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Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids

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Author Info

  • Gerald Häubl

    ()
    (Faculty of Business, University of Alberta, Edmonton, Alberta, Canada T6G 2R6)

  • Valerie Trifts

    ()
    (Faculty of Business, University of Alberta, Edmonton, Alberta, Canada T6G 2R6)

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    Abstract

    Despite the explosive growth of electronic commerce and the rapidly increasing number of consumers who use interactive media (such as the World Wide Web) for prepurchase information search and online shopping, very little is known about how consumers make purchase decisions in such settings. A unique characteristic of online shopping environments is that they allow vendors to create retail interfaces with highly interactive features. One desirable form of interactivity from a consumer perspective is the implementation of sophisticated tools to assist shoppers in their purchase decisions by customizing the electronic shopping environment to their individual preferences. The availability of such tools, which we refer to as for consumers, may lead to a transformation of the way in which shoppers search for product information and make purchase decisions. The primary objective of this paper is to investigate the nature of the effects that interactive decision aids may have on consumer decision making in online shopping environments. While making purchase decisions, consumers are often unable to evaluate all available alternatives in great depth and, thus, tend to use two-stage processes to reach their decisions. At the first stage, consumers typically screen a large set of available products and identify a subset of the most promising alternatives. Subsequently, they evaluate the latter in more depth, perform relative comparisons across products on important attributes, and make a purchase decision. Given the different tasks to be performed in such a two-stage process, interactive tools that provide support to consumers in the following respects are particularly valuable: (1) the initial screening of available products to determine which ones are worth considering further, and (2) the in-depth comparison of selected products before making the actual purchase decision. This paper examines the effects of two decision aids, each designed to assist consumers in performing one of the above tasks, on purchase decision making in an online store. The first interactive tool, a (RA), allows consumers to more efficiently screen the (potentially very large) set of alternatives available in an online shopping environment. Based on self-explicated information about a consumer's own utility function (attribute importance weights and minimum acceptable attribute levels), the RA generates a personalized list of recommended alternatives. The second decision aid, a (CM), is designed to help consumers make in-depth comparisons among selected alternatives. The CM allows consumers to organize attribute information about multiple products in an alternatives × attributes matrix and to have alternatives sorted by any attribute. Based on theoretical and empirical work in marketing, judgment and decision making, psychology, and decision support systems, we develop a set of hypotheses pertaining to the effects of these two decision aids on various aspects of consumer decision making. In particular, we focus on how use of the RA and CM affects consumers' search for product information, the size and quality of their consideration sets, and the quality of their purchase decisions in an online shopping environment. A controlled experiment using a simulated online store was conducted to test the hypotheses. The results indicate that both interactive decision aids have a substantial impact on consumer decision making. As predicted, use of the RA reduces consumers' search effort for product information, decreases the size but increases the quality of their consideration sets, and improves the quality of their purchase decisions. Use of the CM also leads to a decrease in the size but an increase in the quality of consumers' consideration sets, and has a favorable effect on some indicators of decision quality. In sum, our findings suggest that interactive tools designed to assist consumers in the initial screening of available alternatives and to facilitate in-depth comparisons among selected alternatives in an online shopping environment may have strong favorable effects on both the quality the efficiency of purchase decisions—shoppers can make much while expending substantially . This suggests that interactive decision aids have the potential to drastically transform the way in which consumers search for product information and make purchase decisions.

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    File URL: http://dx.doi.org/10.1287/mksc.19.1.4.15178
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 19 (2000)
    Issue (Month): 1 (May)
    Pages: 4-21

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    Handle: RePEc:inm:ormksc:v:19:y:2000:i:1:p:4-21

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    Related research

    Keywords: Decision Making; Online Shopping; Electronic Commerce; Decision Aids; Recommendation Agents; Consumer Behavior; Information Search; Consideration Sets; Information Processing;

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    Citations

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    Cited by:
    1. Sylvie Rolland & Déborah Wallet-Wodka, 2003. "Electronic agents on the Internet: A new way to satisfy the consumer?," Post-Print halshs-00143040, HAL.
    2. Basuroy, Suman & Chatterjee, Subimal, 2008. "Fast and frequent: Investigating box office revenues of motion picture sequels," Journal of Business Research, Elsevier, vol. 61(7), pages 798-803, July.
    3. BALAGUE, Christine & LEE, Janghyuk, 2004. "Dynamic modeling of web purchase behavior and e-mailing impact by Petri net," Les Cahiers de Recherche 804, HEC Paris.
    4. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    5. Chen, Liyun, 2009. "What do we pay for asymmetric information? The evolution of mechanisms in online markets," MPRA Paper 22506, University Library of Munich, Germany.
    6. Joel Steckel & Russell Winer & Randolph Bucklin & Benedict Dellaert & Xavier Drèze & Gerald Häubl & Sandy Jap & John Little & Tom Meyvis & Alan Montgomery & Arvind Rangaswamy, 2005. "Choice in Interactive Environments," Marketing Letters, Springer, vol. 16(3), pages 309-320, December.
    7. Simona Botti & Susan Broniarczyk & Gerald Häubl & Ron Hill & Yanliu Huang & Barbara Kahn & Praveen Kopalle & Donald Lehmann & Joe Urbany & Brian Wansink, 2008. "Choice under restrictions," Marketing Letters, Springer, vol. 19(3), pages 183-199, December.
    8. Wu, Lei-Yu & Chen, Kuan-Yang & Chen, Po-Yuan & Cheng, Shu-Ling, 2014. "Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective," Journal of Business Research, Elsevier, vol. 67(1), pages 2768-2776.
    9. Biswas, Dipayan, 2004. "Economics of information in the Web economy: Towards a new theory?," Journal of Business Research, Elsevier, vol. 57(7), pages 724-733, July.
    10. Nils Reisen & Ulrich Hoffrage & Fred W. Mast, 2008. "Identifying decision strategies in a consumer choice situation," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 3(8), pages 641-658, December.
    11. Broekhuizen, Thijs L.J. & Jager, Wander, 2004. "A conceptual model of channel choice: measuring online and offline shopping value perceptions," Research Report 04F04, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    12. Oliver Hinz & Jochen Eckert, 2010. "The Impact of Search and Recommendation Systems on Sales in Electronic Commerce," Business & Information Systems Engineering, Springer, vol. 2(2), pages 67-77, April.
    13. Jella Pfeiffer & Michael Scholz, 2013. "A Low-Effort Recommendation System with High Accuracy," Business & Information Systems Engineering, Springer, vol. 5(6), pages 397-408, December.
    14. Spiekermann, Sarah & Strobel, Martin & Temme, Dirk, 2002. "Drivers and impediments of consumer online information search: Self-controlled versus agent-based search in a high involvement context," SFB 373 Discussion Papers 2002,37, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    15. Nueno, Jose L & Viscarri, Jesus & Mora, Carlos, 2003. "¿Hay BTC en España?," IESE Research Papers D/499, IESE Business School.
    16. Suk, Kwanho & Yoon, Song-Oh, 2012. "The moderating role of decision task goals in attribute weight convergence," Organizational Behavior and Human Decision Processes, Elsevier, vol. 118(1), pages 37-45.
    17. Elcin Akcura, 2013. "Information effects on consumer willingness to pay for electricity and water service attributes," Working Papers 160, European Bank for Reconstruction and Development, Office of the Chief Economist.

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