Online reputation management: estimating the impact of management responses on consumer reviews

We investigate the relationship between a firm’s use of management responses and its online reputation. We focus on the hotel industry and present several findings. First, hotels are likely to start responding following a negative shock to their ratings. Second, hotels respond to positive, negative, and neutral reviews at roughly the same rate. Third, by exploiting variation in the rate with which hotels respond on different review platforms and variation in the likelihood with which consumers are exposed to management responses, we find a 0.12-star increase in ratings and a 12% increase in review volume for responding hotels. Interestingly, when hotels start responding, they receive fewer but longer negative reviews. To explain this finding, we argue that unsatisfied consumers become less likely to leave short indefensible reviews when hotels are likely to scrutinize them. Our results highlight an interesting trade-off for managers considering responding: fewer negative ratings at the cost of longer and more detailed negative feedback.Accepted manuscrip

Two Essays on the Role of Empathy in Consumer Response to User-Generated Content

Empathy is known to be the basis of all human interactions and an essential component of human psychology. Empathy includes a cognitive component (perspective-taking) and an affective component (e.g., emotional contagion). The two essays of my dissertation investigate how each of these components of empathy affect consumer responses to user-generated content. Essay 1: Although both price and online review ratings are important cues in consumers’ product quality judgment, most previous studies have treated price and review ratings as separate inputs into consumer decision-making. The current research shows that the two cues are intertwined, such that consumers’ perception of the same review rating is different depending on the price of the rated product. Through four experimental studies with a variety of operationalizations, I show that consumers have the tendency to adjust the review rating of higher-priced products upwards compared with that of lower-priced products. For example, the same 4.0- star rating signals a higher-quality product when the price is $37 than when the price is$17, above and beyond the quality signaling effect of the price itself. This price-based bias in review rating perception is attributed to consumers taking the perspective of review writers and to the shared knowledge of review writers taking the price paid into consideration when rating a product. This research extends the existing literature on online reviews by introducing perspective-taking as a metacognitive mechanism that can influence consumers’ responses to online reviews. Essay 2: Companies make significant efforts to encourage positive word-of-mouth (WOM) about their brands on social media. One common tactic is to encourage consumers to post a picture of themselves (i.e., a selfie) with the product on social media. The current research investigates the role of eye gaze in such social media messages in facilitating emotional contagion and its subsequent effects on consumers’ engagement with the content and attitude toward the associated product. Through five online experiments and one lab experiment using facial expression analysis, I show that the mere presence of direct (vs. averted) eye gaze facilitates the transfer of emotions expressed in a positive message, which in turn, leads to positive downstream consequences. I also explore two boundary conditions of this emotional contagion effect, the valence of emotion shown in the selfie and the concurrent cognitive load of the consumer. This research contributes to marketing research by extending our knowledge of eye gaze effects beyond the cognitive mechanisms and attentional effects typically considered in previous studies. It suggests a more primitive, automatic process through emotional contagion

THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

Personalizing online reviews for better customer decision making

Online consumer reviews have become an important source of information for understanding markets and customer preferences. When making purchase decisions, customers increasingly rely on user-generated online reviews; some even consider the information in online reviews more credible and trustworthy than information provided by vendors. Many studies have revealed that online reviews influence demand and sales. Others have shown the possibility of identifying customer interest in product attributes. However, little work has been done to address customer and review diversity in the process of examining reviews. This research intends to answer the research question: how can we solve the problem of customer and review diversity in the context of online reviews to recommend useful reviews based on customer preferences and improve product recommendation? Our approach to the question is through personalization. Similar to other personalization research, we use an attribute-based model to represent products and customer preferences. Unlike existing personalization research that uses a set of pre-defined product attributes, we explore the possibility of a data-driven approach for identifying more comprehensive product attributes from online reviews to model products and customer preferences. Specifically, we introduce a new topic model for product attribute identification and sentiment analysis. By differentiating word co-occurrences at the sentence level from at the document level, the model better identifies interpretable topics. The use of an inference network with shared structure enables the model to predict product attribute ratings accurately. Based on this topic model, we develop attribute-based representations of products, reviews and customer preferences and use them to construct the personalization of online reviews. We examine personalization from the lens of consumer search theory and human information processing theory and test the hypotheses with an experiment. The personalization of online reviews can 1) recommend products matching customer's preferences; 2) improve custom's intention towards recommended products; 3) best distinguish recommended products from products that do not match customer's preferences; and 4) reduce decision effort

Destination image analytics through traveller-generated content

The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users' opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Some Advances in Aspect Analysis of User-Generated Content

Starting from the online reviews associated with an overall rating, the aim is to propose a methodology for detecting the main aspects (or topics) of interest for users, and afterwards to estimate the aspect ratings latently assigned in each review jointly with the weight or emphasis put on each aspect