Executive GDSS: Behavioral considerations at individual,organizational, and environmental levels of analysis

This article emphasizes the importance of behavioral considerations at individual, organizational, and environmental levels of analysis when researching and evaluating the design, implementation, and use of group decision support systems (GDSS) within complex organizations. Discussion is based on interview and archival data collected on an executive level GDSS used within a corporate setting. Issues that are considered generalizable to organizational computing, coordination, and collaboration technologies concern (a) viewing organization participants as strategic, intuitive information processors, (b) understanding the importance of organizational power, politics, and situational constraints on decision making, and (c) appreciating the symbolic value of advanced information technologies to an organization's external environment.     

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.     

An Intelligent Idea Categorizer for Electronic Meeting Systems

Research on group decisions and electronic meeting systems have been increasing rapidly according to the widespread of Internet technology. Although various issues have been raised in empirical research, we will try to solve an issue on idea categorizing in the group decision making process of electronic meeting systems. Idea categorizing used at existing GDSS was performed in a top-down procedure and mostly by participants' manual work. This resulted in tacking as long in idea categorizing as it does for idea generating, clustering an idea in multiple categories, and identifying almost similar redundant categories. However such methods have critical limitation in the electronic meeting systems, so we suggest an intelligent idea categorizing methodology which is a bottom-up approach. This method consists of steps to present idea using keywords, identifying keywords' affinity, computing similarity among ideas, and clustering ideas. This methodology allows participants to interact iteratively for clear manifestation of ambiguous ideas. We also developed a prototype system, IIC (intelligent idea categorizer) and evaluated its performance using the comparison experiments with other systems. IIC is not a general purposed system, but it produces a good result in a given specific domain.     

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.     

Technology and Group Decision Process in Going-Concern Judgements

Accounting and auditing practices are continually being affected by advances in technology. This study empirically examined the effect of group decision processes and technological advances on group going-concern decision making. Groups with access to group decision support systems (GDSS) were compared to groups without access to GDSS for their going-concern judgments. The results show group discussion induced auditors to be more conservative and to consider factors which may have overlooked at the individual level, though neither structure significantly reduced the considerable variance in the individual going-concern judgments. Further, as compared to their counterparts in the face-to-face discussion groups, GDSS groups indicated much higher confidence in their group's final assessment of the client's going-concern status and a higher level of satisfaction and agreement with the group decision processes. The findings suggest that while group discussions did not significantly reduce auditors' considerable variance in going-concern judgments, future research should investigate which explicit models would improve the consensus on going-concern evaluations.     

A Proposal of Toolkit for GDSS Facilitators

Most group decision meetings are perceived to be extremely unproductive in terms of efficiently utilizing the participants’ time and effectively achieving the group decision meeting objectives. Indeed, group decision meetings consume a great deal of time and effort in organizations. These problems occur frequently because effective guidelines or procedures are not used. To overcome these problems, many group decision support systems (GDSS) imbed some facilitation mechanisms and are currently being used with the help of a human facilitator who guides the group members through the decision process. We propose in this paper a toolkit for GDSS facilitators that we integrate in our proposed architecture for distributed GDSS. Based on a model of the decision making processes group facilitation tasks are automated, at least partially in order to increase the ability of inexperienced facilitator to monitor and control the group decision meeting process.     

Developing a Group Decision Support System for Advertising Media Evaluation: A Case in the Middle East

The importance of advertising media evaluation as a multifaceted problem is well known by both academics and practitioners. Although previous studies tried to optimize media evaluation, there still are some gaps and problems to address, particularly in areas of flexibility of models/frameworks, decision making quality, tension management, and agility of the evaluation process. Most of previous studies are based on inflexible models/frameworks that have limitations on number of criteria/alternatives they can consider and type of data they can process. A great volume of the work used arbitrary decision making; arbitrary decision making regarding criteria and media importance may reduce effectiveness of advertising campaigns. Furthermore, the academic literature offers little guidance on group decision aggregation, and tension management during decision making is neglected. Media evaluation is a time taking process and any acceleration will reduce pre-campaign costs. The main aim of this paper is to illustrate how a group decision support system (GDSS) can assist media planners to overcome mentioned problems more systematically. For this purpose, we developed a GDSS that is an integration of three well-known multi-criteria decision making techniques. With a real world case study, we illustrate the performance of the proposed GDSS. Results of our quantitative assessments indicate that the GDSS is flexible, allows decision makers to express their opinions, reduces tension among decision makers, and saves time.     

Influence of Computer-Based Meeting Support on Process and Outcomes for a Divisional Coordinating Group

This research examines the introduction of computer-based group decision support systems (GDSS) to members of a division level coordinating group. Participants performed authentic problem formulation tasks which varied naturally in degree of structuredness, in two non-GDSS meetings then were provided GDSS for four additional meetings. It was proposed that the introduction of the GDSS and task structuredness would influence (1) group process in terms of the amount of divergent and convergent thinking communicated during meetings and (2) perceived outcomes regarding quality, satisfaction, understanding, confidence, and commitment to group positions. Results suggest that GDSS use affected both the total amount and pattern of group communication but not perceived outcomes. Structuredness of the task affected perceived outcomes but neither amount nor patterns of communication. Additional qualitative data regarding perceived of costs and benefits in using GDSS provide richer explanation for study findings and suggest further lines of inquiry.     

Evolution of group performance over time: A repeated measures study of GDSS effects

Many studies in the group decision support system (GDSS) literature have reported on the behavior and performance of ad hoc groups meeting for the first, and, in fact, the only time. Such one‐time studies of groups may not represent their longer term behavior and performance accurately. Adaptive Structuration Theory (AST) conceives of technology use as a social practice that emerges over time. AST suggests that meeting outcomes reflect the extent to which structures offered by the technology (such as GDSS tool sequences, meeting agenda, etc.) are faithfully appropriated by the group. Such faithful appropriation, however, takes time. This article explicitly recognizes the relevance of this appropriation process and reports on a lab study that examined the impact of computer support on group performance over time. In general, results showed that the performance of computer‐supported groups improved over time, whereas the performance of non‐computer‐supported groups stayed the same or declined. The number of alternatives generated by computer‐supported groups increased considerably as they became more proficient in incorporating the technology into group processes. However, the quality of decisions made by computer‐supported groups began to increase slightly, only during the last session. Both these findings suggest that AST is, in fact, a viable theory for studying group behavior and performance over time. Results from this study also point out the need for conducting more longitudinal studies of group processes in the future.     

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.     

Multiple Criteria Decision Making Models in Group Decision Support

Use of multiple criteria decision making (MCDM) models to aid the group decision process was tested. Two multiple criteria group decision support systems (MCGDSS) were studied, one using the AHP/Tchebycheff method of Iz and the other using Kersten's NEGO system. These systems were compared with a commercial GDSS, VisionQuest. VisionQuest does not include multiple criteria tools. To make the study comparable, VisionQuest was augmented with an ad hoc linear programming model that could generate solutions with specified characteristics requested by the using group. The three systems were compared on the dimensions of solution quality and decision support effectiveness.One of the hypotheses was that MCDM models would force participants to examine criteria, preferences, and aspirations more thoroughly, thus leading to decisions of better quality. Subjects using the MCGDSSs were expected to have higher mean quality and effectiveness values. However, the quality and effectiveness values of the VisionQuest/ad hoc system were found to be better on the dimension of effectiveness. Explanations for this result are included in the paper.Another hypothesis was that the AHP/Tchebycheff method of Iz, a value-oriented system, would yield more effective group support than the goal-oriented NEGO system. However, the NEGO system was found to yield solutions with better quality measures than the solutions obtained with the AHP/Tchebycheff system.Observation of the groups using the MCDM systems indicate that both the AHP/Tchebycheff and NEGO methods can be revised to enhance their effectiveness. The primary difficulty encountered with the AHP/Tchebycheff method was in the large number of pairwise comparisons required by AHP. The NEGO method can be enhanced by including specification of desired attainment levels in the first stage of the method. Both MCDM techniques have potential to benefit group decision support by giving using groups a means to design better solutions.     

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.     

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.     

Participants' perceptions on the role of facilitators using Group Decision Support Systems

The impact of facilitation upon group meetings has been a topic of research and debate for over 40 years. However, with the emergence and increasing usage of Group Decision Support Systems (GDSS), the role of facilitators is once more being addressed. Facilitators aim to substantially increase the effectiveness of group working, making meetings more productive and efficient through the management of both content and process. However, while the benefit that facilitators can bring is acknowledged, developing the necessary skills and techniques is more problematic. There are few manuals for would-be faciliators to draw upon, and much of their ability is acquired through experience. This article therefore attempts to increase the knowledge available and to provide facilitators with some practical suggestions. The research focuses on a series of interviews with group members (participants) who had used a particular GDSS—Strategic Options Development and Analysis (SODA)—for organizational decision making. From the analysis of the interviews, a number of reoccurring issues were identified which are presented as a set of learning points. These cover not only the workshop/meeting itself, but also the pre-workshop and post-workshop stages. The article also reflects on the impact which these identified learning points have on design and future GDSS development.     

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.     

Stradin: A Strategic Dynamic and Interactive Decision-Making Process

This article proposes a new strategic decision-making process: Stradin. Stradin uses recent developments in computerized network systems and user-friendly interfaces in order to address management challenges of corporations of the 1990s. Five strategic challenges are addressed: (1) finding the right balance in the centralization-decentralization dilemma; (2) conciliating strategic decisions and their operational implementation; (3) finding a balance between role differentiation and integration; (4) using conflict constructively in order to improve decision making; (5) conciliating emerging strategies and continuity. The article below discusses each of these challenges and shows how Stradin can help in meeting them.     

Information Collection Strategic Design in Experts-assisted Decision Making Paradigm

In an experts-assisted decision making paradigm, the information collection design becomes a strategic variable under a weak assumption that the final decision is dependent on the design used to collect information as well. As a result, the same information of the experts and the decision maker about the problem can potentially produce different final decisions for different information collection designs. The implication is that a decision maker can strategically select a design which serves his/her objective. This paper uses a Bayesian estimation methodology for combining experts' information with the decision maker's prior. An information collection process is designed by setting constraints on this model. Several designs are developed here using such controlled factors as a one-stage versus a two-stage decision process, experts' rank ordering, and group versus individual lobbying/consultation. An example is provided to illustrate the applicability of the concept. It is shown that the information produced in the process of producing a decision can also give insights into the impacts of the decision maker and the experts on the decision.     

Towards a Design Science of Ethical Decision Support

Ethical decision making involves complex emotional, cognitive, social, and philosophical challenges. Even if someone wants to be ethical, he or she may not have clearly articulated what that means, or know how to go about making a decision consistent with his or her values. Information technology may be able to help. A decision support system could offer individuals and groups some guidance, assisting them in making a decision that reflects their underlying values. The first step towards a design science of ethical decision support is to develop a theoretical base on which first-generation systems can be built. This paper brings together work in cognitive, social and moral psychology, information systems, and philosophy relevant to ethical decision making. Attributes of a system that would support ethical decision making are described.     

Decision making under conditions of conflict

A real-world application is employed to explain three general types of decision situations that can arise under conditions of conflict. In addition, meaningful connections and relationships among these areas of decision making are clearly pointed out. To permit useful decision technologies to be employed by practitioners for better understanding and for resolving a variety of actual decision problems, a range of flexible decision support systems is discussed. Subsequently, interesting research developments contained in the upcoming sequence of 12 articles on decision making under conflict are summarized and compared. The research articles not only present unique approaches to decision making involving multiple participants, each of whom may have multiple objectives, but suggest a variety of challenging research problems to be investigated in the future.