Document Type

Journal Article

Publication Title

Journal of Business Research

Volume

125

First Page

354

Last Page

367

Publisher

Elsevier

School

School of Business and Law

RAS ID

32494

Funders

Edith Cowan University - Open Access Support Scheme 2020

Comments

Meek, S., Wilk, V., & Lambert, C. (2021). A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews. Journal of Business Research, 125, 354-367. https://doi.org/10.1016/j.jbusres.2020.12.001

Abstract

© 2020 Edith Cowan University With the proliferation of user generated online reviews, uncovering helpful restaurant reviews is increasingly challenging for potential consumers. Heuristics (such as “Likes”) not only facilitate this process but also enhance the social impact of a review on an Online Opinion Platform. Based on Dual Process Theory and Social Impact Theory, this study explores which contextual and descriptive attributes of restaurant reviews influence the reviewee to accept a review as helpful and thus, “Like” the review. Utilising both qualitative and quantitative methodologies, a big data sample of 58,468 restaurant reviews on Zomato were analysed. Results revealed the informational factor of positive recommendation framing and the normative factors of strong argument quality and moderate recommendation ratings, influence the generation of a reviewee “Like”. This study highlights the important filtering function a heuristic can offer prospective customers which can also result in greater social impact for the Online Opinion Platform.

DOI

10.1016/j.jbusres.2020.12.001

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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