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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

Author

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  • Alireza Abbasi
  • Jorn Altmann

    (Technology Management, Economics, and Policy, College of Engineering, Seoul National University)

  • Liaquat Hossain

Abstract

In this study, we develop a theoretical model based on social network theories and analytical methods for exploring collaboration (co-authorship) networks of scholars. We use measures from social network analysis (SNA) (i.e., normalized degree centrality, normalized closeness centrality, normalized betweenness centrality, normalized eigenvector centrality, average ties strength, and efficiency) for examining the effect of social networks on the (citation-based) performance of scholars in a given discipline (i.e., information systems). Results from our statistical analysis using a Poisson regression model suggest that research performance of scholars (g-index) is positively correlated with four SNA measures except for the normalized betweenness centrality and the normalized closeness centrality measures. Furthermore, it reveals that only normalized degree centrality, efficiency, and average ties strength have a positive significant influence on the g-index (as a performance measure). The normalized eigenvector centrality has a negative significant influence on the g-index. Based on these results, we can imply that scholars, who are connected to many distinct scholars, have a better citation-based performance (g-index) than scholars with fewer connections. Additionally, scholars with large average ties strengths (i.e., repeated co-authorships) show a better research performance than those with low tie strengths (e.g., single co-authorships with many different scholars). The results related to efficiency show that scholars, who maintain a strong co-authorship relationship to only one co-author of a group of linked co-authors, perform better than those researchers with many relationships to the same group of linked co-authors. The negative effect of the normalized eigenvector suggests that scholars should work with many students instead of other well-performing scholars. Consequently, we can state that the professional social network of researchers can be used to predict the future performance of researchers.

Suggested Citation

  • Alireza Abbasi & Jorn Altmann & Liaquat Hossain, 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," TEMEP Discussion Papers 201176, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jun 2011.
    • Handle: RePEc:snv:dp2009:201176
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Tol, Richard S.J., 2008. "A rational, successive g-index applied to economics departments in Ireland," Journal of Informetrics, Elsevier, vol. 2(2), pages 149-155.
    3. Kibae Kim & Jorn Altmann & Junseok Hwang, 2010. "Measuring and Analyzing the Openness of the Web2.0 Service Network for Improving the Innovation Capacity of the Web2.0 System through Collective Intelligence," TEMEP Discussion Papers 201057, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Mar 2010.
    4. Alireza Abbasi & Jörn Altmann & Junseok Hwang, 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, Springer;Akadémiai Kiadó, vol. 83(1), pages 1-13, April.
    5. Jorn Altmann & Alireza Abbasi & Junseok Hwang, 2010. "Evaluating the Productivity of Researchers and their Communities: The RP-Index and the CP-Index," TEMEP Discussion Papers 201048, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jan 2010.
    6. Jason Owen-Smith & Massimo Riccaboni & Fabio Pammolli & Walter W. Powell, 2002. "A Comparison of U.S. and European University-Industry Relations in the Life Sciences," Management Science, INFORMS, vol. 48(1), pages 24-43, January.
    7. Pablo D. Batista & Mônica G. Campiteli & Osame Kinouchi, 2006. "Is it possible to compare researchers with different scientific interests?," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(1), pages 179-189, July.
    8. Alireza Abbasi & Jorn Altmann, 2010. "On the Correlation between Research Performance and Social Network Analysis Measures Applied to Research Collaboration Networks," TEMEP Discussion Papers 201066, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Oct 2010.
    9. Sune Lehmann & Andrew D. Jackson & Benny E. Lautrup, 2006. "Measures for measures," Nature, Nature, vol. 444(7122), pages 1003-1004, December.
    10. Alireza Abbasi & Jorn Altmann & Junseok Hwang, 2009. "Evaluating Scholars Based on their Academic Collaboration Activities: The RC-Index and CC-Index for Quantifying Collaboration Activities of Researchers and Scientific Communities," TEMEP Discussion Papers 200915, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Sep 2009.
    11. Wagner, Caroline S. & Leydesdorff, Loet, 2005. "Network structure, self-organization, and the growth of international collaboration in science," Research Policy, Elsevier, vol. 34(10), pages 1608-1618, December.
    12. Katz, J. Sylvan & Martin, Ben R., 1997. "What is research collaboration?," Research Policy, Elsevier, vol. 26(1), pages 1-18, March.
    13. Ray Reagans & Ezra W. Zuckerman, 2001. "Networks, Diversity, and Productivity: The Social Capital of Corporate R&D Teams," Organization Science, INFORMS, vol. 12(4), pages 502-517, August.
    14. Harold Guetzkow & Herbert A. Simon, 1955. "The Impact of Certain Communication Nets Upon Organization and Performance in Task-Oriented Groups," Management Science, INFORMS, vol. 1(3-4), pages 233-250, 04-07.
    15. Frances Ruane & Richard S.J. Tol, 2007. "Refined (Successive) H-Indices: An Application To Economics In The Republic Of Ireland," Working Papers FNU-130, Research unit Sustainability and Global Change, Hamburg University, revised Mar 2007.
    16. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    17. Antonis Sidiropoulos & Dimitrios Katsaros & Yannis Manolopoulos, 2007. "Generalized Hirsch h-index for disclosing latent facts in citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(2), pages 253-280, August.
    18. Linton Freeman, 1980. "The gatekeeper, pair-dependency and structural centrality," Quality & Quantity: International Journal of Methodology, Springer, vol. 14(4), pages 585-592, August.
    19. Binder, Jj, 1985. "On The Use Of The Multivariate Regression-Model In Event Studies," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 370-383.
    20. Yichuan Jiang, 2008. "Locating active actors in the scientific collaboration communities based on interaction topology analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 74(3), pages 471-482, March.
    21. Gibbons, Michael R., 1982. "Multivariate tests of financial models : A new approach," Journal of Financial Economics, Elsevier, vol. 10(1), pages 3-27, March.
    22. Melin, Goran, 2000. "Pragmatism and self-organization: Research collaboration on the individual level," Research Policy, Elsevier, vol. 29(1), pages 31-40, January.
    23. Tibor Braun & Wolfgang Glänzel & András Schubert, 2006. "A Hirsch-type index for journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 169-173, October.
    24. Frances Ruane & Richard S. J. Tol, 2008. "Rational (successive) h-indices: An application to economics in the Republic of Ireland," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(2), pages 395-405, May.
    25. Alireza Abbasi & Jorn Altmann, 2010. "A Social Network System for Analyzing Publication Activities of Researchers," TEMEP Discussion Papers 201058, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Apr 2010.
    26. Daniel Z. Levin & Rob Cross, 2004. "The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer," Management Science, INFORMS, vol. 50(11), pages 1477-1490, November.
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    More about this item

    Keywords

    Collaboration; citation-based research performance; co-authorship networks; social network analysis measures; regression; correlation.;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • H81 - Public Economics - - Miscellaneous Issues - - - Governmental Loans; Loan Guarantees; Credits; Grants; Bailouts
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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