When disruptive and downstream innovations become frequent, ontological unpredictability becomes increasingly important for innovation policy and strategy.
The analysis of the nature of ontological unpredictability explains why future-oriented technology analysis and foresight frequently fail to grasp socially and economically important technical developments and clariffie why policy strategy,
innovation; creattiv evolution Introduction Predictions about future almost always fail. In this paper, the epistemic and ontological causes for this failure are described and their implications for foresight, innovation policy,
and strategy are explored. The paper introduces the idea of‘ontological unpredictability 'and shows how innovation leads to unpredictability that cannot be removed by more accurate data or incremental improvements in existing predictive models.
Based on the presented analysis, it highlights some methodological implications for future-oriented analysis and policy-making. The paper aims at a conceptual contribution that builds on several disciplines,
ranging from innovation and technology studies to a Bergsonian analysis of creative evolution, theory of autopoietti and anticipatory systems,
The following section then further elaborates the idea of ontological unpredictability in the context of innovation theory,
showing that downstream innovation leads to a practically important form of ontological unpredictability. It then introduces Bergson's model of creative evolution,
The analysis described in this paper essentially indicates that innovation and predictive models are theoretically incompatible.
and innovation, instead of relying on data collected using historically important categories and measurement instruments. Economic and social trends measure what used to be important
To understand how innovation generates progress we have to reconsider some key concepts that underlie future-oriented analysis and strategic management.
and information networks make distributed downstream innovation increasingly visible. Ontological unpredictability thus becomes importaan for technology analysis, foresight,
as well as for characterising the limitations of evidence-based policy-making in innovation-intensive societies and economies.
Ontological unpredictability The nature of ontological unpredictability can most conveniently be understood in the context of innovation theory.
The prototypical narrative of the traditionalwestern model of innovation can be found from the first chapter of Genesis. The 1769 version of King James Bible tells us how cattle
This model of creativity underlies much of innovation research still at present. It assumes that as new entities are brought to life,
Downstream innovation and relational monsters The Genesis essentially depicts a linear model of creation where an‘upstream'heroic innovator is the true source of novelty.
Also the common distinction between radical and incremental innovations implicitly relies on prescient classification of the innovation in question.
For example, the idea that radical innovations emerge as‘hopeful monstrosities'that only gradually realise their true promise (Tushman and Anderson 1986;
In this model, innovation occurs when social practice changes. The history of innovations and technical change shows that‘heroic innovators'are located often in the downstream.
Innovative ideas abound, parallel innovation is unintended frequent uses become drivers of development, and socially and economically important innovations are invented often several times before they eventually start to have real impact.
The true innovative step in general, occurs when a potential user group finds a meaningful way to integrate latent innovative opportunities in the current social practice (Tuomi 2002).
In contrast to the traditional heroic‘upstream'innovation model, downstream models emphasise the active role of current and future users.
In the early work ofvon Hippel (1976,1988), the users were innovative users of existing products.
In models that emphasise the role of social practices and social interaction as the key loci of innovation (Engel 1997;
Innovation and social learning in the context of the local downstream systems of meaning then become key drivers for the evolution of technology.
This view allows for the fact that some innovations are more radical and revolutionary than others.
Some innovations are simple improvements of existing practice. Others however, can appropriately be called revolutions, and their realisation requires power struggles (Hughes 1983;
however, impossible to categorise a particular innovation based on the characteristics of a technical artefact before it is used.
The proper unit of analysis of innovation is thus‘innovation-in use'.'The same artefact Downloaded by University of Bucharest at 04:52 03 december 2014 Foresight in an unpredictable world 739 can be used for many different purposes in many different social practices, each with its own developmental trajectories.
It also shifts the locus of innovation from the‘upstream'to the‘downstream'.'A practical consequence of this relocation of locus of innovation to the downstream is that human upstream inventors rarely know,
or can know, what their inventions will be. The dominant constraints and resources for innovation are often far beyond the reach
and control of heroic upstream creators. Innovations become real in the context of use, and this requires stocks of knowledge
and systems of meaning that are located in communities of users and social practice. The true nature of the beast is revealed only when someone domesticates it.
Ontological expansion and creative evolution Downstream innovation in the history of telephony If asked about the history of the telephone
Innovation as creative evolution According to Schumpeter, innovation can be defined as a historic and irreversible change in the way of doing things.
Although Schumpeter went on to further define innovation as those changes in the production function that cannot be decomposed into infinitesimal steps,
'Many innovation theorists since Schumpeter have focused on the economic aspect of innovattion More broadly, innovation is,
however, about revolution, and it is a fundamentally social phenomenon. Important historical innovations such as fire-making
and the creation of the Phoeniciia alphabet or the wheel are primarily social innovations. Some revolutions remain small
and can be characterised as incremental, parametric, or adaptive innovations. Sometimes revolutions are more radical.
The essence of innovation, however, is in its ontological discontinuity and in its capacity to create directionality in time.
Technical change as élan vital Innovation thus creates phenomenologically new domains of being and action. But what directs
and drives this process? One possibility is to take the Bergsonian model of evolution seriously
In other words, the left-hand side is the generator of innovations, as defined by Schumpeter. The fundamental reason for ontological unpredictability is,
if we take innovation seriously. The Bergsonian rationality includes more than the limited rationality that can exist after ontologies are fixed.
if we want to understand innovation, creativity, and evolution. Ogilvy (2011) has argued recently that scenario developers
Innovation expands the ontological space, making previously invisible aspects of the world visible and relevant for modelling.
Innovation changes the way the natural system itself needs to be constructed. Ontological expansion means that we do need not a better model;
In general, the data required for formal models are available only in domains where innovation has not been important,
only if innovation remains unimportant. For example, data on phone calls or callers could not have been used to predict industry developments
if innovation is unimportaant Specifically, there is little reason to believe that conventional‘impact analysis'models could lead to useful insights if innovation matters.
Downloaded by University of Bucharest at 04:52 03 december 2014 748 I. Tuomi In general facts exist only for natural systems that have associated measurement instruments and established encodings and decodings between the natural system and its formal model.
It is therefore very difficult to formally model systems when innovation matters. Policies that are legitimised by facts,
because it inherently neglects innovation and knowledge creation. When innovation is important, foresight efforts therefore could more appropriately be located around the problem of articulating natural systems,
instead of formulating predictive models. In other words, the focus of future-oriented analysis should be learning, problem redefinition, and innovative construction of new empirically relevant categories, not predictive modelling.
if we also assume that these societies are transforming towards knowledge societies where innovation is an important economic factor.
or economics are structurally unable to encompass ontological expansion and innovation. They should therefore be used with caution.
If innovation is importaant we probably should give relatively little weight for trend extrapolations, what if analyses,
and time-series data and instead facilitate creativity and embrace innovation. Notes 1. Uncertainty, of course, has been a central theme in much of economic theory since Knight.
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influences on future-oriented technology analysis Denis Loveridge a & Ozcan Saritas a a Manchester Institute of Innovation research, Manchester Business school, University of Manchester, Manchester, UK Published online:
influences on future-oriented technology analysis Denis Loveridge*and Ozcan Saritas Manchester Institute of Innovation research, Manchester Business school, University of Manchester, Manchester, UK Future-oriented technology analysis (FTA) deals in phenomenological ignorance
where invention and innovation move at an alarming pace. The contrast to the inexorable, but slower pace of ecological change is stark,
Notes on contributors Denis Loveridge is an Honorary Visiting professor at the Manchester Institute of Innovation research (MIOIR) at the Manchester Business school after 44 years in industry.
Ozcan Saritas is a Research fellow at Manchester Institute of Innovation research (MIOIR) in Manchester Business school, UK.
lessons from the sociology of expectations Harro van Lente a a Department of Innovation studies, Copernicus Institute of Sustainable development, Utrecht University, 3508 TC, Utrecht, The netherlands Published online:
lessons from the sociology of expectations Harro van Lente*Department of Innovation studies, Copernicus Institute of Sustainable development, Utrecht University, 3508 TC Utrecht, The netherlands Foresight can be described as the articulation of possible futures.
technology and innovation studies; technology road mapping; expectations of technological change; foresight; technological change and dynamics 1. Introduction While foresight has been developed into an important instrument for both firms
Innovation studies have shown and discussed how expectations are part and parcel of all professional practices and circulate amongst engineers, board rooms,
Foresight can be characterized as a systemic instrument aiming at enhanced capabilities in innovation systems and their parts.
and reinforce the connectivity of the innovation system. This can be through the creation of new combinations or the enhancement of existing networks.
the daily production of research and innovation Downloaded by University of Bucharest at 05:02 03 december 2014 Navigating foresight in a sea of expectations 771 Table 1. Functions of foresight for policy-making.
which translates the strategic orientation of governmental actors into research and innovation priorities. The arena includes research funding and related agencies and mediates between the governmental actors and the research actors.
but aims to enrich the innovation journey (which includes many choice moments) with more actors, more perspectives and, in general, more reflection (Schot and Rip 1996;
and outcomes become part of innovation races Networking Stakeholder participation tend to reproduce repertoires The newly established networks will start to promote the vision Participants may press their version of the future Building visions Foresight outcomes will not Be built very original visions may have unintended consequences
In the case of priority-setting, foresight will reinforce innovation races: governments tend to follow the choices of other governments.
Notes on contributor Harro van Lente is Socrates Professor of Philosophy of Sustainable development at Maastricht University and Associate professor of Innovation studies at Utrecht University.
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as macro trends in the global environment are translated down into priorities for action in specific innovation fields.
where a specific research unit (an‘Innovation Field')has been established for each of the company business segments.
The main task of each Innovation Field is to elaborate a‘Picture of the Future'for its target segment
Future research efforts can build on literature on innovation and managerial cognition (Tripsas and Gavetti 2000) for investigating how to identify discontinuous drivers
His main research fields are foresight methodologies and strategic management of technology and innovation. He has presented on these themes at international conferences
and International Journal of Foresight and Innovation policy. He has been a consultant for large firms and governmental bodies in Italy and abroad in several foresight projects.
Foresight and innovation in the context of industrial clusters: The case of some Italian districts.
International Journal of Foresight and Innovation policy 3, no. 2: 218 34. Ruff, F. 2006. Corporate foresight:
Integrating the future business environment into innovation and strategy. International Journal of Technology management 34, nos. 3 4: 278 95.
Financial center, 11 andar, Sala 1102, CEP 70712-900, Brazil c Manchester Institute of Innovation research (MIOIR), Manchester Business school, University of Manchester,
cmanchester Institute of Innovation research (MIOIR), Manchester Business school, University of Manchester, UK This paper suggests a dynamic framework of continual learning to enable a business to develop a capacity to anticipate
The continuous improvement cycle found in the PDCA is the key process for driving learning and innovation in an organisation.
and integrated across the net-Collaborative innovation and continuous sustainability performance improvement system, inter-group learning-Values (universal principles) embedded in every process Downloaded by University of Bucharest at 05:04 03 december 2014 A framework, with embedded FTA,
and is now a senior advisor of STI (Science, Technology and Innovation policy and strategy at CGEE.
and practice in RTDI (Research, Technollogy Development and Innovation), business strategy and sustainability, environment management, cleaner production and foresight.
Denis Loveridge is Honorary Visiting professor at Manchester Institute of Innovation research (MIOIR), Manchester Business school, University of Manchester, after 44 years in industry.
Orienting EU innovation systems towards grand challenges and the roles that FTA can play. Science and Public policy 39, no. 2: 140 52.
Phd thesis, Manchester Institute of Innovation research. Shelton, C. 1997. Quantum leaps. Butterworth-Heinemann. SIGMA. 2001. The SIGMA project sustainability in practice.
and sharing through the network to achieve a dynamic process of innovation, learning and continuous improvement Motivation Making available the necessary tools and an environmental where collaborators share responsibility
based on universal principles, cooperation, innovation and continuous learning, but also based on the diverse range of feedback and partnership the company is able to build.
as a consequence, processes of innovation and the changes needed for survival and to sustain competitive responsible advantages in the future.
in the current interlinked innovation meta-system, research and technollog organisations (RTOS) would benefit from developing two systemic capacities:
strategy process Introduction The geographical scales of innovation systems are interlinked currently more than ever. The interrelatedness poses specific challenges for an organisation striving to navigate in this landscape.
and innovation services to enterprises, governments, and other clients. Arnold, Clark, and Jávorka (2010,7) assert that RTOS play important roles in the European innovation system and in de facto European research area policies,
especially by increasing the innovation activities in industry through technology platforms, stretching technoloogica capabilities of companies, and connecting research-based theoretical knowledge with practical knowledge through applications.
We explore the questions of systemic transformations in the context of Finnish RTO, namely VTT Technical research Centre of Finland,
the innovation activity, and indeed the entire activity field of organisations, has been conceptualised as relational practices, that is,
For example, Smits and Kuhlmann (2004,11) argue that innovation is a systemic activity that‘involves a variety of actions within the system,
when roadmapping the futures of a national innovation system. The temporal spans of the roadmaps are also scalable
and different tasks in the innovation network (see Könnölä et al. 2009). It is possible to make a distinction between two roadmapping cultures.
policy perspective Innovation policy roadmapping Roadmap for developing synthesising policy perspectives for public actors Combination of roadmap knowledge spaces depends on the specific aims of the process Forming policy conclusions on the basis of the roadmapping Downloaded by University of Bucharest
This category also contains a methodology of innovation policy roadmapping (see Ahlqvist, Valovirta, and Loikkanen 2012.
and national innovation systems face. In the cases, the targets of the systemic capacities varied according to the different knowledge spaces and roadmap scopes,
and even towards fostering a visionary innovation Downloaded by University of Bucharest at 05:05 03 december 2014 838 T. Ahlqvist et al. culture at the level of nation-states.
The Nordic ICT Foresight aimed to foster the visionary notions of‘Nordic innovation culture'and‘common strategy region'in the context of ICT applications.
His current research focusses on socio-spatial transformations induced by science, technology, and innovation policies. He has published widely in the field of foresight, on topics such as roadmapping, emerging technologies and infrastructures,
Her research focusses on the links between foresight knowledge, corporate strategy, and innovation policy. She holds a Phd from Helsinki Swedish School of economics and Businessadministration and Lic.
Her research relates to future-oriented technology assessment and innovation studies. Her special interest lies in enhancing innovations provoked by societal concerns for the well-being of the ageing society and for cleaner environment.
Johanna Kohl is a senior scientist and a team leader in Foresight and Socio-Technical change team at VTT.
Lecture at the roadmapping course for DIIRD (Department of Industry, Innovation and Regional development), Victoria, Australia.
Publication of Nordic Innovation Centre. Ahlqvist, T.,V. Valovirta, and T. Loikkanen. 2012. Innovation policy roadmapping as a systemic instrument for forwardloookin policy design.
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http://www. tandfonline. com/loi/ctas20 Text mining of information resources to inform Forecasting Innovation Pathways Ying Guo a, Tingting Ma a, Alan L. Porter b & Lu
Ying Guo, Tingting Ma, Alan L. Porter & Lu Huang (2012) Text mining of information resources to inform Forecasting Innovation Pathways, Technology analysis & Strategic management, 24:8, 843-861, DOI:
8 september 2012,843 861 Text mining of information resources to inform Forecasting Innovation Pathways Ying Guoa Tingting Maa, Alan L. Porterb and Lu Huanga*aschool of Management and Economics, Beijing Institute of technology, Beijing, China;
This paper explores the systematisation of the‘Forecasstin Innovation Pathways'analytical approach through the application oftech Mining.
Forecasting Innovation Pathways; Newand Emerging science andtechnologies; Tech Mining; nanotechnology; dye-sensitised solar cells, technology intelligence 1. Introduction New and Emerging science and Technologies(‘NESTS')are studied increasingly because of their potentially important‘emerging applications'.
Capturing and exploring multiple potential innovation pathways show considerable promise as a way of informing technology management and research policy.
We have devised a 4-stage, 10-step approach to Forecast Innovation Pathways(‘FIP'.'This process integrates (a) heavily empirical‘Tech Mining'with (b) heavily expert-based multipath mapping.
These include innovation system modelling, text mining of Science, Technology & Innovation(‘ST&I')information resources, trend analyses, actor analyses,
It explores the promise of this approach through its application to two illustrative innovation situations:
'Anticipating innovation pathways can assist R&d managers as they set priorities, new product managers as they compose development teams,
and national policy-makers as they formulate infrastructures to encourage innovation. 2. Background 2. 1. Tech Mining and FTAS Bibliometrics counting activity levels and identifying patterns in R&d bibliographic records,
and work to relate the content of the data searches to particular innovation process trajectories. Downloaded by University of Bucharest at 05:05 03 december 2014 Text mining of information resources 845 2. 2. Analysing NESTS NESTS comprise a loose category (Foxon et al. 2005;
2. 3. Innovation system conceptual modelling A variety of approaches aim to capture the systemic processes by
which emerging technologies contribute to commercial innovation. To facilitate the analysis of technological change, Hekkert et al. 2007) articulate‘functions of innovation systems'.
'Some researchers look into what kind of innovation transfer is most effective (e g. Liu, Tang, and Zhu 2008;
O'Shea, Chugh, and Allen 2008. Early identification of likely innovations can help discern opportunities, foster energy transitions,
and foresee societal impacts beneficial, as well as undesirable while the course of technology development remains more malleable (Collingridge 1980;
We note several innovation system conceptual modelling efforts pertaining particularly to energy technology, given our case focus on solar cells.
Among the various approaches to capture the essentials of innovation systems, the technology delivery system(‘TDS')has demonstrated enduring value by capturing
and representing (1) key enterprise (to‘deliver'an innovation) and (2) contextual factors (impinging on such delivery).
In our paper, the concept of TDS recognises the inherent uncertainties of innovation pathways. Ezra (1975) offered a TDS to help explain why solar energy innovation in residential housing applications was not notably successful.
Wenk and Kuehn (1977) advance TDS as a form of socio-technical system conceptual modelling to help identify the pivotal elements involved in innovation.
By‘innovation',we mean a novel technical contribution effectively translated into a successful product or process (i e. commercialisation.
Our TDS considers enterprise elements needed to effect innovation and it points out influential factors in the immediate nanobiosensor environment.
We seek key leverage points at which the innovation pathways can be influenced strongly. The elements that appear in a TDS depiction change from application to application.
For instance, Shi, Porter, and Rossini (1985) developed a TDS for microcomputer technology in developing countries,
and available human knowledge of the particular innovation Downloaded by University of Bucharest at 05:05 03 december 2014 846 Y. Guo et al. system, within the context of a more general innovation context (i e. the socioeconomic context in
and potential users (Step E). Figure 1. Framework for forecasting NEST innovation pathways. Downloaded by University of Bucharest at 05:05 03 december 2014 Text mining of information resources 847 Step J,
and stakeholders (Step G). Convening a workshop with multiple perspectives can anchor Step G exploration of alternative innovation pathways.
'That is, what important hurdles must be surmounted along the various innovation pathways? What key policy and/or business management leverage points enhance the prospects of success?
The aim is to synthesise what has been revealed about alternative innovation pathways for the NEST under study.
and expert knowledge to lay out candidate innovation paths (Steps F and G, with J). Detailing how the solar cells function (Step A is treated only briefly (Appendix 2). DSSC R&d (Step C) profiling outputs are illustrated selectively.
with a secondary interest in the DSSC characterisation. 4. 1. Compose TDS (Step B) The TDS approach is akin to other technology innovation system approaches,
but we favour its distinct treatment of (1) the enterprise (organisations with requisite capabilities) to develop the innovation
and events) affecting the success of that innovation process. Clear understanding of both sets of factors offers a valuable decision aid to inform successful NEST management.
Our TDS considers enterprises pursuing DSSC innovations, and it points out influential factors in the immediate solar cell development environment.
which the innovation pathways can be influenced strongly. Figure 2 presents our TDS targeted at the USA we chose this national focus to focus on a set of key actors.
In terms of the enterprises to accomplish commercial innovation based on DSSC technology, we sketch three loose groups of companies.
In the long term, we believe that general Downloaded by University of Bucharest at 05:05 03 december 2014 Text mining of information resources 849 Figure 2. TDS for DSSCS in the USA. economic forces will favour innovation
We worked our way back from that intended innovation to identifying particular attributes that could contribute importantly to it (e g. light-transmitting solar cells.
This could help us to identify potential partners with complementary interests at different places along this technology development progression, thereby serving‘Open innovation'purposes (Chesbrough 2006.
4. 5. Lay out alternative innovation pathway (Step F, with Step J) This stage was completed in two rounds.
This focused on mapping likely innovation avenues, following the process described and demonstrated by Robinson and Propp (2008).
The presentattio aims to locate elements that would add value to the solar cell innovation chain (the y-axis)
4. 6. Explore innovation components (Step G) Figure 7 provides the framework to explore sensitivities and options.
Potential innovation pathwaay can be reshaped; key promising technologies can be identified and positioned in a time frame;
Such visualisation of a pathway can play a central role in exploring innovation routes for a potentially disruptive NEST,
So how do suggested the NEST innovation pathways compare with alternatives? A first step is to broaden the technology assessment beyond the technology alone,
Is the risk framework adequaate The innovation pathways call attention to a need to address such issues that could affect DSSC development
This, in turn, should help array strong candidate innovation pathways. We are investigating DSSC technical component developments through patent analyses that combine text mining, semantic/syntactic analyses,
Rantanen and Domb 2002) to help locate current capabilities along innovation pathways. This paper extends our FIP approach.
It is effective in identifying the first-round potential innovation pathways and then zooming into these through augmented expert engagement exercisses The richness of the data is unquestionable,
and contextual attributes, affecting the prospects for effective applicatioons Drawing attention to innovation pathways (e g.
time horizons for innovation, and scope of study all reinforce the need to adapt these 10 steps to one's priorities.
particularly focusing on how to forecast the likely innovation pathways for emerging nano-related technologies and applications.
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