Effectiveness of product recommendation framing on online retail platforms

Junhui Zhang, M. S. Balaji, Jun Luo, Subhash Jha

Research output: Journal PublicationArticlepeer-review

Abstract

Online retailers often display product recommendations using recommendation framing or signage. Recommendation framing—such as customers who viewed this also viewed or compared similar items—reflects user- or product-related inputs used by the algorithmic product recommender system to identify products for a target customer. The current study examined the effectiveness of norm-based recommendation framing and comparison-based recommendation framing on customers’ click-through intention of the products recommended by online retailers. Four studies were conducted to test the proposed hypotheses. Findings revealed that norm-based recommendation framing is more effective than comparison-based recommendation framing and that the perceived value of the recommendations is the underlying mechanism engendering this result. Furthermore, we observed that the effectiveness of norm-based recommendation framing was only apparent when fewer products were recommended and when the recommended products were highly substitutable for the focal product. Theoretical and managerial implications are discussed regarding online retailers’ efforts to manage improved recommendation-framing strategies.

Original languageEnglish
Pages (from-to)185-197
Number of pages13
JournalJournal of Business Research
Volume153
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Norm-based recommendation framing
  • Online retailers
  • Recommendation framing
  • Recommendation size
  • Recommender system

ASJC Scopus subject areas

  • Marketing

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