A Two-Stage Model of Generating Product Advice : Proposing and Testing the Complementarity Principle

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

9 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)826-862
Journal / PublicationJournal of Management Information Systems
Volume34
Issue number3
Publication statusPublished - Nov 2017

Abstract

Most extant research into product recommendations focuses on how advice from recommendation agents (RAs), consumers, or experts facilitates an initial (or single-stage) screening of available products and provides relevant product recommendations. The literature has largely overlooked the possibility and effects of the second stage of product advice using a recommendation improvement (RI) functionality, during which users can refine and improve the accuracy of the first-stage product recommendations. Thus, our understanding of how users make product choices is incomplete. To rectify this, we propose a two-stage model of generating product advice, and we use it to test what we propose as the complementarity principle. This principle posits that the first-stage recommendations (personalized or nonpersonalized) influence the impact of different types of second-stage RI functionality, which augment the first stage by facilitating either alternative-based or attribute-based processing. Results show that the complementary synergies between the two stages result in higher perceived decision quality, but at the expense of higher perceived decision effort. We contribute to the literature by helping researchers better understand users’ adoption of the second-stage RI functionality in conjunction with first-stage recommendations. In addition, e-commerce designers are advised to provide different and complementary types of recommendation sources and RI functionalities to facilitate online consumers’ decision making.

Research Area(s)

  • online recommenders, perceived decision effort, perceived decision quality, personalized recommendations, product recommendations, recommendation improvement, recommendation sources