In this paper, we open up the organizational attributes that significantly influence the adoption of data mining (DM) technique for financial service industry. The technique of factor analysis was employed to explore the features and multivariate data analysis technique t-test to investigate the hypotheses. Based on the data collected from medium-to large-sized firms, the empirical results confirmed that the organizational size, attitude of data resource, and style of decision-making significantly influence the DM adoption. In addition, it was found that the DM adoption did not significantly affected by the types of both marketing orientation and information orientation in terms of organizational culture. Research implications were also discussed in this research.
Information Technology (IT) has been extensively used in a multitude of applications within various industries, in particular the enhancement of organizational intelligence and decision-making. Many studies have addressed that the organizational features play a fairly important role in the adoption of IT [Thong et al., 1995; Fletcher et al., 1996; Fink, 1998, Chengalur-Smith et al., 1999, Cabrera et al., 2001; Dewett et al., 2001]. These features mainly include size, culture, competition, specialization, functional differentiation, and external integration. While a variety of studies looking at the relation of organizational features and IT adoption have presented that a noteworthy one showed significantly in some specific conditions, but not in all cases, a particular technique of Data Mining (DM) is hardly ever revealed, and thus becomes the motivation of this research.
DM with a descriptive and predictive ability can elicit patterns that are not predictive, but meaningful and decision-supportable in historical data [Fayyad et al., 1996, 1997; Chen et al., 1996]. Basically, the DM mainly consists of five major phases: data collection, data cleaning, data mining, knowledge formulization and knowledge application. The data collection deals primarily with gathering the concerned data such as bank transactions, retailer transactions, Web shopping transactions, etc. The data cleaning is concerned with the consistency of multi-typed datasets, elimination of redundant attributes, refinement and reconstruction of collected datasets, and discretisation of continuous contexts. The DM returns the outputs that entail association, classification, regression, clustering, or summarization. The knowledge reorganization is conducted in the phase of formulization while practical use in the application.
Data mining is one of the important techniques of IT and has been employing in support of management decisions via the discovery of patterns in large databases [Bigus et al., 1996; Chen et al., 1996; Fayyad et al., 1996, 1997; Han et al., 1998; Han et al., 1999]. Pitta (1998) highlights the DM as an important tool that marketers can rely on to reveal patterns in databases while emphasizing the marketing one-to-one strategy. More importantly, the applications in various areas of business depicted in literature in the past few years have also witnessed the increased use of DM. Referable works can be viewed in hotel data mart [Sung et al., 1998], personal bankruptcy prediction [Donato et al., 1999], customer service support [Hui et al., 2000], and the special issue edited by Kohavi et al.,  of an underlying journal. Bigus (1996) and Adriaans et al. (1996) also provides a fundamental concept for the applicability of DM in business problems covering marketing segmentation, customer ranking, real estate pricing, sales forecasting, customer profiling, and prediction of bid behavior of pilots.
It is believed that many industries have been adopting DM as an important management tool to help management decisions. However, it may be more relevant for the DM adoption if an industry can produce tremendous transaction data through organizational activities. …