IDEAS home Printed from https://ideas.repec.org/a/igg/rmj000/v14y2001i1p31-48.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/irmj.2001010104
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    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. Suleyman Ozarslan & P. Erhan Eren, 0. "MobileCDP: A mobile framework for the consumer decision process," Information Systems Frontiers, Springer, vol. 0, pages 1-22.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:rmj000:v:14:y:2001:i:1:p:31-48. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.