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Discusses decisions faced by marketing managers and whether answers to some important questions can be successfully answered. Examines marketing information systems (MIS) components – the data bank, the model bank, the measurement statistics bank, and the system user interface. Posits that there are economic benefits derived from making ‘better’ marketing decisions that result in larger monetary payoffs to the firm. Suggests a systematic impact study be based on analysis of the various steps that have to be taken in constructing a decision model. States MIS aids the marketing manager in specifying the decision model and in implementing this model. Concludes the MIS designer should look at each step in the construction of the decision model in order to estimate the potential impact of the change.

Model management systems (MMS) empower decision makers throughout the problem‐solving phases by providing operations research and management science (OR/MS) models as well as the knowledge to build or use such models. Managerial problem solving typically involves a wide range of modeling activities, i.e., definition, retrieval, modification, execution, modification, and integration of decision models. This research stems from the basic premise that, given the problem, decision aiding software such as MMS can reach its highest level of performance when the necessary modeling activities are adequately supported, subsequently enhancing the quality of the decisions made by the users. Reported in this paper are the results from an experiment involving two versions of MMS used by naïve modelers in two decision‐making settings. Through this study, we learn that the decision‐making behavior of software users, especially the way they develop their decision strategies, is considerably influenced by the capability of the software.

Explains that the focus of decision theory is on the mathematical models. These may be probability based; loss functions or other forms of statistical representations of judgements. Yet, much of decision theory does not lie entirely within any one discipline: it draws on psychology, economics, mathematics, statistics, social sciences and many other areas of study. Investigates investors’ perceptions and attitudes towards real estate. Highlights the important difference between theoretical exposure levels and pragmatic business considerations. Suggests a prescriptive model to explore judgements, beliefs and preferences of decision makers and to inform decision making. Examines the concept of risk and its place in developing a prescriptive model. Maintains that a decision must be judged on factors other than the risk of a single outcome.

Takes the view that managerial decisions are made in a diversity of organizational settings which can best be explained and evaluated in the context of conceptual interdisciplinary decision‐making models, and that such models constitute an appropriate vehicle for explaining the eclectic aspects of managerial decision making in all types of formal organization. Presents a typology of conceptual decision‐making models and evaluates their similarities and differences along with their respective efficacies in various managerial decision‐making contexts. Advances the process model of managerial decision making as the ideal choice for decisions which have significant long‐term consequences for the whole organization.

Purpose: The purpose of the chapter is to determine the specifics of making of managerial decisions in business systems of modern countries of Europe, to compile a European model of decision making in modern business systems, and to determine its capabilities for ensuring optimal decisions.

Methodology: The authors use the method of systemic and problem analysis, the method of comparative analysis (for comparing the European practice of managerial decisions to practices of other regions of the world), and the methods of modeling and formalization.

Conclusions: A European model of making of managerial decisions in modern business systems is compiled; it has the following peculiarities: collective offering, discussing, and making managerial decisions; presence of independent marketing committee that conducts systemic marketing; internal communications of business entities only with the production committee; and sustainability of horizontal connections between committees with the linear organizational structure.

Originality/value: Due to determined peculiarities, such advantages of the European model as making of well-balanced managerial decisions from positions of all interested parties, low expenditures for decision making due to usage of internal outsource, high effectiveness of marketing activities, and integration of managerial staff that allows solving current problems of the business system are achieved. However, in the European model, the process of making of managerial decisions is the longest as compared to other studied regional models. That's why application of the European model of decision making in modern business systems allows achieving high effectiveness of this process in a period of stability, and in case of downward wave of economic cycle, this model cannot ensure high effectiveness of managerial decisions.

It should be re‐emphasized, however, that the [Vroom‐Yetton] model is explicitly normative in character in that it specifies what leaders should do in various organizational circumstances — rather than attempting to summarize what leaders do do and what the effects of those actions are. Thus, if the assumptions in the model about the outcomes which result from various leader behaviors are incorrect, the model will lead to faulty behavioral prescriptions.

This chapter focuses on techniques and technologies to aid groups in making decisions, with an emphasis on computer-based support. Many office workers regularly meet colleagues and clients in virtual meetings using videoconferencing platforms, which enable participants to carry out tasks in a manner similar to a face-to-face meeting. The development of computer-based platforms to facilitate group tasks can be traced back to the 1960s, and while they support group communication, they do not directly support group decision making. In this chapter we distinguish four technologies developed to provide support to group decisions, clustered into two main traditions. Technologies in the task-oriented tradition are mainly concerned with enabling participants to complete tasks to solve the group's decision problem via computer-supported communications. Group Decision Support Systems and social software technologies comprise the task-oriented tradition. Alternately, in the model-driven tradition, participants use computers to build and use a model that acts as a referent to communicate, mostly verbally, about the group's decision problem. System modeling and decision-modeling technologies constitute the model-driven tradition. This chapter sketches the history and guiding ideas of both traditions, and describes their associated technologies. The chapter concludes with questioning if increased availability of online tools will lead to increased use of group decision support technologies, and the differential impact of communication support versus decision support.

Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.