article

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

Authors:

Shuk Ying HoAuthors Info & Claims

Information Systems Research, Volume 16, Issue 3

Pages 271 - 291

https://doi.org/10.1287/isre.1050.0058

Published: 01 September 2005 Publication History

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.

References

[1]

Allen, C., D. Kania, B. Yacckel. 1998. Internet World Guide to One-To-One Web Marketing. John Wiley and Sons, New York.

[2]

Ardissono, L., A. Goy, G. Petrone, M. Segnan. 2002. Personalization in business-to-customer interaction. Comm. ACM 45(5) 52-53.

Digital Library

[3]

Areni, C. S., M. E. Ferrell, J. B. Wilcox. 2000. The persuasive impact of reported group opinions on individuals low vs. high in need for cognition: Rationalization vs. biased elaboration? Psych. Marketing 17(10) 855-875.

[4]

Bargh, J. A. 2002. Losing consciousness: Automatic influences on consumer judgment, behavior, and motivation. J. Consumer Res. 29(2) 280-285.

[5]

Cacioppo, J. T., R. E. Petty. 1982. The need for cognition. J. Personality Soc. Psych. 42 116-130.

[6]

Cacioppo, J. T., R. E. Petty, K. Morris. 1983. Effects of need for cognition on message evaluation, recall, and persuasion. J. Personality Soc. Psych. 45(4) 805-818.

[7]

Cohan, P. S. 2000. E-Profit: High-Payoff Strategies for Capturing the E-Commerce Edge. AMACOM, New York.

[8]

Cohen, A. R., E. Stotland, D. M. Wolfe. 1955. An experimental investigation of need for cognition. J. Abnormal Soc. Psych. 51 291-294.

[9]

Cohen, J. 1988. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Lawrence Erlbaum, Hillsdale, NJ.

[10]

Collins, A. M., E. G. Loftus. 1975. A spreading-activation theory of semantic processing. Psych. Rev. 83 407-428.

[11]

Dillard, J. P., M. Pfau. 2002. The Persuasion Handbook: Developments in Theory and Practice. SAGE, Thousand Oaks, CA.

[12]

Eagly, A. H., S. Chaiken. 1993. The Psychology of Attitudes. Harcourt Brace Jovanovich, Orlando, FL.

[13]

Eastman, C. M. 2002. 30,000 hits may be better than 300: Precision anomalies in Internet searches. J. Amer. Soc. Inform. Sci. Tech. 53(11) 879-882.

Digital Library

[14]

Economist, The. 2004. You're hired (under Technology Quarterly). (September 18) 21-22.

[15]

Ferguson, T. J., G. R. Rule, D. Carlson. 1983. Memory for personally relevant information. J. Personality Soc. Psych. 44(2) 251-261.

[16]

Festa, P. 2003. Report slams Web personalization. http://news.com.com/2100-1038-5090716.html.

[17]

Fletcher, G. J. O., P. Danilovis, G. Fernandez, D. Peterson, G. D. Reeder. 1986. Attributional complexity: An individual difference measure. J. Personality Soc. Psych. 51(4) 875-884.

[18]

Greenwald, A. G., C. Leavitt. 1984. Audience involvement in advertising: Four levels. J. Consumer Res. 11(1) 581-592.

[19]

Haugtvedt, C., R. Petty, R. Cacioppo. 1992. Need for cognition and advertising: Understanding the role of personality variables in consumer behavior. J. Consumer Psych. 1(3) 239-260.

[20]

Hong, W. Y., J. Y. L. Thong, K. Y. Tam. 2004. Does animation attract online consumers' attention? The effect of Flash on information search tasks. Inform. Systems Res. 15(1) 60-86.

Digital Library

[21]

Jupiter Research Reports That Web Site "Personalization" Does Not Always Provide Positive Results. 2003. (October 14). http://www.jupitermedia.com / corporate / releases / 03.10.14-newjupresearch.html.

[22]

Kim, J. W., B. H. Lee, M. J. Shaw, H. L. Chang, M. Nelson. 2001. Application of decision-tree induction techniques to personalized advertisements on Internet storefronts. Internat. J. Electronic Commerce 5(3) 45-62.

Digital Library

[23]

Klein, S. B., J. F. Kihlstrom. 1986. Elaboration, organization, and the self-reference effect in memory. J. Experiment. Psych. 115(1) 26-38.

[24]

Klein, S. B., J. Loftus. 1988. The nature of self-referent encoding: The contributions of elaborative and organizational processes. J. Personality Soc. Psych. 55(1) 5-11.

[25]

Lachman, R., J. L. Lachman, E. C. Butterfield. 1979. Cognitive Psychology and Information Processing. Lawrence Erlbaum, Hillsdale, NJ.

[26]

Larsen, V., N. D. Wright, T. R. Hergert. 2004. Advertising montage: Two theoretical perspectives. Psych. Marketing 21(1) 1-15.

[27]

Ledford, J. A. L. 2002. Content Personalization Market Trends. Faulkner Information Services, Pennsauken, NJ.

[28]

Light, M., M. T. Maybury. 2002. The adaptive web: Personalized multimedia information access. Comm. ACM 45(5) 54-59.

Digital Library

[29]

MacInnis, D. J., B. J. Jaworski. 1989. Information processing from advertisements: Toward an integrative framework. J. Marketing 53(4) 1-23.

[30]

McAlister, L., E. Pessemier. 1982. Variety seeking behavior: An interdisciplinary review. J. Consumer Res. 9(3) 311-323.

[31]

Mick, D. G. 1992. Levels of subjective comprehension in advertising processing and their relations to ad perceptions, attitudes, and memory. J. Consumer Res. 18(4) 411-424.

[32]

Murthi, B. P. S., S. Sarkar. 2003. The role of the management sciences in research on personalization. Management Sci. 49(10) 1344-1362.

Digital Library

[33]

Nachshon, I. 1985. Directional preferences in perception of visual stimuli. Internat. J. Neuroscience 25(3/4) 161-174.

[34]

Narayana, C. L., R. J. Markin. 1975. Consumer behavior and product performance: An alternative conceptualization. J. Marketing 39(4) 1-6.

[35]

Parkes, C. 2001. The power of personalization Web technologies get personalized to increase profits. http://www.esj.com/features/article.asp?EditorialsID=35.

[36]

Peppers, D., M. Rogers. 1997. Enterprise One to One: Tools for Competing in the Interactive Age. Doubleday, New York.

[37]

Petty, R. E., J. T. Cacioppo. 1981. Attitudes and Persuasion: Classic and Contemporary Approaches. W.C. Brown, Dubuque, IA.

[38]

Petty, R. E., J. T. Cacioppo. 1986a. Communication and Persuasion: Central and Peripheral Routes to Attitude Change. Springer Verlag, New York.

[39]

Petty, R. E., J. T. Cacioppo. 1986b. The elaboration likelihood model of persuasion. Adv. Experiment. Soc. Psych. 19 123-205.

[40]

Roehm, M. L., B. Sternthal. 2001. The moderating effect of knowledge and resources on the persuasive impact of analogies. J. Consumer Res. 28(2) 257-272.

[41]

Rogers, T. B., N. A. Kuiper, W. S. Kirker. 1977. Self-reference and the encoding of personal information. J. Personality Soc. Psych. 35 677-688.

[42]

Rust, R. T., K. N. Lemon. 2001. E-service and the customer. Internal J. Electronic Commerce 5(3) 85-101.

Digital Library

[43]

Scharine, A. A., M. K. McBeath. 2002. Right-handers and Americans favor turning to the right. Human Factors 44(2) 248-256.

[44]

Simon, H. A. 1979. Models of Thought. Yale University Press, New Haven, CT.

[45]

Taylor, S. E., S. C. Thompson. 1982. Stalking the elusive "vividness" effect. Psych. Rev. 89(2) 155-181.

[46]

Van der Heijden, A. H. C. 1992. Selective Attention in Vision. Routledge, New York.

[47]

Vecera, S. P., M. J. Farah. 1994. Does visual attention select objects or locations? J. Experiment. Psych.: General 123(2) 146-160.

[48]

Verplanken, B. 1993. Need for cognition and external information search: Responses to time pressure during decision-making. J. Res. Personality 27(September) 238-252.

[49]

Wyer, R. S., T. K. Srull. 1989. Memory and Cognition in Its Social Context. Lawrence Erlbaum, Hillsdale, NJ.

[50]

Zhang, P. A. 2000. The effects of animation on information seeking performance on the world wide web: Securing attention or interfering with primary tasks? J. AIS 1 1-28.

Digital Library

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cover image Information Systems Research

Information Systems Research Volume 16, Issue 3

September 2005

96 pages

ISSN:1526-5536

Issue’s Table of Contents

Publisher

INFORMS

Linthicum, MD, United States

Publication History

Published: 01 September 2005

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https://dl.acm.org/doi/10.1016/j.tele.2023.102050
Fink LNewman LHaran U(2024)Let me decideComputers in Human Behavior10.1016/j.chb.2024.108244156:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.chb.2024.108244
Wu RLi YLiu JWang S(2023)The Impact of Targeted Online Advertising's Pushing Time on Consumers' Browsing IntentionJournal of Global Information Management10.4018/JGIM.31971331:1(1-17)Online publication date: 10-Mar-2023
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