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Influence of Query-Based Decision Aids on Consumer Decision Making in Electronic Commerce

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  • Rex E. Pereira

    (Drake University, USA)

Abstract

This research investigates the influence of computerized search engines on consumer decision making in the electronic commerce environment. The results indicate that by providing well-designed decision aids to consumers, it is possible to significantly increase consumer confidence, satisfaction, and decision quality. Consumers who have access to query-based decision aids perceive increased cost savings and lower cognitive decision effort associated with the purchase decision. The future challenge in developing consumer-oriented computerized decision aids does not reside in technological advances, but rather in developing systems that are useful and appealing to the intended consumer. This is necessary to avoid consumer perceptions of non-utility, and ultimately non-use of the computerized decision aids. The challenge for marketing managers is to provide consumers with information systems that change over time such that they fulfill the consumers’ short-term needs without sacrificing the consumers’ long-term interests.

Suggested Citation

  • Rex E. Pereira, 2001. "Influence of Query-Based Decision Aids on Consumer Decision Making in Electronic Commerce," Information Resources Management Journal (IRMJ), IGI Global, vol. 14(1), pages 31-48, January.
    • Handle: RePEc:igg:rmj000:v:14:y:2001:i:1:p:31-48
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    Citations

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    Cited by:

    1. Suleyman Ozarslan & P. Erhan Eren, 2018. "MobileCDP: A mobile framework for the consumer decision process," Information Systems Frontiers, Springer, vol. 20(4), pages 803-824, August.
    2. Xitong Li & Jörn Grahl & Oliver Hinz, 2022. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Information Systems Research, INFORMS, vol. 33(2), pages 620-637, June.
    3. Suleyman Ozarslan & P. Erhan Eren, 0. "MobileCDP: A mobile framework for the consumer decision process," Information Systems Frontiers, Springer, vol. 0, pages 1-22.
    4. Gaofeng Yi, 2020. "Why are Some Recommendation Systems Preferred?," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 14(2), pages 76-86.
    5. Ourania Vitouladiti, 2015. "The Evolved And More Complex Role Of Travel Agencies And Tour Operators In The Online Era. Effects On Their Marketing Management," Tourism Research Institute, Journal of Tourism Research, vol. 11(1), pages 190-200, September.
    6. Tsao, Wen-Yu, 2013. "The fitness of product information: Evidence from online recommendations," International Journal of Information Management, Elsevier, vol. 33(1), pages 1-9.
    7. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.
    8. Cheng Yi & Zhenhui (Jack) Jiang & Izak Benbasat, 2017. "Designing for Diagnosticity and Serendipity: An Investigation of Social Product-Search Mechanisms," Information Systems Research, INFORMS, vol. 28(2), pages 413-429, June.
    9. Yi, Sangyoon & Kim, Dongyeon & Ju, Jaehyeon, 2022. "Recommendation technologies and consumption diversity: An experimental study on product recommendations, consumer search, and sales diversity," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    10. Gudigantala, Naveen & Song, Jaeki & Jones, Donald, 2011. "User satisfaction with Web-based DSS: The role of cognitive antecedents," International Journal of Information Management, Elsevier, vol. 31(4), pages 327-338.

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