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Smart Agents: When Lower Search Costs for Quality Information Increase Price Sensitivity

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  • Diehl, Kristin
  • Kornish, Laura J
  • Lynch, John G, Jr

Abstract

Recent consumer research suggests that lowering search costs for quality information reduces consumer price sensitivity by creating greater perceived differentiation among brands (e.g., Kaul and Wittink 1995; Lynch and Ariely 2000). We argue that lowering quality search costs by smart agents can have the opposite effect on differentiation and price sensitivity. Smart agents screen through a universe of alternatives, recommending only a handful well-matched to the customer's quality preferences. In this research, we ask and answer the following questions: In markets in which price and quality are uncorrelated, will the use of screening agents increase or decrease prices paid compared to searching from an unordered list of options? Will increasing the size of the store's underlying assortment increase or decrease prices paid when options have been screened on quality? In markets where higher priced goods have higher quality, will the use of screening agents increase or decrease prices paid and quality selected? Experiments 1 and 2 test the effect of quality screening when price and quality are uncorrelated. We then present an analytic model for markets in which price and quality are correlated. We deduce that ordering can cause price and quality to increase or decrease depending on the slope of the price-quality relationship in comparison with the relative importance of price in the utility function. We find support for this model in experiment 3. Copyright 2003 by the University of Chicago.

Suggested Citation

  • Diehl, Kristin & Kornish, Laura J & Lynch, John G, Jr, 2003. " Smart Agents: When Lower Search Costs for Quality Information Increase Price Sensitivity," Journal of Consumer Research, Oxford University Press, vol. 30(1), pages 56-71, June.
  • Handle: RePEc:oup:jconrs:v:30:y:2003:i:1:p:56-71
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    File URL: http://dx.doi.org/10.1086/374698
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    2. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
    3. Eric Johnson & Suzanne Shu & Benedict Dellaert & Craig Fox & Daniel Goldstein & Gerald Häubl & Richard Larrick & John Payne & Ellen Peters & David Schkade & Brian Wansink & Elke Weber, 2012. "Beyond nudges: Tools of a choice architecture," Marketing Letters, Springer, vol. 23(2), pages 487-504, June.
    4. Gavan J. Fitzsimons & Donald R. Lehmann, 2004. "Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses," Marketing Science, INFORMS, vol. 23(1), pages 82-94, September.
    5. Koehler, C.F. & Breugelmans, E. & Dellaert, B.G.C., 2010. "Consumer Acceptance of Recommendations by Interactive Decision Aids: The Joint Role of Temporal Distance and Concrete vs. Abstract Communications," ERIM Report Series Research in Management ERS-2010-041-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Hee Jin Kim & Song Oh Yoon, 2016. "The effect of category label specificity on consumer choice," Marketing Letters, Springer, vol. 27(4), pages 765-777, December.
    7. Bonsall, Peter & Shires, Jeremy & Maule, John & Matthews, Bryan & Beale, Jo, 2007. "Responses to complex pricing signals: Theory, evidence and implications for road pricing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(7), pages 672-683, August.
    8. Vandana Ramachandran & Siva Viswanathan & Sanjay Gosain, 2007. "The Impact of Online Information on the Purchase of Certified Used Cars," Working Papers 07-37, NET Institute.
    9. Viswanath Venkatesh & Ritu Agarwal, 2006. "Turning Visitors into Customers: A Usability-Centric Perspective on Purchase Behavior in Electronic Channels," Management Science, INFORMS, pages 367-382.
    10. Quaschning, Simon & Pandelaere, Mario & Vermeir, Iris, 2014. "When and why attribute sorting affects attribute weights in decision-making," Journal of Business Research, Elsevier, vol. 67(7), pages 1530-1536.
    11. Punj, Girish & Moore, Robert, 2009. "Information search and consideration set formation in a web-based store environment," Journal of Business Research, Elsevier, vol. 62(6), pages 644-650, June.
    12. Arnaud De Bruyn & John C. Liechty & Eelko K. R. E. Huizingh & Gary L. Lilien, 2008. "Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids," Marketing Science, INFORMS, vol. 27(3), pages 443-460, 05-06.
    13. Song Yao & Carl F. Mela, 2008. "A Dynamic Model of Sponsored Search Advertising," Working Papers 08-16, NET Institute, revised Sep 2008.
    14. Homburg, Christian & Allmann, Jan & Klarmann, Martin, 2014. "Internal and external price search in industrial buying: The moderating role of customer satisfaction," Journal of Business Research, Elsevier, vol. 67(8), pages 1581-1588.
    15. Tuck Siong Chung & Roland T. Rust & Michel Wedel, 2009. "My Mobile Music: An Adaptive Personalization System for Digital Audio Players," Marketing Science, INFORMS, vol. 28(1), pages 52-68, 01-02.
    16. Jie Jennifer Zhang & Bing Jing, 2007. "The Impacts of Shopbots on Online Consumer Search," Working Papers 07-34, NET Institute, revised Sep 2007.
    17. Shang, Wenjing & Hooker, Neal H., 2006. "Scales or Stars? Consumer Preferences for Food Quality Signals," 2006 Annual meeting, July 23-26, Long Beach, CA 21237, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    18. Franziska Völckner & Alexander Rühle & Martin Spann, 2012. "To divide or not to divide? The impact of partitioned pricing on the informational and sacrifice effects of price," Marketing Letters, Springer, vol. 23(3), pages 719-730, September.

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