Abstiuct-Organizational databases are pervaded with data of poor quality. However, there has not been an analysis of the data quality literature that provides an overall understanding of the state-of-art research in this area. Using an analogy between product manufacturing and data manufacturing, this paper de-velops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature. This framework consists of seven elements: management responsibili-ties, operation and assurance costs, research and development, production, distribution, personnel management, and legal func-tion. The analysis reveals that most research efforts focus on op-eration and assurance costs, research and development, and pro-duction of data products. Unexplored research topics and unre-solved issues are identified and directions for future research provided. Index Terms-Data quality, data manufacturing, data product,

Information quality (IQ) is critical in organizations. Yet, despite a decade of active research and practice, the field lacks comprehensive methodologies for its assessment and improvement. Here, we develop such a methodology, which we call AIM quality (AIMQ) to form a basis for IQ assessment and benchmarking. The methodology is illustrated through its application to five major organizations. The methodology encompasses a model of IQ, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures. We develop and validate the questionnaire and use it to collect data on the status of organizational IQ. These data are used to assess and benchmark IQ for four quadrants of the model. These analysis techniques are applied to analyze the gap between an organization and best practices. They are also applied to analyze gaps between IS professionals and information consumers. The results of the techniques are useful for determining the best area for IQ improvement activities.

Abstract. Understanding how to implement SPI successfully is arguably the most challenging issue facing the SPI field today. The SPI literature contains many case studies of successful companies and descriptions of their SPI programs. However, there has been no systematic attempt to synthesize and organize the prescriptions offered. The research efforts to date are limited and inconclusive and without adequate theoretical and psychometric justification. This paper provides a synthesis of prescriptions for successful quality management and process improvement found from an extensive review of the quality management, organizational learning, and software process improvement literature. The literature review was confirmed by empirical studies among both researchers and practitioners. The main result is an instrument for measuring the key factors of success in SPI based on data collected from 120 software organizations. The measures were found to have satisfactory psychometric properties. Hence, managers can use the instrument to guide SPI activities in their respective organizations and researchers can use it to build models to relate the facilitating factors to both learning processes and SPI outcomes.

Abstract—Understanding how to implement software process improvement (SPI) successfully is arguably the most challenging issue facing the SPI field today. The SPI literature contains many case studies of successful companies and descriptions of their SPI programs. However, the research efforts to date are limited and inconclusive and without adequate theoretical and psychometric justification. This paper extends and integrates models from prior research by performing an empirical investigation of the key factors for success in SPI. A quantitative survey of 120 software organizations was designed to test the conceptual model and hypotheses of the study. The results indicate that success depends critically on six organizational factors, which explained more than 50 percent of the variance in the outcome variable. The main contribution of the paper is to increase the understanding of the influence of organizational issues by empirically showing that they are at least as important as technology for succeeding with SPI and, thus, to provide researchers and practitioners with important new insights regarding the critical factors of success in SPI.

Quality management has often been advocated as being universally applicable to organizations. This is in contrast with the manufacturing strategy contingency approach of Operations Management which advocates internal and external consistency between manufacturing strategy choices. This article empirically investigates whether quality management practices are contingent on a plant’s manufacturing strategy context, by examining the use of process quality management practices- a critical and distinctive subset of the whole set of quality management practices- across plants representative of different manufacturing strategy contexts. The study strongly suggests that process quality management practices are contingent on a plant’s manufacturing strategy, and identifies mechanisms by which this takes place.

Business performance is affected by the quality management practices of the organization. Holistic programs, including the national quality award programs, six-sigma initiatives, and enterprise resource planning (ERP) applications may characterize the highest level of quality initiatives. Arguably, the most popular quality initiative is the ISO 9000 program. The effect of different levels of quality initiatives on business performance was studied in a stratified sample of 280 firms in the Canadian provinces of Ontario and Quebec. The effect of using these quality initiatives in combination with ISO 9000 was also studied. Finally, the relationship between a firm’s use of enterprise resource applications and performance excellence was examined. The study revealed that, regarding quality initiatives, ISO 9000 certification was deemed positively beneficial only when combined with quality initiatives of the highest level. Further, there is a moderately significant relationship between ERP applications and performance excellence. 1.

Abstract: Data Quality (DQ) is seen as critical to effective business decision-making. However, maintaining DQ is often acknowledged as problematic. Asset data is the key enabler in gaining control of assets. The quality asset data provides the foundation for effective Asset Management (AM). Researches have indicated that achieving AM DQ is the key challenge engineering organisations face today. This paper investigates the DQ issues emerging from the unique nature of engineering AM data. It presents an exploratory research of a large scale national-wide DQ survey on how Australian engineering organisations address DQ issues, and proposes an AM specific DQ framework.

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Innovation has emerged as a ‘hot ’ research topic in the management field. Currently, considerable research effort is being applied to developing better understanding of how it can be effectively developed within organisations. However, recent trends in the literature suggest that there is a lack of convergence of ideas and that the knowledge in the area is still in a relatively ‘untidy’ state. This is despite an excellent foundational literature base. In this paper, the role of research is investigated. An analogous approach is taken whereby some research shortcomings in the area of quality management are examined to demonstrate how similar problems have arisen in the innovation management area, and ways in which researchers in the area can avoid these pitfalls. These, hopefully, will be taken into consideration in future innovation management studies, and will result in increased quality and more credible research findings. Key words: innovation management; quality management; ontology; epistemology; research methodology.

I hereby certify that this material, which I now submit for assessment on the programme of study leading to the award of PhD is entirely my own work and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of my work.