A Study of Apparel Consumer Behaviour in China and Taiwan

To understand consumer behaviour and preferences for apparel shopping in Asia, the current study was undertaken to investigate the following three topics in China and Taiwan: online and offline shopping behaviours, product evaluative criteria and fashion information sources. Data was collected through questionnaire surveys carried out in mainland China and Taiwan. According to the results of this study, both Chinese and Taiwanese women shopped more frequently than men. Chinese consumers shopped more frequently online than did their Taiwanese counterparts. Both Chinese and Taiwanese consumers cited “fit” and “comfort” as the two most important evaluative criteria for clothing, while “brand name’ and “country of origin” were the least important cues. Both Chinese and Taiwanese participants cited “friends” to be their most important fashion information sources, with “siblings” and “parents” being the two least important sources

Critical Success Factors in Data Mining Projects.

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. Chapter 3 describes the research framework and methodology, detailing how the CSFs were identified and validated from more than 1,300 articles published on data mining and related topics. The validated CSFs, organized into a research framework using 7 factors, generate the research questions and hypotheses. Chapter 4 presents analysis and results, along with the chain of evidence for each research question, the quantitative instrument and survey results. In addition, it discusses how the data were collected and analyzed to answer the research questions. Chapter 5 concludes with a summary of the findings, describing assumptions and limitations and suggesting future research