We examine a knowledge management (KM) success model that incorporates the quality of available knowledge and KM systems built to share and reuse knowledge such as determinants of users' perception of usefulness and user satisfaction with an organization's KM practices. Perceived usefulness and user satisfaction, in turn, affect knowledge use, which in our model is a measure of how well knowledge sharing and reuse activities are internalized by an organization. Our model includes organizational support structure as a contributing factor to the success of KM system implementation. Data collected from 150 knowledge workers from a variety of organizations confirmed 10 of 13 hypothesized relationships. Notably, the organizational support factors of leadership commitment, supervisor and coworker support, as well as incentives, directly or indirectly supported shared knowledge quality and knowledge use. In line with the proposed model, the study lends support to the argument that, in addition to KM systems quality, firms must pay careful attention to championing and goal setting as well as designing adequate reward systems for the ultimate success of these efforts. This is one of the first studies that encompasses both the supply (knowledge contribution) and demand (knowledge reuse) sides of KM in the same model. It provides more than anecdotal evidence of factors that determine successful KM system implementations. Unlike earlier studies that only deal with knowledge-sharing incentives or quality of shared knowledge, we present and empirically validate an integrated model that includes knowledge sharing and knowledge quality and their links to the desired outcome--namely, knowledge reuse.
Multiple scenarios are critical to "what-if" analysis. Scenarios are built using alternate versions of a database; each version shows, in detail, the result of a decision or a combination of decisions. Relational database management systems, despite their widespread use, lack the explicit capabilities for what-if analysis. We present a concept called independently updated views (IUVs) for creating multiple scenarios. An IUV corresponds to a version of the database. Each version is manipulated and updated as if it were the "real" database; however, only differences between the version and the real database are stored. This paper describes an experiment for measuring the overhead of using IUVs for supporting what-if analysis for a range of typical views and queries. Results indicate that the overhead is minimal for creating what-if scenarios based on views with aggregate functions (SUM, AVG) and on views that are small subsets of the database. These are the classes of views that are more often used in decision making. For views that require retrieval of entire or large portions of the database, the overhead can be high and special data structures may be required.