Online customers' cognitive differences and their impact on the success of recommendation agents

RAs (recommendation agents) have become a major way to assist customers make online purchase decisions. However, do customers consider an RAs' advice to be as important as managers expect? Which customers, if any, use RAs more frequently? Although these questions are crucial to Website management, sparse knowledge of the answers was found. Based on 316 randomly selected customers, we empirically demonstrated that customers' deliberations were not determined by a single cognitive trait, as examined in past IS studies; their decision was more due a function of innovativeness and involvement. More-involved users were found to adopt an RAs' advice. Managerial implications of these findings are discussed.

[1]  Izak Benbasat,et al.  E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..

[2]  Izak Benbasat,et al.  Understanding Customer Trust in Agent-Mediated Electronic Commerce, Web-Mediated Electronic Commerce, and Traditional Commerce , 2004, Inf. Technol. Manag..

[3]  G. Roehrich Consumer innovativeness: Concepts and measurements , 2004 .

[4]  James C. Wetherbe,et al.  An exploratory field study of differences in information technology use between more- and less-innovative middle managers , 1999, Inf. Manag..

[5]  S. Sriram,et al.  The Moderating Role of Consumer and Product Characteristics on the Value of Customized On-Line Recommendations , 2006, Int. J. Electron. Commer..

[6]  G. Foxall,et al.  Innovativeness and Involvement as Determinants of Website Loyalty: I. A test of the style/involvement model in the context of Internet buying , 2006 .

[7]  Mun Y. Yi,et al.  Understanding the Role of Individual Innovativeness in the Acceptance of IT-Based Innovations: Comparative Analyses of Models and Measures , 2006, Decis. Sci..

[8]  Izak Benbasat,et al.  The Role of Design Characteristics in Shaping Perceptions of Similarity: The Case of Online Shopping Assistants , 2006, J. Assoc. Inf. Syst..

[9]  Ulrike Gretzel,et al.  Persuasion in Recommender Systems , 2006, Int. J. Electron. Commer..

[10]  Mark A. Fuller,et al.  Involvement and Decision-Making Performance with a Decision Aid: The Influence of Social Multimedia, Gender, and Playfulness , 2005, J. Manag. Inf. Syst..

[11]  Sanghyun Lee,et al.  The effect of expertise on consumers' satisfaction with the use of interactive recommendation agents , 2008 .

[12]  Rahul Roy,et al.  Offshore Outsourcing: A Dynamic Causal Model of Counteracting Forces , 2005, J. Manag. Inf. Syst..

[13]  Roger J. Calantone,et al.  A comparison of three models to explain shop‐bot use on the web , 2002 .

[14]  Duen-Ren Liu,et al.  Integrating AHP and data mining for product recommendation based on customer lifetime value , 2005, Inf. Manag..

[15]  Tzyy-Ching Yang,et al.  A system architecture for intelligent browsing on the Web , 2000, Decis. Support Syst..

[16]  Izak Benbasat,et al.  Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs , 2007, J. Manag. Inf. Syst..

[17]  Izak Benbasat,et al.  The Role of Similarity in e-Commerce Interactions: The Case of Online Shopping Assistants , 2005 .

[18]  Yi-Cheng Ku,et al.  Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings , 2006, J. Manag. Inf. Syst..

[19]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[20]  Michael J. Gallivan,et al.  The influence of software developers' creative style on their attitudes to and assimilation of a software process innovation , 2003, Inf. Manag..

[21]  Izak Benbasat,et al.  Attributions of Trust in Decision Support Technologies: A Study of Recommendation Agents for E-Commerce , 2008, J. Manag. Inf. Syst..