Abstract
This paper aims to detect the social mechanisms underlying cooperation in organizational communities. To this purpose, it proposes to apply a longitudinal Social Network Analysis approach based on Stochastic Actor-Oriented Models for network dynamics to Web 2.0 data on interpersonal interaction. The paper claims and demonstrates that such an approach allows alleviating some limitations of current studies. It overcomes the issue of relational missing data. Also, it models directly the network structure as the outcome of actors’ counterparts selection in their neighbourhood. Application is on a virtual community of Italian oncologists who collaborate in resolving diagnoses. Using repository and field data, we reconstruct a network, with clinicians as nodes and emails exchanged as ties. Then, we model cooperation longitudinally. Evidence is provided that emergent behaviors are effectively captured and advantages of this approach are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The rarety of the cancer forms examined implies also a low frequency of information exchange. Network emergence is therefore assumed to be a slow process.
- 2.
Extensions to ordinal data have been proposed, but are still to be documented in the literature.
- 3.
Four degree ordinal scaling, with 1 = not competent at all in the field, …, 4 = very competent. The scale was dichotomised so that 1 and 2 were recoded as not competent and 3 and 4 as competent.
- 4.
We scanned the hospital websites and the 2009 Italian White Book on Cancer Treatments. For each hospital, it reports a list of clinicians expert in any form of cancer.
References
Ahuja, M., & Carley, K. (1999). Network structure in virtual organizations. Organization Science, 10, 741–747.
Barabasi, A. L., & Reka, A. (1999). Emergence of scaling of random networks. Science, 286(5439), 509–512.
Bowman, K. O., & Shenton, L. R. (1985). Encyclopedia of statistical sciences. New York: Wiley.
Contractor, N. S., Wasserman, S., & Faust, K. (2006). Testing multitheoretical, multilevel hypotheses about organizational networks: an analytic framework and empirical example. Academy of Management Review, 31(3), 681–703.
Krackhardt, D. (1994). Graph theoretical dimensions of informal organizations. In K. Carley & M. Prietula (Eds.), Computational organization theory (pp. 89–112). Hillsdale: Lawrence Erlbaum Associates.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press.
Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge: Cambridge University Press.
Monge, P. R., & Contractor, S. N. (2003). Theories of communication networks. Oxford: Oxford University Press.
Norris, J. R. (1997). Markov chains. Cambridge series in statistical and probabilistic mathematics. Cambridge: Cambridge University Press.
Opsahl, T., & Hogan, B. (2011). Growth mechanisms in continuously-observed networks: Communication in a Facebook-like community [arXiv:1010.2141].
Quintane, E., & Kleinbaum, A. M. (2011). Matter over mind? E-mail Data and the Treasurent of Social Networks. Connect, 31(1), 22–46.
Robbins, H., & Monro, S. (1951). A stochastic approximation method. Annals of Mathematical Statistics, 22(3), 400–407.
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.
Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social Networks, 18(1), 69–89.
van Duijn, M. A. J., Gile, K. J., & Handcock, M. S. (2009). A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models. Social Networks, 31(1), 52–62.
Wasko, M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35–57.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zappa, P. (2014). Assessing Cooperation in Open Systems: An Empirical Test in Healthcare. In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-06692-9_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06691-2
Online ISBN: 978-3-319-06692-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)