Workplace friendship, team cohesion, workplace isolation & organisational citizenship behaviour

Abstract

Traditional workplace structures that facilitated face-to-face interactions among organisational members are losing popularity, with hybrid working arrangements becoming the norm (Hopkins & Bardoel, 2003). The post-Covid era has seen a significant rise in remote and hybrid working arrangements, providing employees with more flexibility and autonomy (Kossek & Lautsch, 2018). These contemporary working arrangements, once limited to specific professions, have now fundamentally altered many aspects of work across industries. While alternative working arrangements have been linked to increased job satisfaction and reduced turnover intentions (McNall et al., 2009), they also raise concerns about the potential compromise of social elements within the workplace, such as informal interactions (Kiesler & Cummings, 2002; Will, 2021). This study aims to investigate the effects of different working arrangements—remote, hybrid, and on-site—on Organizational Citizenship Behavior (OCB), a critical aspect of organizational effectiveness (Ndoja & Malekar, 2020). Specifically, the research explores the interplay between workplace friendship, team cohesion, and workplace isolation, and their relationship with OCB. The findings suggest that while the correlations between social dynamics and OCB are evident, the ANOVA results did not show significant differences across all variables, likely due to an inequitable distribution of participants among the different work settings. The significance of the multiple regression model, with workplace friendship emerging as a key predictor, highlights its crucial role in fostering OCB. This study contributes to understanding the social dynamics in modern workplaces and underscores the need for further research with more balanced samples to fully capture the impact of various working conditions on these relationships. Future research should also explore the underlying mechanisms of significant predictors to develop more comp