Optimal Information Acquisition and Sharing Decisions: Joint Reviews on Crowdsourcing Product Design

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Author(s): Jizi Li (Wuhan University of Science and Technology, China), Xiaodie Wang (Huazhong University of Science and Technology, China), Justin Z. Zhang (University of North Florida, USA)and Longyu Li (Fudan University, China)
Copyright: 2024
Volume: 35
Issue: 1
Pages: 34
Source title: Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (City University of Hong Kong, Hong Kong SAR)
DOI: 10.4018/JDM.337971

Keywords: Data Mining and Databases / Databases / Information Science Reference / Library & Information Science

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Abstract

The acquisition and sharing of reviews have significant ramifications for the selection of crowdsourcing designs before mass production. This article studies the optimal decision of a brand enterprise regarding the acquisition/sharing of crowdsourcing design reviews in a supply chain. The authors consider an analytical model where the brand enterprise can privately acquire the manufacturer's review (MR) of crowdsourcing product designs and choose one of two information-sharing schemes—optional or mandatory sharing—to disclose MR to the key opinion leaders (KOLs), which help them to produce fans' reviews (FR). MR and FR integrate into the joint reviews (JR) that impact prospective consumers' purchase intention. The authors find that mandatory sharing significantly harms the brand enterprise's motivation to obtain MR, yet optional sharing is conducive to boosting JR on crowdsourcing designs. In addition, JR has a ceiling value, implying that excessively high FR and MR could not always enhance the effect of JR on crowdsourcing designs.

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Optimal Information Acquisition and Sharing Decisions: Joint Reviews on Crowdsourcing Product Design

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