The good, the bad and the content: Beyond negativity bias in online word-of-mouth.

摘要

My dissertation aims to contribute to a more comprehensive understanding of how consumers make sense of online word-of-mouth. Empirical studies have repeatedly confirmed a negativity bias by which "bad" reviews from prior customers have stronger impact than "good" reviews. Nevertheless, few studies have looked beyond this valence-based approach or examined the rich textual content readily available in online reviews. Given the unavoidable nature of negative reviews, online retailers with limited time and resources need more nuanced recommendations for dealing with them. To address these gaps, I explore the boundary conditions of negativity bias in consumer trust formation, and I examine the impact of emotional arousal and discrete emotions on the perceived diagnosticity of review information. Each essay in my dissertation probes beyond the effect of rating valence and explores the role of textual content.;In the first essay, I explore negativity bias among online consumers evaluating peer information about potential sellers. Drawing on research from impression formation and attribution theory, I propose that although online consumers should be biased towards negative content when evaluating information concerning sellers' integrity, the bias should be reduced for information concerning sellers' competence. Moreover, decrements in trust and purchasing intention caused by a negative review should be more resistant to change if the review is related to integrity than if it is related to competence. In three experiments, participants were provided text reviews (Study 1) or ratings (Study 2 and 3) of sellers in typical online settings, and then asked about their trust toward the sellers. Results supported the proposed hypotheses, suggesting that the universality of negativity bias in a seller review setting has been exaggerated.;In the second essay, I examine the impact of emotional arousal on the perceived helpfulness of text reviews. Building on dimensional theories of emotion and the Yerkes-Dodson law, I propose an inverse U-shaped relationship by which the arousal conveyed in a text review will be associated by readers with lower perceived helpfulness only beyond an optimal level. Furthermore, I propose that the detrimental effect of arousal is present for negative reviews even when objective review content is controlled for, and that perceptions of reviewer rationality underlie this effect. To test these hypotheses, two studies were conducted in the context of Apple's mobile application market. In Study 1, I collected actual review data from Apple's App Store, coded those reviews for arousal using text analysis tools, and examined the non-linear relationship between arousal and review helpfulness. In Study 2, I experimentally manipulated the emotional arousal of reviews at moderate to high levels while holding objective content constant, and I measured the process variable of perceived rationality. Results were largely consistent with the hypotheses. This essay reveals the necessity of considering emotional arousal when evaluating review helpfulness, and the results carry important practical implications.;In the third essay, I explore effects of the emotions embedded in a seller review on its perceived helpfulness to readers. Drawing on frameworks in the emotion and cognitive processing literatures, I propose that over and above the well-known negativity bias, the impact of discrete emotions in a review will vary, and that one source of this variance is perceptions of reviewers' cognitive effort. I focus on the roles of two distinct, negative emotions common to seller reviews: anxiety and anger. In Studies 1 and 2, experimental methods were utilized to identify and explain the differential impact of anxiety and anger in terms of perceived reviewer effort. In Study 3, actual seller reviews from Yahoo! Shopping websites were collected to examine the relationship between emotional review content and helpfulness ratings. These findings demonstrate the importance of discriminating between discrete emotions in online word-of-mouth, and they have important repercussions for consumers and online retailers.