Despite hospitality and tourism researchers’ recent attempts on examining different aspects of online word-of-mouth [WOM], its impact on hotel sales remains largely unknown in the existing literature. To fill this void, we conduct a study to empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, we develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. Our results indicate a significant relationship between online consumer reviews and business performance of hotels.

The impact of online user reviews on hotel room sales [Summary]

Social interaction-based consumer decision-making model in social commerce

This study examines two types of information commonly used by group-buying sites to induce purchasing. The first study indicates the number of people who have bought a deal ("buy" information). The...

Informational and Normative Social Influence in Group-Buying: Evidence from Self-Reported and EEG Data

Consumer perceived value, involvement, trust, susceptibility to interpersonal influence, and intention to participate in online group buying

Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.

Text Mining for Big Data Analysis in Financial Sector: A Literature Review

Since its introduction 10 years ago, group-buying websites, where buyers with similar purchase interests congregate online to obtain group discounts, have metamorphosed into several variants. The most popular variant is the deal-of-the-day group-buying website, where there is only one product/service being offered each day. Starting in the United States in 2008, this new group-buying variant has rapidly achieved tremendous success and has been widely adopted in various countries. At the end of August 2010, there were more than 1000 deal-of-the-day group-buying websites in the most competitive online marketplace, i.e., China. How exactly do buyers behave on these websites? How can deal-of-the-day group-buying website providers take advantage of buyers’ behavior? Based on herd behavior, we collected and analyzed over 500 hourly orders on the most popular deal-of-the-day group-buying website in Beijing. We found that auction times and new orders for each hour have an inverted-U relationship. Moreover, we discovered that the number of existing orders will only have a positive effect on the number of new orders during the first half of the day. Contributions to research and implications for group-buying website providers are presented in the paper.

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Buyers’ purchasing time and herd behavior on deal-of-the-day group-buying websites

Enterprise social media usage: The motives and the moderating role of public social media experience

Excessive use of online video streaming services: Impact of recommender system use, psychological factors, and motives

Consumers’ evaluation of online review helpfulness has been widely examined. The extant literature suggests that the attributes of review content (e.g. review length and extremity) influence review helpfulness. However, review length cannot fully reflect the richness of the review content. Anchoring on information diagnosticity and extremity bias, this study aims to explore the effect of review comprehensiveness on its helpfulness.,Field observations were obtained from 11,812 online restaurant reviews on a popular restaurant review platform. A controlled experiment was conducted to further delineate the effect of review comprehensiveness.,Review comprehensiveness moderates the effects of review length and an extremely negative review on helpfulness. It also confirms that for reviews of the same length, one covering more aspects is perceived by consumers as more helpful.,Different aspects of information in a review can efficiently assist decision-making. The results suggest that review platforms can better design their interface by providing separate areas for different product aspects. The platform can then receive more comprehensive and helpful reviews and increase the diagnosticity of these.,The study enriches the literature by introducing review comprehensiveness and examining the joint effects of review length and comprehensiveness on helpfulness. It also contributes to the literature by indicating how to reduce the effect of review extremity.

Online Review Helpfulness: The Moderating Effects of Review Comprehensiveness

Online social lending has facilitated the ability of borrowers to reach lenders for financing support. With the increasing number of social lending projects, it is becoming very difficult for lenders to find appropriate projects to invest in, and for borrowers to get the funds they need. Project recommendation techniques provide a promising way to solve this problem to some degree, by recommending borrowers’ projects to lenders who are able to invest. Unfortunately, current loan project recommendations only explore some structured information to match borrowers and lenders, so they cannot achieve a satisfactory way to solve the problem very well. In this study, we innovatively mine a huge amount of unstructured data, the text data of borrowers’ and lenders’ motivations, to provide loan project recommendations that solve the problem of mismatches between borrowers and lenders. We present a motivation-based recommendation approach that uses text mining and classifier techniques to identify borrowers’ and lenders’ motivations. Using a dataset from the well-known social lending platform Kiva, our experiment results show that, compared with prior works, the proposed approach improves project recommendations in inactive lender groups and unpopular loan groups, which shows the superiority of the proposed approach in addressing data sparsity and cold start problems in loan project recommendations. This study thus initiates an attempt to solve the information overload problem and improve matching between borrowers and lenders through mining big unstructured text data found in a large number of P2P platforms.

Yi Liu

Papers

Buyers’ purchasing time and herd behavior on deal-of-the-day group-buying websites

Enterprise social media usage: The motives and the moderating role of public social media experience

Excessive use of online video streaming services: Impact of recommender system use, psychological factors, and motives

Online Review Helpfulness: The Moderating Effects of Review Comprehensiveness

Mining Social Lending Motivations for Loan Project Recommendations