The Impact of Ambivalent Attitudes on the Helpfulness of Web-Based Reviews: Secondary Analysis of Data From a Large Physician Review Website - Wei Dong; Yongmei Liu; Zhangxiang Zhu; Xianye Cao (JMIR Publications, 2023)
BackgroundPreviously, most studies used 5-star and 1-star ratings to represent reviewers' positive and negative attitudes, respectively. However, this premise is not always true because individuals' attitudes have more than one dimension. In particular, given the credence traits of medical service, to build durable physician-patient relationships, patients may rate their physicians with high scores to avoid lowering their physicians' web-based ratings and help build their physicians' web-based reputations. Some patients may express complaints only in review texts, resulting in ambivalence, such as conflicting feelings, beliefs, and reactions toward physicians. Thus, web-based rating platforms for medical services may face more ambivalence than platforms for search or experience goods. ObjectiveOn the basis of the tripartite model of attitudes and uncertainty reduction theory, this study aims to consider both the numerical rating and sentiment of each web-based review to explore whether there is ambivalence and how ambivalent attitudes influence the helpfulness of web-based reviews. MethodsThis study collected 114,378 reviews of 3906 physicians on a large physician review website. Then, based on existing literature, we operationalized numerical ratings as the cognitive dimension of attitudes and sentiment in review texts as the affective dimension of attitudes. Several econometric models, including the ordinary least squares model, logistic regression model, and Tobit model, were used to test our research model. ResultsFirst, this study confirmed the existence of ambivalence in each web-based review. Then, by measuring ambivalence through the inconsistency between the numerical rating and sentiment for each review, this study found that the ambivalence in different web-based reviews has a different impact on the helpfulness of the reviews. Specifically, for reviews with positive emotional valence, the higher the degree of inconsistency between the numerical rating and sentiment, the greater the helpfulness is (βpositive 1=.046; P<.001). For reviews with negative and neutral emotional valence, the impact is opposite, that is, the higher the degree of inconsistency between the numerical rating and sentiment, the lesser the helpfulness is (βnegative 1=−.059, P<.001; βneutral 1=−.030, P=.22). Considering the traits of the data, the results were also verified using the logistic regression model (θpositive 1=0.056, P=.005; θnegative 1=−0.080, P<.001; θneutral 1=−0.060, P=.03) and Tobit model. ConclusionsThis study confirmed the existence of ambivalence between the cognitive and affective dimensions in single reviews and found that for reviews with positive emotional valence, the ambivalent attitudes lead to more helpfulness, but for reviews with negative and neutral emotion valence, the ambivalence attitudes lead to less helpfulness. The results contribute to the web-based review literature and inspire a better design for rating mechanisms in review websites to enhance the helpfulness of reviews.