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While problem and decision analysis has attracted considerable interest in general management fields, it is not a topic commonly found in the marketing management literature. Problem understanding and definition determine management action, and therefore deserve greater attention. Addresses the key issues in marketing management problem analysis by showing why problem definition is important; outlining the nature of marketing problems and the difficulties involved in addressing them; and providing guidelines for management and research practitioners. A diagrammatic review of several problem and decision models provides a broad view of the complex processes involved. One critical factor which comes to the fore in the discussion is the necessity for decision makers and analysts to collaborate, and several techniques for such co‐operation are presented.

Managers are increasingly presented with complex, ambiguous decision problems that affect multiple stakeholder groups. Such problems cannot be tackled solely by classical approaches that prescribe rational methods to weigh evidence and select an optimal course of action. Yet most courses on decision making still focus on these methods. This paper draws attention to the complementary nature of rational decision making and sensemaking techniques in management decision making, and describes a practical pedagogy that demonstrates how the two can be integrated into management curricula.

Based on an in-depth review of relevant research, the authors propose a conceptual model that highlights the complementary nature of rational and sensemaking methods for making decisions relating to complex and ambiguous problems. They then describe a course on decision making as an illustration of how the model can inform decision making pedagogy.

Decision makers need to think of their decision problems in terms of two distinct types of uncertainty: those for which uncertainty can be quantified and those for which it cannot. When faced with the latter, decisions are best made by working with relevant stakeholders to collectively frame the problem using practical sensemaking tools prior to applying rational decision making techniques to address it. Decision making under ambiguity is an iterative, social process requiring a combination of rational decision making methods and sensemaking techniques.

The paper seeks to increase awareness about the complementary nature of sensemaking and rational decision making. It emphasizes the need to integrate the two in management curricula and provides details on how this can be done via an example of a course implemented at an Australian Business School. The techniques described will also be of interest to practitioners.

The paper describes a practical pedagogy that blends rational decision making and collective sensemaking techniques in a way that fosters managers’ decision making skills in contexts characterized by ambiguity.

The proper identification of a decision problem is paramount to finding a course of action or solution. This paper attempts to capture the general laws or principles that define decision problems. These principles are then used to establish a decision classification system called the decision‐order taxonomy.

The decision‐order taxonomy is developed by performing a content analysis on the seminal literature in the natural, social, and applied sciences. By identifying the semantic descriptors used to partition various domains, an implicit taxonomy for proper identification of decision problems is hypothesized.

The multidimensional taxonomic classification system and defined nomenclature, together with the identification process, comprise the complete decision‐order taxonomy developed in this paper. While applying the decision‐order taxonomy to an actual decision problem, insights are exposed which will guide the decision maker toward appropriate solution methodologies.

The theoretical foundation developed can be used to promote future research in decision classification. By providing a theoretically derived model, rich opportunities to test the taxonomy empirically are offered. Researchers are also given a foundation upon which they can build interdisciplinary decision models.

For practitioners, the decision‐order taxonomy provides a new paradigm for communicating decision problems across disciplinary boundaries. The taxonomy also provides guidance to the practitioner as they search for appropriate solution methodologies in unfamiliar disciplines.

The establishment of a useful decision‐problem taxonomy is a significant contribution to understanding the multidimensional interdisciplinary nature of real world decision problems. The original classifications will promote cross disciplinary communication, a central element in business success.

Examines the effects of decision unstructuralness (unstructuredness + noisiness) on decision‐support systems (DSS) adoption. Suggests that end‐users are sensitive to “unstructuralness” when they select a decision support approach. Problem structuring at the intelligence phase generates the first signal about DSS usefulness. If this signal is in favour of DSS, the manager either immediately adopts the DSS, or performs problem solving at the design phase. At this phase a new signal will be generated thus, confirming or denying DSS usefulness. This confirms that problem structuring prevails in making the DSS adoption decision. That is, in making the DSS adoption decision, priority is given to problem structuring in the intelligence phase.

Identifying the state of alignment, when there is misalignment, and the path to achieve alignment are of central importance to decision makers today. This paper seeks to offer decision makers some actionable guidance in narrowing the search for possible solution methodologies and to develop a generalized decision alignment framework that can be applied to real decision problems.

Alignment is viewed as a goal of decision makers and the correct matching of decision and action is essential to achieving consistently high performance. Drawing on parallels with the duality problem in linear programming, decision alignment is defined. The decision alignment framework is theoretically developed using examples from a diverse application set, including quantitative research, decision making, education, and e‐commerce.

The evidence shows that good research conforms to the decision alignment framework and poor research violates it. Similarly, good decisions conform to the decision alignment framework and poor decisions violate it. The decision alignment framework guides decision makers in constraining and redefining problems to optimize outcome performance, and shows the importance of addressing the dual problem of learning and understanding the phenomena.

The theoretical foundation developed can be used to promote future research in decision alignment. By providing a theoretically derived framework, rich opportunities for empirical testing are offered. Researchers are also given guidance on how alignment research can be conducted.

The examples presented highlight the prescriptive, communicative, and descriptive value of the decision alignment framework. Practitioners are provided with examples for using the decision alignment framework to build toolboxes of approaches that can be aligned to a characterization of real‐world decision problems to improve performance.

The introduction of a decision alignment framework is a significant contribution to the management decision literature. By introducing a decision alignment framework, the rather ambiguous term alignment is precisely defined as the matching of decision problem characterization (primal problem) with the approach possibility set (dual problem).

We extend the classical garbage can model to examine how individual differences in ability and motivation will influence organizational performance. We find that spontaneous coordination provided by an organized anarchy is superior when agents are equally competent. The Weberian bureaucracy of planned coordination is effective when problems require specialist knowledge. However, errors in matching problems to specialized agents are a central challenge for bureaucracies. Actual organizations, therefore, combine elements of organized anarchies and bureaucracies. Heterogeneous motivation compounds coordination problems, but is usually less important than competence. Our findings point to matching and interactive learning as fruitful areas for further study.

This study aims to develop a decision making model and approach for logistics planning problem which naturally involves two or more decision units at a hierarchical structure. Such a decision problem in practice often involves uncertain and imprecise factors with the parameters of a bilevel decision model, either in the objective functions or constraints.

This paper proposes a fuzzy bilevel decision making model for a general logistics planning problem and develops a fuzzy number based Kth‐best approach to find an optimal solution for the proposed fuzzy bilevel decision problem.

The proposed approach illustrates an optimal solution in logistics management, which meets maximally/minimally the objectives of both supplier and distributor (or other parts of the logistics chain). The proposed fuzzy bilevel decision approach can have a wide range of logistics management applications.

The decision model, approach and system will be further tested for some more complicated real cases in the future.

The proposed fuzzy bilevel decision model and approach are new, which offer theoretical and practice help to logistics management.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.

Proposes that decision making is part of all management tasks and that it is particularly important for emergency managers as they often need to take decisions quickly on very inadequate information. Briefly reviews some of the particular problems of emergency decision. Looks at the usefulness of Vroom and Yetton’s decision process model for emergencies, before proposing a simplified problem classification based on three problem characteristics. Concludes by reviewing a collection of “emergency” decisions and analysing some of the common factors to suggest a number of simple action rules to be used in conjunction with the simplified decision process model proposed, the “emergency manager’s decision cube”.

“Statistical decision theory is a theory of decision‐making, i.e., of selecting among alternatives. It is not a theory of problem solving, i.e., of finding the cause of a particular set of symptoms. Thus, if the problem or opportunity is defined poorly even the best analysis thereafter will be of limited value. In fact, it could be detrimental”.