@article {3723,
	title = {Effects of Hierarchical Levels on Social Network Structures within Communities of Learning},
	journal = {Frontline Learning Research},
	volume = {Learning through Networks},
	year = {2014},
	abstract = {Facilitating an interpersonal knowledge transfer among employees constitutes a key building block in setting up organizational training initiatives. With practitioners and researchers looking for innovative training methods, online Communities of Learning (CoL) have been promoted as a promising methodology to foster this kind of transfer. However, past research has only provided limited data from actual organizations and largely neglected characteristics that constitute a major obstacle to such collaborative processes, namely participants{\textquoteright} hierarchical levels. The current study addresses these shortcomings by providing empirical evidence from 25 CoL of an online training program, provided for 249 staff members of a global organization. Using social network analysis, we are able to show significant differences in participants{\textquoteright} network behaviour and position based on their hierarchical rank. This translates into higher in- and out-degree network ties, as well as centrality scores among participants from higher up the hierarchical ladder. Finally, based on a longitudinal analysis of all indicated network measures, our results indicate that the main trend develops predominately during the first half of the training program. By incorporating these insights into the implementation of future CoL, it is not only possible to anticipate participants{\textquoteright} behaviour. Our findings also allow to draw conclusions about how collaborative activities within CoL should be designed and facilitated, in order to provide employees with a valuable learning experience.},
	keywords = {Centrality, Community of Learning, Hierarchical Positions, Longitudinal Analysis, Social Network Analysis},
	url = {http://journals.sfu.ca/flr/index.php/journal/article/view/85},
	author = {Martin Rehm and Wim Gijselaers and Mien Segers}
}
