The Effects of Integrating Cognitive Feedback and Multi-attribute Utility-Based Multicriteria Decision-Making Methods in GDSS

Cognitive conflicts arise within groups because the members of a group view a problem from different perspectives, even when they have similar interests in achieving a goal. Disagreement within a group may occur due to: (a) differing judgment policies among the members, (b) inconsistency by any member in using a judgment policy, (c) group process losses that prevent group members from understanding each other better, or (d) limited processing capability which may prevent group members from processing all information effectively. Disagreement is especially likely when policies, processes, or information are conflicting in nature.A level 2 GDSS to aid judging in cognitive conflict tasks is presented that combines cognitive feedback and Multi-attribute utility (MAU) theory based multicriteria decision-making techniques with the communication structure and activity-structuring capabilities of a level 1 GDSS. Though cognitive feedback and MAU methods have been used separately to help groups resolve cognitive conflicts, never before have the two decision aids been used together in a computer-based collaborative system.The contributory effects of the components of this GDSS design were empirically tested in a laboratory setting. Three treatments: an unaided face-to-face meeting, a level 1 GDSS supported meeting, and a level 2 GDSS supported meeting were compared in a repeated measures experimental design.Results largely supported the proposed research hypotheses. Some specific findings include: (1) the level 2 GDSS reduced disagreement between group members and improved consistency of judgments better than the other meeting environments did; (2) there was no significant difference in the reduction of disagreement between the level 1 GDSS and face-to-face meetings; and (3) while there was no difference in improvement of consistency of individual judgments between the face-to-face and level 1 GDSS supported meetings, group judgments made in face-to-face meetings were more consistent.     

Contrasting Single User and Networked Group Decision Support Systems for Strategy Making

The use of computers to support group work – as a Group Decision Support System (GDSS) – on strategy making has grown over the last decade. Some GDSS's have a facilitator managing the computer with the group viewing a public screen displaying the debate, problem definition, and agreements of the group as it negotiates strategies. Others involve members of the group in the direct input of data that forms part of the problem definition – data that is then used by the group employing electronic voting and other organizing devices. This paper discusses a real case relating to an organization seeking to reach important agreements about its strategy. The case involved the top management team and over 50 senior managers. The organization used a facilitator driven GDSS for some of this work, and a networked system for other parts. Some of the meetings were video taped, some were observed through one-way mirrors, and all of the participants were interviewed about their reactions to the different systems. This paper reports on some of the significant contrasts between the two approaches.     

Strategic Manipulation of Preference Information in Multi-Criteria Group Decision Methods

When group decisions involve the allocation of resources to group members, the members might have an incentive to strategically distort any information they provide in order to increase their share of resources. The paper compares several multi-criteria group decision methods with respect to this problem. We show, using a computational model, that strategic manipulation of preference information is possible in all of the methods considered, although to a different extent. Furthermore, when the solution a method generates under correct information is not Pareto-optimal, manipulation attempts might even improve the efficiency of outcomes.     

Cooperation support through the use of group decision systems

This article considers the development of the group decision support system (GDSS) field both from organizational and technological perspectives. The growing importance of teamwork, lateral coordination, and activities integration inside modern business organizations is emphasized. Technological and knowledge specialization, quick transformation of business environments, reduction of response time, and so on, are some of the reasons that can explain the renewed relevance of teamwork. Also, the development of information technology (IT) is analyzed in relation to the role it is assuming in supporting group activities. Research in the GDSS field is then introduced. A proposal concerning the identification of three different phases in GDSS studies is developed, ranging from decision rooms to distributed systems. Each phase shows distinctive research topics and application fields, together with different organizational goals. Results of these developments are the growth of potential application areas of GDSS tools. These theoretical considerations, together with empirical experiences coming from the study of a real manufacturing environment (an IBM plant where group cooperation plays a fundamental role for production efficiency), constitute the basis for a research GDSS prototype (GROUPS). Prototype features are designed to support executives in facing production‐planning problems through an improvement in communications and in knowledge representation.     

Evaluating a Framework for Multi-Stakeholder Decision Support in Water Resources Management

In this paper we describe a framework for multicriteria modeling and support of multi-stakeholder decision processes. We report on its testing in the development of a new water level management policy for a regulated lake-river system in Finland. In the framework the stakeholders are involved in the decision process from the problem structuring stage to the group consensus seeking stage followed by a stage of seeking public acceptance for the policy. The framework aims at creating an evolutionary learning process. In this paper we also focus on the use of a new interactive method for finding and identifying Pareto-optimal alternatives. Role playing experiments with students are used to test the practical applicability of a negotiation support procedure called the method of improving directions. We also describe the preference programming approach for the aggregation of the stakeholder opinions in the final evaluation of alternatives and consensus seeking.     

Challenging Group Support Systems Research: The Case for Strategic Decision Making

Strategic decision making (SDM) often occurs in groups that can benefit from the use of group support systems (GSS). However, no comprehensive review of this logical intersection has been made. We explore this intersection by viewing GSS research through the lens of SDM. First, SDM is broadly characterized and a model of GSS-mediated SDM is produced. Second, we review empirical GSS research linking these findings to the characteristics of SDM. We conclude that GSS research has not produced sufficient knowledge about group history, heterogeneity, member experience, task type, time pressure, technology or tool effects, and decision consensus for a favorable evaluation of SDM in GSS groups. SDM in GSS groups challenges researchers to study the effects of group processes such as those just mentioned in a context that involves ongoing and established groups, political activity, and a multiplicity of tasks.     

Using the Analytic Hierarchy Process as a Two-phase Integrated Decision Approach for Large Nominal Groups

When decision makers who comprise a large nominal group face an unstructured decision problem and no simultaneous interactive communications are available, problem identification and consensus building are difficult, if not impossible. Few tools are available to assist decision makers in this situation. The Analytic Hierarchy Process (AHP) has typically been used to evaluate a set of alternatives after a decision problem has been structured as a hierarchy with various levels of criteria above the alternatives. With a group of decision makers, AHP has been used to evaluate those alternatives either by consensus building or by combining judgments or priorities using the geometric mean to aggregate their preferences. In this paper, we extend the use of AHP to a situation involving a large nominal group of dispersed decision makers where the entire hierarchy is not defined at the outset. In particular, we use the AHP as an integrative approach to identify the priorities of the various criteria and then use those priorities to screen and consolidate a large set of potential alternatives. This results in considering a reduced set of alternatives that will be affected by the more important criteria. The consolidated set of alternatives is evaluated by each individual in the group using AHP, combined using the geometric mean, and the results are synthesized to obtain the overall priorities of the alternatives. The approach is demonstrated and evaluated in a case study to select an alunmi anniversary gift to the U.S. Coast Guard Academy with a large nominal group of decision-makers dispersed throughout the United States.     

Group Decision Support Using Fuzzy Cognitive Maps for Causal Reasoning

Cognitive maps have been used for analysing and aiding decision-making by investigating causal links among relevant domain concepts. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCMs can be used as tools for both static as well as dynamic analysis of scenarios evolving with time. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.     

An Experimental Study of Executive Decision-Making With Implications for Decision Support

Past research in the field of information systems has explored factors and conditions that are relevant to decision-making in many contexts. However, very little is known about how executives consider, weigh, and integrate these factors. One current school of thought holds that intuition and instincts can play a significant role and that when decision-makers use their instincts, they rely on only a relatively small subset of the cues available to them. This has implications for designing and improving decision support systems, which form a major and widespread element of modern organizational computing. We examine the decision-making policies of professional decision-makers. High-level information technology executives were asked to evaluate the likelihood of making a strategic investment in the face of varying environmental scenarios. Using policy-capturing methodology, we find differences between what the executives thought was important to their decision-making and what is revealed as actually being important. In addition, we find that personal characteristics of risk-taking propensity and innovativeness affect the way the decision-makers integrate information. We argue that the idiosyncratic nature of the executive-environment relationship calls for increased emphasis on developing suitably adapted decision support systems (e.g., business intelligence systems) for executive decision-making.     

Analysis of multiple criteria decision support systems for cooperative groups

This article reviews research on multiple criteria decision support systems for cooperative groups. The systems included in this survey integrate concepts from group decision theory, multiple criteria decision methodology, and decision support system technology. Three dimensions are considered in the analysis of existing research: the multiple criteria decision technique used to generate decision alternatives or to choose from a given set; the method used to facilitate individual compromise and group consensus; and the application category, if any.     

Combining GDSS and Gaming for Decision Support

Both gaming and group (decision) support systems (GDSS) are frequently used to support decision-making and policymaking in multi-actor settings. Despite the fact that there are a number of ways in which gaming and GDSS can be used in a complementary manner, there are only sporadic examples of their combined use. No systematic overview or framework exists in which GDSS are related to the functions of gaming or vice versa. In this article, we examine, why, how and for what purpose GDSS can be used to enrich and improve gaming simulation for decision support, and vice versa. In addition to a review of examples found in the literature, four games are discussed where we combined gaming and GDSS for complex decision making in a multi actor context: incodelta, a game about transportation corridors; infrastratego, a game about a liberalizing electricity market; containers a drift, a game about the planning of a container terminal, and; dubes, a game about sustainable urban renewal. Based on the literature and these four experiences, a classification is presented of (at least) four ways in which GDSS and gaming can be used in a complementary or even mutually corrective, manner: the use of GDSS for game design, for game evaluation, for game operation and the use of gaming for research, testing and training of GDSS.     

Artificial Neural Systems as a Research Paradigm for the Study of Group Decision Support Systems

Artificial neural systems (ANSs) have received interest recently because of their ability to accurately forecast and classify data. Group decision support systems (GDSSs) also have received much interest for their support of group communication and decision making. This paper explores the potential of the ANS as a methodology for modeling the many complex, interrelated research variables involved in the field of GDSS. As an illustration, multilinear regression, an ANS with backpropagation, and an ANS with a genetic algorithm were developed to classify 133 subjects into verbal or GDSS groups based on their responses to a questionnaire. The ANSs with backpropagation and the genetic algorithm achieved higher classification accuracies (81.8 and 90.9%, respectively) than was achieved with multilinear regression (75.8%). Therefore, an ANS may more accurately model the many interrelationships occurring with GDSS group behavior.     

A framework for interactive multiple criteria group decision support

An interactive framework is developed for multiple criteria group decision support. Decision makers are asked to specify aspiration and reservation levels to establish a preferred range for the value of each objective for each decision maker. Group and individual solutions are generated as a function of these levels. Decision makers are asked to react to current solutions by revising their aspiration and reservation levels and additional solutions are generated. The framework is both simpler and more flexible than previous approaches.     

An object-oriented architecture for group decision support systems

An object-oriented GDSS can produce a rich environment for easily accessing software tools or applications running under a Graphical User Interface and can provide communication among meeting participants. This requires definition of (1) objects to manage the meeting and participant sessions, (2) applications that can run under these sessions, and (3) data storage and data communications capability that handle both private and shared data. This article suggests that a GDSS system can operate under three layers of software: an application-layer, a GDSS environment, and an operating system environment. It defines the structure and behavior of the GDSS environment layer in terms of (1) objects and messages used to communicate among these objects and (2) a message passing protocol between the GDSS environment and the other two layers. These specifications result in an architecture that is sufficiently modular to enable addition of various GDSS software. This article also discusses a prototype implementation of the architecture that is developed in Smalltalk and runs under Microsoft's Windows 3.0.     

A framework for thinking about Group Decision Support Systems (GDSS)

Strategic problem solving in organizations is a social process that disturbs established social relationships. Maintaining a negotiated social order is crucial to political feasibility and to emotional commitment from the participants in relation to a solution package. However, Group Decision Support Systems that attend overly to managing social order risk group think through bounded vision. This may be avoided if emotional commitment is also encouraged through participants experiencing the problem situation from multiple perspectives and in relation to alternative solution strategies. Commitment depends upon both means/ends rationality and procedural rationality. This acknowledges the balance in providing support to a group with respect to the negotiation of social order, with the more traditional group decision support for socially negotiating order out of the problem situation. This article argues that effective Group Decision Support Systems must attend to both aspects of creating order. OR modelling methods and the support that can be provided by modern micro-computers offer a new way forward—models can be toys that a group can play with together, enabling them to create knowledge as well as use it.     

Facilitation Methods for Collaborative Modeling Tools

This paper presents results of an ongoing research effort to support effective user involvement during modeling and analysis meetings. Productivity and user participation of traditional group meetings have been limitations imposed by chauffeured facilitation and single-user tools. These tools have been designed for analysts rather than for direct use by non-analyst users. Recently, electronic meeting systems (EMS) modeling tools that allow users to work in parallel to contribute directly during meetings have been developed. Such tools allow more domain experts to participate directly and productively during model development meetings than is possible using the traditional approach. Although previous research has demonstrated that EMS modeling tools may be used to develop some model content, little research had been done on collaborative facilitation methods that employ these tools. This paper presents a comparison of modeling approaches for use with EMS modeling tools and proposes an approach that overcomes significant problems inherent in other approaches. It leverages the productivity enhancement afforded by direct group access and still results in production of complete, integrated, high quality models. This approach allows models to be developed two to four times faster than with traditional modeling support and yet avoids model ambiguities and inconsistencies.     

Technology support for cooperative work: A framework for studying introduction and assimilation in organizations

This article draws on published research on the nature of the innovation process and exploratory field research in 10 companies to develop a framework for research on organizations’ introduction and assimilation of computer‐supported cooperative work technologies. The research reported in this article, part of a much larger study of the general process of innovation in organizations, focuses specifically on the transfer and assimilation of new technology innovations.

Technologies to support group process, communication, and coordination in face‐to‐face group meetings [electronic meeting support systems (EMSS)] were chosen to illustrate the use of the research framework. The article focuses on the transfer of these technologies from R“D units to target organizational units and the alignment of group, technology, and task during assimilation by end‐user groups. Research propositions are developed and discussed. Future articles will present the findings from current research that utilizes the frameworks presented in this article to study the introduction, transfer, and assimilation of EMSS in organizations.     

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

This paper describes a group decision support system based on an additive multi-attribute utility model for identifying a consensus strategy in group decision-making problems where several decision-makers or groups of decision-makers elicit their own preferences separately. On the one hand, the system provides procedures to quantify the DMs or group of DMs preferences separately. This involves assessing the DMs or group of DMs component utilities that represent their preferences regarding the respective possible attribute values and objective weights that represent the relative importance of the criteria. On the other hand, we propose Monte Carlo simulation techniques for identifying a consensus strategy. An iterative process will be carried out, where, after the simulations have been performed, the imprecise component utilities and weights corresponding to the different DMs or groups of DMs are tightened to output more meaningful information in the next simulations to achieve a consensus strategy. Finally, an application to the evaluation of remedial strategies for restoring contaminated aquatic ecosystems illustrates the usefulness and flexibility of this decision support tool.