Shaping digital maturity in smart factories: the impact of employee voice, well-being, and resistance
Md. Nahin Hossain et al.
Abstract
Purpose This paper investigates how employee voice, well-being, and resistance to change influence the journey of digital maturity in smart factories. Whereas existing models have stressed infrastructure and a system-based approach, this research explores a socio-technical perspective in bringing forward human dynamics necessary toward Industry 4.0 transformation. Design/methodology/approach The Employee-Centric Digital Maturity Framework (EC-DMF) has been proposed and tested to integrate the voice of employees, their wellbeing, and resistance to digital maturity structures. Measurement items were adapted from prior studies. Data from 388 employees in 47 smart factories in Bangladesh were collected through a structured survey. The proposed model was tested through Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4.0. Findings The findings show that, in fact, employee voice, well-being, and resistance to change are significant drivers of a smart factory's digital maturity. The study confirms that employee voice enhances both well-being and digital maturity, while well-being positively drives digital maturity and mediates the effects of voice and resistance. In contrast, resistance to change negatively impacts both well-being and digital maturity. Such findings emphasise not only how these factors interplay to impact digital transformation but also leverage the socio-technical evolution of smart factories. Originality/value This research validates the EC-DMF as a socio-technical framework, extending digital maturity models through psychosocial dimensions. It advances theory by positioning employee voice as a structural enabler, highlighting well-being as both predictor and mediator, and reframing resistance as a barrier and diagnostic signal. Practically, it shows that inclusive communication, well-being support, and constructive management of resistance are essential for accelerating digital maturity in smart factories.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.