Employee work engagement in the digital transformation of enterprises: a fuzzy-set qualitative comparative analysis

The study addresses how employees’ work engagement is shaped during enterprise digital transformation, a context characterized by rapid IT-driven changes (e.g., AI, big data, IoT) that alter organizational structures and work processes. The research problem focuses on how job demands (technostress creators) and personal resources (self-efficacy and Big Five personality traits) combine to influence employee engagement. Prior work largely examined linear, single-factor effects and seldom integrated technostress creators into configurational analyses. Grounded in the JD-R model and trait activation theory, the purpose is to identify complex configurations of individual and environmental factors that lead to high (and low) work engagement, offering a more holistic explanation of employees’ states and behaviors critical to successful digital transformation.

Work engagement is defined as a positive, fulfilling, work-related state (vigor, dedication, absorption) and is linked to performance and well-being. It is influenced by individual differences (e.g., age, personality) and person–environment fit. Digital transformation entails disruptive technological change that reconfigures products, processes, and organizational forms, producing both opportunities and risks (e.g., tech complexity, accelerated R&D). Technostress creators reflect pressures from ICT use—techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty—and can harm work–life balance, satisfaction, and increase burnout. The JD-R model posits job demands decrease engagement while job and personal resources (e.g., self-efficacy) enhance it, especially under high demands. Trait activation theory suggests personality traits translate into behaviors when situational cues are present (task, social, organizational contexts), implying that engagement expression depends on context. Personality (Big Five) relates to engagement: extraversion and conscientiousness generally positive; neuroticism negative. Self-efficacy supports proactive coping and sustained engagement under challenge. Existing studies highlight interdependencies among traits and technostress but lack configurational analyses incorporating technostress creators.

Design: A configurational approach using fuzzy-set qualitative comparative analysis (fsQCA) to examine how combinations of technostress creators (job demand), self-efficacy (personal resource), and Big Five personality traits (openness, agreeableness, neuroticism, conscientiousness, extraversion) shape employee work engagement during digital transformation. Measures: All constructs measured on five-point Likert scales. Personality items adapted from Srivastava et al. (2015). Self-efficacy used Schwarzer et al. (1997) general self-efficacy scale (five items). Technostress creators adapted from Tarafdar et al. (2007) across five dimensions (techno-overload, techno-invasion, techno-complexity, techno-insecurity, techno-uncertainty). Work engagement measured with UWES-9 (Schaufeli et al., 2006): vigor, dedication, absorption. Sample and data collection: Online survey via Credamo targeting employees in firms undergoing digital transformation (state-owned, private, joint ventures) in China. 230 questionnaires distributed; 225 valid responses. Demographics: 45.3% male, 54.7% female; majority under 45 (37.3% ≤25; 34.3% 26–35; 19.1% 36–45); >70% with bachelor’s or higher; 76.9% ordinary employees; most in private enterprises. Reliability and validity: Cronbach’s alpha for subscales ≥0.770; overall alpha 0.982, indicating good reliability. CFA (AMOS 24.0) showed adequate fit: χ2/df=1.666 (<3), RMSEA=0.055 (~0.05), GFI=0.761, AGFI=0.729, CFI=0.943, IFI=0.944, TLI=0.938. Convergent validity: factor loadings 0.65–0.902, AVE ≥0.5761, composite reliability ≥0.8015. fsQCA procedures: Direct calibration using three anchors at the 95th, 50th, and 5th percentiles for full membership, crossover, and full non-membership, respectively. Variables calibrated and labeled (e.g., KFX1 openness, SJZ1 neuroticism, YRX1 agreeableness, JZX1 conscientiousness, WXX1 extraversion, GS1 self-efficacy, TC1 technostress creators). A small offset (0.001) was added to membership scores equal to 0.5 to avoid ties. Necessary condition analysis found no single necessary condition (all consistencies <0.9). Truth table with seven conditions (128 possible configurations); thresholds: consistency ≥0.95, frequency ≥3, PRI consistency >0.75. Intermediate solutions reported. Robustness tested by increasing case frequency threshold from 3 to 4; results remained substantively consistent, with slightly higher overall consistencies and slightly lower coverage.

• No single necessary condition for high or low engagement (all necessity consistencies <0.9), indicating configurational causation. • High work engagement: Seven configurations grouped into three pathways; overall solution consistency 0.960 and coverage 0.721. 1) Technostress suppression (~TC): H1a (~SJZ*YRX*WXX*GS*~TC) and H1b (~SJZ*JZX*WXX*GS*~TC). Core role of low technostress; supportive roles of extraversion, self-efficacy, and low neuroticism; agreeableness or conscientiousness as peripheral. 2) Self-efficacy driven (GS): H2a (KFX*YRX*JZX*GS*~TC) and H2b (YRX*JZX*WXX*GS*~TC). Core self-efficacy with supportive agreeableness, conscientiousness, and low technostress; openness or extraversion may supplement. 3) Openness–extraversion driven (KFX*WXX): H3a (KFX*SJZ*YRX*JZX*WXX), H3b (KFX*YRX*JZX*WXX*GS), H3c (KFX*~SJZ*YRX*~JZX*WXX*~TC). Core openness and extraversion; agreeableness supportive; other conditions vary (e.g., self-efficacy, conscientiousness, low neuroticism, low technostress in some variants). • Low work engagement: Six configurations distilled into two pathways; overall solution consistency 0.901 and coverage 0.796. – Self-efficacy inhibitory (absence of GS): NH1a (SJZ*YRX*JZX*~GS*TC), NH1b (KFX*SJZ*YRX*JZX*WXX*~GS), NH1c (KFX*SJZ*YRX*~JZX*WXX*~GS*~TC). Core absence of self-efficacy; agreeableness often present; contextual roles for neuroticism, conscientiousness, extraversion, and technostress vary. – Neuroticism presence with extraversion absence (SJZ & ~WXX): NH2a (~KFX*SJZ*~YRX*~WXX*~GS*TC), NH2b (KFX*SJZ*~JZX*WXX*~GS*TC), NH2c (KFX*SJZ*YRX*JZX*~WXX*GS*~TC). Core presence of neuroticism with absent extraversion drives low engagement, with differing contextual supports (openness, agreeableness, conscientiousness, self-efficacy, technostress). • Measurement quality: Strong reliability (overall α=0.982; subscales ≥0.770) and CFA fit (χ2/df=1.666; RMSEA=0.055; CFI=0.943; TLI=0.938); convergent validity supported (loadings 0.65–0.902; AVE ≥0.5761; CR ≥0.8015). • Sample: 225 employees in digitally transforming firms; thresholds for fsQCA were consistency 0.95, frequency 3, PRI >0.75; robustness confirmed with frequency 4.

Findings support the JD-R model by showing that reducing job demands from technostress and enhancing personal resources (self-efficacy) increase engagement. Trait activation is evident: openness to experience and extraversion emerge as core traits yielding high engagement in contexts rich with technology change and cross-functional collaboration, where these traits are cued. The configurational results demonstrate conjunctural causation and causal asymmetry—multiple, non-linear pathways can produce high engagement, and the absence of those same conditions does not symmetrically yield low engagement. Practically, firms undergoing digital transformation should concurrently mitigate technostress (e.g., reduce overload, invasion, complexity, insecurity, and uncertainty), build employees’ self-efficacy (targeted training, feedback, autonomy), and leverage or recruit personality profiles (openness, extraversion, conscientiousness) aligned with transformation demands. The low-engagement pathways underscore risks when self-efficacy is lacking or when neuroticism is high and extraversion is low, pointing to targeted interventions for vulnerable groups.

The study contributes a configurational understanding of employee work engagement in digital transformation by integrating technostress creators, self-efficacy, and Big Five traits within the JD-R and trait activation frameworks. Three robust pathways promote high engagement: suppressing technostress, strengthening self-efficacy, and leveraging openness with extraversion (with conscientiousness and agreeableness often supportive). Low engagement arises via absent self-efficacy or a combination of neuroticism presence with extraversion absence. The results highlight causal complexity and asymmetry, advancing theory and offering actionable guidance for managing human factors in digital transformation. Future research can incorporate additional drivers (e.g., leadership, organizational resources, digital literacy), use multi-source ratings for personality and engagement, and broaden sampling across industries and cultures to test generalizability.

The analysis focused on main drivers (technostress creators, self-efficacy, Big Five), potentially omitting other relevant antecedents. Self-report, subjective questionnaire data and modifications to established scales may introduce bias. Personality traits were self-assessed; observer ratings might better predict behavior. Future studies should combine self- and other-ratings, expand and diversify samples, and examine additional variables to enrich conclusions.