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Diversity, networks, and innovation: A text analytic approach to measuring expertise diversity

Published online by Cambridge University Press:  15 December 2022

Alina Lungeanu Open the ORCID record for Alina Lungeanu [Opens in a new window] ,
Ryan Whalen Open the ORCID record for Ryan Whalen [Opens in a new window] ,
Y. Jasmine Wu Open the ORCID record for Y. Jasmine Wu [Opens in a new window] ,
Leslie A. DeChurch  and
Noshir S. Contractor Open the ORCID record for Noshir S. Contractor [Opens in a new window]
Alina Lungeanu*
Affiliation:
Northwestern University, Evanston, IL, USA
Ryan Whalen
Affiliation:
The University of Hong Kong, Pokfulam, Hong Kong
Y. Jasmine Wu
Affiliation:
Northwestern University, Evanston, IL, USA
Leslie A. DeChurch
Affiliation:
Northwestern University, Evanston, IL, USA
Noshir S. Contractor
Affiliation:
Northwestern University, Evanston, IL, USA
*
*Corresponding author. Email: alina.lungeanu1@northwestern.edu
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Abstract

Despite the importance of diverse expertise in helping solve difficult interdisciplinary problems, measuring it is challenging and often relies on proxy measures and presumptive correlates of actual knowledge and experience. To address this challenge, we propose a text-based measure that uses researcher’s prior work to estimate their substantive expertise. These expertise estimates are then used to measure team-level expertise diversity by determining similarity or dissimilarity in members’ prior knowledge and skills. Using this measure on 2.8 million team invented patents granted by the US Patent Office, we show evidence of trends in expertise diversity over time and across team sizes, as well as its relationship with the quality and impact of a team’s innovation output.

Keywords

inventor networksnetwork sciencetext analyticsinnovationpatent recordsteam scienceteam expertise diversity
Type
Research Article
Information
Network Science , Volume 11 , Special Issue 1: Scientific Networks , March 2023 , pp. 36 - 64
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

Guest Editor (Special Issue on Scientific Networks): Dmitry Zaytsev

References

Aksnes, D. W. (2006). Citation rates and perceptions of scientific contribution. Journal of the American Society for Information Science and Technology, 57(2), 169–185.CrossRefGoogle Scholar
Bell, S. T. (2007). Deep-level composition variables as predictors of team performance: A meta-analysis. Journal of Applied Psychology, 92(3), 595–615.CrossRefGoogle ScholarPubMed
Bell, S. T., Villado, A. J., Lukasik, M. A., Belau, L., & Briggs, A. L. (2011). Getting specific about demographic diversity variable and team performance relationships: A meta-analysis. Journal of Management, 37(3), 709–743.CrossRefGoogle Scholar
Biscaro, C., & Giupponi, C. (2014). Co-authorship and bibliographic coupling network effects on citations. PLoS One, 9(6), e99502.10.1371/journal.pone.0099502CrossRefGoogle ScholarPubMed
Blau, P. M. (1977). Inequality and heterogeneity: A primitive theory of social structure (Vol. 7). New York: Free Press.Google Scholar
Börner, K., Contractor, N., Falk-Krzesinski, H. J., Fiore, S. M., Hall, K. L., Keyton, J., …Uzzi, B. (2010). A multi-level systems perspective for the science of team science. Science Translational Medicine, 2(49), 49cm24.10.1126/scitranslmed.3001399CrossRefGoogle ScholarPubMed
Bruns, H. C. (2013). Working alone together: Coordination in collaboration across domains of expertise. Academy of Management Journal, 56(1), 62–83.CrossRefGoogle Scholar
Byrne, D., & Griffitt, W. (1973). Interpersonal attraction. Annual review of Psychology, 24(1), 317–336.10.1146/annurev.ps.24.020173.001533CrossRefGoogle Scholar
Cattani, G., Ferriani, S., Mariani, M. M., & Mengoli, S. (2013). Tackling the, Galácticos, effect: Team familiarity and the performance of star-studded projects. Industrial and Corporate Change, 22(6), 1629–1662.CrossRefGoogle Scholar
Chan, T. H., Mihm, J., & Sosa, M. (2020). Revisiting the role of collaboration in creating breakthrough inventions. Manufacturing & Service Operations Management, 23(5), 1005–1331.CrossRefGoogle Scholar
Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.10.1086/228943CrossRefGoogle Scholar
Cox, T. H., & Blake, S. (1991). Managing cultural diversity: Implications for organizational competitiveness. Academy of Management Perspectives, 5(3), 45–56.10.5465/ame.1991.4274465CrossRefGoogle Scholar
Cummings, J. N. (2004). Work groups, structural diversity, and knowledge sharing in a global organization. Management Science, 50(3), 352–364.CrossRefGoogle Scholar
Cummings, J. N., & Kiesler, S. (2005). Collaborative research across disciplinary and organizational boundaries. Social Studies of Science, 35(5), 703–722.CrossRefGoogle Scholar
Cummings, J. N., & Kiesler, S. (2007). Coordination costs and project outcomes in multi-university collaborations. Research Policy, 36(10), 1620–1634.CrossRefGoogle Scholar
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 .CrossRefGoogle Scholar
Cummings, J. N., Kiesler, S., Bosagh Zadeh, R., & Balakrishnan, A. D. (2013). Group heterogeneity increases the risks of large group size: A longitudinal study of productivity in research groups. Psychological Science, 24(6), 880–890.CrossRefGoogle Scholar
DeChurch, L. A., & Mesmer-Magnus, J. R. (2010). The cognitive underpinnings of effective teamwork: A meta-analysis. Journal of Applied Psychology, 95(1), 32–53.CrossRefGoogle ScholarPubMed
Ebadi, A., & Schiffauerova, A. (2015). How to receive more funding for your research? Get connected to the right people!. PLoS One, 10(7), e0133061.CrossRefGoogle Scholar
Espinosa, J. A., Slaughter, S. A., Kraut, R. E., & Herbsleb, J. D. (2007). Familiarity, complexity, and team performance in geographically distributed software development. Organization Science, 18(4), 613–630.CrossRefGoogle Scholar
Faraj, S., & Sproull, L. (2000). Coordinating expertise in software development teams. Management Science, 46(12), 1554–1568.CrossRefGoogle Scholar
Finholt, T. A., & Olson, G. M. (1997). From laboratories to collaboratories: A new organizational form for scientific collaboration. Psychological Science, 8(1), 28–36.CrossRefGoogle Scholar
Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47(1), 117–132.CrossRefGoogle Scholar
Gilson, L. L., Mathieu, J. E., Shalley, C. E., & Ruddy, T. M. (2005). Creativity and standardization: Complementary or conflicting drivers of team effectiveness? Academy of Management Journal, 48(3), 521–531.CrossRefGoogle Scholar
Guimera, 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.CrossRefGoogle ScholarPubMed
Hall, K. L., Vogel, A. L., Huang, G. C., Serrano, K. J., Rice, E. L., Tsakraklides, S. P., & Fiore, S. M. (2018). The science of team science: A review of the empirical evidence and research gaps on collaboration in science. American Psychologist, 73(4), 532–548.CrossRefGoogle ScholarPubMed
Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82–111.CrossRefGoogle Scholar
Harrison, D. A., & Klein, K. J. (2007). What’s the difference? Diversity constructs as separation, variety, or disparity in organizations. Academy of Management Review, 32(4), 1199–1228.CrossRefGoogle Scholar
Harrison, D. A., Mohammed, S., McGrath, J. E., Florey, A. T., & Vanderstoep, S. W. (2003). Time matters in team performance: Effects of member familiarity, entrainment, and task discontinuity on speed and quality. Personnel Psychology, 56(3), 633–669.CrossRefGoogle Scholar
Harrison, D. A., Price, K. H., Gavin, J. H., & Florey, A. T. (2002). Time, teams, and task performance: Changing effects of surface-and deep-level diversity on group functioning. Academy of Management Journal, 45(5), 1029–1045.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Hinnant, C. C., Stvilia, B., Wu, S., Worrall, A., Burnett, G., Burnett, K., …Marty, P. F. (2012). Author-team diversity and the impact of scientific publications: Evidence from physics research at a national science lab. Library & Information Science Research, 34(4), 249–257.CrossRefGoogle Scholar
Hong, L., & Page, S. E. (2004). Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences, 101(46), 16385–16389.CrossRefGoogle ScholarPubMed
Horwitz, S. K. (2005). The compositional impact of team diversity on performance: Theoretical considerations. Human Resource Development Review, 4(2), 219–245.CrossRefGoogle Scholar
Horwitz, S. K., & Horwitz, I. B. (2007). The effects of team diversity on team outcomes: A meta-analytic review of team demography. Journal of Management, 33(6), 987–1015.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Jehn, K. A., Northcraft, G. B., & Neale, M. A. (1999). Why differences make a difference: A field study of diversity, conflict and performance in workgroups. Administrative Science Quarterly, 44(4), 741–763.CrossRefGoogle Scholar
Jones, B. F. (2009). The burden of knowledge and the “death of the renaissance man”: Is innovation getting harder? The Review of Economic Studies, 76(1), 283–317.CrossRefGoogle Scholar
Joshi, A., & Roh, H. (2009). The role of context in work team diversity research: A meta-analytic review. Academy of Management Journal, 52(3), 599–627.CrossRefGoogle Scholar
Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. In International conference on machine learning .Google Scholar
Leahey, E., Beckman, C. M., & Stanko, T. L. (2017). Prominent but less productive: The impact of interdisciplinarity on scientists’ research. Administrative Science Quarterly, 62(1), 105–139.CrossRefGoogle Scholar
Lee, Y.-N., Walsh, J. P., & Wang, J. (2015). Creativity in scientific teams: Unpacking novelty and impact. Research Policy, 44(3), 684–697.CrossRefGoogle Scholar
Littlepage, G., Robison, W., & Reddington, K. (1997). Effects of task experience and group experience on group performance, member ability, and recognition of expertise. Organizational Behavior and Human Decision Processes, 69(2), 133–147.CrossRefGoogle Scholar
Lungeanu, A., Carter, D. R., DeChurch, L. A., & Contractor, N. S. (2018). How team interlock ecosystems shape the assembly of scientific teams: A hypergraph approach. Communication Methods and Measures, 12(2-3), 174–198.CrossRefGoogle ScholarPubMed
Lungeanu, A., & Contractor, N. S. (2015). The effects of diversity and network ties on innovations: The emergence of a new scientific field. American Behavioral Scientist, 59(5), 548–564.CrossRefGoogle ScholarPubMed
Lungeanu, A., Huang, Y., & Contractor, N. S. (2014). Understanding the assembly of interdisciplinary teams and its impact on performance. Journal of Informetrics, 8(1), 59–70.CrossRefGoogle ScholarPubMed
McCorcle, M. D. (1982). Critical issues in the functioning of interdisciplinary groups. Small Group Behavior, 13(3), 291–310.CrossRefGoogle Scholar
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.CrossRefGoogle Scholar
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems.Google Scholar
Miller, M., & Mansilla, V. B. (2004). Thinking across perspectives and disciplines. In Goodwork Project Report Series. Harvard University Cambridge, MA.Google Scholar
Milojević, S. (2014). Principles of scientific research team formation and evolution. Proceedings of the National Academy of Sciences, 111(11), 3984–3989.CrossRefGoogle Scholar
Milojević, S. (2015). Quantifying the cognitive extent of science. Journal of Informetrics, 9(4), 962–973.CrossRefGoogle Scholar
Milojević, S., Sugimoto, C. R., Yan, E., & Ding, Y. (2011). The cognitive structure of library and information science: Analysis of article title words. Journal of the American Society for Information Science and Technology, 62(10), 1933–1953.CrossRefGoogle Scholar
Monge, P. R., Rothman, L. W., Eisenberg, E. M., Miller, K. I., & Kirste, K. K. (1985). The dynamics of organizational proximity. Management Science, 31(9), 1129–1141.CrossRefGoogle Scholar
Montoya, R. M., & Horton, R. S. (2013). A meta-analytic investigation of the processes underlying the similarity-attraction effect. Journal of Social and Personal Relationships, 30(1), 64–94.CrossRefGoogle Scholar
Mukherjee, S., Huang, Y., Neidhardt, J., Uzzi, B., & Contractor, N. (2019). Prior shared success predicts victory in team competitions. Nature Human Behaviour, 3(1), 74–81.CrossRefGoogle ScholarPubMed
Mukherjee, S., Romero, D. M., Jones, B., & Uzzi, B. (2017). The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: The hotspot. Science Advances, 3(4), e1601315.CrossRefGoogle ScholarPubMed
National Academy of Sciences, National Academy of Engineering, & Institute of Medicine (2005). Facilitating interdisciplinary research. Washington, DC: National Academies Press.Google Scholar
O’Reilly, C. A. III, Williams, K. Y., & Barsade, S. (1998). Group demography and innovation: Does diversity help? In Composition. New York: Elsevier.Google Scholar
Pedraza-Fariña, L. G., & Whalen, R. (2020). A network theory of patentability. The University of Chicago Law Review, 87(1), 63–144.Google Scholar
Pelled, L. H. (1996). Demographic diversity, conflict, and work group outcomes: An intervening process theory. Organization Science, 7(6), 615–631.CrossRefGoogle Scholar
Pollach, I. (2012). Taming textual data: The contribution of corpus linguistics to computer-aided text analysis. Organizational Research Methods, 15(2), 263–287.CrossRefGoogle Scholar
Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: The effects of cohesion and range. Administrative Science Quarterly, 48(2), 240–267. doi: 10.2307/3556658.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Schilling, M. A., & Green, E. (2011). Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences. Research Policy, 40(10), 1321–1331.CrossRefGoogle Scholar
Shi, F., Foster, J. G., & Evans, J. A. (2015). Weaving the fabric of science: Dynamic network models of science’s unfolding structure. Social Networks, 43, 73–85.CrossRefGoogle Scholar
Sieweke, J., & Zhao, B. (2015). The impact of team familiarity and team leader experience on team coordination errors: A panel analysis of professional basketball teams. Journal of Organizational Behavior, 36(3), 382–402.CrossRefGoogle Scholar
Singh, J., & Fleming, L. (2010). Lone inventors as sources of breakthroughs: Myth or reality? Management Science, 56(1), 41–56.CrossRefGoogle Scholar
Stahl, G. K., Maznevski, M. L., Voigt, A., & Jonsen, K. (2010). Unraveling the effects of cultural diversity in teams: A meta-analysis of research on multicultural work groups. Journal of International Business Studies, 41(4), 690–709.CrossRefGoogle Scholar
Steiner, I. D. (1972). Group process and productivity. New York: Academic press.Google Scholar
Taylor, A., & Greve, H. R. (2006). Superman or the fantastic four? Knowledge combination and experience in innovative teams. Academy of Management Journal, 49(4), 723–740.CrossRefGoogle Scholar
Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science, 342(6157), 468–472.CrossRefGoogle ScholarPubMed
Van den Bulte, C., & Moenaert, R. K. (1998). The effects of R&D team co-location on communication patterns among R&D, marketing, and manufacturing. Management Science, 44(11-part-2), S1–S18.CrossRefGoogle Scholar
Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., …Börner, K. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5(1), 14–26.CrossRefGoogle Scholar
Webber, S. S., & Donahue, L. M. (2001). Impact of highly and less job-related diversity on work group cohesion and performance: A meta-analysis. Journal of Management, 27(2), 141–162.CrossRefGoogle Scholar
West, M. A., & Anderson, N. R. (1996). Innovation in top management teams. Journal of Applied Psychology, 81(6), 680–693.CrossRefGoogle Scholar
Whalen, R. (2018). Boundary spanning innovation and the patent system: Interdisciplinary challenges for a specialized examination system. Research Policy, 47(7), 1334–1343.CrossRefGoogle Scholar
Whalen, R., Lungeanu, A., DeChurch, L., & Contractor, N. (2020). Patent similarity data and innovation metrics. Journal of Empirical Legal Studies, 17(3), 615–639.CrossRefGoogle Scholar
Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.CrossRefGoogle ScholarPubMed