Date of Award
Fall 12-2020
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Engineering Management & Systems Engineering
Program/Concentration
Engineering Management and Systems Engineering
Committee Director
Andres Sousa-Poza
Committee Member
Andrew J. Collins
Committee Member
Pilar Pazos-Lago
Committee Member
Patrick T. Hester
Abstract
There is no shortage of methods to address messy problems. A messy problem is a system of problems with multiple stakeholders who may hold different views of what is feasible or desirable. Decision-makers in a messy problem are prone to committing an error – especially the Type III error. One of the ways to mitigate the chance of committing the error in a messy problem is to reach a group consensus. Problem Structuring Methods (PSM) are the collections of participatory modeling methods that aim to tackle a messy problem. Despite the positive reports, literature indicates some challenges and criticisms of the effectiveness of PSM applications. One of the main challenges is the difficulty in identifying clear benefits which leads to a lack of interest from a wider community – particularly in the U.S. This study empirically investigates the effectiveness of a PSM in a messy problem to address the present challenges. Confidence can be a proxy to indicate that a group consensus is reached in a messy problem. Experimental research was conducted to assess participants’ problem-solving confidence in a messy problem. The results reveal that participants in the PSM group show a higher level of problem-solving confidence than the control group. It is hoped that the results of this research can inspire and encourage researchers and practitioners in a wider community to acknowledge the effectiveness of PSM, especially in the U.S.
DOI
10.25777/cx7x-z403
ISBN
9798557053105
Recommended Citation
Thaviphoke, Ying.
"An Investigation on the Effectiveness of a Problem Structuring Method in a Group Decision-Making Process"
(2020). Doctor of Philosophy (PhD), Dissertation, Engineering Management & Systems Engineering, Old Dominion University, DOI: 10.25777/cx7x-z403
https://digitalcommons.odu.edu/emse_etds/182
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Business Administration, Management, and Operations Commons, Industrial Engineering Commons, Systems Engineering Commons