Thesis (M.A.)--Marshall University, 2002. / Title from document title page. Document formatted into pages; contains vi, 106 p. Includes bibliographical references (p. 102-106).

One explanation for the increased use of interactive groups in organisations is that benefits are obtained from pooling individuals?? differing knowledge and abilities. However, prior experimental research has established that groups often do not discuss and use information effectively, exhibiting a bias toward information that is commonly known by all group members, rather than information that is unique to individual group members (common information bias). This dissertation investigated whether the provision of quantitative information resulted in improved group performance in two respects. First, it investigated whether quantitative information was discussed and used more than qualitative information. Second, it examined whether the quantification of information reduced the common information bias. This is important because a basic purpose of managerial accounting is to provide information that improves employees?? abilities to make optimal decisions. This dissertation utilised an experimental task known as a ??hidden profile?? to achieve the research objectives. In a hidden profile experiment, each group member receives some information that is common to everyone in the group, and some information that is unique to them. The group must discuss and use members?? unique information in order to uncover the optimal task solution. This dissertation examined the effect of information availability (common or unique) and information type (quantitative or qualitative) on information discussion and use. There were two stages to the experiment. First, individual group members had to make a capital investment decision, and write down their reasons for that decision. Second, groups had to discuss the information, come to a group decision, and write down their reasons for that decision. The results confirmed a common information bias at the group decision level, with groups significantly favouring common information over unique information, for all measures of discussion and information use. In contrast, while a preference for quantitative information was found at the individual decision level, at a group decision level there were no significant differences in the discussion or use of quantitative and qualitative information, with only one exception: significantly more statements were made about quantitative information.

Thesis (M.A.)--Western Seminary, 1993. / Abstract. Includes bibliographical references (leaves 79-85).

Thesis, PlanB (M.S.)--University of Wisconsin--Stout, 2005. / Includes bibliographical references.

The basic problem of decision making is to choose the best alternative from a set of competing alternatives that are evaluated under conflicting criteria. In general, the process is to evaluate decision elements by quantifying the subjective judgments. The Analytic Hierarchy Process (AHP) provides us with a comprehensive framework for solving such problems. As pointed out by Saaty, AHP "enables us to cope with the intuitive, the rational, and the irrational, all at the same time, when we make multicriteria and multiactor decisions". Furthermore, in most organizations decisions are made collectively, regardless of whether the organization is public or private. It is sometimes difficult to achieve consensus among group members, or for all members of a group to meet. The purpose of this dissertation was two-fold: First, we developed a new aggregation method - Minimum Distance Method (MDM) - to support group decision process and to help the decision makers achieve consensus under the framework of AHP. Second, we evaluated the performance of aggregation methods by using accuracy and group disagreement criteria. The evaluations were performed through simulation and empirical tests. MDM • employs the general distance concept, which is very appealing to the compromise nature of a group decision making. • preserves all of the characteristics of the functional equations approach proposed by Aczel and Saaty. • is based on a goal programming model, which is easy to solve by using a commercial software such as LINDO. • provides the weighted membership capability for participants. • allows for sensitivity analysis to investigate the effect of importance levels of decision makers in the group. The conclusions include the following: • Simulation and empirical tests show that the two most important factors in the aggregation of pairwise comparison judgments are the probability distribution of error terms and the aggregation method. • Selection of the appropriate aggregation method can result in significant improvements in decision quality. • The MDM outperforms the other aggregation methods when the pairwise comparison judgments have large variances. • Some of the prioritization methods, such as EV[AA'], EV[A'A], arithmetic and geometric mean of EV[AA'] and EV[A'A], can be dropped from consideration due to their poor performance

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Organizations must consider increasing their decision-making capabilities in order to remain viable in a post-industrial society that Huber characterized as having "more and increasing knowledge, more and increasing complexity, and more and increasing turbulence" (1984). He sees the challenge for managers in the post-industrial environment as learning to make decisions in less time using greater quantities of more complex information. Group Decision Support Systems (GDSSs) represent a computer-based technology that has the potential to increase an organization's decision-making capabilities, and to meet this post-industrial challenge. This dissertation investigated a specific GDSS to study how GDSS technology affects group decision making compared to the more traditional face-to-face group decision making. The research was conducted through the use of a laboratory study comparing face-to-face groups of size six to GDSS groups of the same size. The decision process was the same for both types of groups, i.e., the sequence of steps used to solve the problem was consistent for both. Additionally, all of the groups were given the same task. Process and decision outcomes were measured for the six sets of treatments considered feasible for the manipulation of the communication condition, leadership, and anonymity. The process outcomes included satisfaction, time to decision, consensus, participation and uninhibited comments. The quality of a group's decision was the decision outcome measurement. The major findings of this study are: (1) Decision quality was equivalent for both face-to-face and GDSS groups; (2) Time to decision was greater for GDSS; (3) Consensus was less likely to occur in GDSS groups; (4) Satisfaction was lower in GDSS groups; (5) Participation was more equitable in GDSS groups.

The increasing complexity of decision situations has required organizations to integrate more types of expertise and consider more criteria for effective group decision making. Researchers have begun to examine how computer based support in the form of a Group Decision Support System (GDSS) can enhance the process and outcomes of decision making groups. This dissertation investigated the impact of GDSS for strategic planning impact analysis. The GDSS was based on the Stakeholder Identification and Assumption Surfacing Model. A controlled laboratory experiment was used to compare the process and outcomes of 4-person groups which had GDSS support, comparable manual support, and no support. The experimental task was a policy statement requiring undergraduates to have a personal computer for admittance to a business college. Groups were asked to determine a list of the most critical stakeholders who would be impacted by the policy, and their assumptions regarding the policy statement. Measures were taken on decision outcomes (decision quality, decision time, and satisfaction with the outcomes) and decision process variables (quantity of unique alternatives, distribution of individual participation, and satisfaction with the process). Additionally, observational data was recorded through the use of videotape recordings of the sessions. The major findings of the study are: (1) Decision quality is enhanced when groups use a structured methodology; (2) Decision time was shortest in the unstructured groups, with GDSS groups finishing somewhat faster than manual structured groups; (3) Satisfaction with the outcomes was not different between structured and unstructured groups, however it was higher in the GDSS groups compared to the structured manual groups; (4) Quantity of unique alternatives was much higher in the groups using a structured methodology; (5) Distribution of individual participation was more equal in groups using a structured methodology; and (6) Satisfaction with the process was not different between structured and unstructured groups, however the GDSS groups were more satisfied than the structured manual groups.

In 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.