Related to the post Learning and knowledge management are 2 sides of the same coin
The expectations were that KM would be able to improve growth and innovation in organizations, productivity and efficiency, customer relationships, employee learning, satisfaction and retention, and management decision-making. However, KM has not demonstrated any competitive advantage to the organizations that have invested in it and most of the KM initiatives have failed (Pollard, 2003). Such failures basically result from the practice to see KM mainly as a technology issue and not as social interaction between people (Delmonte and Aronson, 2004). Malhotra (2005) presents two paradigms of current KM implementation that have characterized the technology-push model of KM. The inputs-driven paradigm with its primary focuses on technologies such as digital repositories, databases, intranets, and groupware systems and the processing-driven paradigm of KM with its focus on best practices, rules, procedures, workflow optimization, and automation of manual processes.
The inputs-driven paradigm focuses on the technology based, static and pre-defined representation of knowledge. Thereby, a significant amount of attention is placed on implementing platforms and repositories to capture, store, control, manage, and re-use structured knowledge. In this view, knowledge is assimilated to objects (Nabeth et al., 2002) and knowledge management systems are not really managing knowledge but information and a large part of what is presented as being KM is often simply information management under a new label (Kimble et al. 2001; Wilson, 2002; Malhotra, 2005). Information is explicit knowledge that is easily expressed, captured, stored and re-used. In the KM literature, there is wide recognition that only a small fraction of valuable knowledge is explicit and there is a huge mass of high-quality knowledge embedded in people, which is not easily expressible and cannot be recorded in a codified form. Additionally, many companies are discovering that the real gold in KM is not in building platforms, distributing documents or combining repositories, but in sharing ideas and insights that are not documented and hard to articulate (McDermott, 2001). This undocumented, hard-to-articulate knowledge is what has been called tacit knowledge. Polanyi (1967) is often cited when describing tacit knowledge. For Polanyi, "we can know more than we can tell". Drucker (1969) disputes the notion that tacit knowledge can be managed. Nonaka and Takeuchi (1995) state that tacit knowledge differs from information in that it resides in people and can thus only be created, sustained, emerged, and shared through socialisation.
Even capturing knowledge that may be expressed, codified and stored is not without its problems. Capturing knowledge in a codified form is time and effort consuming. Additionally, knowledge can be isolated from its context and it can rapidly become out-of-date, obsolete, and useless. Busy knowledge workers have often been asked to make explicit the implicit knowledge that guides their daily work. They have to interrupt their work and try instead to get familiar with a central, feature-rich, and often difficult to use KM system and then focus on how to use a given template to e.g. write a report or classify a document. Often, a knowledge worker does not have the willingness to do this extra job. And, if she is willing to take the time to capture her knowledge, the result will likely be static documents that are general and out-of-context. In the KM literature, it has already been pointed out that knowledge is context sensitive. Codification of knowledge in the form of information tends to abstract knowledge from the context in which it acquires its specific meaning and that provides the common ground for understanding between individuals (Devlin, 1991). It is quite possible to have knowledge that makes sense and is useful in one context, and makes no sense at all and is utterly useless in another (Owen, 2001). Wenger (1998) also stresses that information stored in explicit ways is only a small part of the picture.
The processing-driven paradigm of KM has its primary focus on the automation of the processes of (a) archiving best practices and past success stories to guide future decisions and actions and (b) getting the right information to the right person at the right time. Malhotra (2000, 2004) points out that it is impossible to build a system that predicts who the right person at the right time even is, let alone what constitutes the right information. Pollard (2003) states that, unlike the work world of the last two centuries, most employees today either come into their jobs knowing more than their boss about how to do it, or quickly acquire such superior knowledge from their peers and from personal experience on-the-job. Every job today, every process, is unique, and therefore the expectation that KM systems could capture best practices, success stories, and lessons learned that could be reapplied by others again and again was unrealistic. In the same direction, Siemens (2006) stresses that yesterday’s solutions don’t always work today. In today’s world, knowledge life is short; it survives only a short period of time before it is outdated.
Chatti, M. A., Jarke, M. and Frosch-Wilke, D. (2007) ‘The future of e-Learning: a shift to knowledge networking and social software‘, To be published in the International Journal of Knowledge and Learning IJKL 3(4).
Pollard, D. (2003) ‘The Future of Knowledge Management‘, How to save the world blog, Discussion paper.
Delmonte, A.J. and Aronson, J.E. (2004) ‘The Relationship between Social Interaction and Knowledge Management System Success’, Journal of Knowledge Management Practice, Vol. 5/2004
Malhotra, Y. (2005). ‘Integrating knowledge management technologies in organizational business processes: getting real time enterprises to deliver real business performance‘, Journal of Knowledge Management, Volume 9 Number 1 2005 pp. 7-28, MCB University Press.
Nabeth, T., Angehrn, A. and Roda, C. (2002) ‘Towards Personalized, Socially Aware and Active Knowledge Management Systems‘. In: Challenges and Achievements in E-business and E-work, p. 884-891
Kimble, C., Hildreth, P. and Wright, P. (2001). ‘Communities of practice: going virtual‘, Chapter 13 in Knowledge Management and Business Model Innovation, Idea Group Publishing, Hershey (USA)/London (UK), 2001, pp 220 - 234.
Wilson, T.D. (2002) ‘The nonsense of 'knowledge management’, Information Research, 8(1), paper no. 144.
McDermott, R. (2001) ‘Knowing in Communities: 10 Critical Success Factors in Building Communities of Practice‘, Community Intelligence Labs.
Polanyi, M., (1967) The Tacit Dimension,
Drucker, P.F. (1969) The age of discontinuity: guidelines to our changing society,
Devlin, K. (1991) Logic and information, Cambridge University Press, 1991.
Owen. J.M. (2001) ‘Tacit knowledge in action: basic notions of knowledge sharing in computer supported work environments‘, Proceedings of the European CSCW Workshop on 'Managing tacit knowledge,
Wenger, E. (1998) Communities of practice: Learning, meaning and identity, Cambridge University Press,
Malhotra, Y. (2000) ‘Knowledge Management for [E-]Business Performance. Information Strategy‘, The Executives Journal, v. 16(4), Summer 2000, pp. 5-16.
Malhotra, Y. (2004) ‘Why Knowledge Management Systems Fail? Enablers and Constraints of Knowledge Management in Human Enterprises’, In Michael E.D. Koenig & T. Kanti Srikantaiah (Eds.), Knowledge Management Lessons Learned: What Works and What Doesn't, Information Today Inc. (American Society for Information Science and Technology Monograph Series), 87- 112, 2004.
Siemens, G. (2006) Knowing Knowledge, Lulu.com, ISBN: 978-1-4303-0230-8.