Marginalization of end-use technologies in energy innovation for climate protection

Mitigating climate change requires directed innovation efforts to develop and deploy energy technologies. Innovation activities are directed towards the outcome of climate protection by public institutions, policies and resources that in turn shape market behaviour. We analyse diverse indicators of activity throughout the innovation system to assess these efforts. We find efficient end-use technologies contribute large potential emission reductions and provide higher social returns on investment than energy-supply technologies. Yet public institutions, policies and financial resources pervasively privilege energy-supply technologies. Directed innovation efforts are strikingly misaligned with the needs of an emissions-constrained world. Significantly greater effort is needed to develop the full potential of efficient end-use technologies

Comparing nuclear power trajectories in Germany and the UK: from ‘regimes' to ‘democracies’ in sociotechnical transitions and Discontinuities

This paper focuses on arguably the single most striking contrast in contemporary major energy politics in Europe (and even the developed world as a whole): the starkly differing civil nuclear policies of Germany and the UK. Germany is seeking entirely to phase out nuclear power by 2022. Yet the UK advocates a ‘nuclear renaissance’, promoting the most ambitious new nuclear construction programme in Western Europe.Here,this paper poses a simple yet quite fundamental question: what are the particular divergent conditions most strongly implicated in the contrasting developments in these two countries. With nuclear playing such an iconic role in historical discussions over technological continuity and transformation, answering this may assist in wider understandings of sociotechnical incumbency and discontinuity in the burgeoning field of‘sustainability transitions’. To this end, an ‘abductive’ approach is taken: deploying nine potentially relevant criteria for understanding the different directions pursued in Germany and the UK. Together constituted by 30 parameters spanning literatures related to socio-technical regimes in general as well as nuclear technology in particular, the criteria are divided into those that are ‘internal’ and ‘external’ to the ‘focal regime configuration’ of nuclear power and associated ‘challenger technologies’ like renewables. It is ‘internal’ criteria that are emphasised in conventional sociotechnical regime theory, with ‘external’ criteria relatively less well explored. Asking under each criterion whether attempted discontinuation of nuclear power would be more likely in Germany or the UK, a clear picture emerges. ‘Internal’ criteria suggest attempted nuclear discontinuation should be more likely in the UK than in Germany– the reverse of what is occurring. ‘External’ criteria are more aligned with observed dynamics –especially those relating to military nuclear commitments and broader ‘qualities of democracy’. Despite many differences of framing concerning exactly what constitutes ‘democracy’, a rich political science literature on this point is unanimous in characterising Germany more positively than the UK. Although based only on a single case,a potentially important question is nonetheless raised as to whether sociotechnical regime theory might usefully give greater attention to the general importance of various aspects of democracy in constituting conditions for significant technological discontinuities and transformations. If so, the policy implications are significant. A number of important areas are identified for future research, including the roles of diverse understandings and specific aspects of democracy and the particular relevance of military nuclear commitments– whose under-discussion in civil nuclear policy literatures raises its own questions of democratic accountability

Open-system orchestration as a relational source of sensing capabilities: Evidence from a venture association

Research on innovation networks has highlighted the pivotal role that actors with more prominence and power, such as hub firms, may play in orchestrating the activities of other network members along a collective innovation effort. Our study examined the undertheorized, but no less important, type of orchestration that characterizes other organizations, such as business incubators and venture associations, who seek to support the dispersed entrepreneurial efforts of network members. We refer to this type as ‘open-system’ orchestration, as opposed to the commonly studied ‘closed-system’ type performed by hub firms. Our findings reveal how the processes of open-system orchestration differ markedly from those of closed-system orchestration, and detail how these processes influence the micro-foundations of network members’ sensing capabilities. By doing so, we also offer empirical substantiation and theoretical elaboration to the idea that dynamic capabilities might not reside exclusively inside firms, but could be co-created relationally with other parties in the business ecosystem

The role of venture capitalists in the regional innovation ecosystem : a comparison of networking patterns between private and publicly backed venture capital funds

This paper empirically examines the development of social networks among venture capitalists and other professionals of the regional innovation ecosystem. Using an online survey of venture capitalists, the article considers their networking behaviour, focusing particularly on the distinction between those employed by private and those employed by publicly backed venture capital funds, and on the composition and spatial search of their networks. It investigates whether the frequency of interaction between venture capitalists and other members of the innovation ecosystem is associated with the nature of the venture capital funds. The paper provides the first detailed investigation of the relationship between different types of venture capitalists and other players of the innovation ecosystem such as universities incubators, research institutes, and business support organisations. The results show that there are distinctive differences within the two seemingly similar professional groups (private and public venture capitalists), and public dependence of the venture capital fund is strongly and significantly associated with higher volumes of interactions. The more publicly dependent a fund is, the more it interacts with other players of the innovation system. This finding has important implications for both academics and practitioners and suggests that publicly backed funds have a wider role to play in mobilising the different players of the regional innovation ecosystem

Who leads research productivity growth? Guidelines for R&D policy-makers

[EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). Public financing of cooperative R&D projects in Spain: the concerted projects under the national R&D plan. Research Policy, 30, 625–641.Arbel, A. (1981). Policy evaluation in the dynamic input–output model. International Journal of Systems Science, 12, 255–260.Arnold, E. (2004). Evaluation research and innovation policy: A systems world needs systems evaluations. Research Evaluation, 13, 3–17.Arrow, J. K. (1962). Economic welfare and the allocation of resources for inventions. In R. Nelson (Ed.), The rate and direction of inventive activity: Economic and social factor (pp. 609–625). Princeton: Princeton University Press and NBER.Autio, E. (1997). New, technology-based firms in innovation networks symplectic and generative impacts. Research Policy, 26, 263–281.Balk, B. (2001). Scale efficiency and productivity change. Journal of Productivity Analysis, 15, 153–183.Balzat, M., & Hanusch, H. (2004). Recent trends in the research on national innovation systems. Journal of Evolutionary Economics, 14, 197–210.Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking. Scandinavian Journal of Economics, 94, S211–S228.Bergek, A., Carlsson, B., Lindmark, S., Rickne, A., & Jacobsson, S. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37, 407–429.Bonaccorsi, A., & Daraio, C. (2005). Exploring size and agglomeration effects on public research productivity. Scientometrics, 63(1), 87–120.Buisseret, T. J., Cameron, H., & Georghiou, L. (1995). What difference does it make? Additionality in the public support of R&D in large firms. International Journal of Technology Management, 10, 587–600.Bustelo, M. (2006). The potential role of standards and guidelines in the development of an evaluation culture in Spain. Evaluation, 12, 437–453.Chavas, J. P., & Cox, T. M. (1999). A generalized distance function and the analysis of production efficiency. Southern Economic Journal, 66, 295–318.CICYT. (1987). Programa Nacional de Tecnología de los Alimentos. Madrid: Ministerio de Educación y Ciencia.CICYT (1988). Plan Nacional de Investigación Científica y Desarrollo Tecnológico 1988–1991. Ministerio de Educación y Ciencia, Secretaría de Estado de Universidades e Investigación, Madrid.Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-software. Boston: Kluwer Academic Publishers.David, P., Mowery, D., & Steinmueller, W. E. (1994). Analyzing the economic payoffs from basic research. In D. Mowery (Ed.), Science and technology policy in interdependent economies (pp. 57–78). Boston: Kluwer Academic Publishers.Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263–279.Edquist, C., & Hommen, L. (2008). Comparing national systems of innovation in Asia and Europe: Theory and comparative framework. In C. Edquist & L. Hommen (Eds.), Small country innovation systems: Globalisation, change and policy in Asia and Europe (pp. 1–28). Cheltenham: Edward Elgar.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84, 66–83.Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General, 120(3), 253–281.Førsund, F. R. (1993). Productivity growth in Norwegian ferries. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 352–373). New York: Oxford University Press.Førsund, F. R. (1997). The Malmquist productivity index, TFP and scale. University of Oslo, Oslo: Working Paper, Department of Economics and Business Administration.Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. London: Printer Publishers.García-Martínez, M., & Briz, J. (2000). Innovation in the Spanish food & drink industry. International Food and Agribusiness Management Review, 3, 155–176.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage Publications.Grammatikopoulos, V., Kousteiios, A., Tsigilis, N., & Theodorakis, Y. (2004). Applying dynamic evaluation approach in education. Studies in Educational Evaluation, 30, 255–263.Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81–101.Grimpe, C., & Sofka, W. (2007). Search patterns and absorptive capacity: A comparison of low- and high-technology firms from thirteen European countries. Discussion paper no. 07-062. Centre for European Economic Research (ZEW), Mannheim, Germany.Guan, J., & Wang, J. (2004). Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59(1), 131–155.Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.Jiménez-Sáez, F. (2005). Una Evaluación del Programa Nacional de Tecnología de Alimentos: análisis de la articulación fomentada sobre el Sistema Alimentario de Innovación en España. PhD dissertation, Servicio de Publicaciones de la Universidad Politécnica de Valencia, Valencia.Jiménez-Sáez, F., Zabala-Iturriagagoitia, J. M., Zofío, J. L., & Castro-Martínez, E. (2011). Evaluating research efficiency within National R&D Programmes. Research Policy, 40, 230–241.Kao, C. (2008). Efficiency analysis of university departments: An empirical study. OMEGA, 36, 653–664.Kuhlmann, S. (2003). Evaluation of research and innovation policies: A discussion of trends with examples from Germany. International Journal of Technology Management, 26, 131–149.Laitinen, E. K. (2002). A dynamic performance measurement system: Evidence from small Finnish technology companies. Scandinavian Journal of Management, 18, 65–99.Laranja, M., Uyarra, E., & Flanagan, K. (2008). Policies for science, technology and innovation: Translating rationales into regional policies in a multi-level setting. Research Policy, 37(5), 823–835.Lee, T.-L., & von Tunzelman, N. (2005). A dynamic analytic approach to national innovation systems: The IC industry in Taiwan. Research Policy, 34, 425–440.Lipsey, R., & Carlaw, K. (1998). A structuralist assessment of technology policies: Taking Schumpeter seriously on policy. Ottawa: Industry Canada Research Publications Program.Lipsey, R., Carlaw, K., & Bekar, C. (2005). Economic transformations: General purpose technologies and long term economic growth. Oxford: Oxford University Press.Lundvall, B. Å. (1992). National systems of innovation: Toward a theory of innovation and interactive learning. London: Printer Publishers.Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31, 213–231.Markard, J., & Truffer, B. (2008). Actor-oriented analysis of innovation systems: Exploring micro-meso level linkages in the case of stationary fuel cells. Technology Analysis & Strategic Management, 20, 443–464.Metcalfe, J. S. (2002). Equilibrium and evolutionary foundations of competition and technology policy: New perspectives on the division of labour and the innovation process. CRIC Working Papers series, University of Manchester.Miettinen, R. (1999). The riddle of things. Activity theory and actor network theory as approaches of studying innovations. Mind, Culture and Activity, 6, 170–195.Molas-Gallart, J., & Davies, A. (2006). Toward theory-led evaluation: The experience of European science, technology, and innovation policies. American Journal of Evaluation, 27, 64–82.Mytelka, L. K., & Smith, K. (2002). Policy learning and innovation theory: An interactive and co-evolving process. Research Policy, 31, 1467–1479.Olazarán, M., Lavía, C., & Otero, B. (2004). ¿Hacia una segunda transición en la ciencia? Política científica y grupos de investigación. Revista Española de Sociología, 4, 143–172.Potts, J. (2007). The innovation system & economic evolution. Productivity commission submission, public support for science & innovation, productivity commission, Camberra.Ray, S., & Desli, E. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. American Economic Review, 87(5), 1033–1039.Rip, A., & Nederhof, A. J. (1986). Between dirigism and laissez-faire: Effects of implementing the science policy priority for biotechnology in the Netherlands. Research Policy, 15, 253–268.Schmidt, E. K., Graversen, E. K., & Langberg, K. (2003). Innovation and dynamics in public research environments in Denmark: A research-policy perspective. Science and Public Policy, 30, 107–116.Schmoch, U., & Schubert, T. (2009). Sustainability of incentives for excellent research—The German case. Scientometrics, 81(1), 195–218.Shephard, R. (1970). Theory of cost and production functions. New Jersey: Princeton University Press.Simar, L., & Wilson, P. W. (1998). Productivity growth in industrialized countries. Discussion paper 9810, Universite Catholique de Louvain, Belgium.Van Raan, A. F. J. (2000). R&D evaluation at the beginning of the new century. Research Evaluation, 8, 81–86.Zofio, J. L. (2007). Malmquist productivity index decompositions: A unifying framework. Applied Economics, 39, 2371–2387.Zofio, J. L., & Lovell, C. A. K. (1998). Yet another Malmquist productivity index decomposition. Working paper, Department of Economics, University of Georgia, Athens, GA 30602, USA.Zofio, J. L., & Lovell, C. A. K. (2001). Graph efficiency and productivity measures: An application to US agriculture. Applied Economics, 33(10), 1433–1442.Zofio, J. L., & Prieto, A. M. (2006). Return to dollar, generalized distance function and the Fisher productivity index. Spanish Economic Review, 8, 113–138

The graduation performance of technology business incubators in China's three tier cities: the role of incubator funding, technical support, and entrepreneurial mentoring

This study examines the effects of technology business incubator (TBI)’s funding, technical support and entrepreneurial mentoring on the graduation performance of new technology-based firms in China’s three tier cities. Using new dataset on all TBIs and incubated new technology-based firms from government surveys conducted over five consecutive years from 2009 to 2013 combined with archival and hand-collected data, we find the effects of incubator services on the early growth of new technology-based firms vary according to the local context. Technical support facilities and entrepreneurial mentoring from TBIs are found to have significantly and positively influenced the early development of the firms in the four most affluent tier 1 cities, whilst these effects become less pronounced for the tier 2 and tier 3 cities. These two services are also found to influence graduation performance in the government and university types of TBI respectively. Results support the notion that the effectiveness of an incubators services is shaped by the level of a city’s socio-economic development and that the city location of a TBI does impact the graduation performance of its incubatees

The Governance of Global Innovation Systems: Putting Knowledge in Context

Technological innovation increasingly depends on multiscalar actor networks and institutions. However, the developers of many conceptual frameworks explaining innovation success have paid only limited attention to this new reality, due to their focus on regions and countries as agents that shape innovation governance and as containers that provide institutional conditions for innovation success. In particular, innovation systems literature has been criticized in this respect. In the present chapter, we refer to the recently formulated Global Innovation Systems approach, which enables researchers to capture the emergence of system resources across spatial scales. With this framework, we emphasize that beyond the focus on knowledge generation processes, a better understanding of valuation processes is necessary to guide governance structures for generating new technologies and products. This is particularly true for sectors that are oriented towards confronting grand challenges, such as cleantech industries

Exploring the role of instrument design and instrument interaction for eco-innovation: a survey-based analysis of renewable energy innovation in Germany

Empirical research on eco-innovation has produced a substantive body of literature on the relevance of regulation for stimulating such innovation. Much of this work on the role of policy for eco-innovation relies on econometric analyses of company survey data. In this regard, the eco-innovation module introduced in 2008/9 in the Community Innova-tion Survey serves as an important data source that has helped improve our under-standing of the role of environmental and innovation policy for eco-innovation in the Eu-ropean Union (EU). However, so far, this data source has provided only limited oppor-tunities to generate insights into the role of instrument design and instrument interaction for eco-innovation. In this chapter, we present a first attempt to measure such aspects in a company innovation survey based on the example of renewable energy innovation in Germany. In particular, we explore to what extent the design of the German Renewa-ble Energy Sources Act (and the interaction of its feed-in tariffs with the EU emissions trading system) correlates with innovation in renewable power generation technologies. We find instrument design features but not instrument type to be related to eco-innovation. In addition, our exploratory study provides evidence for an interaction effect between climate policy and renewables support policy. Based on these findings, we discuss implications for future research on the role of policy in eco-innovation