Many phenomena in human resource development (HRD) research unfold in the social context. Most of the variables HRD researchers study, even if these are individual-level variables, are inevitably affected by the formal and informal network of actors in which an individual finds oneself. This relational influence is commonly ignored in studies of performance, learning, change, and other questions.
The Solution
Social networks analysis (SNA) is a methodology that makes it possible to take the relational aspect into account. It allows researchers to model the social capital of an actor and examine how connectivity and position in the network interacts with or influences important outcomes.
The Stakeholders
Researchers in the field of HRD will be able to uncover a wealth of new information and gain a new perspective by including SNA in their toolkit. This article provides the researchers with an introduction to the methods and provides suggestions for their application.
BastianM.HeymannS.JacomyM. (2009). Gephi: An open source software for exploring and manipulating networks. Paper presented at International AAAI Conference on Weblogs and Social Media. San Jose, California, (pp. 361-362). Menlo Park, Californi: The AAAI Press.
2.
BlagusN.ŠubeljL.BajecM. (2012). Self-similar scaling of density in complex real-world networks. Physica A: Statistical Mechanics and Its Applications, 391, 2794-2802.
3.
BonacichP. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92, 1170-1182. doi:10.2307/2780000
4.
BorgattiS. P.EverettM. G.FreemanL. C. (2002). Ucinet for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies.
5.
BrassD. J.BurkhardtM. E. (1992). Centrality and power in organizations. In NohriaN.EcclesR. G. (Eds.), Networks and organizations: Structure, form and action (pp. 191-215). Boston, MA: Harvard Business School Press.
6.
BrassD. J.GalaskiewiczJ.GreveH. R.TsaiW. (2004). Taking stock of networks and organizations: A multilevel perspective. Academy of management journal, 47(6), 795-817.
7.
BurtR. S. (1992). Structural holes. Cambridge, MA: Harvard University Press.
8.
BurtR. S. (1997). The contingent value of social capital. Administrative Science Quarterly, 42, 339-365. doi:10.1016/S0191-3085(00)22009-1
9.
BurtR. S. (2000). The network structure of social capital. Research in Organizational Behavior, 22, 345-423. doi:10.1016/S0191-3085(00)22009-1
10.
ButtsC. T. (2008). Network: A package for managing relational data in R. Journal of Statistical Software, 24(2), 1-36.
CarringtonP. J.ScottJ.WassermanS. (Eds.). (2005). Models and methods in social network analysis. New York, NY: Cambridge University Press.
13.
CookK. S.EmersonR. M. (1978). Power, equity, and commitment in exchange networks. American Sociological Review, 43, 721-739.
14.
CrossR.ParkerA.PrusakL.BorgattiS. P. (2001). Knowing what we know: Supporting knowledge creation and sharing in social networks. Organizational Dynamics, 30, 100-120. doi:10.1016/S0090-2616(01)00046-8
15.
CsardiG.NepuszT. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9.
16.
FeldS. L. (1997). Structural embeddedness and stability of interpersonal. Social Networks, 19, 91-95. doi:10.1016/S0378-8733(96)00293-6
17.
FrankO.StraussD. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832-842. doi:10.1080/01621459.1986.10478342
18.
FreemanL. C.WhiteD.RomneyA. K. (1989). Research methods in social network analysis. Fairfax, VA: George Mason University Press.
19.
HansenM. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44, 82-102. doi:10.2307/2667032
20.
HatalaJ. P. (2006). Social network analysis in human resource development: A new methodology. Human Resource Development Review, 5, 45-71. doi:10.1177/1534484305284318
21.
HollandP. W.LaskeyK. B.LeinhardtS. (1983). Stochastic blockmodels: First steps. Social Networks, 5, 109-137. doi:10.1016/0378-8733(83)90021-7
22.
HollandP. W.LeinhardtS. (1981). An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association, 76, 33-50. doi:10.1080/01621459.1981.10477598
23.
HoltonE. F.III. (2003). Where are the theory-building research methods articles? [Editorial]. Human Resource Development Review, 2, 227-228. doi:10.1177/1534484303257107
24.
HunterD. R.IIIHandcockM. S.ButtsC. T.GoodreauS. M.MorrisM. (2008). ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software, 24(3), 1-29.
25.
IbarraH.AndrewsS. B. (1993). Power, social influence and sensemaking: Effects of network centrality and proximity on employee perceptions. Administrative Science Quarterly, 38, 277-303. doi:10.2307/2393414
26.
KoehlyL. M.ShivyV. A. (1998). Social network analysis: A new methodology for counseling research. Journal of Counseling Psychology, 45, 3-17. doi:10.1037/0022-0167.45.1.3
27.
KrackhardtD. (1987). Cognitive social structures. Social Networks, 9, 109-134. doi:10.1016/0378-8733(87)90009-8
28.
KrackhardtD. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35, 342-369. doi:10.2307/2393394
29.
LynhamS. A. (2002). The general method of theory-building research in applied disciplines. Advances in Developing Human Resources, 4, 221-241. doi:10.1177/1523422302043002
30.
MizruchiM. S. (2000). The stability of the American business elite: Discrimination, competence or connections?Industrial and Corporate Change, 9, 545-553.
31.
MorganD. L.NealM. B.CarderP. (1997). The stability of core and peripheral networks over time. Social Networks, 19, 9-25. doi:10.1016/S0378-8733(96)00288-2
32.
NewmanM. E. (2003). Ego-centered networks and the ripple effect. Social Networks, 25, 83-95.
33.
PariseS. (2007). Knowledge management and human resource development: An application in social network analysis methods. Advances in Developing Human Resources, 9, 359-383. doi:10.1177/1523422307304106
34.
PodolnyJ.BaronJ. (1997). Networks and mobility in the workplace. American Sociological Review, 62, 673-693. doi:10.2307/2657354
35.
ReioT. G. (2010). The threat of common method variance bias to theory building. Human Resource Development Review, 9, 405-411. doi:10.1177/1534484310380331
36.
RothwellW. J.StavrosJ. M.SullivanR. L.SullivanA. (Eds.). (2009). Practicing organization development: A guide for leading change (Vol. 34). Hoboken, NJ: Wiley.
37.
ScottJ. (2000). Social network analysis: A handbook (2nd ed.). London, England: SAGE.
38.
ScottJ. (2011). Social network analysis: Developments, advances, and prospects. Social Network Analysis and Mining, 1, 21-26. doi:10.1007/s13278-010-0012-6
39.
SimoninB. L. (1999). Ambiguity and the process of knowledge transfer in strategic alliances. Strategic Management Journal, 20, 595-623. doi:10.1002/(SICI)1097-0266(199907)20:7
40.
SnijdersC. (1996). Trust and commitments (Vol. 33). West Lafayette, IN: Purdue University Press.
41.
StasserG.VaughanS. I.StewartD. (2000). Pooling unshared information: The benefits of knowing how access to information is distributed among group members. Organizational Behavior and Human Decision Processes, 82, 102-116. doi:10.1006/obhd.2000.2890
42.
StraussD.IkedaM. (1990). Pseudolikelihood estimation for social networks. Journal of the American Statistical Association, 85, 204-212. doi:10.1080/01621459.1990.10475327
43.
SuitorJ.KeetonS. (1997). Once a friend, always a friend? Effects of homophily on women’s support networks across a decade. Social Networks, 19, 51-62. doi:10.1016/S0378-8733(96)00290-0
44.
TorracoR. J. (2004). Challenges and choices for theoretical research in human resource development. Human Resource Development Quarterly, 15, 171-188. doi:10.1002/hrdq.1097
45.
VermuntJ. K.KalmijnM. (2006). Random effects models for personal networks. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 2, 34-41. doi:10.1027/1614-2241.2.1.34
46.
WassermanS.FaustK. (1994). Social network analysis: Methods and applications. New York, NY: Cambridge University Press.
47.
WassermanS.PattisonP. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p. Psychometrika, 61, 401-425. doi:10.1007/BF02294547
48.
WellmanB.WongR.TindallD.NazerN. (1997). A decade of network change: Turnover, persistence and stability in personal communities. Social Networks, 19, 27-50. doi:10.1016/S0378-8733(96)00289-4
