Modeling the Metrics of Individual, Organizational and Technological Knowledge Sharing Barriers: An Analytical Network Process Approach

Introduction

In a knowledge-driven economy, knowledge is considered as the economic resource and the only source of competitive advantage for an engineering industry (Singh & Kant, 2009). Knowledge sharing is the key of successful KM (Singh & Kant, 2008b). Riege (2005) has stated that the identification and recognition of KSBs plays an important role in the success of KM strategy. Knowledge sharing practices often seem to fail because industries try to fit KM strategy and knowledge sharing practices in their existing culture. Nonaka and Konno (1998) have expressed that knowledge is possessed by individuals and the transfer of individual’s knowledge into organizational knowledge depends upon the employee’s knowledge sharing attitude. At present, knowledge sharing is very crucial for an engineering industry but still individuals do not share their knowledge because they aware of their value. (Davenport, 1995). Knowledge sharing can help the individuals to remain valuable in the engineering industries. Gibbert and Krause (2002) have argued that knowledge workers cannot be enforced to share their knowledge but can be motivated to share. Ruggles (1998) has said that motivational methods to encourage knowledge sharing attitude but changing the attitude of individuals is one of the biggest challenges for the success of knowledge sharing and KM strategy. Hence, it is important to know about barriers which hinder the knowledge sharing because it is not easy to translate the individual’s tacit knowledge (resides in the human mind) into organizational knowledge.

The identification and recognition of KSBs provide guidelines to the senior managers to audit existing knowledge sharing practices to identify bottle-necks and improving the overall effectiveness of knowledge sharing activities (Riege, 2005). Individual KSBs are originating from individual behavior or employee’s perceptions and actions. At an individual level, KSBs include internal resistance, lack of trust, lack of motivation and a gap in awareness and knowledge. The organizational knowledge sharing is the key factor for success of KM strategy which depends upon the corporate culture (Singh et al., 2006). At an organizational level, KSBs consist of poor leadership, internal and external competitiveness, existing organizational culture and resources. Finally, Singh and Kant (2008a) have explained that technology is an important enabler for successful knowledge sharing in engineering industries. At technological level, KSBs consist of lack of integration of technology, reluctance to use of IT systems and lack of training.

At present, the knowledge sharing objectives and strategies are not incorporated in the business strategy of the engineering industries due to lacking in metrics and identification of KSBs. Thus, it is necessary to identify and measure the effectiveness of KSBs. KSBs are categorized in to three domains such as individual, organizational and technological due to their equal effect on knowledge sharing in the engineering industries. In Table 1, the comparison among individual, organizational and technological KSBs has been done as per identified attributes.