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). The impact of cluster connectedness on firm innovation: R&D effort and outcomes in the textile industry. Entrepreneurship and Regional Development. 24(7-8):685-704. doi:10.1080/08985626.2012.710260S685704247-8Agarwal, R., Audretsch, D., & Sarkar, M. B. (2007). The process of creative construction: knowledge spillovers, entrepreneurship, and economic growth. Strategic Entrepreneurship Journal, 1(3-4), 263-286. doi:10.1002/sej.36Aharonson, B. S., Baum, J. A. C., & Feldman, M. P. (2007). Desperately seeking spillovers? Increasing returns, industrial organization and the location of new entrants in geographic and technological space. Industrial and Corporate Change, 16(1), 89-130. doi:10.1093/icc/dtl034Albino, V., Carbonara, N., & Giannoccaro, I. (2006). Innovation in industrial districts: An agent-based simulation model. International Journal of Production Economics, 104(1), 30-45. doi:10.1016/j.ijpe.2004.12.023Audretsch, D. B., & Lehmann, E. E. (2005). Does the Knowledge Spillover Theory of Entrepreneurship hold for regions? Research Policy, 34(8), 1191-1202. doi:10.1016/j.respol.2005.03.012Bell, G. G. (2005). Clusters, networks, and firm innovativeness. Strategic Management Journal, 26(3), 287-295. doi:10.1002/smj.448Bell, M., & Albu, M. (1999). Knowledge Systems and Technological Dynamism in Industrial Clusters in Developing Countries. World Development, 27(9), 1715-1734. doi:10.1016/s0305-750x(99)00073-xBelussi, F., & Arcangeli, F. (1998). A typology of networks: flexible and evolutionary firms. Research Policy, 27(4), 415-428. doi:10.1016/s0048-7333(98)00074-2Cantwell, J., & Piscitello, L. (2005). Recent Location of Foreign-owned Research and Development Activities by Large Multinational Corporations in the European Regions: The Role of Spillovers and Externalities. Regional Studies, 39(1), 1-16. doi:10.1080/0034340052000320824Boschma, R. A., & ter Wal, A. L. J. (2007). Knowledge Networks and Innovative Performance in an Industrial District: The Case of a Footwear District in the South of Italy. Industry & Innovation, 14(2), 177-199. doi:10.1080/13662710701253441Brass, D. J. (1984). Being in the Right Place: A Structural Analysis of Individual Influence in an Organization. Administrative Science Quarterly, 29(4), 518. doi:10.2307/2392937Breschi, S. (2001). Knowledge Spillovers and Local Innovation Systems: A Critical Survey. Industrial and Corporate Change, 10(4), 975-1005. doi:10.1093/icc/10.4.975CALANTONE, R. (1997). New product activities and performance: The moderating role of environmental hostility. Journal of Product Innovation Management, 14(3), 179-189. doi:10.1016/s0737-6782(97)00004-0Chell, E., & Baines, S. (2000). Networking, entrepreneurship and microbusiness behaviour. Entrepreneurship & Regional Development, 12(3), 195-215. doi:10.1080/089856200413464Chung, S. (Andy), Singh, H., & Lee, K. (2000). Complementarity, status similarity and social capital as drivers of alliance formation. Strategic Management Journal, 21(1), 1-22. doi:10.1002/(sici)1097-0266(200001)21:13.0.co;2-pCockburn, I. M., & Henderson, R. M. (2003). Absorptive Capacity, Coauthoring Behavior, and the Organization of Research in Drug Discovery. The Journal of Industrial Economics, 46(2), 157-182. doi:10.1111/1467-6451.00067Cohen, W. M., & Levinthal, D. A. (1989). Innovation and Learning: The Two Faces of R & D. The Economic Journal, 99(397), 569. doi:10.2307/2233763Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553Coleman, J. S. (1988). Social Capital in the Creation of Human Capital. American Journal of Sociology, 94, S95-S120. doi:10.1086/228943Coombs, J. E., Deeds, D. L., & Duane Ireland, R. (2009). Placing the choice between exploration and exploitation in context: a study of geography and new product development. Strategic Entrepreneurship Journal, 3(3), 261-279. doi:10.1002/sej.74Crestanello, P., & Tattara, G. (2011). Industrial Clusters and the Governance of the Global Value Chain: The Romania–Veneto Network in Footwear and Clothing. Regional Studies, 45(2), 187-203. doi:10.1080/00343401003596299Dierickx, I., & Cool, K. (1989). Asset Stock Accumulation and Sustainability of Competitive Advantage. Management Science, 35(12), 1504-1511. doi:10.1287/mnsc.35.12.1504Dyer, J. H., & Singh, H. (1998). The Relational View: Cooperative Strategy and Sources of Interorganizational Competitive Advantage. The Academy of Management Review, 23(4), 660. doi:10.2307/259056Eraydin, A., & Armatli-Köroğlu, B. (2005). Innovation, networking and the new industrial clusters: the characteristics of networks and local innovation capabilities in the Turkish industrial clusters. Entrepreneurship & Regional Development, 17(4), 237-266. doi:10.1080/08985620500202632Evenson, R. E., & Kislev, Y. (1973). Research and Productivity in Wheat and Maize. Journal of Political Economy, 81(6), 1309-1329. doi:10.1086/260129Expósito-Langa, M., Molina-Morales, F. X., & Capó-Vicedo, J. (2011). New Product Development and Absorptive Capacity in Industrial Districts: A Multidimensional Approach. Regional Studies, 45(3), 319-331. doi:10.1080/00343400903241535Foss, N. J. (1996). Higher-order industrial Capabilities and competitive advantage. Journal of Industry Studies, 3(1), 1-20. doi:10.1080/13662719600000001George, G., Robley Wood, D., & Khan, R. (2001). Networking strategy of boards: implications for small and medium-sized enterprises. Entrepreneurship & Regional Development, 13(3), 269-285. doi:10.1080/08985620110058115Giuliani, E. 2005. The structure of cluster knowledge networks: Uneven and selective, not pervasive and collective. DRUID Working Paper no. 05-11Giuliani, E., & Bell, M. (2005). The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Research Policy, 34(1), 47-68. doi:10.1016/j.respol.2004.10.008Glasmeier, 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)90070-7Grant, R. M. (1996). Prospering in Dynamically-Competitive Environments: Organizational Capability as Knowledge Integration. Organization Science, 7(4), 375-387. doi:10.1287/orsc.7.4.375Guerrieri, P., & Pietrobelli, C. (2004). Industrial districts’ evolution and technological regimes: Italy and Taiwan. Technovation, 24(11), 899-914. doi:10.1016/s0166-4972(03)00048-8Huggins, R., & Johnston, A. (2010). Knowledge flow and inter-firm networks: The influence of network resources, spatial proximity and firm size. Entrepreneurship & Regional Development, 22(5), 457-484. doi:10.1080/08985620903171350Ibarra, H. (1992). Homophily and Differential Returns: Sex Differences in Network Structure and Access in an Advertising Firm. Administrative Science Quarterly, 37(3), 422. doi:10.2307/2393451Lane, P. J., & Lubatkin, M. (1998). Relative absorptive capacity and interorganizational learning. Strategic Management Journal, 19(5), 461-477. doi:10.1002/(sici)1097-0266(199805)19:53.0.co;2-lLechner, C., Frankenberger, K., & Floyd, S. W. (2010). Task Contingencies in the Curvilinear Relationships Between Intergroup Networks and Initiative Performance. Academy of Management Journal, 53(4), 865-889. doi:10.5465/amj.2010.52814620Levin, D. Z., & Cross, R. (2004). The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer. Management Science, 50(11), 1477-1490. doi:10.1287/mnsc.1030.0136Madill, J. J., Haines, G. H., & Riding, A. L. (2004). Networks and linkages among firms and organizations in the Ottawa-region technology cluster. Entrepreneurship & Regional Development, 16(5), 351-368. doi:10.1080/0898562042000188414Maskell, P. (1998). Low-Tech Competitive Advantages and the Role Of Proximity. European Urban and Regional Studies, 5(2), 99-118. doi:10.1177/096977649800500201Maskell, P. (2001). Towards a Knowledge-based Theory of the Geographical Cluster. Industrial and Corporate Change, 10(4), 921-943. doi:10.1093/icc/10.4.921McEvily, B., & Marcus, A. (2005). Embedded ties and the acquisition of competitive capabilities. Strategic Management Journal, 26(11), 1033-1055. doi:10.1002/smj.484McEvily, B., & Zaheer, A. (1999). Bridging ties: a source of firm heterogeneity in competitive capabilities. Strategic Management Journal, 20(12), 1133-1156. doi:10.1002/(sici)1097-0266(199912)20:123.0.co;2-7Xavier Molina-Morales, F., & Teresa Martínez-Fernández, M. (2006). Industrial districts: something more than a neighbourhood. Entrepreneurship & Regional Development, 18(6), 503-524. doi:10.1080/08985620600884750Molina-Morales, F. X., & Martínez-Fernández, M. T. (2009). Too much love in the neighborhood can hurt: how an excess of intensity and trust in relationships may produce negative effects on firms. Strategic Management Journal, 30(9), 1013-1023. doi:10.1002/smj.766Morrison, A. (2008). Gatekeepers of Knowledgewithin Industrial Districts: Who They Are, How They Interact. Regional Studies, 42(6), 817-835. doi:10.1080/00343400701654178Morrison, A., & Rabellotti, R. (2009). Knowledge and Information Networks in an Italian Wine Cluster. European Planning Studies, 17(7), 983-1006. doi:10.1080/09654310902949265Mowery, D. C., Oxley, J. E., & Silverman, B. S. (1996). Strategic alliances and interfirm knowledge transfer. Strategic Management Journal, 17(S2), 77-91. doi:10.1002/smj.4250171108Nahapiet, J., & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational Advantage. The Academy of Management Review, 23(2), 242. doi:10.2307/259373O’Connor, G. C. (1998). Market Learning and Radical Innovation: A Cross Case Comparison of Eight Radical Innovation Projects. Journal of Product Innovation Management, 15(2), 151-166. doi:10.1111/1540-5885.1520151Oba, B., & Semerciöz, F. (2005). Antecedents of trust in industrial districts: an empirical analysis of inter-firm relations in a Turkish industrial district. Entrepreneurship & Regional Development, 17(3), 163-182. doi:10.1080/08985620500102964Parrilli, M. D. (2009). Collective efficiency, policy inducement and social embeddedness: Drivers for the development of industrial districts. Entrepreneurship & Regional Development, 21(1), 1-24. doi:10.1080/08985620801886513Podolny, J. M., & Baron, J. N. (1997). Resources and Relationships: Social Networks and Mobility in the Workplace. American Sociological Review, 62(5), 673. doi:10.2307/2657354Porter, M. E. (1990). The Competitive Advantage of Nations. doi:10.1007/978-1-349-11336-1Pouder, R., & St. John, C. H. (1996). Hot Spots and Blind Spots: Geographical Clusters of Firms and Innovation. Academy of Management Review, 21(4), 1192-1225. doi:10.5465/amr.1996.9704071867Torre, A., & Rallet, A. (2005). Proximity and Localization. Regional Studies, 39(1), 47-59. doi:10.1080/0034340052000320842Rosenkopf, L., & Almeida, P. (2003). Overcoming Local Search Through Alliances and Mobility. Management Science, 49(6), 751-766. doi:10.1287/mnsc.49.6.751.16026Rosenthal, S. S., & Strange, W. C. (2003). Geography, Industrial Organization, and Agglomeration. Review of Economics and Statistics, 85(2), 377-393. doi:10.1162/003465303765299882Rowley, T., Behrens, D., & Krackhardt, D. (2000). Redundant governance structures: an analysis of structural and relational embeddedness in the steel and semiconductor industries. Strategic Management Journal, 21(3), 369-386. doi:10.1002/(sici)1097-0266(200003)21:33.0.co;2-mRusso, M. (1985). Technical change and the industrial district: The role of interfirm relations in the growth and transformation of ceramic tile production in Italy. Research Policy, 14(6), 329-343. doi:10.1016/0048-7333(85)90003-4Sammarra, A., & Belussi, F. (2006). Evolution and relocation in fashion-led Italian districts: evidence from two case-studies. Entrepreneurship & Regional Development, 18(6), 543-562. doi:10.1080/08985620600884685Simmie, J. (2004). Innovation and Clustering in the Globalised International Economy. Urban Studies, 41(5-6), 1095-1112. doi:10.1080/00420980410001675823Sparrowe, R. T., Liden, R. C., Wayne, S. J., & Kraimer, M. L. (2001). SOCIAL NETWORKS AND THE PERFORMANCE OF INDIVIDUALS AND GROUPS. Academy of Management Journal, 44(2), 316-325. doi:10.2307/3069458STABER, U. (2007). Contextualizing Research on Social Capital in Regional Clusters. International Journal of Urban and Regional Research, 31(3), 505-521. doi:10.1111/j.1468-2427.2007.00742.xStock, G. N., Greis, N. P., & Fischer, W. A. (2001). Absorptive capacity and new product development. The Journal of High Technology Management Research, 12(1), 77-91. doi:10.1016/s1047-8310(00)00040-7Tallman, S., Jenkins, M., Henry, N., & Pinch, S. (2004). Knowledge, Clusters, and Competitive Advantage. The Academy of Management Review, 29(2), 258. doi:10.2307/20159032Thompson, P., & Fox-Kean, M. (2005). Patent Citations and the Geography of Knowledge Spillovers: A Reassessment. American Economic Review, 95(1), 450-460. doi:10.1257/0002828053828509Tsai, W. (2001). KNOWLEDGE TRANSFER IN INTRAORGANIZATIONAL NETWORKS: EFFECTS OF NETWORK POSITION AND ABSORPTIVE CAPACITY ON BUSINESS UNIT INNOVATION AND PERFORMANCE. Academy of Management Journal, 44(5), 996-1004. doi:10.2307/3069443Tsai, W., & Ghoshal, S. (1998). SOCIAL CAPITAL AND VALUE CREATION: THE ROLE OF INTRAFIRM NETWORKS. Academy of Management Journal, 41(4), 464-476. doi:10.2307/257085Tushman, M., & Nadler, D. (1986). Organizing for Innovation. California Management Review, 28(3), 74-92. doi:10.2307/41165203Uzzi, B. (1997). Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. Administrative Science Quarterly, 42(1), 35. doi:10.2307/2393808Varaldo, R., & Ferrucci, L. (1996). The evolutionary nature of the firm within industrial districts. European Planning Studies, 4(1), 27-34. doi:10.1080/09654319608720327Waxell, A., & Malmberg, A. (2007). What is global and what is local in knowledge-generating interaction? The case of the biotech cluster in Uppsala, Sweden. Entrepreneurship & Regional Development, 19(2), 137-159. doi:10.1080/08985620601061184Yli-Renko, H., Autio, E., & Sapienza, H. J. (2001). Social capital, knowledge acquisition, and knowledge exploitation in young technology-based firms. Strategic Management Journal, 22(6-7), 587-613. doi:10.1002/smj.183ZUCKER, L. 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.

Pure-glue hidden valleys through the Higgs portal

We consider the possibility that the Higgs boson can act as a link to a hidden sector in the context of pure-glue hidden valley models. In these models the standard model is weakly coupled, through loops of heavy messengers fields, to a hidden sector whose low energy dynamics is described by a pure-Yang-Mills theory. Such a hidden sector contains several metastable hidden glueballs. In this work we shall extend earlier results on hidden valleys to include couplings of the messengers to the standard model Higgs sector. The effective interactions at one-loop couple the hidden gluons to the standard model particles through the Higgs sector. These couplings in turn induce hidden glueball decays to fermion pairs, or cascade decays with multiple Higgs emission. The presence of effective operators of different mass dimensions, often competing with each other, together with a great diversity of states, leads to a great variability in the lifetimes and decay modes of the hidden glueballs. We find that most of the operators considered in this paper are not heavily constrained by precision electroweak physics, therefore leaving plenty of room in the parameter space to be explored by the future experiments at the LHC.Comment: 44 pages, 16 figures. Major revision for JHEP, corrected an error in Eq. 5.1, comments adde

MAXIPOL: a balloon-borne experiment for measuring the polarization anisotropy of the cosmic microwave background radiation

We discuss MAXIPOL, a bolometric balloon-borne experiment designed to measure the E-mode polarization anisotropy of the cosmic microwave background radiation (CMB) on angular scales of 10 arcmin to 2 degrees. MAXIPOL is the first CMB experiment to collect data with a polarimeter that utilizes a rotating half-wave plate and fixed wire-grid polarizer. We present the instrument design, elaborate on the polarimeter strategy and show the instrument performance during flight with some time domain data. Our primary data set was collected during a 26 hour turnaround flight that was launched from the National Scientific Ballooning Facility in Ft. Sumner, New Mexico in May 2003. During this flight five regions of the sky were mapped. Data analysis is in progress

Socio-Economic Instability and the Scaling of Energy Use with Population Size

The size of the human population is relevant to the development of a sustainable world, yet the forces setting growth or declines in the human population are poorly understood. Generally, population growth rates depend on whether new individuals compete for the same energy (leading to Malthusian or density-dependent growth) or help to generate new energy (leading to exponential and super-exponential growth). It has been hypothesized that exponential and super-exponential growth in humans has resulted from carrying capacity, which is in part determined by energy availability, keeping pace with or exceeding the rate of population growth. We evaluated the relationship between energy use and population size for countries with long records of both and the world as a whole to assess whether energy yields are consistent with the idea of an increasing carrying capacity. We find that on average energy use has indeed kept pace with population size over long time periods. We also show, however, that the energy-population scaling exponent plummets during, and its temporal variability increases preceding, periods of social, political, technological, and environmental change. We suggest that efforts to increase the reliability of future energy yields may be essential for stabilizing both population growth and the global socio-economic system

Fludarabine Modulates Immune Response and Extends In Vivo Survival of Adoptively Transferred CD8 T Cells in Patients with Metastatic Melanoma

Adoptive T cell therapy involving the use of ex vivo generated antigen-specific cytotoxic T lymphocytes provides a promising approach to immunotherapy. It has become increasingly apparent that anti-tumor efficacy using adoptively transferred T cells is linked to their duration of in vivo persistence and can only be achieved when combined with some form of pre-infusion patient conditioning regimen. An optimal conditioning regimen that provides a positive benefit without serious toxicities has yet to be defined. We have established a unique clinical model that allows for evaluation of a given conditioning regimen on adoptively transferred T cells in humans. In this first-in-human study (FHCRC #1796), we evaluate the use of fludarabine, an FDA-approved reagent with predictable lymphodepleting kinetics and duration of action, as a conditioning regimen that promotes homeostatic upregulation of cytokines and growth signals contributing to in vivo T cell persistence.We conducted a phase I study in patients with refractory metastatic melanoma. Patients received two infusions of a single tumor-reactive antigen-specific CTL clone expanded to 10(10)/m(2); the first infusion was given without fludarabine conditioning, and the second CTL infusion was given after a course of fludarabine (25 mg/m(2)/dayx5 days). This design permits intra-patient comparison of in vivo T cell persistence pre- and post-fludarabine. Nineteen CTL infusions were administered to ten patients. No serious toxicities were observed. Three of nine evaluable patients experienced minor response or stable disease for periods of 5.8-11.0 months with two additional patients demonstrating delayed disease stabilization. The median overall survival in this heavily pre-treated population was 9.7 months. Fludarabine led to a 2.9 fold improvement in the in vivo persistence of transferred CTL clones from a median of 4.5 days (range 0-38+) to 13.0 days (range 2-63+) (p<0.05). Fludarabine lymphodepletion increased plasma levels of the homeostatic cytokines IL-7 and IL-15. Surprisingly, fludarabine also increased the relative percentage of CD4+ T cells expressing the regulatory protein Foxp3.Lymphodepletion with fludarabine enhances transferred T cell persistence but suggest that additional improvements to optimize T cell survival and address regulatory T cells are critical in providing anti-tumor efficacy.ClinicalTrials.gov NCT00317759