Research motive and purpose

Business intelligence (BI) is an emerging topic, and is the top priority on the agenda related to technology initiatives that covers 36 industries in 41 countries in 2012. BI skills are extremely important to organizations. Successful enterprises leverage BI technology. However, empirical studies on BI remain scarce. Therefore, one of the objectives of this study is to conduct a pioneering empirical investigation of a systematic method for the construction of rules for the prediction of business intelligence system effectiveness (BISE) in the context of BI implementation to forecast BI performance. The systematic work conducted in this study first determined measurement items of BISE, then adopted statistical analysis to create a prototype model of prediction, and then conducted data mining techniques to form data structures and refined the prototype model to increase model predictive power.

Based on the prediction models with a set of rules for evaluation of the effectiveness of BI solutions, this study also attempts to help BI managers master the critical attributes of the BISE to achieve successful BI. From a BI solutions implementation perspective, the important issues facing an enterprise are to enhance BI capabilities via effectively monitoring BI solution implementation, including identifying critical indicators and assessing the BISE to measure BI performance and thus determine the direction of BI system improvement. Organizations require technical capabilities to achieve BI success. Although most BI systems integration, information delivery, and analysis techniques have already been incorporated into the commercial BI and analytics platforms offered by Microsoft, IBM, Oracle etc., the greatest challenge for most organizations is not technology, but rather the ability to apply or application of new technologies. Previous studies have shown that investment in information technology has not yielded clear benefits in the context of transitional economies. Additionally, numerous academics and practitioners have evaluated the outcomes of BI implementation. Unsurprisingly, evaluation results regarding the contribution of BI to organizational performance have been inconsistent. While BI success remains unrealized in numerous organizations, this study sought to provide a direction to improve the implementation performance of BI solutions through effective management of the BISE. More importantly, from monitoring to mastering, the critical predictive indicators of the BISE are essential to BI success. If those indicators could be effectively managed by constructing prediction models and rules for assessing the BISE in the BI implementation, BI performance might improve. Therefore, this study attempts to resolve the above problems to help enterprises achieve BI success.