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.
Key words: DSS; marketing models; decision quality; decision process; resource allocation
The determination and allocation of a budget (of time or resources, financial or otherwise) is a pervasive human activity. For example, we must all determine our budget for food, necessities, and leisure activities and allocate those budgets within those categories. We must also determine how much of our time we will work each week and how much of our remaining time we will spend with our children, surfing the Internet, watching television, and the like. Firms continually face such resource allocation challenges. They must determine how much to spend on new product development and how to allocate those funds across projects and time. Charitable organizations must determine what their development budget should be and what past donors or prospects to target. Manufacturers must decide how much plant capacity to invest in and where that capacity should be placed.
The determination of the budget and the allocation of that budget are tasks that are straightforward to define conceptually and mathematically, but not at all that simple for humans to perform "optimally" without some decision aid. Indeed, such decisions helped form Simon's (1955) view of satisficing behavior, where he states that "there is a complete lack of evidence that in actual human choice situations these computations can be or are in fact performed" (p. 105). It is perhaps not surprising, therefore, that a search on Google on June 14, 2004, for "resource allocation" and "software" turned up nearly 390,000 links. While it would seem then, that in an area of such importance, we would have substantial and definitive evidence about the benefits and costs of using decision support aids for resource allocation in various application domains, such is not the case. For example, Agarwal et al. (1992) describe critical elements missing in systems to help support the choice of management information system (MIS) projects under resource constraints, a resource allocation task; Muckstadt et al. (2001) describe at least five key elements...
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