Impact of Prior Reviews on the Subsequent

reputation systems have been recognized as successful online review communities and word-of-mouth channels. Our study draws upon the elaboration likelihood model to analyze the extent that the characteristics of reviewers and their early reviews reduce or worsen the bias of subsequent online reviews. Investigating the sources of this bias and ways to mitigate it is of considerable importance given the previously established significant impact of online reviews on consumers’ purchasing decisions and on businesses’ profitability. Based on a panel data set of 744 individual consumers collected from Yelp, we used the Markov chain Monte Carlo simulation method to develop and empirically test a system of simultaneous models of consumer review behavior. Our results reveal that male reviewers or those who lack experience, geographic mobility, or social connectedness are more prone to being influenced by prior reviews. We also found that longer and more frequent reviews can reduce online reviews’ biases. This paper is among the first to examine the moderating effects of reviewer and review characteristics on the relationship between prior reviews and subsequent reviews. Practically, this study offers businesses effective customer relationship management strategies to improve their reputations and expand their clientele. KeY worDS anD phraSeS: consumer review, elaboration likelihood model, hierarchical modeling, MCMC simulation, reputation systems, simultaneous equations model. the ubiquitY anD afforDabiLitY of the internet and mobile connectivity have contributed to the transformation of the World Wide Web into an interactive medium for connecting people. This seamless connectivity has fueled an online crowd movement, referred to as “crowdsourcing” [23, 40], that encompasses, among other functions, individuals’ willingly rating products and services. These contributions, loosely grouped under the umbrella term “word of mouth” (WOM), are defined as any form of “informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services and/or their sellers” [62, p. 261]. Electronic WOM (eWOM), a particular WOM phenomenon whereby consumers share their experiences with other consumers online [39], has become “an important source of information to consumers, substituting and complementing other forms of business-to-consumer and offline WOM communication about product quality” [16, p. 345]. reputation systems, such as Yelp, have been recognized as successful eWOM channels [19, 20, 21, 22, 38, 41]. Yelp, like most online review communities, provides its reviewers with the infrastructure backbone and the technological means to share with others their experiences with a variety of products and services. Considerable prior research has theorized about and empirically validated the significant effect that online reviews have on consumers’ purchasing decisions and on businesses’ profitability [10, 24, 60]. Despite their significant influence on sales, IMPACT OF PrIOr rEVIEWS ON SuBSEquENT rEVIEWS IN rEPuTATION SYSTEMS 281 some of these online reviews have been plagued by self-selection bias [20, 27, 41, 46]. Li and hitt [41] explained that early adopters of products are usually those who are most eager to experiment with them, but this eagerness is not truly representative of the market as a whole. This self-selection bias causes early reviews of products and services to be positively biased, thus misleading potential customers. As the rating environment matures, online reviews have been shown to gradually become less positive [28, 45]. Dellarocas et al. [20] documented the presence of this self-selection bias in Yahoo’s reviewer ratings during the first weekend of the release of a movie. understanding the characteristics and determinants of online reviews is imperative to mitigate their bias, especially considering the acclaimed importance of Web 2.0 for online purchases and the high speed with which information travels online. This understanding will enable practitioners to develop effective strategies that leverage these determinants to boost their revenues and increase their profitability. Our thorough survey of the literature revealed that no prior study has attempted to investigate the online review process and its significant determinants. The purpose of this study is to develop and test a model of the determinants of online reviews in reputation systems and to fill this gap in the literature. In searching for a theoretical underpinning for our effort to understand the mechanics by which prior Yelp reviewers’ ratings influence subsequent reviews, we found support for our research in the elaboration likelihood model (ELM) [51]. We selected the ELM as the underlying theory for our study for the following two reasons, enumerated in Bhattacherjee and Sanford [8]: (1) the ELM studies two routes of influence processes, the central route and the peripheral route, and their effects on individuals’ perceptions and behaviors; and (2) the ELM captures differential outcomes of these influence processes based on individual and message characteristics. Individuals who scrutinize and critically deliberate on their experiences with products and services are said to choose a central deliberation route. The ratings of these centrally deliberating reviewers are not likely to be swayed by prior reviewers’ ratings. however, individuals who simply rely on cues or on others’ judgment to make decisions and who think superficially about their experiences are said to adopt a peripheral deliberation route; in other words, they use peripheral cues as heuristics [35, p. 51]. These less-involved reviewers are more likely to be influenced by prior reviewers’ ratings. Although the ELM illustrates well how personal characteristics can change the decision-making process, it does not discuss how the characteristics of a written message can also affect this process. Practitioners need to consider both individual differences and variations in the written message when characterizing online review behavior. Motivated by this practical need, this study extends the ELM by investigating the moderating effect of the online reviews. This paper makes several contributions to theory, methodology, and practice. From a theoretical perspective, to the best of our knowledge, ours is the first study to examine the moderating variables that affect the extent to which online reviews are more or less independent of previous reviews. This is important, given prior findings that online reviews significantly affect sales and profitability [14, 16, 18, 24, 29, 50, 66]. To support our model and develop our hypotheses, we extend the applicability of the ELM 282 MA, KhANSA, DENg, AND KIM to the context of online reviews, thus paving the way for future research along similar lines. We extend the ELM by accounting for not only reviewer characteristics but also the characteristics of the written reviews. Accordingly, we categorize the moderators between prior reviews and subsequent reviews into those that characterize the reviewers and the others that relate to the reviews themselves. In the process, we propose new variables that gauge reviewers’ social connectedness, geographic mobility, and time since their last review. Our work complements prior studies that have investigated the direct effects of reviewers’ experience, gender, and the length of their reviews. From a methodological perspective, we tested our hypotheses using a multilevel model and the Markov chain Monte Carlo (MCMC) simulation method. This method has several advantages over the previous models used to study consumer review behaviors. First, this is the first time that a multilevel structured model estimated by an MCMC simulation method has been used to analyze consumer review behavior in the information systems (IS) field. This model is robustly specified using the ordered probit model. Second, this model effectively corrects for any reviewer self-selection bias, and thus is preferred over the ordinary least squares (OLS) framework because OLS generally produces inconsistent estimations. Third, our model successfully demonstrates the advantage of the hierarchical modeling framework. It predicts ratings correctly over 50 percent of the time, which is significantly better than the more common 20 percent accuracy. Finally, we show that our model outperforms traditional modeling frameworks by over 18 percent. The remainder of the paper is organized as follows. In the next section, we discuss the ELM as our theoretical framework, elaborate on the rationale for selecting reviewer and review characteristics as moderators of the relationship between the average of prior ratings and subsequent ratings, and develop our hypotheses and model. We then present our data and variables, followed by the modeling of customer rating dynamics, including the rationale for choosing and specifying the models. In the subsequent section, we report our results, model diagnostics, and ad hoc analyses. We conclude this paper by discussing the significance of the study, its limitations, and potential avenues for further research. Theoretical Framework and hypotheses figure 1 iLLuStrateS our conceptuaL MoDeL that relates consumers’ prior expectations of a product or a service, as measured by the average rating of prior reviews, to subsequent ratings and to two types of moderators that affect this relationship: reviewer characteristics and review characteristics. It has been shown that without any other dependable and readily available way to assess a product or a service before consumption, consumers tend to build their expectations on the average rating of prior reviews [1]. These prior expectations serve as a foundation, or level of reference, for postconsumption evaluations. Much empirical research (e.g., [11, 52]) supports such a positive and direct impact of prior expectations on subse