When Personalization Backfires

Item Type text; Electronic Dissertation

Authors Yi, John Jongsei

Publisher The University of Arizona.

Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Download date 30/09/2021 10:10:38

Link to Item http://hdl.handle.net/10150/641698

WHEN PERSONALIZATION BACKFIRES

John Yi

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF MARKETING

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR OF PHILOSOPHY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2020

ACKNOWLEDGEMENTS I would like to express my sincere thanks to my advisor, Caleb Warren, for his guidance and invaluable feedback. Without his persistent support, I would not be who I am today. I would also like to thank the committee members, Merrie Brucks, Jeff Stone, and Anastasiya

Pocheptsova Ghosh for their expertise and support. Thanks also to Ignacio Luri and Kristen

Lane, the best cohort ever, for making through the whole process together as a team until the end. My gratitude also goes to other professors, students, and staff in the Department of

Marketing at the Eller College of Management, University of Arizona. Finally, I am indebted to my family for their constant support, patience, and love. Thank you so much for the help over the years.

TABLE OF CONTENTS

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

ABSTRACT ...... 9

CHAPTER 1 INTRODUCTION ...... 10

CHAPTER 2 PERSONALIZATION, CUSTOMIZATION, AND ONE-TO-ONE MARKETING ...... 12

CHAPTER 3 IDENTITY AND IDENTITY THREATS ...... 23 Identities and the Self ...... 24 Identity Salience ...... 26 Identities Differ in Value ...... 28 Identity Threats ...... 31

CHAPTER 4 PERSONALIZATION AS A SOURCE OF IDENTITY THREAT ...... 38

CHAPTER 5 STUDY 1 ...... 41

Study 1a- Do consumers respond negatively to personalized service when purchasing hemorrhoid cream compared to cough medicine? ...... 41 Method ...... 42 Results ...... 44

Study 1b- Do consumers respond negatively to personalized service when purchasing hemorrhoid cream compared to water? ...... 44 Method ...... 45 Results ...... 45

Study 1c- Do consumers still respond negatively to personalized service when the consumers’ names are not mentioned in the process? ...... 46 Method ...... 46 Results ...... 47

CHAPTER 6 STUDY 2- DO MALES RESPOND NEGATIVELY TO PERSONALIZED MUSIC SERVICE RECOMMENDING “GIRL’S NIGHT” PLAYLIST? ...... 48

Method ...... 49 Results ...... 53

CHAPTER 7 STUDY 3 - WHY DO UNDERGRADUATES RESPOND NEGATIVELY TO PERSONALIZED MUSIC SERVICE RECOMMENDING TWEEN MUSIC? ...... 54 Method ...... 57 Results ...... 61

CHAPTER 8 OVERVIEW OF STUDY 1 TO 3 ...... 65

CHAPTER 9 STUDY 4- DO CONSUMERS RESPOND NEGATIVELY TO PERSONALIZED SERVICE ASSOCIATED WITH A THREATENING IDENTITY BECAUSE OF ONLY PRIVACY CONCERNS?...... 66 Method ...... 67 Results ...... 67

CHAPTER 10 STUDY 5- DO CONSUMERS WHO RECEIVE INACCURATE BOOK RECOMMENDATIONS REACT NEGATIVELY TO A PERSONALIZED SERVICE? ...... 70 Method ...... 72 Results ...... 81

CHAPTER 11 STUDY 6- DO CONSUMERS WHO RECEIVE AN INACCURATE RECOMMENDATION REACT NEGATIVELY TO AN ONLINE MUSIC SERVICE? ...... 84 Method ...... 85 Results ...... 86

CHAPTER 12 STUDY 7- DO CONSUMERS WHO RECEIVE THREATENING PERSONALIZED ADVERTISEMENTS ENGAGE IN COMPENSATORY CONSUMPTION? ...... 88 Method ...... 90 Results ...... 99

CHAPTER 13 GENERAL DISCUSSIONS ...... 102 Limitations ...... 103 Future research ...... 108 Implications ...... 111 Conclusion ...... 113

APPENDIX A - STIMULI FROM STUDY 1A, 1B, 1C, AND 4...... 114

APPENDIX B - STIMULI FROM STUDY 2 AND 3 ...... 116

APPENDIX C - STIMULI FOR STUDY 5...... 119

APPENDIX D - PRETEST RESULTS FOR STUDY 5 ...... 132

APPENDIX E - NON-PERSONALIZED ADVERTISEMENT SHOWN IN STUDY 7 ...... 134

REFERENCES ...... 135

LIST OF TABLES

Table 1 Summary of study 2 and 3 ...... 57

Table 2 Study 5 stimuli ...... 80

Table 3 Stimuli added in study 6 ...... 86

Table 4 Pretest result for each artists ...... 92 Table 5 Focal advertisements by condition ...... 98

LIST OF FIGURES

Figure 1 Examples of customizing Nike ...... 16

Figure 2 Customizing your behavioral targeted advertisement ...... 17

Figure 3 Starbucks Frappuccino happy hour filter ...... 19

Figure 4 Initial conceptualization of how identities are valued ...... 28 Figure 5 Categorization of how identities are valued ...... 29

Figure 6 Devaluation threat ...... 32

Figure 7 Rejection threat ...... 34

Figure 8 Categorization threat ...... 35 Figure 9 Summary of dependent variable, attitude to the pharmacy, in study 1 ...... 48

Figure 10 Study 2 procedure ...... 52

Figure 11 Dependent variable, attitude to the pharmacy, in study 2 ...... 54

Figure 12 Guideline for the response time task ...... 60 Figure 13 Attitude toward music service Z in study 3 ...... 62

Figure 14 Response time average by condition ...... 63

Figure 15 Moderated mediation in study 3 ...... 64

Figure 16 Dependent variable, attitude to the pharmacy, in study 4 ...... 70 Figure 17 Flow of study 5 ...... 76

Figure 18 Example of the personality test questions ...... 77

Figure 19 Choices for the feared identity ...... 78

Figure 20 Dependent variable, attitude toward the service, in study 6 ...... 88 Figure 21 Flow of study 7 ...... 93

Figure 22 AdChoice icon ...... 93

Figure 23 Loading sequence ...... 95

Figure 24 Example of the personalized advertisements ...... 96

Figure 25 Dependent variable, attitude toward the advertised product, in study 7 ...... 100 Figure 26 Number of young artists chosen ...... 101

ABSTRACT

Technological advances have enabled firms to track information about individual consumers and offer personalized recommendations, products, and services to those consumers.

Intuitively, individual-level personalization should better serve the unique needs of each consumer (Arora et al. 2008; Franke, Schreier, and Kaiser 2010; Pine 2011), thereby increasing consumer loyalty and firm profitability.

I illustrate a problem with technologies that enable personalization: personalized products, services, and experiences (hereafter, “products”) can threaten the self by highlighting aspects of a consumer’s identity that they may not like. Receiving a personalized product heightens consumers’ awareness of their past behavior, which increases the accessibility of the identity related to this behavior. But consumers may not always like or aspire to have the identity that a personalized product activates. Being associated with undesirable identities can pose a threat to the self, and consequently, consumers attempt to avoid products and behaviors that are linked to these negative identities (White and Argo 2009). We, therefore, hypothesize that personalized products will backfire when the personalization activates a feared identity, causing consumers to avoid using the product.

Our findings contribute to theory on personalization (Arora et al., 2008) and associated concepts such as mass customization and one-to-one marketing (Franke et al. 2010; Pine 2011) and also work on identity-related consumption (White and Argo 2009; White, Argo, and

Sengupta 2012; White and Dahl 2007), showing personalized products can highlight a feared identity, which risks repelling consumers.

CHAPTER 1

INTRODUCTION

John loves listening to music on Spotify, an online streaming service that provides almost limitless choices. Sometimes John indulges in what he considers to be a “guilty pleasure” by listening to girly songs like Taylor Swift’s “Love Story” and Lady Gaga’s "The Edge of

Glory.” One day, John notices that Spotify has recommended personalized playlists just for him called “Girl Power Anthems” and “Girl’s Night Out.” While John was happy to see some of the songs he liked, the playlist recommendations also gave him an uncomfortable feeling. Why did

John feel this way, and how would this influence John’s subsequent consumption behavior with

Spotify?

John’s story above illustrates how personalization, changes made by a firm to cater to an individual consumer based on the consumer’s past behavior, such as providing individualized recommendations, can hurt customers’ attitude toward the music service. Was it the composition of the songs in the suggested playlist or the fact that an algorithm was dictating him what to listen to? To understand what is happening, we need to tackle what personalization is.

Personalization has been and is currently prevalent in the service domain. Service marketers, even before the dawn of information technology, considered personalization to be an important part of a “good service” (Surprenant and Solomon 1987) and stressed the importance of building personal relationships with the individual consumer (Subramanian and Marquardt

1999). Store owners or retailers could benefit from building a long-term relationship with customers by personalizing their services (Subramanian and Marquardt 1999). For instance, Ritz

Carlton hotel earned its fame for providing one of the most personalized services. The story still remains an industry legend where the hotel not only prepared a birthday cake for the daughter of the guest but also matched the favorite balloon color for the little girl (The Ritz-Carlton

Leadership Center 2014). Also, Cheers, a fictional bar in Boston, was known to be a space

“where everybody knows your name.” At this bar, all the regular customers and the employees knew each other and shared everything as if they were a family. This illustrates how personalized services aimed to achieve consumer loyalty and build a long-term relationship with consumers.

Personalization is quickly becoming the standard in other markets as well. With the current advance in information technology, personalization has grown to influence both offline and online products as well. Over the years, the costs of tracking and storing information of an individual consumer have come down significantly, enabling firms to provide personalized products and services like never before. For example, Pandora and Spotify provide music catered to the individual consumer. When consumers search keywords on Google, Google incorporates consumers’ past searches to personalize the search results. For example, when you are searching for a movie to watch on Google, Google will automatically search nearby movie theaters and offer possible timeslots with the actual link to reserve the tickets. Netflix, an online movie streaming service, provides personalized recommendations for each consumer. If you have been watching the new Star Wars movies, the recommendation system will suggest the original trilogies and other science fiction films. Netflix will even personalize the artwork for the movie based on what genre you prefer. Even with the same movie like Good Will Hunting, if you have a history of watching romance movies, the image on the webpage will highlight the relationship between the main leads. On the other hand, if you have watched more comedy movies in the past, Netflix will provide you an image with Robin Williams, a famous actor known for his

comic performances. Amazon, one of the largest online retailers, offers personalized product recommendations for each individual. If you have started buying licorice candies lately, Amazon will make sure to offer a different variety of licorice candies to you.

Some researchers argue that personalization is the ultimate level of segmentation where the firm provides what the individual customer wants (Pine 2011; Pine, Victor, and Boynton

1993). Even a fast-food restaurant like McDonald’s has decided to acquire Dynamic Yield, a leading company in personalization and decision logic technology, to provide a more personalized experience (McDonald’s Corporation 2019). Now, MacDonald’s Drive-Thru or a kiosk will be suggesting additional items based on the consumer’s choices. McDonald’s aims to provide an experience similar to that of Amazon where personalized recommendations are based on an individual consumer’s past purchases. Now, McDonald’s will offer a discount for an extra

French fries to go with after the personalization system notices your frequent purchase of fries.

Will McDonald’s attempts to provide consumers a personalized product and service be successful? To answer this question, as well as the questions about John’s reaction to Spotify’s personalized playlists, we first review the literature on personalization.

CHAPTER 2

PERSONALIZATION, CUSTOMIZATION, AND ONE-TO-ONE MARKETING

When John received Spotify’s recommended playlist, he noticed that the list contained artists he had listened to before. The playlist not only included songs from Taylor Swift who he

listened to frequently but also other female artists like Adriana Grande and Lana Del Rey. Also, the playlist was titled “Girls Night out.” What made Spotify’s personalized playlist different from other conventional playlists? In this chapter, we discuss what personalization is, how it compares to similar constructs, like customization and one-to-one marketing, and what the literature says about it.

Personalization is part of a bigger strategy in which firms try to adjust their marketing mix (product, price, promotions, or distribution strategy) to meet the needs of individual consumers. Many names that describe firms’ efforts to meet the individual needs of each consumer. Ranging from mass customization (Hart 1996; Pine et al. 1993; Syam, Ruan, and Hess

2005), customerization (Wind and Rangaswamy 2001), personalized service (Surprenant and

Solomon 1987), personalization (Kramer, Spolter-Weisfeld, and Thakkar 2007; Sackmann,

Strüker, and Accorsi 2006) to one-to-one marketing (Arora et al. 2008; Peppers, Rogers, and

Dorf 1999), all of these terms converge on the theme of individualizing the firm’s product and service to meet the individual consumer’s needs. Since there are only minor differences among these terms, I am going to refer to one-to-one marketing as ‘any tailoring of one or more aspects of the firm’s marketing mix to the individual customer (Arora et al. 2008; Peppers et al. 1999), and I will regard customization and personalization as different ways that firms can implement one-to-one marketing.

For one-to-one marketing to work, firms must meet individual needs. However, to discover what the individual needs, firms must obtain individual consumer information; firms can either directly ask consumers about their preferences and needs or infer these preferences from consumers’ prior behavior.

Personalization refers to when a firm changes one or more element in its marketing mix to cater to an individual consumer based on the consumer’s past behavior. However, when a firm has no access to the consumer’s explicit order, it has to rely on other sources of information. This is why personalization relies heavily on the consumer’s past behavior. At the same time, firms need to take the initiative and offer personalized products and services to the consumers based on their inference from the available information. For example, when Nike is trying to provide a personalized product to the consumer, Nike cannot expect the consumer to fill up an order form.

Instead, Nike needs to generate a list of recommended products based on the past purchases the consumer has made. If the consumer had browsed and ordered Air Max shoes in pink color before, Nike may provide a list of other products similar to Air Max in pink to the consumer. As you can see, the changes to the marketing mix in personalized products and services are led by the firm, not the consumer, to cater to the individual consumer based on his or her past behavior.

On the other hand, customization refers to when the same changes are led by consumers themselves (Arora et al. 2008; Peppers et al. 1999). In other words, personalization is the firm- driven changes to the product or service to meet the individual consumer need, whereas customization is changes led by consumers themselves. In both cases, Spotify needs to know what type of songs John likes to ensure providing all the songs he prefers. For this purpose, firms could directly give John a chance to create his own customized playlist. When John customizes his own playlist, Spotify just needs to keep the songs he likes available. So when firms have access to the consumer’s explicit preferences, firms can provide customized services and products where consumers are given initiatives to decide what they need. A consumer can order a customized pair of shoes from Nike.com and decide what canvas, color, and overall design they would like.

Before we move on to discuss the specifics of one-to-one marketing, I have to mention mass-customization, a more operations management focused approach, which has been studied at length as well. Compared to other conceptualizations, the concept of mass-customization focuses on delivering an individual consumer’s explicitly specified preference at no additional cost. While mass-customized products and services should be adjusted to what each customer stated, an equivalent amount of importance is put on cost (Hart 1996; Pine et al. 1993). Before the advent of information technology, it was costly for firms to adjust their production line to cater to the individual consumer. For instance, to produce Nike shoes in 10 different colors for individual consumers, there was bound to be an additional cost incurred. This is why much research on mass-customization emphasized on continuous improvement (Pine et al. 1993), modularized production line (Peppers et al. 1999), and flexible process (Da Silveira, Borenstein, and Fogliatto 2001; Syam et al. 2005) to achieve this goal. Ideally, customized red Nike sneakers should not be any more expensive than a common white shoe to achieve mass-customization. In a way, mass-customization is often a prerequisite to successfully delivering one-to-one marketing

(Wind and Rangaswamy 2001). While more recent conceptualizations on mass-customization do stress providing on what the consumer wants (Pine 2011), it is still true that mass-customization centers around building the capability and capacity to provide mass-customized products and services. This is why I am using the term one-to-one marketing instead of mass-customization in the following part even though I might be referring to work that uses the term “mass- customization.”

To understand the dynamics of one-to-one marketing, it will help to see how each element in the marketing mix can benefit from one-to-one marketing at its application. First, products can be both personalized and customized. For instance, Netflix personalizes its product

by creating a different recommended playlist of movies for each consumer. If you have been watching action movies lately, Netflix will create a list of other action movies that you have not yet watched. The Ritz Carlton hotel also personalizes its service by remembering how to accommodate each customer. For example, if John asks for an extra sheet of blankets, the hotel will ensure that extra blankets will be prepared for his next visit as well. Ritz Carlton’s staff meets daily to share information on each guest, making sure that the guests are treated in a way they preferred during their last visit (Piccoli and Watson 2008).

Figure 1 Examples of customizing Nike

As mentioned before, plenty of work demonstrates the benefits of customization for consumers. In a sense, consumers are given agency to tailor products and services to their own liking. A good example would be where Nike lets its online consumers customize their own shoes. On the Nike website, consumers can change colors on the vamp, tongue, quarter, tip, eyestay, foxing, swoosh, backstab, laces, lining, strap, midsole, outsole, and Lace Dubrae, which are all parts of a sneaker. On top of all the customizable options, you can also put your initials on it (see Figure 1 above).

Second, promotions can also be personalized and customized. Online behaviorally targeted advertisements are one example where firms utilize an individual consumer’s past behavior to offer advertisements that will be relevant to the individual (Boerman, Kruikemeier, and Zuiderveen Borgesius 2017). For example, if you were searching for Hawaii as a possible candidate for vacation, you will receive a behaviorally targeted advertisement on hotels in

Hawaii. Existing work has shown how consumers will incorporate what was implied in the personalized advertisements. When consumers received a behaviorally targeted advertisement that implied sophisticated taste, they were more likely to purchase the advertised product

(Summers, Smith, and Reczek 2016). Customization, on the other hand, will be giving control to the consumer on what promotion or advertisement the customer would like to receive. For instance, consumers can actually customize the type of advertisements they receive by opting out for a certain type of advertisement (as shown below Figure 2).

Figure 2 Customizing your behavioral targeted advertisement

Third, prices can be both personalized and customized. Retailers like Orbitz, a travel search engine, offer personalized prices for each consumer based on their past behavior. Airlines will also charge different prices based on the individual consumer’s online search behavior

(Mohammed 2017). For example, firms can offer a lower price to seal the deal for someone who has been browsing for hours. However, personalized pricing can often be perceived as “price discrimination” to some people since consumers often don’t have control over the suggested

price. For example, when Orbitz, a travel website, showed higher-priced hotels to Mac users compared to PC users, consumers were concerned that they had no control over their own price

(Martha C. White 2012). Rather than dictating different prices for different consumers, customized pricing lets customers name their own price. For example, Priceline introduced a

“Name-Your-Own-Price” feature where consumers could suggest a price for the flight or accommodation. Consumers could be rejected with their initial offer but can keep trying until both the firm and the consumer reach a threshold price where both parties are satisfied. Although not perfect, this is one way to provide consumers the agency to choose their own prices for the product or service.

Lastly, related to place (distribution), firms also provide personalized products and services depending on the geolocation of the consumer. For instance, firms offer different products for different regions. In winter, firms offer sweaters but they offer shorts for those connecting from Australia. Also, Snapchat, a multimedia messaging app, offers geo location- specific filters for consumers. For example, Starbucks Frappuccino Happy Hours filter worked only within the vicinity of a Starbucks during the ‘happy hour’ time (see Figure 3 below).

On the other hand, firms can let consumers customize delivery. For instance, Amazon provides the Prime membership for those who frequently order products. On top of the Amazon prime membership, consumers are given a choice to pay extra for a quicker delivery, up to one- day delivery.

Figure 3 Starbucks Frappuccino happy hour filter

Research suggests that one-to-one marketing, which includes both personalization and customization, can benefit firms and consumers for three reasons. First, one-to-one marketing can better serve consumers’ needs and reduce the cost of searching options. One-to-one marketing and related constructs all stress the importance of focusing on a single consumer (Hart

1996; Kramer 2007; Lynch and Ariely 2000; Peppers et al. 1999; Subramanian and Marquardt

1999; Syam et al. 2005; Wind and Rangaswamy 2001). To serve the need of an individual consumer, the firm must match individual preference. When firms recommended products and services like wine through their own recommendation agent or interactive home shopping system, consumers evaluated the products and services to be of higher quality both in an off-line and online context (Alba et al. 1997; Häubl and Trifts 2000; Lynch and Ariely 2000).

Specifically, one-to-one marketing offers an interactive context that increases the perceived value of the product or the service (Alba et al. 1997). Firms can also aid a consumer’s decision making

with a personalized recommendation list based on how much weight is put on each product attribute. Consumers felt that the recommended list resulted in higher perceived decision quality at a lower search cost (Häubl and Trifts 2000). Also, a personalized recommended list of digital cameras (Kramer et al. 2007) was received much more favorably by students than the list targeted for the whole student group. Consumers also preferred self-designed T-shirts over off- the-shelf design T-shirts (Franke et al. 2010). Empirical data also support that one-to-one marketing better satisfies individual consumer needs. Personalized coupons based on past household purchase history performed 2.5 times better than a uniform coupon optimized for every household (Rossi, McCulloch, and Allenby 2008). Also, personalized promotional e-mail content increased the expected click-through rate by 62% compared to the original design

(Ansari and Mela 2003). Analytical work supports the claim that one-to-one marketing will help firms to accurately evaluate consumers’ valuation and provide a better price for each individual consumer (Shaffer and Zhang 1995).

Second, one-to-one marketing can promote consumer loyalty. Past strategies on segmentation or targeted marketing would often focus on a specific group of people, leveraging on a particular ethnic group, gender, or social class (Aaker, Brumbaugh, and Grier 2000;

Deshpandé and Stayman 1994; Royne Stafford 1996). In contrast, one-to-one marketing is an

“extreme case of segmentation where the target segment is one” (Arora et al. 2008). Such dedicated focus on the individual should lead to higher satisfaction and long-term relationship with each individual consumer (Peppers et al. 1999), leading to greater retention and consumer loyalty since the cost of switching would be high (Ball, Coelho, and Vilares 2006; Lynch and

Ariely 2000). For example, if a clerk in a bookstore consistently recommends new books released by your favorite author, you will be reluctant to visit another bookstore. If you were to

switch to another store, you will have to spend time and effort where there is still uncertainty that you will receive an equivalent level of personalized service, which contributes to higher consumer loyalty. This will also drive consumers to be more forgiving to product and service failures (Ball et al. 2006). Third, one-to-one marketing can provide agency to consumers.

When consumers can customize products and services, consumers are taking an active role in the production process, and consumers did feel like they had “significant control over the design process (Franke et al. 2010).” In these cases, consumers can either explicitly state their preference or contribute to designing the products or services. Consumers gain agency when they proactively take part in product and service design processes. In such cases, consumers are given a chance to specify what they want explicitly, where the firm needs to focus more on delivering these specifications. Most people have an inherent need for agency (Baumeister 1998) and when people are deprived of their agency, this can affect people negatively. This also ties into the self- determination theory which stresses the importance of autonomy as a ‘basic psychological need’

(Deci and Ryan 1985). For example, when consumers had no choice on the type of massage from a list of Swedish, Deep Tissue, Five Elements, and Reflexology massage, consumers were less excited compared to when they had control over their choice (Botti and McGill 2010). Also, when consumers felt like they had no freedom of choice, consumers became reactant as the lack of choice threatened personal freedom (Brehm 1966). Consumers also perceived the task of customization to be more creative and enjoyable (Dahl and Moreau 2007) as consumers view participation as co-production (Bendapudi and Leone 2003; Etgar 2008). Stream of research on customization has also shown that consumers customizing their own products and services increases their perceived ownership and empowerment of the product (Fuchs, Prandelli, and

Schreier 2010). This not only increases the perceived utility of the product (Dellaert and

Stremersch 2005) but also spills over to the global value of the firm as well (Merle et al. 2010).

For example, when consumers were given a chance to customize their purchases, they perceived the customized computers to be higher in utility (Dellaert and Stremersch 2005) and customized shoes to be higher in hedonic and creative values (Merle et al. 2010).

Conversely, when personalized products and services threaten consumers’ perceived agency, consumers may react negatively. This is where personalization and customization differ based on whether consumers have agency, and this difference puts firms providing personalized products and services at potential risk. Personalization comes from the firm and not from the consumer, which inherently carries the problems of agency. Advertisements targeted to a specific identity can threaten consumer’s agency when the message feels forced. When a product was advertised to the environmentally conscious consumers as “the only good choice for green consumers,” consumers were actually less likely to purchase the advertised product. In this context, explicit identity targeted advertisement undermined the “perception of agency” for the consumers because the firm was forcing a specific label to the consumers (Bhattacharjee, Berger, and Menon 2014). Similarly, when consumers were told that their choice was being predicted by the artificial intelligence based on their browsing history, consumers felt like their sense of free will was being threatened. This led consumers to be less favorable toward the artificial- intelligence-powered recommendation system compared to when the artificial intelligence was using consumer’s past purchases only (Schrift, Wertenbroch, and Zwebner 2019).

Consumers also perceived agency to be threatened when consumers’ privacy was compromised (Kim, Barasz, and John 2018), especially when consumers had no control over their information used to generate the advertisement. For example, when consumers were told

that the targeted advertisement was based on the firm’s inference utilizing the information on consumers’ internet connection and browsing history, consumers were less likely to visit the advertised website compared to when consumers themselves specified the information about themselves. Another problem with lack of agency is that the firm suggests an identity of who the customer. Unlike customization where consumers have full control over their consumption experience, in a way, personalization is a message from the firm. As prior work has shown, personalized products and services imply personal messages of who you are (Summers et al.

2016).

We learned from this chapter that Spotify actively provided a personalized playlist for

John based on his past listening behavior. Spotify’s personalization effort included creating a unique playlist cover for John and selecting songs of other artists (e.g., Adriana Grande and Lana

Del Rey) John might be interested. Unlike customized playlists, for which John himself selects the songs, personalized playlists are provided by the firm.

CHAPTER 3

IDENTITY AND IDENTITY THREATS

Let’s go back to the moment when John received a personalized playlist called “Girl’s night out” after listening to Lady Gaga and Taylor Swift. When John read the title of the playlist with all the female pop stars, he started thinking about his male identity and started to worry that he might not be behaving manly enough. What did John think about himself and what part of

himself was concerned? In this chapter, we cover what identities are and how they can be threatened.

Identities and the Self

The literature uses the term “self” to describe any beliefs, evaluations, perceptions, and thoughts, that a person has about himself or herself (James 1890; Swann and Bosson 2010).

Researchers have used different approaches to try to conceptualize the self, including self- concept (Markus and Kunda 1986a; McGuire et al. 1978), self-knowledge (Oyserman 2007), self-schema, and self-identity (Markus 1977). For example, Oyserman (2007) defines self- concept as the ‘cognitive structures that are used to make sense of the world based on one’s goals and protection of one’s basic worth, which can include content, attitude, or evaluative judgments.’ Also, self-schema is ‘cognitive generalization about the self, derived from past experiences that organize and guide the processing of self-related information contained in an individual’s social experience’ (Markus 1977). Despite minor differences, all these terms point to a systemic knowledge structure used to make decisions in contexts related to the self.

The self is often made up of multiple identities (Markus and Kunda 1986b; Oyserman

2007). For example, John is a man, PhD student, warm-hearted person, son, history buff, gamer,

Asian, and fundamentally a human being. Identity is a “category label that a consumer self- associates either by choice or endowment” (Reed and Forehand 2012). John can choose to be a

“PhD student” and be “arduous,” but was also born as a “human being” and an “American.”

While the term identity is also often interchangeably used with self and self-concept, identity could be viewed as the building blocks of the self which is often used at a broader level

(Oyserman 2007). For example, John can hold a self as an American. However, John’s American

self is comprised of multiple identities; Midwesterner, Arizonian, and Tucsonian. At the lowest level, John might even define his identity by living on Park Street. All these different identities contain distinct attributes and associations.

Identities can be concrete or abstract (Reed et al. 2012). Identities can originate from a specific social group that was endowed to you as people do not get to choose their nationalities, family, ethnicity, and gender. American, son, Asian, and male identity are all good examples of a relatively concrete identity. However, identities can also originate from an abstract figure or group. For instance, for a working parent, a fictional character like Superman can be a symbol of competence to carry out both tasks of raising a child and a full-time job. Celebrities or famous historical figures can also be a reference for abstract identities. For example, Mahatma Gandhi can be a symbol of humility and perseverance for forming one’s identity. Here, Mahatma Gandhi represents a group of preserving people against hardships. Similarly, imagined others can be used as a reference as well. People can imagine others in the field who are more successful than themselves as reference. In comparison, a student working hard at school can imagine a group of students who have failed the curriculum as a group to stay away from.

People learn about their identities both from how others label them and from their own behavior. As mentioned before, people are endowed with some labels by being human, nationality, gender, and ethnic groups. You learn that you are part of a label called human beings when you are treated equivalently to others. You are labeled as an American on your social security card and will be given the rights and privileges as an American citizen within the society. People can also learn about their identities from their own behaviors (Bem 1972). When people were labeled as ‘generous’ after donating to the Heart Association, people were more

likely to donate more money for multiple sclerosis patients two weeks after compared to those labeled as ‘uncharitable’ (Kraut 1973). When people learned that they were engaging in charitable behavior and labeled as charitable, people were likely to infer that they indeed hold a

‘generous’ identity. Similarly, when students learned that they were doing great in arithmetic, students performed better consistently one day after and two weeks later (Miller, Brickman, and

Bolen 1975). Again, students inferred about their studious identity from their own behavior that they are doing well in arithmetic.

Identity Salience

People hold multiple identities, and these identities differ in terms of how salient they are. The salience of a specific identity depends on how much the identity is accessible to the individual and whether the identity has a good fit with the context (Oakes 1987). Three factors increase the extent to which a specific identity becomes salient.

First, identities that a person considers important are likely to be more chronically

“accessible” to that person (Forehand et al. 2002). Accessibility refers to the readiness of the identity to be activated in the person (Oakes 1987). For example, if being an American is a valuable part of John’s identity, this American identity will come to mind more quickly and more often than if being an American were not an important part of John’s identity. Similarly, those who strongly identify as being Hispanic are more likely to be favorable toward brands advertised to Hispanics. A stronger association with a group will drive a specific identity to be more salient as well, guiding subsequent behaviors (Deshpande, Hoyer, and Donthu 1986). Similarly, those who associated themselves with African Americans also liked the advertisements by African

American celebrities, like Bill Cosby (pre-scandal) and Sugar Ray Leonard, more than Anglo-

Americans (Williams and Qualls 1989). Also, students in fraternity or sorority responded faster to Greek life compared to non-Greeks (Smith and Henry 1996). People will even go as far to expect themselves to score higher in a made-up personality trait when they were told that the group they belonged to performed well (Schmader and Major 1999).

Second, distinct identities are likely to be salient. For example, being American is not a salient identity in Tucson, Arizona because most of the people are American. However, 70 miles down south in Nogales, Mexico, American identity becomes salient because most of the people are probably Mexican. As an identity becomes more distinct in a specific context, it is more likely to be salient (McGuire et al. 1978). For example, in a group of two women, female identity is not as salient because everybody in the current context shares that identity. However, when three men join the group, the two women in the group begin to focus on their similarity as their female identity is now distinct within the context. Other research has also shown that participants would choose adjectives related to their own gender (e.g., masculine, competitive, assertive, feminine, sympathetic, helpful) to explain themselves when most of the people in the conversation are of the other sex (Hogg and Turner 1987). In other words, when your group membership is “distinctive,” it is likely to become a more salient identity. For instance, compared to white Americans, Hispanics and African Americans were more likely to spontaneously mention their ethnic identity when asked to openly describe themselves (McGuire et al. 1978).

Third, and most relevant to my research questions, situational cues like words, visual images, and symbols can make different identities salient. For “multi-cultural” Asian students in

Hong Kong, seeing a Chinese Dragon could activate a Chinese identity, whereas seeing an

American flag activates an American identity (Hong et al. 2000). Surprisingly, exposure to symbols of a relevant outgroup can also make the ingroup identity salient. For example, when

Rutgers students saw a flag of their rival team (Princeton), their membership to their own university became more salient (Cialdini et al. 1976; Wilder and Shapiro 1984).

Identities Differ in Value

Me Not Me

Ingroup Outgroup

(Positive) (Negative)

Figure 4 Initial conceptualization of how identities are valued

In addition to differing in accessibility and salience, identities differ in value and the extent to which people value them. People value the groups they strongly associate with (i.e., in- groups), and the identities related to these groups are typically valued more than identities related to groups with whom they do not associate (i.e., out-groups; Tajfel and Turner 1979). Even in a minimal group setting where people are artificially split into two groups, people use the outgroup as a reference to be different from. In these early works, the ingroup membership would be automatically considered as the identity people wanted to possess. On the other spectrum, outgroup was the group people did not want to belong to (White et al. 2012; White and Dahl

2007). The outgroup is what people consider as “not me,” ranging from rival schools, foreigners,

immigrants, specific ethnic groups to even the opposite gender (as shown in Figure 4)

However, ingroup identities are not always valued more than outgroup identities. There are cases when people want to be something that they are not. For example, John aspires to be as successful as Tom Hanks even though John is neither an actor nor a Caucasian. Here Tom Hanks is the individual outgroup member whom John aspires to be because Hanks symbolizes an abstract group of successful people (Kirmani and Smith 2009). Acknowledging that not all outgroups are equally valued, I propose a matrix (Figure 5) based on two dimensions: (1) whether people consider the identity to be a part of themselves (me vs. not me) and (2) whether the identity is perceived positively or negatively (valence of the identity). Let us take a look at each of the four types.

Me Not Me

Positive Associative identity Aspirational identity

Negative Feared identity Dissociative identity

Figure 5 Categorization of how identities are valued

First, an associative identity is what people consider to be part of ‘me’ in a positive way.

This is similar to what I previously introduced as ingroup, simply the group people belong to.

Indeed, the ingroup will often be the associative identity people want to have. As identities are

closely related to how people feel about themselves, people are often motivated to have a positive view of the group they belong to. We have observed this basic motivation in the aforementioned studies in minimal groups (Tajfel and Turner 1979). Associative identities include social identities as well. At a local level, people can take pride as a New Yorker, and at a national level, people can feel patriotic to be an American.

Second, an aspirational identity is a social category that a person values despite believing that the category does not apply to them. Celebrities are just one example of aspirational outgroups people identify and aspire to be a part of. Depending on whether individuals perceive there are means to achieve the aspirational identity, individuals might abandon their current ingroup identity and try to move on to an aspirational identity. (Tajfel and

Turner 1979). For a PhD student, an assistant professor can be an aspirational identity. A successful scholar would be another example of a more abstract case of aspirational identity. Just as Tom Hanks represents a group of competent and successful people to John, aspirational identity includes both concrete and abstract identities that are not part of ourselves.

Third, a dissociative identity is a social category that a person neither values nor believes it applies to them. Dissociative identities are very similar to how outgroups are conceptualized

(Tajfel and Turner 1986; Turner 1991). People want to ‘disidentify’ and avoid being associated with groups related to this identity (Englis and Solomon 1995). Outgroups like rival schools, foreigners, specific ethnic groups, and the opposite gender mentioned before are examples of specific dissociative identities. Again, these dissociative identities can be very specific or abstract. While being jobless will be a relatively concrete dissociative identity for a PhD student, being incompetent and non-productive will be a more abstract dissociative identity. On a separate

note, there are also irrelevant outgroups (not-me) consisting of “not me” identities that a person neither thinks is good nor bad. For Rutgers students, the Yale football team was the dissociative outgroup, while New York Yankees, a professional baseball team, were the irrelevant outgroup

(Wilder and Shapiro 1984).

Fourth, and most relevant to my research questions, a feared identity is a social category that a person dislikes but believes to apply to oneself. It might sound unnatural that a person will consider a negative identity to be part of himself or herself, but research on stereotype has demonstrated how people often associate with identities that they view unfavorably (Murphy,

Steele, and Gross 2007). For example, a female student will be aware of a negative stereotype that women are poor at math but cannot simply deny her identity as a woman. Similarly, an

African American is aware of the stereotype that African Americans perform worse in intellectual domains but cannot easily deny one’s ethnic identity. People may also identify with negatively valued social categories that they are not proud of. Guilty pleasure consumption could be an example of this phenomenon. Guilty pleasure consumption refers to consumption that consumers feel ‘ashamed and uncomfortable’ but cannot resist continuing (McCoy and

Scarborough 2014). The consumption practices that consumers are not proud of range from watching a sensational reality television show like America's Next Top Model, eating unhealthy food, and to excessive drinking. Prior work has also shown how people often associate guilt with pleasure (Goldsmith, Cho, and Dhar 2012), where any product or service that feels guilty should be more pleasurable. This suggests that some hedonic consumption could make people feel guilty and aware of their own feared identities.

Identity Threats

Identities are threatened when something shakes a positive view of who you are or who you want to be. The literature uses several different terms to describe identity threats, including dissonance, shaken self, and self-discrepancy. Dissonance results from inconsistencies between mental representations (Festinger 1957; Steele 1988; Thibodeau and Aronson 1992). For instance, when undergraduate students wrote an essay supporting an increase in tuition, most students who previously thought that tuition should be lowered felt dissonant. Shaken self is a result of a threat to a stable self-view. For example, recalling one’s past unethical behavior could shake a stable view of the self as a moral individual (Zhong and Liljenquist 2006). Writing with one’s non-dominant hand also threatens a stable self-view of oneself as being competent (Gao,

Wheeler, and Shiv 2009). Lastly, self-discrepancy occurs when there are inconsistencies between the actual and ideal self (Higgins 1987). When people were reminded of ideal attributes they would like to possess compared to the ones they actually own, people felt discrepant about the inconsistency. All of these terms illustrate that a threat to a positive and stable view of self can come in many different ways.

Me Not Me

Positive Associative identity Aspirational identity

Devaluation

Negative Feared identity Dissociative identity

Figure 6 Devaluation threat

Identity threat can occur in three major ways using our matrix on identity categorization.

First, devaluation can happen when an associative or aspirational identity is disparaged. This can happen when someone suggests that the associative or aspirational identity is not good and as a result poses a threat to the value of the identity (refer to Figure 6). One of the most common causes of devaluation threat is induced when a person is exposed to negative information about a group that they belong to (Crocker and Luhtanen 1990; Doosje, Ellemers, and Spears 1995;

White and Argo 2009). For example, when psychology students were told that psychology majors were less intelligent than business students, psychology students faced an identity threat

(Spears, Doosje, and Ellemers 1997). Students would even feel a threat when they were told that their group performed worse than an outgroup on a fictional trait measure called “surgency”

(Schmader and Major 1999). Other work has demonstrated how stereotypes of racial groups or gender could cause identity threat as well. For instance, white males felt threatened when articles discussed that Asians outperform whites in math (Aronson et al. 1999) and women felt threatened as well when they were told that women are bad in math in general (Spencer, Steele, and Quinn 1999; White and Argo 2009). More recent work has shown that the threat will loom larger for those who strongly identify with the group (Dalton and Huang 2014). For instance, students who strongly identify as a university student will feel threatened when they learn that the school is suffering in quality of students, instruction, and community contribution compared to other schools in the region. For those who associate strongly with being an American, watching a scene from the movie Rocky, where the American protagonist loses to a Russian boxer, was sufficient to cause threat (Branscombe and Wann 1994). People can similarly feel a threat when someone criticizes their aspirational identity. For example, people who look up to

Mahatma Gandhi will feel devaluation threat if someone were to call Gandhi a racist.

Me Not Me

Rejection Positive Associative identity Aspirational identity

Negative Feared identity Dissociative identity

Figure 7 Rejection threat

Second, a rejection threat occurs when someone or something suggests that a person does not possess an identity that they would like to possess (i.e., when they are told that an associative identity is an aspirational identity). An example might be a situation when people are told that they are not part of something good (intelligent, attractive, a member of an aspirational group, etc.; refer to Figure 7) that they have thought to belong to. The aspirational groups can range from dream jobs, schools, sorority/fraternity, brands, products to abstract groups of successful people. For instance, consumers can feel threatened by being rejected by an aspirational brand like Louis Vuitton. When the participants read a scenario where the brand representative was skeptical about whether the participants had actually purchased Louis Vuitton products, participants felt a rejection threat from the brand Louis Vuitton (Ward and Dahl 2014).

People similarly experience rejection threats if someone or something suggests that they don’t have some desirable quality, trait, or characteristic (Bushman and Baumeister 1998; Cohen et al.

1985; Duval and Duval 1987; Fitch 1970; Gao et al. 2009; Gibbons et al. 2002; Morse and

Gergen 1970; Pelham and Wachsmuth 1995; Tesser and Collins 1988; Vallacher and Solodky

1979). For example, students suffered a rejection threat when they were told that their essay is

“is one of the worst essays I have read” (Bushman and Baumeister 1998), their IQ test results were at 42nd percentile (Cohen et al. 1985), they lacked artistic and quantitative ability (Duval and Duval 1987), or that they performed at the 29th percentile compared to others on a scale that supposedly predicted success after graduation (Gibbons et al. 2002; Morse and Gergen 1970;

Pelham and Wachsmuth 1995; Tesser and Collins 1988). In all of these contexts, people felt identity threat because they received information suggesting that they do not belong to a group that they value.

Positive Associative identity Aspirational identity

Categorization

Negative Feared identity Dissociative identity

Figure 8 Categorization threat

Third, a categorization threat occurs when someone or something suggests that you possess a feared identity (refer to Figure 8). People can feel a categorization threat by being associated with groups that they do not want to be a part of (Carver, Lawrence, and Scheier

1999; Ellemers, Spears, and Doosje 2002; White and Dahl 2006, 2007). For instance, when males were offered steak called ‘lady’s cut,’ they were likely to feel threatened because it

associated them with a female identity, an identity that most men try to avoid (Coskuner-Balli and Thompson 2012; Vandello and Bosson 2013). Men were similarly more likely to avoid other products associated with a female identity, and as a result, were more likely to choose a cup of

Joe over a Café Latte, Coke Zero over Diet Coke, and Classic Martini over a Cosmopolitan (Gal and Wilkie 2010). Especially for those who do not have a strong association with a minority group, being categorized under the preexisting stereotype itself can be a threat to the individual

(Ellemers et al. 2002). For example, high ranking women in companies wanted to emphasize their self-descriptive traits (e.g., competitive and ambitious) and avoided categorization by gender.

People can use either direct or indirect means to deal with the threatened identity

(Mandel et al. 2017). Direct means include addressing or trying to fix the source of the threat.

For example, getting plastic surgery and working out at a gym would be a direct means of responding to an identity threat about your appearance. Similarly, when a student’s competence is threatened because they receive a bad grade, she could purchase brain training to perform better for the next exam (Kim and Gal 2014).

Indirect means include symbolic-self-completion, fluid compensation, dissociation, and escapism. Symbolic self-completion is a way to cope with the threat by engaging in behaviors that signal mastery on the threatened dimension. For example, MBA students who got bad grades tried to compensate by purchasing expensive suits to indicate business success (Wicklund and

Gollwitzer 1982). Fluid compensation, including self-affirmation, copes with an identity threat by reinforcing other identities that are not threatened (Steele 1988). For example, rather than focusing on the bad grades, people can purchase a luxurious good reinforce their identity as a

successful and fashionable person.

Another indirect means that people use to cope with a threatened identity is abandoning the identity (dissociation). For instance, when people are told that they performed 42nd percentile on an IQ test, they tended to attribute the result to difficulty and luck rather than to their own ability (Cohen et al. 1985; Federoff and Harvey 1976). People with high self-esteem are especially likely to attribute poor performance to chance rather than thinking it reflects who they are (Fitch 1970). Another way that people abandon a threatened identity is to avoid engaging in behaviors and buying products that are associated with the threatened identity. For example, when women read that women are bad at math, they were less likely to purchase women identity-relevant items like Cosmopolitan magazine (White and Argo 2009). Similarly, after writing about their gender identity, women were less likely to donate to ovarian cancer research and took longer to process breast cancer advertisements (Puntoni, Sweldens, and Tavassoli

2011).

As will be discussed next, the observation that people abandon objects and behaviors associated with a threatened identity has an important implication for how people will respond to personalization. We learned from this chapter that John possesses multiple identities comprising who he is. While each identity differs in value, different identities can be salient depending on the context. When John received a personalized playlist, it is likely that his gender identity was activated. The personalized playlist in this context might have activated John’s feared identity for taste in feminine music, which was likely to pose a categorization threat and may motivate him to try to distance himself from this threatened identity.

CHAPTER 4

PERSONALIZATION AS A SOURCE OF IDENTITY THREAT

Let us go back to John’s story again. What would happen when John received the personalized playlist? Instead of customizing his own music playlist on Spotify full of girly songs on his own free will, John received a playlist named “Girl’s Night Out” provided by

Spotify. This would imply that John had been listening to feminine music, which threatened

John’s masculine identity. Moreover, because the personalized playlist was based on John’s personal listening history, the threat to John loomed larger. How will John respond to this threat?

As I discuss in this chapter, I predict that John is likely to avoid using the music service to distance himself from his threatened gender identity.

Personalized products and services highlight the consumers’ identity. When firms provide personalized options to consumers, consumers are likely to focus on the implications of these recommendations. For instance, when you notice Amazon recommending a list of cooking tools like cast-iron skillet, glassware, rolling pin, and a kitchen counter, you realize that you are a devoted home cook. Personalized products and services make consumers conscious of firms making inferences about the individual consumer’s identity (Summers et al. 2016). For example, when consumers received personalized advertisements after searching for a list of identity- relevant products they would like to own (e.g., footwear, books, and cloths), they inferred that they were receiving the advertisements because of their previous search online. When the advertisements implied a sophisticated taste, the personalized service highlighted the sophisticated identity. Especially, personalized products and services are based on consumers’

prior behavior and are likely to draw more awareness and attention to identities associated with their past behavior. This would make the implied identity more salient to the consumer. When

John received a playlist called “Guy’s Night Out,” it is likely to activate his male identity.

Therefore:

H1: Personalized products and service are likely to activate identities implied by the personalization.

Salient identities are likely to influence consumers’ judgements and decisions (Forehand et al. 2002; Puntoni et al. 2011). For example, when males were told: “It's important to stand by your own conviction,” this statement activated the male identity and related concepts to be agentic and self-focused. This activation drove the males to be favorable toward advertisements that stressed how the mouthwash will “kill germs and bacteria that cause decay” over a message that stressed how the mouthwash will “provide pleasing fresh breath” (Meyers-Levy 1988). In another case, when Asians read questions about their ancestry and language they spoke, they were more likely to perform better in a math test compared to when there was no activation of any identity (Shih et al. 2002). Similarly, Asian women performed worse when they were asked questions about their gender instead of their ethnicity (Shih, Pittinsky, and Ambady 1999).

However, there is a fundamental difference between personalization and customization: agency. If John were customizing the playlist by himself, even with a selection of the same ratio of girly songs, he would not name it “Girl’s Night Out.” If anything, he might have named it

“Guy’s Night Out.” Some identities are given to us (e.g., gender, nationality, and ethnicity),

whereas some identities are what we choose on our own (e.g., University of Arizona student,

PhD student, and a history buff; Reed and Forehand 2012). Customization empowers consumers to selectively choose or avoid different identities with the products and services. However, when

John received a recommended playlist from Spotify, he had no direct control over its personalized components. In this case, the title “Girl’s Night Out” and the album art associated with it could suggest a negative identity to John.

So, what would happen when personalized services or products implied something negative about who you are? As mentioned before, consumers often hold feared identities they are not proud of. These could be watching sensational reality television shows to eating unhealthy food, these are consumption practices that consumers are not proud of. When firms provide a personalized playlist for cheap reality shows, it will activate consumer’s negative identity of watching cheap television. As mentioned earlier, people can feel identity threat when they are associated with groups that they do not want to be a part of (Ellemers et al. 2002; White and Dahl 2006, 2007). Because consumers have less control over the personalized products and services, personalization might suggest a negative identity to consumers. Since the link between the self and the behavior is clear in personalized products and services, consumers are more likely to feel personally responsible. Past work has shown that the effect of dissociative identity threat was stronger when the individual was personally responsible. For example, consumers felt they were being cheap for using a coupon on a date even when it meant saving money

(Ashworth, Darke, and Schaller 2005). Therefore, I propose that personalization can cause an identity threat, specifically a categorization threat, when the recommended content implies a negative identity. To avoid this identity threat, consumers are likely to dissociate themselves from the source of the threat, in this case, the firm offering the personalized product or service.

Therefore, I hypothesize:

H2: When personalized products and services activate a negative identity, it will result in a negative attitude toward the firm.

When there is identity threat, it is likely to activate the threatened identity (more salient) compared to when there is no threat (H1). When the personalized products and services highlight the feared identity, people are likely to respond more quickly to the constructs related to the feared identity as well. Therefore:

H3: Personalized products and services will cause the negative identity to be more salient and accessible, and this negative identity will mediate the influence of personalization on the attitude toward the firm.

CHAPTER 5 STUDY 1

Study 1a- Do consumers respond negatively to personalized service when purchasing

hemorrhoid cream compared to cough medicine?

I hypothesize that personalized service can hurt consumer attitude toward the firm when the service evokes negative identity. For example, buying hemorrhoid cream regularly and receiving personalized service from the clerk may activate the negative identity of suffering from hemorrhoids. On the other hand, buying cough medicine regularly implies that you are suffering from cold, a more common and less stigmatized identity compared to having hemorrhoids. This study examines when personalized service can hurt consumer attitude toward the product, service, and overall experience. It tests whether personalization can backfire when it implies something negative about oneself.

Method

Three hundred two MTurk participants (푀푎푔푒= 36.29, 44.7% female) were randomly assigned to a condition in a 2 (personalized vs. not-personalized) × 2 (threatening vs. non- threatening) between-subjects experiment.

To be able to manipulate personalization in a service context, the survey asked for participants’ first names or pseudonyms for later uses in the scenario in the main study.

Specifically, participants read, “What's your first name? (If you do not want to tell us your real name, you can use a pseudonym that will serve as your name for the purpose of the study.)” On the next page, participants were told to imagine visiting a pharmacy to buy either hemorrhoid cream or cough medicine. Participants read that they had visited this store before to buy either hemorrhoid cream or cough medicine in each condition. Specifically, participants read, “Imagine that you are going to a pharmacy to buy hemorrhoid cream/cough medicine. You have come to

this pharmacy a few times before for the same product.” Hemorrhoid cream was chosen as a category to imply that the participant regularly suffers from hemorrhoids, an identity that most people would like to avoid (i.e., feared identity). While cold medicine also implies that one is sick from cold, everyone gets colds, which makes the identity of “someone who gets colds” less threatening.

Next, participants read, “You quickly find the product you were looking for.” Then, the next sentence was varied to manipulate personalization by using each participant’s own name. In the personalization condition, participants read, “When you step in the counter and hand the product over, you realize that the clerk recognizes you and greets you using your name. “Hi,

[first name], did you find everything you need? Are you here for another batch of hemorrhoid cream/cough medicine?” To strengthen the manipulation, the clerk at the end also said, “Thank you for shopping with us! Please come again, [first name]." Participants in the control condition instead read the same content without the clerk recognizing the customer. For example, the clerk in the scenario would ask, “Hi, did you find everything you need?” without mentioning the participant’s name or the product that they frequently purchase.

After reading the whole scenario, participants read: “Please answer the following questions based on your experience at the pharmacy.” Participants subsequently indicated the intention to intention repurchase the same product, intention to revisit the pharmacy, and how satisfied with the purchase on a 7-point scale (1=unlikely, 7=likely/ 1=not at all, 7=very  = .88).

Specifically, participants read, “How likely are you to purchase the same product in the future?

How likely are you to revisit this pharmacy in the future? How satisfied are you with your purchase?” Lastly, participants answered a few questions on whether the clerk recognized and

identified the participants in the scenario and demographic information.

Results

As predicted, a 2 (personalized vs. not-personalized) × 2 (threatening vs. non- threatening) ANOVA on attitude toward the pharmacy revealed a significant interaction (F

(1,298) = 30.07, p < .01). For hemorrhoid cream, attitude toward the non-personalized service

(M = 5.95) was more favorable than the personalized service (M = 4.76; F(1,298)=33.04, p

< .01). With cough medicine, attitude toward the personalized service (M = 5.98) was not significantly different from that toward the non-personalized service (M = 6.05; F(1,298)=.12, p

= .73).

These results show that personalization can actually hurt consumers’ overall attitude toward the product and the store as well. This finding is consistent with hypothesis 2 and sheds light on how personalization can negatively influence consumers’ attitude.

Study 1b- Do consumers respond negatively to personalized service when purchasing

hemorrhoid cream compared to water?

Study1b employed a different control condition for identity threat. While hemorrhoid cream implies something negative about the self, cough medicine also sends a message that the consumer is sick from a chronic case of cold. On the other hand, water implies that consumer has

a healthy habit of drinking water regularly, which makes the personalized service less threatening to the consumer. Other than switching water as the control product, the rest of the procedure was similar to study 1a.

Method

Three hundred ninety-eight undergraduate students (푀푎푔푒= 19.7, 43.8% female) were randomly assigned to a condition in a 2 (personalized vs. popular) × 2 (identity threat: hemorrhoid cream vs. water) between-subjects experiment. The process was identical to study 1a except for the control condition for identity threat used water instead of cough medicine.

Results

Consistent with my prediction and the results of study 1a, a 2 (personalized vs. not- personalized) × 2 (threatening vs. non-threatening) ANOVA on attitude toward the pharmacy revealed a significant interaction (F(1,395) = 11.26, p < .01). With hemorrhoid cream, attitude toward the non-personalized service (M = 5.36) was more favorable than that toward the personalized service (M = 4.94; F(1,395)=6.03, p < .05). With water, attitude toward the personalized service (M = 6.02) was more favorable than that toward the non-personalized service (M = 5.61, F(1,395)=5.24, p < .05).

These results replicated that personalization can actually hurt a consumer’s overall attitude toward the product and the store. This finding is consistent with the results in the previous study.

Study 1c- Do consumers still respond negatively to personalized service when the

consumers’ names are not mentioned in the process?

Study 1c was designed to confirm that the effect was driven not merely by people’s strong associations with their own names. While remembering a consumer’s name is one way for a firm to personalize service, some consumers might regard it as an invasion of their privacy.

Thus, the ‘name-calling’ task was deleted from the scenarios, but I continued to manipulate personalization by having the clerk use a personalized or generic greeting when interacting with the consumer.

Method

Three hundred MTurk participants (푀푎푔푒= 37.97, 37.33% female) were randomly assigned to a condition in a 2 (personalization: remembering past purchase vs. standard service)

× 2 (identity threat: hemorrhoid cream vs. water) between-subjects experiment. The process was identical to study 1b except for the clerk in the scenario who did not mention the participant’s name.

Results

Replicating the previous studies, a 2(personalized vs. non-personalized) × 2 (threatening vs. non-threatening) ANOVA on attitude toward the pharmacy revealed a significant interaction

(F(1,296) = 9.15, p < .01). With hemorrhoid cream, consumer attitude was more favorable for the non-personalized service (M = 5.95) than for the personalized service (M = 4.96;

F(1,296)=23.47, p < .05). With water, consumer attitude did and the differ between (M = 6.06) and the non-personalized service (M = 6.18; F(1,296)=.32, p = .57) conditions. These results show that it is not just calling consumers by their names but personalization that hurts consumer attitude toward the product and the store. This finding is consistent with what we have found in previous studies (refer to Figure 9 below).

As a whole, Study 1a, 1b, and 1c demonstrate how people respond negatively to personalized services that might convey an undesirable identity, like being unhealthy. The results replicated the negative effect of personalization with different ways to operationalize personalization across multiple products. In study 1a and 1b, the negative response to the personalized service for purchasing hemorrhoid cream was consistent across purchasing cough medicine or water as a comparison. Study 1c demonstrated how the effect was not dependent on the clerk remembering the customer’s name. Interestingly, in study b, personalized service actually increased the attitude toward the pharmacy when participants were buying water in the scenarios. While this effect is not consistently observed in study 1c, there is room for future work to explore when personalization positively influences customers.

7 Study 1a Study 1b Study 1c ** ** ** 6 **

1 Threat No Threat Threat No Threat Threat No Threat Personalized Not Personalized Figure 9 Summary of dependent variable, attitude to the pharmacy, in study 1

CHAPTER 6 STUDY 2- Do males respond negatively to personalized music service recommending “Girl’s Night” playlist?

The aim of study 2 is to replicate that finding that personalization can backfire not only

in a public context but also in a private context. While study 1 shows that personalized service

can backfire when negative identity is implied, it is also a relatively public context where people

could feel more easily shameful. To demonstrate the effect with actual behavioral choices in an

online music streaming context, this study explored how consumers react to personalized music

recommendations. With the rise of information technology, online music platforms are one of the

most popular spaces for personalization as well. Study 2 focuses on when a personalized playlist implies a negative identity and how this influences a consumer’s attitude toward the music service. This study employs a personalized recommendation playlist that might reveal a feminine identity to males who listen to songs regularly. Men often feel threatened when they are associated with the opposite sex (Coskuner-Balli and Thompson 2012; Vandello and Bosson

2013). As previous studies have shown, males tend to avoid choosing options that might signal a feminine identity. For instance, men will choose a Grilled Blackened Porterhouse Steak over a

Vitello Carciofi and Asparagus (Gal and Wilkie 2010) to avoid a feminine identity (White and

Dahl 2006). Based on this rationale, the experiment employs a feminine identity in music that is shameful to males who listen to music frequently. The prediction is that the personalized playlist will hurt consumer attitude toward the music service when the personalized recommendation implies a feminine identity.

Method

Five hundred twenty male participants (푀푎푔푒= 34.9) who listen to music regularly were recruited from MTurk. Participants were assigned to a condition in a 2 (personalization: personalized vs. popular) × 2 (threatening vs. non-threatening) between-subjects design experiment.

Participants read that the experimenter was creating a music service and was interested in getting their actual feedback on the new service. The music service was called "Z" and the name of this service could not be revealed at the moment to get a more accurate measure of their

impression. Participants read that “Z” was similar to major music streaming services such as

Spotify and Apple music but was dedicated to sharing more of the profit to the artists. Also, “Z” offered diverse playlists based on artists, genres and themes. Next, participants read, “Because of the large volume of music available, it can be difficult for people to find the right playlist.”

Then, the explanation about “Z’ was varied to manipulate personalization. Participants in the personalization condition read, “Consequently, Z recommends songs and playlists based on the personal music preferences of each person who uses the service. The purpose of this study is to collect information about your personal preferences in order to help build Z's personalized playlist recommendations.” Participants in the control condition read, “Consequently, Z recommends songs and playlists based on what is popular amongst people who use the service.”

Then, participants in both conditions read, “In the next part of the survey, you will have the opportunity to listen to songs by two different artists. For each pair of songs, you will be asked to select the song or artist that you like better. Simply choose the song or the artist that appeals more to you personally.” Participants in the personalization condition read, “Z will use your choices to recommend personalized playlists customized just for people like you,” whereas those in the control condition read that “Z will use the average choices of everyone to recommend playlists that most listeners will enjoy.”

In the next stage, participants were given multiple chances (total seven) to choose between two artists who were both well-known and similarly successful. This stage was necessary to credibly create personalized playlists. I considered multiple artists who were on the top of Billboard over the years by utilizing YouTube, Spotify, Apple Music, and Chartmetric.

Chartmetric is an online service that combines hundreds of thousands of real-time data points

across iTunes, Spotify, YouTube, Apple Music, Google, Deezer, and SoundCloud, and it provides specific performance on each artist. Utilizing such information, I selected artists who were equivalently popular across different music services like Spotify and Apple Music. For example, participants were asked to choose between a song by Justin Timberlake and a song by Bruno

Mars.

Specifically, participants read, “Which artist do you like better? You can listen to the song below for your reference. It is up to you. (You can listen to up to 30 seconds of each song by clicking on the video). Please choose the artist that you prefer by selecting the box around the video.” After participants made choices among four sets of male artists and three female artists, they were told that they will be given playlists that “Z” has recommended to them. Again, participants in the personalization condition read, “In the next page, you will see multiple playlists that Z has personalized for you based on the choices you have made.” Those in the control condition read, “In the next page, you will see multiple playlists that Z thinks will be the most popular.”

In the next section, participants saw two playlists suggested for them. For those in the personalization condition, the playlist was called “Your Personalized Playlist

Recommendations.” Those in the control condition were given “Popular Playlists.” In both conditions, I included the songs of the artist that participants chose to make the playlist more convincing. Then, they got songs that were popular among the general public but could also be perceived as feminine. For this purpose, I focused on artists who came across as feminine but were popular and well-established across gender. Songs suggested included works from famous artists like Ariana Grande, David Guetta, Luis Fonsi, etc.(refer to Table 1 for the full list).

Identity threat was manipulated through a feminine playlist title while all else (i.e., the songs on the playlists) were kept constant. Those in the identity threat condition were given playlists called “Girls’ Night” and “Girl Power Anthem,” whereas those in the control condition received playlists named “Night Out” and “Mood Booster.”.”

After receiving the recommended playlist, participants read, “Based on your experience with Z, how do you evaluate the music service as a whole?” Participants subsequently indicated their attitude (three items on unfavorable/favorable, dislike/like, and bad/good) toward the music service “Z” and likelihood to try out the music service in the future on a 7-point scale

(1=unlikely, 7=likely;  = .97). Lastly, participants completed questions on age, first language, or any comments on the study itself. Figure 10 below summarizes the overall procedure of study 2.

Figure 10 Study 2 procedure

Results

Like study 1, the prediction was that personalized service and products associated with a feared identity will lower consumers’ general attitude toward the service. Consistent with this prediction, a 2 (personalized vs. popular) × 2 (threatening vs. non-threatening) ANOVA on attitude toward the music service “Z” revealed significant interaction (F (1,516) = 10.51, p

< .01). When participants received playlists associated with women, participants’ attitude toward the non-personalized playlist (M = 4.64) was more favorable than that toward the personalized playlist (M = 3.74; F (1,516) = 28.85, p < .05). When participants received playlists associated with gender-neutral playlist, participants’ attitude toward the non-personalized playlist (M =

4.81) was not significantly different from that toward the personalized playlist (M = 4.64; F

(1,516) = .66, p =.42). These results seem to suggest that personalization might pose an identity threat to the self where a consumer’s attitude toward the service itself is lowered. This finding is consistent with the findings in the previous study, and it also shows that personalized music recommendations can threaten people at a private level as well. Unlike study 1 where participants interacted with the clerk in a public setting, study 2 demonstrates that the threat can be induced in a private context.

6 ** 5

1 No Threat Threat Not personalized Personalized

Figure 11 Dependent variable, attitude to the pharmacy, in study 2

CHAPTER 7 STUDY 3 - Why do undergraduates respond negatively to personalized music service recommending tween music?

Study 3 aimed to demonstrate that the activation of a feared identity mediates the effect of personalized service on consumer’s attitude toward the music platform. Study 3 also tried to replicate the findings from study 2 using a different sample (college students) and a different feared identity (being immature) associated with the playlist. The procedure was identical to study 2 except that the playlist recommendations in the negative identity condition were linked to teenagers (e.g., “Almost Sweet 16” vs. “Almost 21”) rather than women. The artists in the choice

task were slightly changed to provide more realistic lists for college students. Details for studies

2 and 3 are summarized in Table 1. Also, to measure whether feared identities were activated with personalized products and services, a response latency task was used. Participants threatened with the personalized playlist will be more likely to respond quickly to questions related to the threatened aspect of their identity, age-related identities in this study particularly.

Study 2 Study 3

Artists used for Justin Timberlake vs. Bruno Mars Ed Sheeran vs. Bruno Mars choice task Ariana Grande vs. Camila Cabello The Weekend vs. twenty one pilots Coldplay vs. John Mayer LMFAO vs. DJ Snake Kacey Musgraves vs. HAIM Alessia Cara vs. Lorde Portugal. The Man vs. George Ezra Justin Timberlake vs. The Chainsmokers Florence+The Machine vs. SZA Little Mix vs. Cardi B Maroon 5 vs. Ed Sheeran *Those underlined were reflected in the actual recommended playlist Suggested playlist #1 Mi Gente (feat. Beyonce) I Know What You Did Last Summer J Balvin, Willy William, Beyonce Shawn Mendes, Camila Cabello

New Rules Used To Love You Dua Lipa Gwen Stefani

Side to Side CAN'T STOP THE FEELING! Ariana Grande Justin Timberlake

Despacito Cheap Thrills Luis Fonsi, Daddy Yankee, Justin Bieber Sia

Want You Back In The Name Of Love HAIM Martin Garrix, Bebe Rexha

Say My Name Oh Cecilia (Breaking My Heart) David Guetta, Bebe Rexha, J Balvin The Vamps

SUBEME LA RADIO Still Falling For You ENrique Iglesias, Descemer Bueno, Zion & Ellie Goulding Lennox 24K Magic Down Bruno Mars Fifth Harmony, Gucci Mane Superficial Love 4 Life (feat. Graham Candy) Ruth B. Robin Schulz, Graham Candy Fresh Eyes Power Andy Grammer Litle Mix, Stormzy Don't Wanna Know Mama Maroon 5 Jonas Blue, WIlliam SInge Scars to Your Beautiful Allessia Cara

Songs added to the Side to Side Sing list depending on the Ariana Grande Ed Sheeeran participant’s actual Vs. Vs. choice Havana Count on me Camila Cabello Bruno Mars

Follow Your Arrow Scars to Your Beautiful Kacey Musgraves Allessia Cara Vs. Vs. Want You Back Royals HAIM Lorde Suggested playlist #2 The Greatest SICKO MODE (Clean) (constant) Sia Travis Scott Million Reasons Finesse Lada Gaga Bruno Mars, Cardi B Confident One Kiss Demi Lovato Calvin Harris and Dua Lipa I Love It (feat. Charli XCX) The Middle Icona Pop, Charli XCX Zedd, Maren Morris, Grey Something In The Way You Move Like I Do Ellie Goulding David Guetta, Martin Garrix, Brooks Scars To Your Beautiful Sunflower Alessia Cara Post Malone, Swae Lee Love Myself Uptown Funk (Clean) Hailee Steinfeld Mark Ronson, Bruno Mars Halo MIA (feat. Drake) Beyonce Bad Bunny, Drake Worth It One Last Time Fifth Harmony, Kid Ink Ariana Grande Natural Imagine Dragons A Lot 21 Savage First Off (Clean) Future Threat condition playlist covers

Girl Power Anthem

No threat condition playlist covers

Mood Booster

Table 1 Summary of study 2 and 3

Method

Two hundred ninety-one undergraduate students (푀푎푔푒= 21.34, 46.74% female) were assigned to a condition in a 2 (personalized vs. popular) × 2 (threatening vs. non-threatening) between-subjects design experiment. Most of the process was identical to study 2, but a few

artists were added and taken out to match the college student audience. Instead of Coldplay, John

Mayer, Kacey, Musgraves, HAIM, artists that the undergraduate students might not be aware of, the experiment added LMFAO, DJ Snake, Alessia Cara, Lorde, and The Weekend, who were likely to be popular for both undergraduate students and teenagers. Chartmetric was utilized to confirm that these artists were popular among the younger generation (from teenagers to mid- twenties).

Before the experiment started, participants specified their age, gender, which year they were in college (Freshman, sophomore, junior, senior, and super senior), and their native language. This information was later piped into the questionnaire for the response latency task related to questions about age in general. After a similar process of study 2, two playlists were suggested for them. For those in the personalization condition, the playlist was called “Your

Personalized Playlist Recommendations.” Those in the control condition were given “Popular

Playlists.” In both conditions, songs of the artist that participants chose were included to make the playlist more convincing. For instance, if the participant chose Bruno Mars, they would see a song by Bruno Mars in the personalized or popular playlist. Then, they received songs that were popular among college students as well as teenagers. Songs in the recommended playlist included works from famous artists like Ed Sheeran, Justin Timberlake, Maroon 5, Ellie

Goulding, etc.

I changed the playlist titles to manipulate identity threat while everything else was kept constant.

Those in the identity threat condition were given playlists called “Almost Sweet 16” and “Tween

Party,” whereas those in the control condition received playlists named “Almost 21” and

“College Party”. After receiving the recommended playlist, the study instructed “In a survey you

completed earlier, a music service called Z recommended two playlists for you to listen to. We'd like you to think back to this survey and the music service's recommendations when answering the following questions.” Participants read, “Based on your experience with Z, how do you evaluate the music service as a whole?” Participants subsequently indicated their attitude (three items on unfavorable/favorable, dislike/like, and bad/good) toward the music service “Z” and likelihood to try out the music service in the future on a 7-point scale (1=unlikely, 7=likely; 

= .97).

To measure the activation of the threatened identity, after answering their attitude toward the music service, participants were told that they would participate in another part of the study.

Specifically, they read, “In this next study, we would like to learn more about you. In the following task, you will be only using two keys to answer multiple questions: In the next part, you will have to answer as accurately and quickly as possible. Remember that you will have to be fast but also correct in your answers.” Then, participants were instructed to put their fingers on the keyboard

‘e’ and ‘i’. They were told to “In the next task, you will need to answer using two keys: e and i.

Please put any of the fingers you feel comfortable with on each of the keys. From now on, ‘e’ means YES and ‘i’ means NO.” To familiarize the participants, participants were told to “Press

"E" (YES) to proceed.” From this point, participants could only proceed using ‘e’ or ‘i,’ and each page measured how long the participants took to answer. Subsequently, participants were reminded again to use the two keys to answer a few practice questions to practice themselves using these two keys. Participants read “Now, let's practice with a few examples. Just answer the following questions with ‘e’ means YES and ‘i’ means NO. Press “i” to proceed.” Four practice questions included “Press "E", Press "I", Are you human? Are you a robot?” Before the actual task, the participants were reminded again of the basic instructions. Specifically, “Now, we will be actually

asking you these questions. Please respond as QUICKLY and ACCURATELY as you can. As you all know, now answer the following questions with e and i. ‘e’ means YES and ‘i’ means NO. Press

"I" to begin.”

Figure 12 Guideline for the response time task

Before the actual task, there were four buffer questions. These included, “Is today is a weekday? Is tomorrow a weekday? Is today sunny? Is today cloudy?” Each time the question was asked, they saw a figure in Figure 12. After the buffer questions, there were six questions related to the participant’s age, which was directly related to the threatened identity of maturity. These questions included, “Are you a Sophomore/Senior/Sophomore/Senior/ Super senior (depending on what the answered earlier)? Are you a freshman (freshman cannot participate in this survey)? Are you [participant’s age]? Are you 15? Are you a college student? Are you a high school student?”

Other unrelated questions included, “Are you a male? Are you a female? Is English your native language? Is Russian your native language? Are you in Arizona right now? Are you in Michigan now?” A total of twelve questions in random order were presented to the participants and response latency to each question was recorded.

Also, participants answered questions on the content Z provided. Specifically, participants read, “To what extent do you agree? I would not want to be seen as a person who listens to the playlists that Z recommended, I would feel embarrassed to listen to the playlists

that Z recommended, Z gave me customized recommendations, Z recommended playlists to me personally” on a 7-point scale (1=strongly disagree, 7=strongly agree). Lastly, participants indicated how often they use different music platforms (Spotify, Pandora, Apple Music,

Soundcloud, Tidal, and YouTube) on a 3-point scale (1=I don’t use the service, 2=I use the free/trial version, 3=I use paid version) and how many hours per week on average they streamed music (1=less than an hour, 2=1 to 2 hours, 3=2 to 3 hours, 4=4 to 5 hours, 5=5 hours or more).

Results

Before analysis, thirty participants who answered the basic questions and age-related items on the response time task incorrectly (e.g., “Is Russian your native langue? Are you a freshman? Are you in Michigan?”) had to be screened out. A 2 (personalized vs. popular) × 2

(threatening vs. non-threatening) ANOVA on attitude toward the music service “Z” revealed significant interaction (F(1,257) = 9.93, p < .01). When the music service recommended playlists associated with immaturity, participants were less interested in the music service if they were told that the recommendations were personalized based on their previous choices rather than the most popular choices (M = 4.47 vs. 5.13; F(1,257)=6.18, p < .05). For playlists associated with college students (maturity), participants showed more favorable attitude toward the personalized playlist (M= 5.4) over the popular playlist (M = 4.9; F(1,257)=3.87, p =.05, shown in Figure

13).

6 ** ** 5

1 No Threat Threat Not personalized Personalized

Figure 13 Attitude toward music service Z in study 3

To analyze how the personalized music playlist activated maturity identity, response times to age-related questions were averaged (Figure 14). Specifically, response time to six questions about their year in college, age, and student status were averaged to represent how much the age-related identity was accessible at the time. I initially transformed the raw data by base-10 logarithm since response time is known to be noisy and prone to external influences

(Fazio 1990; Ratcliff 1993). However, I observed the equivalent results both pre- and post- transformation and hereon report the results based on the original response time data.

1.20

1.00 **

0.80

0.60

0.40

0.20

0.00 No Threat Threat Not personalized Personalized

Figure 14 Response time average by condition

I ran a mediation analysis (Hayes 2017) with personalization (personalized playlist = 1; popular playlist= 0) as the independent variable, identity threat (immaturity implied = 1; maturity implied= 0) as the moderator, activation of the threatened identity (response time) as the mediator, and attitude toward the music service as the dependent variable. The first model (a path; personalization to identity activation) showed a significant interaction between personalization and identity threat on the threatened identity activation (b = -.08, SE = .36, t = -

2.13, p = .03). The second model (b path; identity activation to attitude toward the firm) showed a significant effect of identity activation (response time; b = 1.52, SE = .61, t = 2.48, p = .01).

Bootstrapping results (Hayes 2012) confirmed that the indirect effect was significant only when participant’s identity was threatened (“Almost Sweet 16”; 95% CI = -.27 to .01). With no threat

(“Almost 21”), the indirect effect was not significant (95% CI = -.04 to .11).

Figure 15 Moderated mediation in study 3

Inconsistent with hypothesis 1, I found that the personalization does not always increase identity accessibility as there was no main effect of personalization on response time related to age. Specifically, participants responded quicker to question related to age only when they received personalized recommendations to listen to the immature playlists (e.g., “Almost Sweet

16). This subsequently led to negatively affecting the attitude toward the firm’s product (b = -.08,

SE = .36, t = -2.13, p = .03). On the other hand, participants who received non-threatening personalized recommendation (e.g., “Almost 21) responded no quicker than participants who received the popular, non-threatening recommendation (b = .01, SE = .02, t = .59, p = .55).

The results from the response time task suggest that undergraduates who felt threatened by the personalized playlist implying immaturity were more likely to respond quickly to questions related to age. Specifically, the “Almost Sweet 16” playlist caused students to answer questions related to age quickly only when they were told that the playlist implying an undesirable identity was ‘personalized’ just for them. When undergraduates received a

personalized playlist implying that they might have an immature and “not of age” identity, undergraduates are likely to think about their own feared identity. It is unlikely that undergraduates thought that the recommended system was inaccurate, as it does not make sense they would be responding more quickly to questions about an otherwise unthreatened identity

(e.g., questions related to their age).

CHAPTER 8

OVERVIEW OF STUDY 1 TO 3

Studies 1 to 3 demonstrated how personalized products and services can be negatively received by participants both in an offline service context and online music service setting.

However, existing studies have three potential problems. First, the results in studies 1a and 1b could be driven by participant’s privacy concerns, especially when dealing with sensitive products like hemorrhoid cream. Research has demonstrated that consumers respond negatively to personalization when it comes at the cost of their privacy (Kim et al. 2018). Although we partially addressed this concern by removing the participant’s name from the personalized service in study 1c, we wanted to conduct a stronger test of whether participants dislike the personalized service for hemorrhoid cream because participants are concerned about their privacy, their identity feels threatened, or both of these reasons.

Second, the results of studies 2 and 3 could have been driven by another variable than identity threat; inaccuracy. There is a possibility that the participants perceived the personalized

recommendation with negative identity as not accurately reflecting their preferences. Third, studies 2 and 3 assumed that males would feel threatened to be classified as feminine (study 2), and undergraduate students would feel threatened to be classified as teenagers (study 3), but neither study directly measured whether the participants’ felt threatened by these identities.

While these assumptions are based on existing work (Dunn, White, and Dahl 2013; White and

Dahl 2007), a new generation of people may not find these identities as threatening. For instance, gender’s boundary has been expanding recently, and many men may not feel threatened to have a feminine taste in music. The new studies tried to deal with these three problems by 1) directly measuring privacy concerns (study 4), 2) manipulating inaccuracy to test whether the negative reaction to personalization is driven by inaccuracy (studies 5 and 6), 3) measuring whether participants attempt to compensate for a threatened aspect of their identity (study 7), and 4) pretesting and manipulating multiple identity threats based on the pretests (study 5).

CHAPTER 9 STUDY 4- Do consumers respond negatively to personalized service associated with a threatening identity because of only privacy concerns?

In study 4, I tested whether participants were experiencing an identity threat when they received personalized service for purchasing hemorrhoid cream at the pharmacy in studies 1a-1c.

In studies 1a-1c, participants continuously displayed a negative attitude toward the pharmacy; however, it is unclear why this was the case. I hypothesize that the negative identity (of having hemorrhoids) becomes more accessible when participants receive a personalized service (H2), driving people to feel ashamed. Shame is a self-conscious emotion connected to an individual’s

negative self (Fischer and Tangney 1995), and this is why I use shame as an indirect measure of threat to self. In addition, participants also answered questions on privacy concerns, as recent research has shown privacy to be one of the prominent reasons why people avoid personalized products and services (Kim et al. 2018; Song et al. 2016). In this way, I will be able to test whether either or both of these mechanisms are driving the effect.

Method

Three hundred two MTurk participants (푀푎푔푒= 35.47, 38.1% female) were randomly assigned to a condition in a 2 (personalization: personalized vs. control) × 2 (identity threat: hemorrhoid cream vs. water) between-subjects experiment. The process was identical to study 1 b. However, participants also answered questions related to how they would have felt in the pharmacy on 7-point scales (1=not ashamed, humiliated, proud, not guilt-ridden, not culpable, not remorseful, 7= ashamed, humiliated, proud, guilt-ridden, culpable, remorseful). Participants also answered the following questions: “I would be worried that the pharmacy might compromise my privacy” and “This situation would make me concerned about privacy.” on a 7- point scale (1=strongly disagree, 7=strongly agree). Lastly, participants finished by filling out questions on demographic information.

Results

A 2(personalized vs. popular) × 2 (threatening vs. non-threatening) ANOVA on attitude toward the pharmacy revealed a significant interaction (F(1,298) = 28.24, p < .01). Again, with hemorrhoid cream, participants’ attitude was more favorable for the non-personalized service (M

= 5.89) than for the personalized service (M = 4.81; F(1,298)=45.1, p < .01). With water, participants’ attitude did and the differ between personalized (M = 6.19) and the non- personalized service (M = 5.96; F(1,298)=.61, p = .44).

I also tested the process by measuring the extent to which participants’ felt ashamed by the purchase (averaged value of how much people felt ashamed and humiliated; r =.89) and concerned about their privacy. I ran the mediation analysis (Hayes 2017) with personalization

(personalized playlist = 1; popular playlist= 0) as the independent variable, threat (hemorrhoid cream= 1; water = 0) as the moderator, shame (indirect indicator of identity threat) and privacy concerns as the mediators, and attitude toward the pharmacy as the dependent variable.

Specifically, the first path (a path; personalization to shame / privacy concern) showed a significant interaction between personalization and threat on the shame (b = 1.29, SE = .41, t =

3.15, p < .01) and privacy concern (b = 1.5, SE = .39, t = 3.79, p < .01). The second path (b path; shame / privacy concern to attitude toward the pharmacy) showed significant effects of both shame (b = -.19, SE = .04, t =-4.58, p < .01) and privacy concern (b = -.2, SE = .04, t =-4.7, p

< .01). Bootstrapping results (Hayes 2012) confirmed that the indirect effect was significant when participant’s identity was threatened (i.e., when they were buying hemorrhoid cream) for both shame and privacy concern (95% CI = -.08 to -.42, -.18 to -.57). With no threat (when participants were buying water), the indirect effect was not significant for either shame or privacy concern (95% CI = -.05 to .15, -.18 to .05, respectively).

A 2(personalized vs. popular) × 2 (threatening vs. non-threatening) ANOVA on shame also revealed a significant interaction (F(1,298) = 9.91, p < .01). With hemorrhoid cream, participants felt more ashamed with personalized service (M = 3.96) than non-personalized service (M = 2.88; F(1,298)=13.83, p < .01). With water, participants did not feel anymore ashamed for receiving personalized (M = 1.8) than non-personalized service (M = 2;

F(1,298)=.52, p = .47).

While I replicate Kim et al. (2018), work that stresses the importance of privacy concerns, I also demonstrate how people also feel a threat when they receive personalized service associated with a negative identity. I find that participants were feeling not only privacy concerns but also shame, a negative emotion about who they are, which negatively influences their evaluation of the pharmacy.

7 ** 6

1 Threat No Threat Personalized Not Personalized

Figure 16 Dependent variable, attitude to the pharmacy, in study 4

CHAPTER 10 STUDY 5- Do consumers who receive inaccurate book recommendations react negatively to a personalized service?

The aim of study 5 is to replicate the negative reaction toward personalization in another online context (online audiobook service) and test an alternative explanation. I hypothesized that people react negatively to personalized products and services because of identity threat, however, the results of study 2 and 3 could have been driven by the perceived inaccuracy of the service.

When participants perceived that the recommended product or service is not what they want, they may have reacted negatively to the personalized service even if their identity was not threatened. In other words, it may have not been identity threat but inaccuracy that was driving

the negative reaction toward personalized products and services.

In order to test whether participants are reacting negatively to the service because of identity threat or inaccuracy, I decided to directly manipulate inaccuracy in study 5. While the music streaming service was a relevant context for testing the hypothesis, I wanted to expand testing the hypothesis in another online setting. The reason behind this change was twofold.

First, the taste for music varies by person drastically. For instance, some men might enjoy listening to artists who are considered feminine. While studies 2 and 3 convinced the participants that they were receiving personalized service, everyone differs on what they like or dislike to listen to. Second, the identity threat manipulation in the previous studies assumes that men feel threatened to be associated with women and undergraduate students fear being categorized as teenagers. Although the literature suggests that many men and undergraduate students do feel threatened by these identities, we did not directly measure whether our participants felt threatened. Another limitation of studies 2 and 3 is that the task for providing personalized recommendations took significant time and resources on the experimenter’s side. For example, participants had to listen to fourteen songs to decide to make choices among the fourteen artists.

In the new study, I aimed to solve this issue by finding a context with more flexibility. After exploring possible content relevant to threatening identities, I decided to focus on book recommendations because there are plenty of books related to negative identities, like being arrogant, boring, and shy.

Study 5 offered participants a hypothetical online bookstore with an audiobook streaming service. To justify providing recommendations to the participants, I administered a personality test before the book recommendations. Then, participants either received a personalized or

popular recommendation, ostensibly either based on their personality test or not. As part of the personality test, I asked participants what negative personality traits they were fearful of. This was to ensure that the participants would receive books associated with an accurate negative identity. For instance, if the participant chose that they were afraid of being lazy, the study provided recommendations related to laziness in the negative accurate condition. In addition, I also included a condition where the participants received an explicitly accurate or inaccurate recommendation. For the accurate non-threatening condition, undergraduate students were recommended books related to living in Tucson whereas those in the inaccurate condition were recommended books about being elderly.

I hypothesize that participants should only react negatively to the personalized product or service when the recommendations are both threatening and accurate, as they should feel less threatened by a recommendation that they believe is inaccurate, even if the recommendation is negative. On the other hand, if inaccuracy is the reason why people do not like the personalized product and services, then a personalized recommendation should hurt attitudes even when the recommendation is inaccurate.

Method

Pretest

Before starting this study, it was important to understand what types of identities undergraduate students perceive negatively. In our previous studies, I assumed that men felt

threatened to be classified as women, and undergraduate students felt threatened to be classified as teenagers. However, I had to find identities that actually threaten undergraduate students in order to provide a personalized product or service with an accurate threatening identity in the focal experiment. For this purpose, I searched books associated with negative identities that could be accurate for undergraduate students, such as being arrogant, boring, and lazy. Some undergraduates may fear that they are arrogant sometimes, such as being a ‘jerk’ to others. However, others may fear that they are too boring, such as being ‘timid’ and shy. Still, others might fear that they are lazy, such as being ‘unproductive.’ I also searched for books related to a negative identity that was clearly inaccurate for twenty-year-old undergraduate students: being old. Lastly, I searched for books related to a non-threatening identity that would be accurate for students at the University of

Arizona: living in Arizona. In total, I tested twenty-one books and all the book titles are included in Appendix D.

Ninety-five undergraduate students (푀푎푔푒= 20.7, Female=52.6%) were recruited to answer how much they agreed that the following traits describe them: old, timid, boring, arrogant, lazy, unproductive, living in Tucson. Participants answered the questions of “to what degree does the description fit you at least some of the time?” and “in general, are the following descriptions considered negative, neutral, or positive?” on a 7-point scale (1=strongly negative,

7=strongly positive / 1=strongly disagree, 7=strongly agree). Afterward, participants answered what type of person (a boring, arrogant, lazy, elderly, local person, someone like me, someone dissimilar to me) would read each of the different books (e.g., You Are Boring, Don’t be that

Dick, Stop Being Lazy, Day Trips, Elderhood) on a 7-point scale (1=definitely not, 7=definitely yes).

Recall that I predicted that undergraduate students would consider living in Arizona, but not being elderly, as congruent with their identity. Indeed, the results show that most students viewed themselves as living in Arizona (“I live in Tucson, I spend a lot of time in Arizona”; M=6.19) but not as being old (M=1.25). Also, students viewed being boring (M=2.64), arrogant (M=1.47), lazy (M=1.97) and old (M=3.12) as being negative. They viewed living in Arizona as neutral

(M=4.27). As expected, the negative identities showed a low fit overall: boring (M=2.75), arrogant

(M=2.64), lazy (M=2.68). Although these negative identities showed a low fit overall, there was a wide range of variance (between 2 to 3 compared to .49 for being old) between participants; most participants thought that at least one of these negative traits accurately described them (60% of participants also scored at mid-point or above on one of these three traits). These results also highlight how the study will need to actually personalize which of these negative identities participants receive, as none of the negative identities on its own threatened a majority of the sample.

I asked the students to evaluate twenty-one books by their covers in order to confirm whether the students associated each book with a specific negative identity (What type of person do you think would read this book?; e.g., a boring person, arrogant person, lazy person, elderly person, person who lives in Tucson, someone like me, someone dissimilar like me).

In total, I chose fifteen books to represent accurate negative, accurate neutral, and inaccurate negative identities. First, I chose nine books (three for each trait) that most participants associated with being arrogant, boring, and lazy, respectively, to use for “accurate negative” condition. Second, I chose the three books that most participants associated with being old to use for the “inaccurate negative” condition. Lastly, I chose the three books that participants most

associated with living in Arizona to use for “accurate neutral,” condition. For instance, a book titled How Not to Be a Jerk scored 4.6 on how much people agreed that arrogant type of people would read it compared to Being Boring Sucks, which scored 2.58 (full scores in Appendix D).

Main study

One hundred-nine undergraduate students (푀푎푔푒= 20.98, 53.1% female) were randomly assigned to a condition in a 2 (personalized vs. popular) x 3 (Accurate threatening vs. accurate non-threatening vs. accurate threatening) between-subjects design. The purpose of the study was to demonstrate that the proposed effect is not driven by inaccuracy. If my hypothesis is correct, then participants who are provided with inaccurate recommendations should respond the same regardless of whether or not the recommendations are personalized.

First, participants read that the experimenter is creating a new online bookstore that recommends different types of audiobooks to customers. Participants were told that the service is called "Z" and the name of this service could not be revealed now in order to get a more accurate measure of their impression. Then, the explanation about “Z’ was varied to manipulate personalization. Participants in the personalization condition read, “Consequently, Z recommends books based on the personality of each person who uses the service. The purpose of this study is to collect information about your personality in order to help build Z's personalized recommendations.” Participants in the control condition read, “Consequently, Z recommends books and lists based on what is popular amongst people who use the service.”

Then, participants in both conditions read, “Z asks each member the following questions

when setting up their profile. (Please click the arrows to begin Z's survey).” To simulate actually

creating a profile for the new service, participants answered demographics questions on age,

gender, year in college, the state they legally reside in, where they live during the semester, and

their native language. Next, participants completed a “personality test.” Specifically, they read, “In

the following section, you will be asked different questions about who you are. Please choose the

images that you feel most accurately answers the question. Even if none of the responses is a

perfect fit for you, choose the answer that you think best reflects who you are.” I included the

personality test for two reasons. First, it made the personalized recommendations later in the study

more believable. Second, the test provided a way to measure whether participants viewed

themselves as being more boring, lazy, or arrogant so the study could recommend a negative

identity that participants perceived to be accurate in the accurate/threat condition (summarized in

Figure 17).

Figure 17 Flow of study 5

Participants chose images that best answer the personality questions. For instance,

participants chose an image (see Figure 18 below) that best answers the question, “How would

you make the most of a morning off?” The other questions were similar; each participant chose an image that best described his or her personality. I chose questions from an actual personality test that uses images (the whole list of questions and pictures can be found in Appendix C).

Figure 18 Example of the personality test questions

Embedded in the personality test is one question that measures the negative identity participants fear they might have. Again, these were identities that undergraduate students in our pretest feared they might have: being arrogant, boring, or lazy. In this question, participants read,

“I am more worried about being seen as ___.” The question shows three figures representing each negative identity captioned “unproductive”, “timid”, and “arrogant.” Each choice had a fitting stick figure to illustrate each choice (See Figure 19 below).

Lazy identity Boring identity Arrogant identity

Feared

identity

choices

Figure 19 Choices for the feared identity

After participants finished the personality test, they read that they would be receiving a list of books “Z” has recommended to them. Again, participants in the personalization condition read,

“In the next page, you will see list of books that Z has personalized for you based on the choices you have made.” Those in the control condition read, “In the next page, you will see list of books that Z thinks will be the most popular.” For those in the personalization condition, the book list called “Your Personalized Book Recommendations.” Those in the non-personalized condition will receive “Popular Books.”

Then, depending on the condition assigned to them, they received book recommendations that implied an accurate neutral identity, an accurate threatening identity, or an inaccurate negative identity. Participants in the inaccurate condition received books about being old. Participants in the accurate neutral condition received books about Arizona. Participants in the accurate threatening condition received books based on the feared identity that they selected during the personality test. Participants who feared being seen as arrogant viewed books related to being arrogant. Participants who feared being seen as boring saw books related to being boring.

Participants who feared being lazy saw books related to being lazy (shown in Table 2).

Condition Identity Books shown based on their choices

Arrogant

* By gender, either one will be

shown

Accurate

threat Boring

Lazy

Accurate Living in no threat Arizona

Inaccurate Old negative

Table 2 Study 5 stimuli

After receiving the recommendations, participants read, “Based on your experience with

Z, how do you evaluate the book service as a whole?” Participants subsequently indicated their

attitude (three items on unfavorable/favorable, dislike/like, and bad/good) toward the book service

“Z” and the likelihood that they would try out the service in the future on a 7-point scale

(1=unlikely, 7=likely). Lastly, participants completed a short PANAS scale (Watson et al. 1988),

questions on how often do they read self-help books, whether “Z” recommended books to them

personally, and gave them personalized recommendations on a 7-point scale (1= never/ unlikely,

7= always/ likely), I measured items related to each participant’s concern for being arrogant,

boring, or lazy to measure how much these identities actually threatened the participants. I used

the PANAS scale to figure out whether the personalized book recommendations with a negative

identity influenced the participant’s emotional valence, which would offer additional evidence that the participants were feeling threatened.

Results

A 2 (personalized vs. popular) × 3 (accurate threatening vs. accurate non-threatening vs. inaccurate negative) ANOVA on the manipulation check for personalization did not show any significant main effect of personalization. Participants did not see any significant difference between personalization and the non-personalized recommendations (M = 2.89 vs. 2.62;

F(2,173)=.95, p=.33). This was a serious issue as it suggests that the participants were not buying the manipulation. Consequently, even if we do observe a difference in the dependent variable (attitudes towards the book service), it will be unclear how to interpret it. On the other hand, there was a significant main effect by the identity type (F (2,173) = 5.48, p < .01) on the manipulation check for personalization. Specifically, participants found the inaccurate negative recommendations (books for older people; M=2.1) to be less personalized than accurate non- threatening recommendations (books for local tourism; M=3.14) and accurate threatening recommendations (books for the chosen threat; M=3.03; F(2,173)=5.48, p<.01). While I tried to manipulate inaccuracy, the participants did not believe that the inaccurate recommendations were actually personalized. Despite these problems with the personalization manipulation, I report the following results on the dependent variable.

A 2 (personalized vs. popular) × 3 (accurate threatening vs. accurate non-threatening vs. inaccurate negative) ANOVA on the attitude toward the service did not reveal the predicted

interaction (F (2,173) = .25, p = .78). However, there was a significant main effect of personalization on the attitude toward the book service, as people held a relatively negative attitude toward the personalized book recommendation (M = 3.32) compared to a non- personalized service (M = 4.32; F(1,173)=16.88, p = .00). There was also a significant main effect of identity type (F (2,173) = 7.87, p < .01). Specifically, participants reported a more favorable attitude when they received accurate non-threatening recommendations (books for local tourism; M=4.51) compared to receiving inaccurate negative recommendations (books for older people; M=3.42) or accurate threatening recommendations (books about being arrogant, boring, or lazy; M=3.53; F(2,173)=7.87, p<.01). However, there was no significant difference between inaccurate negative recommendations (books for older people) and the accurate threatening recommendations (books for the chosen threat). Also, when the analysis was limited to inaccurate recommendations (books for older people), there was an effect of personalization

(F(1,158)=4.97, p = .03). Participants perceived the book service to be worse when they received personalized compared to popular recommendations of books for the elderly (M=2.93 vs. 3.91).

While I cannot make inferences from a study that failed to manipulated personalization successfully, this study also fails to rule out inaccuracy as one way people react negatively to personalization.

The PANAS scale measures positive and negative affect. Positive affect was an average of nine items (feeling interested, excited, strong, enthusiastic, active, determined, proud, inspired, and attentive; M = 4.32, α =.94). Negative affect was an average of eleven items

(distressed, upset, guilty, scared, hostile, irritable, alert, ashamed, nervous, jittery, and afraid; M

= 1.53, α =.91). A 2 (personalized vs. popular) × 3 (Accurate threatening vs. accurate non- threatening vs. accurate threatening) ANOVA on both the positive and negative affect did not

reveal the predicted interaction (F (2,173) = .4, p = .67; F (2,173) = .2.28, p = .11 respectively).

However, there was a significant main effect of personalization on positive affect, as people experienced less positive emotion (M = 1.76) after receiving a personalized compared to a non- personalized service (M = 2.06; F(1,173)=4.65, p <.05). There was also a significant main effect of identity type (F (2,173) = 4.37, p < .05). Specifically, people held a relatively less positive emotion toward the book recommendation related to inaccurate negative identity (being old; M =

1.69) compared to accurate neutral (living in Arizona; M = 1.87) and accurate threatening identity (being arrogant, boring, and lazy; M = 2.06; F (2,173) = 4.37, p <.05). There were no significant effects of personalization or identity type on negative affect.

The results show that the participants did not buy the cover story, that the service is providing personalized recommendations. Still, there was a negative main effect of personalization on the attitude toward the firm. This offers more evidence that the cover story, the content, or both were not properly calibrated. Compared to the previous music studies, which provided two playlists consisting of fourteen songs each, study 5 may have lacked the content to make the cover story believable. I believe this is why the study failed to manipulate personalization but still observed negative effects from it. Lastly, compared to the previous study, in which all the content was personalized yet held constant content across conditions (e.g., everyone received two songs based on the participant’s choices), study 5 tried to directly manipulate the content across conditions, which was a new way to test personalization. In retrospect, participants could have been also skeptical about “Z” since it offered only three book recommendations. For these reasons, it is hard to conclude the effects observed in the study are due to personalization and I decided to return to the music service paradigm for the subsequent studies.

CHAPTER 11 STUDY 6- Do consumers who receive an inaccurate recommendation react negatively to an online music service?

The aim of study 6 is again to test whether personalization is backfiring because consumers feel threatened or simply because they think the personalized recommendations are inaccurate. Because the new book recommendation study failed to successfully manipulate personalization, I went back to testing the inaccuracy explanation using a setting and study design (i.e., the music streaming service) that had worked before.

Like the online book study, I manipulate accuracy independent of negative identity. The study design is similar to Study 2 and 3, however, I added a condition in which the participants received an inaccurate list of recommended songs. The list included songs by Bach, Beethoven,

Mozart, and other classical composers that most undergraduate students do not frequently listen to. I selected Classical music because the literature shows that listening to Classical music is associated with high cultural capital, which implies that it does not have a negative association

(Bourdieu and Passeron 1979). At the same time, undergraduate students would likely perceive a playlist of Classical music to be an inaccurate recommendation.

I hypothesize that participants should react the same to the inaccurate recommendations regardless of whether or not they are personalized. If inaccuracy is the reason why people do not like the personalized product and services, on the other hand, then any personalized recommendation that is inaccurate should hurt attitudes regardless of whether the recommendation is threatening or not. For instance, if the alternative explanation holds, participants will show a negative attitude toward the music service when they receive a

personalized list of classical music, which is unrelated to the set of choices they made earlier in the study.

Method

Three hundred sixty-two undergraduate students were assigned to a condition in a 2

(personalized vs. popular) × 3 (identity threat: present/immature vs. none/mature vs. inaccurate/neutral classic music) between-subjects design experiment. The study design was identical to studies 2 and 3, but two playlists titled Classic Essentials and Easy Classical were added in the inaccurate condition. In this condition, the playlist included songs by Ludwig van

Beethoven, Johann Sebastian Bach, George Frideric Handel, etc. All of the Classical music recommendations were intended to be different from what the undergraduate students listen to

(as shown in Table 3).

Playlist and the songs

Beethoven Symphony no 7 - II Suite española, Op.47: 3. Sevilla Ludwig van Beethoven (Sevillanas) Isaac Albéniz Prelude in C Major, BWV 846 Johann Sebastian Bach Canon in D Major, P.37 Johann Pachelbel

HWV 437: III. Sarabande George Frideric Handel Requiem in D Minor, Op. 48: In Paradisum Ständchen in D Minor- S. 560 Gabriel Fauré Franz Liszt Peer Gynt Suite No. 1, Op. 46: Morning The Gadfly Suite, Op. 97a: VIII. Mood - Arr. for Piano Romance Edvard Grieg Dmitri Shostakovich Keyboard Sonata in C Minor, Serenade for Strings in C major, Op. 48: Hob.XVI:20: III. Finale. Allegro II. Walzer: Moderato, tempo di valse Franz Joseph Haydn Pyotr Ilyich Tchaikovsky Romeo & Juliet, Op. 64, Act I: No. 19, Concerto For Violin And Strings In E, Balcony Scene Op.8, No.1, R.269 "La Primavera": 1. Sergei Prokofiev Allegro Antonio Vivaldi Waltz in C-Sharp Minor, Op. 64 No. 2 Frédéric Chopin Widmung, Op. 25 No. 1 (Arr. Liszt, S. 566a) The Planets, Op. 32: II. Venus, the Robert Schumann Bringer of Peace Gustav Holst 3 Pieces in Baroque Style: No. 1. Aria Krzysztof Penderecki Overture to "A Midsummer Night's Dream", Op. 21: Tempo primo Ombra Mai Fu Felix Mendelssohn Malena Ernman Préludes, Op. 23: No. 10 in G-Flat Serenade for Strings in E Major, Op. 22, Major B. 52: II. Tempo di valse Sergei Rachmaninoff Antonín Dvořák Fantasía para un Gentilhombre: IIa. Clarinet Concerto in A Major, K. 622: II. Españoleta Adagio Joaquín Rodrigo Wolfgang Amadeus Mozart Suite bergamasque, L. 75: No. 3, Clair de lune Claude Debussy Table 3 Stimuli added in study 6

Results

A 2 (personalized vs. popular) × 3 (threatening vs. non-threatening vs. inaccurate)

ANOVA on the manipulation check for personalization showed a significant interaction (F

(2,357) = 3.47, p = .03). Specifically, participants found the recommendations to be personalized when they received non-threatening playlists (“Almost 21”; M = 4.86 vs. 4.16; F(1,357)=5.64, p

<.05). However, those receiving threatening playlists (“Almost Sweet 16”; M = 4.44 vs. 3.93;

F(1,357)=2.9, p =.09) or inaccurate playlists (“Classical Essentials”; M = 2.86 vs. 3.2;

F(1,357)=1.3, p =.26) did not show any significant differences. Overall, participants did not see a significant difference between personalization and the non-personalized recommendations (M =

4.05 vs. 3.76; F(1,357)=.5, p=.092). This was concerning, as the personalization manipulation failed in two of the three conditions. Although again there are issues with the manipulation of personalization, I report the following results on the dependent variable.

A 2 (personalized vs. popular) × 3 (Threatening vs. non-threatening vs. inaccurate)

ANOVA on attitude toward the music service “Z” revealed a significant interaction (F(1,357) =

11.91, p < .01). However, this effect was driven by the inaccurate condition where people received recommendations for a classical music playlist. Participants who received a personalized playlist with classic music held a less favorable attitude toward the service (M =

2.63) than those who receive the playlist as non-personalized (M = 4.18; F(1,357)= 33.73, p<.01). However, there was no significant difference by personalization when they received a threatening playlist (“Almost sweet 16”; M = 4.4 vs. 4.6 ; F(1,357)= .61, p=.44) or an accurate non-threatening playlist (“Almost 21”; M = 5.18 vs. 4.97 ; F(1,357)= .61, p=.43). The study thus failed to replicate the effect observed in studies 2 and 3.

Based on these results, I cannot rule out the possibility that participants have a less favorable impression of the personalized service because they think it is inaccurate rather than because the personalized recommendations threaten their identity. In other words, when the firm is claiming to provide personalized product or service, it must make sure that the participants see the difference from the non-personalized offering. In a way, when a firm promises a personalized offering, it is also raising the expectations of the consumers. In study 6, the operationalization of

an inaccurate recommendation might have gone too far to make the participants feel betrayed because the recommendations did not seem believable. This might be because in the previous studies, and the other conditions, participants received recommendations that were actually personalized based on the songs they selected earlier in the study. However, in this study, participants in the inaccurate condition received recommendations that were unrelated to what they had chosen earlier.

5 ** 4

1 Inaccurate No threat Threat Not Personalized Personalized

Figure 20 Dependent variable, attitude toward the service, in study 6

CHAPTER 12 STUDY 7- Do consumers who receive threatening personalized advertisements engage in compensatory consumption?

The aim of study 7 was to provide further evidence that people feel threatened when they receive a personalized product and service associated with a negative identity. While studies 2 and 3 provide evidence that people have a negative attitude towards a music service when they received a personalized playlist associated with a negative identity (e.g., being feminine or immature), study 7 aimed to further support the argument that participants respond negatively to personalized recommendation because their identities are threatened by showing that consumers who receive a threatening personalized recommendation attempt to compensate by engaging in behaviors that go against threatening identity. If people feel an identity threat from receiving personalization with negative identity, this should also drive people to seek compensatory consumption. Prior work has demonstrated that people will engage in symbolic self-completion when they feel threatened (Mandel et al. 2017). For instance, when MBA students feel like they are not successful enough, they resorted to other products that signaled success, like expensive suits and watches (Wicklund and Gollwitzer 1982). In other words, when feeling threatened, people often try to compensate for the identity threat by consuming products that reinforce their threatened identity. For instance, males threatened with a feminine playlist, like those from study

2, could try to purchase products or services related to masculinity. An increase in compensatory consumption, thus, would provide additional evidence that identity threat is driving the negative reaction toward personalized products and services.

In study 7, I decided to use being old as a threatening identity. Generally, the market glorifies being young and associates youth with competence and high-status but associated being old with a variety of negative traits (Cuddy, Norton, and Fiske 2005; North and Fiske 2013).

Consequently, older consumers, especially those who do not yet consider themselves old, might avoid products and services that signal and old identity. For instance, when marketers used terms

like “golden years” or “senior citizens”, older consumers were less likely to participate in the program (Marrs 1984). Thus, I decided to manipulate threat by reminding relatively older participants that they are getting old via personalized advertisements related to age. To make the recommendations appear more accurate, I recruited participants who were over fifty-five years old.

If participants over age fifty-five felt threatened by personalized advertisements suggesting an old identity, then I hypothesize that participants would engage in compensatory behaviors, such as listening to more artists that young people listen to. For instance, one way that older participants could compensate is by listening to Taylor Swift instead of Jerry Lee Lewis. Compensatory consumption would provide indirect evidence that older consumers feel threatened by personalized advertisements.

Method

Pretest

To identify songs for the compensatory consumption measure, I had to understand which artists my sample associates with young and old people. First, I chose artists potentially associated with older listeners based on Rolling Stone magazine’s list of 100 Greatest Artists and artists potentially associated with younger listeners based on the current rankings on Spotify

(Rolling Stone 2010; Spotify 2020). This helped me identify an initial list of 56 artists, including

The Rolling Stones, Bob Dylan, Led Zeppelin, The Beach Boys, Billie Eilish, Ariana Grande, and Cardi B. Then, I recruited one hundred people from Prolific over fifty-five years old (푀푎푔푒=

59.19, 58% female) to tell me whether they associate each of these artists with young people, old

people, both, or neither. Specifically, participants answered “whether young or old people mostly listen to the specific artist” on a 4-point scale (1= mostly young people listen to this artist, 2= never heard about the artist, 3= mostly old people listen to this artist, 4= everybody listens to this artist). Out of the fifty-six artists, I picked the fifteen artists who the highest percentage of participants answered “mostly young people listen to this” and the fifteen artists who the highest percentage of participants answered “mostly old people listen to this” (see Table 4). I used this list of thirty artists to measure compensatory consumption at the end of the study by having participants select songs by ten of these artists to create a playlist.

Associated with older people Associated with younger people Jerry Lee Lewis (96% Old, 4% Everyone) Justin Bieber (94% young, 6% Everyone) Tammy Wynette (93% Old, 5% Everyone, 2% No Ariana Grande (89% Young, 5% No Knowledge, Knowledge) 5% Everyone, 1% Old) The Byrds (91% Old, 6% No Knowledge, 3% Katy Perry (83% young, 11% Everyone, 5% No Everyone) Knowledge, 1% Old) Chubby Checker (91% Old, 6% Everyone, 3% No Selena Gomez (80% Young, 14% No Knowledge, Knowledge) 6% Everyone) Chuck Berry (89% Old, 9% Everyone, 2% No Cardi B (73% Young, 26% no Knowledge, 1% Knowledge) Everyone) Ray Charles (87% Old, 12% Everyone, 1% No Ke$ha (70% Young, 29% No Knowledge, 1% Knowledge) Everyone) Crosby, Stills, and Nash (87% Old, 7% Everyone, Taylor Swift (71% Young, 27% Everyone, 1% No 6% No Knowledge) Knowledge, Old) Pasty Cline (87% Old, 7% No Knowledge, 6% Drake (65% Young, 24% No Knowledge, 10% Everyone) Everyone, 1% Old) Righteous Brothers (86% Old, 10% Everyone, 4% Lady Gaga (59% Young, 39% Everyone, 2% Old) No Knowledge) The Who (85% Old, 13% Everyone, 1% No Billie Eilish (55% Young, 40% No Knowledge, 2% Knowledge, 1% Young) Everyone) John Denver (82% Old, 17% Everyone, 1% No Mark Ronson (52% Young, 17% No Knowledge, Knowledge) 30% Everyone) Waylon Jennings and Willie Nelson (81% Old, 13% Carly Rae Jepsen (50% Young, 45% No Everyone, 6% No Knowledge) Knowledge, 4% Everyone, 1% Old) Marvin Gaye (80% Old, 18% Everyone, 2% No Lorde (48% Young, 43% No Knowledge, 8% Knowledge) Everyone, 1% Old)

Don McLean (80% Old, 10% Everyone, 10% No Post Malone (46% Young, 54% No Knowledge) Knowledge) The Ronettes (79% Old, 17% No Knowledge, 3% Daft Punk (45% Young, 41% No Knowledge, 10% Everyone, 1% Young) Everyone, 4% Old)

Table 4 Pretest result for each artists

Main study

This study used a 2 (personalized vs. not-personalized) x 2 (threatening vs. not threatening) between-subjects design. To manipulate personalization, I used AdChoices, a logo indicating that an advertisement has been personalized for the individual user. The study was inspired by Summers et al. (2016), who used behaviorally targeted advertisements to personalize marketing communications to study participants. I decided to use a similar paradigm in which the participants receive personalized advertisements ostensibly based on their browsing history.

To manipulate threat, I used advertisements that implied that the participants were becoming old.

In the study, participants in the “threatening” condition received an advertisement for a senior village. To ensure that the advertisements are threatening and avoid inaccuracy issue, I also recruited six hundred fifty-nine people who are over fifty-five from Prolific (푀푎푔푒= 60.93,

61.3% female). In the control condition, participants saw an advertisement for a service that pays people money to participate in online surveys, a service that should seem relevant for participants who use Prolific (overall flow is shown in Figure 21)

Figure 21 Flow of study 7

First, I explained the concept of behaviorally targeted advertising because it was

essential for the participants to understand what a personalized advertisement is. Participants

read that AdChoice is a form of personalized online advertisements based on the sites the

participants have visited while browsing the internet. Furthermore, this type of advertisement

included the AdChoices icon (Figure 22 below).

Figure 22 AdChoice icon

Participants also learned that when they see an AdChoice logo, it means that the online

advertisement is based on their own past behavior. Subsequently, participants read that

AdChoices provides personalized advertisements just for them and requires ‘cookies’ that

contain their past online behavior. Participants answered whether they were willing to provide permission to use their cookies for the latter part of the study. Although the study did not actually incorporate the participants’ cookies, this instruction was important to ensure that the participants find the following personalized advertisements believable. Subsequently, participants answered whether they had cleared their cookies in their browser recently. If participants were aware of how cookies work and had intentionally cleared their cookies, the following manipulation of personalization would not seem believable to these participants.

Consequently, I excluded participants who refused to give permission to their cookies or who said that they cleared their cookies.

The remaining participants read that they would be seeing different types of advertisements.

These advertisements included a mixture of generic (non-personalized) online advertisements and personalized advertisements that varied for each individual. To make this process more believable, the study instructed participants that it would take a few seconds to scan their browsing history to provide personalized advertisements and showed them an animated image imitating a loading sequence (Figure 23 below).

Figure 23 Loading sequence

After the loading sequence, which took a few seconds, the first advertisement was shown to the participant. This advertisement was personalized for everyone, based on his or her browser information. Everybody received an advertisement for ‘read&write’, containing the AdChoice logo along with a personalized slogan based on their browser. For example, if the participant was using chrome, the advertisement read, “If you like using chrome, you’ll love read&write.”

Depending on the web browser used by the participant (Chrome, Firefox, Internet Explorer,

Microsoft Edge, Safari, or Opera), everyone received a different personalized advertisement

(Figure 24). Afterward, participants rated how likely they are to purchase the service advertised on a 7-point scale (1=unlikely, 7=likely). Participants also answered whether the advertisement was personalized or not (1=Yes, 2=No). This question served as an attention check to ensure that the participants understood the concept of AdChoices. Since the advertisement contained the

AdChoices logo, if the participants were aware of the instructions, they should have been able to tell that the advertisement was personalized for them. Those who do not correctly recognize the

personalized advertisement were asked to leave the study.

Figure 24 Example of the personalized advertisements

After evaluating the first advertisement, participants saw a Toyota advertisement without the AdChoices logo. Again, participants rated how likely they are to purchase the product advertised on a 7-point scale (1=unlikely, 7=likely). Participants also answered whether the advertisement was personalized or not (1=Yes, 2=No).

Lastly, participants saw a third advertisement. Participants in the personalized condition received this advertisement with the AdChoices logo, whereas participants in the control condition did not see the AdChoices logo. Participants received different advertisements depending on the identity threat condition. Those in the non-threatening condition received an advertisement for an online survey platform called Psurvey. Since the participants were all using a survey platform to

earn money, this advertisement should be seen as accurate but not threatening. On the other hand, those in the identity threat condition received an advertisement for a senior village. Specifically, the ad for ‘Nurssio Senior Village’ offered independent living, assisted living, and hospice service, along with a slogan, “It’s time to start thinking about your future.” Because participants were all over fifty-five-years-old, I hoped the threatening advertisement would also seem accurate. All of the advertisements are included in Table 5. Again, participants rated how interested they were in the service advertised on a 7-point scale (1=unlikely, 7=likely). Participants also answered whether the advertisement was personalized or not (1=Yes, 0=No).

In the last part of the study, participants were asked to create their own music playlist.

Participants choose ten songs from a list of the 30 artists that I identified in the pretest. Specifically, participants read, “Please create a playlist [or a mixtape] by selecting 10 songs from a list of 30 popular songs. If you do not know all of the songs, pick the songs that you think you would want to listen to.” After choosing ten songs, participants received an image showing what the playlist would look like. Lastly, participants answer how familiar they were with the artists, whether they had trouble choosing, and whether they had chosen randomly for all of the choices.

Control (not personalized) Personalized

No Threat

Threat

Table 5 Focal advertisements by condition

Results

Before the analysis, two hundred eighty-nine participants were dropped either because they were not willing to share their cookies, said they have cleared their cookies recently or answered that the first advertisement was not personalized. While I screened out these participants to ensure that the participants believed the cover story, there were too many people who did not believe the personalized advertisements were real. The first personalized advertisement was intended to be an attention check to ensure the participants understood what a personalized advertisement was. However, many participants commented that they did not think an advertisement for obscure software called “read&write” was personalized just because it had a logo of the web browser they were using. I believe this might have been one reason that the study did not work as well as I hoped.

A 2 (personalized vs. popular) × 3 (threatening vs. non-threatening) ANOVA on the manipulation check for personalization showed a significant effect of personalization.

Participants saw a significant difference between personalization and the non-personalized advertisements (M = .87 vs. .21; F (1,355) = 306.31, p < .01). There was a significant effect by the threatening identity. Participants perceived and the non-threatening advertisements (online survey advertisements) to be more personalized than the threatening advertisements (nursing home advertisement; M = .63 vs. .44; F (1,355) = 24.63, p < .01). The interaction was not significant (F (1,355) = .001, p = .979).

A 2 (personalization) × 2 (identity threat) ANOVA on attitude toward the advertised service did not reveal the predicted interaction (F(1,355) = 1.6, p =.21). When the personalized

100

advertisement was associated threatening identity (nursing home), participants did not show any significant difference in their interest to the nursing home whether they were told that the advertisement was personalized or not (M = 1.74 vs. 1.69; F(1,355)=.06, p = .80). When the advertisement was associated with a non-threatening identity (user of an online survey platform), participants showed a more favorable attitude toward the personalized advertisement (M= 4.94) over the generic one (M = 4.52; F(1,355)=4.13, p =.043, shown in the figure below).

** 5

1 No Threat Threat Not Personalized Personalized

Figure 25 Dependent variable, attitude toward the advertised product, in study 7

A 2 (personalization) × 2 (identity threat) ANOVA on the number of young artists that participants chose for the playlist also did not reveal the predicted interaction (F(1,355) = .42, p

=.52). Neither the threat manipulation (no threat vs. threat; M=2.9 vs.2.75; F(1,355) = .35, p

=.56) nor the personalization manipulation (not personalized vs. personalized; M=2.8 vs.2.86;

101

F(1,355) = .06, p =.81) influenced the number of young artists the participants chose for their playlist.

1 No Threat Threat Not Personalized Personalized

Figure 26 Number of young artists chosen

While the study did not show a significant interaction between threat and personalization, I believe this might be due to a floor effect because participants simply had no interest in the nursing home, regardless of whether or not the ad seemed personalized. The extremely low attitude toward the nursing home makes it hard to observe any effects of personalization. Future studies should explore services that are more likely to appeal to participants than the nursing home in this study. On the other hand, when the advertisement actually provided a more neutral service like Psurvey, a fictional online survey platform for earning more money, people liked the service better when the ad was personalized. This suggests

102

a potential opportunity for future studies to explore when personalization increases attitude toward the firm. On another note, many people did not believe that the advertisements were personalized based on their browsing history. While I did my best to design a study that mimicked collecting their cookies from their past online browsing history, many did not believe this. Also, many thought that the personalized advertisement with “read&write” was not actually based on their past browsing behavior. To provide a more effective manipulation for personalization, I would have to find content that is more relatable to the participants in the sample.

CHAPTER 13

GENERAL DISCUSSIONS

The results of nine experiments clarify the concept of personalization and address when personalized services and products can pose an identity threat to consumers. Personalized products and services can activate a feared identity, which risks repelling consumers from the firm. People responded negatively to personalized services that might convey a feared identity, like having hemorrhoids (study 1a-1c, 4). Different types of people, including men and undergraduate students, also responded negatively to an online music service offering personalized recommendations associated with threatening feminine and tween identities (study

2, 3, and 4). Personalized products and services with threatening identities can activate a feared identity, like being immature (study 3). When such feared identity became more accessible, people felt ashamed of themselves (study 4), which led to a negative attitude toward the firm.

103

However, participants in studies 2 and 3 could have responded negatively to the firm because they thought that the recommendations were inaccurate not because they were threatening. In an attempt to deal with this alternative explanation by inaccuracy, I manipulated inaccuracy directly

(studies 5 and 6) in the context of online book and music stream service. Also, I measured whether participants attempt to compensate for a threatened aspect of their identity (study 7).

Unfortunately, these studies were unable to rule this explanation out, possibly because the operationalization of personalization was inconsistent in studies 5 to 7.

Limitations

Studies 5 to 7 failed to replicate the negative effect of personalization on consumer’s attitude toward the firm possibly because the studies did not incorporate the core components of personalization. Personalization was defined as firm-made changes to one or more element in its marketing mix to cater to an individual consumer based on the consumer’s past behavior, and study 1 to 4 have successfully operationalized this concept in three ways.

First, the personalized product or service had a clear link to past behavior. Firms offer personalized offerings based on consumers’ past behavior. Over the years, consumers have become aware of how multiple services like Amazon, Spotify, or Netflix provide personalized products and services. People know that their behaviors feed into what the firms offer and over the years, and this is how consumers learn that they might be closer to having a feared identity, like being sick or immature. In studies 1 and 4, the participant read from the scenario that they have been returning to the store repeatedly to purchase a specific item, like hemorrhoid cream.

104

Even when this scenario was hypothetical, it was clear that people were feeling negative emotions about the self, such as shame. In studies 2 and 3, participants chose between a set of multiple artists, which led to the personalized recommendation. Participants likely believed that the choices made were reflected in the recommendation when they saw the artists they have chosen appear on the recommended playlist. However, in study 5 (online audiobook study) and 7

(advertisement study), participants did not engage in any apparent behavior linked to the personalized offering. Specifically, in study 5, participants engaged in a personality test, seemingly unrelated behavior to the recommendation. In study 6 (online music streaming study with classical music), most undergraduate students probably did not listen to classical music and did not see that there were any links with their past listening history. Also, in study 7

(advertisement study), those over fifty-five did not buy the cover story that the recommended advertised was based on their past browsing history as there were hundred forty-two participants dropped out because of this reason. Some of the participants commented that although they noticed logo for a personalized advertisement, they did not think it was relevant to what they have been browsing. This links to our second condition for personalization to work.

Second, participants believed that the product or service was personalized. This is also closely related to the inaccuracy problem raised from the results of study 2 and 3 (online music streaming study), where people could have perceived the recommendation as inaccurate to what they want. As mentioned before, people are familiar with interacting with companies like

Amazon, Spotify, and Netflix that provide personalized content, and people’s expectations for personalized products and services have grown to a certain level as well. However, even with their most sophisticated algorithms, firms often have trouble providing the right type of content for their consumers. Especially in domains of music and book where people possess a unique

105

taste, people might not find the recommended content relevant or accurate to what they want.

Studies 1 and 4 did not suffer from this problem because participants in the study all received personalized service in the scenario. Also, studies 2 and 3 provided songs that were viable across conditions. For instance, One Last Song by Ariana Grande is both feminine but popular among the majority of the audience. In other words, the content provided was as believable as being personalized or not. On top of this, participants received two songs from the artists they had chosen earlier across conditions. However, in studies 5 to 7, participants received disparate content, such as books about arrogance, shyness, and lazy, or advertisements about a nursing home or an online survey platform, or a playlist for classic music. This is probably why the participants in study 5 did not consider the recommendations as personalized compared to the non-personalized condition. Also, in study 7, participants over fifty-five did not think that the nursing home was a relevant recommendation to them at all. This implies that people have a certain level of expectation for personalized products and services and reacted negatively when the content fails to deliver what it promises. Therefore, the results suggest that inaccuracy is an important factor for personalization and the personalized content should at least seem related to the participants. Again, personalization is firm’s attempt to change one or more element in its marketing mix to cater to an individual consumer based on the consumer’s past behavior. In summary, studies 5 to 7 have failed to show the link between personalized offering and the consumer’s past behavior and consumers did not perceive the provided product or service as personalized.

Third, participants received categorization threat from receiving personalized products and services associated with feared identities. In the beginning, I hypothesized that people feel a categorization threat from learning that they might actually own a feared identity, a social label

106

that people dislike but believe applies to themselves. Since personalized products and services highlight the link between people’s own past behaviors and both the feared identity, people likely feel threatened and try to avoid the source of the threat. In studies 1 and 4, participants reading the scenario did not want to be categorized as someone who has hemorrhoids. In study 2, males did not want to be categorized as having a feminine taste. In study 3, undergraduate students did not want to be categorized as being immature and young. Personalization here poses a categorization threat by functioning as the medium of the feared identity. I believe this is the unique nature of personalization since people often do not know well about themselves and learn from their own behaviors (Bem 1972). There is indirect evidence on how people feel a categorization threat from learning about themselves from personalized products and services. In study 4, participants displayed a higher level of shame when they read they have received personalized service from a pharmacy. This effect holds while controlling privacy concerns (Kim et al. 2018), another key factor to people reacting negatively to personalization. Undergraduate students in study 3 (online music streaming study) also responded quicker to questions related to age when they received a personalized playlist named ‘Almost Sweet 16’, which shows that they were concerned with their age identity. However, when undergraduate students received a personalized playlist name ‘Almost 21’, they did not respond any quicker to questions on age compared to a not personalized playlist. These studies suggest that participants faced threat from being categorized as someone with hemorrhoid or a childish taste (feared identity) through personalized products and services. Identities presented in study 5 (online audiobook study) and

7 (advertisement study) were also related to feared identities (being arrogant, shy, lazy, or old).

However, in these sets of studies, participants did not avoid the personalized book recommendations and advertisements. As mentioned before, study 5 and 7 failed to meet the

107

necessary conditions for manipulating personalization (clear link to past behavior and minimum accuracy). On top of this, participants in study 5 could have found the list of books (e.g., Being

Boring Sucks So Stop it, How Not to Be a Jerk, Stop Being Lazy) more helpful to improve themselves than feel threatened about themselves. There were only three books included in the recommendation, which probably led to a lower attitude toward the service when it promised a personalized list. Participants in study 7 also responded negatively to personalized advertisements for a nursing home.

This is why accuracy must be acknowledged as an essential component of personalization and this is also connected to the consumer’s past behavior. I initially hypothesized that categorization threat should be the sole reason why consumers would react negatively to personalized products and services. In study 6 (online music streaming study with classical music), the alternative account was tested by providing classical music as an inaccurate set of personalized recommendations. Results showed that even when there is no threat involved

(classical music having no negative associations to undergraduate students), participants still responded negatively to the same list of classical songs when they received it as personalized compared to not. Results of studies 5 and 7 also point that the content (e.g., books and advertisements) used to manipulate categorization threat should be clearly linked to the participant’s past behavior. The book recommendations of study 5 did not seem accurate to what the undergraduate students have been reading. The advertisements participants in study 7 saw were again not in line with what they have been browsing recently. In summary, accuracy is a necessary condition for personalization to pose categorization threat to participants by highlighting their feared identities. After all, if the stage light is not working, the actor will not be even aware of being in the spotlight.

108

Future research

Future studies must calibrate the personalized content so that the participants perceive the personalized recommendations as believable and accurate, and clearly see the link to their past behavior. Ideally, study 5 (online audiobook service) should have made a clear connection between the recommendations and the participant’s past behavior. For instance, instead of a personality test, participants could have made different choices about books related to negative identities. Then, when the participants received personalized book recommendations related to their feared identity, participants will think that they are actually receiving personalized service.

Results of study 6 (music streaming service with classical music) again point how a minimum level of accuracy is required to convince the participants are receiving personalized products and services. Study 7 (advertisement study) should have provided an advertisement that the audience finds it relevant and accurate to what they have browsed before. With these pointers in mind, future studies should first establish a certain level of believability (accuracy) and this could be accomplished in two ways.

I plan to run additional studies that have the following features. First, the study could use a task that is reflected in the personalized content participants receive later. These are similar to study 2 and 3 where participants made choices of different artists after listening to them to receive personalized music recommendations. However, the task (behavior) needs to be close enough to the personalized content so that people can clearly see the link. For instance, the personality test in study 5 was not close enough to convince the participants are receiving

109

personalized book recommendations. As mentioned before, before receiving personalized content (songs or books), participants should be asked to engage in each behavior (e.g., listening, browsing, or reading) related to the product or service.

Second, the study could focus on a group of people who have engaged in specific behavior to avoid the issue of accuracy. For example, people who have searched about the debt before could be recruited and tested to see if they react negatively to a personalized advertisement. Originally, study 7 focused on those over fifty-five with a similar goal to provide personalized product or service that might be considered more accurate and relevant to the audience. I assumed that a homogenous group is more likely to engage in similar behaviors and the group itself would also be more likely to perceive that the recommendations associated with the group as relevant to them. However, I chose nursing home as a sure way to ensure manipulating categorization threat, and many answered that it was irrelevant to what they had browsed online before (past behavior). That’s why the future studies should focus on the participant’s past behavior, and even if the audience thought that the recommendations were accurate, there is a chance that the participants avoided the association because they did feel threatened (Dalton and Huang 2014). However, at an empirical level, it is hard to distinguish if the participants perceive that the personalized offering as inaccurate because the participants felt threatened or the recommendations had no link to the past behavior. This is one of the dilemmas of lab experiments. While lab experiments provide a controlled setting to test the effect of personalization and categorization threat, there are limitations to convincing the participants that the offerings.

Along these lines, I aim to run a field experiment. Currently, I am working with a local

110

credit union that offers loans to people. In this joint project, I hope to test if personalized emails can pose a categorization threat to their potential customers. Especially, I focus on those who participated in a public event held by the credit union to create a clear link to their past behavior.

In this event, the credit union engages with potential customers and collect email address for future contact. The event is targeted to those who need a loan and I believe the personalized email could highlight a feared identity, such as not being able to independently sustain themselves. I plan to send out two sets of emails that are either personalized or not. The personalized email will highlight the receiver’s past behavior and offer a personalized deal for them (e.g., 1.9% rate just for you for joining us today!) while the generic email will remain anonymous (e.g., 1.9% lowest rate guaranteed). If the hypothesized effect holds, those who have participated in this event will hold a negative attitude toward personalized emails and will be less likely to open the mail.

On another direction, future researchers can investigate how consumers perceive personalized products and services associated with a positive identity (Summers et al. 2016).

This paper focused on when personalized products hurt the attitude toward the firm, however, studies 1b, 3, and 7 demonstrated that personalized products and services can also be perceived positively when they signal a neutral identity (e.g., drinking water regularly, listening to music for 21, and taking online surveys for money). However, this positive effect of personalization is not consistently observed across the other sets of studies. This might be why in study 3, personalization with ‘being 21’ did not activate age identity in which participants did not respond any quicker to questions related to age. In the music streaming service studies, participants were not receiving fully catered content, as all of them were receiving the same list of songs with few based on their previous choices. So, everyone was receiving the same content but some were told

111

that the recommendations were personalized and while the others were not. If these songs better matched to what participants liked to listen to, they probably would have responded more positively to the service. For future studies focusing on the positive effects of personalization, researchers should make sure to calibrate the content so that the participants will like what they receive. Future researchers could utilize identities that consumers might want, like being smart, healthy, and competent to better understand the positive effects of personalization. Existing work has shown that consumers do respond more positively to personalized advertisements associated with desirable identities, such as having a refined taste (Summers et al. 2016). In my dissertation, most of the identities used for the control condition were neutral (e.g., someone who has a cold, listens to music for twenty-year-olds, listens to playlist called ‘Night out, Mood Booster’). So to reliably demonstrate the positive effects of personalization, researchers should calibrate the personalized content with identity that people might find desirable. Then, it will be easier to determine if people respond to personalization positively because of identity accessibility as well.

Implications

My findings contribute to the literature on personalization (Arora et al., 2008; Baek &

Morimoto, 2012) and associated concepts such as mass customization and one-to-one marketing

(Franke et al. 2010; Pine 2011) and also work on identity-related consumption (White and Argo

2009; White et al. 2012; White and Dahl 2007). While some work has focused on identities people try to dissociate with (e.g., men avoiding a feminine identity), my research tests how

112

consumers respond when a product or service they are using signals an identity they might possess yet are reluctant to acknowledge. Similar work in the past has called this the “feared self” (Carver et al. 1999), where people feel concerned that they might become a person with negative qualities. When people realize that they are becoming closer to these negative identities, they are motivated to deviate from them. For example, someone who is fearful of becoming unpopular will try to avoid any behavior related to being a ‘geek’ or a ‘nerd’(Berger 2008;

Berger and Heath 2008). I find that personalized products and services associated with threatening identities can motivate people to avoid any association with feared selves. Also, my research expands the literature on personalization by illuminating another factor that can negatively influence people’s perception of personalization other than privacy concerns (Kim et al. 2018). Even when the firms have the trust of consumers, firms could still deliver personalized products and services that might backfire by threatening the consumers’ identity.

Personalized products and services are an intimate yet dangerous tool; they pose both a threat and an opportunity for firms. The negative aspects of personalization are not often discussed since most of the work focuses on either stressing the benefits of implementing the broad concept of personalization or improving the performance of specific algorithms. My research adds insight into what these conceptual papers or algorithms based on a consumer’s past behavior might overlook. Algorithms learn from consumers’ repeated behaviors. When John orders take-out from “Big-Fat Burrito’ three times a week, Jennifer purchases Frosty from

Wendy’s every Friday midnight, and Ignacio watches The Bachelor on Sundays, firms will send out personalized offerings based on this information. As this paper has demonstrated, John,

Jennifer, and Ignacio might find this threatening because it reminds them of a part of themselves about which they are ashamed. Consumers feel ‘ashamed and uncomfortable’ but cannot resist

113

continuing these guilty pleasure consumption (McCoy and Scarborough 2014). Firms negligent of this paper’s findings will blindly trust the algorithm to personally offer “Just for John” deal at

“Big-Fat Burrito.” This does not mean that Jennifer does not like getting a Frosty at Wendy’s, but she does not like to be reminded through personalized messages. Our findings suggest that firms could bypass this problem by just offering personalized content without mentioning this explicitly. In Studies 2 and 3, participants were happy with receiving a personalized list of “Girl

Power” music when they were told that they were receiving a “popular” list of music. In reality, participants were receiving the same personalized content but did not feel threatened because they were not aware the content was personalized. Firms can reap the benefit of personalization without upsetting their regular customers if they keep the message more discreet.

Conclusion

Existing literature related to personalization generally claims that consumers should perceive personalization positively. However, there is a lack of work that directly tests this notion. My research complements recent work on how personalization can raise privacy concerns (Kim et al. 2018) by revealing another reason why personalization can backfire In addition, I show that consumers feel threatened by receiving personalized products and services related to negative identities, and the accessibility of the negative identity drives the effect.

Therefore, personalization with negative identities has implications for both identity researchers and most firms engaging in personalization.

114

Appendix A - Stimuli from Study 1a, 1b, 1c, and 4

Imagine that you are going to a pharmacy to buy hemorrhoid cream.

You have come to this pharmacy a few times before for the same product.

You quickly find the product you were looking for.

When you step in the counter and hand the product over, you realize that the clerk recognizes you and greets you using your name.

Personalization condition

“Hi, [Participant’s name/ not present in study 1c], did you find everything you need? Are you here for another batch of [hemorrhoid cream/cough medicine/water]?”

You exchange a bit of small talk and pay the clerk.

"Thank for shopping at with us! Please come again, [Participant’s name/ not present in study

1c], "

Control condition

Imagine that you are going to a pharmacy to buy hemorrhoid cream.

You have come to this pharmacy a few times before for the same product.

115

You quickly find the product you were looking for.

When you step in the counter and hand the product over, you realize that the clerk does not seem to recognize you.

“Hi, did you find everything you need?”

You exchange a bit of small talk and pay the clerk.

"Thanks for shopping with us! Please come again!"

116

Appendix B - Stimuli from Study 2 and 3

We are researching people's response to online music services. In particular, we want to know what you think about a new music streaming service, which we are calling "Z". (Note that

"Z" is a pseudonym. We are not revealing the name of this service in order to get a more accurate measure of your impression.)

Z offers a music listening experience similar to Spotify and Apple Music. Listeners can stream a wide range of songs on different devices, such as PCs, smartphones, and tablets. The service offers a similar selection of music as its competitors, but it passes along a higher percentage of revenue to the artists. Consequently, musicians and record labels are hoping that more listeners will adopt Z rather than use Spotify and Apple Music.

Z offers a variety of curated playlists based on artists, genres, and even special themes.

For example, there are playlists for electronic music, classical music, mood, and Christmas.

Personalized condition

Because of the large volume of music available, it can be difficult for people to find the right playlist. Consequently, Z recommends songs and playlists based on the personal music preferences of each person who uses the service.

The purpose of this study is to collect information about your personal preferences in order to help build Z's personalized playlist recommendations.

117

In the next part of the survey, you will have the opportunity to listen to songs by two different artists. For each pair of songs, you will be asked to select the song or artist that you like better.

Simply choose the song or the artist that appeals more to you personally. Z will use your choices to recommend personalized playlists customized just for people like you.

The options you see will depend on their previous choices and will be updated as you progress.

Please make sure you have access to audio and proceed to the next page.

Control condition

Because of the large volume of music available, it can be difficult for people to find the right playlist. Consequently, Z recommends songs and playlists based on what is popular amongst people who use the service.

The purpose of this study is to collect information about most people's music preferences in order to help Z recommend popular playlists.

In the next part of the survey, you will have the opportunity to listen to songs by two different artists. For each pair of songs, you will be asked to select the song or artist that you like better.

Simply choose the song or the artist that appeals more to you personally. Z will use the average

118

choices of everyone to recommend playlists that most listeners will enjoy.

The options you see will depend on their previous choices and will be updated as you progress.

Please make sure you have access to audio and proceed to the next page.

119

Appendix C - Stimuli for Study 5

We are researching people's response to online book services. In particular, we want to know what you think about a new online service that streams audio books. We are calling this service

"Z", although "Z" is a pseudonym. (We are not revealing the name of this service in order to get a more accurate measure of your impression.)

Z offers a large collection of books that its members can stream, similar to the way that people can stream songs on a service like Spotify. Members can listen to any audio book in Z's extensive library on a PC, smartphone, or tablet.

Personalized condition

Because of the large volume of books available, it can be difficult for people to find the right books to listen to. Consequently, Z recommends books based on the personal preferences of each person who uses the service.

In order to make recommendations that better match your preferences, Z asks users to report their demographic information and complete a personality test when they sign up for the service.

Z will use your answers to make customized book recommendations just for you.

120

Control condition

Because of the large volume of books available, it can be difficult for people to find the right books to read. Consequently, Z recommends books based on what is popular amongst people who use the service.

In order to make recommendations that better match the preferences of most people, Z asks users to report their demographic information and complete a personality test when they sign up for the service. Z will use the most common answers to make book recommendations that they think will appeal to most users.

For all conditions

Z asks each member the following questions when setting up their profile.

(Please click the arrows to begin Z's survey)

How old are you? (in numbers; e.g. 15)

In what state do you legally reside? (This may or may not be where you live during the semester)?

121

1) Arizona 2) Other (please write the state in the response box)

Where do you live during the semester? (This may or may not be your legal residence)?

1) Tucson 2) Outside of Tucson, but in Arizona3) Outside Arizona

What is your native language?

Personality test

In the following section, you will be asked different questions about who you are. Please choose the images that you feel most accurately answers the question. Even if none of the responses is a perfect fit for you, choose the answer that you think best reflects who you are.

How would you make the most of a morning off?

122

What is more likely to be your desk?

If you're meeting a friend at 6 o'clock, when are you more likely to arrive?

How large is your vocabulary?

123

I am more worried about being seen as _____.

How vivid is your imagination?

124

How do you tend to act in social situations?

If life was a play, who would you be?

125

How strongly do you feel emotion?

What would it look like if you wrote a list of your faults?

How would you lead?

126

Which of these makes society work?

How do you feel when you're stressed?

Description of the audiobook service

With Z, you can keep reading from more than 100,000 titles!

When you log into Z, you will be able to browse multiple categories where you will discover books from mysteries, thrillers, romance, fiction, nonfiction, and more.

You can listen to different books on smartphone, tablet, PC, Mac, and even in your car, on the go.

127

128

Whatever device you are using, when you log-in to Z's system, you have access to all the content

Z has to offer. One of the many things you can do is to browse different genres.

However, with more than 100,000 choices, it can be overwhelming!

That is why Z goes one step more to provide you with recommendations to start from

Personalization condition.

So, Z has personalized recommendations for you based on the choices you made earlier.

Please click below to view your personalized book recommendations

129

Control Condition

So, Z has suggested recommendations for you based on the most popular books being

130

streamed today.

Please click below to view Z's popular book recommendations.

131

132

Appendix D - Pretest results for Study 5

Fit of each book

Title Boring Arrogant Lazy Elderly Living in Similar Dissimilar Tucson to me to me 100 Things to Do in 3.54 3.15 2.22 5 6.17 4.47 3.98 Tucson Before you Die Moon's Guide to Tucson 4.44 2.58 2.53 5.73 6.13 3.17 4.55 Day Trips from Phoenix, 3.35 2.76 2.17 5.27 5.89 4.33 3.84 Tucson & Flagstaff Arizona and Grand 3.89 2.71 2.33 5.35 5.72 3.79 4.16 Canyon Eighty Somethings 3.71 2.11 2.13 6.56 5.03 1.78 5.18 Elderhood 3.89 2.4 2.34 6.58 4.91 2.08 4.85 I'm Too Young to be 70 3.55 2.85 2.52 6.35 4.76 2.11 5.02 A Little Old Lady 3.71 2.4 2.8 5.98 4.63 2.09 4.85 Crotchy Old Man 3.71 2.75 2.74 5.58 4.45 2.47 4.67 Insane Success for Lazy 4.22 3.03 5.64 3.08 4.22 3.77 4.31 People Laziness Gene 4.02 2.86 5.12 3.94 4.21 3.33 4.17 Stop Procrastinating 3.81 2.94 4.81 3.17 4.12 3.94 4.14 Being Boring Sucks 5.56 2.58 3.32 3.34 4.08 2.65 4.63 You Are Boring 5.61 2.63 2.97 3.67 4.07 2.66 4.63 How to Stop 3.81 2.81 4.47 2.85 4.05 4.17 3.97 Procrastination Stop Being Lazy 3.87 2.91 5.06 2.96 4.02 3.26 4.45 Nerdy, Shy, and Socially 4.66 2.55 2.92 3.17 3.99 3.04 4.43 Inappropriate Not to Be an asshole 3.17 4.69 2.94 2.98 3.99 3.53 4.12 Don't Be that Dick 2.99 4.91 2.76 2.77 3.95 3.55 4.29 Don't Be that Bitch 3.03 4.74 2.78 2.19 3.92 3.51 4.39 How Not to Be a Jerk 3.27 4.61 2.6 3.33 3.63 2.92 4.32

133

Fit of each negative identity

7 6.19 6 5 4 3 2.75 2.64 2.68

2 1.25 1 Elderly Shy Arrogant Lazy Living in Arizona

Valence of each negative identity

7 6 5 4.27 4 3.12 3 2.64 1.97 2 1.47 1 Elderly Shy Arrogant Lazy Living in Arizona

134

Appendix E - Non-personalized advertisement shown in study 7

135

REFERENCES

Aaker, Jennifer L., Anne M. Brumbaugh, and Sonya A. Grier (2000), “Nontarget Markets and

Viewer Distinctiveness: The Impact of Target Marketing on Advertising Attitudes,” Journal

of Consumer Psychology, 9(3), 127–40.

Alba, Joseph, John Lynch, Barton Weitz, Chris Janiszewski, Richard Lutz, Alan Sawyer, and Stacy

Wood (1997), “Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives

to Participate in Electronic Marketplaces,” Journal of Marketing, 61(3), 38–53.

Ansari, Asim and Carl F. Mela (2003), “E-Customization,” Journal of Marketing Research, 40(2),

131–45.

Aronson, Joshua, Michael J Lustina, Catherine Good, Kelli Keough, Claude M Steele, and Joseph

Brown (1999), “When White Men Can’t Do Math: Necessary and Sufficient Factors in

Stereotype Threat,” Journal of experimental social psychology, 35(1), 29–46.

Arora, Neeraj, Xavier Dreze, Anindya Ghose, James D. Hess, Raghuram Iyengar, Bing Jing,

Yogesh Joshi, V. Kumar, Nicholas Lurie, Scott Neslin, S. Sajeesh, Meng Su, Niladri Syam,

Jacquelyn Thomas, and Z. John Zhang (2008), “Putting One-to-One Marketing to Work:

Personalization, Customization, and Choice,” Marketing Letters, 19(3–4), 305–21.

Ashworth, Laurence, Peter R. Darke, and Mark Schaller (2005), “No One Wants to Look Cheap:

Trade-Offs Between Social Disincentives and the Economic and Psychological Incentives to

Redeem Coupons,” Journal of Consumer Psychology, 15(4), 295–306.

Baek, Tae Hyun and Mariko Morimoto (2012), “Stay Away From Me,” Journal of Advertising,

136

41(1), 59–76.

Ball, Dwayne, Pedro S. Coelho, and Manuel J. Vilares (2006), “Service Personalization and

Loyalty,” Journal of Services Marketing, 20(6), 391–403.

Baumeister, Roy F (1998), “The Self,” in The Handbook of Social Psychology, ed. Daniel T.

Gilbert, Susan T. Fiske, and Gardner Lindzey, New York: McGraw-Hill, 680–740.

Bem, Daryl (1972), “Self-Perception Theory,” Advances in Experimental Social Psychology, 6, 1–

62.

Bendapudi, Neeli and Robert P. Leone (2003), “Psychological Implications of Customer

Participation in Co-Production,” Journal of Marketing, 67(1), 14–28.

Berger, Jonah (2008), “Identity Signaling, Social Influence, and Social Contagion,”

Understanding peer influence in children and adolescents, 181–99.

Berger, Jonah and Chip Heath (2008), “Who Drives Divergence? Identity Signaling, Outgroup

Dissimilarity, and the Abandonment of Cultural Tastes.,” Journal of Personality and Social

Psychology, 95(3), 593–607.

Bhattacharjee, Amit, Jonah Berger, and Geeta Menon (2014), “When Identity Marketing Backfires:

Consumer Agency in Identity Expression,” Journal of Consumer Research, 41(2), 294–309.

Boerman, Sophie C., Sanne Kruikemeier, and Frederik J. Zuiderveen Borgesius (2017), “Online

Behavioral Advertising: A Literature Review and Research Agenda,” Journal of Advertising,

46(3), 363–76.

Botti, Simona and Ann L. McGill (2010), “The Locus of Choice: Personal Causality and

137

Satisfaction with Hedonic and Utilitarian Decisions,” Journal of Consumer Research, 37(6),

1065–78.

Bourdieu, Pierre and Jean Claude Passeron (1979), The Inheritors: French Students and Their

Relation to Culture, Univ of Chicago Pr.

Branscombe, Nyla R. and Daniel L. Wann (1994), “Collective Self-Esteem Consequences of

Outgroup Derogation When a Valued Social Identity Is on Trial,” European Journal of Social

Psychology, 24(6), 641–57.

Brehm, J W (1966), “A Theory of Psychological Reactance,” New York: Academic, l, 6.

Bushman, Brad J and Roy F Baumeister (1998), “Threatened Egotism, Narcissism, Self-Esteem,

and Direct and Displaced Aggression: Does Self-Love or Self-Hate Lead to Violence?,”

Journal of personality and social psychology, 75(1), 219.

Carver, Charles S, John W Lawrence, and Michael F Scheier (1999), Self-Discrepancies and Affect:

Incorporating the Role of Feared Selves.

Cialdini, Robert B, Richard J Borden, Avril Thorne, Marcus Randall Walker, Stephen Freeman,

and Lloyd Reynolds Sloan (1976), “Basking in Reflected Glory: Three (Football) Field

Studies.,” Journal of personality and social psychology, 34(3), 366.

Cohen, Jerry L., Nancy Dowling, Gregory Bishop, and William J. Maney (1985), “Causal

Attributions,” Personality and Social Psychology Bulletin, 11(4), 369–78.

Coskuner-Balli, Gokcen and Craig J. Thompson (2012), “The Status Costs of Subordinate Cultural

Capital: At-Home Fathers’ Collective Pursuit of Cultural Legitimacy through Capitalizing

Consumption Practices,” Journal of Consumer Research, 40(1), 19–41.

138

Crocker, Jennifer and Riia Luhtanen (1990), “Collective Self-Esteem and Ingroup Bias.,” Journal

of Personality and Social Psychology, 58(1), 60–67.

Cuddy, Amy J. C., Michael I. Norton, and Susan T. Fiske (2005), “This Old Stereotype: The

Pervasiveness and Persistence of the Elderly Stereotype,” Journal of Social Issues, 61(2),

267–85.

Dahl, Darren W. and C. Page Moreau (2007), “Thinking inside the Box: Why Consumers Enjoy

Constrained Creative Experiences,” Journal of Marketing Research, 44(3), 357–69.

Dalton, Amy N and Li Huang (2014), “Motivated Forgetting in Response to Social Identity Threat,”

Journal of Consumer Research, 40(April), 1017–38.

Deci, Edward and Richard Ryan (1985), Intrinsic Motivation and Self-Determination in Human

Behavior, New York: Plenum.

Dellaert, Benedict G. C. and Stefan Stremersch (2005), “Marketing Mass-Customized Products:

Striking a Balance between Utility and Complexity,” Journal of Marketing Research, 42(2),

219–27.

Deshpande, Rohit, Wayne D Hoyer, and Naveen Donthu (1986), “The Intensity of Ethnic

Affiliation: A Study of the Sociology of Hispanic Consumption,” Journal of Consumer

Research, 13.

Deshpandé, Rohit and Douglas M. Stayman (1994), “A Tale of Two Cities: Distinctiveness Theory

and Advertising Effectiveness,” Journal of Marketing Research, 31(1), 57–64.

Doosje, Bertjan, Naomi Ellemers, and Russell Spears (1995), “Perceived Intragroup Variability as

a Function of Group Status and Identification,” Journal of experimental social psychology,

139

31(5), 410–36.

Dunn, Lea, Katherine White, and Darren W Dahl (2013), “Dissociating from Undesired Consumer

Identities,” The Routledge Companion to Identity and Consumption, 273.

Duval, T Shelley and Virginia Hensley Duval (1987), “Level of Perceived Coping Ability and

Attribution for Negative Events,” Journal of Social and Clinical Psychology, 5(4), 452–68.

Ellemers, Naomi, Russell Spears, and Bertjan Doosje (2002), “Self and Social Identity,” Annual

Review of Psychology, 53(1), 161–86.

Englis, Basil G. and Michael R. Solomon (1995), “To Be and Not to Be: Lifestyle Imagery,

Reference Groups, and The Clustering of America,” Journal of Advertising, 24(1), 13–28.

Etgar, Michael (2008), “A Descriptive Model of the Consumer Co-Production Process,” Journal

of the Academy of Marketing Science, 36(1), 97–108.

Fazio, Russell H. (1990), A Practical Guide to the Use of Response Latency in Social

Psychological Research., Research methods in personality and social psychology.

Federoff, Nancy A and John H Harvey (1976), “Focus of Attention, Self-Esteem, and the

Attribution of Causality,” Journal of Research in Personality, 10(3), 336–45.

Festinger, Leon (1957), “An Introduction to the Theory of Dissonance,” in A Theory of Cognitive

Dissonance, Stanford, California: Stanford University Press, 3–21.

Fischer, Kurt W. and June Price Tangney (1995), “Self-Conscious Emotions and the Affect

Revolution: Framework and Overview,” in The Self-Conscious Emotions: Theory and

Research, 3–22.

140

Fitch, Gordon (1970), “Effects of Self-Esteem, Perceived Performance, and Choice on Causal

Attributions.,” Journal of personality and social psychology, 16(2), 311.

Forehand, Mark R., Rohit Deshpandé, I I Reed, and Americus Reed (2002), “Identity Salience and

the Influence of Differential Activation of the Social Self-Schema on Advertising Response.,”

Journal of Applied psychology, 87(6), 1086.

Franke, Nikolaus, Martin Schreier, and Ulrike Kaiser (2010), “The ‘I Designed It Myself’ Effect

in Mass Customization,” Management Science, 56(1), 125–40.

Fuchs, Christoph, Emanuela Prandelli, and Martin Schreier (2010), “The Psychological Effects of

Empowerment Strategies on Consumers’ Product Demand,” Journal of Marketing, 74(1), 65–

79.

Gal, David and James Wilkie (2010), “Real Men Don’t Eat Quiche,” Social Psychological and

Personality Science, 1(4), 291–301.

Gao, Leilei, S. Christian Wheeler, and Baba Shiv (2009), “The ‘Shaken Self’: Product Choices as

a Means of Restoring Self-View Confidence,” Journal of Consumer Research, 36(1), 29–38.

Gibbons, Frederick X, David J Lane, Meg Gerrard, Monica Reis-Bergan, Carrie L Lautrup, Nancy

A Pexa, and Hart Blanton (2002), “Comparison-Level Preferences after Performance: Is

Downward Comparison Theory Still Useful?,” Journal of personality and social psychology,

83(4), 865.

Goldsmith, Kelly, Eunice Kim Cho, and Ravi Dhar (2012), “When Guilt Begets Pleasure: The

Positive Effect of a Negative Emotion,” Journal of Marketing Research, 49(6), 872–81.

Hart, Christopher W (1996), “Made to Order,” Marketing management, 5(2), 10.

141

Häubl, Gerald and Valerie Trifts (2000), “Consumer Decision Making in Online Shopping

Environments: The Effects of Interactive Decision Aids,” Marketing Science, 19(1), 4–21.

Hayes, Andrew F (2017), Introduction to Mediation, Moderation, and Conditional Process

Analysis: A Regression-Based Approach, Guilford Publications.

Higgins, E. Tory (1987), “Self-Discrepancy: A Theory Relating Self and Affect.,” Psychological

Review, 94(3), 319–40.

Hogg, Michael A. and John C. Turner (1987), “Intergroup Behaviour, Self-Stereotyping and the

Salience of Social Categories,” British Journal of Social Psychology, 26(4), 325–40.

Hong, Ying-Yi, Michael W Morris, Chi-Yue Chiu, and Ver6nica Benet-Martfnez (2000),

“Multicultural Minds A Dynamic Constructivist Approach to Culture and Cognition.”

James, William (1890), The Principles of Psychology, New York, NY: Henry Holt and Company.

Kim, Soo and David Gal (2014), “From Compensatory Consumption to Adaptive Consumption:

The Role of Self-Acceptance in Resolving Self-Deficits,” Journal of Consumer Research,

41(2), 526–42.

Kim, Tami, Kate Barasz, and Leslie K John (2018), “Why Am I Seeing This Ad? The Effect of Ad

Transparency on Ad Effectiveness,” ed. Eileen Fischer and Andrea Morales, Journal of

Consumer Research, 45(5), 906–32.

Kirmani, Amna and Robert H Smith (2009), “The Self and the Brand,” Journal of Consumer

Psychology, 19, 271–75.

Kramer, Thomas (2007), “The Effect of Measurement Task Transparency on Preference

142

Construction and Evaluations of Personalized Recommendations,” Journal of Marketing

Research, 44(2), 224–33.

Kramer, Thomas, Suri Spolter-Weisfeld, and Maneesh Thakkar (2007), “The Effect of Cultural

Orientation on Consumer Responses to Personalization,” Marketing Science, 26(2), 246.

Kraut, Robert E. (1973), “Effects of Social Labeling on Giving to Charity,” Journal of

Experimental Social Psychology, 9(6), 551–62.

Lynch, John G. and Dan Ariely (2000), “Wine Online: Search Costs Affect Competition on Price,

Quality, and Distribution,” Marketing Science, 19(1), 83–103.

Mandel, Naomi, Derek D. Rucker, Jonathan Levav, and Adam D. Galinsky (2017), “The

Compensatory Consumer Behavior Model: How Self-Discrepancies Drive Consumer

Behavior,” Journal of Consumer Psychology, 27(1), 133–46.

Markus, Hazel (1977), “Self-Schemata and Processing Information about the Self.,” Journal of

Personality and Social Psychology, 35(2), 63–78.

Markus, Hazel and Ziva Kunda (1986a), “Stability and Malleability of the Self-Concept,” Journal

of Personality and Social Psychology, 51(4), 858–66.

——— (1986b), “Stability and Malleability of the Self-Concept,” Journal of Personality and

Social Psychology, 51(4), 858–66.

Marrs, Carolyn H (1984), “Developing a Profitable Program for the Senior Market,” Journal of

Retail Banking, 6(1 & 2), 25–35.

Martha C. White (2012), “Orbitz Targets Mac Users With More Expensive Search Results,” Time,

143

http://business.time.com/2012/06/26/orbitz-shows-higher-prices-to-mac-users/.

McCoy, Charles Allan and Roscoe C. Scarborough (2014), “Watching ‘Bad’ Television: Ironic

Consumption, Camp, and Guilty Pleasures,” Poetics, 47, 41–59.

McDonald’s Corporation (2019), “McDonald’s to Acquire Dynamic Yield, Will Use Decision

Technology to Increase Personalization and Improve Customer Experience,”

https://news.mcdonalds.com/news-releases/news-release-details/dynamic-yield-acquisition-

release.

McGuire, William J., Claire V. McGuire, Pamela Child, and Terry Fujioka (1978), “Salience of

Ethnicity in the Spontaneous Self-Concept as a Function of One’s Ethnic Distinctiveness in

the Social Environment,” Journal of Personality and Social Psychology, 36(5), 511–20.

Merle, Aurélie, Jean-Louis Chandon, Elyette Roux, and Fabrice Alizon (2010), “Perceived Value

of the Mass-Customized Product and Mass Customization Experience for Individual

Consumers,” Production and Operations Management, 19(5), 503–14.

Meyers-Levy, Joan (1988), “The Influence of Sex Roles on Judgment,” Journal of consumer

research, 14(4), 522–30.

Miller, Richard L, Philip Brickman, and Diana Bolen (1975), “Attribution versus Persuasion as a

Means for Modifying Behavior.,” Journal of personality and social psychology, 31(3), 430–

41.

Mohammed, Rafi (2017), “How Retailers Use Personalized Prices to Test What You’re Willing to

Pay,” Harvard Business Review, October.

Morse, Stan and Kenneth J Gergen (1970), “Social Comparison, Self-Consistency, and the Concept

144

of Self.,” Journal of personality and social psychology, 16(1), 148.

Murphy, Mary C, Claude M Steele, and James J Gross (2007), “Signaling Threat: How Situational

Cues Affect Women in Math, Science, and Engineering Settings,” Psychological science,

18(10), 879–85.

North, Michael S. and Susan T. Fiske (2013), “Act Your (Old) Age: Prescriptive, Ageist Biases

Over Succession, Consumption, and Identity,” Personality and Social Psychology Bulletin,

39(6), 720–34.

Oakes, Penelope J (1987), “The Salience of Social Categories,” in Rediscovering the Social Group:

A Self-Categorization Theory, Oxford: Basil Blackwell, 117–41.

Oyserman, Daphna (2007), “Self, Self-Concept and Identity,” Social psychology: Handbook of

basic principles, 2, 432–53.

Pelham, Brett W. and Jeff Orson Wachsmuth (1995), “The Waxing and Waning of the Social Self:

Assimilation and Contrast in Social Comparison.,” Journal of Personality and Social

Psychology, 69(5), 825–38.

Peppers, D., M. Rogers, and B. Dorf (1999), “Is Your Company Ready for One-To-One

Marketing?,” Harvard business review, 77(1), 151–60.

Piccoli, Gabriele and Richard T Watson (2008), “Profit from Customer Data by Identifying

Strategic Opportunities and Adopting the ‘Born Digital’ Approach,” MIS Quarterly Executive,

7(3), 113–22.

Pine, B Joseph (2011), “Beyond Mass Customization,” Harvard Business Review,

https://hbr.org/2011/05/beyond-mass-customization.

145

Pine, B Joseph, Bart Victor, and Andrew C Boynton (1993), “Making Mass Customization Work,”

Harvard business review, 71(5), 108–11.

Puntoni, Stefano, Steven Sweldens, and Nader T. Tavassoli (2011), “Gender Identity Salience and

Perceived Vulnerability to Breast Cancer,” Journal of Marketing Research, 48(3), 413–24.

Ratcliff, Roger (1993), “Methods for Dealing With Reaction Time Outliers,” Psychological

Bulletin, 114(3), 510–32.

Reed, Americus and Mark R. Forehand (2012), “Identity-Based Consumer Behavior,”

International Journal of Research in Marketing, 29(4), 310–21.

Reed, Americus, Mark R. Forehand, Stefano Puntoni, Luk Warlop, Americus Reed, Mark R.

Forehand, Stefano Puntoni, and Luk Warlop (2012), “Identity-Based Consumer Behavior,”

International Journal of Research in Marketing, 29(4), 310–21.

Rolling Stone (2010), “100 Greatest Artists- The Beatles, Eminem and More of the Best of the

Best,” Rolling Stone, https://www.rollingstone.com/music/music-lists/100-greatest-artists-

147446/bob-dylan-10-31068/.

Rossi, Peter E., Robert E. McCulloch, and Greg M. Allenby (2008), “The Value of Purchase

History Data in Target Marketing,” Marketing Science, 15(4), 321–40.

Royne Stafford, Marla (1996), “Demographic Discriminators of Service Quality in the Banking

Industry,” Journal of Services Marketing, 10(4), 6–22.

Sackmann, Stefan, Jens Strüker, and Rafael Accorsi (2006), “Personalization in Privacy-Aware

Highly Dynamic Systems,” Communications of the ACM, 49(9), 32–38.

146

Schmader, Toni and Brenda Major (1999), “The Impact of Ingroup vs Outgroup Performance on

Personal Values,” Journal of Experimental Social Psychology, 35, 47–67.

Schrift, Rom, Klaus Wertenbroch, and Yonat Zwebner (2019), “The Dark Side of Microtargeting:

Predicting Consumers’ Preferences Threatens Their Sense of Free Will,” in When Marketing

Alienates Consumers, Association for Consumer Research.

Shaffer, Greg and Z. John Zhang (1995), “Competitive Coupon Targeting,” Marketing Science,

14(4), 395–416.

Shih, Margaret, Todd L. Pittinsky, and Nalini Ambady (1999), “Stereotype Susceptibility: Identity

Salience and Shifts in Quantitative Performance,” Psychological Science, 10(1), 80–83.

Shih, Margaret, Jennifer A. Richeson, Nalini Ambady, Kentaro Fujita, and Heather M. Gray (2002),

“Stereotype Performance Boosts: The Impact of Self-Relevance and the Manner of

Stereotype Activation,” Journal of Personality and Social Psychology, 83(3), 638–47.

Da Silveira, Giovani, Denis Borenstein, and Flávio S Fogliatto (2001), “Mass Customization:

Literature Review and Research Directions,” International Journal of Production Economics,

72(1), 1–13.

Smith, Eliot R. and Susan Henry (1996), “An In-Group Becomes Part of the Self: Response Time

Evidence,” Personality and Social Psychology Bulletin, 22(6), 635–42.

Song, Ji Hee, Hye Young Kim, Sahangsoon Kim, Sung Won Lee, and Jong-Ho Lee (2016),

“Effects of Personalized E-Mail Messages on Privacy Risk: Moderating Roles of Control and

Intimacy,” Marketing Letters, 27(1), 89–101.

Spears, Russell, Bertjan Doosje, and Naomi Ellemers (1997), “Self-Stereotyping in the Face of

147

Threats to Group Status and Distinctiveness: The Role of Group Identification,” Personality

and social psychology bulletin, 23(5), 538–53.

Spencer, Steven J, Claude M Steele, and Diane M Quinn (1999), “Stereotype Threat and Women’s

Math Performance,” Journal of experimental social psychology, 35(1), 4–28.

Spotify (2020), “Spotify Charts,” https://spotifycharts.com/regional.

Steele, Claude M. (1988), “The Psychology of Self-Affirmation: Sustaining the Integrity of the

Self,” Advances in Experimental Social Psychology, 21(C), 261–302.

Subramanian, Alka and Raymond A. Marquardt (1999), “The Personalizing Shopper and

Marketplace Relationships,” Journal of Segmentation in Marketing, 3(2), 5–21.

Summers, Christopher A., Robert W. Smith, and Rebecca Walker Reczek (2016), “An Audience

of One: Behaviorally Targeted Ads as Implied Social Labels,” Journal of Consumer Research,

43(1), 156–78.

Surprenant, Carol F. and Michael R. Solomon (1987), “Predictability and Personalization in the

Service Encounter,” Journal of Marketing, 51(2), 86.

Swann, William and Jennifer Bosson (2010), “Self and Identity,” in Handbook of Social

Psychology.

Syam, Niladri B., Ranran Ruan, and James D. Hess (2005), “Customized Products: A Competitive

Analysis,” Marketing Science, 24(4), 569–84.

Tajfel, Henri and John Turner (1979), “An Integrative Theory of Intergroup Conflict,” in The

Social Psychology of Intergroup Relations, ed. M. A. Hogg and D. Abrams, New York, NY:

148

Psychology Press, 33–47.

——— (1986), “The Social Identity Theory of Intergroup Relations,” Psychology of intergroup

relations, 7–24.

Tesser, Abraham and James E Collins (1988), “Emotion in Social Reflection and Comparison

Situations: Intuitive, Systematic, and Exploratory Approaches.,” Journal of personality and

social psychology, 55(5), 695.

The Ritz-Carlton Leadership Center (2014), “The Ritz-Carlton: A Tradition of Storytelling,”

http://ritzcarltonleadershipcenter.com/tag/wow-story-2/.

Thibodeau, R. and E. Aronson (1992), “Taking a Closer Look: Reasserting the Role of the Self-

Concept in Dissonance Theory,” Personality and Social Psychology Bulletin, 591–602.

Turner, John (1991), Social Influence., Thomson Brooks/Cole Publishing Co.

Vallacher, Robin R and Maurice Solodky (1979), “Objective Self-Awareness, Standards of

Evaluation, and Moral Behavior,” Journal of Experimental Social Psychology, 15(3), 254–

62.

Vandello, Joseph A and Jennifer K Bosson (2013), “Hard Won and Easily Lost: A Review and

Synthesis of Theory and Research on Precarious Manhood.,” Psychology of Men &

Masculinity, 14(2), 101.

Ward, Morgan K and Darren W Dahl (2014), “Should the Devil Sell Prada? Retail Rejection

Increases Aspiring Consumers’ Desire for the Brand,” Journal of Consumer Research, 41(3),

590–609.

149

Watson, D, LA Clark, A Tellegen - Journal of personality and Social, and Undefined 1988 (1988),

“Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS

Scales.,” Journal of Personality and Social Psychology, 54(6), 1063–70.

White, Katherine, Jennifer J. Argo, and Jaideep Sengupta (2012), “Dissociative versus Associative

Responses to Social Identity Threat: The Role of Consumer Self-Construal,” Journal of

Consumer Research, 39(4), 704–19.

White, Katherine and Jennifer J Argo (2009), “Social Identity Threat and Consumer Preferences,”

Journal of Consumer Psychology, 19(July), 313–25.

White, Katherine and Darren Dahl (2007), “Are All Out-Groups Created Equal? Consumer

Identity and Dissociative Influence,” Journal of Consumer Research, 34(4), 525–36.

White, Katherine and Darren W. Dahl (2006), “To Be or Not Be? The Influence of Dissociative

Reference Groups on Consumer Preferences,” Journal of Consumer Psychology, 16(4), 404–

14.

Wicklund, Robert A. and Peter M. Gollwitzer (1982), Symbolic Self-Completion, L. Erlbaum

Associates.

Wilder, David A. and Peter N. Shapiro (1984), “Role of Out-Group Cues in Determining Social

Identity,” Journal of Personality and Social Psychology, 47(2), 342–48.

Williams, Jerome D. and William J. Qualls (1989), “Middle-Class Black Consumers and Intensity

of Ethnic Identification,” Psychology and Marketing, 6(4), 263–86.

Wind, Jerry and Arvind Rangaswamy (2001), “Customerization: The next Revolution in Mass

Customization,” Journal of interactive marketing, 15(1), 13–32.

150

Zhong, Chen-Bo and Katie Liljenquist (2006), “Washing Away Your Sins: Threatened Morality

and Physical Cleansing,” Science, 313(5792), 1451–52.