Multiplexity Definition: Multiplex ties can be any combination of two or more types of Definition: Multiplex ties can be any combination of two or more types of relationships; for example, they may provide general work‐related information and technical advice. However, they are often thought of as having an information or advice (instrumental) component and a social support or friendship (affective) component. Friendship network
Advice network
FFriendship‐only i d hi l ties
M lti l ti Multiplex ties
Ad i Advice‐only ties l ti
The extent to which a multiplex advice and friendship network occurs differs by individual. Some people’s friendship network is a subset of their advice network whereas other people keep their friendship and advice advice network, whereas other people keep their friendship and advice‐ seeking relationships largely separate. Low multiplexity Medium multiplexity High multiplexity
Friendship network
Advice network
Friendship network
Advice network
Friendship network
Advice network
Implications of Multiplexity Multiplex relationships can act as conduits of high‐value information: e.g., information that is tacit or “sticky” and that is not easily, readily, or quickly transferred: Multiplex ties are “stronger” than ones with only one element flowing through them, and hence they can facilitate the flow of high value information or problem‐ so g ac solving activities. es Multiplex ties and boundary spanning: Ties that span organizational boundaries tend to be weaker because they are less likely to be influenced by collaborative norms associated with reputation. Advice‐ seeking ties that also have a friendship component increase the level of trust as well as enhancing the likelihood that information transferred will be of high value. Multiplex ties are more resilient: Multiplex ties are more resilient and are more likely to withstand changes such as relocation and new job roles. Multiplex ties that become dormant are more easily revived. revived Multiplex ties and individual performance: Because multiplex ties facilitate the flow of high‐value information, they generally result in individuals’ being more innovative in their work as well as finding solutions faster. This results in higher levels of performance. g p Multiplex ties and organizational commitment, engagement and exit: Employees with more multiplex ties are likely to be committed to the organization, engaged with their work, and less likely to exit the organization. Multiplex relationships can be constraining: l l l h b Though multiplex ties can increase the flow of high‐value information among employees, they are costly to maintain in time and energy. Having too many multiplex ties can result in lower levels of performance. Multiplex ties are more difficult to break, so when job roles change and there is a need for network reconfiguration these ties are the least flexible need for network reconfiguration, these ties are the least flexible.
Identifying Multiplex Ties Constructing multiplex networks in UCINET • In this analysis we are going to combine info_GE_4 a dataset of information ties dichotomized at 4 and above with friend_GE_4 a dataset of friendship ties dichotomized at 4 and above. • Step 1. Tools > Command Line/Matrix Algebra • Step 2. In command line, input the name of the new dataset, e.g., “infoGE4_friendGE4” then type “=“ and then type “info_GE_4*friend_GE_4”. (Note: Use dichotomized data only) yp _ _ _ _ ( y) • Note: Instead of typing the existing dataset names, you can use the browse function to find and select them (this avoids any problems with mistyping network names).
Analyzing multiplex network data in UCINET Once you have constructed your multiplex dataset, you can analyze it the same way as with other networks. It is worthwhile comparing the multiplex analysis to the uniplex informationn and friendship networks to see the difference. • Analysis 1. Degree centrality. Network > Centrality and Power > Degree. Then in the input network box type “infoGE4 box type infoGE4_friendGE4 friendGE4 ” or click or click “…” and select the multiplex network. and select the multiplex network • Analysis 2. Density. Network > Cohesion > Density > Density Overall. Then in the input network box, type “infoGE4_friendGE4 ” or click “…” and select the multiplex network. • Analysis 3. Boundary spanning roles. Network > Ego networks > G&F Brokerage. In the input network box, type “infoGE4_friendGE4 ” or click “…” and select the multiplex network. In the input attribute box, type “attrib” or click “…” and select “attrib”, then choose a column, e.g., gender.
Identifying Multiplex Ties Analyzing multiplex networks in NetDraw • Option 1. Construct your multiplex network in UCINET (see previous page) and then import it into NetDraw. • Step 1. File > Open > UCINET dataset > Network • Step 2. Click ‐ open folder icon • Step 3. Choose network dataset (infoGE4_friendGE4 .##h), then click OK S 3 Ch kd (i f G f i dG h) h li k O • Step 4. You can then use the various drawing and formatting options in NetDraw as you would with any network • Option 2. Join matrices in UCINET and then draw combined network in NetDraw. • Step 1. In UCINET Step 1. In UCINET select: Data > Join > Join Matrices select: Data > Join > Join Matrices • Step 2. In the datasets to join box click “…” and select your two networks e.g. “info_GE_4” and friend_GE_4” (Note: Use dichotomized data only). • Step 3. In the output datasets box type “infoGE4_and_friendGE4” and then click OK. • Step 4. In NetDraw select File > Open > UCINET dataset > Network • Step 5. Click ‐ open folder icon • Step 6. Choose network dataset (infoGE4_and_friendGE4 .##h), then click OK • Step 7. In the in the Rels tab (top right) there are two networks. You can view one type or both. • Step 8. Select: Properties > Lines > Color > Relation, and then click apply. This routine gives different colors to the three types of lines (information only, friendship only, multiplex).
Bibliography Borgatti, S. P., Everett, M. G., & Johnson, J. C. . 2013. Analyzing social networks. Los Angeles, CA: Sage. Kuwabara, K., Sheldon, O., & Luo, J. (2010). Multiplex exchange relations. In E. J. Lawler & S. R. Thye (Eds.), Advances in group processes (pp. 239–268). Bingley, United Kingdom: Emerald Group. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380. Lazega E & Pattison P E (1999) Multiplexity generalized exchange and cooperation in Lazega, E., & Pattison, P. E. (1999). Multiplexity, generalized exchange and cooperation in organizations: A case study. Social Networks, 21, 67–90. Lee, S., & Lee, C. (2015). Creative interaction and multiplexity in intraorganizational networks. Management Communication Quarterly, 29, 56–83. Lee, S., & Monge, P. (2011). The coevolution of multiplex communication networks in organizational communities. Journal of Communication, 61, 758–779. LePine, J. A., Methot, J. R., Crawford, E. R., & Buckman, B. (2012). A model of positive relationships in teams: The role of instrumental, friendship, and multiplex social network ties. In L. T. Eby and T. D. Allen (Eds.), Personal relationships: The effect of supervisory, co‐worker, team, customer and nonwork exchanges on employee attitudes, behavior, and well‐being. New York, NY: Routledge. Methot, J. R., LePine, J. A., Podsakoff, N. P., & Christian, J. S. (2015). Are workplace friendships a mixed blessing? Exploring tradeoffs of multiplex relationships and their associations with job performance. Personnel Psychology, forthcoming. Shah, N. P., Parker, A. & Waldstrøm, C. 2016. Examining the Overlap: Individual Performance Benefits of Multiplex Relationships. Management Communication Quarterly, forthcoming. Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42, 35‐67. , , , ( ) y pp g , Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, United Kingdom: Cambridge University Press. Andrew Parker, PhD, is an assistant professor at Grenoble Ecole de Management. He has conducted network analysis in over 100 multinational organizations and government agencies. He was a Senior Consultant at IBM’s Institute for Knowledge Management, a research fellow at the Network Roundtable at the University of Vi i i Virginia and an advisor to the Knowledge and Innovation Network at Warwick Business d d i t th K l d dI ti N t k tW i kB i School. His research has appeared in Science, Organization Studies, Journal of Applied Psychology, Journal of Applied Behavioral Science, Social Networks, Management Communication Quarterly, Sloan Management Review, Organizational Dynamics and California Management Review. He is also the co‐author of The Hidden Power of Social Networks. He received his PhD from Stanford University.