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.     

Group Decision Making with Dispersion in the Analytic Hierarchy Process

With group judgments in the context of the Analytic Hierarchy Process (AHP) one would hope for broad consensus among the decision makers. However, in practice this will not always be the case, and significant dispersion may exist among the judgments. Too much dispersion violates the principle of Pareto Optimality at the comparison level and/or matrix level, and if this happens, then the group may be homogenous in some comparisons and heterogeneous in others. The question then arises as to what would be an appropriate aggregation scheme when a consensus cannot be reached and the decision makers are either unwilling or unable to revise their judgments. In particular, the traditional aggregation via the geometric mean has been shown to be inappropriate in such situations. In this paper, we propose a new method for aggregating judgments when the raw geometric mean cannot be used. Our work is motivated by a supply chain problem of managing spare parts in the nuclear power generation sector and can be applied whenever the AHP is used with judgments from multiple decision makers. The method makes use of principal components analysis (PCA) to combine the judgments into one aggregated value for each pairwise comparison. We show that this approach is equivalent to using a weighted geometric mean with the weights obtained from the PCA.     

A New Linear Programming Method for Weights Generation and Group Decision Making in the Analytic Hierarchy Process

This paper proposes a new linear programming method entitled by LP-GW-AHP for weights generation in analytic hierarchy process (AHP) which employs concepts from data envelopment analysis. We propose four specially constructed linear programming (LP) models which are used to derive weight vector from a pair-wise comparison matrix or a group of them. We can use both interval and relative importance weights for each decision maker in LP-GW-AHP. In this method, solving only one LP model is enough for local weights derivation from pair-wise comparison matrices. Five numerical examples are examined to illustrate the potential applications of the LP-GW-AHP method. We show that not only derived weights of the new method have slight differences than Saaty’s eigenvector weights but sometimes they are better than eigenvector method weights in the fitting performance index as well. LP-GW-AHP is compared with a method which has been recently proposed for the weights derivation in the group AHP and it becomes obvious that LP-GW-AHP provides better weights. The simple additive weighting method is utilized to aggregate local weights without the need to normalize them.     

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.     

A Mixed-Data Evaluation in Group TOPSIS with Differentiated Decision Power

This main objective of this paper is to provide decision support for mixed data in group Technique for Order Preference by Similarity to Idea Solution (TOPSIS) with differentiated decision power. We use a signum function to compare the ordinal performance of alternatives on any qualitative criterion, or the partial information provided by decision makers. The proposed process for ordinal information is uniformly coherent with the traditional TOPSIS steps, preserving the characteristic of distance-based utilities. Ordinal weights are also considered herein, and the decision power of the group members is formulated by their weights under an agreement in the group. Two examples demonstrate that the proposed approach has some benefits and achieves robustness with two types of sensitivity analyses. Some discussions and their limitations to the approach are also provided.     

The Effect of a Quantitative Decision Aid – Analytic Hierarchy Process – on Group Polarization

The objective of this paper is to examine group polarization in subjects who have access to a quantitative decision aid, comparing to subjects who do not. Group polarization indicates the opinion of a group involved in a decision process will tend to be more extreme in the direction of the norm than the initial opinions of its members. Most of previous studies were focused on the theoretical explanation of this effect. Two theories are generally proposed to explain it: social comparison (SC) and persuasive arguments (PA). As we know, no study has been worked on how it can be dealt with. In this study, we use Analytic Hierarchy Process (AHP) as a quantitative decision aid in a group process to examine the changes of this effect. Two hundred and forty undergraduate students participated in this study. Results show that persuasive argument was significantly reduced by the use of AHP, while no significant effect was found in group polarization and social comparison. The implications of the findings are further discussed.     

Coalition Analysis in Group Decision Support

GMCR II, a decision support system based on the Graph Model for Conflict Resolution, now contains an algorithm for Coalition Analysis to alert users that certain decision-makers would find it both feasible and beneficial to co-ordinate their actions. The theory and implementation of Coalition Analysis in GMCR II are discussed and illustrated.     

Menu Dependence and Group Decision Making

This paper is concerned with the problem of group decision making. We introduce the notion of a collective system rule. A collective system rule maps each preference profile to a group-preference system, which is a collection of social preferences on the subsets of the alternatives. By formulating the Arrovian conditions, we show the Arrow-type impossibility theorems. We also discuss how our approach is related to the standard group decision-making process.     

A Multi-Criteria Group Decision Framework for Partner Grouping When Sharing Facilities

The site location is an important strategic decision for a company and usually incorporates several conflicting criteria. Therefore, multicriteria decisions methods are more and more used in this context. On the same time, another increasing trend is cooperative relationships where partners or even rivals work together to collectively enhance their performances by sharing resources. This paper presents a novel multi-phase approach to aid partners to address the following questions in the location problem context: ‘Where and with whom should the company locate its business?’ A case study is described where final year students are looking for a flat to share in London.     

A Group Cooperative Decision Support System Based on Extended Contract Net

With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Furthermore, many decision-makers begin to seek cooperators on the Internet to help them to solve complex decision problem. In the paper, we propose a research framework for Group Cooperative Decision Support System (GCDSS) and determine three layers such as task layer, cooperator layer and resource layer to characterize it and demonstrate how electronic market could facilitate cooperation in GCDSS. Then we present the negotiation model of GCDSS and propose a task decomposition method based on which one can decompose a complex problem into a formal task AND/OR tree. We also propose a task assignment method based on extended Contract Net to perform task allocation in GCDSS and describe the group cooperative decision-making process in Knowledge Query and Manipulation Language (KQML). An optimal policy is designed to find the optimal solution of task AND/OR tree and cooperative problem solving process is illustrated with an example for education development plan in China. Finally, we conclude with a brief overview of research contribution, along with some proposals for further research.     

A Deviation-Based Approach to Intuitionistic Fuzzy Multiple Attribute Group Decision Making

The aim of this article is to investigate the approach to multiple attribute group decision making (MAGDM) with intuitionistic fuzzy information. We first introduce a deviation measure between two intuitionistic fuzzy numbers, and then utilize the intuitionistic fuzzy hybrid aggregation operator to aggregate all individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. Based on the deviation measure, we develop an optimization model by which a straightforward formula for deriving attribute weights can be obtained. Furthermore, based on the intuitionistic fuzzy weighted averaging operator and information theory, we utilize the score function and accuracy function to give an approach to ranking the given alternatives and then selecting the most desirable one(s). In addition, we extend the above results to MAGDM with interval-valued intuitionistic fuzzy information.     

Developing Group Ordered Weighted Averaging Operator Weights for Group Decision Support

A few single decision-making methods under uncertainty (SDMUU) are available in the literature. The reason for such scarcity seems to be mainly due to too insufficient information to induce a reasonable result for effective decision support. Moreover their final outcomes on the same SDMUU problem may be different depending on which method is applied. A group decision-making method under uncertainty (GDMUU) extends a SDMUU in a sense that a group of individuals, each expressing different opinions, work together to solve a relevant problem. As in a SDMUU, we find that just a few methods are available to solve a GDMUU problem. In the paper, the ordered weighted averaging (OWA) method, originally devised for use with the SDMUU problem, is considered to deal with a GDMUU problem where individuals of a group express their degree of optimism in terms of attitudinal characters. We first find the extreme points corresponding to the attitudinal character and then solve a quadratic mathematical program which minimizes a squared distance from each extreme point identified. Thus the resulting OWA operator weights for the group are located at the center of the weights-space constructed by attitudinal characters. This idea is further extended to deal with uncertain attitudinal character expressed in the form of interval in situations where it is difficult to reliably obtain a precise attitudinal character due to time pressure and a limited domain knowledge and so on.     

A Framework for Adopting Collaboration 2.0 Tools for Virtual Group Decision Making

Decision making in virtual teams is gaining momentum due to globalization, mobility of employees, and the need for collective and rapid decision making by members who are in different locations. These factors resulted in a proliferation of virtual team software support tools for decision making, the latest of which is social software (also known as collaboration 2.0), which includes tools such as wikis, blogs, microblogs, discussion forums, and social networking platforms. This paper describes the potential use of collaboration 2.0 software for improving the process and the specific tasks in virtual group decision making. The paper proposes a framework for exploring the fitness between social software and the major activities in the group decision making process and how such tools can be successfully adopted. Specifically, we use a fit-viability model to help assessing whether social software fit a decision task and what organizational factors are important for such tools to be effective. Representative research issues related to the use of such tools are also presented.     

Group Decision and Negotiation in Strategy Making

For a large number of organisations effective delivery of strategy depends upon the psychological and emotional commitment of the Top Management Team (TMT) thus involving group negotiation and decision making. Computer based group support promoting open debate and developing commitment is therefore important and illustrated through examples from the presenter's large number of strategic interventions.     

Ethical Decision Making: A Comparison of Computer- Supported and Face-to-face Group

This study compares computer-supported groups, i.e., groups using group support systems (GSS), and face-to-face groups using ethical decision-making tasks. A laboratory experiment was conducted using five-person groups of information systems professionals. Face-to-face (FTF) and GSS groups were compared in terms of their decision outcomes and group members' reactions. The results revealed that computer-supported and face-to-face groups showed no significant difference in terms of the decision outcomes of choice shift and decision polarity. However, FTF groups reached their decisions more quickly and they were more successful in attaining group consensus than GSS groups. Subjects evaluated face-to-face communication more favorably than GSS interaction on most post-group measures related to perceived group processes and satisfaction. Despite these outcomes, some possibilities for using GSS technology in an ethical decision making context are examined.     

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.     

A Fuzzy Multi-Criteria Decision Making Model for IS Student Group Project Assessment

To accommodate the criterion-referenced student group project assessment approach, this paper proposes a fuzzy group Multi-Criteria Decision Making (MCDM) model. The proposed model can be used to solve student group project assessment problem in particular and generic MCDM problems in general, where the criteria used are often different and can be scored with multiple preference formats. The proposed fuzzy group MCDM model supports seven different preference formats, including preference ordering, utility vector, linguistic term vector, selected subset, fuzzy selected subset, fuzzy preference relation, and normal preference relation. It has been firstly used in the context of Information Systems (IS) student group project assessment.