Kurtz, C.F.; Snowden, D.J. 2003. The New Dynamics of ... Snowden, David; Stanbridge, Peter. 2004. The Landscape of ... Edited by Leigh Keeble. London:.
Collaboration and Knowledge Governance:
How the Digital Environment Re-Frames the Future of Knowledge Work, Enables Social Computing and A Shift from Management to Governance. 1.0 Introduction ....the leadership required is not provided by a single individual, no matter how steely his or her gaze is, but rather the ability of the team to stay determined and motivated, to find new ways to accomplish their goal as the situation changes, to switch to new goals if that’s what’s called for, to be resilient as members of the team are injured or taken up with other tasks. For Burgess, these are desirable characteristics of a team, not of any one individual. Leadership for Burgess is distributed throughout the team, so that leadership becomes a property of a unit the way robustness is a property of an organism.
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David Weinberger (2012, p.161-2)
We are in the midst of an exponential evolution in communication technologies that are forming a digital environment. This environment will make conversation and collaboration much more dominant in how we generate, discover, capture, analyze, distil, exapt, validate and apply knowledge and how we should structure the work environment. This environment is making an abundance of resources and relationships easily accessible which challenges our concepts of learning, including how we teach, coach, and credentialize knowing. Increasingly people expect to be able to work, share, and learn where and when they want or need to. It is revolutionizing the way that information can be synthesised, stored, shared and retrieved. For organizations seeking to thrive in this environment that also includes accelerating change, interdependence and complexity - agility and speed are vital. Keeping human efforts aligned, while simultaneously promoting agility and innovation, will require more types of collaboration in increasingly complex environments. To do this is a more challenging task than one more ‘reengineering’ of the organizational structure. Managing and stewarding the conditions that shape the communicative and working relationships that enable people to engage in conversations and collaboration as they are need and to search and find information that they require is the challenge facing organizations today. It is a st
complex problem of culture and structure. This paper argues that the structures and cultures of 21 century organizations will have to be programmable. To begin with we must understand that: Knowledge is socially constructed, the result of the complex social interactions. The more enabling the environment, the easier it is for people to apply, generate and exchange their knowledge. This increases
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motivation and openness to innovation. An enabling environment means more self-directed collaboration. This requires governance guidelines or principles that enable agile programming of roles and accountabilities of people. This is as important, if not more so, than the attention placed on management of resources and activities. Knowledge governance for social computing aims at shaping and enabling conditions for selforganization of a complex adaptive system including transdisciplinary & cross-jurisdictional self-forming groups that engage and enrich the intrinsic motivations of people.
2.0 The Digital Environment Figure 2.0, depicts the trajectories of three broad domains of the digital environment. In the upper blue wedge reside ‘Internet’ things-with-things (including all manner of ‘smart’ machines). In the lower wedge are digital technologies of social connection – Web 2.0 or social media networks. In the middle wedge arises the domain of ‘Big Data’ and information enabled by technologies of the virtual, including: the semantic web; augmented or ‘mixed’ reality; social computing (structured massive collaborations); and massive data analytics. The semantic web aims to create the development of ‘smart’ or ‘meta’ data, data that can ‘communicate’ with other data such that their inter-operability is seamless. These domains constitute the emerging digital environment that promises to also be eventually ubiquitous.
Figure 2.0 – Kevin Kelly’s (modified) Trajectories of the Emerging Digital Environment
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The figure purposefully presents no specific predicted timelines. What is important for the development of a 3
‘shaping strategy’ is less an attempt to predict particular timelines and much more to understand the ‘inevitable’
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trajectories of change. The figure permits a robust view of how the digital landscape will inevitably evolve – the timing and specific technologies of this evolution are too inter-dependent on too many variables and social conditions, for any form of accurate prediction. There are many possibilities for these ‘inevitable’ trajectories to suffer significant delays or detours, including socio-political decisions shaping Internet access and the technology investment climate. On the other hand there are many reasons that could accelerate these technologies. The vertical axis represents the connectedness of things. Not just the increasingly powerful computers, but the increasingly inexpensive processors and sensors. It also include new computational capabilities including quantum computing, ‘DNA’ computing, and the potential that the newly found and rapidly developing fundamental electronic element, the ‘memristor’ has for enable a powerful ‘learning’ computer. In essence we have moved from a world where the CEO of IBM visualized a world market for computers to be ‘about five’ computers – to a world with countless and ubiquitous computational devices, that in turn can likely again become a world that is a single computing environment.
2.2
Web 3.0
The middle ‘white area’ of Figure 2.0, represents the increasingly ‘dense’ informational environment – the environment of ‘Big Data’ (including meta-data and meta-meta-data, etc.). With a smart phone a person can move with a GPS in hand, use the camera to translate signs or identify products & places, read bar- & QR-codes to get more information, compare prices and post reviews, and so much more. Web 3.0 will likely arise where the Internet of things overlaps with the social computing enable by social networks. st
While Web 2.0 arose at the dawn of the 21 century as ‘Web Pages’ were transformed into ‘Web Spaces’ – virtual spaces of interaction. The next iteration of the Web – Web 3.0 is still being defined – but one could argue soundly that it will include a number of dimensions, including: Semantic Capability, Augmented Reality, and Machine Learning.
2.2.1 Semantic Capability With the advent of metadata new types of search become possible. For example, today Google work to provide the ‘needle’ in the haystack of information as it seeks to gives us the ‘answer’ we are seeking to our question. Semantic search on the other hand – will not only give us the ‘needle’ but will also reveal the structure of the
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haystack the needle is embedded in. Semantic types of search will reveal the complex relationships of our question’s answer to a vast sea of other information. Semantic capability will enable new forms of information assemblage, info-mash-ups. New economic services will arise because of what can be done to present visualizations and digestible form of patterns within an exponential growing sea of ‘Big Data’.
2.2.2
Augmented Reality
This year Google will be releasing a commercial affordable ‘heads-up display’ – Google Glasses. These glasses may well inaugurate popular augmented reality the way the iPhone turn the phone into an Internet platform. What this enables is the creation of a new informational layer that we will be able to see (and perhaps hear). This will make all three layers of the web (1-3) visible as an environment, within which people will flow.
2.2.3
Machine Learning
Machine learning, based on massive data and statistical processing has created a new approach not feasible even 15 years ago. The utility of massive statistical analysis is partly the result of Moore’s law outlining the doubling of computational power and corresponding decreasing in cost of computing. Adding this capacity for machine learning – Web 3.0 includes a new dynamic type of ‘application’ one that is able to learn to customize itself to an individual user.
2.2.4
Summary – Social Computing, Collective Intelligence, Emergent Social Consciousness
By the combining the top two layers (the blue and white layers) with the exponential increase in our density of communication and connectedness, we see a new social-digital environment emerging. The bottom axis of Figure 2.0 represents the connections between people. Humans have always formed and depended on a finite number of close ties. However, one could argue that civilization begins with a capacity to include and structure a formation of ‘loose’ ties, ties that are less personal, as well as an ability to engage in an apparently ever-widening circle of loose4
ties and somewhat trusted impersonal exchanges . Kelly, hints at more than loose-ties and impersonal exchange by labeling the far end of the connection between people axis the ‘Noosphere’. Introduced by Teilhard de Chardin in 1922, it was meant to denote the "sphere of human thought". Many others have more recently written on this topic (a search of the term ‘global brain’ on Amazon.com, will produce at least a dozen books on this topic). More recently, the ‘Arab Awakening’ evokes powerful images of self-organizing.
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Social media, and the rapid emergence of related ‘Big Data’ are enabling conditions for making the structures of social consciousness visible.
2.3 The Economics of Organizational Structure – Collapse of Transaction Costs Coase (1990) explained that the firm arose in a market economy because of transaction costs (including search, negotiating terms, coordination, enforcement and communication). By sharing purpose; dividing labour; and providing control through roles, responsibilities and methods of communication, the firm was efficient. The digital environment collapses the transaction costs that formed the economic rationale of the hierarchic organization. This change in the conditions of change, enables new modes of production. The classic managerial structure – the command hierarchy is now unduly restrictive.
.3.0
The Digital Environment – A theory of an Intensive Media
Intensive dimensions are the measurable domains such as temperature, pressure, density or connectivity. Intensive dimensions are important in that they are subject to a certain type of change referred to as phase transitions. How can phase transitions occur in a social context? A simple example, as human populations reach certain levels of density we see phase transitions in the possibilities for increasing divisions of labour, varieties of possible exchanges and corresponding social institutional frameworks. Other types of ‘density’ can impact a social context even those with a relatively stable population density. The emergence of new types of communication ‘densities’, new ways to increase connectedness and/or new capabilities that lower transaction cost thresholds (such as search, coordination and communication), can also produce types of phase transitions in social conditions. The concept of phase transition arising from related to changes in population, connectiveness and communication ‘densities’ provides us with a useful overarching theory, and helps us to understand the potential of the emerging ubiquitous digital environment. This theory of the digital environment, sees it as an enabling a progression into an era of almost costless hyper-connectivity enabling a hyper-division-of-labour with a corresponding hyper-exchange producing a requisite hyper-knowledge-metabolism. The consequence that this entails is that organizations must become programmable systems to harness the power of human capital in the digital environment
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3.1
Hyperspecialization and Non-Routine Work
The digital environment enables new ways to extend the power of the division of labour are being realized – making hyperspecialization more feasible. Speed and quality improves as more can be done in parallel by people who are good at what they do. Productivity increases with better use of people’s knowledge, time and motivation. Recent Gartner research noted that currently 25% of work in organizations is non-routine and predicted that this will increase to 40% by 2015. As information technologies automate ever more routine work the emphasis shifts to the real value that people add – analytical, social interaction, capacity to discover, innovate, collaborate, persuade and learn. This also means a better capability to form ‘one-of’ assemblages of talent and experience to deal with unique problems as they arise – in essence programmable social computing in complex adaptive systems. The Digital Environment – disrupts the industrial economy and the structure of its organizations with an almost costless hyper-connectivity enabling a hyper-division-of-labour (hyperspecialization) entailing a requisite hyper-exchange, producing a hyper-knowledge-metabolism. The management challenge involves developing an institutional framework enabling dynamically programmable work processes for more discrete and non-routine types of work by wider varieties of expertise and knowledge.
The Nature of Knowledge While tacit knowledge can be possessed by itself, explicit knowledge must rely on being tacitly understood and applied. Hence all knowledge is either tacit or rooted in tacit knowledge. A wholly explicit knowledge is unthinkable. Michael Polanyi “Knowing and Being” (p.144) …knowledge will live not in the final article but in that web of discussion, debate, elucidation and disagreement. It’s messy. …Knowledge has inherited many other of the web’s properties. It is now linked across all boundaries, it is unsettled, it never comes fully to rest or agreement, and we can see that it is bigger 5 than any of us could ever traverse. David Weinberger Shannon’s information theory provides a useful concept to distinguish the difference between knowledge and information. Shannon was not concerned with content or meaning but with ensuring that the ‘original’ signal, sent throughout its series of transformations through various media, would be the same signal that was received. Thus information can undergo processes of transformation with a guarantee of no error. In this view information management can be more usefully characterized as ensuring that artifacts (material or digital) that are created, stored, exchanged are those that were originally created and contain no error – e.g. a library ensuring the books haven’t be altered and are ordered as they were intended to be.
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Knowledge is better conceived to be more like language – something which must be translated without a 6
guarantee of distortion or error . This is relevant to the world of science where each established discipline has its own content domains, perspectives and corresponding language of concepts and terms. Unlike the problems of information theory – language and knowledge are also constituted through metaphors (cross-domain mappings of ‘knowledge’), frames, narratives, heuristics, etc. as inextricably part of the way humans communicate and express themselves. The issue of knowledge as language raises the question of whether explicit scientific knowledge can ever contain the richness and depth of all human knowledge. Recently, Collins (2010) extended the understanding of tacit knowledge, arguing that tacit knowledge only becomes evident because of the development of explicit knowledge, but also makes the point (as already noted) that explicit knowledge is more clearly understood as ‘information’. Most importantly Collins discusses three types of tacit knowledge (TK):
Relational tacit knowledge (RTK);
Somatic tacit knowledge (STK);
Collective tacit knowledge (CTK)
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RELATIONAL TACIT KNOWLEDGE (RTK) – is knowledge that is in principle explainable, and can be made explicit. There is a significant problem however; we don’t always know what knowledge is important to make explicit. On other occasions we simply can’t explain everything despite it all being explicable. Collins gives an example from his research of how scientist learned to build the transversely excited atmospheric pressure carbon dioxide (TEA laser). What Collins found was that scientist who used only scientific publications failed to build these lasers (Collins, 2010. p.149-150). The particular publications related to this work included a great deal of detail such as the cross-section and machining instruction for the electrodes as well as manufacturers’ parts numbers for off the self parts. Success of those seeking to replicate what the successful lab had accomplished was achieved only after those scientist from unsuccessful labs who were able to time socially interacting with scientist who had successfully built a working laser. Thus learning/education relies more on ‘enculturation’ rather than an ‘algorithmical model’ of knowledge transfer: ”learning to build the laser was like learning a new language or culture, rather than absorbing discrete new pieces of information (Collins 2010. p. 150).” Learning as enculturation
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is necessary because of the inability to develop a fully explicable knowledge as a consequence of the ‘rules regress’ principle – that “rules can never contain all the rules for their own application(Collins 2010. p. 150).” The necessity for socialization includes access to ‘ostensive’ knowledge – what can be learned only by having it literally pointed to, whether, object, state, process or practice, because the description would be too complicated if possible at all. Rrelational tacit knowledge also involves issues such as codifiability, and teachability. SOMATIC TACIT KNOWLEDGE (STK) – includes bodily skill or experience, such as the classic example of riding a bike. We don’t learn to ride a bike from reading or being told about it. It helps to watch someone ride and we can be given guidance (e.g. look in the distance, start on a small hill and coast, etc.). Another example is asking a touch typist to write out the letters of the alphabet as they are laid out on the keyboard (without looking). Most people are unable to do this. Somatic tacit knowledge is essentially what we normally consider as tacit knowledge. It is also the basis of the understanding that how we ‘SAY’ we do things is usually not how we ‘Actually’ do things. This tension between explaining how and actually doing has tended to place excessive focus on the body as the seat of the tacit and in this way limit our understanding of the concept. It remains the generally accepted view, and assumes that most tacit knowledge is explicable – for example, Nonaka and Takeuchi (1995) state that they show how 8
tacit knowledge can be converted to explicit knowledge . The fact that some tacit knowledge has been successfully explicated (if only in very proscribed ways) has contributed to the powerful hold on the perception of knowledge manageability . However, this approach to understanding tacit knowledge remains simplistic rather than simple. COLLECTIVE TACIT KNOWLEDGE (CTK) – includes social, contextual and enculturational knowledge. Collins uses the examples of learning to ride a bike in traffic or the acquisition of fluency in a language (which arises in social contexts, disciplinary fields, and includes the capacity to apply conceptual metaphors). The fundamental distinction related to CTK is between sensory motor expertise versus expertise related to social life and conditions. The realization that to be human is to be social, brings with it the unique human capacity to form and be formed by social rules and even more significantly to adapt to and thrive in multiple and evolving contexts of rules. What that means is that human are capable of ‘polymorphic’ actions “actions that require different behaviours for successful instantiations depending on context and require different interpretations of the same behaviour depending on context” (Collins, 2010. p. 125). In this regard collective tacit knowledge is the central domain of the
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tacit and ultimately is beyond a full explication. Both collective and somatic tacit knowledge involve depths that simply cannot be made explicit. Because of this, they can only be learned through 'socialization/enculturation'. Cognition is the most socially-conditioned activity of man, and knowledge is the paramount social creation. The very structure of language presents a compelling philosophy characteristic of that community, and even a single word can represent a complex theory…. every epistemological theory is trivial that does not take the sociological dependence of all cognition into account in a fundamental and detailed manner.” (Fleck 1935, p. 42 in Douglas 1987, p. 12 quoted in Håkanson, 2006, p.7 – footnote 6).
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Core to collective tacit knowledge is social interaction. Collins argues that social interactional expertise (social skill) is essential in any society founded on significant divisions of labour. Once social/interactional expertise is understood it become almost ubiquitously evident as the most effective method of acquiring CTK. One could argue that the disciplinary structures of post-graduate education shape expert knowledge through lineages of practice and instrumentation, developing the tacit fluencies upon which bodies of explicit knowledge float.
6.0 Conclusion The digital environment creates new conditions for organizations, because of the collapse of traditional transaction costs – the primary economic basis for the classic hierarchical managerial efficiency. Collaboration and knowledge 10
governance is a necessary institutional innovation providing a framework based on responsible autonomy. The condition that collaboration and knowledge governance fosters will enable ‘knowers’ to collaborate and innovate through empowered self-organization to improve knowledge flow when and where it is needed. Knowledge governance shifts from a content-centric concept of knowledge management toward a people-centric concept that recognizes knowledge resides in people and is constructed in social contexts. Knowledge cannot be managed directly. What can be managed is the environment in which it is generated, applied and exchanged.
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The argument this paper proposes is that if organizations are going to be able to fully leverage the emerging digital environment, to fully develop the potential of flowing tacit knowledge and know-how and harness the power of social computing, they will require new ways to design how works is accomplished. To do this will require a governance framework enabling new and more dynamically programmable organizational architectures to foster greater subsidiarity and a more integrated, agile environment. Most important, a programmable architecture is necessary for tacit individual and collective knowledge to be more efficiently and effectively generated, discovered, captured, analyzed, distilled, exapted, exchanged, validated and applied. Why Will An Organization Flourish In A Digital Environment? Because it enables a self-governing knowledge commons where knowledge flows where and when needed through an effective culture of collaboration and self-organization. The economic rationale for the organization in the digital environment is the increasing knowledge and social returns that arise as members share: Common purpose, nearness, stable trusted relationships, common language create collective tacit knowledge – a ‘knowledge commons’, and work that is geared to intrinsic motivations and invests in continual learning for knowledge-intensive processes foster deeper engagement and commitment. st
To flourish in the 21 century the organization must provide conditions where the whole is greater than the sum of its parts – where people can engage and commit to a bigger enterprise. If Social Media is the Medium – Then Social Computing is the Message Entailing That Organizations Become Programmable Complex Adaptive Systems
Endnotes 1
David Weinberger (2012, p161-2) in an interview with Lieutenant Colonel Anthony Burgess Director of the Army Center for the Advancement of Leader Development and Organizational Learning (CALDOL) and co-founder of CompanyCommand.com. 2 Kevin Kelly’s original graph was labelled “Two Strands of Connectionism” http://www.kk.org/thetechnium/archives/2009/01/two_strands_of.php (last accessed 21 May 2012) Kelly’s site operates under the creative commons licence see http://kk.org/ 3 See John Hagel III, John Seely Brown, Lang Davison. http://www.johnseelybrown.com/shapingstrategy.pdf (last accessed 21 May, 2012) 4 Douglass North (1982, 1990, 2010) makes this argument as the basis of the rise and success of a market-based political economy. 5 From “Why Untidiness is Good for Us’. New Scientist. 11 Feb 2012 p.31 referring to Weinberger (2012a): 6 The inability to guarantee against error involves the domain of meaning which Shannon did not address. Not only are some word not directly ‘translatable’ from one language domain to another, but human also add meaning to what they perceive (e.g. we can see things in an ink-blot – which is also an important source of new knowledge and innovation). Only through ongoing conversation can enough redundancy be obtained to establish a good enough translations between knowledge domains. 7 It would be safe to consider collective as equivalent with the term 'social' 8 Ikujiro Nonaka and Hirotaka Takeuchi. 1995.”The knowledge-creating company: How Japanese companies create the dynamics of innovation” Oxford University Press. They state this even in the book’s description http://www.oup.com/us/catalog/general/?cp=24297&view=usa&ci=0195092694 (last accessed 21 May, 2012) 9 This is a quote in Håkanson, Lars. 2006. “The Knowledge Based View Revisited: Knowledge Governance and the Theory of the Firm”. Paper presented at the Annual Conference on Corporate Strategy (ACCS 2006). Berlin, May 19-20, 2006. p.7 footnote 6. 10 See: Fairtlough 2007; Verdon 2005; Verdon et al 2007, 2009; and Okros, Verdon, Chouinard. 2011.
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