The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness

Tsekouras, Dimitrios; Gutt, Dominik; Heimbach, Irina Article — Published Version The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness Journal of the Academy of Marketing Science Provided in Cooperation with: Springer Nature Suggested Citation: Tsekouras, Dimitrios; Gutt, Dominik; Heimbach, Irina (2024) : The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness, Journal of the Academy of Marketing Science, ISSN 1552-7824, Springer US, New York, NY, Vol. 52, Iss. 6, pp. 1651-1672, https://doi.org/10.1007/s11747-024-01027-8 This Version is available at: https://hdl.handle.net/10419/315700 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ ORIGINAL EMPIRICAL RESEARCH Journal of the Academy of Marketing Science (2024) 52:1651–1672 https://doi.org/10.1007/s11747-024-01027-8 Introduction Conversational agents or, more colloquially, chatbots, are “software-based system[s] designed to interact with humans using natural language” (Feine et al., 2019, p. 3). They aim to simulate human conversation by integrating a language model and computational algorithms to mimic informal communication, or chats, and execute tasks between a human user and a computer using natural language (Araujo, 2018). Firms widely use chatbots across various functions and contexts, including marketing (Thomaz et al., 2020), sales (Gartner, 2018; Luo et al., 2019), finance (Luo et al., 2019), health care (Health Europa, 2019), and education (Tourangeau et al., 2003). Firms that introduce chatbots in the context of customerfirm interactions are often trying to improve their operational efficiency (reducing costs and customer response times) and increase their customer satisfaction and revenues (Radziwill & Benton, 2017; Reddy, 2017). Dimitrios Tsekouras, Dominik Gutt and Irina Heimbach contributed equally. Yany Gregoire served as Area Editor for this article. Dominik Gutt [email protected] Dimitrios Tsekouras [email protected] Irina Heimbach [email protected] 1 Erasmus University Rotterdam, 3000 DR, Rotterdam Postbus 1738, Netherlands 2 WHU – Otto Beisheim School of Management, Burgplatz 2, 56179 Vallendar, Germany Abstract Companies are increasingly introducing conversational reviews—reviews solicited via chatbots—to gain customer feedback. However, little is known about how chatbot-mediated solicitation influences rating valence and review helpfulness compared to conventional online forms. Therefore, we conceptualized these review solicitation media on the continuum of anthropomorphism and investigated how various levels of anthropomorphism affect rating valence and review helpfulness, showing that more anthropomorphic media lead to more positive and less helpful reviews. We found that moderate levels of anthropomorphism lead to increased interaction enjoyment, and high levels increase social presence, thus inflating the rating valence and decreasing review helpfulness. Further, the effect of anthropomorphism remains robust across review solicitors’ salience (sellers vs. platforms) and expressed emotionality in conversations. Our study is among the first to investigate chatbots as a new form of technology to solicit online reviews, providing insights to inform various stakeholders of the advantages, drawbacks, and potential ethical concerns of anthropomorphic technology in customer feedback solicitation. Keywords Product reviews · Chatbots · Review solicitation · Anthropomorphism · Social presence · Interaction enjoyment Received: 26 July 2022 / Accepted: 10 May 2024 / Published online: 6 June 2024 © The Author(s) 2024 The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness DimitriosTsekouras1· DominikGutt1· IrinaHeimbach2 1 3 Journal of the Academy of Marketing Science (2024) 52:1651–1672 Notwithstanding these motives, if the recently emerging literature on chatbots has taught us one thing, it is that their introduction rarely comes without side effects, be they negative or positive. On the encouraging side, firms that introduce chatbots may experience positive investor responses (Fotheringham & Wiles, 2022). Also, chatbots increase affective trust and improve consumer perceptions (Hildebrand & Bergner, 2021). On the downside, chatbots can prompt unethical consumer behavior (Kim et al., 2022), decrease customer satisfaction among angry customers (Crolic et al., 2022), increase customers’ assertiveness in negotiations (Schanke et al., 2021), and decrease customer purchases when chatbot use is disclosed (Luo et al., 2019). Therefore, firms should carefully consider whether to introduce chatbots in a particular domain. One trending area that deserves attention is that of conversational reviews—the acquisition of customer reviews via chatbots (Haptik, 2018)—because multiple e-commerce firms are increasingly implementing this practice, both on their platforms and via third-party ones, such as Facebook Messenger (Zhang, 2018). Online firms have always been interested in motivating their consumers to share their product or service experiences (Burtch et al., 2018; Gutt et al., 2019; Tsekouras, 2017) because the online review volume and valence as well as the informational value (e.g., review helpfulness) can facilitate consumer decision-making and increase future sales (You et al., 2015; Forman et al., 2008). Consequently, the goal of this study is to examine whether and how the use of chatbots for online review collection affects these key metrics of the resulting reviews. Motivated by the previous research on chatbots, we concentrate on the level of human likeness—hereafter, anthropomorphism—which is the central aspect in the design of chatbots. One can equip chatbots with a humanlike identity and visual appearance, thus adding a personal touch and enabling them to hold a humanlike conversation (Feine et al., 2019). Because previous research has shown that the effects of anthropomorphic design of products and technology on consumer responses are ambiguous (Luo et al., 2019; Schanke et al., 2021; Crolic et al., 2022), this study aims to answer the following research question: How does the anthropomorphism of review solicitation media affect online rating valence and review helpfulness? Across four studies, we show that anthropomorphism engendered in chatbots positively biases online ratings compared to a conventional review form. We trace two mechanisms through which this bias operates. At a moderate level of anthropomorphism, the bias operates through perceived interaction enjoyment—“the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (Davis et al., 1992, p. 1113), whereas at a high level of anthropomorphism, the bias operates through increased social presence—the “degree of salience of the other person in a mediated communication and the consequent salience of their interpersonal interaction” (Short et al., 1976, p. 65). We also demonstrate that plausible behavioral interventions intended to mute the interaction enjoyment mechanism and the social presence mechanism are unsuccessful at mitigating the bias. Finally, we show that readers assess chatbot-mediated reviews as less helpful than reviews generated using conventional forms. We suggest the fact that human-chatbot interaction leads to shorter reviews, which in turn convey fewer rich arguments, as a potential explanation for this effect. With these findings, we argue for several theoretical contributions to literature. First, we contribute to the stream of literature on the effects of anthropomorphism on consumer behavior. To the best of our knowledge, we are one of the first to conceptualize and present empirical evidence for how anthropomorphism affects consumers’ online reviewing behavior. Although past studies predominantly focused on how anthropomorphism affects consumer perception of firms, such as in terms of affective trust (Hildebrand & Bergner, 2021) and satisfaction (Crolic et al., 2022), this study extends the anthropomorphism effects to consumer perception of products. Second, we unveil increased social presence and interaction enjoyment as the two mechanisms responsible for the anthropomorphism effect. Although social presence has been a mainstay in studies on anthropomorphism (Blut et al., 2020), we introduce the construct of interaction enjoyment to the discussion of anthropomorphism effects. In particular, we identify boundary conditions of anthropomorphism for when the interaction enjoyment mechanism actually dominates social presence and vice versa. Third, the results respond to the call for research on the effects of artificial intelligence (AI)-enabled technology on customer-firm interactions (Luo et al., 2019). Our findings aid in understanding the role of novel review solicitation media and extend the evidence on consumer susceptibility to the reviewing environment when generating product ratings (Gutt et al., 2019; Ransbotham et al., 2019; Tsekouras, 2017). Beyond the contributions to literature, our findings provide meaningful practical implications to managers, consumers, and policymakers. Managers need to be aware that using anthropomorphic chatbots to solicit reviews positively biases their products’ ratings. At the surface, positive ratings may be desirable due to their 1 3 1652 Journal of the Academy of Marketing Science (2024) 52:1651–1672 widely documented effects on sales (Zhu & Zhang, 2010), but managers risk misrepresenting product quality to their customers, raising serious ethical concerns and risking long-term damage to a firm’s brand positioning and image (Bazaarvoice, 2022). As shown in the field of fake reviews (He et al., 2022; Luca & Zervas, 2016; Mayzlin et al., 2014), sellers of weak brands and non-branded products, especially, might disproportionally deploy chatbots to receive better ratings and thus improve sales. What may further disincentivize managers from employing anthropomorphic chatbots, though, is our finding that they decrease review helpfulness. This latter discovery may work as a natural deterrent for the use of anthropomorphic chatbots. If consumers use positively biased ratings that are unhelpful toward informing their decision-making, they can make bad purchase decisions. To mentally discount the positivity bias when screening a list of reviews, consumers would need to know which ratings were collected by means of chatbots. Finally, policymakers might need to step in to establish regulations that keep fraudulent businesses from positively biasing their ratings and harm customer decision-making. Although many government bodies, such as the US Federal Trade Commission’s (FTC) and German Federal Cartel Office (Bundeskartellamt), have issued guidelines arguing for keeping online reviews accurate and unbiased, guidelines such as the FTC’s (FTC, 2024) guide on soliciting reviews may be adapted to incorporate guidelines on the ethical use of chatbots for review solicitation. The remainder of this paper is structured as follows. First, we locate our research within the context of existing literature and supply a theoretical background. Next, we present the methods and results from four empirical studies. Finally, we discuss the implications of our research, comment on the limitations, and offer directions for future research. Related literature Our study pertains to two streams of literature. The first one is concerned with ethical issues in online review collection, and the second is concerned with the effects of anthropomorphic conversational agents on consumer behavior. Ethical issues in online review collection In light of the beneficial sales effect of online reviews for businesses (Zhu & Zhang, 2010), some may disregard ethical concerns and increase their average rating valence or the number of reviews by malicious practices. The most popular practice is to collect positive fake reviews. Studies report that between 5% (Anderson & Simester, 2014) and 15% (Luca & Zervas, 2016) of all reviews might be fake. Despite ethical concerns, fake reviews are effective for businesses. Faking positive reviews can increase a business’s visibility in review-based rankings by up to 40% (Lappas et al., 2016) and can increase a business’s sales by up to 16% (He et al., 2022). This raises serious ethical concerns because it shows that engaging in fraudulent review collection can pay off for businesses. Businesses most likely to collect fake reviews include those that are non-branded (Mayzlin et al., 2014), those that rely heavily on reviews (Luca & Zervas, 2016), and those that face high competition (Luca & Zervas, 2016). Also, sellers oftentimes collect positive fake reviews, especially for low-quality products (He et al., 2022), which is particularly detrimental to the consumer who may be lured into buying poorly made products. Unlike fake reviews, which represent an intentional deception of buyers, our study aims to show how a deliberate anthropomorphic design of chatbots to collect customer reviews might generate unintended negative side effects in the form of inflated product ratings. Given that online sellers often experiment with new technologies via A/B tests, the discoverers of the effect will be incentivized to keep their discovery a secret and utilize it for their purposes. This utilization of highly anthropomorphic chatbots to collect product reviews raises ethical concerns because other sellers will have a competitive disadvantage and buyers will be exposed to distorted product reviews. Therefore, our research aims at raising awareness for this phenomenon and at informing all stakeholders, buyers, sellers, and policymakers about the existence of these effects such that each stakeholder group can make an informed response to the usage of anthropomorphic technology. The effects of chatbot anthropomorphism on consumer behavior A growing number of empirical studies examine how conversational agent anthropomorphism affects consumer perception of the firm and subsequent consumer behavior (see Table 1). Crolic et al. (2022) showed that high levels of anthropomorphism are less effective in interactions with angry customers because of the heightened efficacy expectancies of those chatbots. When facing angry consumers, anthropomorphic chatbots decrease customer satisfaction, firm evaluations, and purchase intention. Regardless of the current emotional status, chatbots can also reduce consumers’ perceptions of anticipatory guilt toward firms when making false claims (Kim et 1 3 1653 Journal of the Academy of Marketing Science (2024) 52:1651–1672 Besides the effects of chatbots on customers’ firm perceptions, a recurring theme in the literature is that a growing number of studies build on social presence theory (Short et al., 1976). Even though the literature converges on anthropomorphic cues in chatbots engendering social presence, nuanced, context-dependent effects are visible. For example, chatbots with anthropomorphic cues exert significantly higher social presence than chatbots without them, but only when the anthropomorphic chatbots are also framed as intelligent (Araujo, 2018). By contrast, in the context of investing, even static bots without the capability to interact with humans can be perceived as significantly more socially present when they are equipped with anthropomorphic cues (Adam et al., 2019). Customers perceive chatbots that have response time delays as more socially present than those without anthropomorphic cues (Gnewuch et al., 2022). Similar to this literature stream, we build on social presence theory, but we complement our theory basis with a new psychological process—interaction enjoyment—next to social presence. Prior studies have predominantly compared al., 2022). As a result of reduced anticipatory guilt, consumers tend to provide information that is manufactured to their advantage or claim coupons they are ineligible for (Kim et al., 2022). However, this effect dampens as anthropomorphism increases, suggesting that chatbot anthropomorphism may instill a sense of accountability with the consumer. Yet, customer responses to chatbots may not always be detrimental. In the context of financial services, conversational agents can improve firm perceptions in terms of affective trust and perceived firm benevolence (Hildebrand & Bergner, 2021). Although all these studies have delivered important insights on the effect of chatbots on consumer behavior, they have primarily focused on the perception of the firm. None of these studies has examined whether chatbots can affect the perceptions of a firm’s products. The only exception is a recent study which shows that consumers feel more satisfied with the review process when they use chatbots, and the review length depends on the structure of the review text environment (Sachdeva et al., 2024). Table 1 Summary of previous research on the effect of chatbot anthropomorphism on consumer behavior Study Context Theory Basis Experimental chatbot conditions Psychological process Dependent variable Finding Crolic et al. (2022) Conversational agent for customer service of angry customers Functionalist theory of emotion, Appraisal theory Low vs. high anthropomorphic bot Expectations Customer satisfaction, firm evaluation, purchase intention Chatbots decrease the customer satisfaction, firm evaluation, and purchase intention of angry customers. Kim et al. (2022) Conversational agent for customer service Unethical consumer behavior Human vs. chatbot Anticipatory guilt Providing false information, claim coupon eligibility Chatbots make customers provide information that is manufactured to their advantage. The effect decreases as chatbots become more anthropomorphic. Hildebrand and Bergner (2021) Conversational agent for financial advice Speech formation, turn-taking Low vs. medium vs. high anthropomorphic bot Affective trust Firm perception, investment behavior Conversational agents positively affect firm perception and investment behavior. Sachdeva et al. (2024) Conversational agents to solicit online reviews Genre rules Web form vs. chatbot Cognitive effort Perception of review process, review characteristics Chatbots are perceived as more efficient than forms. Structuring review text boxes may increase review length. Araujo (2018) Conversational shopping assistant Embodiment Low vs. high anthropomorphic bot Social presence Emotional connection, service satisfaction Higher anthropomorphism leads to stronger emotional connection with a firm. Adam et al. (2019) Conversational agent for financial advice Anthropomorphism, anchoring Low vs. medium vs. high anthropomorphic bot Social presence Investment volume Anthropomorphism leads to higher investment volume through social presence. Gnewuch et al. (2022) Conversational agent for customer service Social Response Theory, Expectancy Violation Theory Low vs. high anthropomorphic bot Social presence Intention to use Response time delay increases intention to use through increased social presence This study Conversational agent to solicit online reviews Impression management, evaluative conditioning theory, common ground Review form vs. moderate vs. high anthropomorphic bot Social presence, interaction enjoyment Rating valence, review helpfulness Anthropomorphism increases rating valence, especially for low-quality products, and decreases review helpfulness. 1 3 1654 Journal of the Academy of Marketing Science (2024) 52:1651–1672 more anthropomorphic that communication technologies are, the more humans treat them as social actors, relating to them in the same manner as they would with humans (Nass & Moon, 2000; Oh et al., 2018). The CASA paradigm represents an important premise to the development of the hypotheses that follow. The same principles that govern humans interacting with each other, such as social presence and impression management (H1, H2), enjoyment through human interaction (H3), and the feeling of common ground (H6, H7), can be applied to the humanchatbot context. Social presence Designers purposefully endow technology with various anthropomorphic cues to increase social presence, which scholars believe positively affects social reactions and communication outcomes (Oh et al., 2018; Thomaz et al., 2020; Von der Pütten et al., 2010). Increased social presence makes conversational partners more salient, converging toward face-to-face communication. That shift, in turn, triggers the activation of impression management, which describes a communicator’s desire to make a favorable impression on the audience (Berger, 2014; Tedeschi, 2013). Humans often conform to audience expectations to receive a reward, such as being liked, or to avoid punishment, such as social disapproval (Cialdini & Goldstein, 2004). Adjusting one’s expressed opinions toward the audience’s perspectives can prevent negative evaluations of oneself by others. Feeling the presence of a “real” person makes the conversational partner more identifiable and accountable. As people strive to maintain a positive self-concept (Leary & Kowalski, 1990), they tend to give favorable evaluations and refrain from unfavorable news in exchanges with a salient conversational chatbots with and without anthropomorphic cues. By adding a conventional review form as the low anthropomorphic condition, we cover a broader range of anthropomorphism on distinct levels (low, moderate, high) than has been typically used in past literature. Conceptual model and hypotheses In this section, we delineate the study’s hypotheses. Figure 1 depicts our conceptual model. We follow recent marketing literature in building on the “computers-are-social-actors” (CASA) paradigm (Nass et al., 1994) to theorize on how consumers interact with anthropomorphized technology (Miao et al., 2022; Novak & Hoffman, 2019; Noble et al., 2022). According to CASA, anthropomorphic cues lead humans to relate to computers as they do with other humans (Reeves & Nass, 1996) and shape their interactions with chatbots with the same social and psychological dynamics that characterize human-to-human interactions. Past evidence has suggested that humans perceive computers as teammates when performing online tasks (Nass et al., 1996); they react to computer movements as they would to human motions (Reeves & Nass, 1996) and apply gender stereotypes and social traits, such as politeness, to computers (Nass et al., 1994). The main reason for treating computers as humans lies in the mindless reactions to social traits that computers exhibit (Nass & Moon, 2000), where the term mindless refers to “an overreliance on categories and distinctions drawn in the past” (Langer, 1992, p. 289). Humans can distinguish humans based on characteristic social traits and behaviors; these include physical traits, psychological cues, language sophistication, social dynamics, and social roles (Epley et al., 2007; Fogg, 2002; Reeves & Nass, 1996). Thus, the Fig. 1 Conceptual model 1 3 1655 Journal of the Academy of Marketing Science (2024) 52:1651–1672 sender spill over to the gift receiver and, subsequently, to the gift itself. In the context of product reviews, Woolley and Sharif (2021) showed that small financial incentives increase the joy of writing reviews and, subsequently, positively affect rating valence. Based on this evidence, we formulated hypothesis H3 as follows: H3 The positive effect of anthropomorphism in the review solicitation medium on rating valence is mediated via perceived interaction enjoyment. Naturally, the question of how to compare the two mechanisms of social presence and interaction enjoyment emerges. Under which circumstances will one mechanism dominate the other and vice versa? In this direction, prior literature suggests that the interactivity of a medium is a strong predictor of perceived interaction enjoyment (Coursaris & Sung, 2012). This relationship has been found in a variety of domains, such as website use (Coursaris & Sung, 2012), virtual reality use (Jang & Park, 2019), and digital artistic experiences (Gonzales et al., 2019). Interactivity is part of anthropomorphism (Kim & Sundar, 2012), but its increase may not be linear over our three conditions. The investigations that follow are designed to explore three levels of anthropomorphism (low vs. moderate vs. high) conditions. The increase in interactivity is arguably strongest comparing a low anthropomorphic medium (a conventional review form) to a moderately anthropomorphic medium (a chatbot). The chatbot is much more interactive than the form because of its ability to mimic a conversation. By contrast, both the moderately anthropomorphic and the highly anthropomorphic chatbots are interactive. Social presence, on the other hand, should increase continuously over three anthropomorphism conditions because each condition adds features that make the medium more human-like. Hence, we hypothesize that the interaction enjoyment mechanism is stronger than the social presence mechanism when comparing low to moderately anthropomorphic conditions. However, the social presence mechanism will be stronger than the interaction enjoyment mechanism when comparing moderate to highly anthropomorphic conditions. Consequently, H4 and H5 are formalized as follows: H4 The mediation through interaction enjoyment is stronger than the mediation through social presence when comparing low to moderately anthropomorphic review solicitation media H5 The mediation through social presence is stronger than the mediation through interaction enjoyment when partner (Heath, 1996; Rosen & Tesser, 1972). Previous research has revealed how increases in perceived social presence generated with the use of a chatbot may lead to socially desirable responses (Schuetzler et al., 2018). Consequently, we expect reviewers who are interacting with chatbots to adapt their reviews positively, such that they are perceived favorably and circumvent negative judgment from the review solicitor. Further, we anticipate that a higher level of anthropomorphism in the review solicitation medium (operationalized by the deployment of chatbots) positively affects reviewers’ evaluations via increased social presence. Therefore: H1 A higher level of anthropomorphism in the review solicitation medium leads to higher rating valence. H2 The positive effect of anthropomorphism in the review solicitation medium on rating valence is mediated by social presence. Interaction enjoyment In general, a communication medium endowed with rich anthropomorphic features not only evokes a feeling of being in the presence of another person but also leads to perceptions of the conversation as fun and enjoyable (Blut et al., 2020; Van Doorn et al., 2017; Van Pinxteren et al., 2020). One can explain this effect by considering people’s preference of interacting with real humans. Qiu and Benbasat (2009) showed that the humanlike appearance and voice output of virtual recommendation agents are associated with increased enjoyment. Jin (2010) revealed that the presence of an educational virtual agent in an interactive test is associated with higher student enjoyment. Therefore, we expected the review solicitation medium endowed with anthropomorphic cues to enhance the perceptions of enjoyment. Further, the enjoyment of interacting with an anthropomorphic medium might have a positive effect on the review rating. Evaluative conditioning theory predicts that a positively laden stimulus presented with another stimulus positively affects the evaluation of the second stimulus (De Houwer et al., 2001; Hofmann et al., 2010). In this vein, a solicitation medium deliberately designed as an interactive, likable, and humanlike conversational partner might affect the resulting product review, a general phenomenon widely observed in the field of relationship marketing (Palmatier et al., 2006). For example, customers who experience enjoyable interactions with service employees are more likely to provide positive word-ofmouth reviews (Gremler & Gwinner, 2000). Howard and Gengler (2001) showed how positive emotions from a gift 1 3 1656 Journal of the Academy of Marketing Science (2024) 52:1651–1672 the increased social presence of chatbots, the feeling of increased common ground can unfold. Common ground describes the knowledge, beliefs, and suppositions that communicators share and know that they share (Krauss and Fussel 1996). For example, we describe an object differently to a person who is in our physical or conversational proximity (“Take the red one”) rather than one who is not (“Take the red ball when you enter the room”). Therefore, people need fewer words to communicate the same message in a condition that is more anthropomorphic (higher increased feeling of common ground) than in one with less anthropomorphism (decreased feeling of common ground). Therefore: H6 A higher level of anthropomorphism in the review solicitation medium leads to lower review helpfulness. H7 The negative effect of anthropomorphism in the review solicitation medium on review helpfulness is mediated via review length. Overview of studies We conducted four studies (see Table 2 for an overview) to examine the effects of the level of anthropomorphism in review solicitation media. We conceptualized them along the continuum of anthropomorphism, with conventional forms and highly humanlike chatbots on the lower and higher ends, respectively. (See Table 3 for the overview of anthropomorphism manipulations.) In Study 1, we tested the main effect of anthropomorphism in the review solicitation medium on rating valence in a field experiment. We found that chatbots, representing more anthropomorphic solicitation media, generate higher ratings than conventional forms (H1). In Study 2, using a controlled experimental setting, we confirmed the positive effect of anthropomorphism on rating valence (H1) and showed that this effect is mediated by interaction enjoyment (H3) for low versus moderate levels of anthropomorphism (H4) and by social presence (H2) for moderate versus high levels of anthropomorphism (H5). We tested the effects of expressed emotionality for two reasons. First, we wanted to rule out the confounding effects of anthropomorphism and expressed emotionality. Second, we tested expressed emotionality’s role as a potentially actionable moderator to mute the effects of increased interaction enjoyment. In Study 3, we tested solicitor salience as a potential actionable moderator to mute the effects of increased social presence for moderate versus high levels of anthropomorphism. Finally, in Study 4, we examined the effect of solicitation medium comparing moderate to highly anthropomorphic review solicitation media. Review helpfulness Finally, anthropomorphism may affect the perception of reviews, particularly their helpfulness. A large body of literature discusses the determining characteristics of helpfulness for online reviews (Hong et al., 2017). Among these characteristics are the review’s age (Archak et al., 2011), the rating valence (Kuan et al., 2015), the readability (Archak et al., 2011), and the review length (Mudambi & Schuff, 2010). Among these four, the most important precursor for helpfulness is the review length. Other things equal, past studies suggest that longer reviews are more helpful because they contain more information and have improved diagnosticity (Mudambi & Schuff, 2010). Social presence likely affects review length. Triggered by Table 2 Summary of studies Study Conditions Product Findings 1 Low vs. high anthropomorphism Teaching video evaluations Product ratings are higher for review solicitation media with higher anthropomorphism. 2 3 (low vs. moderate vs. high anthropomorphism) × 2 (emotional vs. non-emotional) Short movies Product ratings are higher for review solicitation media with higher anthropomorphism. Interaction enjoyment (social presence) mediates the effect of anthropomorphism strongest for low vs. moderate (moderate vs. high) anthropomorphism. Emotionality does not affect the results. 3 2 (moderate vs. high anthropomorphism) × 2 (seller vs. platform) Short movies Product ratings are higher for review solicitation media with higher anthropomorphism. Social presence mediates the effect of anthropomorphism, but this mediation cannot be muted by a moderation with solicitor salience. 4 2 (low vs. moderate anthropomorphism) AMT worker survey reviews Reviews solicited via review solicitation media with higher anthropomorphism are less helpful. This effect is mediated by review length. Note An AMT worker refers to a worker on Amazon Mechanical Turk 1 3 1657 Journal of the Academy of Marketing Science (2024) 52:1651–1672 same payment, and the experiments differed only in the deployed review solicitation media. Hence, the issue of payment did not confound the identification of the effects of anthropomorphism. Study 1 In Study 1, we conducted a field experiment to test whether higher anthropomorphism in the review solicitation medium affects rating valence (H1). The context of the study was course evaluations that students provided. We conducted the study at a medium-sized German university in the midterm of an undergraduate course in statistics between November 22 and November 26, 2022. Because the course introduced learning videos for students, and to ensure consistency with later studies, we asked students to evaluate those videos. Experimental design The instructor asked the students via the course platform to click on a link to rate the course videos. Once students clicked on the provided link, they were randomly allocated to a high (using a chatbot with a high level of anthropomorphism) or a low (using a conventional form) anthropomorphism condition (see Appendix I). We manipulated anthropomorphism via the use of a chatbot, which equaled 1 if the students gave their ratings through anthropomorphism on review readers’ perceptions of review helpfulness (H6 and H7). Our studies offer comprehensive evidence of how anthropomorphism influences product ratings and helpfulness and identify the potential behavioral mechanisms. The general aim of our research was not to test the effects of single anthropomorphic cues on social presence or perceived interaction enjoyment but how increases in these two factors, which are triggered by the anthropomorphism of the review solicitation medium, can lead to higher ratings. For the design of various chatbot configurations, we used a combination of cues to ensure a certain level of anthropomorphism. Our approach is similar to that of Schanke et al. (2021) and is justified by the findings of Seeger et al. (2021), who showed that single cues are insufficient in achieving satisfactory levels of anthropomorphism. Table 3 presents an overview of the anthropomorphic cues used to configure the chatbots in the three studies, and Appendix I provides screen shots of the experimental manipulations of each study. In each study, the participants were paid according to the platform guidelines at the time of data collection, based on the expected length of the studies (Palan & Schitter, 2018). Prior literature has revealed that financial incentives can induce a positive bias in ratings (Burtch et al., 2018; Khern-am-nuai et al., 2018). However, in our study, all the participants received the Table 3 Overview of anthropomorphic cues of the solicitation media across studies Level of Anthropomorphism Low Moderate High Anthropomorphic Cue Design Elements Design Elements Design Elements Source Visual Humanlike appearance No Chatbot icon Real photo of service agent Feine et al. (2019) Gender No indication No indication Female agent Fogg (2002) Human name No No Yes (Emma) Araujo (2018) Emoticons No use Moderate use Frequent use Feine et al. (2019) Verbal Conversational skill No Yes, no first person (e.g., “Let’s get started.” / “Good to hear you liked the survey.” / “Seems like you were not very pleased.” / “Ok, good to know.”) Yes, with the use of first person (e.g., “I am happy you liked the movie” / “We would like to hear more!”) Feine et al. (2019) Schuetzler et al. (2018) Self-introduction No No Yes. (“I’m Emma.”) Feine et al. (2019) Opinion conformity No Yes, no first person (e.g., “Good feedback.”) Yes, first person (e.g., “I totally agree with you.”) Feine et al. (2019) Thanking No Yes (e.g., “Thank you very much for providing your valuable feedback!”) Yes, with use of user’s name. (e.g., “Amazing, thank you so much for your input, [name].”) Feine et al. (2019) Greeting and farewell No Yes (e.g., “Hello there!”) Yes, with use of user’s name. (e.g., “Nice to meet you, [name]!”) Feine et al. (2019) Lexical diversity No Yes Yes Feine et al. (2019) Invisible Response time Immediate proceeding to the next page Dots blinking before each message is sent Dots blinking before each message is sent / “Is typing…” shown before message is sent Feine et al. (2019) 1 3 1658 Journal of the Academy of Marketing Science (2024) 52:1651–1672 significant positive relationship. These results provide initial evidence in support of H6 and H7. The results were robust when we applied a random effects model specification, when we included the interaction between the solicitation medium’s anthropomorphism and product quality, and when we controlled for the order of the displayed review and the helpfulness measure of the previous review. (To account for learning effects, see Appendix H.)11 Mediation In Table 4, we found that reviews solicited via more anthropomorphic media were shorter and perceived as less helpful than those generated via less anthropomorphic means. Next, we employed mediation analyses to investigate whether the shorter length of chatbot reviews (as we found previously) was responsible for their decreased helpfulness. We implemented mediation analyses using a Generalized Structural Equation (GSEM) model to accommodate for the panel nature of the data (Palmer & Sterne, 2015).12 The results are shown in Fig. 4. We found a significant negative effect of moderate anthropomorphism on review length and a positive effect of review length on helpfulness. The negative direct effect of anthropomorphism on helpfulness was nonsignificant, suggesting a mediation effect of review length. In summary, we found a negative and significant indirect effect of moderate anthropomorphism (compared to low) on review helpfulness through review length (b = –0.28, SE = 0.08, 95% LLCI = –0.51, and 95% ULCI = –0.21). This supports H7. Discussion In Study 4, we complemented the characterization of the anthropomorphism effect on reviews by examining review helpfulness. Review helpfulness is of vital importance to firms and review platforms because it supports customer decision-making (Hong et al., 2017; Forman et al., 2008; Mudambi & Schuff, 2010). We showed that review readers considered reviews collected via chatbots less helpful. Our results suggest that the decrease in helpfulness is driven by chatbot-generated reviews being shorter. In summary, using chatbots to collect reviews may backfire for marketers because they decrease the resulting review helpfulness. 11 We also collected evaluations of review credibility, persuasiveness, reviewer trustworthiness, and their intentions regarding choosing this task based on the reviews. The results were qualitatively consistent when we used them as dependent variables (see Appendix H). 12 We confirmed these results when using 5,000 bootstrapped samples (Hayes, 2012), including user fixed effects. separate pages. Multiple participants independently evaluated the reviews, allowing us to control for respondent fixed effects and eliminate any reader idiosyncrasies that otherwise would have biased our results. We took the measures of anthropomorphism in the original review solicitation, as well as the rating valence and the review length (measured as the log-transformed number of words), from the first step of this study. We measured the reviews’ helpfulness using three items established in prior literature (“The review is helpful / useful / informative,” α = 0.93; Yin et al., 2014). Results A total of 303 workers completed the study. Each participant evaluated multiple reviews, generated via low or moderate anthropomorphism, constructing a total panel dataset of 1,515 review impressions. Due to the panel structure of the data, we chose a worker-level fixed effects model to analyze the effect of anthropomorphism on review helpfulness. We present the results of separate regressions in Table 4. In Column 1, we found that reviews generated via moderately anthropomorphic media were 42% ( ( e −0.54 − 1) ∗ 100 = − 41.7)shorter than reviews generated via media with low anthropomorphism. Next, in Column 2, we could see those reviews from the moderate anthropomorphism condition were less helpful than those from the low anthropomorphism condition. Finally, in Column 3 we regressed review helpfulness on review length and found a Table 4 The effect of anthropomorphism on review helpfulness Dependent Variable (DV) DV (1) (2) (3) (ln) Review Length (in words) Review Helpfulness Anthropomorphism (Moderate) –0.54*** (0.05) –0.16* (0.07) 0.03 (0.07) Product Quality (low) 0.09 (0.05) –0.11 (0.07) –0.14** (0.10) (ln) Review Length (in words) 0.36*** (0.05) Constant 3.09*** (0.04) 5.20** (0.06) 5.23** (0.07) Participant FE Yes Yes Yes No. of Impressions 1,515 1,515 1,515 No. of Participants 303 303 303 R² 0.10 0.01 0.03 Note (a) Robust standard errors are in parentheses. *p < .05; **p < .01. (b) A Breusch–Pagan test rejects the homoscedasticity of the error terms. (c) Among the 1,515 impressions, 1,193 were impressions of reviews, and the remainder were those of additional feedback. Our results are robust when removing the additional feedback data points 1 3 1665 Journal of the Academy of Marketing Science (2024) 52:1651–1672 General discussion Chatbots have garnered notable industry interest as a new AI-enabled tool at the company-customer interface. They promise a low-cost, highly automated solution tailored to business and customer needs and represent an appealing new application for businesses, as evidenced by their rising use (Nirale, 2018). Notwithstanding the promising potential of robotic automation, we should not overlook their potential side effects. Gathering and soliciting positive and informative online reviews is one of the most significant challenges that businesses face (Gutt et al., 2019). With the rise of chatbots, the environment in which reviews are solicited from customers awaits fundamental changes toward a more anthropomorphic, conversational direction. In this study, we sought to assess whether such changes affect the rating valence and review helpfulness. Not only did we isolate the effect of anthropomorphism on rating valence, but we uncovered two theoretical mechanisms governing the interaction between humans and (chat) robots. Single-paper meta-analysis To test the overall validity of the effect of review solicitation medium anthropomorphism on rating valence, we conducted a single paper meta-analysis (SPM; McShane & Böckenholt, 2017). We standardized the dependent variables to account for variation in the rating scales. The SPM showed that, over four studies, the rating valence increases as the medium becomes more anthropomorphic. More precisely, we show that, compared to low anthropomorphism, moderate (estimate = 0.35, s = 0.13, z = 2.87, p = .00) and high (estimate = 0.62, s = 0.15, z = 4.26, p = .00) anthropomorphism in the review solicitation medium significantly increases the rating valence. High anthropomorphism also increases rating valence compared to moderate anthropomorphism (estimate = 0.27, s = 0.14, z = 1.93, p = .05)13. Figure 5 presents the graphical summary of the contrast estimates across the conditions. 13 The SPM when contrasting low versus moderate or high anthropomorphic conditions (i.e., no chatbot vs. chatbot) shows a significant increase in rating valence (estimate = 0.44, s = 0.11, z = 3.91, p = .00). Fig. 5 Single paper meta-analysis graphical summary Fig. 4 Study 4 mediation analyses 1 3 1666 Journal of the Academy of Marketing Science (2024) 52:1651–1672 other, that future research can draw upon. Importantly, the mechanism of interaction enjoyment highlights a potential conflict of aims in a firm’s use of anthropomorphic technology. Although it may seem trite, firms naturally try to make interactions with the firm pleasant for the consumers (Ran & Wan, 2023). That is especially true for new technology (Tonietto & Barasch, 2021) to encourage consumers to adopt and embrace it (Cai et al., 2022). In the context of online reviews, this strategy has a negative tradeoff because it biases rating valence and decreases review helpfulness. Third, we contribute to the literature that has focused on online rating biases induced through the design of the online review environment (Gutt et al., 2019). Previous literature focused on the dimensionality of scales (Chen et al., 2018; Schneider et al., 2021) or online review templates (Poniatowski et al., 2019). These studies offer converging evidence that the static features provided in the interface for rating products can greatly affect consumer evaluations. We extend this stream of literature with our study on the effects of dynamic, AI-enabled, anthropomorphic review environment features. Thereby, we also respond to the call for research of AI-enabled technology on customer-firm interactions (Luo et al., 2019). Finally, we contribute to the literature on unethical review collection (Luca & Zervas, 2016). Recent studies have focused on purchasing fake reviews (He et al., 2022) and the conditions that make firms more or less likely to fake reviews (Luca & Zervas, 2016; Mayzlin et al., 2014). We complement this literature by studying how deploying anthropomorphic chatbot technology positively biases online ratings and may hence constitute an unethical way to collect reviews. Practical implications Our results present several practical implications to managers, consumers, and policymakers. To managers, our results imply that employing anthropomorphic technology to solicit reviews positively biases ratings. Managers who continue this practice knowing our results deliberately misrepresent the online ratings of their products raise ethical concerns because these ratings cannot be trusted. This contradicts the standard principles of ethical marketing that hold trust as an essential foundation (Gundlach & Murphy, 1993). In the long term, collecting reviews and ratings that misrepresent a product may cause serious damage to a brand’s positioning and image (Bazaarvoice, 2022). Even if anthropomorphic technology becomes more widely known as an effective way of acquiring positive reviews, all sellers will want to use it, and no one will gain a competitive advantage with its Using four studies, we found that the anthropomorphism in the review solicitation medium increases rating valence. Our results suggest that, at a moderate level of anthropomorphism, the rating valence increase is driven by customers’ interaction enjoyment. At high levels of anthropomorphism, however, the increased social presence is the prevalent driver (next to enjoyment) of the increase in rating valence. In both cases, the effects prove difficult to mute. Reducing the emotionality of chatbots cannot mute the interaction enjoyment mechanism, and decreasing the solicitor salience cannot mute the social presence mechanism. Finally, our results suggest that the anthropomorphism bias also affects review helpfulness. Increasing the solicitation medium anthropomorphism from low to medium decreases the helpfulness of the reviews. This effect is fully mediated by review length, which decreases when using anthropomorphic solicitation media. The rise of chatbots warrants prudent elaboration of their pros and cons. Our paper is one step in that direction and primarily draws attention to the possible downsides of the anthropomorphism of such technologies. To the best of our knowledge, we are among the first to examine the consequences of anthropomorphic technology use for product perception by customers. Theoretical contributions Four theoretical contributions emerge from our findings. First, to the best of our knowledge, we are the first to investigate how anthropomorphism affects consumers’ online reviewing behavior. Thereby, we extend the scope of the literature on the effects of anthropomorphism on consumer behavior. Whereas previous literature investigated how anthropomorphism in chatbots affects consumers’ perception of the firm (Crolic et al., 2022; Fotheringham & Wiles, 2022; Hildebrand & Bergner, 2021), we focused on how anthropomorphism affects the perception of a firm’s products. Second, although previous studies investigated the effects of anthropomorphism on consumer behavior (Crolic et al., 2022; Kim et al., 2016; Kwak et al., 2015; Schanke et al., 2021), whether the mechanisms responsible for the effects of anthropomorphism are consistent across the anthropomorphism continuum remained an open question. Our results demonstrate that this is not necessarily the case, documenting the distinct effects on consumer behavior at moderate (interaction enjoyment) and high (social presence) levels of anthropomorphism. Thereby, we offer a more finegrained characterization of anthropomorphism effects and provide a conceptualization of two channels: interaction enjoyment and social presence, including a description of when we would expect one mechanism to dominate the 1 3 1667 Journal of the Academy of Marketing Science (2024) 52:1651–1672 biases via chatbot solicitation. The latter seems difficult based on our results. Limitations and future research This research presents some limitations that provide fruitful avenues for future research. First, the generalizability of our findings is restricted by the boundaries of our research setting. These boundaries arise naturally from (1) certain limitations in sample representativeness emerging from the data collection platforms (Paolacci et al., 2010); (2) the product categories of the context of our study, including videos and the survey task; and (3) the design of solicitation media. Possible avenues to increase our study’s generalizability involve the following: (a) including a more diverse and heterogeneous sample of participants with varying levels of experience and technology savviness; (b) applying different product categories for review solicitation, which may depend on the nature of the product; and (c) systematically varying numerous design aspects of the implemented solicitation media. Second, consumers’ behavioral response can change over time. Considering firms’ high chatbot adoption and the popularization of ChatGPT, consumers might increasingly get used to chatbots. This can have repercussions for the mechanisms we identify, such as interaction enjoyment. Future research is needed. Third, chatbot-mediated review length was significantly shorter only in Studies 2 and 4. We offer three possible explanations which can be further explored in future research. First, in line with recent evidence (Sachdeva et al., 2024), review length may depend on the structure of the review text solicitation (i.e., number of questions asked in the review process). Although we kept the review structure consistent within our studies, this finding suggests that even minor variations in the review environment may affect the length of the provided reviews. Further, Study 1 used German participants and Study 3 used a UK sample, whereas Studies 2 and 4 used US samples. This points to cultural differences being a potential reason for the inconsistencies. Indeed, there are indications for such effects in writing product review texts (Wang et al., 2019). Finally, the results may be attributed to small statistical power as the effect was insignificant in studies with the smallest sample. In summary, following McShane et al. (2024), we concluded that the cumulative evidence of the studies substantiates that anthropomorphism can decrease review length, and when it does it lowers review helpfulness. Future studies could further investigate the boundary conditions of the negative effect of anthropomorphism on review length. Fourth, we conducted our study on a micro level to accurately elucidate the mechanisms and behavioral responses use, the buyers still stand to lose when all reviews become more positive and less discriminating.14 The incentive to misrepresent or disguise product quality might be particularly tempting for companies whose products will or have received low ratings.15 Anthropomorphic technology would arguably be more effective for those companies at increasing product ratings than for companies whose products will obtain high ratings anyway. Nevertheless, our study also demonstrates some natural deterrents to such unethical practice. First, reviews obtained through chatbot solicitation are less helpful, which limits their value to the firm (Forman et al., 2008). Second, online reviews are an important source for product improvement and innovation. For example, hotels improve their quality based on online reviews (Ananthakrishnan et al., 2023), and app developers innovate their apps on that basis (Karanam et al., 2021). Ratings and reviews misrepresenting the underlying product threaten sustained improvement and innovation. Consumers may be harmed in their decision-making when they face positively biased reviews. They may buy products based on positive ratings and end up disappointed with the product quality. This can extend beyond the context of business-to-consumer (B2C) commerce. As our field experiment shows, students choosing courses based on evaluations may also be misguided by positive evaluations using chatbots. This would be especially problematic if some product managers or course instructors used chatbots, but others refrained from their use. In this situation, the products or courses using chatbots would stand to improve their ratings over those that did not. Taken together, our results call for the attention of policymakers to prevent managers from nefarious deployment of chatbots and consumers from harm. Recently, government bodies around the world (FTC, German Federal Cartel Office) have published guidelines on how to keep online reviews accurate and unbiased. As of today, they offer no advice on how to deal with anthropomorphized chatbots, but our study could be a force for positive change. Our results also extend to platform owners. Akin to governments, they need to put platform governance mechanisms in place that enhance the welfare of their micro-economy (Foerderer et al., 2018). In particular, online review platforms like Yelp (Yelp, 2024) and e-commerce platforms like Amazon (Amazon, 2024) invest a lot of effort toward keeping their reviews unbiased and genuine. To ward off potential biases through chatbots, they would need to either ban chatbots or offer instructions on how to eliminate rating 14 We thank an anonymous reviewer for drawing our attention toward the discussion of possible effects on demand and supply sides. 15 To substantiate this, further analyses in Appendix G show that the anthropomorphism bias is particularly strong for low-quality products. 1 3 1668 Journal of the Academy of Marketing Science (2024) 52:1651–1672 Conference on Information System and Technology (CIST) 2020, the Conference on Digital Experimentation @MIT (CODE@MIT) 2020, the Symposium on Statistical Challenges in E-Commerce Research (SCECR) 2020, and the European Conference on Information Systems (ECIS) 2020. Financial support from the Erasmus Research Institute for Management (ERIM) is gratefully acknowledged. Declarations Conflict of interest The authors declare that they have no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/. References Adam, M., Toutaoui, J., Pfeuffer, N., & Hinz, O. (2019). 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