The Impact of Customized Price Promotion and Functional

THE IMPACT OF CUSTOMIZED PRICE PROMOTION AND FUNCTIONAL

IMPULSIVITY ON EVALUATION OF DEALS: AN EMPIRICAL INVESTIGATION

DORCIA E. BOLTON

Bachelor of Business Administration

University of Technology

August 2005

Master of Business Administration

Florida International University

April 2010

Submitted in partial fulfillment of requirement for the

DOCTOR OF BUSINESS ADMINSTRATION IN MARKETING

at the

CLEVELAND STATE UNIVERSITY

AUGUST 2018

We hereby approve this dissertation

For

Dorcia E. Bolton

Candidate for the Doctor of Business Administration degree

for the Department of

Marketing

And

CLEVELAND STATE UNIVERSITY’S

College of Graduate Studies by

______Committee Chair, Dr. Sreedhar Madhavaram Department of Marketing/ June 22, 2018

______Dr. Jungsil Choi Department of Marketing/ June 22, 2018

______Dr. Jieun Park Department of Marketing/ June 22, 2018

______Dr. Vishag Badrinarayan Department of Marketing, Texas State University/ June 22, 2018

June 22, 2018 ______Date of Defense

ACKNOWLEDGEMENT

A heartfelt thank you to my advisor, Dr. Sreedhar Madhavaram, for the tremendous support and

guidance throughout my journey at CSU.

Thanks to Dr. Choi, Dr. Park, and Dr. Badrinarayanan for the constructive feedback and

suggestions.

Dedicated to my parents, husband, and daughters who sacrificed so much and stood by me every

step of the way.

iii

THE IMPACT OF CUSTOMIZED PRICE PROMOTION AND FUNCTIONAL

IMPULSIVITY ON EVALUATION OF DEALS: AN EMPIRICAL INVESTIGATION

DORCIA E. BOLTON

ABSTRACT

The marketplace has seen several developments in technologies that facilitate firms’ ability to customize prices to target consumers. However, despite improvements in targeting efficiency, many firms still struggle with effective customization of prices. While many firms embrace customized price promotion as a strategy to offer exclusive prices to select customers, the related consequences for firms and consumers remain relatively unexplored. Research suggests that consumers generally prefer more exclusive to more inclusive deals. However, little is known about how individual differences and promotional design factors influence consumer response to customized price promotion deals. In addition, research now suggests that consumers place more emphasis on immediate versus delayed gratification. Furthermore, although the literature is rich with research on impulsivity, consumer researchers are yet to examine the impact of functional impulsivity despite delineation in the psychology domain differentiating it from dysfunctional impulsivity.

This dissertation examines the customized price promotion strategies of firms and the related consequences for firms and consumers. Specifically, the dissertation is framed on two issues: (1) the hitherto, unexplored factors that can potentially impact consumer response to customized price promotion, and (2) the impact of exclusivity on affect-based consequences.

Overall, this research has several implications for consumer research, marketing theory, and strategy. It draws attention to the impact of the customization of pricing strategies, and the likely shifts in consumer values and decision-making processes. Also, it examines the role of

iv previously unexplored concepts such as functional impulsivity and the exclusivity effect on deal evaluation. In addition, it provides empirical evidence in support of theories that can provide actionable insights to help firms with effective price customization.

TABLE OF CONTENTS

Page

ABSTRACT……………………………………………………………………………………iv

LIST OF TABLES…….……………………………………………………………………….x

LIST OF FIGURES…………………………………………………………………………….xi

CHAPTER

I. INTRODUCTION

1.1 Overview…………………………………………………………………...1

1.2 Research Purpose and Contribution……………………………………5

II. LITERATURE REVIEW

2.1 Sales Promotion……………………………………………………………11

2.2 Price Promotion……………..……………………………………………..12

2.2.1 Targeting and Price Promotion…………………………………..14

2.3 Customized Promotion and Targeted Price Promotion. …………………..18

2.3.1 Customized Price Promotion…………………………………..19

2.3.2 Level of Granularity of Customized Price Promotion…………21

2.3.3 Customized Price Promotion and Consumer Response.………23

2.4 Impulsivity……………….…………………………………………….30

2.4.1 Functional and Dysfunctional Impulsivity……….……………35

2.4.2 Impulsivity and Price Promotions……………….……………..37

2.4.3 Functional Impulsivity and Customized Price Promotion……..39

2.5 Overview of Theoretical Background…………………………………45

2.5.1 Motivation …………… ………………………………………45

2.5.1.1 Emotional Arousal……………………………………..46

2.5.2 Self-Concept …………………………………………………..49

2.5.3 Self-Identity ……………….…………………………………..50

2.5.3.1 Self-enhancement………………………………………50

2.5.4 Adaptation-level and Assimilation and Contrast………………54

2.6 Promotion Frame………………………………………………………55

III. RESEARCH MODEL AND HYPOTHESES DEVELOPMENT

3.1 Overview ………………………….………………………………..…59

3.2 Study 1: The Moderating Role of Functional Impulsivity……….…....61

3.2.1 Pretest of Exclusivity…………………………………………....61

3.2.2 Main Study………………………………………………………62

3.3 Study 2: The Impact of Customized Price Promotion…………………64

3.4 Study 3: The Mediators of the Exclusivity Effect……….……………65

3.4.1 The Mediating Role of Positive Emotional Arousal…………..66

3.4.2 The Mediating Role of Self-enhancement……………….……67

3.5 Study 4: The Impact of Promotion Frame..…………………………...67

IV. DATA ANALYSIS

4.1 Overview of Sample and Data Analysis………………………………71

vii

4.1.1 Overview of Survey Research and Experimental Designs……71

4.1.2 Internal Consistency…………………….…………………….72

4.1.3 Validity………………………………………………………..73

4.2 Data Analysis and Model Assumptions ………..…………………….73

4.3 Pretest for Data Collection and Analysis……………………………..74

4.4 Overview of Study 1………………………………………………….76

4.4.1 Procedure for Study 1…………………………………………76

4.4.2 Measures in Study 1…………………………………………..77

4.4.3 Study 1 Results ……………………………………………….78

4.5 Overview of Study 2………………………………………………….81

4.5.1 Procedure for Study 2…………………………………………81

4.5.2 Measures in Study 2…………………………………………..82

4.5.3 Study 2 Results …………..……………………………….….82

4.6 Overview of Study 3………………………………………………….83

4.6.1 Procedure for Study 3………………………………………...84

4.6.2 Measures in Study 3………………………………………….85

4.6.3 Study 3 Results ………………………………………………85

4.7 Overview of Study 4…………………………………………………91

4.7.1 Procedure for Study 4…………………………………….….92

4.7.2 Measures in Study 4………………………………………….93

4.7.3 Study 4 Results ………………………………………………93

V. DISCUSSION

5.1 General Discussion………………………………………..…………98

viii

5.2 The Importance of Deal Exclusivity in Price Promotions ………….…99

5.3 Impulsivity and Marketing Strategy……………….…………………102

5.4 Contribution to Scholarship………………………………….……....104

5.5 Contributions to Practice……………………………………………..106

5.6 Limitations and Future Research……………………………………..107

REFERENCES……………………………………………………………………….111

APPENDIX…………………………………………………………………………..130

LIST OF TABLES

Table Page

I. Summary of Literature on Customized and Targeted Price Promotions 26

II. Definitions of Functional Impulsivity and Similar Terms 41

III. Impulsivity: Insights from the Psychology Domain 42

IV. Select Research on Functional Impulsivity 42

V. Select Research on Self-Enhancement 52

VI. Summary of Study Design and Hypotheses 69

LIST OF FIGURES

Figure Page

1. Theoretical Model 60

2. Main effects of Deal Exclusivity and Functional Impulsivity 80

3. Interaction of Deal Exclusivity and Functional Impulsivity- Study 1 80

4. The Impact of Customized Price Promotion 83

5. Conceptual Model for Model 6 (Hayes 2013) 87

6. The Mediation Effects of Deal Exclusivity on Deal Response 89

7. Interaction of Deal Exclusivity and Functional Impulsivity- Study 4 96

8. The Effect of Deal Exclusivity, Functional Impulsivity and Deal Frame 97

CHAPTER I

INTRODUCTION AND CONTRIBUTIONS

1.1 Overview

Manufacturers and retailers often embrace customized price promotion as a strategy to offer exclusive prices to select customers (Barone and Roy 2010a, 2010b; Fruchter and Zhang 2004).

Emerging developments in marketing technologies have now enhanced firms’ ability to customize promotions to consumers in more efficient, but not necessarily more effective ways

(Sharma 2017). Projections suggest that firms will continue to engage in customized price promotion, with individualized offers to customers expected to increase by over 30% by 2021

(Kharif 2013). In fact, Kroger, the largest traditional grocery retailer in the U.S., has seen over

$10 billion in revenues from customized price promotions (Groenfeldt 2013). And, companies usually report an average of $8 in extra revenues for every $1 they give away (Kharif 2013), making price promotion a standard marketing tool.

Many consumers are aware of the types of price promotions firms usually offer. For example, there is often “a one-day sale” at Macy’s, where customized price offers are directed to select customers via catalogs, personalized emails, or direct mail, and can be redeemed online or in store. Similarly, JCPenny customers can often “get their penney’s worth,” through price discounts which feature products at the cost of one cent. And, Kohl’s “Yes2You” reward members are often offered 15-30% off the sale price of items, in addition to other discounts, if

1 they pay with their Kohl’s credit card. In some instances, firms offer very deep exclusive (vs. across the board, all-inclusive) discounts on a regular basis, which provide customers with the opportunity to engage in extensive, impulsive shopping. However, should companies continue to offer their customers exclusive customized price promotion deals? And, what are the contingencies that affect consumer response to customized price promotion deals?

The use of price promotions by firms has been shown to increase consumer purchases

(e.g., Heilman, Nakamoto, and Rao 2002). To this extent, marketing companies such as

Dunnhumby and Catalina Marketing assess billions of consumer and product purchase data to offer customers a variety of coupons and discounted prices through various retailers. For example, Dunnhumy places great emphasis on understanding the “consumer’s DNA,” in which past purchases provide insights on need and desires for future products. Similarly, Catalina

Marketing offers coupons to retailers’ customers in the form of competitive (i.e., when the customer did not purchase the promoted brand on the previous purchase occasion) or loyalty

(i.e., when the customer bought the promoted brand on the last shopping occasion) discounts.

These discounts are based on the tracking of consumers’ past purchases (Ansari and Mela 2013;

Osuna, González, and Capizzani 2016), and are often linked to unplanned purchases (Rook

1987).

Notwithstanding, in addition to the dynamics and complexities of understanding consumer in-store purchase behavior, marketing academics and practitioners now face a new era of marketing. Email marketing and E-commerce are becoming more popular, with online retail sales exceeding $340 billion in 2015 (US Department of Commerce). As such, while several retailers including major department and traditional mall-based stores experienced a decline in store sales in 2016, online sales increased. For example, Macy’s experienced a 7.4% drop in

2 sales for the first quarter of 2016, while its online deals for the same period showed a double- digit increase (Pasquarelli 2016). As a result, marketers are now, more than ever, concerned about the impact of e-commerce and developing technologies on firm pricing strategies.

The Internet now facilitates firms’ ability to dynamically access consumers, even those outside a firm’s primary markets, for the customization of products, services, and prices. Many cases highlight the growing impact of digital marketing and technology on targeting and pricing strategies, and the likely effect on the consumer decision process. Consumers are also socially connected and have access to a world of information, including information related to price promotions deals. To this extent, some marketers are even concerned about firm promotions that end up in the hands of untargeted consumers (Thompson, Gooner, and Kim 2015), as many firms try to include only some consumers in their price promotion campaigns (Barone and Roy 2010a,

2010b; Feinberg, Krishna, and Zhang 2002).

Consumers generally have a high level of participation in price promotion campaigns

(Anderson and Simester 2004; Simonson 2005). However, technology now affords both marketers and consumers the ability to communicate regularly, to get information, and to make the best of price promotions across various markets. For example, Alibaba, arguably the largest e-commerce company in the world and user of virtual reality shopping, reported sales exceeding

$17 billion on its platform for the predominantly Chinese Singles Day in 2016. This amount exceeded the over $13 billion in sales reported for the 2016 major U.S. shopping holidays (Black

Friday and Cyber Monday) across various retailer platforms (Bain 2016). Furthermore, many firms now try to customize the consumer experience and encourage consumers to “Buy Online and Pickup in Store” (e.g., Kohl’s, Macy’s, and Nordstrom). Therefore, as technology advances,

3 so do price promotion targeting activities, which are now facilitated by adaptive marketing, web interactivity, digitization, and networking (Ansari and Mela 2003).

Given these technological advancements, many firms seek new ways to make their price promotion campaigns more effective. Firms now offer price promotions both online and offline

(Zhang and Wedel 2009), and deliver them through various methods including email, mobile messages, or direct mail. However, consumers tend to prefer promotions that are provided online and via email (Smith 2016). This dissertation focuses on promotions delivered to consumers via email. To this extent, the advancement in firms’ promotional strategies also facilitates the tracking of consumers while they are online allowing firms to offer personalized prices. As such, through adaptive marketing, firms are “continuously revising and updating new product information and price offerings in real-time and satisfying customer demands” (Kumar et al.

2016, p. 25). For example, consumers can use apps and visit a website to redeem coupons. Also, websites such as Flipp.com allows consumers to customize and manage their shopping budgets by finding price promotions and building their shopping lists before they visit a store. Therefore, customized price promotion is now more likely to be associated with several affect-based consequences.

Overall, the effectiveness of customized price promotions for firms and consumers rests on factors such as the effectiveness of the promotional design to include exclusive vs. more inclusive deals, the use of different types of promotion frames, and a sound understanding of individual consumer differences.

1.2 Purpose of Research and Contributions

This dissertation attempts to address two of the major issues in price promotion research: i) whom to target ii) how to make price promotions more effective (Grewal et al. 2011). For Dost et al. (2014, p.393), targeting the “right” consumers is a core part of marketing. And, “it is necessary to identify and target those consumers who will offer the desired response to any given marketing action.” Furthermore, “of the many potential decision variables in a direct marketing campaign, the most important is the selection of the recipients to target” (Dost et al. 2014, p.

394). Therefore, the research examines whether exclusive price promotion deals are effective only for certain segment(s) of consumers and the associated deal characteristics that are at play to influence consumer decisions.

Emerging technologies and the growth in e-commerce have attracted research on consumer decisions in online settings (e.g., Darley, Blankson, and Luethge 2010; Haubl and

Trifts 2000). However, for Obermiller, Arnesen and Cohen (2012, p.14) what makes customized price promotion “worthy of renewed attention is the potential for widespread application, primarily as a consequence of developments in technology.” In addition, the advances have been linked to impulsive behavior (Kacen and Lee 2002), and thus require the further attention of researchers. Furthermore, many firms fail to realize the benefits of their targeted promotion campaigns and struggle to retain a healthy portfolio of customers (Avery, Fournier, and

Wittenbraker 2014). Therefore, marketers should be aware of the contingencies for the effective use of a customized price promotion strategy to better understand when this approach will elicit more favorable results (Barone and Roy 2010b; Hoffman and Nokav 2000).

Customized price promotion is regarded as a form of targeted price promotions with either a modest level of exclusivity to select segment(s) of customers or a high level of

5 exclusivity to individual customers (Barone and Roy 2010a, 2010b). However, despite its increasing popularity, little is known about how customized price promotions may impact consumer response and choice to seek immediate or delayed gratification. That is, most studies on customized price promotion have focused on developing systems to optimize firm return (e.g.,

Fruchter and Zhang 2004; Zhang and Krishnamurthi 2004; Zhang and Wedel 2009).

Furthermore, several studies have focused on the impact of price promotion on retailers’ bottom line (e.g., Walters and Mackenzie 1998), brands (e.g., Montaner and Pina 2011; Zoellner and Schaefers 2015), and the size and makeup of consumers’ shopping baskets (e.g., Keilman et al. 2002; Ramanathan and Dhar 2010). These studies generally suggest several benefits to firms as they optimize their campaign strategies, without much emphasis on the ultimate impact on consumers and their decision processes.

In fact, Barone and Roy (2010b, p.122), in assessing individual differences that affect consumer response to the customization of prices, posit that “little work has explored individual difference factors that characterize people.” Accordingly, increased research in this area “affords a means of identifying theoretically relevant variables that moderate consumers’ tendencies.”

Furthermore, the information found can be “useful in developing strategies aimed at more effectively and efficiently delivering targeted deals to the marketplace” Barone and Roy (2010b, p.122). To add to this research stream, this research examines other unexplored factors which may help firms build more effective price customization strategies.

Specifically, the dissertation focuses on the offering of deals to existing customers.

Findings suggest that it is often more valuable for firms to offer better deals to present vs. prospective customers (Tsai and Lee 2007). In addition, the theory of dual entitlement

(Kahneman, Knetsch, and Thaler 1986) suggests that consumers with purchase histories with

6 firms believe that they are more entitled to exclusive deals than those consumers who are new to the firm. Hence, in this context, perception of unfairness is unlikely to be a significant factor

(Tsai and Lee 2007). That is, with the receipt of an exclusive offer, consumer perception of fairness or advantage are likely to result from a focus on self-comparison (Kahneman et al. 1986;

Tsai and Lee 2007). Furthermore, “satisfaction, pleasure, and happiness accompany advantaged unfairness” (Tsai and Lee 2007, p.488). Therefore, this research also presents opportunities for marketers to effectively segment their market based on a better understanding of the psychological and affective influences of customized price promotion strategies.

Only a few studies have sought to examine the psychological and/or behavioral impact of a customized promotion strategy (e.g., Feinberg et al. 2002). Here, consumers have reported several benefits from price promotion. These include economic and cognitive related benefits such as perceived value, as well as affective benefits such as the positive emotions associated with getting a good deal. That is, the “deals” associated with price promotions may positively impact a consumer’s self-view, especially when s/he feels that s/he has made a good choice (Lee and Tsai 2014). However, researchers have given sparse attention to the factors that may impact consumer response to customized price promotion strategies (Aydinli, Bertini, and Lambrecht

2014; Barone and Roy 2010a, 2010b).

Therefore, this dissertation examines the customized price promotion strategies of firms and the related consequences for consumers. Specifically, the dissertation is framed on two issues: (1) the hitherto, unexplored factors that can potentially impact consumer response to customized price promotion and (2) the impact of customized price promotion on affect-based consequences. First, while research shows that factors such as need for uniqueness, self– construal, relationship equity, and attitude toward deal group help explain the exclusivity effect

(Barone and Roy 2010 a, 2010b), in the context of emerging technologies that enhance firms’ price targeting abilities, the impact of several other factors remains unclear. Also, with the dominant use of customized price promotion by firms and the projected growth, the identification of other factors which are likely to influence how consumers respond to customized price promotion can help firms devise more effective pricing strategies. Therefore, this research examines the influence of the following factor on consumer response to customized price promotions: promotion frame.

Second, research suggests that the majority of studies on customized price promotion, and by extension price promotion, have taken a more economic (Zhang and Krishnamurthi 2004) and behavioral perspective (Feinberg et al. 2002), with little focus on the affective route

(psychological perspective) through which price can impact consumer decisions (e.g., Aydinli et al. 2014). In fact, for Chandon, Wansink, and Laurent (2000), there are both monetary and nonmonetary consumer benefits from price promotions. To advance research in this area, this dissertation also examines the moderating role of functional impulsivity on the relationship between exclusivity and the evaluation of customized price promotion offers. In light of Rook’s

(1987) seminal article, there is a rich body of research on impulsivity. However, despite

Dickman’s (1990) delineation of functional vs. dysfunctional impulsivity, consumer researchers are yet to examine the impact of functional impulsivity, which suggests that consumers high on functional impulsivity may actually benefit from their impulsive tendencies related to customized price promotion. As such, functional impulsivity is examined as a potential moderator because it is likely to affect a consumer’s tendency to use the exclusivity associated with a deal as a basis for his/her response to customized price promotion deals (Barone and Roy

2010a, 2010b). Overall, the dissertation seeks to offer firms insights into whether they should use customized price promotions and how to make price customization more effective.

Research Questions

Relative to the research gap, the following questions are addressed.

i) How does functional impulsivity impact the relationship between customized price

promotion and deal response?

ii) How do factors such as promotion frame impact functional impulsivity’s effect on the

relationship between customized price promotion and deal evaluation?

Significance of Research

This dissertation focuses on the customized price promotion strategies of firms and the related consequences for consumers. The major anticipated contributions of the research are as follows:

i) To provide an empirical test of factors that are likely to influence consumer response

to customized price promotion strategies. These strategies are one of the most

dominant marketing tactics used by firms to target consumers in the age of

technology. Insights can help firms build more effective customized prices and help

sustain healthier relationships with consumers.

ii) To provide a key focus on the psychological perspective and affect-based

consequences of firms’ customization strategies. Research has predominantly focused

on building optimum pricing models for firms. This stream of research has largely

ignored the impact of firms’ customization strategies on affective consequences for

consumers. That is, most of the studies on price promotions have been focused on

firms’ competitive perspective and optimal targeting strategies, with little focus on

the consumers’ psychological responses and perspectives (Tsai and Lee 2007). iii) To shed light on the different dimensions of impulsivity. Consumer researchers are

yet to address the different dimensions of impulsivity, and the likely impact on

consumer response to market offerings. To the best of my knowledge, this

dissertation provides the first empirical test of the influence of functional impulsivity

in the consumer research domain. The understanding of the differences between

functional and dysfunctional impulsivity can provide several insights to marketers in

understanding the impact of the negative versus positive consequences of consumers’

tendencies to act quickly or to seek instant gratification. iv) To examine the impact of factors (i.e., promotion frame) that are under the control of

manufacturers and retailers and are likely to interact with consumer individual

differences to impact their response to customized price promotions. v) To provide a framework that can help marketers better understand factors that may

help determine when the customization of prices may help, hurt, or muddle results.

CHAPTER II

LITERATURE REVIEW

2.1 Sales Promotion

Sales promotion has been studied in marketing for decades (e.g., Edwards and Howard

1946; Ramanathan and Dhar 2010). To this extent, researchers have focused on its many forms, which include i) flash sales that are offered for a limited time (e.g., Shi and Chen 2015), ii) buy more, save more deals (e.g., DelVecchio 2005), iii) price match guarantees (Haruvy and

Leszczyc 2016), iv) holiday promotions (Rodriguez 2014), v) coupon give-away when a consumer spends a specified amount (Lee and Ariely 2006; Osuna, González, and Capizzani

2016), vi) free shipping (Khan, Lewis, and Singh 2009), vii) price matching (Ferguson 2014), and viii) discounted products in the form of price promotions (Goodman and Moody 1970;

Palazon and Delgado-Ballester 2009).

This research focuses on price promotion, which is arguably the most important and commonly used type of sales promotion (Zhang and Wedel 2009; Zoellner and Schaefers 2015).

However, given the advancements in technology that now facilitate more efficient targeting of consumers, and consumers’ increased motivation to seek instant gratification, the study examines price promotion in its customized form.

2.2 Price Promotion

Price promotions are “temporary price reductions offered to consumers” (Srinivasan et al.

2004). The study of price promotion is not new to marketers and has long been a major focus of academic research (Goodman and Moody 1970). Firms embrace price promotions as a means of increasing sales in both online and offline settings (Grewal et al. 2011). Price promotions

“translate to real economic savings, guide buying decisions, encourage trial of new products, and make consumers feel smart and good about themselves” (Lee and Tsai 2014, p. 943). With marketing analytics, firms have access to vast amounts of data, which provide both challenges and opportunities for marketers. Grewal et al. (2011, p. S44) proposed three key questions that emerge as firms focus on innovations in price promotions: i) “what price and promotion model to use” (i.e., firms decide on a model such as a dynamic pricing model and/or a model based on exclusivity). ii) “how to design effective promotions” (i.e., a focus on both online and offline promotion elements such as using reference prices, and the phrasing and presentation of prices in order to increase promotion effectiveness), and very importantly, iii) “whom to target” (i.e., firms decide whether to use loyalty program data to target customers and/or track consumers’ online behaviors to target them individually). Overall, these questions address the efficiency and effectiveness of price promotion campaigns.

The literature on the use of promotional models to optimize price targeting effectiveness suggests that the type of pricing model used is key in determining the success of a price promotion strategy. Firms use a variety of models, including direct vs. indirect price reduction

(e.g., Zoellner and Schaefers 2015), and exclusive vs. all-inclusive deals (Barone and Roy 2010a,

2010b; Feinberg et al. 2002; Steinhoff and Palmatier 2014). For example, Zoellner and Schaefers

(2015) found that direct vs. indirect price reduction (e.g., item) had the stronger impact on sale,

12 and Barone and Roy (2010a, 2010b) found that exclusive price promotions were generally more preferred than inclusive price promotions.

The effectiveness of a price promotion strategy is also often a function of the inferences consumers make regarding factors such as price, product, and brand quality in the absence of other cues (Raghubir, Inman, and Grande 2004). Consequently, there has been a substantial focus on how firms should effectively design their price promotion campaigns. Of common interest are: i) the presentation and framing of the promoted price (e.g., percent off or dollar off, inclusion of reference price with the sale price, and the colors and font used), and ii) the online vs. offline promotion features (Grewal et al. 2011).

Studies show that firms also use other price promotional strategies such as “everyday low prices,” where prices remain relatively stable as a form of promotion, and the “hi-lo” strategy in which prices are generally higher than normal and reduced frequently as a form of promotion

(e.g., Tsiros and Hardesty 2010). Firms are also known to use free offers or other promotions.

Research findings suggest that free promotions are more salient than monetary promotions and are thus less susceptible to negative information on quality than monetary discounts (Chandran, and Morwitz 2006).

Research on price promotions presented in percentage off (e.g., Jung and Lee 2010), and a dollar amount off terms (e.g., Chen, Monroe, and Lou 1998), suggest that these are usually offered using coupons or discount promotions (Chen et al. 1998). Furthermore, Hardesty and

Bearden (2003) recommend the use of percent price discounts when large discounts are being applied. This research focuses on price discounts offered for a limited time, which is one of the most popular types of price promotions used in modern marketing and customized price promotion campaigns (Aydinli et al. 2014). In this context, the dissertation also examines the

13 impact of consumer individual differences on the response to customized price promotional offers.

Price promotions impact consumers in several ways, including a direct impact on their consumption experiences (e.g., Darke and Dahl 2003; Lee and Tsai 2014) and their information processing (Aydinli et al. 2014). Consumer benefits of price promotion range from monetary

(e.g., Zoellner and Schaefers 2015) to nonmonetary benefits (Aydinli et al. 2014; Feinberg et al.

2002). Many studies present econometric models that focus on the monetary value associated with price promotions (e.g., Blattberg and Neslin 1989).). However, research now suggests that nonmonetary factors may also help explain consumer response to price promotion deals (Barone and Roy 2010a, 2010b; Chandon, Wansink, and Laurent 2000). Therefore, while price promotions tend to encourage brand switching and increase the purchase of the brand of interest

(Darke and Dahl 2003), individual differences may moderate the impact of price promotion on the evaluation of deals and subsequent brand choice (Aydinli et al., 2014; Barone and Roy 2010).

Accordingly, researchers have examined several factors that impact the effectiveness of price promotions, including the consumer’s self-belief (e.g., Lee and Tsai 2014), self-concept

(Barone and Roy 2010b), and perceived price fairness or unfairness (Chen, Tsai, and Chuang

2010). However, while these factors have been explored, the influence of several others remain unclear. As such, this dissertation presents a model that incorporates both monetary and nonmonetary factors that are likely to impact consumer response to customized price promotions.

2.2.1 Targeting and Price Promotions

The concept of targeting customers to enhance customer-firm relationships is not new to the marketing literature (e.g., Berry 1983; Bult and Wansbeek 1995; Gupta 1988; Homburg,

Droll, and Totzek 2008; Simonson 2005). Now, often referred to as the targeting of a firm’s product and services to different individuals and/or groups within a target market (Ansari and

Mela 2003; Fruchter and Zhang 2004), targeted marketing has been used by marketers as a dominant promotional activity (Bult and Wansbeek 1995), with increased use as technology advances. Targeted marketing promotions now include permission-based email communication

(Ansari and Mela 2003) and the personalization of price offers to individual consumers (Ansari and Mela 2003; Smith 2016). However, while marketers have used price promotion for decades to optimize their market offering (Goodman and Moody 1970), developments in marketing analytics and intelligent agent technologies have now enhanced firms’ ability to target consumers

(Kumar et al. 2015).

“Targeting activities include any marketing mix activity applied to a selected group of consumers, with the intention to increase their purchase probabilities” (Dost et al. 2014, p. 394).

Consequently, firms collect a vast amount of data on consumers, including their past purchases

(Acquisti and Varian 2005; Rossi, McCulloch, and Allenby 1996), which are likely to provide insights into consumer feelings and behavioral responses to marketing initiatives (Fruchter and

Zhang 2004). However, despite the huge investments in these efforts, many firms fail in their efforts to effectively utilize the collected information and make prudent strategic targeting decisions (e.g., Alvarez, and Vázquez Casielles 2005; Avery et al. 2014; Khan, Lewis, and Singh

2009; Thompson, Gooner, and Kim 2015).

Targeted promotion is formerly defined as “the practice of offering different prices to prospective and present customers” (Feinberg et al. 2002, p. 278). For Tsai and Lee (2007), targeted price promotions differ from price promotions as they “provide promotions only for specific targeted customer segments” (Tsai and Lee 2007, p. 481). With targeted promotions,

15 firms can often avoid the trade-off between charging higher prices to everyone, which may result in consumer switching, and charging a lower price to everyone, which may not be an optimal strategy given that some consumers are not price sensitive (Feinberg et al. 2002; Krishna,

Feinberg, and Zhang 2007). As such, research has suggested that the offering of dynamic targeted pricing based on consumer purchase history can improve profitability for both online and offline retailers (e.g. Chen and Zhang 2009; Cheng and Dogan 2008).

Targeted promotions have been studied as a form of “gift giving, in which the gift-giver is a firm instead of an individual” (Tsai and Lee 2007, p. 484). Firms often decide to use targeted promotions as a function of their loyalty programs or through third party marketing companies such as Catalina Marketing, which offers retailers’ customers promotions in the absence of loyalty programs (Dowling and Uncles 1997; Grewal et al. 2011). In addition, there is also the question of whether firms should target individuals or segments, which also presents mixed results (e.g., Rossi et al. 1996; Zhang and Wedel 2009). Specifically, while Rossi et al. (1996) argued in support of highly customized promotions, Zhang and Wedel (2009) suggest that segment-level customization may be more optimal, given the small benefit advantage of individual customization.

In the context of targeted price promotion, the majority of studies have focused on the economic perspective, with some emphasis on promotional models and the economic route through which price impacts consumer decisions (e.g., Dungchun and Hsiao-Ching 2007; Zhang and Wedel 2009). There have been far fewer studies on the behavioral and affective

(psychological) perspectives, and the related routes through which price impacts decisions (e.g.,

Feinberg et al. 2002; Aydinli et al. 2015). Specifically, studies on the economic perspective of price promotions have largely focused on the impact of targeted price promotions on price

16 competition (Zhang and Krishnamurthi 2004) and firm optimization strategy as to whether it may be optimal to target switchers and/or loyal customers (e.g., Fruchter and Zhang 2004;

Krishna, Feinberg, and Zhang 2007; Zhang and Krishnamurthi 2004; Zhang and Wedel 2009).

These have usually presented mixed findings (e.g., Taylor 2003; Schaffer and Zhang 2000). In addition, there is some debate on whether firms should target switchers (e.g., Taylor 2003) or loyal customers (Shaffer and Zhang 2000) in their targeted marketing campaigns.

In their study, Krishna et al. (2007, pp. 1409-1410) define loyal consumers as “those who purchased from the firm in the last period,” and switchers as those consumers who “purchased from the other firm in the last period.” Krishna et al. (2007) found that, through the loyalty effect, keeping prices constant to loyal customers while increasing prices to switchers may have a positive impact on repurchase behavior. Furthermore, a firm’s decision to target loyal customers or switchers is also based on the nature of the competitive environment (Krishna et al.

2007).

The economic theory of choice suggests that consumers’ decisions to take up a price promotional offer is “exclusively” based on the price offered (Feinberg et al. 2002; Thaler 1980).

To extend research in the area, Feinberg et al. (2002) focused on a behavioral perspective, which is a deviation from the economic theory of rational consumer choice. They examined the impact of consumers’ awareness of discounts that are offered to other customers on the optimization of firm promotional strategies. They found evidence of betrayal effect when companies offer price discounts to competitors’ customers, and a jealousy effect when another firm offers price discounts to its customers (Feinberg et al. 2002).

Notwithstanding, researchers have given little focus on the affective consequences related to the feelings and emotions associated with receiving a customized offer (Aydinli et al. 2014;

Raghubir 1998), and how such feelings may interact with individual consumer differences to impact response to customized price promotion deals. That is, for Batra and Ray (1986, p.234)

“affective responses (ARs) should supplement the cognitive responses.” Here, affective responses represent the moods and feelings evoked by marketing stimuli (Batra and Ray 1986)

2.3 Customized Promotions and Targeted Price Promotions

With the advent of technology, firms customize products (Hildebrand, Haubl, and

Herrmann 2014), communications (Ansari and Mela 2003), and promotions (Zhang and Wedel

2009) to consumers at different levels of granularity (Zhang and Wedel 2009). Researchers have long established that a firm’s ability to customize products and prices require knowledge of individual consumer preferences and needs (Simonson 2005; Wallin Andreassen 1995). These customization activities have been found to positively impact both consumer and firm outcomes

(Oliver, Rust, and Varki 1998; Sheth 2001).

The selective offering of products, communication, and special prices to some customers

(vs. all customers) may increase the loyalty effect, and thus repurchase behavior (Krishna et al.

2007). For example, customized products positively impact consumer evaluation and purchase intentions (e.g., Hildebrand et al. 2014; Wilcox and Song 2011) and offer higher customer benefits (e.g., Franke, Keinz, and Steger 2009). Among its several benefits, customized communication attracts customers and aids effective customer decisions through the reduction of information overload (Ansari and Mela 2003). However, this research focuses on the customization of price promotions. This area of marketing is growing and presents several implications for firms and consumers yet is vastly understudied.

2.3.1 Customized Price Promotion

“A customized price is, simultaneously, the price the firm wants to charge and the price the consumer is willing to pay” (Obermiller et al. 2012, p.13). Customized price promotions involve the identification of discount prices that are likely to interest select customers or groups of customers with the aim of maximizing the promotion effect. It is regarded as a form of targeted price promotion with a modest level of exclusivity to select segment(s) of customers or a high level of exclusivity to individual customers (Barone and Roy 2010a, 2010b). Furthermore, the customization of prices to select customers “put targeted customers in an advantaged condition and non-targeted customers in a disadvantaged condition” (Tsai and Lee 2007, p.482).

However, despite the potential for firms and consumers to gain from its effective use, the literature on the customization of price promotion remains sparse. In addition, while there is some consensus on the exclusivity effect, researchers are yet to offer a precise conceptualization for customized price promotion. As such, this dissertation offers the following definition for customized price promotion: customized price promotion is the process of targeting special prices to select customers or groups of customers with the aim of maximizing the promotion effect.

Firms offer exclusive customized price promotions to their valued individual customers based on their transaction histories (Barone and Roy 2010a, 2010b). Consumers generally prefer more exclusive deals (those offered only to select individual customers or groups of customers) to more inclusive deals (those that are undifferentiated) (Barone and Roy 2010a, 2010b). Thus, they are likely to respond more favorably to promotion deals that are more exclusive than inclusive. However, with the increased use of customized price promotion by firms, more research is warranted on the conditions by which exclusive deals are viewed more positively or

19 negatively than inclusive deals (Barone and Roy 2010b). In addition, researchers often differ in their perspectives on the potential benefits of giving some consumers price advantages over others. For example, in the context of price discrimination, Shaffer and Zhang (2000) suggest that the strategy may benefit individual firms but may also negatively impact the competitive landscape of the market. In contrast, Ghose and Huang (2006) suggest that the personalization of prices may be good for competition. Similarly, while stockpiling (a probable result of offering a price promotion) may be a concern for some retailers, researchers suggest that such a situation may lead to consumer benefits. That is, when consumers wait for promoted prices, they may engage in increased information processing and delayed gratification (Chen and Zhang 2007;

Obermiller et al. 2012). These findings suggest several consequences for a customized price promotion strategy.

Extant research suggest that customized price promotion strategies present several consequences for firms and consumers. Customized price promotions offer firms the ability to reduce the impact of competitive pressure and improve the likelihood of targeting the right customer at the right time to help sustain customer-firm relationships (Ansari and Mela 2003).

More specifically, customized price promotions afford firms several prudent opportunities to personalize products, prices, and services in a targeted way. These strategies have been shown to: i) increase marketing effectiveness and customer loyalty (e.g., Wedel and Kannan 2016;

Zhang and Wedel 2009), ii) increase firm revenue (Homburg et al. 2008; Khan et al. 2009), iii) increase the number of stores consumers visit and the number of items purchased (Rossi et al.

1996; Zhang and Wedel 2009; Zhang and Krishnamurthi 2004), as well as iv) increase the ability to excite consumers and reduce the likelihood of them switching to the competition (Lee and

Tsai 2014). From the advancements in technology, firms can also improve the timing of customized promotions to make targeting more efficient (Zhang and Krishnamurthi 2004).

However, the consequences for consumers may be more dynamic. For example, while customized deals suggest the exclusivity of an offer to deal recipients (Chatterjee and McGinnis

2010), today’s consumers are likely to weigh the costs and benefits of a customized relationship with a firm (Simonson 2005). As such, the effectiveness of customized price promotions is likely impacted by individual consumer differences (Khan et al. 2009) such as the type of impulsivity

(Dickman 1990), the depth and framing of the deals (DelVecchio, Krishnan, and Smith 2007), and the level of exclusivity and/or granularity of the targeting design (Zhang and Wedel 2009).

2.3.2 Level of Granularity and the Exclusivity of Customized Price Promotion

Firms’ decisions to customize prices include determining the level of granularity to which the prices are customized: mass-level customization, segment-level customization, and individual-level customization (Zhang and Wedel 2009). Mass-level customization is evidenced by the offering of the same discount to all relevant consumers. Segment-level customization is used when the customized offer is made to the relevant selected segment(s) of consumers. At this level, customized price promotions are based on the selection of customers who are recent purchasers (Obermiller et al. 2012). Finally, individual-level customization is used to personalize offers to each individual consumer (see Zhang, and Krishnamurthi 2004; Zhang and Wedel

2009). This research adopts the more modest segment level of exclusivity versus the most exclusive individual level (Barone and Roy 2010a, 2010b; Zhang and Wedel 2009).

Firms adopt customized price promotion strategies at the different levels of granularity in various online and offline (store based) contexts. Researchers have also begun to examine the

21 effectiveness of these strategies both in the online and offline settings (Barone and Roy 2010a, b;

Zhang and Wedel 2009). For example, Zhang and Wedel (2009) assessed the effectiveness of customized offers at the three granular levels in an online versus an offline setting. However, this dissertation seeks to contribute to the literature on the customization of prices by examining the impact of customized price promotions at the segment level of price customization based on deal exclusivity, individual difference, and other deal characteristics.

Exclusivity “refers to the degree to which access to the product can be controlled and restricted to a group of consumers according to some criteria” (Lamberton and Rose 2012, p.

110). In fact, according to the Merriam-Webster dictionary, exclusivity means “the quality of being limited to people of wealth or high social class.” Furthermore, exclusivity is synonymous with individuality, selectness, exceptionality, distinctiveness, and uniqueness.

Exclusivity also highlights the offering of different prices to targeted vs. untargeted customers. And, offering exclusive deals to select customers suggests “advantaged inequality,” in which “the focal (i.e., targeted) customer receives a more favorable promotion than other customers.” In the research context, this may induce perceptions of advantaged price inequity

(vs. disadvantaged price inequity), which ultimately impacts emotions, evaluations, and behavior

(Martins 1995; Tsai and Lee 2007). Therefore, high levels of exclusivity may provide some consumers with an increased level of confidence, high sense of status and/or increased self- enhancement. On the other hand, inclusive deals are often offered to the general market and suggest “equality,” whereby “the focal customer receives the same promotion as other customers” (Tsai and Lee 2007, p. 483). Such deals are therefore less likely to have a similar impact on segments of consumers who may have a tendency toward advantageous inequity.

2.3.3 The Impact of Customized Price Promotion on Consumer

The effectiveness of a customized promotion strategy depends on how consumers respond to it (Thompson et al. 2015). Prior research has suggested that consumers differ in their preferences toward customized price promotions (Barone and Roy 2010a, 2010b; Chatterjee and

McGinnis 2010). Customized price promotion has been found to increase the perception of deal value and purchase intention (Chatterjee and McGinnis 2010). However, while many consumers are value seekers (Hsee 1999), their evaluations of target price offers are often related to individual psychological and social factors (Barone and Roy 2010). Further, other factors related to the promotional offer include the framing of the promotion (DelVecchio et al. 2007; Suri,

Swaminathan, and Monroe 2004) and the offers’ perceived fit and attractiveness (Simonson

2005). For example, consumers also tend to prefer and are more willing to expend more effort to get larger (vs. smaller) discounts (DelVecchio et al. 2007; Kahneman and Tversky 1984;

Thompson et al. 2015). This makes price an essential value cue, which impacts consumer decision making in several ways (Lee and Zhao 2014; Thaler 1985; Raghubir 1998).

Research on the impact of price on consumer decision making proposes that the paths include: i) the economic route (e.g., Kahneman and Tversky 1984), which suggest that consumers assess the value of acquiring a particular market offering and use price as a reference point. This reference point is often derived from past experiences and expectations (Kahneman and Tversky 1984), ii) the informative route (e.g., Dawar and Parker 1994; Rao and Monroe

1988), which suggests that consumers may use inferences about price in the absence of other cues (Olsen 1978), and iii) the affective route, which focuses on consumers feelings and emotions toward the price (Darke and Dahl 2003).

In addition, impact of the affective route may be based on whether the consumer feels that s/he has used his/her skills to get a good deal, and offers several implications for studies on functional impulsivity (Schindler 1998). More specifically, extant research suggests that consumer evaluations and satisfaction with price discounts are a function of several factors including whether the consumers attribute the discounts to their own skills (Schindler 1998), perceived luck (Darke and Freedman 1995), or the perceived fairness of the deal (e.g., Darke and

Dahl 2003). Consumers are also likely to focus on the attractiveness of the offer, especially when their preferences for a product category or offer is poorly developed (Simonson 2005).

Notwithstanding, most research on customized price promotion has focused on models to improve and optimize the customization strategy (e.g., Fruchter and Zhang 2004; Khan et al.

2009). For example, Fruchter and Zhang (2004) suggest that in the context of a dynamic service competition, a uniform targeting strategy (offensive- to acquire customers vs. defensive- to retain customers) may not be appropriate or applicable to all firms. Instead, the optimal strategy may be based on factors such as market share and customer profitability. Hence, they suggest that firms with larger market shares would likely benefit from a focus on customer retention, while smaller firms should focus on acquiring customers. However, in the event of no advantages in market share, firms should focus on customer retention (Fruchter and Zhang 2004).

In addition to economic factors, several other factors, such as noneconomic and psychological factors, may influence consumers’ evaluation of customized price promotion deals

(Darke and Dahl 2003; Odekerken-Schröder, De Wulf and Schumacher 2003). These include the presentation format of the deal and the regular price (Coulter and Norberg 2009), social consideration and perceptions related to the exclusivity of the deal (Barone and Roy 2010), and perception of fairness (Darke and Dahl 2003). Specifically, Darke and Dahl (2003) found that

24 consumers considered factors other than economic factors such as perceived fairness when evaluating price promotions. For example, the exclusivity effects result from a consumer’s preference for exclusive (vs. inclusive) deals, in which the positive feelings derived from being included in an exclusive deal influence the evaluation of the deal. In addition, the deal exclusivity effect is based on a consumer’s identification with the exclusive group, and the value assigned to membership of that group (Barone and Roy 2010a).

Consumer individual factors such as gender and self-construal also impact consumer evaluation of deals (Barone and Roy 2010). Specifically, Barone and Roy (2010a) found that male and independent consumers were more likely to prefer exclusive deals, while females and interdependents had lower evaluations for exclusive deals and therefore were more likely to respond better to more inclusive deals. However, given that consumer values have shifted toward a greater focus on immediate gratification and the recent themes in advertising that highlight exclusivity, the influence of other factors on consumer response to customized price promotions

(such as functional impulsivity) remain unclear.

Table I. Summary of Literature on Customized and Targeted Price Promotions

Source Journal Type of Dependent Independent Summary Study Variable Variable and Focus

Thompson, Journal of Empirical Promotion YMMY-type Focused on Gooner, and the (content evaluation deals; untargeted Kim (2015) Academy of analysis of Promotion consumer Marketing over 1000 Characteristics response to Science discussions Discount % targeted from the Cheapest/not promotions highest cheapest traffic “deals” website) Lee and Tsai Journal of Empirical- Consumption Immediate and The time delay (2014) Consumer experiments enjoyment delayed effect between price Research of price promotion promotion discount and consumption influences enjoyment Chen, Tsai, Social Empirical- Perceived Missing a Perceived and Chuang Behavior & experiments Price price price (2010) Personality: unfairness promotion unfairness is An greater when International consumers Journal attribute missing a promotion to the seller. Such perceptions may induce anger Fruchter and Journal of Empirical Mathematical Mathematical Firms’ Zhang (2004) Services (differential models models promotional Research game strategies modeling) should depend on factors such as market share and redemption rate of promotions

Source Journal Type of Dependent Independent Summary Study Variable Variable and Focus Dungchun and Journal of Empirical- Perceived Customer Firms are Hsiao-Ching, Product and experiments unfairness; type; likely to (2007) Brand Purchase Perception of realize greater Management intention unfairness benefits from Mediator: the offering of Perceived targeted value; promotions to Negative present vs. emotions potential customers. Scales provided for all constructs DelVecchio, Journal of Empirical- Expected Promotion Following a Krishnan, and Marketing modeling future price; depth (low deep discount, Smith (2007) plus Choice 13%; high price experiments 43%) expectation is lowered substantially more when cents off promotion is used than when % off promotion is used (Scales Used) Zhang and Journal of Empirical Profit Purchase Effectiveness Wedel (2009) Marketing with a joint potential of incidence, of customized Research model of promotion Brand choice; price purchase and Purchase promotion at incidence, quantity all three levels choice, and of granularity quantity in online and offline Chandran and Journal of Empirical- Purchase Type of Free vs. Morwitz Consumer experiments Likelihood promotion monetary (2006) Research (Sensitivity (free promotions to negative promotions vs. are more contextual discount) salient and are information) thus less susceptible to negative information.

Source Journal Type of Dependent Independent Summary Study Variable Variable and Focus

Zhang and Marketing Empirical Optimal Purchase In an online Krishnamurthi Science Modeling of promotion incidence, setting, variety (2004) purchase per Brand choice, seeking incidence, household and Purchase behaviors are choice, and quantity likely to quantity change over time among households. The authors focused on the timing of customized promotion at the household level. Hardesty and Journal of Empirical- Perceived Promotional Recommended Bearden Retailing experiments value level and type the use of % (2003) price discounts when large discounts are being applied. Feinberg, Journal of Empirical Purchase 3 firm Jealousy and Krishna, and Marketing Model- probability; promotion betrayal are Zhang (2002) Research Markovian Preference types: potential framework for firm Promoting to behavioral and (evaluation) switchers (S), consequences experiment only to loyal of firms’ (L) or not at promotional all (N) strategies

Pauwels, Journal of Empirical- Brand Sales Immediate The sales Hanssens, and Marketing modeling (Brand (short term components of Siddarth Research using choice, changes in choice, (2002) ACNielsen purchase sale), quantity, and household quantity, and permanent, incidence do scanner data category and not carry a incidence) adjustment permanent promotional promotion effects effect, especially in mature markets.

Source Journal Type of Dependent Independent Summary Study Variable Variable and Focus Nijs et al. Marketing Empirical- Category Price The frequency (2001) Science vector demand promotions of promotions models (short run vs. may only be long run) effective in the short run. However, price promotion effectiveness may be greater for perishable goods Raghubir and Journal of Empirical- Brand Brand past For pretrial Corfman Marketing experiments evaluation promotion brand (1999) Research (currently and evaluations, frequency) novices are more likely to use price promotion as a cue for product quality than product category experts

Distinctions among Customized Price Promotion, Targeted Price Promotion, and Loyalty

Programs

Lewis (2004, p. 281) suggests that a loyalty program is a form of “dynamically oriented promotions.” A loyalty program is formally defined by Meyer-Waarden and Benavent (2009, p.

346) as “an integrated system of individualized marketing actions that aims to increase customers’ loyalty through personalized relationships that stimulate their purchase behavior,” and “as any institutionalized incentive system that attempts to enhance consumers' consumption behavior over time beyond the direct effects of changes to the price or the core offering”

(Henderson, Beck, and Palmatier 2011, p.258).

Firms offering loyalty programs offer benefits to customers that fall outside the domain of price promotions. For example, some loyalty programs allow consumers to stock-up on points to earn a reward, etc. Similarly, price promotions are usually undifferentiated offers, commonly found in newspapers or in stores, without being specifically targeted to any individual customer or group(s) of prospective customers. On the other hand, targeted price promotions refer to the practice of offering different prices to loyal or prospective customers (Feinberg et al. 2002).

Dynamic Pricing Models: are pricing models that “update prices frequently based on changing supply or demand characteristics” (Grewal et al. 2011, p. S46). For Grewal et al. (2011, p. S46), “in the future, prices in stores might automatically shift up and down based on costs, inventory levels, and consumer spending habits.” On a similar note, Weisstein, Monroe, and

Kukar-Kinney (2013, p. 501) refer to dynamic pricing as a retailer’s ability to engage in the practice of “changing prices of the same product depending on demand and on consumers’ individual characteristics, such as time and frequency of service usage and loyalty status.”

2.4 Impulsivity

Consumers differ in their level of impulsivity as a function of the accessibility of pleasure seeking vs. self-regulation goals (Zhang and Shrum 2008). In the psychological domain, studies tend to highlight three main categories of impulsivity, as highlighted by Moeller et al. (2001, p.

1784). These include i) “punished and/or extinction paradigms -perseverance of a response that is punished or unrewarded”, ii) “reward-choice paradigms, in which impulsivity is defined as preference for a small immediate reward over a larger delayed reward”, and iii) “response disinhibition/attentional paradigms, in which impulsivity is defined either as making responses that are premature or as the inability to withhold a response.” As such, impulsivity is largely seen

30 as a negative trait and often described as an individual’s irresponsible actions, failure to consider potential risks associated with his/her actions, and a low tolerance for the idea of delaying rewards (Gray 1987).

In the context of reward and choice, consumers are often faced with situations in which they voluntarily choose to defer, rather than engage in immediate gratification (Loewenstein

1987). However, such decisions become especially difficult when choices differ on more than one dimension (Van den Bergh, Dewitte, and Warlop 2008). The preference for immediate versus delayed gratification has important implications for consumer well-being and firm marketing strategy decisions. Immediate gratification is found to be related to self-control problems, the over consumption of consumer goods, and the possible engagement in injurious consumption (O'Donoghue and Rabin 2000). As such, immediate gratification tendencies are likely to be associated with a focus on short term goals, which are inconsistent with long term goals. In addition, some changes in consumer behavior can be explained by a preference for immediate gratification (O'Donoghue and Rabin 2000). In contrast, a decision to delay gratification is impacted by a discounted view and uncertainty of the future (Frederick,

Loewenstein, and O'Donoghue 2002).

Research on impulsivity also suggests that making a reward salient increases impatience

(Baumeister 2002; Hoch and Loewenstein 1991). Mischel and Ebbesen (1970) found that children chose an immediate inferior reward over a superior delayed reward when the reward was exposed. In contrast, most children delayed gratification by resisting the inferior reward and waited for the superior reward when the reward was not seen (Metcalfe and Mischel 1999).

Furthermore, Baumesiter (2002) suggests that some sales strategies may promote immediate

31 gratification. To this extent, contextual cues and situational factors can indeed increase the urge to act impulsively (Ramanathan and Menon 2006).

In addition, affect increases impatience (Chang and Pham 2013). Specifically, Chang and

Pham (2013) found that affect-laden choices are more preferred in the proximate vs. distant choice events as consumers depend more on feeling in near choices than in distant choices. This research posits that exclusive customized price promotions may inhibit patience because of the visible and personalized targeted rewards. More specifically, the dissertation posits that making a customized price promotion more exclusive increases the salience of the reward, and therefore increases favorability responses, especially among consumers who are high on functional impulsivity.

Impulse Buying

While the dissertation focuses on impulsivity as a trait, it also examines how the concept has been studied in the marketing and consumer research domains. An examination of the consumer research literature suggests that impulsivity is largely shown to have a disruptive impact on consumer behavior, particularly buying behaviors (e.g., Rook 1987). Specifically,

Rook (1987) found that consumers may still engage in impulsive buying despite being fully aware of the potential negative consequences and possible challenges from financial or other problems associated with the behavior. That is, “highly impulsive buyers are likely to be unreflective in their thinking, to be emotionally attracted to the object and to desire immediate gratification” (Kacen and Lee 2002, p. 164). To this extent, researchers have argued that more research is warranted on factors that may influence impulsive buying behavior. The premise is that while many consumers do not act on their impulsive tendencies, a vast number of U.S.

32 consumers do, as indicated by rising personal debts. Further, it warrants additional research on factors related to the overarching concept of trait impulsivity.

Consequently, researchers have argued that most of the products purchased in the U.S. are a result of impulsive consumer behavior (Kacen and Lee 2002, p.163), and consumers continue to buy unneeded or unwanted products on impulse (Soll, Keeney, and Larrick 2013).

Defined as “a sudden, compelling, hedonically complex purchasing behavior in which the rapidity of the impulse purchase decision process precludes thoughtful, deliberate consideration of all information and choice alternatives,” (Kacen and Lee 2002, p.163), impulsive buying behaviors are often unplanned (Wolman 1973) and triggered by a visual confrontation (Rook

1987).

For many researchers, impulsive buying is a type of “unplanned” buying that takes place in a store setting and is based on factors such as in-store stimuli (Bell, Corsten and Knox 2011;

Zhang, Winterich, and Mittal 2010). However, firms often communicate customized promotional offers to their targeted customers via personalized emails or direct mail (Ansari and Mela 2003;

Grewal et al. 2011; Smith 2015). In this context, researchers have found browsing behavior and online shopping to be associated with impulse buying (e.g., Madhavaram and Laverie 2004).

However, consumers also differ in their propensity to take up promotional offers online; therefore, these online offers may not elicit the same type of impulsive buying as simply being in-store. For example, Wei and Chen (2015) found that consumers with high impulsivity may be less inclined to take up a sales offer than consumers who exhibit more caution.

Earlier research suggests that a number factors, including demographics (e.g., Wood

1998), can help explain impulse buying, which is highly related to trait impulsivity. Further, the items consumers tend to buy on impulse come from several product categories (Bellenger,

Robertson, and Hirschman 1978), especially those that are symbolic in nature and help express the consumers’ ideal self (Dittmar, Beattie, and Friese 1996). Bell et al. (2011) also posit that conditions related to unplanned buying are often a result of factors occurring outside of the store that are related to the retailer. For example, when consumers choose a retailer based on discounts offered and are focused on that particular store (goals associated with store), impulse or unplanned purchases may be greater (Bell et. al. 2011).

To further understand the nature of impulsivity and impulse buying, researchers have also examined consumer decisions that are influenced by both cognition and affect (e.g., Shiv and

Fedorikhin 1999; Swann et al. 1987). Mental processes underlying impulsive behavior suggest a cognitive perspective and a dual role of affective and cognitive perspective. The body of work on the cognitive perspective suggests that consumers discount future rewards and overvalue immediate ones (e.g. Li 2007; Pyone and Isen 2011). Proponents of the affective and the cognitive perspective argue that affective desires compete with the willpower not to engage in impulsive behavior (Hoch and Loewenstein 1991). Exclusive customized price promotions may thus be an affect-based temptation to consumers.

Furthermore, while cognitive decisions are associated with thinking and interpreting information, affective choices are made based on moods (both positive and negative), feelings, and emotions (Coley and Burgess 2003; Rock and Gardner 1993). However, the two processes exist on a continuum (Coley and Burgess 2003). Impulsive buying becomes more likely with dominant affective versus cognitive choices (Coley and Burgess 2003). That is, buyers who are more susceptible to emotions and moods are more likely to purchase items on impulse (Youn and Faber 2000). For example, Gardner and Rook (1988) found impulsive buying to be associated with boredom and frustration. However, these researchers have failed to distinguish

34 between dysfunctional and functional impulsivity (Dickman 1990). That is, there are “at least two distinct and independent forms of impulsivity” (Vigil-Colet and Codorniu-Raga 2004, p.

1432). To this extent, the impulsivity trait may be positive and enhance the consumer decision making processes. As indicated earlier, consumer researchers are yet to examine the differences in the dimensions of impulsivity and the likely influence on consumer evaluations and choice.

Accordingly, while dysfunctional impulsivity is likely to be associated with high affect- laden decisions, functional impulsivity is likely related to decisions based on the combination of affect and cognition (Dickman 1990). In addition, the different processes by which impulsive choices are made present several implications for firms. While affective choices may be impacted by store design, advertising themes, and promotional designs (such as exclusive vs. more inclusive deals), cognitive choices are likely to involve some amount of thinking and interpretation (Coley and Burgess 2003). As such, the examination of functional impulsivity can provide several implications for academicians and practitioners.

2.4.1 Delineation of Functional and Dysfunctional Impulsivity

There are two forms of independent trait impulsivity: functional and dysfunctional impulsivity (Dickman 1990; Reeves 2007). In the self-reported domain, Dickman (1990) conceptualized impulsivity as a multidimensional construct and showed that the trait can be associated with not only negative outcomes, but also positive outcomes. As such, while impulsivity was largely studied in the context of a negative trait with several potential damaging effects, Dickman (1990) offered a different perspective by providing a clear distinction between functional and dysfunctional impulsivity traits and suggesting a scale to help researchers advance the theory. In support of Dickman’s (1990) findings, Colzato et al. (2010) found support for the

35 difference between functional and dysfunctional impulsivity and suggested that this might help explain mixed results in previous research that did not separate the two subtypes of impulsivity.

For Winkel et al. (2010, p.79), “Functional impulsivity refers to the tendency to act quickly with positive consequences, and has been found to be positively associated with enthusiasm, adventurousness, and extraversion. Dysfunctional impulsivity, on the other hand, refers to the tendency to act hastily with negative consequences.” Similarly, Maccallum et al.

(2007, p. 1836), assessed the difference between functional and dysfunctional impulsivity and suggest that “functional impulsivity may represent a protective trait, reducing the net impact of errors with a series of quick and calculated adaptive decision.” Dysfunctional impulsivity, however, is the “propensity to respond quickly, carelessly and with inattentiveness…, (which) may constitute a trait that contributes to the exacerbation of negative consequences.” This is because dysfunctional impulsivity is the result of an “individual’s inability to plan, reflect on the implications of actions and delay gratification in an adaptive manner” (Maccallum et al. (2007, p. 1836).

That is, functional impulsivity is associated with extraversion, speed, accuracy, and is a source of pride (Dickman 1990; Smillie and Jackson 2006). People with high scores on the trait, also called functional impulsives, are considered “bright” individuals who benefit from their impulsivity (Dickman 1990). That is, functional impulsivity may be associated with positive outcomes from the associated behaviors (Smillie and Jackson 2006). Further, researchers have argued that high functional impulsivity is likely related to risk-taking (Claes, Vertommen, and

Braspenning 2000).

Therefore, functional impulsivity may be a useful trait, which helps individuals function

(Dickman 1990; Maccallum et al. 2007). In fact, high functional impulsivity is characterized by

36 efficient behavioral control and positive consequences, even in ambiguous situations.”

Furthermore, high functional impulsives may also adapt to task requirements when a situation is deemed appropriate and therefore exhibit the tendency to engage in deliberate and methodical thinking (Claes et al. 2000; Dickman 1990). On the other hand, people high on the dysfunctional impulsivity trait, also called dysfunctional impulsives, differ in their personality and cognition from those high on the functional impulsivity trait. The dysfunctional trait is seen as a “bad” trait associated with difficulties like information processing strategies that are rapid and inaccurate

(Dickman 1990, 2000). The trait is also conceptualized as the “inability to inhibit impulsive behavior” (Reeves 2007). As such, dysfunctional impulsives are shown to have lower levels of adaptiveness than functional impulsives and are thus unlikely to benefit from their impulsive behavior (Dickman 1990).

Other researchers have also found support for the distinction between functional and dysfunctional impulsivity. For example, dysfunctional impulsivity was found to be more related to depression than was functional impulsivity (Lester 1992), extraversion impulsivity (a trait associated with functional impulsivity) was related to individuals paying close attention to risks, and psychotic impulsivity (a trait associated with functional impulsivity) was related to decision- making without the calculation of the associated risks (Eysenck 1993). However, researchers are yet to examine the differences in functional and dysfunctional impulsivity in the consumer researcher domain. This dissertation attempts to contribute to this research gap.

2.4.2 Impulsivity and Consumer Response to Price Promotion

Impulsivity is based on information processing as a function of one’s personality

(Whiteside and Lynam 2001). Impulsivity influences consumer cognitive processes and

37 information processing (Reeves 2007). Recent research suggests that price promotion, while providing an incentive for consumers to make purchases, may also serve as “a disincentive to think”, and therefore “dumbs down” the purchasing process (Aydinli et al. 2014). More specifically, Aydinli et al. (2014) argued that price promotions are likely to reduce processing motivation because the consequences that are usually deliberated on are reduced, and affective responses are likely to increase. That is, price promotions may lead consumers to prefer products that are affect laden (Aydinli et al. 2014). Specifically, Aydinli et al. (2014, p. 93) found that

“price promotion ultimately places greater emphasis on the affective responses that products spontaneously trigger”. Similarly, this dissertation argues that customized price promotions that are marketed as exclusive are also rich in affect and should receive more favorable responses than those that are more inclusive or undifferentiated.

Price promotion deals that are customized and offered on an exclusive basis have been found to receive more favorable responses than traditional, all-inclusive deals (Barone and Roy

2010a, 2010b). The present research extends research in this area by examining the moderating impact of functional impulsivity, a personality trait known to affect response to environmental stimuli. Therefore, while impulsivity research has focused mostly on dysfunctional impulsivity and its host of negative consequences, marketers have failed to examine the role of functional impulsivity, which may relate to quick decision making when it is optimal (Dickman 1990). As such, the research examines whether the relationship between deal exclusivity and deal response changes at different levels of functional impulsivity--the key moderating variable.

2.4.3 Functional Impulsivity and Customized Price Promotion

For Reeves (2007, p. 57) “an individual high on functional impulsivity and low on dysfunctional impulsivity might feel comfortable and perform well in a fast-paced environment where quick thinking is required, but would also demonstrate the ability to engage in slow, deliberate consideration of ideas when appropriate in a different situation.” On the other hand, a person high on dysfunctional impulsivity may not be able to inhibit the impulsivity behavior as required by the latter condition (Reeves 2007). Researchers who have examined the differences between functional and dysfunctional impulsivity have found that in information processing and speed related activities, “individuals with high scores in functional impulsivity did not use a strategy based on speed” (Vigil-Colet and Codorniu-Raga 2004, p. 1437). In addition,

“functional impulsivity does not suggest anything about the inability to control impulsive responses” (Reeves 2007, p.57). Therefore, the requirements of the situation help determine whether high functional impulsive individuals adopt a strategy of speed over accuracy (Dickman

1990; Reeves 2007).

In the context of making customized price promotions more effective, and to build more sustainable relationships with consumers, a focus on the inability of consumers to inhibit impulsive behavior is not an optimal strategy. Instead, receiving an exclusive offer suggests the need to assess how consumers may act quickly when they perceive the situation to be optimal, and the effect of such firm strategy on affect-based consequences, which are shown to influence consumer decisions. For example, Bressolles, Durrieu and Giraud (2006) found functional impulsive buying relates to high levels of customer satisfaction. In contrast, dysfunctional impulsive buying more likely leads to high levels of remorse and withdrawal from the firm

(Bressolles et al. 2006).

Functional impulsivity is linked to narcissism, a tendency to be self-centered and a craving for attention and admiration (Jones and Paulhus 2011). It also highly relates to extraversion, enthusiasm and adventurousness (Dickman 1985, 1990; Jones and Paulhus 2011;

Revees 2007). Extraverts are excitement seeking individuals who like to be the center of things

(Eysenck 1990). Extroverts also have a focus on, and are stimulated by external factors, gaining pleasure from factors outside the self (Eysenck 1990). Individuals scoring high on functional impulsivity are also likely to exhibit higher levels of psychopathic egoism, a tendency to focus on self-interest (Jones and Paulhus 2011; Vigil-Colet, Morales-Vives and Tous 2008). Therefore, factors such as the receipt of a customized price promotion deal are expected to relate to some stimulation and pleasure among high functional impulsives. Exclusive price promotions are also usually reward driven and targeted to firms’ best customers. On the other hand, more inclusive promotions are often geared toward increasing sales and are less tailored to a particular customer need.

For both theoretical and practical reasons, I selected functional impulsivity as a distinct form of trait impulsivity to examine the effect of impulsivity on the relationship between deal exclusivity and deal response. When promotions are customized to individual consumers and offered on an exclusive basis, consumers generally have a more positive attitude toward them

(Barone and Roy 2010a, 2010b). This relationship is likely because highly exclusive deals are finely tailored to meet a specific customer or a select small group of customers’ needs. They are often short-lived and require the recipient to act quickly to gain the reward. Exclusive price promotions are also affect-laden and should signify the condition of advantageous inequity (Tsai and Lee 2007; Zhang and Shrum 2008). Thus, high functional impulsive consumers should view the receipt of an exclusive deal as a way to act quickly and benefit from a favorable outcome. As

40 such, I extend the body of knowledge in the areas of deal exclusivity and functional impulsivity and propose that functional impulsivity will moderate the effect of deal exclusivity on deal response. Formally stated,

H1: Functional impulsivity moderates the effect of deal exclusivity on the response to customized price promotion deals such that, when functional impulsivity is high, customized price promotion deals that are greater in exclusivity will receive more favorable responses than deals lower in exclusivity.

Table II. Definitions of Functional Impulsivity and Similar Terms

Functional impulsivity Burnett Heyes et al. (2012, Impulsivity “the p.1) predisposition to act with a low or inadequate degree of deliberation, forethought, or control.” Functional impulsivity “refers to the tendency to act rapidly with little forethought when this style of responding might be optimal.”

Compulsive Buying Dittmar (2005b, p.467-468) A “compensatory behaviour, where individuals attempt to deal with identity and mood problems through buying material goods”

Compulsive Buying Moeller et al (2001, p.1784 Compulsive behaviors are those “in which planning occurs before the behavior.”

Table III. Impulsivity: Insights from the Psychology Domain

Source Journal Contribution Patton, Stanford Journal of Clinical Psychology Formulated a scale that measures Barratt (1995) impulsivity in three ways: Cognitive, impulsiveness- inattention, and instability. Defined impulsivity in the context of making quick cognitive decisions

Dickman (1990) Journal of Personality and Social Distinguished between functional Psychology and dysfunctional impulsivity in the self-report domain.

Gray (1987) Journal of Research in Personality Impulsivity may be related to a lower tolerance to delay reward.

Eysenck and British Journal of Social and Subdivided impulsivity into four Eysenck (1978) Clinical Psychology specific dimensions: narrow impulsiveness, risk-taking, non- planning, and liveliness

Table IV. Select Research on Functional Impulsivity

Source Journal Type of Dependent Independent Conclusion/Finding Study Variable Variable Lester The Journal of Empirical Suicidal Impulsivity- Depression inventory (1993) General thoughts and functional is negatively Psychology attempts and correlated with dysfunctional functional impulsivity. Further, depressed participants show more dysfunctional (vs. functional) impulsivity

Source Journal Type of Dependent Independent Summary and Study Variable Variable Focus

Dear The Journal of Empirical Depression Functional When depression is (2000) Psychology and suicidal and not accounted for, a ideation dysfunctional stronger relationship impulsivity exists between dysfunctional impulsivity and suicidal ideation than for functional impulsivity and suicidal ideation Claes et Personality Empirical Functional and al. (2000) and Individual - factor dysfunctional Differences analysis impulsivity are two independent personality factors. P.34. FI shows strong correlation with venturesomeness (risk taking behavior)

McAliste, British Empirical Extradyadic Dysfunctional Dysfunctional Pachana, Journal of inclination impulsivity impulsivity & Jackson Psychology (kissing and moderates the (2005) sexual NB* relationship between activity) Functional sex-love-marriage Impulsivity and extradyadic was measured inclination to support Dickman’s (1990) delineation

Source Journal Type of Dependent Independent Summary and Study Variable Variable Focus

Vigil- Personality Empirical Reaction Functional vs. Findings show the Colet and and Individual Time Dysfunctional lack of inhibition that Codorniu- Differences Impulsivity is characteristic of Raga individuals with high (2004) levels of anger to be associated with dysfunctional (not functional) impulsivity.

Reeves International Empirical Number of Functional Found functional (2007) Journal of Items Impulsivity impulsivity to be Selection and attempted; positively related to Assessment accuracy the number of items participants attempted, and also the number of items that were correct on an ability test. Bressolles, Journal of Empirical Dimensions Online The functional nature Durrieu & Customer of electronic customer of customer buying Giraud Behavior service satisfaction impulsivity (2007) Website and buying moderates the quality impulse relationships between website quality and customer satisfaction, and website quality and impulse buying

Cosi et al. Psychological Empirical Impulsivity Functional In the self-report (2008) Reports in children and domain, the and dysfunctional distinction between adolescence impulsivity functional and dysfunctional impulsivity may not hold until adulthood

2.5 Theoretical Foundations

The theoretical framework in this research aims to explain why exclusivity may influence consumer response to customized price promotion and the mechanisms by which the effects are likely to occur. Specifically, the research looks at affect-based theories for motivation, emotion, and self-enhancement. Furthermore, to understand the effects of price framing, the research examines the adaptation-level and assimilation- contrast theories. Marketing researchers often use theories of motivation and assimilation-contrast to help explain many aspects of consumer behavior. This research focuses on both affect-based and cognitive consequences of customized price promotion. Hence, these theories are used to explain the mechanisms involved.

2.5.1 Motivation

This research focuses on the impact of customized price promotion on affect-based consequences and response to deals. As such, theories in motivation can provide insights into the research context. According to the Mcguire’s Psychological Motives, motivation can be divided into four categories: i) those which are cognitive or affective in nature, ii) those which are focused on preservation to maintain equilibrium, iii) those that are internally driven versus based on environmental factors and, iv) those that are linked to the achievement of a new internal or external relationship (McGuire 1976; Hawkins, Mothersbaugh, and Best 2013). For Hawkins et al. (2013, p. 354), while cognitive motives “focus on the person’s need for being adaptively oriented toward the environment and achieving a sense of meaning,” affective motives “deal with the need to reach a satisfying feeling state and obtain personal goals.” This dissertation largely focuses on affect-based motives, as well as cognitive motives, which together can help explain consumer response to customized price promotion deals.

2.5.1.1 Emotional Arousal

Research generally suggests that there are three dimensions of emotions: i) pleasure, ii) arousal, and iii) dominance (PAD) (Mehrabian and Russell 1974). However, “the PAD does not purport to measure emotions per se; instead, it assesses the perceived pleasure, arousal, and dominance elicited by a set of environmental stimuli” Richins (1997, p. 128). Notwithstanding, most studies now focus on emotional valence and arousal (Neidenthal 2008; Smith and Ellsworth

1985) as important factors to the study of consumer behavior (Yin, Bond, and Zhang 2017). And, although these two dimensions sometime covary, they are independent factors (Russell 1980;

Yin et al. 2017, p. 448). For example, while valence is characterized by positive or negative states (Brosch, Pourtois, and Sander 2010; Hawkins and Mothersbaugh 2013), arousal captures the extent to which an experience “energizes” an individual (Brosch et al. 2010). To this extent, an emotional stimulus may be categorized as a “threat or an opportunity for growth and expansion” (Brosch et al. 2010, p. 2), and thus is of interest to marketers.

In the marketing domain, the study of emotion has gained some momentum and suggests several implications for marketing relationships and firm outcomes (e.g., Chen and Ayoko 2012;

Brosch et al. 2010; Palmatier et al. 2009; Reimann, Schilke, and Thomas 2010). For example, emotional arousal has been found to impact several factors, such as consumers’ response to brands (Reimann et al. 2012), product attribution type (Choi et al. 2016), and perception of consumer reviews (Yin et al. 2017). Specifically, for Reimann et al. (2012), emotional arousal is highest at the beginning phases of brand relationships and is likely to diminish over time. In the context of advertising, Choi et al. (2016) assessed the impact of emotional arousal levels on the efficiency of product attribute types. They found that “the impact of product attribute type on product evaluations is primarily driven by the emotional arousal levels” (Choi et al. 2016, 78).

And, Yin et al. (2017, p. 448), found that experienced emotional arousal captures an individual’s subjective feelings, which may impact the perceived helpfulness of consumer reviews.

Furthermore, research on affective states highlights the importance of understanding the differences between i) emotional pleasure and ii) emotional arousal (Fiore, Yah, and Yoh 2000;

Russell, Weiss, and Mendelsohn 1989).

For Fiore et al. (2000, p.35 italics added), “emotional pleasure is the evaluation dimension of affect referring to the degree to which one feels good, happy, or satisfied, whereas emotional arousal refers to the degree to which one feels stimulated, excited or alert in the situation.” This dissertation focuses on the arousal dimension of emotion, and by extension, its activation as a result of the receipt of a customized price promotion (Hawkins et al. 2013).

Specifically, the research examines positive emotional arousal as an affective outcome of advantaged price inequity, which is likely associated with the receipt of an exclusive customized price promotion deal.

More exclusive customized price promotion deals are marketed as restrictive and available to a select individual or segment(s) of consumers. On the other hand, inclusive deals are general promotions that are not restricted and open to many customers (Barone and Roy

2010a, 2010b). In this research, positive emotion is defined as the “the egoism-based pleasure of receiving a relatively favored treatment” (Tsai and Lee 2007, p. 484). Furthermore, “positive emotions are induced by the advantaged condition” (Tsai and Lee 2007, p. 484). To this extent, the dissertation posits that deal exclusivity is likely to have a positive effect on positive emotional arousal. Accordingly:

H2a: Deals that are greater in exclusivity are associated with higher levels of emotional arousal than deals that are lower in exclusivity.

Furthermore, the dissertation examines positive emotional arousal and self-enhancement as serial mediators of the relationship between deal exclusivity and deal response. Specifically, the research posits that in this sequence, positive emotional arousal and self-enhancement mediate deal exclusivity’s effect on deal response. Emotional arousal has a direct impact on feelings and experiences (Clark 1982; Mandler 1975). Additionally, emotional arousal has been linked to individuals’ feelings, judgment, and behavior (Clark 1982). More specifically, in assessing feeling states and their impact, Clark (1982) argued for autonomic arousal as a consequence of an event or thought (Clark, p. 236). In this dissertation, I propose that the receipt of an exclusive deal should be associated with positive emotional which would impact self- enhancement to make people feel good about themselves (Barone and Roy 2010a). Furthermore, when people feel good about themselves, they tend to view others more positively and assign higher favorability ratings to products or marketing offers (Clarke 1982; Schiffenbauer 1974;

Veitch and Griffitt 1976). For example, Clark (1982, p. 264) suggests that “when we are feeling good, we tend to behave in a more positive fashion and to perceive the world more favorably than would otherwise be the case.” Also, positive emotional arousal and its effect may result in lower levels of depressive feelings about ones’ self (Pettit et al. 2001). Moreover, “people want to maximize the extent to which they can view themselves positively" (Kernis et al. 1993, p.

1192).

Deal exclusivity is likely to have a positive impact on positive emotional and self- enhancement, such that as deal response will increase as the level of exclusivity increases.

Consumers who receive exclusive (vs. inclusive) deal should exhibit higher levels of positive emotional arousal than consumers who receive a more inclusive deal. Because positive

48 emotional arousal is associated with excitement and alertness in a situation, the research posits that it will likely have a direct impact on consumers’ self-enhance. Specifically, it is the positive emotional arousal that is associated with the receipt of an exclusivity deal that will make consumers feel better about themselves, which reflects self-enhancement.

2.5.2 Self-Concept

Self-concept is defined as “the cognitive and affective understanding of who and what we are” (Malar et al. 2011, p. 36). Two forms of self-concepts – namely, the actual and ideal self- guides are often discussed in the literature following Higgins (1987) self-discrepancy theory, which suggests three domains of the self (actual, ideal, and ought-self), and two standpoints

(yourself and your significant other). Specifically, Higgins (1987, p.320-321) suggests that the individual self often includes an i) actual self- the “representation of the attributes that someone

(yourself or another) believes you actually possess” ii) ideal self- the “representation of the attributes that someone (yourself or another) would like you, ideally, to possess” and, iii) ought self- the “representation of the attributes that someone (yourself or another) believes you should or ought to possess.”

Following these definitions and other studies, Malar et al. (2011, p.36) examined the actual and ideal self-guides and suggest that the actual self is “based on the preconceived reality of oneself,” while the ideal self is “shaped by imagination of ideals and goals related to what a person believes that he or she would like to be or aspire to become.” Further, while the ideal self is associated with rewards and a motivation toward maximizing positive outcomes and minimizing negative ones, the actual self arises when both the ideal and ought-self (i.e., a focus on reducing negative outcomes) are not met (Brendl, Higgins, and Lemm 1995). As such, gaps

49 between these two forms of self-concepts are often referred to as self-discrepancies and are related to several emotional outcomes (Higgins 1987).

2.5.3. Social Identity Theory

Social identification is described as an “individual perception of actual or symbolic belongingness to a group” (Marzocchi, Morandin, and Bergami 2013, p. 95). According to

Bagozzi and Dholakia (2006), self-identification has the components of i) cognitive, ii) evaluative, and iii) emotional, which allows “individuals to define themselves as members of social categories and ascribe to themselves characteristics that are typical of those categories”

(Marzocchi et al. 2013, p.95).

2.5.3.1 Self-Enhancement

Self-enhancement is derived from the social identity theory, which highlights the importance of group membership and favoritism related to an individual’s need for status

(Wolter and Cronin 2016). Similarly, self-perception theory (Bem 1972) suggests that consumers come to realize aspects of their attitudes and behavioral intentions as a function of their past behaviors and the situations in which they occurred (Chawdhary and Dall’Olmo Riley 2015).

Factors such as self-esteem impact the level of need for self-enhancement in consumers. That is, consumers with lower levels of self-esteem are likely to have a higher need to self-enhance and therefore seek experiences that will increase their self-confidence (Shrauger 1975). Furthermore, in a digital world, consumers often share the goal of self-esteem (Ofek and Wathieu 2010).

As a type of motivation, self-enhancement impacts self-esteem and makes people feel good about themselves (Rogers 1951). People attach themselves to others and objects they perceive as valuable and are likely to increase their self -esteem (Chaplin and John 2007). The

50 acceptance of marketing offers have symbolic meanings to consumers (Grubb and Grathwhohl

1967). These positive symbols are likely to impact consumer emotions and then make them feel good about themselves (Oliver and Westbrook 1993, Sherman, Mathur, and Smith 1997)

Self-enhancement has also been examined from a cultural perspective, where findings show a positive relationship between self-enhancement and independent self-construal. In addition, American consumers are more likely exposed to factors that are associated with self- enhancement and are thus more likely to engage in self-enhancement than are Japanese consumers (Kitayama et al. 1997). Further factors such as materialism have been linked indirectly to self-enhancement (Chaplin and John 2007). And, when there is a threat to one’s social self, people are likely to use material possession to enhance the self (Dommer and

Swaminathan 2013).

Self-enhancement has also been studied in the context of both positive and negative

WOM (e.g., Angelis et al. 2012; Chawdhary and Dall’Olmo Riley 2015; Dubois, Bonezzi, and

De Angelis 2016). For example, for Angelis et al. (2012, p.522), “consumers often consider the marketplace as a route to self-express and fulfill psychological needs,” and “an important aspect of a person’s self-enhancement involves managing his or her representation of the self in social interactions to create good impressions and gain positive recognition from others.”

Research also suggests that self-enhancement is a self-motive that drives its affective dimension of customer-company identification (Wolter and Cronin 2016). To expand research in this area, this dissertation posits that consumers can perceive the receipt of an exclusive price promotion offer as a means of enhancing the self. Specifically, I predict that the extent to which high functional impulsive consumers with a high need to enhance the self, evaluate customized price promotion offers depending on the level of exclusivity that is associated with the

51 promotion. That is, customized price promotions are generally considered positive personal outcomes (Simonson 2005) and provide ways for consumers to self-enhance. When consumers link the self to positive outcomes, they can maintain their positive self-view (Angelis et al. 2012;

Brown, Collins, and Schmidt 1988).

Table V. Select Research on Self-Enhancement

Source Definition DV Scales Context and Conclusion Provide Y/N Angelis et “Self-enhancement WOM Valence Y Context: WOM al. (2012) refers to the (generation of positive WOM Self-enhancement is a basic basic human need to vs. transmission human motive that leads feel good about of negative individuals to generate positive oneself and is WOM) WOM regarding their own manifested in the experiences, and to pass on desire to bolster or negative ones. That is, self- improve enhancement drives WOM, but it operates differently in its the self-concept, generation and transmission achieve a positive self-image, and maintainself- esteem”-p.552 Dubois, None shown Number of Y Context: Social distance. Bonezzi, & positive and De Angelis negative Social distance helps (2016) thoughts determine consumers psychological motives to protect others (close others) versus to self-enhance (distant others) when interacting with others. Overall the results show that high interpersonal closeness (IC) can raise more negative WOM than low interpersonal closeness, which tends to generate more positive WOM than with high interpersonal closeness.

Source Journal Type of Study Dependent Independent Variable Variable

Chawdhary Used the definition Self- N/A Context: WOM & Tie Strength & by Alexandrov et al. Enhancement Dall’Olmo (2013, p. 533): Both positive and negative Riley “self-enhancement WOM impacts self- (2015) as the ‘degree to enhancement, with the Future WOM negative condition having a which a person intentions expects that stronger impact than the projecting a good negative. image to others can be accomplished by sharing information Overall, the authors about brands’.” -see investigated the impact of p.1021. WOM on self-enhancement Wolter & Used the definition Skepticism Y Context: customer– company Cronin by (Reid and Hogg identification (CCI). (2016) 2005, p. 804): towards negative “motivation to information Self-enhancement is a self- maintain or increase about a motive that drives its affective the positivity, or company; dimension of customer- decrease the Willingness to company identification. provide negativity, of the The results show that the self” –see p.401. negative/positive WOM; wear affective dimension is company logo; positively related to pay price willingness to pay a price CCI (affective): “as premium; premium. an affectively attitudinal loyal positive connection between the identity The authors posit that “using a of an organization company to define one’s self is and the evaluation a different from using a customer applies to company to feel good about him or herself as one’s self.”-p.402. reflected by positive self-conscious emotions.”-p.402

H2b: Deals that are greater in exclusivity are associated with higher levels of self-enhancement than deals that are lower in exclusivity.

H3: Positive emotional arousal and self-enhancement mediate the effect of exclusivity on the response to customized price promotion deals.

2.5.4 Adaptation-Level and Assimilation-Contrast Theory

Adaptation-Level Theory (Helson 1964) suggests that price judgments are formed from the comparison of an advertised reference price with an internal reference price that is formed from recent experiences. For Janiszewski and Lichtenstein (1999, p.335), “the reference price is hypothesized to be the norm that serves as a neutral point comparison, such that prices below it are evaluated as low (relatively inexpensive) and prices above it are evaluated as high.”

Specifically, Janiszewski and Lichtenstein (1999) found that changes in the level of attractiveness for a market price are mediated by a change in the internal reference price.

Therefore, in the context of a customized price promotion, consumer responses are likely to be affected by factors other than the exclusivity of the offer. As such, the dissertation examines the frame of the price promotion discounts from the context of the assimilation-contrast theory.

The assimilation-contrast theory (Hovland, Harvey, and Sherif 1957; Sherif, Taub, and

Hovland 1958) has been used in the context of sale promotion for a number of decades (e.g.,

Kalyanaram and Little 1994; Lattin and Bucklin 1989) and to assess the impact of external reference prices on consumers’ internal reference prices, which subsequently impacts the response to price promotions. That is, the assimilation-contrast theory “explains how an internal reference price might change” (Grewal et al. 1998). For example, researchers have used the theory to explain several behavioral consequences in promotion such as comparative pricing

(e.g., Della Bitta, Monroe, and McGinnis 1981), believability of the advertised regular price

(e.g., Blair and Landon 1979; Lichtenstein, Burton, and Karson 1991), disparities between expectations and objective product performance (Anderson 1973), and assimilation of reference price (Chandrashekaran and Grewal 2003). For Della Bitta (1981, p. 418), “If the sale (lower) price is perceived as a reasonable substitute for the reference (higher) price, buyers may perceive a bargain (assimilation effect).” Further, “if the buyers perceive the sale price as belonging to another product-price category, they may not believe that the sale price is a reduction from the advertised reference price (contrast effect)”. Therefore, retailers and manufactures use several semantic cues such as framing and discount depth to increase consumer perceptions of bargain.

Similarly, Blair and Landon (1981) found that respondents were skeptical about large gaps in the regular and promoted price, with larger gaps resulting in higher levels of skepticism. Hence, consumers tend to discount the displayed regular price by at least 25% when forming their judgements about the real value of a promotion. For Anderson (1973), there is a rejection threshold for disparity in expected and actual product performance. Once this threshold is reached, consumers’ perception of product favorability will be lower than when the disparity is much smaller. In addition, for Chandrashekaran and Grewal (2003), the assimilation process offers several implications, including the design of price promotion of the depth of the discount and the point of comparison.

2.6 Promotion Frame

Customized price promotions may not be effective with many groups of customers outside of other deal characteristics. The presentation of a promotional price affects consumer evaluations and response (Barnes 1975; Della Bitta et al. 1981; Thaler 1985). That is, consumers

55 respond in different ways to similar prices that are framed differently (e.g., Choi and Mattlia

2012; Sinha and Smith 2000). For Sinha and Smith (2000, p. 261), “typically, transaction value is stipulated in terms of perceived savings resulting from the transaction because it is the perceptual difference between the item price and the internal reference price of the buyer.”

Furthermore, “it is expected that the type of deal description will significantly affect a consumer's transaction value of the deal” (Sinha and Smith 2000, p. 261). In fact, both the percent off and dollar off price discounts may influence deal response and perceived savings

(Krishna et al. 2002). While deal response is the composite measure of deal attitude and intention

(Barone and Roy 2010b), perceived savings is the “difference between reference price and retail price” (Sinha and Smith 2000, p. 267). Research suggests that perception of savings is an essential measure in price framing (e.g., Gupta and Cooper 1992; Krishna et al. 2002; Sinha and

Smith 2000; Thaler 1985; Yadav and Monroe 1993). As such, perceived savings is included as a dependent variable in the examination of the effect of price promotion framing.

Della Bitta et al. (1981, p.418) suggest that framing provides semantic cues that impact consumers’ ability to evaluate a price promotion. Furthermore, an individual’s perception and interpretation of an offer impact behavioral responses. Della Bitta et al. (1981, p.418) suggest that “semantic cues include any direct reference to the price differences, e.g., "x% off" or "$y off." As such, two of the most frequently used discount framing suggested by research are percent (%) off discounts and dollar ($) off discounts. Della Bitta et al.’s (1981) findings show that responses differ when prices are framed in relative (% off) versus absolute ($ off) terms.

However, the findings failed to suggest the mechanisms that drive the differences in response to the relative versus absolute terms.

Consequently, firms often wrestle with decisions on the absolute vs. relative savings associated with price promotion discounts. However, Morwitz, Greenleaf, and Johnson (1998) suggest that dollar off price promotions are perceived as easier to calculate or require no mathematical calculations. In contrast, percent off discounts often require the consumer to make some calculation and are thus perceived to be more difficult to process and more time consuming

(Morwitz et al. 1998). Subsequently, the absolute savings from these discounts may be undervalued (Choi and Mattlia 2012; Morwitz et al. 1998).

Overall, there is much debate in the literature on the effectiveness of promotion frame.

Several factors such as the price of the product (Chen, Monroe, and Lou 1998) are also likely to impact consumer perception of price discounts presented in percentage (%) off or dollar ($) off terms. For example, Chen et al. (1998, p.353) found that for high-priced products, consumers

“indicated that a price reduction framed in dollar terms seemed more significant than the same price reduction framed in percentage terms.” However, the results were opposite for low-priced products, in which consumers had a higher perceived value for percent (%) off discounts (Chen et al. 1998).

Furthermore, consumer expectations are formed in a number of ways, and the consequences of promotion frame also extends to the perception of future prices. That is, the format of price promotions impacts consumer perception of future prices, such that percentage discounts are likely to be more associated with higher future prices than dollar discounts

(DelVecchio et al. 2007). In addition, promotion framing is effective in reducing the negative impact of promotion such that, following a deep discount, price expectation is lowered substantially more when cents off promotion is used than when a percent off promotion is used.

This is because consumers are more likely to calculate a revised price in the cents off promotion

57 frame than in a percentage off promotion frame (DelVecchio et al. 2007). Overall, research also shows that price salience influences both the perception and evaluation of deals, especially in the cases of multidimensional prices (Kim and Kachersky 2006). This suggests that customized price promotion deals, (often presented in multidimensional forms such as $25 regular, 25% off) are likely to be evaluated on the multidimensionality of the price as well.

One limitation of promotion framing is the impact on willingness to pay. That is, while consumers may prefer one type of price presentation over another, willingness to buy may not be significantly different across different conditions (e.g., Berkowitz and Walton 1980; Della Bitta et al. 1981; Keiser and Krum 1976). Therefore, the identification of factors that influence consumers' response to customized price promotions can help firms devise better promotional pricing strategies and shed light on the impact of price promotion frame on consumer perception of savings and deal evaluation.

Building on previous research, I propose that the framing of the customized price promotion will affect the interaction of deal exclusivity and functional impulsivity on deal response and perceived savings. Specifically, I propose that interaction of deal exclusivity and functional impulsivity will be significant only for deals framed in dollar off terms, but not for deals framed in percentage off terms. This proposition is because of the calculation requirements of percentage off deals (Morwitz et al. 1998), which may serve as a deterrence to functionally impulsive consumers (Dickman 1990) and may also reduce the impact of deal exclusivity

(Barone and Roy 2010). Formally stated,

H4: For deals framed as dollar off, the interaction of functional impulsivity and exclusivity will be significant. However, for deals framed as percentage off, the interaction should be attenuated.

CHAPTER III

RESEARCH MODEL AND HYPOTHESIS DEVELOPMENT

3.1 Overview

The research seeks to identify conditions under which exclusive customized deals may be viewed more favorably by targeted consumers. Later studies will also assess deal characteristics

(e.g., discount frame) that may also influence consumer response to these deals. As such, the main goals of the dissertation research are to i) explore the potential moderating effect of functional impulsivity on the relationship between exclusivity and consumer response to customized price promotion deals ii) explore the relationships between customized price promotion and affect-based consequences of positive emotional arousal and self-enhancement iii) assess the promotion frame as a boundary condition to the moderating role of functional impulsivity. This research focuses on the use of customized price promotion in the modest form of exclusivity--the offering of special prices to segments of customers based on their purchase histories.

The research applies survey and experimental methods to test the conceptual model.

Study 1 explores the moderating role of functional impulsivity. Study 2 explores the correlational relationships among customized price promotions and the affect-based consequences, such as positive emotional arousal and self-enhancement. Study 3 examines the mediators of the effect of

exclusivity on the response to customized price promotion deals. Study 4 explores the interaction

effects and a boundary condition.

Figure 1. Theoretical Model

Emotional Self- Arousal enhancement (H2a, H3) (H2b, H3)

Study 2, Study 3

CPP (deal exclusivity) Deal Response

Promotion Frame (H4)

Study 4

Functional Impulsivity (H1)

Study 1

3.2. Study 1: The Moderating Role of Functional Impulsivity

3.2.1 Pretest of the Exclusivity Effect

Following Lamberton and Rose (2012, p.112), I refer to low exclusivity as a system that is “generally open to anyone by virtue of citizenship or location.” In contrast, high exclusivity refers to access to “a system that is restricted to people with certain status, characteristics or relationships.” In the case of the dissertation, consumers are past purchasers of a retailer.

Design: 2 (customized price promotion deals: exclusive vs. inclusive) X 2 response

(intention to attend promotion event vs. evaluation) mixed analysis of variance. Customized price promotions vary in exclusivity: modest or more inclusive customized price deals are those targeted to a segment(s) of customers; high or more exclusive customized price promotion deals are those targeted to individual customers.

DV: Deal response = composite measure deal evaluation and intention to attend the event (See

Barone and Roy 2010b).

Method: The aim of the pretest is to establish that consumers view customized price promotion deals as more exclusive than the traditional, all-inclusive price promotion deal.

Pretest: Following Barone and Roy (2010a, 2010b), participants will be randomly assigned to one of two conditions: 2 (more exclusive vs. inclusive). Participants will be asked to assume they received an email for a price promotion from a retail department store from which they recently made a purchase.

Deal evaluation measure: My opinion of this sales promotion is: 1 2 3 4 5 6 7 8 9 1) Bad Good 2) Negative Positive 3) Unfavorable Favorable

Likelihood of attending a promotions event: How likely are you to attend the event? 0 1 2 3 4 5 6 7 8 9 10 Not at all likely Very likely Deal exclusivity manipulation: Participants will complete four items to include: The sales promotion was… 1 2 3 4 5 6 7 8 9 1) Available to many customers Available to very few customers 2) Inclusive Exclusive 3) Not at all Restrictive Restrictive 4) Not at all Selective Selective

Exclusivity effect is defined as a “recipient’s preference for targeted discounts”- Barone and Roy 2010b, p.79

3.2.2 Main Study: The Moderating Role of Functional Impulsivity

The aim of Study 1 is to test the moderating effect of functional impulsivity on the relationship between exclusivity and deal response. Functional impulsivity is chosen as a potential moderator as it is likely to affect a consumer’s tendency to use the exclusivity associated with a deal as a basis for his/her positive emotional arousal and self-enhancement

(Barone and Roy 2010b; Dickman 1990; Wells 1964).

Functional impulsivity is based on information processing as a function of one’s personality (Whiteside and Lynam 2001). It is often linked to narcissism, a tendency to be self- centered and a craving for attention and admiration (Jones and Paulhus 2011). Furthermore, in the psychology domain, narcissism is related to the concept of consumer entitlement (Boyd and

Helms 2005; Butori 2010). In this context, exclusivity refers to special treatment and gaining additional advantages (Butori 2010). High functional impulsive consumers are thus more likely to be susceptible to the urge to respond when the deal is designed as exclusive.

As suggested in H1, high functional impulsive consumers should view the receipt of an exclusive deal as a way to act quickly and to benefit from a favorable outcome. This is likely because highly exclusive deals are finely tailored to meet a specific customer, or a small group of customers’ needs, and give some consumers advantages over others. They are also often short- lived and require the recipient to act quickly to gain the reward. On the other hand, low exclusive deals are available to a greater number of consumers and should have less impact or positive emotional arousal and self-enhancement among high functional impulsive consumers.

Main Study Design: The Moderating Role of Functional Impulsivity

The approach in Study 1 is to assess functional impulsivity, a personality trait known to affect response to environmental stimuli. Specifically, the study examines whether the relationship between deal exclusivity and response to customized price promotion changes at different levels of the moderating variable, functional impulsivity, as predicted by H1.

2 (price promotion deal: exclusive vs. inclusive) X Continuous (functional impulsivity) design is used. As a continuous moderating variable, functional impulsivity is centered (Cohen et al.

2003). This ensures that the moderator has a meaningful zero point, which is necessary for interpreting the main effects. Participants will complete the deal evaluation manipulations from the pretest. In this study, I use the DII impulsivity scale as suggested by Dickman (1990).

According to Colzato et al. (2010, p.786), in comparison to other measures of impulsivity, “the

DII inventory seems to provide a more comprehensive and differentiated picture of impulsivity patterns.” For this scale, higher means indicate higher levels of functional impulsivity.

Functional impulsivity using the 9-item scale developed by Dickman (1990):

NB* Reversed scores will be used for the negatively worded items.

I don't like to make decisions quickly, even simple decisions, such as choosing what to wear, or what to have for dinner

I am good at taking advantage of unexpected opportunities, where you have to do something immediately or lose your chance Most of the time, I can put my thoughts into words very rapidly. I am uncomfortable when I have to make up my mind rapidly I like to take part in really fast-paced conversations, where you don't have much time to think before you speak I don't like to do things quickly, even when I am doing something that is not very difficult I would enjoy working at a job that required me to make a lot of split-second decisions I like sports and games in which you have to choose your next move very quickly I have often missed out on opportunities because I couldn't make up my mind fast enough. People have admired me because I can think quickly I try to avoid activities where you have to act without much time to think first

3.3 Study 2: The Impact of Customized Price Promotion

The main goal of Study 2 is to explore the potential relationships among customized price promotion deals and the affect-based consequences of: i) positive emotional arousal, and ii) self-enhancement. Research suggests that exclusive deals are likely to be positively associated with positive emotional arousal. Further, themes in marketing can make salient the gap between the actual and ideal self-concepts. And, since exclusive deals are favored by consumers who value being a part of a select group, I expect that the receipt of a customized offer should be positively associated with positive emotional arousal and self-enhancement.

Therefore, as suggested by H2a-b, deals that are greater in exclusivity should lead to a) higher levels of positive emotional arousal, and ii) higher levels of self-enhancement than deals that are more inclusive.

A series of one-way ANOVAs will be used to investigate the relationships:

i) Emotional arousal will be measured on a 7-point Likert scale anchored by 1 =

“strongly disagree,” and 9 = “strongly agree” adapted from Wells (1964)

Wells (1964) items include:

a) “This ad is very appealing to me” b) “I would probably skip this ad if I saw it in a magazine” c) “This is a heart-warming ad” d) “This ad makes me want to buy the brand it features” e) “This ad has little interest for me” f) “I dislike this ad” g) “This ad makes me feel good” h) “This is a wonderful ad” i) “This is the kind of ad you forget easily” j) “This is a fascinating ad” k) “I’m tired of this kind of advertising” l) “This ad leaves me cold”

ii) Self-enhancement will be measured by a scale adapted from Barone and Roy

(2010b): Following Barone and Roy (2010b), self-enhancement will be measured

by a 2-item, 9-point scale, anchored by 1 “strongly disagree,” and 9 = “strongly

agree.” Respondents will indicate the extent to which being included in the

promotion made them feel good about themselves.

3.4 Study 3: The Mediators of the Impact of the Exclusivity Effect

The goal of Study 3 is to test the mediators of the exclusivity effect. Because of the complexities associated with consumer response to customized price promotion deals, I expect there to be multiple mediators of the effect of exclusivity on deal evaluation. Following Hayes and Rockwood (2017), a multiple mediator model is examined. Here the mediators are operating

65 as causal links (Hayes and Rockwood 2017, p.45). Specifically, the study uses a serial mediation model.

Following the recommendations of Hayes and Rockwood (2007), I estimate the indirect effects in one serial multiple mediator model instead of estimating a set of simple mediation models as it would reduce the correspondence between the theory and the model and the simpler models would likely be mis-specified. In addition, by “estimating a model with multiple mediators, it is possible to compare the size of indirect effects through different mediators”, which “can be useful for competitive theory testing” (Hayes and Rockwood 2017, p.

46). The study is a 2 (customized price promotion deal: exclusive vs. inclusive) X continuous functional impulsivity. However, the measures of positive emotional arousal and self- enhancement will be taken. The scenario described participants’ relationship with the fictitious retailer. Below are descriptions of the mediating variables in the causal multiple mediator model.

3.4.1 The Mediating Role of Positive Emotional Arousal

Emotional arousal refers to “the degree to which one feels stimulated, excited, or alert in the situation” (Fiore et al. 2000, p.35). As suggested by H3, positive emotional arousal should mediate exclusivity’s effect on the response to customized price promotion deals. To this extent, the high level of exclusivity associated with a deal is likely to elicit positive emotional arousal.

Specifically, I expect that the significant effect of deal exclusivity on deal evaluation to be diminished when positive emotional arousal is included in the model, rendering the effect no longer significant. Emotional arousal will be measured by Well’s (1964) scale as used in Study

3.4.2 The Mediating Role of Self-enhancement

Self-enhancement is a type of motivation that impacts self-esteem and makes people feel good about themselves (Rogers 1951). Furthermore, the acceptance of marketing offers are likely to have symbolic meanings to consumers. Therefore, the receipt of an exclusive customized price promotion should be viewed as a way to enhance the self. As such, the dissertation posits that, as suggested by H3, self-enhancement should also mediate the effect of exclusivity on the response to customized price promotion deals. Specifically, the exclusivity associated with the deal is likely to lead to increased positive emotional arousal, which is then likely to lead to an enhanced self for the deal recipient. Increased levels of self-enhancement are then likely to impact deal response for the mediated effect. As such, the dissertation examines the serial mediation effect of positive emotion and self-enhancement on the relationship between deal exclusivity and deal response. Therefore, I expect that the significant effect of deal exclusivity on deal evaluation to be attenuated when self-enhancement is included in the model, removing the main effect of deal exclusivity. Self-enhancement is measured by a scale adapted from Barone and Roy (2010), as used in Study 1.

3.5 Study 4: The impact of Promotion Frame- Deal Evaluation across Level of Functional Impulsivity By Type of Customized Price Promotion By Price Framing (H4; 0-9 Scale) The aim of Study 4 is to test the three-way interaction of the exclusivity effect, functional impulsivity, and promotion frame. Research suggests that dollar off price promotions are perceived as easier to calculate or require no mathematical calculations. In contrast, percent off discounts often require the consumer to make some calculation and are thus perceived to be more difficult to process and more time consuming. Consequently, as suggested by H4, the interaction

67 of exclusivity and functional impulsivity should be significant in the dollar off frame. However, the interaction should be attenuated for the percent off promotions.

The study will resemble the Study 1, except that the promotion frame will be introduced.

The discount for the promotion will be manipulated as $60 off or 20% off a promoted product of

$300 (hotel stay). This moderate level of discount and price was used because of the inverted U finding in information processing and consumer response to price promotion (Grewal et al. 1996;

Hardesty and Bearden 2003). Here, discounts that are both low and extremely high are related to lower information processing. On the other hand, moderate discount levels are likely to lead to more elaborative information processing (Hardesty and Bearden 1996). Also, since specific prices are used, the measurement of perceived savings (Berkowitz and Walton 1980), will be included in the study. Furthermore, “both the percent of deal and the amount of deal positively influence perceived deal savings” (Krishna et al. 2008, p. 101), and perceived savings “is the most common method of measuring deal reaction” (Krishna et al. 2008, p. 104). More specifically, the context of the research is expanded to capture consumer perception of savings since framed prices are used, which is consistent with previous research (Krishna et al. 2008). Furthermore, unlike the previous studies which do not include specific prices (i.e., they were based on an invitation for a one-day sale- see Barone and Roy 2010a, 2010b), Study 4 uses manipulated prices framed in dollar off and percentage off terms (DelVecchio et al. 2007).

Table VI. Summary of Study Design and Hypotheses

Study and Hypotheses Theoretical Method Description Support Study 1: The exclusivity effect of Equity Theory 2 (customized price Pretest and customized price promotion is with focus on self- promotion deals: manipulation positively associated with the reward and exclusive vs. test evaluation of customized price advantageous inclusive) X 2 promotion deals equity (Adams response (intention 1965; Bolton and to attend promotion DV: Response favorability (a Ockenfels 2000) event vs. evaluation) composite of intention to attend and mixed analysis of evaluation- see Barone and Roy variance with 2010) promotion type as the between subject measure and response type the within-subject variable Study 1: Main H1: Functional impulsivity Impulsivity and 2 (customized price Study: The moderates the effect of deal information promotion deals: moderating exclusivity on the evaluation of processing exclusive vs. role of customized price promotion deals (Dickman 1990) inclusive) X functional such that, when functional continuous impulsivity impulsivity is high, customized Trait theories (functional price promotion deals that are (Eysenck and impulsivity) design. DV: deal greater in exclusivity will receive Eysenck 1963) response more favorable responses than deals Higher scores will (collapsing of lower in exclusivity. indicate high deal attitude functional and intention impulsivity to attend)

Study and Hypotheses Theoretical Method Description Support

Study 2- The H2a: Deals that are greater in Motivation One-Way ANOVAs impact of exclusivity are associated with (McGuire 1976) customized higher levels of emotional arousal 2 (customized price price than deals that are lower in Self-Concept promotion deals: promotion exclusivity. (Higgins 1987) exclusive vs. Self-discrepancy inclusive) (Dittmar 2005) H2b: Deals that are greater in Comparison of exclusivity are associated with Self-confidence means higher levels of self-enhancement (Swann et al. than deals that are lower in 1987; Sirgy 1982) exclusivity.

Study 3- The Positive emotional arousal and self- 2 (customized price mediating enhancement mediate the Motivation promotion deals: roles of relationship between deal (McGuire 1976) exclusive vs. positive exclusivity and deal response. inclusive) X emotional Self-Concept continuous arousal and (Higgins 1987) (functional self- Self-discrepancy impulsivity) design. enhancement (Dittmar 2005) Emotional arousal Self-confidence and self- (Swann et al. enhancement are 1987; Sirgy 1982) measured. Study 4: The H4: For deals framed as dollar off, Assimilation- Three-way impact of the interaction of functional Contrast Theory interaction promotion impulsivity and exclusivity will be (Hovland, Harvey, frame significant. However, for deals and Sherif 1957) 2 (customized price framed as percentage off, the promotion: exclusive DV: deal interaction should be attenuated. Promotion frame vs. inclusive) x 2 response; effects (Chen, (promotion framing: perceived Monroe, and Lou % off vs. dollar off) savings 1998; Tversky and x continuous Kahneman 1974; (functional Morwitz et al. impulsivity) 1998)

CHAPTER IV

SAMPLE AND DATA ANALYSIS

4.1 Overview of Sample and Data Analysis

This dissertation seeks to uncover the individual and promotional design factors that help explain consumer response to customized price promotion deals based on the level of exclusivity associated with the deals. Specifically, the research seeks to uncover insights on how to make the targeting of prices to consumers more effective, given marketers’ increased challenges in structuring their promotional campaigns. The studies are conducted using survey and experiments. Survey design is used because it allows the researcher to collect data on who, why, and how factors. A repeat-measure design was not used. Hence, the research takes the form of a cross-sectional study design. Respondents were recruited from Amazon Mechanical Turk

(MTurk), an online panel, and were paid a small monetary fee to participate in the study. The following is a brief overview of survey methods and the instruments used in the studies, as well as an overview of experiments, ANOVA, and regression analysis.

4.1.1 Overview Survey Research and Experimental Designs

The use of a survey method provides the opportunity to directly measure consumer responses in a cross-sectional nature. The cross-sectional survey allows for the collection of data at a single point in time (Visser et al. 2000). Here, a sample is drawn from a population of

71 interest--in this case, U.S. consumers. This method allows the researcher to examine differences in subgroups and relationships among the variables involved in the study (Baron and Kenny

1986; Visser et al. 2000). While cross-sectional studies have certain limitations, they offer researchers the opportunity to target respondents in a relatively effective way. Also, this method is frequently used to address marketing research questions (Palmatier et al. 2006).

To reduce the impact of the common errors in survey research, I took care to minimize the effect of systematic measurement errors (non-sampling errors), which usually result from common mistakes in the research design, such as measurement and sampling design errors.

Furthermore, I considered the length of the survey (attention check included), and the wording and ordering of the questions.

Similarly, experiments are commonly used for the examination of psychological and individual differences in consumer research (e.g., Barone and Roy 2010a, 2010; Zhang et al.

2010). Four experiments are conducted to test the hypotheses in the model. Specifically, a between-subjects design is employed in which the subjects are randomly assigned to each experimental cell. Before the primary studies, a pretest was conducted to test that the measures in the survey were both valid and reliable.

4.1.2 Internal Consistency

The constructs in the model are evaluated to test the internal consistency of the scales used in the study. Reliability is assessed by whether the variance in the scores is attributable to the common source. As such, the scales are tested for internal consistency following established research (e.g., Edwards and Bagozzi 2000; DeVellis 2012; Hair et al. 2010; Viswanathan 2005).

For this analysis, the coefficient alphas are recorded, and the inter-item correlations and the item-

72 total correlations are examined (Gerbing and Anderson 1998). Established scales are used in the model, which yields coefficient alphas above .70. Overall, the scales used in this research are subject to the recommendations of Bagazzi and Yi (1988) and Edwards and Bagozzi (2000) for minimum inter-item correlations of .50 and item-total correlations of .60.

4.1.3 Validity

Validity determines the types of survey questions used in the research (Viswanathan

2005). Construct validity is determined by whether the scales are measuring what they purport to measure (Churchill 1979; Viswanathan 2005). Established multi-item scales are used to measure the variables in the study. Traditionally, several types of validity measures are used in research.

These include face and content, convergent, predictive, concurrent, pragmatic, construct, and discriminant validity (Viswanathan 2005). For example, from the theoretical background, the scales appear to have face validity (look right and sample appropriate) (DeVellis 2012;

Viswanathan 2005).

4.2 Data Analysis

ANOVA designs are used for the analyses. The research also uses regression analysis and a conditional Process model in SPSS (Preacher and Hayes 2004, 2008) to examine the components in the model. Many marketing studies now use the Process models to analyze their work (e.g., Han, Lalwani, and Duhachek 2017). The Process model uses confidence intervals

(percentage method) and reports unstandardized coefficients (Preacher and Hayes 2004, 2008).

Also, its bootstrap confidence is now preferred to the Sobel test (Hayes 2013). However, the

73 assumptions of the ANOVA model are first examined. 4.2.1 The assumptions of the ANOVA model

Several assumptions of the ANOVA model need to be met to rely on the study findings.

These include normality, homogeneity in variance, independence, and sample size (Hair et al.

1998). Specifically, the model assumes normality in that errors are normally distributed with zero means. Here the p-values are dependent on this normality assumption. I use the

Kolmogorov-Smirnov and Shapiro-Wilk indicators and the plots to determine whether these assumptions are violated. For homogeneity, the model assumes equal variance in errors. That is, the errors have the same variance. In the same way, the observations examined in the model are all independent of each other. The scores at one level are not affected by the scores at other levels, and the samples are not related. To test this assumption, the model is assessed for confounding factors which may lead to the violation of this assumption. Furthermore, randomization is used to reduce confounding factors.

4.3 Pretest for Data Collection and Analysis

Manipulation tests for deal exclusivity constructs in the model were conducted as a part of the pretests. A sample of 160 participants was recruited from Amazon Mechanical Turk

(MTurk) to complete the study. This panel has been used in several consumer research studies and generally “provides access to data whose quality is equivalent to that of data generated through traditional sampling approaches” Steinhoff and Palmatier (2014, p.96). MTurk panel

(crowdsourcing system) has over 200,000 workers who span across many regions and countries

(57% U.S based), are younger in age (62% between 18-30) and tend to hold a bachelor’s degree or higher (52%) (Ross et al. 2010). However, only U.S based workers were recruited for the

74 study. Also, in order to participate, participants had to meet the qualification of at least 100 approved hits, which also helped ensure the quality of the participants who were involved in the studies. In addition, attention checks were used to remove participants who were not paying attention from the study.

Following Barone and Roy (2010a), the manipulation check for deal exclusivity was done by varying the salience of the level of exclusivity associated with each deal condition.

Here, as a constraint to the study, Barone and Roy (2010b, p. 124) explained the conditions for the labeling of exclusive vs. inclusive deals, which is also adopted in the study.

Barone and Roy (2010b) stated the following:

Deal exclusivity falls on a continuum ranging from promotions provided to one

person to offers that are available to virtually all consumers in a market. Although

our instantiations represent "more exclusive" and "more inclusive" offers, for

expositional ease, we use the term "exclusive" or "inclusive" to refer to our deal

manipulations throughout this article. Note also that because our instantiations do

not occupy the endpoints of this continuum, our studies provide a conservative

test of deal exclusivity effects. (p. 124)

Participants were asked to imagine that they received an invitation from a retailer they had recently bought from. In the more exclusive condition, respondents received an invitation that stated that the deal was “by invitation only,” while those in the more inclusive condition received an invitation that was available to the “general public.” Participants then rated the level of exclusivity associated with each deal on a 4-item, 9-point scale for deal exclusivity. The items in the scale were “the sale promotion was: 1 = available to many customers, 9 = available to few customers; 1 = inclusive, 9 = exclusive; 1 = not at all restricted, 9 = restricted; and 1 = not at all

75 selective, 9 = selective,” with lower means indicating more inclusive deals and higher means indicating more exclusive deals (see Barone and Roy 2010a). Thus, the manipulation of exclusivity worked as intended. The deal exclusivity scale was internally consistent (α = .91).

The results of the 2 (deal condition: exclusive vs. inclusive) between subject ANOM design pretest showed that participants in the inclusive condition viewed the deal invitation as more inclusive (M = 3.05), while participants in the exclusive condition rated the deal invitation as a more exclusive deal (M = 6.95), F(1, 158) = 156.97, p < = .001.

4.4 Study 1: Overview

I conducted Study 1 to test the proposed hypothesis that functional impulsivity moderates the relationship between deal exclusivity and deal response. This study uses an online experiment to isolate the effects of functional impulsivity and to examine its moderating influence on consumer response to customized price promotion deals. Experimental designs provide several advantages in testing causal theories. To analyze the result, I employed analysis of variance (ANOVA).

4.4.1 Procedure for Study 1

Study 1 was a 2 (customized price promotion deals: exclusive vs. inclusive) X continuous

(functional impulsivity) between-subjects design, in which higher scores indicate a high level of functional impulsivity. A total of two hundred and twelve (212) participants took part in the study (Gender: Male =64%; 53.8% between 26-34 years old) for a small monetary payment.

Participants were randomly assigned to one of two groups (i.e., exclusive vs. inclusive deal condition), and were asked to imagine that they received a promotional offer from an electronics retailer they recently purchased from. This scenario manipulated participants’ perception of the

76 level of exclusivity associated with a price promotion deal that they received based on their previous purchases at the retailer. Following Barone and Roy (2010a, 2010b), the name of the retailer was deliberately not given to rule out potential confounding effects.

4.4.2 Measures in Study 1

Participants responded to several measures on bipolar and Likert scales to assess the constructs in the study: deal exclusivity, functional impulsivity, and deal response (attitude toward the deal and intention to attend). For this study, I also included the manipulation of deal exclusivity, in which participants responded to a 4-item, 9-point semantic differential scale which suggests the extent to which they believed the promotion was: 1) “available to many customers = 1; available to few customers = 9”, 2) “inclusive = 1; exclusive = 9”, 3) “not at all restricted = 1; restricted = 9”, 4) “not at all selective = 1; selective = 9” (see Barone and Roy

2010b). The results suggest that the manipulation worked. Participants in the exclusive condition rated the deal as more exclusive (M = 7.18); participants in the inclusive condition rated the deal as more inclusive (M = 5.54), F(1, 210) = 39.03; p < .001). For this study, lower means are related to lower levels of exclusivity. High vs. low functional impulsive participants were identified using Dickman’s (1990) DII scales, in which participants completed a nine-item Likert measure on a 7-point scale, ranging from “strongly disagree” = 1 to “strongly disagree” = 7.

Following established studies, I used a median split for the classification of higher versus lower levels of the measure (e.g., Barone and Roy 2010b, Escalas and Bettman 2005), in which higher values indicated high functional impulsivity and lower values indicated low functional impulsivity.

The dependent variables in the study are deal evaluation and intention to attend, which were combined to form deal response. Deal evaluation was measured on a 3-item, 9-point

77 semantic differential scale, in which participants indicated their overall attitude toward the promotion: “my opinion of this sales promotion is: 1) bad = 1; good = 9, 2) negative=1; positive

= 9, 3) unfavorable = 1; favorable = 9.” These measures were collapsed to form overall deal response (Barone and Roy 2010b). Following this, participants indicated the likelihood of attending the promotional event on a single item, 9-point semantic differential scale: “how likely are you to attend the event?: 1) not at all likely =1; very likely = 9.” (see Barone and Roy 2010a,

2010b).

4.4.3 Study 1 Results

Consistent with Barone and Roy’s (2010) findings, participants had a more favorable deal response toward the exclusive (M = 6.93) than the inclusive deal (M = 4.95), F(1, 210) = 53.30, p < .01. The findings indicate a significant main effect of deal exclusivity but not for functional impulsivity. Deal response was similar for consumers who scored high (M = 6.03) and low (M =

5.90) functional impulsivity F(1, 210) = .197; p > .05.

Test of H1. Following Spiller et al. (2013), to test the moderating role of functional impulsivity a linear regression analysis was used to regress deal response on the functional impulsivity dummy variable with low (0) or high (1), deal exclusivity, and their interaction. A two-way interaction between functional impulsivity and deal exclusivity on deal response was found (B = -1.257, SE = .18, BootCI: -1.620 ~ -.895, t(208) = -6.84, p <. 01). To further test the interaction, a spotlight analysis (Aiken and West 1991) at one standard deviation above and below the mean of functional impulsivity was used. The spotlight analysis examines the differences in slopes for functional impulsivity, which is a continuous predictor variable.

Specifically, I test the effect of deal exclusivity at different levels of functional impulsivity using the Process model by Hayes (2013). I compared the mean deal response in the exclusive and

78 inclusive groups for high and low values of functional impulsivity. Conditional effects show that the interaction of deal exclusivity and functional impulsivity was significant at high (B = -3.636,

BootCI: -4.131 ~ 2.952; SE = .34, t(208) = 10.48, p <. 05), but not low (B = -.277, BootCI: -

.9605 ~ .406; SE = .34, t(208) = -.79, p > .05) levels of functional impulsivity. The slope for functional impulsivity was significant and positive in the exclusive deal condition, which suggests that deal response is likely to increase as functional impulsivity increases.

Overall, the results showed that consumers who score high on functional impulsivity had a significantly higher preference for exclusive (M = 7.73) vs. inclusive (M = 4.19) customized price promotion deals. However, when functional impulsivity is low, deal response was similar regardless of whether the deal was exclusive (M = 5.92) or more inclusive (M = 5.87). I predicted that the level of exclusivity associated with a deal would influence deal response for consumers who score high, but not low, on functional impulsivity. Therefore, the study results provide support for H1.

Discussion

The study answers the question of whether the magnitude of the difference between deal exclusivity (i.e., consumer response to exclusive vs. inclusive deals) depends, in part, on consumers’ level of functional impulsivity. As predicted, a regression analysis showed a significant difference whereby consumers who score high on functional impulsivity had a more favorable deal response when the deal was offered as exclusive vs. inclusive. That is, I find that the functional impulsivity effect on deal response is more likely to be evident when the consumer scores high on functional impulsivity. Additionally, under the condition of low functional impulsivity, there is no significant difference in deal response for exclusive vs. inclusive deals.

Furthermore, as consumers move from low to high functional impulsivity, the effect of

79 exclusivity emerges, which provides several implications for marketing practitioners and researchers.

Figure 2. Main effects of Deal Exclusivity and Functional Impulsivity a) Main effect of deal exclusivity b) Main effect of functional Impulsivity

Figure 3. Interaction of Deal Exclusivity and Functional Impulsivity

4.5 Overview of Study 2- The Impact of Customized Price Promotion

In Study 2, I explore the potential relationships among deal exclusivity and the affect- based consequences of i) positive emotional arousal and ii) self-enhancement. Many studies lack implications for affective consequences of marketing actions. For example, Peterson (1995) argued that, while cognitive reasons are the main factors associated with consumers’ choice to engage in relationships with firms, “many others are affective.” Furthermore, these usually unexplored affective dimensions of firms’ ability to engage consumers in long-term relationships may offer great promise. In this study, I examine the affective consequences of firm-customized price promotion strategies.

4.5.1 Procedure for Study 2

The study is a 2(customized price promotion deals: exclusive vs. inclusive) between-subjects design. A series of One-Way ANOVA is used to explore the related hypotheses (H2a and H2b).

One hundred and four (68% male, 60% between ages 25-44) respondents were recruited on

Amazon Mechanical Turk (MTurk). The respondents all had a U.S. bachelor’s degree or higher.

The procedures in the study are similar to those in Study 1. Following Barone and Roy (2010a,

2010b), participants were randomly assigned to one of two conditions: 2 (more exclusive vs. inclusive). They were asked to assume they received an email invitation for a price promotion from a furniture retail store that they usually buy from. After participants reviewed the promotion, they rated the level of exclusivity associated with the deal. Subsequently, measures of participants’ level of positive emotional arousal and self-enhancement were taken. Finally, participants completed the deal response measures and provided demographic information.

4.5.2 Measures in Study 2

Established multi-item scales were used to measure the constructs. All the items used 7- point Likert scales. Deal response was measured by the combination of deal attitude and intention as used in Study 1 (α =. 95). Deal exclusivity was measured by the 4-item scale used in

Study 1 (α=.98). Positive emotional arousal was measured on a 7-point Likert scale anchored by

1= “strongly disagree,” and 9 = “strongly agree,” adapted from Wells (1964a), (α = .92). Self- enhancement was measured by a 2-item, 9-point scale, anchored by 1 “strongly disagree,” and 9

= “strongly agree,” adopted from Barone and Roy (2010), (α = .90). Finally, attitude toward retailer was measured by a 9-point, 4-item scale anchored by “very bad…very good”; “very undesirable… very desirable”; “very unfavorable…very favorable”; “very negative…very positive.”

4.5.3 Study 2 Results

Results show that exclusive deals were more likely to be associated with higher levels of positive emotional arousal and self-enhancement than more inclusive deals.

For positive emotional arousal, participants in exclusive deal condition indicated higher levels of positive emotional arousal (M = 5.16) than participants in the inclusive group (M = 4.66), F(1,

103) = 4.78, p < .05. For self-enhancement, participants in the exclusive condition indicated a higher level of self-enhancement (M = 4.55) than those in the inclusive condition (M = 3.69),

F(1, 103) = 7.56, p < .05. Furthermore, as a control variable, I measured attitude toward electronics retailers. The results suggest that attitude toward electronics retailers was similar in the exclusive group (M = 6.63) and inclusive condition (M = 6.20), F(1, 103) = 1.53, p > .05.

Discussion

While many studies focus on the cognitive consequences of price promotions, this study shows that the targeting and framing of price promotions have affective consequences for consumers.

Specifically, Study 2 provides support for increased positive emotional arousal and self- enhancement as consequences of deal exclusivity.

Figure 4. The Impact of Customized Price Promotion a) The impact of deal exclusivity on b) The impact of deal exclusivity on Self-enhancement Positive Emotional Arousal

4.6 Study 3: Overview

Like Studies 1 & 2, this study uses an online experiment to isolate the effects of functional impulsivity and to assess the mechanisms by deal exclusivity affect deal response.

Study 3 examines the causal mediation effects of positive emotional arousal and self- enhancement. To analyze the results, I use model 6 of the Hayes (2013) PROCESS model, using

83 the bootstrapping procedure (Preacher, Rucker, and Hayes 2007). This approach “facilitates procedures that compute a 95% confidence interval (CI) around the indirect effect, and if a CI does not include zero, it indicates mediation” (Han et al. 2017, p.186).

Specifically, in the serial mediation model, I used an asymmetric bootstrap test of mediation with a 95% confidence interval (Hayes 2017; Hamby, Daniloski and Brinberg 2015;

Zhao, Lynch and Chen 2010). For these models, the indirect effect is the only measure necessary to establish mediation (Hamby et al. 2015; Zhao et al. 2010). That is, by citing Zhao et al.

(2010), Hamby et al. (2015, p. 1246) note that “review of the mediation literature maintains that the sole condition necessary to establish mediation is a significant indirect effect.” Besides, more recent consumer research now suggest support for serial (vs. parallel) mediator models to help explain more complex relationships among marketing phenomena (e.g., Hamby et al. 2015,

Jiang, Hoegg and Dahl 2013; Smith, Newman and Dhar 2015; Madzharov, Block and Morrin

2015).

4.6.1 Procedure for Study 3

Study 3 was a 2 (customized price promotion deals: exclusive vs. inclusive) X continuous

(functional impulsivity) design, in which higher scores indicate a high level of functional impulsivity. A total of 261 participants took part in the study (57.1% male; 69.8% between the ages 26-35) for a small monetary payment. Participants were randomly assigned to one of two groups (i.e., exclusive vs. inclusive deal condition), and were asked to imagine that they received a promotional offer from an electronics retailer that they have purchased from on several occasions. Similar to studies 1 & 2, the scenario manipulated deal exclusivity as suggested by

Barone and Roy (2010a, 2010b).

4.6.2 Measures in Study 3

Participants responded to established measures on bipolar and Likert scales to assess the constructs in the study: deal exclusivity, functional impulsivity, and deal response (attitude toward the deal and intention to attend. For the manipulation of deal exclusivity, participants responded to a 4-item, 9-point semantic differential scale which suggests the extent to which they believed the promotion was: 1) “available to many customers =1; available to few customers = 9”, 2) “inclusive = 1; exclusive = 9”, 3) “not at all restricted = 1; restricted = 9”, 4)

“selective = 1; not at all selective = 9” (see Barone and Roy 2010b). The results suggest that the manipulation worked with participants in the exclusive condition rating the deal as more exclusive (M = 7.39) than participants in the inclusive deal condition (M = 3.20); F(1, 259) =

258.20; p < .01. The measures of deal exclusivity, functional impulsivity, and deal response were comparable to those in the previous studies.

4.6.3 Study 3 Results

To establish mediating effects, a researcher must follow specific steps. Following Baron and Kenny (1986), I first determined that a relationship exist between causal variable, deal exclusivity (CPP groups: exclusive vs. inclusive) and the outcome variable, deal response. Next,

I show that deal exclusivity is correlated with each mediator in the model. Here, I use each mediator as an outcome variable in a regression equation with deal exclusivity as the causal variable. Next, I show that each mediator affects deal response by using each mediator as a predictor variable. Finally, I examine if each mediator completely mediates the relationship between deal exclusivity and deal response by controlling for the mediator and establishing a zero effect.

The results suggest a significant impact of deal exclusivity as deal response was greater in the exclusive (M = 7.26) than in the inclusive condition (M = 5.71), F(1, 259) = 43.65; p < .01.

The results also show that the exclusive deal (M = 5.61) was associated with greater levels of positive emotional arousal than the more inclusive deals (M = 4.64), F(1, 259) = 54.41; p < .01.

Furthermore, the exclusive deal (M = 5.28) was associated with greater levels of self- enhancement than the more inclusive deal (M = 3.95), F(1, 259) = 53.77; p < .01. The results provide additional supports for Study 2. Next, the results suggest that positive emotional arousal is a significant predictor of self- enhancement β =. 62, SE = .069, t(259) = 12.74, p < .01. And, self-enhancement helps predict deal response β =. 71, SE = .056, t(259) = 16.24, p < .01.

Deal Response. Furthermore, in support of Study 1, the data suggest an interaction between deal exclusivity and functional impulsivity. Here, I used model 1 of the PROCESS macro Hayes (2013) as a simple moderation model that mean centers the continuous IV and tested at one standard deviation above and below the mean. Like Study 1, the results indicate a significant interaction of deal exclusivity and functional impulsivity B = -.80, t(257) = -2.23, p =

.0264. Furthermore, the confidence interval did not include zero (BootCI: -1.50 ~ -.094), which suggests a significant result.

Positive Emotional Arousal. Study results suggest a main effect of deal exclusivity (B = -

.7939, SE = .1814, t(257) = -4.37, p < .01, BootCI: -1.15 ~ -.43), but not for functional impulsivity (B = .6685, SE = .4062, t(257) = 1.64, p < .05, BootCI: -.1314 ~ 1.4684), nor the interaction of deal exclusivity and functional impulsivity (B = -.2914, SE = .2560, t(257) = -1.13, p > .05, BootCI: -.7956 ~ .2127).

Self-enhancement. Similar to the results for positive emotional arousal, the results for self-enhancement suggests a main effect of deal exclusivity (B = -1.291, SE = .2560, t(257) = -

5.043, p < .01, BootCI: -1.7951 ~ -.7869), but not for functional impulsivity (B = .4972, SE =

.5731, t(257) = .86, p > .05, BootCI: -.6314 ~ 1.6258), nor the interaction of deal exclusivity and functional impulsivity (B = -.0531, SE = .3612, t(257) = -.14, p > .05, BootCI: -.7643 ~

.6582).

Test of H3. Process Model 6 was used to analyze the following:

 Indirect effect of X on Y through M1 only

 Indirect effect of X on Y through M1 and M2

 Direct effect of X on Y (c')

Serial Mediation Analysis. A serial multiple mediator model (Hayes 2017), from deal exclusivity to positive emotional arousal to self-enhancement to deal response was analyzed. In this model, positive emotional arousal and self-enhancement act as with mediators (in that sequence).

Figure 5. Conceptual Model for Model 6 (Hayes 2013)

Self- PEM Enhancement M1 M2

Deal Exclusivity Deal Response

Indirect effect X on Y through M1 only. Model 6 results suggest a mediation of positive emotional arousal on the relationship between deal exclusivity and deal response (B = -.80, SE =

.14, BootCI: -1.121 ~ -.528, p < .05). Furthermore, the effect of deal exclusivity on response disappeared when positive emotional arousal was included in the model.

Indirect effect of X on Y through M1 and M2. Model 6 results suggest a significant serial mediation effect of positive emotional arousal and self-enhancement on the effect of deal exclusivity on deal response. That is, the indirect effect of X on Y through positive emotional arousal and self-enhancement (see Ind3 Group_nu -> PEM -> SelF_enh ->

DEAL_RES) is significant (B = .38, SE = .082, BootCI: -.557 ~ -.236, p < .05). Overall, Model 6 analyses suggest positive emotional arousal (B = .85, SE = .12, BootCI: .5992 ~ 1.1055, t(257) =

6.63, p < .01) and self-enhancement (B = .52, SE = .07, BootCI: .3709 ~ .6805, t(257) = 6.68, p

< .01) as significant mediators of the impact of deal exclusivity on deal response. When the mediators were included in the model, the impact of deal exclusivity (as indicated by group number), which was previously significant, becomes non-significant (i.e., the effect disappeared).

Direct effect of X on Y(c'). Model 6 results also suggest that the direct effect of deal exclusivity on deal response (c' = -.023, p = .78, ns; 95%, BootCI: -.3927 ~ -.2954) is lower than the total effect of deal exclusivity on deal response (c = -.758, p < .02; 95%, BootCI: -

2.022 ~ -1.091). Therefore, c is greater than c', an indication of mediation effect.

Figure 6. The Mediation Effects of Deal Exclusivity on Deal Response

The Interaction of Deal Exclusivity and Functional Impulsivity

Discussion

Study 3 shows the mechanism by which deal exclusivity impacts deal evaluation. The results indicate that positive emotional arousal and self-enhancement mediate the effect of deal exclusivity on deal response. The result therefore suggest support for H3. Specifically, Study 3 results show that the variation in deal exclusivity affected positive emotional arousal, which in turn affects self-enhancement. Also, higher levels of self-enhancement lead to increased deal response. Overall, the results from the serial mediation analyses demonstrate positive emotional arousal and self-enhancement as the underlying mechanisms by which deal exclusivity affect deal response.

As an implication for future research, I examined the indirect effects of functional impulsivity using Process Model 8. The indirect effects (GROUP: deal exclusivity -> PEM

-> DEAL_RES) suggest that the impact of positive emotional arousal is strongest at higher levels of functional impulsivity. Specifically, the effect of positive emotional arousal is strongest at a high level of functional impulsivity (B = .92, BootCI: -1.3185 ~ -.5010) vs. at the moderate level (B = .78, BootCI: -1.0748 ~ -.4933) and low level of functional impulsivity (B = .63,

BootCI: -1.0679 ~ .2532). Similar results were found for the indirect effects of self- enhancement, which also shows an increased effect at higher levels of functional impulsivity (see tables). These findings suggest that the effect of mediators would be more evident for consumers who score high, but not low, on functional impulsivity.

4.7 Study 4- Overview: The Impact of Promotion Frame

The goal of Study 4 is to test a possible three-way interaction of deal exclusivity, functional impulsivity, and deal frame. Promotion frame is often used by marketers to enhance

91 price promotion strategies. In addition, “besides the actual price, how the price offering is presented to consumers also affects consumer evaluation of the product offering” (Krishna et al.

2002, p. 101). Accordingly, the study measures both deal response and perceived savings as dependent variables. While deal response captures deal attitude and intention, perceived savings capture the perceived transaction value of the deal (Sinha and Smith 2000). The study, therefore, includes factors that marketers can manipulate to structure effective price customizations.

4.7.1 Procedure for Study 4

Four hundred and thirty-seven participants (60.5% male; 72.9 % between the ages 25-34) were recruited from Amazon Mechanical Turk (MTurk) to participate in the study for a small monetary contribution. Participants were based in the U.S. The study is a 2 (customized price promotion deals: exclusive vs. inclusive) X 2 (deal frame: dollar off vs. percentage off) X continuous functional impulsivity between-subjects design. Following Barone and Roy (2010), participants were randomly assigned to one of 2 deal conditions. Following DelVecchio,

Krishnan, and Smith (2007), participants received either a dollar-off or a percent-off discount.

They were asked to assume they received an email invitation for a price promotion from a hotel

(Steinhoff and Palmatier 2016). First, information was collected on the participant’s level of functional impulsivity (mean centered). After the measure was taken, participants reviewed the promotion and then rated the level of exclusivity associated with the deal. Subsequently, measures of participant’s level of positive emotional arousal and self-enhancement were taken.

Finally, participants completed the measures for deal response and perceived savings and provided their demographic information.

4.7.2 Measures in Study 4

The dependent measures in Study 4 are deal response and perceived savings. Similar to the previous studies, deal response (α = .96) was measured as a combination of attitude and likelihood to take up the offer (see Barone and Roy 2010b). Perceived savings (α = .86) was measured by a 2-item, 9-point bipolar scale. Items include “Overall, the deal: - represents no savings at all: represents extremely large savings,” and “Overall, the deal: - Offers trivial savings: Offers significant savings” (Berkowitz and Walton 1980). Positive emotional arousal was measured on a 7-point Likert scale anchored by 1= “strongly disagree,” and 9 = “strongly agree,” adapted from Wells (1964a), (α =. 92). Self-enhancement was measured by a 2-item, 9- point scale, anchored by 1 “strongly disagree,” and 9 = “strongly agree,” adopted from Barone and Roy (2010), (α=.88). The scale manipulation for deal exclusivity was reliable (α = .94). And, attitude toward hotels (α = .90) was measured by a 4-item, 9-point scale: “overall, I think hotels are... - very bad: very good, negative: positive; undesirable: desirable; unfavorable: favorable.”

4.7.3 Study 4 Results

The results show that the manipulation worked. Participants in the exclusive condition rated the deal as more exclusive (M = 7.11) than those in the inclusive condition (M = 4.12); F(1,

433) = 179.84, p < .01. Furthermore, as a control variable, I measured attitude toward hotels.

The results suggest that attitude toward hotels was similar in the exclusive group (M = 7.34) and inclusive condition (M = 7.28), F(1, 433) = .21, p > .05.

Deal response: In support of the previous studies, deal response was higher in the exclusive (M = 7.14) than in the inclusive condition (M = 4.93), F(1, 433) = 138.98, p < .01.

The results also show a main effect of deal exclusivity on deal response (B = -1.75, t(432) = -

6.75, p < .001), and a significant interaction of deal exclusivity and functional impulsivity on deal response (B = .76, t(432) = -2.14, p <. 05). In addition, the Hayes Process Model 3 multiple moderator model (2 moderators included) show additional support for the interaction of deal exclusivity and functional impulsivity (B = -2.3959, SE = 1.10, t(428) = -2.174, BootCI: -.4.562

~ -.2299, p < .05). However, the three-way interaction was not significant (B = 1.0438, SE =

.6999, t(428) = 1.49, BootCI: -.3319 ~ 2.4194, p > .05).

Perceived savings: The results suggest a significant interaction of deal exclusivity and functional impulsivity (B = -2.0822, SE = .9612, t(433) = -2.17, BootCI: -3.9714 ~ -.1930, p

< .0308). The results show a marginally significant three-way interaction of deal exclusivity, functional impulsivity and deal frame (B = 1.1079, SE = .6104, t(428) = 1.81, BootCI: -.0919 ~

2.307, p = .070). Although there is some debate over the use of marginal significance and its meaning, recent research suggests its continuous use (e.g., Han et al. 2017, JCR). Therefore, I further examine this interaction.

The test of conditional deal exclusivity by functional impulsivity (X*W), the interaction was examined at the two levels of framing: dollar off (coded as 1, in the table) and percent off

(coded as 2, in the table). In support of H4, the analysis suggests that the interaction of deal exclusivity and functional impulsivity was significant only in the dollar off framing condition

(F(1, 428) = 5.17; p < .05), but not in the percent off framing condition (F (1, 428) = .095, p >

.05).

Next, to further examine the three-way interaction, I followed the procedure suggested by established research (e.g., Fitzsimons 2008; Han et al. 2017, p.187; Spiller et al. 2013) to split the data into two groups based on the level of deal framing: dollar off, percent off. For these

94 studies, separate regression analyses are conducted with deal exclusivity, functional impulsivity, and their interaction as independent variables. The dependent variable is perceived savings.

In the dollar off frame (N=221; within this condition, N=120 high scores on functional impulsivity and N=101 low scores on functional impulsivity), the analyses show no main effect for exclusivity (B = -.1570, SE = .30, t(216) = .51, BootCI: -.4404 ~ .7544, p > .05). However, there was a significant main effect of functional impulsivity (B = 1.433, SE = .63, t(216) = 2.24,

BootCI: .1762 ~ 2.6914, p < .05), and a significant interaction of deal exclusivity and functional impulsivity (B = -.9977, SE = .41, t(211) = -2.42, BootCI: -1.8072 ~ -.1881, p < .05).

Finally, in the percent off frame condition, (N=216; within this condition N=114 high functional impulsivity and N=102 low functional impulsivity), the analyses show no significant main effect for exclusivity (B = -.1581, SE = .32, t(211) = -.48, BootCI: -.8045 ~ .4884, p >

.05), functional impulsivity (B = -.0608, SE = .71, t(211) = -.08, BootCI: -1.475 ~ .1.354, p >

.05), and the interaction of deal exclusivity and functional impulsivity (B =.100, SE = .45, t(211)

= .22, BootCI: -.7906 ~ .9920, p > .05).

Discussion

The three-way interaction of deal exclusivity, functional impulsivity, and price promotion frame was only significant for perceived savings and not for deal response. However, the findings support H4, which posits that the interaction of deal exclusivity and deal response would be significant for deals framed as dollar off, but not for deals framed as percentage off.

These results are likely because of the complicated and time-consuming efforts for functionally impulsive consumers to calculate and process percent off discount information. Additionally, the findings show that for the dollar off price priming, the interaction of deal exclusivity and functional impulsivity on perceived savings was significant only at high (but not low) levels of

95 functional impulsivity. Furthermore, deal exclusivity did not significantly impact perceived savings. Under the percentage off framing condition, the findings suggest that there is no significant effect of deal exclusivity nor of its interaction with functional impulsivity on both deal response and perceived savings.

Figure 7. Interaction of deal exclusivity and functional impulsivity

a) Deal response

b) Perceived savings

Figure 8. The Effect of Deal Exclusivity, Functional Impulsivity and Deal Frame on

Perceived Savings.

CHAPTER V

DISCUSSION

5.1 General Discussion

The dissertation demonstrates that customized price promotions deals that differ on exclusivity (more exclusive vs. more inclusive) can systematically affect consumer preferences.

Experimental designs show that exclusive (vs. inclusive) deals may lead to higher deal evaluations (pretest). However, the higher preference for exclusive deals is more likely for consumers who score high (vs. low) on functional impulsivity, which indicates the significant moderating role of functional impulsivity on the effect of deal exclusivity on deal response

(Study 1). Exclusive deals (vs. inclusive deals) are also likely to be associated with higher levels of positive emotional arousal and self-enhancement- two key affective consequences of firms’ marketing strategy (Study 2). Furthermore, the research shows that the effect of deal exclusivity on deal response is driven by both (in a sequential manner) positive emotional arousal and self- enhancement (Study 3). Finally, the research findings show that a critical promotion design factor, price framing, may create a boundary condition for the interaction of deal exclusivity and functional impulsivity (Study 4).

The research makes essential contributions to several areas of consumer research and marketing. While research in the psychology domain focuses on the rich dimensions of impulsivity, previous consumer and marketing research has mainly focused on dysfunctional

98 impulsivity, largely ignoring the potential impact of functional impulsivity. Previous studies on exclusive deals have also ignored the potential impact of promotional design factors in the context of deal exclusivity.

The research also has several practical implications for marketing practitioners. While firms continue to struggle with making the customization of pricing more effective (which impacts the decision to target consumers), consumers continue to express a need for firms to offer them customized and exclusive deals. The research findings suggest that marketers should carefully consider individual consumer differences in impulsivity during the selection of themes and general framing of their customized price promotion campaigns. The understanding of consumers who are likely to be functionally impulsive will help firms better target them over time.

The dissertation offers empirical evidence in support of the views that firms can increase the effectiveness of customized price promotion by incorporating several strategies.

Consequently, this research takes both a cognitive and affective approach to include both individual differences and promotional design factors to understand the impact of customized price promotion. To the best of my knowledge, this is the first examination of functional impulsivity in the consumer research domain, and the first study of price framing in the context of deal exclusivity.

5.2 The Importance of Deal Exclusivity in Price Promotions

As firms continue to collect vast amounts of data on consumers, they often struggle with the complexities of customized price offerings, in which both exclusive and inclusive deals carry several advantages and disadvantages. According to a 2017 KMPG study, the offering of more

99 exclusive deals is the second most important factor to consumers, only following exceptional and consistent customer support. However, while firms have increased the efficiencies related to their customized pricing strategies, the effectiveness is often overshadowed.

Research suggests that strategies to improve the effectiveness of price customization include i) assessing the history of customer data, ii) using cues such as individual consumer difference (Dickman 1990), iii) managing the timing dimension of the customized offer (Park and Gupta 2011), and iv) including general promotional design factors such as price framing in campaigns (DelVecchio et al. 2007). However, while several studies have highlighted the importance of many of these strategies (e.g., Barone and Roy 2010a, 2010b; DelVecchio et al.

2007), the majority of research has focused on the cognitive factors as the exclusive reason for consumer response to price promotion.

Only a few studies have focused on the psychological influencers of consumers’ attitudes and behaviors toward price promotions. In fact, earlier research called for more studies on the affect-based consequences of firms’ price promotion strategies (e.g., Raghubir et al. 2004).

However, only a few researchers have yielded to the call for a more balanced view of both the cognitive and affective influencers of consumer decisions related to price promotions (e.g.,

Aydinli et al. 2014; Barone and Roy 2010; Feinberg et al. 2002; Steinhoff and Palmatier 2014).

For example, Barone and Roy (2010a, 2010b) assessed the effect of deal exclusivity and found it to influence consumer response to promotions, suggesting the significant impact of nonmonetary factors on consumer response to deals (Barone and Roy 2010a 201b).

More specifically, Barone and Roy (2010a, 2010b) found that the level of exclusivity associated with a customized price promotion deal is likely to affect deal attitude and overall deal favorability. However, they also found that individual differences may moderate

100 exclusivity’s effect on deal response and called for more research in the area. This dissertation sought to contribute to research on deal exclusivity by introducing functional impulsivity as a potential moderator of the effect of deal exclusivity on deal response. In addition, the current research also shows that a while consumers who score high on functional impulsivity may have a greater preference for exclusive than inclusive deal, the framing (% off or $ off terms) of the deal may play a role. Specifically, the research findings suggest a three-way interaction of deal exclusivity, functional impulsivity, and deal framing on perceived savings. According, the exclusivity themes in the design of promotional material matter to consumers, especially for those who score high on functional impulsivity.

Deal exclusivity may also be associated with the affective consequences of positive emotional arousal and increased levels of self-enhancement. Preliminary study findings show that consumers in the exclusive deal condition expressed more thoughts than consumers in the inclusive deal condition. Additionally, some participants, particularly those in the exclusive deal condition, stated that the deal made them “feel special,” and “happy.” Furthermore, the findings support the hypotheses that deal exclusivity would be associated with higher levels of both positive emotional arousal and self-enhancement.

Overall, the research findings highlight the need for more studies on the affective influences, such as deal exclusivity, of marketing strategies. Consumers also vary in the types and levels of personality trait, emotion, need for recognition, image consciousness, and confidence which are all associated with functional impulsivity. The understanding of how consumer difference factors may interact with promotional design elements to influence attitudes and behavioral responses toward customized prices is critical to the development of key pricing strategies.

101

5.3 Impulsivity and Marketing Strategy

Marketing researchers have, for decades, examined the impact of impulsivity on firm strategies and consumer well-being. However, while the psychology domain captures many aspects and consequences of impulsivity, marketing academicians have only highlighted the effects of impulsivity as a unidimensional construct. In the psychology domain, a vast literature exists which supports impulsivity as a multidimensional construct. This literature provides evidence for the distinct differences between functional and dysfunctional impulsivity, which represents two forms of trait impulsivity.

While dysfunctional impulsivity is classified as an inability to control the urge to act quickly without attention, functional impulsivity highlights a positive trait where people may respond quickly, but only when such action is deemed optimal. That is, consumers who score high on functional impulsivity can also engage in slow, methodical processing and may benefit from their impulsive tendencies (Dickman 1990). Surprisingly, however, consumer researchers have generally overlooked the differences between the two types of trait impulsivity, despite compelling evidence in the psychology domain.

A few earlier research on impulsivity classifies individuals as impulsive vs. non- impulsive (e.g., Eyesneck et al. 1987). In fact, a search on EBSCO's Business Source Complete using the keyword "non-impulsive" yielded only ten results- of which only seven were academic articles. Also, in these studies, there is a general lack of definitions, scales, or measurements for non-impulsivity or non-impulsive individuals (see Berman 2013, Corvi et al. 2012, Verbeken

2012). For example, Corvi et al. (2012, p. 483) suggest that “compared to a relatively non- impulsive individual, a highly impulsive person would discount future rewards more (i.e., s/he would consider a future reward as less valuable).” However, like other studies, no definitions,

102 conceptualizations, or measurements were given. Similarly, Bergh (2006, p. 3) suggests “a non- impulsive individual is able to adjust behavioral inhibition according to the situation, while an impulsive individual displays a more consistent failure to inhibit behavior or ignore interference.” Again, no measurement or other theoretical support was provided.

Notwithstanding, Van den Broek, Bradshaw, and Szabadi (1992, p.171), in one of the only studies that attempts to measure “non-impulsivity” classified the twenty-eight participants in their studies as impulsive or non-impulsive “on the basis of their ‘impulsiveness’ (I) indices on the Matching Familiar Figures Test (MFFT Ss).” Here, “Ss were assigned to the impulsive group if their I score exceeded + 1.OO and the remainder were assigned to the non-impulsive group.” This study was an extension of an earlier study (N= 8), in which participants were

“selected on the basis of their performance on the Matching Familiar Figures Test” (Van den

Broek, Bradshaw, C. M., and Szabadi 1987). However, further support for these measures is lacking.

Therefore, the dissertation focuses on impulsivity and by extension the influence of functional impulsivity on consumer behavior. Explicitly, the dissertation adopts the premise that individuals fall on a continuum of low to high levels of functional impulsivity (Dickman 1990;

Della Bitta et al. 1981). As such, the research does not attempt to examine dysfunctional impulsivity nor non-impulsivity. That is, although I do not rule-out dysfunctional or non- impulsivity, the dissertation focuses on functional impulsivity, a vastly understudied concept with the potential to increase our understanding of impulsivity in the marketing domain.

Consumers may score high on functional impulsivity, which may affect their tendencies and responses toward customized price offers. In fact, the results of the dissertation may help explain consumers’ lack of support for some price promotion. That is, while some firms carefully

103 design their price promotions and with the customer in mind, other firms’ promotions lack evidence of careful considerations in their designs and themes. Overall, increased knowledge of the differences between functional and dysfunctional impulsivity can help aid the designs of effective pricing strategies. Factors such as themes used, timeliness of the promotion, and the value and framing of the discount will likely influence functionally impulsive consumers in different ways.

5.4 Contribution of Research to Scholarship

The dissertation provides the first empirical test of functional impulsivity in the consumer research domain. Notably, the research contributes to a better understanding of a key, yet understudied, individual difference factor that affects consumer response to customized price promotion. Prior marketing researchers have relied on a unidimensional view to study the impact of impulsivity on consumer behavior. Specifically, this research expands the study of impulsivity in the consumer research domain by assessing the effect of functional impulsivity in the context of the customization of price promotions.

Consistent with previous findings, deal exclusivity matters, such that exclusive deals are generally favored to inclusive deals (Barone and Roy 2010a, 2010b). However, this research demonstrates that the level of exclusivity associated with a customized price promotion deal is more likely to affect the deal response for consumers who score high, but not, on functional impulsivity. Therefore, the research identifies another moderator for the impact of deal exclusivity on deal response. As such, the research answers call for more research in the context of individual differences that affect consumer response to the customization of prices. For

104 example, Barone and Roy (2010b, p. 122) stated that in the context of price promotion, “little work has explored individual difference factors that characterize people.”

Additionally, this research contributes to explanations for the effect of deal exclusivity on deal response. Specifically, a serial mediator model shows that positive emotional arousal and self-enhancement (in this order) mediate deal exclusivity’s impact on deal response.

Furthermore, the research findings indicate that promotion framing is likely to affect the interaction of deal exclusivity and functional impulsivity on consumer perception of savings. As such, the dissertation contributes to the limited research on deal exclusivity in the marketing domain. Additionally, the research identifies potential affective and cognitive factors that impacts deal exclusivity.

More specifically, in Study 1, I found functional impulsivity to be a significant moderator for the impact of deal exclusivity on deal response. This finding suggests a need for additional research on the delineation of functional impulsivity in the consumer research domain. In Study

2, I find that the offering of exclusive deals can generate higher responses on measures of positive emotional arousal and self-enhancement than inclusive deals. This finding supports the call for marketing researchers to include more affective (vs. cognitive) consequences in their price promotion research to increase understanding of the impact of marketing strategies on the consumer decision-making process. In Study 3, I also found positive emotional arousal and self- enhancement as two significant mediators (serial mediation) of the exclusivity effect. This finding provides additional support for theories which seek to explain the psychological components of marketing communications. Finally, in Study 4, I found price framing to be a boundary condition for the interaction of deal exclusivity and functional impulsivity. That is, I found that consumers who score high on functional impulsivity would likely perceive a price

105 discount framed in dollar off terms to offer more significant savings than the same promotion framed in percentage off terms.

5.5 Contribution of Research to Practice

The research has several important contributions for practice. Price promotion is one of the main strategies used by firms in today’s competitive marketplace. However, firms continue to struggle with the use of different strategies to make the customization of prices more effective.

Both efficiency and effectiveness of pricing strategies afford firms opportunities to target and build relationships with key customers. This research provides empirical evidence in support of promotional design factors that can aid deal response.

Accordingly, the research findings provide marketers with insights on how consumers are likely to respond to customized price promotions based i) on the level of exclusivity associated with the deal, ii) the framing of the price offer, and iii) individual consumer differences.

Consistent with previous work, consumers generally value exclusive deals (Barone and Roy

2010b). This research also provides a test of price promotion framing in the context of exclusive deals, which can guide practitioners on price framing effects for marketing strategy. For example, a marketer can manipulate the level of exclusivity associated with a price promotion campaign and also manage the themes for customized price promotion strategies by controlling the levels of emotional and self-focus messages. Marketers can also manipulate the framing of customized price promotion into dollar off vs. percentage off terms, which is likely to impact the perception of savings associated with the deal.

The research also provides support for theories that highlight the importance for firms to understand individual consumer differences and to factor these differences in the formulation of

106 marketing strategies. The test of functional impulsivity suggests that consumers are likely to vary on the level of impulsivity. Increased understanding in this area can help firms to structure their promotional campaigns better.

Overall, the research findings suggest that price promotion may not necessarily “dumb down” the information processing of impulsive consumers. In fact, the design and framing of the price promotion may lead to differences in deal response for some consumers, especially for those who score high on functional impulsivity. Accordingly, marketers need to structure price promotion deals in ways that increase the likelihood that functionally impulsive consumers will perceive them to be optimal. For example, the results show that consumers who score high (vs. low) on functional impulsivity are more likely to respond to exclusive versus inclusive deals.

Furthermore, price framing matters for high functionally impulsive consumers. That is, for deals that are exclusive, the marketer would likely find more success in their strategies to target high functional impulsive consumers when the exclusive deal is framed in the dollar (vs. percentage) off terms. Many of these are factors within the control of the firm. As such, when firms offer price promotion, themes and promotional strategies that heightens positive emotional arousal and self-enhancement may work best to influence high functionally impulsive consumers.

5.6 Limitations and Future Research

Besides the contributions, the research has some limitations. The studies use cross- sectional designs. The limitations of cross-sectional designs include equivalence issues.

Recommended solutions include the random assignment of individuals to the different treatment conditions, statistical adjustments, and repeated measure designs. While participants were randomly assigned to the treatment groups, longitudinal studies can offer more robust results.

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The studies also include only conservative test of deal exclusivity (Barone and Roy 2010b).

Furthermore, deal response may be different across a range of product categories. To further study deal exclusivity and functional impulsivity, researchers could test their effect in loyalty programs that capture a wide range of consumer products. Hence, field experiments and use of actual consumer database may provide more significant and generalizable results.

Another limitation of the present research may relate to the effect of deal exclusivity on positive emotional arousal and self-enhancement over time. For example, the effect of deal exclusivity on positive emotional arousal may be greater in the earlier stages of firms’ use of exclusive deals as a relationship building strategy. Furthermore, the ability of these deals to make consumers feel good about themselves may also reduce over time. Therefore, moderating variables such as relationship length and relationship equity could affect the mediators in the model.

Additionally, the findings in Study 4 did not support the three-way interaction of deal exclusivity, functional impulsivity, and promotion frame on deal response. The three-way interaction was marginally significant only on perceived savings. Deal response captures attitude and likelihood to attend a price promotion event. Since the deal manipulation in Study 4 uses both a reference and a sale, perceived savings may have provided a more robust measure of consumer evaluation (Sinha and Smith 2000). Furthermore, in their meta-analysis, Krishna et al.

(2002, p.104) suggest that perceived savings “is the most common method of measuring deal reaction.” Furthermore, deals framed in both percentage off and dollar off terms are likely to impact perceived saving (Della Bitta et al. 1981; Thaler 1985; Krishna et al. 2002). Also, based on previous research, Study 4 results may be explained by the dominant impact of price framing on consumer perception, evaluation, and response (Thaler 1985). Overall, the effect of the price

108 framing and the cues they signal may reduce the impact of deal exclusivity, especially when functionally impulsive individuals need to expend time to make savings calculations.

Accordingly, additional research in the area of price framing and functional impulsivity is warranted.

Future Research

The research offers insights into the effectiveness of customized price promotion strategies. Effective customized pricing strategies can ultimately help firms and consumers achieve favorable outcomes. Specifically, successful price customization allows firms to engage their customers better and to devise marketing material that is both relevant and valuable to them. Also, the benefits of customized price to consumers include satisfaction and increased relevance of marketing messages.

The limitations mentioned above offer opportunities for future research. First, future investigations could include field experiments to increase the nomological validity of the presented results. Furthermore, for more significant implications, future research could also include longitudinal designs to track respondents over time. The findings from longitudinal investigations on deal exclusivity could provide additional insights into its long-term effect on affect-based consequences such as positive emotional arousal and self-enhancement. That is, research might ask at what point does the positive impact of deal exclusivity on emotional arousal and self-enhancement weakens.

Second, the recruited participants in the studies were younger consumers. Future research could examine the impact of deal exclusivity in the context of older consumer segments.

Additionally, the studies were conducted solely in the context of online email price promotion.

More research is warranted to test the robustness of the findings across other digital settings.

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Third, future research could examine price threshold in the context of customized price promotion. For example, does the use of threshold pricing impact deal exclusivity’s effect on deal response? And, does price threshold impact the interaction of deal exclusivity and functional impulsivity? Fourth, and finally, future research could explore other potential moderating factors, such as materialism on the impact of deal exclusivity on the response to customized price promotion deals. Overall, additional boundary conditions can help firms plan contingencies for the effectiveness of their customized pricing strategies.

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APPENDIX

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APPENDIX A

STUDY SUMMARIES

i) Study 1:

Run MATRIX procedure:

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 1 Y : D_RESP X : D_GR W : FI_CAT

Sample Size: 212

************************************************************************** OUTCOME VARIABLE: D_RESP

Model Summary R R-sq MSE F df1 df2 p .6000 .3601 3.1446 39.0093 3.0000 208.0000 .0000

Model coeff se t p LLCI ULCI constant 5.9651 .5735 10.4003 .0000 4.8344 7.0958 D_GR -.0442 .3639 -.1216 .9034 -.7616 .6732 FI_CAT 5.3115 .7690 6.9073 .0000 3.7955 6.8275 Int_1 -3.5012 .4900 -7.1449 .0000 -4.4673 -2.5351

Product terms key: Int_1 : D_GR x FI_CAT

Covariance matrix of regression parameter estimates: constant D_GR FI_CAT Int_1 constant .3290 -.1979 -.3290 .1979 D_GR -.1979 .1324 .1979 -.1324 FI_CAT -.3290 .1979 .5913 -.3572 Int_1 .1979 -.1324 -.3572 .2401

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W .1571 51.0499 1.0000 208.0000 .0000 ------Focal predict: D_GR (X) Mod var: FI_CAT (W)

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Conditional effects of the focal predictor at values of the moderator(s):

FI_CAT Effect se t p LLCI ULCI .0000 -.0442 .3639 -.1216 .9034 -.7616 .6732 1.0000 -3.5454 .3282 -10.8032 .0000 -4.1924 -2.8984

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ D_GR FI_CAT D_RESP . BEGIN DATA. 1.0000 .0000 5.9208 2.0000 .0000 5.8766 1.0000 1.0000 7.7311 2.0000 1.0000 4.1857 END DATA. GRAPH/SCATTERPLOT= D_GR WITH D_RESP BY FI_CAT .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

------END MATRIX -----

iii) Study 2

Data tables for Study 2

Report PEM D_GR Mean N Std. Deviation Minimum Maximum Exclusive 5.1699 51 1.08294 2.33 7 Inclusive 4.6667 53 1.26845 1 7 Total 4.9135 104 1.20229 1 7

ANOVA Tablea Sum of Mean Squares df Square F Sig. PEM * Between Groups (Combined) 6.583 1 6.583 4.718 .032 D_GR Within Groups 142.305 102 1.395

Total 148.888 103

132 a. With fewer than three groups, linearity measures for PEM * D_GR cannot be computed.

Report S_ENH D_GR Mean N Std. Deviation Minimum Maximum Exclusive 4.5588 51 1.45137 2 7 Inclusive 3.6981 53 1.72186 1 7 Total 4.1202 104 1.64508 1 7

ANOVA Tablea Sum of Mean Squares df Square F Sig. S_ENH * Between (Combined) 19.254 1 19.254 7.568 .007 D_GR Groups Within Groups 259.493 102 2.544 Total 278.748 103 a. With fewer than three groups, linearity measures for S_ENH * D_GR cannot be computed.

` Report Ret_ATT D_GR Mean N Std. Deviation Minimum Maximum Exclusive 6.6373 51 1.80576 1 9 Inclusive 6.2075 53 1.73187 1 9 Total 6.4183 104 1.77306 1 9

ANOVA Tablea Sum of Mean Squares df Square F Sig. Ret_ATT * Between (Combined) 4.799 1 4.799 1.534 .218 D_GR Groups Within Groups 319.006 102 3.128 Total 323.805 103 a. With fewer than three groups, linearity measures for Ret_ATT * D_GR cannot be computed. iii) Study 3

133

Run MATRIX procedure: The effect of X on Y through MI and M2

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 6 Y : DEAL_RES X : Group_nu M1 : PEM M2 : SelF_enh

Sample Size: 261

************************************************************************** OUTCOME VARIABLE: PEM

Model Summary R R-sq MSE F(HC3) df1 df2 p .4167 .1736 1.0780 54.1515 1.0000 259.0000 .0000

Model coeff se(HC3) t p LLCI ULCI constant 6.5580 .1969 33.3131 .0000 6.1704 6.9457 Group_nu -.9482 .1289 -7.3588 .0000 -1.2019 -.6945

Covariance matrix of regression parameter estimates: constant Group_nu constant .0388 -.0240 Group_nu -.0240 .0166

************************************************************************** OUTCOME VARIABLE: SelF_enh

Model Summary R R-sq MSE F(HC3) df1 df2 p .6441 .4148 1.5273 81.4771 2.0000 258.0000 .0000

Model coeff se(HC3) t p LLCI ULCI constant 1.5920 .5158 3.0864 .0022 .5763 2.6077 Group_nu -.6064 .1617 -3.7499 .0002 -.9248 -.2880 PEM .7653 .0746 10.2581 .0000 .6184 .9123

Covariance matrix of regression parameter estimates: constant Group_nu PEM constant .2661 -.0570 -.0347 Group_nu -.0570 .0261 .0040 PEM -.0347 .0040 .0056

************************************************************************** OUTCOME VARIABLE: DEAL_RES

Model Summary R R-sq MSE F(HC3) df1 df2 p .8027 .6443 1.5179 144.1457 3.0000 257.0000 .0000

Model coeff se(HC3) t p LLCI ULCI constant -.2453 .6479 -.3786 .7053 -1.5212 1.0305 Group_nu -.0487 .1747 -.2785 .7809 -.3927 .2954 PEM .8523 .1286 6.6304 .0000 .5992 1.1055 SelF_enh .5257 .0786 6.6872 .0000 .3709 .6805

134

Covariance matrix of regression parameter estimates: constant Group_nu PEM SelF_enh constant .4198 -.0786 -.0563 .0026 Group_nu -.0786 .0305 .0028 .0032 PEM -.0563 .0028 .0165 -.0073 SelF_enh .0026 .0032 -.0073 .0062

************************** TOTAL EFFECT MODEL **************************** OUTCOME VARIABLE: DEAL_RES

Model Summary R R-sq MSE F(HC3) df1 df2 p .3798 .1442 3.6235 43.4293 1.0000 259.0000 .0000

Model coeff se(HC3) t p LLCI ULCI constant 8.8197 .3625 24.3331 .0000 8.1060 9.5334 Group_nu -1.5571 .2363 -6.5901 .0000 -2.0224 -1.0918

Covariance matrix of regression parameter estimates: constant Group_nu constant .1314 -.0810 Group_nu -.0810 .0558

************** TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y **************

Total effect of X on Y Effect se(HC3) t p LLCI ULCI c_ps -1.5571 .2363 -6.5901 .0000 -2.0224 -1.0918 -.7582

Direct effect of X on Y Effect se(HC3) t p LLCI ULCI c'_ps -.0487 .1747 -.2785 .7809 -.3927 .2954 -.0237

Indirect effect(s) of X on Y: Effect BootSE BootLLCI BootULCI TOTAL -1.5084 .1855 -1.8798 -1.1479 Ind1 -.8082 .1497 -1.1210 -.5285 Ind2 -.3188 .0903 -.5109 -.1529 Ind3 -.3815 .0823 -.5577 -.2361 (C1) -.4894 .1910 -.8699 -.1202 (C2) -.4267 .1673 -.7680 -.0960 (C3) .0627 .1147 -.1533 .3011

Partially standardized indirect effect(s) of X on Y: Effect BootSE BootLLCI BootULCI TOTAL -.7345 .0787 -.8877 -.5751 Ind1 -.3935 .0686 -.5311 -.2646 Ind2 -.1552 .0439 -.2475 -.0750 Ind3 -.1857 .0377 -.2673 -.1180 (C1) -.2383 .0913 -.4143 -.0587 (C2) -.2078 .0808 -.3671 -.0471 (C3) .0305 .0556 -.0756 .1453

Specific indirect effect contrast definition(s): (C1) Ind1 minus Ind2 (C2) Ind1 minus Ind3 (C3) Ind2 minus Ind3

Indirect effect key: Ind1 Group_nu -> PEM -> DEAL_RES Ind2 Group_nu -> SelF_enh -> DEAL_RES Ind3 Group_nu -> PEM -> SelF_enh -> DEAL_RES

************************************************************************** Bootstrap estimates were saved to a file

Map of column names to model coefficients: Conseqnt Antecdnt COL1 PEM constant

135

COL2 PEM Group_nu COL3 SelF_enh constant COL4 SelF_enh Group_nu COL5 SelF_enh PEM COL6 DEAL_RES constant COL7 DEAL_RES Group_nu COL8 DEAL_RES PEM COL9 DEAL_RES SelF_enh

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

Number of bootstrap samples for percentile bootstrap confidence intervals: 5000

NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator was used.

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

Report DEAL_RESP CPP_Group Mean N Std. Deviation Minimum Maximum Exclusive Condition 7.2626 129 1.79537 1.88 9 Inclusive Condition 5.7055 132 2.00364 1 9 Total 6.4751 261 2.05378 1 9

ANOVA Tablea Sum of Mean Squares df Square F Sig. DEAL_RESP * Between (Combined 158.183 1 158.183 43.654 .000 CPP_Group Groups ) Within Groups 938.499 259 3.624 Total 1096.682 260 a. With fewer than three groups, linearity measures for DEAL_RESP * CPP_Group cannot be computed.

Report DEAL_RESP CPP_Group Mean N Std. Deviation Minimum Maximum Exclusive Condition 7.2626 129 1.79537 1.88 9 Inclusive Condition 5.7055 132 2.00364 1 9 Total 6.4751 261 2.05378 1 9

136

ANOVA Tablea Sum of Mean Squares df Square F Sig. DEAL_RESP * Between (Combined 158.183 1 158.183 43.654 .000 CPP_Group Groups ) Within Groups 938.499 259 3.624

Total 1096.682 260 a. With fewer than three groups, linearity measures for DEAL_RESP * CPP_Group cannot be computed.

Measures of Association Eta Eta Squared DEAL_RESP * CPP_Group .380 .144

Report P_EMOTION CPP_Group Mean N Std. Deviation Minimum Maximum Exclusive Condition 5.6098 129 .97216 2.50 7 Inclusive Condition 4.6616 132 1.09899 1 6.67 Total 5.1303 261 1.13992 1 7

ANOVA Tablea Sum of Mean Squares df Square F Sig. P_EMOTION * Between (Combined 58.658 1 58.658 54.416 .000 CPP_Group Groups ) Within Groups 279.191 259 1.078

Total 337.849 260 a. With fewer than three groups, linearity measures for P_EMOTION * CPP_Group cannot be computed.

137

Report SelF_enhancement CPP_Group Mean N Std. Deviation Minimum Maximum Exclusive Condition 5.279 129 1.2499 1 7 Inclusive Condition 3.947 132 1.6523 1 6.5 Total 4.605 261 1.6093 1 7

ANOVA Tablea Sum of Mean Squares df Square F Sig. SelF_enh * Between (Combined 115.770 1 115.770 53.776 .000 CPP_Group Groups ) Within Groups 557.582 259 2.153 Total 673.352 260 a. With fewer than three groups, linearity measures for SelF_enh * CPP_Group cannot be computed.

Correlations P_EMOTION SelF_enh P_EMOTION Pearson Correlation 1 .621** Sig. (2-tailed) .000 Sum of Squares and Cross- 337.849 296.084 products Covariance 1.299 1.139 N 261 261 SelF_enh Pearson Correlation .621** 1 Sig. (2-tailed) .000 Sum of Squares and Cross- 296.084 673.352 products Covariance 1.139 2.590 N 261 261 **. Correlation is significant at the 0.01 level (2-tailed).

ANOVAa Sum of Model Squares df Mean Square F Sig.

138

1 Regression 259.483 1 259.483 162.385 .000b Residual 413.870 259 1.598 Total 673.352 260 a. Dependent Variable: SelF_enh b. Predictors: (Constant), P_EMOTION.

Coefficientsa Unstandardized Standardized 95.0% Confidence Interval Coefficients Coefficients for B Model B Std. Error Beta t Sig. Lower Bound Upper Bound 1 (Constant) .109 .361 .302 .763 -.602 .821 P_EMOTIO .876 .069 .621 12.743 .000 .741 1.012 N a. Dependent Variable: SelF_enh

ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 553.623 1 553.623 264.038 .000b Residual 543.059 259 2.097 Total 1096.682 260 a. Dependent Variable: DEAL_RESP b. Predictors: (Constant), SelF_enh

Coefficientsa Unstandardized Standardized 95.0% Confidence Interval Coefficients Coefficients for B Model B Std. Error Beta t Sig. Lower Bound Upper Bound 1 (Constant) 2.299 .272 8.448 .000 1.763 2.835 SelF_enh .907 .056 .711 16.249 .000 .797 1.017 a. Dependent Variable: DEAL_RESP

iv) Study 4

139

Perceived Savings

Run MATRIX procedure:

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 3 Y : Per_SAV X : EXC W : FI_CAT Z : Framing

Covariates: Hotel_AT

Sample Size: 437

************************************************************************** OUTCOME VARIABLE: Per_SAV

Model Summary R R-sq MSE F df1 df2 p .4755 .2261 2.5007 15.6292 8.0000 428.0000 .0000

Model coeff se t p LLCI ULCI constant 1.9978 1.1923 1.6756 .0945 -.3456 4.3413 EXC .4499 .7066 .6367 .5247 -.9390 1.8387 FI_CAT 2.9442 1.4998 1.9631 .0503 -.0037 5.8920 Int_1 -2.0822 .9612 -2.1663 .0308 -3.9714 -.1930 Framing .1167 .7087 .1647 .8692 -1.2762 1.5097 Int_2 -.3122 .4466 -.6990 .4849 -1.1900 .5656 Int_3 -1.5300 .9583 -1.5966 .1111 -3.4135 .3535 Int_4 1.1079 .6104 1.8150 .0702 -.0919 2.3078 Hotel_AT .5713 .0539 10.6041 .0000 .4654 .6772

Product terms key: Int_1 : EXC x FI_CAT Int_2 : EXC x Framing Int_3 : FI_CAT x Framing Int_4 : EXC x FI_CAT x Framing

Covariance matrix of regression parameter estimates: constant EXC FI_CAT Int_1 Framing Int_2 Int_3 Int_4 Hotel_AT constant 1.4215 -.7580 -1.2517 .7674 -.7578 .4587 .7671 -.4665 -.0244 EXC -.7580 .4993 .7404 -.5003 .4470 -.2995 -.4479 .3003 .0025 FI_CAT -1.2517 .7404 2.2494 -1.3659 .7423 -.4474 -1.3622 .8251 .0043 Int_1 .7674 -.5003 -1.3659 .9239 -.4478 .3001 .8256 -.5566 -.0036 Framing -.7578 .4470 .7423 -.4478 .5023 -.3005 -.5031 .3012 .0022 Int_2 .4587 -.2995 -.4474 .3001 -.3005 .1994 .3011 -.2000 -.0016 Int_3 .7671 -.4479 -1.3622 .8256 -.5031 .3011 .9183 -.5547 -.0033 Int_4 -.4665 .3003 .8251 -.5566 .3012 -.2000 -.5547 .3726 .0026 Hotel_AT -.0244 .0025 .0043 -.0036 .0022 -.0016 -.0033 .0026 .0029

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W*Z .0060 3.2941 1.0000 428.0000 .0702 ------Focal predict: EXC (X) Mod var: FI_CAT (W) Mod var: Framing (Z)

Test of conditional X*W interaction at value(s) of Z: Framing Effect F df1 df2 p 1.0000 -.9743 5.1760 1.0000 428.0000 .0234 2.0000 .1337 .0950 1.0000 428.0000 .7581

140

Conditional effects of the focal predictor at values of the moderator(s):

FI_CAT Framing Effect se t p LLCI ULCI .0000 1.0000 .1377 .3159 .4359 .6631 -.4832 .7586 .0000 2.0000 -.1745 .3150 -.5540 .5799 -.7935 .4446 1.0000 1.0000 -.8366 .2889 -2.8957 .0040 -1.4044 -.2687 1.0000 2.0000 -.0408 .2977 -.1371 .8911 -.6259 .5443

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ EXC FI_CAT Framing Per_SAV . BEGIN DATA. 1.0000 .0000 1.0000 6.4321 2.0000 .0000 1.0000 6.5698 1.0000 .0000 2.0000 6.2367 2.0000 .0000 2.0000 6.0622 1.0000 1.0000 1.0000 6.8720 2.0000 1.0000 1.0000 6.0355 1.0000 1.0000 2.0000 6.2545 2.0000 1.0000 2.0000 6.2137 END DATA. GRAPH/SCATTERPLOT= EXC WITH Per_SAV BY FI_CAT /PANEL ROWVAR= Framing .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

Deal Response

Run MATRIX procedure:

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 3 Y : D_RESP X : EXC W : FI_CAT Z : Framing

Covariates: Hotel_AT

Sample Size: 437

************************************************************************** OUTCOME VARIABLE: D_RESP

Model Summary R R-sq MSE F df1 df2 p .5867 .3442 3.2872 28.0797 8.0000 428.0000 .0000

141

Model coeff se t p LLCI ULCI constant 5.0603 1.3670 3.7019 .0002 2.3735 7.7471 EXC -.5432 .8101 -.6705 .5029 -2.1355 1.0492 FI_CAT 4.0595 1.7195 2.3608 .0187 .6797 7.4393 Int_1 -2.3959 1.1020 -2.1741 .0302 -4.5620 -.2299 Framing .6834 .8125 .8411 .4007 -.9136 2.2805 Int_2 -.7826 .5120 -1.5284 .1271 -1.7890 .2238 Int_3 -2.0834 1.0987 -1.8963 .0586 -4.2429 .0760 Int_4 1.0438 .6999 1.4914 .1366 -.3319 2.4194 Hotel_AT .3695 .0618 5.9824 .0000 .2481 .4909

Product terms key: Int_1 : EXC x FI_CAT Int_2 : EXC x Framing Int_3 : FI_CAT x Framing Int_4 : EXC x FI_CAT x Framing

Covariance matrix of regression parameter estimates: constant EXC FI_CAT Int_1 Framing Int_2 Int_3 Int_4 Hotel_AT constant 1.8686 -.9964 -1.6454 1.0087 -.9961 .6030 1.0084 -.6132 -.0321 EXC -.9964 .6563 .9732 -.6576 .5875 -.3936 -.5888 .3947 .0033 FI_CAT -1.6454 .9732 2.9568 -1.7955 .9757 -.5881 -1.7906 1.0846 .0056 Int_1 1.0087 -.6576 -1.7955 1.2144 -.5887 .3945 1.0852 -.7316 -.0048 Framing -.9961 .5875 .9757 -.5887 .6602 -.3949 -.6613 .3959 .0029 Int_2 .6030 -.3936 -.5881 .3945 -.3949 .2622 .3958 -.2629 -.0021 Int_3 1.0084 -.5888 -1.7906 1.0852 -.6613 .3958 1.2071 -.7291 -.0044 Int_4 -.6132 .3947 1.0846 -.7316 .3959 -.2629 -.7291 .4898 .0034 Hotel_AT -.0321 .0033 .0056 -.0048 .0029 -.0021 -.0044 .0034 .0038

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W*Z .0034 2.2242 1.0000 428.0000 .1366 ------Focal predict: EXC (X) Mod var: FI_CAT (W) Mod var: Framing (Z)

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ EXC FI_CAT Framing D_RESP . BEGIN DATA. 1.0000 .0000 1.0000 7.1216 2.0000 .0000 1.0000 5.7959 1.0000 .0000 2.0000 7.0225 2.0000 .0000 2.0000 4.9142 1.0000 1.0000 1.0000 7.7456 2.0000 1.0000 1.0000 5.0677 1.0000 1.0000 2.0000 6.6068 2.0000 1.0000 2.0000 4.1902 END DATA. GRAPH/SCATTERPLOT= EXC WITH D_RESP BY FI_CAT /PANEL ROWVAR= Framing .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

Further Examination of the 3-way interaction of Deal Exclusivity, Functional Impulsivity, and Promotion Frame on Perceived Savings

142

Run MATRIX procedure: Dollar off Condition only (Procedure by Han et al. 2017)- DV= Perceived Savings

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 1 Y : Per_SAV X : EXC W : FI_CAT

Covariates: Hotel_AT

Sample Size: 221

************************************************************************** OUTCOME VARIABLE: Per_SAV

Model Summary R R-sq MSE F df1 df2 p .5340 .2852 2.2957 21.5421 4.0000 216.0000 .0000

Model coeff se t p LLCI ULCI constant 1.6405 .7221 2.2716 .0241 .2171 3.0638 EXC .1570 .3031 .5180 .6050 -.4404 .7544 FI_CAT 1.4338 .6381 2.2471 .0256 .1762 2.6914 Int_1 -.9977 .4107 -2.4289 .0160 -1.8072 -.1881 Hotel_AT .6333 .0719 8.8061 .0000 .4915 .7750

Product terms key: Int_1 : EXC x FI_CAT

Covariance matrix of regression parameter estimates: constant EXC FI_CAT Int_1 Hotel_AT constant .5215 -.1462 -.2314 .1488 -.0396 EXC -.1462 .0919 .1344 -.0920 .0016 FI_CAT -.2314 .1344 .4071 -.2481 .0016 Int_1 .1488 -.0920 -.2481 .1687 -.0020 Hotel_AT -.0396 .0016 .0016 -.0020 .0052

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W .0195 5.8998 1.0000 216.0000 .0160 ------Focal predict: EXC (X) Mod var: FI_CAT (W)

Conditional effects of the focal predictor at values of the moderator(s):

FI_CAT Effect se t p LLCI ULCI .0000 .1570 .3031 .5180 .6050 -.4404 .7544 1.0000 -.8407 .2768 -3.0368 .0027 -1.3863 -.2950

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ EXC FI_CAT Per_SAV .

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BEGIN DATA. 1.0000 .0000 6.4346 2.0000 .0000 6.5916 1.0000 1.0000 6.8707 2.0000 1.0000 6.0301 END DATA. GRAPH/SCATTERPLOT= EXC WITH Per_SAV BY FI_CAT .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. ------END MATRIX -----

Run MATRIX procedure: Percent off frame Only. DV- Perceived Savings

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 1 Y : Per_SAV (percent off frame) X : EXC W : FI_CAT

Covariates: Hotel_AT

Sample Size: 216

************************************************************************** OUTCOME VARIABLE: Per_SAV

Model Summary R R-sq MSE F df1 df2 p .4000 .1600 2.7057 10.0487 4.0000 211.0000 .0000

Model coeff se t p LLCI ULCI constant 2.6846 .7684 3.4939 .0006 1.1700 4.1993 EXC -.1581 .3279 -.4821 .6303 -.8045 .4884 FI_CAT -.0608 .7174 -.0848 .9325 -1.4751 1.3534 Int_1 .1007 .4521 .2227 .8240 -.7906 .9920 Hotel_AT .5054 .0805 6.2792 .0000 .3468 .6641

Product terms key: Int_1 : EXC x FI_CAT

Covariance matrix of regression parameter estimates: constant EXC FI_CAT Int_1 Hotel_AT constant .5904 -.1549 -.2464 .1436 -.0446 EXC -.1549 .1075 .1673 -.1079 -.0016 FI_CAT -.2464 .1673 .5147 -.3080 -.0054 Int_1 .1436 -.1079 -.3080 .2044 .0032 Hotel_AT -.0446 -.0016 -.0054 .0032 .0065

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W .0002 .0496 1.0000 211.0000 .8240 ------Focal predict: EXC (X) Mod var: FI_CAT (W)

144

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ EXC FI_CAT Per_SAV . BEGIN DATA. 1.0000 .0000 6.2213 2.0000 .0000 6.0632 1.0000 1.0000 6.2611 2.0000 1.0000 6.2037 END DATA. GRAPH/SCATTERPLOT= EXC WITH Per_SAV BY FI_CAT .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

Run MATRIX procedure:

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 3 Y : D_RESP X : EXC W : FI_CAT Z : Framing

Covariates: Hotel_AT

Sample Size: 437

************************************************************************** OUTCOME VARIABLE: D_RESP

Model Summary R R-sq MSE F df1 df2 p .5867 .3442 3.2872 28.0797 8.0000 428.0000 .0000

Product terms key: Int_1 : EXC x FI_CAT Int_2 : EXC x Framing Int_3 : FI_CAT x Framing

145

Int_4 : EXC x FI_CAT x Framing

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W*Z .0034 2.2242 1.0000 428.0000 .1366 ------Focal predict: EXC (X) Mod var: FI_CAT (W) Mod var: Framing (Z)

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

PERCEIVED SAVINGS

Run MATRIX procedure:

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 3 Y : Per_SAV X : EXC W : FI_CAT Z : Framing

Covariates:

146

Hotel_AT

Sample Size: 437

************************************************************************** OUTCOME VARIABLE: Per_SAV

Model Summary R R-sq MSE F df1 df2 p .4755 .2261 2.5007 15.6292 8.0000 428.0000 .0000

Product terms key: Int_1 : EXC x FI_CAT Int_2 : EXC x Framing Int_3 : FI_CAT x Framing Int_4 : EXC x FI_CAT x Framing

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W*Z .0060 3.2941 1.0000 428.0000 .0702 ------Focal predict: EXC (X) Mod var: FI_CAT (W) Mod var: Framing (Z)

Test of conditional X*W interaction at value(s) of Z: Framing Effect F df1 df2 p 1.0000 -.9743 5.1760 1.0000 428.0000 .0234 2.0000 .1337 .0950 1.0000 428.0000 .7581

Conditional effects of the focal predictor at values of the moderator(s):

Data for visualizing the conditional effect of the focal predictor:

147

Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ EXC FI_CAT Framing Per_SAV BEGIN DATA. 1.0000 .0000 1.0000 6.4321 2.0000 .0000 1.0000 6.5698 1.0000 .0000 2.0000 6.2367 2.0000 .0000 2.0000 6.0622 1.0000 1.0000 1.0000 6.8720 2.0000 1.0000 1.0000 6.0355 1.0000 1.0000 2.0000 6.2545 2.0000 1.0000 2.0000 6.2137 END DATA. GRAPH/SCATTERPLOT= EXC WITH Per_SAV BY FI_CAT /PANEL ROWVAR= Framing .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

Run MATRIX procedure:

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 3 Y : Per_SAV X : EXC W : FI_CAT Z : Framing

Covariates: Hotel_AT

Sample Size: 437

************************************************************************** OUTCOME VARIABLE: Per_SAV

Model Summary R R-sq MSE F df1 df2 p .4755 .2261 2.5007 15.6292 8.0000 428.0000 .0000

Product terms key: Int_1 : EXC x FI_CAT Int_2 : EXC x Framing

148

Int_3 : FI_CAT x Framing Int_4 : EXC x FI_CAT x Framing

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W*Z .0060 3.2941 1.0000 428.0000 .0702 ------Focal predict: EXC (X) Mod var: FI_CAT (W) Mod var: Framing (Z)

Test of conditional X*W interaction at value(s) of Z: Framing Effect F df1 df2 p 1.0000 -.9743 5.1760 1.0000 428.0000 .0234 2.0000 .1337 .0950 1.0000 428.0000 .7581

Conditional effects of the focal predictor at values of the moderator(s):

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

Run MATRIX procedure:

**************** PROCESS Procedure for SPSS Version 3.00 *****************

149

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 1 Y : D_RESP X : D_GR W : FI_CAT

Sample Size: 212

************************************************************************** OUTCOME VARIABLE: D_RESP

Model Summary R R-sq MSE F df1 df2 p .6000 .3601 3.1446 39.0093 3.0000 208.0000 .0000

Product terms key: Int_1 : D_GR x FI_CAT

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W .1571 51.0499 1.0000 208.0000 .0000 ------Focal predict: D_GR (X) Mod var: FI_CAT (W)

Conditional effects of the focal predictor at values of the moderator(s):

FI_CAT Effect se t p LLCI ULCI .0000 -.0442 .3639 -.1216 .9034 -.7616 .6732 1.0000 -3.5454 .3282 -10.8032 .0000 -4.1924 -2.8984

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ D_GR FI_CAT D_RESP . BEGIN DATA. 1.0000 .0000 5.9208 2.0000 .0000 5.8766 1.0000 1.0000 7.7311 2.0000 1.0000 4.1857 END DATA. GRAPH/SCATTERPLOT= D_GR WITH D_RESP BY FI_CAT .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

------END MATRIX -----

150

Report

D_RESP

D_GR Mean N Std. Deviation

Exclusive 6.9339 109 1.73249

Inclusive 4.9573 103 2.19405

Total 5.9736 212 2.20092

Report

Lev-EXC

D_GR Mean N Std. Deviation

Exclusive 7.1881 109 1.42366

Inclusive 5.5437 103 2.32512

Total 6.3892 212 2.08074

ANOVA Table

Sum of

Squares df Mean Square F Sig.

Lev-EXC * D_GR Between Groups (Combined) 143.197 1 143.197 39.037 .000

Within Groups 770.323 210 3.668

Total 913.520 211

Report

D_RESP

FI_CAT Mean N Std. Deviation

Low_FI 5.8989 95 1.51162

High_FI 6.0342 117 2.63652

Total 5.9736 212 2.20092

ANOVA Table

Sum of Squares df Mean Square F Sig.

151

D_RESP * FI_CAT Between Groups (Combined) .959 1 .959 .197 .657

Within Groups 1021.133 210 4.863

Total 1022.092 211

Run MATRIX procedure: (Percentage Frame)

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 1 Y : Per_SAV X : EXC W : FI_CAT

Covariates: Hotel_AT

Sample Size: 216

************************************************************************** OUTCOME VARIABLE: Per_SAV

Model Summary R R-sq MSE F df1 df2 p .4000 .1600 2.7057 10.0487 4.0000 211.0000 .0000

Product terms key: Int_1 : EXC x FI_CAT

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W .0002 .0496 1.0000 211.0000 .8240 ------Focal predict: EXC (X) Mod var: FI_CAT (W)

Data for visualizing the conditional effect of the focal predictor:

152

Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ EXC FI_CAT Per_SAV . BEGIN DATA. 1.0000 .0000 6.2213 2.0000 .0000 6.0632 1.0000 1.0000 6.2611 2.0000 1.0000 6.2037 END DATA. GRAPH/SCATTERPLOT= EXC WITH Per_SAV BY FI_CAT .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

Run MATRIX procedure: (Dollar frame)

**************** PROCESS Procedure for SPSS Version 3.00 *****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

************************************************************************** Model : 1 Y : Per_SAV X : EXC W : FI_CAT

Covariates: Hotel_AT

Sample Size: 221

************************************************************************** OUTCOME VARIABLE: Per_SAV

Model Summary R R-sq MSE F df1 df2 p .5340 .2852 2.2957 21.5421 4.0000 216.0000 .0000

Product terms key: Int_1 : EXC x FI_CAT

153

Hotel_AT -.0396 .0016 .0016 -.0020 .0052

Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W .0195 5.8998 1.0000 216.0000 .0160 ------Focal predict: EXC (X) Mod var: FI_CAT (W)

Conditional effects of the focal predictor at values of the moderator(s):

FI_CAT Effect se t p LLCI ULCI .0000 .1570 .3031 .5180 .6050 -.4404 .7544 1.0000 -.8407 .2768 -3.0368 .0027 -1.3863 -.2950

Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot.

DATA LIST FREE/ EXC FI_CAT Per_SAV . BEGIN DATA. 1.0000 .0000 6.4346 2.0000 .0000 6.5916 1.0000 1.0000 6.8707 2.0000 1.0000 6.0301 END DATA. GRAPH/SCATTERPLOT= EXC WITH Per_SAV BY FI_CAT .

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output: 95.0000

NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended.

------END MATRIX -----

154