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.
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