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The Quality Double Whammy

Author

Listed:
  • William Boulding

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Ajay Kalra

    (Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Richard Staelin

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Abstract

This research focuses on how consumers perceive the quality level of a complex stimulus (in our case, a service encounter) and how this perception affects consumers' overall assessment of the quality level of the firm. As such, it should be of interest to consumer behavior theorists as well as to those involved in marketing management issues. We start our presentation by developing a normative Bayesian data integration and updating model somewhat similar to that put forth by Rust et al. (1999). The major constructs of interest in this model are the person's belief about the overall service quality level of a firm and what will happen on the next service encounter. Two major results following from this baseline normative model are that the updated means of these two beliefs are identical to the dynamic updating equations put forth by Boulding et al. (1993), and that an underlying assumption of this model is that consumers form unbiased perceptions of the complex stimulus. Next, based on substantial empirical evidence from the behavioral literature, we incorporate into this baseline model a process by which consumers form nonrandom perceptions. Specifically, we postulate that a person's perception of a complex service encounter is a blend of the objective dimensions of the service encounter and the person's prior overall belief about the quality level of the firm. The relative weights placed on these two factors are determined at least in part by the experience level of the customer and the complexity of the service encounter. We then use this expanded model to compare and test different models of service quality and provide deeper insights into the process by which consumers form perceptions of both the service transaction and the overall service quality level of a firm. We test this model using data from two different experiments. In each experiment we manipulate the service provided and the person's initial and . Using both obtrusive and unobtrusive measures of the underlying constructs of our model, we employ multiple tests to determine the veracity of our expanded model relative to the baseline model. These tests support the expanded model specification. As found previously, and as predicted by a Bayesian updating process, consumers' prior beliefs influence their cumulative overall opinion of service quality (e.g., Anderson and Sullivan 1993, Bolton and Drew 1991, Boulding et al. 1993, Rust et al. 1999). Perhaps more importantly, these prior beliefs also influence their perceptions of the data themselves, which in turn affect their new (updated) overall opinion. These two different influences of prior beliefs on cumulative evaluations of quality constitute the “double whammy” referred to in the title. This double whammy effect has major managerial significance. Specifically, we see from our model development that all activities of a firm will be perceived in light of a person's prior beliefs and that these priors will be double counted relative to our baseline model. Thus, any marketing action taken by a firm will be perceived more positively or negatively depending on the person's prior belief about the quality level of the firm. As such, our model provides a formal explanation for the notion of brand equity as “differential leverage” in marketing activities as proposed by Keller (1993).

Suggested Citation

  • William Boulding & Ajay Kalra & Richard Staelin, 1999. "The Quality Double Whammy," Marketing Science, INFORMS, vol. 18(4), pages 463-484.
  • Handle: RePEc:inm:ormksc:v:18:y:1999:i:4:p:463-484
    DOI: 10.1287/mksc.18.4.463
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    References listed on IDEAS

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    Cited by:

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    2. Bolton, R.N. & Lemo, K.N. & Verhoef, P.C., 2002. "The Theoretical Underpinnings of Customer Asset Management," ERIM Report Series Research in Management ERS-2002-80-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.
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    5. Bolton, Ruth N. & Gustafsson, Anders & Tarasi, Crina O. & Witell, Lars, 2022. "Managing a Global Retail Brand in Different Markets: Meta-Analyses of Customer Responses to Service Encounters," Journal of Retailing, Elsevier, vol. 98(2), pages 294-314.
    6. Teck H. Ho & Xin Wang & Colin F. Camerer, 2008. "Individual Differences in EWA Learning with Partial Payoff Information," Economic Journal, Royal Economic Society, vol. 118(525), pages 37-59, January.
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    8. Russo, J.E. & Yong, Kevyn, 2011. "The distortion of information to support an emerging evaluation of risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 132-139, May.
    9. Bond, Samuel D. & Carlson, Kurt A. & Meloy, Margaret G. & Russo, J. Edward & Tanner, Robin J., 2007. "Information distortion in the evaluation of a single option," Organizational Behavior and Human Decision Processes, Elsevier, vol. 102(2), pages 240-254, March.
    10. Kopalle, Praveen K. & Lehmann, Donald R., 2015. "The Truth Hurts: How Customers May Lose From Honest Advertising," International Journal of Research in Marketing, Elsevier, vol. 32(3), pages 251-262.
    11. Gupta, Shaphali & Leszkiewicz, Agata & Kumar, V. & Bijmolt, Tammo & Potapov, Dmitriy, 2020. "Digital Analytics: Modeling for Insights and New Methods," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 26-43.
    12. Rong Chen & Jianmin Jia, 2012. "Regret and performance uncertainty in consumer repeat choice," Marketing Letters, Springer, vol. 23(1), pages 353-365, March.
    13. Ajay Kalra & Mengze Shi & Kannan Srinivasan, 2003. "Salesforce Compensation Scheme and Consumer Inferences," Management Science, INFORMS, vol. 49(5), pages 655-672, May.
    14. Carlson, Kurt A. & Pearo, Lisa Klein, 2004. "Limiting predecisional distortion by prior valuation of attribute components," Organizational Behavior and Human Decision Processes, Elsevier, vol. 94(1), pages 48-59, May.
    15. Azimian, Alireza & Kilgour, D. Marc & Noori, Hamid, 2016. "Mitigating contagion risk by investing in the safety of rivals," European Journal of Operational Research, Elsevier, vol. 254(3), pages 935-945.
    16. Vanhouche, Wouter & Alba, Joseph W., 2009. "Generalizing from negative experiences," International Journal of Research in Marketing, Elsevier, vol. 26(3), pages 238-244.
    17. Nuno Camacho & Bas Donkers & Stefan Stremersch, 2011. "Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality," Marketing Science, INFORMS, vol. 30(2), pages 305-320, 03-04.
    18. Sullivan, Ursula Y. & Thomas, Jacquelyn S., 2004. "Customer Migration: An Empirical Investigation across Multiple Channels," Working Papers 04-0112, University of Illinois at Urbana-Champaign, College of Business.
    19. Chakravarthi Narasimhan & Chuan He & Eric Anderson & Lyle Brenner & Preyas Desai & Dmitri Kuksov & Paul Messinger & Sridhar Moorthy & Joseph Nunes & Yuval Rottenstreich & Richard Staelin & George Wu &, 2005. "Incorporating Behavioral Anomalies in Strategic Models," Marketing Letters, Springer, vol. 16(3), pages 361-373, December.
    20. Biswas, Dipayan & Pechmann, Cornelia, 2012. "What do these clinical trial results mean? How product efficacy judgments are affected by data partitioning, framing, and quantification," Organizational Behavior and Human Decision Processes, Elsevier, vol. 117(2), pages 341-350.
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    22. Duncan Simester & Juanjuan Zhang, 2010. "Why Are Bad Products So Hard to Kill?," Management Science, INFORMS, vol. 56(7), pages 1161-1179, July.
    23. Praveen K. Kopalle & Donald R. Lehmann, 2006. "Setting Quality Expectations When Entering a Market: What Should the Promise Be?," Marketing Science, INFORMS, vol. 25(1), pages 8-24, 01-02.

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