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Technology-Augmented Choice: How Digital Innovations Are Transforming Consumer Decision Processes

Author

Listed:
  • Shiri Melumad

    (University of Pennsylvania)

  • Rhonda Hadi

    (University of Oxford)

  • Christian Hildebrand

    (University of St. Gallen)

  • Adrian F. Ward

    (University of Texas at Austin)

Abstract

This paper provides an overview of recent research that explores how digital technologies such as mobile devices, wearables, voice technology, and recommendation agents are transforming consumer decision-making. We advance a conceptual model of technology-augmented choice that describes how the three Ms of technology—mediums (i.e., device types), modalities (i.e., interaction interfaces), and modifiers (i.e., intelligent agents)—are becoming increasingly integral elements of consumer decision processes. For instance, today’s new technologies often help curate consideration sets, shape how options are evaluated, and even guide choices themselves. As a result, market choices must now be viewed as a joint function of both consumer preferences and the characteristics of the technological environment in which those preferences are expressed. Examples of empirical research are reviewed that characterize the interdependencies between technology and decision-making, including how smartphones transform user-generated content, voice technology affects consumer search, haptic interfaces shape product preferences, and search engines alter confidence in choice.

Suggested Citation

  • Shiri Melumad & Rhonda Hadi & Christian Hildebrand & Adrian F. Ward, 2021. "Technology-Augmented Choice: How Digital Innovations Are Transforming Consumer Decision Processes," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 7(3), pages 90-101, October.
  • Handle: RePEc:spr:custns:v:7:y:2021:i:3:d:10.1007_s40547-020-00107-4
    DOI: 10.1007/s40547-020-00107-4
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    1. John G. Lynch , Jr. & Dan Ariely, 2000. "Wine Online: Search Costs Affect Competition on Price, Quality, and Distribution," Marketing Science, INFORMS, vol. 19(1), pages 83-103, April.
    2. Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. "Constructive Consumer Choice Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 187-217, December.
    3. Sam Ransbotham & Nicholas H. Lurie & Hongju Liu, 2019. "Creation and Consumption of Mobile Word of Mouth: How Are Mobile Reviews Different?," Marketing Science, INFORMS, vol. 38(5), pages 773-792, September.
    4. repec:cup:judgdm:v:9:y:2014:i:2:p:167-175 is not listed on IDEAS
    5. Oliver Rutz & Randolph Bucklin, 2012. "Does banner advertising affect browsing for brands? clickstream choice model says yes, for some," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 231-257, June.
    6. Burke, Raymond R, et al, 1992. "Comparing Dynamic Consumer Choice in Real and Computer-Simulated Environments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(1), pages 71-82, June.
    7. Erik Brynjolfsson & Kristina McElheran, 2016. "The Rapid Adoption of Data-Driven Decision-Making," American Economic Review, American Economic Association, vol. 106(5), pages 133-139, May.
    8. Robert Meyer & Kenneth Broad & Ben Orlove & Nada Petrovic, 2013. "Dynamic Simulation as an Approach to Understanding Hurricane Risk Response: Insights from the Stormview Lab," Risk Analysis, John Wiley & Sons, vol. 33(8), pages 1532-1552, August.
    9. Kahn, Barbara E & Isen, Alice M, 1993. "The Influence of Positive Affect on Variety Seeking among Safe, Enjoyable Products," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(2), pages 257-270, September.
    10. Hildebrand, Christian & Efthymiou, Fotis & Busquet, Francesc & Hampton, William H. & Hoffman, Donna L. & Novak, Thomas P., 2020. "Voice analytics in business research: Conceptual foundations, acoustic feature extraction, and applications," Journal of Business Research, Elsevier, vol. 121(C), pages 364-374.
    11. Jordan Etkin, 2016. "The Hidden Cost of Personal Quantification," Journal of Consumer Research, Oxford University Press, vol. 42(6), pages 967-984.
    12. Mulcahy, Rory Francis & Riedel, Aimee S., 2020. "‘Touch it, swipe it, shake it’: Does the emergence of haptic touch in mobile retailing advertising improve its effectiveness?," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    13. Cassie Mogilner & Jennifer Aaker & Sepandar D. Kamvar, 2012. "How Happiness Affects Choice," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 39(2), pages 429-443.
    14. Cowan, Ruth Schwartz, 1997. "A Social History of American Technology," OUP Catalogue, Oxford University Press, number 9780195046052.
    15. Jie Xu & Min Ding, 2019. "Using the Double Transparency of Autonomous Vehicles to Increase Fairness and Social Welfare," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 6(1), pages 26-35, June.
    16. Amos Tversky & Itamar Simonson, 1993. "Context-Dependent Preferences," Management Science, INFORMS, vol. 39(10), pages 1179-1189, October.
    17. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    18. Donna L Hoffman & Thomas P Novak & Eileen FischerEditor & Robert KozinetsAssociate Editor, 2018. "Consumer and Object Experience in the Internet of Things: An Assemblage Theory Approach," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1178-1204.
    19. Goodwin, Paul & Sinan Gönül, M. & Önkal, Dilek, 2013. "Antecedents and effects of trust in forecasting advice," International Journal of Forecasting, Elsevier, vol. 29(2), pages 354-366.
    20. Andrew Prahl & Lyn Van Swol, 2017. "Understanding algorithm aversion: When is advice from automation discounted?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 691-702, September.
    21. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
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