This study examines users’ perceptions of Chatbots in China, with a particular focus on the factors influencing their acceptance and usage. Grounded in the Technology Acceptance Model (TAM), we analyze data from 542 online responses to explore the roles of Perceived Authenticity, usefulness, and risk in shaping user behavior toward AI-driven Chatbots. Using linear regression and mediation analyses, our findings indicate that both Perceived Authenticity and Perceived Usefulness positively impact users’ behavioral intentions, while Perceived Risk has a negative influence. Notably, Perceived Usefulness serves as a mediator between behavioral intentions and both Perceived Authenticity and Perceived Risk. These results contribute to the growing body of research on AI and e-commerce by providing empirical evidence of the key factors affecting Chatbot adoption. The study offers valuable implications for developers and marketers, suggesting that enhancing Perceived Authenticity and usefulness while addressing Perceived Risks can improve user acceptance. These insights are particularly pertinent for AI practitioners aiming to refine Chatbot technology and expand its application across various sectors.

This study investigates how the characteristics of mobile devices influence consumer review-posting behavior. We propose that the characteristics of mobile devices affect a consumer’s perceived review-posting cost in terms of time and cognitive effort. Our empirical analysis, using field data from Booking.com, shows that the relative ratio of extremely positive and negative reviews via mobile devices is significantly higher than those through nonmobile devices. Of interest, the increased ratio of extremely negative reviews is smaller than that of extremely positive reviews. Our subsequent analyses, using three experimental studies, reveal that our empirical findings can be explained by the changes in perceived review-posting cost via mobile devices. This study provides theoretical implications by demonstrating how the development of mobile technology influences the review rating extremity and revealing its underlying mechanism, which is perceived as the review-posting cost. Our findings also provide useful managerial insights for e-commerce companies in terms of building an effective strategy to encourage consumers to post reviews based on changes in consumers’ perceived review-posting costs.

Purpose: Prior literature on online customer reviews (OCRs) suggests that individuals are socially influenced by information shared by others. Given that the online environment brings together users from different cultures, understanding how users differ in their processing and generation of OCRs across cultures is imperative. Specifically, this paper explores how cross-cultural differences influence OCR generation when there are inconsistencies between recent and overall review ratings.Design/methodology/approach: The authors employ an empirical study and an experimental approach to test the predictions. For the empirical study (Study 1), the authors collected and analyzed actual review data from an online hotel review platform, Booking.com. This was followed by an experimental study (Study 2) in which the authors manipulated the thinking style represented by each cultural orientation to further explain how and why cross-cultural differences exist.Findings: The results show that compared with the review ratings of users from collectivist cultures, those of users from individualistic cultures are more likely to follow recent review ratings. Based on the experimental study, the authors further find that such cross-cultural differences in OCR generation are driven by differences in thinking style.Originality/value: This research extends the literature by demonstrating the cross-cultural differences in individuals' herding tendencies in OCR generation. The authors also add to the literature by showing in which direction OCR herding occurs when there is a discrepancy between overall and recent review ratings. From a managerial perspective, the findings provide guidelines for online platforms serving the global market on predicting customers' OCR generation and constructing appropriate response strategies.

Product type plays a critical role in shaping how consumers generate, perceive, and utilize online reviews in decision-making. While previous studies have examined various review features, this study highlights the distinct effects of product classification—search goods, experience goods, and credence goods—on both review generation and perceived helpfulness. Drawing on Product Classification Theory, as well as Self-Determination Theory and the Theory of Planned Behavior, we analyze how review characteristics such as text length and photo inclusion vary across product types and influence consumer perceptions. Using a large-scale dataset of verified Amazon reviews, we find that consumers are more likely to produce longer and more visually rich reviews for search and experience goods than for credence goods, which are harder to evaluate and, thus, elicit less elaborate content. In terms of review helpfulness, reviews for experience goods are rated as more helpful than those for credence goods, while those for search goods are seen as less helpful. Furthermore, review length significantly boosts helpfulness for search goods, while photo inclusion enhances it for experience goods. These findings contribute to review effectiveness research by emphasizing the moderating role of product type and offering actionable insights for e-commerce platforms to improve review design and consumer decision-making.