Evaluating the Wisdom of Strangers: The Perceived Credibility of Online Consumer Reviews on Yelp

Lim, Young Shin; Heide, Brandon Van Der

doi:10.1111/jcc4.12093

Cited by 131 publications

(95 citation statements)

References 41 publications

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“…Based on the assessments of attribution theory, consumers discount a review if they attribute product endorsement to the reviewer's intent to persuade rather than the product performance (Kelley, 1967). Previous studies suggest that the formation of credibility perception takes several routes and the factor structure of credibility depends on the communication context, and is influenced by the characteristics of source, receiver, and message (Lim & Van Der Heide, 2015;Willemsen et al, 2012).…”

Section: Source Credibility Of Consumer Reviewsmentioning

confidence: 99%

How credible are online product reviews? The effects of self-generated and system-generated cues on source credibility evaluation

Yan

2016

Computers in Human Behavior

140

110

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Section: Source Credibility Of Consumer Reviewsmentioning

confidence: 99%

How credible are online product reviews? The effects of self-generated and system-generated cues on source credibility evaluation

Yan

2016

Computers in Human Behavior

140

110

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“…Advisors in each set have entirely different review balances (see Table 2). Although these advisors' profiles cannot present the characteristics of the whole dataset, using a small amount of typical experiment material is acceptable in many studies [9,47].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Given the uncertainty regarding the source of online reviews, buyers are motivated to assess the credibility of advisors based on accessible pieces of information [9]. Many online marketplaces (e.g., Amazon, Taobao) allow buyers to visit advisors' profile page.…”

Section: Introductionmentioning

confidence: 99%

How buyers perceive the credibility of advisors in online marketplace: review balance, review count and misattribution

Noorian

Vassileva

et al. 2015

J Trust Manag

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In an online marketplace, buyers rely heavily on reviews posted by previous buyers (referred to as advisors). The advisor's credibility determines the persuasiveness of reviews. Much work has addressed the evaluation of advisors' credibility based on their static profile information, but little attention has been paid to the effect of the information about the history of advisors' reviews. We conducted three sub-studies to evaluate how the advisors' review balance (proportion of positive reviews) affects the buyer's judgement of advisor's credibility (e.g., trustworthiness, expertise). The result of study 1 shows that advisors with mixed positive and negative reviews are perceived to be more trustworthy, and those with extremely positive or negative review balance are perceived to be less trustworthy. Moreover, the perceived expertise of the advisor increases as the review balance turns from positive to negative; yet buyers perceive advisors with extremely negative review balance as low in expertise. Study 2 finds that buyers might be more inclined to misattribute low trustworthiness to low expertise when they are processing high number of reviews. Finally, study 3 explains the misattribution phenomenon and suggests that perceived expertise has close relationship with affective trust. Both theoretical and practical implications are discussed.

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“…2). Specifically, the most popular ratings for restaurants and There are fraud reviews in online review websites (e.g., [12,15]). According to a recent study focusing on restaurant reviews in the metropolitan area of Boston, US, roughly 16% of restaurant reviews on Yelp are suspicious reviews [15].…”

Section: Datamentioning

confidence: 99%

“…is. In this regard, some researchers have tried to use Yelp reviews to analyze ratings of business venues from different perspectives, including influence of consumer reviews on purchase decisions (e.g., [14,20]), fraud and credibility of reviews (e.g., [12,15]), prediction of venue ratings (see [6,7,9,10,16,29]), venue recommendation [4,13,22,30]. Although more and more researchers are interested in Yelp data, exploiting geospatial information of Yelp data is missing in most of the aforementioned studies.…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of users-generated ratings of yelp venues

Sun

Paule

2017

Open geospatial data, softw. stand.

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Background: With popular location-based services on smart phones, users are willing to leave comments on the business venues (e.g., restaurants, shops, bars, etc.) that they visited. Reviews of users on Yelp venues somewhat indicate satisfaction of customers with services of those venues. Those reviews could be used to reflect service quality of business venues. Geo-localized venues could tell researchers where and how good a business venue is. Methods: In terms of a spatial analysis of venues' ratings, this paper explored geographic patterns of ratings of Yelp business venues in a city-wide region. Specifically, we identified clusters of high and low ratings and explored spatial patterns of clusters of high ratings for different venue categories (i.e., restaurants, fast foods and bars). Results: In this study, we undertook an analysis of Yelp ratings in Phoenix, USA. The empirical results indicate that spatial clusters of high ratings tend to be differently distributed between different categories of Yelp venues. More specifically, bars within or near the city centre are likely to get high ratings. Moreover, although hot spots and cold spots of restaurants and fast foods both tend to be randomly distributed over space, spatial distribution of restaurants' ratings tends to be more similar to that of bars' ratings. Conclusion: Mapping Yelp's business venues with ratings provides a new way to understand spatial patterns of service quality of business or public venues at a large spatial scale.

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Evaluating the Wisdom of Strangers: The Perceived Credibility of Online Consumer Reviews on Yelp

Cited by 131 publications

References 41 publications

How credible are online product reviews? The effects of self-generated and system-generated cues on source credibility evaluation

How credible are online product reviews? The effects of self-generated and system-generated cues on source credibility evaluation

How buyers perceive the credibility of advisors in online marketplace: review balance, review count and misattribution

Spatial analysis of users-generated ratings of yelp venues

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