DSS effectiveness in marketing resource allocation decisions: Reality vs. perception

Gary L. Lilien, Arvind Rangaswamy, Gerrit H. Van Bruggen, Katrin Starke

Research output: Contribution to journalReview articlepeer-review

75 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)216-235
Number of pages20
JournalInformation Systems Research
Volume15
Issue number3
DOIs
StatePublished - Sep 2004

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

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