The Relative Effect of the Convergence of Product Recommendations from Various Online Sources

• 成果类型: Article
• 署名作者: Xu, Jingjun (David); Benbasat, Izak; Cenfetelli, Ronald T.
• 署名单位: City University of Hong Kong; University of British Columbia
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2020.1790192
• 发表日期: 2020
• 页码: 788-819
• 关键词: word-of-mouth; common method variance; consumer reviews; TASK COMPLEXITY; 2-stage model; fake news; INFORMATION; perceptions; DECISION; uncertainty

摘要

Most previous studies about online product recommendation sources (recommendation agents [RAs], consumers, and experts) have been limited to the evaluation by a single source on a website. Thus, the relative influence of convergent recommendations from different sources on consumers' acceptance of the advice remains largely unknown. We draw upon and extend the product uncertainty model to theorize how the convergence of recommendations from various sources differentially influences customers' acceptance of recommendations. Our experiments show that the recommendation convergence between RAs and experts leads to the greater recommendation acceptance of the jointly recommended products than the convergence between experts and consumers or convergence between RAs and consumers. The rationale is that RAs best reduce fit uncertainty, and experts best reduce description and performance uncertainties. Experts and RAs complement each other by reducing all three dimensions of product uncertainty. Online merchants are advised to incorporate multiple sources into their websites, including sources (i.e., RAs and experts) that play complementary roles in reducing product uncertainty.