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dc.contributor.advisorNunamaker, J. F.en_US
dc.contributor.authorHong, Ilyoo Barry.
dc.creatorHong, Ilyoo Barry.en_US
dc.date.accessioned2011-10-31T17:17:25Z
dc.date.available2011-10-31T17:17:25Z
dc.date.issued1989en_US
dc.identifier.urihttp://hdl.handle.net/10150/184752
dc.description.abstractIn modern organizations where managers must constantly be dealing with an overload of information, it is often observed that participants in group decision processes either are not clearly aware of their specific preferences or that they are not capable of properly formulating those preferences. When this happens, inconsistent or incomplete expression of personal preferences and their use in decision making may lead to an unjustifiable outcome for the group. Due to this problem, the strengths and effectiveness of GDSS-supported group meetings may, in some situations, not be apparent. This dissertation develops a new approach to supporting group decision making, focusing on preference knowledge of individual participants in a group. A system architecture for the design of an MCDM (Multiple Criteria Decision Making) GDSS which facilitates the process of eliciting, formulating, utilizing, aggregating, and analyzing preferences for individuals within groups is presented. The architecture integrates multi-criteria decision making paradigms with a group decision support environment. A prototype has been developed in order to demonstrate the design feasibility of an architecture that centers around four phases of choice making: alternative generation, preference specification, alternative evaluation, and preference aggregation. The prototype is designed to support managerial choice and judgment processes in collaborative meetings. The intended problem domain of the model is semi-structured managerial decisions for which decision variables (attributes) can be represented in quantitative terms to some extent, yet for which evaluation of alternatives requires a high degree of intuition and personal analysis. The process of prototyping the proposed architecture and the results from a qualitative study have provided some instructive conclusions relating to MCDM GDSS design: (1) support for human choice strategies can be integrated into a GDSS, (2) appropriate management of preferences of group participants will facilitate collaborative decision processes, (3) hierarchical decomposition of a decision problem can provide structure to a problem and thereby reduce problem complexity, and (4) managerial decisions are appropriate problems to which the current approach can be applied.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectGroup decision making.en_US
dc.subjectMultiple criteria decision making.en_US
dc.titleComputerized group decision support for managerial choice/judgment tasks through facilitated preference formulation and utilization.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.identifier.oclc702670196en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberVogel, Douglas R.en_US
dc.contributor.committeememberGeorge, Joeyen_US
dc.contributor.committeememberTansik, Daviden_US
dc.contributor.committeememberTorres, Daviden_US
dc.identifier.proquest9000131en_US
thesis.degree.disciplineBusiness Administrationen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
dc.description.notethis item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at repository@u.library.arizona.edu.
dc.description.admin-noteOriginal file replaced with corrected file August 2023.
refterms.dateFOA2018-08-22T21:15:34Z
html.description.abstractIn modern organizations where managers must constantly be dealing with an overload of information, it is often observed that participants in group decision processes either are not clearly aware of their specific preferences or that they are not capable of properly formulating those preferences. When this happens, inconsistent or incomplete expression of personal preferences and their use in decision making may lead to an unjustifiable outcome for the group. Due to this problem, the strengths and effectiveness of GDSS-supported group meetings may, in some situations, not be apparent. This dissertation develops a new approach to supporting group decision making, focusing on preference knowledge of individual participants in a group. A system architecture for the design of an MCDM (Multiple Criteria Decision Making) GDSS which facilitates the process of eliciting, formulating, utilizing, aggregating, and analyzing preferences for individuals within groups is presented. The architecture integrates multi-criteria decision making paradigms with a group decision support environment. A prototype has been developed in order to demonstrate the design feasibility of an architecture that centers around four phases of choice making: alternative generation, preference specification, alternative evaluation, and preference aggregation. The prototype is designed to support managerial choice and judgment processes in collaborative meetings. The intended problem domain of the model is semi-structured managerial decisions for which decision variables (attributes) can be represented in quantitative terms to some extent, yet for which evaluation of alternatives requires a high degree of intuition and personal analysis. The process of prototyping the proposed architecture and the results from a qualitative study have provided some instructive conclusions relating to MCDM GDSS design: (1) support for human choice strategies can be integrated into a GDSS, (2) appropriate management of preferences of group participants will facilitate collaborative decision processes, (3) hierarchical decomposition of a decision problem can provide structure to a problem and thereby reduce problem complexity, and (4) managerial decisions are appropriate problems to which the current approach can be applied.


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