Digital Transformation and Employee Wellbeing in SMEs: An Empirical Study

Thesis Abstract

Digital transformation (DT) initiatives increasingly reshape organizational workflows, workforce dynamics, and well-being outcomes in small and medium-sized enterprises (SMEs). While DT promises efficiency and competitive advantage, its implementation may simultaneously affect employee stress, job satisfaction, engagement, and psychosocial safety, potentially altering overall wellbeing. This study addresses the gap in nuanced, empirically grounded evidence on how DT adoption, digital work environments, and related organizational practices influence employee wellbeing within SMEs. The aim is to examine the relationships between DT extent, digital work conditions, and wellbeing indicators, and to identify moderating and mediating processes that shape these outcomes. Specific objectives are (1) to measure the degree of DT implementation across core domains (digital tools, automation, data analytics, and digital communication platforms) in a representative sample of SMEs; (2) to assess employee wellbeing across multiple dimensions (psychological wellbeing, job satisfaction, work-life balance, and perceived burnout); (3) to evaluate the direct and indirect effects of DT on wellbeing via digital work conditions (workload, autonomy, system usability, perceived organizational support); (4) to test the moderating roles of managerial support, digital literacy, and organizational change climate; and (5) to develop a parsimonious conceptual model integrating Technology Acceptance Theory and the Job Demands-Resources (JD-R) framework to explain wellbeing outcomes in DT contexts. The methodology employs a cross-sectional, multilevel study design combining quantitative and qualitative components. The population comprises SMEs with between 10 and 250 employees across manufacturing and service sectors within a defined metropolitan region. A stratified random sample of 200 SMEs will be invited to participate, from which 1,200 employees will be surveyed (approximately 6–8 respondents per firm) to achieve robust cross-firm comparisons. Data collection will utilize a structured questionnaire incorporating validated scales DT maturity (customized scale capturing tool use, process automation, data analytics, and digital collaboration), the Utrecht Work Engagement Scale, the Maslach Burnout Inventory-General Survey, and the Warwick-Edinburgh Mental Wellbeing Scale, alongside measures of perceived workload, autonomy, system usability, and perceived organizational support. Complementary semi-structured interviews with 20–25 HR managers and 25–30 frontline employees will illuminate contextual mechanisms and triangulate survey findings. Validity and reliability will be established through pre-testing, confirmatory factor analysis (CFA) for construct validity, and Cronbach’s alpha coefficients above 0.70. Data will be analyzed using structural equation modeling (SEM) to test direct, indirect, and moderating effects within the JD-R framework, and multilevel modeling to account for nested data (employees within SMEs). Thematic analysis will be applied to interview transcripts to extract contextual themes related to change fatigue, learning curves, and perceived organizational justice during digital transitions. Expected findings include (a) a curvilinear (inverted-U) association between DT maturity and wellbeing, where moderate DT sophistication enhances wellbeing but excessive automation escalates stress and burnout if not accompanied by supportive practices; (b) significant mediation by digital work conditions, with workload increase and system usability as key pathways; (c) positive moderation by managerial support, digital literacy, and a strong change climate, buffering adverse wellbeing effects; and (d) differential effects by sector and firm size, with service SMEs showing distinct patterns due to customer-facing digital interfaces. The study will contribute to knowledge by integrating the JD-R model with Technology Acceptance Theory in SME DT contexts, offering a validated, transferable model for predicting wellbeing outcomes during digital transitions. It will provide practical implications for SMEs, including targeted workforce development, change management strategies, and investment in user-centered digital tools and supervisor training. The conclusion will emphasize balancing efficiency gains with employee wellbeing through inclusive change leadership, continuous digital literacy programs, and structured psychosocial support. Recommendations include designing staggered DT rollouts, ensuring participatory decision-making, enhancing IT help desks, and monitoring wellbeing indicators as a core metric of DT success.