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DSS Effectiveness in Marketing Resource Allocation Decisions: Reality vs. Perception

Author

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  • Gary L. Lilien

    (The Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802)

  • Arvind Rangaswamy

    (The Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802)

  • Gerrit H. Van Bruggen

    (Rotterdam School of Management, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands)

  • Katrin Starke

    (The Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802)

Abstract

We study the process by which model-based decision support systems (DSSs) influence managerial decision making in the context of marketing budgeting and resource allocation. We focus on identifying whether and how DSSs influence the decision process (e.g., cognitive effort deployed, discussion quality, and decision alternatives considered) and, as a result, how these DSSs influence decision outcomes (e.g., profit and satisfaction both with the decision process and the outcome). We study two specific marketing resource allocation decisions in a laboratory context: sales effort allocation and customer targeting. We find that decision makers who use high-quality, model-based DSSs make objectively better decisions than do decision makers who only have access to a generic decision tool (Microsoft Excel). However, their subjective evaluations (perceptions) of both their decisions and the processes that lead to those decisions do not necessarily improve as a result of DSS use. And expert judges, serving as surrogates for top management, have a difficult time assessing the objective quality of those decisions.Our results suggest that what managers get from a high-quality DSS may be substantially better than what they see. To increase the inclination for managerial adoption and use of DSS, we must get users to “see” the benefits of using a DSS. Our results also suggest two ways to bridge the perception-reality gap: (1) improve the perceived value of the decision process by designing DSSs both to encourage discussion (e.g., by providing explanation and support for alternative recommendations) as well as to reduce the perceived complexity of the problem so that managers invest more cognitive effort in exploring additional options and (2) provide feedback on the likely market/business outcomes of various decision options.

Suggested Citation

  • Gary L. Lilien & Arvind Rangaswamy & Gerrit H. Van Bruggen & Katrin Starke, 2004. "DSS Effectiveness in Marketing Resource Allocation Decisions: Reality vs. Perception," Information Systems Research, INFORMS, vol. 15(3), pages 216-235, September.
    • Handle: RePEc:inm:orisre:v:15:y:2004:i:3:p:216-235
    DOI: 10.1287/isre.1040.0026
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    References listed on IDEAS

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    2. Ian Wilkinson & Louise Young, 2012. "Toward A Normative Theory of Normative Marketing Theory," Papers 1205.5821, arXiv.org.
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    5. Kayande, U. & de Bruyn, A. & Lilien, G.L. & Rangaswamy, A. & van Bruggen, G.H., 2006. "How Feedback Can Improve Managerial Evaluations of Model-based Marketing Decision Support Systems," ERIM Report Series Research in Management ERS-2006-039-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. Céline Bérard & L.M., Cloutier & Luc Cassivi, 2017. "The effects of using system dynamics-based decision support models: testing policy-makers’ boundaries in a complex situation," Post-Print hal-02128255, HAL.
    7. Korbinian Dress & Stefan Lessmann & Hans-Jorg von Mettenheim, 2017. "Residual Value Forecasting Using Asymmetric Cost Functions," Papers 1707.02736, arXiv.org.
    8. Dress, Korbinian & Lessmann, Stefan & von Mettenheim, Hans-Jörg, 2018. "Residual value forecasting using asymmetric cost functions," International Journal of Forecasting, Elsevier, vol. 34(4), pages 551-565.
    9. Aksoy, Lerzan & Cooil, Bruce & Lurie, Nicholas H., 2011. "Decision Quality Measures in Recommendation Agents Research," Journal of Interactive Marketing, Elsevier, vol. 25(2), pages 110-122.
    10. 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.
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    12. Luiz Moutinho & Paulo Rita & Shuliang Li, 2006. "Strategic diagnostics and management decision making: a hybrid knowledge‐based approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(3), pages 129-155, July.
    13. Céline Bérard, 2010. "Complexité et décision participative : le cas du système de la propriété intellectuelle des innovations biotechnologiques," Post-Print halshs-00519036, HAL.
    14. Ujwal Kayande & Arnaud De Bruyn & Gary L. Lilien & Arvind Rangaswamy & Gerrit H. van Bruggen, 2009. "How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations," Information Systems Research, INFORMS, vol. 20(4), pages 527-546, December.
    15. Dong, Weimin & Swain, Scott D. & Berger, Paul D., 2007. "The role of channel quality in customer equity management," Journal of Business Research, Elsevier, vol. 60(12), pages 1243-1252, December.
    16. Lessmann, Stefan & Coussement, Kristof & De Bock, Koen W. & Haupt, Johannes, 2018. "Targeting customers for profit: An ensemble learning framework to support marketing decision making," IRTG 1792 Discussion Papers 2018-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Céline Bérard & Martin Cloutier L. & Luc Cassivi, 2017. "The effects of using system dynamics-based decision support models: testing policy-makers’ boundaries in a complex situation," Post-Print halshs-01666605, HAL.

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