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The ability to understand the causes of customers' complaints is critical for hotels to improve their service quality, customer satisfaction, and revenue. This study adopts a novel structural topic model text analysis method to analyze 27,864 hotel reviews in New York City, and show that it leads to improved inference on consumer dissatisfaction. Our results reveal 10 topics, whose appearances in the negative reviews are substantially higher than those in the positive reviews. In addition, we investigate how customer complaints vary across different hotel grades. Results indicate that customer complaints for high-end hotels are mainly related to service issues, whereas customers of low-end hotels are frequently annoyed by facility-related problems. This research contributes to the hospitality literature by enhancing our understanding of the aspects of hotel customers’ dissatisfaction through rigorous statistical analysis, their correlations, and importance for different hotel grades.

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.

This study investigates the collective influences of cultural, hotel, and reviewer characteristics on online ratings in the hotel sector. Based on over 243,000 TripAdvisor reviews for hotels in 24 US cities, we empirically find a negative relationship between the reviewers' power distance and their online hotel ratings, thereby indicating that cultural factor plays a significant role in the customers’ online rating behavior. The negative effect of power distance on online hotel ratings is weaker for chained hotels than for independent hotels. This negative effect is also weaker for reviewers with more travel experience than for those with less travel experience. The robustness check demonstrates that these findings are applicable for ratings on product features that involve staff interactions, such as service, value, rooms, and cleanliness.

Hotel managers increasingly compose rote responses (e.g., similar responses repeated from prior responses) to reviews when dealing with customers' opinions while improving efficiency and saving cost. However, such responses may be ineffective because they are unspecific and add little information to reviewers' comments. To address this dilemma, we examine the degree of managers' rote responses to identify the similarity of managers' response texts. Basing on panel data of 334,671 reviews and 169,794 manager responses from 868 hotels in TripAdvisor, we find that customers leave fewer reviews and lower review valence during the subsequent month, when the level of roteness of managers’ responses is higher. However, rote responses only decrease the volume of subsequent positive reviews but have no influence on the volume of negative reviews. Moreover, such effects are weaker for chain hotels than for independent ones. Therefore, managers should differentiate their responses to online reviews, particularly for independent hotels.

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.

In this study, we utilize the employee online review data from Glassdoor to examine whether stock market capitalizes the intangible asset value of employee satisfaction for high-contact service firms. We hypothesize that in the hospitality and tourism industry, employee satisfaction can efficiently motivate employees to deliver high-quality service and improve the employee retention, thereby leading to greater shareholder value. Our finding is consistent with this prediction that stock market investors indeed incorporate the intangible value of employee satisfaction into the valuation models. In addition, we find that the value of such intangible asset manifests in firm’s subsequent profitability, resulting in improved return on asset (ROA). We further decompose ROA into profit margin and asset turnover to explore the pathways by which employee satisfaction affects shareholder value. We find that employee satisfaction can improve both profit margin and asset turnover for high-contact service firms. Overall, our study suggests that employee satisfaction can be an important intangible asset that contributes to the service firms’ long-term value.

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.

We use the system GMM estimator to analyze the size-growth relationship of 21,948 stores from 14 industries on Taobao.com.Data analysis shows that Gibrat's law tends to be rejected and the smaller stores grow faster.The applicability of the law is contingent upon store age and size: it tends to hold for larger and older stores but fails for smaller and younger ones.We explain the size-growth relationship from the viewpoints of trust building and store survival. This study examines Gibrat's law regarding size-growth relationships in the consumer-to-consumer (C2C) online marketplace. Using dynamic panel data models, we analyze 21,948 e-merchants from 14 industries on Taobao.com. The data analysis shows that Gibrat's law holds for large and mature stores when their size and age exceed certain threshold, but it generally fails to apply to stores whose size and age are below certain threshold. For those small stores, they grow faster than large ones in the C2C e-commerce. Results of the study provide insights into the competitive dynamics and industry structure of the C2C online marketplace.

This paper studies the temporal evolution of the size distribution of China's listed companies. We first identify a Pareto distribution for the upper-tail distribution. Unexpectedly, we observe that overall the Pareto coefficients decreased over the years from 2001 to 2013, which has not been reported previously in the literature. In particular, the Pareto coefficients dropped significantly during 2001 to 2008, and then fluctuated at the lowest level after 2008. A decreasing Pareto coefficient implies that the firm size inequality of the China's listed companies continuously increases during these years. By analyzing the relationship between the growth and size of firms based on a panel data model, we find that one possible reason causing the Pareto coefficients to decrease is that large firms grow faster than small ones, which is in particularly true during the non-tradable shares reform period. Furthermore, estimation results of the panel data model show that after 2008 large firms grew not as fast as they would before 2008, indicating a possible negative outcome due to the global financial crisis, which affected the growth of large firms. In addition, we examine the newly listed companies and discover that the newly listed companies with size greater than the lower bound of Pareto distribution also contribute to the decrease of the Pareto coefficients.

International audience; This study investigates how free add-on services affect customers’ perceived value in horizontal and vertical competition. We collected 349,879 reviews about over 3000 hotels in 25 U.S. cities from TripAdvisor. Using three balanced data sets generated by coarsened exact matching, the ordered logistic regressions show that free hotel add-on services (including free breakfast, parking, and WiFi) positively affect consumers’ perceived value. However, increased horizontal and vertical competition differentially weakens the positive effects of free add-on services. We not only observe a negative moderating effect of horizontal competition, but also identify three patterns of the marginal effects of these three add-ons in horizontal competition. The moderating effect of vertical competition exists from the higher-grade hotel segment to a lower-grade hotel, but such an effect is insignificant from the lower-grade hotel segment to a higher-grade hotel. Therefore, hotel managers should consider diverse external competitive environments and design appropriate differentiated service strategies.

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.