The digital transformation of enterprises, driven by technologies like blockchain, AI, and big data, presents both opportunities and challenges. While offering competitive advantages, this transition often leads to difficulties at the organizational and employee levels. Employee resistance to new technologies, their learning abilities, and their capacity to leverage personal resources to maintain work engagement under technological pressure are critical considerations. Previous research has primarily focused on linear relationships between individual factors and work engagement, neglecting the configurational effects and the role of technostress. This study addresses this gap by exploring the interplay of job demands (technostress), personal resources (self-efficacy), and personality traits (Big Five) on employee work engagement, employing fsQCA to uncover synergistic effects and causal asymmetries.

The literature review examines work engagement, conceptualized as a positive, fulfilling work-related state associated with positive outcomes. Antecedent variables are explored from the perspectives of job demands (work pressure, particularly technostress stemming from remote work, work-life conflict, and job insecurity) and personal resources (self-efficacy, a positive belief in one's ability to succeed). Personality traits, especially extraversion and conscientiousness, are also highlighted as significant individual differences influencing engagement. The review emphasizes the limitations of traditional linear models and the need for a configurational approach to understand the complex interplay of these factors in the context of digital transformation. Studies on digital transformation, technostress, and its creators are also reviewed, emphasizing the need for a holistic perspective.

This study uses the fuzzy-set qualitative comparative analysis (fsQCA) method, chosen for its ability to handle complex causal relationships and configurations. The fsQCA method is particularly suited to exploring multiple, potentially interdependent antecedent conditions and allows identification of multiple pathways to the same outcome (equifinality) and different pathways to high versus low levels of the outcome (asymmetry). The Job Demands-Resources (JD-R) model and Trait Activation Theory provide the theoretical foundation. Seven variables were selected: technostress creators (techno-overload, techno-invasion, techno-complexity, techno-insecurity, techno-uncertainty), self-efficacy, and the Big Five personality traits. Data were collected through a survey of 225 employees in Chinese enterprises undergoing digital transformation. A five-point Likert scale was used for measurement. Cronbach's alpha was used to assess reliability (all above 0.77), and confirmatory factor analysis (CFA) was performed using AMOS 24.0 to assess construct and convergent validity. The direct calibration method in fsQCA, using 95th, 50th, and 5th percentiles as anchor points, was employed to convert data into fuzzy sets. Consistency thresholds were set at 0.95, with a frequency threshold of 3, and a PRI consistency greater than 0.75. Both necessary condition analysis and configurational analysis were performed using fsQCA software. A robustness analysis was conducted by adjusting the case truncation value from 3 to 4 to verify the stability of the results.

The fsQCA analysis revealed three main pathways to high employee work engagement: 1. **Openness to experience/conscientiousness/extraversion-driven:** This pathway highlights the importance of personality traits. Employees high in openness to experience adapt more easily to new technologies, those high in conscientiousness are intrinsically motivated to high performance, and those high in extraversion thrive in collaborative work environments, which are common in digitally transformed organizations. 2. **Self-efficacy-driven:** High self-efficacy was a central factor in promoting work engagement. Employees confident in their abilities are more likely to embrace challenges, actively participate, and contribute to organizational transformation. 3. **Inhibition of technostress creators:** The absence of technostress was crucial for high engagement. Reducing techno-overload, uncertainty, invasion, complexity, and insecurity is essential for maintaining employee well-being. The analysis of low work engagement identified two main pathways: 1. **Self-efficacy-inhibitory:** The absence of self-efficacy was a core condition in several configurations associated with low engagement. 2. **Neuroticism presence/extraversion absence:** The presence of neuroticism combined with a lack of extraversion significantly contributed to low engagement. A robustness analysis, altering the case truncation value, confirmed the stability of the findings. The overall consistency of solutions improved, showing that the results are relatively robust.

The findings address the research question by identifying specific configurations of individual and contextual factors that drive high and low work engagement during digital transformation. The three pathways to high engagement highlight the importance of a supportive organizational context that reduces technostress, the value of empowering employees with strong self-efficacy, and the influence of specific personality traits that promote adaptability and collaboration. The two pathways to low engagement underscore the detrimental effects of low self-efficacy and the negative influence of neuroticism coupled with a lack of extraversion. The results are significant as they move beyond individual factors to show how configurations of factors interact to influence engagement. The findings offer valuable insights for managing employee engagement in the context of enterprise digital transformation.

This study contributes to the literature by using fsQCA to explore the complex interplay of individual and contextual factors impacting employee work engagement during digital transformation. The identification of three key pathways to high engagement and two to low engagement provides a nuanced understanding of this phenomenon. Future research could investigate the longitudinal aspects of these relationships, explore the mediating or moderating roles of other variables, and examine the generalizability of these findings across different cultural contexts and industries. Further research could investigate intervention strategies to mitigate technostress and enhance employee self-efficacy.

The study's reliance on self-reported data through a questionnaire survey may introduce subjective biases. Future studies might benefit from incorporating multi-source data, including observer ratings of personality traits and objective measures of performance. The sample, while relatively large for fsQCA, was limited to employees in Chinese enterprises. Further research is needed to determine the generalizability of the findings to other cultural and organizational contexts. The study focused on specific drivers of work engagement; other factors could also play a significant role.