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In this study, we focus on the effects of doctors’ provision of online health consultation service (OHCSV) on the review valence provided by patients. We develop a unique panel dataset based on over 3,000,000 reviews for 493,548 doctors on a leading online healthcare community in China. Coarsened exact matching and difference-in-difference estimators are used to determine causality. Findings corroborate that doctors’ provision of OHCSV increases the review valence posted by patients and significantly changes the valence structure by increasing and decreasing the proportion of positive and negative valences, respectively. Effects of OHCSV also differ among various review dimensions and doctors’ hierarchical levels. Doctors at lower hierarchical levels experience stronger positive provision effect than their counterparts. Such findings contribute to literature on sustainable doctor–patient relationship and expert service, and similarly generate implications for the sustainability of health platforms.

Agglomeration benefits firms because it minimizes consumers' search costs and information asymmetry. However, the effects of agglomeration may change due to the considerable amount of information available in online platforms. Thus, drawing upon the theoretical lens of information transparency and agglomeration theory, we investigate how online reviews change the effect of agglomeration on firm performance. A total of 11,528 observations of 467 hotels show that agglomeration density positively affects firm revenue in the hotel sector. However, such an effect decreases with the increase in online review quantity (including volume and length). This change is manifested in two forms, namely, minimizing information asymmetry and increasing competition cost due to information transparency. Moreover, the positive effect of agglomeration density decreases due to the quantity of high-quality (few spelling errors and easy to understand) online reviews, but not with low-quality. We further discover that the substitution effect of online review quantity on agglomeration density is stronger for independent hotels than for branded ones, whereas the effect is similar among hotels with various qualities. Our findings provide implications for the management of online channels in changing the local agglomeration effectiveness.

This study investigates the differential roles of negative and positive reviews on the growth of branded and independent hotels and their underlying interplays. We construct a dataset by combining the revenue data with TripAdvisor review data of hotels in four Texas cities. Based on the resource-based view of firm growth and signaling theory, we obtain several novel findings. Positive reviews exert greater positive effects on the growth of independent hotels than branded hotels, and high growth in turn brings them additional positive reviews. Such a positive feedback loop helps explain why independent hotels benefit more from positive reviews than branded hotels. Negative reviews negatively affect the growth of independent hotels. Although a high growth of branded hotels leads to more negative reviews, the latter does not jeopardize their growth significantly due to the protecting role of brands.

This study explores the antecedents and consequences of health information privacy concerns in online health communities by integrating the dual calculus and protection motivation theories. On the basis of survey data from 337 users, health information privacy concerns, together with informational and emotional support, significantly influence personal health information (PHI) disclosure intention. Privacy concerns are negatively influenced by two coping appraisals (i.e., response efficacy and self-efficacy) and positively affected by two threat appraisals (i.e., perceived vulnerability and perceived severity). The perceived health status differentially moderates the effects of privacy concerns and informational support on the PHI disclosure intention.

Purpose Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms. Design/methodology/approach The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. Findings First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics. Originality/value The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.

This study investigates if reviewers' pattern of rating is consistent over time and predictable. Two interesting results emerge from the econometric analyses using publicly available data from TripAdvisor.com . First, reviewers' rating behavior is consistent over time and across products. Furthermore, most of the variation in their future rating behavior can be explained by their rating behavior in the past rather than by the observed average rating. Second, reviews by reviewers with higher absolute bias in rating in the past receive more helpful votes in future. We further divide the bias in rating into intrinsic bias (driven by intrinsic reviewer characteristics) and extrinsic bias (driven by influences beyond intrinsic bias) and document that intrinsic bias plays a more significant role in influencing helpful votes for reviews than extrinsic bias. Our results are robust to different product categories and different definition of bias. Overall our results indicate that in the online review context, the observed average rating or an attention grabbing strategy may not be as important as believed in the past. This study provides insights into reviewers' rating behavior and prescribes actionable items for online vendors so that they can proactively influence online opinion instead of passively responding to them.

The study uses a novel psychological text-mining approach, language style matching (LSM), to examine the effect of the social interaction of top management team (TMT) members on conservative accounting reporting practices. We posit that similar language styles help to form consensus and social integration, leading to better cooperation in group decision-making. Using 10,531 firm-year observations, we show that there is a positive association between TMT LSM and accounting conservatism, suggesting that TMT members who share similar linguistic styles respond quickly to corporate news on firm value. This implies that TMTs with better language style alignment facilitate social interactions among members to form a consensus view, enhancing decision making on how a firm should react to various corporate events that affect its value. Additional analyses indicate that the impact of LSM on conservative accounting reporting behavior is more pronounced when firms face a more competitive business environment, severe information asymmetry, and higher financial distress. By examining TMT characteristics from the dynamic psycholinguistic perspective, we shed light on the understanding of how executives' cognitive values collectively affect a firm's conservative reporting decisions.

This study examines the collective effects of voice and physician characteristics on patient satisfaction based on 35,597 voice-based medical services provided by physicians in a mobile health community. Results show patient satisfaction is positively influenced by the physician’s speech rate but negatively affected by the average spectral centroid of consultation voice. A fast speaker and speech with neural emotion are more likely to be associated with higher patient satisfaction than a slow speaker and speech with low and high emotion states. However, these effects are weak for physicians with high professional capital, which suggests a substitute role for voice characteristics.