title: Modeling the prediction of business intelligence system effectiveness.
abstract: Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.
authors.affiliation: Department of Information and Finance Management, National Taipei University of Technology, No.1, Sec. 3, Zhongxiao E. Rd., Taipei, 10608 Taiwan.
authors.forename: Sung-Shun
authors.initials: SS
authors.lastname: Weng
authors.affiliation: Department of Information Management, Fu Jen Catholic University, No. 510 Zhongzheng Rd, Xinzhuang Dist., New Taipei City, 24205 Taiwan.
authors.forename: Ming-Hsien
authors.initials: MH
authors.lastname: Yang
authors.affiliation: Department of Accounting, Shih Chien University, No.70 Ta-Chih Street, Chung-Shan District, Taipei, 10462 Taiwan.
authors.forename: Tian-Lih
authors.initials: TL
authors.lastname: Koo
authors.affiliation: Department of Information and Finance Management, National Taipei University of Technology, No.1, Sec. 3, Zhongxiao E. Rd., Taipei, 10608 Taiwan.
authors.forename: Pei-I
authors.identifier: 0000-0003-3783-8221
authors.initials: PI
authors.lastname: Hsiao
keywords: Business intelligence; Decision tree algorithm; Logistic regression analysis; Prediction model