Abstract:
Smart technologies, Artificial Intelligence, Robotics and Algorithms (STARA) have been reshaping industries, including the apparel sector, to achieve efficiency gains and remain competitive, yet have shown mixed effects on employee responses. Prior research has not sufficiently examined employee-level responses, particularly the underlying mechanisms involved in appraising awareness of these technologies. Building on the Challenge–Hindrance Stressor Framework (CHSF), this study investigates a dual mediating mechanism in examining the impact of STARA awareness on employee organisational commitment in the Sri Lankan apparel sector. The data were collected via an online survey from 249 executive-grade employees of three large-scale apparel firms in Sri Lanka using convenience sampling. The data analysis was conducted using Partial Least Squares–Structural Equation Modelling (PLS-SEM). The results reveal that STARA awareness negatively affects organisational commitment, while job insecurity has a direct negative impact on organisational commitment. In contrast, retraining expectations have a direct positive impact on organisational commitment. Furthermore, STARA awareness affects both job insecurity and retraining expectations. Importantly, job insecurity was found to partially mediate the relationship between STARA awareness and organisational commitment, acting as a hindrance stressor in response to STARA awareness, while retraining expectations were found to partially mediate as a challenge stressor in the relationship between STARA awareness and organisational commitment. The study provides a dual perspective in understanding how emerging technologies are perceived and responded to by employees in an uncertain business environment. It challenges the existing one-sided negative employee responses to advanced technologies. The findings offer practical implications for Human Resource (HR) practitioners and process owners to better manage employee responses to technological change in resource-constrained organisational settings.
