The relationship between personal cognition, social context, and knowledge sharing in global communities of practice

The purpose of this study was to identify the structural pathways of personal cognition and social context as they influence knowledge sharing behaviors in communities of practice. Based on the existing literature, ten hypotheses and a conceptual model built on the basis of the social cognitive theory were developed regarding the interrelationships of the five constructs: self-efficacy for knowledge sharing, outcome expectations, sense of community, leadership of a community, and knowledge sharing. The data were collected through an online questionnaire from the employees who have participated in communities of practice in a Fortune 100 corporation. A total of 438 usable questionnaires were collected. Overall, three analyses were conducted in order to prove the given hypotheses: (a) hypothesized measurement model fit, (b) relational and influential associations among the constructs, and (c) structural equation model analysis (SEM). In addition, open-ended responses were analyzed. The results presented that (a) hypothesized measurement models were valid and reliable, (b) personal cognitive factors, self-efficacy and outcome expectations for knowledge sharing, were found to be significant predictors of community members’ sense of community and knowledge sharing behaviors, (c) sense of community had the most significant impact on the knowledge sharing, (d) as the perceived social context, sense of community mediated the effects of personal cognition on knowledge sharing behaviors, and (e) personal cognition and social context jointly contributed to knowledge sharing. In brief, all of the hypotheses were positively supported. A conclusive summary is provided along with contributive discussion. Implications and contributions to HRD researchers and practitioners are discussed, and recommendations are provided.

A path-based framework for analyzing large markov models

Reliability and dependability modeling can be employed during many stages of analysis of a computing system to gain insights into its critical behaviors. To provide useful results, realistic models of systems are often necessarily large and complex. Numerical analysis of these models presents a formidable challenge because the sizes of their state-space descriptions grow exponentially in proportion to the sizes of the models. On the other hand, simulation of the models requires analysis of many trajectories in order to compute statistically correct solutions. This dissertation presents a novel framework for performing both numerical analysis and simulation. The new numerical approach computes bounds on the solutions of transient measures in large continuous-time Markov chains (CTMCs). It extends existing path-based and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship makes it possible for the approach to explore multiple paths at the same time,· thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow it to analyze very large models using a very small amount of storage. Often, path-based techniques must compute many paths to obtain tight bounds. In addition to presenting the basic path-based approach, we also present algorithms for computing more paths and tighter bounds quickly. One resulting approach is based on the concept of path composition whereby precomputed subpaths are composed to compute the whole paths efficiently. Another approach is based on selecting important paths (among a set of many paths) for evaluation. Many path-based techniques suffer from having to evaluate many (unimportant) paths. Evaluating the important ones helps to compute tight bounds efficiently and quickly.