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The impact of individual collaborative activities on knowledge creation and transmission

Abstract

Collaboration is a major factor in the knowledge and innovation creation in emerging science-driven industries where the technology is rapidly changing and constantly evolving, such as nanotechnology. The objective of this work is to investigate the role of individual scientists and their collaborations in enhancing the knowledge flows, and consequently the scientific production. The methodology involves two main phases. First, the data on all the nanotechnology journal publications in Canada was extracted from the SCOPUS database to create the co-authorship network, and then employ statistical data mining techniques to analyze the scientists’ research performance and partnership history. Also, a questionnaire was sent directly to the researchers selected from our database seeking the predominant properties that make a scientist sufficiently attractive to be selected as a research partner. In the second phase, an agent-based model using Netlogo has been developed to study the network in its dynamic context where several factors could be controlled. It was found that scientists in centralized positions in such networks have a considerable positive impact on the knowledge flows, while loyalty and strong connections within a dense local research group negatively affect the knowledge transmission. Star scientists appear to play a substitutive role in the network and are selected when the usual collaborators, i.e., most famous, and trustable partners are scarce or missing.

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Notes

  1. Publish or Perish is a software program that retrieves and analyzes academic citations.

  2. Based on the graph theory; a clique in an undirected graph is a subset of its vertices such that every two vertices in the subset are connected by an edge.

  3. NW is an extended library that can be integrated with models developed in NetLogo to perform the social network analysis. More information and the downloadable files are available at: https://github.com/NetLogo/NW-Extension.

  4. BehaviorSpace is a software tool integrated with NetLogo that allows you to perform experiments with models.

References

  • Abbasi, A., & Altmann, J. (2011). On the correlation between research performance and social network analysis measures applied to research collaboration networks. In 44th Hawaii international conference on systems science (HICSS-44). Hawaii, USA.

  • Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607.

    Article  Google Scholar 

  • Abbasi, A., Altmann, J., & Hwang, J. (2010). Evaluating scholars based on their academic collaboration activities: Two indices, the Rc-index and the Cc-index, for quantifying collaboration activities of researchers and scientific communities. Scientometrics, 83(1), 1–13.

    Article  Google Scholar 

  • Abbasi, A., Chung, K. S. K., & Hossain, L. (2012). Egocentric analysis of co-authorship network structure, position and performance. Journal of Information Processing & Management, 48(4), 671–679.

    Article  Google Scholar 

  • Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.

    Article  Google Scholar 

  • Allen, R. (1983). Collective invention. Journal of Economic Behavior & Organization, 4(1), 1–24.

    Article  Google Scholar 

  • Axelrod, R. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Banks, J. (1998). Handbook of simulation: Principles, methodology, advances, applications, and practice. New York, NY: Wiley.

    Book  Google Scholar 

  • Beaudry, C., & Allaoui, S. (2012). Impact of public and private research funding scientific production: The case of nanotechnology. Research Policy, 41(9), 1589–1606.

    Article  Google Scholar 

  • Beaudry, C., & Kananian, T. S. R. (2013). Follow the (industry) money—the impact of science networks and industry-to-university contracts on academic patenting in nanotechnology and biotechnology. Industry and Innovation, 20(3), 241–260.

    Article  Google Scholar 

  • Beaudry, C., & Schiffauerova, A. (2011). Impacts of collaboration and network indicators on patent quality: The case of Canadian nanotechnology innovation. European Management Journal, 29(5), 362–376.

    Article  Google Scholar 

  • Beaver, D., & Rosen, R. (1979). Studies in scientific collaboration part III. Professionalization and the natural history of modern scientific co-authorship. Scientometrics, 1(3), 231–245.

    Article  Google Scholar 

  • Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America, 99(3), 7280–7287.

    Article  Google Scholar 

  • Breschi, S., & Lissoni, F. (2006). Mobility of inventors and the geography of knowledge spillovers. New evidence on US data. In CESPRI conference. Milan, Italy.

  • Chen, Z., & Guan, J. (2016). The core-peripheral structure of international knowledge flows: Evidence from patent citation data. R&D Management, 46(1), 62–79.

    MathSciNet  Article  Google Scholar 

  • Chung, K. S. K., & Hossain, L. (2009). Measuring performance of knowledge intensive workgroups through social networks. Project Management Journal, 40(2), 34–58.

    Article  Google Scholar 

  • Contandriopoulos, D., Duhoux, A., Larouche, C., & Perroux, M. (2016). The impact of a researcher’s structural position on scientific performance: An empirical analysis. PLoS ONE, 11(8), e0161281.

    Article  Google Scholar 

  • Drejer, I., & Vinding, A. L. (2006). Organisation, “anchoring” of knowledge, and innovative activity in construction. Construction Management and Economics, 24(9), 921–931.

    Article  Google Scholar 

  • Ebadi, A., & Schiffauerova, A. (2015a). How to become an important player in scientific collaboration networks? Journal of Informetrics, 9(4), 809–825.

    Article  Google Scholar 

  • Ebadi, A., & Schiffauerova, A. (2015b). On the relation between the small world structure and scientific activities. PLoS ONE, 10(3), e0121129.

    Article  Google Scholar 

  • Ebadi, A., & Schiffauerova, A. (2016). How to boost scientific production? A statistical analysis of research funding and other influencing factors. Scientometrics, 106(3), 1093–1116.

    Article  Google Scholar 

  • Eslami, H., Ebadi, A., & Schiffauerova, A. (2013). Effect of collaboration network structure on knowledge creation and technological performance: The case of biotechnology in Canada. Scientometrics, 97(1), 99–119.

    Article  Google Scholar 

  • Fitzgibbons, K., & McNiven, C. (2006). Towards a nanotechnology statistical framework. In Blue sky indicators conference II (pp. 25–27). Ottawa, Canada.

  • Fleming, L., King, C., III, & Juda, A. I. (2007). Small worlds and regional innovation. Organization Science, 18(6), 938–954.

    Article  Google Scholar 

  • Fujimoto, R. M., Perumalla, K., Park, A., Wu, H., Ammar, M. H., Riley, G. F. (2003). Large-scale network simulation: how big? How fast? In Modeling, analysis and simulation of computer telecommunications systems, 2003. MASCOTS 2003: 11th IEEE/ACM international symposium. Atlanta, USA. doi: 10.1109/MASCOT.2003.1240649.

  • Gilbert, N., & Troitzsch, K. (1999). Simulation for the social scientist. Buckingham: Open University Press.

    Google Scholar 

  • Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G., & Aan Den Oord, A. (2008). Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density. Research Policy, 37(10), 1717–1731.

    Article  Google Scholar 

  • Glahn, H. R., & Ruth, D. P. (2003). The new digital forecast database of the National Weather Service. Bulletin of the American Meteorological Society, 84(2), 195–201.

    Article  Google Scholar 

  • Glänzel, W., & Winterhager, M. (1992). International collaboration of three east European countries with Germany in the sciences, 1980–1989. Scientometrics, 25(2), 219–227.

    Article  Google Scholar 

  • Gould, R. V., & Fernandez, R. M. (1989). Structures of mediation: A formal approach to brokerage in transaction networks. Sociological Methodology, 19(1989), 89–126.

    Article  Google Scholar 

  • Graf, H. (2011). Gatekeepers in regional networks of innovators. Cambridge Journal of Economics, 35(1), 173–198.

    Article  Google Scholar 

  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  • Guan, J., & Liu, N. (2016). Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy. Research Policy, 45(1), 97–112.

    Article  Google Scholar 

  • Guan, J. C., & Yan, Y. (2016). Technological proximity and recombinative innovation in the alternative energy field. Research Policy, 45(7), 1460–1473.

    Article  Google Scholar 

  • Guan, J., Zuo, K., Chen, K., & Yam, R. C. (2016). Does country-level R&D efficiency benefit from the collaboration network structure? Research Policy, 45(4), 770–784.

    Article  Google Scholar 

  • Hao, Z., Yun, X., & Zhang, H. (2008). An efficient routing mechanism in network simulation. Journal of Simulation, 84(10–11), 511–520.

    Google Scholar 

  • Harzing, A. W. (2007). Publish or Perish. http://www.harzing.com/pop.htm.

  • He, J., & Fallah, M. H. (2009). Is inventor network structure a predictor of cluster evolution? Technological Forecasting and Social Change, 76(1), 91–106.

    Article  Google Scholar 

  • Heikkinen, M. T., Mainela, T., Still, J., & Tähtinen, J. (2007). Roles for managing in mobile service development nets. Industrial Marketing Management, 36(7), 909–925.

    Article  Google Scholar 

  • Henderson, R., & Cockburn, I. (1996). Scale, scope, and spillovers: The determinants of research productivity in drug discovery. The Rand Journal of Economics, 27(1), 32–59.

    Article  Google Scholar 

  • Hess, A. M., & Rothaermel, F. T. (2011). When are assets complementary? Star scientists, strategic alliances, and innovation in the pharmaceutical industry. Strategic Management Journal, 32(8), 895–909.

    Article  Google Scholar 

  • Keller, R. T. (1991). Gatekeeper communication networks and technological innovation: A study of U.S. and Mexican R&D organizations. The Journal of High Technology Management Research, 2(1), 1–13.

    Article  Google Scholar 

  • Kollock, P. (1994). The emergence of exchange structures: An experimental study of uncertainty, commitment, and trust. American Journal of Sociology, 100(2), 313–345.

    Article  Google Scholar 

  • Kumar, S., & Jan, J. M. (2014). Research collaboration networks of two OIC nations: Comparative study between Turkey and Malaysia in the field of ‘Energy fuels’, 2009–2011. Scientometrics, 98(1), 387–414.

    Article  Google Scholar 

  • Landry, R., Traore, N., & Godin, B. (1996). An econometric analysis of the effect of collaboration on academic research productivity. Higher Education, 32(3), 283–301.

    Article  Google Scholar 

  • Manley, K., Mcfallan, S., & Kajewski, S. (2009). Relationship between construction firm strategies and innovation outcomes. Journal of Construction Engineering and Management, 135(8), 764–771.

    Article  Google Scholar 

  • Mat, N. C., Cheung, Y., Scheepers, H. (2009). Partner selection: Criteria for successful collaborative network. In 20th Australian conference on information systems. Melbourne, Australia.

  • Moazami, A., Ebadi, A., & Schiffauerova, A. (2015). A network perspective of academiaindustry nanotechnology collaboration: A comparison of Canada and the United States. Collnet Journal of Scientometrics and Information Management, 9(2), 263–293. doi:10.1080/09737766.2015.1069966.

    Article  Google Scholar 

  • Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociology Review, 69(2), 138–213.

    Article  Google Scholar 

  • Nagpaul, P. S. (2002). Visualizing cooperation networks of Elite institutions in India. Scientometrics, 54(2), 213–228.

    Article  Google Scholar 

  • Porter, A. L., Youtie, J., Shapira, P., & Schoeneck, D. J. (2008). Refining search terms for nanotechnology. Journal of Nanoparticle Research, 10(5), 715–728.

    Article  Google Scholar 

  • Price, D., & Beaver, D. (1966). Collaboration in an invisible college. American Psychologist, 21(11), 1011.

    Article  Google Scholar 

  • Pyka, A., Ebersberger, B., & Hanusch, H. (2004). A conceptual framework to model long-run qualitative change in the energy system. In J. S. Metcalfe & J. Foster (Eds.), Evolution and economic complexity (pp. 191–213). Cheltenham: Edward Elgar.

    Google Scholar 

  • Pyka, A., Gilbert, N., & Ahrweiler, P. (2002). Simulating innovation networks. In A. Pyka & G. Kuppers (Eds.), Innovation networks: Theory and practice (pp. 169–196). Cheltenham: Edward Elgar.

    Google Scholar 

  • Pyka, A., Gilbert, N., & Ahrweiler, P. (2007). Simulating knowledge-generation and distribution processes in innovation collaborations and networks. Cybernetics and Systems: An International Journal, 38(7), 667–693.

    Article  MATH  Google Scholar 

  • Racherla, P., & Hu, C. (2010). A social network perspective of tourism research collaborations. Annals of Tourism Research, 37(4), 1012–1034.

    Article  Google Scholar 

  • Schiffauerova, A., & Beaudry, C. (2011). Star scientists and their positions in the Canadian biotechnology network. Economics of Innovation and New Technology, 20(4), 343–366.

    Article  Google Scholar 

  • Schiffauerova, A., & Beaudry, C. (2012). Collaboration spaces in Canadian biotechnology: A search for gatekeepers. Journal of Engineering and Technology Management, 29(2), 281–306.

    Article  Google Scholar 

  • Schilling, M. A., & Phelps, C. C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53(7), 1113–1126.

    Article  MATH  Google Scholar 

  • Scholz, R., Nokkala, T., Ahrweiler, P., Pyka, A., & Gilbert, N. (2010). The agent-based Nemo model (SKEIN)—simulating european framework programmes. Innovation in complex social systems (pp. 300–314). London: Routledge.

    Google Scholar 

  • Schrempf, B., Kaplan, D., & Schroeder, D. (2013). National, regional, and sectoral systems of innovation—an overview. Report for FP7 Project” Progress”. European Comission. https://www.google.com.co/url.

  • Sonnenwald, D. (2007). Scientific collaboration: A synthesis of challenges and strategies. Annual Review of Information Science and Technology, 41, 643–681.

    Article  Google Scholar 

  • Sosa, R., & Gero, J. S. (2005). A computational study of creativity in design: The role of society. Artificial Intelligence for Engineering Design, Analysis and Manufacturing Journal, 19(04), 229–244.

    Google Scholar 

  • Tahmooresnejad, L., Beaudry, C., & Schiffauerova, A. (2015). The role of public funding in nanotechnology scientific production: Where Canada stands in comparison to the United States. Scientometrics, 102, 753–787.

    Article  Google Scholar 

  • Tajaddod Alizadeh, D., Ghiasi, G., Schiffauerova, A. (2015) The role of individuals in innovation networks: A simulation approach in canadian biotechnology network. In 11 e Congres International de Genie Industriel-CIGI2015. Québec, Canada.

  • Triulzi, G., Pyka, A., & Scholz, R. (2011). R&D and knowledge dynamics in university-industry relationships in biotech and pharmaceuticals: An agent-based model. International Journal of Biotechnology, 13(1–3), 137–179.

    Google Scholar 

  • Van Segbroeck, S., Santos, F. C., Nowé, A., Pacheco, J. M., Lenaerts, T. (2009). The coevolution of loyalty and cooperation. In 2009 IEEE congress on evolutionary computation, 2009. Trondheim, Norway: IEEE.

  • Wang, X. (2013). Forming mechanisms and structures of a knowledge transfer network: Theoretical and simulation research. Journal of Knowledge Management, 17(2), 278–289.

    Article  Google Scholar 

  • Wilensky, U. (1999). Center for connected learning and computer-based modeling. Evanston, IL: Northwestern University.

    Google Scholar 

  • Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology, 60(10), 2107–2118.

    Article  Google Scholar 

  • Zucker, L. G., & Darby, M. R. (1996). Star scientists and institutional transformation: Patterns of invention and innovation in the formation of the biotechnology industry. Proceedings of the National Academy of Sciences, 93(23), 12709–12716.

    Article  Google Scholar 

  • Zucker, L. G., & Darby, M. R. (2005). Socio-economic impact of nanoscale science: Initial results and Nanobank. Washington: National Bureau of Economic Research Inc.

    Book  Google Scholar 

  • Zuckerman, H. (1967). Nobel laureates in science: Patterns of productivity, collaboration, and authorship. American Sociological Review, 32(3), 391–403.

    Article  Google Scholar 

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Correspondence to Nuha Zamzami.

Appendix: List of nanotechnology keywords (based on Moazami et al. 2015)

Appendix: List of nanotechnology keywords (based on Moazami et al. 2015)

Search term Search queries
Nano* terms “nano assembly”, “nano computer”, “nano cubic technology”, “nano molecular machine”, “nano optic”, “nano optical tweezers”, “nano warfare”, “nanoarray”, “nanoassembler”, “nanobarcode”, “nanobarcodes particle”, “nanobioprocess”, “nanobot”, “nanobotics”, “nanobots”, “nanobubble”, “nanobusiness alliance”, “nanobusiness company”, “nanocatalysis”, “nanoceramic”, “nanochemistry”, “nanochip”, “nanocircle”, “nanocluster”, “nanocomputer”, “nanocone”, “nanocontact”, “nanocrystal”, “nanocrystal antenna”, “nanodefense”, “nanodentistry”, “nanodetect”, “nanodevice”, “nanodiamond”, “nanodisaster”, “nanodot”, “nanoelectrospray”, “nanoengineering”, “nanofacture”, “nanofacty”, “nanofiber”, “nanofibre”, “nanofiltration”, “nanofluidic”, “nanofoam”, “nanogate”, “nanogear”, “nanogenomic”, “nanoimaging”, “nanoimprint lithography”, “nanoimprint machine”, “nanoimprinting”, “nanolabel”, “nanolithography”, “nanomachine”, “nanomagnet”, “nanomanipulat”, “nanomanipulation”, “nanomanufacturing”, “nanomaterial”, “nanomechanical”, “nanomot”, “nanoparticles”,nanowire”, “nanope”, “nanope”, “nanopharmaceutical”, “nanophotonic”, “nanophysic”, “nanoplumbing”, “nanoprism”, “nano-ring”, “nanoscale self assembly”, “nanoscale synthesis”, “nanoscience”, “nanoscopic scale”, “nanoscopic scale”, “nanosens”, “nanosheet”, “nanoshell”, “nanosource”, “nanostructure”, “nanostructured”, “nanosurgery”, “nanosystem”, “nanotechism”, “nanotechnology”, “nanotube”, “nanotube bundle”, “nanowalker”, “nanowetting”
Quantum terms “quantum cascade laser”, “quantum coherence”, “quantum computation”, “quantum compute”, “quantum computer”, “quantum 116 computing”, “quantum conduct”, “quantum conductance”, “quantum conductivity”, “quantum confine”, “quantum device”, “quantum dot”, “quantum gate”, “quantum information”, “quantum information process”, “quantum mirage”, “quantum nanophysics”, “quantum nanomechanics”, “quantum system”, “quantum well”
Molecular* terms “molecular assembler”, “molecular machine”, “molecular nanogenerat”, “molecular nanotechnology”, “molecular robotic”, “molecular scale manufacturing”, “molecular systems engineering”, “molecular technology”
Self assembly terms “fluidic self assembly”, “nanoscale self assembly”, “self assembled”
Atomic terms “atomic manipulation”, “atomic nanostructure”
Other terms “biofabrication”, “biomedical nanotechnology”, “biomimetic synthesis”, “biomolecular assembly”, “biomolecular nanoscale computing”, “biomolecular nanotechnology”, “bionems”, “brownian assembly”, “buckminsterfullerene”, “buckyball”, “buckytube”, “c60 molecule”, “carbon nanotubes”, “conductance quantization”, “dna chip”, “electron beam lithography”, “epitaxial film”, “epitaxy”, “fat fingers problem”, “ganic led”, “glyconanotechnology”, “grey.goo”, “immune machine”, “khaki goo”, “laser tweezer”, “limited assembler”, “military nanotech.”, “moletronic”, “naneplicat”, “nanite”, “optical trapping”, “protein design”, “protein engineering”, “proximal probe”, “rotaxane”, “single cell manipulation”, “spin coating”, “stewart platfm”, “sticky fingers problem”, “textronic”, “universal assembler”, “utility fog”, “zettatechnology”

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Zamzami, N., Schiffauerova, A. The impact of individual collaborative activities on knowledge creation and transmission. Scientometrics 111, 1385–1413 (2017). https://doi.org/10.1007/s11192-017-2350-x

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Keywords

  • Scientific collaboration
  • Partnership
  • Productivity
  • Knowledge flows
  • Social network
  • Network structure