Start-ups as technology life cycle indicator for the early stage of application: An analysis of the battery value chain

© 2018 Elsevier Ltd Insights from battery research and development (R&D) need to be transferred into industrial application to create innovations and thus foster e.g. electro mobility. In terms of battery technology transfer, the early phase of application is particularly challenging due to the close intertwining between R&D and application. Therefore, the present study introduces start-ups as an additional indicator to capture the transition from science to industry within the technology life cycle. The findings show that despite highly dynamic R&D activities, technology transfer is only taking place on a very limited level. Surprisingly, start-ups focus on incremental improvements of existing technologies instead of introducing radical breakthrough-technologies. An analysis of the battery value chain reveals that opportunities for start-ups are rather located downstream in the value chain when integrating cells to battery systems and developing applications relying on innovative battery technologies. The findings contribute to the area of technology life cycle analysis explicitly using start-up companies as additional indicator for the critical transfer step from R&D to application. In a similar vein, technology forecasting literature, which is to date mainly focused on R&D, is expanded by a more application-centred perspective that allows identifying transfer opportunities along the technology value chain

On Search of a General Model of Technology Innovation

Working Paper Ircres-CNR 04/2021. This work has the objective to present a general model of technology innovation considering technology as a separated discipline from scientific research and economy. That is justified by the fact that many technologies have been developed without economic purposes but after generating technologies with a great economic importance, and that there are limits to the study of fundamentals of technology only from an economic point of view. The study defines some principles that are at the base of technology and of its innovation. These principles lead to a model considering technology as a time-oriented structure of technological operations, and allowing the definition of concepts such as technological space, technological landscape and space of technologies. From this model it is possible to explain various technological processes including the nature of knowhow and the transfer of technology. The model then defines three organizational structures for innovation concerning the industrial R&D project, the startup-venture capital and the industrial platform systems, and then the stages of the innovation process. It follows the development of a model of technology innovation of a territory, based on loops of fluxes of knowledge and capitals, and in which these three organizational structures are in action. Applications of the general model of technology innovation concern the relation between science and technology, the technological competitivity, the relation between R&D investments and growth, new possible statistical studies, the relation between technology and the environment and the importance of intermediate scientific and technical education. The work terminates giving a perspective of evolution of the organizational structures for innovation toward a system of industrial platforms network

Los actores de los sistemas de I+D+i de las regiones periféricas

Los estudios sobre los sistemas de investigación, desarrollo e innovación (I+D+i) han realizado avances significativos al adoptar una perspectiva regional, con análisis más ajustados de las características de los actores en la estructura económica y social. También la clase política ha prestado más atención a la planificación de las políticas públicas de ciencia, tecnología e innovación, con el propósito de ser más eficientes. Sin embargo, la orientación de los estudios regionales de innovación permanece anclada en los sistemas más exitosos: Silicon Valley, Manchester, Lyon..., sin que sepamos con certeza si estas mismas claves pueden generalizarse a otras regiones, especialmente las periféricas, con escaso o ningún desarrollo industrial y, por tanto, con situaciones muy poco favorables para el desarrollo de las economías del conocimiento. En este trabajo analizamos las actitudes y las opiniones de los actores principales del sistema de I+D+i de Canarias, que es una región ultraperiférica y no industrializada. La metodología basada en grupos de discusión y grupos Delphi nos permitirá establecer la manera de pensar y de actuar de los agentes locales. Podremos mostrar entonces que, además de la estructura de la región, la actitud que adoptan los agentes sociales desempeña un papel fundamental en el desarrollo exitoso del sistema regional de I+D+i.Studies of the R&D and innovation system have been making relevant progress when adopting a regional perspective with more apropiate analysis of the characteristics of the actors in the economic and political structure. Political stakeholders have also attended to planning public policies in science and technology and innovation with the aim of being more efficient. However, orientation of regional studies of innovation is anchored in success systems: Silicon Valley, Manchester, Lyon..., but we do not know certainly if these same key elements could be generalised for other regions, especially, periphery regions with nearly or none industrial development, and, therefore, with a little or no development industrial, and, therefore, with a situation very unfavorable for developing knowledge-based economies. In this work we analyse attitudes and opinions of main actors of Canary R&D and innovation system, which is an ultraperipheral and non industrialised region. The methodology based on discussion and Delphi groups allow us to establish the way of thinking and behaviour of local agents. We will be able to show, thus, in addition to the structure of the region, that the attitude adopted by the social agents play a fundamental role in the develop of the regional R&D and innovation system

Disruptive Innovation in Traditional Clusters: The Case of the Kerajet Ceramic Tile Cluster in Spain

[EN] Academic literature has often emphasized how firms in regional clusters exploit both place-specific local resources and external, world-class knowledge to strengthen their competitiveness by expanding the influence of regional systems of innovation. Innovation based on more complex and disruptive technologies tends to also be based on more open systems that utilize the clusters¿ external networks. However, most of the literature has associated clusters with incremental innovation. This paper will analyze the determinants of disruptive innovation development in traditional (low and medium tech) clusters caused by high-tech entrepreneurs. It will analyze the case of the development of breakthrough innovation, its diffusion in the Spanish ceramic tile cluster, and its consequent diffusion in the industry worldwide. It will examine how market demands, customer orientation, technology diffusion from other industries, industry competitiveness, as well as internal and external networking of clusters can facilitate the development of complex technology within a common set of social capital goals, cognitive schemes, and knowledge. The paper is based on a case study and field work carried out over10 years in the field in the Italian and Spanish tile ceramic clusters. The main contribution of this paper to technology strategy theory will be thorough the utilization of the disruptive technology paradigm in explaining industry changes and sustainability.Albors Garrigós, J.; Hervás Oliver, JL. (2019). Disruptive Innovation in Traditional Clusters: The Case of the Kerajet Ceramic Tile Cluster in Spain. Applied Sciences. 9(24):1-19. https://doi.org/10.3390/app9245513S119924Asheim, B. T., & Coenen, L. (2005). Knowledge bases and regional innovation systems: Comparing Nordic clusters. Research Policy, 34(8), 1173-1190. doi:10.1016/j.respol.2005.03.013Asheim, B. T., Smith, H. L., & Oughton, C. (2011). Regional Innovation Systems: Theory, Empirics and Policy. Regional Studies, 45(7), 875-891. doi:10.1080/00343404.2011.596701Lissoni, F. (2001). Knowledge codification and the geography of innovation: the case of Brescia mechanical cluster. Research Policy, 30(9), 1479-1500. doi:10.1016/s0048-7333(01)00163-9Belussi, F., & Sedita, S. R. (2012). Industrial Districts as Open Learning Systems: Combining Emergent and Deliberate Knowledge Structures. Regional Studies, 46(2), 165-184. doi:10.1080/00343404.2010.497133Steinle, C., & Schiele, H. (2002). When do industries cluster? Research Policy, 31(6), 849-858. doi:10.1016/s0048-7333(01)00151-2Giuliani, E. (2007). The wine industry: persistence of tacit knowledge or increased codification? Some implications for catching-up countries. International Journal of Technology and Globalisation, 3(2/3), 138. doi:10.1504/ijtg.2007.014330Hervas-Oliver, J.-L., & Albors-Garrigos, J. (2008). The role of the firm’s internal and relational capabilities in clusters: when distance and embeddedness are not enough to explain innovation. Journal of Economic Geography, 9(2), 263-283. doi:10.1093/jeg/lbn033Grashof, N., Hesse, K., & Fornahl, D. (2019). Radical or not? The role of clusters in the emergence of radical innovations. European Planning Studies, 27(10), 1904-1923. doi:10.1080/09654313.2019.1631260Dubé, & Paré. (2003). Rigor in Information Systems Positivist Case Research: Current Practices, Trends, and Recommendations. MIS Quarterly, 27(4), 597. doi:10.2307/30036550Molina-Morales, F. X. (2002). Industrial districts and innovation: the case of the Spanish ceramic tiles industry. Entrepreneurship & Regional Development, 14(4), 317-335. doi:10.1080/08985620210144992Estudio Económico del Sector del Azulejo 2018, Castellón (Spanish) http//www.ascer.esHervás-Oliver, J. L., & Albors-Garrigós, J. (2007). Do clusters capabilities matter? An empirical application of the resource-based view in clusters. Entrepreneurship & Regional Development, 19(2), 113-136. doi:10.1080/08985620601137554Oliver, J. L. H., Garrigós, J. A., & Porta, J. I. D. (2008). External Ties and the Reduction of Knowledge Asymmetries among Clusters within Global Value Chains: The Case of the Ceramic Tile District of Castellon. European Planning Studies, 16(4), 507-520. doi:10.1080/09654310801983308Assopiastrelle and Asia Observatore, Corporate Information www.assopiastrelle.itAlbors, J. G. (2002). The Journal of Technology Transfer, 27(3), 263-273. doi:10.1023/a:1015600521407Belussi, F., & Rita Sedita, S. (2008). The Symbiotic Division of Labour between Heterogeneous Districts in the Dutch and Italian Horticultural Industry. Urban Studies, 45(13), 2715-2734. doi:10.1177/0042098008098202Breschi, S., Malerba, F., & Orsenigo, L. (2000). Technological Regimes and Schumpeterian Patterns of Innovation. The Economic Journal, 110(463), 388-410. doi:10.1111/1468-0297.00530Iammarino, S., & McCann, P. (2006). The structure and evolution of industrial clusters: Transactions, technology and knowledge spillovers. Research Policy, 35(7), 1018-1036. doi:10.1016/j.respol.2006.05.004Robertson, P. L., & Patel, P. R. (2007). New wine in old bottles: Technological diffusion in developed economies. Research Policy, 36(5), 708-721. doi:10.1016/j.respol.2007.01.008JOHN, C. H., & POUDER, R. W. (2006). Technology Clusters versus Industry Clusters: Resources, Networks, and Regional Advantages. Growth and Change, 37(2), 141-171. doi:10.1111/j.1468-2257.2006.00313.xBeckman, C. M., & Haunschild, P. R. (2002). Network Learning: The Effects of Partners’ Heterogeneity of Experience on Corporate Acquisitions. Administrative Science Quarterly, 47(1), 92. doi:10.2307/3094892Abernathy, W. J., & Clark, K. B. (1985). Innovation: Mapping the winds of creative destruction. Research Policy, 14(1), 3-22. doi:10.1016/0048-7333(85)90021-6Ettlie, J. E., Bridges, W. P., & O’Keefe, R. D. (1984). Organization Strategy and Structural Differences for Radical Versus Incremental Innovation. Management Science, 30(6), 682-695. doi:10.1287/mnsc.30.6.682Tushman, M. L., & Anderson, P. (1986). Technological Discontinuities and Organizational Environments. Administrative Science Quarterly, 31(3), 439. doi:10.2307/2392832Dewar, R. D., & Dutton, J. E. (1986). The Adoption of Radical and Incremental Innovations: An Empirical Analysis. Management Science, 32(11), 1422-1433. doi:10.1287/mnsc.32.11.1422Green, S. G., Gavin, M. B., & Aiman-Smith, L. (1995). Assessing a multidimensional measure of radical technological innovation. IEEE Transactions on Engineering Management, 42(3), 203-214. doi:10.1109/17.403738Coccia, M. (2015). Radical innovations as drivers of breakthroughs: characteristics and properties of the management of technology leading to superior organisational performance in the discovery process of R&D labs. Technology Analysis & Strategic Management, 28(4), 381-395. doi:10.1080/09537325.2015.1095287Adner, R. (2002). When are technologies disruptive? a demand-based view of the emergence of competition. Strategic Management Journal, 23(8), 667-688. doi:10.1002/smj.246Adner, R., & Kapoor, R. (2015). Innovation ecosystems and the pace of substitution: Re-examining technology S-curves. Strategic Management Journal, 37(4), 625-648. doi:10.1002/smj.2363Danneels, E. (2004). Disruptive Technology Reconsidered: A Critique and Research Agenda. Journal of Product Innovation Management, 21(4), 246-258. doi:10.1111/j.0737-6782.2004.00076.xMarkides, C. (2006). Disruptive Innovation: In Need of Better Theory*. Journal of Product Innovation Management, 23(1), 19-25. doi:10.1111/j.1540-5885.2005.00177.xTellis, G. J. (2006). Disruptive Technology or Visionary Leadership?*. Journal of Product Innovation Management, 23(1), 34-38. doi:10.1111/j.1540-5885.2005.00179.xVon Hippel, E. (1986). Lead Users: A Source of Novel Product Concepts. Management Science, 32(7), 791-805. doi:10.1287/mnsc.32.7.791Chesbrough, H. (2010). Business Model Innovation: Opportunities and Barriers. Long Range Planning, 43(2-3), 354-363. doi:10.1016/j.lrp.2009.07.010Schmidt, G. M., & Druehl, C. T. (2008). When Is a Disruptive Innovation Disruptive? Journal of Product Innovation Management, 25(4), 347-369. doi:10.1111/j.1540-5885.2008.00306.xIsaksen, A. (2018). From success to failure, the disappearance of clusters: a study of a Norwegian boat-building cluster. Cambridge Journal of Regions, Economy and Society, 11(2), 241-255. doi:10.1093/cjres/rsy007Glasmeier, A. (1991). Technological discontinuities and flexible production networks: The case of Switzerland and the world watch industry. Research Policy, 20(5), 469-485. doi:10.1016/0048-7333(91)90070-7Hervas-Oliver, J.-L., & Albors-Garrigos, J. (2014). Are technology gatekeepers renewing clusters? Understanding gatekeepers and their dynamics across cluster life cycles. Entrepreneurship & Regional Development, 26(5-6), 431-452. doi:10.1080/08985626.2014.933489Hervas-Oliver, J.-L., Sempere-Ripoll, F., Estelles-Miguel, S., & Rojas-Alvarado, R. (2019). Radical vs incremental innovation in Marshallian Industrial Districts in the Valencian Region: what prevails? European Planning Studies, 27(10), 1924-1939. doi:10.1080/09654313.2019.1638887Albors, J., & Hervás, J. L. (2006). La industria cerámica europea en el siglo XXI. Retos tecnológicos y desafíos de la próxima década. Boletín de la Sociedad Española de Cerámica y Vidrio, 45(1), 13-21. doi:10.3989/cyv.2006.v45.i1.323Utterback, J. M., & Abernathy, W. J. (1975). A dynamic model of process and product innovation. Omega, 3(6), 639-656. doi:10.1016/0305-0483(75)90068-7Assink, M. (2006). Inhibitors of disruptive innovation capability: a conceptual model. European Journal of Innovation Management, 9(2), 215-233. doi:10.1108/14601060610663587Munari, F., Sobrero, M., & Malipiero, A. (2011). Absorptive capacity and localized spillovers: focal firms as technological gatekeepers in industrial districts. Industrial and Corporate Change, 21(2), 429-462. doi:10.1093/icc/dtr053Landry, R., Amara, N., & Lamari, M. (2002). Does social capital determine innovation? To what extent? Technological Forecasting and Social Change, 69(7), 681-701. doi:10.1016/s0040-1625(01)00170-6Angel, D. P. (2002). Inter-firm Collaboration and Technology Development Partnerships Within US Manufacturing Industries. Regional Studies, 36(4), 333-344. doi:10.1080/00343400220131115Tallman, S., Jenkins, M., Henry, N., & Pinch, S. (2004). Knowledge, Clusters, and Competitive Advantage. Academy of Management Review, 29(2), 258-271. doi:10.5465/amr.2004.12736089Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/239355

A bibliometric overview of how critical success factors influence on enterprise resource planning implementations

[EN] This work conducts bibliometric research into publications during the period 1999 to early 2018. The aim of this study is to help gain a better understanding of the publications covering CSF and ERP implementations all over the world. The study includes the most cited articles, most cited authors and most influential institutions as well as the most prolific countries. A database of 301 articles from 86 different institutions and 48 countries has been documented and analyzed. The results indicate that this field is growing significantly over time and a small number of US institutions are currently the most productive in this field.Vicedo Payà, P.; Gil Gómez, H.; Oltra Badenes, RF.; Guerola-Navarro, V. (2020). A bibliometric overview of how critical success factors influence on enterprise resource planning implementations. Journal of Intelligent & Fuzzy Systems. 38(5):5475-5487. https://doi.org/10.3233/JIFS-179639S54755487385Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205-225. doi:10.1016/s1467-0895(03)00026-5Broadus, R. N. (1987). Toward a definition of «bibliometrics». Scientometrics, 12(5-6), 373-379. doi:10.1007/bf02016680Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569-16572. doi:10.1073/pnas.0507655102Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2015). An overview of fuzzy research with bibliometric indicators. Applied Soft Computing, 27, 420-433. doi:10.1016/j.asoc.2014.10.035Dereli, T., Durmuşoğlu, A., Delibaş, D., & Avlanmaz, N. (2011). An analysis of the papers published inTotal Quality Management & Business Excellencefrom 1995 through 2008. Total Quality Management & Business Excellence, 22(3), 373-386. doi:10.1080/14783363.2010.532337Petersen, C. G., Aase, G. R., & Heiser, D. R. (2011). Journal ranking analyses of operations management research. International Journal of Operations & Production Management, 31(4), 405-422. doi:10.1108/01443571111119533Maloni, M., Carter, C. R., & Kaufmann, L. (2012). Author affiliation in supply chain management and logistics journals: 2008‐2010. International Journal of Physical Distribution & Logistics Management, 42(1), 83-101. doi:10.1108/09600031211202481Hsieh, P.-N., & Chang, P.-L. (2009). An assessment of world-wide research productivity in production and operations management. International Journal of Production Economics, 120(2), 540-551. doi:10.1016/j.ijpe.2009.03.015Merino, M. T. G., do Carmo, M. L. P., & Álvarez, M. V. S. (2006). 25 Years of Technovation: Characterisation and evolution of the journal. Technovation, 26(12), 1303-1316. doi:10.1016/j.technovation.2005.11.005Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Bachrach, D. G. (2008). Scholarly Influence in the Field of Management: A Bibliometric Analysis of the Determinants of University and Author Impact in the Management Literature in the Past Quarter Century. Journal of Management, 34(4), 641-720. doi:10.1177/0149206308319533Goh, C.-H., Holsapple, C. W., Johnson, L. E., & Tanner, J. R. (1997). Evaluating and classifying POM journals. Journal of Operations Management, 15(2), 123-138. doi:10.1016/s0272-6963(96)00102-7Pilkington, A., & Meredith, J. (2008). The evolution of the intellectual structure of operations management-1980-2006: A citation/co-citation analysis. Journal of Operations Management, 27(3), 185-202. doi:10.1016/j.jom.2008.08.001Stonebraker, J. S., Gil, E., Kirkwood, C. W., & Handfield, R. B. (2011). Impact factor as a metric to assess journals where OM research is published. Journal of Operations Management, 30(1-2), 24-43. doi:10.1016/j.jom.2011.05.002Fagerberg, J., Fosaas, M., & Sapprasert, K. (2012). Innovation: Exploring the knowledge base. Research Policy, 41(7), 1132-1153. doi:10.1016/j.respol.2012.03.008Shiau, W.-L., Dwivedi, Y. K., & Tsai, C.-H. (2015). Supply chain management: exploring the intellectual structure. Scientometrics, 105(1), 215-230. doi:10.1007/s11192-015-1680-9Merigó, J. M., Cancino, C. A., Coronado, F., & Urbano, D. (2016). Academic research in innovation: a country analysis. Scientometrics, 108(2), 559-593. doi:10.1007/s11192-016-1984-4Cancino, C., Merigó, J. M., Coronado, F., Dessouky, Y., & Dessouky, M. (2017). Forty years of Computers & Industrial Engineering: A bibliometric analysis. Computers & Industrial Engineering, 113, 614-629. doi:10.1016/j.cie.2017.08.033Laengle, S., Merigó, J. M., Miranda, J., Słowiński, R., Bomze, I., Borgonovo, E., … Teunter, R. (2017). Forty years of the European Journal of Operational Research: A bibliometric overview. European Journal of Operational Research, 262(3), 803-816. doi:10.1016/j.ejor.2017.04.027Martínez-López, F. J., Merigó, J. M., Valenzuela-Fernández, L., & Nicolás, C. (2018). Fifty years of the European Journal of Marketing: a bibliometric analysis. European Journal of Marketing, 52(1/2), 439-468. doi:10.1108/ejm-11-2017-0853Merigó, J. M., Pedrycz, W., Weber, R., & de la Sotta, C. (2018). Fifty years of Information Sciences: A bibliometric overview. Information Sciences, 432, 245-268. doi:10.1016/j.ins.2017.11.054Merigó, J. M., & Yang, J.-B. (2017). A bibliometric analysis of operations research and management science. Omega, 73, 37-48. doi:10.1016/j.omega.2016.12.004Tur-Porcar, A., Mas-Tur, A., Merigó, J. M., Roig-Tierno, N., & Watt, J. (2018). A Bibliometric History of the Journal of Psychology Between 1936 and 2015. The Journal of Psychology, 152(4), 199-225. doi:10.1080/00223980.2018.1440516Valenzuela, L. M., Merigó, J. M., Johnston, W. J., Nicolas, C., & Jaramillo, J. F. (2017). Thirty years of the Journal of Business & Industrial Marketing: a bibliometric analysis. Journal of Business & Industrial Marketing, 32(1), 1-17. doi:10.1108/jbim-04-2016-0079Merigó, J. M., Blanco-Mesa, F., Gil-Lafuente, A. M., & Yager, R. R. (2016). Thirty Years of theInternational Journal of Intelligent Systems: A Bibliometric Review. International Journal of Intelligent Systems, 32(5), 526-554. doi:10.1002/int.21859Wang, W., Laengle, S., Merigó, J. M., Yu, D., Herrera-Viedma, E., Cobo, M. J., & Bouchon-Meunier, B. (2018). A Bibliometric Analysis of the First Twenty-Five Years of the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 26(02), 169-193. doi:10.1142/s0218488518500095Yu D. , Xu Z. , Kao Y. , Lin C.T. , “The Structure and Citation Landscape of IEEE Transactions on Fuzzy Systems (1994–2015)”, IEEE Transactions on Fuzzy Systems 26(2) (2018).Tang, M., Liao, H., & Su, S.-F. (2018). A Bibliometric Overview and Visualization of the International Journal of Fuzzy Systems Between 2007 and 2017. International Journal of Fuzzy Systems, 20(5), 1403-1422. doi:10.1007/s40815-018-0484-5LÓPEZ-HERRERA, A. G., HERRERA-VIEDMA, E., COBO, M. J., MARTÍNEZ, M. A., KOU, G., & SHI, Y. (2012). A CONCEPTUAL SNAPSHOT OF THE FIRST DECADE (2002–2011) OF THE INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING. International Journal of Information Technology & Decision Making, 11(02), 247-270. doi:10.1142/s0219622012400020Cobo, M. J., Martínez, M. A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25years at Knowledge-Based Systems: A bibliometric analysis. Knowledge-Based Systems, 80, 3-13. doi:10.1016/j.knosys.2014.12.035Yu, D., & Shi, S. (2015). Researching the development of Atanassov intuitionistic fuzzy set: Using a citation network analysis. Applied Soft Computing, 32, 189-198. doi:10.1016/j.asoc.2015.03.027Yu, D., Xu, Z., & Wang, W. (2018). Bibliometric analysis of fuzzy theory research in China: A 30-year perspective. Knowledge-Based Systems, 141, 188-199. doi:10.1016/j.knosys.2017.11.018Yu, D. (2015). A scientometrics review on aggregation operator research. Scientometrics, 105(1), 115-133. doi:10.1007/s11192-015-1695-2Zhang, Y., Chen, H., Lu, J., & Zhang, G. (2017). Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016. Knowledge-Based Systems, 133, 255-268. doi:10.1016/j.knosys.2017.07.011Muhuri, P. K., Shukla, A. K., Janmaijaya, M., & Basu, A. (2018). Applied soft computing: A bibliometric analysis of the publications and citations during (2004–2016). Applied Soft Computing, 69, 381-392. doi:10.1016/j.asoc.2018.03.041Van Eck, N. J., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. doi:10.1007/s11192-009-0146-

The role of universities in making industrial districts more dynamic. A case study in Spain

Through this research we aim to contribute to the debate on the role of universities in industrial districts in the context of the new competitive panorama that they are facing. With this objective in mind, we have carried out a study based on a university located within a Spanish textile district, using Social Network Analysis techniques. Of particular relevance are the relationships between companies, of which there is a dense concentration, and the university, which has an important role as mediator within the network.Capó-Vicedo, J.; Molina Morales, FX.; Capó I Vicedo, J. (2013). The role of universities in making industrial districts more dynamic. A case study in Spain. Higher Education. 65(4):417-435. doi:10.1007/s10734-012-9553-0S417435654Abramo, G., D’angelo, C. A., & Di Costa, F. (2011). University-industry research collaboration: A model to assess university capability. Higher Education, 62, 163–181.Abramovsky, L., Kremp, E., López, A., Schmidt, T., & Simpson, H. (2009). 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The impact of cluster connectedness on firm innovation: R&D effort and outcomes in the textile industry

This is an Author's Accepted Manuscript of an article published in "The impact of cluster connectedness on firm innovation: R&D effort and outcomes in the textile industry" version of the article as published in the Entrepreneurship and Regional Development, 2012 september,[copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/08985626.2012.710260"[EN] Recent research into the clustering effect on firms has moved away from a simplistic view to a more complex approach. More realistic and complex causal relationships are now considered when analysing these territorial networks. Specifically, this paper attempts to analyse how cluster connect- edness moderates the relationship of a firm's innovation effort and the results obtained from this effort. We want to question the commonly accepted direct and positive impact of R&D effort, and moreover, we suggest the existence of a saturation effect and that the level of cluster's inter-connectedness in the cluster moderates this effect. We have developed our empirical study focusing on the Spanish textile industrial cluster. This is a complex manufacturing industry that uses relatively low-technology manufacturing and R&D. Our findings suggest that the degree to which a firm is involved with, or connected to, other firms in the cluster can moderate the effect of the R&D effort on its innovation results. More generally, we aim to contribute to the discussion on the degree to which firms should be involved in the cluster network in order to operate efficiently and gain the maximum competitive advantages. Our findings have implications both in recent cluster and network literature as well for institutional policy.Molina Morales, FX.; Expósito Langa, M. (2012). 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G., DARBY, M. R., & ARMSTRONG, J. (1998). GEOGRAPHICALLY LOCALIZED KNOWLEDGE: SPILLOVERS OR MARKETS? Economic Inquiry, 36(1), 65-86. doi:10.1111/j.1465-7295.1998.tb01696.

Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms

[EN] The main aim of this study is to establish the effect of the Exploitation and Exploration; and the influence of these learning flows on the Innovative Outcome (IO). The Innovative Outcome refers to new products, services, processes (or improvements) that the organization has obtained as a result of an innovative process. For this purpose, a relationship model is defined, which is empirically contrasted, and can explains and predicts the cyclical dynamization of learning flows on innovative outcome in knowledge intensive firms. The quantitative test for this model use the data from entrepreneurial firms biotechnology sector. The statistical analysis applies a method based on variance using Partial Least Squares (PLS). Research results confirm the hypotheses, that is, they show a positive dynamic effect between the Exploration and the Innovative as outcomes. In the same vein, they results confirm the presence of the cyclic movement of innovative outcome with the Exploitation.In addition, this research is part of the Project ECO2015-71380-R funded by the Spanish Ministry of Economy, Industry and Competitiveness and the State Research Agency. Co-financed by the European Regional Development Fund (ERDF).Vargas-Mendoza, NY.; Lloria, MB.; Salazar Afanador, A.; Vergara Domínguez, L. (2018). Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms. International Entrepreneurship and Management Journal. 14(4):1053-1069. https://doi.org/10.1007/s11365-018-0496-5S10531069144Alegre, J., & Chiva, R. (2008). Assessing the impact of organizational learning capability on product innovation performance: an empirical test. Technovation, 28, 315–326.Amara, N., Landry, R., Becheikh, N., & Ouimet, M. (2008). Learning and novelty of innovation in established manufacturing SMEs. 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Open innovation: past, present and future trends

[EN] Purpose The purpose of this paper is to provide interested parties with the means of grasping how the literature on open innovation has evolved over the course of time. In this way, the authors furthermore contribute towards a better understanding, scaling and positioning of this field of research. Design/methodology/approach This study applies a combination of bibliometric techniques, such as citations, co-citations and social network analysis in order to map the scientific domain of open innovation. Currently, bibliometric analysis represents a methodology in effect on a global scale to evaluate the existing state of fields of research (Mutschke et al., 2011). This spans the application of quantitative and statistical analysis to publications such as articles and their respective citations and serving to evaluate the performance of research through returning data on all of the activities ongoing in a scientific field with summaries of these data generating a broad perspective on the research activities and impacts, especially as regards the researchers, journals, countries and universities (Hawkins, 1977; Osareh, 1996; Thomsom Reuters, 2008). Findings This research aims to map and analyse the intellectual knowledge held on open innovation. To this end, the authors carried out a bibliometric study with recourse to co-citations. Based on cluster and factorial analyses, it is possible identify and classify the several theoretical perspectives on open innovation across six areas: open innovation concept, open innovation and networks, open innovation and knowledge, open Innovation, and innovation spillovers, open innovation management and open innovation and technology.JoAo J. Ferreira and Cristina I. Fernandes acknowledge the financial support from NECE - Research Unit in Business Sciences funded by the Multiannual Funding Programme of R&D Centres of FCT - Fundacao para a Ciencia e a Tecnologia (Project UID/GES/04630/2013).Fernandes, C.; Ferreira, J.; Peris-Ortiz, M. (2019). Open innovation: past, present and future trends. Journal of Organizational Change Management. 32(5):578-602. https://doi.org/10.1108/JOCM-09-2018-0257S578602325Ahn, J. M., Minshall, T., & Mortara, L. (2017). Understanding the human side of openness: the fit between open innovation modes and CEO characteristics. R&D Management, 47(5), 727-740. doi:10.1111/radm.12264Alexy, O., George, G., & Salter, A. J. (2013). Cui Bono? The Selective Revealing of Knowledge and Its Implications for Innovative Activity. Academy of Management Review, 38(2), 270-291. doi:10.5465/amr.2011.0193Baldwin, C., & von Hippel, E. (2011). Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation. Organization Science, 22(6), 1399-1417. doi:10.1287/orsc.1100.0618Ballell, L., Bates, R. H., Young, R. J., Alvarez-Gomez, D., Alvarez-Ruiz, E., Barroso, V., … Cammack, N. (2013). Fueling Open-Source Drug Discovery: 177 Small-Molecule Leads against Tuberculosis. ChemMedChem, 8(2), 313-321. doi:10.1002/cmdc.201200428Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120. doi:10.1177/014920639101700108Belussi, F., Sammarra, A., & Sedita, S. R. (2010). Learning at the boundaries in an «Open Regional Innovation System»: A focus on firms’ innovation strategies in the Emilia Romagna life science industry. Research Policy, 39(6), 710-721. doi:10.1016/j.respol.2010.01.014Berchicci, L. (2013). Towards an open R&D system: Internal R&D investment, external knowledge acquisition and innovative performance. Research Policy, 42(1), 117-127. doi:10.1016/j.respol.2012.04.017Berkhout, G., Hartmann, D., & Trott, P. (2010). Connecting technological capabilities with market needs using a cyclic innovation model. R&D Management, 40(5), 474-490. doi:10.1111/j.1467-9310.2010.00618.xBerthon, P., Ewing, M. T., & Napoli, J. (2008). Brand Management in Small to Medium-Sized Enterprises*. Journal of Small Business Management, 46(1), 27-45. doi:10.1111/j.1540-627x.2007.00229.xBianchi, M., Campodall’Orto, S., Frattini, F., & Vercesi, P. (2010). Enabling open innovation in small- and medium-sized enterprises: how to find alternative applications for your technologies. R&D Management, 40(4), 414-431. doi:10.1111/j.1467-9310.2010.00613.xChen, J., Chen, Y., & Vanhaverbeke, W. (2011). The influence of scope, depth, and orientation of external technology sources on the innovative performance of Chinese firms. Technovation, 31(8), 362-373. doi:10.1016/j.technovation.2011.03.002Cheng, C.-F., Lai, M.-K., & Wu, W.-Y. (2010). Exploring the impact of innovation strategy on R&D employees’ job satisfaction: A mathematical model and empirical research. Technovation, 30(7-8), 459-470. doi:10.1016/j.technovation.2010.03.006Chesbrough, H. and Bogers, M. (2014), “Explicating open innovation: clarifying an emerging paradigm for understanding innovation”, in Chesbrough, H., Vanhaverbeke, W. and West, J. (Eds), New Frontiers in Open Innovation, Oxford University Press, Oxford, pp. 3-28.Chesbrough, H. (2012). Open Innovation: Where We’ve Been and Where We’re Going. Research-Technology Management, 55(4), 20-27. doi:10.5437/08956308x5504085Chesbrough, H. W., & Appleyard, M. M. (2007). Open Innovation and Strategy. California Management Review, 50(1), 57-76. doi:10.2307/41166416Chiaroni, D., Chiesa, V., & Frattini, F. (2011). The Open Innovation Journey: How firms dynamically implement the emerging innovation management paradigm. Technovation, 31(1), 34-43. doi:10.1016/j.technovation.2009.08.007Christensen, J. F., Olesen, M. H., & Kjær, J. S. (2005). The industrial dynamics of Open Innovation—Evidence from the transformation of consumer electronics. Research Policy, 34(10), 1533-1549. doi:10.1016/j.respol.2005.07.002Cooke, P. (2005). Regionally asymmetric knowledge capabilities and open innovation. Research Policy, 34(8), 1128-1149. doi:10.1016/j.respol.2004.12.005Cooper, R. G. (2008). Perspective: The Stage-Gate®Idea-to-Launch Process—Update, What’s New, and NexGen Systems. Journal of Product Innovation Management, 25(3), 213-232. doi:10.1111/j.1540-5885.2008.00296.xDahlander, L., & Gann, D. M. (2010). How open is innovation? Research Policy, 39(6), 699-709. doi:10.1016/j.respol.2010.01.013Dahlander, L., O’Mahony, S., & Gann, D. M. (2014). One foot in, one foot out: how does individuals’ external search breadth affect innovation outcomes? Strategic Management Journal, 37(2), 280-302. doi:10.1002/smj.2342Di Gangi, P. M., & Wasko, M. (2009). Steal my idea! Organizational adoption of user innovations from a user innovation community: A case study of Dell IdeaStorm. Decision Support Systems, 48(1), 303-312. doi:10.1016/j.dss.2009.04.004Dodgson, M., Gann, D., & Salter, A. (2006). The role of technology in the shift towards open innovation: the case of Procter & Gamble. R and D Management, 36(3), 333-346. doi:10.1111/j.1467-9310.2006.00429.xDoloreux, D., & Melançon, Y. (2008). On the dynamics of innovation in Quebec’s coastal maritime industry. Technovation, 28(4), 231-243. doi:10.1016/j.technovation.2007.10.006Drechsler, W., & Natter, M. (2012). Understanding a firm’s openness decisions in innovation. Journal of Business Research, 65(3), 438-445. doi:10.1016/j.jbusres.2011.11.003Chatenier, E. du, Verstegen, J. A. A. M., Biemans, H. J. A., Mulder, M., & Omta, O. S. W. F. (2010). Identification of competencies for professionals in open innovation teams. R&D Management, 40(3), 271-280. doi:10.1111/j.1467-9310.2010.00590.xEbner, W., Leimeister, J. M., & Krcmar, H. (2009). Community engineering for innovations: the ideas competition as a method to nurture a virtual community for innovations. R&D Management, 39(4), 342-356. doi:10.1111/j.1467-9310.2009.00564.xEnkel, E., Gassmann, O., & Chesbrough, H. (2009). Open R&D and open innovation: exploring the phenomenon. R&D Management, 39(4), 311-316. doi:10.1111/j.1467-9310.2009.00570.xFaems, D., De Visser, M., Andries, P., & Van Looy, B. (2010). 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Conceptualisation of the three-dimensional matrix of collaborative knowledge barriers

[EN] Nowadays, collaborative knowledge management (CKM) is well accepted as a decisive asset in the field of networked enterprises and supply chains. However, few knowledge management initiatives have been performed successfully because, in most cases, the barriers that hinder the CKM process are unknown and misunderstood. Currently, the research reveals different uni- and bi-dimensional barriers' classifications, however multi-dimensional approaches provide a better view of the complexity in the area of CKM. Therefore, this paper proposes the three-dimensional matrix of collaborative knowledge barriers taking into account: (i) perspectives; (ii) levels and (iii) barriers blocks to provide a reference way to audit the CKM barriers, and thus, in further research, focus on the corrections and adjustments to guarantee the success while implementing a CKM project.Sanchis, R.; Sanchis Gisbert, MR.; Poler, R. (2020). 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