研究期間：10108~10207;Decision making with Business Intelligence (BI) has attracted increasing attention in recent years since data are accumulated in organizations in unforeseeable pace. In North America and Europe, BI has surpassed ERP as the most commonly queried IS keywords in Google. Nevertheless, most managers still rely on MIS professionals to provide query reports. The process can be time consuming and error prone. These problems hinder organizations to ripe the full benefit of making rational decisions with numeric data. Since data are accumulated faster than MIS professionals can be trained, the most possible way to tackle the problem is to encourage managers to create BI reports directly. To help managers to do so, BI systems have make query design so easy that no program has to be written and the result can be displayed in an Excel-like interface. However, even with the huge potential benefit and ease of use interface, few managers have really created BI reports personally. The study is therefore designed to study the ways organizations can adopt to encourage managers to created BI reports to boost the efficiency and effectiveness of decision making. In the first year, this study will integrate Expectancy Theory and Social Exchange Theory (SET) to investigate incentives to encourage business managers. Furthermore, the integrated model will be also consolidated with BDB model to study managers’ intention of creating BI reports. On the other hand, the personal tendency of making decisions with numeric data may be effected by personality traits of managers. To boost the usage of BI systems with minimal effort in the early stage of system adoption, organizations therefore may have to focus their resources on the managers who are more likely to fully utilize the systems. Therefore, the second year of this study will integrate Five Factor Model (FFM) and BDB model to investigate managers’ intention on making decision with numerical data to examine this model, we will proceed to use a cross-sectional survey and then perform statistical analysis. The project is expected to find ways to effectively promote in-depth usage of BI systems among managers with suitable personality trait
