Getting Personal in Public!? How Consumers Respond to Public Personalized Advertising in Retail Stores

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Getting Personal in Public!? How Consumers Respond to Public Personalized Advertising in Retail Stores Nicole J. Hess a,d,∗ , Corinne M. Kelley b , Maura L. Scott c , Martin Mende c , Jan H. Schumann d a

Institute of Market-Based Management, Ludwig-Maximilians-Universität München, Kaulbachstraße 45, 80539 Munich, Germany b Gatton College of Business and Economics, University of Kentucky, Lexington, KY 40506-0034, United States c College of Business, Florida State University, Tallahassee, FL 32306, United States d University of Passau, Innstraße 27, 94032 Passau, Germany

Abstract Retailers are now expanding personalized advertising into consumers’ public life (e.g., via digital in-store displays). Little research has examined how consumers respond to such public personalized advertising (PPA). Grounded in theory on impression management and consumers’ self-concept, three experiments examine when and why social presence and configurations of ad-self-congruity affect consumer responses to PPA negatively or positively. This research reveals that (negative/positive) consumer responses are influenced by a new typology of four distinct ad-self-congruity configurations (i.e., threatening ad-self-(in)congruity vs. bolstering ad-self-(in)congruity). Uncovering contingency factors of the effectiveness of PPAs (i.e., social presence and distinct configurations of ad-self-congruity), the results show that personalization in public diminishes favorable consumer response to threatening self-congruent ads; this effect is driven by consumer-perceived embarrassment. In contrast, bolstering selfcongruent ads translate into positive consumer response with social presence; this effect is driven by consumer-perceived flattery. Taken together, the results provide insights into how PPAs influence consumers via the interplay of personalization, social presence, varying advertising appeals and distinct configurations of ad-self-congruity, thus providing meaningful implications on how to effectively implement personalization technologies in retailing. © 2019 New York University. Published by Elsevier Inc. All rights reserved. Keywords: Personalized advertising; Retail technology; Ad-self-congruity; Consumer data; Social presence

Personalized advertising, which is well-established in online marketing, is becoming increasingly important in retail (Galkin 2018; Inman and Nikolova 2017); that is, as the marketplace examples in Table 1 illustrate, retailers are now expanding personalized advertising into consumers’ public life (Grewal, Roggeveen, and Nordfält 2017; Roggeveen and Sethuraman 2018). For example, using audience-measurement systems in instore monitors and in-store digital displays, retailers can conduct a demographic analysis of consumers and use this information for personalized in-store ads (Buckley and Hunter 2011). Tesco installed such an audience measurement system in its 450 petrol ∗

Corresponding author. E-mail addresses: [email protected] (N.J. Hess), [email protected] (C.M. Kelley), [email protected] (M.L. Scott), [email protected] (M. Mende), [email protected] (J.H. Schumann).

stations in the U.K. to provide personalized on-screen advertising to consumers (Hawkes 2013), and 7-Eleven introduced facial recognition technology to its 11,000 stores in Thailand to personalize ads to customers (Chan 2018). Even more-advanced facial recognition technologies allow for the analysis of consumers’ emotional states (McStay 2015). KFC China uses such technology to recommend menu items to its customers based on their biometrics and their (technology-derived) mood (Hawkins 2017). Notably, the market for such retail technologies is growing considerably (Beams and Narisawa 2017; Transparency Market Research 2015), and technology-enabled personalized ads are forecasted to become a common aspect of consumers’ retail experiences in the near future (Inman and Nikolova 2017). Personalized ads offer benefits for consumers and retailers, as they help consumers make more-informed decisions (Grewal, Roggeveen, and Nordfält 2017), provide relevant content (Aguirre et al. 2015) and allow a more engaging shopping experience (Shankar et al. 2011). However, although conversion

https://doi.org/10.1016/j.jretai.2019.11.005 0022-4359/© 2019 New York University. Published by Elsevier Inc. All rights reserved.

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Country/city

Type of consumer tracking

Type of data collected

Type of technology interface

Displayed content

Source

Tesco

UK

Facial recognition, smart screen

Gender, age, attention time

Digital screens at petrol stations

Hawkes (2013)

Japan Railways

Japan

Facial recognition, smart touchscreen

Bahio (M&C Saatchi)

UK, London

Facial recognition, smart screen

Gender, age, context (time, weather), machine learning Interaction

Personalized, interactive ad

McStay (2015)

Real

Germany USA, Santa Monica

Personalized product recommendations Targeted car advertisement

Jansen (2017)

GMC

Facial recognition, smart screen Movement/facial recognition, smart screen

Vending machines positioned in metro stations Digital billboard installed at Oxford Street in London Digital screens at retail stores Digital screen positioned in a shopping mall

Various personalized advertisements (up to 10 seconds in length) Personalized beverage recommendation

Pharmacy Hjärtat

Sweden, Stockholm

Smoke detector, smart screen

Kottasova (2017)

Astra

Germany, Hamburg

Facial recognition, smart screen

Age, gender, height

Digital screen positioned outside a pub

Landsec

UK, London

Image/facial recognition, smart screen

Vehicles, age, emotions

KFC

China

Facial recognition, smart touchscreen

Gender, age, emotions

Digital screen (size of two basketball courts) at Piccadilly Circus Screens positioned in restaurants

Personalized video-advertisements with sound Personalized video-advertisements for females Various personalized advertisements

Hawkins (2017)

Synaps Labs

Russia

Image recognition

Car model, mobile data, license plate

Personalized recommendations from the menu Targeted car advertisements

Gender, age, composition, eye movement Age, gender, expression, composition and engagement Smoke (yes/no)

Digital screen positioned outside a metro station

Digital billboards positioned at roadsides

Ryall (2010)

Brown (2017)

McCarthy (2015)

Fussell (2017)

Johnson (2017)

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Company (Provider)

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Table 1 Marketplace illustrations of personalized advertising in public.

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rates may increase with greater personalization, consumers may also feel discomfort when realizing the corresponding data collection and analyses (e.g., due to an invasion of privacy) (Aguirre et al. 2015). For example, when Tesco announced its plans to use consumer-tracking technologies, consumers were wary as the technology reminded them of science fiction akin the movie ‘Minority Report’ (Cockerton 2013). Thus, when using personalization technology—especially in public spaces such as stores or shopping malls—retailers need to understand how consumers might respond and why, so that firms can employ these technologies effectively (Grewal et al. 2019; Marketing Science Institute 2016; Roggeveen and Sethuraman 2018). Surprisingly, the effects of personalized advertising in public spaces are under-researched in the retailing literature. Prior research on personalized ads—examining personalized e-mail, online-, postal- and phone-marketing (e.g., Aguirre et al. 2015; Bleier and Eisenbeiss 2015; Schumann, von Wangenheim, and Groene 2014; Speck and Elliott 1997)—reveals valuable insights. However, this prior work focused on personalized ads that are typically not received in public. Thus, prior findings may not generalize to the emerging types of personalized ads that consumers receive in public (e.g., via digital in-store displays) for, at least, two reasons: First, social presence—the real, implied, or imagined presence of another person or group (Latané 1981; McFerran et al. 2010)—may alter the assessment of personalized ads. Specifically, personalized content shown in public can be visible to others (e.g., shoppers, salespeople), who might judge the targeted consumer based on the ad’s content. This relevance of social presence stands in sharp contrast to personalized ads online, which consumers typically receive in non-public settings. Second, emerging in-store technologies allow retailers to collect new types of data related to visible consumer attributes (e.g., a consumer’s body shape, gender, age, ethnicity) and then to display corresponding tailored content on in-store displays; we will refer to this type of attribute-derived advertising as public personalized advertising (PPA). For example, a consumer, based on the software analyzing attributes such as age, gender, and body shape, is presented with in-store advertising for fitness gear or a weight loss program. Notably, on a conceptual level, such PPAs are linked to the consumer’s self-presentation and impression management concerns (because these ads are received in public); in addition, because PPAs are based on appearance-related consumer attributes, they are also likely highly relevant to the consumer’s self-concept. Drawing on observations in the popular business press (Cockerton 2013) that suggest negative consumer responses to the emerging technology of public personalized ads, we adopt a risk-sensitive analytical perspective and assume a negativity bias towards PPAs. That is, consumers typically have a propensity to attend to, learn from, and use negative information far more than positive information (Ito et al. 1998; Vaish, Grossmann, and Woodward 2008). As such, we expect a general negative effect of PPAs on consumers’ response. However, even if there were such a general negative effect of PPAs, we suggest that contingency factors might alter (and potentially reverse)

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this effect. Therefore, drawing on prior research (e.g., Thomas, Trump, and Price 2015), we propose that consumer responses to PPAs depend on how an ad resonates with the consumer’s self-concept (i.e., the ad-self-congruity1 ), and which image it signals to others (e.g., co-consumers in the retail store). That is, we examine whether self-concept-congruent image appeals presented in PPAs threaten or bolster consumers’ self-concept and their public impression. Although some research points to the effectiveness of self-concept-congruent advertisements (i.e., ads that resonate with a consumer’s self-concept; Hong and Zinkhan 1995), Thomas, Trump, and Price (2015) show that self-concept-congruent advertising appeals can backfire when they convey an unfavorable self-presentation. What has yet to be examined, however, are the specific circumstances under which consumers sense a PPA as a negative or positive public impression of themselves. Understanding this aspect is crucial for retailers to design effective PPAs. Therefore, we examine the direction (i.e., valence) of ad-self-congruity to understand how PPAs might threaten or bolster a consumer’s self-concept and impression management. Such a nuanced account of ad-selfcongruity and its interplay with social presence is missing from the literature to date (e.g., Kressmann et al. 2006; Sirgy et al. 1997). Against this background, grounded in theory on impression management and self-concept-congruity, we investigate the following research questions: (1) Will consumer reactions to personalized ads (vs. non-personalized ads) differ with social presence (vs. no social presence)? (2) If so, what is the underlying mechanism driving the corresponding consumer response? (3) Will distinct configurations of ad-self-congruity alter consumer response to PPAs? Results from three experiments contribute to the literature in several ways: First, we uncover a general negative effect of PPA and its contingency factors (i.e., social presence and distinct configurations of ad-self-congruity), showing that personalization in public diminishes favorable consumer response to threatening self-congruent ads; we show that this effect is driven by consumer-perceived embarrassment. In contrast, bolstering selfcongruent ads translate into positive consumer response with social presence; this effect is driven by consumer-perceived flattery. By identifying both positive and negative effects of PPAs, we contribute to a deeper and more nuanced scholarly and managerial understanding of the effectiveness of these evolving retail-technologies (e.g., Grewal et al. 2019; Inman and Nikolova 2017; Roggeveen, Nordfält, and Grewal 2016). Second, expanding prior work on self-concept-congruity (Sirgy et al. 1997), we introduce a new typology of four distinct ad-self-congruity configurations (i.e., threatening adself-(in)congruity vs. bolstering ad-self-(in)congruity), thereby demonstrating the importance of capturing the direction (i.e., valence) of ad-self-congruity. This novel account of distinct types of ad-self-congruity provides a more fine-grained understanding of self-concept-related appeals in advertising,

1 Ad-self-congruity is the extent to which the ad is consistent with the consumer’s self-concept (Sirgy et al. 1997).

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especially in public settings (e.g., Thomas, Trump, and Price 2015). Taken together, the findings from our empirical studies provide insights into how novel forms of advertising influence consumers via the interplay of personalization, social presence, and distinct advertising appeals. Thus, we add to research on shopper marketing (e.g., Shankar et al. 2011) and customer experience (e.g., Verhoef et al. 2009); moreover, we answer calls for more research on emerging personalization technologies in retailing (e.g., Grewal, Roggeveen, and Nordfält 2017; Grewal et al. 2019; Roggeveen and Sethuraman 2018). Finally, our work has managerial implications, further illustrated in the “General Discussion.” Fig. 1 provides an overview of our studies. Theoretical Background and Hypotheses Development Research on personalized advertising and its focus on private settings Personalized advertising involves tailoring ads to consumers’ individual preferences based on information about, for example, demographics, past preferences and purchases, as well as prior browsing behavior (Baek and Morimoto 2012; Evans 2009; Schumann, von Wangenheim, and Groene 2014). Personalization has both benefits and drawbacks for consumers and businesses (Baek and Morimoto 2012; Culnan and Armstrong 1999). For example, personalization allows companies to increase the accuracy of their ads, thereby enhancing their relationship marketing (Culnan and Armstrong 1999; Evans 2009). In parallel, consumers benefit from personalized offers as these are tailored to their specific needs, serve as decision aids, and reduce search costs, information overload, and transaction time (Baek and Morimoto 2012). On the other hand, consumers’ privacy concerns might mitigate these benefits (Culnan and Armstrong 1999).2 Notably, prior research has focused on consumer responses toward personalized ads in private settings, which are typically not visible to others (i.e., personalized e-mail-, online-, postal- and phone-marketing). Consequently, prior research offers little insight into how consumers respond to personalized ads in public settings, such as retail stores or shopping malls. We propose that how consumers assess personalized ads is influenced by considerations of social presence and impression management. The influence of social presence and impression management concerns Consumers are influenced by social presence, which refers to the real, implied, or imagined presence of others (Latané 1981; McFerran et al. 2010). For example, salespeople, fam-

ily, friends and even strangers can affect a consumer’s feelings, motives, and behavior (e.g., Kurt, Inman, and Argo 2011; Latané 1981). Social presence can influence consumer cognitions and emotions because it increases a person’s awareness of being a social object (Buss 1980) and it activates impression management goals (e.g., Argo, Dahl, and Manchanda 2005; Puntoni, de Hooge, and Verbeke 2015). In other words, people possess public self-consciousness, such that they are concerned about the way they present themselves to others (e.g., Fenigstein, Scheier, and Buss 1975). Accordingly, people strive to create the best possible impression in public and aim to control how others perceive them by means of impression management (e.g., Latané 1981; Leary and Kowalski 1990). Furthermore, consumers are influenced by an ego-centric bias, which often lets them overestimate the extent to which their impressions (e.g., in terms of actions or appearance) are salient to others (Gilovich, Medvec, and Savitsky 2000). Against this background, we theorize that consumers consider and attach great importance to the impressions they make on others, and the personalized ads in public may become selfpresentation tools if they include self-concept-related appeals (Puntoni, de Hooge, and Verbeke 2015; Thomas, Trump, and Price 2015). More specifically, we expect that personalized (i.e., consumer attribute-based) advertisements under social presence will elicit impression management concerns of the targeted consumer.3 Notably, consumers cannot control the impressions others might form based on the PPA’s content. Consequently, we adopt a risk-sensitive managerial approach to the employment of PPAs and build on insights (e.g., noted in the popular (business) press (e.g., Cockerton 2013)) that point to negative consumer responses to the emerging technology of PPA. Thus, we assume a negativity bias towards PPAs, because prior research on the negativity bias has shown that consumers tend to attend to, learn from, and use negative information more than positive information (Ito et al. 1998; Vaish et al., 2008). We expect this bias will shape consumers’ attitudinal and behavioral responses towards the store that uses PPA technology. Against this background, we anticipate a general negative effect of ad personalization on attitudes and behavioral responses under social presence. We do not expect a similar negative effect of social presence for non-personalized ads as these should not elicit impression management concerns; we hypothesize: H1. Personalization will interact with social presence and influence (a) attitude toward the store and (b) behavioral intentions toward the store, such that consumers who are presented with personalized advertising will be less favorable toward the store under social presence, but this effect will be attenuated when the ad is not personalized. Although H1 proposes a general negative effect of PPAs under social presence, retailers also need to understand under which conditions the negative effect of the interplay between

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Prior studies examined consumer responses to personalized advertising, such as privacy-protective responses and their underlying determinants (e.g., Lwin, Wirtz, and Williams 2007), the phenomenon of advertising avoidance (e.g., Cho and Cheon 2004), and why consumers do not disclose personal information (e.g., Malhotra, Kim, and Agarwal 2004; Mothersbaugh et al. 2012).

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Personalized ads shown on digital screens can—for example, based on facial recognition technology—include identity appeals as they account for a consumer’s demographics (e.g., age, gender) and body metrics (e.g., shape).

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Fig. 1. Overview of studies.

personalization and social presence might reverse and elicit positive effects. We posit that the ad’s congruity with the consumer’s self-concept helps answer this question, as discussed below. The moderating roles of ad appeal and ad-self-congruity The impressions individuals aim to construct in public depend on their self-concept (Latané 1981; Leary and Kowalski 1990), which refers to the “totality of the individual’s thoughts and feelings, having reference to himself as an object” (Rosenberg 1979, p. 9). Self-concept-congruity is defined by the match between a consumer’s self-concept and the image a focal product, brand, store or advertisement conveys about its user (Kressmann et al. 2006). Ads that are congruent (vs. incongruent) with a target’s self-concept tend to be more effective in influencing consumers (Hong and Zinkhan 1995). However, Thomas, Trump, and Price (2015) show that self-concept-congruent advertising appeals can also trigger negative consumer responses toward an ad, if this ad portrays information about consumers that they do not want others to see. In this case, the focal ad is considered to be an unfavorable self-presentation. Thomas, Trump, and Price (2015) label this the ‘dirty-laundry effect’, noting that it is driven by selfpresentation concerns. Although this prior research provides important insights into potential negative effects of ad-selfcongruity, it has not identified specific instances of when and how consumers perceive an ad as a negative or positive selfpresentation. We propose that the corresponding effect and its downstream consequence is contingent on (1) the valence of the advertising appeal (i.e., whether the ad’s appeal is threatening or bolstering) (see examples displayed in Fig. 2, Panel B) and (2) how this appeal resonates with a consumer’s self-concept, as conceptualized next (and displayed in the 2 × 2 matrix in Fig. 2, Panel A).

In terms of the valence of the ad appeal, current advertising practices use a variety of distinct appeals, which range from potentially threatening to bolstering a consumer’s self-concept. For example, Victoria’s Secret ‘Perfect Body’ campaign uses idealized body images, which is an appeal that can threaten a consumer’s self-concept (Luxen 2014). In contrast, Loreal’s ‘You are worth it’ campaign reflects an appeal that bolsters a consumer’s self-concept. In terms of how these appeals resonate with a consumer’s self-concept, we draw on research on self-concept (Sirgy et al. 1997) and social identity threat (White and Argo 2009) to reveal a nuanced account for self-concept in-/congruity. Specifically, expanding prior conceptualizations of ad-self-congruity (Hong and Zinkhan 1995; Hosany and Martin 2012; Kressmann et al. 2006; Sirgy 1982), we conceptualize four ad-self-congruity states: 1 Threatening ad-self-incongruity refers to low perceived congruity between a consumer’s self-concept and a personalized ad that highlights a negative aspect of the consumer (quadrant #1 in Fig. 2). 2 Bolstering ad-self-incongruity refers to low perceived congruity between a consumer’s self-concept and a personalized ad that highlights a positive aspect of the consumer (quadrant #2 in Fig. 2). 3 Threatening ad-self-congruity refers to high perceived congruity between a consumer’s self-concept and a personalized ad that highlights a negative aspect of the consumer (quadrant #3 in Fig. 2). 4 Bolstering ad-self-congruity refers to high perceived congruity between a consumer’s self-concept and a personalized ad that highlights a positive aspect of the consumer (quadrant #4 in Fig. 2).

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Fig. 2. Conceptualization of four distinct ad-self-congruity configurations.

These four ad-self-congruity configurations help explain, in a more nuanced manner than prior work, when and why PPAs drive un-/favorable consumer responses, as discussed next. Predicting unfavorable consumer response via embarrassment We expect that the valence of the in-/congruity (threatening vs. bolstering) will alter consumer response to personalized ads as a function of its interplay with social presence. According to social identity theory, situational aspects may activate a certain facet of an individual’s identity and, in turn, affect how individuals feel and behave (Tajfel et al. 1979; White and Argo 2009). People strive to maintain a positive self-worth (e.g., Tesser 2000) and to present their best impression to others (e.g., Latané 1981; Leary and Kowalski 1990). Consequently, threatening information about a consumer’s self-concept leads

to negative response (e.g., avoidance of products) (White and Argo 2009). With social presence, we expect threatening adself-congruity (quadrant #3) to undermine the consumer’s public impression and to trigger negative reactions. Because people make attributions for the causes of certain situations (Weiner 1985), we expect that consumers report a less favorable response (i.e., less favorable attitude and behavioral intentions) toward the store that uses the PPA technology. In terms of the underlying process we predict that, consistent with theory on impression management, embarrassment drives the negative effect of the interplay between ad appeal, ad-self-congruity, and social presence (Dahl, Manchanda, and Argo 2001; Puntoni, de Hooge, and Verbeke 2015). Notably, the negative effect related to the interplay between ad appeal, ad-self-congruity, and social presence should be mitigated when the threatening advertising appeal does not match the

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consumer’s self-concept (i.e., threatening ad-self-incongruity, quadrant #1). Here, consumers will dissociate their self-concept from the ad’s appeal (Spears, Doosje, and Ellemers 1997) and should not perceive their public impression to be undermined. Furthermore, we expect that for bolstering ad appeals (quadrants #2 and #4), this proposed negative effect will also be attenuated. Predicting favorable consumer response via flattery Under bolstering ad-self-congruity (quadrant #4) we predict that the interplay between ad appeal, ad-self-congruity, and social presence will elicit positive consumer responses. Recent research suggests potential positive effects of social presence in a shopping context (e.g., Esmark and Noble 2018). We expect that personalized advertising that is bolstering and is displayed in public will elicit feelings of flattery, as these appeals highlight a positive aspect of the consumer. Consumers targeted with flattery should evaluate the flatterer positively (i.e., the retailer showing the personalized ad), because individuals have a desire to hear positive aspects about themselves (Fogg and Nass 1997; Vonk 1998). As such, the consumer’s goal to make a positive public impression (e.g., Leary and Kowalski 1990) will be satisfied with bolstering ad-self-congruity (quadrant #4) and should lead to a positive response toward the retailer. However, this positive effect should be attenuated if the advertisement is perceived to have low self-concept-congruity (quadrant #2). In this case, targeted consumers may perceive an ulterior motive of the retailer and discount the flattering message, which can mitigate an otherwise positive reaction (e.g., Campbell and Kirmani 2000; Vonk 1998). Finally, we do not expect a positive effect of the interaction between ad appeal, adself-congruity, and social presence on consumer response under threatening ad-self-(in)congruity (quadrants #1 and #3). Synthesizing the above theoretical rationale, we hypothesize: H2. There will be a three-way interaction between ad appeal, ad-self-congruity, and social presence, such that: (a) when the personalized ad is congruent, there will be an interaction effect between ad appeal and social presence on (i) attitude toward the store and (ii) behavioral intentions; however, (b) when the personalized ad is incongruent, this effect will be attenuated. H3. The effect of ad appeal, ad-self-congruity, and social presence on attitude and behavioral intentions is mediated by (a) embarrassment when the ad is congruent and threatening, and by (b) flattery when the ad is congruent and bolstering. Study 1: The Interaction Between Personalization and Social Presence Study 1 examines the interactive effects of social presence and personalization on consumer attitude and behavioral intentions toward a retailer. We hypothesize (H1) that under personalization consumers will respond less favorably when others are present, but this effect will be attenuated when the ad is not personalized.

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Design, Participants and Procedure Study 1 employed a 2 (social presence: no, yes) × 2 (personalized advertisement: no, yes) between-subjects design. One hundred seventy-six undergraduate students4 (84 females, MAge = 20) participated for credit. To manipulate the personalized ad, we asked participants to enter their age, gender, and body measurements (e.g., height, weight). Participants in the personalized condition were then informed that these metrics would be used to provide them with a personalized ad; participants in the non-personalization condition were not provided with this information. Next, all participants read about a retail shopping experience in which they encounter an ad on a large TV monitor prominently displayed in the store. The ad was described as either being personalized to the participants’ personal physical metrics (age, gender, body type) via a scanning technology, or in the non-personalized condition, all references to personalization were omitted (see Web Appendix 3). Next, we manipulated social presence by presenting the situation as having other shoppers present or not, both in the description and images used (see Web Appendix 3). In the social presence condition the visual stimuli pictured the outline of the participant in front of the ad in the store with other shoppers present. In the no social presence condition, the same picture of the participant standing in front of the screen was used, but no other shoppers were present. No further information on other shoppers was provided to reduce compatibility bias. The ads in our stimuli depicted exercise clothing and were adapted from an actual department store’s clothing advertisement. We used a clothing ad because it addresses aspects of a person’s physical appearance that can be seen by others. Because we used clothing in the ad, all female participants were shown the same ad and, all male participants were shown the same ad. Participants in the personalization condition were informed that this is their personalized advertisement, based on their recorded metrics they had provided in the first phase of the survey. A pretest confirmed that the personalization condition resulted in significantly greater perception of ad personalization, as compared to the non-personalized ad. It also confirmed that the social presence condition resulted in a significantly greater perception of social presence as compared to the no social presence condition (see Web Appendix 1).

4 We use student samples, as prior research shows that self-presentation concerns are of high importance to young consumers, but relatively older consumers also place high importance to convey desired impressions to others (Martin, Leary, Rejeski 2000). We note that a student population represents a sizable consumer segment in retail settings who are particularly keen on their self-image in public, and the personalized advertisements and the depicted products in our studies do target this consumer segment. This is, in turn, of high importance in terms of external validity of the findings of our study. Moreover, we decided to use student samples, as this is a more conservative test. Young consumers should be more open to technological innovation such as in-store consumer tracking, as compared to older consumers (Lee and Coughlin 2015; Uhl, Andrus, and Poulsen 1970), whose reactions should thus be even more negative if different at all.

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Table 2 Measures of studies 1, 2, and 3. Construct

Study & Cronbach’s α

Measurement itemsa

Attitude toward the retailer

Study 1 (α = .97); Study 3 (α = .97)

• • • • • • • • • • • •

Spangenberg, Crowley, and Henderson (1996) Behavioral intentions Adapted from Kaltcheva and Weitz (2006) Ad-self-congruity

Study 1 (α = .91); Study 2 (α = .89) Study 2 (α = .85)

Sirgy et al. (1997) • Embarrassment Adapted from Blair and Roese (2013) Flattery Adapted from Main, Dahl, and Darke (2007)

Study 2 (α = .84); Study 3 (α = .92) Study 3 (α = .94)

• • • • • •

Bad/good (Study 1, Study 3) Unfavorable/favorable (Study 1, Study 3) Negative/positive (Study 1, Study 3) Not valuable/valuable (Study 3) Dislike/like (Study 3) I would enjoy shopping in this store. I would be willing to buy things at this store. I would be willing to recommend this store to my friends. I can identify with the material in the ad. People similar to me would receive the same ad. The kind of person who would receive this ad is similar to me. The kind of person who this ad would be personalized for is very much like me. The image of a person who would receive this ad is highly consistent with how I see myself. Embarrassed Humiliated Distressed Flattered Sweet-talked Praised

a For all items, participants indicated their responses to the items on seven-point Likert scales (1 = “strongly disagree” and 7 = “strongly agree” or 7-point bi-polar scales).

Finally, participants indicated their attitude and behavioral intentions toward the retailer. Attitude toward the retailer was measured on a seven-point, bi-polar scale (bad/good, unfavorable/favorable, negative/positive; Spangenberg, Crowley, and Henderson 1996). We measured behavioral intentions using a three-item scale adapted from Kaltcheva and Weitz (2006). Items included “I would enjoy shopping in this store”, “I would be willing to buy things at this store”, “I would be willing to recommend this store to my friends”. See Table 2 for measurement items of all studies. Results Attitude Toward the Retailer5 A social presence × personalization ANCOVA on attitude toward the retailer (α = .97) revealed the predicted two-way interaction (F(1, 171) = 4.54, p = .04; η2 = .03), see Fig. 3A. The model also revealed a significant main effect of personalization (F(1, 171) = 16.64, p < .001; η2 = .09); the main effect of social presence was NS (F(1, 171) = 1.97, p = .16). To explain the significant two-way interaction, we examined effects at each level of personalization. When the ad is personalized, consumers are less favorable toward the retailer under social presence (MSocialPresence = 3.98 vs. MNoSocialPresence = 4.77; F(1, 171) = 6.20, p = .01); however, 5 Participant gender was included as a covariate, but it was not significant in any of the models. Since we used gender-matched clothing ads (females saw an ad for women’s clothing and males saw an ad for men’s clothing), as a follow-up, we also conducted a 3-way ANOVA, with gender as a third factor. The three-way interaction was non-significant for attitude toward the retailer (p = .94) and for behavioral intentions (p = .59). In subsequent studies we do not vary products by gender.

this effect is attenuated when the ad is not personalized (MSocialPresence = 5.38 vs. MNoSocialPresence = 5.21; F < 1). These findings support hypothesis H1a. Behavioral Intentions A social presence × personalization ANCOVA on behavioral intentions (α = .91) revealed the predicted two-way interaction (F(1, 171) = 6.12, p = .01; η2 = .04), see Fig. 3B. The model also revealed a significant main effect of personalization (F(1, 171) = 10.69, p = .001; η2 = .06); the main effect of social presence was NS (F(1, 171) = 1.75, p = .19). To explain the significant two-way interaction, we examined effects at each level of personalization. When the ad is personalized, consumers have less favorable behavioral intentions toward the retailer under social presence (MSocialPresence = 3.79 vs. MNoSocialPresence = 4.55; F(1, 171) = 7.14, p = .008); this effect is attenuated when the ad is not personalized (MSocialPresence = 4.95 vs. MNoSocialPresence = 4.71; F < 1), supporting hypothesis H1b. Discussion Study 1 supports H1 and demonstrates the effect of the interplay between personalization and social presence on attitudes and behavioral intentions toward a retail store. When the instore advertising is personalized, the presence of other customers results in a less favorable evaluation of the retailer and a decline in behavioral intentions. When the in-store advertising is not personalized, consumers are relatively unaffected by the presence or absence of other people. Follow Up Study The results from Study 1 are consistent with the notion of a negativity bias toward personalization in public, which is

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Fig. 3. Study 1 results: attitude toward the retailer and behavioral intentions.

reflected in a negative effect of PPAs on consumer response. To determine the extent to which the personalized (vs. nonpersonalized) ads in Study 1 were perceived as threatening (i.e., making salient negative information), we conducted a study with eighty-three Amazon Mechanical Turk participants (females = 39, MAge = 36). Participants first reported their age, gender, and body measurements (e.g., height, weight). Then, they were randomly assigned to one of two conditions (personalization: no, yes). We manipulated personalization using the language and advertisement photo from the main study (corresponding to their gender); no information about social presence was provided. Next, participants rated how threatening they

perceived the ad to be. We measured threat level by asking participants how “threatening,” and “judgmental,” the ad is, and how likely the ad would, “remind you of your bad qualities?” “make you feel self-conscious?” and “make you feel like you need to lose weight?” (7-point scale; Cronbach’s α = .93). An ANOVA on the threat index revealed a significant main effect of personalization (MNotPersonalized = 2.63 vs. MPersonalized = 4.30; F(1, 81) = 18.50, p < .001). Thus, the personalized ads were perceived as relatively threatening. For completeness (since as in Study 1, females saw a women’s clothing ad and males saw a men’s clothing ad), we also conducted analyses in which we controlled for gender, and conducted an ANOVA including gender as a second

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factor in the model. In these analyses the respective main effect remained significant, and all other effects were NS. Taken together, Study 1 and the follow up study provide empirical evidence that personalization can increase the relative self-threatening nature of the respective advertisement. In other words, advertisements can be perceived as more threatening to a person’s self-concept when they are personalized versus not. As such, and expanding our analytical scope, we now investigate in greater detail when and why PPAs negatively affect consumer response and examine the interplay between ad appeal, ad-selfcongruity and social presence. To do so, we specifically focus on personalized ads only, but now manipulate the threatening and bolstering nature of the personalized ads’ appeals. Study 2: The Interplay Between Ad Appeal, The Valence of Ad-Self-Congruity, and Social Presence Study 2 examines H2 and H3; more specifically, it has four objectives. First, this study focuses on creating an actual personalized ad based on the participant’s actual appearance. Second, it tests the interplay between the valence of ad-self-congruity as a function of ad appeal (i.e., the appeal being bolstering or threatening) and consumer-perceived congruity (ranging from low to high) (see Fig. 2) and social presence. We predict that when the consumer’s ad-self-congruity is high and the personalized ad is threatening (quadrant #3), the presence of others will negatively affect the consumer’s response toward the retailer. When the consumer’s ad-self-congruity is high and the personalized advertisement is bolstering (quadrant #4), we predict the negative impact of the interaction between ad appeal, ad-selfcongruity, and social presence on the consumer’s response will be attenuated. In parallel, when the consumer’s ad-self-congruity is low (quadrants #1, #2), we do not expect consumer response to vary as a function of social presence or ad appeal. A third goal of Study 2 is to investigate the underlying mechanism by testing the mediating role of consumer-perceived embarrassment. Fourth, Study 2 tests the robustness of the negative effect of the interplay between ad appeal, ad-self-congruity, and social presence by examining personalized ads in the new context of dental advertisements.

the social presence condition, other shoppers were present when the participants’ teeth were scanned by the in-store technology and when the personalized advertisement was provided. In the no social presence condition, no other shoppers were present at any time. The personalized ad displayed via the in-store technology promoted dental products. In order to provide an actual personalization experience, we matched participants to a bolstering or threatening ad condition based on how they rated their teeth at the beginning of the study (i.e., as relatively white or yellow). A pretest confirmed that the whiter tooth shades on the scale, labeled 1 and 2 were considered attractive, shades 3 and 4 were in the middle, and shades 5 through 8 were considered unattractive (see Web Appendix 2). Therefore, participants identifying their own teeth as being most similar to shades 1 or 2 were assigned to the bolstering (i.e., attractive) condition, those indicating that their teeth were most similar to shades 5, 6, 7, or 8 were assigned to the threatening (i.e., unattractive) condition, and those indicating shades 3 or 4 as most similar to their own teeth were randomly assigned to either a bolstering or threatening condition, because the appearance was not clearly attractive or unattractive. The advertisements read as follows (see Web Appendix 4): “Your teeth are [white/yellow]! The scan shows that your teeth [do not have/have] noticeable stains. Your teeth have a [healthy/unhealthy] appearance. We recommend the following products to help [keep your teeth looking/make your teeth look] healthy and white.” After exposure to the advertisement, participants rated the extent to which this ad aligned with their self-concept, to provide a measure of ad-self-congruity (e.g., “People similar to me would receive the same ad,” “The kind of person who would receive this ad is similar to me;” α = .85; Sirgy et al. 1997; see Table 2). Participants also reported their behavioral intentions toward the retailer, using the same items as Study 1. Then, they reported how embarrassed the experience with the personalized ad would make them feel (embarrassed, humiliated, distressed; 1 = strongly disagree, 7 = strongly agree). Finally, they rated their attitude toward the ad (unpleasant/pleasant, unfavorable/favorable, negative/positive; 7-point bi-polar) and reported demographics.

Design, Participants, and Procedure This study employed a 2 (social presence: no, yes) × 2 (ad appeal: bolstering, threatening) × continuous (adself-congruity: measured) between-subjects design. Three hundred and eighty-seven undergraduate students (140 females; MAge = 21) participated. Participants began the study by rating the shade of their own teeth using a scale that displayed a range of eight different tooth shades (see Web Appendix 4). This measure of tooth color enabled us to provide participants with a personalized in-store advertisement, designed based on the condition of their own teeth, later in the study. After reporting the current color of their teeth, participants read a shopping scenario about encountering a personalized instore advertisement for dental products (see Web Appendix 4). In

Results Behavioral Intentions6 A social presence (no, yes) × advertisement appeal (bolstering, threatening) × ad-self-congruity OLS regression on behavioral intentions (α = .89) revealed the predicted significant three-way interaction (b = -.37, t = −2.09 p = .04; η2 = .01), see Fig. 4A. The model also revealed a social presence by ad appeal

6

Participant gender and attitude toward the ad were included as covariates. Gender and attitude toward the ad were significant covariates in the models for behavioral intentions and embarrassment (p’s < .05). When gender and attitude toward the ad are excluded, the pattern of results remains consistent in the models for behavioral intentions and embarrassment.

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Fig. 4. Study 2 results: behavioral intentions and embarrassment.

interaction (b = 1.49, t = 1.92, p = .06; η2 = .01) and a social presence by ad-self-congruity interaction (b = .19, t = 1.75, p = .08; η2 < .001). The main effects of social presence (b = −.94, t = −1.83, p = .07; η2 = .001) and ad-self-congruity (b = .25, t = 3.00, p = .003; η2 = .14) were significant. The other effects in the model were NS (p’s > .13). To explain the significant three-way interaction, we examined effects at high and low levels of ad-self-congruity. For participants with higher levels of ad-self-congruity (+1 SD), a simple interaction effect emerged between social presence and ad appeal (b = −.56, t = 1.85, p = .07). When the ad was threatening, participants reported reduced behavioral intentions toward the retailer under social presence (vs. not; MSocialPresence = 5.09 vs. MNoSocialPresence = 5.52; b = −.43, t = −1.66, p < .10), consistent with H2a. When the ad was bolstering, this negative effect of social presence is attenuated, as behavioral intentions toward the retailer did not differ as a function of social presence (MSocialPresence = 5.31 vs. MNoSocialPresence = 5.17; b = .13, t = .83, p = .41). For those with lower levels of ad-self-congruity (−1 SD), a significant simple interaction effect did not emerge (b = .32, t = 1.16, p = .25), in line with H2b.

ad appeal was significant (b = 1.55, t = 2.12, p = .04; η2 = .001). The other effects in the model were NS (p’s > .11). To explain the significant three-way interaction, we examined effects at low and high ad-self-congruity. For participants with higher levels of ad-self-congruity (+1 SD), a simple interaction effect emerged between social presence and ad appeal (b = .69, t = 1.77, p = .08). When the ad was threatening, participants reported significantly greater embarrassment under social presence (vs. not; MSocialPresence = 4.12 vs. MNoSocialPresence = 3.40; b = .72, t = 2.17, p = .03). When the ad was bolstering, embarrassment did not differ as a function of social presence (MSocialPresence = 3.15 vs. MNoSocialPresence = 3.13; b = .03, t = .13, p = .90). For those with lower levels of ad-selfcongruity (−1 SD), a simple interaction effect emerged (b = −.62, t = −1.76, p = .08). When the ad was threatening, embarrassment did not differ as a function of social presence (MSocialPresence = 3.71 vs. MNoSocialPresence = 3.86; b = −.15, t = −.61, p = .54). Unexpectedly, when the ad was bolstering, participants reported more embarrassment with social presence (vs. not; MSocialPresence = 3.50 vs. MNoSocialPresence = 3.04; b = .46, t = 1.91, p = .06).

Embarrassment A social presence (no, yes) × ad appeal (bolstering, threatening) × ad-self-congruity OLS regression on embarrassment (α = .84) revealed the predicted significant three-way interaction (b = .55, t = 2.43, p = .02; η2 = .02), see Fig. 4B. The model also revealed a significant social presence by ad appeal interaction (b = −2.36, t = −2.38, p = .02; η2 = .02); the main effect of

Moderated Mediation Testing H3a, we conducted moderated mediation analysis to examine whether embarrassment mediates the effects of the social presence × ad appeal × ad-self-congruity interaction on behavioral intentions toward the retailer (Hayes 2013; Process Model 11, 5000 resamples). Gender and attitude toward the ad were control variables, as in the models above. Specifi-

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cally, for a threatening ad, we predicted a mediating role of embarrassment on the relationship between the social presence by ad-self-congruity interaction on behavioral intentions. The bootstrapping analysis revealed that embarrassment mediates the relationship between the highest order interaction and behavioral intentions toward the retailer (a × b = −.0842; 95% CI = −.1849 to −.0049), supporting H3a. Embarrassment also mediated in the conditional high ad-self-congruity, threatening ad path (a × b = −.1108; 95% CI = −.2531 to −.0058), but it did not mediate in the conditional high ad-self-congruity, bolstering ad path (a × b = −.0041; 95% CI = −.0728 to .0629). All other paths were non-significant, consistent with our theorizing. Web Appendix 5 includes the detailed reporting of the mediation paths. Discussion This study reveals a difference in the impact of social presence on shoppers across advertising appeals and the valence of consumers’ perceived ad-self-congruity, consistent with our theory (H2). Social presence reduces favorable intentions toward the retailer and increases embarrassment when the personalized ad is threatening and perceived to be high in ad-self-congruity. In support of H3a, consumers’ negative response toward the retailer under social presence is mediated by increased embarrassment. In other words, when a consumer has a relatively negative selfconcept and the ad relates to this negative self-concept, this causes the consumer to feel embarrassed in the presence of others, thereby adversely impacting behavioral intentions toward the retailer. Overall, these findings offer insights into when and why the interplay between ad appeal, ad-self-congruity, and social presence negatively affects reactions to personalized ads in retail. Study 3: Embarrasment and Flattery Through Public Personalized Ads Focusing exclusively on consumers’ interactions with personalized ads, the objective of Study 3 is two-fold: First, this study further examines the interplay between ad appeal, ad-selfcongruity, and social presence (H2); however, in this study we manipulate (vs. measure) ad-self-congruity. Second, this study offers more nuanced insights into the underlying mechanisms by re-examining the mediating role of embarrassment (H3a), while also revealing the mediating role of perceived flattery (H3b). Design, Participants, and Procedure This study employed a 2 (social presence: no, yes) × 2 (ad appeal: bolstering, threatening) × 2 (ad-self-congruity: low, high) between-subjects design. Four hundred and thirty-six undergraduate business students (212 females; MAge = 20) participated. Participants were randomly assigned to one of eight conditions, in which they read about a consumer encountering a personalized ad (derived from a tooth scan, similar to the one in Study 2).

In the social presence condition, other shoppers were present when the consumer’s teeth were scanned by the personalized ad technology. In the no social presence condition, no other shoppers were present. After being scanned, the consumer was presented with the following bolstering or threatening in-store ad message: “Your teeth are very [white/yellow]! The scan shows that your teeth [do not have/have] noticeable stains. Your teeth have a [healthy/unhealthy] appearance. We recommend the following products to help [keep your teeth looking/make your teeth look] healthy and white.” Participants were also shown a photo of the oral care products. Finally, to manipulate ad-selfcongruity, the target’s response to the ad was shown in a thought cloud stating either disagreement (low congruity condition) or agreement (high congruity condition). Web Appendix 6 presents the full manipulation. Next, participants indicated judgments about the consumer’s attitude toward the retailer, embarrassment, and flattery (see Table 2 for items). Finally, they rated their attitude toward the ad (dislike/like; 7-point bi-polar) and provided their demographic information. Results Attitude Toward the Retailer7 A social presence (no, yes) × ad appeal (bolstering, threatening) × ad-self-congruity (low, high) ANCOVA on attitude toward the retailer (α = .97) revealed the predicted three-way interaction (F(1, 426) = 4.56, p = .03; η2 = .01), see Fig. 5A. The model also revealed a social presence by ad appeal interaction (F(1, 426) = 2.80, p < .10; η2 = .007); the main effects of ad appeal (F(1, 426) = 9.58, p = .002; η2 = .02) and ad-selfcongruity (F(1, 426) = 7.02, p = .008; η2 = .02) were significant. The other effects were NS (p’s > .10). To explain the significant three-way interaction, we examined effects at each level of ad-self-congruity. For the high adself-congruity condition, a simple interaction emerged between social presence and ad appeal (F(1, 426) = 7.42, p = .007). When the ad was threatening, attitudes toward the retailer were less favorable under social presence (vs. not; MSocialPresence = 4.23 vs. MNoSocialPresence = 4.57; F(1, 426) = 3.31, p = .07). When the ad was bolstering, attitudes toward the retailer were significantly more favorable under social presence (vs. not; MSocialPresence = 4.76 vs. MNoSocialPresence = 4.38; F(1, 426) = 4.08, p = .04). For the low ad-self-congruity condition, a significant simple interaction effect did not emerge (F(1, 426) = .10, p = .75). Overall, these results are consistent with our theorizing (H2).

7 Gender and attitude toward the ad were included as covariates. Gender was not a significant covariate (p’s > .10). Attitude toward the ad was a significant covariate (p’s < .001). When gender and attitude toward the ad are excluded, the pattern of results remains consistent in the models for behavioral intentions, embarrassment, and flattery. 8 We conducted additional correlation analyses between embarrassment and flattery. Prior literature suggests that embarrassment may not only arise from a negative experience, but embarrassment could also result from compliments (Lewis, Haviland-Jones and Barrett 2010). In this study, the bolstering, high

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Fig. 5. Study 3 results: attitude toward the retailer, embarrassment, and flattery.

congruity, social presence condition, shows a negative correlation (r = −.53, p < .001), such that increased flattery relates to decreased embarrassment. We also assessed the possibility that embarrassment could result from insincere flattery (i.e., bolstering ads with low congruity) (Chan and Sengupta 2013). Our results show no correlation between flattery and embarrassment in this condition (r = −.01, p = .97). These results rule out (insincere) compliments as potential triggers of embarrassment in the PPA context.

Embarrassment8 A social presence (no, yes) × ad appeal (bolstering, threatening) × ad-self-congruity (low, high) ANCOVA on embarrassment (α = .92) revealed the predicted three-way interaction (F(1, 426) = 4.32, p = .04; η2 = .01), see Fig. 5B. The social presence main effect (F(1, 426) = 9.89, p = .002; η2 = .02) and the ad appeal main effect (F(1, 426) = 121.86, p < .001; η2 = .22) were also significant. The other effects in the model were NS (p’s > .15).

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To explain the significant three-way interaction, we examined effects at low and high levels of ad-self-congruity. For the high ad-self-congruity condition, a simple interaction effect emerged between social presence and ad appeal (F(1, 426) = 5.68, p = .02). When the ad was threatening, participants reported significantly more embarrassment under social presence (vs. not; MSocialPresence = 4.87 vs. MNoSocialPresence = 4.01; F(1, 426) = 16.22, p < .001). When the ad was bolstering, there was no difference in embarrassment as a function of social presence (MSocialPresence = 3.28 vs. MNoSocialPresence = 3.14; F < 1). For the low ad-self-congruity condition, a significant simple interaction effect did not emerge (F(1, 426) = .33, p = .56). Flattery A social presence (no, yes) × ad appeal (bolstering, threatening) × ad-self- congruity (low, high) ANCOVA on flattery (α = .94) revealed the predicted significant three-way interaction (F(1, 426) = 6.68, p = .01; η2 = .02), see Fig. 5C. The model also revealed a social presence by ad-self-congruity interaction (F(1, 426) = 3.17, p = .08; η2 = .007); the main effects of ad appeal (F(1, 426) = 291.64, p < .001; η2 = .41) and ad-selfcongruity (F(1, 426) = 4.19, p = .04; η2 = .01) were significant. The other effects in the model were NS (p’s > .37). To explain the significant three-way interaction, we examined effects at low and high levels of ad-self-congruity. Under high ad-self-congruity, a simple interaction effect emerged between social presence and ad appeal (F(1, 426) = 2.82, p = .09). When the ad was threatening, there was no difference in flattery as a function of social presence (MSocialPresence = 2.23 vs. MNoSocialPresence = 2.20; F < 1). When the ad was bolstering, participants perceived significantly more flattery under social presence (vs. not; MSocialPresence = 4.58 vs. MNoSocialPresence = 4.04; F(1, 426) = 6.34, p = .01). Under low ad-self-congruity, a simple interaction effect emerged (F(1, 426) = 3.87, p = .05). When the ad was threatening, flattery did not differ as a function of social presence (MSocialPresence = 2.63 vs. MNoSocialPresence = 2.42; F < 1). When the ad was bolstering, participants perceived less flattery under social presence (vs. not; MSocialPresence = 4.28 vs. MNoSocialPresence = 4.67; F(1, 426) = 3.39, p = .07). Moderated Mediation Testing H3, we conducted moderated mediation analysis to examine whether perceived embarrassment (H3a) and flattery (H3b) mediate the effects of the social presence × ad appeal × ad-self-congruity interaction on attitude toward the retailer (Hayes 2013; Process Model 11, 5000 resamples). We controlled for gender and attitude toward the ad, as in the models above. The bootstrapping analysis revealed that embarrassment (a × b = −.1328; 95% CI = −.3183 to −.0039) and flattery (a × b = −.1003; 95% CI = −.2583 to −.0045) mediate the relationship between the highest order interaction and attitude toward the retailer. Embarrassment mediated the conditional high ad-self-congruity, threatening ad path (a × b = −.1266; 95% CI = −.2410 to −.0400), supporting H3a. Flattery mediated the conditional high ad-self-congruity, bolstering ad path

(a × b = .0488; 95% CI = .0015 to .1207), supporting H3b. All other paths were NS, consistent with our theorizing. See Web Appendix 5 for detailed reporting of the mediation paths. Discussion This study supports our theorizing (H2, H3) as it shows a difference in the impact of social presence on shoppers across varying ad-self-congruity configurations and deepens our understanding of why the interplay between ad appeals, ad-self-congruity, and social presence influences reactions to personalized ads. Social presence decreases positive attitudes toward the retailer and increases embarrassment when the personalized ad is threatening and congruent. However, social presence increases flattery when the personalized ad is bolstering and congruent, which, in turn, increases the consumer’s attitude toward the retailer. Accordingly, consumers’ negative response to personalized ads under social presence is mediated by embarrassment, and consumers’ positive response to personalized ads is mediated by flattery. General Discussion Although personalized in-store advertising is becoming increasingly important in retailing (Table 1), research on this emerging trend and its downstream effects is scant. Prior work has focused on benefits of digital in-store displays (e.g., enhanced store atmosphere and positive effects on shopping behavior), but it did not consider potential downsides (Dennis et al. 2014; Dennis et al. 2012; Roggeveen, Nordfält, and Grewal 2016). In parallel, marketing research has examined dis-/advantages of personalized advertising (e.g., Bleier and Eisenbeiss 2015; Schumann, von Wangenheim, and Groene 2014), but the effects of personalized advertising in public settings remain under-researched. This is surprising, because thought leaders on customer experience and shopper marketing (e.g., Lemon and Verhoef 2016; Shankar et al. 2011; Verhoef et al. 2009) have pointed to an urgent need to examine how marketing activities influence shoppers along their consumption journey. For example, Shankar et al. (2011) call for more research on how new retail technologies can be used to better influence shoppers at the point of sale; and the Marketing Science Institute (2016, 2018) calls for research on how new types of consumer data and personalization technologies reshape the ways customers interact with companies, brands, products, services, and other customers. Against this background, we examine the interactive effects of advertising technology, social presence, and ad-self-congruity to shed light on when and why public personalized advertisements (PPAs) elicit un-/favorable consumer responses. Theoretical Contributions and Implications We find that personalization under social presence can lead to unfavorable consumer responses (i.e., decreased attitude and behavioral intentions toward a store). To further examine when and why social presence increases un-/favorable

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consumer responses, we adopt a more nuanced conceptual perspective. Specifically, we introduce a novel view of four types of ad-self-congruity (Fig. 2), which expands prior work on selfconcept-congruity (e.g., Hosany and Martin 2012; Kressmann et al. 2006; Sirgy et al. 1997). This typology allows us to demonstrate the importance of accounting for the valence of ad-self-congruity in understanding whether PPAs elicit negative or positive consumer responses: On the one hand, we show that for threatening self-congruent ads, social presence triggers negative consumer response; notably, consumer-perceived embarrassment drives this negative effect of social presence. On the other hand, we find that personalization with social presence can elicit favorable consumer response (i.e., increased attitude and behavioral intentions toward a store), if the ad resonates with a consumer’s self-concept and is perceived as bolstering; this effect is driven by consumer-perceived flattery. This latter finding adds to research on social presence that suggests that social presence of others may elicit positive consumer response via social acceptance (Esmark and Noble 2018). Our results also contribute to the literature on identity appeals in advertising, which includes ambivalent findings. Some research found idealized identity-appeals in advertising to be effective (e.g., Bolton and Reed 2004; Reed et al. 2012), but other research cautions that identity-marketing (e.g., Bhattacharjee, Berger, and Menon 2014; Puntoni, de Hooge, and Verbeke 2015) and self-concept-congruent advertising appeals can backfire (Thomas, Trump, and Price 2015). However, prior research has not always considered contingency factors and their underlying mechanisms, which might influence the effectiveness of advertising appeals that are related to a consumer’s self-concept. We show that it is the corresponding threeway interaction between social presence, consumer-perceived ad-self-congruity, and the focal advertising appeal (threatening/bolstering) that drives whether consumers respond favorably (driven by flattery) or unfavorably (driven by embarrassment). Finally, our findings add to research by Thomas, Trump, and Price (2015), who found that consumers respond unfavorably to self-concept-congruent ads if they dislike the image it portrays of them to a broad audience. We expand this prior work twofold: First, we show the negative effect of social presence on consumer response is diminished for threatening ads that consumers perceive to be incongruent with their self-concept. That is, even if a personalized ad is objectively consistent with a consumer’s physical appearance, but this consumer disagrees with the ad’s portrayal of his/her self-concept (threatening ad-self-incongruity), the negative effect of social presence on the consumer’s response is mitigated. This finding helps explain when self-concept-based advertisements elicit reflected appraisals (i.e., consumers’ inferences about others’ perceptions of them), as in this case, social presence does not seem to drive self-presentation concerns. Additionally, these insights shed further light on the spotlight effect in social judgment (Gilovich, Medvec, and Savitsky 2000). That is, individuals tend to overestimate the extent to which their own impressions (e.g., actions, appearance) are salient to others. If consumers disagree with an ad’s portrayal of their self-concept (ad-self-incongruity), this egocentric bias seems mitigated and consumers are less prone to

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the spotlight effect. Second, although Thomas, Trump, and Price (2015) provide important insights into when consumers judge a focal ad negatively, these authors do not focus on unearthing the mechanisms that drive self-presentation concerns and potential downstream consequences. Expanding this prior research, we show that self-concept-related appeals in advertising might not only hurt the attitude toward the focal ad (Thomas, Trump, and Price 2015); rather, we reveal that advertising-induced embarrassment and flattery can cause important downstream effects for retailers (i.e., on attitude and behavioral intentions toward the retailer); this finding is of considerable managerial relevance for retail managers. Moreover, although Thomas, Trump, and Price (2015) and our research both examine advertisinginduced self-presentation concerns, we depart from Thomas, Trump, and Price (2015) in terms of the focal identity appeal and type of targeting. Thomas, Trump, and Price (2015) build on self-categorization theory, stating that “[. . .] people define themselves in terms of their group memberships and come to perceive themselves as part of the group” (p. 2).9 In contrast, we examine attribute-derived advertising focusing on individual consumers. Furthermore, we build on novel types of data that retailers can collect; these data are related to visible, individual consumer attributes (e.g., a consumer’s body shape, gender, age) and then display tailored content. As such, in light of the emerging technologies related to personalized advertising, our research offers a meaningful contribution to the retailing literature. Managerial Implications In-store communication and advertising are increasingly fundamental for an effective customer experience at the point of sale (Ailawadi et al. 2009). In light of the growing importance of body- and facial-recognition technologies as a platform for in-store advertising (Transparency Market Research 2015), our research identifies important managerial challenges and offers implications on how to implement personalization technologies in retailing. Retail managers need to recognize that personalization in a public setting is different from personalization in a private setting (e.g., online marketing). Therefore, managers cannot simply transfer their insights on the effectiveness of personalization online to an offline context. It is essential for retailers to understand how consumers assess and respond to personalized (vs. non-personalized) ads. Managers ought to consider potential consumer aversion when implementing PPAs as our results point to a negativity bias (i.e., generally, personalization seems to be perceived as negative, even though there may be situations under which it is not) toward PPAs which may threaten a consumer’s self. Our findings suggest that managers need to carefully assess which advertising appeals are used in 9 The authors argue that individuals categorize themselves as part of the ingroup in the ad, and link the image portrayed in the ad to their self-concept. In this perspective, identity-related ads are less likely to target individuals, but they rather target people as group members. Consumers think of the audience for an ad and they make inferences about how non-group members will view the identity portrayed in the ad.

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PPAs (bolstering vs. threatening). We find that the effectiveness of personalized ads in public is contingent on distinct states of ad-self-congruity. Thus, our results help guide managers on how to implement PPAs by presenting bolstering advertising images that resonate with consumers’ self-concept. In this case, retailers can leverage social presence and boost positive consumer response. In other words, in public settings, bolstering (vs. threatening) advertising appeals are preferable to avoid negative downstream consequences for retailers. Moreover, to reduce threat to consumers’ self and to alleviate potential negative effects, retailers may create bolstering shopping environments. Ambient stimuli such as relaxing music, scent and light could add to the creation of an overall bolstering shopping environment, as atmospheric variables influence a wide variety of consumer evaluations and behaviors (e.g., Turley and Milliman 2000; Mattila and Wirtz 2001). Additional bolstering cues such as affirmative in-store messages (e.g., on (virtual) sticky notes in dressing rooms) might also be employed in order to reduce feelings of threat. In parallel, retailers should test emerging technologies with different forms of personalization techniques. In our study, we find negative and positive effects of personalized ads that are based on consumer’s appearance-related attributes (e.g., gender, body shape, color of teeth). Other personalization techniques, such as targeting people’s emotional states, might further elevate the effects demonstrated in our research. In contrast, personalization techniques that are not directly based on observable consumer attributes might be less likely to produce negative outcomes (but potentially also less likely to produce positive outcomes). Finally, firms might adopt a more risk-averse approach to avoid negative effects of personalized in-store advertising by carefully assessing where to place these technologies. By placing the technologies in areas that are less visible to others (e.g., mirrors in dressing rooms might display personalized ads), retailers can mitigate the potential negative effects of social presence. Using small displays (e.g., tablets) might be another strategy for creating more privacy when targeting consumers with personalized content at the point of sale. Of course, if corresponding field tests suggest negative consumer responses to even these approaches, retailers might fall back on more established personalized ads that are displayed in private settings (e.g., sending personalized ads, with permission, to the consumer’s cell phone). Limitations and Further Research Our work has limitations that provide avenues for further research. First, we focus on the general effect of social presence and did not consider characteristics of the audience. For example, certain types of others (e.g., strangers vs. friends) or the number of others may alter the effects. We speculate that facing personalized ads in front of more (vs. less) relevant others and in front of a large (vs. a small) number of others might influence our effects. Second, we study PPAs that are based on physical appearance (e.g., body shape, color of teeth). Future research could exam-

ine which forms of public data collection consumers consider to be more (un-)favorable. For example, emerging technologies can analyze facial expressions and related emotions of consumers, and these forms of data collection might alter our effects. Collecting data on emotions might be perceived as more intrusive than capturing physical appearance, because the internal, psychological condition of a targeted consumer is revealed into public view. More research is needed on these and other emerging data collection methods, to better understand possible (negative and positive) consequences for consumers and firms. Third, our model focused on the mediating role of embarrassment and flattery, but other emotions might also be relevant (e.g., vanity, pride). Moreover, we introduce embarrassment as an underlying mechanism driving negative consumer response to public personalized ads building on prior research. That is, theory on impression management highlights embarrassment as the mechanism driving negative effects of social presence (Dahl, Manchanda, and Argo 2001; Puntoni, de Hooge, and Verbeke 2015). However, additional research could further elucidate our findings, for instance with the help of qualitative research. Fourth, marketing scholars should examine how personalized ads affect observers. Prior research investigated how observers react when they see others being flattered in a retail context (e.g., by a salesperson) (Chan and Sengupta 2013). Future research could examine observer reactions to PPA-induced embarrassment (e.g., might Schadenfreude be relevant?). Fifth, we studied PPAs that featured ego-relevant products. More work is needed to better understand the boundary conditions of the effects of PPAs, for example, by examining different product categories. Sixth, we informed participants in our studies that the focal ad was personalized to them. Our rationale is that retailers will likely need to disclose data tracking practices to consumers in the long run. However, not all retailers experimenting with in-store tracking technologies inform their customers about the data collection and usage. Thus, further research could explore the extent to which consumers perceive ads as customized to them (without being informed about it) and how this alters their response. Do consumers respond differently toward threatening and bolstering self-congruent advertisements, if they are not aware, that the ads are matched to their personal recorded data? Seventh, we used student samples in our studies. Future research could explore whether responses toward personalized advertisements in public differ across different age groups. However, studying new in-store tracking technology via student samples is a conservative test, because young consumers should be more open to technological innovation (Lee and Coughlin 2015; Uhl, Andrus, and Poulsen 1970); hence, effects among older consumers might be even stronger. Finally, Studies 2 and 3 showed differences in behavioral intentions and attitude toward the retailer when consumers were exposed to bolstering and congruent ads in the presence of others (vs. not). Specifically, we did not find that social presence increases behavioral intentions (Study 2), whereas it did improve attitude (Study 3) in this focal experimental condition. This inconsistent effect requires more research. However, we speculate that this unexpected difference could result from the fact that

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Study 2 measured ad-self-congruity, whereas Study 3 manipulated it. Study 3 also used a projective technique to overcome potential barriers that may constrain participants’ expression of feelings, which may have given strength to the effect of flattery on attitude. Further, although they are often highly correlated constructs, attitudes are not perfect predictors of behavioral intentions (Ajzen and Fishbein 2005). Appendix. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/ j.jretai.2019.11.005. References Aguirre, Elizabeth, Dominik Mahr, Dhruv Grewal, Ko de Ruyter and Martin Wetzels (2015), “Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness,” Journal of Retailing, 91 (1), 34–49. Ailawadi, Kusum L., Jonathan P. Beauchamp, Naveen Donthu, Dinesh K. Gauri and Venkatesh Shankar (2009), “Communication and Promotion Decisions in Retailing: A Review and Directions for Future Research,” Journal of Retailing, 85 (1), 42–55. Ajzen, Icek and Martin Fishbein (2005), “The Influence of Attitudes on Behavior,” The Handbook of Attitudes, 173 (221), 31. Argo, Jennifer J., Darren W. Dahl and Rajesh V. Manchanda (2005), “The Influence of a Mere Social Presence in a Retail Context,” Journal of Consumer Research, 32 (2), 207–12. Baek, Tae H. and Mariko Morimoto (2012), “Stay Away from Me,” Journal of Advertising, 41 (1), 59–76. Beams, Molly and Rika Narisawa (2017), Forecast: Enterprise IT Spending for the Retail Market, Worldwide, 2015-2021, 1Q17 Update, https://www. gartner.com/doc/3723439/forecast-enterprise-it-spending-retail 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. Blair, Sean and Neal J. Roese (2013), “Balancing the Basket: The Role of Shopping Basket Composition in Embarrassment,” Journal of Consumer Research, 40 (4), 676–91. Bleier, Alexander and Maik Eisenbeiss (2015), “The Importance of Trust for Personalized Online Advertising,” Journal of Retailing, 91 (3), 390–409. Bolton, Lisa E. and Americus Reed (2004), “Sticky Priors: The Perseverance of Identity Effects on Judgment,” Journal of Marketing Research, 41 (4), 397–410. Brown, Ariella (2017), The Future of Smart Billboards, https://www. campaignlive.com/article/future-smart-billboards/1422247 Buckley, Ben and Matt Hunter (2011), “Say Cheese! Privacy and Facial Recognition,” Computer Law & Security Review, 27 (6), 637–40. Buss, Arnold H. (1980), Self-Consciousness and Social Anxiety, San Francisco, CA: Freeman. Campbell, Margaret C. and Amna Kirmani (2000), “Consumers’ Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent,” Journal of Consumer Research, 27 (1), 69–83. Chan, Elaine and Jaideep Sengupta (2013), “Observing Flattery: A Social Comparison Perspective,” Journal of Consumer Research, 40 (4), 740–58. Chan, Tara F. (2018), 7-Eleven is Bringing Facial-Recognition Technology Pioneered in China to its 11,000 Stores in Thailand, https://www. businessinsider.de/7-eleven-facial-recognition-technology-introduced-inthailand-2018-3?r=US&IR=T Cho, Chang-Hoan and Hongsik J. Cheon (2004), “Why Do People Avoid Advertising on the Internet?,” Journal of Advertising, 33 (4), 89–97.

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