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Multilevel social spaces: The network dynamics of organizational fields

Published online by Cambridge University Press:  06 June 2017

JAMES HOLLWAY ,
ALESSANDRO LOMI ,
FRANCESCA PALLOTTI  and
CHRISTOPH STADTFELD
JAMES HOLLWAY
Affiliation:
Department of International Relations/Political Science, Graduate Institute of International and Development Studies, Chemin Eugène-Rigot 2A, 1211, Genève, Switzerland (e-mail: james.hollway@iheid.ch)
ALESSANDRO LOMI
Affiliation:
University of Italian Switzerland, Via Buffi 13, Lugano 6900, Switzerland (e-mail: alessandro.lomi@usi.ch)
FRANCESCA PALLOTTI
Affiliation:
Department of International Business and Economics, University of Greenwich, Old Royal Naval College, Park Row, London, SE10 9LS, United Kingdom (e-mail: f.pallotti@greenwich.ac.uk)
CHRISTOPH STADTFELD
Affiliation:
Chair of Social Networks, ETH Zürich, Clausiusstrasse 50, 8092, Zürich, Switzerland (e-mail: christoph.stadtfeld@ethz.ch)
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Abstract

In this paper, we seek to advance an updated concept of social space that integrates the multilayer and dynamic statistical network methods currently at the disposal of social network researchers. We demonstrate the analytic value of the new concept of social space that we propose with the help of an illustrative analysis of an organizational field involving organizations' external and internal decisions that congeal into a multilevel system of action that shapes the space of possibilities for other participants in the field. Through these internal and external decisions, organizations seek certain positions in their social space while simultaneously modifying that social space over time. We conclude by arguing that network researchers' choices of goodness-of-fit statistics should reflect a consideration about the dimensions of social space of most interest to the nodes involved.

Keywords

affiliation networkcollaborationcompetitiongoodness-of-fitinterorganizational networkmultilevel networkmultilevel triad censusorganizational changeorganizational fieldssocial space
Type
Research Article
Information
Network Science , Volume 5 , Special Issue 2: Modeling, Analysis, and Mining of Multilayer Networks , June 2017 , pp. 187 - 212
Copyright
Copyright © Cambridge University Press 2017 

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References

Abbott, A. (1997). Of time and space: The contemporary relevance of the Chicago School. Social Forces, 75 (4), 1149–1182.Google Scholar
Batagelj, V., & Bren, M. (1995). Comparing resemblance measures. Journal of Classification, 12 (1), 73–90.CrossRefGoogle Scholar
Baum, J. A. C., Shipilov, A. V., & Rowley, T. J. (2003). Where do small worlds come from? Industrial and Corporate Change, 12 (4), 697–725.CrossRefGoogle Scholar
Baum, J. A., & Mezias, S. J. (1992). Localized competition and organizational failure in the Manhattan hotel industry, 1898-1990. Administrative Science Quarterly, 37 (4), 580–604.CrossRefGoogle Scholar
Block, P. (2015). Reciprocity, transitivity, and the mysterious three-cycle. Social Networks, 40, 163–173.Google Scholar
Block, P., Stadtfeld, C., & Snijders, T. A. B. (Forthcoming). Forms of dependence: Comparing SAOMs and ERGMs from basic principles. Sociological Methods and Research.Google Scholar
Bosk, E. A., Veinot, T., & Iwashyna, T. J. (2011). Which patients, and where: A qualitative study of patient transfers from community hospitals. Medical Care, 49 (6), 592–598.Google Scholar
Bourdieu, P. (1985). The social space and the genesis of groups. Theory and Society, 14 (6), 723–744.Google Scholar
Bourdieu, P. (1989). Social space and symbolic power. Sociological Theory, 7 (1), 14–25.CrossRefGoogle Scholar
Brunson, J. C. (2015). Triadic analysis of affiliation networks. Network Science, 3 (4), 480–508.Google Scholar
Carley, K. (1991). A theory of group stability. American Sociological Review, 56, 331–354.Google Scholar
Carroll, G. (1985). Concentration and specialization: Dynamics of niche width in populations of organizations. American Journal of Sociology, 90 (6), 1262–1283.Google Scholar
Cohen, W., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.Google Scholar
Cohen, W., & Levinthal, D. A. (1994). Fortune favors the prepared firm. Management Science, 40, 227–251.Google Scholar
Conaldi, G., Tonellato, M., and Lomi, A. (2012). Dynamic Models of Affiliation and the Network Structure of Problem Solving in an Open Source Software Project. Organizational Research Methods, 15 (3), 385–412.Google Scholar
Daraganova, G., Pattison, P., Koskinen, J., Mitchell, B., Bill, A., Watts, M., & Baum, S. (2012). Networks and geography: Modelling community network structures as the outcome of both spatial and network processes. Social Networks, 34 (1), 6–17.Google Scholar
Davis, J. A., & Leinhardt, S. (1972). The structure of positive interpersonal relations in small groups. In Berger, J. (Ed.), Sociological theories in progress, (Volume 2, pp. 218–251). Boston: Houghton Mifflin.Google Scholar
Davis, G. F., Yoo, M., & Baker, W. E. (2003). The small world of the American corporate elite, 1982–2001. Strategic Organization, 1 (3), 301–326.Google Scholar
DiMaggio, P. (1986). Structural analysis of organizational fields: A blockmodeling approach. Research in Organizational Behavior, 8, 335–370.Google Scholar
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited. Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48, 147–160.Google Scholar
Dixit, A. (1992). Investment and hysteresis. The Journal of Economic Perspectives, 6 (1), 107–132.CrossRefGoogle Scholar
Feld, S. L. (1981). The focused organization of social ties. American Journal of Sociology, 86, 1015–1035.Google Scholar
Gittell, J. H. (2002). Coordinating mechanisms in care provider groups: Relational mediator and input uncertainty as a moderator of performance effects, Management Science, 48, 1408–1426.Google Scholar
Gluckman, M. (1955). The judicial process among the barotse of northern rhodesia. Manchester: Manchester University Press.Google Scholar
Greve, H. (2005). Interorganizational learning and heterogeneous social structure. Organization Studies, 26, 1025–1047.Google Scholar
Hains, I. M., Marks, A., Georgiou, A., & Westbrook, J. I. (2011). Non-emergency patient transport: What are the quality and safety issues? A systematic review. International Journal for Quality in Health Care, 23 (1), 68–75.Google Scholar
Haunschild, P. R., & Beckman, C. M. (1998). When do interlocks matter? Alternate sources of information and interlock influence. Administrative Science Quarterly, 43, 815–844.Google Scholar
Holland, P. W., & Leinhardt, S. (1970). A method for detecting structure in sociometric data. American Journal Sociology, 70, 492–513.Google Scholar
Hollway, J., & Koskinen, J. H. 2016. Multilevel bilateralism and multilateralism: states' bilateral and multilateral fisheries treaties and their secretariats. In Lazega, E. & Snijders, T. A. B. (Eds.), Multilevel Network Analysis for the Social Sciences (pp. 315–332), Cham: Springer.Google Scholar
Iacobucci, D., & Wasserman, S. (1990). Social networks with two sets of actors. Psychometrika, 55, 707–720.Google Scholar
Ingram, P., & Morris, M. W. (2007). Do people mix at mixers? Structure, homophily, and the “Life of the Party”. Administrative Science Quarterly, 52, 558–585.CrossRefGoogle Scholar
Iwashyna, T. J. (2012). The incomplete infrastructure for interhospital patient transfer. Critical Care Medicine, 40 (8), 2470–2478.Google Scholar
Iwashyna, T. J., & Courey, A. J. (2011). Guided transfer of critically ill patients: Where patients are transferred can be an informed choice. Current Opinion in Critical Care, 17, 641–647.Google Scholar
Iwashyna, T. J., Christie, J. D., Moody, J., Kahn, J. M., & Asch, D. A. (2009). The structure of critical care transfer networks. Medical Care, 47, 787–793.Google Scholar
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks, Journal of Complex Networks, 2 (3), 203–271.CrossRefGoogle Scholar
Kogut, B., Shan, W., & Walker, G. (1992). The make-or-cooperate decision in the context of an industry network. In Nohria, N., & Eccles, R. (Eds.), Networks and organisations: Structure, form and action (pp. 348–365). Boston: Harvard Business School Press.Google Scholar
Lazega, E., Jourda, M.-T., Mounier, L., & Stofer, R. (2008). Catching up with big fish in the big pond? Multi-level network analysis through linked design. Social Networks, 30, 159–176.Google Scholar
Lee, B. Y., McGlone, S. M., Song, Y., Avery, T. R., Eubank, S., Chang, C.-C., . . . Huang, S. S. (2011). Social network analysis of patient sharing among hospitals in orange county, California. American Journal of Public Health, 4, 707–713.CrossRefGoogle Scholar
Lomi, A., & Pallotti, F. (2012). Relational collaboration among spatial multipoint competitors. Social Networks, 34, 101–111.CrossRefGoogle Scholar
Lomi, A., & Stadtfeld, C. (2014). Social networks and social settings. Developing a coevolutionary view. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 66 (1), 395–415.CrossRefGoogle Scholar
Lomi, A., Mascia, D., Vu, D. Q., Pallotti, F., Conaldi, G., & Iwashyna, T. J. (2014). Quality of care and interhospital collaboration: A study of patient transfers in Italy. Medical Care, 52, 407.Google Scholar
Lomi, A., Robins, G., & Tranmer, M. (2016). Introduction to multilevel social networks. Social Networks, 44, 266–268.Google Scholar
Lospinoso, J. A. 2012. Statistical models for social network dynamics. Dissertation: University of Oxford.Google Scholar
Milewicz, K., Hollway, J., Peacock, C., & Snidal, D. (Forthcoming). Beyond trade: The expanding scope of the non-trade agenda in trade agreements. Journal of Conflict Resolution.Google Scholar
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298, 824–827.Google Scholar
Mische, A., & Pattison, P. E. (2000). Composing a civic arena: Publics, projects, and social settings. Poetics, 27, 163–194.Google Scholar
Mische, A., & White, H. (1998). Between conversation and situation: Public switching dynamics across network domains. Social Research, 65 (3), 695–724.Google Scholar
Mitsuhashi, H., & Greve, H., (2009). A matching theory of alliance formation and organizational success: Complementarity and compatibility. Academy of Management Journal, 52 (5), 975–995.Google Scholar
Mohr, J. W. (1994). Soldiers, mothers, tramps and others: Discourse roles in the 1907 New York City charity directory. Poetics, 22, 327–357.Google Scholar
Moody, J. (1998). Matrix methods for calculating the triad census. Social Networks, 20, 291–299.CrossRefGoogle Scholar
Padgett, J., & Ansell, C. (1993). Robust action and the rise of the medici, 1400-1434. The American Journal of Sociology, 98 (6), 1259–1319.Google Scholar
Padgett, J., & Powell, W. (2012). The emergence of organizations and markets. Princeton: Princeton University Press.Google Scholar
Pallotti, F., Tubaro, P., & Lomi, A. (2015). How far do network effects spill over? Evidence from an empirical study of performance differentials in interorganizational networks. European Management Review. (DOI: 10.1111/emre.12052).CrossRefGoogle Scholar
Pattison, P. E., & Robins, G. L. (2002). Neighborhood-based models for social networks. Sociological Methodology, 32, 301–337.Google Scholar
Pattison, P. E., & Robins, G. L. (2004). Building models for social space: Neighbourhood-based models for social networks and affiliation structures. Mathematics and Social Sciences, 42 (168), 11–29.Google Scholar
Pina-Stranger, A., & Lazega, E. (2011). Bringing personalized ties back in: Their added value for biotech entrepreneurs and venture capitalists interorganizational networks. The Sociological Quarterly, 52 (2), 268–292.Google Scholar
Ring, P. S., & Van de Ven, A. H. (1994). Developmental processes of cooperative interorganizational relationships. Academy of Management Review, 19, 90–118.CrossRefGoogle Scholar
Ripley, R. M., Snijders, T. A. B., Boda, Z., Vörös, A., & Preciado Lopez, P. (2015). Manual for RSiena. University of Oxford: Department of Statistics; Nuffield College. Retrieved from http://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf.Google Scholar
Robinson, V., Goel, V., Macdonald, R. D., & Manuel, D. (2009). Inter-facility patient transfers in Ontario: Do you know what your local ambulance is being used for?. Health Policy, 4 (3), 53–66.Google Scholar
Sethi, D., & Subramanian, S. (2014). When place and time matter: How to conduct safe inter-hospital transfer of patients. Saudi Journal of Anaesthesia, 8 (1), 104–113.Google Scholar
Shipilov, A. V., & Li, S. X. (2010). The missing link: The effect of customers on the formation of relationships among producers in the multiplex triads. Organization Science, 23 (2), 472–491.Google Scholar
Snijders, T. A. B. 2001. The statistical evaluation of social network dynamics. Sociological Methodology 31 (1), 361–395.Google Scholar
Snijders, T. A. B. (2016). The multiple flavours of multilevel issues for networks. In Lazega, E., & Snijders, T. A. B. (Eds.), Multi-level network analysis for the social sciences: Theory, methods and applications (pp. 15–46). Cham: Springer.Google Scholar
Snijders, T. A. B., & Stokman, F. N. (1987). Extensions of triad counts to networks with different subsets of points and testing underlying random graph distributions. Social Networks, 9, 249–275.Google Scholar
Snijders, T. A. B., Van de Bunt, G. G. & Steglich, C. E. G. 2010. Introduction to stochastic actor-based models for network dynamics. Social Networks 32 (1), 44–60.Google Scholar
Snijders, T. A. B., Lomi, A., & Torlo', V. J. (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks, 35, 265–276.CrossRefGoogle Scholar
Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32, 44–60.Google Scholar
Sohn, M.-W. (2001). Distance and cosine measures of niche overlap. Social Networks, 23, 141–165.CrossRefGoogle Scholar
Stadtfeld, C., Mascia, D., Pallotti, F., & Lomi, A. (2016). Assimilation and differentiation: A multilevel perspective on organizational and network change. Social Networks, 44, 363–374.Google Scholar
Steglich, C. E. G., Snijders, T. A. B., & Pearson, M. 2010. Dynamic networks and behavior: separating selection from influence. Sociological Methodology 40 (1), 329–393.Google Scholar
Trapido, D. (2007). Competitive embeddedness and the emergence of interfirm cooperation. Social Forces, 86 (1), 165–191.Google Scholar
Uzzi, B. (1996). The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American Sociological Review, 61, 674–698.Google Scholar
Veinot, T. C., Bosk, E. A., Unnikrishnan, K. P., & Iwashyna, T. J. (2012). Revenue, relationships and routines: The social organization of acute myocardial infarction patient transfers in the United States. Social Science & Medicine, 75, 1800–1810.Google Scholar
Verbrugge, L. M. (1979). Multiplexity in adult friendships. Social Forces, 57 (4), 1286–1309.Google Scholar
Wagner, J., Iwashyna, T. J., & Kahn, J. M. (2013). Reasons underlying interhospital transfers to an academic medical intensive care unit. Journal of Critical Care, 28 (2), 202–208.Google Scholar
Wang, P., Robins, G., Pattison, P., & Lazega, E. (2013). Exponential random graph models for multilevel networks. Social Networks, 35, 96–115.Google Scholar
Wasserman, S. (1977). Random directed graph distributions and the triad census in social networks. Journal of Mathematical Sociology, 5, 61–86.Google Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.Google Scholar
Wasserman, S., & Iacobucci, D. (1991). Statistical modelling of one-mode and two-mode networks: Simultaneous analysis of graphs and bipartite graphs. British Journal of Mathematical and Statistical Psychology, 44, 13–43.Google Scholar