• Management Science and Financial Engineering
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• 제10권1호
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• pp.1-23
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• 2004

The changing structure of organization and the increasing diversity of business have forced organizations to have abilities to coordinate dispersed business activities. They have required cooperation and coordination among the functional units in the organization which should involve group decision-making processes. Although many group decision-making support tools and methods have been introduced to enable the collaborative process of group decision-making, they often lack the features supporting the dynamic complexity issue frequently occurring at group decision-making processes. This results in cognitive unfit between the group decision-making tasks and their supporting tools, bringing about mixed results in their effects on group decision-making. This study proposes system dynamics modeling as a group decision-making support tool to deal with the group decision -making tasks having properties of dynamic complexity in terms of cognitive fit theory.

• Asia pacific journal of information systems
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• 제10권1호
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• pp.1-18
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• 2000

One stream of the GDSS(Group Decision Support System) research is to investigate how GDSS affects decision performances of small groups according to task types, support features, meeting facilitation modes and meeting environments. But little study has investigated the effects of group member characteristics on group decision processes and outcomes depending upon whether GDSS is provided or not. To date, most GDSS studies have not controlled group member characteristics(e,g. personality, sex, decision style) in laboratory experiments. However, this study included the decision styles of group members as an independent variable. Therefore, this study investigated how differently members of two different decision styles perceive the use of GDSS in small group meetings through lab experiments. The two decision styles are feeling(F) style and thinking(T) style. We found that the effect of GDSS is a function of individual's decision style only in the communication thoroughness variable. The decision style is a statistically significant factor that can mediate the effects of the group support technology on the perceived communication thoroughness. Specifically, the GDSS is positively related to participants' perception about satisfaction on decision process, goal achievement, communication thoroughness, degree of influence-outward and effort for achieving meeting goals.

• Asia pacific journal of information systems
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• 제9권1호
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• pp.143-163
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• 1999

This study investigates the effect of group size on group performance, here the quality of group decision, Four effects are proposed and tested in a field experimental setting : (1) the relationship between the group size and the distribution of individual's problem-solving ability ; (2) the change of the group decision quality as group size increases ; (3) the relationship between the group decision quality and the quality of the best/worst member as group size increases ; (4) the relationship between the group decision quality and the average quality of individuals in the group as group size increases. Data showed that contrary to the exiting results, group decision quality was not improved with the group size. Rather, it showed a little tendency that group decision quality was worsened with the group size. Data also showed that consensus-oriented group decision making process produced the compromised output. Thus, group decision quality was not better than the average group members'. The opinion of the best member was not accepted. The implications of the findings are discussed for the development of a Korean GSS.

• 한국시뮬레이션학회논문지
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• 제12권1호
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• pp.49-58
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• 2003

There have been growing recognition on the needs of coordination of diverse activities across cross-functional business areas necessarily involving group decision-making processes. Although many group decision-making support tools and methods have been introduced to enable the collaborative processes of group decision-making, they often lack features supporting the dynamic complexity issues. This study proposes system dynamics modeling approach based on simulation techniques to deal with the group decision-making tasks having properties of dynamic complexity.

• 한국정보시스템학회지:정보시스템연구
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• 제5권
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• pp.307-328
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• 1996

Group Decision Support System(GDSS) has been a rapidly emerging field of the 1990's. Whereas conventional Decision Support System(DSS) helps individual decision makers, GDSS are designed to help groups of senior management and professional groups reach consensus. However, empirical researches which required to establish the optimum GDSS specification are scarce. Along with this purpose, this paper was focused to investigate the effect of group decision factors on the efficiency of the group decision. In this paper, task types of group decision which were discussed as major variable at many papers were settled as environmental factor. The result has verified the importance of the role-related factors on the efficiency of the group decision. This fact highlights the need of more researches on the role-related factors in the group decision in the GDSS development.

• 한국경영과학회지
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• 제27권4호
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• pp.111-126
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• 2002

This paper presents an interactive decision procedure to aggregate each group member's preferences when each group member articulates his or her preference information incompletely. An index, an indicative for the preference strength between alternatives, is derived to aid each decision maker to articulate preference information about alternatives. We develop a mathematical programming model that can establish dominance relations when the preference information about values of alternatives, attribute weights, and group member's importance weights are provided incompletely. Also, the preference relation between alternatives is to be considered in the model. Based on the preference strength measure and mathematical model, we develop an interactive group decision support procedure.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.958-965
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• 2003

The rapidly changing business environment has required cooperation and coorduiation among functional units n organizations which should Involve group decision-making processes Although many group derision-making support tools and methods have provided the collaborative capabilities for organizational members, they often lack features supporting the dynamic complexity issue frequently occurring at group decision-making processes This study proposes system dynamics modeling as a group decision-making support tool to deal with the group derision-making tasks having properties of dynamic complexity in terms of cognitive fit theory.

• 한국경영과학회지
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• 제23권4호
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• pp.171-186
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• 1998

While one main stream of research in GDSS (Group Decision Support System) is to investigate how GDSS affects decision-making performances of groups according to task types, support features, meeting facilitation modes and meeting environments. little study h3s been done about how group characteristics affect group decision processes and outcomes depending upon GDSS is provided or not. So far, most GDSS research has considered group characteristics (e.g. personality homogeneity) as given and did not include it as control variables in experiments. Therefore, the objective of this study is to investigate how members of two different groups perceive the use of GDSS in group meetings through lab experiments. The two groups are homogeneous and heterogeneous groups in terms of members' personality mix. This research found that the effect of GDSS is a function of groups' personality homogeneity in regards of the satisfaction on decision process and the communication thoroughness. The support of GDSS and the group homogeneity are proved to influence participant's perception about some dependent variables such as satisfaction on decision process.

• 한국의료질향상학회지
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• 제8권2호
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• pp.186-207
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• 2001

Background : This study presented an analysis of healthcare quality indicators using data mining and a development of decision support system for quality improvement. Method : Specifically, important factors influencing the key quality indicators were identified using a decision tree method for data mining based on 8,405 patients who discharged from a medical center during the period between December 1, 2000 and January 31, 2001. In addition, a decision support system was developed to analyze and monitor trends of these quality indicators using a Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. Result : Among 12 selected quality indicators, decision tree analysis was performed for 3 indicators ; unscheduled readmission due to the same or related condition, unscheduled return to intensive care unit, and inpatient mortality which have a volume bigger than 100 cases during the period. The optimum range of target group in healthcare quality indicators were identified from the gain chart. Important influencing factors for these 3 indicators were: diagnosis, attribute of the disease, and age of the patient in unscheduled returns to ICU group ; and length of stay, diagnosis, and belonging department in inpatient mortality group. Conclusion : We developed a decision support system through analysis of healthcare quality indicators and data mining technique which can be effectively implemented for utilization review and quality management in a healthcare organization. In the future, further number of quality indicators should be developed to effectively support a hospital-wide Continuous Quality Improvement activity. Through these endevours, a decision support system can be developed and the newly developed decision support system should be well integrated with the hospital Order Communication System to support concurrent review, utilization review, quality and risk management.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2003년도 추계공동학술대회
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• pp.239-250
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• 2003

In this research, we propose a hybrid group decision support mechanism (H-GDSM) based on Fuzzy AHP (Analytic Hierarchy Process) and FCM (Fuzzy Cognitive Map). The AHP elicits a corresponding priority vector interpreting the preferred information among the decision makers. Corresponding vector was composed of the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale. However, AHP couldn't represent the causal relationship among information, which were used by decision makers. In contrast to AHP, FCM could represent the causal relationship among variables or information. Therefore, FCMs were successfully developed and used in several ill-structured domains, such as strategic decision-making, policy making, and simulations. Nonetheless, many researchers used subjective and voluntary inputs to simulate the FCM. As a result of subjective inputs, it couldn't avoid the rebukes of businessman. To overcome these limitations, we incorporated the Fuzzy membership functions, AHP and FCM into a H-GDSM. In contrast to current AHP methods and FCMs, the H-GDSM method developed herein could concurrently tackle the pairwise comparison involving causal relationships under a group decision-making environment. The strengths and contributions of our mechanism were 1) handling of qualitative knowledge and causal relationships, 2) extraction of objective input value to simulate the FCM, 3) multi-phase group decision support based on H-GDSM. To validate our proposed mechanism we developed a simple prototype system to support negotiation-based decisions in electronic commerce (EC).