Practice based innovation insights, applications and policy implications understanding practice based innovation and its poli

Practice-Based Innovation: Insights, Applications and Policy Implications Helinaă Melkas l Vesa Harmaakorpi Editors Practice-Based Innovation: Insights, Applications and Policy Implications Editors Prof Helinaă Melkas Lappeenranta University of Technology Lahti School of Innovation Saimaankatu 11 15140 Lahti Finland helina.melkas@lut.fi Prof Vesa Harmaakorpi Lappeenranta University of Technology Lahti School of Innovation Saimaankatu 11 15140 Lahti Finland vesa.harmaakorpi@lut.fi ISBN 978-3-642-21722-7 e-ISBN 978-3-642-21723-4 DOI 10.1007/978-3-642-21723-4 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011938152 # Springer-Verlag Berlin Heidelberg 2012 This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer Violations are liable to prosecution under the German Copyright Law The use of general descriptive names, registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Foreword Today’s innovation environment is changing rapidly and new challenges are emerging The locus of innovation is shifting towards rapidly developing countries and corporate operations are increasingly executed in global value networks Social structures will change due to urbanisation and ageing of the population in industrial countries Demand for environmentally sustainable and tailored solutions will grow as ecological issues move to the forefront in most areas of society Meanwhile technological developments are accelerating, combined with an increasing emphasis on non-technological innovations where usability and user experiences are the main drivers To cope with these challenges extensive renewal through a broad-based view on innovation is needed It will not be sufficient to fund only technological breakthroughs Service-related, design, business, and social innovations should also be emphasised Innovations increasingly emerge in practical contexts where different types of knowledge from different disciplines have to be continuously combined Networking and interaction abilities will be of key importance Tekes – the Finnish Funding Agency for Technology and Innovation – is the most important publicly funded expert organisation for financing research, development and innovation in Finland Tekes aims to boost wide-ranging innovation activities in research communities, industry and service sectors and to work with the top innovative companies and research units to achieve this Every year Tekes funds some 1,500 business research and development projects, and almost 600 public research projects at universities, research institutes and polytechnics The activities are targeted to projects that create the greatest socio-economic impacts in the longer-term Tekes is continuously monitoring developments in its operating environment and supporting innovation policies, among other things, by funding innovation research projects and undertaking foresight schemes In 2012, Tekes new strategy will introduce novel operating modes in public research carried out by universities and research institutes These will help to create v vi Foreword new business opportunities and generate areas of expertise in strategic areas which are vital for Finland The new operating modes aim to create expertise that, on one hand, improves the odds for generating new business activities and companies, and on the other, sets in motion research teams aiming for leading edge expertise that is of key importance for the Finnish business sector Tekes also strongly supports internationalisation of Finnish business and research This book showcases examples of broad-based innovation activities, which is welcome in a situation where the concept of broad-based innovation, and practicebased innovation in particular, still requires improved conceptualisation both in Finland and internationally The chapters in this book – that focus both on macro-, meso- and micro-level perspectives on innovation through contributions by experts from many different countries – guide us on this journey towards a deeper understanding of broad-based and practice-based innovation The book has been cofunded through Tekes innovation research activities and I hope that it can provide new insights and viewpoints about innovation and its drivers in an increasingly challenging and complex world Helsinki, Finland Dr Veli-Pekka Saarnivaara Director General of Tekes Contents Introduction Helinaă Melkas and Vesa Harmaakorpi Part I Insights into Practice-Based Innovation and Innovation Strategies A Pragmatist Theory of Innovation 17 Bart Nooteboom Combining Foresight and Innovation: Developing a Conceptual Model 29 Tuomo Uotila, Martti Maăkimattila, Vesa Harmaakorpi, and Helinaă Melkas Communicating Connections: Social Networks and Innovation Diffusion 49 Pekka Aula and Olli Parviainen Dilemmas of Practice-Based Innovation Policy-Making 65 Re´jean Landry and Nabil Amara Coordination in Innovation Projects 91 Cornelius Herstatt and Norbert Luăhring Measuring the Impact of Innovation Intermediaries: A Case Study of Tekes 117 Margaret Dalziel and Satu Parjanen vii viii Contents Part II Micro-, Meso- and Regional Level Applications Developing a Framework for Innovation and Learning in the Workplace 135 Lotte Darsø and Steen Høyrup Fostering Practice-Based Innovation Through Reflection at Work 155 Per Nilsen and Per-Erik Ellstroăm 10 The Role of Reflection, Reflection on Roles: Practice-Based Innovation Through Theatre-Based Learning 173 Anne Paăssilaă, Tuija Oikarinen, and Russ Vince 11 From the Artists to the Managers: Responsible Collective Innovation Practices, Inspiration Flowing Through Hosting and Harvesting Profound Change 193 Isabelle Mahy 12 Collective Intelligence and Practice-Based Innovation: An Idea Evaluation Method Based on Collective Intelligence 213 Juho Salminen and Vesa Harmaakorpi 13 Users as Sources of Radical Service Innovation 233 Florian Skiba and Cornelius Herstatt 14 Challenges of Bringing Citizen Knowledge into Public Sector Service Innovation 255 Lea Hennala, Suvi Konsti-Laakso, and Vesa Harmaakorpi 15 The Increasing Use of Dramaturgy in Regional Innovation Practice 277 Philip Cooke Part III 16 Case Studies and Policy Implications Service Innovation and Service Design in the German Printing Industry 305 Christina Cramer and Christiane Hipp Contents ix 17 Innovation, Cities and Place: An Empirical Study of the Knowledge System in Vancouver and Its Place on the Pacific Rim 323 Brian Wixted and J Adam Holbrook 18 User-Driven Innovation and Knowledge Integration in Elderly Care Services: A Community Integration Model 345 Koichi Ogasawara 19 A Holistic Model of Innovation Network Management: Action Research in Elderly Health Care 369 Timo Jaărvensivu, Katri Nykaănen, and Rika Rajala 20 Practice-Based Innovations at ‘Sendan No Oka’: Motivation Management and Empowerment Management 393 Hiroo Hagino 21 Innovation Capability and Its Measurement in Finnish SMEs 417 Minna Saunila, Juhani Ukko, and Hannu Rantanen 22 Epilogue: Two Modes of Practice-Based Innovation 437 Vesa Harmaakorpi and Helinaă Melkas 438 V Harmaakorpi and H Melkas opportunities related to innovation is lacking This is especially important in the context of practice-based innovation In the Introduction (Chapter 1), we defined practice-based innovation processes as being triggered by problem-setting in a practical context and conducted in non-linear processes utilising scientific and practical knowledge production and creation in cross-disciplinary innovation networks These processes essentially require combination of knowledge from theory and practice, as well as knowledge from different disciplines In the chapters in this book, practice-based innovation is understood by the authors in various ways This is usual; concepts are incorporated into discussions in various forums already before their contents are clearly defined The concept of practice-based innovation is a good example In the preceding chapters, it is used more or less as a synonym for user-driven or employee-driven innovation, for instance On the other hand, as noted in Chapter 2, the arguments for practicebased innovation are not only practical but also philosophical The author aptly points out that to some, it may seem that practice-based innovation allows for ignorance, incoherence, or even inconsistency Pragmatism, on the other hand, has deep roots as a respectable line of thought in philosophy, recognising that application is part of a learning process where ideas change in their application and yield new ideas, so that application is part of discovery Thus, according to the analysis in Chapter 2, practice-based innovation has a firm foundation in the embodied cognition line in cognitive science and in the pragmatic tradition in philosophy In our view, the concept of practice-based innovation – as defined in the Introduction and understood in various ways in the other chapters – is useful also because innovation researchers and practitioners, as well as funding organisations, for instance, not necessarily (and unfortunately) acquaint themselves with philosophical works The concept may then serve as a bridge between various ‘possible worlds’ The book aimed at variety, presenting a fraction of issues related to practicebased innovation and identifying also needs for further research as well as practice One lesson learned is that all levels from the macro-level to the individual level, and both basic concepts – such as innovation and practice-based innovation – as well as ‘circling’ them with other central concepts require researchers and practitioners’ attention It is not sufficient if innovations are discussed at the upper level of national science and technology policy High-level strategic documents, for instance, may provide quite a narrow view that does not benefit practical actors in companies or public organisations Multi-actor innovation processes require consideration of other levels, too (regional, network, organisational, work community, and individual levels) Achieving and keeping a balance between these levels is vital This book hopefully contributed to building up a stronger foundation for the concepts as well as for future studies Having emphasised the need to combine different levels and concepts, it is also necessary to note the danger of the innovation concept becoming downgraded and of novel innovation policies dissipating (cf Edquist et al 2009) When concepts are transferred back and forth between the academic and the public policy sphere, their character may become even more problematic and vague than they might otherwise 22 Epilogue: Two Modes of Practice-Based Innovation 439 be (cf Miettinen 2002) But what is the overall aim? In innovation research that is, in our view, not that simple a question Is the aim to avoid downgrading, dissipation and vagueness of concepts? That is certainly worthwhile, but if we want to find out how to combine various types of innovation activities into effective and sustainable wholes at various levels, the aim has to be different, and we have to be willing to accept vagueness, too Another lesson learned is that diversity is needed in innovation activities and policies Policies to promote competitiveness should thus allow for diversity and divergence in order to enhance innovativeness, but manage to link diversity into a power that promotes sustainability Diversity at all levels – from, for instance, individual innovation tools to diverse types of knowledge and to organisations, industries, and sectors – is important in this regard (cf Harmaakorpi et al 2011) The need for diversity is also an equality issue at various levels, which should not be forgotten when discussing (practice-based) innovation This issue as such is not investigated in detail in this book, but in, for instance, studies in which wellbeing, learning, and innovativeness are focused on, the discussion and/or the data should be disaggregated by gender Such disaggregation is crucial in order to make differences in women and men’s views, needs, and experiences visible and thus give an informed and sustainable basis for reflection and action related to innovation Otherwise, the relevance of the results may be questionable, and the results may be significantly biased – hiding information that should be available when deciding about organisational development efforts or policies at larger levels This problem of questionable relevance and biased information also applies to existing innovation policy diagnostics that tend to provide data at levels of aggregation that not take into account the diversity of the situations and needs of firms, industries, and regions, as discussed in Chapter The authors note that innovationrelated arguments are typically formulated at a very high level of aggregation that prevents policy-makers from deriving policy prescriptions that would take into account the diversity of situations and needs of different types of organisations in different sectors, industries, regions, and countries National innovation diagnostics may enable reasonable benchmarking of innovation performances across countries The implementation of practice-based innovation policies, however, requires disaggregated data and evidence Such shortcomings need to be addressed by researchers and policy-makers in the future We also need combinations of qualitative and quantitative studies at different levels The results and insights included in this book cover a large spectrum, so it is not appropriate to repeat all of them in this chapter By bringing up just a few here, we did not wish to be unfair In the remainder of this chapter we present a framework with two modes of practice-based innovation and explore their characteristics as compared to science-based innovation It summarises many of the issues discussed in previous chapters and combines them with a view to informing future comprehensive innovation policies and strategies – their planning, implementation, and evaluation – and to promoting their long-term effectiveness and sustainability 440 22.2 V Harmaakorpi and H Melkas Outlining the Pieces of the New Puzzle 22.2.1 Background Lundvall (2007) emphasised that in the current era, there is a need for both strengthening the science-base and promoting experience-based learning; “This is absolutely fundamental when it comes to linking the analysis of national innovation systems to economic development” He suggested the following four new directions for research (on innovation systems): It is necessary to develop a better understanding and more efficient analytical techniques to study institutional ‘complementarity’ and ‘mismatch’ in innovation systems There is a need to deepen the understanding of the production, diffusion, and use of knowledge In this connection the focus should be on interactive learning processes and upon how ‘social capital’ evolves as a basis for interaction within and across organisational boundaries There is a need to understand and develop indicators of how and to what degree workplaces function as learning sites in different national systems A promising research strategy is to link organisational learning, mobility of people, and network formation Networks will always involve interaction between people, and the specific career will have an impact on with whom and how agents interact Our thinking departs from Lundvall’s (2007; cf Lorenz and Lundvall 2006) arguments He strongly supported the idea that understanding processes of experience-based learning is a key to the understanding of the specificities of national innovation systems His analysis thus focused on the following: • • • • Understanding knowledge and learning; The co-evolution of the division of labour, interaction and cooperation; Firms as sites for employee learning; and The weak correlation between strength of the science-base and economic performance We operationalise Lundvall’s arguments into our framework1 as follows: • As the most central factors of innovation activities (‘the corner pieces of the puzzle’) we look into innovation types, modes of knowledge production, innovation models, and proximities and distances For a more detailed description, see Harmaakorpi et al (forthcoming) 22 Epilogue: Two Modes of Practice-Based Innovation 441 • As ‘the other pieces’ we look into economic logics, innovation capital, innovation processes, innovation methods, origins of innovation, fields of expertise, types and conversion of knowledge, knowledge bases, innovation environments, knowledge transfer mechanisms, as well as institutions (target organisations and educational organisations) Lists like this may contain many shortcomings For instance, Edquist (2005) listed ten activities (‘functions’) that should be studied in a systematic manner in terms of their respective ‘causes and determinants’ The list encompasses quite disparate elements including, for instance, forms of knowledge creation and learning, organisational forms, market demand and public policy instruments Lundvall (2007), again, criticised listing of a number of ‘activities’ and studying each of those separately Listing is potentially useful if the aim is to establish a checklist for managers and policy makers (Rickne 2000), but in terms of theoretical understanding, it represents a step backwards since much of what we already know about the innovation process is neglected Lundvall (2007) thus argued that it is not obvious how studying different activities separately would lead to more rigorous theory In addition to – or instead of – our ‘pieces’, many others could be studied, such as organisational set-up of firms, interaction among firms, national specificities such as national education, labour markets, and welfare regimes, or network positioning of firms, inter alia Our list is aimed to be both a theoretical and a practical framework The concepts included in it originate from different levels 22.2.2 The ‘Corner Pieces’ We will not go into detail here concerning the various concepts, such as innovation types, as that is beyond the scope of this Epilogue It is easy to get lost in the jungle of various innovation types that have been identified by scholars In practice, categorising innovations into types is often very difficult, if at all possible Various innovation types usually co-exist Dividing innovation into types is about looking at innovation from different perspectives, and it may contribute to distinguishing cause-effect relationships and co-impacts of various innovations (Pekkarinen and Melkas 2010) It is also beneficial with regard to finding new policy options and directions Policies at different levels need to contribute to and build up skills that cover the entire spectrum of various innovations and – very importantly – build up skills to define which skills and policies are needed 22.2.2.1 Modes of Knowledge Production in Innovation Gibbons and his colleagues (1994) defined two different processes of knowledge production: Mode and Mode Mode is usually a hierarchical process, during which knowledge tends to preserve its form Mode is a more heterarchical 442 V Harmaakorpi and H Melkas process, transient by nature One of the key contrasts between them is that in Mode 1, problem-solving is usually carried out following codes of practice relevant to a particular discipline, and it departs from a homogeneous theoretical basis, while in Mode 2, knowledge production activity is organised around a particular application and is more diffuse by nature Mode combines heterogeneous knowledge interests in a multidisciplinary manner – often in very practical environments Gibbons et al (1994) claimed that Mode knowledge production started emerging from the mid twentieth century, and it is context-driven, problem-focused and interdisciplinary It involves multidisciplinary teams brought together for short periods of time to work on specific problems in the real world Traditional research represents Mode 1, which is academic, investigator-initiated, and discipline-based knowledge production Gibbons et al already noted a shift in knowledge production from Mode to Mode This does not diminish the significance of Mode knowledge production Furthering Mode knowledge production is mainly a task for science policy In practice-based innovation activities, again, Mode is likely to be the mainstream, but tools to support it are not yet very well developed, and until recently, the whole Mode has not been highly valued as compared to Mode According to Gibbons et al (1994), in line with Mode 2, quality control also becomes more context- and use-dependent In a more dispersed institutional space, quality control takes on more transient and temporary forms and fluid norms Essentially, success is defined differently in Mode It includes additional criteria to the traditional one of scientific excellence, such as efficiency or usefulness that are defined in terms of the contributions the work has made to the overall solution of transdisciplinary problems (Gibbons et al 1994) This is taken into account very poorly or not at all in today’s measures and criteria of innovation There is a clear need for new ways to measure results and identify them and new ways to conceive innovation information and knowledge (Lundvall 2007) The notion of Mode knowledge production has attracted considerable interest, and also criticism Also Mode knowledge has been put forward (e.g., GroundwaterSmith and Mockler 2009) According to Carayannis and Alexander (1999; 2004), Mode for knowledge creation, diffusion and use is a multi-lateral, multi-nodal, multi-modal, and multi-level systems approach to the conceptualisation, design and implementation of government–university–industry public-private research and technology development cooperative partnerships According to Jime´nez (2008), Mode knowledge production shares some of the properties of Mode research, but with the distinctive characteristic of being closely linked to current societal needs Mode is bottom-up initiatives, while Mode is top-down (Jime´nez 2008) The criticism concerning the concept of Mode has focused, for instance, on its empirical validity, conceptual strength, and political value (Hessels and Van Lente 2008) Etzkowitz and Leydesdorff (2000) argued that Mode was the material base of science already before its academic institutionalisation, so it is not new Rip (2002), again, argued that the internal coherence of Mode is questionable; multidisciplinary, application-oriented research does not always show organisational diversity or novel types of quality control Descriptive and normative elements 22 Epilogue: Two Modes of Practice-Based Innovation 443 are mixed in Mode discussion, so it has been claimed to be rather a political ideology than a descriptive theory (Godin 1998; Shinn 2002) Perhaps Mode and could be seen as a descriptive theory on policy Gibbons et al (1994) reminded that Mode and interact with each other According to them, specialists trained in interdisciplinary sciences tend to enter Mode knowledge production This leads to the notion that if only or overwhelmingly Mode is supported by public innovation (and economic and science) policies, the contribution to work life of all these specialists in interdisciplinary sciences is undermined and overlooked This group of people is quite large in the society Gibbons et al (1994) further noted that socially distributed knowledge is at the core of Mode It is, in fact, linked to the ways in which IT is revolutionising innovation (Brynjolfsson 2010; also Gibbons and his colleagues referred to that already) as well as to the rise of virtual networks in work life Mode and Mode knowledge production are at the core of the research concerning practice-based innovation, and we will claim that there is a need to reconsider this categorisation 22.2.2.2 STI and DUI Models in Innovation Mode and (as well as Mode 3) are related to DUI (doing, using, interacting) and STI (science, technology, innovation) innovation activities Lundvall (2007) noted that already Adam Smith (1776) distinguished two different modes of innovation One refers to industrial districts where the focus is on experience-based learning (DUI) and the other refers to the national system of research (STI) Lundvall claimed that in international organisations, as in national governments, the strong position of expertise based upon standard economics has contributed to a narrow interpretation of the national system of innovation Innovation indicators reflect outputs such as number of patents or inputs that are easy to measure such as R&D expenditure As to indicators of knowledge, there is a strong bias in favour of knowledge that is explicit The know-how built up through learning by doing, using and interacting is much more difficult to measure Human capital measurements may register formal investment in education, but what people learn at the workplace or as consumers is not easy to capture through standard measurements The absence of indicators makes the area less visible for policy makers, and this contributes to a bias in innovation policy toward promoting STI activities rather than DUI activities In recent studies based on a combination of survey and register data for Danish firms it has been demonstrated that firms that engage in R&D without establishing organisational forms that promote learning, and neglect customer interaction are much less innovative than firms that are strong both in terms of STI and DUI learning (Jensen et al 2007; Lundvall 2007) The fact that science and codified knowledge become increasingly important for more and more firms in different industries – including so-called low-technology ones – does not imply that experience-based learning and tacit knowledge have become less important for innovation (Lundvall 2007) To bring innovations – including science-based innovations – to the market, organisational learning, 444 V Harmaakorpi and H Melkas industrial networks as well as employee participation and competence building are more important than ever (DUI learning) Lundvall argued that the distinction between STI and DUI is fundamental, when it comes to analysing modern innovation systems – and also when it comes to designing management strategy as well as public policy Although DUI is becoming better known, concrete strategy models are few The DUI model is certainly not a competitor of the STI model, but supports rooting of knowledge produced by the STI model, too An important role for DUI is thus to function as follow-up to STI 22.2.2.3 Proximity and Distance in Innovation Since the 1990s, there has been an increasing interest towards the notion of proximity in the context of economic development in general – and innovation in particular As such, the notion of proximity is a sort of an ‘umbrella’ concept (Boschma 2005a) consisting of different dimensions The general idea is that proximity, in whatever form, somehow reduces the uncertainty of economic activity, contributes to solving the problem of coordination between different actors, and facilitates interactive learning and innovation (Boschma 2005b) When analysing the logic and dynamics of innovation, at least four functions of proximity have been identified First, being close to each other helps companies to develop an efficient division of labour and coordinate their actions, facilitating the development of a core of specialised suppliers and partners Second, there are externalities of proximity available to all within a region In particular, these externalities are related to the localised human resources (workforce) and knowhow (Feldman 1994; Torre and Gilly 2000) Third, there is evidence that when companies of the same industry are located close to each other, it forces them to innovate by creating an environment where companies compete, in a positive sense, with each other (see, e.g., Porter 1990) Fourth, and perhaps most importantly, proximity is relevant for the appearance of knowledge spillovers and learning processes between the actors (Audretsch and Feldman 2003; Malmberg and Maskell 2002) It is not clear, however, what the role of physical proximity is in contrast to the other forms of proximity in innovation activities In his discussion on the relevance of proximity for learning, Boschma (2005b) argued that geographical proximity per se is neither a necessary nor sufficient condition for learning Moreover, as Audretsch and Feldman (2003) argued in the context of knowledge spillovers, empirical evidence on the correlation between physical proximity and learning gives us no understanding about the ways in which knowledge spillovers really occur between relevant actors From the point of view of innovation, proximity in its different forms has contradictory effects There are the apparent positive effects of proximity, but on the other hand, it has been persuasively argued that there is a phenomenon of ‘having too much proximity’, that is, a negative side of proximity These negative effects relate to different forms of lock-ins, involuntary spillovers, a certain lack of flexibility, and a possibility of opportunism (see, e.g., Boschma 2005b; Tura and 22 Epilogue: Two Modes of Practice-Based Innovation 445 Harmaakorpi 2005; Adler and Kwon 2000) The possibility of negative effects of proximity suggests that there are equally important conditions of innovation connected to the idea of physical, cognitive or functional distance or diversity between actors Such models of innovation emphasise the generation and maintenance of social, cultural and cognitive diversity as basic functions of innovation policy Boschma (2005b) identified two basic features of distance that are relevant for innovation processes: (1) by enhancing physical, cognitive, social and structural openness, distance enables new ideas and information to be triggered and thus fosters renewal of the regional knowledge base, and (2) distance enables the implementation of new ideas by the formation of an efficient combination of control and flexibility When discussing the roles of proximity and distance in innovation policy, we thus face a critical dilemma: on the one hand, there is a need for mechanisms for enhancing physical, social and cognitive proximity between the relevant actors of innovation processes This is the basic idea behind most of the modern innovation policies and, for example, behind the different models of technology centres and science parks On the other hand, innovation policy has to include mechanisms for enhancing social and cognitive diversity, openness of innovation networks and the ability of an innovation network to connect itself to the wider national and global knowledge base – that is, mechanisms for ensuring sufficient distance between the actors This perspective is not unfamiliar to innovation policy makers, but it has not attracted similar attention as its opposite one In particular, when focusing on the practical models and methods of innovation policy, this imbalance is striking The main intention of innovation policy models appears to be the generation of different forms of closeness However, if the above argument is accepted, the central challenge is not to maximise proximity, but to create an efficient balance between the contradictory purposes of enhancing proximity and distance The interplay between proximity and distance is a very challenging task to manage with regard to supporting practice-based innovation Practice-based innovation is often hybrid by nature, and its effectiveness may become visible only after quite some time has passed Therefore, methods and approaches to managing the interplay between proximity and distance successfully require considerable attention also in future research (Harmaakorpi et al 2011) 22.3 The Need for Two New Sub-Categories On the basis of the multi-faceted research presented in this book and its synthesis, we argue that it is necessary and fruitful to divide Mode knowledge production into two sub-categories to understand the prerequisites of practice-based and broadbased innovation activities, and to support them in practice: • Sub-category 2a contains intellectual cross-fertilisation, for instance in innovation sessions, in which scientific and practical expertise are combined 446 V Harmaakorpi and H Melkas with the help of various ideation and creative methods; such sessions may aim at, for instance, a concrete product or process innovation • Sub-category 2b, again, contains more heterogeneous development of organisations, the effectiveness of which becomes visible more slowly Such development may be conducted with the help of, for instance, applied community-based theatre methods and learning by doing In this kind of development that aims at, for example, organisational and social innovations, every employee – and customer – is an expert Our recategorisation of the modes including sub-categories 2a and 2b is presented in Table 22.1 The points of view on the left include the ‘corner pieces’ and the ‘other pieces’ of the ‘puzzle’ In this Epilogue, we have briefly gone through the corner pieces only; our work is presented in greater detail in Harmaakorpi et al (forthcoming) The footnotes related to Table 22.1 point to just a few of the relevant references Most of the concepts have been discussed also in the preceding chapters When looking at Table 22.1, it needs to be kept in mind that the categorisation is simplified, and the borders between the cells are not always clear or self-evident The categorisation, however, helps in understanding the differences in the modes and the resulting needs for innovation policy The modes are mutually complementary, but each of them needs to be managed In our view, this should be taken into account in future evaluations of innovation policy For instance, if Mode innovation policies are concentrated on at the national level, it is not sufficient Such shortcomings are typical – for instance, many international seminars are pure Mode 1, while Mode 2a and 2b gain much less or non-existent attention in innovation policy Moreover, in international cooperation projects, Mode 2a and 2b may be much more relevant and sustainable to develop than Mode 22.4 Discussion and Conclusions: It Takes Three to Tango The conclusion, thus, is that three different kinds of innovation policies are also needed Things get mixed up, and even false assumptions and negative effects may ensue, if the Mode approach or something related to it is utilised when, for instance, developing public sector services and organisations The three different innovation policy wholes need to ‘dance seamlessly together’ (see also Fig 22.1) Critical elements in this joint dance are, for instance, the importance of brokerage of information and knowledge, and creation of arenas for combinations of the different modes (knowledge conversion mechanisms and bas) Modes 2a and 2b require different types of expertise and brokerage skills, which also have to be focused on In addition to being more and more often practice-based, many innovation processes are more and more open Innovations are increasingly boundary-breaking creations that require knowledge and skills that are hardly found even in one big company Even giants like Nokia are developing innovation systems that integrate customers The customer-driven approach has become more prevalent, Most typical innovation processesb Most typical innovation methods Most typical origins of innovations Most typical fields of expertise Most typical types of knowledge Most typical knowledge basesd Most typical logics of knowledge production Most typical innovation environments Most typical capital Most typical innovation types Most typical fuels of innovation Most typical logics Symbolic Synthetic Heterogeneous knowledge production Analytical Homogeneous knowledge production World class scientific centres Arenas of intellectual cross-fertilisation Arenas of developing organisational innovation in value networks capability (continued) Heterogeneous knowledge production Brokering – general ability to build possible worlds Tacit knowledge World class scientific expertise in Brokering – general ability to build narrow fields possible worlds Explicit knowledge Self-transcending knowledgec Science and related expertise Methods of intellectual crossfertilisation (also virtual) Networks – serendipity – customers Problem-based learning (e.g., culture-based methods) ‘Normal’ staff – customers Interpretative Interpretative Scientific methods Developing innovation capability – breaking ‘silos’ and preventing bottlenecks Social capital – structural capital Related variety – innovation platforms Agglomeration – clusters – economies of scale Intellectual capital – financial capital Analytical Social capital – institutional capital Practice-based innovation (DUI, Mode 2b) Organisational innovations – social innovations – service innovations ‘Near distance’ Practice-based innovation (STI, Mode 1) (DUI, Mode 2a) Radical technological innovations Radical concept innovations – and related concepts technological system innovations Proximity Distance Table 22.1 Modes 1, 2a and 2b, and innovation policya Point of Innovation policy types view Science-based innovation 22 Epilogue: Two Modes of Practice-Based Innovation 447 Science-based innovation (STI, Mode 1) Technology diffusion for the firms of cluster Big companies – technology gaselles Universities Innovation policy types Practice-based innovation (DUI, Mode 2a) Scanning and absorbing technology and market signals SMEs, big companies Practice-based innovation (DUI, Mode 2b) Organisational learning Most typical knowledge transfer mechanisms Most typical target Big companies – SMEs – public and third sector organisations Most typical educational Universities – polytechnics Polytechnics – colleges – vocational education organisations a For further information and descriptions of the background and the concepts, see Harmaakorpi et al (forthcoming) b Lester and Piore (2004) divided innovation processes into analytical and interpretative ones The goal of interpretative innovation is to discover new definitions This process of sense-making is understood to be a fragmented, ongoing, open-ended (and multi-voiced) process of dialogue that emphasises interaction and communication In an interpretative innovation process, incompleteness and distance need to be tolerated, and participants have to be willing to withstand multiple viewpoints and a lack of universal truths – as there may be no single ‘answer’, rather multiple suggestions and proposals c See, e.g., Scharmer (2001); Harmaakorpi and Melkas (2005) d Asheim and Coenen (2005; 2006; see also, e.g., Asheim et al 2005) made a distinction between three types of regional knowledge bases: analytical (sciencebased), synthetic (engineering-based), and symbolic These types indicate different mixes of tacit and codified knowledge, codification possibilities and limits, qualifications and skills, required organisations and institutions involved, as well as specific competitive challenges from the globalising economy, which have different implications for different sectors of industry, and, thus, for the kind of innovation support needed Table 22.1 (continued) Point of view 448 V Harmaakorpi and H Melkas 22 Epilogue: Two Modes of Practice-Based Innovation 449 The new innovation paradigm (practicebased innovation activities) The traditional innovation paradigm (science-and research-based innovation activities) DUI STI Synthetic knowledge basis Analytic knowledge basis Mode 2a & 2b knowledge production Mode knowledge production Fig 22.1 A simplified demonstration of the need for balance between certain conceptual pairs related to innovation but the impact of information and knowledge produced by customers on innovation processes has in practice not necessarily increased The different forms and impacts of customer involvement also have to be focused on carefully Also in the future, contributions to science, technology and centres of expertise are just as important as earlier If, however, increasing investments to furthering the practice-based mainstream of innovations and to combining practice- and sciencebased approaches are not made, much of the innovation potential hiding in innovation networks may fail to be utilised (Harmaakorpi 2010) The same applies to individuals and organisations By definition (see the Introduction), practice-based innovation does not mean that only practice-based knowledge would be combined, but the essential thing is problem-setting in a practical context, and impacts and consequences of this on innovation processes Innovation processes often utilise highly scientific knowledge, but in the case of practice-based innovation, problem-setting is practicebased, and often knowledge from different scientific fields is combined This is a significant difference as compared to science-based innovation, in which problemsetting takes place on the conditions of theoretical knowledge Thus it requires a different kind of investigation and attention (Harmaakorpi 2010) Practice-based innovation activities are typically based on employees, customers, or partner networks in daily operations While science-based innovation activities are 450 V Harmaakorpi and H Melkas strongly based on concentrations of mental capital and raw material born in those, the core issues in practice-based innovation activities are quite different and mainly based on utilisation of social capital Practice-based innovation activities place challenges on the following fields, inter alia: • • • • • • • Finding practice-based innovation potential Developing absorptive capacity Furthering knowledge production in Mode 2a and 2b Forming social capital Forming expertise and roles of actors Enhancement of creativity Development of practice-based innovation policy Edquist et al (2009) noted concerning innovation policies and strategies that caution is needed in the implementation of broad-based policies so as not to add complexity to the support system If we adopt the distinction between Modes 2a and 2b in practice-based innovation, complexity is actually likely to increase However, what is the goal – to avoid complexity or enhance innovation? 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