Abstract:
Sri Lanka’s telecommunication industry plays a critical role in advancing national
connectivity and economic development, with its continued success heavily
dependent on the consistent job performance of employees to deliver reliable services
and meet evolving consumer demands. However, negative workplace dynamics,
particularly workplace ostracism, may undermine employees’ ability and motivation
to perform effectively. Drawing on the Conservation of Resources (COR) theory, this
study investigates the impact of perceived workplace ostracism on employee job
performance, with emotional intelligence examined as a moderating factor. Using a
time-lagged dyadic design, data were collected in two phases from 225 employee–
supervisor pairs across five major telecommunication firms in Sri Lanka. In the first
phase, employees reported their experiences of workplace ostracism and emotional
intelligence. In the second phase, their immediate supervisors provided ratings of the
employees’ job performance. This dyadic approach mitigated common method
variance by separating the sources of the independent and dependent variables while
ensuring data alignment through matched employee–supervisor pairs. Correlation
and regression analyses using SPSS version 25.0 revealed a significant negative
relationship between workplace ostracism and job performance (r = -0.33, p < 0.01),
suggesting that employees who feel excluded are less likely to perform well.
Furthermore, hierarchical regression analysis confirmed that emotional intelligence
significantly moderated this relationship (interaction term β = 0.19, p < 0.05),
indicating that employees with higher emotional intelligence were better able to cope
with ostracism and maintain job performance. Based on the findings, telecom HR
professionals should incorporate emotional intelligence assessments and training into
recruitment and development initiatives to strengthen employee resilience and job
performance. However, since this study relied on self-reported and supervisor-rated
measures, it may be subject to source-specific biases. Future research should adopt
longitudinal, multi-wave designs and incorporate additional data sources.
