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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.

The investigation reported in this dissertation attempts to determine the feasibility of using a distance-based approach like compromise programming for discrete-time dynamic programming problems with multiple objectives. In compromise programming, a function measuring the distance from a generally infeasible ideal solution to the feasible set of the problem is the single objective acting as a surrogate for the set of multiple objectives. Since, in general, there is no single best solution to a multiple objective problem, a framework to generate a family of compromise solutions interactively on a computer is proposed. Various quantities relevant to dynamic compromise programming are defined in precise terms. Dynamic compromise programming problems are computationally difficult to solve because in order to make the distance function decomposable over stages, dimensionality of the state-space must be increased by the number of objectives. To generate compromise solutions, quasi-Newton differential dynamic programming (QDDP), a recently developed variable-metric method for discrete-time optimal control, was employed. QDDP is attractive because no second order or Hessian information is required as input. Instead, Hessian matrices are approximated by first order or gradient information. Since very little is known about its numerical properties, computational experiments were conducted on QDDP. A new strategy for updating Hessian matrix approximations was computationally tested. A constrained QDDP algorithm is proposed, computationally tested, and applied to solve a multiobjective dynamic programming problem with inequality constraints at each stage. The algorithm has the potential for application to the more general discrete-time optimal control problem with stage constraints. The framework for generating compromise solutions interactively was implemented for prototype problems. Because decision maker interaction is crucial in a multiple objective situation, special attention was paid towards developing a man-machine interface using on-screen windows. All implementation and computational testing were done on a UNIX based personal computer.