The impact of intra-team communication and support relationships on team identification and collective efficacy in elite team sport: a social network analysis

ABSTRACT

Team identification and collective efficacy are important determinants of team functioning. A team’s collective efficacy is borne from its shared social identity and improved through iterative, interpersonal exchanges between teammates during training sessions and matches. Our study employed social network analyses to examine the impact of intra-team relationships on team identification and collective efficacy. We adopted a cross-sectional design including four elite teams (N = 67, 79% female) and collected athletes’ data on which teammates they communicated with during matches (match communication relationships) and which teammates they sought informational support from during training sessions (informational support relationships). Regression analyses were conducted to explore the impact of these relationships on team identification and collective efficacy. Communication ties positively predicted team identification, while incoming (i.e., receiving nominations for support from teammates) and outgoing support ties (i.e., perceiving teammates as available for support) were unrelated. In addition, outgoing support ties predicted collective efficacy, while incoming support ties and communication ties were unrelated. Findings are discussed through a social identity lens, with suggestions to curate the training environment with activities that increase the reciprocity of communication relations between certain pairs of teammates to strengthen identification as well as increase the quantity and distribution of outgoing, support-seeking relations to enhance collective efficacy. Network maps of the teams sampled are used to exemplify these suggestions. Future research using social network analyses to track changes in networks over time is encouraged to understand the role of intra-team relationships in team functioning.

KEYWORDS:

• Social network analysis
• teamwork
• social identity
• communication
• informational support

Acknowledgements

We thank Dr Filip Agneessens for his invaluable expertise in Social Network Analysis methodology; Dr. Craig White for his input during the write-up; and, Dr. Anthony Miller and Dr. Niels Mertens for their support during data analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, EJS, upon reasonable request.

Notes

1 Essentially calculates all the ways [i.e., permutations] that the experiment could have come out given that [the variables] were actually independent of [each other], and counts the proportion of all assignments yielding a correlation as large as the one actually observed. So we cannot enumerate all possible permutations. Instead, we sample uniformly from the space of all permutations (Borgatti et al., 2018, pp. 145–146).