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A genuine internal commitment to a learning organisation approach is a necessary but not a sufficient condition for developing dynamic capability. For an organisation to ensure dynamic capability, first it must become a learning organisation in practice, and second it must be confident and opportunistic about applying what it has learned. Team members need to have trust in one another and enjoy the managerial mandate to exploit opportunities as they arise, or experiment with new conditions emerging from the shifting external context that situates the organisation.

Thus, the rhetoric of a learning organisation can be tested on a case-by-case basis (as we do below in regard to English NHS organisations) against what the organisation actually practices. For example, the ill-fated Rover automobile company claimed to be a learning organisation but only established one main feature (maximising the individual learning of its workforce) [45]. By contrast, Chaparral Steel in the USA, a more stable and successful company in the 1990s, reportedly demonstrated its learning organisation credentials and accrued the benefits of dynamic capability [46]. Such claims also are made for BP [47] and, in more guarded terms, for other firms [48]. A critical difference between these companies was that Rover outsourced its attempt at becoming a learning organisation, whereas the other two developed it from their own senior managers. The latter championed and oversaw fidelity to the learning organisation model as a corporate rather than a brought-in managerial initiative. We return to the importance of leadership in a learning organisation later.

Research and development are one aspect of a learning culture. Successful knowledge management, a concomitant or implication of a learning organisation, also is said to increase dynamic capability .  Ownership of intellectual property is a commercial advantage in itself, as is the capacity to deny that knowledge to competitors, but its main use is the utilisation of knowledge to achieve an organisation’s operational goals and strategic aims. The most obvious example of this is knowledge-based decision-making at all levels in an organisation. (The existence of this very journal testifies to the logic discussed here.)

It is generally assumed that the creation of learning organisations requires the combination of all the conditions listed above, not just some of them.

Based on the introductory overview provided of what knowledge management is, it is relatively simple to identify various examples ways in which well-designed and effectively-functioning distance education systems already engage in practices of managing knowledge:

1) Typically, well-functioning distance education systems demand extensive investment of time and resources in rigorous processes of programme and course design and development. These investments usually involve diverse groups of experts, collaborating to produce programmes, courses, modules, and learning materials that enable independent study by learners. They can all justifiably be considered as investments in managing knowledge effectively. Importantly, they represent a process of taking knowledge that once was tacit (curriculum design, learning outcomes, teaching and learning strategies, and subject matter) and making it explicit by documenting it thoroughly. It is possible to leverage even more institutional value from these investments if the resulting materials are stored in a centrally accessible repository. In many cases, this value is not created because the resulting knowledge ‘products’ are not shared or made accessible beyond an individual department or faculty.

2) Cost-effective distance education systems require enrolments of large numbers of students on individual programmes in order to achieve the economies of scale needed to reduce the cost of learning per student. In order to be able to assure quality of delivery in such circumstances, well-functioning distance education programmes create standardized approaches to the way in which learners are supported (within learning materials, through student counselling and administrative systems, during tutorial support, and via feedback on assessment tasks). Again, providing this support typically requires processes of making tacit knowledge explicit, so that it can be documented and shared with often large, de-centralized networks of tutors and facilitators who constitute the primary point of reference between the student and the institution. Such systems are often very sophisticated in the way in which the structure and manage communication across the institution.

3) Provision of distance education and management of communication with large, dispersed groups of students across wide geographical areas also usually requires investments in very efficient administrative systems, which gather and store large volumes of data about learners and learning. In best-case scenarios, such systems will now harness the power of computers and databases to support student administration. Although these systems are not – in and of themselves – knowledge management systems, they are critical sources of data and information about what is happening within the distance education institution. Such systems are potentially enormously valuable building blocks within an overall knowledge management strategy, as they can feed reliable information reports on many critical aspects of the educational process into the institution, thus supporting the creation of learning organizations that are able to adjust how they operate based on knowledge of what is and is not working successfully.

4) Extensive literature has been produced about quality assurance in distance education, and robust, vibrant quality assurance systems are typically a feature of well-functioning distance education systems. Although quality assurance systems are by no means unique to distance education, the requirement to provide high quality learning experiences to large numbers of learners and the involvement of many employees in delivering such experiences has created a strong imperative for their development in such environments. The process of designing a quality assurance strategy is an important precursor to its successful implementation, and should involve a wide range of staff members at various phases. Developing quality assurance systems demands effective knowledge management across the organization.

As these illustrative examples show, distance education systems work hand in hand with knowledge management strategies. It is important to understand that many of the features of well-functioning distance education already constitute effective strategies of managing knowledge. Thus, knowledge management is not a new, ‘high technology’ concept that is beyond the reach of the average distance education institution. Nor is it a concept that should induce fear in distance educators, many of whom have for some time grasped its key principles intuitively in the way in which they have set up and manage distance education systems and programmes. However, many distance education institutions could benefit from approaching knowledge management more explicitly and working systematically to improve how knowledge is managed across the enterprise. The next section in this paper, thus, provides an introductory overview of how to tackle this task effectively. First, however, we provide a brief summary of key issues emerging from the above discussion.

Even in more hospitable environs of the more “rearward” (or at least less directly kinetic) locations of the Long War, analysts are interacting on a more frequent basis with those outside the walls. These interactions all have the potential to generate unique information not otherwise known to the body collective. This is especially prevalent in the homeland security community, in which a unique combination of private industry, non-governmental organizations, and the less connected elements of state and local law enforcement are constantly generating material which simply cannot be found anywhere else. Likewise, in the corporate world of competitive intelligence, collection and analysis are almost always integrated into a single function – often euphemistically referred to as research functions.

The challenges posed by this new dynamic are manifold from classic theoretical perspectives, but the key issue underlying all of these is one of knowledge management. How are the results of these interactions captured and shared, and how does knowledge and insight arise from this process?

The use of the term knowledge management has perhaps doomed many of these efforts from the start. Entire industries exist to facilitate KM systems and processes, each with their own highly paid consultants and patented buzzword solution sets. But offering up technologists, or even methodologists, with the “one true answer” is merely to spill additional blood upon the alter of the irrelevant.

Beyond technology, this is a social problem set. Tools such as wikis and collaboration mechanisms such as Groove have long existed to permit such exchanges at the technical level, but these are not integrated into the processes of the community. Informal exchanges dominate the day in the absence of considered mechanisms, but these are inconsistent and fraught with unexamined dependencies and assumptions. Yes, somehow the system manages to work; but at what opportunity cost – or cost in human lifetimes?

Likewise, this problem will become acute as the community faces its looming challenges of attrition and replacement when the last of the boomer generation slides inexorably into retirement. The community will have to rely on the same mechanisms of experience capture, caveat, validation, and knowledge formation that are critical in a world where the analyst is often thrust into roles where they are functionally, if not programmatically, collectors; but in this case the role will be as a collector of continuity before the human capital which provides it is lost.

Ironically, if ever there was a role for the academics in the community it is here, but outside of the case study method, there have been few efforts to develop and validate the approaches required to ensure preservation of knowledge capital, in the end the very essence of the community’s existence.

The shift to an information economy is creating new challenges for economic development. As knowledge and information play an increasingly important role in economic activity, the needs of businesses have changed. More importantly, the nature of the business opportunities has shifted. These opportunities are twofold:

· to create new information and knowledge-based enterprises, and

· to utilize information and knowledge better in existing companies.

The tools and techniques of Knowledge Management can help economic development practitioners face this new environment. Economic development organizations can use  KM tools to enhance the external communications of local companies, including for e-commerce and marketing. They can also promote the use of KM tools and techniques to help local businesses capture and utilize their knowledge and information assets.

More importantly, KM tools and techniques can be used by economic development practitioners to uncover local information assets and entrepreneurial activities that can serve as the bases for future economic development. Finally, economic development practitioners can use these tools to enhance knowledge sharing among key members of the community and to capture and share tacit knowledge within their own organizations.

Knowledge management can be a powerful tool in economic development—but only if we can harness its power to the unique needs of economic development activities. As this review has shown, the use of KM tools in economic development is just emerging. Just as companies are learning a new way of operating, economic development organizations need to learn and experiment with these tools and techniques. Economic development practitioners should be encouraged and supported in their efforts to use and tailor these tools to meet their own needs and be encouraged to share their successes and failures. In essence, we need to set up a KM process for understanding and sharing best KM practices.

Economic development in the information age requires better use of information and knowledge. It requires unlocking the information and knowledge assets of a community as the driver of local economic development. It also requires unlocking the hidden information and knowledge about a community and about the process of economic development.

The information economy is not about the information technology industries. It is about the use of information and knowledge—formal and tacit—in economic activities. Building a strong local economy means developing and cultivating the local knowledge and information base. KM tools and techniques can provide the foundation upon which to build successful local information-age economy.

Changing factors in location decisions

The shift to a knowledge/information-based economy in changing what business needs as inputs to the production process. No longer are business location decisions based simply on the availability of cheap land, cheap energy, a low-cost labor force, availability of raw materials, or access to transportation. The ability of a locality to supply a company’s need for information and knowledge assets has become paramount in economic development. There are at least three elements involved in the process: an up-to-date IT infrastructure, availability of skilled workers, and a good quality of life.

The IT infrastructure

The starting point for economic development in the information age is the existence of a suitable IT infrastructure. Many people see the Internet as a consumption tool—as a means of recreation, information gathering and shopping. Economic development practitioners know that the information technology infrastructure is also a production tool. Advanced information technologies can make businesses more productive and efficient as well as expand their markets. To take advantage of those opportunities, companies must have access to high-speed telecommunications connections, known as “broadband.”

Having access to broadband is especially important in attracting technology-creating companies. It is also important for the heavy IT using companies. More and more companies are able either to locate so-called back office operations in distant areas or to outsource those operations entirely to companies located in lower-wage areas. On an individual basis, the location of back office operations in more distant areas leads to a sort of telecommuting.

In some areas, especially current commercial and business hubs, broadband has been and is being installed as a matter of course. Other areas, especially rural areas and parts of the inner city, are in danger of being left behind.11 According to a joint study by National Telecommunications and Information Administration (NTIA) and the Agriculture Department’s Rural Utility Service (RUS), “rural areas are currently lagging far behind urban areas in broadband availability. Deployment in rural towns (populations of fewer than 2,500) is more likely to occur than in remote areas outside of towns. These latter areas present a special challenge for broadband deployment.”

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It is therefore interesting to investigate possible differences when it comes to the size of the organization. There has been some earlier research towards this topic, which showed:

“(..) “organizational size” did not have any effect on the ‘initiation’ and ‘use behaviour’ of KMS.” A possible explanation, according to the researchers, is that “the competitive pressure has forced every business and organization practice knowledge management even though there are some differences in format, extent, complexity, advances, and experience of their knowledge management activities.” (Quaddus & Xu, 2007)

Results of the Research

Our research “The Human Factor in Knowledge Processes” investigated the organizational sizes of the participants. We compared the size to the amount of reported knowledge management behavior.

Looking at this graph, there is no real difference between the different organizational sizes and the reported knowledge behavior. This is in line with the earlier findings of Quaddus and Xu. The small organizations reported a lower score at some of the activities, but the error margin was too high to conclude that this is due to their size.

The tendency worth mentioning is the drop in searching for knowledge outside the organization when the organizations are  big: 2500+. Such a tendency is dangerous, because it might lead to inward focused organizations, so-called “knowledge fortresses” (Barnard, 2003), which might have considerable negative effects on the longer term. We will dedicate a post on this in the near future.

Researched by http://www.weknowmore.org/

]]> http://www.knowledge-management.org/does-the-size-of-the-organization-matter-for-knowledge-management.htm/feed 1 http://www.knowledge-management.org/using-a-km-framework-to-measure-behavior-in-knowledge-processes.htm http://www.knowledge-management.org/using-a-km-framework-to-measure-behavior-in-knowledge-processes.htm#comments Fri, 02 Oct 2009 12:31:20 +0000 admin http://www.knowledge-management.org/?p=238 There are many books written on knowledge management and the authors have come up with an enourmous amount of knowledge management frameworks . Some are deceptively simple, others extremely difficult to grasp. The one we find most useful is the very practical model of Collison and Parcell taken from their great book “Learning to Fly”.

We recommend reading this book to fully understand this generative model. There is a summary available through their via their website. According to their model the knowledge management process consists of the following steps [very short outline]:

• People and teams agree on certain objectives. Organization-wide this is the mission and strategy, but on a smaller scale they could be developing a certain policy or building a specific house for instance.
• When people start performing the activities needed to attain the goals they set, they should enter the “knowledge circle”.  Before the team starts a task they could search for experiences with others that did the same assignment once. This promotes learning and prevents people from reinventing the wheel in certain circumstances. During and after the activities reflection on the attained results thusfar and checking with other sources of knowledge might lead to valuable learning opportunities.
• All these learning activities should be connected to some sort of knowledg ebank. If you want to learn before you act, you can withdraw knowledge from this source. If you want to contribute, you will have to submit your knowledge somewhere.
• However, it is not possible to capture all knowledge necesarry and available. Therefore it is important to let your team connect with some sort of network of people that have the required knowledge and experience ready to be tapped.
• Finally the environment or culture of the organization surrounds the model, which is critical to strat and support behavior in knowledge processes.

This model can be applied as follows:

• Preventing wheel reinvention, praticularly in large international organizations
• Accelerating the integration process following an aquisition or merger
• Identifying, capturing and sharing good practice in organizations
• Dealing with cultural barriers that hold back organizations
• To build, nurture and support networks and CoPs
• Creating a culture of continuous improvement and learning
• Involving leadership in the right behaviors to reinforce knowledge-sharing

We have used this model as a framework for our research, since we think it is practical, simple and complete.

Measuring behavior in knowledge processes

In our research we used the following questions to measure behavior in knowledge processes, extracted from the above mentioned model:

• How often do you actively search for prior experiences within your organization before you start a new assignment?
• When you start a new assignment, how often do you actively search for relevant information from outside your organization?
• How often do you thoroughly reflect on your goals, results, and the process when you finish an assignment?
• How often do you formulate learned lessons into a document that is shared within your organization?
• How often are you active in sharing and discussing lessons learned with people from other organizations?
• How often do you actively participate in a group which is focused at sharing work experiences?

All questions where preceded with a short elaboration of the decribed situation.

Results of the research

The research showed the following results:

As you can see in this graph, there is no significant difference between men and women.  Women tend to score a little bit higher on searching for information prior to their tasks, while men score a bit higher on the other four activities, but he differences are very small.

It is interesting to look at the reported frequencies of the knowledge management behavior. When searching for knowledge prior to an assignment searching outside of the organization scored higher than searching inside the organization. We are in the process of further analysing this difference. According to the research people do reflect on their activities, but formulating them into a document, and thus contributing to the above mentioned knowledge bank, scores a lit lower. The same accounts for sharing and discussing lessons learned with other organizations. Not surprisingly the non-profit staff scored higher here than the group of people from the private sector. Finally, though the amount of communication methods have vastly increased over the last years, the participants of the research are not yet using them to the fullest. Futher research is needed what methods and instruments they use and how they use them.

As Davenport and Prusak (1998) put it: “Effective knowledge management cannot take place without extensive behavioral, cultural and organizational change (…) Technology alone won’t make a person with expertise share with others. Technology alone won’t get an employee who is uninterested in seeking knowledge to hop onto a keyboard and searching or browsing.”

Knowledge management is first and foremost a people issue. Does the culture of your organization support ongoing learning and knowledge sharing? Are people motivated and rewarded for creating, sharing and using knowledge? Is there a culture of openness and mutual respect and support? Or is your organization very hierarchical where ‘knowledge is power’ and so people are reluctant to share? Are people under constant pressure to act with no time for knowledge-seeking or reflection? Do they feel inspired to innovate and learn from mistakes, or is there a strong ‘blame and shame’ culture? These questions are essential to ask and to solve. There is a need to further understand the reasons why people engage in knowledge sharing behavior. This study tries to provide some answers.

The research on ‘the human factor in knowledge processes‘ tries to shine a light on the human factors that promote or impede effective behavior of people within different knowledge processes. By taking a multi-cultural focus it wants to provide a cross-section of the world we live in. And on this site you’ll find a diverse overview of relevant psychological theories that are translated to the specific world of knowledge management in development settings. This site publishes some main outcomes of this cross-cultural study. Finally it also gives recommendations how to make the human factor flourish in your organizations.

However, we would also really like your input and experiences in unraveling this people factor in knowledge management. We look forward to sharing and discussing strategies and practice with you.