2021
DOI: 10.2224/sbp.10825
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Exploring extremity and negativity biases in online reviews: Evidence from Yelp. com

Abstract: While some online reviews explicitly praise or criticize a product, others reveal a neutral evaluation. We predicted that extreme reviews would be considered more useful than moderate ones, and that negative reviews would be considered more useful than positive ones. To test these predictions, this study collected a dataset comprising 951,178 reviews of New York restaurants made by 142,286 reviewers on Yelp.com. By combining these two datasets, we incorporated each reviewer’s unique reference point into a mode… Show more

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Cited by 6 publications
(3 citation statements)
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References 33 publications
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“…This also explains why the response time to negative online reviews is shorter than that to positive online reviews. Furthermore, the finding is also consistent with the research conducted by traditional self-report approaches which believed that negative reviews are more useful than ( Casaló et al, 2015b ; Rouliez et al, 2019 ; Roh and Yang, 2021 ) than positive reviews in the aspect of promoting consumers to exclude some products and choose other products.…”
Section: Discussionsupporting
confidence: 87%
“…This also explains why the response time to negative online reviews is shorter than that to positive online reviews. Furthermore, the finding is also consistent with the research conducted by traditional self-report approaches which believed that negative reviews are more useful than ( Casaló et al, 2015b ; Rouliez et al, 2019 ; Roh and Yang, 2021 ) than positive reviews in the aspect of promoting consumers to exclude some products and choose other products.…”
Section: Discussionsupporting
confidence: 87%
“…However, there is a different result from previous research on insignificant influence of service quality againsts E-WOM (Larastanio & Lahindah, 2020;Taryadi & Miftahuddin, 2021). Service quality does not have significant influence on E-WOM under context of hotel online reviews (Roh & Yang, 2021;Roy et al, 2021).…”
Section: Introductioncontrasting
confidence: 60%
“…This construct could be used to build a generalizable model to understand how users evaluate the effectiveness of computer/machine assistance in decision making. Similar constructs have been used to evaluate the effectiveness of mobile apps and artificial intelligence, including the perceived usefulness or helpfulness of customer reviews [45,46], functional value [47], and performance expectancy [48] of mobile apps, as well as competence perception [49], overall reward [50], customer knowledge creation [51], and perceived usefulness [52], to capture the effectiveness of artificial intelligence as assistant tools. While there may be variations in the nomenclature and the contexts used, all of these constructs are based on the idea of measuring the extent to which computers/machines assist users in making informed decisions and, thus, the extent to which users rely on them to reach final decisions.…”
Section: Theoretical Implicationsmentioning
confidence: 99%