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Societal KM – excerpt from upcoming journal article

This is an excerpt from an upcoming article on societal HRD, written with Gary McLean at Texas A&M – comments welcomed 

Knowledge for Public Good

Theorists, including Spender (1996), have argued that knowledge exists for the public good:

While land, labour and capital are private goods, knowledge is often said to be a “public good,” meaning that it is infinitely extensible and its use by one person does not deprive others of its use. (p. 48)

The definition of public good is based on it being non-rivalrous and non-excludable (Olson, 1971). But tacit knowledge would appear to be an excludable resource in the knowledge based view of the organisation, where knowledge is developed for rivalrous advantage, thereby depriving others of its use. However, in a societal view, it is necessary to speak of knowledge based processes in non-rivalrous and non-excludable terms. Using this view, Hess and Ostrom (2006) suggested that intangible knowledge exists as a public good, as, once a discovery emerges, it exists as part of the public domain for use by all.

Complicating this issue is Reichert (2006), who claimed that the tacit dimension of knowledge is excludable, through society and its supporting framework, to geographic location; whereas the explicit dimension exists as an accessible resource unbound by geographic location. A possible foil to this argument could be societal migration, where, for example, an organisation relocates from one geographic location to another, thereby making tacit knowledge transient.

Knowledge, such as scientific knowledge, has also been recognised as what is termed a “common-pool” resource (Dietz et al., 2002, p. 3); a concept informed by economic, behavioural, sociology and social science studies. Hess and Ostrom (2006) explained that the idea of common-pool resources has traditionally been applied to natural resources such as fish or water. However, it has grown in recent times to encompass the concept of knowledge through the term “knowledge commons’” (Reference, date, p. 3), reflecting the complexity and variability of modern knowledge-based resources and the KE. Commons resources exist at the micro level, the family oven as an example, and macro level, such as a library, as a resource with wider societal access. They are also bounded, such as in the case of a library, transbounded, where, for example, wildlife migrate from one continent to another, or unbounded, such as human knowledge (Hess & Ostrom, 2006). Knowledge as an unbounded resource could be debated using Reichert’s (2006) argument that tacit knowledge can become geographically bounded. In discussing knowledge in terms of popular explicit and tacit definitions, the resource could actually exist in all three states. Ultimately, Hess and Ostrom (2006) bind the commons concept to that of public good in stating, “Knowledge is cumulative. With ideas the cumulative effect is ‘public good’. So long as people have access to the vast storehouse (sic.)” (p. 8). We adopted these concepts with the understanding that individuals, organisations, and societies can take the knowledge created and manipulate it for competitive advantage, making it rivalrous and excludable, moving it beyond the realm of public good.

Societal Knowledge Management

Few authors have discussed SKM; the leading exception seems to be Wiig (2006; 2007). It could be argued that it has been explored through the concept of common pool resources, where the societal management of common resources are discussed at the macro-institutional (Central Government) through to the micro-institutional (County Borough Council) level (Agrawal, 2002). However, the link between organisational KM processes and the management of knowledge, or what Hess and Ostrom (2006) termed knowledge-pool resources, appears to be limited to that of Wiig (2006; 2007)

The roots of societal KM are entwined around what the OECD terms “The Four Pillars of the KE”: innovation, technology, human capital, and adaptive capacity (Asgeirsdottir, 2005, p. 2). Figure 1 demonstrates an adaptation of these four pillars as a flow from the founding block of human capital towards adaptive capacity and value creation.

Figure 1. KE drivers

What seems to be universally agreed is, “knowledge is the driver of growth” (Wiig, 2007, p. 142). Furthermore, knowledge drives a knowledge-based society (Wiig, 2007), organisations operating within the knowledge–based view of the firm and individuals looking to improve their personal capital (Spender, 1996). The management of this resource as a common commodity has been of interest to economists and behavioural and social scientists (Hess & Ostrom, 2006) and KM theorists (Wiig, 2006; 2007). It is this link between SKM and organisational KM processes that forms the locus of our enquiry.

Wiig (2007) wrote extensively on requirements for successful participation in the KE, stating that successful participation requires an organic stock of intellectual capital, the motivation of the individual to participate in the needs of society, and the societal culture that feeds the individual’s motivation. The argument is that these elements need to be in place for a society to innovate. However, we contend that an additional driver, the capability of people to contribute to society, is also needed. In comparing Wiig’s work to the OECD’s Pillars of the KE, we identify that another aspect for successful participation is under-defined: adaptive capacity. For if knowledge resources are to be applied for public good, there would seem to be a need to address sustainability, which, following the flow in Figure 1, requires society to address evolving links between innovation, technology, and human capital. Richerson et al. (2002) observed that sustainability is embedded in the culture of the situated context, which impacts the affective and cognitive styles of the individual and the collective. Wiig (2007) missed this; he did allude to the need for adaptive capacity, but couched it in terms of diversity, where “diversity drives the city’s industries and commercial establishments in new directions…to deal with changes in technology and the economy” (p. 149). A simplified approach to successful societal participation in the KE could be to evaluate societal strategy, policy, and activity against the four pillars of the KE. If this could be agreed, then concepts, such as adaptive capacity, become not only pillars for the KE, but also pillars for knowledge for the public good.

Progressing, Wiig posited that, “effective SKM is required to build, maintain, and make the best use of the country’s broad knowledge assets” (p. 150). It could be questioned whether this is any different from the fundamental aims of any KM process. This is acknowledged in Wiig (2007):

In general, SKM shares the same foundation as the private sector KM. Hence, SKM uses approaches developed and perfected in the private sector. Most management, organisational, and operational principles are similar. (p. 151)

If it can be agreed that these similarities exist, then it opens the concept to exploration by researchers using organisational KM models.

Griffiths et al. (2010) put forward the K-Core, an evidence-based generic model for KM. The K-Core (Figure 2) emerged out of a value-based meta-analysis of KM literature (Griffiths & Morse, 2009). Research produced four functions and twelve enablers. The model was designed to address organisational KM research and, particularly, the need for a model that could address processes regardless of sector. The modelling process was compared with a global practitioner survey, and fractal analysis was used to demonstrate self-affinity between organisational sectors (Griffiths & Evans, 2011). It would, therefore, seem to be an acceptable organisational KM model to apply against SKM drivers.

 

Figure 2. K-Core model of knowledge management (Reproduced by permission from Griffiths et al., 2010)

A comparison of Wiig’s (2007) principles for effective SKM (Table 1) with the K-Core model (Figure 2) reveals that several variables within the KM process have been omitted or require further clarification.

Table 1

Wiig’s Principles for Effective SKM

Principle Provide societal leadership to promote local and private KM initiatives without autocratic bureaucracy
  Govern and facilitate building, maintaining, safeguarding, and utilisation of IC assets to support broad societal goals and intents
  Provide societal resources and support priority setting to achieve desired goals
  Pursue initiatives and practices that employ technology and rely on human and social mechanisms, which must be understood for initiatives and practices to be effective
  Rely extensively on educational institutions and infrastructure support such as ICT
  Obtain support by public opinion and voluntary action for best effectiveness
  Support development of higher-order learning and reasoning in addition to widespread literacy as fundamental goals for effective behaviours
Premise 1 Citizens throughout society must be knowledgeable and responsible partners who can understand and judge societal issues independently to participate objectively in the public process
Premise 2 An effective society requires competent public administration to perform normal work and with additional insights and perspectives to apply contextual judgement in non-routine situations
Premise 3 A globally competitive society requires a variety of quality IC assets to build:Competent and well-educated workforces
  State-of-the-art government and industrial programs for basic and applied R&D
  Effective and innovative industrial and commercial establishments
  Effective and justly enforced laws and regulation
Premise 4 Modern technology continually changes society’s culture, systems, procedures and infrastructures….widespread understandings must be built to work with and navigate the new environment

Adapted from Wiig (2007, p. 151).

For example, Wiig (2007) failed to discuss two KM functions: knowledge sharing and the acquisition and storage of knowledge resources. He did state the need to “govern and facilitate building, maintaining, safeguarding and utilisation of [information communication] assets to support broad societal goals and intents” (p. 151). But whether this sufficiently addresses the need for acquisition and storage is perhaps open to debate. There is also no reference to access to knowledge, which is part of the knowledge structure aspect of the K-Core model. Looking toward the generation (innovation) and use of knowledge resources, the access and structure of those resources would appear to be an important variable for consideration.

Perhaps the most critical function missing from Wiig’s principles is that of knowledge sharing. The socialisation of knowledge to facilitate the exchange of knowledge artefacts and the evolution of what is known by society is not discussed, nor is the development of knowledge artefacts to improve societal knowledge stocks. This said, in an apparent disconnect between the discourse set out in his paper and his principles for SKM, he stated that communities of practice are important in order to allow professionals an opportunity to socialise. Another example of a disconnect in Wiig’s principles is his lack of discussion on appropriate space for knowledge storage and development, such as technology parks; the significance of this omission will become clearer in later discussion on institutional clustering and networking. But he does state in his text that cities facilitate the congregation of societal activities in specialised areas. In a further example, he discussed enablers, such as people and technology, but issues of public and private finance needed to seed KM functions are apparently not required.

Wiig’s (2007) work provides a valuable insight into the potential value of SKM. However, Wiig has drawn upon similarities between SKM and organisational KM processes. If this can be accepted, then it must also be accepted that his work, while providing authority for KM processes to be applied at societal levels, appears to suffer from significant gaps that could inhibit full and proper SKM analysis.

Knowledge Regions and Cities

A knowledge region or city is seen as a network of places, people, processes, and purpose enabled by essential conversations between actors (Dvir, 2005). They are seen as a form of competitive advantage, through a sense of place that brings advantage through the exploitation of geographically embedded tacit knowledge (Reichert, 2006). This is supported by Richerson et al. (2002) who stated that “people from different societies behave differently because their habits have been inculcated by long participation in societies with different institutions” (p. 405). This reinforces Reichert’s (2006) claim, made earlier, that knowledge can be bound to a geographic location. Wiig (2007) echoed the pivotal nature of cities as catalysts for knowledge generation, while introducing a caveat that cities often have a surplus of human capital in relation to employment opportunities and infrastructure capabilities, causing slums to develop. Extending this thinking, it could be suggested, as implied later in this article by NESTA (2007), that a disconnect between the indigenous population and economic demands could also cause slums to occur. For example, if the population is under-skilled, they are, therefore, incapable of attracting knowledge intensive employment opportunities that would allow the city or region to prosper. This opinion is supported by Pranab and Dayton-Johnson (2002) who stated that “common-pool resources play a decisive role in determining the livelihood of the …poor” (p. 87).

The idea of knowledge cities or regions has been propagated for several years by both theorists (Wiig, 2006; 2007) and institutions, such as the city of Manchester (www.machesterknowledge.com). Manchester has focused on becoming a knowledge capital, transforming the region through an innovation network that includes regional universities, councils, government agencies, and the airport. Its success in this area was acknowledged with a global award for “Most Admired Knowledge City Region 2009” (www.manchesterknowledge.com). The South East Wales Economic Forum (2005) offered Technology Region Karlsrhue as a further example. Karlsrhue-Pforzheim is a post industrial/mining region situated between Mannheim, Stuttgart, and Strasbourg with a population of one million. The region is effectively a cooperative of nine cities within a 30km radius. The region has used its universities as an incubator for innovation by interfacing with innovation-minded SMEs (small to medium-sized enterprises). The region has created one of Germany’s largest public funded science and engineering research institutions. Significantly, the research centre focuses on “research and development problems of public interest” (p. 44).

It is the idea of knowledge-regions, as opposed to singular cities, that is of interest to us in discussing a knowledge-society. Knowledge regions place a city, or, in the case of the Technologie Region Karlsrhue, a conglomerate of cities, at its hub with outlying towns acting as tributaries that feed the heart of the region. This clustering, or network of practice of similar industries, inform critical decisions and minimise risk, bringing strength to the region (Reichert, 2006).

Regardless of the locus of discussion, being regions or cities, the common theme in theory and practice is the drivers of the KE, as detailed earlier and supported by theorists such as Bounfour and Edvinsson (2005) in their work on intellectual capital for communities. It is also emphasised by Scott (2010) in his Times newspaper article in which, when discussing the strategy for Manchester as a knowledge capital, he reported:

The city decided that it needed to focus on the new, knowledge-based economy…this means a focus on digital, creative, new media and science based activities, along with financial and professional services and advanced manufacturing. (p. 11)

However, Reichert (2006) positioned the power of clusters as the driver for the KE:

A knowledge intensive firm benefits from the proximity to a cluster of related firms because it can exploit…the competitive advantages provided by critical mass. Knowledge industries are thus more likely to locate to cities in order to achieve these advantages. Hence, city-regions have become the main drivers of the KE.

 

Earlier, we discussed geographic location as a form of competitive advantage. This is further enhanced by this idea of clustering and the idea introduced earlier of the impact of a region’s culture upon the cognitive style of individuals within that society. It would seem that clustering could act as an instrument for cultural change, where society can embed new cognitive traditions through the inward migration and networking of similar organisations.

It would, therefore, seem that the KE and Knowledge Regions act in a reciprocal relationship through which they drive and are driven by knowledge. According to the National Endowment for Science Technology and the Arts (NESTA, 2007), this network of people, education, and commerce is key to an adaptive society, where a reciprocal relationship of attraction builds between skilled people and businesses. However, NESTA issued a warning that “in isolation and without intervention, cities that are not part of this [relationship] may fall victim to a downward spiral of economic stagnation and declining skills base” (p. 2). NESTA also linked Knowledge Cities to improved cultural variables through cultural actors, such as higher skilled workers, creative classes, who attract enhanced cultural and consumer standards, as well as an improved quality of life. The strength of education as a partner in the societal KM network is ascribed to by NESTA who moved the case for knowledge cities beyond innovation to one of knowledge usage:

Those cities hosting higher education institutions (HEI) have an inherent advantage as a source of knowledge production. However, an HEI’s primary contribution to its local innovation system is not knowledge, but a workforce capable of exploiting knowledge. Accordingly, the creation and retention of high-quality graduates should form a policy priority. (p. 3)

In a global economy, where competitiveness is heated by technology bringing disparate societies closer together, it is the situated culture of the society that brings competitive advantage to the region or city (Dicken, 2007). Indigenous geographic advantage through variables, such as inward investment in education, taxation policy, skills base, historic culture, tradition, and links between business and education become the currency for competitive advantage (Reichert, 2006).

The more implicit “tacit” forms of knowledge have a geographic dimension which can be positively influenced by policies and framework conditions. Moreover, it seems that for knowledge economies the dimension of “place” has gained importance…especially in an age of globalisation. (p. 8)

 

This reaffirms the ability of a region or city to inform competitive advantage for a wider society.

Macro strategies, such as The Europe 2020 Strategy (Barroso, 2010), The Lisbon Strategy (www.ec.europa.eu), and The Learning Country (WAG, 2001) have been formed to emphasise the importance of knowledge growth and provide direction for national and regional governments. But it is the management of common-pool resources at a local level that economics theorists have targeted as being pivotal to the adaptive capacity and productivity of the nation (Agrawal, 2002).

National governments in nearly all developing countries have turned to local-level common property institutions [local councils and organisations]…as a new policy thrust to decentralise the governance of the environment… Resource management can never be independent of collective human institutions that frame governance, and local users are often the ones with the greatest stakes in sustainability of resources and institutions. (p. 41)

It is this view that is used as authority for examining the management of macro-level edicts within micro-level institutions, such as Borough Councils.

Learning and Knowledge

Education resonates throughout the literature on SKM and knowledge regions and cities as a key component for innovation (Barroso, 2010; WAG, 2001; Wiig, 2007). Wiig (2007) stated:

People are society’s basic knowledge agents and their knowledge growth through formal and informal education are required for competence to tackle challenges in the private and public sectors and are central to function and progress. (p. 147)

Wiig (2007) linked the complexities of a knowledge-based society to the need to develop higher order reasoning. This could be seen as acknowledging the need for a society to develop adaptive capacity, though this is not explicit in Wiig’s text. The emphasis on education as a stimulus in developing knowledge for public good is signalled in the Europe 2020 strategy for smart, sustainable, and inclusive growth (Barroso, 2010):

Smart growth means strengthening knowledge and innovation as drivers of our future growth. This requires improving the quality of our education, strengthening our research performance, promoting innovation and knowledge transfer. (p. 9)

The Europe 2020 strategy explores the need for national and regional governments to improve innovation capability by maximising education opportunities for society as a whole. Spender (1996) saw education as a process whereby previously discovered knowledge can be communicated to mass society. This was acknowledged by the OECD (2008) where tertiary education is observed as a pivotal element in the development of human capital, the development of knowledge stores, the sharing and use of knowledge, and the maintenance of knowledge. The OECD posited that the diversity of education institutions are necessary “to develop a closer relationship between tertiary education and the external world, including greater responsiveness to labour market needs” (p. 2). The OECD also stressed the need for tertiary education institutions to become partners in the strategic development of economic competitiveness, thereby ensuring the responsiveness of the skill base to community needs.

Organisational KM enrols innovation within its network of processes through the function of knowledge generation (Griffiths et al., 2010). The importance of innovation, beyond being integral to the OECD’s Four Pillars of the KE, is supported through the findings of the Premier League of Innovative European Regions; a project conducted by the European Commission and concluded in December, 2008 (www.ec.europa.eu). The list confirms the work of theorists, such as Antonacopolou (2006), among many others, who have suggested that knowledge and learning are aligned, in that the vast majority of the top 22 innovative regions in Europe are linked to universities with strong research traditions. This is demonstrated in the United Kingdom’s three representative regions, all of which have established old and ancient universities: Oxfordshire, Cambridge, and Edinburgh. This suggests that, if knowledge is to be harnessed and utilised for societal good, there will need to be education partners within KM networks. A point highlighted by Scott (2010) in his Times article in which he cited the Chief Executive of the award winning Manchester Knowledge Capital project, “Education is a key selling point for the city as it bids to attract more business to the area” (p. 11).

The responsibility of societal institutions to safeguard the discovery process afforded by education is a point of emphasis for Hess and Ostrom (2006): “The discovery of future knowledge is a common good…the challenge of today’s generation is to keep pathways to discovery open” (p. 8). It is, therefore, clear that any enquiry into SKM must focus on feedback from educational institutions; such an approach informed our research approach.

One comment on “Societal KM – excerpt from upcoming journal article

  1. Sergio Storch
    September 3, 2010

    Dear friends
    I rejoiced at reading this article, because I´ve been writing a bit (non-academically) on what I understand as “Societal Intelligence”. You provided some solid foundations on which to further develop my concept.
    One thing I think would add a powerful layer to your model: deepen the role of processes for getting a full intelligence cycle end-to-end from knowledge creation to knowledge use. I wrote on this in my article (in Portuguese, but you can Google-translate it) “Knowledge Chains – the pipes for collective intelligence” (see link below).

    Additionally, I think the application of Verna Allee´s framework of Value Network Analysis would also enrich your discussion.

    Looking forward to see your work progressing. Few people see KM as transcending the walls of the corporation, and so the best of KM is overlooked. Thanks for your work.

    1) My article´s link: http://sergiostorch.com/artigos/cadeias-de-conhecimento-dutos-para-a-inteligencia-coletiva/
    2) Verna Allee´s link: http://valuenetworks.com/public/blog/207717

    Cheers

    Sergio Storch
    Brazilian Association of Knowledge Management
    Follow me on http://www.twitter.com/sergiostorch

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This entry was posted on September 3, 2010 by in Uncategorized.
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