In the knowledge-driven economy, knowledge becomes the only source to enhance the productivity of industries. Knowledge drives strategies and strategies drive knowledge management (KM). KM is a tool to capture the useful knowledge to translate it into actionable form of the organization by effective sharing and reuse. Smooth knowledge flow and its sharing is the backbone of KM. Technologies play an important role to enhance effective knowledge sharing (KS) within the industry. Therefore, it is important to identify and recognize km technologies (KMTs) in the industries to enhance smooth sharing of tacit as well as explicit knowledge. In this study, 24 KMTs have been identified as basic facilitators of KS and KM. Interpretive structural modeling (ISM) has been used to evolve mutual relationships among the KMTs. Identification of KMTs at the root of the hierarchy (called driving KMTs) and those at the top of the hierarchy (called dependent KMTs) is the main aim of this research. The hierarchy of the KMTs obtained from ISM model will assist the senior managers to integrate them according to their driving power to improve the effectiveness of KM.

With globalization of business, knowledge and its management has become the only source for gaining competitive advantage (Drucker, 1993). The fact that knowledge is an organizational asset no longer lies in the ability to store and retrieve (Squire, 2006). Knowledge drives strategy and strategies drive Knowledge Management (KM) (Tiwana, 2002). KM is about connecting people to people and people to knowledge to create a competitive advantage. Many researchers referred to Knowledge Sharing (KS) as a corner stone of KM. The main objective of KS is to distribute right knowledge from the right people to the right people at the right time (Riege, 2005). KM Technologies (KMTs) have been identified as basic facilitators to enhance effective KS in the industries (Kant and Singh, 2008). The main objective of this research is to identify and recognize the various KMTs and to observe their effectiveness in supporting KS in the industries.

In this study, 24 KMTs have been identified as important facilitators of KS among the employees of the industries. Interpretive Structural Modeling (ISM) methodology is applied to develop a hierarchy of the identified KMTs according to their driving power. Once managers get the hierarchy of the identified KMTs, they need to integrate them according to their driving power. KMTs at the root of hierarchy are called driver technologies; and at the top of the hierarchy are known as dependent technologies. The paper first presents a literature review to identify the KMTs, and then explains the ISM model development and MICMAC analysis. Finally, it concludes with results, managerial implications and scope for future research.

Knowledge Management Journal, Knowledge Management Technologies, Knowledge Sharing (KS), Knowledge Management (KM), KMTechnologies (KMTs), Interpretive Structural Modeling (ISM).