Team Member Perceptions of Alternative Decision Analysis Approaches. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Componation, Paul J.; Dorneich, Michael C.; Nicholls, Gillian M.
• Is Part Of: Engineering management journal. Volume 29:Issue 1(2017)
• Page Start: 45
• Page End: 54
• Publication Date: 2017-01-02
• Subjects: Decision Analysis -- Supplier Selection -- Systems Engineering; Decision Making & Risk Management -- Program & Project Management -- Systems Engineering; Engineering -- Management -- Periodicals; Engineering -- Management; Periodicals; 620.0068
• DOI: 10.1080/10429247.2016.1277887
• Languages: English
• ISSNs: 1042-9247

Abstract

Abstract: This research evaluated team members' perceptions of alternative decision analysis approaches when they were asked to analyze a simple or complex data set under a time constraint. There has been significant work done to understand what criteria should be included in this class of problems. However, less work has been done examining the selection of a specific decision analysis approach. This is an area of concern since research has shown that selecting a different decision analysis approach can change the results even when the same data is analyzed. This research effort is a preliminary look at perceptions of alternative decision analysis approaches. A mixed method study analyzing quantitative and qualitative data was completed. Teams were assigned to use one of five different decision analysis approaches, including Weighted Sum, SMART, TOPSIS, AHP, and ELECTRE I. After applying the approach to either a simple or complex data set, team members were surveyed to determine perceptions including comfort with the approach, its results, and ease/difficulty of explaining the approach to coworkers. Discussions were also held with team members to gain additional insights. The study indicated that team members preferred less technically challenging decision analysis approaches, such as Weighted Sum and to a lesser degree SMART, when they were using a simple data set because they found the approach easier to use and to explain to others. The more technically challenging decision analysis approaches, such as AHP and ELECTRE I, were preferred with a complex data set because they provided better insight into the problem. Teams in this study did selected different alternatives when they used different decision analysis approaches. Demographic data was also collected but was not found to influence results in this study.