Companies have invested large amounts of money on information systems development. Unfortunately, not all information systems developments are successful. Software project failure is frequent and lamentable. Surveys and statistical analysis results underscore the severity and scope of software project failure. Limited research relates software structure to information systems failures. Systematic study of failure provides insights into the causes of IS failure. More importantly, it contributes to better monitoring and control of projects and enhancing the likelihood of the success of management information systems. The underlining theories and literature that contribute to the construction of theoretical framework come from general systems theory, complexity theory, and failure studies. One hundred COBOL programs from a single company are used in the analysis. The program log clearly documents the date, time, and the reasons for changes to the programs. In this study the relationships among the variables of business requirements change, software complexity, program size and the error rate in each phase of software development life cycle are tested. Interpretations of the hypotheses testing are provided as well. The data shows that analysis error and design error occur more often than programming error. Measurement criteria need to be developed at each stage of the …

The increasing awareness of data mining technology, along with the attendant increase in the capturing, warehousing, and utilization of historical data to support evidence-based decision making, is leading many organizations to recognize that the effective use of data is the key element in the next generation of client-server enterprise information technology. The concept of data mining is gaining acceptance in business as a means of seeking higher profits and lower costs. To deploy data mining projects successfully, organizations need to know the key factors for successful data mining. Implementing emerging information systems (IS) can be risky if the critical success factors (CSFs) have been researched insufficiently or documented inadequately. While numerous studies have listed the advantages and described the data mining process, there is little research on the success factors of data mining. This dissertation identifies CSFs in data mining projects. Chapter 1 introduces the history of the data mining process and states the problems, purposes, and significances of this dissertation. Chapter 2 reviews the literature, discusses general concepts of data mining and data mining project contexts, and reviews general concepts of CSF methodologies. It also describes the identification process for the various CSFs used to develop the research framework. …