What makes a useful online review? Implication for travel product websites

Liu, Zhiwei; Park, Sang Won

doi:10.1016/j.tourman.2014.09.020

Cited by 738 publications

(658 citation statements)

References 82 publications

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“…Trust plays an influential role in information source selection for travellers, although findings are mixed in terms of the degree of trustworthiness for available information sources and their effects on information satisfaction. Specifically, there are concerns about the credibility of information provided by websites that provide user-generated content, such as reviews because of the anonymity and questionable motives of information providers, which could result in posts of fake or biased reviews [49,50]; however, online customer reviews are considered, in principle, more reliable and truthful as an information source compared to information provided by company websites [15]. Nevertheless, such websites, although more trustworthy, have been reported to be less informative compared to editorial content providers [51].…”

Section: Capturing Travellers' Online Information Search Process Durimentioning

confidence: 99%

Explaining travellers online information satisfaction: A complexity theory approach on information needs, barriers, sources and personal characteristics

Kourouthanassis

Mikalef

Pappas

et al. 2017

Information & Management

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Please cite this article as: P.E. Kourouthanassis, P. Mikalef, I.O. Pappas, P. Kostagiolas, Explaining travellers online information satisfaction: a complexity theory approach on information needs, barriers, sources and personal characteristics, Information and Management (2017), http://dx.doi.org/10. 1016/j.im.2017.03.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Capturing Travellers' Online Information Search Process Durimentioning

confidence: 99%

Explaining travellers online information satisfaction: A complexity theory approach on information needs, barriers, sources and personal characteristics

Kourouthanassis

Mikalef

Pappas

et al. 2017

Information & Management

View full text Add to dashboard Cite

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“…Ariyasriwatana and Quiroga [40] explored expressions of deliciousness from restaurant reviews based on content analysis and then categorized them into eight primary dimensions. Liu and Park [41] extracted four text readability features from review contents using content analysis, and the results showed that two of the four features had a statistically significant and positive effect on review helpfulness. Document-based sentiment analysis.…”

Section: Content Analysismentioning

confidence: 99%

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Ren

Hong

2017

Sustainability

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With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide "suggestion" reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction "management" obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.

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“…Fourthly, Zhang et al (2010) studied the behavioral intentions of Chinese customers after reading reviews posted on a Chinese rating platform, finding that perceived informativeness and argument strength were additional positive drivers of behavioral intentions, together with the volume of reviews (differently from source credibility, which was found not to be an important factor). Finally, disclosure of identity was another important factor of behavioral intentions (Liu and Park, 2015).…”

Section: The Impact Of Rating Systemsmentioning

confidence: 99%

“…In the case of experience goods, people find difficult to assess the quality before consumption (differently from standardized products -search goods -and from opinions); hence prior information about quality is highly relevant for purchasing choices. Consumers tend to rely on different forms of word-ofmouth (such as online UGCs) to obtain sufficient information and reduce their level of perceived uncertainty (Liu and Park, 2015). Tourism has been one of the first sectors developing online rating platforms and UGC systems, and tourism services (in particularly accomodation) are typical examples of experience goods.…”

Section: Contribution and Hypothesesmentioning

confidence: 99%

Social Influence Bias in Online Ratings: A Field Experiment

2016

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The aim of this paper is to study the empirical phenomenon of rating bubbles, i.e. clustering on extremely positive values in e-commerce platforms and rating web sites. By means of a field experiment that exogenously manipulates prior ratings for a hotel in an important Italian tourism destination, we investigate whether consumers are influenced by prior ratings when evaluating their stay (i.e., social influence bias). Results show that positive social influence exists, and that herd behavior is asymmetric: information on prior positive ratings has a stronger influence on consumers' rating attitude than information on prior mediocre ratings. Furthermore, we are able to exclude any brag-or-moan effect: the behavior of frequent reviewers, on average, is not statistically different from the behavior of consumers who have never posted ratings online. Yet, non-reviewers exhibit a higher influence to excellent prior ratings, thus lending support to the social influence bias interpretation. Finally, also repeat customers are affected by prior ratings, although to a lesser extent with respect to new customers.

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What makes a useful online review? Implication for travel product websites

Cited by 738 publications

References 82 publications

Explaining travellers online information satisfaction: A complexity theory approach on information needs, barriers, sources and personal characteristics

Explaining travellers online information satisfaction: A complexity theory approach on information needs, barriers, sources and personal characteristics

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Social Influence Bias in Online Ratings: A Field Experiment

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