Firm-Generated, User-Generated and Machine Generated Content in the Digital Economy: Analytics, Prediction, Recommendation, and Impact

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    Understanding the Association between Star Ratings and Review Helpfulness: The Perspectives of Expectation Confirmation Theory and Negativity Bias
    ( 2023-01-03) Son, Jaebong ; Lee, Gunwoong
    Consisting of textual, multimedia, and numerical information elements, online consumer reviews (OCR) have been considered an essential information source of products for prospective consumers. Researchers have made significant efforts to comprehend how these information elements are associated with OCRs’ information value or helpfulness. However, there is a paucity of theoretical evidence on consumers’ perception and evaluation of star ratings and their information, even though star ratings as numerical information cues can imply multiple meanings. In this study, we leverage (1) expectation-confirmation theory to delineate star ratings as the extent of consumer satisfaction and (2) negativity bias to explain the relationship between star ratings and helpfulness. Using 45,621 reviews of 20 products across three categories, we empirically find that our theoretical approaches improve our understanding of the effect of star ratings on helpfulness. Therefore, this study contributes to the extant literature on OCRs by providing the theory-based evaluation of star ratings in relation to helpfulness.
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    Bargain Hunting on Black Friday - Making Great Deals and Bragging About Them
    ( 2023-01-03) Züllig, Kilian ; Erlebach, Stefanie ; Kupfer, Alexander ; Zimmermann, Steffen
    Online customer reviews (OCRs) are helpful when they provide an unbiased view on a product. Large-scale shopping events (e.g., Black Friday) generate large volumes of OCRs. We hypothesize that OCRs from such events are biased due to price discounts and smart shopper feelings. To test our hypotheses, we use OCR data of a large US electronics retailer that emerge from Black Friday purchases and regular purchases. We find that numerical ratings from Black Friday purchases are considerably higher. This effect is also observable in an increase of the average numerical rating through Black Friday purchases. We further observe that textual OCR content from Black Friday purchases focuses more on the purchase conditions (e.g., price discounts) at the expense of other, potentially more helpful content. We further provide managerial implications on how retailers may counteract the negative consequences of such biased OCRs on the quality of their OCR systems.
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    How Does the Authenticity in an Online Review Affect Its Helpfulness? A Decision Tree Induction Theory Development Approach
    ( 2023-01-03) Guduru, Rakesh ; Andoh-Baidoo, Francis ; Ayaburi, Emmanuel ; Hughes, Jerald
    Drawing on multi-dimensionality of authenticity, this study focuses on the role of two distinct authenticities: nominal and expressive. We propose that the type of authenticity in a review will vary based on the reviews’ lexical density (word level) and breadth (sentence level). Using the decision tree induction approach, the main and interaction effects of the dimensions and forms of authenticity are examined for their effect on review helpfulness. The preliminary analysis of 470 reviews demonstrate that the lexical density form of expressive authenticity is a predominant predictor of online review helpfulness. Additionally, the effects of expressive authenticity depth, nominal authenticity breadth and depth on online review helpfulness, vary based on the expressive breadth. The decision tree induction approach provides new theoretical insights that extends the frontiers of authenticity and practical implications on online review helpfulness.