The purpose of this research is to remove the ambiguity that clouds the analysis of knowledge management systems (KMSs). This is because of an overall lack of consensus on how KMSs adapt to the new “knowledge explosion” embraced by the booming “Big Data” hype. In this paper, a refreshing synthesis of literature will uncover benefits and identify gaps in current knowledge. These findings will also be of benefit to researchers and industries as it allows for the holistic analysis of a KMS. This systematic literature review collected 54 papers for qualitative analysis. This analysis led to a synthesis of factors evident in the research and how they could be combined and collected as key categories. Once each factor was categorized, the future directions of research was analysed and documented. The primary factors discussed include (a) formal processes, (b) company culture, (c) top‐down support, (d) motivation, (e) clear goals, and (f) quality of KMS. This research has created a baseline for the further evaluation of KMSs in the real world.

Digital learning communities have become a focal point of organizational development. The education industry has begun to follow suit by using the same technologies to enhance the learning process through a deeper process of participation. These technological tools complement sound learning design to bring a wealth of benefits to students. These benefits are not without peril. New technological tools shift common issues of education into online environments. This article reviews recent implementations of digital communities and highlights their influencing factors. The factors are then connected to existing factors in knowledge management literature. The key factors found are A) Student interaction with the community, B) Interaction vs grades and C) Student experiences.

Knowledge is an intangible and vital resource that is an important source of competitive advantage; however, the technologies that help create, store, and transfer knowledge are hindered by unrealistic expectations and ambiguity, and the measurement of knowledge‐sharing activities is both difficult and complex. Compounding this is the deficit of empirical studies on the factors that influence the knowledge‐sharing process. We endeavored to provide empirical evidence on these interactions using a survey developed from a prior extensive systematic literature review. The previously identified factors that were in the current study tested comprised (1) organizational culture, (2) formal processes, (3) top‐down support, (4) motivation, (5) clear strategy, and (6) quality of technology. In order, the most influential factors were organizational culture, top‐down support, motivation, and quality of technology. This study is a promising start to the exploration of the factors used in knowledge sharing and should be expanded to include new industries and contexts.