While online reviews are of paramount importance in brand evaluations and purchase decisions, the impact of a reviewer’s attractiveness is not well understood. To bridge that gap, this paper aims to explore how physical attractiveness cues through profile photos influence customers’ brand evaluations.
The first study assesses the impact of attractiveness and review valence on brand evaluations. The authors used an experimental design and tested the model with an ANCOVA. Study 2 examines the impact of attractiveness in the context of multiple reviews and tests attractiveness heuristic as the underlying mechanism.
The findings indicate that when an attractive (vs less-attractive) reviewer writes a positive review, brand evaluations are enhanced. However, such an effect does not occur with a negative review. With multiple reviews varying in valence, cognitive load activates the use of an attractiveness heuristic when a positive review is written by an attractive (vs less-attractive) reviewer, thus leading to enhanced brand evaluations.
These findings highlight the presence of the attractiveness halo effect in online reviews and offer important implications to social media marketers. While previous studies have largely focused on review characteristics (e.g. star ratings, strength of the argument, etc.), this study focuses on reviewer characteristics (i.e. attractiveness) and cognitive biases associated with online brand evaluations.
Ozanne, M., Liu, S.Q. and Mattila, A.S. (2019), "Are attractive reviewers more persuasive? Examining the role of physical attractiveness in online reviews", Journal of Consumer Marketing, Vol. 36 No. 6, pp. 728-739. https://doi.org/10.1108/JCM-02-2017-2096Download as .RIS
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Who does not like pretty faces? Unconsciously, people use the “beautiful is good” heuristic to guide their everyday decisions, from making a friend to buying a product (Greitemeyer and Kunz, 2013; Zakin, 1983). In advertising, attractive endorsers have a positive influence on customers’ brand attitudes and behavioral intentions (Belch et al., 1987; Buunk and Dijkstra, 2011; Kahle and Homer, 1985; Percy and Rossiter, 1992; Till and Busler, 2000; Trampe et al., 2010). Similarly, attractive employees and shoppers are preferred over less attractive ones, thus leading to a higher purchase intention (Argo et al., 2008; Magnini et al., 2013; Reingen and Kernan, 1993). However, the marketing literature offers little guidance on the role of physical attractiveness in the e-Word of Mouth context. This neglect is surprising given that 92 per cent of online shoppers read online reviews before placing an order and they are 23 times more likely to trust customer-generated content than marketer-generated content (ChannelAdvisor, 2010; eMarketer, 2010). We address this oversight by examining how an online reviewer’s physical attractiveness through profile pictures influences customers’ evaluations.
Drawing on the halo effect (Lucker et al., 1981; Nisbett and Wilson, 1977), we suggest that a reviewer’s physical attractiveness has a favorable impact on brand evaluations. Precisely, we propose that the congruency between a positive review and attractiveness of the reviewer has a favorable impact on brand evaluations. Conversely, such a halo effect is attenuated when the review is negative in valence as negative information tends to induce deeper information processing (Fiske, 1993; Papathanassis and Knolle, 2011). However, when exposed to multiple reviews with mixed valence, customers experience a high cognitive load, which in turn enhances the use of the congruency heuristic between a positive review valence and reviewer attractiveness.
The present study is important for several reasons. First, online reviews are generally regarded as the most trusted source of information during the pre-purchase stage (eMarketer, 2016). Therefore, understanding the impact of reviewer characteristics on the audience’s brand evaluations is crucial. Second, prior research has mainly focused on review characteristics, such as the helpfulness of the review (Zhu and Zhang, 2010), the strength of the reviewer’s arguments (Zhang et al., 2014) or the type of language used (Liu et al., 2018). Yet, the impact of the reviewer’s attractiveness remains unexplored. Third, this study demonstrates that even in the presence of multiple reviews mixed in valence, the congruency heuristic plays a role on brand evaluations. As such, this study highlights the importance of online profile pictures and contributes to the literature on online stereotyping.
Physical attractiveness is a dimension of source credibility, along with trustworthiness and expertize (Ohanian, 1991). Source credibility is an important informational and heuristic cue (Chaiken and Maheswaran, 1994; Pornpitakpan, 2004; Sussman and Siegal, 2003) that is highly relevant in the online environment (Brown et al., 2007; Cheung et al., 2012; Pavlou and Dimoka, 2006). In comparison to systematic processors, heuristic processors judge the validity of the message by relying on accessible contextual information, such as the identity of the source, rather than the information itself.
Several studies have demonstrated that physical attractiveness acts as a heuristic cue in product judgments. For instance, customers tend to evaluate a product more favorably when a highly attractive salesperson showcases the product or when an attractive customer of opposite-sex touches the product (Argo et al., 2008). Similarly, exposure to an attractive (vs less-attractive) endorser results in heightened product/service evaluations (Reinhard et al., 2006; Shavitt et al., 1994) and a greater intention to purchase the endorsed product/brand (Kahle and Homer, 1985). This positive influence of attractiveness is called the halo effect. Such an effect is a form of cognitive bias in which the brain allows specific traits to positively influence the overall evaluation of a person, an idea or an object (Nisbett and Wilson, 1977). The halo effect has been demonstrated in multiple domains. For example, when fast-food restaurants portray themselves to be healthy, customers tend to underestimate the caloric content of their menu items (Chandon and Wansink, 2007). In a similar vein, corporate social responsibility initiatives positively influence perceived product performance even when such initiatives are unrelated to the company’s core activity (Chernev and Blair, 2015).
Regarding attractiveness, Feingold’s (1992) meta-analysis suggests a strong association between physical attractiveness and numerous personality traits and social skills. For example, attractive endorsers are often associated with outgoing personalities which influence positively customers’ brand attitudes and behavioral intentions (Belch et al., 1987; Buunk and Dijkstra, 2011; Kahle and Homer, 1985; Percy and Rossiter, 1992; Till and Busler, 2000; Trampe et al., 2010). Similarly, attractive people are preferred over less attractive people in sales encounters (Reingen and Kernan, 1993) and other types of service encounters (Dommeyer, 2008; Magnini et al., 2013). This is not surprising given that attractiveness is a personal characteristic that is most obvious and accessible to others in social interactions (Dion et al., 1972).
However, in both retail and service contexts, customers are not only exposed to endorsers or employees, but also to other customers. Prior research shows that the presence of other customers influences customers’ service perceptions (Thakor et al., 2008), satisfaction (Grove and Fisk, 1997), behavioral intentions (Brocato et al., 2012; Choi and Mattila, 2016) and product evaluations (Argo et al., 2008). Manipulating the physical appearance of other customers also influences observers’ behavioral intentions (Choi and Mattila, 2016; Day and Stafford, 1997; Thakor et al., 2008). Brocato et al. (2012) further suggest that physical appearance is one of the key dimensions of how customers perceive other customers. In an online context, Naylor et al. (2012) show that the “mere virtual presence” of others on social media influences the target customer’s brand evaluations and purchase intentions. Precisely, they found that demographic characteristics of online brand supporters, such as gender and perceived age, have a positive impact on customers with similar demographics. Given that identities displayed through names and profile pictures on Facebook (FB) are rather transparent (Ellison et al., 2007), we suggest that the reviewer’s attractiveness might influence brand evaluations. Online reviews can be either positive or negative in valence, and therefore, it is important to investigate whether reviewer attractiveness has a similar effect across positive and negative reviews.
Review valence refers to whether the review itself (or a collection of reviews) is positive or negative. Positively valenced messages are pleasant descriptions of experiences, whereas negatively valenced messages contain complains, unpleasantness or denigration of products or services (Anderson, 1998). Ye et al. (2009) show that positive online reviews have a boosting effect on hotel bookings. However, negative (vs positive) information tends to be overemphasized and is more influential when forming overall impressions (Fiske, 1993; Papathanassis and Knolle, 2011). Negative emotions generally involve deeper thinking, and consequently, information is processed more thoroughly (Baumeister et al., 2001; Rozin and Royzman, 2001)
In this research, we propose that the effect of attractiveness as a heuristic cue is reduced when customers are exposed to a negative (vs positive) review. The content of the negative review involves deep processing, thus reducing the impact of heuristic cues such as attractiveness. Consequently, brand evaluations should not be affected by the reviewer’s level of attractiveness. Conversely, shallow processing of positive reviews enhances customers’ reliance on heuristic cues. As a result, brand evaluations should be higher when an attractive (vs less-attractive) reviewer writes a positive review. We call this effect a congruency heuristic and put forth the following hypothesis:
A positive online review written by an attractive (vs less-attractive) reviewer will lead to higher brand evaluations. In contrast, such an effect is not expected for a negative online review.
Our first prediction is examined in the context of a single review. Would the effect hold in the context of multiple reviews? We argue that the position of the positive review written by an attractive reviewer is a determining factor.
Review positioning and cognitive load
Research shows that the positioning of a brand post in a brand fan page (De Vries et al., 2012) or an ad on a website (Rutz and Trusov, 2011) influences the number of likes/comments received and the click-through rates. Posts and ads on the top of the page typically generate the most clicks. Previous research refers to this phenomenon as the “primacy” effect. As an item at the beginning of the list is typically read or heard first, it stays in the short-term memory the longest. Yet, we propose that this effect is particularly true if the first review involves stereotype consistent information.
When individuals are exposed to an abundance of information, they tend to rely on heuristic cues. For example, when customers are under high information load (six reviews vs two reviews), the complexity of the decision-making process is higher leading individuals to search for information confirming what they already know (Fischer et al., 2008; Fischer et al., 2008; Yoon et al., 2011). Many interpersonal evaluations are based on stigma or stereotypes (Gilbert and Hixon, 1991; Macrae et al., 1993; Pratto and Bargh, 1991) such as “what is beautiful is good” (Dion et al., 1972). There are two basic mechanisms to explain why a high cognitive load makes people rely on stereotypes. First, stereotype-consistent (vs inconsistent) information is simply easier to comprehend and to encode in memory. This is particularly true when cognitive resources are low (Macrae et al., 1993; Stangor and Duan, 1991; Stangor and McMillan, 1992). Second, stereotype-inconsistent information challenges social norms, thus giving preference to stereotype-consistent information that is easier to process (Fiske and Neuberg, 1990; Hamilton and Sherman, 1996; Macrae et al., 1994). In sum, stereotype-consistent information is more desirable under a high cognitive load.
We, thus, argue that when exposed to an equal number of positive and negative reviews, brand evaluations will be higher if the first review presents stereotype-consistent (vs inconsistent) information. We put forth the following hypothesis:
In the context of multiple reviews, when the first review presents stereotype-consistent information (“match”), brand evaluations will be higher than when the first review represents stereotype-inconsistent information (“mismatch”).
If the first review includes stereotype-consistent information, the attractiveness cue will be highly salient in the customer’s mind. However, if the first review is void of such information, the impact of the attractiveness cue should be diminished. We propose the following:
The attractiveness cue will mediate the relationship between stereotype-consistent information in the first review and brand evaluations.
To summarize, we argue that attractiveness of the reviewer will have a positive impact on brand evaluations when the review is positive (vs negative) in valence. When multiple reviews vary in valence, cognitive load will activate the search for stereotype-consistent information. Given the salience of the first review, brand evaluations will be higher if the first review contains stereotype-consistent (vs inconsistent) information. The attractiveness cue will explain this effect.
The purpose of the first pilot study was to develop effective stimuli for the manipulation of physical attractiveness. We adopted profile pictures of seven women and seven men from a research database (Minear and Park, 2004). These profile pictures were then modified with Adobe Photoshop to harmonize the background. We recruited 119 US adult customers from Amazon MTurk to participate in the pilot study. In terms of demographics, 42 per cent of the participants were between 18 and 34 years of age, 53 per cent were male and 50 per cent had an annual household income over $50,000.
Participants were asked to evaluate faces in the profile pictures in terms of physical attractiveness (0 = physically unattractive, 10 = physically attractive). Participants evaluated either the pictures of the women or the men. The order of the pictures was also randomized within each condition. Based on the ratings of 59 participants assigned to the female pictures, we selected two female pictures: one physically attractive (M = 6.51, SD = 1.63) and one less-attractive [M = 2.71, SD = 2.06, t(58) = −11.82, p < 0.000]. We also selected one physically attractive male (M = 8.00, SD = 2.12) and one less-attractive [M = 2.53, SD = 2.11; t(59) = −14.77, p < 0.001]. In the main studies, our attractiveness manipulation used both female and male reviewers’ photos as replications to enhance the generalizability of our findings.
The purpose of the second pilot study was to develop effective stimuli for the manipulation of review valence. We retrieved four negative and four positive reviews from a FB fan page of a midscale hotel and inserted a fictitious brand name. We recruited 48 US adult customers from Amazon MTurk. In terms of demographics, 56 per cent of participants were between 18 and 34 years old, 56 per cent were male and 54 per cent had an annual household income over $40,000.
Participants were asked to evaluate the valence of all the reviews (0 = very negative; 10 = very positive). The order of the reviews was randomized. Based on their ratings, we selected the most positive review (M = 1.57, SD = 2.10) and the most negative review (M = 8.48, SD = 1.62) significantly different in valence [t(47) = −13.29, p < 0.001]. In addition, we chose two additional reviews for Study 2, which had two positive and two negative reviews. Specifically, we adopted the reviews with the second highest (M = 8.15, SD = 1.87) and lowest valence ratings (M = 1.85, SD = 1.72) and they were significantly different in valence [t(47) = −17.38, p < 0.001]. Pairewise t-test indicated no differences in valence between the two positive reviews [t(47) = 0.22, p = 0.83, ns] and between the two negative ones [t(47) = −1.25, p = 0.22, ns].
Study design and sample
Study 1 used a 2 (reviewer attractiveness: less-attractive vs attractive) × 2 (review valence: negative vs positive) × 2 (reviewer gender: male vs female) between-subjects design. A total of 462 US adult customers, recruited via Amazon MTurk (Buhrmester et al., 2011; Paolacci et al., 2010), completed this study. In terms of demographics, 61 per cent were between 25 and 34 years old, 53 per cent were male and 47 per cent had an annual household income over $50,000.
Procedures and materials
Participants were prescreened to make sure that they follow a fan page on FB. They were then randomly assigned to one of the eight experimental conditions (See Appendix 1 for sample stimuli). Participants were asked to imagine that they were browsing their FB newsfeed and encountered a post from “Hotel M’s Brand Fan Page”. The post contained an image of a hotel room and a text inviting people to book a room at Hotel M. Below the post, participants could find a review written by a guest who had recently stayed at the hotel. Depending on the conditions, the valence of the review was either positive or negative and the reviewer was either attractive or less-attractive. After reading the online review, participants completed a series of survey questions.
We measured brand evaluations using five items to capture brand liking, brand trust, brand quality, brand desirability and brand purchase likelihood (Dawar and Pillutla, 2000) (Cronbach’s α = 0.95). Given that individuals are more influenced by individuals who share similar attitudes (Hendrick and Page, 1970; Reagor and Clore, 1970) and that attractiveness increases similarity ratings (Miyake and Zuckerman, 1993) we controlled for “perceived similarity” between the participant and the reviewer (“How similar do you feel with the person who wrote the review?”). The use of a single-item measure for perceived similarity is coherent with the C-OAR-SE procedure (Rossiter, 2002). Finally, we controlled for participants’ general attitude toward online reviews, also adapted from Ayeh et al. (2013) (Cronbach’s α = 0.94) and for the two other aspects of source credibility: perceived trustworthiness (five items, Cronbach α: 0.95) and expertise (five items, Cronbach α: 0.92) of the reviewer (see Ayeh et al. (2013) for the list of items). We coded reviewers’ attractiveness (0 = less-attractive, 1 = attractive) and review valence (0 = negative, 1 = positive).
Our theorizing suggests that attractiveness of the reviewer influences brand evaluations when the review is positive (but not negative). When the covariates were inserted in the model, the results of a three-way ANCOVA revealed a significant effect of trustworthiness [F(1,454) = 4.49, p < 0.05] and attitude toward online reviews [F(1,454) = 8.55, p < 0.01]. The other control variables were not significant. The main effect of reviewer gender (p = 0.61) and the three-way interaction (gender of the reviewer, valence of the review and level of attractiveness) (p = 0.59) were insignificant. The two-way interactions involving reviewer gender and level of attractiveness (p = 0.99) and reviewer gender and review valence (p = 0.90) were also insignificant. These results indicate that reviewer gender did not influence the impact of attractiveness and review valence on brand evaluations (Table I).
Given the non-significant results of reviewer gender, we combined the two gender conditions to reduce complexity and to enhance the clarity of our core findings. Results reveal a significant main effect of review valence [F(1,458)=370.20, p < 0.000] and a main effect of attractiveness [F(1,458) = 4.57, p < 0.05] (Table II). More importantly, the interaction of attractiveness and review valence on brand evaluation was significant [F(1,458) = 4.65, p < 0.05] (Figure 1). Simple effect tests demonstrate that brand evaluations are higher when a positive review is written by an attractive reviewer (M = 4.00, SD = 0.62) than by a non-attractive reviewer (M = 3.71, SD = 0.70, p < 0.01). However, when the review is negative in valence, the effect of attractiveness becomes non-significant (Figure 1). Therefore, H1 is supported.
The results of Study 1 indicate that the halo effect is absent in the negative review condition, but present in the positive review condition. As such, attractiveness influences customers’ brand evaluations when the review is positive. To further examine the attractiveness halo effect on positive reviews, Study 2 exposed participants to multiple reviews with a mixed valence.
The goal of the second study is to demonstrate that when the first review contains stereotype-consistent information (vs stereotype-inconsistent information), brand evaluations are enhanced. The attractiveness cue explains such an effect.
Study design and sample
Study 2 used a 2 (attractiveness-valence congruency: match vs mismatch) × 2 (reviewer gender: male vs female) between-subjects design. A total of 164 participants on MTurk participated in the survey in exchange for a small compensation. In term of demographics, 58 per cent were female, 48 per cent were between 25 and 34 years old and 40 per cent had a household income between $30,000 and $60,000.
Procedures and materials
Participants were instructed to view M Hotel’s FB fan page displaying four online reviews (two positive and two negative reviews) written by hotel guests. We manipulated the match (mismatch) of reviewer attractiveness and review valence using the reviewers’ profile photos. The text and order of the reviews were kept constant across experimental conditions, but the profile pictures of the first two reviewers were permuted in each condition. In the match condition, the first positive review was written by an attractive reviewer, whereas the first negative review was written by a reviewer with a lower level of physical attractiveness. In the mismatch condition, the first positive review was written by a reviewer with a lower physical attractiveness and the first negative review by an attractive reviewer. The other two reviews were kept constant: the second positive review was written by an attractive reviewer and the second negative review was written by a less-attractive reviewer. To assess whether gender had an effect, we replicated the design by making the first two reviews written by men (women) and the last two reviews written by women (men). Sample stimuli are shown in Appendix 2.
As in Study 1, participants were prescreened for their use of FB and were randomly assigned to one of the four experimental conditions. Participants were asked to imagine that they were browsing their FB newsfeed and encountered the same post as in Study 1, except that, instead of seeing one review, they saw the four reviews. As an attention check question, we asked participants to assess whether the overall valence of the reviews was either more negative or more positive (1 = more negative, 4 = neutral, 7 = more positive). Results indicated that the overall valence of the review was rather neutral (M = 4.17, SD = 1.15) and the mean value did not differ from the scale mid-point (p > 0.05). Then, participants were asked to indicate their brand evaluations (Cronbach’s α = 0.92). Finally, to assess the effect of the attractiveness cue, participants indicated the overall attractiveness of the reviewers (0 = very unattractive to 10 = very attractive).
Results indicate a significant main effect of attractiveness [F(1, 160)=6.07, p < 0.05], an insignificant effect of reviewer gender [F(1, 160)=3.61, p = 0.06] and an insignificant interaction of effect (p = 0.99). Again, these results indicate that reviewer gender did not influence the impact of the attractiveness cue on brand evaluations. However, brand evaluations were higher when the first positive review was written by an attractive reviewer (i.e. stereotype consistent information) (M = 3.25, SD = 0.84) than when it was written by an unattractive reviewer (M = 2.92, SD = 0.86). These results support H2.
Results of a mediation analysis (PROCESS, Model 4, 5,000 iterations) (Hayes, 2017) indicate a significant indirect effect of stereotype consistent information on brand evaluations through overall perceived attractiveness (effect = 0.05, 95 per cent CI = 0.002 to 0.1485). These results support H3.
Findings from Study 2 demonstrate that when the customer is exposed to stereotype-consistent information (i.e. positive review written by an attractive reviewer) in the first review, brand evaluations are enhanced despite the presence of negative reviews. These results are explained by the attractiveness cue.
The present work examined the joint effects of review valence and physical attractiveness on customers’ brand evaluations. The study findings demonstrate that customers are positively biased when positive reviews are written by attractive (vs less-attractive) reviewers. However, such an effect does not occur when reviews are negative. Consistent with our predictions, the attractiveness cue explains the more favorable brand evaluations. Furthermore, participants’ gender did not influence our results. Overall, the study findings suggest that reviewers’ attractiveness is a characteristic that significantly influences customers’ brand evaluations. These findings have important theoretical and managerial implications.
Previous research has examined what motivates individuals to post online reviews (Bickart and Schindler, 2001; Kim et al., 2011; Ye et al., 2009; Zhang et al., 2014) and the impact of review characteristics on customers’ brand evaluations and purchase intention (Liu et al., 2018; Ye et al., 2009; Zhang et al., 2014; Zhu and Zhang, 2010). Yet, to the best of our knowledge, the effect of reviewer attractiveness has been ignored. One could argue that profile pictures are not a vital component of online reviews. However, social media reports indicate that there are 75 billion items of content posted or shared on FB every day, 400 million tweets sent on Twitter and 2 billion photos stamped with a heart on Instagram (Campbell, 2015). Each of these social network sites uses profile pictures. Furthermore, recent research shows that social media platforms are highly influential when it comes to discovering, reviewing and recommending new products and brands (eMarketer, 2016). Consequently, examining the impact of profile pictures is warranted. Our findings indicate that individuals rely on the attractiveness halo effect to evaluate a brand with positive (but not negative) reviews. Our study extends the literature on the attractiveness halo effect (Lucker et al., 1981; Nisbett and Wilson, 1977; Verhulst et al., 2010) by illustrating its impact in the online review context. Furthermore, while attractiveness is considered as a heuristic cue of source credibility, most studies focusing on online reviews relied on trustworthiness and expertize (Ayeh et al., 2013; Cheung et al., 2012; Sparks et al., 2013; Willemsen et al., 2012). In this study, we show that attractiveness as a cue of source credibility has a significant impact on brand evaluations.
Second, the attractiveness halo effect influenced brand evaluations only when the review was positive (vs negative). Under a light cognitive load (i.e. a single review), customers are less influenced by reviewer attractiveness when the review is negative in valence because negative information triggers deeper levels of cognitive processing (Baumeister et al., 2001; Rozin and Royzman, 2001). However, when multiple reviews are present (i.e. four reviews), cognitive load increases an individual’s reliance on stereotype-consistent information. Consequently, brand evaluations were enhanced when an attractive reviewer posted positive reviews. These findings demonstrate the activation of stereotypes in the online review context and contribute to the general literature on stereotypes in social network sites (Bailey et al., 2013; Tynes and Markoe, 2010).
Third, there were no significant differences on whether the reviewer was a male or a female, suggesting that the attractiveness cue is not linked to either gender.
Finally, we show that the effect of attractiveness is also present in the context of multiple reviews when the first review contains stereotype-consistent information (i.e. an attractive reviewer writing a positive review). This finding contributes to research on the influence of positioning of information on social media (De Vries et al., 2012; Rutz and Trusov, 2011) and demonstrates that this effect is reinforced when the first review represents stereotype-consistent information.
While this research demonstrates the impact of the attractiveness heuristic in the online review context, managers cannot control the “attractiveness” of their customers. However, brand managers should be aware that positive reviews can be influenced by heuristic cues. We show that brand evaluations can be significantly affected by reviewers’ appearance. This attractiveness heuristic prevails even in the presence of multiple reviews. As heuristics tend to influence positive reviews, managers may want to emphasize the content of the review itself. By doing so, customers will be encouraged to attentively read the review and will be less affected by heuristic cues. However, when encouraging customers to focus on the review itself, brand managers should not hint on heuristic cues. An avoidable example is: “Do not focus on profile pictures. Instead, focus on the text of the review”. Such a message will make customers conscious about negative stereotyping and it might simply make them unable to engage in online reviews processing (Steele and Aronson, 1995). A more appropriate message would be: “online reviews are helpful to make a decision, read them attentively”. In sum, social media managers might be able to influence customers’ brand perceptions by leveraging the impact of other customers.
Limitations and future research
As in any research, this study has several limitations. First, we used hypothetical scenarios. Although it would have been optimal to use real situations, doing so would have resulted in a great loss of standardization, making it more difficult to attribute the observed differences to reviewer attractiveness or review valence. Second, in Study 2, we kept constant the second set of reviews. While this enables us to demonstrate the effect of stereotype-consistent information in the first review, we did not specifically test the effect of stereotype-consistent information with the second positive review. Future research should explore this alternative explanation. Our findings provide strong preliminary evidence for the impact of reviewer attractiveness on customers’ brand evaluations.
The perception of attractiveness is also dependent on cultural norms (Cunningham et al., 2002; Tovée et al., 2006). In this research, we asked the US participants to judge reviewers’ attractiveness. In western culture, a high body mass is often perceived as a negative characteristic when judging attractiveness, whereas the opposite is found in rural South Africa (Tovée et al., 2006). Future research should look at the moderating effect of cultural norms on the relationship between a reviewer’s attractiveness and brand evaluations.
Finally, this research used a Hotel post as a stimulus. Staying at a hotel can either be considered as a hedonic (i.e. vacation) or utilitarian (i.e. for work) experience. In general, hedonic experiences are more emotional in nature, whereas utilitarian experiences tend to be more rational in nature (Adaval, 2001; Alba and Williams, 2013; Dhar and Wertenbroch, 2000). In other words, hedonic experiences are more emotionally driven, whereas utilitarian ones are more cognitively driven (Botti and McGill, 2010). However, specific emotions, such as happiness, may reduce the motivation to cognitively process information or tasks (Bodenhausen, 1993). Therefore, it would be interesting to explore whether a hedonic motive accentuates the reliance on heuristic cues such as a reviewer’s attractiveness. Overall, as major social network sites such as FB, Instagram or Twitter offer the possibility to present the self with a profile picture, there are numerous research opportunities to gain a deeper understanding of how other customers’ characteristics influence decision-making in the digital world.
Results of the three-way ANCOVA
|Source||Type III sum of squares||df||Mean square||F||Sig.|
|Attitude toward online reviews||4.395||1||4.395||8.550||0.004|
|Gender of the reviewer (A)||0.137||1||0.137||0.267||0.606|
|Review valence (B)||188.225||1||188.225||366.198||0.000|
|(A) * (B)||0.008||1||0.008||0.016||0.901|
|(A) * (C)||6.219E-6||1||6.219E-6||0.000||0.997|
|(B) * (C)||2.415||1||2.415||4.699||0.031|
|(A) * (B) * (C)||0.156||1||0.156||0.303||0.582|
Note: aR squared = 0.487 (Adjusted R squared = 0.477)
Results of the two-way ANCOVA
|Source||Type III sum of squares||df||Mean square||F||Sig.|
|Attitude toward online reviews||4.37||1||4.37||8.56||0.00|
|Review valence (A)||188.86||1||188.86||370.20||0.00|
|Interaction (A * B)||2.37||1||2.37||4.65||0.03|
Note: aR Squared = 0.486 (Adjusted R squared = 0.481)
Appendix 1. Scenario of a positive review written by an attractive female
Appendix 2. Sample stimuli for Study 2
Adaval, R. (2001), “Sometimes it just feels right: the differential weighting of affect-consistent and affect-inconsistent product information”, Journal of Consumer Research, Vol. 28 No. 1, pp. 1-17.
Alba, J.W. and Williams, E.F. (2013), “Pleasure principles: a review of research on hedonic consumption”, Journal of Consumer Psychology, Vol. 23 No. 1, pp. 2-18.
Anderson, E.W. (1998), “Customer satisfaction and word of mouth”, Journal of Service Research, Vol. 1 No. 1, pp. 5-17.
Argo, J.J., Dahl, D.W. and Morales, A.C. (2008), “Positive consumer contagion: responses to attractive others in a retail context”, Journal of Marketing Research, Vol. 45 No. 6, pp. 690-701.
Ayeh, J.K., Au, N. and Law, R. (2013), “Do We believe in TripAdvisor?’ examining credibility perceptions and online travelers’ attitude toward using user-generated content”, Journal of Travel Research, Vol. 52 No. 4, pp. 437-452.
Bailey, J., Steeves, V., Burkell, J. and Regan, P. (2013), “Negotiating with gender stereotypes on social networking sites from ‘bicycle face’ to facebook”, Journal of Communication Inquiry, Vol. 37 No. 2, pp. 91-112.
Baumeister, R.F., Bratslavsky, E., Finkenauer, C. and Vohs, K.D. (2001), “Bad is stronger than good”, Review of General Psychology, Vol. 5 No. 4, p. 323.
Belch, G.E., Belch, M.A. and Villarreal, A. (1987), “Effects of advertising communications: review of research”, Research in marketing, available at: http://psycnet.apa.org/psycinfo/1988-37629-001 (accessed 27 November 2016).
Bickart, B. and Schindler, R.M. (2001), “Internet forums as influential sources of consumer information”, Journal of Interactive Marketing, Vol. 15 No. 3, pp. 31-40.
Bodenhausen, G.V. (1993), “Emotions, arousal, and stereotypic judgments: a heuristic model of affect and stereotyping”, Affect, Cognition and Stereotyping, Elsevier, pp. 13-37.
Botti, S. and McGill, A.L. (2010), “The locus of choice: personal causality and satisfaction with hedonic and utilitarian decisions”, Journal of Consumer Research, Vol. 37 No. 6, pp. 1065-1078.
Brocato, E.D., Voorhees, C.M. and Baker, J. (2012), “Understanding the influence of cues from other customers in the service experience: a scale development and validation”, Journal of Retailing, Vol. 88 No. 3, pp. 384-398.
Brown, J., Broderick, A.J. and Lee, N. (2007), “Word of mouth communication within online communities: conceptualizing the online social network”, Journal of Interactive Marketing, Vol. 21 No. 3, pp. 2-20.
Buhrmester, M., Kwang, T. and Gosling, S.D. (2011), “Amazon’s Mechanical turk a new source of inexpensive, yet high-quality, data?”, Perspectives on Psychological Science: a Journal of the Association for Psychological Science, Vol. 6 No. 1, pp. 3-5.
Buunk, A.P. and Dijkstra, P. (2011), “Does attractiveness sell? women’s attitude toward a product as a function of model attractiveness, gender priming, and social comparison orientation”, Psychology and Marketing, Vol. 28 No. 9, pp. 958-973.
Campbell, C. (2015), “How online reviews and reputation can support your social media strategy”, Social Media Today, Text, 30 September, available at: www.socialmediatoday.com/marketing/reviewtrackers/2015-09-30/how-online-reviews-and-reputation-can-support-your-social-media (accessed 19 September 2017).
Chaiken, S. and Maheswaran, D. (1994), “Heuristic processing can bias systematic processing: effects of source credibility, argument ambiguity, and task importance on attitude judgment”, Journal of Personality and Social Psychology, Vol. 66 No. 3, p. 460.
Chandon, P. and Wansink, B. (2007), “The biasing health halos of fast-food restaurant health claims: lower calorie estimates and higher side-dish consumption intentions”, Journal of Consumer Research, Vol. 34 No. 3, pp. 301-314.
ChannelAdvisor (2010), “Through the eyes of the consumer: 2010 consumer shopping habits survey”, available at: http://go.channeladvisor.com/rs/channeladvisor/images/us-wpconsumer-survey-2010.pdf (accessed 2 February 2019).
Chernev, A. and Blair, S. (2015), “Doing well by doing good: the benevolent halo of corporate social responsibility”, Journal of Consumer Research, Vol. 41 No. 6, pp. 1412-1425.
Cheung, C.M.-Y., Sia, C.-L. and Kuan, K.K.Y. (2012), “Is this review believable? a study of factors affecting the credibility of online consumer reviews from an ELM perspective”, Journal of the Association for Information Systems, Vol. 13 No. 8, pp. 618-635.
Choi, C. and Mattila, A.S. (2016), “The effects of other customers’ dress style on customers’ approach behaviors”, Cornell Hospitality Quarterly, Vol. 57 No. 2, pp. 211-218.
Cunningham, M.R., Barbee, A.P. and Philhower, C.L. (2002), “Dimensions of facial physical attractiveness: the intersection of biology and culture.”
Dawar, N. and Pillutla, M.M. (2000), “Impact of product-harm crises on brand equity: the moderating role of consumer expectations”, Journal of Marketing Research, Vol. 37 No. 2, pp. 215-226.
Day, E. and Stafford, M.R. (1997), “Age-related cues in retail services advertising: their effects on younger consumers”, Journal of Retailing, Vol. 73 No. 2, pp. 211-233.
De Vries, L., Gensler, S. and Leeflang, P.S. (2012), “Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing”, Journal of Interactive Marketing, Vol. 26 No. 2, pp. 83-91.
Dhar, R. and Wertenbroch, K. (2000), “Consumer choice between hedonic and utilitarian goods”, JMR”, Journal of Marketing Research, Vol. 37 No. 1, pp. 60-71.
Dion, K., Berscheid, E. and Walster, E. (1972), “What is beautiful is good”, Journal of Personality and Social Psychology, Vol. 24 No. 3, pp. 285-290.
Dommeyer, C.J. (2008), “The effects of the researcher’s physical attractiveness and gender on mail survey response”, Psychology and Marketing, Vol. 25 No. 1, pp. 47-70.
Ellison, N.B., Steinfield, C. and Lampe, C. (2007), “The benefits of Facebook ‘friends:’ social capital and college students’ use of online social network sites”, Journal of Computer-Mediated Communication, Vol. 12 No. 4, pp. 1143-1168.
eMarketer (2010), “What makes social media trustworthy? – eMarketer”, available at: www.emarketer.com/Article/What-Makes-Social-Media-Trustworthy/1007863 (accessed 2 February 2019).
eMarketer (2016), “Facebook plays powerful role in US women’s path to purchase – eMarketer”, Emarketer, 25 March, available at: www.emarketer.com/Brief/Facebook-Plays-Powerful-Role-US-Womenrsquos-Path-Purchase/5500866#whats-included (accessed 19 September 2017).
eMarketer (2019), “Social media sways purchasing decisions for US millennials – eMarketer”, EMarketer, available at: www.emarketer.com/Brief/Social-Media-Sways-Purchasing-Decisions-US-Millennials/5500881 (accessed 19 September 2017).
Feingold, A. (1992), “Good-looking people are not what we think”, Psychological Bulletin, Vol. 111 No. 2, p. 304.
Fischer, P., Greitemeyer, T. and Frey, D. (2008), “Self-regulation and selective exposure: the impact of depleted self-regulation resources on confirmatory information processing”, Journal of Personality and Social Psychology, Vol. 94 No. 3, p. 382.
Fischer, P., Schulz-Hardt, S. and Frey, D. (2008), “Selective exposure and information quantity: how different information quantities moderate decision makers’ preference for consistent and inconsistent information”, Journal of Personality and Social Psychology, Vol. 94 No. 2, p. 231.
Fiske, S.T. (1993), “Social cognition and social perception”, Annual Review of Psychology, Vol. 44 No. 1, pp. 155-194.
Fiske, S.T. and Neuberg, S.L. (1990), “A continuum of impression formation, from category-based to individuating processes: influences of information and motivation on attention and interpretation”, Advances in Experimental Social Psychology, Elsevier, Vol. 23, pp. 1-74.
Gilbert, D.T. and Hixon, J.G. (1991), “The trouble of thinking: activation and application of stereotypic beliefs”, Journal of Personality and Social Psychology, Vol. 60 No. 4, p. 509.
Greitemeyer, T. and Kunz, I. (2013), “Name-valence and physical attractiveness in Facebook: their compensatory effects on friendship acceptance”, The Journal of Social Psychology, Vol. 153 No. 3, pp. 257-260.
Grove, S.J. and Fisk, R.P. (1997), “The impact of other customers on service experiences: a critical incident examination of ‘getting along”, Journal of Retailing, Vol. 73 No. 1, pp. 63-85.
Hamilton, D.L. and Sherman, S.J. (1996), “Perceiving persons and groups”, Psychological Review, Vol. 103 No. 2, p. 336.
Hayes, A.F. (2017), Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, Guilford Publications.
Hendrick, C. and Page, H.A. (1970), “Self-esteem, attitude similarity, and attraction”, Journal of Personality, Vol. 38 No. 4, pp. 588-601.
Kahle, L.R. and Homer, P.M. (1985), “Physical attractiveness of the celebrity endorser: a social adaptation perspective”, Journal of Consumer Research, Vol. 11 No. 4, pp. 954-961.
Kim, E.E.K., Mattila, A.S. and Baloglu, S. (2011), “Effects of gender and expertise on consumers’ motivation to read online hotel reviews”, Cornell Hospitality Quarterly, Vol. 52 No. 4, pp. 399-406.
Liu, S.Q., Ozanne, M. and Mattila, A.S. (2018), “Does expressing subjectivity in online reviews enhance persuasion?”, Journal of Consumer Marketing, Vol. 35 No. 4, pp. 403-413.
Lucker, G.W., Beane, W.E. and Helmreich, R.L. (1981), “The strength of the halo effect in physical attractiveness research”, The Journal of Psychology, Vol. 107 No. 1, pp. 69-75.
Macrae, C.N., Hewstone, M. and Griffiths, R.J. (1993), “Processing load and memory for stereotype-based information”, European Journal of Social Psychology, Vol. 23 No. 1, pp. 77-87.
Macrae, C.N., Milne, A.B. and Bodenhausen, G.V. (1994), “Stereotypes as energy-saving devices: a peek inside the cognitive toolbox”, Journal of Personality and Social Psychology, Vol. 66 No. 1, p. 37.
Magnini, V.P., Baker, M. and Karande, K. (2013), “The frontline provider’s appearance a driver of guest perceptions”, Cornell Hospitality Quarterly, Vol. 54 No. 4, pp. 396-405.
Minear, M. and Park, D.C. (2004), “A lifespan database of adult facial stimuli”, Behavior Research Methods, Instruments, & Computers, Vol. 36 No. 4, pp. 630-633.
Miyake, K. and Zuckerman, M. (1993), “Beyond personality impressions: effects of physical and vocal attractiveness on false consensus, social comparison, affiliation, and assumed and perceived similarity”, Journal of Personality, Vol. 61 No. 3, pp. 411-437.
Naylor, R.W., Lamberton, C.P. and West, P.M. (2012), “Beyond the ‘like’ button: the impact of mere virtual presence on brand evaluations and purchase intentions in social media settings”, Journal of Marketing, Vol. 76 No. 6, pp. 105-120.
Nisbett, R.E. and Wilson, T.D. (1977), “The halo effect: evidence for unconscious alteration of judgments”, Journal of Personality and Social Psychology, Vol. 35 No. 4, p. 250.
Ohanian, R. (1991), “The impact of celebrity spokespersons’ perceived image on consumers’ intention to purchase”, Journal of Advertising Research, Vol. 31 No. 1, pp. 46-54.
Paolacci, G., Chandler, J. and Ipeirotis, P.G. (2010), “Running experiments on amazon mechanical turk”, Judgment and Decision Making, Vol. 5 No. 5, pp. 411-419.
Papathanassis, A. and Knolle, F. (2011), “Exploring the adoption and processing of online holiday reviews: a grounded theory approach”, Tourism Management, Vol. 32 No. 2, pp. 215-224.
Pavlou, P.A. and Dimoka, A. (2006), “The nature and role of feedback text comments in online marketplaces: implications for trust building, price premiums, and seller differentiation”, Information Systems Research, Vol. 17 No. 4, pp. 392-414.
Percy, L. and Rossiter, J.R. (1992), “A model of brand awareness and brand attitude advertising strategies”, Psychology and Marketing, Vol. 9 No. 4, pp. 263-274.
Pornpitakpan, C. (2004), “The persuasiveness of source credibility: a critical review of five decades’ evidence”, Journal of Applied Social Psychology, Vol. 34 No. 2, pp. 243-281.
Pratto, F. and Bargh, J.A. (1991), “Stereotyping based on apparently individuating information: trait and global components of sex stereotypes under attention overload”, Journal of Experimental Social Psychology, Vol. 27 No. 1, pp. 26-47.
Reagor, P.A. and Clore, G.L. (1970), “Attraction, test anxiety, and similarity-dissimilarity of test performance”, Psychonomic Science, Vol. 18 No. 4, pp. 219-220.
Reingen, P.H. and Kernan, J.B. (1993), “Social perception and interpersonal influence: some consequences of the physical attractiveness stereotype in a personal selling setting”, Journal of Consumer Psychology, Vol. 2 No. 1, pp. 25-38.
Reinhard, M.-A., Messner, M. and Sporer, S.L. (2006), “Explicit persuasive intent and its impact on success at persuasion – the determining roles of attractiveness and likeableness”, Journal of Consumer Psychology, Vol. 16 No. 3, pp. 249-259.
Rossiter, J.R. (2002), “The C-OAR-SE procedure for scale development in marketing”, International Journal of Research in Marketing, Vol. 19 No. 4, pp. 305-335.
Rozin, P. and Royzman, E.B. (2001), “Negativity bias, negativity dominance, and contagion”, Personality and Social Psychology Review, Vol. 5 No. 4, pp. 296-320.
Rutz, O.J. and Trusov, M. (2011), “Zooming in on paid search ads – a consumer-level model calibrated on aggregated data”, Marketing Science, Vol. 30 No. 5, pp. 789-800.
Shavitt, S., Swan, S., Lowrey, T.M. and Wänke, M. (1994), “The interaction of endorser attractiveness and involvement in persuasion depends on the goal that guides message processing”, Journal of Consumer Psychology, Vol. 3 No. 2, pp. 137-162.
Sparks, B.A., Perkins, H.E. and Buckley, R. (2013), “Online travel reviews as persuasive communication: the effects of content type, source, and certification logos on consumer behavior”, Tourism Management, Vol. 39, pp. 1-9.
Stangor, C. and Duan, C. (1991), “Effects of multiple task demands upon memory for information about social groups”, Journal of Experimental Social Psychology, Vol. 27 No. 4, pp. 357-378.
Stangor, C. and McMillan, D. (1992), “Memory for expectancy-congruent and expectancy-incongruent information: a review of the social and social developmental literatures”, Psychological Bulletin, Vol. 111 No. 1, p. 42.
Steele, C.M. and Aronson, J. (1995), “Stereotype threat and the intellectual test performance of African Americans”, Journal of Personality and Social Psychology, Vol. 69 No. 5, p. 797.
Sussman, S.W. and Siegal, W.S. (2003), “Informational influence in organizations: an integrated approach to knowledge adoption”, Information Systems Research, Vol. 14 No. 1, pp. 47-65.
Thakor, M.V., Suri, R. and Saleh, K. (2008), “Effects of service setting and other consumers’ age on the service perceptions of young consumers”, Journal of Retailing, Vol. 84 No. 2, pp. 137-149.
Till, B.D. and Busler, M. (2000), “The match-up hypothesis: physical attractiveness, expertise, and the role of fit on brand attitude, purchase intent and brand beliefs”, Journal of Advertising, Vol. 29 No. 3, pp. 1-13.
Tovée, M.J., Swami, V., Furnham, A. and Mangalparsad, R. (2006), “Changing perceptions of attractiveness as observers are exposed to a different culture”, Evolution and Human Behavior, Vol. 27 No. 6, pp. 443-456.
Trampe, D., Stapel, D.A., Siero, F.W. and Mulder, H. (2010), “Beauty as a tool: the effect of model attractiveness, product relevance, and elaboration likelihood on advertising effectiveness”, Psychology & Marketing, Vol. 27 No. 12, pp. 1101-1121.
Tynes, B.M. and Markoe, S.L. (2010), “The role of color-blind racial attitudes in reactions to racial discrimination on social network sites”, Journal of Diversity in Higher Education, Vol. 3 No. 1, p. 1.
Verhulst, B., Lodge, M. and Lavine, H. (2010), “The attractiveness halo: why some candidates are perceived more favorably than others”, Journal of Nonverbal Behavior, Vol. 34 No. 2, pp. 111-117.
Willemsen, L.M., Neijens, P.C. and Bronner, F. (2012), “The ironic effect of source identification on the perceived credibility of online product reviewers”, Journal of Computer-Mediated Communication, Vol. 18 No. 1, pp. 16-31.
Ye, Q., Law, R. and Gu, B. (2009), “The impact of online user reviews on hotel room sales”, International Journal of Hospitality Management, Vol. 28 No. 1, pp. 180-182.
Yoon, Y., Sarial-Abi, G. and Gürhan-Canli, Z. (2011), “Effect of regulatory focus on selective information processing”, Journal of Consumer Research, Vol. 39 No. 1, pp. 93-110.
Zakin, D.F. (1983), “Physical attractiveness, sociability, athletic ability, and children’s preference for their peers”, The Journal of Psychology, Vol. 115 No. 1, pp. 117-122.
Zhang, Y., Feick, L. and Mittal, V. (2014), “How males and females differ in their likelihood of transmitting negative word of mouth”, Journal of Consumer Research, Vol. 40 No. 6, pp. 1097-1108.
Zhang, K.Z., Zhao, S.J., Cheung, C.M. and Lee, M.K. (2014), “Examining the influence of online reviews on consumers’ decision-making: a heuristic–systematic model”, Decision Support Systems, Vol. 67, pp. 78-89.
Zhu, F. and Zhang, X. (2010), “Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics”, Journal of Marketing, Vol. 74 No. 2, pp. 133-148.