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.     

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.     

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.     

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.     

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.     

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.     

Organization-Harm vs. Organization-Gain Ethical Issues: An Exploratory Examination of the Effects of Organizational Commitment

The existing literature on the relationship between organizational commitment and ethical decision making suggests that ethical decision makers with higher organizational commitment are less likely to engage in ethically questionable behaviors. The ethical behaviors previously studied in an organizational commitment context have been organization-harm issues in which the organization was harmed and the individual benefited (e.g., overstating an expense report). There is another class of ethical issues in an organizational context, however. These other issues, termed organization-gain issues, focus on the organization obtaining a benefit while outsiders, such as investors, are harmed (e.g., overstating reported revenue). We explore whether individuals with higher organizational commitment are more or less likely to engage in questionable behaviors that benefit the organization. Results of our study indicate that individuals with higher organizational commitment are less likely to engage in ethically questionable behaviors, regardless of whether the behaviors are organization-harm or organizational-gain issues.     

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.     

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.     

Facilitating computer-supported meetings: A cumulative analysis in a multiple-criteria task environment

There have been many instances of the ineffective applications of new information technology. This article describes a program of enhancing the effectiveness of a new technology, Group Decision Support Systems (GDSS), through a series of studies which resulted in improvements in the technology itself as well as in how the technology is supported. Our approach emphasizes human facilitation and facilitative features embedded in the GDSS software.     

Multi-agent interaction processes: From oligopoly theory to decentralized artificial intelligence

This article examines the group processes studied in oligopoly theory and in decentralized artificial intelligence. We develop a unifying perspective for the research on the behavior of autonomous interacting agents. Among the many questions of interest in these disciplines are the ways of creating and reaching cooperation by a group of self-interested independent decentralized agents. In this respect, the models and results of oligopoly theory can also be used both in decentralized artificial intelligence and in many other areas of research, such as group decision making, negotiation support, and organizational theory. In particular, the important idea of reshaping goals with strategic information sharing and transmission—incentive communication—has received little attention outside the field of economics. On the other hand, oligopoly theory and experimental economics can especially benefit from the computational methods and tools of artificial intelligence and modern decision support technology. To demonstrate this we have built a prototype of an experimental market analysis environment. Its potential in the analysis of group processes is illustrated with examples.     

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.     

以团队为核心的水平式组织结构模型研究

在信息经济时代，企业对于信息的传递和反应速度直接影响到决策的准确性，进而影响到企业的生存和发展。以团队为核心的水平式组织结构模型兼具团队和水平式结构双重优势，信息流动顺畅，分权化提高凝聚力，促进创新，压缩管理层为企业节约成本。     

Determinants of Ethical Decision Making: The Relationship of the Perceived Organizational Environment

This study attempts to help explain the ethical decision making of individual employees by determining how the perceived organizational environment is related to that decision. A self- administered questionnaire design was used for gathering data in this study with a sample size of 245 full-time employees. Perceived supervisor expectation, formal policies, and informal policies were used to assess the expressed ethical decision of the respondents. The findings indicate that the perceived organizational environment is significantly related to the ethical decision of the respondent.     

Aggregation and Disaggregation of Preferences for Collective Decision-Making

Group decisions are of longstanding interest to researchers from a wide spectrum of disciplines. Group Decision Support Systems (GDSS) can play a vital role in situations where multiple persons are involved, each having their own private perceptions of the context and the decision problem to be tackled. In such an environment the conflict between the members of the planning group is not an unusual situation. Multiple criteria decision aid (MCDA) methods may be a useful tool in coping with such interpersonal conflicts where the aim is to achieve consensus between the group members. This paper combines two well-known multicriteria methods, based on the notion of aggregation of preferences, in order to construct a consensus seeking methodology for collective decision-making.     

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.