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

Ye, Qiang; Law, Rob; Gu, Bin; Chen, Wei

doi:10.1016/j.chb.2010.04.014

Cited by 907 publications

(613 citation statements)

References 20 publications

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“…Due to the spread and adoption of new consumer-empowering technologies such as social media and mobile devices complaints and dissatisfied experiences can be communicated and distributed instantly within a huge network of other consumers (Van Noort & Willemsen, 2011). The large scale availability of negative O-WOM, combined with the fact that the majority of consumers puts trust into these disclosures when engaging in online buying behavior (Ye, Law, Gu, & Chen, 2011), emphasizes the need for examining negative O-WOM into detail.…”

Section: Negative O-wommentioning

confidence: 99%

Negative online word-of-mouth: Behavioral indicator or emotional release?

Verhagen

Nauta

Feldberg

2013

Computers in Human Behavior

168

118

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The influence of negative online word-of-mouth on the behavior of those receiving it has been addressed extensively in the academic literature. Remarkably, the question whether negative online word-of-mouth should also be seen as a behavioral indicator of its sender remains unaddressed. Answering this question is relevant as it provides companies with insight into the need to engage in interaction with those who negatively express themselves online or whether these expressions should be seen as temporary emotional releases without any future conduct. To fill the existing research gap, this research paper proposes and empirically tests a sender-oriented model, investigating the influence of emotions, negative online word-of-mouth on repatronage and switching behavior. As disclosing negative feedback online may also reflect the sender's motivation to inform the consumer community or to provide constructive feedback to the company responsible for the dissatisfying consumption, community usefulness and company usefulness are included as behavioral moderators. The results of an empirical survey conducted amongst real senders of negative information confirm that negative online word-of-mouth is directly driven by positive and negative emotions and is strongly predictive for the sender's future conduct. The motivation to help other consumers was demonstrated to function as behavioral moderator. The paper concludes with theoretical and managerial implications, and suggests avenues for further research.

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Section: Negative O-wommentioning

confidence: 99%

Negative online word-of-mouth: Behavioral indicator or emotional release?

Verhagen

Nauta

Feldberg

2013

Computers in Human Behavior

168

118

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“…Vermeulen and Seegers (2009) investigated the impact of online hotel reviews on the formation of consumer consideration, and found out that online reviews improve hotel awareness, which ultimately helps travellers to develop their consideration set. Ye et al (2011) estimated the effect of online review features on room sales in hotels based on the assumption that the number of reviews encompasses the linear function of sales. They concluded that review ratings and room prices are important elements to predict room sales online.…”

Section: Online Consumer Reviewsmentioning

confidence: 99%

“…This study argues that consumer reviews are particularly important in purchasing experiential goods (e.g., destinations, hotels, and restaurants) because people find it difficult to assess the quality of the intangible products before consumption. Hence consumers tend to rely on online comments (a form of e-word of mouth) that allow them to obtain sufficient information and have indirect purchasing experiences so as to reduce their level of perceived uncertainty (Ye, Law, Gu & Chen, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Liu¹,

2015

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“…More precisely, 73% to 87% of consumers of product reviews within the tourism domain (e.g. hotel or restaurant reviews) denote that reviews influenced their purchase decision (Pang & Lee, 2008;Zehrer et al 2011;Ye et al 2011). Indeed, the exploitation of available opinions is interesting for companies as well as users (Lin, & Huang, 2006;Carson, 2008).…”

Section: Introductionmentioning

confidence: 99%

Classification of Customer Reviews based on Sentiment Analysis

Gräbner¹,

Zanker

Fliedl

et al. 2012

Information and Communication Technologies in Tourism 2012

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In this paper we propose a system that performs the classification of customer reviews of hotels by means of a sentiment analysis. We elaborate on a process to extract a domainspecific lexicon of semantically relevant words based on a given corpus (Scharl et al., 2003;Pak & Paroubek, 2010). The resulting lexicon backs the sentiment analysis for generating a classification of the reviews. The evaluation of the classification on test data shows that the proposed system performs better compared to a predefined baseline: if a customer review is classified as good or bad the classification is correct with a probability of about 90%.

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

Cited by 907 publications

References 20 publications

Negative online word-of-mouth: Behavioral indicator or emotional release?

Negative online word-of-mouth: Behavioral indicator or emotional release?

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

Classification of Customer Reviews based on Sentiment Analysis

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