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]

Word similarity is broadly used in many applications, such as information retrieval, information extraction, text classification, word sense disambiguation, example -based machine translation, etc. There are two different methods used to compute similarity: one is based on ontology or a semantic taxonomy; the other is based on collocations of words in a corpus. As a lexical knowledgebase with rich semantic information, How-net has been employed in various researches. Unlike other thesauri, such as WordNet and Tongyici Cilin, in which word similarity is defined based on the distance between words in a semantic taxonomy tree, How-net defines a word in a complicated multi-dimensional knowledge description language. As a result, a series of problems arise in the process of word similarity computation using How-net. The difficulties are outlined below: 1. The description of each word consists of a group of sememes. For example, the Chinese word “暗箱(camera obscura)” is described as: “part|部件, #TakePicture|拍攝, %tool|用具 , body|身”, and the Chinese word “寫信 (write a letter)” is described as: “write|寫, ContentProduct=letter|信件”; 2. The meaning of a word is not a simple combination of these sememes. Sememes are organized using a specific knowledge description language. To meet these challenges, our work includes: 1. A study on the How-net knowledge description language. We rewrite the How-net definition of a word in a more structural format, using the abstract data structure of set and feature structure. 2. A study on the algorithm used to compute word similarity based on How-net. The similarity between sememes, that between sets , and that between feature structures are given. To compute the similarity between two sememes, we

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基於《知網》的辭彙語義相似度計算 (Word Similarity Computing Based on How-net).

Online customer reviews (OCRs) are increasingly used by travelers to inform their purchase decisions. However, the vast amount of reviews available nowadays may increase travellers' effort in information processing. In order to facilitate traveller's decisions, social commerce organizations must help travellers rapidly identify the most helpful reviews to reduce their cognitive effort. Academic literature has often documented that negative reviews are judged as helpful by consumers. However, extremely negative reviews are not always perceived as such. This study is the first that unveils what factors moderate the influence of extremely negative reviews on review helpfulness. The study has adopted a sample of 7,455 online customer reviews of hotels to test hypotheses. Findings show that reviews with extremely negative ratings are more likely to be helpful when the review is long and easy to read and when the reviewer is an expert or discloses his identity (geographical origin).

What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics

Insights into the association rules of destinations can help to understand the possibility of tourists visiting a destination after having traveled from another. These insights are crucial for tourism industries to exploit strategies and travel products and offer improved services. Recently, tourism-related, user-generated content (UGC) big data have provided a great opportunity to investigate the travel behavior of tourists on an unparalleled scale. However, existing analyses of the association of destinations or attractions mainly depend on geo-tagged UGC, and only a few have utilized unstructured textual UGC (e.g., online travel reviews) to understand tourist movement patterns. In this study, we derive the association of destinations from online textual travel reviews. A workflow, which includes collecting data from travel service websites, extracting destination sequences from travel reviews, and identifying the frequent association of destinations, is developed to achieve the goal. A case study of Yunnan Province, China is implemented to verify the proposed workflow. The results show that the popular destinations and association of destinations could be identified in Yunnan, demonstrating that unstructured textual online travel reviews can be used to investigate the frequent movement patterns of tourists. Tourism managers can use the findings to optimize travel products and promote destination management.

A Methodological Workflow for Deriving the Association of Tourist Destinations Based on Online Travel Reviews: A Case Study of Yunnan Province, China

With the rapid development of new energy vehicles (NEVs), the market competition in the NEV industry is becoming increasingly fierce. Selecting the right supplier has become a critical aspect for NEV manufacturers. Therefore, based on the user’s demand information, selecting a suitable NEV supplier to support the NEV manufacturer’s management decision is a noteworthy research problem. The purpose of this study is to develop an integrated method for demand-driven NEV supplier selection based on ontology–quality function deployment (QFD)–case-based reasoning (CBR). The method is composed of three parts: 1) construction of domain ontology of NEV component supplier selection criteria based on text information mining; 2) extraction of demand attributes and determination of their weight based on latent Dirichlet allocation (LDA) and Kano model, as well as determination of expected attributes and their weights based on QFD; and 3) selection of an NEV component supplier based on CBR. To illustrate the use of the proposed method, an empirical study on the supplier selection of the XP NEV manufacturer is given. This method is helpful in selecting the most suitable component supplier for NEV manufacturers and relevant decision-makers.

Demand-driven NEV supplier selection: An integrated method based on ontology–QFD–CBR

PurposeIn recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make their product purchase decisions, while for travel service providers (TSPs), monitoring and improving the e-WOM of their travel products is always an important task. Therefore, based on the online review information, how to capture e-WOM of travel products and find out specific ways to improve the e-WOM is a noteworthy research problem. The purpose of this paper is to develop a method for capturing and analyzing e-WOM toward travel products based on sentiment analysis and stochastic dominance.Design/methodology/approachSpecifically, online review information of travel products is first crawled and preprocessed. Second, sentiment strengths of online review information toward travel products concerning each feature are judged. Then, the matrix of structured online review information toward travel products is formed. Further, the matrix of e-WOM comparisons between any two travel products is constructed, and e-WOM ranking concerning each travel product is determined. Finally, trade-off chart models are constructed to conduct the e-WOM improvement analyses concerning the travel products.FindingsAn empirical study based on the online review information toward six travel products crawled from the Tuniu.com website is given to illustrate the use of the proposed method.Originality/valueThe proposed method can not only realize the real-time e-WOM monitoring to travel products but also be useful for TSPs to improve the e-WOM of their travel products.

Yong-Hai Li

Papers

Capturing and analyzing e-WOM for travel products: a method based on sentiment analysis and stochastic dominance

Sentiment analysis-based method for matching creative agri-product scheme demanders and suppliers: A case study from China