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      What makes hotel online reviews credible? : An investigation of the roles of reviewer expertise, review rating consistency and review valence

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      International Journal of Contemporary Hospitality Management
      Emerald

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          Abstract

          Purpose

          This study aims to adopt a cognitive heuristic approach to investigate the interaction effect of a message source characteristic (reviewer expertise [RE]) and two message structure characteristics (review rating consistency [RC] and review valence [RV]) on the perceived credibility of hotel online reviews.

          Design/methodology/approach

          Data were collected from 242 university students and were analyzed by three-way analysis of variance through a 2 × 2 factorial experiments using a simulated hotel review page on TripAdvisor.

          Findings

          Results show a three-way interaction effect of RE, RC and RV on the perceived credibility of hotel online reviews. The main effects of the three factors are also determined. Higher perceived credibility scores are found for negative reviews, reviews written by experts and reviews with a consistent rating.

          Research limitations/implications

          This study adopts an experimental approach and is the first to investigate the three-way interactions of message source and message structure characteristics of online hotel reviews. Data were collected from students in a university in Hong Kong. Results may not be generalizable to other markets.

          Practical implications

          Results suggest that reviews written by experts have higher perceived credibility. Hotels should pay attention to the content of online reviews and the expertise level of reviewers. Efforts should be exerted to create positive experiences for hotel guests that motivate expert reviewers to write positive reviews. Note that negative reviews have higher perceived credibility than positive ones. Hotels should promptly address negative reviews and provide professional responses to reviewers. Platform operators of user-generated content (UGC) should create well-defined reviewer profiles that can serve as cues that communicate the different expertise of reviewers.

          Originality/value

          This study is the first to test the three-way interaction effect of RE, RC and RV on the perceived credibility of hotel online reviews. Results provide recommendations to hotels and UGC operators and enable them to benefit from emerging UGC usage.

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          Most cited references73

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          Structural equation modeling in practice: A review and recommended two-step approach.

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            Judgment under Uncertainty: Heuristics and Biases.

            This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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              The impact of online reviews on hotel booking intentions and perception of trust

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                Author and article information

                Journal
                International Journal of Contemporary Hospitality Management
                IJCHM
                Emerald
                0959-6119
                0959-6119
                January 14 2019
                January 14 2019
                : 31
                : 1
                : 41-60
                Article
                10.1108/IJCHM-10-2017-0671
                0bc2bb21-8989-4d98-b62c-88657d09c211
                © 2019

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