Factors, Effects, And Solutions Of Knowledge Sharing In Various Organizations

Essential Models for Knowledge Sharing Motivation

Knowledge is defined as the development or practice of acquiring, taking, allotting, and the utilization of information anywhere it resides in order to boost various learning and enactment abilities in any organization. Therefore, knowledge sharing stands in for knowledge management practices. In the long run, knowledge not only distributed but also it can be distorted during the process of articulating, interpreting, and its absorption (Iskoujina, Z & Roberts, J 2015). This is why knowledge is regarded as an essential asset for acquiring any firm’s competitive advantage. In this modernized era, there has been hasty transition based on the markets, products, and technology change which requires many firms/organizations to improve their data allocation tactics for adjusting on the current changes worldwide. Subsequently, knowledge sharing has drawn huge research attention. Regardless of knowledge sharing there is also various obstacles that hinder free flow within organizations so that individuals can share knowledge. This is why the paper’s literature will specifically focus on the factors, effects, and possible solutions of knowledge sharing in various organizations.

According to Ajzen (1991), he clearly proposes some essential models that can be used in knowledge sharing centered on two theoretical motivational values. The two theories brought up are planed behavior and self-determination theory. Deci & Ryan, (1985, 2000) also support him by stating the knowledge sharing based motivation ranges depending on one’s level and quality. For instance, Gagne & Deci (2005) study has revealed that motivation has resulted to good behavioral and change in attitude. The model is mainly structured and purposed in nourishing the initial knowledge sharing motivational models considering its quality. On the other hand, they also prove that knowledge sharing has slight resemblance to various behaviors which are helpful in any organization. As a matter of fact, their validation of the use of TBT and SDT models are effective during predicting purpose of sharing knowledge and behavior in organizations.

As was previously stated, Fullwood, R., Rowley, J. and Delbridge, R. (2013), supports the idea by introducing one of his models that will be much of use in colleges and universities. Bock’s models have set out to be a new measurement tool of knowledge sharing in most campuses in the UK. It is more to consider the focus the linkage between knowledge sharing factors and the purpose of sharing such data in UK academics. Fullwood, R., (2013) supports the idea by viewing different departments in UK University colleagues who strictly put emphasis on knowledge sharing. Besides most universities in UK always participate in the process of distribution of skills and useful information by collaborating with various UK firms to aid teaching and learning which boosts knowledge sharing. Actually, some of the organizational and individual factors are closely linked to knowledge sharing with some models using the structural equation model can measure the sharing factors. The paper closely explains the linkage of one’s feelings and goals in which they prove that they are comparatively weaker although theoretically contributes significantly to knowledge sharing. Much less Chee, C., Ho, J. and Vera-Munoz, S. (2008) portray that quality administration makes sure that every high ranked member of state who accommodate the transfer of knowledge and skills in his/her firm is highly appreciated.

Measurement Tool of Knowledge Sharing in Colleges and Universities

Apart from the models mentioned in the above paragraph, there are also individual variables influencing the spread of knowledge among employees of a firm. According to Israilidis, J., Siachou, E., Cooke, L. and Lock, R. (2015) they have presented and examined the variables that impacts employees neglecting knowledge management with respect to the Aerospace and the Defense Industry as shown in the research paper. They have also supported the idea by investigating different classes of workers ignorance by analyzing how various factors can impinge knowledge sharing activity. In addition to that, this will assist most people who are engaged in knowledge sharing fully comprehend various sharing activities by simplifying the development of an effective workplace for studying.

There are many factors which influence knowledge sharing by stimulating knowledge sharing among online communities (Iskoujina, Z & Roberts, J, 2015). Being thoughtful of the dynamics in knowledge sharing is a primary concern of managers that have knowledge such as online communities. Bozionelos, (2002) also tries to shed some light on the linkage between knowledge sharing and online communities and the factors affecting knowledge sharing in an organization. This paper is also seconded by the journal of knowledge management (Fullwood, R., et al., 2013) It exhibits that attitude of and intentions are essential aspects which influence the sharing of relevant data in the UK through linking different perspective of various factors that are thought to have an influence on knowledge sharing. Lin, M., Hung, S. and Chen, C. (2009) think that rewards can be a significant factor that can affect knowledge sharing in Universities.

Factors affecting knowledge sharing can be categorized based on three sets of data which include the dispositional preference, relational effect, lucid calculus, (Cyr, S. Wei and Choo, C. (2010). Additionally, the linkage between target sharing of data hugely amounts to affecting knowledge sharing in any business. Lin, M et al., (2009) further brings out techniques that can be used in evaluating the social values during the process of knowledge sharing. According to Cyr, et al., (2010), they support the idea presented above by critically analyzing some essential knowledge sharing determinants that can be fully utilized in the PVC. He suggested that various factors that affect behavioral processes of knowledge sharing are knowledge self-efficacy, resource availability, and perceived ease of use.

Kang, Morris, & Snell, (2007), clearly links some important human resource management stream about knowledge based competitive advantage with the paper showing motivation of knowledge within organization’s employees centered on job design issues. This is supported by Gagne, M. (2009) in which he focuses specifically on the effect of job design on the employee’s motivation of sharing knowledge to one another. According to Foss, N et al., (2009) all are in support of the HRM rehearses in nourishing knowledge management in different firms. There are various solutions that can be employed in various firms so that data sharing can be uplifted. Some areas that are in urgent need of knowledge sharing solutions are audit firms (Chee C, et al., 2008). By cumulating knowledge sharing in audit firms it will aid in immediate replying to arising challenges which will help people understand more about knowledge sharing.

Generally, knowledge sharing as an activity is often acted individually, since knowledge is controlled by people. And people’s behavior can be defined using the TRA Theory and the TPB Theory. The center position consists of TRA and TPB and always reads out whichever sort of action. According to (Jabbary and Madhoshi, 2014) they suggest that these two theories can be used effectively to predict knowledge sharing behavior. Below are some factors that may hinder knowledge sharing they include:

• The RA (reasoned action) and a PB (planned behavior) theory portrays how individual’s performance is always caused intentionally based on one’s attitude and various subjective models, while TPB is an improved model of TRA. TPB further states that apparent change in behavior is a subsequent factor which must be prioritized during the process of examining an individual’s behavior using TRA.
• The EE (economic exchange) and the SE (Social exchange) theories prove that every individual always tend to behave based on his/her personal desires. In this case knowledge sharing will happen only if the rewards are greater than the costs.
• Rewards – if some factors that endorsed and prevented the transfer or important information then some initial gifts will be measured to amplify one’s skills then these rewards will be for a short term thus providing minimal solutions.
• Technological inventions – despite IT being regarded as one way of learning effectively, on the other hand it is not regarded very useful in this study. Basically knowledge is centered in people’s minds as a social concept rather than a technical concept.
• Job related problems – the level of production based on an individual is determined by an employee performance at work. Job performance affects organization commitment which might affect knowledge sharing.
• An employee turnover intention – this is directly associated to any organization’s willingness in the opposite direction. Hence an increase in the turn over targets will result to a decrease in the organizational commitment. Centered on that, one can finalize by stating that a decrease in the turnover results to a pessimistic value will affect the organization’s obligations and vice versa.
• Psychological effect – Being committed in an organization mainly implies that is creates a positive impact and builds a good relationship in understanding knowledge sharing. Therefore, psychological contract may have a good relationship based on knowledge sharing in an organization.
• Stressors – stress is defined as a reaction to a situation observed to be challenging or threatening to someone’s health, while stressors is the general ecological state of a place which causes arousing damage to a person.  It means that an increase in organizational willingness will result to less stress and other stressors. Therefore, stressors highly inhibit knowledge sharing in organizations.

References:

Chee, C., Ho, J. and Vera-Munoz, S. (2008). Exploring the Extent and Determinants of Knowledge Sharing in Audit Engagements. Asia-Pacific Journal of Accounting & Economics, 15(2), pp.141-160.

Cyr, S., Wei and Choo, C. (2010). The individual and social dynamics of knowledge sharing: an exploratory study. Journal of Documentation, 66(6), pp.824-846.

Foss, N., Minbaeva, D., Pedersen, T. and Reinholt, M. (2009). Encouraging knowledge sharing among employees: How job design matters. Human Resource Management, 48(6), pp.871-893.

Deci & Ryan, (1985). An investigation of factors affecting knowledge sharing amongst UK academics. Journal of Knowledge Management, pp.00-00.

Fullwood, R., Rowley, J. and Delbridge, R. (2013). Knowledge sharing amongst academics in UK universities. Journal of Knowledge Management, 17(1), pp.123-136.

Gagné, M. (2009). A model of knowledge-sharing motivation. Human Resource Management, 48(4), pp.571-589.

Iskoujina, Z. and Roberts, J. (2015). Knowledge sharing in open source software communities: motivations and management. Journal of Knowledge Management, 19(4), pp.791-813.

Israilidis, J., Siachou, E., Cooke, L. and Lock, R. (2015). Individual variables with an impact on knowledge sharing: the critical role of employees’ ignorance. Journal of Knowledge Management, 19(6), pp.1109-1123.

Key factors preventing knowledge sharing in various organizations. (2014). Knowledge Management Research & Practice, 12(1), pp.97-102.

Lin, M., Hung, S. and Chen, C. (2009). Fostering the determinants of knowledge sharing in professional virtual communities. Computers in Human Behavior, 25(4), pp.929-939.

Fullwood, R. and Rowley, J. (2017). An investigation of factors affecting knowledge sharing amongst UK academics. Journal of Knowledge Management, pp.00-00.

Jabbary, N. and Madhoshi, M. (2014). Review of Factors Affecting Knowledge Sharing Behavior. International Journal of Education and Practice, 2(6), pp.126-136.