Author(s): Babin Dhas Devadhasan and Majdi Anwar Quttainah
Purpose—Employee job performance (IWP) is greatly impacted by workplace ostracism (WOS). In the context of the Indian information technology (IT) industry, this study investigates the moderating function of personality inventory (PI) and the mediating function of perceived organizational support (POS). Design/methodology/approach—A structured survey using pre-established measuring scales was used to collect data from 569 IT professionals in southern India. Partial Least Squares Structural Equation Modelling (PLS-SEM) was made easier by the pyPLS Python module, which enabled a thorough examination of the proposed correlations, including mediation and moderation effects. These relationships were represented via a visual path diagram. Findings—The results show that ostracism at work has a significant negative impact on job performance (β = 0.469). This link is partially mediated by perceived organizational support, which has an indirect effect of 0.364. Furthermore, the association between job performance and workplace exclusion is moderated by personality factors in a minor but statistically significant way (moderation effect = -0.008). It's interesting to note that the moderation effect's negative sign implies that some personality qualities may marginally increase the negative effects of ostracism on work performance. Nevertheless, the impact size is small, and its applicability demands careful evaluation. Practical implications—This study emphasizes how crucial it is to build resilient personality qualities and organizational support in order to mitigate the detrimental effects of ostracism on individual working performance. Despite unfavourable interpersonal dynamics, employee outcomes can be maintained with the support of managerial measures such as encouraging inclusive work cultures and resilience-building initiatives. Originality/value—This study adds to the body of knowledge on workplace ostracism by clarifying how personality and perceived organizational support affect job performance. With Python-based tools for PLS-SEM analysis and visualization, it ensures methodological robustness while offering fresh insights into these dynamics in the IT industry.