TOTAL QUALITY MANAGEMENT: A GDSS APPROACH

Teams are a major factor in total quality management (TQM), so any technology that improves team performance facilitates the TQM process. This article describes how group decision support systems (GDSSs) can be used to increase team productivity and effectiveness within the TQM framework.     

Ergodynamics and macroergonomics in analysis of decision‐making efficiency and complexity

The principles of macroergonomics and the laws of ergodynamics are reviewed. The theories of mutual adaptation and transformation dynamics are presented as a complex basis of the ergodynamics, and they are offered as paradigms for macroergonomic evaluation and design of sociotechnical or human‐machine‐environment decision‐making systems (HMES). Criteria and factors of decision‐making efficiency are studied for different cognitive strategies and their transformations in the course of long‐term training and short‐term decision making in emergencies. Sample system criteria and design guidelines, based on the results of both laboratory studies and actual research and design applications, are provided. A combination of macroergonomics and ergodynamics helps ergonomists to meet rapidly growing requirements of practice and overcome a narrow approach to the workstations analysis when a broader view of complex multilevel system leads to success and instead of traditional static approach the analysis of transformations in technologies, management structures, and work skills is needed.     

Social processes and group decision making: anonymity in group decision support systems

Various social processes in decision-making groups are considered detrimental to the quality of decisions. It is often assumed that removing the ability for groups to exert strong social influence on its members improves group decisions. Group Decision Support Systems (GDSSs) are increasingly used to remedy the social faults of the decision-making process in groups. In these systems, anonymity is seen as a tool to reduce the impact of the group over its members, and therefore as the key to improved group performance. This meta-analytic review examines the assumption that anonymity in GDSSs is beneficial for group decision-making on a range of performance indicators. In 6 meta-analyses of 12 independent investigations there is no support for this hypothesis. The only reliable effect of anonymity was to lead to more contributions, especially more critical ones. An alternative model is presented to account for the findings. This model argues that performance in decision-making groups depends on the social context and relevant social norms as well as on system characteristics such as anonymity. It is concluded that the integration of anonymity into phases of group decision support does not guarantee improved performance.     

Social processes and group decision making: anonymity in group decision support systems

Various social processes in decision-making groups are considered detrimental to the quality of decisions. It is often assumed that removing the ability for groups to exert strong social influence on its members improves group decisions. Group Decision Support Systems (GDSSs) are increasingly used to remedy the social faults of the decision-making process in groups. In these systems, anonymity is seen as a tool to reduce the impact of the group over its members, and therefore as the key to improved group performance. This meta-analytic review examines the assumption that anonymity in GDSSs is beneficial for group decision-making on a range of performance indicators. In 6 meta-analyses of 12 independent investigations there is no support for this hypothesis. The only reliable effect of anonymity was to lead to more contributions, especially more critical ones. An alternative model is presented to account for the findings. This model argues that performance in decision-making groups depends on the social context and relevant social norms as well as on system characteristics such as anonymity. It is concluded that the integration of anonymity into phases of group decision support does not guarantee improved performance.     

Revisiting the supplier selection problem: An integrated approach for group decision support

Decision support for supplier selection is a highly researched theme in procurement management literature. However applications of group decision support theories are yet to be explored extensively in this domain. This study proposes an approach for group decision support for the supplier selection problem by integrating fuzzy Analytic Hierarchy Process (AHP) for group decision making and fuzzy goal programming for discriminant analysis. In the first step, the fuzzy AHP theory with the Geometric Mean Method has been used to prioritize and aggregate the preferences of a group of decision makers. Then consensus has been developed between these aggregated priorities using the Ordinal Consensus Improvement Approach. Subsequently, the consensual priorities of this group of decision makers have been integrated with fuzzy goal programming theory for discriminant analysis to provide predictive decision support. Finally it has been shown through a case study how the integrated approach using fuzzy AHP for group decision making and fuzzy goal programming with soft constraints has been more effective as compared to an existing approach for group decision making using only AHP.     

A Jini‐based software framework for developing distributed cooperative decision support systems

Many mission‐critical, decision‐making situations happen in dynamic, rapidly changing, and often unpredictable environments. Military, governmental, and medical contexts are examples of such situations, which can be characterized by highly decentralized, up‐to‐date data sets coming from various sources. Unlike other decision‐making tools, decision support systems (DSS) designed for such situations are challenged by the need to access this decentralized data at any time, from anywhere, under tight time constraints. This paper presents the design of a software framework for developing these kinds of distributed DSSs. The proposed Java‐based framework relies mostly on the Jini technology and its JavaSpaces service. The construction of the framework is presented with UML class diagrams, UML sequence diagrams, and additional explanations. Copyright © 2003 John Wiley & Sons, Ltd.     

The effects of decision support and task contingencies on model formulation: A cognitive perspective

This paper takes a cognitive cost-benefit approach to understanding model formulation. Work in the behavioral decision literature on the role of effort and accuracy in choice tasks indicates that effort, or cognitive cost, is a key factor in understanding decision behavior. However, the model formulation literature does not discuss how effort interacts with other factors, such as task complexity and decision aids, to influence model formulation. In this paper, based on the work on the cost-benefit theories of cognition, we posit that two types of effort, namely that associated with building or formulating a model and that associated with utilizing that model in the solution of a problem, will influence model formulation. We then examine how the methods used in the behavioral decision making literature and the reported findings concerning the interaction of effort with task and decision aids can be utilized to understand model formulation.     

The impact of problem size on decision processes: an experimental investigation on very large choice problems with support of decision support systems

Abstract: Choice problems as a class of decision problems have attracted great attention for the last couple of decades. Among the frameworks and supporting theories used in their study, two have had the greatest impact: bounded rationality and cost–benefit. Both theories could find support from past empirical studies under different conditions or problem environments. In the past studies, problem size has been shown to play an important role in decision‐making. As problem size increases, a decision process may be detoured and the decision outcome may be different. In this paper we investigate the impact of problem size on three important aspects of the computer‐aided decision process – strategy selection, decision time/effort, and decision quality – through very large choice problems.     

Goal programming to model human decision making in ultimatum games

Goal Programming (GP) is applied to modelling the decision making processes in the well‐known Ultimatum Game and some of its variations. The decision model for a player is a Chebychev GP model that balances her individual desires with the mental model she has of the desires of other relevant players. Fairness is modelled as a universal mechanism, allowing players to differ in their belief of what a fair solution should be in any particular game. The model's conceptual framework draws upon elements considered of importance in the field of cognitive neuroscience, and results from the field of psychology are used to further specify the types of goals in the model. Computer simulations of the GP models, testing a number of Ultimatum, Dictator and Double‐Blind Dictator Games, lead to distributions of proposals made and accepted that correspond reasonably well with experimental findings.     

Organizational decision support systems

Decision support systems have traditionally been discussed within the context of individual or group decision making. In this paper we study decision support systems from an organizational perspective. We propose a framework for designing an organizational decision support system that is based on a network of knowledgebased systems. Nodes of this network interact with each other, as well as various other organizational systems, to provide comprehensive decision support. This network is also utilized to provide effective support for formal multi-participant decision making.     

Evaluation of decision support systems in medicine

Evaluation deals with the measurement or judgement of system characteristics and with comparison of these with the frame of reference. Evaluation of medical decision support systems is important because these systems are planned to support human decision making in tasks where information from different sources is combined to support clinicians' decisions concerning diagnosis, therapy planning and monitoring of the disease and treatment processes. As the field of decision support systems is still relatively unexplored, standards or generally accepted methodologies are not yet available for evaluation. Evaluation of medical decision support systems should be approached from the perspectives of knowledge acquisition, system development life-cycle and user-system integrated environment.     

The effect of reasoning logics on real-time decision making

The advent of satellite tracking and communications technology has motivated organizations to investigate centralized real-time support for remote and mobile units and operations. Transportation companies have begun to use satellite tracking systems, production and energy plants are employing intelligent monitoring systems, and on-site emergency managers are being supported by headquarters in their assessment and decision making process. Although different reasoning logics have been proposed for real-time decision making, little attention has been given to the comparative assessment of these approaches. This paper presents empirical evidence on the impact of alternative reasoning logics on individual decision making in real-time. Following the definition of the decision tasks for hazardous operations, commonly used reasoning logics are discussed. Then, results of an empirical comparison of different reasoning approaches are presented. The data were gathered during an experiment with experienced operators. The conclusions of this research are that the selection of the appropriate reasoning logic in support of real-time decision making is crucial     

Putting value of information theory into practice: a methodology for building sequential decision support systems

Abstract: The paper presents a methodology for building sequential decision support systems based on decision theory using value of information (for short, DT‐VOI based SDSSs). DT‐VOI based SDSSs support decision‐makers in difficult problems of sequential decision‐making. In particular we consider the problem of building DT‐VOI based SDSSs which are capable of supporting decisions in critical situations where (1) making a decision entails knowing the states of some critical hypotheses, and such knowledge is acquired by performing suitable tests; (2) test outcomes are uncertain; (3) performing a test entails, in general, some drawbacks, so that a trade‐off exists between such drawbacks and the value of the information provided by the test; (4) performing a test has the side‐effect that it changes the expected benefit from performing other tests; (5) exceptional situations alter probability and utility default values.     

The Influence of workspace awareness on group intellective decision effectiveness

Workspace awareness is an understanding of members’ interactions within a shared workspace, and has been a fundamental concern to researchers examining how groups using group support systems can be more effective as they design and develop physical artifacts. We provide theoretical and empirical evidence that workspace awareness can be a significant factor influencing the effectiveness of groups employing group support systems for the development of intangible artifacts, such as decision quality and consensus. The workspace awareness literature typically does not examine causal relationships within awareness. We develop a theoretical model that divides workspace awareness into three elements (presence, behavior, and insight awareness), which are important for group decision tasks, and provide empirical evidence that: (1) understanding the reasons behind group member behaviors (insight awareness) is key to increasing decision quality and consensus; (2) greater insight awareness can be obtained when an individual is better able to track and characterize others’ behaviors (behavior awareness); and (3) behavior awareness depends on an individual's ability to identify and distinguish among the different individuals within the group (presence awareness). Empirical support is derived from a laboratory experiment.     

An examination of decision-makers' utilization of decision support system output

A study was conducted of 18 decision support systems in major U.S. corporations in order to examine their methods of operation and the ways in which the DSS contribute to the decision making process. These DSS were studied with respect to organizational level of the decision maker, phases of the decision making process, interaction among decision makers using the DSS, requirement for and regularity of DSS use, impact on job tasks and performance, and perceived value of the DSS. Study results provide substantial support for generalized conceptualizations in the literature. Decision support is primarily for upper and/or middle management working in interaction. Use of the DSS is at the discretion of the decision maker and the majority of DSS users are highly satisfied with their systems.     

Naturalistic decision making

OBJECTIVE: This article describes the origins and contributions of the naturalistic decision making (NDM) research approach. BACKGROUND: NDM research emerged in the 1980s to study how people make decisions in real-world settings. Method: The findings and methods used by NDM researchers are presented along with their implications. RESULTS: The NDM framework emphasizes the role of experience in enabling people to rapidly categorize situations to make effective decisions. CONCLUSION: The NDM focus on field settings and its interest in complex conditions provide insights for human factors practitioners about ways to improve performance. APPLICATION: The NDM approach has been used to improve performance through revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support decision making and related cognitive functions.     

Strategies for implementing decision technology systems successfully: a field study in army housing management

A decision technology system (DTS) has been proposed to provide direct support for the entire decision‐making process. In the 1990s, the concept was implemented in stages to facilitate US Army housing management. This implementation has been the first successful field application of the DTS theory. Recent enhancements confirmed earlier insights and offered important new lessons in developing, implementing, and benefiting from DTSs. This paper discusses the original insights and fresh lessons. To provide a context, the article first briefly describes the enhancements and presents a typical, but streamlined, system session. The paper then summarizes the documented gains from implementing the DTS theory, reports the utilized strategies, and infers the implications for the systems analysis and design process.     

Solving discrete multicriteria decision problems based on logic-based decision support systems

This study aims to formulate and solve discrete multicriteria decision making (D-MCDM) problems by utilizing artificial intelligent decision support systems. The major advantage of this approach is that data, functions, many D-MCDM methods, choice rules for methods, and the decision maker's preferences in D-MCDM can be integrated in a logical structure. Besides, based on the modulared D-MCDM method base and data base, the decision maker can flexibly choose suitable methods to solve decision problems. This paper first decomposes D-MCDM problems into alternative-attribute, attribute-criterion, criterion-method-recommendation and choice-method relationships, then transforms these relationships into ‘Data’, ‘Function’ and ‘Rule’ formats of logic-based programs. By following that the typical D-MCDM methods as Dominant method, Lexicographic method, Weighting method, ELECTRE method, TOPSIS method and Method with fuzzy concept are coded in a PROLOG-type language in a consistent format. Some choice rules for these D-MCDM methods are then discussed. Finally, the inference process and the man-machine dialog of this system are analyzed.     

The Role of Intuition and Deliberation for Exploration and Exploitation Success

Advances in social and cognitive psychology and related fields have drawn attention to the role of intuition in organizational decision making. In this study we link intuitive and deliberate decision‐making styles to the success of exploration and exploitation activities, which are understood as two qualitatively opposing strategies that organizations can adopt. We provide empirical evidence that the two opposing strategies are linked to two opposing styles of decision making – intuitive and deliberate. In doing so, we draw on data which we received from 140 entrepreneurs and managing partners of Austrian companies and show that exploration is strongly related to intuitive decision making whereas exploitation draws on both intuitive and deliberate decision making. Based on our findings, we stress the complementarity of the two decision‐making styles, and point out that particularly in the light of the fast‐changing premises in which organizations have to manoeuvre today, decision makers are well advised to use both decision‐making styles to their best benefit.     

Embedding robotic surgery into routine practice and impacts on communication and decision making: a review of the experience of surgical teams

While an increasing number of healthcare providers are purchasing surgical robots because of anticipated improvements in patient outcomes, their implementation into practice is highly variable. In robotic surgery, the surgeon is physically separated from the patient and the rest of the team with the potential to impact communication and decision making in the operating theatre and subsequently patient safety. Drawing on the approach of realist evaluation, in this article we review reports of the experience of surgical teams that have introduced robotic surgery to identify how and in what contexts robotic surgery is successfully integrated into practice and how and in what contexts it affects communication and decision making. Our analysis indicates that, while robotic surgery might bring about a number of benefits, it also creates new challenges. Robotic surgery is associated with increased operation duration, which has implications for patient safety, but strategies to reduce it can be effective with appropriate support from hospital administration and nursing management. The separation of the surgeon from the team can compromise communication but may be overcome through use of standardised communication. While surgeon situation awareness may be affected by the separation, the ergonomic benefits of robotic surgery may reduce stress and tiredness and enhance surgeon decision making. Our review adds to the existing literature by revealing strategies to support the introduction of robotic surgery and contextual factors that need to be in place for these to be effective.