Qiang Ye

The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings

The increasing use of web 2.0 applications has generated numerous online user reviews. Prior studies have revealed the influence of user-generated reviews on the sales of products such as CDs, books, and movies. However, the influence of online user-generated reviews in the tourism industry is still largely unknown both to tourism researchers and practitioners. To bridge this knowledge gap in tourism management, we conducted an empirical study to identify the impact of online user-generated reviews on business performance using data extracted from a major online travel agency in China. The empirical findings show that traveler reviews have a significant impact on online sales, with a 10 percent increase in traveler review ratings boosting online bookings by more than five percent. Our results highlight the importance of online user-generated reviews to business performance in tourism.

Sentiment classification of online reviews to travel destinations by supervised machine learning approaches

The rapid growth in Internet applications in tourism has lead to an enormous amount of personal reviews for travel-related information on the Web. These reviews can appear in different forms like BBS, blogs, Wiki or forum websites. More importantly, the information in these reviews is valuable to both travelers and practitioners for various understanding and planning processes. An intrinsic problem of the overwhelming information on the Internet, however, is information overloading as users are simply unable to read all the available information. Query functions in search engines like Yahoo and Google can help users find some of the reviews that they needed about specific destinations. The returned pages from these search engines are still beyond the visual capacity of humans. In this research, sentiment classification techniques were incorporated into the domain of mining reviews from travel blogs. Specifically, we compared three supervised machine learning algorithms of Naive Bayes, SVM and the character based N-gram model for sentiment classification of the reviews on travel blogs for seven popular travel destinations in the US and Europe. Empirical findings indicated that the SVM and N-gram approaches outperformed the Naive Bayes approach, and that when training datasets had a large number of reviews, all three approaches reached accuracies of at least 80%.

Sentiment classification of Internet restaurant reviews written in Cantonese

Research highlights? Naive Bayes and SVM are used for Cantonese sentiment classification. ? Accuracy is influenced by interaction between classification models and features. ? Naive Bayes classifier achieves as well as or better accuracy than SVM. ? Character-based bigrams are better features than unigrams and trigrams in capturing Cantonese sentiment. Cantonese is an important dialect in some regions of Southern China. Local online users often represent their opinions and experiences on the web with written Cantonese. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product and the Cantonese reviews are unintelligible for Mandarin Chinese speakers.In this paper, standard machine learning techniques naive Bayes and SVM are incorporated into the domain of online Cantonese-written restaurant reviews to automatically classify user reviews as positive or negative. The effects of feature presentations and feature sizes on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The naive Bayes classifier achieves as well as or better accuracy than SVM. Character-based bigrams are proved better features than unigrams and trigrams in capturing Cantonese sentiment orientation.

The influence of hotel price on perceived service quality and value in E-tourism : an empirical investigation based on online traveler reviews

The relationship between price and postpurchase perceptions is an important topic in tourism and hospitality studies. However, little is known about how this relationship operates in an online context. Using 43,726 online reviews covering 774 star-rated hotels, this study empirically investigated the influence of price on customers’ perceptions of service quality and value. The results show that it has a positive impact on perceived quality but has a negative impact on perceived value. Price also has a more significant impact on perceived quality for higher-star, luxury hotels than lower-star, economy establishments. Additionally, it has a significant influence on perceived quality for business travelers but the equivalent value for leisure travelers is not significant.

A Coauthorship Network Analysis of Tourism and Hospitality Research Collaboration

As an essential part of knowledge development, research collaboration inspires knowledge acquisition and dissemination. As such, it attracts an increasing amount of interest from academic researchers. This article applies social network analysis to the investigation of research collaborations among tourism and hospitality scholars. Using articles published in six leading tourism and hospitality journals from 1991 to 2010, the empirical findings provide an overview of research collaborations in this field in terms of coauthorship networks. This study identifies some critical scholars in tourism and hospitality research collaborations. Result of a linear regression shows that research collaborations are significantly associated with researchers’ research outputs. This article also proposes a new method to evaluate researchers’ collaboration strategies on extroversive collaboration and introversive collaboration.

Determinants of Customer Satisfaction in the Hotel Industry: An Application of Online Review Analysis

This study illustrates that determinants of customer satisfaction in hospitality venues can be identified through an analysis of online reviews. Using text mining and content analysis of 42,668 online traveler reviews covering 774 star-rated hotels, the study found that transportation convenience, food and beverage management, convenience to tourist destinations and value for money are identified as excellent factors that customers booking both luxury and budget hotels consider important and for which the performance is much satisfactory to them. Customers paid more attention to, but were less satisfied with, bed, reception services and room size and decoration. Most determinants of customer satisfaction also showed a consensus over luxury versus budget hotels, except for factors referring to lobby and sound insulation. As per its findings, the article concludes by presenting theoretical and managerial implications.

Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach

Sentiment classification aims at mining reviews of customers for a certain product by automatic classifying the reviews into positive or negative opinions. With the fast developing of World Wide Web applications, sentiment classification would have huge opportunity to help people automatic analysis of customers’ opinions from the web information. Automatic opinion mining will benefit to both consumers and sellers. Up to now, it is still a complicated task with great challenge. There are mainly two types of approaches for sentiment classification, machine learning methods and semantic orientation methods. Though some pioneer researches explored the approaches for English movie review classification, few jobs have been done on sentiment classification for Chinese reviews. The improved semantic approach for sentiment classification on movie reviews written in Chinese was proposed in this paper. Data experiment shows the capability of this approach.

The Impact of Seller Reputation on the Performance of online sales: evidence from TaoBao buy-it-now (BIN) data

The understanding of the seller reputation and sales performance relationship is an important topic in C2C market. Previous research has mainly focused on the C2C market in the U.S. The findings of that research have not been validated in countries with different cultural backgrounds such as China. With the explosive growth of electronic markets in China, the study of C2C online sales becomes increasingly important. This paper attempts to understand seller reputation effects on sales price, number of sales, and total revenue, based on But-It-Now (BIN) data collected from TaoBao.com, the largest online auction site in China. Multiple regression analysis is performed to test the significance of the effects. This study revealed cross effects of negative ratings on the performance of a seller. By separating the cross effects of magnitude and reputation of negative ratings, this study demonstrated that positive ratings have a significant positive impact on the performance of sellers, while negative ratings have negative reputation effects and positive magnitude effects. This is the first study that has focused on understanding the Buy-It-Now pricing and seller reputation relationship. Since the BIN feature is the most popular way of selling in online C2C markets in China, we believe the findings of this research will provide insight to researchers performing cross-cultural comparisosn between China and other markets.

AN ANALYSIS OF THE MOST INFLUENTIAL ARTICLES PUBLISHED IN TOURISM JOURNALS FROM 2000 TO 2007: A GOOGLE SCHOLAR APPROACH

This research note reports a study that analyzed the 100 most influential articles, which is operationalized as the most cited publications published in tourism journals from 2000 to 2007. A Google Scholar‐based software system was developed in Java to retrieve the citation information. The empirical findings show that 10.16% of the citations were from Institute for Scientific Information‐listed (ISI) journals, and that 71.64% of them were from neither ISI nor tourism journals. The most popular topics covered by these articles were psychology and tourist behavior, followed by destination image and marketing. This article contributes to the literature by providing an alternative means of assessing the impact of research into tourism.

How Does the Valence of Online Consumer Reviews Matter in Consumer Decision Making? Differences between Search Goods and Experience Goods

Existing empirical studies have drawn inconsistent conclusions about the effect of electronic word-of-mouth valence on consumer decision making. Based on attribution theory and prospect theory, this study attempts to explain this discrepancy through exploring how product type moderates the impact of online consumer reviews valence. Our results from a 2 (Positive reviews vs. Negative reviews) 2 (Search goods vs. Experience goods) experiment design show that the effect of online consumer reviews valence is asymmetrically moderated by product type: The effect of positive reviews is greater for search goods than that for experience goods, whereas the effects of negative reviews have no significant difference between these two types of goods; And the impact difference between negative reviews and positive reviews is greater for experience goods than for search goods. Our study not only confirms the moderating role of product type, but also further explores how product type moderates the effect of reviews valence. We also provide implications for e-marketers.