Integrating attachment style and social networks in the workplace

Abstract

Attachment style, originally rooted in early child-parent relationships, has emerged as a pivotal lens for understanding adult relationships within relationship science. As the extension of attachment theory to the workplace in recent decade, attachment style has begun to show a significant influence on understanding workplace phenomenon. However, the relational nature of attachment style has often been neglected due to the focus has primarily been on work-related outcomes. In order to comprehensively study attachment style and workplace relationships, this thesis integrates social network theory to investigate how attachment style influence intra-organisational networks through three key research questions: (1) how does attachment style influences the formation of intra-organisational networks? (2) how does attachment style influences individuals’ positions in intra-organisational networks? and (3) how does attachment style impact job performance through the influence of social networks? This thesis conducts two studies to address these questions. Study 1 uses longitudinal social network data to examine the influence of attachment style on the formation of social networks, investigating how attachment style affects the out-degree, reciprocity, and multiplexity of friendship and advice networks. Longitudinal analyses results show that both anxious and avoidant attachment styles have significant impacts on the formation of friendship and advice networks. Study 2 investigates the indirect effects of attachment style on job performance via network position. Using an advanced static network model and a mediation model to examine cross-sectional data from a technology company, this study reveals the negative impacts of avoidant attachment on occupying in-degree centrality, thereby diminishing job performance. The integration of attachment style and social networks provides new insights into both attachment theory and micro-foundations of social networks.