December « 2009 « Experiment, Adopt, Achieve

15Dec/090

On the Nature of Knowledge, Public and Private

The following is an academic essay prepared for my Msc in Managment at Birkbeck University.

[Update: This essay received 70%]

The Nature of Knowledge as an Economic Good, Public and Private

by Mark Gibaud

In the 19th century industrialist society, the capacity to generate wealth was inextricably linked to physical property rights. This means that wealth generation was implicitly excludable. In the new intellectually-powered economy knowledge is the source of wealth but must be afforded a special mechanism of protection since new knowledge is expensive to create but often (though not always) easier and cheaper to imitate. It is in society's interest to reward these creators of new knowledge so as to incentivise their ongoing efforts such that new knowledge is continually created. However, society also benefits when knowledge is freely diffused, disseminated and leveraged as shown by the Open Source Software movement. This dilemma has given rise to the debate of knowledge being an economic good, but should it be public or private, or both? This essay attempts to answer this question by further analysing the nature of both public knowledge and private knowledge and qualifies the role of intellectual property rights (IPRs). A deeper analyses the nature of the relationship between public and private knowledge is followed by the policy implications suggested by that relationship.

A 'pure' public good by definition is non-excludable. That is, one's consumption of it does not prohibit another's ability to also enjoy it. Also, it is non-rival, meaning, as more people enjoy it, there is no reduction in supply which may cause others to go without it (public goods exhibiting only one of these characteristics are not noted as 'pure' public goods) (Kaul and Mendoza 2003). Air, for breathing, is an example of a pure public good in that it is neither excludable nor rival, since my breathing the air does not leave less of it for you, nor can I do very much to prohibit you from enjoying it. Though there are kinds of knowledge that do not have these characteristics, the majority of scientific knowledge (including economics and the social sciences) does enjoy these characteristics and is thus a prime public good. Public knowledge generally refers to knowledge coming from academics and researchers in universities, but can also include industry 'best practices' like Total Quality Management or Capability Maturity Model Integration (Matusik 2002). Private knowledge on the other hand is knowledge unique to particular firms and can be wrapped up inside company policies and processes (for eg. Google's "20% time"), and also embodied in services and products (Nelson 1992). Keith Pavitt argues compellingly for the distinction between science and technology, where technology "ecompass[es] both physical artefacts themselves, and the person-embodied knowledge to develop, operate and improve them." (Pavitt 1987). However, the real-world actualisation of that distinction is slightly more difficult to locate. For instance, two researchers may be performing the same basic undertaking of research into a particular topic or field, but one might be working at Google Research and the other at Stanford University (Nelson (1992) makes a similar observation of engineers engaged in research at IBM, AT&T and DuPont at that time). The university researcher is motivated to publish his research and contribute to the stock of knowledge, while the Google researcher is bound to keeping the results of his research private, and allow Google to benefit from the Schumpeterian rents associated with leveraging private knowledge (Nelson 1992). This is done either through patents, in which case the knowledge is public but protected, or through secrecy, by which the knowledge stock is left bereft of the Google researcher's new knowledge altogether (Nelson 1992). This is the distinction between science and technology implicitly proposed by the economists Sir Partha Dasgupta and Paul David when they argued that science contributes to the stock of knowledge, while technology does not (Dasgupta & David 1985). Finally, Dasgupta and David also reason (1985) that science produces knowledge that is more "general and fundamental" in nature and lacking in "immediate practical applicability" than what technological research tends to pursue. A distinction then, not so much of nature, but of goals, and a distinction that is illuminating when considering what direction public policy should take.

We have established that knowledge can be a public good. The fact that it is non-excludable and non-rival, means that it is effectively free, and no profit can thus be generated from public knowledge in and of itself (transmission costs of knowledge is a separate issue). This in turns means that there is no financial incentive to contribute to the stock of public knowledge. Thus, if left to the open market, any contribution will likely not occur and lead to a "tragedy of knowledge", where players might deplete the stock of knowledge (as it is still profitable to embed and evolve ideas from public knowledge into private products) without any replenishment of new knowledge into that stock taking place (David 2001). For an analogy, the same problem affects pollution, where no one company will curb its carbon emissions as it then puts itself at a disadvantage with competing companies in terms of manufacturing output. In matters like these, it is a compelling argument for the state to get involved, as can be seen by the recent lobbying activity in Copenhagen recently for climate change. As knowledge itself is an input to new knowledge (Stiglitz 1999, David 2001), this public good is critical to fostering ongoing innovation, and thus it is imperative that the state facilitate the cultivation of knowledge as a public good, as it does with other public goods. But the state does not directly incentivise this creation process. Rather, academia centres around a compelling code of conduct relating to publication, scientific priority and reputation building. Scientific priority is the phenomenon whereby fame and recognition is accorded to that discoverer who published his discovery first, irrespective of whether one or more other researchers made the same discovery independently at the same or similar times. In Robert K Merton's seminal discussion of scientific priority (1957), he tells of some fierce battles of priority such as Galileo who, in The Assayer, "flayed four other would be rivals" in aid of protecting his discoveries. Notwithstanding with this winner-takes-all approach for scientific discoveries, a steady building of reputation is also very important to scientific researchers. Charles Darwin, the first to develop the theory of evolution through natural selection, is quoted as saying "My love of natural science . . . has been much aided by the ambition to be esteemed by my fellow naturalists." (Barlow 1958). It would appear that the state only has to facilitate this self-promotion of the academic community in order to safeguard a continual contribution to the public stock of knowledge.

Knowledge can also be a private good. Successful companies seek to employ effective strategies to cultivate the generation, capture and dissemination (Nonaka and Takeuchi 1995, Senge 1991) of their private knowledge as this contributes directly to an organisation's competitive advantage (Teece 2000) and is key to providing new products and services (Kessels 2001). Nelson (1992) pointed out that firms make use of intellectual property rights (mainly patenting) and secrecy to pursue profit from their organisational private knowledge. But Mansfield (1985) found that secrecy is often not very effective. He noted after studying 100 American firms that "information concerning the detailed nature and operation of a new product or process generally leaks out within about one year". The argument that the state must safeguard some kind of reward for innovators and their product and process breakthroughs is unassailable, though precisely how and what is the subject of increasing debate. Still dominant in these times though, is the patenting system.

The patenting system intends to encourage innovation and technological breakthrough by firstly creating a temporary monopoly for the inventor or inventors such that they are rewarded for their efforts, and secondly by requiring disclosure of the knowledge that led to the new product or process, such that that knowledge may be diffused to the benefit of wider society (Griliches 1990). It asserts that firms will not invest in innovation activities if they cannot reasonably expect profits to be generated from innovative breakthroughs. If imitators should come into possession of knowledge that innovative firms have invested heavily to develop, these "free-riders" will negatively affect the motivation of firms to invest in innovation (Gault and Von Hippel 2009). It is argued that any abolition of IPRs risks a "tragedy of knowledge", as it might benefit society at the present, since their would be an influx of new knowledge that may well be leveraged usefully. However, without any legal monopoly afforded by IPRs, there would no longer be a strong incentive to engage in expensive research and development and the creation of new knowledge would ebb away, leaving us at a relative standstill before long. Attention must be drawn, however, to the alternatives presented by, among others, Paul David (2001) such as "fair use" exclusions to the public research sector, and "compulsory licensing" which would enable the inventor to be rewarded but also society to benefit from the diffusion of the innovation. Gault and Von Hippel (2009) also cite government R&D subsidies and tax credits as helpful mechanisms to "lower innovator's private costs" and go on to point out the Open Source Software phenomenon, where source code contributors act under agreement to engage in 'free-revealing' of their privately-generated knowledge in an effort to build reputations in their communities which would in turn lead to more income-generating opportunities for both that individual and their full-time employer. Products and processes that are subject to free-revealing also generate valuable feedback from the wider community so that a company might improve that product or process improving its quality and thus potentially improving profitability. De Jong and von Hippel (2009) also present 'free-revealing' to have similarly rewarded innovation in the process equipment industry for high-tech SME's in Holland. It is clear that there is increasing innovation in reward systems for inventiveness and the current debate in IPR circles may well begin a steady journey towards revolutionizing the system.

Leonard-Barton asserts that "few if any companies can build capabilities without importing knowledge from outside its boundaries" (Leonard-Barton 1992). Remembering that firms on the whole seek to retain their own private knowledge, any company wanting to begin to deepen its capability has only one recourse: the public stock of knowledge. It can thus be said that in this aspect, private enterprise depends both initially and continually on the 'communal well' of public knowledge. In addition to the broadly applicable research that is generated in public academia, Nelson (1986) and Levitt et al (1984) found that across a wide range of sectors, there exists a dependency on the public academic infrastructure itself, in that the private sector directly values the training and research-orientated skills acquisition of future industrial researchers, as well as the academic "hive-mind" they bring with them in the form of a network of academic colleagues. It would appear that the nature of the public-private knowledge relationship is largely one of public creation and private consumption. Furthermore, it seems that there is no real risk of a confusion in roles as the institutions are, as discussed earlier, geared towards fundamentally different goals and outputs. Although he notes computer programs (Google Search started life as a university research project) and biotechnology products as exceptions, Nelson (1992) suggests that, "in most fields of technology what gives advantage to a company is a particular manifestation of a generic technology that is tailored to its own circumstance, products and processes." Although de Solla Price (1965) sums up the intent by stating that "scientists are highly motivated to publish but not to read, whereas technologists read assiduously but are not motivated to publish", it must be said that companies occasionally do also publish papers that contribute to public knowledge (Hicks et al. 1996).

Paul David tells us (2001) that the public good nature of public knowledge asserts that the free market cannot responsibly price that commodity such to align with the maximum benefit to society. Thus, the state has to engage in "public patronage for fundamental, exploratory research". This is a noble endeavour but one that subject to fiscal pressure on an ongoing basis, since any 'oversupply' of public knowledge could be wasteful in terms of taxpayer's money. On the other hand, the state must also govern the innovation-reward institution with an even hand. If governments overstate the benefit of innovation to private companies, the scales will tip in favour of turning public knowledge into private profits and we could advance the journey toward a "tragedy of the public knowledge commons". Furthermore, governments would be well advised to pay close attention to the research uncovering new ways by which innovators and inventors could be rewarded, but in addition, downstream innovation is further infused than it is currently, under the contemporary IPR system.

Conclusion:

In summary, given that the over-arching goal is the betterment of society generally, through the continual upgrading of existing, and the invention of new technology, it would appear that a public-private partnership where researchers and academics "push" knowledge of a more fundamental nature and of more general applicability to the stock of public knowledge, and in turn the private sector be incentivised to "pull" from this common stock, perhaps adding it to their internal research (which is of a more focused and specified nature) to then internally develop products which embed the science into new technology, this societal goal is served the best. Consequently, public policy needs to centre around maintaining this balance. By way of simple analogy, it falls to the state to ensure that the tree of knowledge is adequately watered, such that its trunk and branches, the public knowledge, may support the growing of new leaves of private knowledge to the benefit of society at large.

References:

Barlow, N., 1958. The Autobiography of Charles Darwin. London: Collins, 119–120.

Dasgupta, P. et al., 1997. On Institutions that Produce and Disseminate Knowledge. Fondazione Eni Enrico Mattei, Nota di lavoro, 68.

Dasgupta, P. & David, P., 1985. Information Disclosure and the Economics of Science and Technology, C.E.P.R. Discussion Papers. Available at: http://ideas.repec.org/p/cpr/ceprdp/73.html [Accessed November 22, 2009].

David, P.A., 2001. A tragedy of the public knowledge ‘commons’? Global science, intellectual property and the digital technology boomerang. Research Memoranda, 3.

Debackere, K., Clarysse, B. & Rappa, M.A., 1994. Science and industry: a theory of networks and paradigms.

Gault, F. & Hippel, E.A.V., 2009. The Prevalence of User Innovation and Free Innovation Transfers: Implications for Statistical Indicators and Innovation Policy. SSRN eLibrary. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1337232 [Accessed December 15, 2009].

Griliches, Z., 1990. Patent Statistics as Economic Indicators: A Survey. Journal of Economic Literature, 28(4), 1661-1707.

Hicks, D.M., Isard, P.A. & Martin, B.R., 1996. A morphology of Japanese and European corporate research networks. Research Policy, 25(3), 359-378.

de Jong, J. & Hippel, E.A.V., 2009. Free Transfer of User-Developed Process Innovations to Producers: A typical behavior among Dutch High-Tech SMEs. Forthcoming.

Kaul, I. & Mendoza, R.U., 2003. Advancing the concept of public goods. Providing global public goods: Managing globalization, 78.

Leonard-Barton, D., 1992. Core capabilities and core rigidities: A paradox in managing new product development. Strategic management journal, 111–125.

Mansfield, E., 1985. How Rapidly Does New Industrial Technology Leak Out? The Journal of Industrial Economics, 34(2), 217-223.

Nelson, R.R., 1992. What is ‘commercial’and what is ‘public’about technology, and what should be. Technology and the Wealth of Nations, 57–71.

Pavitt, K., 1987. On the nature of technology. University of Sussex.

Matusik. S., 2002. An Empirical Investigation of Firm Public and Private Knowledge. Strategic Management Journal, 23(5), 457-467.

Senge, P.M., 1993. The Fifth Discipline: Art and Practice of the Learning Organization New edition., Random House Business Books.

Stephan, P.E. & Audretsch, D.B., 2000. The economics of science and innovation, Edward Elgar Publishing.

Stiglitz, J.E., 1999. Knowledge as a global public good. Global Public Goods: International cooperation in the 21st century, 308–25.

Filed under: academic No Comments

5Dec/090

Knowledge Management Essay

The following is an academic essay on Knowledge Management prepared for my Msc in Management at Birkbeck University, London.

[Updated 15 Dec with final version]

Organisational Knowledge as One of the Core Assets of our Time:

Implications for Management

by Mark Gibaud

Knowledge is one of the key assets of the intellectually-powered "new" economy. Organisations can achieve critical competitive differentiation if they learn to leverage the collective and cumulative knowledge of their employees. Starting earnestly in the mid 90's, Knowledge Management (KM) has emerged as the key discipline to support this endeavour, but many organisations have not yet fully recognised the benefits of managing their knowledge due to a poor understanding of the nature of workplace knowledge itself and how to effectively manage its capture, storage and distribution within the organisation. This essay aims to explore the implications for management by illuminating the nature of knowledge in the workplace, how it is generated and how to further amplify its generation such that an organisation can be in a better position to capture and diffuse it. Also highlighted are the compelling reasons as to why knowledge should be managed. Finally, one leading strategy, of moving from "channels" to "platforms", is more fully explored through the analysis of a new type of "wiki" software.

Knowledge is defined by the Oxford English Dictionary as "expertise and skills acquired by a person through experience or education". One might think that simply by extending this definition to include the situational context of the workplace, one can then arrive at an accurate assessment of organisational knowledge. However, this is only a part of the story. Organisations amplify knowledge in a number of ways from the cumulative efforts of its staff (Nonaka 1994), potentially creating a knowledge repository contributed to and in turn leveraged by all employees. Michael Polyani popularised the nature of tacit or implicit knowledge which is embodied in an individual and not easily codified or transferred (Polanyi 1966). Later, Ikujiro Nonaka focused on the distinction between tacit knowledge and explicit knowledge, which can be written down and transferred (Ikujiro & Takeuchi 1991). Polyani also argued (1966:55) that all knowledge is either tacit or rooted in tacit knowledge. Pairing this observation with the fact that the world is full of visible codified knowledge, it follows that tacit knowledge could be further broken down into that which is codifiable, albeit with more intensive effort and management, and that which remains uncodifiable, such as intuition gained from experience, vis-à-vis Polanyi's "We can know more than we can tell". Organisations have historically exhibited a high level of explicit knowledge codification such as operational manuals, process documentation and annual financial reports. However, it is the generation, codification and dissemination of knowledge further along the knowledge continuum (Leonard & Sensiper 1999) (that is, increasingly tacit knowledge) that represents the major challenge for contemporary organisations and thus becomes the primary opportunity for knowledge management solutions (Twentyman 2006) (see diagram 1). In turn, the term "Knowledge Management" has many definitions. The common elements of the most-cited definitions centre around defining a strategy for creating, capturing, storing, accessing, and distributing knowledge across individuals, teams and the entire organisation (and sometimes even outside it) in an effort to generate value (increased skill in employees, more innovative products or services, etc) from an organisation's knowledge-based assets (Stankosky 2005, Davenport et al. 2008, O'dell & Grayson 1998, Rumizen 2001, Brooking 1998).

Nonaka, through the development of his SECI model (Nonaka 1994), argued that organisational knowledge can take on a self-generative nature when knowledge is transferred between tacit and tacit, tacit and explicit, and explicit and explicit states. He argued, after closely studying Japanese firms, that organisational knowledge takes on a "spiral" nature when transferring between these states, inferring that this "knowledge conversion" process serves to continually increase the organisation's total stock of knowledge as learning becomes embedded in the organisation's culture. A deeper analysis of Nonaka's SECI model is beyond the scope of this essay, but the key implication for management is that in order to create a useful knowledge base from which employees may leverage value, an organisation must first take the necessary steps to begin to support and facilitate the generation of that knowledge.

Another useful lens through which to define knowledge is with reference to the Knowledge Hierarchy (Zeleny & von Hayek 1980) or DIKW (Data to Information to Knowledge to Wisdom) taxonomy, if only to assert that while an organisation might record data and make sense of that data turning it to information, gaining knowledge from that information is indeed the next level entirely. Consider when Google asked users in a survey whether they would prefer more search results per page (Millis 2006). Users responded that "more is better", so Google lengthened the results from 10 to 30 for some users. However, it turned out that the extra half-second that the page took to load drastically decreased user satisfaction such that users conducted fewer searches. Google took the data from the initial survey, turned it to information (a recommendation to provide more search results per page and thereby increase user satisfaction) but then, only after going forward with an experiment, did Google gain the knowledge that users actually far preferred faster load times than more results per page. The lesson here is that the management of knowledge, is very different to the management of mere data and information.

Much reference is also made to the dynamic nature of organisational knowledge (McInerney 2002), in that it is constantly being updated, upgraded and refreshed, and should be regarded and managed as a living entity rather than a static artifact. Davenport & Prusak argue that knowledge is not only bound to "documents and repositories" but also "routines, processes, practices and norms" (Davenport & Prusak 2000). Knowledge is produced, captured and transferred in many ways, from formal mentoring arrangements, apprenticeships and training programs to informal workplace discussions. Since these workplace practices occur on an ongoing basis, it is imperative that any KM strategy and/or solution needs to facilitate the consistent and continual upgrading and improving of the organisation's knowledge.

There is broad and growing consensus that the strategic management of knowledge assets contributes directly to an organisation's competitive advantage (Teece 2000) and is key to providing new products and services (Kessels 2001). On the other hand, neglecting this need leads to organisations suffering redundancy in effort and repeating past mistakes (Garfield 2007). International Data Corporation, a technology research group, reported that a poor capability to manage knowledge cost the Fortune 500 $12 billion per year (Stewart 2001, cited by McInerney 2002). But knowledge cannot be bought in and of itself; rather, it has to be cultivated in-house (Teece 2000) before any opportunity to embed it into new products or services can be fully exploited. The more salient knowledge an organisation has, and the more accessible it is, the more opportunities employees will find to leverage that knowledge in the innovation of new products and services. This will not only decrease time-to-market and increase quality, but also expand the potential for future improvements to said products and services. It would appear that executives can no longer afford to lack a strategy for managing the collective of their employees' knowledge.

There are many opportunities to both capture and diffuse knowledge already present in most organisations. Any design, development or engineering of a product is fundamentally a knowledge creation process, and the lessons of bringing a product to market are invariably valuable to future efforts (M. Poppendieck & T. Poppendieck 2006). Davenport and Prusak, in their book "Working Knowledge: How Organisations Manage What They Know", highlight the value of interpersonal communication when facilitating knowledge transfer (Davenport & Prusak 2000). Time should be allotted, they argue, to allow staff members to engage in spontaneous conversations, organised group KT (knowledge transfer) sessions, demonstrations, presentations and experience reports, and the idea of "productivity" should be broadened to include these activities. Swap et al. suggest (2001) that storytelling and mentoring are also powerful ways to diffuse knowledge if well managed, and highly recommend the practice of joint-problem solving involving a senior and junior practitioner (Leonard & Swap 2004). A good example of this is in the software development industry where two programmers engage in "pair programming" (Williams & Kessler 2002). Two programmers sit at one computer, taking turns to command the keyboard and mouse while the other reviews the code as it is written. The result is a higher quality piece of work, and a good deal of knowledge transfer between the programmers involved. Peter Senge also advocates creating learning situations (Senge 1993). In his book "The Fifth Discipline", he champions the creation of a "learning organisation" through five key disciplines including "team learning" through dialogue and group discussion. It is clear that there is already a wealth of key opportunities for the capturing of knowledge in the workplace, but the key is to institutionalise the culture of knowledge capture and sharing. Workers should not feel harried or rushed; rather, they must have the opportunity to constantly reflect and analyse how practices and processes might be improved (Garvin 2003). Projects should end with (or be punctuated by) "retrospectives" or "post-mortems" where questions such as "What did we do right?", "What did we do wrong?" and "How can we improve?" are asked and provoke discussion in their answers (Derby et al. 2006). Another key aspect to evolving towards a culture that facilitates KM is the breaking down of boundaries and isolated teams and individuals. Along with the aforementioned communities of practice, working groups provide an opportunity for individuals to share and collaborate with employees outside of their day-to-day teams. Management teams that cultivate this kind of culture will, in addition to fostering innovation, prime themselves for return on investment when engaging in KM practices.

But how exactly do we use these opportunities to capture a deeper set of knowledge? Andrew McAfee recommends that organisations move away from communication "channels" where knowledge is one-to-one, formulated haphazardly and expended quickly, to communication "platforms" where knowledge has a bigger audience (and thus often displays more care in its creation) and can be kept relevant, salient and accessible (McAfee 2006). An instruction sent in an email to a new employee detailing the tools, practices and processes of the organisation is redundant effort, likely to be incomplete, and loses accessibility and visibility rapidly after its immediate use has expired. But a web page on the corporate intranet titled "Things you need to know to start" that is both easy to find, and easy to update, is instantly accessible to anyone, likely to be kept relevant and updated, and also likely to be a product of a group of people rather than one individual, greatly increasing the chance that it is of a higher quality (Mader 2007). A "wiki" is a website that allows users to create and edit HTML pages with rich content for immediate consumption by potentially everyone with a web browser. Wikipedia has built its success and huge popularity on this model. Contemporary wiki products include features like configurable access rights, a WYSIWYG (what-you-see-is-what-you-get) text editor, document revision history and several more features that make it an extremely compelling KM tool for the enterprise. A number of organisations have identified wikis as an excellent tool to facilitate communication and collaboration (Grace 2009) and one of the easiest ways to move from the "channel" of communication (often email) to a "platform" of wiki documents, photo galleries and blog entries. Management teams should look keenly to this and other "Web 2.0" technologies that contribute in bringing people together, as Nonaka's spiral can be greatly infused when large groups of employees, who are otherwise distributed across the organisation, are brought together in the same virtual space.

However, it is important to note that the role of technology in Knowledge Management has historically been misdiagnosed and overstated. Consequently a number of organisations have missed the mark by focusing too much on the technology (Davenport et al. 2008). According to Teech (2000), IT solutions are only an enabler, and they should be implemented not to enable the storage and retrieval of knowledge, but the connection of people. This should be self-evident in today's Facebook dominated world, but as Joel Spolsky says, to promote the successful community-driven collaboration tool he helped create, "A successful Exchange [an installation of the product] depends on whether you [the product buyer] can supply the community", implying that even the best software solution will not create a useful knowledge base without the engagement of a community.

Microsoft chairman Bill Gates framed the challenge precisely in an interview for a Newsweek magazine article in 2006: “While information wants to be free, knowledge is much ‘stickier’ – harder to communicate, more subjective, less easy to define.” He continued that an employee's value to their firm is wrapped up in their tacit knowledge. “Your ability to combine it with the knowledge of co-workers, partners and customers can make the difference between success and failure – for you and your employer.” (Twentyman 2006) The message to management is clear: the cultivation and generation of strategic knowledge, followed by the effective capture, diffusion and consequent amplification will define competitiveness in tomorrow’s businesses. Organisations are highly advised to adopt strategies for the effective management of their employees' collective knowledge, as it is critical to survival in a knowledge-driven economy of the 21st century.

References:

Brooking, A., 1998. Corporate Memory: Strategies For Knowledge Management 1st ed., Cengage Learning Business Press.

Davenport, S.T., Prusak, L. & Strong, B., 2008. Putting Ideas to Work. Wall Street Journal.

Davenport, T.H. & Prusak, L., 1998. Working knowledge: How organizations manage what they know, Harvard Business School Pr.

Derby, E., Larsen, D. & Schwaber, K., 2006. Agile Retrospectives: Making Good Teams Great illustrated edition., Pragmatic Bookshelf.

Elinor Millis, 2006. Google says speed is king - CNET News. CNet News. Available at: http://news.cnet.com/2100-1032_3-6134247.html [Accessed November 16, 2009].

Garfield, S., 2007. Knowledge Management in the Real World. Lecture at Lawrence Technological University. Available online at

http://www.slideshare.net/SGarfield/knowledge-management-in-the-real-world [Accessed November 16, 2009].

Grace, T.P.L., 2009. Wikis as a knowledge management tool. Journal of Knowledge Management, 13(4), 64 - 74.

Kessels, J.W., 2001. Learning in organisations: a corporate curriculum for the knowledge economy. Futures, 33(6), 497–506.

Lave, J. & Wenger, E., 1991. Situated learning: Legitimate peripheral participation, Cambridge Univ Pr.

Leonard, D. & Sensiper, S., 1999. The role of tacit knowledge in group innovation. Knowledge and Strategy, 281.

Leonard, D. & Swap, W., 2004. Deep smarts. IEEE Engineering Management Review, 32(4), 3–10.

Mader, S., 2007. WikiPatterns, John Wiley & Sons.

McAfee, A.P., 2006. Enterprise 2.0: The dawn of emergent collaboration. MIT Sloan Management Review, 47(3), 21.

McElroy, M.W., 2003. The new knowledge management: Complexity, learning, and sustainable innovation, Butterworth-Heinemann.

McInerney, &., 2002. Knowledge Management and the Dynamic Nature of Knowledge. Journal of the American Society for Information Science and Technology, 53(12), 1009–1018.

Nonaka, I., 1994. A Dynamic Theory of Organizational Knowledge Creation. Organization Science, 5(1), 14-37.

O'dell, C. & Grayson, C.J., 1998. If Only We Knew What We Know: The Transfer of Internal Knowledge and Best Practice 1st ed., Free Press.

Polanyi, M., 1966. The tacit dimension. New York.

Poppendieck, M. & Poppendieck, T., 2006. Implementing Lean Software Development: From Concept to Cash (The Addison-Wesley Signature Series), Addison-Wesley Professional.

Rumizen, M.C., 2001. The Complete Idiot's Guide to Knowledge Management 1st ed., Alpha.

Senge, P.M., 1993. The Fifth Discipline: Art and Practice of the Learning Organization New edition., Random House Business Books.

Senge, P.M., 1990. The Leader's New Work: Building Learning Organizations. Sloan Management Review, 32(1), 7-23.

Spolsky, J., 2009. StackOverflow Podcast 70, IT Conversations. Transcript available at: http://blog.stackoverflow.com/2009/10/podcast-70/ [Accessed November 16, 2009].

Stankosky, M., 2005. Creating the Discipline of Knowledge Management: The Latest in University Research, Butterworth-Heinemann.

Swap, W. et al., 2001. Using Mentoring and Storytelling to Transfer Knowledge in the Workplace. Journal of Management Information Systems, 18(1), 95-114.

Teece, D.J., 2000. Strategies for managing knowledge assets: the role of firm structure and industrial context. Long Range Planning, 33(1), 35–54.

Twentyman, J., 2006. Capturing knowledge Knowledge capture is an area that has traditionally been poorly served by supporting software tools. INSIDE KNOWLEDGE, 9(6), 42.

Wenger, E., 1999. Communities of practice: Learning, meaning, and identity, Cambridge Univ Pr.

Williams, L. & Kessler, R., 2002. Pair Programming Illuminated, Addison Wesley.

Zeleny, M. & von Hayek, F.A., 1980. Management support systems: Towards integrated knowledge management. Management, 1(1), 7.