BI System Effectiveness

Three theoretical implications are described below. First, this study adopted a systematic research process and quantitative techniques to model the prediction of BISE in order to overcome inadequacies in the empirical studies on BI, a neglected area in academic research. Particularly, the authors are unaware of any empirical study focused on modeling the prediction of BISE. Using the systematic research process and methodology, this study proposed critical attributes and indicators for predicting and assessing the effectiveness of BI implementation. This study thus complemented the gap in the research on the BI implementation context.

Second, in terms of information technology adoption theory, this study provided an answer to the question of how to effectively construct and acquire the critical indicators of BISE. The main results of this study also provided a sequential research opportunity to study the BI performance in depth. Applying the prediction models and rules of BISE as an empirical research bridge can explore the future influence of BI solution implementation on, for example, organizational performance and innovation performance. The prediction model of BISE can be further expanded using influences on BI performance to establish a comprehensive research model for new BI solution implementation.

Finally, the systematic research process demonstrated that two different predictive models and rules were constructed using data mining techniques and statistical methods that could provide high predictive accuracy in BISE. The DT algorithm identified important attributes of measurement that could be used to refine the prototype of the LR model to improve the predictive accuracy of model.