Scientometrics

, Volume 94, Issue 1, pp 397–421 | Cite as

The motivations for knowledge transfer across borders: the diffusion of data envelopment analysis (DEA) methodology

Article

Abstract

To facilitate technology development, people rely on quick and intensive knowledge interactions without barriers. However, when people need to transfer knowledge from one place to another, geographical distance is a critical barrier to overcome because tacit and invisible characteristics are embedded in certain knowledge and locations. This study explores how social and scientific resources embedded within persons can motivate personal knowledge-diffusion behaviors; that is, bridging resources between locations. To explain cross-border diffusion, this work analyzes knowledge dissemination of the data envelopment analysis (DEA) method. By collecting theoretical and application papers in DEA methodology from the Web of Science data set, this study analyzes the academic network consisting of 610 researchers and identifies author locations, research disciplines, and their mutual linkages to explain the importance of personal specific characteristics in cross-border diffusion. Regression models and network analysis show the advantages of personal research seniority and cross-disciplinary coordinating capabilities for researchers to diffuse knowledge from one region to another. The corresponding brokering capabilities accumulated within domestic area or adjacent nations are also helpful for specifically brokering resources of other farther places.

Keywords

Location Knowledge diffusion Brokerages Network position DEA method 

References

  1. Abbasi, A., & Altmann, J. (2010). On the correlation between research performance and social network analysis measures applied to research collaboration networks. Technology Management, Economics and Policy Papers, TEMEP Discussion Paper, No. 2010:66, Seoul National University, Seoul.Google Scholar
  2. Autant-Bernard, C., & Lesage, J. P. (2010). Quantifying knowledge spillovers using spatial econometric tools. Journal of Regional Science, 20(10), 1–16.Google Scholar
  3. Autant-Bernard, C., & Massard, N. (2009). Underlying mechanisms of knowledge diffusion. IAREG. Working paper, 4.7, 1–38.Google Scholar
  4. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for windows: Software for social network analysis. Harvard: Analytic Technologies.Google Scholar
  5. Bornmann, L., & Daniel, H.-D. (2005). Does the h-index for ranking of scientists really work? Scientometrics, 65(3), 391–392.CrossRefGoogle Scholar
  6. Breschi, S., & Lissoni, F. (2001). Knowledge spillovers and local innovation systems: A critical survey. Industrial and Corporate Change, 10(4), 975–1005.Google Scholar
  7. Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(1), 69–87.CrossRefGoogle Scholar
  8. Burt, R. S. (1992). Structural Holes: The Social Structure of Competition, Cambridge: Harvard University Press.Google Scholar
  9. Burt, R. S. (1997). The contingent value of social capital. Administrative Science Quarterly, 42(2), 339–366.Google Scholar
  10. Burt, R. S. (2010). Neighbor networksCompetitive advantage local and personal. NY: Oxford University Press.Google Scholar
  11. Cantwell, J., & Iammarino, S. (2003). Geographical hierarchies of research locations in the European Union. In J. Cantwell & S. Iammarino (Eds.), Multinational corporations and European regional systems of innovation. London: Routledge.Google Scholar
  12. Cantwell, J. C., Noonan, C., & Zhang, F. (2008). Technological complexity and the restructuring of subsidiary knowledge sourcing in intra-multinational and inter-firm networks. Milan: Academy of International Business.Google Scholar
  13. Cantwell, J., & Piscitello, L. (2005). Collective learning and relational capital in Local innovation processes. Regional Studies, 39(1), 1–16.CrossRefGoogle Scholar
  14. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.MathSciNetMATHCrossRefGoogle Scholar
  15. Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York: Springer Science Business Media.Google Scholar
  16. Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R&D. The Economic Journal, 99, 569–596.CrossRefGoogle Scholar
  17. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning, and innovation. Administrative Science Quarterly, 35, 128–152.CrossRefGoogle Scholar
  18. Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 2(3), 151–157.CrossRefGoogle Scholar
  19. Gould, R., & Fernandez, R. (1989). Structures of mediation: A formal approach to brokerage in transaction networks. Sociological Methodology, 19, 89–126.CrossRefGoogle Scholar
  20. Grant, R. M., Jammine, A. P., & Thomas, H. (1988). Diversity, diversification, and profitability among British manufacturing companies, 1972–84. Academy of Management Journal, 31(4), 771–801.CrossRefGoogle Scholar
  21. Han, Y.-J., & Park, Y. (2006). Patent network analysis of inter-industrial knowledge flows: The case of Korea between traditional and emerging industries. World Patent Information, 28(3), 235–247.CrossRefGoogle Scholar
  22. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.CrossRefGoogle Scholar
  23. Ho, M. H. C., & Verspagen, B. (2006). The role of national border and regions in knowledge flows. In N. Lorenz & A. Lundvall (Eds.), How Europe’s Economies Learn. UK: Oxford University Press.Google Scholar
  24. Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108, 577–598.Google Scholar
  25. Mingers, J. (2009). Measuring the research contribution of management academics using the Hirsch-index. Journal of the Operational Research Society, 60(9), 1143–1153.CrossRefGoogle Scholar
  26. Nomaler, Ö., & Verspagen, B. (2008). Knowledge flows, patent citations, and the impact of science on technology. Economic Systems Research, 20(4), 339–366.CrossRefGoogle Scholar
  27. Saad, G. (2010). Applying the h-index in exploring bibliometric properties of elite marketing scholars. Scientometrics, 83(2), 423–433.CrossRefGoogle Scholar
  28. Sambharya, R. (1995). The combined effect of international diversification and product diversification strategies on the performance of U.S.-based multinational corporations. Management International Review, 35(3), 197–218.Google Scholar
  29. Seiford, L. M. (1996). Data envelopment analysis: the evolution of the state of the art (1978–1995). Journal of Productivity Analysis, 7(2–3), 99–137.CrossRefGoogle Scholar
  30. Singh, J. (2004). Multinational firms and international knowledge diffusion: Evidence using patent citation data. INSEAD working paper.Google Scholar
  31. Sonnenwald, D. (2006). Scientific collaboration: a synthesis of challenges and strategies. Annual Review of Information Science and Technology, 41, 643–681.CrossRefGoogle Scholar
  32. Teece, D. J. (1980). Economies of scope and the scope of the enterprises. Journal of Economic Behavior & Organization, 1, 223–247.CrossRefGoogle Scholar
  33. Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42, 35–67.CrossRefGoogle Scholar
  34. Vanhaverbeke, W., Beerkens, B., & Duysters, G. (2007). Technological capability building through networking strategies within high-tech industries, UNU-MERIT Working Paper Series, 2007-018, 1–43.Google Scholar
  35. Verspagen, B. (1998). European ‘Regional Clubs’: Do they exist, and where are they heading? On economic and technological differences between European regions. In J. Adams & F. Pigliaru (Eds.), Economic changes and growth. UK: Edward Elgar.Google Scholar
  36. Verspagen, B. (2010). The spatial hierarchy of technological change and economic development in Europe. Annual Regional Science, 45, 109–132.CrossRefGoogle Scholar
  37. Verspagen, B., & Schoenmakers, W. (2004). The spatial dimension of patenting by multinational firms in Europe. Journal of Economic Geography, 4, 23–42.CrossRefGoogle Scholar
  38. Yung, J. W., Meyer, P. S., & Ausubel, J. H. (1999). The Loglet Lab software: a tutorial. Technological Forecasting and Social Change, 61(3), 273–295.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2012

Authors and Affiliations

  1. 1.National Taiwan University of Science and TechnologyTaipeiTaiwan

Personalised recommendations