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Little Teams, Big Data: Big Data Provides New Opportunities for Teams Theory

Published online by Cambridge University Press:  17 December 2015

Dorothy R. Carter ,
Raquel Asencio ,
Amy Wax ,
Leslie A. DeChurch  and
Noshir S. Contractor
Dorothy R. Carter*
Affiliation:
Department of Psychology, University of Georgia
Raquel Asencio
Affiliation:
School of Psychology, Georgia Institute of Technology
Amy Wax
Affiliation:
Department of Psychology, California State University, Long Beach
Leslie A. DeChurch
Affiliation:
School of Psychology, Georgia Institute of Technology
Noshir S. Contractor
Affiliation:
Kellogg School of Management, Department of Communication Studies, and Department of Industrial Engineering and Management Studies, Northwestern University
*
Correspondence concerning this article should be addressed to Dorothy R. Carter, Department of Psychology, University of Georgia, 125 Baldwin Street, Athens, GA 30602. E-mail: dorothyrpc@gmail.com
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Extract

Over the past 25 years, industrial and organizational (I-O) psychologists have made great strides forward in the area of teams research. They have developed and tested meso-level theories that explain and predict the behavior of individuals in teams and teams operating within and across organizations. The continued contributions of I-O psychologists to theory and research on teams require us to address the challenges—several of which were well described in the focal article (Guzzo, Fink, King, Tonidandel, & Landis, 2015)—and embrace the opportunities that are being ushered in by big and broad data streams (Hendler, 2013). We suggest that a principal unique value add of the I-O psychologist to the basic scientific endeavor of understanding small teams comes in the form of theory—theories that explain why, when, how, and to what end individuals form relationships needed for teams to function in unison toward the accomplishment of collective goals. Some have argued that the big data revolution means “the end of theory,” suggesting petabyte data render theoretical models obsolete (Anderson, 2008). On the contrary, we submit that big-data enabled social science holds the promise of rapid progress in social science theory, particularly in the area of teams.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 8 , Issue 4 , December 2015 , pp. 550 - 555
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

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References

Aad, G., Abajyan, T., Abbott, B., Abdallah, J., Abdel Khalek, S., Abdelalim, A. A., . . . Zwalinski, L. (2012). Observation of a new particle in the search for the standard model Higgs boson with the ATLAS detector at the LHC. Physics Letters B, 716 (1), 1–29.Google Scholar
Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine. Retrieved from http://archive.wired.com/science/discoveries/magazine/16-07/pb_theoryGoogle 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
Bruckman, A., & Resnick, M. (1995). The MediaMOO project constructionism and professional community. Convergence, 1 (1), 94–109.CrossRefGoogle Scholar
Bryant, S. L., Forte, A., & Bruckman, A. (2005). Becoming Wikipedian: Transformation of participation in a collaborative online encyclopedia. Proceedings of the 2005 International ACM SIGGROUP Conference on Supporting Group Work, 1–10. Retrieved from http://dl.acm.org/citation.cfm?id=1099203CrossRefGoogle Scholar
Cooke, N. J., & Hilton, , , M. L. (Eds.). (2015). Enhancing the effectiveness of team science. Washington, DC: National Academies Press.Google Scholar
Guzzo, R. A., Fink, A. A., King, E., Tonidandel, S., & Landis, R. S. (2015). Big data recommendations for industrial–organizational psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice, 8 (4), 491–508.Google Scholar
Hackman, J. R. (1987). The design of work teams. In Lorsch, J. (Ed.), Handbook of organizational behavior (pp. 315–342). New York, NY: Prentice-Hall.Google Scholar
Hendler, J. (2013). Broad data: Exploring the emerging web of data. Big Data, 1 (1), 18–20.CrossRefGoogle ScholarPubMed
Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings. Newbury Park, CA: Sage.Google Scholar
Ilgen, D. R., Hollenbeck, J. R., Johnson, M., & Jundt, D. (2005). Teams in organizations: From input-process-output models to IMOI models. Annual Review of Psychology, 56, 517–543.Google Scholar
Jacobs, C. (2012, April). A vision for, and progress towards EarthCube. EGU General Assembly Conference Abstracts, 14, 1227.Google Scholar
Jarvenpaa, S. L., Shaw, T. R., & Staples, D. S. (2004). Toward contextualized theories of trust: The role of trust in global virtual teams. Information Systems Research, 15 (3), 250–267.CrossRefGoogle Scholar
Keegan, B., Gergle, D., & Contractor, N. (2012, February). Do editors or articles drive collaboration? Multilevel statistical network analysis of Wikipedia co-authorship. Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, 427–436. Retrieved from http://dl.acm.org/citation.cfm?id=2145204CrossRefGoogle Scholar
Kim, T., McFee, E., Olguin, D., Waber, B., & Pentland, A. (2012). Sociometric badges: Using sensor technology to capture new forms of collaboration. Journal of Organizational Behavior, 33, 412–427.Google Scholar
Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., . . . Horton, J. (2013, February). The future of crowd work. Proceedings of the 2013 Conference on Computer Supported Cooperative Work, 1301–1318. Retrieved from http://dl.acm.org/citation.cfm?id=2441776Google Scholar
Kozlowski, S. W. J., Chao, G. T., Chang, C. D., & Fernandez, R. (in press). Using big data to enhance the science of team effectiveness. In Tonidandel, S., King, E., & Cortina, J. (Eds.), Big data at work: The data science revolution and organizational psychology. New York, NY: Routledge.Google Scholar
Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., . . . Van Alstyne, M. (2009). Life in the network: The coming age of computational social science. Science, 323 (5915), 721–723.Google Scholar
Lurey, J. S., & Raisinghani, M. S. (2001). An empirical study of best practices in virtual teams. Information and Management, 38 (8), 523–544.Google Scholar
Macy, M., DellaPosta, D., & Shi, Y. (2015). Why do liberals drink lattes? American Journal of Sociology, 120 (5), 1473–1511.Google Scholar
Martins, L. L., Gilson, L. L., & Maynard, M. T. (2004). Virtual teams: What do we know and where do we go from here? Journal of Management, 30, 805–835.Google Scholar
McGrath, J. E. (1984). Groups: Interaction and performance. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Mesmer-Magnus, J. R., & DeChurch, L. A. (2009). Information sharing and team performance: A meta-analysis. Journal of Applied Psychology, 94 (2), 535–546.Google Scholar
Pentland, A. (2000). Looking at people: Sensing for ubiquitous and wearable computing. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 22, 107–119.Google Scholar
Smith, J. B. (1994). Collective intelligence in computer-based collaboration. Hillsdale, NJ: Erlbaum.Google Scholar
Steiner, I. D. (1972). Group process and productivity. New York, NY: Academic Press.Google Scholar
Sullivan, S. D., Lungeanu, A., DeChurch, L. A., & Contractor, N. S. (2015). Space, time, and the development of shared leadership networks in multiteam systems. Network Science, 3 (01), 124–155.CrossRefGoogle Scholar
Turek, P., Wierzbicki, A., Nielek, R., Hupa, A., & Datta, A. (2010). Learning about the quality of teamwork from Wikiteams. Social Computing (SocialCom), 2010 IEEE Second International Conference, 17–24. Retrieved from http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5590331Google Scholar
Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., . . . Zhu, X. (2001). The sequences of the human genome. Science, 16 (291), 1304–1351.Google Scholar
Wilkinson, D. M. (2008). Strong regularities in online peer production. Proceedings of the 9th ACM Conference on Electronic Commerce, 302–309.Google Scholar