EXTENDING INSIGHTS ON LOYALTY PROGRAM EFFECTIVENESS By Travis Alan Walkowiak A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Business Administration – Marketing – Doctor of Philosophy 2023 ABSTRACT Loyalty programs are extremely common relationship marketing tools. Their popularity continues to grow, despite limited insight as to what makes some loyalty programs overwhelmingly successful while others fail to prove their worth to managers. Composed of two essays, this dissertation aims to build knowledge on the effectiveness of loyalty programs. The first essay is positioned at the confluence of two literature streams: loyalty programs and service failure and recovery. The essay identifies deficiencies shared by these research streams and explores the influence of loyalty program membership on customer outcomes following service failures. The empirical findings suggest that loyalty program membership attenuates the negative impact of service failures. That is, loyalty program members react to service failures less negatively than nonmember customers. The second essay addresses calls for research examining the influence of cultural factors on consumer preferences surrounding loyalty programs. In this essay, status is viewed as a personal value that is prioritized to a varying degree across individual consumers. The empirical findings show that consumers who prioritize the achievement of status have a stronger preference for brands and firms that offer loyalty programs. Overall, this dissertation extends the understanding of how and under what circumstances loyalty programs effectively influence customer attitudes and purchase behavior. Copyright by TRAVIS ALAN WALKOWIAK 2023 ACKNOWLEDGEMENTS To my parents: Thank you for believing in me. To Dr. Morgeson: Thank you for your guidance and feedback throughout this process. To Dr. Ruvio: Thank you for your availability and valuable input. To Dr. Hult: Thank you for your support at every turn. To Dr. Schrock: Thank you for your unwavering encouragement. iv TABLE OF CONTENTS LIST OF ABBREVIATIONS ........................................................................................................ vi ESSAY ONE: THE ROLE OF LOYALTY PROGRAM MEMBERSHIP IN SERVICE FAILURE AND RECOVERY ....................................................................................................... 1 REFERENCES ......................................................................................................................... 23 APPENDIX ............................................................................................................................... 32 ESSAY TWO: EXPLORING THE EFFECTS OF PERSONAL VALUES ON CONSUMER PREFERENCES FOR LOYALTY PROGRAMS........................................................................ 44 REFERENCES ......................................................................................................................... 61 APPENDIX ............................................................................................................................... 68 v LIST OF ABBREVIATIONS LP = Loyalty Program SFR = Service Failure and Recovery vi ESSAY ONE: THE ROLE OF LOYALTY PROGRAM MEMBERSHIP IN SERVICE FAILURE AND RECOVERY Over the past three decades, loyalty programs (LPs) have become a central feature of firms’ customer relationship management endeavors (Kivetz and Simonson 2003; Kim, Steinhoff, and Palmatier 2020). Defined 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), LPs have exploded in popularity. Over 90% of companies have invested in LPs (Wollan 2017), and consumers in the United States hold an average of 16.6 LP memberships (Navarro 2023). Notably, the popularity of LPs has grown despite longstanding debate as to whether they truly enhance customer loyalty and/or drive firm performance (Dowling and Uncles 1997; Nunes and Drèze 2006b; Shugan 2005). A growing body of evidence ties LPs to positive business outcomes, including customer perceptions reflected in attitudinal measures, customer behavior, and firm performance. LPs have been found to enhance quality and value perceptions (Bolton, Kannan, and Bramlett 2000), perceived customer status (Drèze and Nunes 2009), and attitudinal loyalty (Stathopoulou and Balabanis 2016; Steinhoff and Palmatier 2016). LP effectiveness has also been measured through increased program enrollment (Leenheer et al. 2007), reward redemption (Lal and Bell 2003), customer retention (Verhoef 2003), customer traffic (Drèze and Hoch 1998), and purchase behavior (Kopalle et al. 2012; van Heerde and Bijmolt 2005; Zhang and Breugelmans 2012). Firm-level studies have found that LP implementation is associated with increased sales (Bombaij and Dekimpe 2020), gross profit (Chaudhuri, Voorhees, and Beck 2019), and firm value (Faramarzi and Bhattacharya 2021). 1 The richness of the extant LP literature notwithstanding, there is a notable lack of research exploring the effect of LP membership on one of the most important aspects of service provision – service failure and recovery (SFR). Service failure, defined as “a private service performance that falls below the expectation of one or a few customer(s)” (Khamitov et al. 2020, p. 521), can trigger perceptions of unfairness, negative emotional responses, decreased repurchase intention, and retaliatory behaviors against the firm (Gelbrich 2010; Grègoire and Fisher 2008; Joireman et al. 2013; Komarova Loureiro, Haws, and Bearden 2018; McColl- Kennedy et al. 2009; Schoefer and Diamantopoulos 2008; Surachartkumtonkun, Patterson, and McColl-Kennedy 2013). As minimizing or eliminating these negative outcomes of service failures is crucial for virtually all firms (Tax, Brown, and Chandrashekaran 1998), research generally investigates service failure along with service recovery, defined as “all actions a firm can take to redress the grievances or loss caused by a service failure” (Khamitov et al. 2020, p. 521). This has resulted in a voluminous body of published SFR research (Grègoire and Mattila 2020), with a growing emphasis on handling customer complaints such that negative outcomes are minimized (Morgeson et al. 2020). Given the failure of the extant literature to connect LP membership to service failure and recovery actions by firms, the main research question pursued in this study is: How does loyalty program membership influence customer responses to service failures? We address this question by examining LP membership as a customer-firm relationship attribute that impacts the negative effect of service failure on customer attitudes and behavioral intentions. Specifically, we propose that LP members should be less negatively affected by service failures than nonmembers because LP membership is associated with an enhanced customer-firm relationship commitment. Prior research has yielded results that seem to support this proposition. For example, Kang, Alejandro, 2 and Groza (2015) and Yi and Jeon (2003) find that LP members become loyal to the program itself, which can drive loyalty to the brand or firm. Further, LP members are willing to discount negative experiences (Bolton, Kannan, and Bramlett 2000), and participation in LPs limits the negative effects of variation in service quality (Voorhees et al. 2021). We find that LP membership moderates the negative effects of service failure on customer satisfaction, customer willingness to recommend, and repurchase intentions (loyalty), such that they are weaker (less negative) for LP members than nonmembers. This research contributes to the LP literature by uniquely testing the effects of LP membership in a service failure context. As noted above, past research has found that LP members are influenced less by negative events than nonmembers. However, to our knowledge, no previous studies have examined LP membership as a moderator of negative outcomes of service failures. By adopting the SFR framework of Khamitov et al. (2020), we extend insights on how LP membership drives loyalty outside of established LP effectiveness frameworks (e.g., Kim et al. 2020). Further, while previous SFR studies have tested the moderating roles of other customer relationship factors such as relationship quality and strength (e.g., Grègoire and Fisher 2008; Grègoire, Tripp, and Legoux 2009), past research has not examined LP membership in this way. Our findings imply that there are benefits of increasing LP enrollment beyond those established in past studies and organizing frameworks of LP effectiveness. That is, while many published studies have found positive relationships between LPs and customer outcomes, reducing the negative effects of service failure is an additional benefit of LP membership. In the following sections, we first review the LP and SFR literature to provide context for our study. Next, we introduce a conceptual model in which LP membership is positioned as a relational attribute that diminishes the negative effects of service failure. Then, we use data from 3 the American Customer Satisfaction Index (ACSI) and specify a seemingly unrelated regression model to test our hypotheses. Finally, we present the results of our model and discuss implications for research and practice. Conceptual Background Loyalty programs and service failure and recovery have each received considerable attention from researchers, resulting in substantial bodies of literature. These research areas both focus on customer retention and loyalty, reflected in common dependent variables in empirical studies, including customer attitudes, behavioral intentions, and purchase behavior. Importantly, a common trend in conceptual and theoretical development has emerged across LP effectiveness and SFR research, as both areas have increasingly emphasized a customer relationship management perspective. Loyalty Programs The dramatic increase in LP popularity can be attributed to several factors. First, technological advances make LPs continuously more attractive to firms and consumers. For firms, the internet and mobile devices enable program implementation, communication (Wiebenga and Fennis 2014), and collection of valuable customer data (Stourm et al. 2020). For consumers, technology facilitates program enrollment, access to rewards, and personalized offers and experiences (Chen, Mandler, and Meyer-Waarden 2021; Gabel and Guhl 2022). Second, the competitive landscape drives firms to invest in LPs. Most companies now offer an LP (Wollan 2017), and consumers increasingly expect firms to offer them an opportunity to enroll in an LP (Navarro 2023). Practitioners tend to view LPs as necessary, even if their true effectiveness is unclear (Kopalle and Neslin 2003; McCall and Voorhees 2010; Shugan 2005). Finally, recent research connecting LP implementation to positive organizational performance outcomes is 4 likely to drive continued popularity of such programs (Bombaij and Dekimpe 2020; Chaudhuri, Voorhees, and Beck 2019; Faramarzi and Bhattacharya 2021; Katsikeas et al. 2016). The above factors are encouraging for firms that have invested in LPs, but many questions remain regarding how and under what conditions LPs effectively drive customer loyalty. This is, in part, due to the narrow theoretical or substantive scope of much of the empirical work on LP effectiveness. While insightful, many LP studies yield findings with limited generalizability or practical insights into directions for optimizing LP design and operation. Noting the fragmented nature of the literature, reviews have called for broadened perspectives on LP effectiveness and provided organizing frameworks for future research (e.g., Breugelmans et al. 2015; Henderson, Beck, and Palmatier 2011; Kim, Steinhoff, and Palmatier 2020; McCall and Voorhees 2010). These frameworks highlight that most studies focus on a subset of (a) the main effects of LP design elements on customer outcomes, (b) the psychological mechanisms that mediate LP influence on customer loyalty, and/or (c) the moderating effects of customer factors and LP operational elements. Table 1.1 provides an overview of LP effectiveness research, structured according to the dimensions used to construct prior organizing frameworks. Many empirical studies on LP effectiveness focus on a single program design element and/or psychological mechanism through which LPs influence customer loyalty (McCall and Voorhees 2010; Henderson, Beck, and Palmatier 2011). More recent studies have taken broader perspectives, testing the combined effects of more than one design element, examining more than one psychological mechanism simultaneously, and/or integrating relationship marketing fundamentals (e.g., Steinhoff and Palmatier 2016). 5 Program Design and Execution The organizing frameworks of Breugelmans et al. (2015) and McCall and Voorhees (2010) focus on LP design and execution, which encompass the components and rules of the program. Broadly, these details can be categorized as program structure, point structure, and reward structure. Program structure can take two main forms: frequency reward programs and customer tier programs (i.e., hierarchical LPs). In frequency reward programs, customers simply receive rewards that correspond to their spending. In hierarchical LPs, firms grant membership of elevated tiers (e.g., gold, platinum) to customers who spend at or above a pre-defined threshold. Generally, frequency reward programs are implemented in contexts of low customer commitment and high purchase frequencies (e.g., grocery stores). Conversely, customer tier programs are more often used in high customer involvement contexts (e.g., airlines, hospitality) in which firms aim to cultivate strong customer relationships by giving high-tier members exclusive benefits and preferential treatment (Breugelmans et al. 2015; Kopalle et al. 2012). Program structure also includes enrollment requirements (Dholakia 2006; Lal and Bell 2003; Liu 2007), customer tier configuration (Dréze and Nunes 2009), rules for tier transitions (Wagner, Hennig-Thurau, and Rudolph 2009), and single-firm versus coalition or partnership LPs (Dorotic et al. 2011). Point structure refers to LP specifications, including whether an LP uses points or another unique currency, whether and when points expire, how points are earned, issued, and redeemed, and how points goals are defined (Breugelmans et al. 2015; McCall and Voorhees 2010). Empirical research on point structure has explored these elements of LP design extensively. Examples include studies that compare the effectiveness of points systems to price discounts (Gabel and Guhl 2022; Zhang and Breugelmans 2012), test the effects of clear versus ambiguous 6 points issuance ratios (Bagchi and Li 2011), and examine the influence of goal proximity (i.e., points pressure) and rewarded behavior (i.e., point/reward redemption) (Dorotic et al. 2014 Nastasoiu et al. 2021; Nunes and Dréze 2006a; Taylor and Neslin 2005). LP design also encompasses reward structure, which determines the type, magnitude, and timing of rewards granted to program members (Breugelmans et al. 2015; Dorotic, Bijmolt, and Verhoef 2012; McCall and Voorhees 2010). Empirical studies have compared the effects of reward types and timing extensively (Dorotic et al. 2014; Hwang and Mattila 2018; Keh and Lee 2006; Kivetz 2003; Kumar and Shah 2004; Ma, Li, and Zhang 2018; Minnema, Bijmolt, and Non 2017; Taylor and Neslin 2005; Yi and Jeon 2003). Broadly, the findings of this stream of research suggest the importance of choosing reward types and reward timing that align with the focal firm’s offering, service setting, and LP structure. This is often implemented in practice. For example, airlines offer preferential treatment to high-tier LP members, including early boarding, seat upgrades, and access to exclusive lounges. These experiential or relational rewards take the form of social or psychological benefits (Steinhoff and Palmatier 2016). Psychological Mechanisms and Customer Relationship Management The organizing framework of Henderson, Beck, and Palmatier (2011) focuses on the psychological mechanisms through which LPs influence customer perceptions, attitudes, and loyalty. The authors categorize these mechanisms as loyalty drivers based on habit, status, and relational factors. Importantly, these mechanisms do not include simple price promotions. LPs, by definition, must “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) to drive “true” loyalty (Dick and Basu 1994; Oliver 1999). Although research suggests that LPs can drive loyalty by establishing purchase habits (Lewis 2004; Wood and Neal 2009), more 7 LP research has focused on status and relational factors as drivers of loyalty. This may be attributed to the challenges of operationalizing habit strength. For example, repurchase likelihood or purchase frequency may not accurately capture the formation of one’s purchase habit. Therefore, it is challenging to distinguish a potentially habit-forming effect of an LP reward or feature from its other effects on customer outcomes. Members of hierarchical LPs can earn elevated status, defined as “context-specific and generally desirable elevated ranking within a social hierarchy based on attributed characteristics” (Henderson, Beck, and Palmatier 2011, p. 272). This status is conferred through the membership of high-LP tiers. Research shows that high tier membership has a powerful effect on customer status perceptions and attitudes (Dréze and Nunes 2009), and elevated status serves as a social benefit, especially when it can be signaled to other consumers (Steinhoff and Palmatier 2016). Research has explored the tradeoffs associated with status conferral. For example, Wagner, Hennig-Thurau, and Rudolph (2009) find negative effects of demoting customers following a period of insufficient spending to maintain their status. Further, Steinhoff and Palmatier (2016) take a cross-customer perspective and find that while giving target customers exclusive rewards drives their loyalty, bystander customers who witness these rewards can perceive them as unfair, leading to decreased loyalty. Studies on the tradeoffs of status conferral reflect a trend toward broadened, customer relationship-focused LP research. Steinhoff and Palmatier’s (2016) findings highlight the importance of considering the perceptions of both target and bystander customers. Taking a customer relationship management perspective, research has also expanded conceptualizations of an LP’s role in the customer journey. For example, Steinhoff and Zondag (2021) view LPs as “travel companions” for customers, highlighting their technology-enabled influence across the 8 customers’ journeys through the prepurchase, purchase, and post-purchase stages. Throughout these stages of the customer experience (Lemon and Verhoef 2016; Voorhees et al. 2017), LPs can provide benefits to profitable customers beyond rewards earned by meeting purchase requirements. Reviews of the LP literature have noted the need to take broader conceptual perspectives to fully understand LP effectiveness. Henderson, Beck, and Palmatier (2011) highlight the importance of testing multiple psychological mechanisms simultaneously, and Kim, Steinhoff, and Palmatier (2020) propose a moderating role of customer relationship stages. On the one hand, some studies have responded to these suggestions (e.g., Steinhoff and Palmatier 2016), and future research should continue to move toward comprehensive tests of LP design elements, psychological mechanisms, and moderating customer factors. On the other hand, research has shown the conceptualization of LP effectiveness can be broadened by comparing LP members to nonmembers across contexts. For example, Hernandez-Ortega et al. (2022) find that LP members and nonmembers respond differently to various forms of social media content produced by brands. Research has also shown that LP members tend to react less negatively to negative events (Bolton et al. 2000) and variation in service quality (Voorhees et al. 2021). Extending this approach, we test the role of LP membership outside of established LP effectiveness frameworks, in an SFR context. Service Failure and Recovery Research has investigated SFR extensively over the past three decades, resulting in a mature body of literature of about 1,600 peer-reviewed articles (Grègoire and Mattila 2020). Broadly, the findings of SFR research have illustrated that the stakes are high for firms aiming to mitigate the negatives effects of service failures, including harm to customer perceptions of the 9 firm, negative customer emotions, reduced loyalty, and negative financial outcomes (Bitner 1990; Morgeson et al. 2020; Schoefer and Diamantopoulos 2008; Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998). Marketers face challenges of demanding consumers, and organizations often struggle to provide satisfactory responses to customer complaints following service failures (Van Vaerenbergh et al. 2019). Technological innovations now enable customers to easily influence many other consumers, which can multiply the harm of service failures to brand and firm reputation, further heightening the importance of fielding and addressing customer complaints (Morgeson et al. 2020). Table 1.2 summarizes the three phases of SFR research over the past 30 years (Grègoire and Mattila 2020; Khamitov et al. 2020). The Service Encounter and Service Failure Early SFR research emerged against the backdrop of the massive growth in the service industries, generally low and decreasing consumer satisfaction, and a surge in service quality research (Bitner 1990; Parasuraman, Zeithaml, and Berry 1985; Zeithaml, Berry, and Parasuraman 1988). During this period, studies largely focused on customer evaluations of the service encounter, defined as the “period of time during which a consumer directly interacts with a service” (Shostack 1985, p. 243). That is, the service encounter was generally viewed as a single interaction between the customer and firm. Grounded in the expectation disconfirmation framework (Oliver 1980), services marketing research on service encounters that fail to meet a customer’s expectations (i.e., “negative disconfirmation”) introduced the notion of a service failure (e.g., Bitner, Booms, and Mohr 1994; Bitner, Booms, and Tetreault 1990). This is aligned with contemporary definition of service failure, “a private service performance that falls below the expectation of one or a few customer(s)” (Khamitov et al. 2020, p. 521). 10 The Importance of Service Recovery Service failure and service recovery have generally been studied together (Sivakumar, Li, and Dong 2014). In practice, actions following a failure are crucial, particularly for service- providing firms (Tax, Brown, and Chandrashekaran 1998). Disgruntled customers are increasingly connected to other consumers, and they often use the internet and social media to publicly express their displeasure (Grègoire and Fisher 2008; Morgeson et al. 2020; Schoefer and Diamantopoulos 2008). This highlights that service recovery, defined as “all actions a firm can take to redress the grievances or loss caused by a service failure” (Khamitov et al. 2020, p. 521), is crucial for firms to retain displeased customers (Parasuraman, Berry, and Zeithaml 1991; Dwyer, Schurr, and Oh 1987). Researchers studying SFR comprehensively have taken theoretical perspectives beyond those applied in studies of the service encounter. For example, Tax, Brown, and Chandrashekaran (1998) apply justice theory, finding that all three dimensions of justice are relevant to customer outcomes following complaint incidents. Specifically, customers consider distributive justice (i.e., fairness of received outcomes), procedural justice (i.e., fairness of the processes by which outcomes are determined), and interpersonal justice (i.e., fairness of the interpersonal treatment they receive during the incident) in their evaluations of complaint incidents. Additionally, Smith, Bolton, and Wagner (1999) conceptualize service recovery as a “bundle of resources.” Applying resource exchange theory and prospect theory, the authors find that service failures are best redressed with resources that fit the service failure. That is, customer satisfaction is best restored when deployed resources match customer losses in terms of resource type and magnitude (Smith, Bolton, and Wagner 1999). 11 Relationship Marketing Principles in SFR Research Together, the seminal articles of Smith et al. (1999) and Tax et al. (1998) have served as the foundation for much of the SFR research published in the intervening years. Paralleling the trend in LP research, recent SFR research has taken a customer relationship management perspective in two ways. First, empirical work increasingly considers sequences of multiple “touchpoints,” each of which can constitute a service failure or recovery, positively or negatively influencing customer evaluations. For example, Sivakumar, Li, and Dong (2014) conceptualize service quality over the course of multiple touchpoints, noting the importance of the failure and recovery sequences. Second, customer relationship attributes are often positioned as moderators of the effects of service failures and recoveries. Grègoire and Fisher (2008) and Grègoire, Tripp, and Legoux (2009) exemplify this, finding counterintuitive effects of relationship quality and strength in SFR contexts. This trend is noted in the organizing framework of Khamitov et al. (2020), in which prior relational attributes play a moderating role. Hypothesis Development In this section, we present our conceptual model and hypotheses. Our conceptual model (Figure 1.1) includes customer outcomes (satisfaction, repurchase intentions, and likelihood to recommend) as dependent variables and service failure as the independent variable. LP membership is positioned as a moderator. Two factors support this approach. First, positioning LP membership, a customer-firm relationship attribute, as a moderator is aligned with the organizing framework of Khamitov, Grègoire, and Suri (2020), which is based on prior SFR studies involving relational factors (e.g., Grègoire and Fisher 2008; Grègoire, Tripp, and Legoux 2009). Second, prior studies have found moderating effects of LP-related criteria on the outcomes of various firm actions (e.g., Bolton et al. 2000; Hernandez-Ortega et al. 2022; 12 Voorhees et al. 2021). Broadly, the moderating role of LP membership is based on the idea that LP members’ relationships with the firm are stronger than those of nonmembers. LP members are likely to be more committed to the service provider, in a mindset less prone to negative outcomes of service failures. Negative Outcomes of Service Failure Grounded in justice theory and attribution theory, prior research has established that service failures are reflected in negative customer evaluations of service encounters and negatively affect relationship quality, customer attitudes, and behavioral loyalty (Bitner 1990; Smith, Bolton, and Wagner 1999; Tax, Brown, and Chandrashekaran 1998). Further, SFR research has identified a range of mechanisms that mediate these effects, including reduced value perceptions, blame attributions, injustice/unfairness perceptions, anger, anxiety, disgust, frustration, rage, rumination, and helplessness (Chebat and Slusarczyk 2005; Grègoire and Fisher 2008; Joireman et al. 2013; McColl-Kennedy et al. 2009; Strizhakova et al. 2012). It is well established that these cognitive and emotional processes drive negative outcomes including demands for reparation, negative word of mouth, public complaining, avoidance, desire for revenge, customer retaliation, and reduced customer loyalty (Chebat and Slusarczyk 2005; Gelbrich 2010; Grègoire and Fisher 2008; Komarova Loureiro et al. 2018; Schoefer and Diamantopoulos 2008). Therefore, we hypothesize: H1: Service failure is negatively associated with (a) satisfaction, (b) repurchase intentions, and (c) likelihood to recommend. The Moderating Role of LP Membership A subset of SFR studies have examined the moderating role of customer-firm relationship attributes. For example, Grègoire and Fisher (2008) find that airline customers in high-quality 13 relationships with the firm experience greater feelings of betrayal following a failed service recovery (i.e., double deviation) than customers in low-quality relationships. Similarly, Grègoire, Tripp, and Legoux (2009) find that customers in strong relationships tend to hold stronger “grudges” against firms than customers with weaker relationships to the firm. The findings of Grègoire and Fisher (2008) and Grègoire, Tripp, and Legoux (2009) constitute what they call the “love becomes hate” effect. Research has shown that the opposite, “love is blind,” effect is also possible. That is, close relationships with providers can reduce the negative effects of service failures. For example, Wan, Hui, and Wyer (2011) find that friendship with a service provider can reduce the harm of service failure if the customer attributes the failure to a misunderstanding. Further, Harmeling et al. (2015) find distinct moderating effects of relational expectations. Specifically, high relational expectations exacerbate the effects of negative relational disconfirmations (i.e., provider violations of relational norms), but high relational expectations reduce the effects of product disconfirmations (including service failures). The mixed findings of SFR research on the moderating role of relational factors offer conflicting perspectives on how LP membership might influence the effects of service failure. However, relevant LP research outside of SFR contexts suggests that LP membership has a “love is blind” effect. Three lines of reasoning support this. First, applying behavioral learning theory (Rothschild and Gaidis 1981), Yi and Jeon (2003) find that LP members can become loyal to the program itself, which extends to and results in overall brand loyalty. Kim et al. (2013) also find that the positive effects of perceived LP benefits on customer loyalty are partially or fully mediated by program loyalty. 14 Second, in a financial services context, Bolton, Kannan, and Bramlett (2000) find that LP members are affected less by negative evaluations of the focal firm relative to competition than nonmembers. The authors suggest that “members of loyalty programs are likely to be less knowledgeable — and less certain — about the performance of competing service alternatives than nonmembers because the bulk of their experience is with their current service provider” (p. 98). Finally, applying intimacy theory (Perlman and Fehr 1987), Voorhees et al. (2021) find that LP participation is associated with greater tolerance for variation in service quality. Additionally, LP features that confer elevated status on qualified customers have the same moderating effect. The authors suggest that these effects can be attributed to greater relational commitment among customers who participate consistently in an LP and earn elevated status within the program. This reasoning is further supported by research finding positive long-term effects of customer tier (i.e., hierarchical) programs on attitudinal loyalty, positive attitudes, and relationship commitment (Breugelmans et al. 2015; Kopalle et al. 2012). To the extent that they are invested in status conferred through the LP, we expect that LP members will discount service failures. That is, the negative effect of service failure will be weaker for LP members than nonmembers. Therefore, we hypothesize: H2: Loyalty program membership moderates the negative effects of service failure on (a) cumulative satisfaction, (b) repurchase intentions, and (c) likelihood to recommend, such that they are weaker (i.e., less negative) for members than for nonmembers. 15 Data, Methods, and Results Data To test our hypotheses, we acquired a data set from the American Customer Satisfaction Index (ACSI), including measures of customer expectations and attitudes, customer-company relationship attributes, and complaint information. The data were collected via post consumption surveys between February 2019 and March 2020. Respondents were airline customers who had flown on a commercial airline within three months of the survey. In total, respondents had flown with nine commercial airlines. Our use of ACSI data is in line with published research on customer satisfaction, loyalty, and complaints (Fornell et al. 1996; Fornell, Morgeson, and Hult 2016; Hult et al. 2019; Lariviere et al. 2016; Morgan and Rego 2006; Morgeson et al. 2020) Table 1.3 lists our model’s constructs and corresponding measures. Respondents reported all dependent variables on 10-point scales (e.g., repurchase intentions: 1 = very unlikely, 10 = very likely). Service failure was operationalized in two ways. First, the Complaint variable was dummy-coded based on whether each respondent had submitted a complaint about their service experience with the focal airline (complaint = 1, no complaint = 0), which we use as an indicator of service failure (Morgeson et al. 2020). Second, we obtained the ServFail variable by comparing ratings of overall service quality to expectations of overall service quality. Individuals who rated overall quality lower than their expectations were coded as having experienced a service failure. This is aligned with the definition of service failure and the idea that customer expectations are disconfirmed negatively in such sub-standard service performances (Bitner, Booms, and Mohr 1994; Bitner, Booms, and Tetrealt 1990; Khamitov et al. 2020; Oliver 1980). LP membership was dummy-coded based on whether each respondent was an LP member of the focal airline. 16 Additional control variables were reported via the ACSI survey. Upgraded class was dummy-coded based on the class or cabin in which each respondent’s seat was located. As each airline features unique naming schemes for its seating options, we simplified this measurement to indicate whether each respondent was in an upgraded class within his or her focal airline’s array of options (upgraded class = 1, non-upgraded class = 0). Upgraded class was generally defined as being in a separate cabin (e.g., “first class”). Based on definitions in the transportation literature (Azadian and Vasigh 2019; Bachwich and Wittman 2017), we also distinguish three airline types: legacy airlines, low-cost carriers (LCCs), and ultra-low-cost carriers (ULCCs). Legacy airlines included American Airlines, Alaska Airlines, Delta Air Lines, and United Airlines. LCCs included Jet Blue and Southwest Airlines. ULCCs included Allegiant Air, Frontier Airlines, and Spirit Airlines. Control variables also included passenger gender (male = 1, female = 0), flight purpose (personal = 1, business = 0), and flight type (international = 1, domestic = 0). Frequency distributions and descriptive statistics are listed in Table 1.4 and 1.5. Model Specification To test our hypotheses, we specify a series of seemingly unrelated regression (SUR) models and use STATA statistical software for our analysis. Following established procedures (e.g., Lucia-Palacios, Perez-Lopez, and Polo-Redondo 2020; Sharma et al. 2020), we use SUR to specify three regression equations simultaneously. Per our conceptual model, the SUR includes three dependent variables: satisfaction (Sat), repurchase intention (Rep), and likelihood to recommend (Rec). As we expect the error terms to correlate across the three equations, we use SUR to obtain more efficient estimates than ordinary least squares regression (Zellner 1962; Zellner and Huang 1962). Model 1 is specified as follows: Sati = α1 + β1(Complaint) + β2(LPMember) + β3(Complaint*LPMember) + ε1i 17 Repi = α2 + β1(Complaint) + β2(LPMember) + β3(Complaint*LPMember) + ε2i Reci = α3 + β1(Complaint) + β2(LPMember) + β3(Complaint*LPMember) + ε3i cov(ε1i, ε2i, ε3i) ≠ 0 where Complaint represents whether a service failure occurred, measured as whether an individual submitted a complaint to the airline following a service failure, and LPMember indicates whether an individual is a member of the focal airline’s loyalty program. To improve the robustness of our results, we use our second operationalization of service failure in Model 2. That is, we replace the complaint proxy for service failure with the expectations-based measure of service failure (ServFail). Model 2 is specified as follows: Sati = α1 + β1(ServFail) + β2(LPMember) + β3(ServFail*LPMember) + ε1i Repi = α2 + β1(ServFail) + β2(LPMember) + β3(ServFail*LPMember) + ε2i Reci = α3 + β1(ServFail) + β2(LPMember) + β3(ServFail*LPMember) + ε3i cov(ε1i, ε2i, ε3i) ≠ 0 Model 3 extends Model 1, as specified in the Appendix. Model 3 includes the same dependent variables, independent variable (service failure measured as Complaint), and moderator (LP Membership). We add ticket class, airline type, flight type, flight purpose, and gender as control variables in Model 3. Model 4, also specified in the Appendix, extends Model 2 with the addition of the same control variables. Results The results of Model 1 and Model 2 are summarized in Table 1.6 and Table 1.7, respectively. For Model 1, H1 is supported by a significant, negative effect of service failure (operationalized as customer complaint) on satisfaction (b1 = -2.02, p < .01), repurchase intentions (b1 = -2.01, p < .01), and likelihood to recommend (b1 = -2.15, p < .01). Our central 18 hypothesis, H2, is also supported for Model 1. LP membership moderates the negative effect of service failure on satisfaction (b3 = 1.50, p < .01), repurchase intentions (b3 = 1.40, p < .01), and likelihood to recommend (b3 = 1.55, p < .01). This indicates that the negative effect of service failure is weaker for LP members than nonmembers. These results are consistent across operationalizations of service failure. The results of Model 2, in which service failure is operationalized as a negative discrepancy between overall quality and expected quality, we find a significant, negative effect of service failure on satisfaction (b1 = -1.41, p < .01), repurchase intentions (b1 = -1.29, p < .01), and likelihood to recommend (b1 = -1.39, p < .01), supporting H1. Importantly, Model 2 also yields support for H2, as LP membership moderates the negative effect of service failure on satisfaction (b3 = .51, p < .01), repurchase intentions (b3 = .58, p < .01), and likelihood to recommend (b3 = .56, p < .01). Error terms are significantly correlated across equations in Model 1 and Model 2, supporting the selection of SUR over OLS (Zellner 1962; Zellner and Huang 1962). As shown in the results tables, LP membership has a significant (p < .01), positive main effect on all three dependent variables across Model 1 and Model 2. This effect was not hypothesized. The models yield the same hypothesis test results and show comparable overall fit in terms of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). However, R 2 is consistently higher in Model 2 than Model 1. The inclusion of control variables in Model 3 and Model 4 had no effect on our hypothesis tests and yielded modest improvements in overall model fit (see Table 1.8 and Table 1.9 in appendix). 19 Implications for LP and SFR Research Discussion Our main finding is that the negative effects of service failure are less severe for LP members than nonmembers. This is a unique contribution to the literature on LP effectiveness and SFR because past research has not tested the moderating role of LP membership in a service failure context. Some relevant research has been published in both streams of literature. For example, Hernandez-Ortega et al. (2022) compare LP members and nonmembers, finding differential responses to branded social media posts. SFR research has examined the moderating role of other relational attributes, including relationship quality and relationship strength in service failure contexts (Grègoire and Fisher 2008; Grègoire, Tripp, and Legoux 2009). Finally, Voorhees et al. (2021) examine the effects of service quality variation on customer outcomes and find a moderating effect of LP participation and LP characteristics. However, to our knowledge, no previous study has combined these elements in assessing the effects of LP membership on service failure. Our findings extend insights on how LPs influence customer outcomes beyond published LP effectiveness frameworks. Organizing frameworks have centered on the effects of LP design and execution elements on customer attitudinal and behavioral loyalty (e.g., Breugelmans et al. 2015; McCall and Voorhees 2010), psychological mechanisms that mediate these effects (e.g., Henderson, Beck, and Palmatier 2011), and/or operational elements and customer-firm relationship stages that moderate these effects (e.g., Kim, Steinhoff, and Palmatier 2020). The effects of LP design and execution on customer loyalty do not capture the influence of LP membership in the context of other firm actions. Our findings suggest that the value of LPs not only comes from positive outcomes associated with their design, implementation, and operation, 20 but also the insulation of profitable customers from negative outcomes associated with service failures. This study also contributes to the SFR literature by uniquely positioning LP membership as a relational attribute that influences the effect of service failure on customer outcomes, supporting the framework of Khamitov et al. (2020). In addition to the hypothesized negative effects of service failure and moderating effect of LP membership, we find a positive main effect of LP membership. This contradicts the findings of Bolton et al. (2000), who find in a financial service context “the main effect of being a member of the loyalty program is not statistically significant. In other words, being a member of the loyalty program per se does not directly affect the chances of account retention.” It is possible that this discrepancy could be attributed to differing contexts, as the nature of the negative events studied differs (losses on financial investments versus service failure in an airline context). Additionally, financial services and airlines are broadly different contexts in which customers are likely to feel differing levels of involvement. Yi and Jeon (2003) find that in high- involvement settings, customer loyalty to the LP drives brand loyalty. Managerial Implications For managers, our findings offer an explanation as to how carrying an LP drives organizational performance outcomes. The value of reducing the negative effects of service failures may not be clearly captured in extant marketing metrics. Further, adopting an SFR framework to determine how LP members differentially respond to service failures extends our understanding of LP effectiveness outside of established, LP-specific frameworks (e.g., Kim, Steinhoff, and Palmatier 2020). Therefore, our findings help eliminate the confusion surrounding precisely how LPs might drive positive firm performance (McCall and Voorhees 2010) and are thus worthwhile for firms. 21 Limitations and Future Research The present research has two notable limitations. First, while air travel is a useful context in which to study LPs, future research should consider a broader set of industries and contexts. LP popularity has grown across a vast array of industries, which should be reflected in LP research. Specifically, it is possible that our findings would differ in lower-commitment contexts in which frequency reward programs are utilized rather than hierarchical LPs (Kopalle et al. 2012). Additionally, few LP studies have considered the effects of international contexts and cultural influences on LP effectiveness. Thus, LP effectiveness remains a fruitful area for future research. Second, while our study uniquely tests the role of LP membership in an SFR context, we do not simultaneously control for previously studied aspects of service failure (e.g., failure severity, failure type). Future research could integrate LP membership into a more comprehensive model to test its interactions with various SFR attributes. 22 REFERENCES Azadian, Farshid, and Bijan Vasigh. "The blurring lines between full-service network carriers and low-cost carriers: A financial perspective on business model convergence." Transport Policy 75 (2019): 19-26. Bachwich, Alexander R., and Michael D. Wittman. "The emergence and effects of the ultra-low cost carrier (ULCC) business model in the US airline industry." Journal of Air Transport Management 62 (2017): 155-164. Bagchi, Rajesh, and Xingbo Li. "Illusionary progress in loyalty programs: Magnitudes, reward distances, and step-size ambiguity." Journal of Consumer Research 37, no. 5 (2011): 888-901. Bijmolt, Tammo HA, and Peter C. Verhoef. "Loyalty programs: Current insights, research challenges, and emerging trends." Handbook of marketing decision models (2017): 143- 165. Bijmolt, Tammo HA, Matilda Dorotic, and Peter C. Verhoef. "Loyalty programs: Generalizations on their adoption, effectiveness and design." Foundations and Trends® in Marketing 5, no. 4 (2011): 197-258. Bitner, Mary Jo, Amy L. Ostrom, and Felicia N. Morgan. "Service blueprinting: a practical technique for service innovation." California management review 50, no. 3 (2008): 66- 94. Bitner, Mary Jo, Bernard H. Booms, and Lois A. Mohr. "Critical service encounters: The employee's viewpoint." Journal of marketing 58, no. 4 (1994): 95-106. Bitner, Mary Jo, Bernard H. Booms, and Mary Stanfield Tetreault. "The service encounter: diagnosing favorable and unfavorable incidents." Journal of marketing 54, no. 1 (1990): 71-84. Bitner, Mary Jo. "Evaluating service encounters: the effects of physical surroundings and employee responses." Journal of marketing 54, no. 2 (1990): 69-82. Bolton, Ruth N., P.K. Kannan, and Matthew D. Bramlett. "Implications of loyalty program membership and service experiences for customer retention and value." Journal of the academy of marketing science 28, no. 1 (2000): 95-108. Bombaij, Nick JF, and Marnik G. Dekimpe. "When do loyalty programs work? The moderating role of design, retailer-strategy, and country characteristics." International Journal of Research in Marketing 37, no. 1 (2020): 175-195. Breugelmans, Els, and Yuping Liu-Thompkins. "The effect of loyalty program expiration policy on consumer behavior." Marketing Letters 28, no. 4 (2017): 537-550. 23 Breugelmans, Els, Tammo HA Bijmolt, Jie Zhang, Leonardo J. Basso, Matilda Dorotic, Praveen Kopalle, Alec Minnema, Willem Jan Mijnlieff, and Nancy V. Wünderlich. "Advancing research on loyalty programs: a future research agenda." Marketing Letters 26, no. 2 (2015): 127-139. Chan, Kimmy Wa, Chi Kin Yim, and Taeshik Gong. "An investigation of nonbeneficiary reactions to discretionary preferential treatments." Journal of Service Research 22, no. 4 (2019): 371-387. Chaudhuri, Malika, Clay M. Voorhees, and Jonathan M. Beck. "The effects of loyalty program introduction and design on short-and long-term sales and gross profits." Journal of the Academy of marketing science 47, no. 4 (2019): 640-658. Chebat, Jean-Charles, and Witold Slusarczyk. "How emotions mediate the effects of perceived justice on loyalty in service recovery situations: an empirical study." Journal of business research 58, no. 5 (2005): 664-673. Chen, Yanyan, Timo Mandler, and Lars Meyer-Waarden. "Three decades of research on loyalty programs: A literature review and future research agenda." Journal of Business Research 124 (2021): 179-197. DeWitt, Tom, and Michael K. Brady. "Rethinking service recovery strategies: the effect of rapport on consumer responses to service failure." Journal of Service Research 6, no. 2 (2003): 193-207. Dholakia, Utpal M. "How customer self-determination influences relational marketing outcomes: evidence from longitudinal field studies." Journal of Marketing Research 43, no. 1 (2006): 109-120. Dick, Alan S., and Kunal Basu. "Customer loyalty: toward an integrated conceptual framework." Journal of the academy of marketing science 22, no. 2 (1994): 99-113. Dorotic, Matilda, Dennis Fok, Peter C. Verhoef, and Tammo HA Bijmolt. "Synergistic and cannibalization effects in a partnership loyalty program." Journal of the Academy of Marketing Science 49, no. 5 (2021): 1021-1042. Dorotic, Matilda, Dennis Fok, Peter C. Verhoef, and Tammo HA Bijmolt. "Do vendors benefit from promotions in a multi-vendor loyalty program?." Marketing Letters 22, no. 4 (2011): 341-356. Dorotic, Matilda, Peter C. Verhoef, Dennis Fok, and Tammo HA Bijmolt. "Reward redemption effects in a loyalty program when customers choose how much and when to redeem." International Journal of Research in Marketing 31, no. 4 (2014): 339-355. Dorotic, Matilda, Tammo HA Bijmolt, and Peter C. Verhoef. "Loyalty programmes: Current knowledge and research directions." International Journal of Management Reviews 14, no. 3 (2012): 217-237. 24 Dowling, Grahame R., and Mark Uncles. "Do customer loyalty programs really work?." Sloan management review 38 (1997): 71-82. Drèze, Xavier, and Joseph C. Nunes. "Feeling superior: The impact of loyalty program structure on consumers' perceptions of status." Journal of Consumer Research 35, no. 6 (2009): 890-905. Drèze, Xavier, and Stephen J. Hoch. "Exploiting the installed base using cross-merchandising and category destination programs." International Journal of Research in Marketing 15, no. 5 (1998): 459-471. Dwyer, F. Robert, Paul H. Schurr, and Sejo Oh. "Developing buyer-seller relationships." Journal of marketing 51, no. 2 (1987): 11-27. Eggert, Andreas, Lena Steinhoff, and Ina Garnefeld. "Managing the bright and dark sides of status endowment in hierarchical loyalty programs." Journal of Service Research 18, no. 2 (2015): 210-228. Faramarzi, Ashkan, and Abhi Bhattacharya. "The economic worth of loyalty programs: An event study analysis." Journal of Business Research 123 (2021): 313-323. Fornell, Claes, Michael D. Johnson, Eugene W. Anderson, Jaesung Cha, and Barbara Everitt Bryant. "The American customer satisfaction index: nature, purpose, and findings." Journal of marketing 60, no. 4 (1996): 7-18. Fornell, Claes, Forrest V. Morgeson III, and G. Tomas M. Hult. "Stock returns on customer satisfaction do beat the market: Gauging the effect of a marketing intangible." Journal of Marketing 80, no. 5 (2016): 92-107. Gabel, Sebastian, and Daniel Guhl. "Comparing the effectiveness of rewards and individually targeted coupons in loyalty programs." Journal of Retailing 98, no. 3 (2022): 395-411. Gelbrich, Katja. "Anger, frustration, and helplessness after service failure: coping strategies and effective informational support." Journal of the Academy of Marketing Science 38, no. 5 (2010): 567-585. Grégoire, Yany, and Anna S. Mattila. "Service failure and recovery at the crossroads: recommendations to revitalize the field and its influence." Journal of Service Research 24, no. 3 (2021): 323-328. Grégoire, Yany, and Robert J. Fisher. "Customer betrayal and retaliation: when your best customers become your worst enemies." Journal of the Academy of Marketing Science 36, no. 2 (2008): 247-261. Grégoire, Yany, Thomas M. Tripp, and Renaud Legoux. "When customer love turns into lasting hate: The effects of relationship strength and time on customer revenge and avoidance." Journal of marketing 73, no. 6 (2009): 18-32. 25 Harmeling, Colleen M., Robert W. Palmatier, Mark B. Houston, Mark J. Arnold, and Stephen A. Samaha. "Transformational relationship events." Journal of Marketing 79, no. 5 (2015): 39-62. Henderson, Conor M., Joshua T. Beck, and Robert W. Palmatier. "Review of the theoretical underpinnings of loyalty programs." Journal of Consumer Psychology 21, no. 3 (2011): 256-276. Hernández-Ortega, Blanca I., Michael A. Stanko, Rishika Rishika, Francisco-Jose Molina- Castillo, and José Franco. "Brand-generated social media content and its differential impact on loyalty program members." Journal of the Academy of Marketing Science (2022): 1-20. Hult, G. Tomas M., Pratyush Nidhi Sharma, Forrest V. Morgeson III, and Yufei Zhang. "Antecedents and consequences of customer satisfaction: do they differ across online and offline purchases?." Journal of Retailing 95, no. 1 (2019): 10-23. Hwang, Jiyoung, and Laee Choi. "Having fun while receiving rewards?: Exploration of gamification in loyalty programs for consumer loyalty." Journal of Business Research 106 (2020): 365-376. Hwang, YooHee, and Anna S. Mattila. "Is it my luck or loyalty? The role of culture on customer preferences for loyalty reward types." Journal of Travel Research 57, no. 6 (2018): 769- 778. Joireman, Jeff, Yany Grégoire, Berna Devezer, and Thomas M. Tripp. "When do customers offer firms a “second chance” following a double deviation? The impact of inferred firm motives on customer revenge and reconciliation." Journal of Retailing 89, no. 3 (2013): 315-337. Kang, Jun, Thomas Brashear Alejandro, and Mark D. Groza. "Customer–company identification and the effectiveness of loyalty programs." Journal of Business Research 68, no. 2 (2015): 464-471. Katsikeas, Constantine S., Neil A. Morgan, Leonidas C. Leonidou, and G. Tomas M. Hult. "Assessing performance outcomes in marketing." Journal of marketing 80, no. 2 (2016): 1-20. Keh, Hean Tat, and Yih Hwai Lee. "Do reward programs build loyalty for services?: The moderating effect of satisfaction on type and timing of rewards." Journal of retailing 82, no. 2 (2006): 127-136. Khamitov, Mansur, Yany Grégoire, and Anshu Suri. "A systematic review of brand transgression, service failure recovery and product-harm crisis: integration and guiding insights." Journal of the Academy of Marketing Science 48, no. 3 (2020): 519-542. 26 Kim, Hye-Young, Ji Young Lee, Dooyoung Choi, Juanjuan Wu, and Kim KP Johnson. "Perceived benefits of retail loyalty programs: Their effects on program loyalty and customer loyalty." Journal of Relationship Marketing 12, no. 2 (2013): 95-113. Kim, Jisu J., Lena Steinhoff, and Robert W. Palmatier. "An emerging theory of loyalty program dynamics." Journal of the Academy of Marketing Science 49, no. 1 (2021): 71-95. Kivetz, Ran, and Itamar Simonson. "The idiosyncratic fit heuristic: Effort advantage as a determinant of consumer response to loyalty programs." Journal of marketing research 40, no. 4 (2003): 454-467. Kivetz, Ran. "The effects of effort and intrinsic motivation on risky choice." Marketing Science 22, no. 4 (2003): 477-502. Komarova Loureiro, Yuliya, Kelly L. Haws, and William O. Bearden. "Businesses beware: Consumer immoral retaliation in response to perceived moral violations by companies." Journal of Service Research 21, no. 2 (2018): 184-200. Kopalle, Praveen K., and Scott A. Neslin. “The economic viability of frequency reward programs in a strategic competitive environment.” Review of Marketing Science no. 1 (2003): 1–39. Kopalle, Praveen K., Yacheng Sun, Scott A. Neslin, Baohong Sun, and Vanitha Swaminathan. "The joint sales impact of frequency reward and customer tier components of loyalty programs." Marketing Science 31, no. 2 (2012): 216-235. Kumar, Viswanathan, and Denish Shah. "Building and sustaining profitable customer loyalty for the 21st century." Journal of retailing 80, no. 4 (2004): 317-329. Lal, Rajiv, and David E. Bell. "The impact of frequent shopper programs in grocery retailing." Quantitative marketing and economics 1, no. 2 (2003): 179-202. Lariviere, Bart, Timothy L. Keiningham, Lerzan Aksoy, Atakan Yalçin, Forrest V. Morgeson III, and Sunil Mithas. "Modeling heterogeneity in the satisfaction, loyalty intention, and shareholder value linkage: A cross-industry analysis at the customer and firm levels." Journal of Marketing Research 53, no. 1 (2016): 91-109. Leenheer, Jorna, Harald J. Van Heerde, Tammo HA Bijmolt, and Ale Smidts. "Do loyalty programs really enhance behavioral loyalty? An empirical analysis accounting for self - selecting members." International Journal of Research in Marketing 24, no. 1 (2007): 31- 47. Lemon, Katherine N., and Peter C. Verhoef. "Understanding customer experience throughout the customer journey." Journal of marketing 80, no. 6 (2016): 69-96. Lewis, Michael. "The influence of loyalty programs and short-term promotions on customer retention." Journal of marketing research 41, no. 3 (2004): 281-292. 27 Liu, Yuping, and Rong Yang. "Competing loyalty programs: Impact of market saturation, market share, and category expandability." International Retail and Marketing Review 5, no. 2 (2009): 89-113. Liu, Yuping. "The long-term impact of loyalty programs on consumer purchase behavior and loyalty." Journal of marketing 71, no. 4 (2007): 19-35. Lucia-Palacios, Laura, Raúl Pérez-López, and Yolanda Polo-Redondo. "Does stress matter in mall experience and customer satisfaction?." Journal of Services Marketing (2020). Ma, Baolong, Xiaofei Li, and Lin Zhang. "The effects of loyalty programs in services–a double- edged sword?." Journal of Services Marketing (2017). McCall, Michael, and Clay Voorhees. "The drivers of loyalty program success: An organizing framework and research agenda." Cornell Hospitality Quarterly 51, no. 1 (2010): 35-52. McColl-Kennedy, Janet R., Paul G. Patterson, Amy K. Smith, and Michael K. Brady. "Customer rage episodes: emotions, expressions and behaviors." Journal of Retailing 85, no. 2 (2009): 222-237. Melancon, Joanna Phillips, Stephanie M. Noble, and Charles H. Noble. "Managing rewards to enhance relational worth." Journal of the Academy of Marketing Science 39, no. 3 (2011): 341-362. Minnema, Alec, Tammo HA Bijmolt, and Mariёlle C. Non. "The impact of instant reward programs and bonus premiums on consumer purchase behavior." International journal of Research in Marketing 34, no. 1 (2017): 194-211. Morgan, Neil A., and Lopo Leotte Rego. "The value of different customer satisfaction and loyalty metrics in predicting business performance." Marketing science 25, no. 5 (2006): 426-439. Morgeson III, Forrest V., G. Tomas M. Hult, Sunil Mithas, Timothy Keiningham, and Claes Fornell. "Turning complaining customers into loyal customers: Moderators of the complaint handling–Customer loyalty relationship." Journal of Marketing 84, no. 5 (2020): 79-99. Nastasoiu, Alina, Neil T. Bendle, Charan K. Bagga, Mark Vandenbosch, and Salvador Navarro. "Separating customer heterogeneity, points pressure and rewarded behavior to assess a retail loyalty program." Journal of the Academy of Marketing Science 49, no. 6 (2021): 1132-1150. Navarro, José Gabriel. “Average Memberships in Loyalty Programs in the U.S. 2015-2022.” Statista. January 6, 2023. https://www.statista.com/statistics/618744/average-number-of- loyalty-programs-us-consumers-belong-to/. Nunes, Joseph C., and Xavier Drèze. "The endowed progress effect: How artificial advancement increases effort." Journal of Consumer Research 32, no. 4 (2006a): 504-512. 28 Nunes, Joseph C., and Xavier Drèze. "Your loyalty program is betraying you." Harvard business review 84, no. 4 (2006b): 124-31. Oliver, Richard L. "A cognitive model of the antecedents and consequences of satisfaction decisions." Journal of marketing research 17, no. 4 (1980): 460-469. Oliver, Richard L. "Whence consumer loyalty?." Journal of marketing 63, no. 4_suppl1 (1999): 33-44. Palmeira, Mauricio, Nicolas Pontes, Dominic Thomas, and Shanker Krishnan. "Framing as status or benefits? Consumers’ reactions to hierarchical loyalty program communication." European Journal of Marketing 50, no. 3/4 (2016): 488-508. Parasuraman, A., Valarie A. Zeithaml, and L. Berry. "SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality." 1988 64, no. 1 (1988): 12-40. Parasuraman, Anantharanthan, Valarie A. Zeithaml, and Leonard L. Berry. "A conceptual model of service quality and its implications for future research." Journal of marketing 49, no. 4 (1985): 41-50. Parasuraman, Arun, Leonard L. Berry, and Valarie A. Zeithaml. "Understanding customer expectations of service." Sloan management review 32, no. 3 (1991): 39-48. Perlman, Daniel, and Beverley Fehr. "The development of intimate relationships." (1987). Rothschild, Michael L., and William C. Gaidis. "Behavioral learning theory: Its relevance to marketing and promotions." Journal of marketing 45, no. 2 (1981): 70-78. Schoefer, Klaus, and Adamantios Diamantopoulos. "The role of emotions in translating perceptions of (in) justice into postcomplaint behavioral responses." Journal of service research 11, no. 1 (2008): 91-103. Sharma, Amalesh, Aditya Christopher Moses, Sourav Bikash Borah, and Anirban Adhikary. "Investigating the impact of workforce racial diversity on the organizational corporate social responsibility performance: An institutional logics perspective." Journal of Business Research 107 (2020): 138-152. Shostack, G. Lynn. "Planning the service encounter." The service encounter (1985). Shugan, Steven M. "Brand loyalty programs: are they shams?." Marketing Science 24, no. 2 (2005): 185-193. Sivakumar, K., Mei Li, and Beibei Dong. "Service quality: The impact of frequency, timing, proximity, and sequence of failures and delights." Journal of Marketing 78, no. 1 (2014): 41-58. 29 Smith, Amy K., Ruth N. Bolton, and Janet Wagner. "A model of customer satisfaction with service encounters involving failure and recovery." Journal of marketing research 36, no. 3 (1999): 356-372. Stathopoulou, Anastasia, and George Balabanis. "The effects of loyalty programs on customer satisfaction, trust, and loyalty toward high-and low-end fashion retailers." Journal of Business Research 69, no. 12 (2016): 5801-5808. Steinhoff, Lena, and Marcellis M. Zondag. "Loyalty programs as travel companions: Complementary service features across customer journey stages." Journal of Business Research 129 (2021): 70-82. Steinhoff, Lena, and Robert W. Palmatier. "Understanding loyalty program effectiveness: managing target and bystander effects." Journal of the Academy of Marketing Science 44, no. 1 (2016): 88-107. Stourm, Valeria, Scott A. Neslin, Eric T. Bradlow, Els Breugelmans, So Yeon Chun, Pedro Gardete, P. K. Kannan et al. "Refocusing loyalty programs in the era of big data: a societal lens paradigm." Marketing Letters 31, no. 4 (2020): 405-418. Strizhakova, Yuliya, Yelena Tsarenko, and Julie A. Ruth. "“I’m mad and I can’t get that service failure off my mind” coping and rumination as mediators of anger effects on customer intentions." Journal of Service Research 15, no. 4 (2012): 414-429. Surachartkumtonkun, Jiraporn, Paul G. Patterson, and Janet R. McColl-Kennedy. "Customer rage back-story: linking needs-based cognitive appraisal to service failure type." Journal of Retailing 89, no. 1 (2013): 72-87. Tax, Stephen S., Stephen W. Brown, and Murali Chandrashekaran. "Customer evaluations of service complaint experiences: implications for relationship marketing." Journal of marketing 62, no. 2 (1998): 60-76. Taylor, Gail Ayala, and Scott A. Neslin. "The current and future sales impact of a retail frequency reward program." Journal of Retailing 81, no. 4 (2005): 293-305. Van Heerde, Harald J., and Tammo HA Bijmolt. "Decomposing the promotional revenue bump for loyalty program members versus nonmembers." Journal of Marketing Research 42, no. 4 (2005): 443-457. Van Vaerenbergh, Yves, Dorottya Varga, Arne De Keyser, and Chiara Orsingher. "The service recovery journey: Conceptualization, integration, and directions for future research." Journal of Service Research 22, no. 2 (2019): 103-119. Verhoef, Peter C. "Understanding the effect of customer relationship management efforts on customer retention and customer share development." Journal of marketing 67, no. 4 (2003): 30-45. 30 Voorhees, Clay M., Jonathan M. Beck, Praneet Randhawa, Kristen Bell DeTienne, and Sterling A. Bone. "Assessing the Effects of Service Variability on Consumer Confidence and Behavior." Journal of Service Research 24, no. 3 (2021): 405-420. Voorhees, Clay M., Paul W. Fombelle, Yany Gregoire, Sterling Bone, Anders Gustafsson, Rui Sousa, and Travis Walkowiak. "Service encounters, experiences and the customer journey: Defining the field and a call to expand our lens." Journal of Business Research 79 (2017): 269-280. Wagner, Tillmann, Thorsten Hennig-Thurau, and Thomas Rudolph. "Does customer demotion jeopardize loyalty?." Journal of marketing 73, no. 3 (2009): 69-85. Wan, Lisa C., Michael K. Hui, and Robert S. Wyer Jr. "The role of relationship norms in responses to service failures." Journal of Consumer Research 38, no. 2 (2011): 260-277. Wiebenga, Jacob H., and Bob M. Fennis. "The road traveled, the road ahead, or simply on the road? When progress framing affects motivation in goal pursuit." Journal of Consumer Psychology 24, no. 1 (2014): 49-62. Wollan, Robert, Phil Davis, Fabio De Angelis, and Kevin Quiring. “Seeing Beyond the Loyalty Illusion: It’s Time You Invest More Wisely.” Accenture. 2017. https://www.azpiral.com/wp-content/uploads/2017/05/Accenture-Strategy-GCPR- Customer-Loyalty.pdf/. Wood, Wendy, and David T. Neal. "The habitual consumer." Journal of Consumer Psychology 19, no. 4 (2009): 579-592. Yi, Youjae, and Hoseong Jeon. "Effects of loyalty programs on value perception, program loyalty, and brand loyalty." Journal of the academy of marketing science 31, no. 3 (2003): 229-240. Zellner, Arnold, and David S. Huang. "Further properties of efficient estimators for seemingly unrelated regression equations." International Economic Review 3, no. 3 (1962): 300- 313. Zellner, Arnold. "An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias." Journal of the American statistical Association 57, no. 298 (1962): 348-368. Zhang, Jie, and Els Breugelmans. "The impact of an item-based loyalty program on consumer purchase behavior." Journal of Marketing research 49, no. 1 (2012): 50-65. 31 APPENDIX Table 1.1: Overview of Loyalty Program Effectiveness Research Program Design and Execution Psychological Mechanisms Habit Lewis (2004) Wood and Neal (2009) Status Dréze and Nunes (2009) Eggert, Steinhoff, and Garnefeld (2015) Kumar and Shah (2004) Steinhoff and Palmatier (2016) Wagner, Hennig-Thurau, and Rudolph (2009) Relationship Eggert, Steinhoff, and Garnefeld (2015) Hernandez-Ortega et al. (2022) Steinhoff and Palmatier (2016) Steinhoff and Zondag (2021) Voorhees et al. (2021) Wagner, Hennig-Thurau, and Rudolph (2009) Program Structure Dréze and Nunes (2009) Dorotic et al. (2011) Kopalle et al. (2012) Lal and Bell (2003) Liu (2007) Point Structure Breugelmans and Liu-Thompkins (2017) Dorotic et al. (2014) Nastasoiu et al. (2021) Nunes and Dréze (2006a) Zhang and Breugelmans (2012) Reward Structure Bagchi and Li (2011) Hwang and Choi (2020) Hwang and Mattila (2018) Keh and Lee (2006) Kivetz (2003) Kumar and Shah (2004) Ma, Li, and Zhang (2018) Minnema, Bijmolt, and Non (2017) Taylor and Neslin (2005) Yi and Jeon (2003) 32 Table 1.2: Overview of Service Failure and Recovery Research The Service Encounter Failure and Recovery Customer Relationship Management Focal Phenomena - Service Quality - Service Failure - Failure Severity - Recovery Strategies - Double Deviations - Relationship Strength/Quality - Sequences of SFR - Complaint Handling Primary Theoretical Perspective(s) Exemplary Research Expectation Disconfirmation Bitner (1990) Bitner et al. (1990) Bitner et al. (1994) Shostack (1985) Attribution Theory, Justice Theory DeWitt and Brady (2003) Tax et al. (1998) Smith et al. (1999) Relationship Marketing Grégoire and Fisher (2008) Joireman et al. (2013) Morgeson et al. (2020) Sivakumar, Li, and Dong (2014) 33 Construct Dependent Variables Satisfaction Table 1.3: Constructs and Measures Measure Sat: How satisfied are you with [Focal Airline]? 10-point scale: 1 = Very dissatisfied, 10 = Very satisfied Repurchase Intentions Rep: The next time you are going to travel by air, how likely is it that it will be with [Focal Airline]? 10-point scale: 1 = Very unlikely, 10 = Very likely Likelihood to Recommend Rec: How likely is it that you would recommend [Focal Airline]? 10-point scale: 1 = Very unlikely, 10 = Very likely Independent Variables Service Failure (Complaint) Service Failure (ServFail) Moderator Complaint: 1 = Customer submitted a complaint, 0 = Customer did not submit a complaint ServFail: 1 = Overall quality was lower than expectations, 0 = Overall quality was equal to or greater than expectations LP Membership LPMember: 1 = Member of the focal airline’s loyalty program, 0 = Nonmember Control Variables Class Class: 1 = Customer’s ticket was for a premium seat, 0 = Customer’s ticket was for a basic seat. Legacy Legacy: 1 = Customer flew with a legacy airline, 0 otherwise Ultra Low-cost Carrier ULCC: 1 = Customer flew with an ultra low-cost carrier airline, 0 otherwise Flight Type FlightType: 1 = International flight, 0 = Domestic flight Purpose Gender Purpose: 1 = Personal flight, 0 = Business flight Gender: 1 = Female, 0 = Male 34 Figure 1.1: Conceptual Model 35 Table 1.4: Frequency Distributions Variable Freq. Percent Airline Type Legacy Low Cost Carrier Ultra Low Cost Carrier Total Class Premium Basic Total Purpose Personal Business Total Flight Type Domestic International Total Gender Female Male Total LP Member Nonmember Member Total Service Failure (ServFail) No Failure Failure Total Service Failure (Complaint) No Failure Failure Total 3,939 2,195 1,546 7,680 5,826 1,656 7,482 1,873 6,276 8,149 6,454 1,657 8,111 4,144 4,005 8,149 3,668 4,489 8,157 5,905 2,252 8,157 6,858 1,271 8,129 51.3 28.6 20.1 100.0 77.9 22.1 100.0 23.0 77.0 100.0 79.6 20.4 100.0 50.9 49.1 100.0 45.0 55.0 100.0 72.4 27.6 100.0 84.4 15.6 100.0 Note: Sample sizes vary due to missing data. 36 Table 1.5: Descriptive Statistics and Correlation Matrix Variable 1 2 3 Mean 1. Satisfaction 2. Repurchase Intentions 3. Likelihood to Recommend n = 8,034 0.719 0.781 0.819 8.130 8.184 8.016 SD 1.812 2.026 2.094 37 Table 1.6: Model 1 Results Intercept Complaint LP Membership Comp. * LP Mem. R2 DV: Satisfaction Std. Error .03 .11 .04 .13 Estimate 8.04** -2.02** .45** 1.50** .068 DV: Repurchase Intentions Estimate Std. Error DV: Likelihood to Rec. Estimate Std. Error .03 .12 .05 .14 7.96** -2.01** .70** 1.40** .073 .04 .13 .05 .15 7.80** -2.15** .67** 1.55** .071 81885.72 81969.43 AIC BIC *p < .05 **p < .01 n = 7,908 Breusch-Pagan test of independence: χ2(3) = 13,637.46, p < .001 38 Intercept Service Failure LP Membership SF * LP Mem. R2 DV: Satisfaction Std. Error .03 .07 .05 .09 Estimate 8.24** -1.41** .39** .51** .100 Table 1.7: Model 2 Results DV: Repurchase Intentions Estimate Std. Error .04 .07 .05 .10 8.13** -1.29** .59** .58** .081 81893.23 81976.96 AIC BIC *p < .05 **p < .01 n = 7,925 Breusch-Pagan test of independence: χ2(3) = 13,552.84, p < .001 DV: Likelihood to Rec. Std. Error .04 .08 .05 .10 Estimate 7.99** -1.39** .59** .56** .085 39 Model 3 Specification Sati = α1 + β1(Complaint) + β2(LPMember) + β3(Complaint *LPMember) + β4(Class) + β5(Legacy) + β6(ULCC) + β7(FlightType) + β8(Purpose) + β9(Gender) + ε1i Repi = α2 + β1(Complaint) + β2(LPMember) + β3(Complaint *LPMember) + β4(Class) + β5(Legacy) + β6(ULCC) + β7(FlightType) + β8(Purpose) + β9(Gender) + ε2i Reci = α3 + β1(Complaint) + β2(LPMember) + β3(Complaint *LPMember) + β4(Class) + β5(Legacy) + β6(ULCC) + β7(FlightType) + β8(Purpose) + β9(Gender) + ε3i cov(ε1i, ε2i, ε3i) ≠ 0 40 Table 1.8: Model 3 Results DV: Satisfaction DV: Repurchase Intentions DV: Likelihood to Rec. Intercept Complaint LP Membership Comp. * LP Mem. Class Legacy ULCC Flight Type Purpose Gender R2 Std. Error .07 .11 .04 .13 .05 .05 .06 .05 .05 .04 Estimate 8.55** -1.90** .32** 1.35** .22** -.31** -.67** .03 -.22** .06 .085 Estimate 8.56** -1.99** .51** 1.35** .29** -.38** -.89** .04 -.17** .08 .101 Std. Error .08 .13 .05 .14 .06 .05 .07 .06 .06 .05 Std. Error .08 .13 .05 .15 .06 .05 .07 .06 .06 .05 Estimate 8.53** -2.10** .47** 1.46** .31** -.45** -1.03** .03 -.26** .07 .107 73928.70 74135.33 AIC BIC *p < .05 **p < .01 n = 7,242 Breusch-Pagan test of independence: χ2(3) = 12,341.92, p < .001 41 Model 4 Specification Sati = α1 + β1(ServFail) + β2(LPMember) + β3(ServFail*LPMember) + β4(Class) + β5(Legacy) + β6(ULCC) + β7(FlightType) + β8(Purpose) + β9(Gender) + ε1i Repi = α2 + β1(ServFail) + β2(LPMember) + β3(ServFail*LPMember) + β4(Class) + β5(Legacy) + β6(ULCC) + β7(FlightType) + β8(Purpose) + β9(Gender) + ε2i Reci = α3 + β1(ServFail) + β2(LPMember) + β3(ServFail*LPMember) + β4(Class) + β5(Legacy) + β6(ULCC) + β7(FlightType) + β8(Purpose) + β9(Gender) + ε3i cov(ε1i, ε2i, ε3i) ≠ 0 42 Intercept Service Failure LP Membership SF * LP Mem. Class Legacy ULCC Flight Type Purpose Gender R2 DV: Satisfaction Std. Error .07 .07 .05 .09 .05 .05 .06 .05 .05 .04 Estimate 8.72** -1.39** .24** .51** .20** -.28** -.75** -.01 -.16** .03 .121 Table 1.9: Model 4 Results DV: Repurchase Intentions Estimate Std. Error .08 .08 .05 .10 .06 .05 .07 .06 .06 .05 8.69** -1.28** .39** .59** .26** -.37** -.99** -.01 -.09 .04 .110 73919.16 74125.86 AIC BIC *p < .05 **p < .01 n = 7,257 Breusch-Pagan test of independence: χ2(3) = 12,246.52, p < .001 DV: Likelihood to Rec. Std. Error .08 .08 .06 .10 .06 .05 .07 .06 .06 .05 Estimate 8.66** -1.33** .39** .52** .28** -.44** -1.13** -.01 -.19** .04 .120 43 ESSAY TWO: EXPLORING THE EFFECTS OF PERSONAL VALUES ON CONSUMER PREFERENCES FOR LOYALTY PROGRAMS The popularity of loyalty programs (LPs) has increased dramatically over the past three decades. Although early LP research raised questions regarding the capacity of LPs to increase customer attitudinal and behavioral loyalty (Berman 2006; Dowling and Uncles 1997; Nunes and Drèze 2006; Sharp and Sharp 1997; Shugan 2005; Stauss, Schmidt, and Schoeler 2005; Uncles, Dowling, and Hammond 2003), recent work suggests that investments in LPs are justified. Research has linked LPs to positive customer attitudinal outcomes (Drèze and Nunes 2009; García Gómez, Gutiérrez Arranz, and Gutiérrez Cillán 2006; Stathopoulou and Balabanis 2016; Steinhoff and Palmatier 2016), customer engagement and purchase behavior (Belli et al. 2022; Kopalle et al. 2012; Leenheer et al. 2007; van Heerde and Bijmolt 2005; Zhang and Breugelmans 2012), and positive firm performance outcomes (Bombaij and Dekimpe 2020; Chaudhuri, Voorhees, and Beck 2019; Faramarzi and Bhattacharya 2021). Further, consumers increasingly expect the option to enroll in LPs (Navarro 2023), pressuring most firms to offer LPs in order to remain competitive. Consumers even become loyal to an LP in addition to their loyalty to a brand or company (Evanschitzky et al. 2012; Kang, Alejandro, and Groza 2015; Rosenbaum, Ostrom, and Kuntze 2005; Yi and Jeon 2003). LP research increasingly focuses on how and under what conditions LPs drive customer loyalty. Specifically, much of the LP literature investigates the psychological mechanisms through which LPs influence attitudinal and behavioral loyalty. A widely studied mechanism is status, defined as “context-specific and generally desirable elevated ranking within a social hierarchy based on attributed characteristics” (Henderson, Beck, and Palmatier 2011, p. 272). Firms often structure LPs such that a set of tiers (e.g., gold, silver, bronze) defines a structured 44 hierarchy of members and membership. Through repeat purchases, members can advance to higher tiers, elevating their status within the program. Customers who achieve elevated status are then granted access to exclusive services and other benefits beyond the tangible rewards earned within the program. Thus, LPs provide an opportunity for firms and customers to enhance their relationships. For firms, LPs define rules for prioritizing select customers and granting them preferential treatment. For customers, LPs allow access to special benefits, rewards, and feelings of superiority relative to other customers. Most status-focused LP research examines the effects of status conferral at the individual level (e.g., Drèze and Nunes 2009, Steinhoff and Palmatier 2016). Conversely, LP research incorporating cultural factors primarily focuses on country-level differences in consumer preferences for LPs (Beck, Chapman, and Palmatier 2015; Thompson and Chmura 2015). Researchers have called for relationship marketing and LP research that integrates cultural theories across national, organizational, and individual levels (Chen, Mandler, and Meyer- Waarden 2021; Samaha, Beck, and Palmatier 2014). Against this backdrop, it is surprising that research has not yet examined the joint effects of status as a personal value and country-level cultural differences in consumer preferences regarding LPs. To address this deficiency in the literature, we center the present research on the following research question: How do individuals’ personal values and country-level cultural differences influence consumer preferences for loyalty programs? In line with the LP literature and applying Schwartz’s (1992, 2012) theory of universal basic values, we hypothesize a positive relationship between valuing status and the importance of LPs. Additionally, we integrate Hofstede’s (1980) cultural dimension of power distance (PDI), defined as “the extent to which power is distributed unequally in institutions, organizations, and social structures” (Jain 45 and Jain 2018, p. 136). We hypothesize that PDI will moderate the positive relationship between status and LP importance. Based on these hypotheses, we develop a conceptual model that uniquely integrates individual-level values and country-level cultural differences. We test our model using a dataset collected by an international market research firm, including over 77,000 responses from residents of 25 countries. Our findings suggest that individuals who value status place greater importance on LPs. Additionally, we find that PDI moderates this relationship, such that the positive effect of valuing status is stronger in high-PDI countries and weaker in low-PDI countries. These results are consistent across a series of regression models, suggesting that the relationship is robust to control variables, including other personal values and demographic factors. In the following sections, we first review the role of status as it has been studied in the LP literature. Second, we introduce Schwartz’s (1992, 2012) theory of universal basic values and highlight the important distinction between viewing status as a psychological mechanism and viewing status as a personal value. Next, we present our conceptual model and hypotheses. Then, we detail the dataset and series of regression models we use to test our model. Finally, we discuss our findings and their implications for research and practice. Status, Exclusivity, and Preferential Treatment Conceptual Background Status is defined as “context-specific and generally desirable elevated ranking within a social hierarchy based on attributed characteristics” (Henderson, Beck, and Palmatier 2011, p. 272). Henderson, Beck, and Palmatier (2011) highlight three important components of this definition. First, status is positional, as it involves ranking individuals in a hierarchy. Second, the achievement of status is generally desirable because individuals with elevated status gain access 46 to greater resources, social connections, and control over others than those with lower status. Therefore, elevated status can be a powerful motivator across individuals and situations (Anderson et al. 2001). Third, status is dependent on social context. That is, one may attain elevated local status that does not change his or her status in a broader social context. For example, accomplishments that gain one respect within a specialized field may not influence perceptions of the broader social collective outside that group. Further, one’s status can be elevated publicly or privately (Harbaugh 1998; Henderson, Beck, and Palmatier 2011). For instance, receiving an award or job promotion enhances an individual’s status publicly. On the other hand, an individual who receives acceptance letters from multiple universities has his or her status enhanced only privately. Firms design and structure LPs to facilitate status conferral, defined as “actions that provide status or legitimacy to a target” (Tiedens 2001, p. 86). In the context of LPs, profitable customers are the target, and programs offer elevated status to entice them to increase or maintain purchase behavior. A common form of status conferral takes place in hierarchical LPs, which define a set of customer tiers (Breugelmans et al. 2015; Dréze and Nunes 2009; Eggert, Steinhoff, and Garnefeld 2015; McCall and Voorhees 2010; Wagner, Hennig-Thurau, and Rudolph 2009). Members earn access to tiers according to the frequency and/or quantity of their purchases, which allows them preferential treatment in the form of upgraded services, special events, exclusive rewards, and personalized experiences. Further, tiers are often labelled with status-laden terms that highlight the positional nature of status within a program (e.g., platinum, gold, silver, bronze). When a member enters a higher tier in a hierarchical LP, his or her status perceptions are shaped by the tier’s exclusive benefits and the program’s emphasis on status through its design. 47 Conferring status on customers according to their positions within an LP is a mode of customer prioritization, determining which customers gain access to preferential treatment. Therefore, status-focused LP research fits within a broader stream of literature on preferential treatment, defined as “the practice of giving selective customers’ elevated social status recognition and/or additional or enhanced products and services above and beyond standard firm value propositions and customer service practices” (Lacey, Suh, and Morgan 2007, p. 242-243). The effects of preferential treatment as a customer relationship management tactic have been studied extensively in marketing and related fields. For example, in the tourism and hospitality industries, it is common for firms to provide preferential treatment to select customers (Mattila, Hanks, and Zhang 2013; Zhang and Hanks 2015). Further, preferential treatment is not always granted based on purchase history or any other objective merit (Colliander, Söderlund, and Marder 2019). Firms may surprise customers with unearned preferential treatment (e.g., unexpected upgrades) in an effort to delight them. Table 2.1 summarizes studies on status, exclusivity, and preferential treatment in customer relationship management. Research on preferential treatment of customers has two clear themes. First, granting preferential treatment can have the intended consequence of positive affective responses, satisfaction, and attitudinal loyalty among targeted customers (Kim and Mattila 2010). Second, granting preferential treatment to a subset of customers can result in negative, unintended consequences. For example, customers who witness the receipt of preferential treatment can react negatively, often due to perceptions of unfairness (Colliander, Söderlund, and Marder 2019). Further, recipients of unearned preferential treatment can experience feelings of social discomfort or guilt (Jiang, Hoegg, and Dahl 2013; Mattila, Hanks, and Zhang 2013; Zhang and Hanks 2015). These findings are aligned with the predictions of justice theory and equity theory 48 because granting preferential treatment to a subset of customers promotes perceptions that some customers do not receive outcomes that are proportional to their inputs (Adams 1965). The findings of status-focused LP research are largely aligned with those of research on preferential treatment in general. That is, through status conferral, LPs can achieve the intended outcomes of feelings of superiority, positive attitudinal outcomes, and customer loyalty among target customers (Dréze and Nunes 2009; Söderlund 2019). Status conferral in LPs can also yield unintended consequences. Bystander customers can perceive unfairness when they witness high- status LP members receiving exclusive rewards (Lacey and Sneath 2006; Steinhoff and Palmatier 2016). Additionally, conferring elevated status on profitable customers can lead to feelings of entitlement, driving increased service costs and decreased profit (Ma, Li, and Zhang 2018; Wetzel, Hammerschmidt, and Zablah 2014). Further, otherwise loyal customers who do not make sufficient purchases to maintain elevated status can be driven away by the negative experience of status demotion (Wagner, Hennig-Thurau, and Rudolph 2009). While status- focused LP research has adopted a variety of theoretical perspectives, social comparison theory (Festinger 1954), social identity theory (Tajfel and Turner 1979, 1986), and equity theory (Adams 1965) have guided much of the published research on the role of status in LP effectiveness. Personal Values Values are conceptualized as criteria that motivate, justify, and inform action and are used to evaluate oneself and others (Rokeach 1973; Schwartz 1992). A blend of shared and unique personal experiences influences how an individual prioritizes values. Schwartz’s (1992, 2012) theory of universal basic values delineates the following six core features that define values and distinguish values from norms, attitudes, and attributes. 49 1. Values constitute beliefs that are inherently tied to affect. 2. Values refer to goals that are generally desirable. 3. Values apply across situations and contexts. 4. Values play the role of criteria that shape assessment and choice of actions and actions of others. 5. Individuals rank values according to their importance. 6. The consideration of multiple values and their relative importance influences an individual’s actions. Based on these criteria, Schwartz developed a set of ten universal basic values. Table 2.2 includes descriptions of the fundamental goals associated with each value, along with example dimensions of each value. Prior research has examined the effects of various personal values and beliefs on consumption preferences, attitudes, and behaviors. For example, Haws, Winterich, and Naylor (2014) develop a specialized scale for measuring the extent to which consumers value environmental protection in their consumption choices. The scale, largely in line with the basic value of universalism (Schwartz 1992, 2012), has been impactful in the growing literature on green consumption (Barari et al. 2020; Lunde 2018; Rana and Paul 2020; Sharma 2021). Status as a Personal Value vs. Psychological Mechanism The influence of personal values is largely unexplored in the LP literature. Studies on LP effectiveness generally center on how program design features (e.g., tier configurations, reward types) influence customer attitudes and purchase behavior through multiple psychological mechanisms. Due to the complexity of LP design and structure, various cognitive and affective mechanisms are activated simultaneously, influencing consumer responses to LP features (Kim, Steinhoff, and Palmatier 2021). Extensive research has aimed to disentangle these mechanisms, 50 including status (Dréze and Nunes 2009), gratitude (Palmatier et al. 2009), unfairness perceptions (Steinhoff and Palmatier 2016), and habit (Henderson, Beck, and Palmatier 2011). Although this has resulted in a rich body of literature, organizing frameworks of LP effectiveness tend to ignore variation in the extent to which individuals value status. Throughout the LP literature, status is viewed as a psychological mechanism through which LPs influence customer attitudes and behavior. This is reflected in conceptual models and organizing frameworks in which status is positioned as a mediator (e.g., Kim, Steinhoff, and Palmatier 2021; Steinhoff and Palmatier 2016). In this research, we view status as a personal value, positioned as a preexisting condition. That is, this research focuses on the extent to which individuals prioritize the achievement of status and its influence on consumption preferences. This distinction is crucial because it determines how status is operationalized. Generally, in the LP literature, researchers measure status as the objective or perceived extent to which a customer’s status is elevated within a hierarchy defined by an LP’s structure. In this research, status is measured as the extent to which an individual values the attainment of status across contexts and social hierarchies. Therefore, we use the term status to label individuals’ valuation of status in our analysis. Hypothesis Development Prior research has found considerable variation in consumer preferences regarding the opportunity to enhance their status through their purchase behavior. In fact, some consumers prefer that customers receive equal treatment (Barone and Roy 2010a; Barone and Roy 2010b; Henderson, Beck, and Palmatier 2011). This finding is in line with Schwartz’s (2012) statement : “Values influence action when they are relevant in the context (hence likely to be activated) and important to the actor.” (p. 4). Attaining elevated status through consumption behavior is not 51 equally important across individuals, so it is reasonable to expect this variation to influence preferences for brands that offer LPs. We expect that consumers who value the achievement of status will be predisposed to prefer brands or firms that offer LPs. In addition to ten universal basic values, Schwartz’s (1992, 1994) model delineates higher-order value categories. Status is a dimension of power, which is positioned alongside achievement in the broader category self-enhancement. Self-enhancement reflects desire to gain a favorable position relative to others (Schwartz 1994). Self-enhancement is positioned opposite self-transcendence (Schwartz et al. 2001). Centered on one’s tendency to view others as equals and have concern for their well-being, self-transcendence includes the values of universalism and benevolence (Schwartz and Bilsky 1987, 1990). Because self-enhancement and self- transcendence are fundamentally opposed to one another, Schwartz (1994) posits that one cannot pursue both simultaneously without conflict. This further supports the idea that consumers who differ in the extent to which they prioritize the attainment of status will exhibit different preferences regarding LP offerings because the status-focused nature of many LPs can polarize consumers with differing views of status. Therefore, we hypothesize: H1: Status is positively related to loyalty program importance among customers. In addition to individual-level values, consumption preferences are influenced by broader cultural factors. Prior research finds country-level variation in terms of LP preferences. For example, Thompson and Chmura (2015) use Hofstede’s (1980) cultural dimensions to test for differences in consumer preferences for LPs. Based on the idea that LPs are built into firms’ value propositions, and consumers fundamentally differ in terms of their preferences for value propositions (De Mooij and Hofstede 2002), Thompson and Chmura (2015) find that consumers from countries high in masculinity and uncertainty avoidance prefer promotional offers over 52 LPs. Additionally, Bombaij and Dekimpe (2020) find that the positive sales impact of LPs is stronger in countries that are high in individualism and long-term orientation. Chen, Mandler, and Meyer-Waarden (2021) suggest that multiple cultural theories should be adopted to test the moderating role of cultural differences. In line with this reasoning, we expect that country-level differences in PDI will influence consumer mindsets, such that the impact of their desire for status will be amplified or diminished. Specifically, the relationship between valuing status and LP importance will be stronger among individuals in high-PDI countries because hierarchies and inequality are more readily accepted in high-PDI countries. Conversely, the relationship between status and LP importance will be weaker in low-PDI countries, where hierarchies and inequality are less accepted. Therefore, we hypothesize: H2: Power distance moderates the positive relationship between status and loyalty program importance among customers, such that the relationship is stronger in high- power distance countries and weaker in low-power distance countries. Data, Methods, and Results Data We acquired data from an international market research company. The dataset, collected via survey, included measures of individual values that aggregate to Schwartz’s (1992, 2012) ten universal basic values. Additionally, respondents rated three items related to the role of LPs in their consumption choices and post consumption satisfaction ratings for multiple recently purchased brands. Variables used in our analysis are listed in Table 2.3, along with the operationalization of each variable. Data were collected from residents of 25 countries (see Table 2.4 and Table 2.5, which summarize responses by country and frequency distributions for demographics). In addition to the survey data, we collected PDI values for all 25 countries in the 53 sample. In line with prior research (Bombaij and Dekimpe 2020), we use PDI values from Hoftede, Hofstede, and Minkov (2010). PDI scores averaged 60.7 and ranged from 31 (Sweden) to 93 (Russia). We then dummy-coded countries as high and low-PDI (see Table 2.3). Model Specification To test H1, we specified a series of three regression models. The dependent variable in all three models is LP Importance, which is measured as the extent to which offering an LP influences respondent preference for the brand. Status is included as an independent variable, along with power and wealth across all three models. We controlled for power and wealth because they aggregate to the higher order value of power and are positively correlated with one another (see Table 2.6, Figure 2.1). Model 1 Model 1 provides a baseline test of the relationship between status and LP importance. To ensure that the positive relationship between status and LP importance is not attributed to values closely related to status, we control for power and wealth. We specify Model 1 as follows: LPImportancei = β 0 + β1(Status) + β2(Power) + β3(Wealth) + εi Model 2 Model 2 extends Model 1 by adding two important control variables. Specifically, we control for the influence family and friends have on respondents’ choice to use LPs. By controlling for LP recommendations from family and friends, we address the possibility that these influences explain the positive relationship between status and LP importance. We specify Model 2 as follows: LPImportancei = β 0 + β1(Status) + β2(Power) + β3(Wealth) + β4(FamilyRecLPs) + β5(FriendsRecLPs) + εi 54 Model 3 Model 3 tests the robustness of Model 2 by controlling for demographic and socioeconomic factors. Model 3 includes the same values and LP recommendation variables as Model 2, along with additional demographic and socioeconomic control variables, including marital status, parental status, and education. We specify Model 3 as follows: LPImportancei = β 0 + β1(Status) + β2(Power) + β3(Wealth) + β4(FamilyRecLPs) + β5(FriendsRecLPs) + β6(Married) + β7(LivingTogether) + β8(Divorced) + β9(Widowed) + β10(Parent) + β11(HighSchool) + β12(College) + β13(Postgraduate) + εi Model 4 To test H2, we specified two additional models. In Model 4, we extend Model 1 by testing for an interaction effect between status and PDI. We specify Model 4 as follows: LPImportancei = β 0 + β1(Status) + β2(Power) + β3(Wealth) + β4(PDI) + β5(Status*PDI) + εi Model 5 Model 5 is an extension of Model 4. Model 5 includes family and friend recommendations to use LPs as control variables. We specify Model 5 as follows: LPImportancei = β 0 + β1(Status) + β2(Power) + β3(Wealth) + β4(PDI) + β5(Status*PDI) + β6(FamilyRecLPs) + β7(FriendsRecLPs) + εi 55 Results Model 1 Results The results of Model 1 are summarized in Table 2.7. H1 is supported by a positive effect of status on LP importance (b1 = .04, p < .01). The effects of power and wealth are both near zero and nonsignificant (b2 < .01, p > .05; b3 < .01, p > .05). Model 2 Results The results of Model 2 are summarized in Table 2.8. In support of H 1, the positive effect of status on LP importance is significant (b1 = .04, p < .01). Relative to Model 1, R2 is considerably higher (26.4%). While these results show that LP recommendations from family and friends account for most of the explained variance in LP importance, the hypothesized positive effect of status on LP importance remains statistically significant in Model 2. Model 3 Results The results of Model 3 are summarized in Table 2.9. The effect of status on LP importance remains positive and significant in Model 3 (b1 = .03, p < .01), supporting H1. Overall, controlling for marital status, parental status, and education has a minimal effect on the results. The additional control variables increase R2 by only 0.1%, and the effect of status on LP importance is unaffected. Model 4 Results The results of Model 4 are summarized in Table 2.10. H1 is supported by a positive effect of status on LP importance (b1 = .02, p < .01). The effects of power and wealth are both near zero and nonsignificant (b2 < .01, p > .05; b3 < .01, p > .05). H2 is supported, as we find a positive interaction effect between status and PDI (b5 = .05, p < .01). Additionally, we find a 56 significant, negative main effect of PDI on LP importance (b4 = -.07, p < .01). This effect was not hypothesized. Model 5 Results The results of Model 5 (Table 2.11) further support both hypotheses. H1 is supported by a positive effect of status on LP importance (b1 = .02, p < .01). The effects of power and wealth are both near zero and nonsignificant (b2 = -.01, p > .05; b3 = .01, p > .05). H2 is supported, as we find a positive interaction effect between status and PDI (b5 = .04, p < .01). In line with the results of Model 2, the addition of family and friend LP recommendations as control variables substantially increased R2 to 26.4%, but this did not affect the results of our hypothesis tests. Like Model 4, Model 5 shows a significant, negative main effect of PDI on LP importance b4 = .05, p < .01). Implications for Loyalty Program Research Discussion The present research has two main findings. First, we find that the extent to which consumers prioritize the achievement of status is positively related to preference for brands that offer LPs. By applying Schwartz’s (1992, 2012) theory of universal basic values, we uniquely position status as a preexisting condition that influences consumer perceptions of LP offerings. This differs from the way status is viewed throughout the LP literature. Generally, LP research views status as a psychological mechanism through which LPs influence customer loyalty. Conceptual models and organizing frameworks in the LP literature consistently position status as a mediator (e.g., Kim, Steinhoff, and Palmatier 2020; Steinhoff and Palmatier 2016). Thus, our findings fundamentally differ from those presented in prior studies. 57 Second, we find that country-level PDI moderates the relationship between status as a personal value and LP importance. This is a significant finding because prior LP research on the effects of cultural differences focuses on country-level factors but does not integrate individual- level factors (e.g., Bombaij and Dekimpe 2020; Thompson and Chmura 2015). This contribution to the LP literature is aligned with Samaha, Beck, and Palmatier’s (2014) emphasis on the importance of studying cultural factors in relationship marketing effectiveness across national, regional, organizational, and individual levels. Overall, our findings address Chen, Mandler, and Meyer-Waarden’s (2021) suggestion: “Future research should adopt established cultural theories, such as Hofstede (2001) dimensions of national culture, Schwartz (1992) theory of basic values, or Trompenaars (1993) model of national culture differences, when investigating the moderating role of specific cultural dimensions.” (p. 190). Managerial Implications Our findings have significant implications for the design and operation of LPs. Managers need to consider the extent to which target customers value the achievement of status. Our findings suggest that in practice, LPs designed to incentivize customers with status enhancement are not likely to be equally effective across individuals who differ in terms of how they prioritize the attainment of status. The extensive LP literature on status has found strong effects of various program structures and tier configurations (Dréze and Nunes 2009), member promotion and demotion (Wagner, Hennig-Thurau, and Rudolph 2009), and the exclusivity and visibility of membership benefits (Steinhoff and Palmatier 2016). However, managers should not assume that all target customers value the attainment of status within an LP equally. Additionally, managers should consider the joint effects of individual-level views of status and country-level cultural factors. Although our findings indicate a positive relationship between customer valuation of 58 status and LP importance, the strength of this relationship varies across countries. Specifically, we find that this relationship is stronger in high-PDI countries than low-PDI countries. Overall, our findings help explain variation in consumer preferences for LPs. Limitations and Future Research The present research has some notable limitations. First, the results of our models indicate a relatively low proportion of the variance in LP importance is explained by status, wealth, and power collectively (see Table 2.7, Table 2.10). Despite this, the positive relationship between status and LP importance is robust across models that include LP-related control variables and demographic variables (Table 2.8, Table 2.9). Further, this relationship holds when testing the interaction effect between status and country-level PDI (Table 2.10, Table 2.11). Second, our method limits our ability to infer a causal relationship between the valuation of status and preference for brands that offer LPs. Although our models do not have the internal validity of an experimental approach, the nature of our sample makes our findings compelling. Our sample of over 77,000 responses came from 25 countries, ensuring considerable variation in the extent to which individuals value status. Finally, in addition to our primary findings, the results of our analysis indicate a significant, negative main effect of PDI. Although this effect is counterintuitive and was not hypothesized, it may be understood through future research on the interplay of individual and higher-level cultural factors in LP preferences. Our findings suggest that future LP research should take a broader view of status by addressing two distinct components. First, status should be viewed as a psychological mechanism, as in prior LP research. LPs are designed to motivate purchase behavior by offering elevated status and exclusive benefits associated with it to customers who make sufficient purchases. It is well established in the literature that status mediates the effects of LP design 59 features on customer loyalty (Kim, Steinhoff, and Palmatier 2020). Second, research should consider the valuation of status, which varies across individuals. In future organizing frameworks of LP effectiveness, the valuation of status should be positioned as a mod erator. This is in line with calls for research on cultural factors in LP effectiveness (e.g., Chen, Mandler, and Meyer- Waarden 2021). Taken together, these components imply that the effect of status-focused LP features can be understood as the product of (a) the extent to which a target customer prioritizes status as a personal value, and (b) the extent to which one’s status is elevated. Future research should also consider cultural factors across multiple levels. This research uniquely examines the joint effects of individual-level values and country-level PDI. Extensions of our findings could result from comprehensively testing the interplay of a broader range of personal values and higher-level cultural factors. 60 REFERENCES Adams, J. Stacy. "Inequity in social exchange." In Advances in experimental social psychology, vol. 2, pp. 267-299. Academic Press, 1965. Anderson, Cameron, Oliver P. John, Dacher Keltner, and Ann M. Kring. "Who attains social status? Effects of personality and physical attractiveness in social groups." Journal of personality and social psychology 81, no. 1 (2001): 116. Barari, Mojtaba, Mitchell Ross, Sara Thaichon, and Jiraporn Surachartkumtonkun. "A meta‐ analysis of customer engagement behaviour." International Journal of Consumer Studies 45, no. 4 (2021): 457-477. Barone, Michael J., and Tirthankar Roy. "Does exclusivity always pay off? Exclusive price promotions and consumer response." Journal of Marketing 74, no. 2 (2010a): 121-132. Barone, Michael J., and Tirthankar Roy. "The effect of deal exclusivity on consumer response to targeted price promotions: A social identification perspective." Journal of Consumer Psychology 20, no. 1 (2010b): 78-89. Beck, Joshua T., Kelly Chapman, and Robert W. Palmatier. "Understanding relationship marketing and loyalty program effectiveness in global markets." Journal of International Marketing 23, no. 3 (2015): 1-21. Belli, Alex, Anne-Maree O’Rourke, François A. Carrillat, Ljubomir Pupovac, Valentyna Melnyk, and Ekaterina Napolova. "40 years of loyalty programs: how effective are they? Generalizations from a meta-analysis." Journal of the Academy of Marketing Science 50, no. 1 (2022): 147-173. Berman, Barry. "Developing an effective customer loyalty program." California management review 49, no. 1 (2006): 123-148. Bombaij, Nick JF, and Marnik G. Dekimpe. "When do loyalty programs work? The moderating role of design, retailer-strategy, and country characteristics." International Journal of Research in Marketing 37, no. 1 (2020): 175-195. Breugelmans, Els, Tammo HA Bijmolt, Jie Zhang, Leonardo J. Basso, Matilda Dorotic, Praveen Kopalle, Alec Minnema, Willem Jan Mijnlieff, and Nancy V. Wünderlich. "Advancing research on loyalty programs: a future research agenda." Marketing Letters 26, no. 2 (2015): 127-139. Chaudhuri, Malika, Clay M. Voorhees, and Jonathan M. Beck. "The effects of loyalty program introduction and design on short-and long-term sales and gross profits." Journal of the Academy of marketing science 47, no. 4 (2019): 640-658. Chen, Yanyan, Timo Mandler, and Lars Meyer-Waarden. "Three decades of research on loyalty programs: A literature review and future research agenda." Journal of Business Research 124 (2021): 179-197. 61 Chun, So Yeon, and Anton Ovchinnikov. "Strategic consumers, revenue management, and the design of loyalty programs." Management Science 65, no. 9 (2019): 3969-3987. Colliander, Jonas, Magnus Söderlund, and Ben Marder. "Watching others receive unearned superior treatment: Examining the effects on tourists who receive less than their peers." Journal of Travel Research 58, no. 7 (2019): 1175-1192. De Mooij, Marieke, and Geert Hofstede. "Convergence and divergence in consumer behavior: implications for international retailing." Journal of retailing 78, no. 1 (2002): 61-69. Dowling, Grahame R., and Mark Uncles. "Do customer loyalty programs really work?." Sloan management review 38 (1997): 71-82. Drèze, Xavier, and Joseph C. Nunes. "Feeling superior: The impact of loyalty program structure on consumers' perceptions of status." Journal of Consumer Research 35, no. 6 (2009): 890-905. Eggert, Andreas, Lena Steinhoff, and Ina Garnefeld. "Managing the bright and dark sides of status endowment in hierarchical loyalty programs." Journal of Service Research 18, no. 2 (2015): 210-228. Evanschitzky, Heiner, Balasubramanian Ramaseshan, David M. Woisetschläger, Verena Richelsen, Markus Blut, and Christof Backhaus. "Consequences of customer loyalty to the loyalty program and to the company." Journal of the academy of marketing science 40 (2012): 625-638. Faramarzi, Ashkan, and Abhi Bhattacharya. "The economic worth of loyalty programs: An event study analysis." Journal of Business Research 123 (2021): 313-323. Festinger, Leon. "A theory of social comparison processes." Human relations 7, no. 2 (1954): 117-140. García Gómez, Blanca, Ana Gutiérrez Arranz, and Jesús Gutiérrez Cillán. "The role of loyalty programs in behavioral and affective loyalty." Journal of consumer marketing 23, no. 7 (2006): 387-396. Harbaugh, William T. "What do donations buy?: A model of philanthropy based on prestige and warm glow." Journal of public economics 67, no. 2 (1998): 269-284. Haverila, Matti J., Kai Haverila, Caitlin McLaughlin, and Hailey Tran. "The impact of tangible and intangible rewards on online loyalty program, brand engagement, and attitudinal loyalty." Journal of Marketing Analytics 10, no. 1 (2022): 64-81. Haws, Kelly L., Karen Page Winterich, and Rebecca Walker Naylor. "Seeing the world through GREEN-tinted glasses: Green consumption values and responses to environmentally friendly products." Journal of consumer psychology 24, no. 3 (2014): 336-354. 62 Henderson, Conor M., Joshua T. Beck, and Robert W. Palmatier. "Review of the theoretical underpinnings of loyalty programs." Journal of Consumer Psychology 21, no. 3 (2011): 256-276. Hofstede, Geert. "Culture and organizations." International studies of management & organization 10, no. 4 (1980): 15-41. Hofstede, Geert. Culture's consequences: Comparing values, behaviors, institutions and organizations across nations. sage, 2001. Hoftede, Geert, Gert Jan Hofstede, and Michael Minkov. Cultures and organizations: software of the mind: intercultural cooperation and its importance for survival. McGraw-Hill, 2010. Jain, Shalini Sarin, and Shailendra Pratap Jain. "Power distance belief and preference for transparency." Journal of Business Research 89 (2018): 135-142. Jiang, Lan, JoAndrea Hoegg, and Darren W. Dahl. "Consumer reaction to unearned preferential treatment." Journal of Consumer Research 40, no. 3 (2013): 412-427. Kang, Jun, Thomas Brashear Alejandro, and Mark D. Groza. "Customer–company identification and the effectiveness of loyalty programs." Journal of Business Research 68, no. 2 (2015): 464-471. Kim, Youngsun Sean, and Melissa A. Baker. "I earn it, but they just get it: Loyalty program customer reactions to unearned preferential treatment in the social servicescape." Cornell Hospitality Quarterly 61, no. 1 (2020): 84-97. Kim, Min Gyung, and Anna S. Mattila. "The impact of mood states and surprise cues on satisfaction." International Journal of Hospitality Management 29, no. 3 (2010): 432- 436. Kim, Jisu J., Lena Steinhoff, and Robert W. Palmatier. "An emerging theory of loyalty program dynamics." Journal of the Academy of Marketing Science 49 (2021): 71-95. Kopalle, Praveen K., Yacheng Sun, Scott A. Neslin, Baohong Sun, and Vanitha Swaminathan. "The joint sales impact of frequency reward and customer tier components of loyalty programs." Marketing Science 31, no. 2 (2012): 216-235. Lacey, Russell, and Julie Z. Sneath. "Customer loyalty programs: are they fair to consumers?." Journal of consumer marketing 23, no. 7 (2006): 458-464. Leenheer, Jorna, Harald J. Van Heerde, Tammo HA Bijmolt, and Ale Smidts. "Do loyalty programs really enhance behavioral loyalty? An empirical analysis accounting for self - selecting members." International Journal of Research in Marketing 24, no. 1 (2007): 31- 47. 63 Lunde, Matthew B. "Sustainability in marketing: A systematic review unifying 20 years of theoretical and substantive contributions (1997–2016)." AMS review 8, no. 3-4 (2018): 85-110. Ma, Baolong, Xiaofei Li, and Lin Zhang. "The effects of loyalty programs in services–a double- edged sword?." Journal of Services Marketing 32, no. 3 (2018): 300-310. Mattila, Anna S., Lydia Hanks, and Lu Zhang. "Existential guilt and preferential treatment: The case of an airline upgrade." Journal of Travel Research 52, no. 5 (2013): 591-599. McCall, Michael, and Clay Voorhees. "The drivers of loyalty program success: An organizing framework and research agenda." Cornell Hospitality Quarterly 51, no. 1 (2010): 35-52. Navarro, José Gabriel. “Average Memberships in Loyalty Programs in the U.S. 2015-2022.” Statista. January 6, 2023. https://www.statista.com/statistics/618744/average-number-of- loyalty-programs-us-consumers-belong-to/. Nunes, Joseph C., and Xavier Drèze. "Your loyalty program is betraying you." Harvard business review 84, no. 4 (2006): 124-31. Palmatier, Robert W., Cheryl Burke Jarvis, Jennifer R. Bechkoff, and Frank R. Kardes. "The role of customer gratitude in relationship marketing." Journal of marketing 73, no. 5 (2009): 1-18. Palmeira, Mauricio, Nicolas Pontes, Dominic Thomas, and Shanker Krishnan. "Framing as status or benefits? Consumers’ reactions to hierarchical loyalty program communication." European Journal of Marketing 50, no. 3/4 (2016): 488-508. Park, Jeong-Yeol, and SooCheong Shawn Jang. "You got a free upgrade? What about me? The consequences of unearned preferential treatment." Tourism Management 50 (2015): 59- 68. Rana, Jyoti, and Justin Paul. "Health motive and the purchase of organic food: A meta‐analytic review." International Journal of Consumer Studies 44, no. 2 (2020): 162-171. Rokeach, Milton. The nature of human values. Free press, 1973. Rosenbaum, Mark S., Amy L. Ostrom, and Ronald Kuntze. "Loyalty programs and a sense of community." Journal of Services Marketing 19, no. 4 (2005): 222-233. Sagiv, Lilach, and Shalom H. Schwartz. "Value priorities and subjective well‐being: Direct relations and congruity effects." European journal of social psychology 30, no. 2 (2000): 177-198. Samaha, Stephen A., Joshua T. Beck, and Robert W. Palmatier. "The role of culture in international relationship marketing." Journal of Marketing 78, no. 5 (2014): 78-98. 64 Schwartz, Shalom H. "An overview of the Schwartz theory of basic values." Online readings in Psychology and Culture 2, no. 1 (2012): 11. Schwartz, Shalom H. "Are there universal aspects in the structure and contents of human values?." Journal of social issues 50, no. 4 (1994): 19-45. Schwartz, Shalom H. "Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries." In Advances in experimental social psychology, vol. 25, pp. 1-65. Academic Press, 1992. Schwartz, Shalom H., and Wolfgang Bilsky. "Toward a universal psychological structure of human values." Journal of personality and social psychology 53, no. 3 (1987): 550. Schwartz, Shalom H., and Wolfgang Bilsky. "Toward a theory of the universal content and structure of values: Extensions and cross-cultural replications." Journal of personality and social psychology 58, no. 5 (1990): 878. Schwartz, Shalom H., Gila Melech, Arielle Lehmann, Steven Burgess, Mari Harris, and Vicki Owens. "Extending the cross-cultural validity of the theory of basic human values with a different method of measurement." Journal of cross-cultural psychology 32, no. 5 (2001): 519-542. Sharma, Ajai Pal. "Consumers’ purchase behaviour and green marketing: A synthesis, review and agenda." International Journal of Consumer Studies 45, no. 6 (2021): 1217-1238. Sharp, Byron, and Anne Sharp. "Loyalty programs and their impact on repeat-purchase loyalty patterns." International journal of Research in Marketing 14, no. 5 (1997): 473-486. Shugan, Steven M. "Brand loyalty programs: are they shams?." Marketing Science 24, no. 2 (2005): 185-193. Söderlund, Magnus. "Can the label ‘member’in a loyalty program context boost customer satisfaction?." The International Review of Retail, Distribution and Consumer Research 29, no. 3 (2019): 340-357. Stathopoulou, Anastasia, and George Balabanis. "The effects of loyalty programs on customer satisfaction, trust, and loyalty toward high-and low-end fashion retailers." Journal of Business Research 69, no. 12 (2016): 5801-5808. Stauss, Bernd, Maxie Schmidt, and Andreas Schoeler. "Customer frustration in loyalty programs." International Journal of Service Industry Management 16, no. 3 (2005): 229- 252. Steinhoff, Lena, and Robert W. Palmatier. "Understanding loyalty program effectiveness: managing target and bystander effects." Journal of the Academy of Marketing Science 44, no. 1 (2016): 88-107. 65 Tajfel, H., and J. Turner. "An integrative theory of interpersonal conflict." Psychology of intergroup relations (1986): 7-24. Tajfel, Henri, John C. Turner, William G. Austin, and Stephen Worchel. "An integrative theory of intergroup conflict." Organizational identity: A reader 56, no. 65 (1979): 9780203505984-16. Thompson, Frauke Mattison, and Thorsten Chmura. "Loyalty programs in emerging and developed markets: the impact of cultural values on loyalty program choice." Journal of International Marketing 23, no. 3 (2015): 87-103. Tiedens, Larissa Z. "Anger and advancement versus sadness and subjugation: the effect of negative emotion expressions on social status conferral." Journal of personality and social psychology 80, no. 1 (2001): 86. Trompenaars, Fons. Riding the waves of culture: Understanding cultural diversity in business. London, UK: The Economist Books, 1993. Uncles, Mark D., Grahame R. Dowling, and Kathy Hammond. "Customer loyalty and customer loyalty programs." Journal of consumer marketing 20, no. 4 (2003): 294-316. Van Heerde, Harald J., and Tammo HA Bijmolt. "Decomposing the promotional revenue bump for loyalty program members versus nonmembers." Journal of Marketing Research 42, no. 4 (2005): 443-457. Viswanathan, Vijay, F. Javier Sese, and Manfred Krafft. "Social influence in the adoption of a B2B loyalty program: The role of elite status members." International Journal of Research in Marketing 34, no. 4 (2017): 901-918. Viswanathan, Vijay, Kim Koetterheinrich, Tammo Bijmolt, Manfred Krafft, and F. Javier Sese. "Quantifying the effect of status in a multi-tier loyalty program." Industrial Marketing Management 104 (2022): 376-383. Wagner, Tillmann, Thorsten Hennig-Thurau, and Thomas Rudolph. "Does customer demotion jeopardize loyalty?." Journal of marketing 73, no. 3 (2009): 69-85. Wetzel, Hauke A., Maik Hammerschmidt, and Alex R. Zablah. "Gratitude versus entitlement: A dual process model of the profitability implications of customer prioritization." Journal of Marketing 78, no. 2 (2014): 1-19. Yi, Youjae, and Hoseong Jeon. "Effects of loyalty programs on value perception, program loyalty, and brand loyalty." Journal of the academy of marketing science 31, no. 3 (2003): 229-240. Zhang, Jie, and Els Breugelmans. "The impact of an item-based loyalty program on consumer purchase behavior." Journal of Marketing research 49, no. 1 (2012): 50-65. 66 Zhang, Lu, and Lydia Hanks. "Unearned preferential treatment: The moderating role of power." Cornell Hospitality Quarterly 56, no. 3 (2015): 309-319. 67 APPENDIX Table 2.1: Overview of Status, Exclusivity, and Preferential Treatment Research Author(s) Theoretical/Conceptual Background Primary Findings Viswanathan et al. (2022) Social Comparison Theory, Social Exchange Theory In hierarchical LPs, reaching higher tiers increases share of wallet, especially for new customers in elite tiers. Haverila et al. (2022) Social Exchange Theory Both tangible and intangible rewards drive positive evaluations of online LPs. Tangible rewards have a stronger impact on attitudinal loyalty to the LP. Kim and Baker (2020) Equity Theory, Justice Theory, Social Comparison Theory Granting high-value, unearned preferential treatment to nonmember customers undermines loyalty among LP members through negative status and justice perceptions. Chun and Ovchinnikov (2019) Strategic Consumer Behavior, Status-Seeking Behavior LPs in which customers qualify for premium status based on spending drive higher profits than LPs that confer premium status on customers based on purchase quantities. This effect is driven by both increased spending per customer and growth of customer segments targeting premium status. Söderlund (2019) Social Identity Theory, Self- Categorization Theory, Social Comparison Theory Using the term "member" evokes a higher sense of belonging and customer satisfaction among members than nonmembers. Ma, Li, and Zhang (2018) Social Exchange Theory, Complexity Theory LPs are associated with positive customer relationship outcomes, but status- focused LP features can drive entitlement among members, leading to unrealistic expectations among high-status customers. Viswanathan et al. (2017) Social Comparison Theory, Social Influence LP members with elite status have disproportionate influence on nonmember customer decisions to join hierarchical LPs. 68 Table 2.1 (cont’d): Overview of Status, Exclusivity, and Preferential Treatment Research Author(s) Steinhoff and Palmatier (2016) Theoretical/Conceptual Background Social Comparison Theory, Social Identity Theory, Equity Theory Palmeira et al. (2016) Framing Theory, Social Comparison Theory Colliander, Söderlund, and Marder (2019) Justice Theory, Equity Theory Zhang and Hanks (2015) Social Power Park and Jang (2015) Social Comparison Theory Mattila, Hanks, and Zhang (2013) Justice Theory Primary Findings LPs influence target and bystander customers differently. While status- laden, visible rewards drive loyalty and sales through status perceptions, bystander customers who witness others' benefits are affected negatively. Customer reactions to hierarchical LP implementation depend on whether they expect to qualify for an elevated tier and whether they are primed to think about the program in terms of benefits or status. Customers who do not expect to qualify for elevated tier membership have negative reactions to hierarchical LPs when prompted to think about status. Customers who observe other customers receiving unearned superior treatment perceive lower levels of justice and are less satisfied with service exchanges than those who observe equal treatment of other customers. Satisfaction among recipients of a surprise upgrade can be reduced if others do not receive the upgrade, particularly when the recipient is friends with the non-recipients and does not have control over whether they receive an upgrade. High-value spontaneous free upgrades drive feelings of envy in other customers who witness the receipt of the upgrade, both when the recipient is a stranger and when the recipient is a friend of the bystander customer. Recipients of an unexpected airline upgrade who are high in guilt- proneness can feel existential guilt, especially when non-recipients include individuals with whom the recipient has a close relationship. 69 Table 2.1 (cont’d): Overview of Status, Exclusivity, and Preferential Treatment Research Author(s) Theoretical/Conceptual Background Jiang, Hoegg, and Dahl (2013) Social Influence, Social Comparison Theory Wagner, Hennig- Thurau, and Rudolph (2009) Wetzel, Hammerschmidt, and Zablah (2014) Prospect Theory, Emotions Theory Social Exchange Theory Barone and Roy (2010a) Equity Theory, Justice Theory Barone and Roy (2010b) Equity Theory Primary Findings Receiving unearned preferential treatment in front of others can result in feelings of social discomfort. Status demotion in hierarchical LPs has a stronger negative effect than the positive impact of status upgrades. Loyal customers can be pushed away by the negative experience of demotion for failure to meet spending requirements. Customer prioritization affects profit via two mechanisms: One positive, via customer gratitude and increased sales, and one negative, via customer entitlement and increased service costs. Preference for exclusive promotions is stronger among consumers with independent self-construal and among male consumers who have established purchase history with the provider. Preference for exclusive deals is moderated by need for uniqueness such that consumers high in need for uniqueness have a stronger preference for exclusive deals than those low in need for uniqueness. 70 Value Self-Direction Stimulation Hedonism Table 2.2: Schwartz’s 10 Universal Basic Values Thinking and acting independently; choosing one's own path Dimensions: Creativity, Curiosity, Freedom, Independence Defining Goal/Description Taking on life's challenges; finding excitement Dimensions: Excitement, Variety, Daring Seeking enjoyment in life; self-indulgence Dimensions: Pleasure, Enjoying Life, Self-Indulgent Achievement Finding success per social benchmarks; showing competence Dimensions: Intelligence, Self-Respect, Social Recognition Power Security Conformity Tradition Benevolence Universalism Attaining prestige and social status; obtaining control over people and resources Dimensions: Authority, Status, Wealth, Social Power Achieving stability and safety in society and in personal relationships Dimensions: Social Order, Cleanliness, Sense of Belonging Resisting impulsive behaviors; living within norms Dimensions: Self-Discipline, Obedience, Politeness Having respect for established customs or religious influences Dimensions: Devoutness, Humility, Respect for Tradition Ensuring the well-being of socially proximal persons Dimensions: Honesty, Responsibility, Loyalty, Helpfulness Appreciating, understanding, and protecting all people and the natural world Dimensions: Environmental Protection, Unity with Nature, Equality, Inner Harmony 71 Variable Dependent Variable Loyalty Program Importance Independent Variables Status Power Wealth Table 2.3: Variable Names and Measures Measure LPImportance: To what extent does a brand offering an LP influence your decision to purchase the brand? 1 = Does not impact my decision at all, 5 = One of the most critical factors in my decision Status: 1 = Opposed to my values, 2 = Somewhat important, 3 = Important, 4 = Very important, 5 = Extremely important Power: 1 = Opposed to my values, 2 = Somewhat important, 3 = Important, 4 = Very important, 5 = Extremely important Wealth: 1 = Opposed to my values, 2 = Somewhat important, 3 = Important, 4 = Very important, 5 = Extremely important Family Recommends LPs FamRecLPs: My close family recommends loyalty programs. 1 = Strongly disagree, 7 = Strongly agree Friends Recommend LPs FriendsRecLPs: Friends recommend loyalty programs to me. 1 = Power Distance PDI: Median-split, Dummy-coded with low-PDI as base Strongly disagree, 7 = Strongly agree condition. Control Variables Marital status Married Living Together Divorced Widowed Current marital status, dummy-coded with single as base condition Married: 1 = Married, 0 otherwise Living Together: 1 = Living Together, 0 otherwise Divorced: 1 = Divorced, 0 otherwise Widowed: 1 = Widowed, 0 otherwise Parental status Parent: 1 = Parent, 0 otherwise Education High School College Postgraduate Highest level of school completed, dummy-coded with less than high school as base condition High School: 1 = Completed high school, 0 otherwise College: 1 = Holds college degree, 0 otherwise Postgraduate: 1 = Holds postgraduate degree, 0 otherwise 72 Table 2.4: Responses by Country Country Freq. Australia Brazil Canada China Czechia Egypt France Germany India Indonesia Italy Japan South Korea Mexico Poland Romania Russia South Africa Spain Sweden Taiwan Thailand Turkey UK USA Total 3,468 3,416 3,416 3,392 3,516 3,492 3,864 3,456 3,384 3,528 3,202 2,562 2,583 2,541 2,622 2,523 2,628 2,568 2,598 2,559 2,733 2,604 2,607 2,649 5,148 77,059 Percent 4.5 4.4 4.4 4.4 4.6 4.5 5.0 4.5 4.4 4.6 4.2 3.3 3.4 3.3 3.4 3.3 3.4 3.3 3.4 3.3 3.5 3.4 3.4 3.4 6.7 100.0 Note: Sample size differs from results tables due to missing data. 73 Table 2.5: Frequency Distributions Variable Freq. Percent Marital Status Single Married Living together Separated, divorced Widowed Total 23,992 41,693 4,887 4,271 2,216 77,059 31.1 54.1 6.3 5.5 2.9 100.0 Parental Status Yes, parent No Total Education 48,162 28,897 77,059 62.5 37.5 100.0 Primary or less Some high school College Post-graduate Total 13,886 39,319 21,311 2,543 77,059 18.0 51.0 27.7 3.3 100.0 Note: Sample size differs from results tables due to missing data. 74 Table 2.6: Descriptive Statistics and Correlation Matrix 1 2 3 4 5 6 .023** .024** .044** .387** .418** .488** .481** .014** .003 .502** .005 -.002 .012** .006 .238** Mean 3.529 2.258 2.731 2.567 3.860 3.930 SD .948 .977 1.086 1.029 1.189 1.218 Variable 1. LP Importance 2. Power 3. Wealth 4. Status 5. FamRecLPs 6. FriendsRecLPs *p < .05 **p < .01 n = 74,167 75 Figure 2.1: Conceptual Model 76 Table 2.7: Model 1 Results Std. Error .01 .00 .00 .00 t 310.44 .29 .27 9.67 Estimate 3.42** .00 .00 .04** .002 Table 2.8: Model 2 Results Std. Error .02 .00 .00 .00 .00 .00 t 93.53 -.93 1.39 9.88 93.71 106.46 Estimate 1.44** .00 .01 .04** .24** .27** .264 Intercept Power Wealth Status R2 *p < .05 **p < .01 n = 74,167 Intercept Power Wealth Status Family Rec LPs Friends Rec LPs R2 *p < .05 **p < .01 n = 74,167 77 Table 2.9: Model 3 Results Estimate 1.40** .00 .01 .03** .24** .27** Std. Error .02 .00 .00 .00 .00 .00 t 78.77 -.85 1.68 9.08 93.82 106.54 .01 .01 .02 .02 .01 .01 .01 .02 4.01 .45 .01 1.95 -4.21 6.28 7.01 1.22 .04** .01 .00 .04 -.04** .05** .07** .02 .265 Intercept Power Wealth Status Family Rec LPs Friends Rec LPs Married Living Together Divorced Widowed Parent High School College Postgraduate R2 *p < .05 **p < .01 n = 74,167 78 Table 2.10: Model 4 Results Std. Error .01 .01 .00 .00 .02 .01 t 248.32 3.02 -.57 .27 -3.67 8.02 Estimate 3.46** .02** .00 .00 -.07** .05** .004 Table 2.11: Model 5 Results Std. Error .02 .00 .00 .00 .02 .01 .00 .00 t 86.13 4.39 -1.49 1.38 -3.34 6.02 93.57 106.22 Estimate 1.48** .02** -.01 .01 -.05** .04** .24** .27** .264 Intercept Status Power Wealth PDI Status*PDI R2 *p < .05 **p < .01 n = 74,167 Intercept Status Power Wealth PDI Status*PDI Family Rec LPs Friends Rec LPs R2 *p < .05 **p < .01 n = 74,167 79