Hostname: page-component-7c8c6479df-24hb2 Total loading time: 0 Render date: 2024-03-29T13:01:52.277Z Has data issue: false hasContentIssue false

Tenure trumps title: Assessing the logic of informant selection in inter-organizational network research

Published online by Cambridge University Press:  17 September 2019

Amanda M. Beacom*
Affiliation:
Boston, MA 02127, USA
Thomas W. Valente
Affiliation:
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90034, USA(email: tvalente@usc.edu)†
*
*Corresponding author. Email: abeacom@gmail.com

Abstract

Researchers collecting survey data on inter-organizational networks typically choose a single informant with the most senior job title in each organization from whom to obtain a report of the organization’s inter-organizational ties. This approach to informant selection is based on the logic that greater seniority confers greater knowledge of inter-organizational relationships. The present study investigated the wisdom of this logic, using data in which multiple informants’ reports of inter-organizational network ties were collected for each organization. We calculated the degree of agreement in network reports between the informant with the most senior job title and a second informant in the organization. To determine if alternative criteria to seniority serve as better approaches to informant selection, we assessed other potential predictors of agreement in informants’ reports. Results indicated that (1) informants’ perceptions of the network differed significantly according to job title, suggesting little agreement between senior informants and their more junior colleagues; and (2) greater informant tenure in the network and industry were associated with greater agreement among informants. These results call a common data collection practice into question and suggest that tenure may trump title as a criterion for informant selection in inter-organizational network research.

Type
Original Article
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Amanda M. Beacom was affiliated with Boston College and University of Southern California when portions of this research were conducted.

References

Alker, H. R. (1969). A typology of ecological fallacies. In Dogan, M. & Rokkan, S. (Eds.), Quantitative ecological analysis in the social sciences (pp. 6986). Cambridge: MIT Press.Google Scholar
Bell, G. G., & Zaheer, A. (2007). Geography, networks, and knowledge flow. Organization Science, 18(6), 955972. doi:10.1287/orsc.1070.0308 CrossRefGoogle Scholar
Bernard, H. R., Killworth, P. D., Kronenfeld, D., & Sailer, L. (1984). The problem of informant accuracy: The validity of retrospective data. Annual Review of Anthropology, 13, 495517. doi:10.1146/annurev.an.13.100184.002431 CrossRefGoogle Scholar
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies.Google Scholar
Brands, R. A. (2013). Cognitive social structures in social network research: A review. Journal of Organizational Behavior, 34, S82S103. doi:10.1002/job.1890 CrossRefGoogle Scholar
Burt, R. S., Kilduff, M., & Tasselli, S. (2013). Social network analysis: Foundations and frontiers on advantage. Annual Review of Psychology, 64, 527547. doi:10.1146/annurev-psych-113011-143828 CrossRefGoogle ScholarPubMed
Butts, C. T. (2003). Network inference, error, and informant (in)accuracy: A Bayesian approach. Social Networks, 25, 103140. doi:10.1016/S0378-8733(02)00038-2 CrossRefGoogle Scholar
Butts, C. T. (2009). Revisiting the foundations of network analysis. Science, 325, 414416. doi:10.1126/science.1171022 CrossRefGoogle ScholarPubMed
Butts, C. T. (2016). Package “sna”: Tools for social network analysis, version 2.4. Retrieved from https://cran.r-project.org/web/packages/sna/sna.pdfGoogle Scholar
Casciaro, T. (1998). Seeing things clearly: Social structure, personality, and accuracy in social network perception. Social Networks, 20, 331351. doi:10.1016/S0378-8733(98)00008-2 CrossRefGoogle Scholar
Eagle, N., Pentland, A. S., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 1527415278. doi:10.1073/pnas.0900282106 CrossRefGoogle ScholarPubMed
Freeman, L. C., Romney, A. K., & Freeman, S. C. (1987). Cognitive structure and informant accuracy. American Anthropologist, 89(2), 310325. doi:10.1525/aa.1987.89.2.02a00020 CrossRefGoogle Scholar
Heaney, M. T. (2014). Multiplex networks and interest group influence reputation: An exponential random graph model. Social Networks, 36, 6681. doi:10.1016/j.socnet.2012.11.003 CrossRefGoogle Scholar
Howison, J., Wiggins, A., & Crowston, K. (2011). Validity issues in the use of social network analysis with digital trace data. Journal of the Association for Information Systems, 12(12), 767797. doi:10.17705/1jais.00282 CrossRefGoogle Scholar
Ihm, J., Shumate, M., Bello-Bravo, J., Atouba, Y., Malick Ba, N., Dabire-Binso, C. L., & Pittendrigh, B. R. (2014). How do service providers and clients perceive interorganizational networks? Voluntas, 26, 17691785. doi:10.1007/s11266-014-9515-5 CrossRefGoogle Scholar
Kadushin, C., Lindholm, M., Ryan, D., Brodsky, A., & Saxe, L. (2005). Why it is so difficult to form effective community coalitions. City & Community, 4(3), 255275.CrossRefGoogle Scholar
Klein, K. J., & Kozlowski, S. W. J. (2000). From micro to meso: Critical steps in conceptualizing and conducting multilevel research. Organizational Research Methods, 3(3), 211236. doi:10.1177/109442810033001 CrossRefGoogle Scholar
Klein, K. J., Palmer, S. L., & Conn, A. B. (2000). Interorganizational relationships: A multilevel perspective. In Klein, K. J. & Kozlowski, S. W. J. (Eds.), Multilevel theory, research, and methods in organizations (pp. 267307). San Francisco, CA: Jossey-Bass.Google Scholar
Koehly, L. M., & Pattison, P. (2005). Random graph models for social networks: Multiple relations or multiple raters. In Carrington, P. J., Scott, J., & Wasserman, S. (Eds.), Models and methods in social network analysis (pp. 162191). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Krackhardt, D. (1987a). Cognitive social structures. Social Networks, 9(2), 109134. doi:10.1016/0378-8733(87)90009-8 CrossRefGoogle Scholar
Krackhardt, D. (1987b). QAP partialling as a test of spuriousness. Social Networks, 9, 171186. doi:10.1016/0378-8733(87)90012-8 CrossRefGoogle Scholar
Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35(2), 342369. doi:10.2307/2393394 CrossRefGoogle Scholar
Kumar, N., Stern, L. W., & Anderson, J. C. (1993). Conducting interorganizational research using key informants. Academy of Management Journal, 36(6), 16331651. doi:10.2307/256824 Google Scholar
Lazega, E., & Snijders, T. A. B. (Eds.). (2016). Multilevel network analysis for the social sciences: Theory, methods and applications. Cham, Switzerland: Springer.CrossRefGoogle Scholar
LeBreton, J. M., & Senter, J. L. (2008). Answers to 20 questions about interrater reliability and interrater agreement. Organizational Research Methods, 11(4), 815852. doi:10.1177/1094428106296642 CrossRefGoogle Scholar
Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 1322. doi:10.1093/biomet/73.1.13 CrossRefGoogle Scholar
Luke, D. A. (2004). Multilevel modeling. Thousand Oaks, CA: Sage Publications.CrossRefGoogle Scholar
Marsden, P. V. (1990). Network data and measurement. Annual Review of Sociology, 16, 435463. doi:10.1146/annurev.so.16.080190.002251 CrossRefGoogle Scholar
Marsden, P. V. (2005). Recent developments in network measurement. In Carrington, P. J., Scott, J., & Wasserman, S. (Eds.), Models and methods in social network analysis (pp. 830). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Mohammed, S., Ferzandi, L., & Hamilton, K. (2010). Metaphor no more: A 15-year review of the team mental model construct. Journal of Management, 36(4), 876910. doi:10.1177/0149206309356804 CrossRefGoogle Scholar
Monge, P. R., & Contractor, N. S. (2003). Theories of communication networks. Oxford: Oxford University Press.Google Scholar
Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford University Press.CrossRefGoogle Scholar
Oerlemans, L. A. G., Casciaro, T., Rank, O. N., & Brennecke, J. (2015). European Group for Organizational Studies Standing Working Group 07: Multi-level network research. Retrieved from https://www.egosnet.org/jart/prj3/egos/main.jart?rel=de&content-id=1472701767727&reserve-mode=activeGoogle Scholar
Paruchuri, S., Goossen, M. C., & Phelps, C. (2019). Conceptual foundations of multilevel social networks. In Humphrey, S. E. & LeBreton, J. M. (Eds.), Handbook of multilevel theory, measurement, and analysis (pp. 201221). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Phelps, C., Heidl, R., & Wadhwa, A. (2012). Knowledge, networks, and knowledge networks: A review and research agenda. Journal of Management, 38(4), 11151166. doi:10.1177/0149206311432640 CrossRefGoogle Scholar
Powell, W. W., White, D. R., Koput, K. W., & Owen-Smith, J. (2005). Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American Journal of Sociology, 110(4), 11321205. doi:10.1086/421508 CrossRefGoogle Scholar
Romney, A. K., & Faust, K. (1982). Predicting the structure of a communications network from recalled data. Social Networks, 4, 285304. doi:10.1016/0378-8733(82)90015-6 CrossRefGoogle Scholar
Shumate, M. (2012). The evolution of the HIV/AIDS NGO hyperlink network. Journal of Computer-Mediated Communication, 17, 120134. doi:10.1111/j.1083-6101.2011.01569.x CrossRefGoogle Scholar
Simpson, B., & Borch, C. (2005). Does power affect perception in social networks? Two arguments and an experimental test. Social Psychology Quarterly, 68(3), 278287. doi:10.1177/019027250506800306 CrossRefGoogle Scholar
Simpson, B., Markovsky, B., & Steketee, M. (2011). Power and the perception of social networks. Social Networks, 33, 166171. doi:10.1016/j.socnet.2010.10.007 CrossRefGoogle Scholar
Stevens, G. D., Rice, K., & Cousineau, M. R. (2007). Children’s health initiatives in California: The experiences of local coalitions pursuing universal coverage for children. American Journal of Public Health, 97(4), 738743. doi:10.2105/AJPH.2006.088690 CrossRefGoogle ScholarPubMed
Valente, T. W. (2010). Social networks and health: Models, methods, and applications. New York: Oxford University Press.CrossRefGoogle Scholar
Valente, T. W., Coronges, K. A., Stevens, G. D., & Cousineau, M. R. (2008). Collaboration and competition in a children’s health initiative coalition: A network analysis. Evaluation and Program Planning, 31, 392402. doi:10.1016/j.evalprogplan.2008.06.002 CrossRefGoogle Scholar
Walsh, J. P. (1995). Managerial and organizational cognition: Notes from a trip down memory lane. Organization Science, 6(3), 280321. doi:10.1287/orsc.6.3.280 CrossRefGoogle Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Zeger, S. L., & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42(1), 121130. doi:10.2307/2531248 CrossRefGoogle ScholarPubMed