The energizing role of digital leadership: Digital mindset and eustress as dual mechanisms of digital work engagement in technology-intensive hotels

Abstract

Background

The rapid digital transformation of the hospitality industry has intensified the need for employees to adapt to technology-driven work environments while maintaining high levels of engagement. In this context, digital leadership serves as a critical organizational resource that can shape employees’ perceptions and responses to digital demands.

Objective

This study examines the impact of digital leadership on hotel employees’ digital work engagement, with a focus on the mediating roles of digital mindset and eustress. Drawing on the Job Demands–Resources (JD-R) theory, the research explores how leadership in digital contexts can enhance employee engagement in technology-driven work environments.

Methods

Data were collected from 380 full-time employees working in five-star hotels that extensively utilize digital technologies in their daily operations and service delivery. Partial least squares structural equation modeling (PLS-SEM) was employed using WarpPLS version 8 to analyze the proposed relationships.

Results

Findings reveal that digital leadership has a positive effect on employees’ digital work engagement, digital mindset, and eustress. Moreover, both digital mindset and eustress were found to positively influence digital work engagement. Mediation analysis indicates that employees’ digital mindset and eustress significantly mediate the relationship between digital leadership and digital work engagement.

Conclusion

This study underscores the critical role of digital leadership in fostering a technologically adaptive workforce and highlights the importance of promoting a positive digital mindset and manageable stress levels to enhance employee engagement in the hotel industry.

Keywords

digital leadership digital work engagement digital mindset eustress hospitality industry

Introduction

The rapid digital transformation in the hospitality industry has reshaped how hotel employees perform their roles, interact with guests, and engage with organizational systems.^1–4 Technology-enabled services, including artificial intelligence (AI), digital booking platforms, and automated guest interactions, have become integral to service delivery in five-star hotels, increasing both the complexity and expectations of employee roles.^5–7 While these changes present opportunities for enhanced efficiency and customer satisfaction, they also pose challenges for employee adaptation, motivation, and engagement, highlighting the need for effective leadership that can navigate this digitally intensive environment.^8,9

Digital leadership, defined as the ability of leaders to leverage digital tools, foster technological competencies, and guide employees in digitally mediated work contexts, has emerged as a critical determinant of employee engagement and performance.^10–12 Unlike traditional leadership approaches, digital leadership specifically addresses the demands of technology-intensive work, emphasizing digital skills, autonomy, and learning orientation to support employees in navigating complex digital tasks.^8,13,14 Despite growing interest, empirical research on digital leadership in the hospitality industry remains limited, with much of the existing literature focusing on general leadership styles or examining digital leadership in corporate or educational contexts rather than frontline hotel operations.^15–18

Moreover, while employee engagement has been widely studied as an outcome of leadership, most studies overlook the mechanisms through which digital leadership influences engagement in technology-rich settings.^19–21 In particular, there is a need to examine the role of cognitive and affective mechanisms, such as digital mindset—employees’ attitudes and competencies toward digital technologies—and eustress, a positive form of stress that energizes and motivates employees.^18,22,23 While some studies have explored mediators like alienation, motivation, or psychological empowerment in digital contexts,^19,24 the literature lacks an integrated framework that simultaneously considers both personal resources and psychological responses in explaining the effects of digital leadership on digital work engagement in hospitality.

Another critical research gap is the context-specific investigation in five-star hotels, where service quality and employee-guest interactions are highly dependent on employees’ ability to effectively integrate digital tools into their work.^25,26 Existing studies often generalize findings from other sectors or fail to account for the unique challenges posed by hospitality work, such as high customer contact, variable work demands, and rapid service expectations.^1,3,7

Although prior studies have established the importance of digital leadership for enhancing employee outcomes, the underlying mechanisms through which digital leadership promotes digital work engagement remain insufficiently understood, particularly in hospitality settings characterized by intensive technology use and high service expectations. Existing research has predominantly emphasized mediating variables such as employee empowerment, learning orientation, digital capabilities, and psychological readiness. However, these studies have generally focused on either cognitive or motivational processes in isolation. Consequently, limited attention has been devoted to understanding how employees’ cognitive orientation toward digital technologies and their positive emotional responses to digital challenges jointly explain the effectiveness of digital leadership.

To address this gap, the present study draws on the Job Demands–Resources (JD-R) theory to examine digital mindset and employee eustress as two complementary mediating mechanisms linking digital leadership to digital work engagement. Digital mindset represents employees’ beliefs, openness, and growth orientation toward digital transformation, whereas employee eustress reflects positive psychological responses to challenging but manageable digital work demands. By integrating these cognitive and affective pathways within a single theoretical framework and testing the model among employees working in technology-intensive five-star hotels in Egypt, this study offers a more nuanced understanding of how digital leadership fosters engagement in contemporary digital workplaces.

Therefore, this study aims to investigate how digital leadership influences hotel employees’ digital work engagement, with a particular focus on the mediating roles of digital mindset and eustress. Specifically, it seeks to examine how digital leadership, as a key job resource, fosters employees’ cognitive readiness and positive psychological responses in digitally intensive environments, thereby enhancing engagement. By empirically testing these mechanisms in five-star hotels, the study intends to extend the Job Demands–Resources (JD-R) framework in hospitality contexts and provide actionable insights for hotel management on how to leverage digital leadership to optimize employee engagement, adaptability, and performance in technology-driven work settings.

Literature review and hypotheses development

Underpinning theory: The Job Demands–Resources (JD-R) theory

The Job Demands–Resources (JD-R) Theory posits that employee well-being and performance are determined by the dynamic interplay between job demands, which require sustained physical, cognitive, or emotional effort, and job resources, which facilitate goal attainment, reduce demands, and promote personal growth.^27–29 Job resources are particularly critical because they not only buffer the detrimental effects of high demands but also initiate a motivational process that enhances engagement, performance, and positive work outcomes.^9,27

In the context of digital workplaces, digital leadership can be conceptualized as a pivotal job resource. By providing guidance, support, autonomy, and clarity in navigating digital work environments, digital leaders equip employees with the tools and confidence to handle complex technological demands.^8,12,14,30 Through fostering digital skills, empowering employees, and facilitating access to technology, digital leaders reduce hindering demands while simultaneously enhancing motivational resources. This supportive environment encourages employees’ digital work engagement, cultivates a digital mindset characterized by adaptability, learning orientation, and openness toward technology, and stimulates eustress, a positive form of stress that energizes employees to perform effectively.^22,23,31

Extending the JD-R perspective, personal resources—such as resilience, optimism, and cognitive flexibility—mediate the motivational effects of job resources on engagement.^28,32 In digital work contexts, employees’ digital mindset and experience of eustress serve as key personal resources, enhancing their capacity to manage digital demands, sustain intrinsic motivation, and maintain high engagement levels.^18,33,34 Digital leadership contributes indirectly to engagement by shaping these personal resources, demonstrating how leadership behaviors can influence both the work environment and employees’ internal capacities^21,35,36

Overall, JD-R Theory provides a robust lens to understand the mechanisms through which digital leadership affects employee outcomes. By fostering a high-resource digital work environment, leaders can transform technological challenges into opportunities for growth, engagement, and innovation, highlighting the synergistic role of job and personal resources in driving sustainable digital performance.^7,24,37

Digital leadership and employees’ digital work engagement

Digital leadership is increasingly recognized as a critical factor for guiding employees in technology-intensive work environments.^8,10,11 It combines strategic use of digital tools, emotional intelligence, and supportive leadership behaviors to facilitate employee adaptation to the digital workplace.^15,36,38 Leaders who demonstrate digital competence, provide resources, and foster learning opportunities can enhance employees’ engagement in digitally mediated tasks.^21,35 Engagement in this context is defined as a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption in digital work activities.^25,39

Digital work engagement is conceptualized as a context-specific adaptation of the broader work engagement construct. Similar to traditional work engagement, it encompasses vigor, dedication, and absorption. However, digital work engagement specifically refers to employees’ positive, fulfilling, and persistent involvement in technology-mediated work activities and digital job tasks.^39,40 Therefore, while rooted in the general engagement literature, digital work engagement captures employees’ psychological investment in digitally intensive work environments.

Prior studies highlight that employees are more likely to be motivated, proactive, and committed when leaders demonstrate strong digital capabilities and provide clear guidance on digital work processes.^26,37,41 Based on this evidence, it is proposed that digital leadership directly influences employees’ digital work engagement. Consequently, the following hypothesis is formulated:

Digital leadership positively influences employees’ digital work engagement.

Digital leadership and employees’ digital mindset

Digital mindset refers to employees’ cognitive orientation toward digital technologies, characterized by openness to technological change, recognition of the transformative potential of digital innovation, and a growth-oriented attitude toward digitalization.^34,42 Importantly, digital mindset is conceptually distinct from related constructs. Digital literacy refers to the technical ability to use digital tools and technologies, whereas digital competence encompasses a broader set of digital knowledge and skills required to perform digital tasks effectively.⁴⁰ Similarly, digital self-efficacy reflects employees’ confidence in their capability to use digital technologies successfully.⁴³ In contrast, digital mindset is not concerned with what employees can do or how confident they are in doing it, but rather with how they perceive, interpret, and embrace digital transformation. Consequently, employees may possess strong digital skills while exhibiting a limited digital mindset, or conversely, demonstrates a strong digital mindset despite having only moderate technical capabilities. This distinction positions digital mindset as a cognitive antecedent that shapes employees’ willingness to engage with and adapt to digital transformation rather than as a capability-based construct.

Employees with a strong digital mindset view technology as an enabler for problem-solving, creativity, and innovation.^12,43 Digital leaders play a crucial role in cultivating this mindset by modeling digital fluency, promoting digital learning opportunities, and encouraging adaptive behaviors in response to technological change.^36,44 Empirical research demonstrates that leadership practices in digital contexts positively influence employees’ perceptions of their digital self-efficacy and readiness for digital work.^8,20,42 By fostering an environment where employees feel competent and confident in using digital tools, leaders enhance their capacity to adopt a proactive, growth-oriented digital mindset, which is essential for successful digital transformation in organizations.^37,45 Thus, digital leadership is expected to positively influence employees’ digital mindset. Consequently, the following hypothesis is formulated:

Digital leadership positively influences employees’ digital mindset.

Digital leadership and employees’ eustress

Eustress refers to a positive form of stress that energizes employees and motivates them to perform effectively.^22,46 Unlike distress, eustress is associated with manageable challenges and opportunities for growth.^47,48 According to the JD-R theory, job resources stimulate motivation and help employees manage job demands effectively.^27,28 In digital workplaces, digital leadership may shape not only the availability of resources but also employees’ psychological appraisal of digital challenges. Rather than perceiving technological changes as threatening or overwhelming, employees supported by digitally competent leaders may interpret digital demands as manageable and growth-promoting. This positive appraisal gives rise to eustress, a beneficial form of stress characterized by motivation, enthusiasm, and a sense of challenge.^22,23 Therefore, the present study conceptualizes eustress not as a stable personal resource but as an affective psychological response that mediates the relationship between digital leadership and digital work engagement.

Digital leaders can foster eustress by designing digital tasks that are challenging but achievable, providing autonomy, and offering support for employees to navigate technological complexities.^31,49 Moreover, leaders’ emotional intelligence and guidance in digital work reduce feelings of overwhelm and enable employees to experience technology-driven challenges as motivating rather than threatening.^33,38 Research shows that when employees experience eustress, they demonstrate higher energy, focus, and willingness to engage in innovative behaviors, leading to improved performance in digital tasks.^14,22,32 Therefore, digital leadership is proposed to positively influence employees’ experience of eustress. Consequently, the following hypothesis is formulated:

Digital leadership positively influences employees’ eustress.

Employees’ digital mindset and digital work engagement

A well-developed digital mindset enables employees to perceive technology as an opportunity rather than a threat, thereby enhancing engagement in digital work tasks.^18,34,40 Employees with a digital mindset are more likely to invest effort, display resilience in facing digital challenges, and remain absorbed in work activities.^20,43 Prior studies indicate that fostering digital competencies, confidence, and adaptability strengthens employees’ psychological investment in their work, particularly in highly digitalized environments.^13,42 Employees with a growth-oriented digital mindset experience greater intrinsic motivation and are more willing to learn and adopt new technologies, which is a key antecedent of digital work engagement.^12,37 Thus, digital mindset is expected to have a positive effect on employees’ engagement in digital work. Consequently, the following hypothesis is formulated:

Employees’ digital mindset positively influences their digital work engagement.

Employees’ eustress and digital work engagement

Eustress has been widely acknowledged as a motivational factor that enhances focus, creativity, and proactive behavior at work.^21–23 Unlike distress, which impairs performance, eustress energizes employees, helping them approach tasks with enthusiasm and persistence.^9,32,47,50 Empirical studies highlight that employees who perceive manageable challenges as opportunities for growth show higher levels of engagement and task involvement.^31,46,48 In digital workplaces, eustress arising from technology use encourages employees to embrace new digital tools, innovate, and sustain attention in complex digital tasks.^7,37,51 Accordingly, eustress is hypothesized to enhance employees’ engagement in digital work. Consequently, the following hypothesis is formulated:

Employees’ eustress positively influences their digital work engagement.

Digital mindset and eustress as mediators

Digital mindset acts as a cognitive and motivational mechanism through which digital leadership affects employees’ work engagement.^14,18,34 Digital leaders provide resources, learning opportunities, and supportive guidance that enhance employees’ digital competencies, attitudes, and confidence.^36,44 Employees with a strong digital mindset interpret challenges as opportunities for growth, remain focused on tasks, and sustain motivation in the face of technological complexity.^20,43 Prior research demonstrates that digital mindset fully or partially mediates the effect of leadership on engagement and performance outcomes in digital contexts.^13,21,52 Therefore, it is proposed that digital mindset mediates the relationship between digital leadership and employees’ digital work engagement.

Eustress serves as an affective mechanism linking leadership behaviors to engagement outcomes.^21–23 Digital leaders influence employees’ appraisal of digital tasks, ensuring that challenges are perceived as manageable and motivating.^31,33,49 Employees experiencing eustress demonstrate heightened energy, focus, and involvement in work activities.^9,32,46 Empirical evidence suggests that eustress mediates the effect of leadership on engagement by converting technological and cognitive demands into motivating experiences.^7,14,24 Thus, it is hypothesized that eustress mediates the impact of digital leadership on digital work engagement.

Consequently, the following hypotheses are formulated:

Employees’ digital mindset mediates the relationship between digital leadership and digital work engagement.

Employees’ eustress mediates the relationship between digital leadership and digital work engagement.

Existing studies have linked digital leadership to employee engagement through mediators such as empowerment, digital capabilities, organizational learning, motivation, and psychological readiness.^18,29,36,52 However, these studies have predominantly focused on either capability-based or motivational mechanisms. Comparatively less attention has been devoted to examining how employees’ cognitive orientation toward digital transformation and their positive emotional responses to digital challenges jointly explain the effectiveness of digital leadership. By simultaneously examining digital mindset and employee eustress as cognitive and affective mediating mechanisms, respectively, the present study provides a more comprehensive explanation of how digital leadership fosters digital work engagement in technology-intensive hospitality environments.

The theoretical framework of the study is illustrated below in Figure 1.

Methodology

Survey instrument and measures

Data were collected using a structured survey instrument designed to operationalize the constructs included in the research model. The questionnaire comprised two main sections. The first section contained items measuring the core study variables—digital leadership, digital mindset, employee eustress, and employee digital work engagement—while the second section gathered respondents’ demographic information, including gender, age, and educational level. The survey consisted of 38 measurement items in total (see Appendix A), which were adapted from previously validated scales to enhance measurement reliability and construct validity. All items were measured using a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Digital leadership was measured using a seven-item scale adapted from Claassen et al.⁸ Digital mindset was operationalized through a 15-item scale derived from Valta et al.³⁴ Employee eustress was assessed using a 10-item scale adapted from Almazrouei,²² while digital work engagement was measured using a 6-item scale adapted from Zia et al.⁵²

To ensure linguistic accuracy and conceptual consistency, the questionnaire was translated into Arabic using a rigorous back-translation approach based on Brislin’s⁵³ guidelines. The initial translation from English to Arabic was performed by two bilingual experts, after which an independent translator converted the Arabic version back into English. The original and back-translated versions were then compared to identify and resolve any discrepancies, ensuring semantic equivalence and cultural suitability. The finalized questionnaire was further evaluated by academic scholars and experienced five-star hotel professionals, who verified the clarity, relevance, and appropriateness of the survey items for the study’s target respondents.

Sample and data collection

This study examines the proposed research model within the context of five-star hotels. The target population consists of full-time employees working in officially classified five-star hotels that extensively utilize digital technologies in their daily operations and service delivery. To ensure meaningful responses, participants were required to have at least 6 months of work experience in their current hotel⁵⁴ and regular interaction with digital systems such as property management systems, reservation platforms, point-of-sale systems, or other hotel-related digital applications. Employees with less than 6 months of organizational tenure were excluded from the study. This criterion was adopted because newly hired employees may have had limited interaction with their supervisors and insufficient exposure to the digital systems, technologies, and work practices implemented within their hotels. Consequently, their perceptions of digital leadership, digital mindset, eustress, and digital work engagement may not yet have fully developed. Similar tenure thresholds have been adopted in previous organizational behavior research to ensure that respondents possess sufficient experience to provide reliable evaluations of workplace phenomena. In addition, only employees who report to a direct supervisor were included, as this enables accurate assessment of perceived digital leadership. Respondents were drawn from operational and administrative departments, including front office, housekeeping, food and beverage, sales and marketing, and information technology, where digital tools are integral to job performance. This context is particularly suitable for examining digital leadership, employee eustress, digital work engagement, and digital mindset, as five-star hotels operate in high-pressure, technology-intensive service environments that demand continuous digital competence, employee adaptability, and strong leadership support.

Owing to the wide geographical distribution of five-star hotels across Egypt and the associated practical limitations, a convenience sampling technique was employed. This non-probability approach is commonly applied in organizational research when access to respondents is constrained and the target population is geographically dispersed. The study sample was drawn from employees working in technology-intensive five-star hotels in Egypt. Data were collected from 20 five-hotels operating in the Greater Cairo region of Egypt. The number of respondents obtained from each hotel ranged from approximately 18–20 employees, resulting in a final sample of 380 valid questionnaires. This distribution helped ensure adequate representation across participating hotels while reducing the likelihood that the findings were disproportionately influenced by a single organization.

The unit of analysis in this study is the individual employee. Although respondents were nested within hotels and departments, the primary objective was to examine employees’ individual perceptions regarding digital leadership, digital mindset, eustress, and digital work engagement. Preliminary examination of the data did not indicate substantial clustering effects across hotels or departments. Nevertheless, future studies may employ multilevel analytical approaches to explicitly investigate organizational-level influences on these relationships.

Access to participating hotels was obtained through formal communication with hotel management and human resource departments. After receiving verbal organizational approval, questionnaires were distributed on-site directly to employees who met the study criteria. HR departments facilitated access to employees but did not select respondents or influence their participation. Prior to participation, respondents were informed about the academic purpose of the study, assured that their responses would remain anonymous and confidential, and informed that participation was entirely voluntary.

In total, 500 questionnaires were distributed, of which 380 were deemed complete and usable, yielding a response rate of 76%. Following the guideline proposed by Hair et al.,⁵⁵ which recommends a minimum respondent-to-item ratio of 10:1, the final sample size of 380 respondents is adequate for analyzing the 38 measurement items included in the research model and provides sufficient statistical power for subsequent analyses.

The study adhered to the ethical principles of The Declaration of Helsinki, ensuring voluntary participation and obtaining informed consent from all respondents. Participants were guaranteed anonymity, and the data collected was exclusively for academic use.

Data analysis

The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), an analytical technique well suited for testing complex research models, accommodating moderate sample sizes, and handling data that may not meet normality assumptions.^56,57 The analysis was conducted using WarpPLS version 8.0, which enabled simultaneous assessment of the measurement model and the structural model. The measurement model was evaluated in terms of reliability and validity, while the structural model was examined to test the proposed hypotheses and relationships among the study constructs.

Common method variance

To minimize the risk of common method variance (CMV), both procedural and statistical remedies were applied. Procedurally, respondents were assured of anonymity and confidentiality and were informed that there were no right or wrong answers, thereby reducing evaluation apprehension and social desirability bias. The questionnaire items were carefully worded to be clear, concise, and contextually appropriate for employees in five-star hotels, which helped to reduce ambiguity and method-related bias. In addition, the measurement items for the key constructs were adapted from different validated sources, and the questionnaire was structured to psychologically separate the predictor and criterion variables.

Statistically, several post hoc tests were conducted to assess the presence of CMV. Harman’s single-factor test indicated that no single factor accounted for the majority of the variance, suggesting that CMV was not a serious concern. Furthermore, Variance Inflation Factor (VIF) values for all latent constructs were below the conservative threshold of 3.3, indicating the absence of multicollinearity and common method bias. Taken together, these procedural and statistical controls provide reasonable assurance that CMV is unlikely to threaten the validity of the study findings.

Results

Participants’ profile

Table 1 summarizes the demographic characteristics of the 380 hotel employees who participated in the study. The sample was predominantly male, with men accounting for nearly three-fifths of the participants, while females represented just over two-fifths. Most respondents were between 30 and 45 years of age, followed by those aged 45–60, whereas employees under 30 formed the smallest age group. In terms of educational background, the majority held an undergraduate degree, a smaller proportion had completed high school or less, and only a limited number possessed a postgraduate qualification or higher.

Table 1.

Participant’s profile (N = 380 hotel employees).

Demographics	Category	Frequency	Percent
Gender	Male	223	58.68
Gender	Female	157	41.32
Age	18< 30 years	98	25.79
	30:<45 years	182	47.89
	45:60 years	100	26.32
Education	High school or below	114	30.00
	Undergraduate degree	224	58.95
	Postgraduate degree or above	42	11.05

Measurement model

Table 2 presents the psychometric assessment of the study constructs, including indicator loadings, reliability, convergent validity, and multicollinearity. All constructs demonstrate acceptable to strong internal consistency, as reflected by high composite reliability (CR) and Cronbach’s alpha (CA) values that exceed recommended thresholds (>0.70). The indicator loadings are generally moderate to high, indicating that the items adequately represent their respective constructs (>0.60). Convergent validity is supported, with average variance extracted (AVE) values meeting or exceeding acceptable levels across the constructs (>0.50). Additionally, the variance inflation factor (VIF) values remain below critical limits (<3.3), suggesting that multicollinearity is not a concern. Overall, the results confirm that the measurement model exhibits satisfactory reliability and validity.

Table 2.

Results of psychometric properties.

Construct	Indicators	Loading	CR	CA	AVE	VIF
Digital leadership (DL)	DL1	0.605	0.903	0.874	0.573	1.867
	DL2	0.783
	DL3	0.842
	DL4	0.733
	DL5	0.830
	DL6	0.726
	DL7	0.756
Digital work engagement (DWE)	DWE.1	0.871	0.915	0.888	0.645	2.018
	DWE.2	0.788
	DWE.3	0.848
	DWE.4	0.693
	DWE.5	0.836
	DWE.6	0.768
Digital mindset (DM)	DM1	0.755	0.953	0.946	0.594	1.656
	DM2	0.742
	DM3	0.869
	DM4	0.761
	DM5	0.837
	DM6	0.687
	DM7	0.753
	DM8	0.682
	DM9	0.668
	DM10	0.871
	DM11	0.678
	DM12	0.842
	DM13	0.856
	DM14	0.857
	DM15	0.643
Employee eustress (EEU)	EEU1	0.656	0.902	0.878	0.507	2.216
	EEU2	0.712
	EEU3	0.658
	EEU4	0.779
	EEU5	0.692
	EEU6	0.691
	EEU7	0.748
	EEU8	0.707
	EEU9	0.724
	EEU10	0.742

Table 3 reports the correlations among the latent variables along with the square roots of the AVE based on the Fornell and Larcker criterion. The diagonal values represent the square roots of AVE for each construct and are higher than the corresponding inter-construct correlations, indicating adequate discriminant validity. Overall, the results confirm that each construct is empirically distinct while maintaining theoretically consistent associations with the others.

Table 3.

Correlations among latent variables with the square root of AVEs.

Construct	DL	EEU	DM	DWE
Digital leadership (DL)	0.757
Employee Eustress (EEU)	0.590	0.712
Digital mindset (DM)	0.581	0.512	0.771
Digital Work Engagement (DWE)	0.531	0.684	0.490	0.803

Table 4 presents the discriminant validity assessment using the heterotrait–monotrait (HTMT) ratio. All HTMT values among the constructs are well below the conservative threshold of 0.85 and remain comfortably under the maximum acceptable limit of 0.90, indicating strong discriminant validity. Overall, the results provide further evidence that the measurement model demonstrates satisfactory discriminant validity.

Table 4.

Discriminant validity (HTMT).

Construct	DL	EEU	DM
Digital leadership (DL)
Employee Eustress (EEU)	0.685
Digital mindset (DM)	0.641	0.586
Digital Work Engagement (DWE)	0.605	0.772	0.536

Model fit

Appendix B presents the model fit and quality indices used to evaluate the overall adequacy of the structural model, following the criteria proposed by Kock.⁵⁸ The average path coefficient (APC) is statistically significant, indicating that the relationships specified in the model are meaningful. Similarly, the average R-squared (ARS) and average adjusted R-squared (AARS) values are both significant, suggesting that the model explains a strong proportion of variance in the endogenous constructs.

Regarding collinearity, both the average block VIF (AVIF) and the average full collinearity VIF (AFVIF) fall well below the recommended thresholds, confirming the absence of multicollinearity concerns. The Tenenhaus goodness-of-fit (GoF) value exceeds the criterion for a large effect size, demonstrating strong overall model fit. In addition, the Sympson’s paradox ratio (SPR), R-squared contribution ratio (RSCR), statistical suppression ratio (SSR), and nonlinear bivariate causality direction ratio (NLBCDR) all achieve their ideal values. Collectively, these results indicate that the model meets all recommended quality criteria and exhibits satisfactory explanatory performance, robustness, and predictive validity.

The predictive assessment (see Appendix B) indicates that the model possesses acceptable predictive relevance for the endogenous constructs. Accordingly, the findings suggest that the proposed framework provides a meaningful explanation of employees’ digital work engagement within the examined context, although further validation using out-of-sample prediction techniques in different hospitality settings is warranted.

Structural model and hypotheses testing

Table 5 and Figure 2 provide a comprehensive overview of the direct relationships proposed in the structural model, including their statistical significance, strength, and practical relevance. Overall, all hypothesized paths are positive and statistically significant at the 1% level, offering strong empirical support for the proposed framework.

Table 5.

Direct effects.

H	Structural paths	Path coefficient (β)	P value	95% CI (LL–UL)	Effect size (f²)	Result
H1	DL → DWE	0.13	<0.01	0.030–0.228	0.077	Supported
H2	DL → DM	0.59	<0.01	0.494–0.680	0.345	Supported
H3	DL → EEU	0.72	<0.01	0.625–0.807	0.512	Supported
H4	DM → DWE	0.15	<0.01	0.054–0.251	0.075	Supported
H5	EEU → DWE	0.52	<0.01	0.430–0.617	0.364	Supported
R² DM (0.34), EEU^: (0.51), DWE (0.52)
Q² DM: (0.351), EEU (0.502), DWE (0.521)

First, digital leadership → digital work engagement (H1) shows a positive path coefficient (β = 0.13), indicating that digital leadership directly enhances employees’ engagement in digital work activities. Although the effect size is relatively small (f² = 0.077), the relationship is statistically meaningful, suggesting that leadership practices alone contribute to engagement, even if their direct influence is partially complemented by other factors in the model.

Second, digital leadership → digital mindset (H2) demonstrates a strong and highly significant relationship (β = 0.59) with a large effect size (f² = 0.345). This finding implies that digital leadership plays a critical role in shaping employees’ openness, adaptability, and cognitive readiness toward digital technologies. Leaders who actively promote digital vision, support, and innovation strongly influence the development of a digital mindset among employees.

Third, digital leadership → employee eustress (H3) reveals the strongest direct effect in the model (β = 0.72), accompanied by a large effect size (f² = 0.512). This result indicates that effective digital leadership significantly fosters positive stress experiences, such as challenge, motivation, and enthusiasm, rather than harmful distress. It highlights leadership as a key driver in creating work environments where digital demands are perceived as stimulating and manageable.

Fourth, digital mindset → digital work engagement (H4) is positive and significant (β = 0.15), though the effect size is small (f² = 0.075). This suggests that employees who possess a stronger digital mindset are more likely to be engaged in digital tasks, yet this influence is moderate compared to other predictors. The result implies that mindset contributes to engagement but works more effectively in combination with other factors, such as leadership and eustress.

Finally, employee eustress → digital work engagement (H5) shows a strong positive relationship (β = 0.52) with a large effect size (f² = 0.364). This finding underscores the importance of positive stress as a central mechanism through which employees become energized, focused, and deeply involved in digital work activities. Among the predictors of digital work engagement, employee eustress emerges as one of the most influential factors.

The explanatory power of the model is further supported by the R² values. Digital leadership explains 34% of the variance in digital mindset and 51% of the variance in employee eustress, indicating strong predictive strength. Together, digital leadership, digital mindset, and employee eustress account for 52% of the variance in digital work engagement, demonstrating that the model provides a strong explanation of engagement outcomes. The R² values indicate that the model exhibits satisfactory explanatory capability for the endogenous constructs. Following the guidelines proposed by Sarstedt et al.,⁵⁸ the obtained R² values suggest moderate-to-high levels of explained variance, indicating that the proposed model captures an important proportion of the variance in digital mindset, employee eustress, and digital work engagement.

Lastly, all endogenous constructs exhibited Q² values greater than zero, indicating that the model possesses predictive relevance.⁵⁹

Table 6 reports the results of the mediation analysis using the bootstrapping procedure recommended by Preacher and Hayes,⁶⁰ focusing on the indirect effects of digital leadership on digital work engagement through digital mindset and employee eustress. The results provide strong evidence for the presence of partial mediation in both hypothesized relationships.

Table 6.

Mediation analysis’ Bootstrapped Confidence Interval (CI).

Hypo.	Indirect path	Path a	Path b	Indirect effect	SE	t value	95% CI (LL–UL)	Mediation
H6	DL → DM → DWE	0.590	0.150	0.089	0.036	2.458	0.018–0.159	Partial
H7	DL → EEU → DWE	0.720	0.520	0.374	0.034	11.012	0.308–0.441	Partial

For H6, the indirect path from digital leadership to digital work engagement through digital mindset (DL → DM → DWE) is positive and statistically significant. Digital leadership has a strong effect on digital mindset (path a = 0.590), while digital mindset, in turn, has a positive effect on digital work engagement (path b = 0.150). The resulting indirect effect is 0.089, with a relatively small standard error, and the associated t-value exceeds the critical threshold. Importantly, the 95% bootstrapped confidence interval does not include zero, confirming the significance of the mediation effect. This finding indicates that digital mindset partially transmits the influence of digital leadership to digital work engagement.

For H7, the mediation effect through employee eustress (DL → EEU → DWE) is notably stronger. Digital leadership exhibits a strong effect on employee eustress (path a = 0.720), and employee eustress strongly predicts digital work engagement (path b = 0.520). The indirect effect is large (0.374) and highly significant, as reflected by a high t-value and a narrow confidence interval. The bootstrapped confidence interval again excludes zero, providing robust support for mediation. This result suggests that employee eustress serves as a powerful mechanism through which digital leadership enhances digital work engagement.

Overall, the mediation analysis indicates that digital mindset and employee eustress partially mediate the relationship between digital leadership and digital work engagement. Although the direct effect of digital leadership remains statistically significant after including the mediators, its magnitude (β = 0.13) is relatively modest. This finding suggests that digital leadership exhibits a statistically significant but modest direct effect on digital work engagement, indicating that its influence is primarily transmitted through cognitive and affective mediating mechanisms.

Discussion

The present study contributes to the growing literature on digital leadership and employee engagement by empirically demonstrating that digital leadership significantly enhances hotel employees’ digital work engagement, digital mindset, and eustress. These findings reinforce the argument that leadership is a critical determinant of employee outcomes in the digital era and extend prior research in hospitality that highlights the strategic role of leadership in navigating technological change.^8,12,25 Specifically, our results suggest that digital leaders provide the guidance, autonomy, and technological support necessary for employees to thrive in digitally mediated roles, corroborating recent evidence that leadership support and digital competencies directly contribute to operational performance and employee engagement in five-star hotel contexts.^26,29

While previous studies have largely focused on traditional leadership styles, such as transformational or inclusive leadership, and their influence on retention and innovation^16,18 our study identifies digital leadership as a distinct antecedent of digital work engagement. This distinction is particularly salient in contemporary hospitality settings, where employees’ roles are increasingly mediated by technology, and service delivery depends on both human interaction and digital proficiency.^3,5 By demonstrating that digital leadership fosters not only engagement but also a growth-oriented digital mindset, our findings underscore the necessity of leadership that actively cultivates technological adaptability and learning orientation among employees.^34,43

Consistent with the Job Demands–Resources (JD-R) framework, our results indicate that digital leadership functions as a critical job resource, enhancing engagement directly while simultaneously nurturing employees’ psychological resources—namely, digital mindset and eustress.^21,27,28,31 This aligns with prior studies demonstrating that job resources such as leadership support, training, and autonomy positively influence employee motivation and engagement during digital transformation.^19,20 Importantly, by emphasizing positive stress responses (eustress) rather than negative stress or alienation, our findings offer a more constructive perspective on technology’s role in work engagement, suggesting that digital demands can be framed as opportunities for growth when employees are supported by capable digital leaders.^22,23,33

The observed mediating role of digital mindset and eustress further highlights that engagement in digital contexts is a product of both cognitive and affective processes. Employees’ readiness to embrace digital technologies (digital mindset) and their capacity to convert challenges into motivating experiences (eustress) are essential mechanisms through which digital leadership translates into higher engagement.^14,18,34 This finding resonates with broader digital transformation research emphasizing the psychological dimensions of technology adoption, such as empowerment, motivation, and learning orientation, as key drivers of performance outcomes.^19,36,37

An important finding concerns the relative strength of the two mediating mechanisms. The indirect effect of digital leadership on digital work engagement through employee eustress is strongly larger than the indirect effect through digital mindset. This result suggests that employees’ positive emotional responses to digital challenges constitute the dominant pathway through which digital leadership enhances digital work engagement. While fostering a digital mindset remains important, the findings indicate that leaders’ ability to create psychologically energizing and motivating digital work experiences may have a more pronounced influence on employees’ engagement in technology-intensive environments.

Finally, our results offer an important counterbalance to literature highlighting the “dark side” of digitalization, such as technostress, emotional strain, and perceived dehumanization in digitally mediated hotel work.^7,61,62 By demonstrating that digital leadership enhances eustress and fosters engagement, our study suggests that supportive leadership can mitigate the negative consequences of digital job demands, turning potential stressors into energizing experiences that promote learning, innovation, and sustained performance.^14,31,49 This highlights the strategic importance of cultivating digital leadership competencies within hospitality organizations to achieve both human and technological synergy.

Theoretical implications

This study makes several important theoretical contributions to the literature on digital leadership, employee engagement, and the Job Demands–Resources (JD-R) framework. First, the findings extend the application of JD-R theory to technology-intensive hospitality settings by demonstrating that digital leadership functions as an important job resource that fosters employees’ digital work engagement. While previous JD-R studies have predominantly examined traditional forms of leadership and general work engagement, the present study shows that leadership characterized by digital vision, technological adaptability, and support for digital transformation is positively associated with employees’ engagement in digitally mediated work activities. Therefore, the study broadens the applicability of JD-R theory to contemporary work environments in which digital technologies are increasingly embedded in employees’ daily tasks and interactions.

Second, this study contributes to the JD-R and digital leadership literature by simultaneously integrating cognitive and affective mechanisms that explain how digital leadership translates into digital work engagement. Specifically, digital mindset and employee eustress are conceptualized as distinct mediating pathways representing employees’ cognitive orientation toward digital transformation and their positive emotional responses to digital challenges, respectively. By incorporating these two complementary mechanisms within a single theoretical framework, the study advances a more comprehensive understanding of employee adaptation to digital transformation. The findings suggest that employees’ engagement in digital work is not solely determined by the availability of organizational resources but is also influenced by how employees perceive digital change and emotionally respond to digital work demands.

Third, the findings provide additional theoretical insight into the role of employee eustress within digital work environments. Rather than conceptualizing eustress as a stable personal resource, this study positions it as a positive psychological response that emerges when employees perceive digital challenges as manageable and growth-promoting. The results indicate that eustress serves as an important affective mechanism linking digital leadership to digital work engagement and, notably, represents the stronger of the two mediating pathways examined in this study. This finding enriches the JD-R literature by emphasizing the importance of employees’ positive emotional experiences in shaping engagement outcomes in digitally intensive workplaces.

Fourth, this study contributes contextual insights by providing empirical evidence from technology-intensive five-star hotels in Egypt, a setting that remains relatively underexplored in the digital leadership literature. The findings demonstrate that digital leadership, digital mindset, and employee eustress are important factors associated with digital work engagement within this specific organizational context. However, the present study does not seek to establish cross-cultural differences or to claim universal applicability of these relationships. Instead, the findings offer a contextual understanding of how digital leadership operates in hospitality organizations undergoing digital transformation and provides a foundation for future comparative studies examining whether these relationships differ across cultural or institutional settings.

Finally, this research advances digital leadership theory by demonstrating that the effectiveness of digital leadership extends beyond direct supervisory influence. The findings suggest that digital leadership shapes employees’ engagement primarily by fostering a positive cognitive orientation toward digital transformation and by encouraging positive psychological responses to digital challenges. Consequently, the study provides a richer theoretical explanation of the psychological mechanisms through which digital leadership influences employee outcomes and offers a stronger foundation for future research on leadership dynamics in digital workplaces.

Practical implications

The findings offer several focused implications for hotel managers aiming to enhance digital work engagement under realistic operational constraints. First, strengthening digital leadership capabilities at the supervisory level emerges as a central priority. Since leadership behavior in digital contexts is closely linked to employee engagement outcomes, hotels should invest in structured internal development initiatives that equip managers with practical competencies in digital communication, technology-enabled coordination, and support for digital task execution. Such development can be delivered through short, in-house training sessions, peer learning among department heads, and embedded coaching practices that reinforce day-to-day digital leadership behaviors.

Second, the results suggest the importance of creating work conditions that provide employees with constructive and manageable digital challenges. When employees are exposed to tasks that require learning and adaptation, but remain achievable, engagement is more likely to increase. Hotel managers can operationalize this by designing work assignments that gradually expand digital responsibilities, ensuring that employees are supported through clear guidance and timely feedback. Regular supervisory check-ins can also help employees interpret challenges as opportunities for learning rather than sources of strain, which aligns with the positive engagement patterns observed in the study.

Third, building employee confidence in using digital tools is a key mechanism for sustaining engagement. The findings imply that when employees feel capable in handling digital systems, their willingness to invest effort and participate actively in digital work increases. Hotels can therefore focus on hands-on practice opportunities, simplified digital workflows, and continuous on-the-job support that reduces uncertainty and strengthens self-efficacy. Encouraging collaborative problem-solving among employees when dealing with digital systems can further reinforce learning and shared confidence.

Overall, the practical message is relatively straightforward: digital engagement in hotels is best supported through capable leadership, structured exposure to manageable digital challenges, and consistent reinforcement of employees’ confidence in using technology in their daily work.

Limitations and future research avenues

While this study provides valuable insights into the role of digital leadership, digital mindset, and eustress in shaping hotel employees’ digital work engagement, several limitations warrant careful consideration. First, the cross-sectional design constrains the ability to infer causal relationships. Digital work engagement is likely dynamic, influenced by evolving technologies and fluctuating job demands. Longitudinal studies are needed to capture temporal patterns and examine how digital leadership practices impact engagement over time, including potential feedback loops between employee behavior and leadership strategies.

Second, the study is contextually limited to full-time employees in five-star hotels in Egypt, a setting characterized by high service standards and extensive digital adoption. This focus may obscure how digital leadership functions in smaller hotels, mid-scale properties, or in regions with lower technological infrastructure. Comparative cross-cultural research is critical to identify boundary conditions of the model and to understand how organizational culture, technological maturity, and labor market characteristics moderate these relationships.

Third, while the study examined digital mindset and eustress as mediators, the model does not account for other cognitive, emotional, or contextual mechanisms. Variables such as digital resilience, psychological empowerment, perceived organizational support, or technology-related anxiety may interact with leadership behaviors in shaping engagement. Future research should explore these mediators and moderators, including potential non-linear or conditional effects, to advance theoretical sophistication.

Fourth, the study assumes a uniform effect of digital leadership across all employees, neglecting individual differences such as age, digital literacy, personality traits, or generational perspectives. Future research could adopt a more granular approach, investigating how personal and demographic factors influence the efficacy of digital leadership strategies. Additionally, emerging digital technologies such as AI-driven tools, robotics, and metaverse-based service platforms introduce new challenges and opportunities, suggesting a need for studies that examine digital leadership in rapidly evolving technological contexts.

Finally, the possibility of self-selection bias cannot be completely ruled out. Employees who possess greater digital confidence or more positive attitudes toward digital technologies may have been more willing to participate in the survey. Consequently, the sample may overrepresent employees who are more receptive to digital transformation. Future studies are encouraged to employ probability sampling techniques or longitudinal research designs to minimize this potential bias and enhance the generalizability of the findings.

Footnotes

ORCID iD

Wagih M. E. Salama

Funding

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (KFU261033).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Appendix A

Appendix B

References

Jonathan

. The transformation of the hospitality industry in facing the digital technology era: opportunities, challenges, and innovations. AIRA (Artificial Intelligence Research and Applied Learning) 2025; 4(2): 79–95.

Khairy

Lee

Al-Romeedy

. Leader STARA competence and green competitiveness in tourism and hotel enterprises: leveraging green creativity and human capital. J Hospit Tour Insights. 2025; 8(6): 2338–2358.

Radović

Vujko

Stanišić

, et al. Digital hospitality as a socio-technical system: aligning technology and HR to drive guest perceptions and workforce dynamics. World 2025; 6(4): 134.

Khan

Soomro

Pitafi

. AI and employee performance: the role of knowledge and techno-pessimism in the digital age. J Knowl Manag 2026; 30(1): 172–191.

Almokdad

Mouloudj

Lee

. Rehumanizing AI-Driven service: how employee presence shapes consumer perceptions in digital hospitality settings. J Theor Appl Electron Commer Res 2025; 20(3): 209.

Khairy

Fayyad

El Sawy

. AI awareness and work withdrawal in hotel enterprises: unpacking the roles of psychological contract breach, job crafting, and resilience. Curr Issues Tourism. 2025; 1–21.

Shi

. The bright and dark sides of digital technology embedded in jobs: a daily investigation from hotel industry. Tour Manag 2025; 110: 105203.

Claassen

Dos Anjos

Kettschau

, et al. How to evaluate digital leadership: a cross-sectional study. J Occup Med Toxicol 2021; 16(1): 44.

Bai-Ngern

Tubtiang

. Leadership in the digital era. In: 2020 5th International STEM Education Conference (iSTEM-Ed). IEEE, 2020, November, pp. 127–130.

10.

Petry

. Digital leadership. In: Knowledge management in digital change: New findings and practical cases. Springer International Publishing, 2018, pp. 209–218.

11.

Hensellek

. Digital leadership: a framework for successful leadership in the digital age. Journal of Media Management and Entrepreneurship (JMME) 2020; 2(1): 1–15.

12.

Hamza

Karadas

. Digital leadership, AI integration, and cyberloafing: pathways to sustainable innovation in SMEs within resource-constrained economies. Sustainability 2025; 17(20): 9171.

13.

Shin

Mollah

Choi

. Sustainability and organizational performance in South Korea: the effect of digital leadership on digital culture and employees’ digital capabilities. Sustainability 2023; 15(3): 2027.

14.

Khan

Wang

Khan

, et al. Digital leadership enhances employee empowerment, techno-work engagement, and sustainability: SEM analysis in public healthcare. Inquiry: The Journal of Health Care Organization, Provision, and Financing 2025; 62: 00469580251317653.

15.

Oberer

. Leadership 4.0: digital leaders in the age of industry 4.0. International Journal of Organizational Leadership 2018.

16.

Gabriel

. The impact of transformational leadership on generation Z employee retention and innovative behaviour: a case of Malaysian hotel industry. International Journal of Multicultural and Multireligious Understanding 2022; 9(4): 35.

17.

Jasim

Khairy

Fayyad

, et al. Digital leadership and creative performance in tourism and hotel enterprises: leveraging strategic agility and organizational learning culture. Geo J Tour Geosites 2024; 54: 872–884.

18.

David

Lahindah

. Agile leadership and digital mindset: their impact on employee performance through employee engagement. Amkop Management Accounting Review (AMAR) 2025; 5(1): 182–207.

19.

Chen

. Hospitality employees and digital transformation: the mediating roles of alienation and motivation. Int J Hospit Manag 2024; 119: 103731.

20.

Shang

. Understanding employee digital learning engagement and innovative work behavior in hospitality sectors: a machine learning based multistage approach. Int J Hospit Manag 2025; 125: 103985.

21.

Khan

Shamsi

. Leading the innovation: role of eustress and work engagement as simultaneous mediators between JD-R and innovative work behavior. International Research Journal of Management and Social Sciences 2022; 3(2): 218–228.

22.

Almazrouei

SAS

. The effect of emotional intelligence dimensions on enhancing employees’ eustress at work. Information Management and Business Review 2017; 9(4): 18–27.

23.

Tarafdar

Stich

Maier

, et al. Techno‐eustress creators: conceptualization and empirical validation. Inf Syst J 2024; 34(6): 2097–2131.

24.

Salama

Suliman

Hassanin

, et al. Catalyzing green competitiveness in hotel and tourism businesses: leveraging digital dynamic capability and green creativity. Environment and Social Psychology 2024; 9(9): 2978.

25.

Aly

. Employee engagement mediate: the impact of digital leadership and leadership diversity in shaping operational performance. The International Journal of Tourism and Hospitality Studies 2024; 8(1): 34–56.

26.

Munir

Firdaus

Suhardi

, et al. The influence of digital leadership, work-life balance, employee engagement, and organizational learning on employee performance with job satisfaction as a moderating variable. International Journal of Applied Finance and Business Studies 2024; 12(3): 204–216.

27.

Bakker

Demerouti

. The job demands‐resources model: state of the art. J Manag Psychol 2007; 22(3): 309–328.

28.

Bakker

Demerouti

. Job demands–resources theory. Wellbeing: a complete reference guide, 2014, pp. 1–28.

29.

Wang

Ding

Kong

. Understanding technostress and employee well-being in digital work: the roles of work exhaustion and workplace knowledge diversity. Int J Manpow 2023; 44(2): 334–353.

30.

Wang

Mansor

Leong

. Linking digital leadership and employee digital performance in SMEs in China: the chain-mediating role of high-involvement human resource management practice and employee dynamic capability. Heliyon 2024; 10(16): e36026.

31.

Hasanein

Khairy

Al-Romeedy

, et al. Working smarter with AI in hotel industry: how awareness fuels eustress, task crafting, and adaptation. Societies 2026; 16(1): 36.

32.

Zhao

Yang

, et al. Where are eustress and distress from? An examination of smart healthcare system use. Ind Manag Data Syst 2026; 126: 1–27.

33.

Nascimento

Correia

Califf

. Techno-eustress under remote work: a longitudinal study in higher education teachers. Educ Inf Technol 2025; 30: 1–38.

34.

Valta

Hildebrandt

Maier

. Fostering the digital mindset to mitigate technostress: an empirical study of empowering individuals for using digital technologies. Internet Res 2024; 34(6): 2341–2369.

35.

Bilginoglu

Yozgat

. Exploring the interplay between technostress, digital leadership, and innovative work behavior. Human-Centric, Sustainable, and Resilient Organizations in the Digital Age. IGI Global Scientific Publishing, 2025, pp. 209–234.

36.

Hamouche

Bani-Melhem

Haj-Salem

, et al. Digital leadership and employee creativity: exploring the role of digital literacy and learning orientation. Employee Relat 2026; 48(1): 150–173.

37.

Yang

, et al. The impact of middle managers’ digital leadership on employee work engagement. Front Psychol 2024; 15: 1368442.

38.

Ertiö

Eriksson

Rowan

, et al. The role of digital leaders’ emotional intelligence in mitigating employee technostress. Bus Horiz 2024; 67(4): 399–409.

39.

Wei

, et al. Employee work engagement in the digital transformation of enterprises: a fuzzy-set qualitative comparative analysis. Hum Soc Sci Commun 2024; 11(1): 1–15.

40.

Chan

Hooi

Ngui

. Do digital literacies matter in employee engagement in digitalised workplace? J Asia Bus Stud 2021; 15(3): 523–540.

41.

Karafakıoğlu

Fındıklı

. The mediating role of work engagement in the relationship between digital leadership and innovative behavior and organizational agility. International Journal of Organizational Leadership 2024; 13(1): 1–21.

42.

Wong

Solberg

Traavik

. Individuals' fixed digital mindset, internal HRM alignment and feelings of helplessness in virtual teams. Inf Technol People 2022; 35(6): 1693–1713.

43.

Liu

Kamioka

. The effects of employees' digital growth mindset and supervisors’ coaching behaviour on digital self-efficacy. Technol Soc 2025; 81: 102875.

44.

Narbariya

Vohra

Chandra Kruse

. Towards readiness for digital transformation: exploring the interplay of leadership. Mindfulness, and Resilience 2025; 64(13): 126–161.

45.

Shen

Deng

Xiao

, et al. Driving green digital innovation in higher education: the influence of leadership and dynamic capabilities on cultivating a green digital mindset and knowledge sharing for sustainable practices. BMC Psychol 2025; 13(1): 1–19.

46.

Venkatesh

Ram

. Eustress: a unique dimension to stress management. Voice of Research 2015; 4(2): 26–29.

47.

Quinones

Rodríguez-Carvajal

Griffiths

. Testing a eustress–distress emotion regulation model in British and Spanish front-line employees. Int J Stress Manag 2017; 24(S1): 1–28.

48.

Hakim

Maharani

Patiro

SPS

. The influence of burnout, Eustress/distress and employee wellbeing on employee performance. Quantitative Economics and Management Studies 2025; 6(3): 334–342.

49.

Farmanesh

Vehbi

Solati Dehkordi

. Uprooting technostress: Digital leadership empowering employee well-being in the era of industry 4.0. Sustainability 2025; 17(19): 8868.

50.

Parker

Ragsdale

. Effects of distress and eustress on changes in fatigue from waking to working. Appl Psychol: Health and Well‐Being 2015; 7(3): 293–315.

51.

Salama

Khairy

Mansour

, et al. How AI awareness drives employee intention to leave in hotels: the mediating roles of job burnout and psychological contract breach. Geo J Tour Geosites 2025; 60: 1206–1220.

52.

Zia

Memon

Mirza

, et al. Digital job resources, digital engagement, digital leadership, and innovative work behaviour: a serial mediation model. Eur J Innovat Manag 2025; 28(8): 3192–3216.

53.

Brislin

. Translation and content analysis of oral and written materials. Methodology 1980: 389–444.

54.

Morrison

. Newcomer information seeking: exploring types, modes, sources, and outcomes. Acad Manag J 1993; 36(3): 557–589.

55.

Hair

Black

Babin

, et al. Multivariate data analysis. 7th ed., 2010.

56.

Hair

J, JF

Matthews

, et al. PLS-SEM or CB-SEM: updated guidelines on which method to use. Int J Multivar Data Anal 2017; 1(2): 107–123.

57.

Ali

Rasoolimanesh

Sarstedt

, et al. An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. Int J Contemp Hospit Manag 2018; 30(1): 514–538.

58.

Kock

. WarpPLS user manual: version 7.0. ScriptWarp Systems, 2021.

59.

Sarstedt

Ringle

Hair

. Partial least squares structural equation modeling. Handbook of market research. Springer International Publishing, 2021, pp. 587–632.

60.

Preacher

Hayes

. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods 2008; 40(3): 879–891.

61.

Lyu

Liu

Chen

. Investigating the effect of AI technostress on employee’s working behaviors: a stress response model. J Hosp Tour Technol 2026: 1–30.

62.

Seah

RYT

Yuen

. Mental wellbeing in digital workplaces: the role of digital resources, technostress, and burnout. Technol Soc 2025; 81: 102844.