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Managing knowledge efficiently and effectively is considered a core competence for organizations to survive in the long run. The evolution and implementation of Knowledge Management (KM) is still in its infancy stage in most developing countries. Therefore, it is not easy to compose a comprehensive and applicable KM framework. The main purpose of this study is to disclose the core processes of KM and figure out the relationship between these processes and KM performance, based on data collected from 200 employees. This paper considers the important issue of how managers can measure the value of their organization's knowledge and discusses approaches to measure or manage knowledge. The results reveal that there is a positive relationship between KM processes (sharing, generation and storage) and KM performance. The paper emphasizes the importance of knowledge sharing and distribution, particularly for organizations in the manufacturing sector and also indicates that there is a positive relationship between the managerial role in production and organizational performance.
Introduction
In the last few decades, we have been witnessing a socio-economic transformation based on knowledge. Advanced information and communication technologies are at the heart of this transformation (Mansell and Wehn, 1998), which has been dramatically changing the established world order following the industrial revolution (Hope and Hope, 1997). In the new order, knowledge is considered as the fundamental means of wealth creation and prosperity and one of the most important driving forces for business success (Riege, 2007). Knowledge is the only meaningful factor of production in the "post-capitalist" society, where the main challenge will be the productivity of knowledge work and knowledge workers (Drucker, 1993). From the business perspective, an organization's success to a great extent depends on its capability to leverage knowledge and produce value from its knowledge resources. In an economy where the only certainty is uncertainty, the one sure source of lasting competitive advantage is knowledge (Nonaka, 1998). When markets shift, technologies proliferate, competitors multiply and products become obsolete almost overnight. Successful companies are those that consistently create new knowledge, disseminate it widely throughout the organization and quickly embody it in new technologies and products (Nonaka, 1998). According to Storey and Barnett (2000), Knowledge Management (KM) is the only promising medium of gaining sustainable competitive advantage for organizations in the long run.
The development of information technologies during the past few years has enabled many organizations to improve both the understanding and the dissemination of information. The development of powerful databases allows information to be organized in a manner that improves access, increases speed of retrieval and expands searching flexibility. Furthermore, the Internet now provides a vehicle for the sharing of information across geographical distance that encourages collaboration between people and organizations. Manufacturing agencies across the United States have begun to adopt innovative knowledge management technologies to aid in the management of manufacturing information. The nature of the managerial task is to develop component knowledge, as well as to develop architectural knowledge critical for the integration of one or more interacting sets of routines. The role of the primary leader is to support the creation and application of networks to examine knowledge gaps. In addition, effective leadership identifies and breaks down obstacles to maintaining healthy network interaction. It seems that the time has come to move from the more simplistic view of data or information assets towards the more dynamic, path dependent and complex role of knowledge in the value creation process of modern businesses.
Knowledge Management in Practice
The knowledge management challenge is twofold, depending on the type of knowledge involved. Explicit knowledge is that which is codified, written down, held as a computer record or expressed in some other tangible form. Explicit knowledge has the advantage that it is easily reproducible and therefore easily disseminated around the organization. However, it then has to be internalized by individuals and applied in specific contexts. Tacit knowledge is less diffusible and either needs converting as far as possible into explicit knowledge, or needs to be transferred through mechanisms like observation, personal communications, on-the-job learning and so on. Its very intangibility makes its management a challenge.
Explicit knowledge involves a systematic approach to organizing information, making it available and disseminating it. The approach is systematic, lends itself to computerization and appeals to technologists. Tacit knowledge is implicit, highly personal and hard to formalize. Subjective insights, intuitions and hunches fall into this category of knowledge. A tricky challenge is how to manage tacit knowledge. Knowledge management in this sense could be considered an oxymoron, since one cannot really manage personal knowledge organizationally. Tacit knowledge is in people's heads and when "people walk," or leave the organization, they take their knowledge with them. The two complementary KM approaches to deal with tacit knowledge are:
1. Converting it to a more explicit form--in documents, processes, databases, etc.
2. Enhancing tacit knowledge flow through better human interaction, so that knowledge is diffused around the organization and not held in the heads of a few. In Japan, various "socialization" activities support this kind of knowledge flow that by its very nature sparks the generation of new ideas and knowledge. Add some basic elements of good human resource management, including a stimulating environment, personal development plans, motivation and suitable reward and recognition systems (such as knowledge sharing awards and stock options) and there is less chance of your best knowledge workers wanting to leave.
Purpose of the Paper
While there are many processes of KM, such as gathering, searching, filtering, conceptualizing, projecting and transferring (Park and Kim, 2006), this study presents KM as a combination of five specific processes. These are:
* knowledge sharing and distribution,
* knowledge generation and development,
* knowledge codification and storage,
* organization leader roles and
* reward systems
The main purpose is to compose a useful and comprehensive model for KM in terms of its core processes. The paper analyzes empirical evidence of these processes on KM performance based on the data collected from ZAND A. CO in Iran. This company manufactures and distributes agricultural items and has about 300 employees.
The Knowledge Sharing Process
KM could be viewed as a suite of sub-processes that together make up the field of Knowledge Management. Some examples of KM sub-processes are knowledge publishing, knowledge acquisition (for expert systems) and knowledge discovery (through data mining). Tacit knowledge is formally defined as knowledge that is personal, experiential and context specific. Explicit knowledge is knowledge that has been codified, articulated, or published in some way. Knowledge sharing is fundamental to the sharing of best practices, creating new knowledge and achieving shared learning. Knowledge sharing is mostly achieved through tacit-to-tacit communication, though clearly knowledge sharing can also be achieved through the tacit to explicit to tacit conversion loop shown in Exhibit 1.
[ILLUSTRATION OMITTED]
The tacit to explicit to tacit cycle, however, is by far the least effective means of knowledge sharing. If we were to rely on this cycle alone, the knowledge sharing effectiveness would be less than 10 percent (Nonaka & Takeuchi, 1995). It is generally accepted that we know far more than we can make explicit. Experience over the past 10 to 15 years with knowledge-based expert systems reinforces this view. Even with the most advanced knowledge acquisition tools and techniques available to us, there would be few knowledge engineers who would claim to have captured, in computational form, more than a shallow layer of knowledge from their human expert subjects.
Ultimately we cannot progress without some measure for the tacit-to-tacit knowledge transfer process, given the impact it has on effective knowledge sharing. Because the tacit-to-tacit knowledge transfer process is a social process, it is useful to look in the area of social science for measurement assistance. Good examples of tacit-to-tacit knowledge sharing can be seen when observing good teams or partnerships at work. Nonaka & Takeuchi (1995) describe how the number of social interactions is directly correlated with the degree of trust and commitment to the network and ultimately, the performance of that network. Likewise, the density of social interactions is directly correlated with the degree of tacit-to-tacit knowledge sharing. In a business context, social interactions would include business meetings, seminars, conferences as well as traditional social events.
In measuring social interactions, organizations often find that organizing and managing explicit knowledge is a very difficult task. Content is the beginning step that leads to knowledge reuse and contributes to knowledge efficiency. As these same organizations evolve their KM efforts, they begin to see how to more effectively share and transfer tacit knowledge. Sharing experience and expertise provides a higher competitive advantage and decreases time to competency. The obvious lesson is that KM is not just about sharing documents. Knowledge cannot exist without information as well as experience. Knowledge is information in action. It includes what people know about any process or approach. With good information, people can make better decisions and take intelligent action. Knowledge management is a systematic process to:
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