Article ID: | iaor19972026 |
Country: | United Kingdom |
Volume: | 24 |
Issue: | 5 |
Start Page Number: | 523 |
End Page Number: | 538 |
Publication Date: | Oct 1996 |
Journal: | OMEGA |
Authors: | Tavana M., Kennedy D.T., Jogelekar P. |
Keywords: | personnel & manpower planning, artificial intelligence: decision support, analytic hierarchy process |
In many developed countries, today’s socioeconomic environment has expended the role of the technical manager. Organizations capable of recruiting technical managers with adequate management education and interpersonal skills, in addition to technical expertise, are more likely to be successful in managing their limited resources. A technical manager’s success is also dependent on the manager’s acceptance by his/her subordinates, peers, and superiors, and the decision to hire a technical manager should be made with their participation. Many of these individuals have little background or experience in hiring, and they need appropriate decision support. This paper presents a framework to help a group of decision makers define and articulate a hierarchy of hiring criteria and subcriteria and rate each of the candidates on that hierarchy. To improve consistency among group members, the proposed group decision support system combines the Analytic Hierarchy Process (AHP) with the Delphi principles of anonymous feedback and iteration, Given the decision makers’ desire for a consensus choice, the framework deviates from the normal practice of AHP, and uses the Maximize Agreement Heuristic to arrive at the final ranking of the candidates. An application to the ranking of nurse manager candidates at a hospital in the United States is presented.