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Information Sciences Letters

Information Sciences Letters

Abstract

Data mining is the process of discovering patterns from large sets of data, based on methods at the intersection of machine learning, statistics, and database systems. As a form of knowledge discovery, the process uncovers concealed patterns to forecast possible results. To meet this objective, this study has applied a cross-sectional quantitative research approach. The data was gathered from managers in the fields of Information Technology (IT) and information systems (IS) of large companies operating in different e-commerce, digital businesses, and marketing in Jordan. The data was then gathered and analyzed. With a total of 309 responses collected in this study, the results were reached using structural equation modeling via Analysis of Moments Structure (AMOS V.21). The proposed conceptual model confirmed that all the identified variables associated with positive coefficients of data mining adoption with data warehouse, data accuracy, perceived usefulness, perceived ease of use, as well as Information System performance. Moreover, the study concluded with research insights related to this topic with further suggested research directed to expand the grasp in this field, and provide deeper understanding of the data mining related issues.

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