Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective

Kar Yan Tam () and Shuk Ying Ho ()
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Kar Yan Tam: Department of Information and Systems Management, School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Shuk Ying Ho: Department of Accounting and Business Information Systems, Faculty of Economics and Commerce, University of Melbourne, Victoria 3010, Australia

Information Systems Research, 2005, vol. 16, issue 3, 271-291

Abstract: With advances in tracking and database technologies, firms are increasingly able to understand their customers and translate this understanding into products and services that appeal to them. Technologies such as collaborative filtering, data mining, and click-stream analysis enable firms to customize their offerings at the individual level. While there has been a lot of hype about web personalization recently, our understanding of its effectiveness is far from conclusive. Drawing on the elaboration likelihood model (ELM) literature, this research takes the view that the interaction between a firm and its customers is one of communicating a persuasive message to the customers driven by business objectives. In particular, we examine three major elements of a web personalization strategy: level of preference matching, recommendation set size, and sorting cue. These elements can be manipulated by a firm in implementing its personalization strategy. This research also investigates a personal disposition, need for cognition, which plays a role in assessing the effectiveness of web personalization. Research hypotheses are tested using 1,000 subjects in three field experiments based on a ring-tone download website. Our findings indicate the saliency of these variables in different stages of the persuasion process. Theoretical and practical implications of the findings are discussed.

Keywords: web personalization; elaboration likelihood model; persuasion; preference matching; human computer interaction; recommendation set size; sorting cue (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (72)

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