| Summary of contributions of this study: This study contributes new knowledge regarding the prescriptive utility of business intelligence systems and explicit knowledge objects in evidence-based management, and proposes a framework to better facilitate the management of business intelligence systems geared toward a more efficient and effective use of explicit knowledge. The focus of this study is toward optimizing the use of business intelligence systems and explicit knowledge objects (i.e., prescriptive), and not necessarily on optimal decision making (i.e., normative) or the behavior of the decision maker (i.e., descriptive).;Abstract: Many public sector programs fail to leverage their business intelligence systems and explicit knowledge objects to drive efficiency and effectiveness. Given the decline in federal budgets and the need for effective government, federal programs look to business intelligence as an evidence-based decision-making practice to lead to a more lean government, improving efficiency in cost and effectiveness in delivering results. However, cost overruns, technical obstacles, and next-generation information challenges stemming from pervasive computing can reduce any perceived value of utilizing explicit knowledge systems to support evidence in decision making. Through the evaluation of five diverse projects tasked to address the use of evidence in decision-making practices, this research shows that achieving contextualization of information requirements, stakeholder alignment, and the complexity/feasibility of information integration are key factors that should be analyzed to improve the evidence-based decision-making practice in government programs, and may be accomplished through a systematic approach, such as the rationalization of business intelligence systems. Thus, a rationalization framework is provided to facilitate the management of business intelligence systems geared toward a more efficient and effective use of explicit knowledge. |
