With the rapid advance of information technologies, human beings increasingly rely on computers to accumulate, process, and make use of data. Knowledge discovery techniques have been proposed to automatically search large volumes of data for patterns.Group Support Systems (GSS) play an important role in streamlining group activities and improving group outcomes. Various attempts have been made to help automate several tasks in group activities under GSS environment. One of the approaches is to apply automatic approach to idea organization task in GSS.This research designed and tested an SVD-enabled system to automate the idea organization task in GSS. Specifically, this research was conducted to examine how the idea organization task, typically regarded as the most labor-intensive and cognitively demanding in group problems solving, can be automated using a system enabled by singular value decomposition techniques. For the purpose of evaluation, we compared the performance of our automated approach using SVD algorithm against that of human subjects. Two separate experiments were conducted to evaluate the performance of the automatic approach on two essential components of an idea organization; namely, generation of topic categories and placement of relevant comments into their respective categories.The general conclusion that can be made from this research is that idea organization in group problem solving can be facilitated both efficiently and effectively with the SVD-enabled system that can automatically generate topic categories and place relevant comments into their respective topic categories. Therefore, our automatic approach may provide a useful and promising tool for the idea organization task in GSS.

This study examines the impact of organisational culture and GSS (Group Support Systems) on small group decision-making as measured by group decision outcomes. Laboratory experiments were designed to investigate how two types of groups selected on the basis of having apparently different organisational cultures make decisions on a preference task, in situations where they were given one of two different means of support (face-to-face GSS support or manually structured support) to aid their decision-making. Organisational culture and GSS are the independent variables, whilst the dependent outcome variables were perceived decision quality, decision process satisfaction, decision satisfaction, unified commitment, collaborative climate, and group cohesiveness. The research results showed that manually-supported groups, those who were given simple physical decision-support aids and worked face-to-face, had higher perceived decision quality and decision process satisfaction but no significant differences in unified commitment, decision satisfaction, collaborative climate, and group cohesiveness were found when compared to face-to-face GSS???supported groups who were given electronic aids. One of the two groups drawn from environments where organisational culture was expected to be quite different, that is the Kensington Campus group had higher perceived decision quality, decision process satisfaction, unified commitment, and group cohesiveness but no significant differences in decision satisfaction and collaborative climate when compared with groups drawn from a campus of the same university, ADFA (Australian Defence Force Academy) Campus, one which is attended only by military students. Whilst compared with those of manually structured support on group outcomes, the impacts of GSS on group outcomes weakened when they performed a preference task, organisational culture was found to have a significant impact on group outcomes when both face-to-face GSS and manual settings were considered.

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This study examines the impact of organisational culture and GSS (Group Support Systems) on small group decision-making as measured by group decision outcomes. Laboratory experiments were designed to investigate how two types of groups selected on the basis of having apparently different organisational cultures make decisions on a preference task, in situations where they were given one of two different means of support (face-to-face GSS support or manually structured support) to aid their decision-making. Organisational culture and GSS are the independent variables, whilst the dependent outcome variables were perceived decision quality, decision process satisfaction, decision satisfaction, unified commitment, collaborative climate, and group cohesiveness. The research results showed that manually-supported groups, those who were given simple physical decision-support aids and worked face-to-face, had higher perceived decision quality and decision process satisfaction but no significant differences in unified commitment, decision satisfaction, collaborative climate, and group cohesiveness were found when compared to face-to-face GSS???supported groups who were given electronic aids. One of the two groups drawn from environments where organisational culture was expected to be quite different, that is the Kensington Campus group had higher perceived decision quality, decision process satisfaction, unified commitment, and group cohesiveness but no significant differences in decision satisfaction and collaborative climate when compared with groups drawn from a campus of the same university, ADFA (Australian Defence Force Academy) Campus, one which is attended only by military students. Whilst compared with those of manually structured support on group outcomes, the impacts of GSS on group outcomes weakened when they performed a preference task, organisational culture was found to have a significant impact on group outcomes when both face-to-face GSS and manual settings were considered.

This research compared the use of an electronic meeting system tool to a manual group process in large and small groups in a controlled laboratory experiment. Outcomes measured include the quality of decision, the time taken in various stages of the decision making process, and group member satisfaction. A research model of the variables influencing group decision making was developed. The six independent variables included in this model are group size, the rule by which the group makes a decision, the incentives driving the group, the distribution of useful information within the group, the task complexity, and the meeting support (electronic or manual). In this research group size and method of support were manipulated, while the other variables were controlled. A decision-making task was developed for this research to specify and manipulate the six independent variables. The task described a product mix problem in which information on each product was given to group members. The group shared information and jointly determined an outcome. The group used an unanimous decision rule to choose a solution. A numerical outcome was used to objectively measure decision quality. Each member of the group received a cash payoff determined by the group's solution as incentive in accomplishing the task. All groups found the optimal solution. The simplicity of the task may have minimized the differences found between groups. There was no significant difference in general member satisfaction or time to decision. Prior knowledge was found to influence general member satisfaction and the time needed for the group to share information. Members of large groups perceived more uneven distribution of participation than members of small groups. Voting differences were very large: large groups took significantly more votes than small groups, and electronic groups took significantly more votes than manual groups. "Conjunctive" and "disjunctive" task descriptions are used to discuss task/tool interaction.

This thesis has two distinct research threads. One thread examines the effectiveness of technology support on the performance of focus groups. Unlike previous research, the work described in this thesis addresses the fundamental issue that groups are social systems, and that comprehensive measurement of the effectiveness of group activities requires assessment of both the task-oriented and social aspects of the group activity. In this research, four different communication modes are used to compare group effectiveness. The second research thread in this thesis is the use of Systems Thinking, and specifically Soft Systems Methodology (SSM), as the framework for inquiring into the effects of technology support on group effectiveness. The strategy in this thesis for developing and evaluating hypotheses extends the general descriptions and guidance in the literature on using SSM for hypothesis testing. Systems thinking also provides the basis for examining the prevailing ???profile deviation??? view that the better the fit between the group task and the technology support the greater the group performance. Using the six perspectives of fit developed by Venkatraman (1989), the most common GSS models and other models developed to examine Task-Technology Fit (TTF) are analysed. The results show that group performance models are most often tested from a ???profile deviation??? perspective and TTF models developed from a profile deviation perspective claim to have predictive and descriptive validity for assessing the level of group performance. To assess whether an SSM based approach can improve the predictive and/or descriptive analysis of the impact of technology support on group work, a field experiment was conducted at the Australian Defence Force Academy. Twenty focus groups of officer cadets assessed their military training program using a GSS in one of four communication modes. The results showed little predictive or descriptive support for the profile deviation perspective of TTF when measuring the group???s overall effectiveness, task effectiveness, participant satisfaction or group relations. The alternative ???gestalt??? perspective, operationalised in this research by using SSM, provided a more comprehensive approach to examining the effectiveness of technology support for group work.

This thesis has two distinct research threads. One thread examines the effectiveness of technology support on the performance of focus groups. Unlike previous research, the work described in this thesis addresses the fundamental issue that groups are social systems, and that comprehensive measurement of the effectiveness of group activities requires assessment of both the task-oriented and social aspects of the group activity. In this research, four different communication modes are used to compare group effectiveness. The second research thread in this thesis is the use of Systems Thinking, and specifically Soft Systems Methodology (SSM), as the framework for inquiring into the effects of technology support on group effectiveness. The strategy in this thesis for developing and evaluating hypotheses extends the general descriptions and guidance in the literature on using SSM for hypothesis testing. Systems thinking also provides the basis for examining the prevailing ???profile deviation??? view that the better the fit between the group task and the technology support the greater the group performance. Using the six perspectives of fit developed by Venkatraman (1989), the most common GSS models and other models developed to examine Task-Technology Fit (TTF) are analysed. The results show that group performance models are most often tested from a ???profile deviation??? perspective and TTF models developed from a profile deviation perspective claim to have predictive and descriptive validity for assessing the level of group performance. To assess whether an SSM based approach can improve the predictive and/or descriptive analysis of the impact of technology support on group work, a field experiment was conducted at the Australian Defence Force Academy. Twenty focus groups of officer cadets assessed their military training program using a GSS in one of four communication modes. The results showed little predictive or descriptive support for the profile deviation perspective of TTF when measuring the group???s overall effectiveness, task effectiveness, participant satisfaction or group relations. The alternative ???gestalt??? perspective, operationalised in this research by using SSM, provided a more comprehensive approach to examining the effectiveness of technology support for group work.

The mobile phone has been regarded as one of the most significant inventions in the field of communications and information technology over the past decade. Due to the rapid growth of mobile phone subscribers, hundreds of phone models have been introduced. Therefore, customers may find it difficult to select the most appropriate mobile phone because of information overload. The aim of this study is to investigate web support for customers who are selecting a mobile phone. Firstly, all the models of mobile phones in the New Zealand market were identified by visiting shops and local websites. Secondly, a list of all the features of these mobile phones was collated from local shops, websites and magazines. This list was categorised into mobile phone properties and performance criteria. An experiment then compared three different selection support methods: A (mobile phone catalogue), B (mobile phone property selection) and C (mobile phone property and performance criteria selection). The results of the experiment revealed that selection support methods B and C had higher overall satisfaction ratings than selection support method A; both methods B and C had similar satisfaction ratings. The results also suggested that males and females select their mobile phones differently, though there was no gender preference in selection support methods.

Complex Systems-of-Systems, which form the subject matter of the research presented here, may be considered to be systems that are constituted of several components, each of which could themself be regarded as being a complete system in its own right. These component systems are typically largely managed independently of one another. They each have their own intrinsic goals which they are capable of fulfilling autonomously. These goals are additional to any to which they might contribute as part of the overall System-of-Systems that is being investigated. Systems-of-Systems are epitomised by features such as the prevalence of complex webs of mutual interdependency amongst their component systems, the involvement of a broad diversity of stakeholders and the participation of a number of independent decision-makers, each of whom competes with the others for strictly limited resources. Furthermore, the components of Systems-of-Systems may often be found to be widely scattered across an extensive geographical region. Sometimes such systems may even span global distances. Complex Systems-of-Systems of such a nature would appear to be becoming ever more prevalent in the increasingly interconnected and ever shrinking world which is engendered, in particular, by the proliferation of modern information and communication technology. Systems-of-Systems may arise in both commercial and public sector contexts, hence commercial organisations and governments, alike, confront the daunting challenge of planning, establishing and maintaining novel systems of this type. However, it is to be anticipated that significant difficulties might be encountered in applying existing systems engineering methods, which were designed for dealing with far more monolithic types of system, to the treatment of Systems-of- Systems. Instead, the successful achievement of acceptable resolutions to the various Systems-of-Systems issues and conundrums with which the responsible planners can be expected to be confronted would appear to demand the application of sophisticated distributed decision aids, in order to alleviate the the intolerable burden which the decision-makers would otherwise be forced to endure. Such aids need to be able to take a genuinely fair and unbiased account of the interests of a multitude of stakeholders. At the same time, they must be able to satisfactorily accommodate the enforcement of a range of highly involved and intricate constraints upon the various alternative interventions which might potentially be contemplated. In order to underpin the development of decision aids of the kind demanded, it has been sought to establish some basic theoretical foundations for Systems-of-Systems. This body of theory has then been applied to the identification of the methods which might be most suitable for the treatment of Systems-of-Systems questions. Finally, some decision-support tools which are intended to facilitate the implementation of the methods that have been recommended have been described. Any attempt to dictate a single â??bestâ?? intervention to the decision-makers would generally be totally unacceptable. As a result, the objective which has been pursued has been to seek to filter out and identify a relatively sparing number of what would appear to be the most reasonable alternatives from some much more profuse range of possibilities. These may then be afforded more intensive scrutiny by the pertinent decision-makers.

A data warehouse is a single data store, where data from multiple data sources is integrated for online business analytical processing (OLAP) of an entire organisation. The rationale being single and integrated is to ensure a consistent view of the organisational business performance independent from different angels of business perspectives. Due to its wide coverage of subjects, data warehouse design is a highly complex, lengthy and error-prone process. Furthermore, the business analytical tasks change over time, which results in changes in the requirements for the OLAP systems. Thus, data warehouse and OLAP systems are rather dynamic and the design process is continuous. In this thesis, we propose a method that is integrated, formal and application-tailored to overcome the complexity problem, deal with the system dynamics, improve the quality of the system and the chance of success. Our method comprises three important parts: the general ASMs method with types, the application tailored design framework for data warehouse and OLAP, and the schema integration method with a set of provably correct refinement rules. By using the ASM method, we are able to model both data and operations in a uniform conceptual framework, which enables us to design an integrated approach for data warehouse and OLAP design. The freedom given by the ASM method allows us to model the system at an abstract level that is easy to understand for both users and designers. More specifically, the language allows us to use the terms from the user domain not biased by the terms used in computer systems. The pseudo-code like transition rules, which gives the simplest form of operational semantics in ASMs, give the closeness to programming languages for designers to understand. Furthermore, these rules are rooted in mathematics to assist in improving the quality of the system design. By extending the ASMs with types, the modelling language is tailored for data warehouse with the terms that are well developed for data-intensive applications, which makes it easy to model the schema evolution as refinements in the dynamic data warehouse design. By providing the application-tailored design framework, we break down the design complexity by business processes (also called subjects in data warehousing) and design concerns. By designing the data warehouse by subjects, our method resembles Kimball's "bottom-up" approach. However, with the schema integration method, our method resolves the stovepipe issue of the approach. By building up a data warehouse iteratively in an integrated framework, our method not only results in an integrated data warehouse, but also resolves the issues of complexity and delayed ROI (Return On Investment) in Inmon's "top-down" approach. By dealing with the user change requests in the same way as new subjects, and modelling data and operations explicitly in a three-tier architecture, namely the data sources, the data warehouse and the OLAP (online Analytical Processing), our method facilitates dynamic design with system integrity. By introducing a notion of refinement specific to schema evolution, namely schema refinement, for capturing the notion of schema dominance in schema integration, we are able to build a set of correctness-proven refinement rules. By providing the set of refinement rules, we simplify the designers's work in correctness design verification. Nevertheless, we do not aim for a complete set due to the fact that there are many different ways for schema integration, and neither a prescribed way of integration to allow designer favored design. Furthermore, given its °exibility in the process, our method can be extended for new emerging design issues easily.