Journal article
Subdivided or aggregated online review systems: Which is better for online takeaway vendors?
Electronic commerce research, Vol.20(4), pp.1-30
07/14/2018
DOI: 10.1007/s10660-018-9314-y
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.
Details
- Title: Subtitle
- Subdivided or aggregated online review systems: Which is better for online takeaway vendors?
- Creators
- Hongpeng WangRong DuJin LiWeiguo Fan
- Resource Type
- Journal article
- Publication Details
- Electronic commerce research, Vol.20(4), pp.1-30
- DOI
- 10.1007/s10660-018-9314-y
- ISSN
- 1389-5753
- eISSN
- 1572-9362
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 71771184; name: Humanities and Social Science Talent Plan, award: ER42015060002; name: Natural Science Basic Research Plan in Shaanxi Province of China, award: 2017JQ7012; name: Chinese Fundamental Research Funds for the Central Universities, award: JB180602
- Language
- English
- Date published
- 07/14/2018
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083893902771
Metrics
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