Impacts of GSS generic structures and task types on group communication process and outcome: Some expected and unexpected research findings

The contingency nature of GSS use implies that some GSS structures may be more suitable for supporting some task types than others. Research specifically examining effects of the GSS-task fit on group process and outcome has been inadequate in GSS research literature. This research, therefore, focused on the GSS-task fit. Two task types were used to explore the fit: an intellective task that was chosen to elicit the good fit and a preference task which was chosen to elicit the poor fit. Half groups were provided with computer (GSS) support and the other half was not. Research results reported some expected and unexpected findings. The good GSS-task fit for the intellective task enhanced group communication in group process whereas the poor fit for the preference task reduced group communication, as expected. However, the good fit for an intellective task failed to increase group satisfaction, relative to the poor fit for a preference task, which was unexpected. A possible reason for the unexpected results was suggested and discussed. The research findings provide empirical evidences to support the argument that GSS may not be studied as a global entity in the future. Instead, more research needs to be done on GSS-task fit to explore matches between specific GSS structures and specific task types.     

Impacts of GSS generic structures and task types on group communication process and outcome: some expected and unexpected research findings

The contingency nature of GSS use implies that some GSS structures may be more suitable for supporting some task types than others. Research specifically examining effects of the GSS-task fit on group process and outcome has been inadequate in GSS research literature. This research, therefore, focused on the GSS-task fit. Two task types were used to explore the fit: an intellective task that was chosen to elicit the good fit and a preference task which was chosen to elicit the poor fit. Half groups were provided with computer (GSS) support and the other half was not. Research results reported some expected and unexpected findings. The good GSS-task fit for the intellective task enhanced group communication in group process whereas the poor fit for the preference task reduced group communication, as expected. However, the good fit for an intellective task failed to increase group satisfaction, relative to the poor fit for a preference task, which was unexpected. A possible reason for the unexpected results was suggested and discussed. The research findings provide empirical evidences to support the argument that GSS may not be studied as a global entity in the future. Instead, more research needs to be done on GSS-task fit to explore matches between specific GSS structures and specific task types.     

Opening the black box of system usage: user adaptation to disruptive IT

According to Benbasat and Barki (2007), systems usage has remained a black box in spite of the fact that the construct lies at the heart of a host of studies in the field. We know very little about how exactly users cope with information technology (IT), especially disruptive IT. To answer such questions, we grounded our current work in Beaudry and Pinsonneault's Coping Model of User Adaptation (2005; CMUA), a model that explains user strategies appraising an IT event. These strategies are a response to threats and opportunities embedded in the IT event and are impacted by the level of control users have over the situation. In the current study, following CMUA, we develop and test measures for a deeper understanding of systems usage and user adaptation to IT through a 2 × 2 laboratory experiment. Overall, we found strong support for the CMUA model.     

Interactive group decision process with evolutionary database

This paper presents interactive procedures for solving a multiple criteria group decision making (MCGDM) problem with incomplete information when multiple decision makers are involved. It is difficult for group members participating in the decision making process to articulate their preferences with cardinal values. Therefore, we represent their preferences with utility ranges obtained by solving linear programming (LP) problems with incompletely specified information, find conflicting judgments, if any in their specified information, and suggest interaction processes to help the group reach a consensus. This paper will provide an algorithmic basis for a normative and interactive knowledge based group decision support system.     

Interpretation and knowledge discovery from the multilayer perceptron network: Opening the black box

This paper interprets the outputs from the multilayer perceptron (MLP) network by finding the input data features at the input layer of the network which activate the hidden layer feature detectors. This leads directly to the deduction of the significant data inputs, the inputs that the network actually uses to perform the input/output mapping for a classification task, and the discovery of the most significant of these data inputs. The analysis presents a method for providing explanations for the network outputs and for representing the knowledge learned by the network in the form of significant input data relationships. During network development the explanation facilities and data relationships can be used for network validation and verification, and after development, for rule induction and data mining where this method provides a potential tool for knowledge discovery in databases (KDD).     

Opening the black box of neural networks for remote sensing image classification

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a ‘black box’. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classification decisions explicitly in the form of fuzzy ‘if-then’ rules. However, the construction of a knowledge base, especially the fine-tuning of the fuzzy set parameters of the fuzzy rules in a fuzzy expert system, is a tedious and subjective process. This research has developed a new, improved neuro-fuzzy image classification system based on the synergism between neural networks and fuzzy expert systems. It incorporates the best of both technologies and compensates for the shortcomings of each. The learning algorithms of neural networks developed here are used to automate the derivation of fuzzy set parameters for the fuzzy ‘if-then’ rules in a fuzzy expert system. The rules obtained, in symbolic form, facilitate the understanding of the neural network based image classification system. In addition, the image classification accuracy obtained from the improved neuro-fuzzy system was significantly superior to those of the back-propagation based neural network and the maximum likelihood approaches.     

SODPM: A sequence-oriented decision process model for unstructured group decision problems

Many approaches to the solutions of modern unstructured decision problems mainly involve modelling, information technology and group behaviour. The work of group decision-making can be viewed as a type of process plan that is reflected by its problem structure within which the thinking space is wide and innovative. This paper presents a Sequence-Oriented Decision Process Model (SODPM) that is based on the defined sequence of problem elements to help solve unstructured problems. A GDSSP (Group Decision Support System for Personnel Promotion) that embeds a predefined mechanism to perform decision process, decision model and decision choice is developed to experimentally demonstrate the SODPM. Empirical evaluation was conducted to derive the results for the research problems that include efficiency and group satisfaction. There were remarkable results: the SODPM can serve as a facilitative vehicle for opinion convergence and group satisfaction is highly positive. While the demonstrated domain for the example presented is personnel promotion, the proposed SODPM can be utilized to help solve the similar class of unstructured problems in other domains also.     

SODPM: a sequence-oriented decision process model for unstructured group decision problems

Many approaches to the solutions of modern unstructured decision problems mainly involve modelling, information technology and group behaviour. The work of group decision-making can be viewed as a type of process plan that is reflected by its problem structure within which the thinking space is wide and innovative. This paper presents a Sequence-Oriented Decision Process Model (SODPM) that is based on the defined sequence of problem elements to help solve unstructured problems. A GDSSP (Group Decision Support System for Personnel Promotion) that embeds a predefined mechanism to perform decision process, decision model and decision choice is developed to experimentally demonstrate the SODPM. Empirical evaluation was conducted to derive the results for the research problems that include efficiency and group satisfaction. There were remarkable results: the SODPM can serve as a facilitative vehicle for opinion convergence and group satisfaction is highly positive. While the demonstrated domain for the example presented is personnel promotion, the proposed SODPM can be utilized to help solve the similar class of unstructured problems in other domains also.     

Organizational form, incentives and the management of information technology: Opening the black box of outsourcing

In this work, we attempt to show how operations researchers can effectively manage the production costs of computing services. The thesis of this paper is that an outsourcing firm, by virtue of the fact that it manages the IT function for multiple firms, is privileged to information not available to the focal firm. We derive the conditions under which this privileged information allows the partner firm to construct superior incentives for its employees, resulting in superior IT management. Further, we detail the circumstances under which outsourcing will not provide additional benefit, and what sorts of partners are likely to provide the greatest benefit. The two main findings are that for low levels of uncertainty, both in-house and relational management are equally acceptable. However, as uncertainty increases, the value of relational management increases. Conclusions are drawn and extensions are proposed, related to economies of scale and transactions costs.     

The fusion process with heterogeneous preference structures in group decision making: A survey

In group decision making (GDM), decision makers who have different experiential, cultural and educational backgrounds will naturally provide their preference information by heterogeneous preference structures (e.g., utility values, preference orderings, numerical preference relations and multigranular linguistic preference relations). To date, many studies have discussed GDM problems with heterogeneous preference structures. To provide a clear perspective on the fusion process with heterogeneous preference structures in GDM, this paper presents a review of three types of fusion approaches: the indirect approach, the optimization-based approach and the direct approach. Moreover, with respect to insights gained from prior researches, several open problems are proposed for the future research.     

Beyond the black box: open implementation

Encapsulation, informally known as black-box abstraction, is a widely known and accepted principle. It is a basic tenet of software design, underlying approaches to portability and reuse. However, many practitioners find themselves violating it in order to achieve performance requirements in a practical manner. The gap between theory and practice must be filled. Open implementation is a controversial new approach that claims to do just that. The paper provides some ideas to spark further debate on black-box abstraction     

Beyond the black box: knowledge overlaps in software outsourcing

The black-box approach - that is, the use of formal project requirements to transfer knowledge about the application problem domain from the client to the vendor organization - has long been the mainstay of outsourced software development. We've studied 209 custom application development projects in 209 global software development organizations. We used the study results to develop detailed guidelines for improving outsourcing practice and a client-vendor knowledge congruence assessment framework. The study's key finding is that effective outsourcing requires knowledge congruence - that is, a good fit in terms of business and technical knowledge across the client-vendor dyad. This uniquely extends the notion of the product/process fit from the manufacturing management arena into the more knowledge-driven domain of software engineering.     

Analytic hierarchy process: It can work for group decision support systems

Most group problems are complex and unstructured and are difficult to find solutions, GDSS, systems for group decision making support, have more concentrated on the development of technological factor such as communication support, electronic meeting support, etc. And there have been relatively few researches about group problem modeling and group decision-aid. We cannot guarantee the value of GDSS just with the support in technological basis. The AHP has been suggested as an group problem modeling tool. In this paper authors applied AHP in real world group problems, investigated its value. and suggested the elements of GDSS software.     

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.     

Effects of individuals’ psychological states on their satisfaction with the GSS process

The use of group support systems (GSS) to improve the outcomes of, and satisfaction with, meetings is increasing. Our empirical study, drawing on the theory of job design, was initiated to investigate the relative contribution of both individual and group characteristics on GSS participants’ satisfaction with the process. Our field study found that when employees used a GSS to brainstorm on an issue of concern, two individual characteristics (locus of control and personal innovativeness) and one group characteristic (group cohesion) had significant impact on user satisfaction with the meeting process. Locus of control, however, was found to have an impact in the opposite direction to our assumptions. Computer anxiety, an individual characteristic, was not found to be significant.     

Planning facilities layout in clinical laboratories using a group decision making process

A group decision making process used for planning the facilities layout in clinical laboratories is described. While quantitative and computer-based models can be used for planning alternative layout plans, considerations of qualitative and personal factors should be given during the layout finalization phase. The visual evaluation of detailed architectural drawings of alternative layout plans, by a group of the laboratory employees, can result in generating ideas for modifications. These modifications are incorporated in a new plan. The process of evaluation of a modified plan, generating ideas for further improvements, and readjusting the layout plan would result in an effective laboratory layout. This can increase employee morale and productivity. The group decision making process was successfully applied to redesigning the facility layout in a hospital clinical laboratory.     

A peek into the black box: exploring classifiers by randomization

Classifiers are often opaque and cannot easily be inspected to gain understanding of which factors are of importance. We propose an efficient iterative algorithm to find the attributes and dependencies used by any classifier when making predictions. The performance and utility of the algorithm is demonstrated on two synthetic and 26 real-world datasets, using 15 commonly used learning algorithms to generate the classifiers. The empirical investigation shows that the novel algorithm is indeed able to find groupings of interacting attributes exploited by the different classifiers. These groupings allow for finding similarities among classifiers for a single dataset as well as for determining the extent to which different classifiers exploit such interactions in general.     

Checking inside the black box: regression testing by comparing value spectra

Comparing behaviors of program versions has become an important task in software maintenance and regression testing. Black-box program outputs have been used to characterize program behaviors and they are compared over program versions in traditional regression testing. Program spectra have recently been proposed to characterize a program's behavior inside the black box. Comparing program spectra of program versions offers insights into the internal behavioral differences between versions. In this paper, we present a new class of program spectra, value spectra, that enriches the existing program spectra family. We compare the value spectra of a program's old version and new version to detect internal behavioral deviations in the new version. We use a deviation-propagation call tree to present the deviation details. Based on the deviation-propagation call tree, we propose two heuristics to locate deviation roots, which are program locations that trigger the behavioral deviations. We also use path spectra (previously proposed program spectra) to approximate the program states in value spectra. We then similarly compare path spectra to detect behavioral deviations and locate deviation roots in the new version. We have conducted an experiment on eight C programs to evaluate our spectra-comparison approach. The results show that both value-spectra-comparison and path-spectra-comparison approaches can effectively expose program behavioral differences between program versions even when their program outputs are the same, and our value-spectra-comparison approach reports deviation roots with high accuracy for most programs.