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Scientometrics

, Volume 114, Issue 1, pp 181–216 | Cite as

Factors influencing the formation of intra-institutional formal research groups: group prediction from collaboration, organisational, and topical networks

  • Hector G. CeballosEmail author
  • Sara E. Garza
  • Francisco J. Cantu
Article

Abstract

The factors that foster successful scientific collaboration and teamwork have been studied extensively. However, these factors have been studied in isolation and it is not clear to what extent one factor is more relevant than other in the formation of research groups. In this work we propose a new methodology based on network analysis to simultaneously evaluate multiple factors considered relevant in the conformation of formal research groups. Our methodology is supported on structural, statistical, and correlation analysis. In addition to validating our methodology with a case study at a research-teaching university, we introduce a new network to represent the success of scientific collaboration that produces the best prediction in group formation. Our methodology and the results obtained can be used for organising researchers in a university that seeks to strengthen its research strategy.

Keywords

Scientific collaboration Network analysis Graph clustering Research groups Complex networks 

Mathematics Subject Classification

62H30 91D30 90C27 

JEL Classification

C61 C88 

References

  1. Ancona, D. G., & Caldwell, D. F. (1992). Demography and design: Predictors of new product team performance. Organization Science, 3(3), 321–341.CrossRefGoogle Scholar
  2. Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006). Group formation in large social networks: Membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’06 (pp. 44–54). New York, NY: ACM.  https://doi.org/10.1145/1150402.1150412.
  3. Balland, P. A. (2011). Proximity and the evolution of collaboration networks: Evidence from research and development projects within the global navigation satellite system (GNSS) industry. Regional Studies, 46(6), 741–756.CrossRefGoogle Scholar
  4. Beaver, D. D. (2001). Reflections on scientific collaboration (and its study): Past, present, and future. Scientometrics, 52(3), 365–377.  https://doi.org/10.1023/A:1014254214337.CrossRefGoogle Scholar
  5. Bercovitz, J., & Feldman, M. (2011). The mechanisms of collaboration in inventive teams: Composition, social networks, and geography. Research Policy, 40(1), 81–93.  https://doi.org/10.1016/j.respol.2010.09.008. Special section on Heterogeneity and University-Industry Relations.CrossRefGoogle Scholar
  6. Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. http://stacks.iop.org/1742-5468/2008/i=10/a=P10008.
  7. Bonner, B. L., Baumann, M. R., & Dalal, R. S. (2002). The effects of member expertise on group decision-making and performance. Organizational Behavior and Human Decision Processes, 88(2), 719–736.CrossRefGoogle Scholar
  8. Calero, C., Buter, R., Cabello Valdés, C., & Noyons, E. (2006). How to identify research groups using publication analysis: An example in the field of nanotechnology. Scientometrics, 66(2), 365–376.CrossRefGoogle Scholar
  9. Campion, M. A., Medsker, G. J., & Higgs, A. C. (1993). Relations between work group characteristics and effectiveness: Implications for designing effective work groups. Personnel Psychology, 46(4), 823–847.CrossRefGoogle Scholar
  10. Cantu, F., & Ceballos, H. (2012). A framework for fostering multidisciplinary research collaboration and scientific networking within university environs. In J. Leibowitz (Ed.), Knowledge Management Handbook: Collaboration and Scientific Networking, Chap 12 (pp. 207–217). Boca Raton: CRC Press.CrossRefGoogle Scholar
  11. Cantu, F., Ceballos, H., Mora, S., & Escoffie, M. (2005). A knowledge-based information system for managing research programs and value creation in a university environment. In Proceedings of the Americas conference on information systems, AMCIS (Vol. 2005, pp. 781–791).Google Scholar
  12. Cantu, F., Bustani, A., Molina, A., & Moreira, H. (2009). A knowledge-based development model: The research chair strategy. Journal of Knowledge Management, 13(1), 154–170.CrossRefGoogle Scholar
  13. Casciaro, T., & Lobo, M. S. (2008). When competence is irrelevant: The role of interpersonal affect in task-related ties. Administrative Science Quarterly, 53(4), 655–684.CrossRefGoogle Scholar
  14. Ceballos, H., Fangmeyer, J., Galeano, N., Juarez, E., & Cantu-Ortiz, F. (2017). Impelling research productivity and impact through collaboration: A scientometric case study of knowledge management. Knowledge Management Research and Practice, 15(3), 346–355.CrossRefGoogle Scholar
  15. Cho, P. S., Do, H. H. N., Chandrasekaran, M. K., & Kan, M. Y. (2013). Identifying research facilitators in an emerging Asian Research Area. Scientometrics, 97(1), 75–97.  https://doi.org/10.1007/s11192-013-1051-3.CrossRefGoogle Scholar
  16. Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(066), 111.  https://doi.org/10.1103/PhysRevE.70.066111.Google Scholar
  17. Cohen, S. G., & Bailey, D. E. (1997). What makes teams work: Group effectiveness research from the shop floor to the executive suite. Journal of Management, 23(3), 239–290.  https://doi.org/10.1016/S0149-2063(97)90034-9. A special issue of the journal of management.CrossRefGoogle Scholar
  18. Cummings, J. N., & Kiesler, S. (2008). Who collaborates successfully?: Prior experience reduces collaboration barriers in distributed interdisciplinary research. In: Proceedings of the 2008 ACM conference on computer supported cooperative work, CSCW’08 (pp. 437–446). New York, NY: ACM.  https://doi.org/10.1145/1460563.1460633.
  19. Dahlander, L., & McFarland, D. A. (2013). Ties that last tie formation and persistence in research collaborations over time. Administrative Science Quarterly, 58(1), 69–110.CrossRefGoogle Scholar
  20. Etzkowitz, H. (2003). Research groups as ’quasi-firms’: The invention of the entrepreneurial university. Research Policy, 32(1), 109–121.CrossRefGoogle Scholar
  21. Etzkowitz, H., & Leydesdorff, L. (1998). The endless transition: A ’triple helix’ of niversity industry government relations. Minerva, 36(3), 203–208.CrossRefGoogle Scholar
  22. Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From national systems and ’mode 2’ to a triple helix of university–industry–government relations. Research Policy, 29(2), 109–123.CrossRefGoogle Scholar
  23. Garza, S. E., & Schaeffer, S. E. (2016). Local bilateral clustering for identifying research topics and groups from bibliographical data. Knowledge and Information Systems, 48(1), 179–199.  https://doi.org/10.1007/s10115-015-0867-y.CrossRefGoogle Scholar
  24. Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.  https://doi.org/10.1073/pnas.122653799.MathSciNetzbMATHCrossRefGoogle Scholar
  25. Gruenfeld, D. H., Mannix, E. A., Williams, K. Y., & Neale, M. A. (1996). Group composition and decision making: How member familiarity and information distribution affect process and performance. Organizational Behavior and Human Decision Processes, 67(1), 1–15.  https://doi.org/10.1006/obhd.1996.0061.CrossRefGoogle Scholar
  26. Guimerà, R., Uzzi, B., Spiro, J., & Amaral, L. A. N. (2005). Team assembly mechanisms determine collaboration network structure and team performance. Science, 308(5722), 697–702.  https://doi.org/10.1126/science.1106340.CrossRefGoogle Scholar
  27. Hahn, J., Moon, J. Y., Zhang, C. (2006). Impact of social ties on open source project team formation. In IFIP international conference on open source systems (pp. 307–317). Springer.Google Scholar
  28. Heredia, R. M., & Vinueza, P. C. (2015). A proposal model to monitor interdisciplinary research projects in Latin American Universities. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje, 10(3), 102–108.CrossRefGoogle Scholar
  29. Hinds, P. J., Carley, K. M., Krackhardt, D., & Wholey, D. (2000). Choosing work group members: Balancing similarity, competence, and familiarity. Organizational Behavior and Human Decision Processes, 81(2), 226–251.  https://doi.org/10.1006/obhd.1999.2875.CrossRefGoogle Scholar
  30. Huckman, R. S., Staats, B. R., & Upton, D. M. (2009). Team familiarity, role experience, and performance: Evidence from Indian software services. Management Science, 55(1), 85–100.  https://doi.org/10.1287/mnsc.1080.0921.CrossRefGoogle Scholar
  31. Johnson, N. F., Xu, C., Zhao, Z., Ducheneaut, N., Yee, N., Tita, G., et al. (2009). Human group formation in online guilds and offline gangs driven by a common team dynamic. Physical Review E, 79(066), 117.  https://doi.org/10.1103/PhysRevE.79.066117.Google Scholar
  32. Jones, B. F., Wuchty, S., & Uzzi, B. (2008). Multi-university research teams: Shifting impact, geography, and stratification in science. Science, 322(5905), 1259–1262.CrossRefGoogle Scholar
  33. Kairam, S. R., Wang, D. J., & Leskovec, J. (2012). The life and death of online groups: Predicting group growth and longevity. In Proceedings of the fifth ACM international conference on web search and data mining, WSDM’12 (pp. 673–682). New York, NY: ACM.  https://doi.org/10.1145/2124295.2124374.
  34. Katz, J. S. (1994). Geographic Proximity and Scientific Collaboration. Scientometrics, 31(1), 31–43.MathSciNetCrossRefGoogle Scholar
  35. Katz, J. S., & Hicks, D. (1997). How much is a collaboration worth? A calibrated bibliometric model. In Proceedings of the sixth conference OD the international society for scientometric and informetric (pp. 163–175).Google Scholar
  36. Katzenbach, J. R., & Smith, D. K. (1993a). The discipline of teams. Massachusetts: Harvard Business Press.Google Scholar
  37. Katzenbach, J. R., & Smith, D. K. (1993b). The wisdom of teams: Creating the high-performance organization. Massachusetts: Harvard Business Press.Google Scholar
  38. Klein, J. T. (2008). Evaluation of interdisciplinary and transdisciplinary research: A literature review. American Journal of Preventive Medicine, 35(2), S116–S123.CrossRefGoogle Scholar
  39. Levine, J. M., & Moreland, R. L. (1991). Culture and socialization in work groups. In L. Resnick, J. Levine, & S. Teasley (Eds.), Perspectives on socially shared cognition (pp. 257–279). Washington: American Psychological Association.CrossRefGoogle Scholar
  40. Leydesdorff, L. (2013). Triple helix of university-industry-government relations. Encyclopedia of creativity, innovation and entrepreneurship (pp. 1844–1851). Springer.Google Scholar
  41. Liang, L., & Zhu, L. (2002). Major factors affecting China’s inter-regional research collaboration: Regional scientific productivity and geographical proximity. Scientometrics, 55(2), 287–316.  https://doi.org/10.1023/A:1019623925759.CrossRefGoogle Scholar
  42. Margolin, D., Ognyanoya, K., Huang, M., Huang, Y., & Contractor, N. (2012). Team formation and performance on Nanohub: A network selection challenge in scientific communities. In B. Vedres & M. Scotti (Eds.), Networks in social policy problems (pp. 80–100). Cambridge: Cambridge University Press.Google Scholar
  43. Martin, B. R. (2012). Are universities and university research under threat? Towards an evolutionary model of university speciation. Cambridge Journal of Economics, 36(3), 543–565.CrossRefGoogle Scholar
  44. Martin, T., Ball, B., Karrer, B., & Newman, M. E. J. (2013). Coauthorship and citation patterns in the Physical Review. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics.  https://doi.org/10.1103/PhysRevE.88.012814, arXiv:1304.0473.
  45. Martín-Sempere, M. J., Garzón-García, B., & Rey-Rocha, J. (2008). Team consolidation, social integration and scientists’ research performance: An empirical study in the biology and biomedicine field. Scientometrics, 76(3), 457–482.  https://doi.org/10.1007/s11192-007-1866-x.CrossRefGoogle Scholar
  46. Mattessich, P. W., & Monsey, B. R. (1992). Collaboration: What makes it work. In A review of research literature on factors influencing successful collaboration. Minnesota: ERIC.Google Scholar
  47. Newman, M. E. J. (2001). Clustering and preferential attachment in growing networks. Physical Review E, 64, 0104209v1.Google Scholar
  48. Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(Suppl 1), 5200–5205.  https://doi.org/10.1073/pnas.0307545100.CrossRefGoogle Scholar
  49. Ordóñez-Matamoros, H. G., Cozzens, S. E., & Garcia, M. (2010). International co-authorship and research team performance in Colombia. Review of Policy Research, 27(4), 415–431.CrossRefGoogle Scholar
  50. Owens, D. A., Mannix, E. A., & Neale, M. A. (1998). Strategic formation of groups: Issues in task performance and team member selection. Research on Managing Groups and Teams, 1, 149–165.Google Scholar
  51. Palla, G., Barabási, A. L., & Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136), 664–667.CrossRefGoogle Scholar
  52. Perianes-Rodriguez, A., Chinchilla-Rodriguez, Z., Vargas-Quesada, B., Olmeda-Gomez, C., & Moya-Anegon, F. (2009). Synthetic hybrid indicators based on scientific collaboration to quantify and evaluate individual research results. Journal of Informetrics, 3(2), 91–101.CrossRefGoogle Scholar
  53. Putnam, L. L. (1992). Rethinking the nature of groups in organizations. Small Group Communication: A Reader, 6, 57–66.Google Scholar
  54. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America, 101(9), 2658–2663.  https://doi.org/10.1073/pnas.0400054101.CrossRefGoogle Scholar
  55. Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76(036), 106.  https://doi.org/10.1103/PhysRevE.76.036106.Google Scholar
  56. Reagans, R., Zuckerman, E., & McEvily, B. (2004). How to make the team: Social networks vs. demography as criteria for designing effective teams. Administrative Science Quarterly, 49(1), 101–133.Google Scholar
  57. Rey-Rocha, J., Garzón-García, B., & Martín-Sempere, M. J. (2006). Scientists’ performance and consolidation of research teams in Biology and Biomedicine at the Spanish Council for Scientific Research. Scientometrics, 69(2), 183–212.CrossRefGoogle Scholar
  58. Rey-Rocha, J., Garzón-García, B., & José Martín-Sempere, M. (2007). Exploring social integration as a determinant of research activity, performance and prestige of scientists. Empirical evidence in the Biology and Biomedicine field. Scientometrics, 72(1), 59–80.  https://doi.org/10.1007/s11192-007-1703-2.CrossRefGoogle Scholar
  59. Ruef, M., Aldrich, H. E., & Carter, N. M. (2003). The structure of founding teams: Homophily, strong ties, and isolation among us entrepreneurs. American Sociological Review, 68, 195–222.CrossRefGoogle Scholar
  60. Sam, C., & Van Der Sijde, P. (2014). Understanding the concept of the entrepreneurial university from the perspective of higher education models. Higher Education, 68(6), 891–908.CrossRefGoogle Scholar
  61. Sarigöl, E., Pfitzner, R., Scholtes, I., Garas, A., & Schweitzer, F. (2014). Predicting scientific success based on coauthorship networks. EPJ Data Science, 3(1), 1–16.CrossRefGoogle Scholar
  62. Schaeffer, S. E. (2005). Stochastic local clustering for massive graphs (pp. 354–360). Berlin, Heidelberg: Springer.  https://doi.org/10.1007/11430919_42.
  63. Shah, P. P., & Jehn, K. A. (1993). Do friends perform better than acquaintances? The interaction of friendship, conflict, and task. Group Decision and Negotiation, 2(2), 149–165.  https://doi.org/10.1007/BF01884769.CrossRefGoogle Scholar
  64. Sonnentag, S., & Volmer, J. (2009). Individual-level predictors of task-related teamwork processes: The role of expertise and self-efficacy in team meetings. Group & Organization Management, 34(1), 37–66.CrossRefGoogle Scholar
  65. Stokols, D., Misra, S., Moser, R. P., Hall, K. L., & Taylor, B. K. (2008). The ecology of team science: Understanding contextual influences on transdisciplinary collaboration. American Journal of Preventive Medicine, 35(2), S96–S115.CrossRefGoogle Scholar
  66. Sun, Y., & Liu, K. (2016). Proximity effect, preferential attachment and path dependence in inter-regional network: A case of China’s technology transaction. Scientometrics, 108(1), 201–220.  https://doi.org/10.1007/s11192-016-1951-0.CrossRefGoogle Scholar
  67. Tomas-Folch, M., Mentado-Labao, T., & Ruiz-Ruiz, J. M. (2015). Las buenas prácticas en gestión de la investigación de las universidades mejores situadas en los rankings. Archivos Analíticos de Políticas Educativas, 23(105), 1–23.Google Scholar
  68. Uddin, S., Hossain, L., & Rasmussen, K. (2013). Network effects on scientific collaborations. PLOS ONE, 8(2), e57546.  https://doi.org/10.1371/journal.pone.0057546.CrossRefGoogle Scholar
  69. Verbree, M., Horlings, E., Groenewegen, P., Van der Weijden, E., & van den Besselaar, P. (2015). Organizational factors influencing scholarly performance: A multivariate study of biomedical research groups. Scientometrics, 102(1), 25–49.CrossRefGoogle Scholar
  70. Volmer, J., & Sonnentag, S. (2011). The role of star performers in software design teams. Journal of Managerial Psychology, 26(3), 219–234.CrossRefGoogle Scholar
  71. Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 366–391.MathSciNetGoogle Scholar
  72. Waltman, L., & Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the Association for Information Science and Technology, 63(12), 2378–2392.CrossRefGoogle Scholar
  73. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge University Press.zbMATHCrossRefGoogle Scholar
  74. Yu, Q., Shao, H., & Duan, Z. (2011). Research groups of oncology co-authorship network in China. Scientometrics, 89(2), 553–567.CrossRefGoogle Scholar
  75. Zhang, Y., Chen, K., Zhu, G., Yam, R. C. M., & Guan, J. (2016). Inter-organizational scientific collaborations and policy effects: An ego-network evolutionary perspective of the Chinese Academy of Sciences. Scientometrics, 108(3), 1383–1415.  https://doi.org/10.1007/s11192-016-2022-2.CrossRefGoogle Scholar
  76. Zhu, M., Huang, Y., & Contractor, N. S. (2013). Motivations for self-assembling into project teams. Social Networks, 35(2), 251–264.  https://doi.org/10.1016/j.socnet.2013.03.001. Special issue on advances in two-mode social networks.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  • Hector G. Ceballos
    • 1
    Email author
  • Sara E. Garza
    • 2
  • Francisco J. Cantu
    • 1
  1. 1.Tecnologico de MonterreyMonterreyMexico
  2. 2.School of Mechanical and Electrical Engineering (FIME)Universidad Autónoma de Nuevo León (UANL)San Nicolás de los GarzaMexico

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