel Deal Bookings Through Online Travel Intermediaries

Consumer Behavior of Hotel Deal Bookings through Online Travel Intermediaries

Hsiang-Ting Chen, M.S.

A Dissertation In Hospitality Administration Submitted to the Graduate Faculty of Texas Tech University In Partial Fulfillment of the Requirement for the Degree of Doctor of Philosophy

Approved

Dr. Kelly Virginia Phelan Chair of Committee

Dr. Tun-Min Jai

Dr. Hyo Jung Chang

Dr. Mark Sheridan Dean of the Graduate School

May, 2014

Texas Tech University, Hsiang-Ting Chen, May 2014

ACKNOWLEDGEMENT There are many people who have contributed to my pursuit of the doctoral degree, and I sincerely thank all of you. I would not have made it this far through such a challenging and stressful process without your support and encouragement.

I am fortunate to have Dr. Kelly Virginia Phelan as my mentor, the dissertation chair and my boss. I would be perplexed in my quest for a PhD if she was not the one who led me in the right direction. More than just an advisor, she is also my role model and good friend. I thank her for spending a numerous amount of time and effort guiding my research and editing my articles. Without her continuous support and encouragement, I would not be able to see my potential to be successful in my pursuit.

I am grateful to have Dr. Tun-Min (Catherine) Jai and Dr. Hyo Jung (Julie)

Chang to be my committee members. I sincerely appreciate your counsel, guidance and inspiration. Thank you for your wise insight and generous help during this whole process.

I also want to give thanks to my fellow graduate students in the “KVP” PhD support group. The time we spent sharing our graduate student life will always be a precious and memorable moment to me. Special thanks to Dr. James Brian Aday, who always stands by me and willingly supports me no matter what I may ask.

Thanks to my family with their unconditional love and support. I always think about my mother when I am writing because she was the first teacher who taught me Texas Tech University, Hsiang-Ting Chen, May 2014

how to write. It has been 18 years since I lost her and I miss her every day of my life.

She is the reason I keep striving toward my dream of completing the doctoral degree.

Finally, to my soul mate, Joseph Arthur Fiscus, thank you for your understanding and support during the whole process. You always listen to my complaints, deal with my emotions and respond to my annoying requests. You are an amazing person, and I cannot ask for anything more. I am so blessed to have you in my life.

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Texas Tech University, Hsiang-Ting Chen, May 2014

TABLE OF CONTENTS ACKNOWLEDGEMENT ...... ii

LIST OF TABLES ...... x

LIST OF FIGURES ...... xii

I. CHAPTER 1: INTRODUCTION ...... 1

1.1 Background ...... 1

1.2 Statement of the Problem ...... 5

1.2 Purpose of Study ...... 5

1.4 Study Objectives ...... 6

1.5 Significance of the study...... 6

1.6 The definitions of key terms ...... 7

II. CHAPTER 2: REVIEW OF LITERATURE ...... 9

2.1 E-commerce in the tourism industry ...... 9

2.1.1 Online travel agencies ...... 10

2.1.2 Meta-search websites ...... 11

2.1.3 Social media websites ...... 12

2.1.4 Flash sale, private sales and last-minute websites ...... 12

2.1.5 The impact of Internet on pricing strategies ...... 13

2.2 Online consumer decision-making process ...... 16

2.2.1 Consumer perceived value and awareness of sales promotions ...... 19

2.2.2 Deal searching...... 20

2.2.3 Consumers’ deal proneness ...... 22

2.2.4 Evaluation and comparison process ...... 24

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2.3 Theoretic foundation of research ...... 25

2.3.1 Transaction and acquisition utility theory ...... 25

2.3.2 Goal-setting theory ...... 27

2.3.3 Theory of planned behavior ...... 27

2.3.4 The model of goal-directed behavior (MGB) ...... 29

2.3.5 Proposed conceptual framework and research propositions ...... 32

III. CHAPTER 3: METHODOLOGY ...... 46

3.1 Model development ...... 46

3.2 Measurement development and procedure ...... 47

3.3 Pilot study ...... 53

3.3.1 Data collection ...... 53

3.3.2 Data screening...... 54

3.4 Full study ...... 54

3.4.1 Questionnaire revision ...... 54

3.4.2 Data collection ...... 56

3.4.3 Data screening...... 57

IV. CHAPTER 4: RESULTS ...... 58

4.1 Pilot study ...... 58

4.1.1 Sample ...... 58

4.1.2 Assumption testing ...... 60

4.1.3 Exploratory factor analysis (EFA) ...... 62

4.1.4 Confirmatory factor analysis (CFA) ...... 66

4.2 Full Study ...... 69

4.2.1 Sample ...... 69

Texas Tech University, Hsiang-Ting Chen, May 2014

4.2.2 Assumption testing ...... 71

4.2.3 EFA ...... 73

4.2.4 CFA ...... 77

4.2.5.1 Structural Equation Model ...... 80

4.2.5.2 Gender comparisons ...... 84

4.2.5.3 Age group comparisons ...... 87

V. CHAPTER 5: CONCLUSION AND IMPLICATIONS ...... 88

REFERENCES ...... 98

APPENDIX A ...... 114

APPENDIX B ...... 116

Texas Tech University, Hsiang-Ting Chen, May 2014

ABSTRACT

Electronic commerce has revolutionized the distribution of travel products and affected the way travelers search and purchase those products. The prevalence of

Internet usage energizes the rapid growth and potential business opportunities of the online travel market. The emerging operators of online travel bookings have become a global phenomenon and represent a significant percent of global travel sales.

Online travel intermediaries are third-party websites that provide travel information and reservation services for consumers. Such websites not only provide consumers with one-stop shopping, but also allow them to compare a variety of product choices, brands and prices. Since online travel intermediaries feature the convenience of variety seeking and competitive pricing, they attract an enormous amount of online consumers to search for product deals and promotions. Consumers have been educated and encouraged to look up prices and compare product values extensively before making a final purchase decision. In this highly competitive environment, an understanding of online consumer behavior is vital for developing effective marketing strategies. Previous studies have widely investigated consumer purchase intentions in online shopping. However, limited research addresses consumer behavior in the deal-purchasing segment.

The current research aimed to examine consumers’ motivation and intention to search and book hotel deals through online travel intermediaries. Specifically, this research examined: (1) how the cognitive, emotional and social factors influence

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Texas Tech University, Hsiang-Ting Chen, May 2014 consumers’ decision making process while they search and book hotel deals through online travel intermediaries; (2) how consumers’ deal proneness influences their motivation and intention to book hotel deals and (3) what characteristics influence consumers’ deal-purchasing behavior through online travel intermediaries.

The research framework proposed a modified Model of Goal-Directed

Behavior (MGB) theory to comprehend the causes of deal-purchasing behavior in the tourism industry. A quantitative research method was employed to measure the cognitive, emotional and social factors that influenced motivation and also how motivation mediated these factors toward booking intention.

Based upon the findings, consumers’ attitudes and perceived self-efficacy were two substantial factors that influenced motivation to book hotel deals online, which in turn, impacted their future intention. The results indicated that consumer deal proneness and value consciousness drove them to search and book hotel promotions online. Also, the ability to acquire information from sales promotions and comparison shopping significantly influenced consumers’ tendency toward deal-purchasing. To help consumers easily navigate the site and recognize product value, online travel intermediaries may address the quality of information, usability and functionality of the websites. In this way, consumers’ positive attitudes and self-confidence may increase and positively influence their purchase intention.

Overall, the emotional state did not show a direct or significant relationship in consumers’ motivation to book hotel deals. However, the male consumers’ motivation

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Texas Tech University, Hsiang-Ting Chen, May 2014 toward hotel deal bookings was influenced by the positive emotion construct.

Surprisingly, female consumers were not influenced by emotional components. The traditional view of gender differences in consumer behavior states that females are more greatly influenced by emotional gratification, and they perceive higher deal proneness than males. These extant studies contradict the current research, which found males show a higher tendency toward booking hotel deals when they perceived positive emotional involvement.

The social influence was shown to have different results in terms of the consumers’ ages. The Millennial Generation tended to proactively share information and interact with other consumers via the Internet; they showed a tendency of being a marketplace influencer. However, Baby Boomers preferred to receive information, opinions and advice from a trusted source, which provide a social conformity in their decision making process. Therefore, hospitality marketers should differentiate the age differences and develop appropriate online marketing strategies.

Finally, motivation played a mediating role that strongly effected consumers’ attitudes and perceived self-efficacy in regards to their intent to book hotel deals. This research provided a theoretical and practical contribution of MGB theory applied to explain the causal-effect relationship between motivation and intention in the field of consumer behavior.

Texas Tech University, Hsiang-Ting Chen, May 2014

LIST OF TABLES

1. Table 3.1 Measurement Items in the pilot study…………………………….58

2. Table 3.2 Measurement Items in the full study……………………………...65

3. Table 4.1 Pilot Study demographic information…………………………….69

4. Table 4.2 Pilot study: number of online travel websites visited before booking

a hotel and frequency of travel within the past 12 months…………………..70

5. Table 4.3 Pilot study: means, standard deviation and construct inter-

correlations………...... 71

6. Table 4.4 Pilot study: mean, standard deviation, factor loading and Cronbach’s

Alpha of indicators……………………………………………………………73

7. Table 4.5 Pilot study: unstandardized and standardized coefficients for

CFA…………………………………………………………………………..76

8. Table 4.6 Full study: demographic information……………………………...79

9. Table 4.7 Full study: number of online travel websites visited before booking a

hotel and frequency of travel within the past 12 months……………………..70

10. Table 4.8 Full study: means, standard deviation and construct inter-

correlations………...... 71

11. Table 4.9 Full study: mean, standard deviation, cactor loading and Cronbach’s

Alpha of indicators……………………………………………………………73

12. Table 4.10 Full study: unstandardized and standardized coefficients for

CFA…………………………………………………………………………...76

Texas Tech University, Hsiang-Ting Chen, May 2014

13. Table 4.11 Unstandardized coefficients, standardized coefficients and

significance levels of direct effects…………………………………………...91

14. Table 4.12 Unstandardized coefficients, standardized coefficients and

significance levels of indirect effects…………………………………………91

15. Table 4.13 Decomposition of effects with standardizes values………………92

16. Table 4.14 Structural equations results for age effect models…...…………...97

Texas Tech University, Hsiang-Ting Chen, May 2014

LIST OF FIGURES

1. Figure 1: A modified model of online consumer behavior and decision

making………………………………………………………………………...30

2. Figure 2: The theory of model of goal-directed behavior………………….....42

3. Figure 3: The proposed framework……………………………………...... 45

4. Figure 4.1 The CFA results from the pilot study…………………………...... 80

5. Figure 4.2 The CFA results from the full study………………………………91

6. Figure 4.3 The structural equation model…………………………………….96

7. Figure 4.4 The structural equation model of the male group…………………97

8. Figure 4.5 The structural equation model of the female group……………….98

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Texas Tech University, Hsiang-Ting Chen, May 2014

CHAPTER 1

INTRODUCTION

1.1 Background

The growth of e-commerce over the past decade has significantly influenced product distribution channels within the hospitality and tourism industry (Green &

Lomanno, 2012; Kracht & Wang, 2010). Due to the rapid development of the digital era, Internet saturation has become paramount, allowing travel products to predominately shift from offline to online distribution channels. The emerging online travel booking operators has become a global phenomenon and represents one third of total global travel sales (yStats, 2012). This quickly expanding trend has increased

20% in Europe and 50% in the Asia-pacific region between 2010 and 2011 (yStats,

2012). The online travel booking phenomenon is even more apparent in the United

States, where 83% of leisure travelers and 76% of business travelers utilize the

Internet as a major tool for arranging their travel plans (Google & Ipsos Media CT,

20121). Online travel booking in the United States grossed $103 billion in 2012, an increase of 9% over the previous year (comScore, 2013).

Since the online environment provides flexibility and accessibility, it is easy for travelers to browse and purchase travel products and services with only a few clicks of the mouse. Air tickets, car rentals or accommodation can be researched, evaluated and reserved through the Internet 24 hours a day, seven days a week. Online consumers tend to utilize numerous websites as a primary tool for booking travel

Texas Tech University, Hsiang-Ting Chen, May 2014 products due to the variety of product offerings, quick price comparisons, time savings and ease of use when requesting services to fulfill their needs (Toh, Raven & DeKay,

2011). Online booking availability not only benefits customers by making travel arrangements easier, it also increases the profits of businesses such as airlines, hotels and other package tour companies (Hotelmarketing, 2012).

Research shows 32% of hotel revenue is generated through online bookings

(TravelClick, 2012). Not only do hotels use their own websites as a major distribution channel, but they also utilize online travel intermediaries because these third-parties have the ability to reach worldwide customers and boost occupancy rates (Travel

Technology Association, 2012). In addition, online travel intermediaries have been viewed as effective marketing channels to sell distressed inventory and reach customers hotels may not be able to connect with directly (Kotler, Bowen, & Makens,

2010). Hotel bookings through online travel intermediaries increased 14% in 2013 and continue to demonstrate demand by customers (TravelClick, 2013).

The so-called “billboard effect” of online travel intermediaries indicates there are advantages of offering hotels market access (O’Connor, 2003). However, hotels have been facing the challenge of attracting consumers to book directly through their reservation systems. With the rapid growth of e-commerce business in the hospitality and tourism industry, hotels have struggled with maximizing revenue and managing consumer relationships due to the expansion of online travel intermediaries (McCartan,

2013). The controversial arguments of business partnerships between hotels and online

Texas Tech University, Hsiang-Ting Chen, May 2014 travel intermediaries raise concerns that hotels struggle to maintain profitability in the long term (Green & Lomanno, 2012; Lee, Guillet, & Law, 2012). In addition, price transparency of online channels adds more pressure to hotel room rates and thereby forces hotels to keep rate parity in all channels, keep online rates as low as possible, or provide “low price guarantees” on hotel websites (Green & Lomanno, 2012). To cope with these challenges, researchers suggest hotels should rethink their pricing strategies, identify the value proposition from consumers, understand consumers’ responsive behavior toward deal offerings and improve hotels’ sales and promotional tactics

(Buhalis & Law, 2008; McCartan, 2013).

Online travel intermediaries are perceived by customers as offering a variety of choices at low prices. As such, they are considered by many as the dominant choice when it comes to making travel arrangements (Law, Chan, & Goh, 2007). These travel intermediaries consist of third-party travel agencies (e.g., Hotel.com), social media sites (e.g., Tripadvisor.com), search engines (e.g., Google), meta-search sites

(e.g., Kayak.com) and flash sale sites (e.g., Groupon Getaway). The earning potential of online travel intermediaries should not be neglected. In 2010 online travel agencies

(OTA) earned nearly $40 billion in sales in the U.S. which was equivalent to 15% of the total U.S. travel market (PhoCusWright, 2011). One of the biggest OTAs, Priceline, had a profit growth of 59% in 2011 (CNN Money, 2011) while Expedia enjoys 60 million visits to the company’s website each month (Expedia Annual Report, 2011).

Texas Tech University, Hsiang-Ting Chen, May 2014

Since the Internet provides consumers the opportunity to retrieve extensive information, consumers’ propensity to search for discounted travel products has been heightened. Sixty-six percent (66%) of travelers claim they are willing to search for deals before booking travel products and seek the best value for their money (Google

& Ipsos Media CT, 2012). In addition, research shows customers visit 17 travel websites on average to search product information before booking (eMarketer1, 2013).

Third party intermediaries such as online travel agencies and meta-search engines have become particularly effective channels for finding attractive hotel discounts in recent years (Bennett, 2007; Schaal, 2012).

However, there is still little evidence which suggests how to transform browsers into buyers. Research indicates most consumers are concerned with acquiring good value for their money instead of solely seeking the lowest possible price (Gupta & Kim, 2010; Peterson, 2011). More than half of Internet users tend to search and redeem coupons to obtain discounts while purchasing products online; travelers will visit travel-coupon websites and look up deal information while planning their trips (eMarketer2, 2013). Another study showed 80% of affluent travelers frequently compare online travel pricing before making a purchase decision (Google

& Ipsos Media CT, 20122). Understanding consumers’ perceptions regarding product value and how consumers evaluate discounted products may prove valuable information to online travel service providers. However, most service providers still focus on how to slash prices as their main promotional marketing strategies instead of understanding consumer deal-searching behavior (Peterson, 2011). Misaligning

Texas Tech University, Hsiang-Ting Chen, May 2014 customers’ needs and wants may cause online intermediaries to struggle to maintain competitive pricing and profitability.

1.2 Statement of the Problem

Despite these staggering figures which showcase the prevalence of online travel bookings, there is limited research regarding consumers’ deal-purchasing behavior. Specifically, there is no published scholarly research related to consumer behavior on searching and purchasing discounted products from online travel intermediaries. The factors which motivate consumers to engage in searching for hotel deals online and the causes leading to bookings are still unexplored. Thus, this current research is designed to bridge this gap in the literature and provide marketing implications for hospitality researchers.

1.2 Purpose of Study

The primary goal of this study was to (1) define deal-purchasing behavior by combining the various perspectives which have been utilized in the consumer decision making process and (2) develop an integrated model to understand consumer deal- purchasing behavior in hotel bookings through online travel intermediaries. The results of this study aimed to contribute theoretical and managerial implications of online consumer behavior in the hospitality industry.

This research examined the following research questions:

Texas Tech University, Hsiang-Ting Chen, May 2014

1. How do cognitive, emotional and social factors influence consumers’

decision making process while they search and book hotel deals through

online travel intermediaries?

2. How does a consumer’s deal proneness influence their motivation and

intention to book hotel deals?

3. What demographic characteristics influence consumers’ deal-purchasing

behavior through online travel intermediaries?

1.4 Study Objectives

This research examined consumer behavior related to online hotel deal purchases, particularly through online travel intermediaries such as online travel agencies (OTA) and meta-search sites. Based on the perspectives from consumer psychology, the quantitative survey research method was developed and employed to discover the latent factors of deal purchasing behavior, understand how these factors influence purchase motivation and intention, and acquire information regarding the demographic characteristics of deal-prone consumers. A pilot study and full study were used to test the hypotheses and answer the research questions. Specific details regarding the data collection and analysis were discussed in the methodology section.

1.5 Significance of the study

Online travel intermediaries play a significant role in the development of e- tourism; it has transformed the distribution channel of travel products and changed the way consumers search for product information. Pricing has been a critical issue in the

Texas Tech University, Hsiang-Ting Chen, May 2014 online travel market due to price transparency, which allows consumers to easily obtain information about market offerings. Although some hospitality operators have implemented rate parity across different distribution channels, consumers tend to look for more choices and switch products or channels in hopes of identifying a better deal.

It is indubitable that consumers have gained more bargaining power in the online marketplace.

This research is important as it focuses on understanding the consumer decision making process in purchasing online travel products. Previous research related to e-tourism addressed the evolution of the Internet within the hospitality and tourism industry, the concentration on improvement, effectiveness and usefulness of suppliers’ websites. There is limited research related to consumer psychological factors which incite their motivation to purchase discounted travel products.

Investigating such latent motives and consumers’ characteristics may provide useful insight for hospitality operators in shaping their marketing strategies aimed at long- term profitability.

1.6 The definitions of key terms

A. E-commerce: The general term for an industry where the buying and selling

process is supported by an electronic medium such as the Internet or another

type of computer network system (Kotler et al., 2010).

Texas Tech University, Hsiang-Ting Chen, May 2014

B. E-tourism: The tourism industry that is supported by Information

Communication Technology (ICTs) and applies e-commerce developments for

tourism organizations and destinations (Buhalis & Law, 2008).

C. Meta-search site: Meta-search websites simultaneously gather information

from multiple websites based on customer preferences to generate a list of

travel products best suited to the individual consumer (Christodoulidou et al.,

2007).

D. Online travel agencies: Internet-based firms that provide and sell travel

arrangement services to consumers. They are business partners, vendors or

wholesalers for tourism-related companies, such as hotels, airlines and car

rentals (Carroll & Siguaw, 2003; Law et al., 2007; Lee et al., 2012).

E. Online travel intermediaries: Tourism companies that are Internet-based and

use an electronic medium as a major distribution channel. These include

Internet companies, global-distribution systems, travel agencies and

distribution-service companies (Carroll & Siguaw, 2003; Law et al., 2007).

Texas Tech University, Hsiang-Ting Chen, May 2014

CHAPTER 2

REVIEW OF LITERATURE

2.1 E-commerce in the tourism industry

Due to the rapid development of information technology in the past two decades, the growth of tourism e-commerce has expanded dramatically (Buhalis &

Law, 2008; Dale, 2003). Before the explosion of the Internet era, global distribution systems (GDS) fulfilled a significant role in coordinating functions between brick- and-mortar travel agencies and travel suppliers (i.e., airlines and hotels).

Since the late 1990s, the development of Internet and information communication technologies evolved, changing the way hospitality firms conduct business in the global competitive market. Hospitality organizations, travel destinations and tourism enterprises shifted their business operations toward electronic distribution. Major hotel chains adopted aggressive direct sales techniques via their websites, rather than depending on travel intermediaries as was traditionally practiced.

The replacement of traditional travel intermediaries by an organization taking back control of its own distribution is referred to as “disintermediation” (Kracht & Wang,

2009; Tse, 2003). Disintermediation is now widely practiced as hotels and airlines often prefer to utilize direct electronic distribution to reduce costs and enhance customer satisfaction and retention (Carroll & Siguaw, 2003).

Online direct distribution is especially valuable for those hospitality companies with loyalty programs as a visit to a firm’s website exposes visitors to special reward

Texas Tech University, Hsiang-Ting Chen, May 2014 program incentives. Conversely, loyalty programs allow firms to maintain a database with customer information which is helpful in improving service and sales performance (Toh et al, 2011). Engaging customers through developing online loyalty programs not only permits service providers to offer individualized services to meet customers’ needs, but also provides more intimate communication with customers

(Carroll & Siguaw, 2003). Thus, researchers have suggested that travel service providers should take advantage of using the Internet to build effective customer relationships and increase customer loyalty (Dunn, Baloglu, Brewer & Qu, 2009).

The prevalence of Internet usage has also resulted in increasing online business opportunities. Online travel retailers, such as online travel agencies, account for more than half of online travel sales (Rao and Smith, 2005). The term “reintermediation” is used to describe the role online travel intermediaries play as a middleman in distributing travel products for hospitality suppliers. There are four different classifications of online travel intermediaries: online travel agencies, meta-search engine sites, social media sites and flash sale sites (Kracht & Wang, 2010; Rao &

Smith, 2005).

2.1.1 Online travel agencies

Online travel agencies (OTAs) include companies such as Orbitz.com,

Expedia.com, Hotel.com, Travelocity.com and Priceline.com. OTAs adopt one or more business models: a merchant, commissionable or opaque selling model (Lee et al., 2012; Starkov & Price, 2003). In the merchant model, OTAs purchase travel

Texas Tech University, Hsiang-Ting Chen, May 2014 products at a discounted rate and market those products to the consumer at a higher price. This is different from a commissionable model where the OTAs arrange bookings for suppliers and received a percentage of the sale in the form of commission on each transaction (Law et al., 2007; Lee et al., 2012). In the opaque selling model consumers purchase travel products by stating their preferred price point without knowing any product information until the purchase is completed. Opaque selling offers a deep discount since the purchased product has many limitations. For instance, the consumer may be unaware of the hotel name and exact location before purchase and the inability to receive a refund or exchange means the customer is subject to considerable risk (Anderson & Wilson, 2011).

2.1.2 Meta-search websites

Meta-search websites simultaneously gather information from multiple websites based on customer preferences to generate a list of travel products best suited to the individual consumer (Christodoulidou et al., 2007). Kayak,com,

Bookingbuddy.com, Google.com and Momondo.com are some of the most popular meta-search sites. These sites streamline the search process for customers, allowing them to easily view and compare information for numerous products simultaneously.

Meta-search websites are limited in that customers cannot complete booking transactions, but they are referred to the specific travel website where the purchase may be completed. Researchers state some customers may feel they receive a better

Texas Tech University, Hsiang-Ting Chen, May 2014 value in terms of price and convenience by using meta-search websites

(Christodoulidou et al., 2007).

2.1.3 Social media websites

Social media websites represent user-generated-content platforms for customers to share travel-related information, product reviews and comments and personal experience (Xiang & Gretel, 2010). Major hotel chains have recognized social media as an important marketing channel and subsequently launched their own social media pages on Facebook and Twitter to encourage customers to utilize the direct booking function (Revinate, 2012). Tripadvisor.com, one of the most popular travel social media websites, incorporates user-generated-content and information search functions on its website. Hotel customers provide comments and ratings based on their experience, satisfaction and revisit intention (Jeong & Jeon 2008). In addition, customers are able to search hotels based on prices, location and star level. Other social media sites, such as Biddingfortravel.com and Betterbidding.com provide enormous user-generated-content from experienced customers who have booked through online travel agencies. These customers share their experiences, giving readers advice on searching and booking a better value of travel products online.

2.1.4 Flash sale, private sales and last-minute websites

Flash sale and private sale websites offer products for sale at a reduced cost during a limited time period, typically in 24 to 48 hours (Martinez & Kim, 2012).

Consumers must register as members and subsequently receive daily deal offers via e-

Texas Tech University, Hsiang-Ting Chen, May 2014 mail, text message, or notifications via mobile applications. Product offerings through flash and private sale websites are often discounted travel packages. These websites target bargain shoppers who desire inclusive products with greatly discounted prices

(Martinez & Kim 2012). Groupon-Getaways, Living Social-Escape and Gilt-JetSetter are some of the most commonly utilized flash and private sale websites. There are several reasons hotels offer flash sales including increasing off-season occupancy, raising the property’s profile, reaching a new segment of customers and competition

(TravelClick1, 2012).

Last-minute deal websites, such as Lastminute.com and Travelzoo.com provide competitive prices designed to sell distressed inventory before the perishability date (Last minute travel, 2013). These websites target consumers who are highly flexible with their travel plans, and who prefer to wait for last-minute sales instead of booking in advance at a higher price.

2.1.5 The impact of Internet on pricing strategies

Price-discrimination is prevalent in the hospitality industry; it is a significant tool for stimulating demand and maximizing revenue opportunities for organizations

(Kotler et al., 2010). Hotels, airlines and other travel providers employ different pricing strategies to generate desired profits. For example, hotels employ a room rate structure based on demand and consumers’ price sensitivity. Research shows price dispersion and differentiation is common among tourism providers in the online environment (Clemons, Hann, & Hitt, 2002; Toh, Raven, & DeKay, 2011). However,

Texas Tech University, Hsiang-Ting Chen, May 2014 this can be challenging for hotels as room rates and product information can be easily retrieved by online consumers. In a recent study, hotel revenue managers reported

“price wars” were the most critical issue for management (Kimes, 2009). In order to stay competitive, hotels have to consistently monitor market conditions and make room rate adjustments accordingly.

In the online travel market, travelers tend to make decisions strategically to ensure the purchased product is worth the money (Lee, Bai, & Murphy, 2012).

Consequently, consumers’ perceived values and risks are major factors which influence their behavior while they shop online (Forsythe, Liu, Shannon, & Gardner,

2006). Moreover, online consumers tend to have different risk-reduction strategies to reduce perceived risk caused by uncertainty while purchasing products online (Kim,

Qu, & Kim, 2009; Schindler, 1989; Zheng, Favier, Huang, & Coat, 2012). Some consumers who hesitate to book a hotel room while they browse online travel websites believe good deals will eventually show up if they just wait (Mayock, 2011). Chen,

Schwartz and Vargas (2011) investigated how deal-seeking travelers utilize searching and booking strategies against hotel cancellation policies. Their research indicated deal-seeking travelers continue to search for better deals to optimize their booking decision (Chen et al., 2011). As a result, tourism suppliers are facing more knowledgeable and strategic consumers who understand how to take advantage of suppliers’ pricing strategies (Anderson & Wilson, 2011; Smith & Rupp, 2003).

Texas Tech University, Hsiang-Ting Chen, May 2014

Since consumers have become technologically savvy and quickly learn how to find the best prices via various Internet platforms, hotels must embrace dynamic pricing strategies across all distribution channels (Forgacs, 2010). Dynamic pricing is not only a useful tool in attracting price-sensitive customers, but also can effectively increase hotel revenue by using analytical pricing models which focus on real-time reservations, customer booking history and customers’ characteristics (Mourier, 2013).

To reach price optimization, dynamic pricing requires hotels’ understanding of customers’ willingness to pay and the perceived value of a hotel room (Forgacs, 2010;

Noone, McGuire and Rohlfs, 2011). As a result, hotels’ pricing strategies have encompassed a customer-centric approach which addresses consumer buying behavior, preferences and expectations in terms of the effect of promotional activities (Noone et al., 2011).

Dynamic pricing strategies are also embraced by online travel agencies when selling excess inventory (Kasavana & Singh, 2001; Sahut & Hikkerova, 2009) Sahut

& Hikkerova (2009) summarized four main types of pricing strategies used by online travel product distributors: low cost products with a minimum level of quality or within a limited time period, last-minute selling, selling opaque products and providing auction sales. Perhaps the best dynamic pricing practice among online travel agencies is the reverse auction technique used by Priceline.com. Priceline’s reverse auction allows buyers to determine a price they are willing to pay. Through a negotiation process the seller and buyer reach an agreed upon price and complete the sale. Thus, online travel agencies can instantly adjust prices according to what a

Texas Tech University, Hsiang-Ting Chen, May 2014 customer is willing to pay. Since hotel rooms are perishable products, online travel agencies must consider the lifespan of a product and adopt innovative pricing strategies to maximize revenue.

2.2 Online consumer decision-making process

Traditionally, the consumer decision-making process (CDP) model stated that consumers go through three stages before they decide to purchase a good or service: need recognition, search for information and evaluation of alternatives (Blackwell,

Miniard, & Engel; 2006). These three stages are considered a cognitive function which guides consumers through the purchase decision. Consumers first recognize their needs and desires, then begin searching for solutions to satisfy their wants. The external search, which plays an important role in acquiring information, involves consumers reviewing all possible alternatives.

The widespread use of the Internet has directly affected the traditional CDP, and it now often plays an integral role in assisting consumers with their information search process. According to one study, more than 40% of consumers believe the

Internet strongly influences their decision making (Blackwell et al., 2006, p.115).

Since the Internet and new technologies evolve with time, online consumer behavior is expected to become increasingly more complex. The factors, such as economics (time spent and monetary cost), computing (search engines or online third-party agents) and psychology (search efforts) all influence online consumers’ decision making processes

(Punj, 2012). Researchers suggest it is necessary to investigate different facets to

Texas Tech University, Hsiang-Ting Chen, May 2014 understand the complexity of consumer decision-making (Hansen, 2005; Grant, Clarke,

& Kyriazis, 2007).

Darley, Blankson and Luethge (2010) presented a comprehensive model and review of online consumer behavior and the decision making process. By extending the traditional CDP model, the modified model of online consumer behavior recognized that external factors (i.e. individual characteristics, social influences, situational and economic factors and online environment) impact the decision-making process in an online environment. Consumer decision-making has evolved into a sophisticated and dynamic process in the online environment. In Figure 1, the model demonstrates exactly how the online and traditional consumer behavior and decision making has transformed (Darley et al., 2010). In particular, information processing and alternative evaluation are important factors which need more research to contribute a better understanding of online consumer behavior (Darley et al., 2010;

Grant et al., 2007).

In terms of purchasing online travel products, Beldona, Morrison and O’Leary

(2005) argued that a consumer’s motivation to book online travel products depends upon the consumer’s past experience and the heterogeneity of travel products.

Consumers attach both informational (e.g., product detailed descriptions) and transactional aspects (e.g., ease of booking) to evaluate travel products. While consumers evaluate complex alternatives, such as accommodations or travel packages, their motivations are driven by informational aspects. Specifically, consumers are

Texas Tech University, Hsiang-Ting Chen, May 2014 more willing to search and acquire detailed information before booking accommodations or bundled travel products online.

Figure 1: A modified model of online consumer behavior and decision making.

Source: Darley, Blankson, & Luethge (2010).

Texas Tech University, Hsiang-Ting Chen, May 2014

2.2.1 Consumer perceived value and awareness of sales promotions

The concept of consumer perceived value has been recognized as an important component in setting pricing strategies. A value-based pricing strategy focuses on enhancing consumers’ perception of value; that is, consumers’ overall assessment of the product value should be greater than the price they actually pay. Hospitality marketers often apply this concept when developing sales promotions to increase revenues and consumer satisfaction (Shoemaker, Lewis & Yesawich, 2007).

Sales promotions in hositality e-commerce includes numerous formats such as price-reductions, coupon codes, premiums, sweepstakes, extra loyalty program points, milliage accumulation and bundling packages (Christou 2011;, Kotler et al., 2010).

Online sales promotions possess appealing features and innovative ways to attract consumers to visit suppliers’ websites (Sigala, 2013). For instance, the emerging trend of promoting online deals available for a certain time period and with limited inventory has become popular with the rise of LivingSocial and Groupon. These sales generate a feeling of product scarcity and increases consumers’ likelihood of impulsive purchases (Sigala, 2013).

Researchers have also investigated how promotional messages act as stimuli which affect a consumer’s evaluation process (Choi, Ge & Messinger, 2010; Dastidar

& Datta, 2008; Nusair, Yoon, Naipaul & Parsa, 2010). Price formats, advertisements and types of incentives influence whether consumers respond favorably or unfavorably to marketing messages. Therefore, marketers have focused on developing

Texas Tech University, Hsiang-Ting Chen, May 2014 various forms of sales promotions to trigger consumers’ psychological processes and manipulate their attitudes toward acquiring products (Dastidar & Datta, 2008; Raju &

Hastak, 1980).

Nevertheless, some research indicates consumers avoid acquiring discounted products due to their inferences of product quality based upon price (Tellis & Gaeth,

1990). Price is perceived as an indicator of quality; consumers tend to equate low prices with low quality, thus prompting concerns about undesirable outcomes if they purchase discounted products (Zeithaml, 1988; Lichtenstein, Ridgway & Netemeyer,

1993).

2.2.2 Deal searching

Consumers’ deal-searching behavior can be categorized as either active or passive (Schneider & Currim, 1991). In active deal-searching, consumers are sensitive to sales promotions and willing to enthusiastically search for deals. By contrast, passive searching refers to consumers who wait to receive promotional information distributed by companies. Instead of proactively seeking deal-relevant information, passive customers tend to respond to direct contacts by marketers, such as marketers sending out individual e-mails to connect with customers. Hospitality firms have recognized the value of both types of customers and instituted methods for attracting each segment through Internet marketing campaigns (Hawkins & Mothersbaugh, 2010;

Shoemaker et al., 2007). For instance, hotels use paid advertisements on travel websites to target the consumers who tend to actively search for deals on these sites.

Texas Tech University, Hsiang-Ting Chen, May 2014

However, passive customers will not be exposed to these ads as they don’t visit travel sites. In order to reach the passive customer segment hotel marketers may attempt a more direct distribution method by sending promotional incentives (i.e., digital coupons) through electronic mail using customer contact information.

Deal searching involves a trade-off between benefits (i.e. potential savings) and costs (i.e. search efforts). Search efforts represent opportunity costs, which include time, as well as physical and psychological efforts. In other words, consumers must be able to attribute these efforts with a monetary savings (Blackwell et al., 2006;

Hawkins & Mothersbaugh, 2010; Punj, 2012). According to the cost-benefit paradigm, consumers search for decision-relevant information until the perceived benefits outweigh search efforts (Fortin, 2000; Jiang, 2003; Stigler, 1961). Online consumers tend to utilize the Internet as a major information search tool to help them make a quality decision and generate different search strategies, such as using third-party websites or search engines as facilitated tools to match consumers’ needs and reduce search efforts (Jiang, 2003; Punj, 2012). As a result, E-commerce marketers have realized that providing convenient, easy to use and time-saving features on their websites can reduce consumers’ time spent searching, assist them in comparison shopping and motivate them to make a purchase (Blackwell et al., 2006; Park &

Gretzel, 2010; Punj, 2012). Alternatively, due to the development of information transparency and rapid data transmission on the Internet, consumers are encouraged to do more price and product comparisons in the stage of pre-purchase evaluation

(Buhalis & Law, 2008; Jiang, 2003; Sharma and Krishnan; 2001). Therefore,

Texas Tech University, Hsiang-Ting Chen, May 2014 consumers’ deal-searching proneness has been increasingly fostered and affects individuals’ decision-making.

2.2.3 Consumers’ deal proneness

Deal proneness is defined in psychology as a propensity to respond sensitively to discount pricing such as coupons or other types of sales promotions (Lichtenstein,

Netemeyer & Burton, 1990). Lichtenstein et al., (1993) identified five major factors that influence consumers’ increased propensity to respond to sales promotions: value consciousness, price consciousness, coupon proneness, sale proneness and price mavenism. Value consciousness refers to consumers’ perceptions of price and quality evaluation. Price consciousness is related to consumers’ emphasis on low prices as opposed to product quality. Coupon and sale proneness reflect consumers’ propensity to apply coupons and take advantage of sales promotions. Price mavenism is defined as consumers’ sensitivity to price information and their tendency to forward that information along to other consumers. These factors all associate with consumers’ positive attitudes toward responding to sales promotions. Not only do these factors depict the psychological traits of deal-prone consumers, but can also be used to explain consumer deal-responsive behavior (Lichtenstein et al., 1993).

For deal-prone consumers, finding the “best deal” is their shopping goal; they seek value which exceeds the price they pay for a product or service (Kwon & Kwon,

2013). There are two different perspectives regarding how deal-prone consumers evaluate promotions: heuristic processing and heightened processing (DelVecchio,

Texas Tech University, Hsiang-Ting Chen, May 2014

2005). With heuristic processing deal-prone consumers tend to respond to promotional cues in an effort to simplify decisions. Since consumers are often trying to optimize outcomes and minimize mental efforts while making a decision, they select promoted brands instead of assessing price and quality (DelVecchio, 2005). A good application of heuristic processing is showcased by online travel agencies with opaque selling features, such as Hotwire. The website simply provides a single discounted price for a certain star-level hotel room and consumers can choose a hotel room based upon their budget. Thus, the selling price is most important. This practice is different from heightened processing in which deal-prone consumers tend to carefully evaluate all costs and benefits and integrate this information into their decision making process

(DelVecchio, 2005). Both heuristic and heighted processing influence deal-prone consumers while they are exposed in a shopping environment.

Research has extensively studied coupon usage and purchase intention, word- of-month, customer relationship and brand loyalty (Bawa, Srinivasan & Srivastava,

1997; Fortin, 2000; Kwon & Kwon, 2013; Lichtenstein et al., 1993; Martinez &

Montaner, 2006; Yi & Yoo, 2011; Wirtz & Chew, 2002). The tendency of deal proneness not only predisposes consumers to have a more favorable attitude towards deal hunting, but also motivates consumers to proactively respond to sales promotions, leading to actual purchase and showing higher brand loyalty. It has been argued that consumers respond to promotion types differently; however, research indicates consumers who have the high tendency toward deal proneness have more favorable

Texas Tech University, Hsiang-Ting Chen, May 2014 attitudes toward the brand with sales promotions than a brand without, regardless of the type of promotion (Lichtenstein, Burton, & Netemeyer, 1997; Yi & Yoo, 2011).

2.2.4 Evaluation and comparison process

Rational choice theory states that a consumer’s evaluation of alternatives is “to identify or discover the one optimal choice” through searching relevant information and weighing costs and values ((Hawkins & Mothersbaugh, 2010, p.550). It also indicates consumers are expected to engage in rational and logical processes that assist them in making decisions during an evaluation and comparison process (Tversky &

Kahneman, 1986). However, other researchers have suggested that consumers’ decision-making process also involves emotional responses, such as their feelings or experience in terms of the product features. As an affective system, these emotional responses influence consumers’ judgment (Darke, Chattopadhyay & Ashworth, 2006;

Hansen, 2005). Furthermore, cognitive and affective systems are also underlined in the concepts of utilitarian and hedonic shopping values which are the paradigm in consumer behavior research explaining how consumers make brand choices (Sarkar,

2011).

Chandon, Wansink, & Laurent (2000) distiguish how consumers perceive utiliarian and hedonic benefits when they evaluate sales promotions. Utilitarian benefits are “instrumental, functional and cognitive,” whereas hedonic benefits are

“experiential and affective” (p.66). Marketers utilize these two types of benefits when featuring sales promotions. For example, a promotional message focusing on utiliarian

Texas Tech University, Hsiang-Ting Chen, May 2014 benefits may address the savings, values of quality or convience. In constrast, marketers address the entertainment or emotional gratification achieved when browsing sales promotions as hedonic benefits (Chandon et al., 2000).

Other researchers conclude that utilitarian values comprise consumption utility, news utility, anticipation utility and memory utility (Hsee & Tsai, 2008). Consumption utility refers to a consumer’s perception of the value gained from the consumption. An example of consumption utility would be when a customer books a hotel room and receives monetary savings through coupon redemption. News utility refers to perceived benefits associated with informed messages (e.g., advertisements of monetary savings) while anticipation utility is the perceived value during consumption

(e.g., desired discounted levels). Finally, memory utility shows consumers’ past experience creates assimilation effects by responding to the current experience. These five dimensions have been utilized to explain consumer evaluation processes and decision-making.

2.3 Theoretic foundation of research

2.3.1 Transaction and acquisition utility theory

Thaler (1985) proposed that consumer behavior is shaped by a series of cognitive activities that are combined with possible consequences during a consumer’s decision making process. This evaluative process is modeled by a mental accounting system and referred to as “transaction utility” and “acquisition utility” (Thaler, 1985).

Transaction utility refers to the difference between the selling price and the reference

Texas Tech University, Hsiang-Ting Chen, May 2014 price which a consumer expects to pay; it represents the feeling of pleasure or disappointment with the deal (Gupta & Kim, 2010; Fortin, 2000; Thaler, 1985). The value of transaction utility depends on whether or not a consumer subjectively perceives that he or she is getting a good deal for the product or service.

Conversely, acquisition utility refers to the economic aspect of the sales transaction (Gupta & Kim, 2010; Fortin, 2000; Thaler, 1985). Transaction and acquisition utility theory has been widely applied in consumer behavior and marketing research (Liu & Soman, 2008). The theory has been used to explain how consumers evaluate and react to different types of promotions by sellers and why some consumers avoid purchasing discounted products (Liu & Soman, 2008; Thaler, 1985). Research related to coupon usage and deal assessment has also applied transaction and acquisition utility theory to understand value conscious and deal prone consumers

(Lichtenstein et al., 1990).

Researchers have argued that traditional economic theories fail to fully explain why consumers positively or negatively respond to sales promotions (Lichtenstein et al., 1990, Liu & Soman, 2008). Consumers may be influenced by relevant information and experience, and they continuously consider both the utilitarian and hedonic benefits while evaluating the sales promotions. Gupta & Kim (2010) adopted transaction utility theory to interpret online consumers’ perceived value and purchase intentions. In their study, both monetary and non-monetary benefits influenced

Texas Tech University, Hsiang-Ting Chen, May 2014 consumers’ value perception which was an intrinsic factor motivating them to purchase products online (Gupta & Kim, 2010).

2.3.2 Goal-setting theory

The goal-setting theory of motivation was originally developed to describe the relationship between goal attainment and job performance; it also provides an explanation of workplace motivation (Lunenburg, 2011). Goal-setting theory specifies that an individual’s values and intentions are cognitive components influencing behavior (Baumgartner & Pieters, 2008). Specifically, goals lead people to persist in accomplishing a task and avoiding an undesireable outcome. In addition, one’s satisfaction of achieving goals gives him or her further motivation to continue pursuing these goals (Lunenburg, 2011). Goal-setting theory is also applied in the field of consumer psychology (Baumgartner & Pieters, 2008, Bagozzi & Dholakia, 1992).

Pavlou & Fygenson (2006) stated that purchasing a certain product from a website is a goal-oriented behavior, and obtaining product information is a consequence in order to achieve this goal. While consumers recognize their desires, they form intentions to pursue a purchasing behavior, which assists in fulfilling their desires (Baumgartner &

Pieters, 2008; Pavlou & Fygenson, 2006). Thus, the goal-setting has been viewed as an important stage in activating consumers’ motivation and influencing their behavior.

2.3.3 Theory of planned behavior

The theory of reasoned action (TRA) and theory of planned behavior (TPB) are both based on expectancy-value theories of goal setting and designed to predict

Texas Tech University, Hsiang-Ting Chen, May 2014 voluntary behavior. In TRA, there are two determinants of consumer behavior: 1.) attitude influences one’ intention toward buying a product and 2.) social subjective norms determine one’s intention to purchase or not buy a product (Fishbein and Ajzen,

1975). Later, Ajzen modified TRA and proposed perceived behavioral control as another major determent of predicting one’s behavior. The TPB as an extension of

TRA explains how one’s judgment of a behavioral difficulty affects his or her behavioral intention (Ajzen, 1991). In sum, attitudes, subjective norm and perception of behavior control lead to the formation of a behavioral intention (Ajzen, 1991).

TRA and TPB have been widely applied in the field of social psychology of consumer behavior. Many TRA and TPB research has focused on buying familiar versus unfamiliar products (Arvola, Lahteenmaki, & Tuorlia, 1999), examining travelers’ behavior and destination choices (Lam & Hsu, 2004), clipping coupons online (Fortin, 2000), e-commerce adoption (Pavlou & Fygenson, 2006), online grocery buying intention (Hansen, Jensen, & Stubbe Solgaard, 2004) and online consumers’ behavior (Wen, 2006).

TRA and TPB address how people’s cognitive evaluation of consequences leads to either pursuing a certain behavior or not. However, it has been argued that they do not specify how intentions are produced to influence actions. Researchers have argued that most consumer behavior is goal-oriented (Bagozzi & Dholakia, 1992). A goal is an “internal representation of desirable states that people try to attain and undesirable states that they try to avoid” (Baumgartner & Pieters, 2008, p.368). In an attempt to attain a desirable state, consumers need to trade something in exchange for

Texas Tech University, Hsiang-Ting Chen, May 2014 achievingwhat they want. For instance, consumers search relevent information and consider all possible alternatives in order to make a decision which is best fit to their goals. Since TRA and TPB were not designed to be goal-directed models, the goal attainment was ignored in these theories. Thus, TRA and TPB are not applicable to explain goal-directed behavior and do not explicitly describe the psychological process of intention formation (Bagozzi, Gurhan-Canli, & Priester, 2002; Baumgartner

& Pieters, 2008).

2.3.4 The model of goal-directed behavior (MGB)

Perugini and Bagozzi (2001) extended TRA and TPB to provide a comprehensive perspective of consumer behavior called the model of goal-directed behavior (MGB) (Bagozzi et al., 2002). Similar to TRA and TPB, MGB addresses human intention and behavior; however, there are several differences between TPB and MGB. MGB proposes that a volatile desire provides a motivational impetus to influence an individual’s intentions (Perugini & Bagozzi, 2001). From the psychological perspective, motivation is a desired state which elicits intention and provides direction to a particular behavior (Bagozzi & Dholakia, 1992). Thus, given a strong motivational impetus, an individual’s intrinsic attitudes, emotions, subjective norms and perceived behavior control will be energized and transformed toward a behavioral intention (Perugini & Bagozzi, 2001). During the consumer decision making process, an individual’s motives will be driven toward the intention once the need is recognized. Consequently, an intention is targeted at a specific outcome

Texas Tech University, Hsiang-Ting Chen, May 2014 through “the execution of instrumental acts” (Bagozzi et al., 2002, p.83). The MGB is displayed in Figure 2.

Figure 2: Model of Goal-Directed Behavior. Adapted from Perugini & Bagozzi (2001).

According to the model, desires can be explained by one of three motivations: attitudes, anticipated emotions and subjective norms. Desires also play a mediating role between these three latent factors and perceived behavior control toward intention and behavior (Bagozzi, Gopinath, & Nyer, 1999; Wang, Hong, & Wei, 2010).

Secondly, MGB postulates that frequency of past behavior is a predictor of desires, intentions and behavior (Perugini & Bagozzi, 2001). Frequency of past behavior stands that an individual’s past experience will reflect his or her behavioral patterns, thus, affecting the individual’s intentions and future behavior. Recency of past

Texas Tech University, Hsiang-Ting Chen, May 2014 behavior indicates that the individual’s most recent experience will constantly influence his or her future behavior (Perugini et al., 2001).

The MGB provides a greater understanding of how intention and behavior are produced, expanding the TPB and providing a more powerful explanation of human behavior than TRA or TPB (Bagozzi et al., 2002; Kim, Lee, Lee, & Song, 2012).

Hunter (2006) utilized MGB to examine how the role of anticipated emotion, desire and intention interact with a shopping center image and frequency of consumers’ visits.

The results showed that desire and positive anticipated emotions were important variables in predicting consumers’ intention to visit the shopping center. Taylor,

Hunter and Longfellow (2006) adapted MGB and examined consumer loyalty in a business-to-business (B2B) service context. In their study desires were strongly influenced by attitudes, positive anticipated emotions and subjective norms. Recent studies also applied MGB to examine tourist behavior in the hospitality industry (Kim,

Lee, Lee, & Song, 2012; Song, Lee, Norman, & Han, 2012; Xie, Bagozzi, & Ostli,

2013). These studies provide satisfactory support that desire is a vital impetus in the intention-formation process.

Although MGB has been credited as broadening and deepening the traditional attitude-behavior paradigms to explain consumer behavior, it has not been extensively applied in consumer or marketing research (Baumgartner & Pieters, 2008; Hunter,

2006). Presently, there is no research which utilizes MGB to investigate online consumer behavior and buying intention. Thus, the current study is designed to

Texas Tech University, Hsiang-Ting Chen, May 2014 combine salient factors which may influence online deal-searching behavior, along with the variables from MGB to produce a holistic framework of online consumer behavior.

2.3.5 Proposed conceptual framework and research propositions

The proposed framework for this study is displayed in Figure 3. This study proposed a modified MGB model to investigate consumer deal-searching motivations and booking intentions.

Texas Tech University, Hsiang-Ting Chen, May 2014

Figure 3: The proposed framework

Texas Tech University, Hsiang-Ting Chen, May 2014

Attitudes

Based on TRA and TPB, attitude has a direct effect on an individual’s behavior

(Ajzen & Fishbein, 1980; Ajzen, 1991). Attitude is defined as “the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question”

(Ajzen, 1991, p.188). A consumer’s perception and belief about the consequence of evaluation and actions determines one’s attitude (Bagozzi et al., 2002). Social psychologists stated that attitdues are determined by beliefs and values which are associated with certain attributes, objects, or events (Ajzen, 1991; Bagozzi et al., 2002).

These beliefs and values reflect a person’s subjective attributes and explain why someone makes a particular choice when selecting among multiple alternatives.

Consumers’ attitudes toward brand perferences can be an important determinant of their purchase decisions (Ajzen, 1991). In the field of consumer behavior, the construct of attititude plays a central role in explaining consumers’ buying motivations, particularly in the area of product evaluation, changing attitudes and determinants of brand opinion

(Ajzen, 2008).

A consumer’s belief and evaluation of a subject are determined primarily through learning and knowledge acquisition (Perugini & Bagozzi, 2001). While consumers evaluate possible alternatives prior to making a purchase decision, they are highly engaged in a cognitive thinking process (Hawkins & Mothersbaugh, 2010). Research has shown that perceived benefits, costs and risks are major constructs in cognitive attitude formation (Forsythe et al., 2006; Hansen, 2005; Javadi, Dolatabadi, Nourbakhsh,

Texas Tech University, Hsiang-Ting Chen, May 2014

Poursaeei & Asadollahi, 2012; Jayawardhena, 2004; Kim et al., 2009; Pires, Staton, &

Eckford, 2004). Forsythe et al. (2006) summarized the main constructs of perceived benefits and risks through a comprehensive literature review and a qualitative inqury. In their study, four percieved benefits (shopping convenience, product selection, comfort of shopping and enjoyment) and three percieved risks (financial risk, product risk and time/convenience risk) were identified as the determinants of influencing consumer online shopping behavior (Forsythe et al., 2006). Jayawardhena (2004) also conducted research regarding how personal values influence online consumer shopping attitudes and behaviors. The research indicates that the significant constructs of attitudes include considering a variety of products, comparison shopping, finding the best price for goods and a quick purchase determination.

One of the behavioral theories related to consumers’ attitudes and beliefs structure is the theory of cognitive dissonance. The theory of cognitive dissonance was first introduced by Festinger in 1957 and based upon the belief that individuals seek to maintain consistency between information obtained and internal beliefs (Festinger, 1957).

Dissonance arises when individuals have an experience that contrasts their preconceived notions. When consumers observe a price which is different from what they expected they utilize strategies to reduce the feeling of dissonance. Continuous information searching, shopping-around for savings, or changing price expectations are strategies adopted by consumers when they perceive diverse prices in a marketplace (Lindsey-

Mullikin, 2003). Consumers who are demanding low prices continuously seek information among online travel websites (Christodoulidou et al., 2007).

Texas Tech University, Hsiang-Ting Chen, May 2014

When consumers search hotel deals or related discount information, their ultimate goal is to locate the best deal. Value-maximization, justification of spending and other benefits (i.e. quality benefit, convenience benefit) frame consumers beliefs to continue searching deals (Chandon et al., 2000; Kwon & Kwon, 2013). In addition, consumers’ attitudes toward deals are related to deal pronesses and value consiousness (Lichtenstein et al., 1990; Sigala, 2013). The increased propensity of deal pronesses and value consiousness directly affects consumers’ attitudes toward purchase (Lichtenstein et al.,

1990). Thus, the more positive benefits consumers perceive, the more favorable attitudes they hold, resulting in stronger motivations to make a purchase decision. In order to measure consumers’ attitudes toward booking deals, the propensity of spending time and effort, the preception of searching hotel deals online and the price/quality evaluations are considered in this research.

H1: Attitudes (ATT) have a significant positive influence on a consumer’s

motivation to book hotel deals through online travel intermediaries.

Emotions

Emotions are defined as a mental state of readiness that is affected by an individual’s appraisal of events and thoughts (Bagozzi et al., 2002). Specifically, emotions are subjective feelings that relate to individuals’ needs, motivation and experience. These feelings (i.e. joyful, excited, guilty, etc.) are associated with how consumers feel when they evaluate, buy and consume a product (Hawkins &

Mothersbaugh, 2010; Ruth, 2001). Previous studies indicate emotional experiences can

Texas Tech University, Hsiang-Ting Chen, May 2014 be evoked and aroused by different environmental stimuli (Eroglu, Machleit & Davis,

2003; Kawaf & Tagg, 2012). For example, virtual layout and design of a travel website may influence consumers’ feelings of pleasure while browsing the site (Manganari,

Siomlos, Rigopoulou & Vrechopoulos, 2011). Moreover, consumers’ positive and negative emotions may influence their perceptions toward sales promotions and product advertisements, which in turn, influences their purchase decisions (Chuang & Lin, 2007).

Thus, consumers’ emotional state has been viewed as a vital factor that affects decision- making.

Researchers have argued that anticipated emotions are different from affective attitudes (Perugini & Bagozzi, 2001). While affective attitudes are attained by experience acquisition or through repeated behavioral reinforcement, emotions are dynamic and directly affect an individual’s desire to complete a certain task (Baggozi et al., 1999; Hansen, 2005; Kim et al., 2012). By performing a behavior, a consequence and subsequent feedback will generate emotional responses. These anticipated positive and negative emotions are the result of consumers’ information processing, evaluation and decision justification. Researchers have suggested possible emotional responses should be examined when attempting to understand consumer behavior (Bettman, Luce, &

Payne, 2008; Hansen, 2005). The effects of emotions have been found to significantly influence consumers’ purchase decisions about food products (Hansen, 2005), grocery products (Chuang, Kung, & Sun, 2008), online merchandise (Mazaheri, 2012) and other various product categories (Hawkins & Mothersbaugh, 2010).

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Maximizing the positive emotion and minimizing the negative emotion experienced while making a decision are two metagoals in the decision making process

(Bettman et al., 2008, p.590). Bagozzi, Baumgartner, & Pieters (1998) proposed that different types of emotions will be produced when a decision maker pursues a certain behavior. Positive emotions are hypothesized to affect a consumer’s willingness and motivation to pursue a particular behavior. When consumers recognize positive emotions while seeking sales promotions their feelings of pleasure and enjoyment may be enhanced, inducing the purchase (Fortin, 2000).

However, a consumer is unlikely to pursue a certain behavior which he or she anticipates would cause negative emotional outcomes. Previous research argued that a consumer may experience negative emotions while facing choices (Bettman et al., 2008;

Cai and Cude, 2011). Consumers try to avoid the feeling of regret or disappointment from making a wrong choice such as when buying discounted products and worrying that the lower price indicates poor quality. While it is believed positive emotions entice time and effort spent deal-searching, negative emotions may cut the search short.

H2: Positive Emotions (PEM) have a significant positive influence on a

consumer’s motivation to book hotel deals through online travel intermediaries.

H3: Negative Emotions (NEM) have a significant negative influence on a

consumer’s motivation to book hotel deals through online travel intermediaries.

Market Mavens

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The concept of market mavens is defined as individuals who willingly acquire product-related information and share that knowledge and information with other consumers (Feick & Price, 1987). Market mavens are characterized as information seekers, highly involved in the purchase process and heavy coupon users who are easily reached through advertisements. Consumers who have higher market maven propensities are more aware of new product offerings and actively respond to promotions than consumers with low market maven propensity (Barnes & Pressey, 2012; Williams &

Slama, 1995).

The consumer segment of market mavens draw a special interest to the online market because they are capable of influencing other consumers. Market mavens perceive themselves as experts or smart shoppers who influence other people’s purchase decisions

(Clark & Goldsmith, 2005; Feick & Price, 1987; Williams & Slama, 1995). They also tend to gather and share information with others in order to gain appreciation and approval from their peers (Lichetenstein, Ridgway, & Netemeyer, 1993; Schindler, 1989;

Hawkins & Mothersbaugh, 2010). Market mavens not only represent a type of normative influence on other consumers by providing their opinion, they are also heavily influenced by marketing information (Clark & Goldsmith, 2005). According to Clark & Goldsmith’s research, market mavens are more likely to conform to expectations and be influenced by others. This indicates that the market maven can be a type of “subjective norm,” which determines how an individual feels and reacts to perceived normative influence (Bagozzi et al., 2002).

Texas Tech University, Hsiang-Ting Chen, May 2014

The concept of the market maven is also applied in investigating why consumers search online hotel promotions. In Christou’s study (2011), the market maven was a critical variable that influenced consumers when searching and purchasing hotel deals online. The consciousness of retrieving promotions fuels consumers to actively look for more deal information and enhance their feeling of competence about getting good deals

(Christou, 2011). Therefore, the construct of market mavens is considered a factor that affects consumers’ motivation to book hotel deals.

H4: Market Mavens (MM) have a significant positive influence on a consumer’s

motivation to book hotel deals through online travel intermediaries.

Subjective norms

In TRA, TPB and MGB, subjective norms have been examined as a salient predictor which determines an individual’s intention and behavior (Bagozzi et al., 2002).

Subjective norms are defined as a person’s obligation to perform or not perform a behavior based on perceived social pressure (Ajzen, 1991). Normative belief indicates that obtaining other people’s approval and agreement is vital to one’s behavioral intention.

Previous studies have confirmed subjective norms affect online shopping behavior (Chen

& Chang, 2005; Shim, Eastlick, Lotz, Warrington, 2001) and coupon usage (Fortin, 2000;

Sigala, 2013). Xie et al. (2013) found many consumers’ consumption decisions were strongly affected by social influences. Zhang, Pan, Smith and Lee (2009) stated most travelers actively seek online reviews and recommendations before making a hotel reservation. In addition, price-sensitive customers tend to collect price-related

Texas Tech University, Hsiang-Ting Chen, May 2014 information from other customers in user-generated content websites, such as social media sites (Toh et al., 2011). Thus, in this research, subjective norms are defined as the extent to which consumers will engage in deal-purchasing behavior when they perceive approval from others. These norms are hypothesized to influence consumers’ motivation to book hotel deals.

H5: Subjective norms (SN) have a significant positive influence on a consumer’s

motivation to book hotel deals through online travel intermediaries.

Perceived self-efficacy

In TPB, perceived behavior control (PBC) is defined as “people’s perception of the ease or difficulty of performing the behavior of interest” (Ajzen, 1991, p.183). PBC can be interpreted as the degree that people believe whether or not they have control over an action (Bagozzi et al., 2002). Although Ajzen states the concept of perceived behavior control is close to the concept of self-efficacy, researchers have argued they are not entirely synonymous (Armitage & Conner, 2001; Bagozzi et al., 2002). Perceived self- efficacy is viewed as one’s ability and confidence to complete a certain task, similar to the concept of “outcome expectancies” (Bagozzi et al., 2002, p.73). Armitage & Conner

(1999) have distinguished the differences between perceived behavior control and self- efficacy. In their study, perceived behavior control and self-efficacy represent the different facet of performing a particular behavior. Self-efficacy can be defined as confidence in one’s ability to carry out a behavior; it also indicates consumers’ abilities to acquire marketplace information (Armitage & Conner, 1999; Bearden & Hardesty, 2001;

Texas Tech University, Hsiang-Ting Chen, May 2014

Clark, Goldsmith, & Goldsmith, 2008). Vijayaharathy (2004) defined the concept of self- efficacy as “ a consumer’s self-assessment of his/her capabilities to shop online” (p.751).

Self-efficacy was found to have positive and significant influence on consumers’ intention toward online shopping (Vijayaharathy, 2004). Therefore, in deal-purchasing behavior, one’s competence in searching information and obtaining online deals may influence that individual’s motivation and subsequently, booking intention.

H6: Perceived self-efficacy (PSE) has a significant positive influence on a

consumer’s motivation to book hotel deals through online travel intermediaries.

Frequency of past behavior

Past behavior contributes directly to desire and intention because consumers have favorable or unfavorable behavioral propensities due to past experiences (Perugini &

Bagozzi., 2001). The frequency of past behavior reflects strength of various habits and the likelihood of repeated performance; thus, it has been suggested as an indicator of intention which induces an individual to pursue a certain behavior (Bamberg, Ajzen, &

Schmidt, 2003; Lam & Hsu, 2004; Kim et al., 2012; Perugini & Bagozzi., 2001).

Researchers state that the more often previous behavior has been performed, the more likely it influences an individual’s motivation to pursuing the same behavior in the future.

Past behavior was also found to have a strong and direct impact on consumers’ intention to conduct online information searches and shopping (Shim et al., 2001).

In a study of consumers’ e-coupon redemption while shopping online, the frequency of past behavior played an important role in predicting usage intentions (Chen

Texas Tech University, Hsiang-Ting Chen, May 2014

& Lu, 2011). Based on previous studies, there is a reasonable assumption that consumers’ motivation and intention may be affected by their past experience searching and booking hotel deals through online travel intermediaries.

H7: Frequency of past behavior (FPB) has a significant positive influence on a

consumer’s motivation to book hotel deals through online travel intermediaries.

H8: Frequency of past behavior (FPB) has a significant positive influence on a

consumer’s intention to book hotel deals through online travel intermediaries.

Motivation

Several researchers have examined which factors fuel consumers’ motivation to purchase products online (Smith, 2004). Previous studies identified information and economic motivations as being the primary influences upon consumers’ purchase intentions while using the Internet as a preferred shopping channel (Ganesh, Reynolds

Luckeet, & Pomirleanu, 2010; Joines, Scherer, & Scheufele, 2003; Smith, 2004; Parsons,

2002).

Information motivation refers to online consumers’ tendency to search for information about a product and service to help them make a better or more efficient purchase decision (Joines et al., 2003). Consumers may continue to search for information to reinforce and justify their decisions (Jiang, 2002). For online travelers, information motivation affects comparison shopping behavior (Chatterjee & Wang, 2012;

Park & Gretzel, 2010). Consumers’ comparison shopping behavior is also related to

Texas Tech University, Hsiang-Ting Chen, May 2014 economic motivation, which involves evaluating deals to save money and maximize the value of money spent (Joines et al., 2003). For instance, price-oriented consumers who desire a hotel room with a discount price are motived to search around until they are satisfied with an advertised deal.

Intention

Intention refers to the likelihood that an individual will do something; it is an expectation that he or she will act a certain way in the future (Bagozzi et al., 2002). Based on TRA and TPB, behavioral and normative beliefs facilitate and guide one’s intention, which is the most accurate predictor of actual behavior (Ajzen, 1991). Gollwitzer &

Brandstätter (1997) states that an intention is defined as “people’s attempts to realize their wishes and desires,” and it stimulates behavioral responses (p.148). Intention promotes the initiation and execution of goal-directed behavior (Gollwitzer

&Brandstätter 1997).

From the psychological perspective, motivation is a desired state which elicits intention and provides direction to a particular behavior (Bagozzi & Dholakia, 1992).

Given a strong motivational impetus, an individual’s intrinsic attitudes, emotions, subjective norms and self –efficacy will be energized and transformed toward a behavioral intention (Perugini & Bagozzi, 2001). In other words, motivations are the proximal causes of intentions (Xie et al., 2013). During a consumer decision making process, an individual’s motives are driven to the intention once the need is recognized.

Consequently, an intention is targeted at a specific outcome through “the execution of

Texas Tech University, Hsiang-Ting Chen, May 2014 instrumental acts” (Bagozzi et al., 2002, p.83). It is assumed a stronger motivation will incite a more pronounced intention, which eventually leads to performing a behavior.

H9: Consumers’ motivation (MOV) has a positive significant influence on

consumers’ intention (BI) to book hotel deals through online travel intermediaries.

Texas Tech University, Hsiang-Ting Chen, May 2014

CHAPTER 3

METHODOLOGY The purpose of this study was to identify: (1) the factors that influence consumer decision making while searching and booking hotel deals through online travel intermediaries; (2) how cognitive, emotional and social components motivate consumers to book online hotel deals; and (3) how consumers’ intention to purchase hotel deals is stimulated. The hypothesized model was structured based on MGB and a literature review relevant to consumer behavior. In an attempt to understand consumers’ propensity for searching and booking hotel deals, a self-reported survey method was utilized in this research. A pilot study was conducted to test the construction of measurement items. An initial structural model was identified. Based upon the findings from the pilot study, the survey and model were modified. The full study was subsequently implemented to examine the stated research questions.

3.1 Model development

An extensive literature review regarding consumer behavior provided the foundation for the hypothesized model. The model consisted of nine constructs: attitudes, positive emotions, negative emotions, market mavens, subjective norms, perceived self- efficacy, frequency of past behavior, motivation and intention. Based upon previous behavioral theories, attitude is considered as a cognitive mechanism in an evaluation process. Emotional states also influence one’s volitions and initial action (Bagozzi et al.,

2002). Market mavens and subjective norms represent individuals’ expectations or social

Texas Tech University, Hsiang-Ting Chen, May 2014 pressures from others which affect whether a consumer performs a certain behavior or chooses not to do so (Clark & Goldsmith, 2005). Thus, cognitive, emotional and social components are three main aspects to predict one’s behavior. Perceived self-efficacy was established to measure an individual’s confidence and ability to pursue a behavior

(Armitage & Conner, 1999).

Motivation plays a mediating role, which is influenced by cognitive, emotional and social components toward an intention to engage in a behavior. Motivation also serves as an important and supportive role to induce one’s behavioral intention. Intention indicates one’s commitment to pursue a goal, which ultimately leads to actual behavior.

Finally, frequency of past behavior reflects a determinant based on prior experience, which has a significant effect on an individual’s motivation and intention to book hotel deals through online travel intermediaries.

3.2 Measurement development and procedure

The measurements in the proposed theoretical model of this research were developed based upon extant scholarly literature related to consumers’ decision-making and purchase process, tendency toward deal acquisitions, transaction and acquisition utility theory, Model of Goal-Directed Behavior (MGB) and other behavior related theories. The measure of attitudes was established to recognize consumers’ perceptions toward deal acquisitions, including perceived benefits, perceived risks, cognitive evaluation and affective attitudes (Harris & Mowen (2001); Jayawardhena (2004); Kim et al., 2009; Lichtenstein et al., 1993). Positive and negative emotions identified consumers’

Texas Tech University, Hsiang-Ting Chen, May 2014 emotional expectations toward booking hotel deals online (Kim et al., 2012; Taylor, 2007;

Tsang, Tsai & Leung, 2011; Perugini & Bagozzi, 2001). The market mavens variable detected consumers’ propensity to obtain information related to product promotions and be experts/opinion leaders in the marketplace (Lichetenstein et al., 1993). Subjective norms represent social obligations which allow consumers to comply with normative pressure (Perugini & Bagozzi, 2001). The construct of perceived self-efficacy is adapted from previous studies (Armitage & Conner, 1999; Bearden, Hardesty, & Rose, 2001) to identify self-confidence and the ability to search for hotel deals online. Motivation indicates the likelihood and willingness to search and book hotel deals online (Laroche,

Zgolli, Cervellon & Kim, 2003; Perugini & Bagozzi, 2001). Finally, intention determines the likelihood of booking hotel deals in the near future (Perugini & Bagozzi, 2001). A total of 45 items were included in the questionnaire. Table 3.1 shows the development of measurement scales based upon previous research.

Table 3.1 Measurement Items in the pilot study Measures and Items Source Cronbach’s Alpha Attitude(ATT) ATT1 When I book a hotel online, I Adapted from Lichtenstein .84; .86 like to ensure I get the best et al. (1993); Harris & value. Mowen (2001) ATT2 I always check hotel prices Adapted from Lichtenstein .84; .86 through travel websites to be et al. (1993); Harris & sure I get the best value. Mowen (2001) ATT3 Finding a good deal is usually Adapted from Lichtenstein .84; .86 worth the time and effort. et al. (1993); Harris & Mowen (2001) ATT4 When purchasing a product, I Adopted from Lichtenstein .84; .86 always try to maximize the et al. (1993); Harris & quality I get for the money I Mowen (2001)

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spend. ATT5 Using travel websites is a Adapted from Shim et al., .78 convenient way to search (2001) product/price information. ATT6 Using travel websites is easy Adapted from Shim et al., .73 for finding a good deal on a (2001) hotel room. ATT7 When I book a discounted Adapted from Kim et al., .78 hotel room through travel 2009 websites, I am concerned that these websites may not provide the level of quality/service I am expecting. ATT8 When I book a hotel room Adapted from Kim et al., .87 through travel intermediaries, 2009 I am concerned about the security of my personal information (e.g. credit card details). ATT9 Searching hotel deals over the Adapted from Kim et al., .78 web takes too long or is a 2009 waste of time. ATT10 I enjoy spending time Adapted from .79; .94 comparing prices through Jayawardhena (2004); different websites. Lee(2000) ATT11 Searching online deals gives Adapted from Lee (2000) .94 me pleasure. ATT12 Searching online deals is Adapted from Harris & .93 exciting. Mowen (2001) Positive emotions (PEM) PEM1 I am happy when I am able to Adapted from Tsang et al. .80 find a hotel room with a (2012) lower price than I expected. PEM2 I am satisfied when I am able Adapted from Tsang et al. .80 to secure a lower price than (2012) others who pay full price. PEM3 I am excited when I am able Adapted from Perugini & .75 to find a good deal for my Bagozzi (2001) money. PEM4 I am thrilled when I book Adapted from Taylor .95 hotel deals through online (2007) travel websites.

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Negative emotions (NEM) NEM1 I regret booking hotel deals Adapted from Taylor .95 through travel websites. (2007) NEM2 I feel uncomfortable booking Adapted from Taylor .95 hotel deals through travel (2007) websites. NEM3 When I don’t find a good deal Adapted from Kim et .88 through travel websites, I am al.(2012) disappointed. NEM4 When I didn’t find a good Adapted from Kim et .88 deal through travel websites, I al.,(2012) feel angry. Market Mavens (MM) MM1 People ask me for Adapted from Lichetenstein .90 information about prices on et al., (1993) different types of products. MM2 I am considered somewhat of Adapted from Lichetenstein .90 an expert when it comes to et al., (1993) knowing the prices of products. MM3 I think I am better able than Adapted from Lichetenstein .90 most people to tell someone et al., (1993) where to shop and how to find the best deal. MM4 My friends think of me as a Adapted from Lichetenstein .90 good source of price et al., (1993) information for online deals. MM5 I like helping people by Adapted from Lichetenstein .90 providing them with price et al., (1993) information about online deals. Subjective norms (SN) SN1 People whose opinions I Adapted from Perugini & .91 value approve of me Bagozzi (2001) searching for deals online. SN2 People whose opinions I Adapted from Perugini & .91 value approve of me Bagozzi (2001) searching for hotel deals before booking. SN3 Most people who are Adapted from Perugini & .91 important to me search for Bagozzi (2001) hotel deals through travel

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websites. SN4 Most people who are Adapted from Perugini & .91 important to me think I Bagozzi (2001) should book hotel deals through travel websites. Perceived self-efficacy PSE1 I know where to find the Adapted from Bearden, .80 information I need prior to Hardesty, & Rose (2001) making a purchase. PSE2 I trust my own judgment Adapted from Bearden, .72 when selecting products to Hardesty, & Rose (2001) purchase. PSE3 I believe I can effectively Adapted from Armitage & .83 search for information online. Conner (1999) PSE4 I am confident I am able to Adapted from Armitage & .87 find good deals online. Conner (1999) Motivation MOV1 I am likely to use travel Adapted from Laroche et .92 websites to search for hotel al., (2003) deals. MOV2 I am likely to visit different Adapted from Laroche et .92 travel websites to take al., (2003) advantage of low prices. MOV3 I prefer to search hotel deals Adapted from Perugini & .97 online before making a Bagozzi (2001); Taylor et reservation. al., (2007) MOV4 I am more likely to make a Adapted from Perugini & .94 purchase on a website that Bagozzi (2001); Taylor et provides good deals. al., (2006) Intention BI1 In the future, I plan to book a Adapted from Tsang et al., .80 hotel room through travel (2011) websites. BI2 In the future, I plan to book Adapted from Tsang et al., .80 hotel rooms through travel (2011) websites that provide good deals. BI3 After searching hotels Adapted from Perugini & .92 through travel websites, I Bagozzi (2001); Taylor et consider booking a hotel al., (2006) room on one of the websites

Texas Tech University, Hsiang-Ting Chen, May 2014

which I visited. BI4 I am willing to book hotel Adapted from Perugini & .92 deals online in the near Bagozzi (2001); Taylor et future. al., (2006)

A pilot study was conducted to test the appropriateness of the measurement items.

Internet users with experience booking hotel rooms through online travel intermediaries were the target population. Therefore, the questionnaire included two filter questions. The first filter question was used to determine whether participants primarily use online travel websites for travel booking. Those participants who did not use online travel intermediaries were thanked for their interest and excluded from taking the questionnaire.

For those participants who answered the first filter question positively, they were asked a second filter question that confirm their age to be 18 years or older.

In order to allow participants to evoke a buying situation regarding searching and booking hotel deals through online travel intermediaries, participants were presented with a scenario, which indicated they were the decision makers. The buying situation specified that participants were going to book a hotel room for a leisure trip. In addition, they had enough time for search and compare various hotel deals. The scenario presented to respondents at the start of the survey follows:

“There are many different choices regarding prices and features of hotel rooms

on online travel websites. Suppose you were going to have a leisurely vacation

with one of your close friends. Your friend put you in charge of making the travel

arrangements. Thus, you can select the price, location and star level of the hotel

Texas Tech University, Hsiang-Ting Chen, May 2014

yourself. Since you and your friend intend to share all the expenses during the trip,

you would like to obtain the best deal by searching for hotel rooms online. You

have plenty of time, about three months prior to the trip, to make all the travel

arrangements. Keep these details in mind as you answer the following questions.”

Participants were asked to answer questions designed to address the nine constructs pertaining to consumer deal-purchasing behavior via travel websites. A 7-point

Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree) was used to record participants’ responses. The question related to frequency of past behavior of online hotel deal bookings was measured from 1 (never) to 7 (every time). The final section of the survey included demographic questions, such as gender, age, education level, online shopping frequency, preferences of promotion types, participation of hotel/online travel website loyalty programs, times of travel in the last year, household annual income and ethnicity.

3.3 Pilot study

3.3.1 Data collection

Data collection was conducted from April to June, 2013. The pilot study applied a convenience sampling technique; a self-report questionnaire was distributed to a student population in a southwestern university. College students have been identified as being technologically savvy, particularly because they tend to search for information via the

Internet (Seock & Bailey, 2008). Therefore, the student population was utilized for the pilot study. The participants either filled out hard copy questionnaires or an online survey

Texas Tech University, Hsiang-Ting Chen, May 2014 accessed via a link provided. The survey was distributed to approximately 400 students.

A total of 305 questionnaires were collected, yielding a 76% response rate. Since the survey consisted of two filter questions to ensure the sample met the requirements, a total of 162 participants had positive responses to both filter questions. Thus, the usable response rate was 53%.

3.3.2 Data screening

The frequency check of all variables was conducted to identify mis-entered and missing data. There was no mis-entered data found. However, eight surveys included more than 5% missing data. Thus, these 8 surveys were removed from the data set.

Normality tests identified the 9 univariate outliers which were deleted from the data set.

Multivariate outliers were assessed using Mahalanobis distance. There was one multivariate outlier found and removed. After deleting the outliers, the sample size was reduced to 144. Tests for the assumption of linearity indicated nothing out of the norm.

The test for multicollinearity revealed no variable was highly correlated (r <|.70|). Thus, the muliticillinearity was not an issue in this study.

3.4 Full study

3.4.1 Questionnaire revision

Based on CFA, EFA and reliability results from the pilot study, the measurement items in the questionnaire were revised. The initial questionnaire consisted of 45 measurement items. However, only 33 items were utilized in the full study. A total of 9

Texas Tech University, Hsiang-Ting Chen, May 2014 items were removed due to the inadequate fitness of the CFA. The questions related to frequency of past behavior were revised to a single question which represented an observed variable. The revised items, codes and descriptions which were used in the full study are displayed in Table 3.2.

Table 3.2. Measurement Items in the full study

Factor Item code Measures and Items When I book a hotel online, I like to ensure I get the best Attitudes ATT1 value. I always check hotel prices through travel websites to be sure ATT2 I get the best value. ATT3 Finding a good deal is usually worth the time and effort. When purchasing a product, I always try to maximize the ATT4 quality I get for the money I spend. Using travel websites is a convenient way to search ATT5 product/price information. Using travel websites is easy for finding a good deal on a ATT6 hotel room. Positive I am happy when I am able to find a hotel room with a lower PEM1 Emotions price than I expected. I am satisfied when I am able to secure a lower price than PEM2 others who pay full price. I am excited when I am able to find a good deal for my PEM3 money. Negative NEM1 I regret booking hotel deals through travel websites. Emotions I feel uncomfortable booking hotel deals through travel NEM2 websites. People ask me for information about prices on different types Market Mavens MM1 of products. I am considered somewhat of an expert when it comes to MM2 knowing the prices of products. I think I am better able than most people to tell someone MM3 where to shop and how to find the best deal. My friends think of me as a good source of price information MM4 for online deals. I like helping people by providing them with price MM5 information about online deals.

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Subjective People whose opinions I value approve of me searching for SN1 norms deals online. People whose opinions I value approve of me searching for SN2 hotel deals before booking. Most people who are important to me search for hotel deals SN3 through travel websites. Most people who are important to me think I should book SN4 hotel deals through travel websites. Perceived self- I know where to find the information I need prior to making PSE1 efficacy a purchase. I trust my own judgment when selecting products to PSE2 purchase. PSE3 I believe I can effectively search for information online. PSE4 I am confident I am able to find good deals online. I am likely to use online travel websites for searching hotel Motivation MOV1 deals. I am likely to visit different online travel websites to take MOV2 advantage of low prices. I prefer to search hotel deals before making an online MOV3 reservation. I am likely to make a purchase on a website that provides MOV4 good deals. In the future, I plan to book a hotel room through travel Intention BI1 websites. In the future, I plan to book hotel rooms through travel BI2 websites that provide good deals. After searching hotels through travel websites, I will BI3 consider booking a hotel room on one of the websites which I visited. BI4 I am willing to book hotel deals online in the near future.

3.4.2 Data collection

The revised questionnaire was distributed via Qualtrics Survey Software

Company. Participants were recruited from a panel of Internet users developed by

Qualtrics. Since the purpose of this study was to understand online consumer deal- purchasing behavior through travel intermediaries, an online survey distribution method

Texas Tech University, Hsiang-Ting Chen, May 2014 was considered appropriate (Bosnjak et al., 2007; Park & Gretzel, 2010). The full study utilized non-probability, web-based quota-sampling from a nationwide consumer panel.

The type of sampling technique was utilized due to the desire to solicit responses from participants who had online hotel booking experience in the past 12 months. As this research aimed to identify online consumer behavior, the quota-sampling represented a specific portion of the population. In addition, employing a convenience quota-sampling method provided representative samples in conducting online travel research (Lohmann

& Schmücker, 2009). The data collection was conducted in September, 2013. A total of

603 completed responses were collected.

3.4.3 Data screening

The data were examined for univariate and multivariate outliers. By conducting an analysis of normal distribution, a total of 9 univariate outliers were identified and deleted from the dataset. In addition, 13 multivariate outliers were found and deleted by using Mahalanobis distance. A total of 581 usable questionnaires were utilized in the full study. Reliability and validity was tested using Crobabch’s alpha. The alpha coefficients ranged from .848 to .919. The test for multicollinearity revealed that no variables were highly correlated (r <|.70|). The data confirmed no straight lining problem, such as selecting 7=strongly agree on all questions, or having missing answers on more than 5% on all questions. The statistical software IBM SPSS (version 21) was used to analyze the data for normality, outliers and test statistical assumptions. Mplus (version 7) was utilized to run confirmatory factor analysis (CFA) and structural equation modeling (SEM).

Texas Tech University, Hsiang-Ting Chen, May 2014

CHAPTER 4

RESULTS

4.1 Pilot study

4.1.1 Sample

The survey was distributed to approximately 400 students. A total of 305 questionnaires were collected, yielding a 76% response rate. Since the survey consisted of two filter questions to ensure the samples were over 18 years old and had hotel booking experience through online travel websites in the past 12 months , only 162 responses were usable. Thus, the usable response rate was 53%. A total of 144 usable questionnaires were collected between April and June 2013. As the aim of the pilot test was to determine the appropriateness and effectiveness of the survey instrument a small sample size was sufficient. Since the participants in the pilot study were primarily recruited from a student population, the majority of the sample (65%) was between 21 and 25 years of age and in college pursuing a bachelor’s degree (65%) at the time of data collection. The remaining respondents possessed a bachelor’s degree (22%) or master’s degree or higher (13%). Most individuals identified themselves as Caucasian (64%). As expected from the young sample, 43% reported their household income to be less than

$24,999 annually. Table 4.1 shows the participants’ demographic information regarding to their age, ethnicity, education and income level.

Texas Tech University, Hsiang-Ting Chen, May 2014

Table 4.1 Pilot Stud: demographic information (N=144)

Variable Frequency Percent Age 18-20 15 10% 21-25 93 65% 26-29 20 14% 30+ 16 11% Ethnic Group African/African American 10 7% Asian 16 11% Caucasian 92 64% Hispanic 20 14% Other 6 4% Education In College 93 65% Bachelor's Degree 32 22% Master's Degree or Beyond 19 13% Household Income Less than $24,999 62 43% $25,000-$49,999 24 17% $50,000-$74,999 16 11% $75,000-$99,999 10 7% $100,000-$124,999 9 6% Greater than $125,000 23 16%

Table 4.2 provides information related to the frequency of travel within the past

12 months. More than one-third of participants (35%) stated they had traveled 3-4 times within the past year. When asked about how often they visit travel websites before booking a hotel, 67% claimed they browse 3-4 websites before purchasing.

Texas Tech University, Hsiang-Ting Chen, May 2014

Table 4.2 Pilot study: number of online travel websites visited before booking a hotel and frequency of travel within the past 12 months (N=144) Variable Frequency Percent Number of visits to online travel websites 1-2 24 17% 3-4 97 67% 5-6 18 13% 7+ 5 3% Trips taken within the past 12 months 1-2 29 20% 3-4 50 35% 5-6 40 28% 7-8 8 6% 9+ 17 6%

4.1.2 Assumption testing

A frequency check of all variables was conducted to identify incorrectly entered data. There were no inaccuracies found in the data entry. For the assumption of multivariate normality, the Kolmogorov-Smirnov (K-S) test was conducted. The subscales of negative emotion, perceived self-efficacy, motivation and intention demonstrated negatively skewed layout with leptokurtic tendencies. However, the

Kurtosis value in the pilot study ranged from -0.14 to 1.81 and the Skewness ranged from

-1.21 to 0.24. Based on Kline’s (2005) recommendations that Kurtosis measure below 10 and Skewness under 3, the data was considered to be normally distributed. To test the relationship among the variables, an inter-correlation test was conducted (Table 4.3). The results indicated the variables were correlated. Further tests revealed VIF<3, which indicate multicollinearity was not a concern (O’Brien, 2007).

Texas Tech University, Hsiang-Ting Chen, May 2014

Table 4.3 Pilot study: Means, Standard Deviation and Construct Inter-Correlations

Variables Mean SD 1 2 3 4 5 6 7 8 1. SubATT 62.24 .63  2. SubPEM 23.90 .27 .59**  3. SubNEM 12.85 .27 .14 -.02  4. SubMM 20.49 .54 .42** .35** .17*  5. SubSN 19.75 .38 .32** .44** .03 .53**  6. SubPSE 23.01 .29 .28** .47** -.31** .37** .51**  7. SubMOV 24.47 .29 .44** .62** -.10 .27** .39** .53**  8. SubBI 24.90 .27 .44** .56** -.02 .26** .41** .54** .77**  Note: N=114, SubATT= Attitudes sub score; SubPEM=Positive emotions sub score; SubNEM=Negative emotions sub score; SubMM=Market Maven sub score; SubSN=Subjective norms sub score; SubMOV=Motivation sub score; SubBI=Behavioral intention sub score. * p<.05, **<.01.

Texas Tech University, Hsiang-Ting Chen, May 2014

4.1.3 Exploratory factor analysis (EFA)

Exploratory factor analysis (EFA) was performed by utilizing principal factor extraction with oblique rotation (direct oblimin). Principal axis factoring extraction was used prior to principal factors extraction to estimate the number of factors and factorability of the correlation matrices. To meet the standard requirement of factor loading, the cutoff point was set at .40 and the eigenvalues over Kaiser’ criterion of 1 was applied. Based on the results of the EFA, six indicators were identified for attitudes, three positive emotion indicators, two negative emotion indicators, five market maven indicators, four social norm indicators, four perceived self-efficacy indicators, four indicators of motivations and four indicators of behavioral intention. These indicators were subsequently selected to perform the confirmatory factor analysis. The measurements for each indicator were sufficiently reliable according to Cronbach’s alpha which ranged from .81 to .90. The result of the factor loadings is shown in Table 4.4.

Texas Tech University, Hsiang-Ting Chen, May 2014

Table 4.4 Pilot study: Mean, Standard Deviation, Factor loading and Cronbach’s Alpha of Indicators

Constructs Code Indicators Mean SD Factor Loadings  Attitudes ATT1 When I book a hotel online, I like to 6.39 .97 .61 .87 ensure I get the best value. ATT2 I always check hotel prices through 6.18 1.04 .78 travel websites to be sure I get the best value. ATT3 Finding a good deal is usually worth the 6.29 .88 .75 time and effort. ATT4 When purchasing a product, I always try 6.20 .89 .72 to maximize the quality I get for the money I spend. ATT5 Using travel websites is a convenient 6.38 .79 .74 way to search product/price information. ATT6 Using travel websites is easy for finding 6.17 .89 .63 a good deal on a hotel room. Positive Emotions PEM1 I am happy when I am able to find a 6.23 .88 .79 .89 hotel room with a lower price than I expected. PEM2 I am satisfied when I am able to secure a 6.26 .92 .86 lower price than others who pay full price. PEM3 I am excited when I am able to find a 6.33 .79 .92 good deal for my money.

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Negative NEM1 I regret booking hotel deals through 2.30 1.18 .82 .81 travel websites. Emotions NEM2 I feel uncomfortable booking hotel deals 2.45 1.26 .84 through travel websites. Market Mavens MM1 People ask me for information about 3.99 1.56 .70 .88 prices on different types of products. MM2 I am considered somewhat of an expert 3.65 1.54 .78 when it comes to knowing the prices of products. MM3 I think I am better able than most people 4.25 1.51 .82 to tell someone where to shop and how to find the best deal. MM4 My friends think of me as a good source 4.10 1.63 .87 of price information for online deals. MM5 I like helping people by providing them 4.59 1.65 .73 with price information about online deals. Subjective norms SN1 People whose opinions I value approve 5.01 1.28 .86 .87 of me searching for deals online. SN2 People whose opinions I value approve 5.08 1.33 .81 of me searching for hotel deals before booking. SN3 Most people who are important to me 4.94 1.37 .79 search for hotel deals through travel websites. SN4 Most people who are important to me 4.83 1.33 .73 think I should book hotel deals through travel websites. Perceived PSE1 I know where to find the information I 5.46 1.04 .65 .87 need prior to making a purchase.

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Self-efficacy PSE2 I trust my own judgment when selecting 5.73 1.00 .78 products to purchase. PSE3 I believe I can effectively search for 5.94 1.02 .90 information online. PSE4 I am confident I am able to find good 5.92 .98 .84 deals online. Motivations MOV1 I am likely to use online travel websites 6.08 1.00 .79 .89 for searching hotel deals. MOV2 I am likely to visit different online travel 6.11 .97 .77 websites to take advantage of low prices. MOV3 I prefer to search hotel deals before 6.17 1.02 .84 making an online reservation. MOV4 I am likely to make a purchase on a 6.19 .89 .86 website that provides good deals. Behavioral BI1 In the future, I plan to book a hotel room 6.23 .93 .85 .90 through travel websites. Intentions BI2 In the future, I plan to book hotel rooms 6.31 .83 .88 through travel websites that provide good deals. BI3 After searching hotels through travel 6.20 .92 .76 websites, I will consider booking a hotel room on one of the websites which I visited. BI4 I am willing to book hotel deals online 6.24 .86 .80 in the near future.

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4.1.4 Confirmatory factor analysis (CFA)

A confirmatory factor analysis (CFA) was conducted using Mplus (version 7).

Based on the results of the CFA, eight variables were identified: attitude, positive emotion, negative emotion, market mavens, social norms, perceived self-efficacy, motivation and behavioral intention. The model was estimated by using the maximum- likelihood method. The results indicated the model had a mediocre fit to the data, χ 2

(436) = 878.33, p < .0001, CFI = .870, RMSEA = .084 (90% CI .076-.092). MacCallum,

Browne, & Sugawara (1996) noted that RMSEA values ranging from .08 to .10 is acceptable. According the MacCallum et al.’s requirements, all factor loadings were significant, which supported the convergent validity of the model (Anderson and

Gerbing, 1988). Unstandardized and standardized coefficients estimates are provided in

Table 4.5. Figure 4.1 demonstrates the results of CFA.

Table 4.5 Pilot study: unstandardized and standardized coefficients for CFA Observed Latent Construct B SE β Est/S.E. p-value Variable ATT1 Attitude 1 0 0.63 11.38 <.001 ATT2 Attitude 1.404 0.175 0.828 25.24 <.001 ATT3 Attitude 1.077 0.145 0.75 17.68 <.001 ATT4 Attitude 1.116 0.147 0.766 19.00 <.001 ATT5 Attitude 0.994 0.133 0.764 18.83 <.001 ATT6 Attitude 0.89 0.143 0.614 10.68 <.001 PEM1 Positive Emotion 1 0 0.851 29.13 <.001 PEM2 Positive Emotion 1.02 0.087 0.824 25.47 <.001

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PEM3 Positive Emotion 0.937 0.071 0.891 36.27 <.001 NEM1 Negative Emotion 1 0 0.986 14.92 <.001 NEM2 Negative Emotion 0.757 0.121 0.695 10.95 <.001 MM1 Market Maven 1 0 0.678 13.50 <.001 MM2 Market Maven 1.08 0.133 0.742 17.28 <.001 MM3 Market Maven 1.193 0.136 0.837 26.24 <.001 MM4 Market Maven 1.347 0.147 0.878 32.81 <.001 MM5 Market Maven 1.163 0.147 0.747 17.42 <.001 SN1 Subjective norms 1 0 0.95 54.31 <.001 SN2 Subjective norms 0.976 0.055 0.896 42.57 <.001 SN3 Subjective norms 0.734 0.08 0.652 12.53 <.001 SN4 Subjective norms 0.646 0.082 0.591 10.03 <.001 PSE1 Perceived Self-efficacy 1 0 0.654 12.24 <.001 PSE2 Perceived Self-efficacy 1.105 0.141 0.756 18.70 <.001 PSE3 Perceived Self-efficacy 1.368 0.158 0.913 38.92 <.001 PSE4 Perceived Self-efficacy 1.208 0.142 0.844 28.23 <.001 MOV1 Motivation 1 0 0.79 22.14 <.001 MOV2 Motivation 0.931 0.095 0.758 19.13 <.001 MOV3 Motivation 1.002 0.099 0.779 21.06 <.001 MOV4 Motivation 0.98 0.083 0.876 35.61 <.001 BI1 Behavioral Intention 1 0 0.892 41.83 <.001 BI2 Behavioral Intention 0.931 0.057 0.912 48.28 <.001 BI3 Behavioral Intention 0.81 0.076 0.724 16.88 <.001 BI4 Behavioral Intention 0.789 0.069 0.76 19.67 <.001

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Figure 4.1 The CFA results from the pilot study

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4.2 Full Study

4.2.1 Sample

A total of 603 completed questionnaires were collected from a nationwide online consumer panel in September 2013. After data screening, 22 questionnaires were identified as outliers and removed from the data set. Only 581 usable questionnaires were included in the data analysis. Table 4.6 shows the participants’ demographic information regarding their age, ethnicity, education and income level. Fifty-two percent (52%) of the participants were 45 and above. Most participants held Associates and Bachelor’s degrees

(57%). The second largest group of participants held a Master’s degree or higher (28%).

The ethnic background of the sample was overwhelmingly Caucasian (78%). The largest group of participants (23%) reported their annual income as between $50,000 and

$74,999 annually. Exactly half of the participants (50%) reported their annual income was $75,000 or above.

Table 4.6 Full study: demographic information (N=581)

Variable Frequency Percent Gender Male 290 50% Female 291 50% Age 18-24 71 12% 25-34 102 18% 35-44 104 18% 45-54 113 19% 55 and above 191 33% Ethnic Group

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African/African American 35 6% Asian 50 9% Caucasian 451 78% Hispanic 25 4% Other 18 3% Education High school graduate 67 12% In college 16 3% College associate degree 172 30% Bachelor's Degree 154 27% Master's Degree 132 23% Beyond Master’s Degree 31 5% Annual Income Less than $24,999 40 7% $25,000-$49,999 119 20% $50,000-$74,999 132 23% $75,000-$99,999 117 20% $100,000-$124,999 73 13% $120,000-$149,999 40 7% $150,000-$174,999 22 4% $175,000-$199,999 17 3% Greater than $200,000 20 3%

The largest group of participants (30%) stated they had traveled 1-2 times within the past year, followed by the group of participants who had traveled 3-4 times (27%) and

5-6 times (22%). When asked about how often they visit online travel websites before booking a hotel, fifty-seven percent (57%) claimed they browse 3-4 websites before purchasing. Both of these figures were similar to findings from the pilot test. Table 4.7 provides information related to the frequency of travel and online travel information search within the previous 12 months.

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Table 4.7 Number of online travel websites visited before booking a hotel and frequency of travel within the past 12 months (N=581) Variable Frequency Percent Number of visits to online travel websites 1-2 121 21% 3-4 333 57% 5-6 92 16% 7+ 29 5% Missing data 6 1% Trips taken within the past 12 months 1-2 175 30% 3-4 156 27% 5-6 127 22% 7-8 43 7% 9+ 72 12% Missing data 8 1%

4.2.2 Assumption testing

The Kolmogorov-Smirnov (K-S) test was conducted to assess normality. All subscales of the constructs showed negatively skewed layout with leptokurtic tendencies.

Kurtosis values ranged between -0.46 and 1.38 and Skewness ranged from -1.31 to -.31; both were within acceptable limits (Kline, 2005). To test the relationship between variables an inter-correlation test was conducted (Table 4.8). Results indicated the variables were correlated to one another. The attitude construct was highly correlated with several variables: positive emotions, perceived self-efficacy, motivation and behavioral intention (r > |0.70|). Field (2009) stated that a critical multicollinear problem exists if a correlation between two variables is greater than .90. In addition, while conducting a multicollinearity test, the results showed that VIF ranged from 1 to 4, which indicated multicollinearity was not a concern (O’brien, 2007; Myer, 1990).

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Table 4.8 Full study: Means, Standard Deviation and Construct Inter-Correlations

Variables Mean SD 1 2 3 4 5 6 7 8 1. MeanATT 6.17 .80  2. MeanPEM 6.31 .82 .81**  3. MeanNEM 5.48 1.56 .41** .05*  4. MeanMM 5.05 1.19 .36** .32** .35**  5. MeanSN 5.32 1.00 .52** .47** .15** .46**  6. MeanPSE 6.04 .83 .75** .68** .37** .40** .52**  7. MeanMOV 6.13 .82 .82** .76** .36** .34** .54** .79**  8. MeanBI 6.16 .83 .74** .67** .49** .33** .51** .72** .77**  Note: N=581, MeanATT= Attitudes mean score; MeanPEM=Positive emotions mean score; MeanNEM=Negative emotions mean score; MeanMM=Market Maven mean score; MeanSN=Subjective norms mean score; MeanMOV=Motivation mean score; MeanBI=Behavioral intention mean score. * p<.05, **<.01.

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4.2.3 EFA

Exploratory factor analysis (EFA) was performed by utilizing principal factor extraction with oblique rotation (direct oblimin). The technique of principal axis factoring extraction was selected due to its ability to estimate communalities among factors (Pett,

Lackey, & Sullivan, 2003). Thus, principal factor extraction determined the number of factors and factorability of the correlation matrices. To meet the standard requirement of factor loading, the cutoff point was set at .40 and the eigenvalues over Kaiser’s criterion of 1 was applied. Based on the result of the EFA, six indicators measured attitude, three positive emotion indicators, two negative emotion indicators, five market maven indicators, four social norm indicators, four perceived self-efficacy indicators, four motivation indicators and four indicators of behavioral intentions emerged. These indicators were subsequently selected to perform the confirmatory factor analysis. These results were identical to the EFA results from the pilot study, thus the hypothesized model with 33 indicators was considered appropriate for use in the full study. Moreover, the measurements indicated sufficient reliability according to Cronbach’s alpha which ranged from .85 to .92. The factor loadings are shown in Table 4.9.

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Table 4.9 Full study: Mean, Standard Deviation, Factor loading and Cronbach’s Alpha of Indicators

Constructs Code Indicators Mean SD Factor  Loadings Attitudes ATT1 When I book a hotel online, I like to 6.21 .89 .80 .92 ensure I get the best value. ATT2 I always check hotel prices through 6.14 .99 .83 travel websites to be sure I get the best value. ATT3 Finding a good deal is usually worth the 6.25 .96 .85 time and effort. ATT4 When purchasing a product, I always try 6.15 1.00 .79 to maximize the quality I get for the money I spend. ATT5 Using travel websites is a convenient 6.21 .94 .82 way to search product/price information. ATT6 Using travel websites is easy for finding 6.09 .96 .73 a good deal on a hotel room. Positive Emotions PEM1 I am happy when I am able to find a 6.38 .91 .83 .92 hotel room with a lower price than I expected. PEM2 I am satisfied when I am able to secure a 6.22 .94 .82 lower price than others who pay full price. PEM3 I am excited when I am able to find a 6.32 .89 .87 good deal for my money.

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Negative NEM1 I regret booking hotel deals through 5.44 1.65 .88 .88 travel websites. Emotions NEM2 I feel uncomfortable booking hotel deals 5.52 1.66 .88 through travel websites. Market Mavens MM1 People ask me for information about 4.97 1.40 .81 .91 prices on different types of products. MM2 I am considered somewhat of an expert 4.82 1.40 .86 when it comes to knowing the prices of products. MM3 I think I am better able than most people 5.20 1.29 .82 to tell someone where to shop and how to find the best deal. MM4 My friends think of me as a good source 5.06 1.39 .88 of price information for online deals. MM5 I like helping people by providing them 5.25 1.38 .80 with price information about online deals. Subjective norms SN1 People whose opinions I value approve 5.40 1.20 .83 .88 of me searching for deals online. SN2 People whose opinions I value approve 5.50 1.19 .83 of me searching for hotel deals before booking. SN3 Most people who are important to me 5.23 1.23 .69 search for hotel deals through travel websites. SN4 Most people who are important to me 5.18 1.21 .72 think I should book hotel deals through travel websites. Perceived PSE1 I know where to find the information I 5.85 1.03 .73 .85 need prior to making a purchase.

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Self-efficacy PSE2 I trust my own judgment when selecting 6.02 .95 .84 products to purchase. PSE3 I believe I can effectively search for 6.20 .90 .87 information online. PSE4 I am confident I am able to find good 6.11 .96 .83 deals online. Motivations MOV1 I am likely to use online travel websites 6.17 .96 .83 .89 for searching hotel deals. MOV2 I am likely to visit different online travel 6.15 .96 .86 websites to take advantage of low prices. MOV3 I prefer to search hotel deals before 6.15 1.01 .76 making an online reservation. MOV4 I am likely to make a purchase on a 6.07 .95 .71 website that provides good deals. Behavioral BI1 In the future, I plan to book a hotel room 6.13 .93 .87 .87 through travel websites. Intentions BI2 In the future, I plan to book hotel rooms 6.20 .93 .88 through travel websites that provide good deals. BI3 After searching hotels through travel 6.12 .93 .84 websites, I will consider booking a hotel room on one of the websites which I visited. BI4 I am willing to book hotel deals online 6.18 1.00 .79 in the near future.

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4.2.4 CFA

A confirmatory factor analysis was conducted using Mplus (version 7). Eight variables, the same eight variables from the pilot test, were identified in the CFA: attitudes, positive emotions, negative emotions, market mavens, social norms, perceived self-efficacy, motivations and behavioral intentions. The model was estimated using the maximum-likelihood method. Results indicated the model had a good fit to the data, χ 2

(528) = 14993.596, p < .0001, CFI = .934, TLI=.926, RMSEA = .06 (90% CI .056-.063),

SRMR=.08. All factor loadings were significant, which supported the convergent validity of the model (Anderson and Gerbing, 1988). Unstandardized and standardized coefficient estimates are provided in Table 4.10. Figure 4.2 demonstrates the results of the CFA from the full study.

Table 4.10 Full study: unstandardized and standardized coefficients for CFA Observed Latent Construct B SE β Est./S.E p-value Variable ATT1 Attitude 1 0 0.800 48.67 <.001 ATT2 Attitude 1.147 0.050 0.825 55.82 <.001 ATT3 Attitude 1.102 0.048 0.823 55.24 <.001 ATT4 Attitude 1.116 0.052 0.791 46.71 <.001 ATT5 Attitude 1.116 0.049 0.829 57.26 <.001 ATT6 Attitude 1.018 0.051 0.749 38.15 <.001 PEM1 Positive Emotion 1 0 0.849 59.25 <.001 PEM2 Positive Emotion 0.981 0.042 0.812 48.52 <.001 PEM3 Positive Emotion 0.956 0.040 0.819 50.03 <.001 NEM1 Negative Emotion 1 0 0.857 34.54 <.001

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NEM2 Negative Emotion 1.051 0.063 0.897 36.41 <.001 MM1 Market Maven 1 0 0.807 47.71 <.001 MM2 Market Maven 1.046 0.044 0.846 58.41 <.001 MM3 Market Maven 0.944 0.042 0.828 53.92 <.001 MM4 Market Maven 1.087 0.045 0.88 72.43 <.001 MM5 Market Maven 0.986 0.046 0.805 47.58 <.001 SN1 Subjective norms 1 0 0.977 55.93 <.001 SN2 Subjective norms 0.977 0.038 0.730 54.32 <.001 SN3 Subjective norms 0.730 0.048 0.782 21.49 <.001 SN4 Subjective norms 0.782 0.046 0.977 25.23 <.001 PSE1 Perceived Self-efficacy 1 0 0.726 33.38 <.001 PSE2 Perceived Self-efficacy 1.026 0.053 0.810 48.96 <.001 PSE3 Perceived Self-efficacy 1.029 0.051 0.854 62.21 <.001 PSE4 Perceived Self-efficacy 1.075 0.054 0.850 61.43 <.001 MOV1 Motivation 1 0 0.85 63.59 <.001 MOV2 Motivation 0.941 0.04 0.792 45.00 <.001 MOV3 Motivation 0.924 0.044 0.741 36.12 <.001 MOV4 Motivation 0.866 0.041 0.748 37.89 <.001 BI1 Behavioral Intention 1 0 0.855 63.85 <.001 BI2 Behavioral Intention 1.019 0.037 0.871 70.36 <.001 BI3 Behavioral Intention 0.974 0.039 0.833 55.93 <.001 BI4 Behavioral Intention 1.008 0.044 0.79 44.89 <.001

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Figure 4.2 The CFA results from the full study

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4.2.5.1 Structural Equation Model

After completing the CFA, a structural model was tested utilizing Mplus (Version

7). Maximum likelihood estimation was employed to estimate the model. The result of the structural equation model indicates the hypothesized model was a good fit, χ 2 (528) =

14993.596, p < .001, CFI = .935, TLI=.927,RMSEA = .059 (90% CI .055-.063),

SRMR=.087. The results from SEM also indicate the model paths yield significant parameter estimates. The construct of attitude, subjective norms and perceived self-

efficacy had a significant and positive relationship with motivation (βatt→mov =.423, p<.01;

βsn→mov =.093, p<.01; βpse→mov =.46, p<.01). Therefore, Hypotheses 1, 5, 6 were supported.

However, the positive and negative emotion constructs were not statistically

significant for predicting motivation (βpem→mov =.118, n.s.; βnem→mov =-.033, n.s.) Thus,

Hypothesis 2 and 3 were not supported. The market maven construct produced a

significant but negative relationship with motivation (βmm→mov=-.059, p<.01). Hypothesis

4 proposed that the market maven construct had a positive relationship toward motivation; however, the result indicated an opposite outcome. Thus, Hypothesis 4 was not supported.

The frequency of past behavior had significant and positive relationships with both

motivation and behavioral intention (βfqb→mov=.093, p<.01; βfqb→bi=.069, p<.05).

Therefore, Hypotheses 7 and 8 were supported. Finally, the motivation construct

strongly influenced participants’ intention on booking hotel deals (βmov→bi=.884, p<.01).

Thus, Hypothesis 9 was supported.

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It was hypothesized that motivation played an important mediator and influenced behavioral intentions to book hotel deals. The construct of negative emotions and subjective norms were not found to have any effect on the motivation which influences behavioral intention (p>.01). Other constructs, including attitudes, positive emotions, market mavens and perceived self-efficacy had significant indirect effects on behavioral intention through motivation. The results indicated that the construct of motivation fully mediated attitudes, market mavens and perceived self-efficacy toward behavioral intention. A summary of direct and indirect effects are demonstrated in Table 4.11 and

Table 4.12.

Table 4.11 Unstandardized Coefficients, Standardized Coefficients and Significance levels of Direct Effects

Hypothesis Direct Effect B β Est/S.E. p Result Path H1 ATT MOV .466 0.423 3.97 .00 Supported H2 PEM MOV 0.118 0.118 1.28 .20 Not supported H3 NEMMOV -0.018 -0.033 -1.24 .21 Not supported H4 MMMOV -0.059 -0.085 -3.14 .00 Not supported H5 SNMOV 0.07 0.093 3.03 .00 Supported H6 PSEMOV 0.482 0.46 8.51 .00 Supported H7 MOVBI .854 .884 57.93 .00 Supported H8 FPBMOV .053 .093 3.67 .04 Supported H9 FPBBI .038 .069 2.10 .00 Supported

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Table 4.12 Unstandardized Coefficients, Standardized Coefficients and Significance Levels of Indirect Effects

Hypothesis Indirect Effect B β Est/S.E. p Result Path H1 ATT MOVBI .427 .400 2.29 .02 Supported H2 PEM MOVBI .077 .079 0.87 .38 Not supported H3 NEMMOVBI -.038 -.072 -1.95 .05 Not supported H4 MMMOVBI -.063 -.094 -2.25 .02 Supported H5 SNMOVBI .039 .054 1.58 .11 Not supported H6 PSEMOVBI .431 .425 2.74 .00 Supported

Table 4.13 Decomposition of Effects with Standardizes Values

Constructs Direct Effect Indirect Effect Total Effect BI BI BI ATT -.123 .40* .277* PEM .079 .079 .162 NEM .155 -.072 .082* MM .072 -.094* -.022* SN .095* .054 .148** PSE -.095 .425** .330** MOV .884** -- --

Note: * p<.05, **<.01.

The structural equation model is shown in Figure 4.3. The final structural paths explained approximately 94% of the variance in motivation (R2=.944) and 80% of the variance in motivation which influenced behavioral intentions (R2=.797).

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Figure 4.3 The structural equation model. χ 2 (528) = 14993.596, p < .001, CFI = .94, TLI=.93, RMSEA = .059 (90% CI .055- .063), SRMR=.08. Note: →significant ---> non-significant *p<.05 **p<.01

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4.2.5.2 Gender comparisons

Since previous studies indicate that gender differences have effects on consumer deal-purchasing behavior (Hill & Harmon, 2007), in this current research, male and female respondents were compared to test the effect of gender. Among male participants, positive emotions showed a positive and significant relationship on motivation (β=.21, p<.01). This finding contradicted females who displayed no significance between positive emotions and motivation.

Past behavior proved to be another variation between the sexes. While males demonstrated positive and significant relationships between past behavior and motivation and behavioral intention (βmalefpb→mov =.10, p<.01; βmalefpb→bi =.14, p<.01), past behavior only affected motivation of females (βfemalefpb→mov =.07, p<.01), not their behavioral intention (βfemalefpb→bi=.01, n.s.). Overall, the model for the male group explained 3% higher variance on motivation and 11% higher variance on behavior intention than the females. The structural equation models of the male and female groups are shown in

Figure 4.4 and 4.5, respectively.

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Figure 4.4 The structural equation model of the male group. χ 2 (468) = 926.696, p < .001, CFI = .94, TLI=.93, RMSEA = .058 (90% CI .053-.064), SRMR=.09. Note: →significant ---> non-significant *p<.05 **p<.01

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Figure 4.5 The structural equation model of the female group. χ 2 (466) = 938.280, p < .001, CFI = .94, TLI=.93,RMSEA = .059 (90% CI .054-.064), SRMR=.08. Note: →significant ---> non-significant *p<.05 **p<.01

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4.2.5.3 Age group comparisons

The result of the correlational analysis indicated there was a statistically significant and negative relationship between age and the construct of market maven, r

=-22, p (two-tailed) <.01. Therefore, three age groups were compared to see the effect of age: 18 to 34 year olds, 35 to 54 and 55 year olds and above. In the youngest age group only attitudes and perceived self-efficacy were found to have a significant effect on motivation (β =.50, p<.01; β =.37, p<.01). The market maven construct showed a positive parameter estimate, but it did not significantly affect motivation. In the 35-44 age group, attitudes, market mavens and perceived self-efficacy showed significant relationships towards motivation (β =.38, p<.01; β =-.17, p<.01; β =.68, p<.01). However, the constructs of positive emotions, negative emotions and subjective norms were not significant. In 55 year olds and older, market mavens, social norms and perceived self- efficacy showed significant relationships toward motivation (β =-.11 p<.05; β =.23, p<.01; β =.38, p<.01). Yet, the constructs of attitudes, positive and negative emotions fail to support the relationships toward the motivation. Overall, perceived self-efficacy was the only variable which was significant across all age groups. A summary of age groups are compared in Table 4.13.

Table 4.14 Structural Equations Results for Age Effect Models Age groups N χ 2 df CFI RMSEA SRMR p 18 to 34 173 939.749 472 .90 .08 .09 <.01 35 to 54 217 941.308 472 .92 .07 .09 <.01 55 and above 191 843.743 472 .93 .06 .09 <.01

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CHAPTER 5

CONCLUSION AND IMPLICATIONS This research examined the process by which consumers form an intention toward deal-purchasing and their decision-making when booking hotel deals online.

Specifically, this study aimed to identify: (1) the factors that influence consumer decision making while searching and booking hotel deals through online travel intermediaries; (2) how cognitive, emotional and social components motivate consumers to book online hotel deals; and (3) how consumers’ intent to purchase hotel deals is stimulated. In addition, the study aimed to identify the demographic characteristics that influence consumers’ deal-purchasing behavior with online travel intermediaries.

Findings demonstrated consumers’ attitudes and perceived self-efficacy were two substantial factors that influenced consumers’ motivation toward booking hotel deals, which in turn, impacted future intention. Motivation played a mediating role that strongly drove consumers’ attitudes and perceived self-efficacy in regard to their intent to book hotel deals. Both subjective norms and frequency of past behavior directly influenced motivation and intention to book hotel deals online. The findings also showed that gender and age differences existed in consumers’ deal-purchasing behavior.

In this research, attitudes represented the cognitive component that significantly influenced consumers’ motivation. It indicated that consumer deal proneness and value consciousness drove them to search and book hotel promotions online. Since consumers recognized the variety of sales promotions and the ease of

Texas Tech University, Hsiang-Ting Chen, May 2014 comparison shopping in the online travel market, they tended to utilize online information to seek a better deal and engage in assessments of sales promotions. This research also elaborated on previous studies which showed that consumers’ deal- purchasing behavior was determined through a series of cognitive tasks (Chandon et al., 2000; Christou, 2011; Lichtenstein et al., 1993). Consumers’ judgment of value was mainly determined by comparing the market value of a product and the attractiveness of the deal offered. Therefore, hospitality marketers should take into account consumer perception of price and value evaluation when developing the features of hotel deals to persuade a purchase. The presentation of sales promotions, the reference pricing points and the depth of price discounts may influence consumers’ deal evaluations. Marketers may find this information beneficial in developing effective pricing and promotional strategies.

Two emotional components, positive and negative emotions, did not show a direct or significant relationship in consumers’ motivation to book hotel deals, which indicated that consumer deal-purchasing behavior was mainly influenced by their cognitive state instead of affective state. However, the strength of the relationship between attitudes and positive emotions was strong at .93. This finding showed there was a notable correlation between the cognitive and affective components. Prior research applied the cognitive-affective-conative approach to examine how consumers’ deal evaluation and deal proneness directly influenced consumers’ emotional states

(Christou, 2011; Laroche et al., 2003). Consumers’ attitudes toward deal-purchasing may increase their emotional gratification, which induces their purchase intention.

Thus, a further analysis of the causal relationship between attitudes and positive

Texas Tech University, Hsiang-Ting Chen, May 2014 emotions can be investigated to understand how consumers’ cognitive evaluations induce their inclination toward deal-purchasing behavior.

The concept of market maven represented the individual’s propensity to acquire product-related information and share that knowledge and information with other consumers (Feick & Price, 1987). This notion was based on an individual’s perception of what his or her behavior symbolized in a social sense. In this research, market maven was hypothesized as having a positive influence on consumers’ motivation toward hotel deal bookings. However, the result showed that market maven held a negative and weak relationship toward motivation. The possible interpretation regarding this result is the age difference among participants.

Since the construct of market maven demonstrated a negative correlation with participants’ ages, the scale of market maven may be affected by age differences.

Compared to other generations, the Millennial Generation tends to proactively share information and interact with other consumers by using Internet-based platforms

(Sago, 2010). Due to the majority of participants (70%) in this research being from both the Generation X and the Baby Boomer Generation, there may have been skewed results that showed a negative relationship between market maven and motivation. In contrast to previous research which claimed there was no age difference in terms of consumers with the market maven propensity, this research established age difference may influence consumers’ tendency to be a marketplace influencer.

This research suggests that marketers should differentiate customer segmentation by generation. Differences in age may influence consumers’ preference

Texas Tech University, Hsiang-Ting Chen, May 2014 toward selecting online travel websites as an information resource, thereby also having an influence on their strategies of searching for and booking discounted travel products. For instance, since Millennials are characterized as technology adopters and savvy shoppers, they are more likely to share their purchase experience on user- generated content websites (e.g., social network or consumer review sites) (eMarkter,

2012). In contrast, Baby Boomers rely on receiving information from experts who they know and trust (Business Wire, 2008). Thus, marketers should adopt different online marketing strategies and select appropriate communication channels in terms of age differences. Marketers could create a marketing campaign via social media targeted toward Millennials to encourage them to share their travel experiences. Baby

Boomers, who often prefer to pass product information to their friends and family via the emails (eMarkter, 2012) could be targeted by sending personalized email messages.

Subjective norms showed a direct effect on motivation and intention; it also indicated that motivation partially mediated the relationship between subjective norms and intention. The construct of subjective norms represented the influence of perceived social pressure associated with performing a certain behavior. The results from this research echoed previous consumer behavior studies that found social conformity was an important factor in a decision making process. Hospitality marketers could use this information to develop word-of-mouth marketing strategies.

Designing a referral incentive program to encourage customers to recommend their purchase to other customers may be one such initiative. Referral incentive programs encourage existing customers to spread positive word-of-mouth and attract new customers. Online travel intermediaries could offer a referral reward that would

Texas Tech University, Hsiang-Ting Chen, May 2014 motivate customers to recommend their purchases to others. New customers who are susceptible to social conformity often seek endorsement before purchasing. Thus, online travel intermediaries could optimize their word-of-mouth strategies in order to promote sales. Furthermore, research indicates that providing referral incentives to customers is a more effective marketing tactic than price reductions (Biyalogorsky,

Gerstner & Libai, 2001; Ryu & Feick, 2007).

Perceived self-efficacy demonstrated a strong influence on consumers’ motivation to book online hotel deals. The concept of perceived self-efficacy involved consumer self-confidence and perceived ability to facilitate consumers in a decision- making process. The ability to acquire information from sales promotions and comparison shopping significantly influenced consumers’ tendency toward deal- purchasing. Moreover, the results from the SEM analysis showed perceived self- efficacy and attitudes were highly correlated (β=.86). This analysis indicated that consumers’ cognitive evaluations connect with their capacities to process information while they search online hotel deals. This result was also consistent with Chatterjee &

Wang’s study (2012), which showed that hotel customers required increased effort when inspecting hotel deals and were inclined to buy from the website that delivers the best value. While consumers perceive high competency when searching and comparing hotel deals, they have more positive attitudes toward acquiring these deals.

For deal-seeking consumers, meta-search websites and search engines have been viewed as the most efficient tools for searching product pricing and information

(eMarketer3, 2013). Thus, the implication for hotels is to consider creating strategic alignments with the meta-search websites to drive potential consumers directly to the

Texas Tech University, Hsiang-Ting Chen, May 2014 hotels’ own websites. Hotels’ online marketing strategies could focus on increasing brand awareness and highlighting product value on meta-search sites.

This current research also provided evidence that consumers’ need for information and engagement during search activities were critical aspects in the prepurchase stage of consumer behavior. This information could provide insight for hospitality marketers offering sufficient promotional information for deal-seeking consumers. Online travel intermediaries may address the quality of information, usability and functionality of the websites to help consumers easily navigate the site and recognize product value. In this way, consumers’ positive attitudes and self- confidence may increase, stimulating purchase intention.

Motivation was hypothesized as a mediator that led cognition, emotion, subjective norms and perceived self-efficacy toward intention. Findings showed motivation fully mediated attitudes and perceived self-efficacy and partially mediated subjective norms toward intention. Consistent with previous research that applied

MGB theory, motivation is an important impetus when forming intention in this research. It also explains how motivation is influenced by antecedents and entices consumers’ intention to book hotel deals. Once an individual’s motivation is induced, his or her behavioral tendency can be predicted. Since the MGB is a relatively new behavioral theory, this research provides a theoretical and practical contribution of

MGB theory applied to explain the causal-effect relationship between motivation and intention in consumer behavior.

Texas Tech University, Hsiang-Ting Chen, May 2014

Results also demonstrated gender differences existed in consumers’ deal- purchasing behavior. The proposed model accounted for more variance in motivation and intention among males than females. Of particular note, male consumers’ motivation toward hotel deal bookings was influenced by the positive emotion construct. Surprisingly, female consumers were not influenced by emotional components. The traditional view of gender differences in consumer behavior states that females are more influenced by emotional gratification and perceive higher deal proneness than males (Lee et al., 2012; Harmon & Hill, 2003). These extant studies contradict this research which found males show a higher tendency toward booking hotel deals when they perceive positive emotional involvement. In addition, past experience created a larger impact on males than females. These findings suggest travel service providers should profile their customers in terms of gender and past purchases to tailor appropriate promotional messages.

Regarding age comparisons, three generations: Millennials (18-34 age group);

Generation X (35-54 age group) and the Baby Boomers (55 and above age group) showed different influencing factors toward deal-purchasing behavior. Millennials were strongly influenced by cognitive attitudes toward acquiring sales promotions, yet

Baby Boomers were affected by social components in their decision-making process.

Generation X was significantly influenced by both cognitive and social components.

Previous research indicated Millennials and younger Generation Xers are characterized by “a strong sense of independence and autonomy” (Williams & Page,

2011, p6). They tend to trust their own judgments, which are what ultimately lead to make a purchase decision. Alternatively, Baby Boomers prefer to obtain information

Texas Tech University, Hsiang-Ting Chen, May 2014 in the form of trusted opinions and advice. Listening to people’s experience and consultation will help them alleviate any perceived risk and uncertainty before purchasing travel products (Beldoba, Nusair & Demicco, 2009).

This demographic meaning of consumers’ deal-purchasing behavior can provide beneficial insight for hospitality marketers. In order to develop effective marketing messages, online travel intermediaries need to recognize the different purchase behaviors across generations. For Millennials, the overall value assessment of a deal offer was the determinant while searching and booking hotels. Since

Millennials are more technologically savvy and proactively seek information via the

Internet, the functionality, navigation, and interaction of an online travel website may be important to them. Conversely, in this research, Baby Boomers were more influenced by obtaining other people’s approval and agreement. Baby Boomers may prefer to maintain ties with their family and friends they have known for a long time, instead of building new ties with people (Nyemba, Mukwasi, Mosiane & Chigona,

2011). Therefore, word-of-mouth marketing could be a more powerful tactic while targeting the Baby Boomer segment. For instance, hospitality marketers could encourage customers to share their opinions and personal experiences about products and services, or refer promotional messages to their friends and family. Using personal recommendations could reinforce the credibility and trust of online travel websites, which in turn, could influence purchase decisions.

Limitations and future research

Texas Tech University, Hsiang-Ting Chen, May 2014

This research consisted of several limitations. One limitation was that the type of hotel deals provided by online travel intermediaries were not specified. Consumers may have had different behavioral responses if different types of sales promotions were presented. For instance, research suggests consumers may have the tendency to make impulsive purchases when they see time-limited or scarcity of online deals

(Sigala, 2013). Consumers’ affective responses, such as the anxiety of missing a deal, may have encouraged and motivated them to immediately make a purchase decision.

Therefore, the emotional aspect may significantly influence consumers’ motivation to book deals. This concept may provide a direction for future research to investigate the relationships between the design of hotel deals and consumers’ preferences, intent to purchase and satisfaction.

Secondly, online travel intermediaries include Internet companies, global- distribution systems, travel agencies and distribution-service companies. While a filter question was used to ensure participants had booking experience through online travel intermediaries in the past 12 months, it did not specify which websites they had utilized. Consumers’ experiences may vary significantly depending on the website visited, thus influencing their perceptions of deals purchased. In future research, studies could investigate consumer perceived benefits and risks of acquiring hotel deals through a certain type of travel channel, such as through search-engines.

In this research, the concept of market maven was hypothesized as a latent variable that affects consumers’ motivation with booking hotel deals. However, some research suggests a dichotomy should be used to measure personality traits of the market maven (i.e., using a mean score to divide non-market mavens and market

Texas Tech University, Hsiang-Ting Chen, May 2014 mavens). Therefore, the group comparison could be conducted to interpret the relationships between market mavens and consumer deal-purchasing behavior. In addition, the concept of market maven may be influenced by individuals’ personality and cultural backgrounds. Cal (2003) indicated cultural differences may cause some market mavens to resent being labeled as shopping experts always in search of better deals. Jin & Sternquist, (2003) stated that individualism and collectivism influence consumers’ propensity to be a market maven. Thus, future research could address cultural differences and obtain a better understanding of how the concept of market maven influences consumer deal-purchasing behavior. Finally, this research only utilized a quantitative research method. Future studies could include a qualitative component which would be valuable in obtaining more in-depth information regarding deal-prone consumers’ psychological characteristics and behavioral tendencies.

Texas Tech University, Hsiang-Ting Chen, May 2014

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purchase behaviours. International Journal of Consumer Studies, 32(2), 113- 121. Sharma, V. M., & Krishnan, K. S. (2001). Recognizing the importance of consumer bargaining: strategice marketing implications. Journal of Marketing Theory & Practice, 9(1), 24-37. Shih, H.-P., & Jin, B.-H. (2011). Driving goal-directed and experiential online shopping. Journal of Organizational Computing and Electronic Commerce, 21(2), 136-157. Shim, S., Eastlick, M.A., Lotz, S.L., & Warrington, P. (2001). An online prepurchase intentions model: the role of intention to search. Journal of Retailing, 77, 397- 416. Shoemaker, S., Lewis, R. C., & Yesawich, P. C. (2007). Marketing leadership in hospitality and tourism. Prentice-Hall. Sigala, M. (2013). A framework for designing and implementing effective online coupons in tourism and hospitality. Journal of Vacation Marketing, 19 (2), 165-180. Smith, A. D. (2004). Information exchanges associated with Internet travel marketplaces. Online Information Review, 28(4), 292-300. Smith, A. D., & Rupp, W. T. (2003). Strategic online customer decision making: leveraging the transformational power of the Internet. Online Information Review, 27(6), 418-432. Song, H. J., Lee, C. K., Norman, W. C., & Han, H. (2012). The Role of Responsible Gambling Strategy in Forming Behavioral Intention An Application of a Model of Goal-Directed Behavior. Journal of Travel Research, 51(4), 512-523. Starkov, M., & Price, J. (2003). In search of the internet intelligence report that makes sense, growing online distribution drives demand for new intelligence tools. Hotel Online Special Report. Stigler, G.J. (1961). The economic of information. Journal of Politica Economics, 19 (June), 213-225.

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Taylor, S. A. (2007). The addition of anticipated regret to attitudinally based, goal- directed models of information search behaviours under conditions of uncertainty and risk. The British Journal of Social Psychology, 46,739-68. Taylor, S. A., Hunter, G. L., & Longfellow, T. A. (2006). Testing an expanded attitude model of goal-directed behavior in a loyalty context. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 19, 18. Tellis, G.J., & Gaeth, G.J. (1990). Best value, price-seeking, and price aversion: the impact of information and learning on consumer choices. Journal of Marketing, 54 (2), 34-45. Thaler, R. (1985). Mental accounting and consumer choice. Marketing science, 4(3), 199-214. Toh, R. S., Raven, P., & DeKay, F. (2011). Selling rooms: Hotels vs. third-party websites. Cornell Hospitality Quarterly, 52(2), 181-189. Travel Technology Association. (2012). Online travel to represent a third of total global travel market. Retrieved February 24, 2013 from Travelclick (2012). Flash Sale Sites Lose Popularity with Hoteliers TravelClick survey shows dissatisfaction with group discount model. Retrived from http://www.travelclick.com/en/news-events/press-release/flash-sale-sites-lose- popularity-hoteliers-travelclick-survey-shows-dissatisfaction-group Travelclick (2013). Transient travlers continue to book more hotel rooms online in the second quarter 2013. Retrieved from http://www.travelclick.com/en/news- events/press-releases/transient-travelers-continue-book-more-hotel-rooms- online-q2-2013 Tsang, N. K. F., Tsai, H., & Leung, F. (2011). A critical investigation of the bargaining behavior of tourists: The case of Hong Kong open-air markets. J. Travel Tour. Mark. Journal of Travel and Tourism Marketing, 28(1), 27-47. Tse, A. C. B. (2003). Disintermediation of travel agents in the hotel industry. International Journal of Hospitality Management, 22(4), 453-460. Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. Journal of business, 59(4), 251-278. U.S. Census Bureau. (2011, May 26). E-Stats.U.S. Census Bureau: E-Stats. Retrieved

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Verma, R. (2010). Customer Choice Modeling in Hospitality Services: A review of past research and discussion of some new applications. Cornell Hospitality Quarterly, 51(4), 470-478. Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Information & Management, 41(6), 747-762. Wakefield, K. L., & Inman, J. J. (2003). Situational price sensitivity: the role of consumption occasion, social context and income. Journal of Retailing, 79(4), 199-212. Wang, Y. J., Hong, S., & Wei, J. (2010). A broadened model of goal-directed behavior: Incorporated the conative force into consumer research. Review of Business Research, 10(1), 142-150. Wen, H. (2006). A comprehensive structural model of factors affecting online consumer travel purchasing. (Order No. 3226634, University of Nevada, Las Vegas). ProQuest Dissertations and Theses, , 229-229 p. Retrieved from http://search.proquest.com/docview/304963488?accountid=7098. (prod.academic_MSTAR_304963488). Wen, I. (2009). Factors affecting the online travel buying decision: a review. International Journal of Contemporary Hospitality Management, 21(6/7), 752- 765. Williams, K. C., & Page, R. A. (2011). Marketing to the generations. Journal of Behavioral Studies in Business, 5(1), 1-17. Williams, T. G., & Slama, M. E. (1995). Market mavens' purchase decision evaluative criteria: implications for brand and store promotion efforts. Journal of Consumer Marketing, 12(3), 4-21. Wirtz, J., & Chew, P. (2002). The effects of incentives, deal proneness, satisfaction and tie strength on word-of-mouth behaviour. International Journal of Service Industry Management, 13(2), 141-162. Xie, C., Bagozzi, R. P., & Ø stli, J. (2013). Cognitive, Emotional, and Sociocultural Processes in Consumption. Psychology & Marketing, 30(1), 12-25.

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Yi, Y., & Yoo, J. (2011). The long‐term effects of sales promotions on brand attitude across monetary and non‐monetary promotions. Psychology & Marketing, 28(9), 879-896. yStats (2012). Global Online Travel Report. Retrieved from http://ystats.com/en/reports/preview.php?reportId=927 Zheng, L., Favier, M., Huang, P., & Coat, F. (2012). Chinese consumer perceived risk and risk relievers in e-shopping for clothing. Journal of Electronic Commerce Research, 13(3), 255-274.

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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 Phalen, Ph.D. email: [email protected] Co-investigator: Hsiang-Ting (Shatina) Chen email: [email protected] Department: Nutrition, Hospitality, and Retailing Phone number: (806) 742-3068 Mail stop: 1240 Proposal Title: Consumer Behavior on Booking Hotel Deals through Online Travel Intermediaries

I. Rationale

The growth of e-commerce over the past decade has significantly influenced product distribution channels within the hospitality and tourism industry (Green & Lomanno, 2012; Kracht & Wang, 2010). Due to the rapid development of the digital era, Internet saturation has become paramount, allowing travel products to predominately shift from offline to online distribution channels. In the United States, 83% of leisure travelers and 76% of business travelers utilize the Internet as a major tool for arranging their travel plans (Google & Ipsos Media CT, 2012). Since the online environment provides flexibility and accessibility, it is easy for travelers to browse and purchase various travel products by a few clicks of the mouse. Air tickets, car rental, or accommodation can be arranged and reservations made through the Internet 24 a day, seven days a week. Additionally, customers can search product/service information, read reviews, and compare prices or promotions using numerous online platforms. Online travel booking not only benefits travelers by making travel arrangements easier, it also increases the profits of businesses such as airlines, hotels, and other package tour companies (Hotelmarketing, 2012).

Hotels utilize online travel intermediaries because these third-parties have the ability to reach worldwide customers and boost occupancy rates (Travel Technology Association, 2012). Online travel intermediaries consist of third-party travel agencies, social media sites (e.g., Tripadvisor.com), search engines (e.g., Google), meta-search sites (e.g., Kayak.com), and flash sale sites (e.g., Groupon Getaway). The earning potential of online travel intermediaries should not be neglected. For example, the emergence of online travel agencies (OTA) reached nearly $40 billion sales in the U.S in 2010, which was equivalent to 15% of the total U.S. travel market (PhoCusWright, 2011). Online travel intermediaries are perceived by customers as offering a variety of choices at low prices. As such, they are considered by many as the dominant choice

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Texas Tech University, Hsiang-Ting Chen, May 2014 when it comes to making travel arrangements (Law, Chan, & Goh, 2007). Since the Internet provides consumers the opportunity to retrieve extensive information, consumers’ propensity to search for discounted travel products has been heightened and rapidly accelerated in an online travel market. Sixty-six percent (66%) of travelers claim they spend more time searching for deals before booking travel products, and they seek the best value for their money (Google & Ipsos Media CT, 2012). In addition, research shows on average travelers visit 17 travel websites to search product information before booking (eMarketer, 2013). However, research shows most online travel intermediaries focus on developing price-cutting strategies, instead of understanding consumers’ perceptions of product value and how consumers evaluate discounted products (Peterson, 2011). Misaligning customers’ needs and wants will consequently cause these online intermediaries to struggle to maintain competitive pricing. In other words, most consumers are concerned with acquiring good value for their money, rather than solely seeking a low price.

Despite these staggering figures which showcase the prevalence of online travel bookings, there is limited research regarding consumers’ deal-purchasing behavior. Specifically, there is no published scholarly research related to consumer behavior on searching and purchasing discounted online travel products. Thus, the primary goal of this study is to (1) define deal-purchasing behavior by combining the various perspectives which have been utilized in the consumer decision making process and (2) develop an integrated model to understand consumer deal-purchasing behavior of hotel bookings through online travel intermediaries. The results of this study aim to contribute theoretical and managerial implications of online consumer behavior in the hospitality industry.

This research intends to examine the following research questions:

1. How do cognitive, emotional, and social factors influence consumers’ decision making while they search and book hotel deals through online travel intermediaries? 2. How does a consumer’s deal proneness influence their motivation and intention to book hotel deals? 3. What demographic characteristics influence consumers’ deal-purchasing behavior through online travel intermediaries? This research examines consumer behavior related to purchasing online hotel deals, particularly through online travel intermediaries such as online travel agency (OTA) sites and meta-search sites. Based on the perspectives from consumer psychology, the quantitative survey research method is developed and employed to discover the latent factors of deal purchasing behavior, understand how these factors influence purchasing motivation and intention, and acquire information of deal-prone consumers’ characteristics. A pilot study and a full study will be used to test the

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hypotheses and answer the research questions. The specific details regarding the data collection and analysis will be discussed in the methodology section.

This research is important as it focuses on understanding the consumer decision process when purchasing online travel products. Previous research related to e-tourism has addressed the phenomenon of Internet evolution and transformation in the hospitality and tourism industry, which has concentrated on the improvement, effectiveness, and usefulness of suppliers’ websites. There is limited research related to the psychological factors which affect consumers’ motivation to purchase discounted travel products. Investigating such latent motives and consumers’ characteristics may provide useful insight for hospitality operators to reevaluate their marketing strategies and their value chains for a long-term profitability.

II. Subjects

(a) Sample for the survey method A sample of approximately 300 students will be recruited to participate in the pilot study. In the full study, approximately 600 participants from a nationwide consumer panel will be recruited through Qualtrics Survey Company. All participants must be 18 or older and have experience booking hotel rooms through online travel websites within the past 12 months.

(b) Recruitment procedures In the pilot study, survey distribution and collection will occur in classrooms at Texas Tech University. Hospitality faculty will be contacted ahead of time and asked permission to distribute a paper survey following their classes. Hospitality students will be invited to participate in the survey and be asked to return the survey before the next class session begins. i.e. the next day or the next class meeting (such as Monday if the survey is distributed on a Friday). An oral presentation explaining the purpose of the study will be delivered before distributing the survey (Attachment A). A drop box to collect the surveys will be located near the door on the way out. The researcher will pick up the box containing the completed surveys the end of the next class.

In the full study, a website link containing the online survey will be distributed by Qualtrics Survey Company. The participants will read a brief description of the survey information before answering the survey questions (Attachment B).

III. Procedures:

(a) Describe step-by-step all procedures involving these subjects Data will be collected by utilizing a self-administered survey during a specified data collection period. Each survey will contain a total of 60 questions. The printed

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survey is identical to the online questionnaire (Attachment C). Participants in the pilot test will have the printed version of the survey. If they disagree with the survey information sheet or select “No” for any of the filter questions, text included in the survey will direct them to stop immediately and return their survey. A similar procedure will be utilized in the online survey. Participants will read a paragraph which includes the survey information, if they disagree with the survey instructions or select “No” to the filter question, they will be thanked and exit the survey.

This survey is intended to understand consumer perceptions related to booking hotel deals through online travel intermediaries. For the purpose of this study, it is vital that the participants have booked hotels through online travel intermediaries within the past 12 months. Thus, the survey will contain a filter question which asks participants about this detail. If the participant selects “No” as their answer choice they will be asked to return the survey (in-person) or will be redirected to the Qualtrics webpage (online). Additionally, the online and paper surveys 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 Qualtrics webpage and those filling out the survey in person will be asked to return their incomplete survey.

In order to control situational variables or other effects of compounding variables which are not intended to be tested in this study, participants will be presented with a scenario about a buying situation before they start to answer related questions. The scenario is also shown in Attachment C.

The online survey information sheet (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.

(b) Potential risks: This research presents no risks beyond those of everyday life.

Texas Tech University, Hsiang-Ting Chen, May 2014

participation in this research.

IV. Adverse Events and Liability

The proposed research does not involve risks exceeding the ordinary risks of everyday life and no specific liability plan is offered.

V. Consent Form

None

References eMarketer (2013). Hotels strive to own organic search results. Retrieved March 1 2013 from http://www.emarketer.com/Article/Hotels-Strive-Own-Organic-Search- Results/1009694.

Green C.E., & Lomanno, M.V. (2012). Distribution channel analysis: a guide for hotels. An AH&LA and STR spcecial repor, HSMAI Foundation.

Goolge and Ipsos MediaCT. (2012).The Traveler's Road to Decision. Avaliable at:www.thinkwithgoolge.com.

Hotelmarketing (2012). Online travel market projected to reach US$533.8 billion by 2018. Avalible at: http://hotelmarketing.com/index.php/content/article/online_travel_market_proj ected_to_reach_us533.8_billion_by_2018

Law, R., Chan, I., & Goh, C. (2007). Where to find the lowest hotel room rates on the internet? The case of Hong Kong. International Journal of Contemporary Hospitality Management, 19(6/7), 495-506.

Phocuswright (2012). U.S. Online Travel Overview Twelfth Edition. PhoCusWright Inc.

Travel Technology Association. (2012). Online travel to represent a third of total global travel market. Retrieved February 24, 2013 from

http://traveltechnologyassociation.org/news/online-travel-to-represent-a-third- of-total-global-travel-market-1

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Attachment A: An oral presentation before the survey distribution

Hello,

My name is Shatina Chen, and I am currently a doctoral candidate in Hospitality Administration program at Texas Tech University. I am conducting a study to obtain information on consumers’ perspectives toward searching and booking hotel deals through online travel websites. We are interested in participants who are at least 18 years old and have booked hotels through online travel websites within the past year. The survey will take approximately 15-25 minutes to complete. Please drop the completed survey in a drop box before the next time this class meets. The drop box is located near the door on the way out. I will pick up the box containing the completed surveys at the end of the next class meeting (tomorrow). Participation is voluntary and anonymous. 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. All responses will be kept anonymous. No personal data will be asked and the information obtained will be kept confidential for the research purpose.

This study has been approved by the Texas Tech University Human Research Protection Program.

If you have any questions or if you would like to know the results of the study, please contact Dr. Kelly Phelan or Shatina Chen at 806-742-3068 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 (806-742-2064).

Thank you for your participation.

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Attachment B: An information sheet for online consumer survey

We are conducting a study to obtain information on consumers’ perspectives toward searching and booking hotel deals through online travel websites. We are interested in participants who are at least 18 years old and have booked hotels through online travel websites within the past year. The survey will take approximately 15-25 minutes to complete. Participation is voluntary and anonymous. 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. All responses will be kept anonymous. No personal data will be asked and the information obtained will be kept confidential for the research purpose.

Participants must be at least 18 years of age to participate in the survey. Please follow the link below to access the survey: [LINK]

This study has been approved by the Texas Tech University Human Research Protection Program.

If you have any questions or if you would like to know the results of the study, please contact Dr. Kelly Phelan or Shatina Chen at 806-742-3068 or email at [email protected].

Thank you for your participation.

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Attachment C: Consumer Survey

We are conducting a study to obtain information on how consumers search and book online hotel deals and which criteria most influences purchase decision. Online travel intermediaries include online travel agency websites, such as Hotels.com, Expedia, Orbitz, Travelocity or Priceline, and meta-search websites, such as Kayak, Bookingbuddy, or Travelzoo. This survey will take approximately 15-25 minutes to complete. Participation in this survey is voluntary. 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. 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. 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. We would appreciate complete responses as they will help with this research and provide information regarding online consumer behavior. If you have any questions, please contact Dr. Kelly Phelan or Shatina Chen at 806-742-3068 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 (806-742-2064).

 Yes, I am willing to respond to the survey (Please go to the next page)  No, I am not willing to respond to the survey (If selected, STOP HERE, Return survey to the drop box) (Online version: Exit the survey)

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1. Have you booked a hotel room from an online travel website such as Hotels.com, Expedia, Orbitz, Priceline, Hotwire, Kayak, Bookingbuddy, or Travelzoo within the past 12 months?

 Yes  No (If selected, STOP HERE, Return survey to the drop box) (Online version: Exit the survey)

2. Age Verification: 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 (If selected, STOP HERE, Return survey to the drop box) (Online version: Exit the survey)

3. Before deciding to book a hotel room on the website, I will

 search hotel deals/promotions on two or more online travel websites  search hotel deals/promotions on only one specific online travel website

Please read the following scenario and then select the answer which best suits you. (Choose only one answer per question)

There are many different choices regarding prices and features of hotel rooms on online travel websites. Suppose you were going to have a leisurely vacation with one of your close friends. Your friend put you in charge of making the travel arrangements. Thus, you can select the price, location, and star level of the hotel yourself. Since you and your friend intend to share all the expenses during the trip, you would like to obtain the best deal by searching for hotel rooms online. You have plenty of time, about three months prior to the trip, to make all the travel arrangements. Keep these details in mind as you answer the following questions.

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Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree Agree Agree nor Disagree When I book a        hotel online, I like to ensure I get the best value. I always check        hotel prices through online travel websites to be sure I get the best value. Finding a good        deal is usually worth the time and effort. When purchasing        a product, I always try to maximize the quality I get for the money I spend. Using travel        websites is a convenient way to search product/price information. Using travel        websites is easy for finding a good deal on a hotel room. When I book a        discounted hotel room through online travel websites, I am concerned that these websites may not provide the level of quality/service I am expecting. When I book a        hotel room through travel intermediaries, I am concerned about sending the

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Texas Tech University, Hsiang-Ting Chen, May 2014 security of my personal information (e.g. credit card details). Searching hotel        deals over the web takes too long or is a waste of time. I enjoy spending        time comparing prices through different websites. Searching online        deals gives me pleasure. Searching online        deals is exciting. I am happy when        I am able to find a hotel room with a lower price than I expected. I am satisfied        when I am able to secure a lower price than others who pay full price. I am excited when        I am able to find a good deal for my money. I am thrilled        when I book hotel deals through travel websites. I regret booking        hotel deals through travel websites. I feel        uncomfortable booking hotel deals through travel websites. When I don’t find        a good deal through online travel intermediaries, I am disappointed.

Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree Agree Agree nor

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Disagree When I didn’t        find a good deal through online travel websites, I feel angry. People ask me for        information about prices on different types of products. I am considered        somewhat of an expert when it comes to knowing the prices of products. I think I am        better able than most people to tell someone where to shop and how to find the best deal. My friends think        of me as a good source of price information for online deals. I like helping        people by providing them with price information about online deals. People whose        opinions I value approve of me searching for deals online. People whose        opinions I value approve of me searching for hotel deals before booking. Most people who        are important to me search for hotel deals through online travel websites. Most people who        are important to me think I should book hotel deals through online travel websites.

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I know where to        find the information I need prior to making a purchase. I trust my own        judgment when selecting products to purchase. I believe I can        effectively search for information online. I am confident I        am able to find good deals online. In the past, I        searched for hotel deals via the Internet prior to making a reservation. In the past, I        looked for hotel deals through travel websites. I have purchased        products online in the past 12 months. I have booked        hotel rooms through travel websites in the past 12 months. I am more likely        to use travel websites for searching hotel deals.

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Strongly Disagree Somewhat Neither Somewhat Agree Strongly Disagree Disagree Agree Agree Agree nor Disagree I am likely to visit        different travel websites to take advantage of low prices. I prefer to search        hotel deals before making an online reservation. I am more likely        to make a purchase on a website that provides good deals. In the future, I        plan to book a hotel room through travel websites. In the future, I        plan to book hotel rooms through online travel websites that provide good deals. After searching        hotels through online travel websites, I will consider booking a hotel room on one of the websites which I visited. I am willing to        book hotel deals online in the near future. When I search        hotel deals online, I only consider booking a brand- name hotel through an travel website. When I search        hotel deals online, I only consider

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 Which star level of hotels do you usually book online? (Check all that apply)

 1 star  2 stars  3 stars  4 stars  5 stars and above

 What types of sales promotion do you prefer to use on a travel agency website? (Check all that apply)

 Percentage price-cut ( e.g., 20% off)  Coupon codes  Price-reduction (e.g., $200$160)  Gift/reward card  Free-booking fee  Earn points on the loyalty program

 Do you belong to any hotel loyalty program, such as Hilton HHonors /Choice Hotel Privileges/ IHG Priority Club Rewards? ○ Yes ○ No

 Do you belong to any online travel websites loyalty program, such as Expedia Rewards/Hotels.com Welcome Rewards/Priceline Rewards? ○ Yes ○ No

 How many online travel websites may you visit in order to search for hotel information? Please enter the number of websites:______

 How many times did you travel last year? ______times

 In the past year, how often you did book hotel rooms through online travel intermediary sites prior to travel?

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○ Never ○ Rarely ○ Occasionally ○ Sometimes ○Frequently ○ Usually ○Every time

 Please enter your age:______

 Your 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  Bachelor Degree  Some Master's level  Master Degree  Beyond Master Degree

 Ethnicity  African/African American  Asian  Caucasian  Hispanic  Other

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