Initiating scientific collaborations across career levels and disciplines — a network analysis on behavioral data

Collaborations are essential in research, especially in answering increasingly complex questions that require integrating knowledge from different disciplines and that engage multiple stakeholders. Fostering such collaboration between newcomers and established researchers helps keep scientific communities alive while opening the way to innovation. But this is a challenge for scientific communities, especially as little is known about the onset of such collaborations. Prior social network research suggests that face-to-face interaction at scientific events as well as both network-driven selection patterns (reciprocity and transitivity) and patterns of active selection of specific others (homophily / heterophily) may be important. Learning science research implies, moreover, that selecting appropriate collaboration partners may require group awareness. In a field study at two scientific events on technology-enhanced learning (Alpine Rendez-Vous 2011 and 2013) including N = 5736 relations between 287 researchers, we investigated how researchers selected future collaboration partners, looking specifically at the role of career level, disciplinary background, and selection patterns. Face-to-face contact was measured using RFID devices. Additionally, a group awareness intervention was experimentally varied. Data was analyzed using RSiena and meta-analyses. The results showed that transitivity, reciprocity and contact duration are relevant for the identification of new potential collaboration partners. PhD students were less often chosen as new potential collaboration partners, and researchers with a background in Information Technology selected fewer new potential collaboration partners. However, group awareness support balanced this disciplinary difference. Theoretical, methodological, and practical implications of these findings are discussed