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Companies have increasingly advocated social media technologies to transform businesses and improve organizational performance. This study scrutinizes the predictive relationships between social media and firm equity value, the relative effects of social media metrics compared with conventional online behavioral metrics, and the dynamics of these relationships. The results derived from vector autoregressive models suggest that social media-based metrics (Web blogs and consumer ratings) are significant leading indicators of firm equity value. Interestingly, conventional online behavioral metrics (Google searches and Web traffic) are found to have a significant yet substantially weaker predictive relationship with firm equity value than social media metrics. We also find that social media has a faster predictive value, i.e., shorter “wear-in” time, than conventional online media. These findings are robust to a consistent set of volume-based measures (total blog posts, rating volume, total page views, and search intensity). Collectively, this study proffers new insights for senior executives with respect to firm equity valuations and the transformative power of social media.

The hotel industry continues to develop strategies for addressing consumer-generated online reviews, and particularly responding to poor reviews, which can have a damaging effect on a hotel’s reputation. To gain a greater understanding of the dynamics of poor reviews, this study analyzed 1,946 one-star reviews from ten popular online review websites, as well as 225 management responses from eighty-six Washington, D.C., hotels. A comprehensive complaint framework found that the most common complaints related to front desk staff, bathroom issues, room cleanliness, and guestroom noise issues. Complaints were also analyzed by hotel characteristics, including chain-scale segments, and reviewer characteristics, including purpose of travel and geographic location. Examining the reviews, highly rated hotels often respond to online complaints with appreciation, apologies, and explanations for what had gone wrong. Compensation adjustments are rarely mentioned by any hotel. The increasingly prominent role of social media necessitates that hotels use online reviews for market research and service recovery opportunities, regardless of whether they respond publicly.

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Online user-generated content in various social media websites, such as consumer experiences, user feedback, and product reviews, has increasingly become the primary information source for both consumers and businesses. In this study, we aim to look beyond the quantitative summary and unidimensional interpretation of online user reviews to provide a more comprehensive view of online user-generated content. Moreover, we would like to extend the current literature to the more customer-driven service industries, particularly the hotel industry. We obtain a unique and extensive dataset of online user reviews for hotels across various review sites and over long time periods. We use the sentiment analysis technique to decompose user reviews into different dimensions to measure hotel service quality and performance based on the SERVPERF model. Those dimensions are then incorporated into econometrics models to examine their effect in shaping users’ overall evaluation and content-generating behavior. The results suggest that different dimensions of user reviews have significantly different effects in forming user evaluation and driving content generation. This paper demonstrates the importance of using textual data to measure consumers’ relative preferences for service quality and evaluate service performance.

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