Subdivided or aggregated online review systems: Which is better for online takeaway vendors?

Hongpeng Wang, Rong Du, Jin Li () and Weiguo Fan
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Hongpeng Wang: Xidian University
Rong Du: Xidian University
Jin Li: Xidian University
Weiguo Fan: University of Iowa

Electronic Commerce Research, 2020, vol. 20, issue 4, No 10, 915-944

Abstract: Abstract This paper examines the role of a subdivided or aggregated online review system to help online takeaway vendors select the most appropriate information strategy. First, we develop two models to depict the interaction between online vendors’ information strategies and consumers’ responses. Second, we take the multidimensional product attributes with their corresponding weights into consideration and illustrate that the sensitivity to product misfits, instead of the relative importance of product attributes, dominates profit maximization. Third, we make a comparison to find the most appropriate scenario to adopt a full or partial information strategy. When a large number of consumers satisfy the delivery time performance, an aggregated review system will be a better choice. Otherwise, vendors are advised to host a subdivided review system. Finally, we universally identify a variance boundary in the rating-star review system, which not only prevents consumers from expressing their real feelings but also makes observing consumer feedback and strategic adjustments inconvenient for online vendors.

Keywords: Online review systems; Information strategies; Multidimensional attributes; Variance boundary (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10660-018-9314-y

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