knowledge

Knowledge

Existing knowledge (9)
Expert knowledge (11)
Explicit knowledge (10)
Incomplete knowledge (5)
Knowledge (1059)
Knowledge creation (21)
Knowledge diffusion (12)
Knowledge dynamics (19)
Knowledge exchange (5)
Knowledge generation (9)
Knowledge making (3)
Knowledge management (33)
Knowledge production (23)
Knowledge sharing (20)
Knowledge triangle (3)
New knowledge (20)
Relevant knowledge (5)
Strategic knowledge (6)
Tacit knowledge (24)
Validating knowledge (3)

and knowledge sharing elements of studies which added to the value of the policy formulation outcomes. In addition to a complex combination of techniques

where the best available and often diverging sources of knowledge ought to be brought together in an explicit way

and their contributions to be positioned in a comprehensive framework. 7 A fact-based foundation is thus as crucial for the credibility of foresight as a critical assessment of the sources of knowledge.

The even more traditional technology foresight standpoint developed from a more linear understanding of innovation does handle many of the challenges we discuss excellently (using best expert knowledge,

Thirdly, adaptive planning takes into account the accumulation of knowledge and thus at least over time should be able to overcome the impressionism of conventional foresight,

if too harsh requirements to build on well established scientific results are imposed on foresight At the stage of impressionist knowledge,

but by being sensitive to the degree of maturity of knowledge, it suggests more offensive action at later stages.

K. M. Weber/Technological forecasting & Social Change 75 (2008) 462 482 information on any particular fire and consequently it will have to build on more generic knowledge on different sorts of fires, their probabilities and the like.

in order to protect its knowledge and improve its ability to act. These internal processes need equally the support of foresight specialists and should

Socio-technical knowledge base, i e. the entirety of the distributed knowledge that is available to the different actors;

The process dynamics, by which these different elements of innovation systems are coupled. 3. 2. 3. Phase 2:

K. M. Weber/Technological forecasting & Social Change 75 (2008) 462 482 While there are many ways to use such knowledge,

Plausible in the sense that they start from (aspects of) the current situation and develop in ways consistent with established knowledge.

to the identification of new socio-technical options, to the growing knowledge and understanding of their impacts, to the design of new types of policy options and to their integration into portfolios.

It allows adapting to the advancement of knowledge in the course of an AF process,

and to build knowledge on how such processes can be enacted best so that the coordination tools can attain the objectives that have been placed on them.

which only uncertain and incomplete knowledge exists. It is structured based on a survey of expert groups and makes use of the implicit knowledge of participants.

Hence the Delphi method has both quantitative and qualitative dimensions. There is not a single method, but all agree that a Delphi study requires an expert survey in at least two or more rounds.

knowledge of the technology and market drivers) are generally uncertain 9, 21,22. New s&t are defined not by eventual application but characterised by‘generic richness,

and outlook for the multi-path mapping approach. 8 Robust in the sense that it is informed by knowledge of path dynamics of new

knowledge of path dynamics need to be integrated into a process of controlled speculation in combination with other analyses.

and proximity will allow for knowledge exchange and the building up of trust. The workshop participants pointed out that there are attempts at all four innovation chains.

Knowledge creation, diffusion and absorption; Social capital and networking; The evolution of strategies to cope with or escape from the negative consequences of a‘risk society'.

and factors increasing the role of knowledge in society; a group of factors leading to innovation-based growth;

'4 Notwithstanding the importance of knowledge in previous types of societies, several authors acknowledge a shift in the economic structure of modern societies away from a‘material'input driven economy towards a knowledge-based

They associate the increasing role of knowledge in the emerging knowledge societies with the increased density

and complexity of the ways information and knowledge is mediated, especially through developments in information and communication technologies, the increasing importance of knowledge-based industries and the service sector,

and exploited are qualitative elements such as creativity and knowledge creation, knowledge diffusion and absorption, and a range of related skills and competences,

which in turn call for new organisation forms and working environments. These factors seem to be interlinked with each other in a cycle reinforcing their development.

They are considered important prerequisites for knowledge-based economic growth because knowledge-based economic growth is considered typically to be dependent on innovation.

This in turn reveals the importance not only of technological innovations, but also of social changes and the building of social 4 For an analysis of the major findings of the literature review see 1. 541 E. Amanatidou,

K. Guy/Technological forecasting & Social Change 75 (2008) 539 557 capital as key factors underpinning the more technical features of the knowledge-based economy and the realisation of a‘knowledge society'.

On the other hand, knowledge seems to have a dual, paradoxical role in modern societies, in that while increasing reliance is placed upon it,

The paradox stems from the fact that knowledge use and creation is associated inherently with uncertainty.

The literature suggests that the emerging knowledge societies are also‘risk societies',characterised by decision-making conducted within an environment of increasingly uncertain or incomplete knowledge.

Dealing with uncertainty and partial or incomplete knowledge needs collaboration and the strategic alignment of actors.

This is conceived both in terms of sharing knowledge and uncertainties but also in terms of identifying alternative solutions and commonly agreeing on actions to avoid undesired consequences.

'The findings of the literature review on what constitutes the major characteristics of the emerging knowledge and risk societies are synthesised into a conceptual framework describing a more participatory‘knowledge society'.

In parallel, knowledge and skills, are acquired through a variety of learning processes. Foresight is referred increasingly to as such a process,

Policy-making in the emerging knowledge societies needs to take account of uncertainty and lack of knowledge. It has to engage all interested and potentially affected stakeholders in this endeavour.

Foresight also acknowledges that knowledge is constructed‘socially'.'8 By bringing together all interested parties it facilitates knowledge diffusion and production among diverse groups with different backgrounds.

It also allows‘non-expert'knowledge and society's perceptions, interests, concerns and fears to be taken into account.

It meets the need to move beyond reliable knowledge towards‘socially robust'knowledge9 and provides a space for knowledge representation, mediation and co-production by integrating different knowledge sources and types.

Thus, it facilitates trans-disciplinary practice and‘extended peer review'10 an important response to the increasing complexity of scientific knowledge production.

Additionally, it enforces the active engagement of relevant actors strengthening their communication and collaboration via constructive discussions and joint decision-making.

It enables the alignment of all stakeholders'endeavours such that they can influence underlying trends. Foresight can have an impact on the ways in

which policy-making deals with uncertainty and lack of knowledge by promoting more participatory governance. Foresight also encourages the emergence of the new types of affiliations

Knowledge creation, absorption and diffusion and through these to the increasingly dominant role of knowledge; Social capital and networking

This is illustrated in Fig. 2. 8 For an analysis of the social construction of knowledge and related implications on definitions,

Increasing creativity, knowledge diffusion and absorption Facilitate thinking out of the box Development of new ways of thinking Challenge mindsets Creating a language

and a‘reservoir'of knowledge was created which can be drawn upon whenever the need arises. In terms of factors that affected the implementation of the process, its outcomes and its overall success,

and promote new knowledge and learning;(b) coping with greater Fig. 5. An impact assessment framework for foresight systems capable of enhancing a more participatory‘knowledge society'.

and selective) is determined by limitations of knowledge and 12 Socio-technical constituencies are defined as dynamic ensembles of technical and social constituents machines, instruments, institutions,

Knowledge creation, absorption and diffusion and through these to the increasingly dominant role of knowledge within modern societies;

Foresight as a Tool for the Management of Knowledge Flows and Innovation (FORMAKIN), Final report under the TSER Programme Stage II, 2001.5 H. Cameron, L. Georghiou, M. Keenan,

Public policy in Knowledge-based Economies. Foundations and Frameworks, Edward Elgar, 2003.12 H. Nowotny, P. Scott, M. Gibbons, Rethinking Science.

Knowledge and the Public in an Age of Uncertainty, Polity Press, Cambridge, 2001.13 A. Guimaraes Pereira, S. Funtowicz, Quality assurance by extended peer review:

and validating new scientific knowledge, but other actors have also become major research performers. Meanwhile, the notion of research has been extended considerably,

Their diverse accumulated knowledge and experience, as well as distinct viewpoints are indispensable for building policy-relevant visions.

Multilevel governance 1. Introduction The first universities emerged as responses to the need to harness the expanding intellectual forces of the era to the increasingly demanding knowledge requirements of the surrounding society

The very idea behind participatory programmes is to bring together different stakeholders with their diverse sets of accumuulate knowledge and experience,

& Social Change 75 (2008) 558 582 2. The role of universities in knowledge production 2. 1. The changing landscape of research systems Universities have traditionally been key players in

producing and validating new scientific knowledge. 11 From the point of viewofr&dand innovation (RTDI) processes, they have focussed on twomain activities:

using and validating knowledge, learning, learning capabilities, and learning systems, etc. 13,14, 27 30. Notwithstanding the above general considerations on the principal role of universities in creating knowledge,

one should not overlook the significant diversity across the EU at least in three aspects: the balance of research activities between universities and other players;

plus other research performers do play a major role in producing knowledge. In other words teaching and research nowadays are intertwined only'at a fewer number 15 Obviously,

and partly because the crucial role of other research actors in producing knowledge. To understand the role of universities,

In brief, too much emphasis on‘forcing'appropriability of publicly financed research is likely (a) to slow down the rate of knowledge generation;(

when legitimating and validating knowledge. Besides conventional academic researchers, knowledge is produced by a wide variety of players, e g. think tanks, private research organisations, nonprofit organisations, government agencies, consultancy companies, market research organisations, patients'groups, various

NGOS, trade associattions interest groups. These pieces of knowledge are used by some of these organisations themselves (government agencies,

firms'labs), sold to other parties (contract research organisations, consultancies) or exploited in political/societal processes for advocating/pursuing certain views or interests (NGOS, trade associations).

From a different angle, these pieces of knowledge are diffused also, and thus subject to different types of validation procedures (formal/informal;

and hence‘the rules of the game'are changing in legitimating knowledge, different possible future states can be considered:(

a) nonacademic sources of knowledge are considered fully legitimate, i e. academic research loses its power to validate knowledge;(

b) knowledge either from academic or nonacademic sources is accepted only in society if validated by conventional academic rules and players;(

c a clear separation between knowledge created by credible academic organisations and nonacademic ones, the former enjoying a higher status 28.5.

Changing set of evaluation criteria. Depending on the speed and extent of changes envisaged above, especially (1)( 3),

One of the specific guidelines is to improve the knowledge and innovation for growth. More specific areas of interventions include:

As for legitimisation and validation of knowledge largely unchanged universities would push hard to maintain their centuries-old monopoly to validate knowledge;

yet, a number of other organisations e g. think tanks, private research organisations, private nonprofit research organisations, government laboratories, consultancy firms, patient organisations, various NGOS, trade associations and interest groups

increasingly produce knowledge. Four options can be envisaged for unchanged universities, following the considerations presented in 28:

a) they progressively lose their power to validate knowledge produced outside their domain b) they maintain their power to validate knowledge produced outside their domain c a new public authority is set up to validate knowledge produced by a large variety of actors d

) a clear separation of knowledge produced by universities (and other credible research organisations), on the one hand, and knowledge produced by other sources with a‘lower status',on the other.

Radically reformed universities, in contrast, would seek partnerships with other knowledge producers, as well as government agencies and NGOS to establish new rules and organisations,

if necessary to validate knowledge jointly, and a mutually acceptable way. 32 Universities, obviously, have a certain level of autonomy in choosing their strategies. 33 Teaching activities of these two types of universities,

using the same structure, are considered in 21.575 A. Havas/Technological forecasting & Social Change 75 (2008) 558 582 The methods, approaches,

and new issues are now in the centre of analysis, such as knowledge, knowledge production and use; learning, learning capabilities and learning systems;

using and validating knowledge; etc. As both the activities of universities and their environment are undergoing fundamental changes,

and thus the accumulated knowledge and experience, as well as distinct viewpoints and approaches of the major stakeholders involved in these strategic dialogues,

transmitting, disseminating and applying knowledge, and hence their contribution to socioeconomic development, major stakeholders need to be involved

of knowledge, Communication from the Commission, COM (2003) 58 final, Brussels, 5 february 2003.3 EC, The European research area:

of Knowledge, Sage Publications, London, 1994.31 L. Sanz-Menéndez, The Future of Key Actors in the European research area:

Universities and Strategic knowledge Creation: Specialization and Performance in Europe, Edward Elgar, Cheltenham, 2007.33 K. Pavitt,

Towards a Knowledge-Based Economy, Office for Official Publications of the European communities, Luxembourg, 2003.39 OECD, Main Science and Technology indicators, OECD, Paris, 2006.40 M. Thorne (Ed.),Universities

and knowledge in order to make FTA more policy relevant. The rising importance of FTA is reflected in the interest for the Third International Seville FTA Conference.

disciplines and intellectual traditions and paradigms and this we believe is consistent with the dynamism of transition to a knowledge-based economy;

In many respects the BMBF foresight demonstrates how in practice many of the new approaches are actively engaging a changing view of policy for the knowledge economy.

Annele Eerola is a Senior Research scientist of the knowledge center‘Organisations, Networks and Innovation systems'at VTT Technical research Centre of Finland.

being also the Deputy Technology manager of the knowledge center since 2007. She holds a Phd from the Helsinki Swedish School of economics and Business administration.

Swanson explored knowledge discovery by exploring database links 10. Swanson demonstrates integrative capability by demonstrating new links between technologies, inherent in the data,

Consider a knowledge base of technology where components of technologies are described and linkages between the technologies are identified.

This description of a knowledge base describes many repositories of scientific and technological information, including: the Internet, science and technology databases, patent databases, newswires,

It extends and elaborates upon the procedures described by these authors for discovering new linkages of knowledge through use of a structured representation of science and technology,

Likewise, in terms of knowledge production, researchers form multi-disciplinary teams devoted to specific problems and specific contexts 12.

it is necessary for responsive organizations to restructure themselves to exploit this knowledge environment. The actual economic and institutional arrangements necessary to create flexible and distributed networks may have been captured in the regional development literature 13.

In the following paragraphs some ideas about the organization of technological knowledge is described; this knowledge is coupled with the institutional environment of distributed knowledge production.

This review suggests an important avenue for research in this article, and in future research: creating software solutions to help innovative organizations develop new technologies within an open innovation environment.

Knowledge is structured hierarchically. A shared knowledge base, developed by multiple, decentralized actors may be stored in a network form.

This network, although highly diffuse, may be accessed by all players for personal as well as community betterment. Hierarchies are one form of technological structure confirmed by theories and practice.

The knowledge base may be equated with a network structure: the nodes are technologies, and the edges are the component relationships that are present between the respective technologies.

Without a theory of the data the technology analyst cannot distinguish between meaningful structure and possibly accidental corruption of the knowledge base.

This knowledge is stored in databases of science and technology. New future-oriented technology analysis techniques, such as the approach suggested here,

and yet the biggest promise of these sources of information may be the diffuse and distributed information they contain about the current state of the knowledge of the community.

This diffuse knowledge is networked, and relatively unstructured. What is needed therefore, are techniques for extracting these networks,

and accurately structuring the knowledge so that it can be used for analysis, design and forecasting. If this hypothesis of distributed knowledge bases is correct,

then it implies certain things about the information and infrastructural needs of organizations engaged in open innovation.

Such organizations, not surprisingly, need access to these distributed databases of knowledge. Unlike conventional, disciplinary researchers, these organizations do need not necessarily the database to gain access to individual pieces of information

and magnify the resident knowledge of distributed communities of peers. The premise of available, but decentralized, knowledge of science and technology is something

which can be tested through the use of machine learning techniques. These concepts are explored further in this paper with a case exploring a software technology known as AJAX.

Should the hypothesis of distributed knowledge be demonstrably true, and therefore the value proposition of software for design support be made clear,

but not yet observed in the Wikipedia knowledge base are shown below in Table 2. Fig. 6. Consensus diagram for Ajax Technologies. 1145 S w. Cunningham/Technological forecasting

It recognized these new changes without explicit linkages in the knowledge base of technologies. Thus, the hierarchical random graph approach may provide a new forecasting, analysis and design technique for architectural innovation.

Claim Claimant Data Scientific and technical knowledge consists of a set of interdependent claims Popper 31 Networks of knowledge can be structured readily from science

and technology databases using techniques such as hierarchical random graphs Knowledge claims are heterogenous in character Derrida 32 Networks built upon science

and technology databases are very heterogeneous in character Technologists have a wealth of tacit knowledge, built upon practice,

which they struggle to encode within the network of scientific progress Polanyi 35 Changes in technology in this case are manifested in changes in network structure Knowledge is built upon the configuration of knowledge claims,

The section to follow examines the claim that knowledge resides on networks, as a series of claims or propositions.

Some knowledge is directly accessible, while other knowledge is tacit: either unexpressed, or resident in a diffuse way across a network of scientific claims.

The role of scientists, engineers, and innovators is to enhance the coherence of this network.

which bridge knowledge and increase coherence between related fields may be increasingly more difficult to formulate.

The status of knowledge is a matter of prolonged and fundamental discussion in philosophy of science.

The objectivist would argue that knowledge resides in the world at large and it is the role of the scientist to absorb this knowledge according to his or her capabilities.

This position is seen increasingly as untenable. The primary challenge to the position questions the status of empiricismas a certain route to knowledge.

Other philosophers, such as Karl Popper, argue that the practice of naive empiricism is impossible 31.

This perspective then, suggests that knowledge is a network of interlocked claims. Only some of these claims may be anchored in observation,

A subjective account of knowledge suggests two things. First, many claims cannot be anchored directly in empiricism.

Because explicit knowledge is encoded in claims, the existence of the claim can be verified through recourse to knowledge bases of science and technology.

Tacit knowledge entails knowledge about the configuration of claims 34. Since the subjectivist perspective on knowledge is conditional on entirety networks of knowledge claims,

tacit knowledge is required if the truth or falsity of a specific claim is to be evaluated. Further

and not shared across a scientific community. Polanyi's account of science and technology has technologists laboring at the interface of claims,

and actual physical artifacts 35. The tacit knowledge of actual practice is encoded only partially in the network of competing claims.

The raw data of a hierarchical random graph, when applied to a network of science and technology information, is likely to be material as well as semiotic in character. 1147 S w. Cunningham/Technological forecasting

& Social Change 76 (2009) 1138 1149 If tacit knowledge has based a character upon the configuration of knowledge claims, then methods (such as the hierarchical graph)

which enable exploration of these networks are needed as support tools. The alternative approach would be to expressly encode the configuration within the database of science and technology.

This approach may demand an unrealistic level of unanimity about the status and relatedness of specific knowledge claims. 7. Conclusions The paper concludes that the proposed method,

a hierarchical random graph, is a useful way for structuring diffuse knowledge bases of science and technology.

In the case discussed, the technique did appear to anticipate significant standards setting activity, as well as presaging a significant reorganization of the science and technology database to better match technological progress.

The success of the method is itself dependent upon the collected, distributed activities of innovators, the activities

or the mode 2 of future knowledge production. The hierarchical random graph model is missing a model of the actor.

The author appreciates helpful discussion from Jan Kwakkel on the epistemology of knowledge networks. References 1 M. C. Roco, Key note address:

Researching a New paradigm Oxford, Oxford university Press, 2006.12 M. Gibbons, C. Limoges, H. Nowotny, S. Schwartzman, P. Scott, The New Production of Knowledge:

Manage. 42 (3)( 1995) 203 214.31 K. Popper, Objective Knowledge: An Evolutionary approach, Oxford university Press, Oxford, 1972.32 J. Derrida, Of Grammatology, John Hopkins University Press, Balitmore, MA, 1974.33 M. Callon, J. P. Courtial

Philosophical Papers, Two Volumes, Cambridge university Press, Cambridge, 1978.35 M. Polanyi, Personal Knowledge: Towards a Post-Critical Philosophy, University of Chicago Press, Chicago, 1958.

They consist of rules, standards, scientific knowledge, engineering practices, technologies and skills that determine a stable context in which highly complex system configurations can develop 12.

Socio-technical regimes create advantages for system development as they tend to reduce the costs of knowledge generation and the political costs of decision making 13.

a widening of participation in the assessment phase is necessary to include local knowledge for the establishment of regional specific scenarios 6 as well as for multi perspective assessment of options.

target, and action knowledge 55 to the decision makers and render the major trade-offs that could appear over the lifetime of an infrastructure transparently.

a wider group of stakeholders is involved 59 to diversify the knowledge sources 30. The relevant groups are identified in the situation analysis (e g. by using system constellation methods 60 to identify the roles, intentions, power and interactions among the most influential and affected 1153 E. Störmer et al./

The inclusion of selected additional stakeholders in the workshops was appreciated for tapping into a broader knowledge base

knowledge and ignorance in organizational foresight, Futures 38 (8)( 2006) 942 955.29 W. Xiang, K c. Clarke, The use of scenarios in land-use planning, Environ.

The multiple backgrounds of the team widen the knowledge base, but also increase the challenges of communication and cooperation.

Creating a wider knowledge base and learning across disciplines is, however, considered important in order to develop more proactive and systemic risk assessment that covers even new types of emerging risks (incl. risks related to new technologies and their introduction to the market).

and the better utilization of the partly overlapping, complementary competence and knowledge bases, are among the objectives of the exercise.

The SLC differentiates knowledge making, sense making and decision making as well as diffusion/acting on environment and interaction with other actors in real world. 1164 R. Koivisto et al./

/Technological forecasting & Social Change 76 (2009) 1163 1176 Both the SECI and SLC model emphasize the shared knowledge making.

and synthesising knowledge. Anticipation considers previous analysis and aims at defining possible and/or desired futures.

and expert panels are used widely in the generation phase to generate new knowledge. In the action phase, technology roadmaps, backcasting, narrative scenarios and others are useful methods to disseminate the visions of the future.

1 but has moved increasingly towards providing useful knowledge for actively shaping technology. Consequently, concepts such as participatory technology assessment, construuctiv technology assessment, discursive TA,

which the participants are selected based on their relevant knowledge and experience of the industrial process. The pertinent literature and other kinds of external expert knowledge are consulted also as deemed necessary Traditionally,

risks are identified and removed, and risk analysis methods are designed to be a tool of systematic risk identificcatio process.

These projects are chosen based on the authors'knowledge, experiences and/or involvement in the projects. The results of this analysis are presented in the following section. 3. 1. Integrated Risk Reduction of Information-based Infrastructure Systems (IRRIIS) A case study of the use of foresight

because these methods require more detailed knowledge of the target to be analysed: the process, the technology, people, the environment and so on should be known as fully as possible.

SWOT analysis is used then to deepen the knowledge of strengths opportunities, threats and weaknesses of the new innovation.

however, to be able to manage a multiplicity of the uncertain knowledge sources. Scientific knowledge concerning natural changes constitutes different scenarios of the future

and social knowledge can also be formulated into various scenarios depicting the potential futures. A strategy to handle this multiplicity requires selection.

In the context of the Nordic hydropower production and distribution, for instance, the most threatening scenarios are selected for the risk estimation and evaluation process.

The ultimate meaning of this phase is to arrange the knowledge in such a form that it is easy to use in decision making.

and accuracy of the analysis. The foresight process as well as the risk assessment process is a knowledge making process.

Both models organise the knowledge making in three dimensional space generating the knowledge from personal and proprietary to common sense and public,

from tacit to explicit knowledge through sense making and field building, dialogue and interaction. The core idea is to share the knowledge

and create in that way developed knowledge which is more than the sum of its elements. In the method scale the common ground is seen in the multitude of the methods and in the hierarchy of methods:

scanning or mapping like methods are used in the early phases of the foresight or risk analysis processes

and construct the knowledge create an understanding and share it in networks of people. Futures and safe situations,

and creating the best possible knowledge of the future and risks, and being all the time aware of the possible threats and opportunities of the complex world,

and to create knowledge to help decision making in defining management strategies concerning the changes the future may cause.

To create and arrange the knowledge about risks in order to help the corporate decision making. To identify possible future developments, driving forces, emerging technologies, barriers, threats and opportunities related to a broader socio-technoeconnomi system.

To arrange the knowledge in such a format that is easy to use in decision making. Results A report where identified

SWOT analysis, benchmarking, expert panels (new knowledge creation) Technology roadmaps, backcasting, narrative scenarios (visions of the future) Constructive technology assessment,

The Handbook of Technology foresight, PRIME Series on Research and Innovation policy, Edward Elgar, Cheltenham, UK, 2008.8 I. Nonaka, H. Takeutchi, The Knowledge-creating Company, Oxford university Press, New york, 1995.9

A Framework for Learning in Organizations, Institutions and Culture, Routledge, London, 1995.41 M. H. Boisot, Knowledge Asset:

Dr. Annele Eerola is Senior Research scientist and Deputy Technology manager of the knowledge centre‘Organisations, Networks and Innovation systems'at VTT.

including the links between foresight knowledge, corporate strategy and innovation policy. She graduated in Helsinki University of Technology and holds a Phd from Helsinki Swedish School of economics and Business administration