http://repub.eur.nl/res/aut/854/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/15059/ 2008-12-01T00:00:00Z Model-based decision support systems (DSSs) improve performance in many contexts that are datarich, uncertain, and require repetitive decisions. But such DSSs are often not designed to help users understand and internalize the underlying factors driving DSS recommendations. Users then feel uncertain about DSS recommendations, leading them to possibly avoid using the system. We argue that a DSS must be designed to induce an alignment of a decision maker’s mental model with the decision model embedded in the DSS. Such an alignment requires effort from the decision maker and guidance from the DSS. We experimentally evaluate two DSS design characteristics that facilitate such alignment: (i) feedback on the upside potential for performance improvement and (ii) feedback on corrective actions to improve decisions. We show that, in tandem, these two types of DSS feedback induce decision makers to align their mental models with the decision model, a process we call deep learning, whereas individually these two types of feedback have little effect on deep learning. We also show that deep learning, in turn, improves user evaluations of the DSS. We discuss how our findings can potentially lead to DSS design improvements and better returns on DSS investments. http://repub.eur.nl/res/pub/7893/ 2006-08-14T00:00:00Z Marketing managers often provide much poorer evaluations of model-based marketing decision support systems (MDSSs) than are warranted by the objective performance of those systems. We show that a reason for this discrepant evaluation may be that MDSSs are often not designed to help users understand and internalize the underlying factors driving the MDSS results and related recommendations. Thus, there is likely to be a gap between a marketing manager’s mental model and the decision model embedded in the MDSS. We suggest that this gap is an important reason for the poor subjective evaluations of MDSSs, even when the MDSSs are of high objective quality, ultimately resulting in unreasonably low levels of MDSS adoption and use. We propose that to have impact, an MDSS should not only be of high objective quality, but should also help reduce any mental model-MDSS model gap. We evaluate two design characteristics that together lead model-users to update their mental models and reduce the mental model-MDSS gap, resulting in better MDSS evaluations: providing feedback on the upside potential for performance improvement and providing specific suggestions for corrective actions to better align the user's mental model with the MDSS. We hypothesize that, in tandem, these two types of MDSS feedback induce marketing managers to update their mental models, a process we call deep learning, whereas individually, these two types of feedback will have much smaller effects on deep learning. We validate our framework in an experimental setting, using a realistic MDSS in the context of a direct marketing decision problem. We then discuss how our findings can lead to design improvements and better returns on investments in MDSSs such as CRM systems, Revenue Management systems, pricing decision support systems, and the like. http://repub.eur.nl/res/pub/11083/ 2005-12-01T00:00:00Z In the early 21st century, firms are thinking seriously and practically about an interactive marketing paradigm—one that integrates mass scale with individual responsiveness. The focus of this paper is on how this interactive environment is changing the customer decision-making process. With the increased amount of information available, the existence of sophisticated decision aids such as intelligent agents, and more latitude in how to interact beyond the basic desktop and laptop computers (e.g., personal digital assistants, cellular phones, tablet computers), customers have more choices than ever about how, when, and how much to interact with companies and each other. In this paper, we attempt to cover a few of the major areas of research on how customers make decisions in these environments. http://repub.eur.nl/res/pub/15060/ 2005-03-29T00:00:00Z During the past decade, customers have become familiar with using various interface technologies, such as Web sites and wireless devices, to interact with firms. Increasingly, they choose the times and the channels through which they deal with firms for different aspects of their interactions. It is becoming common for customers to use different channels at different stages of their decision-and-shopping cycles, for example, using Web sites to obtain information but making purchases offline; in the past they typically obtained all their channel services from a single integrated channel at all stages of their decision process. We refer to customers who use more than one channel to interact with firms as multichannel customers, and marketing strategies to reach such customers as multichannel marketing. According to a study by Doubleclick (2004), the incidence of multichannel shopping among online shoppers increased from 56% to 65% between the 2002 and 2003 holiday seasons. http://repub.eur.nl/res/pub/2665/ 2004-09-01T00:00:00Z 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. http://repub.eur.nl/res/pub/2669/ 2002-02-01T00:00:00Z New developments in marketing management support systems (MMSSs) have provided the marketer with a growing supply of tools that can enrich decision making. In this paper, we describe the concept of marketing engineering — an approach to solving marketing decision problems — popularized by Lilien and Rangaswamy [Lilien GL,Rangaswamy A. Marketing engineering: computer-assisted marketing analysis and planning. Reading, MA: Addison-Wesley, 1998.]. We describe how marketing engineering harnesses marketing data and knowledge to facilitate decision making. We provide several illustrations of the successful application of the marketing engineering concept. We also summarize developments that we believe will further encourage the adoption of the marketing engineering concept and tools for both teaching about marketing decision making, and for improving the practice of marketing decision making. We conclude with some challenges for the academic research community. http://repub.eur.nl/res/pub/94/ 2001-06-08T00:00:00Z We study how and why model-based Decision Support Systems (DSSs) influence managerial decision making, in the context of marketing budgeting and resource allocation. We consider several questions: (1) What does it mean for a DSS to be "good?"; (2) What is the relationship between an anchor or reference condition, DSS-supported recommendation and decision quality? (3) How does a DSS influence the decision process, and how does the process influence outcomes? (4) Is the effect of the DSS on the decision process and outcome robust, or context specific? We test hypotheses about the effects of DSSs in a controlled experiment with two award winning DSSs and find that, (1) DSSs improve users' objective decision outcomes (an index of likely realized revenue or profit); (2) DSS users often do not report enhanced subjective perceptions of outcomes; (3) DSSs, that provide feedback in the form of specific recommendations and their associated projected benefits had a stronger effect both on the decision making process and on the outcomes. Our results suggest that although managers actually achieve improved outcomes from DSS use, they may not perceive that the DSS has improved the outcomes. Therefore, there may be limited interest in managerial uses of DSSs, unless they are designed to: (1) encourage discussion (e.g., by providing explanations and support for the recommendations), (2) provide feedback to users on likely marketplace results, and (3) help reduce the perceived complexity of the problem so that managers will consider more alternatives and invest more cognitive effort in searching for improved outcomes.