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Erschienen in: Electronic Commerce Research 4/2020

14.07.2018

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

verfasst von: Hongpeng Wang, Rong Du, Jin Li, Weiguo Fan

Erschienen in: Electronic Commerce Research | Ausgabe 4/2020

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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.

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Metadaten
Titel
Subdivided or aggregated online review systems: Which is better for online takeaway vendors?
verfasst von
Hongpeng Wang
Rong Du
Jin Li
Weiguo Fan
Publikationsdatum
14.07.2018
Verlag
Springer US
Erschienen in
Electronic Commerce Research / Ausgabe 4/2020
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI
https://doi.org/10.1007/s10660-018-9314-y

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