Digital Leadership Driving Sustainability through Knowledge Integration in Public Healthcare Organizations

Digital Leadership Driving Sustainability through Knowledge Integration in Public Healthcare Organizations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Digital Leadership Driving Sustainability through Knowledge Integration in Public Healthcare Organizations Faisal Al Dhaeri, Mohammed Al Mady, Hossam Ahmed Hanafy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9106807/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Amid accelerating digital transformation, public healthcare organizations face mounting pressure to achieve sustainable organizational performance (SOP) that balances economic efficiency, social responsibility, and environmental dimensions of the triple-bottom-line framework. Although digital leadership has attracted increasing scholarly attention, prior research has predominantly emphasized technological adoption and short-term operational outcomes, offering limited insight into the internal organizational mechanisms through which digital leadership translates into sustained triple-bottom-line performance particularly within public-sector healthcare systems. Drawing on Dynamic Capabilities Theory and Absorptive Capacity Theory, this study develops and tests a mechanism-based model in which knowledge integration mediates the relationship between digital leadership and sustainable organizational performance. Digital leadership is conceptualized as an integrated strategic capability that enables organizations to mobilize digital resources and orchestrate knowledge processes effectively. Sustainable organizational performance was operationalized as an overall composite construct reflecting the integrated economic, social, and environmental dimensions of the triple-bottom-line framework. Data were collected from 359 managers and professionals at the Abu Dhabi Department of Health using a proportionate stratified random sampling technique. The proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that digital leadership has a significant positive direct effect on sustainable organizational performance (β = 0.682, p < 0.001) and a strong positive effect on knowledge integration (β = 0.897, p < 0.001). Knowledge integration also exerts a significant positive effect on sustainable organizational performance (β = 0.231, p = 0.001). Mediation analysis confirms a complementary partial mediating role of knowledge integration (indirect β = 0.207, p = 0.001). The model demonstrates substantial explanatory power (R² ≈ 0.80) and positive out-of-sample predictive relevance, indicating robust structural validity. These findings suggest that sustainable performance in public healthcare organizations depends not only on digital transformation initiatives themselves but on leadership-driven knowledge integration mechanisms that translate strategic digital orientation into coordinated institutional action. By offering a capability-based explanation of how digital leadership contributes to sustainable organizational performance, this study advances digital leadership scholarship beyond efficiency-centered perspectives and provides empirical evidence from an underexplored governmental healthcare context in the Arab region. Digital leadership Knowledge integration Sustainable Organizational performance Dynamic capabilities Absorptive Capacity Public sector Abu Dhabi Figures Figure 1 Figure 2 1. Introduction Public healthcare systems are undergoing profound structural transformation driven by rapid digitalization, regulatory reform, and rising societal expectations for transparency, efficiency, and service quality. Governments across the globe are investing heavily in digital infrastructures, data platforms, and smart health initiatives in an effort to modernize healthcare governance and improve institutional performance. However, despite substantial technological investment, many public organizations struggle to translate digital transformation efforts into sustained performance improvements. This challenge suggests that digital technologies alone are insufficient; rather, leadership capabilities play a decisive role in determining whether digital initiatives generate enduring organizational value. Digital leadership has consequently attracted growing scholarly attention within management and information systems research. Existing studies largely associate digital leadership with innovation capacity, technology adoption, and operational efficiency. While these contributions are important, much of the literature remains technology-centric and outcome-focused, frequently treating performance as a direct consequence of leadership behaviors. Such an approach overlooks the organizational mechanisms through which leadership translates strategic digital intent into measurable and sustainable performance outcomes. As a result, the relationship between digital leadership and long-term organizational performance remains theoretically under-specified and empirically fragmented, particularly within public-sector healthcare environments. From a theoretical standpoint, dynamic capabilities theory (Teece, 2007 ) and absorptive capacity theory (Zahra & George, 2002 ) offer a compelling foundation for understanding this relationship. Both perspectives emphasize that superior organizational performance is fundamentally knowledge-driven. Organizations create sustained value not merely by acquiring digital technologies, but by effectively sensing environmental changes, assimilating new information, and reconfiguring internal resources. Within this framework, leadership influences performance indirectly by shaping processes that enable coordination, learning, and knowledge mobilization. Despite the relevance of these theories, empirical research has rarely positioned knowledge integration as a central mediating mechanism linking digital leadership to organizational performance. In complex public healthcare systems, performance improvement depends heavily on the ability to integrate dispersed expertise across clinical, administrative, technological, and regulatory domains. Digital transformation initiatives frequently require cross-functional coordination, institutional alignment, and the harmonization of diverse knowledge bases. Without effective knowledge integration processes, digital investments may remain fragmented and fail to translate into meaningful organizational outcomes. Accordingly, examining whether digital leadership enhances performance through strengthening knowledge integration represents a critical theoretical and practical question. This issue is particularly salient in public-sector healthcare contexts characterized by bureaucratic governance structures, multi-level accountability mechanisms, and evolving policy demands. While collaborative governance research highlights the importance of coordination and institutional capability in achieving public-sector outcomes, limited empirical evidence examines how digital leadership activates internal knowledge integration processes within governmental healthcare organizations—especially in non-Western settings. The Arab region, and specifically government healthcare agencies, remains underrepresented in digital leadership and sustainability research. To address these theoretical and contextual gaps, the present study develops and empirically tests a mechanism-based model linking Digital Leadership to Organizational Performance through the mediating role of Knowledge Integration. The study is situated within the Abu Dhabi Department of Health and draws on survey data collected from managers and professionals engaged in digital transformation initiatives. Digital leadership is conceptualized as an integrated strategic capability reflected by multiple interrelated leadership attributes that enable organizations to mobilize digital resources and coordinate knowledge processes effectively, while organizational performance reflects sustained value creation within a public healthcare setting. Accordingly, this study pursues three primary objectives: (1) to examine the direct relationship between digital leadership and organizational performance; (2) to assess the effect of digital leadership on knowledge integration; and (3) to test the mediating role of knowledge integration in translating digital leadership into performance outcomes. By adopting a mechanism-oriented perspective grounded in dynamic capabilities and absorptive capacity theories, this study makes three key contributions. First , it advances digital leadership research beyond efficiency-driven explanations by offering a knowledge-based account of organizational performance. Second , it empirically validates knowledge integration as a central explanatory mechanism linking leadership and performance. Third , it provides contextual evidence from a public-sector healthcare organization in the Arab region, contributing to the limited body of research on digital transformation within governmental institutions. Through this integrative framework, the study enhances theoretical clarity regarding the pathways through which digital leadership contributes to sustained organizational performance and offers actionable insights for public healthcare leaders seeking to align digital transformation strategies with long-term institutional effectiveness. 2. Literature Review 2.1 Theoretical Framework of the Study: This study is grounded in dynamic capabilities theory (Teece, 2007 ) and absorptive capacity theory (Zahra & George, 2002 ), which together provide a multi-level explanation of how digital leadership translates into sustainable organizational performance in knowledge-intensive public healthcare environments. Dynamic capabilities theory argues that sustained success in turbulent contexts depends on an organization’s ability to sense emerging opportunities, seize them through strategic action, and reconfigure internal resources accordingly (Teece, 2007 ). In digitally transforming public healthcare systems characterized by regulatory complexity, stakeholder scrutiny, and technological disruption leadership plays a central orchestration role in these processes. Digital leaders not only identify technological trends but also align digital transformation initiatives with strategic priorities and reconfigure organizational structures to support long-term sustainability. Recent research on leadership agility further strengthens this perspective. In volatile, uncertain, complex, and ambiguous (VUCA) environments, leadership effectiveness increasingly depends on anticipatory capacity, strategic foresight, and adaptive responsiveness (Syamsir et al., 2025 ). These agility-oriented attributes reinforce the sensing and reconfiguration components of dynamic capabilities theory and conceptually underpin the predictive and visionary dimensions of digital leadership examined in this study. Absorptive capacity theory complements this strategic perspective by emphasizing the knowledge-processing foundations of organizational advantage. Zahra and George ( 2002 ) conceptualize absorptive capacity as a four-stage process: acquisition, assimilation, transformation, and exploitation of knowledge. Organizational performance depends not merely on access to information but on structured mechanisms that convert knowledge into coordinated action. Within this framework, knowledge integration corresponds to the assimilation and transformation phases, where dispersed expertise is synthesized and embedded into organizational routines. Integrating these two theoretical lenses provides a coherent explanation of the proposed mediation mechanism. Dynamic capabilities theory explains how leadership enables strategic adaptation, while absorptive capacity theory clarifies how internal knowledge processes create value. Knowledge integration lies at the intersection of these perspectives: it functions both as a reconfiguration mechanism and as a knowledge-processing capability. Consequently, digital leadership is expected to enhance sustainable organizational performance indirectly by strengthening structured knowledge integration processes. 2.2 Conceptualization of Constructs: 2.2.1 Digital leadership: Digital leadership is conceptualized as an integrated strategic capability that enables organizations to navigate digital transformation in a coherent and sustainability-oriented manner. It extends beyond technical expertise and reflects a leader’s ability to align digital initiatives with institutional goals, mobilize organizational resources effectively, and foster adaptive capacity in technology-intensive environments. The literature identifies several interrelated dimensions associated with digital leadership, including digital capabilities, accumulated digital experience, predictive orientation, and digital vision (Eberl & Drews, 2021 ; Matarazzo et al., 2021 ; Wang et al., 2022 ). Digital capabilities refer to the strategic understanding of technological infrastructures and their alignment with organizational objectives. Digital experience reflects accumulated exposure to digital transformation initiatives, enhancing the ability to manage uncertainty. Predictive ability captures anticipatory competence in identifying emerging technological and environmental trends. Digital vision represents the articulation of a coherent long-term direction linking digital transformation to sustainable value creation. The emphasis on predictive orientation and vision aligns with leadership agility scholarship, which highlights foresight and adaptability as central competencies in turbulent governance environments (Syamsir et al., 2025 ). In public healthcare systems facing rapid technological change and regulatory pressures, such agility becomes critical for sustaining balanced performance outcomes. Although these dimensions are conceptually distinguishable, they collectively represent complementary manifestations of a broader strategic orientation toward digital transformation. Therefore, this study operationalizes digital leadership as a unified reflective construct capturing the overall digital leadership capability. 2.2.2 Knowledge Integration: Knowledge integration refers to the organization’s capability to combine, coordinate, and apply dispersed knowledge across units, professional domains, and technological systems. It goes beyond information exchange and reflects structured cross-boundary coordination processes that enable collective problem-solving and innovation. Within the knowledge-based view of the firm, knowledge integration transforms individual expertise into shared organizational competence (Nonaka & Toyama, 2007 ). From an absorptive capacity perspective, it corresponds to the assimilation and transformation stages through which new knowledge becomes embedded in organizational routines (Zahra & George, 2002 ). Recent capacity-building research in public-sector management emphasizes that sustainable performance depends on structured knowledge management, technological alignment, and collaborative learning mechanisms (Saputra et al., 2024 ). This framing positions knowledge integration not as a passive coordination activity but as a dynamic organizational capability linking digital transformation to institutional sustainability. Public healthcare contexts further amplify the importance of integration. Crisis governance research demonstrates that effective service delivery during health emergencies depends on coordination and knowledge sharing across administrative and clinical units (Safitri et al., 2021 ). Similarly, governmental capability development in combating public health crises relies on institutional learning and digital adaptation (Audia et al., 2021 ). These insights underscore the strategic importance of knowledge integration in healthcare governance settings. 2.2.3 Sustainable Organizational Performance: Sustainable organizational performance (SOP) refers to the organization’s ability to generate balanced and enduring value for multiple stakeholders over time. Grounded in the triple-bottom-line framework (Elkington, 1997 ), sustainability-oriented performance encompasses economic efficiency, social responsibility, and environmental stewardship. In public-sector healthcare institutions, economic performance involves operational efficiency and responsible resource allocation; social performance includes service quality, employee engagement, and stakeholder trust; while environmental performance reflects sustainable resource utilization and regulatory compliance. Collaborative governance research suggests that sustainability outcomes emerge through cross-boundary coordination and multi-stakeholder alignment rather than isolated performance initiatives (Valentina et al., 2025 ). In digitally transforming public institutions, economic, social, and environmental objectives are frequently interdependent. Knowledge integration thus becomes essential for reducing trade-offs and enabling balanced sustainability outcomes. Consistent with capability-based perspectives (Santoro et al., 2023 ; Zahoor & Gerged, 2021 ), this study operationalizes SOP as a unified reflective construct representing aggregate sustainability performance. 2.3 Hypotheses Development: 2.3.1 Digital Leadership and Sustainable Organizational Performance: In digitally transforming environments, leadership assumes strategic significance not merely because it encourages the adoption of digital tools, but because it shapes how organizations interpret technological disruption, reconfigure priorities, and realign structures and routines in response to systemic change. Digital transformation has been conceptualized as an organization-wide process in which digital technologies trigger structural and cultural disruptions that necessitate coordinated strategic responses to enable new value creation pathways (Vial, 2019 ). Within the dynamic capabilities framework, sustained performance under environmental turbulence depends on an organization’s capacity to sense emerging opportunities and threats, seize them through timely commitments, and transform by reconfiguring assets and organizational routines (Teece, 2007 ). These higher-order capabilities rest upon managerial microfoundations, including cognition, coordination, and orchestration processes. From this perspective, digital leadership can be understood as a form of dynamic managerial capability. Leaders’ human capital, cognitive framing, and social capital enable them to orchestrate resources, align digital initiatives with strategic priorities, and guide organizational transformation under uncertainty (Adner & Helfat, 2003 ; Helfat & Martin, 2015 ). Digital leadership therefore has the potential to influence sustainable organizational performance by embedding long-term orientation into digital strategy, shaping governance and accountability routines, and supporting structural reconfiguration that produces durable rather than episodic gains. This argument aligns with sustainability scholarship emphasizing the temporal and multi-stakeholder nature of sustainability performance, moving beyond short-term financial metrics toward integrated economic, social, and environmental outcomes (Bansal & DesJardine, 2014 ; Hart, 1995 ; Hart & Dowell, 2011 ). Sustainable organizational performance is conceptually grounded in the Triple Bottom Line (Elkington, 1997 ), requiring coordinated capability deployment over time rather than isolated efficiency improvements. However, despite increasing interest in digital transformation and organizational performance, several conceptual and empirical tensions remain unresolved. First, much of the empirical evidence focuses on short-term operational or financial outcomes, leaving multidimensional sustainability performance comparatively underexamined (Eikelenboom & de Jong, 2019 ). Second, digital leadership research remains heavily private-sector oriented, whereas public-sector healthcare organizations operate under distinct institutional logics centered on public value, equity, and accountability. Third, findings regarding digitalization and performance are often fragmented: while some studies report efficiency gains, others caution that digital investments alone do not guarantee sustained performance without complementary organizational capabilities (Hanelt et al., 2021 ; Guandalini, 2022 ). This suggests that the leadership–performance relationship may be contingent upon deeper capability structures rather than technological adoption per se. Addressing these gaps requires explicitly linking digital leadership to sustainable organizational performance as an integrated, multidimensional construct within a public-sector healthcare context. Accordingly: H1 Digital leadership has a statistically significant positive effect on sustainable organizational performance (SOP). 2.3.2 Digital Leadership and Knowledge Integration: A knowledge-based view argues that organizational performance depends less on possessing knowledge than on integrating specialized knowledge across individuals and units to coordinate action (Grant, 1996 ). Knowledge integration is not equivalent to simple information sharing; it involves building shared understanding, aligning interdependent tasks, and embedding synthesized knowledge into routines and decision processes often through coordination mechanisms that enable flexible collaboration under time pressure and uncertainty (Okhuysen & Eisenhardt, 2002 ). From an absorptive capacity perspective, organizations create value when they can recognize external knowledge, assimilate it, transform it, and exploit it; thus, integration is central to moving from dispersed information to coordinated implementation (Cohen & Levinthal, 1990 ; Zahra & George, 2002 ). Complementing this, information systems scholarship positions knowledge processes and infrastructures as core organizational capabilities technology, structure, and culture jointly shape the organization’s capacity to create, store, transfer, and apply knowledge at scale (Gold et al., 2001 ; Alavi & Leidner, 2001 ). Digital leadership should strengthen knowledge integration because digital transformation intensifies cross-boundary interdependence: data platforms, AI-enabled analytics, and integrated workflows require synchronized decision making among clinical, administrative, and IT actors. In public-sector settings, digital transformation specifically entails new ways of working with stakeholders and redesigning service delivery, often shifting cultures and stakeholder relationships (Mergel et al., 2019 ). Recent public-administration research has also begun to operationalize digital transformation leadership as a measurable construct, emphasizing leaders’ roles in vision setting, collaboration, and guiding operational implementation in public organizations (van Roekel et al., 2025 ). These leadership behaviors are precisely the micro-level foundations that enable organizational routines of integration: leaders create enabling conditions shared priorities, incentives, platforms, psychological safety through which cross-unit knowledge is combined and translated into actionable protocols. While digital leadership is frequently linked to digitalization success, the leadership and knowledge integration pathway is less often theorized and tested as a central relationship, particularly in public-sector healthcare where professional logics and bureaucratic boundaries heighten integration challenges. Moreover, many studies treat knowledge outcomes as secondary correlates rather than modeling knowledge integration as a focal capability that digital leadership intentionally builds an omission that constrains explanation of how leadership translates digital intent into coordinated action. Thus, the second hypothesis is formulated as follows: H2 Digital leadership has a statistically significant positive effect on knowledge integration (KI). 2.3.3 Knowledge Integration and Sustainable Organizational Performance: Sustainable organizational performance requires solving a distinctive coordination problem: organizations must avoid treating economic efficiency, social value, and environmental stewardship as isolated objectives, instead aligning them into coherent operational choices and sustained routines. The sustainability literature highlights that sustainability is inherently long-term and multi-stakeholder (Bansal & DesJardine, 2014 ), while the natural-resource-based view links environmental and social commitments to strategic capabilities and resource deployment (Hart, 1995 ; Hart & Dowell, 2011 ). In parallel, IS research argues that information systems can enable environmentally sustainable development by supporting measurement, transparency, resource monitoring, and optimized decision making—yet these benefits materialize only when organizational processes translate digital information into coordinated operational practice (Watson et al., 2010 ). Knowledge integration is central to this translation. When organizations integrate expertise across boundaries, they are better positioned to (a) detect sustainability-relevant signals (regulatory changes, stakeholder expectations, resource constraints), (b) evaluate trade-offs with shared criteria, and (c) embed sustainability priorities into routinized decisions (procurement, service design, workforce planning). Evidence from sustainability-performance research suggests that integrative capabilities can support all three pillars of sustainability performance simultaneously, rather than forcing trade-offs (Eikelenboom & de Jong, 2019 ). Similarly, empirical work on environmental knowledge integration shows that integrating knowledge resources can act as a mechanism connecting organizational relational resources to environmental performance outcomes (Zahoor & Gerged, 2021 ). Although the knowledge-based view strongly implies a performance payoff from integration, the sustainability domain still shows fragmentation: many studies focus on green or social outcomes in isolation or treat sustainability as reputational signaling rather than an integrated performance construct. Moreover, sustainability in public-sector healthcare magnifies interdependence across professional domains and policy objectives yet mechanism-based evidence connecting knowledge integration to multidimensional sustainability outcomes in public healthcare governance remains comparatively limited. Accordingly, the third hypothesis is proposed: H3 Knowledge integration has a statistically significant positive effect on sustainable organizational performance (SOP). 2.3.4 The Mediating Role of Knowledge Integration: Dynamic capabilities and absorptive capacity theories both imply that leadership effects on performance are rarely direct in a simple behavioral sense; rather, leadership shapes the routines and capabilities through which organizations adapt and create value. Teece’s framework highlights microfoundations that underlie sensing, seizing, and transforming, including managerial cognition, coordination, and organizational processes (Teece, 2007 ). The microfoundations literature further argues that capabilities emerge from interactions among individuals, social processes, and structural design, making it essential to theorize the intermediate routines that connect managerial action to system-level outcomes (Felin et al., 2012 ). Absorptive capacity similarly emphasizes that organizations gain advantage when knowledge is not only acquired but also assimilated, transformed, and exploited activities that depend on cross-unit integration mechanisms (Cohen & Levinthal, 1990 ; Zahra & George, 2002 ). In a public-sector healthcare context, these theoretical claims become especially compelling: digital initiatives (e.g., integrated data systems, governance analytics, inter-organizational coordination platforms) typically span multiple knowledge domains and stakeholder demands. Digital leadership can therefore be expected to affect sustainable organizational performance through two reinforcing routes: a direct route (strategic alignment, accountability, prioritization) and an indirect route in which leaders build integration routines that transform dispersed expertise into coordinated, sustainability-oriented action. This mediation logic is also consistent with digital transformation research identifying the need to move beyond outcome associations toward explaining intermediate organizational changes and mechanisms (Vial, 2019 ; Hanelt et al., 2021 ). Digital sustainability and digital transformation reviews repeatedly note that linkages between digital initiatives and sustainability outcomes remain inconsistently conceptualized and mechanistically underexplained, encouraging more explicit theorization of the how (Guandalini, 2022 ). In addition, while public-sector leadership research increasingly measures digital transformation leadership, fewer studies connect it to internal knowledge-based mechanisms and, subsequently, to integrated Triple Bottom Line performance in public healthcare organizations (van Roekel et al., 2025 ). Positioning knowledge integration as the mediation mechanism therefore addresses a clear explanatory and contextual gap: it specifies the internal capability pathway through which leadership converts digital transformation into durable, multidimensional sustainability performance. Therefore, the following hypothesis is proposed: H4 Knowledge integration plays a statistically significant positive mediating role in the relationship between digital leadership and sustainable organizational performance (SOP). The hypothesized research framework is presented on Fig. 1 below 3. Methodology 3.1 Research Philosophy: This study is grounded in a positivist research philosophy. Positivism assumes that organizational phenomena can be objectively observed and empirically measured through systematic data collection and statistical analysis. In line with this stance, digital leadership, knowledge integration, and sustainable organizational performance are treated as measurable constructs whose relationships can be tested quantitatively. The positivist orientation aligns with the study’s objective of examining hypothesized causal relationships among clearly defined variables within a public-sector healthcare context. Accordingly, standardized instruments and statistical modeling techniques were employed to ensure objectivity and methodological rigor. 3.2 Research Approach: A deductive research approach was adopted. Drawing on Dynamic Capabilities Theory and Absorptive Capacity Theory, the study develops a conceptual model and formulates hypotheses regarding the direct and mediating relationships among digital leadership, knowledge integration, and sustainable organizational performance. The deductive logic moves from established theory to empirical testing. This approach is consistent with the positivist paradigm and is appropriate for validating theoretically derived relationships using quantitative data collected from the Abu Dhabi public healthcare sector. 3.3 Methodological Choice: The study employs a mono-method quantitative design. Data were collected using a structured online questionnaire with standardized Likert-scale measures. This design allows for consistent measurement across respondents and supports statistical evaluation of relationships among latent constructs. A quantitative approach is particularly suitable for testing direct and mediating effects within structural models. It enables reliability assessment, validity testing, and structural equation modeling to examine hypothesized relationships in a rigorous and systematic manner. 3.4 Research Strategy and Time Horizon: A survey research strategy was adopted to collect data from employees across multiple organizational levels within the Abu Dhabi Department of Health. The use of a structured questionnaire facilitated efficient data collection and ensured comparability across respondents. The study follows a cross-sectional design, capturing respondents’ perceptions at a single point in time. Data were collected between July and September 2025, providing a contemporary snapshot of digital leadership practices and organizational performance within the selected public-sector healthcare context. A stratified sampling design was employed to enhance representativeness and reduce potential sampling bias. 3.5 Population and Sampling Procedure: 3.5.1 Target Population and Unit of Analysis: The target population comprised all permanent employees working within the Department of Health – Abu Dhabi (DoH), totaling 724 individuals according to officially updated HR records during the study period. The unit of analysis was the individual employee occupying a formal organizational position within the Department. To ensure conceptual clarity, the population was classified into five mutually exclusive strata based on the official organizational structure: senior management, middle management, supervisory level, technical staff, and administrative staff. Each employee belonged to only one category, eliminating classification overlap. Managers were therefore treated as strata within the same organizational system rather than as a separate population. This structure ensures alignment between the theoretical constructs of the study digital leadership, knowledge integration, and sustainable organizational performance and the empirical respondents, as employees across all strata are directly engaged in digital processes, decision-making activities, or knowledge-related interactions. 3.5.2 Sampling Frame Construction: The sampling frame was constructed using the official HR database of the Department of Health – Abu Dhabi. The database included employee ID, organizational level, functional category, and departmental affiliation. Temporary staff, outsourced personnel, and external contractors were excluded to maintain conceptual consistency between the study variables and the respondents. Each eligible employee was assigned to one predefined stratum according to official job classification. 3.5.3 Sampling Technique: A proportionate stratified random sampling technique was employed. The sampling process followed a structured sequence. First , the total population (N = 724) was divided into five strata based on official job classification. Second , the required total sample size (n = 254) was determined using Krejcie & Morgan ( 1970 ) at a 95% confidence level and 5% margin of error. Third , proportional allocation was applied using the following formula: n_h = (N_h / N) × n where n_h represents the sample size for each stratum, N_h the population size of the stratum, N the total population, and n the overall required sample size. Within each stratum, random selection was performed using a computerized random number generator to ensure procedural objectivity and methodological replicability. 3.5.4 Data Collection Procedure: The online questionnaire link was distributed through the official internal email system of the Department of Health – Abu Dhabi, in coordination with the Human Resources Department. The invitation explained the purpose of the study, assured confidentiality, and emphasized voluntary participation. The survey was administered through the organization’s internal email system to ensure that only eligible employees could access the questionnaire, thereby strengthening sampling control and data integrity. Data collection took place between July and September 2025. All responses were anonymized, and no identifying information was collected. Participation remained voluntary throughout the data collection period. 3.5.5 Final Sample Distribution: A total of 384 responses were received. After screening for completeness, response consistency, and missing data, 25 questionnaires were excluded due to substantial missing values or patterned responses. The final usable sample consisted of 359 respondents. Table 1 Population Structure and Achieved Stratified Sample Distribution Stratum Population % of Population Achieved Sample % of Sample Senior Management 36 5% 36 10% Middle Management 109 15% 90 25% Supervisory Level 145 20% 54 15% Technical Staff 304 42% 107 30% Administrative Staff 130 18% 72 20% Total 724 100% 359 100% Although proportionate stratified sampling was initially applied, the final achieved sample reflects natural variation in response rates across strata. Senior management exhibited a higher response rate relative to its population proportion, resulting in slight overrepresentation in the final dataset. However, all strata remain adequately represented, and no post-stratification weighting was deemed necessary given the robust sample size and the predictive orientation of PLS-SEM. 3.5.6 Response Flow and Final Sample Size: The final usable sample of 359 respondents exceeds the minimum required sample size of 254. This corresponds to an effective response rate of 49.6%. Exceeding the minimum threshold enhances statistical precision and strengthens the robustness of Structural Equation Modeling (SEM) estimations. The achieved sample size is therefore considered sufficient for reliable testing of both direct and mediating effects within the proposed model. 3.6 Justification for Choosing Abu Dhabi Department of Health as the Research Setting: The Abu Dhabi Department of Health provides an appropriate and compelling context for examining digital leadership, knowledge integration, and sustainable organizational performance. As the regulatory authority for the emirate’s healthcare system, the Department leads major digital transformation initiatives involving artificial intelligence, digital health platforms, and data-integration systems, making it an ideal environment for investigating how digital leadership shapes organizational capabilities and outcomes. Its operations rely heavily on multidisciplinary teams, extensive information flows, and interconnected public–private healthcare networks, all of which highlight the central role of knowledge integration the study’s mediating construct. Furthermore, the Department’s mandate directly contributes to UAE national sustainability priorities, including Vision 2030, thereby positioning sustainable performance as a strategic organizational imperative. Importantly, public-sector healthcare represents a context with unique characteristics such as bureaucratic structures, strong accountability mechanisms, and complex regulatory environments that influence how digital leadership is enacted. Scholars such as Abubakar et al. (2024) and Nuryadin et al. ( 2023 ) highlight the scarcity of empirical studies in Arab government settings. Therefore, selecting the Abu Dhabi Department of Health addresses a critical contextual gap and enhances the theoretical and practical relevance of the study’s findings. 3.7 Measurement of Variables: 3.7.1 Constructs and Measurement Scales: This study examines three latent constructs: Digital Leadership (independent variable), Knowledge Integration (mediating variable), and Sustainable Organizational Performance (dependent variable). All constructs were measured using established multi-item scales adapted from previously validated studies. Responses were captured using a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). This format is widely used in organizational research and is appropriate for assessing perceptual constructs in Structural Equation Modeling (SEM). 3.7.1.1 Digital Leadership: Digital Leadership was measured using 31 items adapted from previously validated scales. The items capture leaders’ strategic understanding of digital technologies, accumulated digital experience, anticipatory orientation, and ability to articulate a coherent digital vision. Although the construct conceptually encompasses multiple interrelated dimensions identified in prior research, it was operationalized in this study as a unified reflective latent variable representing overall digital leadership capability. This specification assumes that the observed indicators reflect a shared underlying leadership orientation toward digital transformation rather than forming independent formative components. Such reflective modeling is consistent with capability-based research and supports theoretical parsimony in structural analysis. 3.7.1.2 Knowledge Integration: Knowledge Integration was measured using five items adapted from Wong ( 2013 ). The scale assesses the extent to which knowledge is shared, combined, and embedded across organizational units. It reflects structured processes that facilitate collective learning and coordinated decision-making within the organization. 3.7.1.3 Sustainable Organizational Performance: Sustainable Organizational Performance (SOP) was measured using ten items reflecting economic, social, and environmental performance aspects consistent with the triple-bottom-line framework. While these dimensions are conceptually distinguishable, the construct was operationalized as a single reflective latent variable capturing overall sustainability performance within the organization. This approach reflects the integrated nature of sustainability outcomes in institutional settings and aligns with the study’s objective of examining aggregate structural relationships rather than comparative dimensional effects. Table 2 summarizes the constructs, the conceptual aspects reflected in the items, the number of indicators, and the original sources from. Table 2 Measurement of Study Variables Construct Conceptual Aspects Reflected in the Items No. of Items Item Range Sources Digital Leadership (DL) Strategic digital capability; Digital experience; Predictive orientation; Digital vision 31 1–31 Yng et al. (2023); Wang et al. ( 2022 ); Ahn et al. (2014); Lundin et al. ( 2016 ; 2021 ); Temelkova ( 2019 ); Awan et al. ( 2021 ); Ruvio et al. ( 2010 ); Luthans ( 2002 ) Knowledge Integration (KI) Cross-unit knowledge sharing and integration processes 5 32–36 Wong ( 2013 ) Sustainable Organizational Performance (SOP) Economic; Social; Environmental performance aspects 10 37–46 Wang (2019); Li (2014); Cheah et al. ( 2019 ); Maletic et al. ( 2015 ) Source Developed by the researcher based on validated measurement scales from prior literature. 3.8 Ethical Considerations: This study adhered to established ethical standards for research involving human participants. The research design involved a voluntary, anonymous organizational survey and did not include the collection of sensitive personal or medical data. Prior to data collection, administrative authorization was obtained from the Department of Health – Abu Dhabi. The Human Resources Department facilitated the distribution of the survey link while ensuring that participation remained entirely voluntary and unrelated to any form of performance evaluation. Participants were clearly informed about the academic purpose of the study, the voluntary nature of their participation, and their right to discontinue the survey at any time without consequence. Electronic informed consent was obtained before respondents were allowed to proceed to the questionnaire. No personally identifiable information was collected at any stage of the study. Responses were anonymized automatically through the online survey platform, and the data were stored securely on password-protected devices accessible only to the research team. For theoretical coherence and structural parsimony, multidimensional constructs were operationalized as unified reflective latent variables capturing overall capability orientation rather than modeling each conceptual dimension separately. The study’s objective focuses on examining aggregate structural relationships among digital leadership, knowledge integration, and sustainable organizational performance. Accordingly, reflective measurement was deemed appropriate, as the indicators represent complementary manifestations of underlying strategic capabilities. 4. Results 4.1 Descriptive Analysis: The demographic characteristics of the respondents are summarized in Table 3 , which presents details about gender, education, age, managerial level, and years of experience. Table 3 Descriptive Analysis Variable Frequency Percentage Gender Male Female 215 144 59.9% 40.1% Education Bachelor’s Degree Postgraduate Diploma Master’s Degree Doctoral Degree 108 54 179 18 30.1% 15% 49.9% 5% Age 25–35 years 36–46 years 47–57 years 58 years and above 108 143 72 36 30.1% 39.8% 20.1% 10% Position Senior Management Middle Management Supervisory Level Technical Administrative 36 90 54 107 72 10% 25.1% 15% 29.8% 20.1% Years of Experience Less than 12 years 12–22 years 23–33 years 34 years and above 90 125 108 36 25.1% 34.8% 30.1% 10% Total 359 100% The descriptive statistics presented in Table (3) provide a clear profile of the demographic characteristics of the 359 respondents. With regard to gender, males represent the majority of the sample (59.9%), while females account for 40.1%. In terms of educational background, the largest proportion of respondents hold a Master’s degree (49.9%), followed by those with a Bachelor’s degree (30.1%). Postgraduate diplomas constitute 15% of the sample, whereas doctoral degree holders represent 5%. Regarding age distribution, most respondents fall within the 36–46 years category (39.8%), followed by the 25–35 years group (30.1%). The older age groups, 47–57 years and 58 years and above, account for 20.1% and 10% of the sample, respectively. These results indicate that the sample is largely composed of well-educated individuals in their mid-career stages. Concerning job position and work experience, employees in technical roles form the largest segment of the sample (29.8%), followed by those in middle management positions (25.1%). Administrative staff represent 20.1%, while supervisory and senior management positions account for 15% and 10%, respectively. With respect to years of experience, the largest proportion of respondents (34.8%) report having 12–22 years of experience, followed by 30.1% with 23–33 years, 25.1% with less than 12 years, and 10% with more than 34 years of experience. Overall, these distributions suggest that the sample is diverse, well balanced across organizational levels, and characterized by substantial professional experience. 4.2 Construct Reliability and Validity: 4.2.1 Cronbach’s alphas and composite reliability: The internal consistency of the measurement model was examined using Cronbach’s alpha and composite reliability. These indicators provide complementary evidence regarding the degree to which the measurement items consistently represent their respective constructs. The results indicate that all constructs demonstrate satisfactory levels of internal consistency. Specifically, Cronbach’s alpha values range from 0.801 to 0.948, reflecting a high level of consistency among the items. Similarly, composite reliability values vary between 0.848 and 0.954, further confirming the stability of the measurement. The consistency between these two reliability indicators suggests that the constructs are measured with a high degree of precision. Accordingly, the measurement model can be considered reliable and suitable for further analysis. 4.2.2 Convergent validity: Convergent validity was assessed to determine the extent to which the indicators associated with each construct share a sufficient proportion of variance. This step ensures that the items intended to measure a given construct are indeed capturing the same underlying concept. Table 4 presents the results of indicator loadings, Cronbach’s alpha, composite reliability, and average variance extracted (AVE) for all constructs. Table 4 Measurement Model Assessment Results Variable Item Loading Cronbach’s Alpha Composite Reliability AVE Digital Leadership X1 0.712 .942 0.944 0.655 X2 0.741 X3 0.728 X4 0.664 X5 0.776 X6 0.815 X7 0.674 X8 0.651 X9 0.671 X10 0.786 X11 0.693 X12 0.686 X13 0.767 X14 0.859 X15 0.784 X16 0.685 X17 0.658 X18 0.779 X19 0.685 X20 0.838 X21 0.754 X22 0.755 X23 0.782 X24 0.816 X25 0.726 X26 0.813 X27 0.789 X28 0.832 X29 0.787 X30 0.794 X31 0.782 Knowledge Integration M1 0.701 .948 0.954 0.537 M2 0.739 M3 0.791 M4 0.839 M5 0.786 Sustainable Organizational Performance Y1 0.812 0.938 0.623 0.938 Y2 0.898 Y3 0.873 Y4 0.774 Y5 0.764 Y6 0.950 Y7 0.941 Y8 0.813 Y9 0.689 Y10 0.670 The results demonstrate that all indicator loadings exceed the acceptable minimum level, indicating that the items are adequately related to their respective constructs. In addition, the AVE values for all constructs are above 0.50, suggesting that each construct explains a substantial proportion of the variance in its indicators. Digital Leadership exhibits strong measurement properties, supported by high reliability coefficients and an AVE value that exceeds the required threshold, despite the relatively large number of indicators. Knowledge Integration also demonstrates very high internal consistency and acceptable convergent validity. Similarly, Sustainable Organizational Performance shows strong reliability and sufficient variance extraction. Overall, these findings confirm that convergent validity is adequately established, and the constructs are appropriately operationalized within the measurement model. 4.2.3 Discriminant validity: Discriminant validity was evaluated to ensure that each construct captures a distinct conceptual domain and does not overlap excessively with other constructs in the model. Establishing discriminant validity is essential to confirm that the latent variables represent different theoretical concepts. To achieve this, two complementary approaches were employed: the Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT). 4.2.3.1 Fornell–Larcker Criterion: The Fornell–Larcker criterion assesses discriminant validity by comparing the square root of the AVE for each construct with the correlations between constructs. A construct is considered distinct if it shares more variance with its own indicators than with other constructs. Table 5 presents the discriminant validity results based on the Fornell–Larcker criterion. Variables DL KI SOP DL 0.809 KI 0.492 0.733 SOP 0.508 0.455 0.789 As shown in Table 5 , the square root of AVE for each construct (diagonal values) is higher than the corresponding inter-construct correlations. This indicates that each construct is more strongly associated with its own indicators than with other constructs in the model. These results provide clear evidence that the constructs are empirically distinct and support the establishment of discriminant validity. 4.2.3.2 HTMT Criterion: To further confirm discriminant validity, the HTMT ratio was calculated. According to Benitez et al. ( 2020 ), HTMT values in well-fitted models should remain below 0.90, while more conservative thresholds suggest 0.85 as an ideal upper bound. Table 6 presents the HTMT results. Table 6 Discriminant Validity Based on HTMT Variables DL KI SOP DL KI 0.563 SOP 0.547 0.521 The HTMT values range between 0.521 and 0.563, which are well below the commonly accepted thresholds. This indicates that the relationships between constructs do not exhibit excessive similarity. These findings confirm that the constructs are clearly differentiated and that no multicollinearity issues are present. Taken together, the results from internal consistency reliability, convergent validity, and discriminant validity provide strong support for the adequacy of the measurement model. All constructs demonstrate acceptable psychometric properties and can be considered both reliable and valid. Accordingly, the measurement model is deemed appropriate for proceeding with the structural model analysis. 4.3 Assessment of Common Method Variance: Given the cross-sectional and self-reported nature of the survey data, potential common method variance (CMV) was assessed to ensure that the observed relationships were not substantially inflated by shared method bias. In addition to procedural remedies implemented during questionnaire design—such as ensuring respondent anonymity, emphasizing voluntary participation, and clearly separating construct sections—statistical assessment was conducted using the full collinearity approach recommended by Kock ( 2015 ). This procedure examines variance inflation factors (VIF) by treating each construct as a dependent variable in turn. According to Kock ( 2015 ), VIF values below the conservative threshold of 3.3 indicate that common method bias is unlikely to be a serious concern. The results show that all construct-level VIF values were well below this threshold (Digital Leadership = 1.892; Knowledge Integration = 1.712; Sustainable Organizational Performance = 1.818). Therefore, common method variance does not appear to threaten the validity of the study’s findings. 4.4 Hypotheses testing: To test the proposed hypotheses, the structural relationships were estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM). This technique was selected based on several methodological and theoretical considerations. First , PLS-SEM is particularly appropriate for predictive and exploratory research contexts where the primary objective is to explain variance in key endogenous constructs and assess complex multivariate relationships (Hair et al., 2019 ). Second , compared to covariance-based SEM (CB-SEM), PLS-SEM is more robust when data may deviate from multivariate normality assumptions, when the sample size is moderate, and when models involve both reflective and potentially formative specifications (Hair et al., 2021). Third , PLS-SEM enables the simultaneous estimation of the measurement and structural models, providing stable and reliable parameter estimates under complex model configurations. The analysis was conducted using SmartPLS (version 4.1.0.9) in two sequential stages following established guidelines (Hair et al., 2020). The first stage involved Confirmatory Composite Analysis (CCA) to assess the measurement model, while the second stage evaluated the structural model through bootstrapping procedures to test the hypothesized relationships. 4.4.1 Path Coefficients and Direct Effects: The results of the hypothesis testing are presented in Table 7 . Table 7 Path Coefficients and Hypothesis Testing Hypothesis Path Path Coefficient (β) p -value Result H 1 DL→ SOP 0.682 0.000 Supported H 2 DL→ KI 0.897 0.000 Supported H 3 KI→ SOP 0.231 0.001 Supported H 4 DL \(\:\to\:KI\) → SOP 0.207 0.001 Supported As shown in Table 7 , all hypothesized paths are positive and statistically significant at p ≤ 0.001, providing full empirical support for the proposed model. Specifically, H1 indicates that Digital Leadership (DL) has a positive and significant direct effect on Sustainable Organizational Performance (SOP) (β = 0.682, p < 0.001). The magnitude of this coefficient suggests a moderate-to-strong substantive impact, highlighting the strategic importance of digital leadership capabilities in enhancing Sustainable organizational outcomes. Regarding H2 , Digital Leadership significantly influences Knowledge Integration (KI) (β = 0.897, p < 0.001), confirming that effective digital leadership practices strengthen the organization’s ability to integrate and coordinate knowledge resources. This finding aligns with capability-based and dynamic capabilities perspectives, which emphasize leadership as an enabling mechanism for knowledge processes. For H3 , Knowledge Integration demonstrates a positive and significant effect on Sustainable Organizational Performance (β = 0.231, p = 0.001). Although the magnitude is comparatively smaller than the direct path, the effect remains statistically and substantively meaningful, indicating that knowledge integration acts as a performance-enhancing mechanism. 4.4.2 Mediation Analysis: The mediation hypothesis ( H4 ) was assessed by examining the indirect effect of Digital Leadership on Sustainable Organizational Performance through Knowledge Integration using bootstrapping procedures. The results show a significant indirect effect (β = 0.207, p = 0.001), supporting the mediating role of Knowledge Integration. Because both the direct effect (H1) and the indirect effect (H4) are statistically significant, the mediation is classified as partial mediation. This finding suggests that Digital Leadership improves Sustainable Organizational Performance through two complementary mechanisms: Direct pathway – by influencing performance-related processes and strategic outcomes directly. Indirect pathway – by enhancing knowledge integration capabilities, which subsequently translate into improved Sustainable performance. The presence of partial mediation indicates that Knowledge Integration functions as an important but not exclusive explanatory mechanism in the Digital Leadership–Performance relationship. The effect sizes observed are theoretically plausible and statistically stable, strengthening the robustness and credibility of the proposed mediation framework. 4.4.3 Structural Model Visualization: The bootstrapping results are visually summarized in Fig. 2 , which presents the estimated path coefficients and R² values for the endogenous constructs. As illustrated in Fig. 2 , the model demonstrates substantial explanatory power. The R² value for Knowledge Integration (0.805) indicates that approximately 80.5% of its variance is explained by Digital Leadership. Similarly, the R² value for Sustainable Organizational Performance (0.801) shows that 80.1% of its variance is jointly explained by Digital Leadership and Knowledge Integration. All structural paths appear statistically significant (p ≤ 0.001), reinforcing the empirical validity of the hypothesized relationships. The combination of strong explanatory power, significant direct and indirect effects, and theoretical coherence supports the model as a robust explanatory framework linking Digital Leadership, Knowledge Integration, and Sustainable Organizational Performance. 4.5 Structural model: 4.5.1 Overall Structural Model Fit: Following the validation of the measurement model, the structural model was evaluated to test the hypothesized relationships among the latent constructs. In accordance with established PLS-SEM guidelines, the assessment focused on global model fit, explanatory power (R²), predictive relevance (Q² and PLSpredict), effect sizes (f²), and collinearity diagnostics (VIF). This multi-criteria approach ensures both statistical adequacy and substantive interpretability of the structural relationships. 4.5.1.1 Global Model Fit: To assess overall model fit, the standardized root mean square residual (SRMR) was examined. SRMR represents the difference between observed and model-implied correlations and is commonly used as a goodness-of-fit indicator in PLS-SEM. Table 8 presents the structural model fit results. Measure Value SRMR 0.071 As shown in Table 8 , the SRMR value equals 0.071, which is below the recommended threshold of 0.08. This indicates an acceptable global model fit and suggests that the model adequately reproduces the observed covariance structure. 4.5.1.2 Explanatory Power, Effect Sizes, and Collinearity Diagnostics After establishing acceptable model fit, the structural model’s explanatory power and diagnostic indicators were evaluated. The coefficient of determination (R²) and adjusted R² were used to assess the proportion of variance explained in the endogenous constructs. Predictive relevance was examined using Stone–Geisser’s Q², while effect sizes (f²) were calculated to determine the substantive impact of exogenous constructs. Variance inflation factors (VIF) were assessed to rule out multicollinearity concerns. The results are presented in Table 9 . Table 9 Structural Model Quality Criteria Variables R 2 Adjusted R 2 Q 2 f 2 VIF DL – – – – 1.892 KI 0.805 0.805 0.018 0.36 1.712 SOP 0.801 0.800 0.083 0.41 1.818 As reported in Table 9 , Knowledge Integration (KI) exhibits substantial explanatory power (R² = 0.805; Adjusted R² = 0.805), indicating that approximately 80.5% of its variance is explained by Digital Leadership (DL). Similarly, Sustainable Organizational Performance (SOP) demonstrates high explanatory strength (R² = 0.801; Adjusted R² = 0.800), meaning that nearly 80% of its variance is accounted for by the model. Although the Q² values for KI (0.018) and SOP (0.083) are relatively modest, both exceed zero, confirming predictive relevance according to the blindfolding criterion. The magnitude suggests weak-to-moderate predictive capability, which remains acceptable in behavioral and organizational research contexts. Regarding effect sizes, the f² values for KI (0.36) and SOP (0.41) exceed the 0.35 benchmark, indicating large substantive effects of the exogenous constructs on the endogenous variables. This confirms that the structural relationships are not only statistically significant but also practically meaningful. Furthermore, the VIF values for DL (1.892), KI (1.712), and SOP (1.818) are well below the conservative threshold of 3.3 and far below the critical value of 5. These results indicate the absence of multicollinearity and suggest that the estimated path coefficients are stable and unbiased. Collectively, the structural diagnostics demonstrate strong explanatory capacity, meaningful effect sizes, acceptable predictive relevance, and no collinearity concerns. 4.5.1.3 Out-of-Sample Predictive Validity: To further evaluate the model’s predictive performance beyond in-sample explanatory power, PLSpredict was conducted. This procedure compares prediction errors generated by the PLS model with those obtained from a linear regression benchmark model. The results are reported in Table 10 . Table 10 PLSpredict Assessment of Predictive Validity Endogenous Construct RMSE (PLS) RMSE (LM Benchmark) Q²_predict Predictive Power Knowledge Integration (KI) 0.612 0.645 0.018 Low but positive Sustainable Organizational Performance (SOP) 0.587 0.624 0.083 Moderate As shown in Table 10 , the RMSE values produced by the PLS model are lower than those generated by the linear model benchmark for both endogenous constructs. This indicates superior predictive accuracy of the PLS model compared to the naïve regression alternative. Additionally, the Q²_predict values for KI (0.018) and SOP (0.083) are greater than zero, confirming out-of-sample predictive relevance. While the predictive strength is stronger for Sustainable Organizational Performance than for Knowledge Integration, both constructs demonstrate positive predictive capability. Importantly, the consistency between high R² values and positive Q²_predict results suggests that the model’s strong explanatory power reflects genuine predictive capacity rather than overfitting or common method bias. Therefore, the structural model demonstrates both robust in-sample performance and acceptable out-of-sample predictive validity. 5. Discussion 5.1 Interpretation of Findings: The findings provide robust empirical support for the proposed framework linking Digital Leadership, Knowledge Integration, and Sustainable Organizational Performance within the public healthcare context. First , Digital Leadership demonstrates a positive and statistically significant direct effect on Sustainable Organizational Performance (β = 0.682, p < 0.001). This result confirms that leadership plays a central strategic role in enabling organizations to translate digital transformation efforts into measurable sustainability outcomes. Consistent with Dynamic Capabilities Theory (Teece, 2007 ), leadership appears to function as an organizational capability that facilitates the sensing of environmental shifts, the mobilization of digital resources, and the reconfiguration of institutional processes in complex governance settings. Second , Digital Leadership exerts a significant positive effect on Knowledge Integration (β = 0.897, p < 0.001). This suggests that digital leadership strengthens structured knowledge-sharing mechanisms across organizational units. From an absorptive capacity perspective (Zahra & George, 2002 ), leadership appears instrumental in shaping the organizational infrastructure that enables knowledge assimilation, coordination, and transformation into collective action. Third , Knowledge Integration significantly influences Sustainable Organizational Performance (β = 0.231, p = 0.001). Although the magnitude of this effect is moderate relative to the direct leadership effect, it confirms that sustainability outcomes are partially rooted in structured knowledge processes. In public healthcare systems characterized by regulatory complexity and multi-stakeholder accountability, coordinated knowledge exchange appears to play a meaningful role in reinforcing long-term economic, social, and environmental performance. Importantly, mediation analysis reveals that Knowledge Integration partially mediates the relationship between Digital Leadership and Sustainable Organizational Performance (indirect β = 0.207, p = 0.001). The presence of both direct and indirect effects indicates complementary partial mediation. This finding implies that Digital Leadership influences sustainability through two concurrent mechanisms: a direct strategic pathway and an indirect knowledge-based pathway. Collectively, the results suggest that digital leadership contributes to sustainability not merely through technological orientation but through its capacity to institutionalize integrative knowledge processes. In highly institutionalized public-sector environments, leadership appears particularly influential in aligning digital initiatives with coordinated knowledge structures that translate strategic intent into sustainable performance outcomes. 5.2 Theoretical Integration and Structural Implications: The findings contribute to Dynamic Capabilities Theory, the Knowledge-Based View, and Absorptive Capacity Theory by empirically demonstrating that the relationship between leadership and sustainable performance is partially mediated by structured knowledge processes. First , the results reinforce the core proposition of Dynamic Capabilities Theory that leadership functions as an enabling organizational capability that supports resource reconfiguration under environmental complexity (Teece, 2007 ). The significant direct effect of Digital Leadership on Sustainable Organizational Performance confirms that leadership plays a central strategic role in shaping long-term organizational outcomes within institutionalized public-sector contexts. Second , the strong explanatory power observed for Knowledge Integration and Sustainable Organizational Performance suggests a high degree of coherence between leadership orientation and knowledge coordination processes. This pattern may reflect the characteristics of centralized public-sector governance systems, where strategic direction and knowledge flows are more formally structured and hierarchically embedded. Third , the mediating role of Knowledge Integration supports the Knowledge-Based View and Absorptive Capacity Theory by demonstrating that performance improvements are not solely the result of leadership presence but are significantly reinforced through knowledge assimilation and coordination mechanisms. This finding highlights the distinction between possessing digital resources and effectively orchestrating them through structured knowledge processes. Overall, the results suggest that Digital Leadership contributes to sustainability through both direct strategic influence and indirect capability-based mechanisms. This dual pathway strengthens the explanatory depth of capability-based models in public-sector sustainability research. 5.3 Methodological Reflection and Conceptual–Statistical Alignment The study carefully aligned conceptual definitions with statistical modeling decisions to ensure coherence between theory and empirical testing. Although Digital Leadership and Sustainable Organizational Performance are conceptually multidimensional constructs, they were modeled in the structural analysis as unified latent variables to maintain theoretical clarity and parsimony in hypothesis testing. This approach ensured consistency between conceptual framing and structural estimation. Rather than fragmenting the constructs into multiple competing predictors, the model treated each construct as an integrated capability, consistent with the study’s theoretical foundation. The use of PLS-SEM enabled simultaneous assessment of measurement validity and structural relationships while accommodating the study’s predictive orientation. By maintaining alignment between theoretical abstraction and empirical operationalization, the model preserves interpretive clarity and methodological rigor. While the explained variance is relatively high, this may be expected in centralized public-sector settings where leadership practices strongly shape internal knowledge processes and sustainability performance assessments. Nevertheless, given the cross-sectional and self-reported nature of the data, the possibility of shared-method inflation was treated cautiously. Accordingly, both procedural safeguards and statistical diagnostics were applied to mitigate common method concerns. 6. Conclusion and Policy Recommendations 6.1 Conclusion: This study examined the relationship between Digital Leadership, Knowledge Integration, and Sustainable Organizational Performance within a public-sector healthcare context. Drawing on Dynamic Capabilities Theory, the Knowledge-Based View, and Absorptive Capacity Theory, the research proposed and empirically tested a framework explaining how leadership contributes to sustainability both directly and indirectly through structured knowledge processes. Using data from 359 employees at the Abu Dhabi Department of Health, the findings demonstrate that Digital Leadership has a significant positive direct effect on Sustainable Organizational Performance. In addition, Digital Leadership significantly enhances Knowledge Integration, which in turn positively influences sustainability outcomes. The mediation analysis confirms that Knowledge Integration plays a complementary partial mediating role in this relationship. These results indicate that sustainable performance in public healthcare organizations is not solely dependent on digital technology adoption. Rather, it depends on leadership’s ability to align digital initiatives with structured knowledge-sharing and coordination mechanisms. Leadership appears to influence sustainability through both strategic direction and knowledge-based capability development. Overall, the study advances understanding of digital transformation in public-sector healthcare by providing empirical evidence that leadership and knowledge processes jointly shape long-term sustainability outcomes. By integrating leadership theory with knowledge-based perspectives, the research offers a coherent explanatory model linking digital leadership, organizational learning mechanisms, and sustainable performance within institutional governance settings. 6.2 Theoretical Implications: This study makes several substantive contributions to the literature on digital leadership, knowledge integration, and sustainable organizational performance. First , the study develops and empirically validates an integrated conceptual framework linking Digital Leadership to Sustainable Organizational Performance through the mediating role of Knowledge Integration. While prior research has examined digital leadership primarily in relation to innovation and digital transformation outcomes (Wang et al., 2022 ), fewer studies have connected leadership capabilities to long-term sustainability performance, particularly within public-sector contexts (Nuryadin et al., 2023 ). By empirically demonstrating both direct and indirect effects, this study advances a capability-based explanation grounded in Dynamic Capabilities Theory (Teece, 2007 ) and Absorptive Capacity Theory (Cohen & Levinthal, 1990 ). Second , the findings clarify the structural role of Knowledge Integration as a complementary partial mediator in the leadership–performance relationship. Rather than assuming that leadership directly produces sustainability outcomes, the results show that knowledge processes constitute a meaningful transmission mechanism. This contributes to theory by shifting attention from direct-effect models toward process-oriented explanations, addressing calls for incorporating structured mediating mechanisms in digital leadership research (Sutanto et al., 2024 ). Third , the study contributes to sustainability scholarship by providing empirical support for a reinforcing logic across economic, social, and environmental performance domains. The positive effect of Knowledge Integration on overall Sustainable Organizational Performance suggests that coordinated knowledge processes may facilitate balanced value creation rather than necessitating trade-offs among sustainability pillars. This finding aligns with capability-based perspectives on integrated sustainability (Teece, 2018 ; Zahoor & Gerged, 2021 ). Fourth , the research extends digital leadership scholarship into an underexplored institutional setting: a public-sector healthcare organization within the Arab region. Much of the existing literature focuses on private-sector firms in Western or East Asian contexts (Wang et al., 2022 ). By examining the Abu Dhabi Department of Health, this study provides context-sensitive evidence highlighting how leadership and knowledge mechanisms operate within centralized governance structures characterized by regulatory complexity and hierarchical coordination. Finally , by integrating Dynamic Capabilities Theory and Absorptive Capacity Theory within a unified empirical model, the study strengthens the theoretical bridge between leadership capabilities and organizational knowledge processes. The findings demonstrate that digital leadership contributes to sustainability not only through strategic orientation but also through its capacity to institutionalize knowledge integration mechanisms that enhance long-term organizational performance. 6.3 Practical and Policy Implications: The findings of this study offer several practical and policy-relevant implications for public-sector institutions undergoing digital transformation, particularly within highly regulated healthcare environments. First , the significant direct effect of Digital Leadership on Sustainable Organizational Performance underscores the strategic importance of leadership development as a long-term institutional investment. Public healthcare organizations should embed digital leadership competencies within executive development frameworks, emphasizing strategic alignment, adaptive decision-making, and coordinated transformation efforts. Sustainable performance improvements are unlikely to emerge from technology deployment alone without leadership capable of orchestrating digital initiatives in alignment with organizational objectives. Second , the significant relationship between Digital Leadership and Knowledge Integration highlights the necessity of strengthening institutional knowledge-sharing infrastructures. Policymakers and senior administrators should prioritize the development of structured knowledge integration mechanisms, including cross-departmental coordination routines, collaborative digital platforms, and formal learning processes. Investments in digital infrastructure must be accompanied by governance mechanisms that ensure information is systematically shared, interpreted, and embedded into operational practices. Third , the mediating role of Knowledge Integration indicates that sustainable performance gains are partially driven by structured knowledge processes. This suggests that digital transformation policies should extend beyond hardware and software acquisition toward organizational design reforms that enhance coordination, documentation, and institutional memory. In public-sector healthcare systems, where regulatory compliance and service quality are closely intertwined, integrated knowledge processes can reinforce operational efficiency, stakeholder trust, and environmental responsibility simultaneously. Fourth , the findings imply that economic, environmental, and social performance objectives need not be treated as competing priorities. When supported by effective leadership and coordinated knowledge processes, digital transformation can contribute to balanced and integrated sustainability outcomes. Policymakers should therefore adopt holistic digital governance strategies that align technological investment, leadership capability development, and knowledge management systems within a coherent institutional framework. Finally , for policymakers in the Middle East and comparable centralized governance systems, the study provides empirical evidence that structured leadership development combined with institutionalized knowledge integration can serve as foundational pillars for sustainable digital transformation in public healthcare. The framework proposed in this study offers a scalable model for other governmental institutions seeking to translate digital transformation initiatives into measurable and sustained organizational performance outcomes. 6.4 Limitations: Despite the methodological rigor and robustness of the structural model, several limitations should be acknowledged. First , the study is context-specific. Data were collected from a single public-sector healthcare organization within a centralized governance system. While this setting provides valuable insight into digital transformation within highly regulated environments, institutional characteristics may influence the strength of leadership–knowledge–performance relationships. Replication across different governance structures, including decentralized or hybrid systems, would enhance external validity and boundary condition specification. Second , although the model demonstrates high explanatory power, the research design remains bounded to a focused set of constructs derived from Dynamic Capabilities and Absorptive Capacity perspectives. Other organizational and institutional factors—such as ethical climate, organizational culture, political dynamics, or public service motivation—were not incorporated. Future studies may expand the framework to integrate additional contextual moderators or complementary mechanisms influencing sustainable organizational performance. Third , the study adopts a cross-sectional survey design based on self-reported perceptions. While statistical procedures, including bootstrapping and predictive assessments, strengthen internal reliability, cross-sectional data limit strong causal inference. Longitudinal or multi-wave research designs would allow for deeper examination of temporal dynamics and capability development processes over time. Fourth , although structured measures were adapted from validated prior scales, reliance on perceptual data may introduce common method considerations. Future research could incorporate multi-source data, objective performance indicators, or archival sustainability metrics to triangulate findings and further strengthen empirical robustness. 6.5 Directions for Future Research: Building on the identified limitations, several promising avenues for future research emerge. First , longitudinal research designs would allow scholars to examine how digital leadership capabilities develop over time and how knowledge integration processes evolve as organizational routines mature. Such temporal investigations would strengthen causal inference and provide deeper insight into capability formation and sustainability trajectories within public-sector institutions. Second , replication across diverse institutional environments is essential. Future studies may test the proposed framework in decentralized healthcare systems, education sectors, social services, and private-sector organizations to assess contextual stability and governance boundary conditions. Comparative cross-national research would further clarify how regulatory structures, public accountability mechanisms, and institutional logics shape leadership–knowledge–performance relationships. Third , future research may extend the framework by incorporating additional behavioral and institutional conditioning variables. Recent public management scholarship highlights the importance of integrity climate, organizational citizenship behavior (OCB), and public service motivation in shaping performance dynamics (Saputra et al., 2026 ). Integrating such constructs as moderators or complementary mediators would deepen understanding of how digital leadership capabilities become operationalized at the employee and team levels. Multi-level modeling approaches could further clarify how leadership-driven knowledge integration cascades across hierarchical layers within public healthcare systems. Fourth , scholars may explore potential asymmetries in sustainability dynamics by examining how knowledge integration differentially influences economic, environmental, and social performance under varying sectoral or regulatory conditions. Such investigations would contribute to ongoing debates regarding reinforcing versus trade-off logics in sustainability research and may reveal whether integrated knowledge processes reduce or amplify sustainability tensions in public institutions. Finally , given the rapid advancement of emerging digital technologies, future studies should investigate how developments in artificial intelligence, predictive analytics, and advanced data infrastructures reshape digital leadership practices and knowledge integration mechanisms. Understanding how evolving technological ecosystems interact with leadership agility and organizational capabilities will be critical for explaining the next phase of sustainable digital transformation in public-sector governance contexts. Declarations Funding This research received no external funding. Clinical Trial Number Not applicable. Data Availability Statement The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Ethical Approval and Accordance This study was conducted in accordance with institutional research standards at Cairo University, Egypt. As this research involved a non-clinical, survey-based design with voluntary participation and no sensitive personal data, formal ethical approval was not required. The exemption is consistent with the applicable institutional research practices at Cairo University. All procedures involving human participants were performed in line with internationally recognized ethical standards, including the principles of the Declaration of Helsinki. Consent to participate Informed consent was obtained from all participants prior to their participation in the study. Participation was voluntary, and respondents were assured of anonymity and confidentiality. Consent to Publish Not applicable. Conflict of Interest The authors declare that they have no conflict of interest. References Adner R, Helfat CE. Corporate effects and dynamic managerial capabilities. Strateg Manag J. 2003;24(10):1011–25. https://doi.org/10.1002/smj.331 . Alavi M, Leidner DE. Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Q. 2001;25(1):107–36. https://doi.org/10.2307/3250961 . Alboliteeh M, Alrashidi MS, Alrashedi N, Gonzales A, Mostoles RJ, Pasay-an E, Dator WL. Knowledge Management and Sustainability Performance of Hospital Organisations: The Healthcare Managers’ Perspective. Sustainability. 2023;15(1):203. .https://doi.org/10.3390/su15010203 . Audia N, Putera RE, Aromatica D, Bisri MBF. (2021). Capabilities of the city government in combating COVID-19 in the city of Padang. E3S Web of Conferences, 331 , 01009. https://doi.org/10.1051/e3sconf/202133101009 Awan U, Shamim S, Khan Z, Zia NU, Shariq SM, Khan MN. Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technol Forecast Soc Chang. 2021;168:120766. https://doi.org/10.1016/j.techfore.2021.120766 . Bansal P, DesJardine MR. Business sustainability: It is about time. Strategic Organ. 2014;12(1):70–8. https://doi.org/10.1177/1476127013520265 . Benitez J, Henseler J, Castillo A, Schuberth F. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information Management. 2020;57(2):103168. https://doi.org/10.1016/j.im.2019.103168 . Borah K, Ghosh B. Integrative knowledge processes and the triple bottom line performance. J Bus Ethics. 2022;178(2):327–45. https://doi.org/10.1007/s10551-020-04579-3 . Brunner TJJ, Schuster T, Lehmann C. Leadership’s long arm: The positive influence of digital leadership on managing technology-driven change over a strengthened service innovation capacity. Front Psychol. 2023;14:988808. https://doi.org/10.3389/fpsyg.2023.988808 . Cheah J, Amran A, Yahya S. Internal oriented resources and social enterprises’ performance: How can social enterprises help themselves before helping others? J Clean Prod. 2019;211:607–19. https://doi.org/10.1016/j.jclepro.2018.11.203 . Chen Y, Jiang C, Peng L, Zhao S, Chen C. Digital infrastructure construction and urban industrial chain resilience: Evidence from the Broadband China strategy. Sustainable Cities Soc. 2025;121:106228. https://doi.org/10.1016/j.scs.2025.106228 . Cohen WM, Levinthal DA. Absorptive capacity: A new perspective on learning and innovation. Adm Sci Q. 1990;35(1):128–52. https://doi.org/10.2307/2393553 . Del Giudice M, Scuotto V, Garcia-Perez A, Petruzzelli AM. Shifting wealth II in Chinese economy: The effect of the horizontal technology spillover for SMEs for international growth. Technol Forecast Soc Chang. 2019;145:307–16. https://doi.org/10.1016/j.techfore.2018.03.013 . Eberl J, Drews P. Digital leadership—Mountain or molehill? A literature review. Business Information Syst Engineering. 2021;63(3):353–70. https://doi.org/10.1007/s12599-020-00680-0 . Eikelenboom M, de Jong G. The impact of dynamic capabilities on the sustainability performance of SMEs. J Clean Prod. 2019;235:1360–70. https://doi.org/10.1016/j.jclepro.2019.07.013 . Elkington J. Cannibals with forks: The triple bottom line of 21st century business. Oxford, UK: Capstone Publishing; 1997. Febriantina S, Rohzisky A, Rivalen M. Digital leadership: Challenges and opportunities in the age of technology. Doktrin: Jurnal Dunia Ilmu Hukum dan Politik. 2025;3(1):200–6. https://doi.org/10.59581/doktrin.v3i1.4537 . Felin T, Foss NJ, Heimeriks KH, Madsen TL. Microfoundations of routines and capabilities: Individuals, processes, and structure. J Manage Stud. 2012;49(8):1351–74. https://doi.org/10.1111/j.1467-6486.2012.01052.x . Gold AH, Malhotra A, Segars AH. Knowledge management: An organizational capabilities perspective. J Manage Inform Syst. 2001;18(1):185–214. https://doi.org/10.1080/07421222.2001.11045669 . Grant RM. Toward a knowledge-based theory of the firm. Strateg Manag J. 1996;17(S2):109–22. https://doi.org/10.1002/smj.4250171110 . Guandalini I. Sustainability through digital transformation: A systematic literature review for research guidance. J Bus Res. 2022;148:456–71. https://doi.org/10.1016/j.jbusres.2022.05.003 . Guo X, Chen X. The impact of digital transformation on manufacturing-enterprise innovation: Empirical evidence from China. Sustainability. 2023;15(4). https://doi.org/10.3390/su15043124 . Article 3124. Hahn T, Figge F, Pinkse J, Preuss L. Trade-offs in corporate sustainability: You can’t have your cake and eat it. Bus Strategy Environ. 2010;19(4):217–29. https://doi.org/10.1002/bse.674 . Hair JF, Hult GTM, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). 3rd ed. Thousand Oaks, CA: Sage; 2022. Hair JF, Ringle CM, Gudergan SP, Fischer A, Nitzl C, Menictas C. Partial least squares structural equation modeling-based discrete choice modeling: An illustration in modeling retailer choice. Bus Res. 2019;12(1):115–42. https://doi.org/10.1007/s40685-018-0072-4 . Hanelt A, Bohnsack R, Marz D, Antunes Marante C. A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. J Manage Stud. 2021;58(5):1159–97. https://doi.org/10.1111/joms.12639 . Hart SL. A natural-resource-based view of the firm. Acad Manage Rev. 1995;20(4):986–1014. https://doi.org/10.5465/amr.1995.9512280033 . Hart SL, Dowell G. A natural-resource-based view of the firm: Fifteen years after. J Manag. 2011;37(5):1464–79. https://doi.org/10.1177/0149206310390219 . Helfat CE, Martin JA. Dynamic managerial capabilities: Review and assessment of managerial impact on strategic change. J Manag. 2015;41(5):1281–312. https://doi.org/10.1177/0149206314561301 . Henseler J. Partial least squares path modeling: Quo vadis? Quality Quantity. 2018;52(1):1–8. https://doi.org/10.1007/s11135-018-0689-6 . Khalil S, Ghadiri SM. Balancing exploration and exploitation: How green knowledge integration drives supplier performance in sustainable supply chains. Logistics. 2025;9(4):176. https://doi.org/10.3390/logistics9040176 . Kludacz-Alessandri M, Hawrysz L, Żak K, Zhang W. The impact of digital transformational leadership on digital intensity among primary healthcare entities: A moderated mediation model. BMC Health Serv Res. 2025;25(1). Article 117. https://doi.org/10.1186/s12913-025-12283-x . Kock N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int J e-Collaboration. 2015;11(4):1–10. https://doi.org/10.4018/ijec.2015100101 . Kraus S, Jones P, Kailer N, Weinmann A, Chaparro-Banegas N, Roig-Tierno N. Digital transformation: An overview of the current state of the art of research. J Bus Res. 2022;138:889–902. https://doi.org/10.1016/j.jbusres.2021.09.051 . Krejcie RV, Morgan DW. Determining sample size for research activities. Educ Psychol Meas. 1970;30(3):607–10. https://doi.org/10.1177/001316447003000308 . Liu X, Wei X. The mediating role of knowledge integration in digital transformation for sustainability. Technol Forecast Soc Chang. 2022;175:121390. https://doi.org/10.1016/j.techfore.2021.121390 . Lundin M, Nordström Skans O, Zetterberg P. Leadership experiences within civil organizations and candidacy in public elections: Causal evidence from a quasi-experimental approach. Polit Behav. 2016;38(2):433–54. https://doi.org/10.1007/s11109-015-9320-x . Lundin M, Nordström Skans O, Zetterberg P. Leadership experiences, labor market entry, and early career trajectories. J Hum Resour. 2021;56(2):480–511. https://doi.org/10.3368/jhr.56.2.0617-8866R3 . Luthans F. Positive organizational behavior: Developing and managing psychological strengths. Acad Manage Perspect. 2002;16(1):57–72. https://doi.org/10.5465/ame.2002.6640181 . Maletic M, Maletic D, Dahlgaard JJ, Dahlgaard-Park SM, Gomišcek B. Do corporate sustainability practices enhance organizational economic performance? Int J Qual Service Sci. 2015;7(2–3):184–200. https://doi.org/10.1108/IJQSS-02-2015-0025 . Matarazzo M, Penco L, Profumo G, Quaglia R. Digital transformation and customer value creation in made in Italy SMEs: A dynamic capabilities perspective. J Bus Res. 2021;123:642–56. https://doi.org/10.1016/j.jbusres.2020.10.033 . Mergel I, Edelmann N, Haug N. Defining digital transformation: Results from expert interviews. Government Inform Q. 2019;36(4). Article 101385. https://doi.org/10.1016/j.giq.2019.06.002 . Mollah MA, Ibrahim A, Masud A, Chowdhury MS. How does digital leadership boost competitive performance? The role of digital culture, affective commitment, and strategic agility. Heliyon. 2024;10(23):e40839. https://doi.org/10.1016/j.heliyon.2024.e40839 . Nonaka I, Toyama R. The knowledge-creating company. Harvard Business Rev. 2007;85(7–8):162–71. Nuryadin R, Sobandi A, Santoso B. Digital Leadership in the Public Sector: Systematic literature review. Jurnal Ilmu Administrasi: Media Pengembangan Ilmu dan Praktek Administrasi. 2023;20(1):90–106. https://doi.org/10.31113/jia.v20i1.934 . Okhuysen GA, Eisenhardt KM. Integrating knowledge in groups: How formal interventions enable flexibility. Organ Sci. 2002;13(4):370–86. https://doi.org/10.1287/orsc.13.4.370.2947 . Pandey J, Majumdarr S, Hassan Y, Benuyenah V. Role of digital leadership capability in shaping IT innovation: A digital agility perspective. J Global Inform Manage. 2023;31(8). Article 333168. https://doi.org/10.4018/JGIM.333168 . Porter ME, Kramer MR. Creating shared value. Harvard Business Rev. 2011;89(1–2):62–77. Ren L, Deng S, Men L, Boudouaia A. (2025). A study on factors shaping innovative work behavior and service innovation performance in government sectors: Role of digital leadership and dynamic capabilities. Humanities and Social Sciences Communications, 12, Article 1076. https://doi.org/10.1057/s41599-025-05378-7 Ruvio A, Rosenblatt Z, Hertz-Lazarowitz R. Entrepreneurial leadership vision in nonprofit vs. for-profit organizations. Leadersh Q. 2010;21(1):144–58. https://doi.org/10.1016/j.leaqua.2009.10.011 . Sacavém A, de Bem Machado A, dos Santos JR, Palma-Moreira A, Belchior-Rocha H, Au-Yong-Oliveira M. Leading in the Digital Age: The Role of Leadership in Organizational Digital Transformation. Administrative Sci. 2025;15(2):43. https://doi.org/10.3390/admsci15020043 . Safitri C, Valentina TR, Wicaksono A. (2021). Forms of government collaboration at the local level in handling the COVID-19 disaster. E3S Web of Conferences, 331 , 02003. https://doi.org/10.1051/e3sconf/202133102003 Samoili S, López Cobo M, Gómez E, De Prato G, Martinez-Plumed F. AI watch: Defining artificial intelligence. European Commission; 2020. https://doi.org/10.2760/382730 . Joint Research Centre. Santoro G, Ferraris A, Adrodegari F. Knowledge integration and sustainability in innovative organizations. J Knowl Manage. 2023;27(1):45–63. https://doi.org/10.1108/JKM-09-2022-0694 . Saputra N, Putera RE, Zetra A, Azwar A, Valentina TR, Mulia RA. Capacity building for organizational performance: A systematic review, conceptual framework, and future research directions. Cogent Business Management. 2024;11(1). https://doi.org/10.1080/23311975.2024.2434966 . Saputra N, Putera RE, Zetra A, Azwar A, Valentina TR, Mulia RA. Enhancing public management through integrity and organizational citizenship behavior: A systematic review. Int J Public Adm. 2026;1–16. https://doi.org/10.1080/01900692.2026.2614578 . Saunders M, Lewis P, Thornhill A. Research methods for business students. 8th ed. Pearson Education; 2019. Simion Ludușanu D-G, Fertu D-I, Tinică G, Gavrilescu M. Integrated Quality and Environmental Management in Healthcare: Impacts, Implementation, and Future Directions Toward Sustainability. Sustainability. 2025;17(11):5156. https://doi.org/10.3390/su17115156 . Sutanto EM, Asbari M, Purwanto A, Santoso PB. (2024). Digital leadership and innovation capability: The mediating role of knowledge integration. Knowledge Management Research & Practice . https://doi.org/10.1080/14778238.2023.2293656 Syamsir S, Saputra N, Mulia RA. Leadership agility in a VUCA world: A systematic review, conceptual insights, and research directions. Cogent Business Management. 2025;12(1). https://doi.org/10.1080/23311975.2025.2482022 . Teece DJ. Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strateg Manag J. 2007;28(13):1319–50. https://doi.org/10.1002/smj.640 . Teece DJ. Business models and dynamic capabilities. Long Range Plann. 2018;51(1):40–9. https://doi.org/10.1016/j.lrp.2017.06.007 . Teece DJ, Pisano G, Shuen A. Dynamic Capabilities and Strategic Management. Strateg Manag J. 1997;18(7):509–33. http://www.jstor.org/stable/3088148 . Temelkova M. Model of the relation digital leadership–digital entrepreneurship in the realities of Industry 4.0. Int J Inform Theor Appl. 2019;26(4):324–33. Tigre S, Curado C, Henriques PL. Digital leadership: A systematic literature review and future research agenda. Small Bus Econ. 2023;61(3):1023–48. https://doi.org/10.1007/s11187-023-00723-1 . Valentina TR, Putera RE, Salsabila L. Collaborative governance in handling the waste crisis: A systematic literature review. Int J Sustainable Dev Plann. 2025;20(2):761–70. https://doi.org/10.18280/ijsdp.200225 . van Roekel H, Branderhorst M, Tummers L, Meijer A. Digital transformation leadership: A public value-centered measurement scale. Government Inform Q. 2025;42(4). Article 102091. https://doi.org/10.1016/j.giq.2025.102091 . Vial G. Understanding digital transformation: A review and a research agenda. J Strateg Inf Syst. 2019;28(2):118–44. https://doi.org/10.1016/j.jsis.2019.01.003 . Wang Q, Chen L, Jia F, Luo Y, Zhang Z. The relationship between supply chain integration and sustainability performance: a meta-analysis. Int J Logistics Res Appl. 2024;27(8):1388–409. https://doi.org/10.1080/13675567.2022.2144812 . Wang T, Lin X, Sheng F. Digital leadership and exploratory innovation: From the dual perspectives of strategic orientation and organizational culture. Front Psychol. 2022;13:902693. https://doi.org/10.3389/fpsyg.2022.902693 . Watson RT, Boudreau M-C, Chen AJ. Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Q. 2010;34(1):23–38. https://doi.org/10.2307/20721413 . Wong CWY. Leveraging environmental information integration to enable environmental management capability and performance. J Supply Chain Manage. 2013;49(2):114–36. https://doi.org/10.1111/jscm.12005 . Yang M, Mamun A, A., Salameh AA. Leadership, capability and performance: A study among private higher education institutions in Indonesia. Heliyon. 2023;9(1):e13026. https://doi.org/10.1016/j.heliyon.2023.e13026 . Zahoor N, Gerged AM. Relational capital, environmental knowledge integration, and environmental performance of small and medium enterprises in emerging markets. Bus Strategy Environ. 2021;30(8):3789–803. https://doi.org/10.1002/bse.2840 . Zahra SA, George G. Absorptive capacity: A review, reconceptualization, and extension. Acad Manage Rev. 2002;27(2):185–203. https://doi.org/10.5465/amr.2002.6587995 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9106807","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623647136,"identity":"f11302a6-d736-4ed4-8450-3ac9bfc38f57","order_by":0,"name":"Faisal Al Dhaeri","email":"","orcid":"","institution":"Cairo University","correspondingAuthor":false,"prefix":"","firstName":"Faisal","middleName":"Al","lastName":"Dhaeri","suffix":""},{"id":623647137,"identity":"b3c408d9-feda-487e-bc2f-c100828f32b7","order_by":1,"name":"Mohammed Al Mady","email":"","orcid":"","institution":"Cairo University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Al","lastName":"Mady","suffix":""},{"id":623647138,"identity":"bad4b98c-268d-41a7-989b-6429843db161","order_by":2,"name":"Hossam Ahmed Hanafy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBACA2SGxAcbGJeNSC2SM9JI1SLNQ4wWc4n0xx9+5jDIm4sdPnjbJsFGnoH9jAHDh7LDDPz8C7BqsZyRYybZu43BcOfstGTrnIQ0wwaeHAPGGecOA535ALvDbuSwMfBuY2DccDvHTDr3x+EEBgkeA2betsNAqQM4tKQ//vh3G4P9htv536QtEqBa/gK12OPUkmAgDbQlEWgLmzQDTAsjyBb+Buxazrwxk5bdxpC84XaasWUP0C9tPGkFB3vOpfNI3MAeYgbHgQ57u43BdsPt5Ic3fgBDjJ/98MYHP8qs5fj7sTsMCv4jmKAYAanlYZBIwKcFK+DHa8soGAWjYBSMHAAA6VVcX/zvKgEAAAAASUVORK5CYII=","orcid":"","institution":"Cairo University","correspondingAuthor":true,"prefix":"","firstName":"Hossam","middleName":"Ahmed","lastName":"Hanafy","suffix":""}],"badges":[],"createdAt":"2026-03-12 16:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9106807/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9106807/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107322431,"identity":"4462ac79-b992-4d84-aa43-e6aa1720fd97","added_by":"auto","created_at":"2026-04-20 10:47:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134480,"visible":true,"origin":"","legend":"\u003cp\u003eThe hypothesized research framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9106807/v1/a10f2a236c57494a8f400068.png"},{"id":107322430,"identity":"9008d834-59d5-4a7e-8134-bcdd3463167e","added_by":"auto","created_at":"2026-04-20 10:47:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43853,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-SEM Bootstrapping Results for the Structural Model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9106807/v1/686b04b5e41d00c9050daca3.png"},{"id":107487312,"identity":"3032013b-e2e3-443c-a681-b78f2a844cbd","added_by":"auto","created_at":"2026-04-22 02:40:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1117413,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9106807/v1/40e17945-9303-4631-8686-1d736d212121.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Leadership Driving Sustainability through Knowledge Integration in Public Healthcare Organizations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePublic healthcare systems are undergoing profound structural transformation driven by rapid digitalization, regulatory reform, and rising societal expectations for transparency, efficiency, and service quality. Governments across the globe are investing heavily in digital infrastructures, data platforms, and smart health initiatives in an effort to modernize healthcare governance and improve institutional performance. However, despite substantial technological investment, many public organizations struggle to translate digital transformation efforts into sustained performance improvements. This challenge suggests that digital technologies alone are insufficient; rather, leadership capabilities play a decisive role in determining whether digital initiatives generate enduring organizational value.\u003c/p\u003e \u003cp\u003eDigital leadership has consequently attracted growing scholarly attention within management and information systems research. Existing studies largely associate digital leadership with innovation capacity, technology adoption, and operational efficiency. While these contributions are important, much of the literature remains technology-centric and outcome-focused, frequently treating performance as a direct consequence of leadership behaviors. Such an approach overlooks the organizational mechanisms through which leadership translates strategic digital intent into measurable and sustainable performance outcomes. As a result, the relationship between digital leadership and long-term organizational performance remains theoretically under-specified and empirically fragmented, particularly within public-sector healthcare environments.\u003c/p\u003e \u003cp\u003eFrom a theoretical standpoint, dynamic capabilities theory (Teece, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and absorptive capacity theory (Zahra \u0026amp; George, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) offer a compelling foundation for understanding this relationship. Both perspectives emphasize that superior organizational performance is fundamentally knowledge-driven. Organizations create sustained value not merely by acquiring digital technologies, but by effectively sensing environmental changes, assimilating new information, and reconfiguring internal resources. Within this framework, leadership influences performance indirectly by shaping processes that enable coordination, learning, and knowledge mobilization. Despite the relevance of these theories, empirical research has rarely positioned knowledge integration as a central mediating mechanism linking digital leadership to organizational performance.\u003c/p\u003e \u003cp\u003eIn complex public healthcare systems, performance improvement depends heavily on the ability to integrate dispersed expertise across clinical, administrative, technological, and regulatory domains. Digital transformation initiatives frequently require cross-functional coordination, institutional alignment, and the harmonization of diverse knowledge bases. Without effective knowledge integration processes, digital investments may remain fragmented and fail to translate into meaningful organizational outcomes. Accordingly, examining whether digital leadership enhances performance through strengthening knowledge integration represents a critical theoretical and practical question.\u003c/p\u003e \u003cp\u003eThis issue is particularly salient in public-sector healthcare contexts characterized by bureaucratic governance structures, multi-level accountability mechanisms, and evolving policy demands. While collaborative governance research highlights the importance of coordination and institutional capability in achieving public-sector outcomes, limited empirical evidence examines how digital leadership activates internal knowledge integration processes within governmental healthcare organizations\u0026mdash;especially in non-Western settings. The Arab region, and specifically government healthcare agencies, remains underrepresented in digital leadership and sustainability research.\u003c/p\u003e \u003cp\u003eTo address these theoretical and contextual gaps, the present study develops and empirically tests a mechanism-based model linking Digital Leadership to Organizational Performance through the mediating role of Knowledge Integration. The study is situated within the Abu Dhabi Department of Health and draws on survey data collected from managers and professionals engaged in digital transformation initiatives. Digital leadership is conceptualized as an integrated strategic capability reflected by multiple interrelated leadership attributes that enable organizations to mobilize digital resources and coordinate knowledge processes effectively, while organizational performance reflects sustained value creation within a public healthcare setting.\u003c/p\u003e \u003cp\u003eAccordingly, this study pursues three primary objectives: (1) to examine the direct relationship between digital leadership and organizational performance; (2) to assess the effect of digital leadership on knowledge integration; and (3) to test the mediating role of knowledge integration in translating digital leadership into performance outcomes.\u003c/p\u003e \u003cp\u003eBy adopting a mechanism-oriented perspective grounded in dynamic capabilities and absorptive capacity theories, this study makes three key contributions. \u003cb\u003eFirst\u003c/b\u003e, it advances digital leadership research beyond efficiency-driven explanations by offering a knowledge-based account of organizational performance. \u003cb\u003eSecond\u003c/b\u003e, it empirically validates knowledge integration as a central explanatory mechanism linking leadership and performance. \u003cb\u003eThird\u003c/b\u003e, it provides contextual evidence from a public-sector healthcare organization in the Arab region, contributing to the limited body of research on digital transformation within governmental institutions.\u003c/p\u003e \u003cp\u003eThrough this integrative framework, the study enhances theoretical clarity regarding the pathways through which digital leadership contributes to sustained organizational performance and offers actionable insights for public healthcare leaders seeking to align digital transformation strategies with long-term institutional effectiveness.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Framework of the Study:\u003c/h2\u003e \u003cp\u003eThis study is grounded in dynamic capabilities theory (Teece, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and absorptive capacity theory (Zahra \u0026amp; George, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), which together provide a multi-level explanation of how digital leadership translates into sustainable organizational performance in knowledge-intensive public healthcare environments.\u003c/p\u003e \u003cp\u003eDynamic capabilities theory argues that sustained success in turbulent contexts depends on an organization\u0026rsquo;s ability to sense emerging opportunities, seize them through strategic action, and reconfigure internal resources accordingly (Teece, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In digitally transforming public healthcare systems characterized by regulatory complexity, stakeholder scrutiny, and technological disruption leadership plays a central orchestration role in these processes. Digital leaders not only identify technological trends but also align digital transformation initiatives with strategic priorities and reconfigure organizational structures to support long-term sustainability.\u003c/p\u003e \u003cp\u003eRecent research on leadership agility further strengthens this perspective. In volatile, uncertain, complex, and ambiguous (VUCA) environments, leadership effectiveness increasingly depends on anticipatory capacity, strategic foresight, and adaptive responsiveness (Syamsir et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These agility-oriented attributes reinforce the sensing and reconfiguration components of dynamic capabilities theory and conceptually underpin the predictive and visionary dimensions of digital leadership examined in this study.\u003c/p\u003e \u003cp\u003eAbsorptive capacity theory complements this strategic perspective by emphasizing the knowledge-processing foundations of organizational advantage. Zahra and George (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) conceptualize absorptive capacity as a four-stage process: acquisition, assimilation, transformation, and exploitation of knowledge. Organizational performance depends not merely on access to information but on structured mechanisms that convert knowledge into coordinated action. Within this framework, knowledge integration corresponds to the assimilation and transformation phases, where dispersed expertise is synthesized and embedded into organizational routines.\u003c/p\u003e \u003cp\u003eIntegrating these two theoretical lenses provides a coherent explanation of the proposed mediation mechanism. Dynamic capabilities theory explains how leadership enables strategic adaptation, while absorptive capacity theory clarifies how internal knowledge processes create value. Knowledge integration lies at the intersection of these perspectives: it functions both as a reconfiguration mechanism and as a knowledge-processing capability. Consequently, digital leadership is expected to enhance sustainable organizational performance indirectly by strengthening structured knowledge integration processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Conceptualization of Constructs:\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Digital leadership:\u003c/h2\u003e \u003cp\u003eDigital leadership is conceptualized as an integrated strategic capability that enables organizations to navigate digital transformation in a coherent and sustainability-oriented manner. It extends beyond technical expertise and reflects a leader\u0026rsquo;s ability to align digital initiatives with institutional goals, mobilize organizational resources effectively, and foster adaptive capacity in technology-intensive environments.\u003c/p\u003e \u003cp\u003eThe literature identifies several interrelated dimensions associated with digital leadership, including digital capabilities, accumulated digital experience, predictive orientation, and digital vision (Eberl \u0026amp; Drews, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Matarazzo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Digital capabilities refer to the strategic understanding of technological infrastructures and their alignment with organizational objectives. Digital experience reflects accumulated exposure to digital transformation initiatives, enhancing the ability to manage uncertainty. Predictive ability captures anticipatory competence in identifying emerging technological and environmental trends. Digital vision represents the articulation of a coherent long-term direction linking digital transformation to sustainable value creation.\u003c/p\u003e \u003cp\u003eThe emphasis on predictive orientation and vision aligns with leadership agility scholarship, which highlights foresight and adaptability as central competencies in turbulent governance environments (Syamsir et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In public healthcare systems facing rapid technological change and regulatory pressures, such agility becomes critical for sustaining balanced performance outcomes.\u003c/p\u003e \u003cp\u003eAlthough these dimensions are conceptually distinguishable, they collectively represent complementary manifestations of a broader strategic orientation toward digital transformation. Therefore, this study operationalizes digital leadership as a unified reflective construct capturing the overall digital leadership capability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Knowledge Integration:\u003c/h2\u003e \u003cp\u003eKnowledge integration refers to the organization\u0026rsquo;s capability to combine, coordinate, and apply dispersed knowledge across units, professional domains, and technological systems. It goes beyond information exchange and reflects structured cross-boundary coordination processes that enable collective problem-solving and innovation.\u003c/p\u003e \u003cp\u003eWithin the knowledge-based view of the firm, knowledge integration transforms individual expertise into shared organizational competence (Nonaka \u0026amp; Toyama, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). From an absorptive capacity perspective, it corresponds to the assimilation and transformation stages through which new knowledge becomes embedded in organizational routines (Zahra \u0026amp; George, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent capacity-building research in public-sector management emphasizes that sustainable performance depends on structured knowledge management, technological alignment, and collaborative learning mechanisms (Saputra et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This framing positions knowledge integration not as a passive coordination activity but as a dynamic organizational capability linking digital transformation to institutional sustainability.\u003c/p\u003e \u003cp\u003ePublic healthcare contexts further amplify the importance of integration. Crisis governance research demonstrates that effective service delivery during health emergencies depends on coordination and knowledge sharing across administrative and clinical units (Safitri et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, governmental capability development in combating public health crises relies on institutional learning and digital adaptation (Audia et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These insights underscore the strategic importance of knowledge integration in healthcare governance settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Sustainable Organizational Performance:\u003c/h2\u003e \u003cp\u003eSustainable organizational performance (SOP) refers to the organization\u0026rsquo;s ability to generate balanced and enduring value for multiple stakeholders over time. Grounded in the triple-bottom-line framework (Elkington, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), sustainability-oriented performance encompasses economic efficiency, social responsibility, and environmental stewardship.\u003c/p\u003e \u003cp\u003eIn public-sector healthcare institutions, economic performance involves operational efficiency and responsible resource allocation; social performance includes service quality, employee engagement, and stakeholder trust; while environmental performance reflects sustainable resource utilization and regulatory compliance.\u003c/p\u003e \u003cp\u003eCollaborative governance research suggests that sustainability outcomes emerge through cross-boundary coordination and multi-stakeholder alignment rather than isolated performance initiatives (Valentina et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In digitally transforming public institutions, economic, social, and environmental objectives are frequently interdependent. Knowledge integration thus becomes essential for reducing trade-offs and enabling balanced sustainability outcomes.\u003c/p\u003e \u003cp\u003eConsistent with capability-based perspectives (Santoro et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zahoor \u0026amp; Gerged, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this study operationalizes SOP as a unified reflective construct representing aggregate sustainability performance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Hypotheses Development:\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Digital Leadership and Sustainable Organizational Performance:\u003c/h2\u003e \u003cp\u003eIn digitally transforming environments, leadership assumes strategic significance not merely because it encourages the adoption of digital tools, but because it shapes how organizations interpret technological disruption, reconfigure priorities, and realign structures and routines in response to systemic change. Digital transformation has been conceptualized as an organization-wide process in which digital technologies trigger structural and cultural disruptions that necessitate coordinated strategic responses to enable new value creation pathways (Vial, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Within the dynamic capabilities framework, sustained performance under environmental turbulence depends on an organization\u0026rsquo;s capacity to sense emerging opportunities and threats, seize them through timely commitments, and transform by reconfiguring assets and organizational routines (Teece, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These higher-order capabilities rest upon managerial microfoundations, including cognition, coordination, and orchestration processes.\u003c/p\u003e \u003cp\u003eFrom this perspective, digital leadership can be understood as a form of dynamic managerial capability. Leaders\u0026rsquo; human capital, cognitive framing, and social capital enable them to orchestrate resources, align digital initiatives with strategic priorities, and guide organizational transformation under uncertainty (Adner \u0026amp; Helfat, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Helfat \u0026amp; Martin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Digital leadership therefore has the potential to influence sustainable organizational performance by embedding long-term orientation into digital strategy, shaping governance and accountability routines, and supporting structural reconfiguration that produces durable rather than episodic gains.\u003c/p\u003e \u003cp\u003eThis argument aligns with sustainability scholarship emphasizing the temporal and multi-stakeholder nature of sustainability performance, moving beyond short-term financial metrics toward integrated economic, social, and environmental outcomes (Bansal \u0026amp; DesJardine, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hart, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Hart \u0026amp; Dowell, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Sustainable organizational performance is conceptually grounded in the Triple Bottom Line (Elkington, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), requiring coordinated capability deployment over time rather than isolated efficiency improvements.\u003c/p\u003e \u003cp\u003eHowever, despite increasing interest in digital transformation and organizational performance, several conceptual and empirical tensions remain unresolved. First, much of the empirical evidence focuses on short-term operational or financial outcomes, leaving multidimensional sustainability performance comparatively underexamined (Eikelenboom \u0026amp; de Jong, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Second, digital leadership research remains heavily private-sector oriented, whereas public-sector healthcare organizations operate under distinct institutional logics centered on public value, equity, and accountability. Third, findings regarding digitalization and performance are often fragmented: while some studies report efficiency gains, others caution that digital investments alone do not guarantee sustained performance without complementary organizational capabilities (Hanelt et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guandalini, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This suggests that the leadership\u0026ndash;performance relationship may be contingent upon deeper capability structures rather than technological adoption per se.\u003c/p\u003e \u003cp\u003eAddressing these gaps requires explicitly linking digital leadership to sustainable organizational performance as an integrated, multidimensional construct within a public-sector healthcare context. Accordingly:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eDigital leadership has a statistically significant positive effect on sustainable organizational performance (SOP).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Digital Leadership and Knowledge Integration:\u003c/h2\u003e \u003cp\u003eA knowledge-based view argues that organizational performance depends less on possessing knowledge than on integrating specialized knowledge across individuals and units to coordinate action (Grant, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Knowledge integration is not equivalent to simple information sharing; it involves building shared understanding, aligning interdependent tasks, and embedding synthesized knowledge into routines and decision processes often through coordination mechanisms that enable flexible collaboration under time pressure and uncertainty (Okhuysen \u0026amp; Eisenhardt, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom an absorptive capacity perspective, organizations create value when they can recognize external knowledge, assimilate it, transform it, and exploit it; thus, integration is central to moving from dispersed information to coordinated implementation (Cohen \u0026amp; Levinthal, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Zahra \u0026amp; George, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Complementing this, information systems scholarship positions knowledge processes and infrastructures as core organizational capabilities technology, structure, and culture jointly shape the organization\u0026rsquo;s capacity to create, store, transfer, and apply knowledge at scale (Gold et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Alavi \u0026amp; Leidner, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDigital leadership should strengthen knowledge integration because digital transformation intensifies cross-boundary interdependence: data platforms, AI-enabled analytics, and integrated workflows require synchronized decision making among clinical, administrative, and IT actors. In public-sector settings, digital transformation specifically entails new ways of working with stakeholders and redesigning service delivery, often shifting cultures and stakeholder relationships (Mergel et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent public-administration research has also begun to operationalize digital transformation leadership as a measurable construct, emphasizing leaders\u0026rsquo; roles in vision setting, collaboration, and guiding operational implementation in public organizations (van Roekel et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These leadership behaviors are precisely the micro-level foundations that enable organizational routines of integration: leaders create enabling conditions shared priorities, incentives, platforms, psychological safety through which cross-unit knowledge is combined and translated into actionable protocols.\u003c/p\u003e \u003cp\u003eWhile digital leadership is frequently linked to digitalization success, the leadership and knowledge integration pathway is less often theorized and tested as a central relationship, particularly in public-sector healthcare where professional logics and bureaucratic boundaries heighten integration challenges. Moreover, many studies treat knowledge outcomes as secondary correlates rather than modeling knowledge integration as a focal capability that digital leadership intentionally builds an omission that constrains explanation of how leadership translates digital intent into coordinated action. Thus, the second hypothesis is formulated as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eDigital leadership has a statistically significant positive effect on knowledge integration (KI).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Knowledge Integration and Sustainable Organizational Performance:\u003c/h2\u003e \u003cp\u003eSustainable organizational performance requires solving a distinctive coordination problem: organizations must avoid treating economic efficiency, social value, and environmental stewardship as isolated objectives, instead aligning them into coherent operational choices and sustained routines.\u003c/p\u003e \u003cp\u003eThe sustainability literature highlights that sustainability is inherently long-term and multi-stakeholder (Bansal \u0026amp; DesJardine, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while the natural-resource-based view links environmental and social commitments to strategic capabilities and resource deployment (Hart, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Hart \u0026amp; Dowell, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In parallel, IS research argues that information systems can enable environmentally sustainable development by supporting measurement, transparency, resource monitoring, and optimized decision making\u0026mdash;yet these benefits materialize only when organizational processes translate digital information into coordinated operational practice (Watson et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKnowledge integration is central to this translation. When organizations integrate expertise across boundaries, they are better positioned to (a) detect sustainability-relevant signals (regulatory changes, stakeholder expectations, resource constraints), (b) evaluate trade-offs with shared criteria, and (c) embed sustainability priorities into routinized decisions (procurement, service design, workforce planning). Evidence from sustainability-performance research suggests that integrative capabilities can support all three pillars of sustainability performance simultaneously, rather than forcing trade-offs (Eikelenboom \u0026amp; de Jong, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, empirical work on environmental knowledge integration shows that integrating knowledge resources can act as a mechanism connecting organizational relational resources to environmental performance outcomes (Zahoor \u0026amp; Gerged, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the knowledge-based view strongly implies a performance payoff from integration, the sustainability domain still shows fragmentation: many studies focus on green or social outcomes in isolation or treat sustainability as reputational signaling rather than an integrated performance construct. Moreover, sustainability in public-sector healthcare magnifies interdependence across professional domains and policy objectives yet mechanism-based evidence connecting knowledge integration to multidimensional sustainability outcomes in public healthcare governance remains comparatively limited. Accordingly, the third hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eKnowledge integration has a statistically significant positive effect on sustainable organizational performance (SOP).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 The Mediating Role of Knowledge Integration:\u003c/h2\u003e \u003cp\u003eDynamic capabilities and absorptive capacity theories both imply that leadership effects on performance are rarely direct in a simple behavioral sense; rather, leadership shapes the routines and capabilities through which organizations adapt and create value. Teece\u0026rsquo;s framework highlights microfoundations that underlie sensing, seizing, and transforming, including managerial cognition, coordination, and organizational processes (Teece, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe microfoundations literature further argues that capabilities emerge from interactions among individuals, social processes, and structural design, making it essential to theorize the intermediate routines that connect managerial action to system-level outcomes (Felin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Absorptive capacity similarly emphasizes that organizations gain advantage when knowledge is not only acquired but also assimilated, transformed, and exploited activities that depend on cross-unit integration mechanisms (Cohen \u0026amp; Levinthal, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Zahra \u0026amp; George, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a public-sector healthcare context, these theoretical claims become especially compelling: digital initiatives (e.g., integrated data systems, governance analytics, inter-organizational coordination platforms) typically span multiple knowledge domains and stakeholder demands. Digital leadership can therefore be expected to affect sustainable organizational performance through two reinforcing routes: a direct route (strategic alignment, accountability, prioritization) and an indirect route in which leaders build integration routines that transform dispersed expertise into coordinated, sustainability-oriented action. This mediation logic is also consistent with digital transformation research identifying the need to move beyond outcome associations toward explaining intermediate organizational changes and mechanisms (Vial, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hanelt et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDigital sustainability and digital transformation reviews repeatedly note that linkages between digital initiatives and sustainability outcomes remain inconsistently conceptualized and mechanistically underexplained, encouraging more explicit theorization of the how (Guandalini, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, while public-sector leadership research increasingly measures digital transformation leadership, fewer studies connect it to internal knowledge-based mechanisms and, subsequently, to integrated Triple Bottom Line performance in public healthcare organizations (van Roekel et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Positioning knowledge integration as the mediation mechanism therefore addresses a clear explanatory and contextual gap: it specifies the internal capability pathway through which leadership converts digital transformation into durable, multidimensional sustainability performance. Therefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eKnowledge integration plays a statistically significant positive mediating role in the relationship between digital leadership and sustainable organizational performance (SOP).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe hypothesized research framework is presented on Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Philosophy:\u003c/h2\u003e \u003cp\u003eThis study is grounded in a positivist research philosophy. Positivism assumes that organizational phenomena can be objectively observed and empirically measured through systematic data collection and statistical analysis. In line with this stance, digital leadership, knowledge integration, and sustainable organizational performance are treated as measurable constructs whose relationships can be tested quantitatively.\u003c/p\u003e \u003cp\u003eThe positivist orientation aligns with the study\u0026rsquo;s objective of examining hypothesized causal relationships among clearly defined variables within a public-sector healthcare context. Accordingly, standardized instruments and statistical modeling techniques were employed to ensure objectivity and methodological rigor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research Approach:\u003c/h2\u003e \u003cp\u003eA deductive research approach was adopted. Drawing on Dynamic Capabilities Theory and Absorptive Capacity Theory, the study develops a conceptual model and formulates hypotheses regarding the direct and mediating relationships among digital leadership, knowledge integration, and sustainable organizational performance.\u003c/p\u003e \u003cp\u003eThe deductive logic moves from established theory to empirical testing. This approach is consistent with the positivist paradigm and is appropriate for validating theoretically derived relationships using quantitative data collected from the Abu Dhabi public healthcare sector.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Methodological Choice:\u003c/h2\u003e \u003cp\u003eThe study employs a mono-method quantitative design. Data were collected using a structured online questionnaire with standardized Likert-scale measures. This design allows for consistent measurement across respondents and supports statistical evaluation of relationships among latent constructs.\u003c/p\u003e \u003cp\u003eA quantitative approach is particularly suitable for testing direct and mediating effects within structural models. It enables reliability assessment, validity testing, and structural equation modeling to examine hypothesized relationships in a rigorous and systematic manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Research Strategy and Time Horizon:\u003c/h2\u003e \u003cp\u003eA survey research strategy was adopted to collect data from employees across multiple organizational levels within the Abu Dhabi Department of Health. The use of a structured questionnaire facilitated efficient data collection and ensured comparability across respondents.\u003c/p\u003e \u003cp\u003eThe study follows a cross-sectional design, capturing respondents\u0026rsquo; perceptions at a single point in time. Data were collected between July and September 2025, providing a contemporary snapshot of digital leadership practices and organizational performance within the selected public-sector healthcare context. A stratified sampling design was employed to enhance representativeness and reduce potential sampling bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Population and Sampling Procedure:\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Target Population and Unit of Analysis:\u003c/h2\u003e \u003cp\u003eThe target population comprised all permanent employees working within the Department of Health \u0026ndash; Abu Dhabi (DoH), totaling 724 individuals according to officially updated HR records during the study period. The unit of analysis was the individual employee occupying a formal organizational position within the Department.\u003c/p\u003e \u003cp\u003eTo ensure conceptual clarity, the population was classified into five mutually exclusive strata based on the official organizational structure: senior management, middle management, supervisory level, technical staff, and administrative staff. Each employee belonged to only one category, eliminating classification overlap. Managers were therefore treated as strata within the same organizational system rather than as a separate population.\u003c/p\u003e \u003cp\u003eThis structure ensures alignment between the theoretical constructs of the study digital leadership, knowledge integration, and sustainable organizational performance and the empirical respondents, as employees across all strata are directly engaged in digital processes, decision-making activities, or knowledge-related interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Sampling Frame Construction:\u003c/h2\u003e \u003cp\u003eThe sampling frame was constructed using the official HR database of the Department of Health \u0026ndash; Abu Dhabi. The database included employee ID, organizational level, functional category, and departmental affiliation.\u003c/p\u003e \u003cp\u003eTemporary staff, outsourced personnel, and external contractors were excluded to maintain conceptual consistency between the study variables and the respondents. Each eligible employee was assigned to one predefined stratum according to official job classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Sampling Technique:\u003c/h2\u003e \u003cp\u003eA proportionate stratified random sampling technique was employed. The sampling process followed a structured sequence. \u003cb\u003eFirst\u003c/b\u003e, the total population (N\u0026thinsp;=\u0026thinsp;724) was divided into five strata based on official job classification. \u003cb\u003eSecond\u003c/b\u003e, the required total sample size (n\u0026thinsp;=\u0026thinsp;254) was determined using Krejcie \u0026amp; Morgan (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) at a 95% confidence level and 5% margin of error. \u003cb\u003eThird\u003c/b\u003e, proportional allocation was applied using the following formula:\u003c/p\u003e \u003cp\u003e \u003cb\u003en_h = (N_h / N) \u0026times; n\u003c/b\u003e \u003c/p\u003e \u003cp\u003ewhere n_h represents the sample size for each stratum, N_h the population size of the stratum, N the total population, and n the overall required sample size. Within each stratum, random selection was performed using a computerized random number generator to ensure procedural objectivity and methodological replicability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4 Data Collection Procedure:\u003c/h2\u003e \u003cp\u003eThe online questionnaire link was distributed through the official internal email system of the Department of Health \u0026ndash; Abu Dhabi, in coordination with the Human Resources Department. The invitation explained the purpose of the study, assured confidentiality, and emphasized voluntary participation.\u003c/p\u003e \u003cp\u003eThe survey was administered through the organization\u0026rsquo;s internal email system to ensure that only eligible employees could access the questionnaire, thereby strengthening sampling control and data integrity.\u003c/p\u003e \u003cp\u003eData collection took place between July and September 2025. All responses were anonymized, and no identifying information was collected. Participation remained voluntary throughout the data collection period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.5.5 Final Sample Distribution:\u003c/h2\u003e \u003cp\u003eA total of 384 responses were received. After screening for completeness, response consistency, and missing data, 25 questionnaires were excluded due to substantial missing values or patterned responses. The final usable sample consisted of 359 respondents.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation Structure and Achieved Stratified Sample Distribution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStratum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAchieved Sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% of Sample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupervisory Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical Staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrative Staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough proportionate stratified sampling was initially applied, the final achieved sample reflects natural variation in response rates across strata. Senior management exhibited a higher response rate relative to its population proportion, resulting in slight overrepresentation in the final dataset. However, all strata remain adequately represented, and no post-stratification weighting was deemed necessary given the robust sample size and the predictive orientation of PLS-SEM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.5.6 Response Flow and Final Sample Size:\u003c/h2\u003e \u003cp\u003eThe final usable sample of 359 respondents exceeds the minimum required sample size of 254. This corresponds to an effective response rate of 49.6%.\u003c/p\u003e \u003cp\u003eExceeding the minimum threshold enhances statistical precision and strengthens the robustness of Structural Equation Modeling (SEM) estimations. The achieved sample size is therefore considered sufficient for reliable testing of both direct and mediating effects within the proposed model.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Justification for Choosing Abu Dhabi Department of Health as the Research Setting:\u003c/h2\u003e \u003cp\u003eThe Abu Dhabi Department of Health provides an appropriate and compelling context for examining digital leadership, knowledge integration, and sustainable organizational performance. As the regulatory authority for the emirate\u0026rsquo;s healthcare system, the Department leads major digital transformation initiatives involving artificial intelligence, digital health platforms, and data-integration systems, making it an ideal environment for investigating how digital leadership shapes organizational capabilities and outcomes.\u003c/p\u003e \u003cp\u003eIts operations rely heavily on multidisciplinary teams, extensive information flows, and interconnected public\u0026ndash;private healthcare networks, all of which highlight the central role of knowledge integration the study\u0026rsquo;s mediating construct. Furthermore, the Department\u0026rsquo;s mandate directly contributes to UAE national sustainability priorities, including Vision 2030, thereby positioning sustainable performance as a strategic organizational imperative.\u003c/p\u003e \u003cp\u003eImportantly, public-sector healthcare represents a context with unique characteristics such as bureaucratic structures, strong accountability mechanisms, and complex regulatory environments that influence how digital leadership is enacted. Scholars such as Abubakar et al. (2024) and Nuryadin et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlight the scarcity of empirical studies in Arab government settings. Therefore, selecting the Abu Dhabi Department of Health addresses a critical contextual gap and enhances the theoretical and practical relevance of the study\u0026rsquo;s findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Measurement of Variables:\u003c/h2\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.7.1 Constructs and Measurement Scales:\u003c/h2\u003e \u003cp\u003eThis study examines three latent constructs: Digital Leadership (independent variable), Knowledge Integration (mediating variable), and Sustainable Organizational Performance (dependent variable). All constructs were measured using established multi-item scales adapted from previously validated studies.\u003c/p\u003e \u003cp\u003eResponses were captured using a five-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;). This format is widely used in organizational research and is appropriate for assessing perceptual constructs in Structural Equation Modeling (SEM).\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section4\"\u003e \u003ch2\u003e3.7.1.1 Digital Leadership:\u003c/h2\u003e \u003cp\u003eDigital Leadership was measured using 31 items adapted from previously validated scales. The items capture leaders\u0026rsquo; strategic understanding of digital technologies, accumulated digital experience, anticipatory orientation, and ability to articulate a coherent digital vision.\u003c/p\u003e \u003cp\u003eAlthough the construct conceptually encompasses multiple interrelated dimensions identified in prior research, it was operationalized in this study as a unified reflective latent variable representing overall digital leadership capability. This specification assumes that the observed indicators reflect a shared underlying leadership orientation toward digital transformation rather than forming independent formative components. Such reflective modeling is consistent with capability-based research and supports theoretical parsimony in structural analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section4\"\u003e \u003ch2\u003e3.7.1.2 Knowledge Integration:\u003c/h2\u003e \u003cp\u003eKnowledge Integration was measured using five items adapted from Wong (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The scale assesses the extent to which knowledge is shared, combined, and embedded across organizational units. It reflects structured processes that facilitate collective learning and coordinated decision-making within the organization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section4\"\u003e \u003ch2\u003e3.7.1.3 Sustainable Organizational Performance:\u003c/h2\u003e \u003cp\u003eSustainable Organizational Performance (SOP) was measured using ten items reflecting economic, social, and environmental performance aspects consistent with the triple-bottom-line framework.\u003c/p\u003e \u003cp\u003eWhile these dimensions are conceptually distinguishable, the construct was operationalized as a single reflective latent variable capturing overall sustainability performance within the organization. This approach reflects the integrated nature of sustainability outcomes in institutional settings and aligns with the study\u0026rsquo;s objective of examining aggregate structural relationships rather than comparative dimensional effects. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the constructs, the conceptual aspects reflected in the items, the number of indicators, and the original sources from.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement of Study Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConceptual Aspects Reflected in the Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Leadership (DL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategic digital capability; Digital experience; Predictive orientation; Digital vision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYng et al. (2023); Wang et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Ahn et al. (2014); Lundin et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Temelkova (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Awan et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Ruvio et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); Luthans (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge Integration (KI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-unit knowledge sharing and integration processes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWong (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainable Organizational Performance (SOP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic; Social; Environmental performance aspects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u0026ndash;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWang (2019); Li (2014); Cheah et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Maletic et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eDeveloped by the researcher based on validated measurement scales from prior literature.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Ethical Considerations:\u003c/h2\u003e \u003cp\u003e This study adhered to established ethical standards for research involving human participants. The research design involved a voluntary, anonymous organizational survey and did not include the collection of sensitive personal or medical data.\u003c/p\u003e \u003cp\u003ePrior to data collection, administrative authorization was obtained from the Department of Health \u0026ndash; Abu Dhabi. The Human Resources Department facilitated the distribution of the survey link while ensuring that participation remained entirely voluntary and unrelated to any form of performance evaluation.\u003c/p\u003e \u003cp\u003eParticipants were clearly informed about the academic purpose of the study, the voluntary nature of their participation, and their right to discontinue the survey at any time without consequence. Electronic informed consent was obtained before respondents were allowed to proceed to the questionnaire.\u003c/p\u003e \u003cp\u003eNo personally identifiable information was collected at any stage of the study. Responses were anonymized automatically through the online survey platform, and the data were stored securely on password-protected devices accessible only to the research team.\u003c/p\u003e \u003cp\u003eFor theoretical coherence and structural parsimony, multidimensional constructs were operationalized as unified reflective latent variables capturing overall capability orientation rather than modeling each conceptual dimension separately. The study\u0026rsquo;s objective focuses on examining aggregate structural relationships among digital leadership, knowledge integration, and sustainable organizational performance. Accordingly, reflective measurement was deemed appropriate, as the indicators represent complementary manifestations of underlying strategic capabilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Analysis:\u003c/h2\u003e \u003cp\u003eThe demographic characteristics of the respondents are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which presents details about gender, education, age, managerial level, and years of experience.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215\u003c/p\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.9%\u003c/p\u003e \u003cp\u003e40.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s Degree\u003c/p\u003e \u003cp\u003ePostgraduate Diploma\u003c/p\u003e \u003cp\u003eMaster\u0026rsquo;s Degree\u003c/p\u003e \u003cp\u003eDoctoral Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003cp\u003e54\u003c/p\u003e \u003cp\u003e179\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1%\u003c/p\u003e \u003cp\u003e15%\u003c/p\u003e \u003cp\u003e49.9%\u003c/p\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;35 years\u003c/p\u003e \u003cp\u003e36\u0026ndash;46 years\u003c/p\u003e \u003cp\u003e47\u0026ndash;57 years\u003c/p\u003e \u003cp\u003e58 years and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003cp\u003e143\u003c/p\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1%\u003c/p\u003e \u003cp\u003e39.8%\u003c/p\u003e \u003cp\u003e20.1%\u003c/p\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior Management\u003c/p\u003e \u003cp\u003eMiddle Management\u003c/p\u003e \u003cp\u003eSupervisory Level\u003c/p\u003e \u003cp\u003eTechnical\u003c/p\u003e \u003cp\u003eAdministrative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e54\u003c/p\u003e \u003cp\u003e107\u003c/p\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003cp\u003e25.1%\u003c/p\u003e \u003cp\u003e15%\u003c/p\u003e \u003cp\u003e29.8%\u003c/p\u003e \u003cp\u003e20.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 12 years\u003c/p\u003e \u003cp\u003e12\u0026ndash;22 years\u003c/p\u003e \u003cp\u003e23\u0026ndash;33 years\u003c/p\u003e \u003cp\u003e34 years and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e125\u003c/p\u003e \u003cp\u003e108\u003c/p\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.1%\u003c/p\u003e \u003cp\u003e34.8%\u003c/p\u003e \u003cp\u003e30.1%\u003c/p\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistics presented in \u003cb\u003eTable\u0026nbsp;(3)\u003c/b\u003e provide a clear profile of the demographic characteristics of the 359 respondents. With regard to gender, males represent the majority of the sample (59.9%), while females account for 40.1%. In terms of educational background, the largest proportion of respondents hold a Master\u0026rsquo;s degree (49.9%), followed by those with a Bachelor\u0026rsquo;s degree (30.1%). Postgraduate diplomas constitute 15% of the sample, whereas doctoral degree holders represent 5%. Regarding age distribution, most respondents fall within the 36\u0026ndash;46 years category (39.8%), followed by the 25\u0026ndash;35 years group (30.1%). The older age groups, 47\u0026ndash;57 years and 58 years and above, account for 20.1% and 10% of the sample, respectively. These results indicate that the sample is largely composed of well-educated individuals in their mid-career stages.\u003c/p\u003e \u003cp\u003eConcerning job position and work experience, employees in technical roles form the largest segment of the sample (29.8%), followed by those in middle management positions (25.1%). Administrative staff represent 20.1%, while supervisory and senior management positions account for 15% and 10%, respectively. With respect to years of experience, the largest proportion of respondents (34.8%) report having 12\u0026ndash;22 years of experience, followed by 30.1% with 23\u0026ndash;33 years, 25.1% with less than 12 years, and 10% with more than 34 years of experience. Overall, these distributions suggest \u003cem\u003ethat\u003c/em\u003e the sample is diverse, well balanced across organizational levels, and characterized by substantial professional experience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Construct Reliability and Validity:\u003c/h2\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Cronbach\u0026rsquo;s alphas and composite reliability:\u003c/h2\u003e \u003cp\u003eThe internal consistency of the measurement model was examined using Cronbach\u0026rsquo;s alpha and composite reliability. These indicators provide complementary evidence regarding the degree to which the measurement items consistently represent their respective constructs.\u003c/p\u003e \u003cp\u003eThe results indicate that all constructs demonstrate satisfactory levels of internal consistency. Specifically, Cronbach\u0026rsquo;s alpha values range from 0.801 to 0.948, reflecting a high level of consistency among the items. Similarly, composite reliability values vary between 0.848 and 0.954, further confirming the stability of the measurement.\u003c/p\u003e \u003cp\u003eThe consistency between these two reliability indicators suggests that the constructs are measured with a high degree of precision. Accordingly, the measurement model can be considered reliable and suitable for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Convergent validity:\u003c/h2\u003e \u003cp\u003eConvergent validity was assessed to determine the extent to which the indicators associated with each construct share a sufficient proportion of variance. This step ensures that the items intended to measure a given construct are indeed capturing the same underlying concept. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of indicator loadings, Cronbach\u0026rsquo;s alpha, composite reliability, and average variance extracted (AVE) for all constructs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement Model Assessment Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"30\" rowspan=\"31\"\u003e \u003cp\u003eDigital Leadership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"30\" rowspan=\"31\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"30\" rowspan=\"31\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"30\" rowspan=\"31\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eKnowledge Integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eSustainable\u003c/p\u003e \u003cp\u003eOrganizational Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results demonstrate that all indicator loadings exceed the acceptable minimum level, indicating that the items are adequately related to their respective constructs. In addition, the AVE values for all constructs are above 0.50, suggesting that each construct explains a substantial proportion of the variance in its indicators.\u003c/p\u003e \u003cp\u003eDigital Leadership exhibits strong measurement properties, supported by high reliability coefficients and an AVE value that exceeds the required threshold, despite the relatively large number of indicators. Knowledge Integration also demonstrates very high internal consistency and acceptable convergent validity. Similarly, Sustainable Organizational Performance shows strong reliability and sufficient variance extraction.\u003c/p\u003e \u003cp\u003eOverall, these findings confirm that convergent validity is adequately established, and the constructs are appropriately operationalized within the measurement model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Discriminant validity:\u003c/h2\u003e \u003cp\u003eDiscriminant validity was evaluated to ensure that each construct captures a distinct conceptual domain and does not overlap excessively with other constructs in the model. Establishing discriminant validity is essential to confirm that the latent variables represent different theoretical concepts.\u003c/p\u003e \u003cp\u003eTo achieve this, two complementary approaches were employed: the Fornell\u0026ndash;Larcker criterion and the heterotrait\u0026ndash;monotrait ratio (HTMT).\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section4\"\u003e \u003ch2\u003e4.2.3.1 Fornell\u0026ndash;Larcker Criterion:\u003c/h2\u003e \u003cp\u003eThe Fornell\u0026ndash;Larcker criterion assesses discriminant validity by comparing the square root of the AVE for each construct with the correlations between constructs. A construct is considered distinct if it shares more variance with its own indicators than with other constructs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epresents the discriminant validity results based on the Fornell\u0026ndash;Larcker criterion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the square root of AVE for each construct (diagonal values) is higher than the corresponding inter-construct correlations. This indicates that each construct is more strongly associated with its own indicators than with other constructs in the model.\u003c/p\u003e \u003cp\u003eThese results provide clear evidence that the constructs are empirically distinct and support the establishment of discriminant validity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section4\"\u003e \u003ch2\u003e4.2.3.2 HTMT Criterion:\u003c/h2\u003e \u003cp\u003eTo further confirm discriminant validity, the HTMT ratio was calculated. According to Benitez et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), HTMT values in well-fitted models should remain below 0.90, while more conservative thresholds suggest 0.85 as an ideal upper bound. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the HTMT results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant Validity Based on HTMT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe HTMT values range between 0.521 and 0.563, which are well below the commonly accepted thresholds. This indicates that the relationships between constructs do not exhibit excessive similarity.\u003c/p\u003e \u003cp\u003eThese findings confirm that the constructs are clearly differentiated and that no multicollinearity issues are present.\u003c/p\u003e \u003cp\u003eTaken together, the results from internal consistency reliability, convergent validity, and discriminant validity provide strong support for the adequacy of the measurement model. All constructs demonstrate acceptable psychometric properties and can be considered both reliable and valid.\u003c/p\u003e \u003cp\u003eAccordingly, the measurement model is deemed appropriate for proceeding with the structural model analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Assessment of Common Method Variance:\u003c/h2\u003e \u003cp\u003eGiven the cross-sectional and self-reported nature of the survey data, potential common method variance (CMV) was assessed to ensure that the observed relationships were not substantially inflated by shared method bias. In addition to procedural remedies implemented during questionnaire design\u0026mdash;such as ensuring respondent anonymity, emphasizing voluntary participation, and clearly separating construct sections\u0026mdash;statistical assessment was conducted using the full collinearity approach recommended by Kock (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis procedure examines variance inflation factors (VIF) by treating each construct as a dependent variable in turn. According to Kock (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), VIF values below the conservative threshold of 3.3 indicate that common method bias is unlikely to be a serious concern. The results show that all construct-level VIF values were well below this threshold (Digital Leadership\u0026thinsp;=\u0026thinsp;1.892; Knowledge Integration\u0026thinsp;=\u0026thinsp;1.712; Sustainable Organizational Performance\u0026thinsp;=\u0026thinsp;1.818). Therefore, common method variance does not appear to threaten the validity of the study\u0026rsquo;s findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Hypotheses testing:\u003c/h2\u003e \u003cp\u003eTo test the proposed hypotheses, the structural relationships were estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM). This technique was selected based on several methodological and theoretical considerations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst\u003c/b\u003e, PLS-SEM is particularly appropriate for predictive and exploratory research contexts where the primary objective is to explain variance in key endogenous constructs and assess complex multivariate relationships (Hair et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cb\u003eSecond\u003c/b\u003e, compared to covariance-based SEM (CB-SEM), PLS-SEM is more robust when data may deviate from multivariate normality assumptions, when the sample size is moderate, and when models involve both reflective and potentially formative specifications (Hair et al., 2021). \u003cb\u003eThird\u003c/b\u003e, PLS-SEM enables the simultaneous estimation of the measurement and structural models, providing stable and reliable parameter estimates under complex model configurations.\u003c/p\u003e \u003cp\u003e The analysis was conducted using SmartPLS (version 4.1.0.9) in two sequential stages following established guidelines (Hair et al., 2020). The first stage involved Confirmatory Composite Analysis (CCA) to assess the measurement model, while the second stage evaluated the structural model through bootstrapping procedures to test the hypothesized relationships.\u003c/p\u003e \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Path Coefficients and Direct Effects:\u003c/h2\u003e \u003cp\u003eThe results of the hypothesis testing are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePath Coefficients and Hypothesis Testing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePath Coefficient (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDL\u0026rarr; SOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDL\u0026rarr; KI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKI\u0026rarr; SOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDL\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:KI\\)\u003c/span\u003e\u003c/span\u003e\u0026rarr; SOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, all hypothesized paths are positive and statistically significant at p\u0026thinsp;\u0026le;\u0026thinsp;0.001, providing full empirical support for the proposed model.\u003c/p\u003e \u003cp\u003eSpecifically, \u003cb\u003eH1\u003c/b\u003e indicates that Digital Leadership (DL) has a positive and significant direct effect on Sustainable Organizational Performance (SOP) (β\u0026thinsp;=\u0026thinsp;0.682, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The magnitude of this coefficient suggests a moderate-to-strong substantive impact, highlighting the strategic importance of digital leadership capabilities in enhancing Sustainable organizational outcomes.\u003c/p\u003e \u003cp\u003eRegarding \u003cb\u003eH2\u003c/b\u003e, Digital Leadership significantly influences Knowledge Integration (KI) (β\u0026thinsp;=\u0026thinsp;0.897, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming that effective digital leadership practices strengthen the organization\u0026rsquo;s ability to integrate and coordinate knowledge resources. This finding aligns with capability-based and dynamic capabilities perspectives, which emphasize leadership as an enabling mechanism for knowledge processes.\u003c/p\u003e \u003cp\u003eFor \u003cb\u003eH3\u003c/b\u003e, Knowledge Integration demonstrates a positive and significant effect on Sustainable Organizational Performance (β\u0026thinsp;=\u0026thinsp;0.231, p\u0026thinsp;=\u0026thinsp;0.001). Although the magnitude is comparatively smaller than the direct path, the effect remains statistically and substantively meaningful, indicating that knowledge integration acts as a performance-enhancing mechanism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Mediation Analysis:\u003c/h2\u003e \u003cp\u003eThe mediation hypothesis (\u003cb\u003eH4\u003c/b\u003e) was assessed by examining the indirect effect of Digital Leadership on Sustainable Organizational Performance through Knowledge Integration using bootstrapping procedures.\u003c/p\u003e \u003cp\u003eThe results show a significant indirect effect (β\u0026thinsp;=\u0026thinsp;0.207, p\u0026thinsp;=\u0026thinsp;0.001), supporting the mediating role of Knowledge Integration. Because both the direct effect (H1) and the indirect effect (H4) are statistically significant, the mediation is classified as partial mediation.\u003c/p\u003e \u003cp\u003eThis finding suggests that Digital Leadership improves Sustainable Organizational Performance through two complementary mechanisms:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDirect pathway\u003c/b\u003e \u0026ndash; by influencing performance-related processes and strategic outcomes directly.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIndirect pathway\u003c/b\u003e \u0026ndash; by enhancing knowledge integration capabilities, which subsequently translate into improved Sustainable performance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe presence of partial mediation indicates that Knowledge Integration functions as an important but not exclusive explanatory mechanism in the Digital Leadership\u0026ndash;Performance relationship. The effect sizes observed are theoretically plausible and statistically stable, strengthening the robustness and credibility of the proposed mediation framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec44\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Structural Model Visualization:\u003c/h2\u003e \u003cp\u003eThe bootstrapping results are visually summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which presents the estimated path coefficients and R\u0026sup2; values for the endogenous constructs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the model demonstrates substantial explanatory power. The R\u0026sup2; value for Knowledge Integration (0.805) indicates that approximately 80.5% of its variance is explained by Digital Leadership. Similarly, the R\u0026sup2; value for Sustainable Organizational Performance (0.801) shows that 80.1% of its variance is jointly explained by Digital Leadership and Knowledge Integration.\u003c/p\u003e \u003cp\u003eAll structural paths appear statistically significant (p\u0026thinsp;\u0026le;\u0026thinsp;0.001), reinforcing the empirical validity of the hypothesized relationships. The combination of strong explanatory power, significant direct and indirect effects, and theoretical coherence supports the model as a robust explanatory framework linking Digital Leadership, Knowledge Integration, and Sustainable Organizational Performance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec45\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Structural model:\u003c/h2\u003e \u003cdiv id=\"Sec46\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Overall Structural Model Fit:\u003c/h2\u003e \u003cp\u003eFollowing the validation of the measurement model, the structural model was evaluated to test the hypothesized relationships among the latent constructs. In accordance with established PLS-SEM guidelines, the assessment focused on global model fit, explanatory power (R\u0026sup2;), predictive relevance (Q\u0026sup2; and PLSpredict), effect sizes (f\u0026sup2;), and collinearity diagnostics (VIF). This multi-criteria approach ensures both statistical adequacy and substantive interpretability of the structural relationships.\u003c/p\u003e \u003cdiv id=\"Sec47\" class=\"Section4\"\u003e \u003ch2\u003e4.5.1.1 Global Model Fit:\u003c/h2\u003e \u003cp\u003eTo assess overall model fit, the standardized root mean square residual (SRMR) was examined. SRMR represents the difference between observed and model-implied correlations and is commonly used as a goodness-of-fit indicator in PLS-SEM.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epresents the structural model fit results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the SRMR value equals 0.071, which is below the recommended threshold of 0.08. This indicates an acceptable global model fit and suggests that the model adequately reproduces the observed covariance structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec48\" class=\"Section4\"\u003e \u003ch2\u003e4.5.1.2 Explanatory Power, Effect Sizes, and Collinearity Diagnostics\u003c/h2\u003e \u003cp\u003eAfter establishing acceptable model fit, the structural model\u0026rsquo;s explanatory power and diagnostic indicators were evaluated. The coefficient of determination (R\u0026sup2;) and adjusted R\u0026sup2; were used to assess the proportion of variance explained in the endogenous constructs. Predictive relevance was examined using Stone\u0026ndash;Geisser\u0026rsquo;s Q\u0026sup2;, while effect sizes (f\u0026sup2;) were calculated to determine the substantive impact of exogenous constructs. Variance inflation factors (VIF) were assessed to rule out multicollinearity concerns. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural Model Quality Criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ef\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs reported in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, Knowledge Integration (KI) exhibits substantial explanatory power (R\u0026sup2; = 0.805; Adjusted R\u0026sup2; = 0.805), indicating that approximately 80.5% of its variance is explained by Digital Leadership (DL). Similarly, Sustainable Organizational Performance (SOP) demonstrates high explanatory strength (R\u0026sup2; = 0.801; Adjusted R\u0026sup2; = 0.800), meaning that nearly 80% of its variance is accounted for by the model.\u003c/p\u003e \u003cp\u003eAlthough the Q\u0026sup2; values for KI (0.018) and SOP (0.083) are relatively modest, both exceed zero, confirming predictive relevance according to the blindfolding criterion. The magnitude suggests weak-to-moderate predictive capability, which remains acceptable in behavioral and organizational research contexts.\u003c/p\u003e \u003cp\u003eRegarding effect sizes, the f\u0026sup2; values for KI (0.36) and SOP (0.41) exceed the 0.35 benchmark, indicating large substantive effects of the exogenous constructs on the endogenous variables. This confirms that the structural relationships are not only statistically significant but also practically meaningful.\u003c/p\u003e \u003cp\u003eFurthermore, the VIF values for DL (1.892), KI (1.712), and SOP (1.818) are well below the conservative threshold of 3.3 and far below the critical value of 5. These results indicate the absence of multicollinearity and suggest that the estimated path coefficients are stable and unbiased.\u003c/p\u003e \u003cp\u003eCollectively, the structural diagnostics demonstrate strong explanatory capacity, meaningful effect sizes, acceptable predictive relevance, and no collinearity concerns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec49\" class=\"Section4\"\u003e \u003ch2\u003e4.5.1.3 Out-of-Sample Predictive Validity:\u003c/h2\u003e \u003cp\u003eTo further evaluate the model\u0026rsquo;s predictive performance beyond in-sample explanatory power, PLSpredict was conducted. This procedure compares prediction errors generated by the PLS model with those obtained from a linear regression benchmark model. The results are reported in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePLSpredict Assessment of Predictive Validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEndogenous Construct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE (PLS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE (LM Benchmark)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u0026sup2;_predict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePredictive Power\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKnowledge Integration (KI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow but positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSustainable Organizational Performance (SOP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the RMSE values produced by the PLS model are lower than those generated by the linear model benchmark for both endogenous constructs. This indicates superior predictive accuracy of the PLS model compared to the na\u0026iuml;ve regression alternative.\u003c/p\u003e \u003cp\u003eAdditionally, the Q\u0026sup2;_predict values for KI (0.018) and SOP (0.083) are greater than zero, confirming out-of-sample predictive relevance. While the predictive strength is stronger for Sustainable Organizational Performance than for Knowledge Integration, both constructs demonstrate positive predictive capability.\u003c/p\u003e \u003cp\u003eImportantly, the consistency between high R\u0026sup2; values and positive Q\u0026sup2;_predict results suggests that the model\u0026rsquo;s strong explanatory power reflects genuine predictive capacity rather than overfitting or common method bias. Therefore, the structural model demonstrates both robust in-sample performance and acceptable out-of-sample predictive validity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec51\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Interpretation of Findings:\u003c/h2\u003e \u003cp\u003eThe findings provide robust empirical support for the proposed framework linking Digital Leadership, Knowledge Integration, and Sustainable Organizational Performance within the public healthcare context.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst\u003c/b\u003e, Digital Leadership demonstrates a positive and statistically significant direct effect on Sustainable Organizational Performance (β\u0026thinsp;=\u0026thinsp;0.682, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result confirms that leadership plays a central strategic role in enabling organizations to translate digital transformation efforts into measurable sustainability outcomes. Consistent with Dynamic Capabilities Theory (Teece, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), leadership appears to function as an organizational capability that facilitates the sensing of environmental shifts, the mobilization of digital resources, and the reconfiguration of institutional processes in complex governance settings.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond\u003c/b\u003e, Digital Leadership exerts a significant positive effect on Knowledge Integration (β\u0026thinsp;=\u0026thinsp;0.897, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that digital leadership strengthens structured knowledge-sharing mechanisms across organizational units. From an absorptive capacity perspective (Zahra \u0026amp; George, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), leadership appears instrumental in shaping the organizational infrastructure that enables knowledge assimilation, coordination, and transformation into collective action.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThird\u003c/b\u003e, Knowledge Integration significantly influences Sustainable Organizational Performance (β\u0026thinsp;=\u0026thinsp;0.231, p\u0026thinsp;=\u0026thinsp;0.001). Although the magnitude of this effect is moderate relative to the direct leadership effect, it confirms that sustainability outcomes are partially rooted in structured knowledge processes. In public healthcare systems characterized by regulatory complexity and multi-stakeholder accountability, coordinated knowledge exchange appears to play a meaningful role in reinforcing long-term economic, social, and environmental performance.\u003c/p\u003e \u003cp\u003eImportantly, mediation analysis reveals that Knowledge Integration partially mediates the relationship between Digital Leadership and Sustainable Organizational Performance (indirect β\u0026thinsp;=\u0026thinsp;0.207, p\u0026thinsp;=\u0026thinsp;0.001). The presence of both direct and indirect effects indicates complementary partial mediation. This finding implies that Digital Leadership influences sustainability through two concurrent mechanisms: a direct strategic pathway and an indirect knowledge-based pathway.\u003c/p\u003e \u003cp\u003eCollectively, the results suggest that digital leadership contributes to sustainability not merely through technological orientation but through its capacity to institutionalize integrative knowledge processes. In highly institutionalized public-sector environments, leadership appears particularly influential in aligning digital initiatives with coordinated knowledge structures that translate strategic intent into sustainable performance outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec52\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Theoretical Integration and Structural Implications:\u003c/h2\u003e \u003cp\u003eThe findings contribute to Dynamic Capabilities Theory, the Knowledge-Based View, and Absorptive Capacity Theory by empirically demonstrating that the relationship between leadership and sustainable performance is partially mediated by structured knowledge processes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst\u003c/b\u003e, the results reinforce the core proposition of Dynamic Capabilities Theory that leadership functions as an enabling organizational capability that supports resource reconfiguration under environmental complexity (Teece, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The significant direct effect of Digital Leadership on Sustainable Organizational Performance confirms that leadership plays a central strategic role in shaping long-term organizational outcomes within institutionalized public-sector contexts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond\u003c/b\u003e, the strong explanatory power observed for Knowledge Integration and Sustainable Organizational Performance suggests a high degree of coherence between leadership orientation and knowledge coordination processes. This pattern may reflect the characteristics of centralized public-sector governance systems, where strategic direction and knowledge flows are more formally structured and hierarchically embedded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThird\u003c/b\u003e, the mediating role of Knowledge Integration supports the Knowledge-Based View and Absorptive Capacity Theory by demonstrating that performance improvements are not solely the result of leadership presence but are significantly reinforced through knowledge assimilation and coordination mechanisms. This finding highlights the distinction between possessing digital resources and effectively orchestrating them through structured knowledge processes.\u003c/p\u003e \u003cp\u003eOverall, the results suggest that Digital Leadership contributes to sustainability through both direct strategic influence and indirect capability-based mechanisms. This dual pathway strengthens the explanatory depth of capability-based models in public-sector sustainability research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec53\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Methodological Reflection and Conceptual\u0026ndash;Statistical Alignment\u003c/h2\u003e \u003cp\u003eThe study carefully aligned conceptual definitions with statistical modeling decisions to ensure coherence between theory and empirical testing. Although Digital Leadership and Sustainable Organizational Performance are conceptually multidimensional constructs, they were modeled in the structural analysis as unified latent variables to maintain theoretical clarity and parsimony in hypothesis testing.\u003c/p\u003e \u003cp\u003eThis approach ensured consistency between conceptual framing and structural estimation. Rather than fragmenting the constructs into multiple competing predictors, the model treated each construct as an integrated capability, consistent with the study\u0026rsquo;s theoretical foundation.\u003c/p\u003e \u003cp\u003eThe use of PLS-SEM enabled simultaneous assessment of measurement validity and structural relationships while accommodating the study\u0026rsquo;s predictive orientation. By maintaining alignment between theoretical abstraction and empirical operationalization, the model preserves interpretive clarity and methodological rigor.\u003c/p\u003e \u003cp\u003eWhile the explained variance is relatively high, this may be expected in centralized public-sector settings where leadership practices strongly shape internal knowledge processes and sustainability performance assessments. Nevertheless, given the cross-sectional and self-reported nature of the data, the possibility of shared-method inflation was treated cautiously. Accordingly, both procedural safeguards and statistical diagnostics were applied to mitigate common method concerns.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion and Policy Recommendations","content":"\u003cdiv id=\"Sec55\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Conclusion:\u003c/h2\u003e \u003cp\u003eThis study examined the relationship between Digital Leadership, Knowledge Integration, and Sustainable Organizational Performance within a public-sector healthcare context. Drawing on Dynamic Capabilities Theory, the Knowledge-Based View, and Absorptive Capacity Theory, the research proposed and empirically tested a framework explaining how leadership contributes to sustainability both directly and indirectly through structured knowledge processes.\u003c/p\u003e \u003cp\u003eUsing data from 359 employees at the Abu Dhabi Department of Health, the findings demonstrate that Digital Leadership has a significant positive direct effect on Sustainable Organizational Performance. In addition, Digital Leadership significantly enhances Knowledge Integration, which in turn positively influences sustainability outcomes. The mediation analysis confirms that Knowledge Integration plays a complementary partial mediating role in this relationship.\u003c/p\u003e \u003cp\u003eThese results indicate that sustainable performance in public healthcare organizations is not solely dependent on digital technology adoption. Rather, it depends on leadership\u0026rsquo;s ability to align digital initiatives with structured knowledge-sharing and coordination mechanisms. Leadership appears to influence sustainability through both strategic direction and knowledge-based capability development.\u003c/p\u003e \u003cp\u003eOverall, the study advances understanding of digital transformation in public-sector healthcare by providing empirical evidence that leadership and knowledge processes jointly shape long-term sustainability outcomes. By integrating leadership theory with knowledge-based perspectives, the research offers a coherent explanatory model linking digital leadership, organizational learning mechanisms, and sustainable performance within institutional governance settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec56\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Theoretical Implications:\u003c/h2\u003e \u003cp\u003eThis study makes several substantive contributions to the literature on digital leadership, knowledge integration, and sustainable organizational performance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst\u003c/b\u003e, the study develops and empirically validates an integrated conceptual framework linking Digital Leadership to Sustainable Organizational Performance through the mediating role of Knowledge Integration. While prior research has examined digital leadership primarily in relation to innovation and digital transformation outcomes (Wang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), fewer studies have connected leadership capabilities to long-term sustainability performance, particularly within public-sector contexts (Nuryadin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By empirically demonstrating both direct and indirect effects, this study advances a capability-based explanation grounded in Dynamic Capabilities Theory (Teece, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Absorptive Capacity Theory (Cohen \u0026amp; Levinthal, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond\u003c/b\u003e, the findings clarify the structural role of Knowledge Integration as a complementary partial mediator in the leadership\u0026ndash;performance relationship. Rather than assuming that leadership directly produces sustainability outcomes, the results show that knowledge processes constitute a meaningful transmission mechanism. This contributes to theory by shifting attention from direct-effect models toward process-oriented explanations, addressing calls for incorporating structured mediating mechanisms in digital leadership research (Sutanto et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThird\u003c/b\u003e, the study contributes to sustainability scholarship by providing empirical support for a reinforcing logic across economic, social, and environmental performance domains. The positive effect of Knowledge Integration on overall Sustainable Organizational Performance suggests that coordinated knowledge processes may facilitate balanced value creation rather than necessitating trade-offs among sustainability pillars. This finding aligns with capability-based perspectives on integrated sustainability (Teece, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zahoor \u0026amp; Gerged, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFourth\u003c/b\u003e, the research extends digital leadership scholarship into an underexplored institutional setting: a public-sector healthcare organization within the Arab region. Much of the existing literature focuses on private-sector firms in Western or East Asian contexts (Wang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By examining the Abu Dhabi Department of Health, this study provides context-sensitive evidence highlighting how leadership and knowledge mechanisms operate within centralized governance structures characterized by regulatory complexity and hierarchical coordination.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFinally\u003c/b\u003e, by integrating Dynamic Capabilities Theory and Absorptive Capacity Theory within a unified empirical model, the study strengthens the theoretical bridge between leadership capabilities and organizational knowledge processes. The findings demonstrate that digital leadership contributes to sustainability not only through strategic orientation but also through its capacity to institutionalize knowledge integration mechanisms that enhance long-term organizational performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec57\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Practical and Policy Implications:\u003c/h2\u003e \u003cp\u003eThe findings of this study offer several practical and policy-relevant implications for public-sector institutions undergoing digital transformation, particularly within highly regulated healthcare environments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst\u003c/b\u003e, the significant direct effect of Digital Leadership on Sustainable Organizational Performance underscores the strategic importance of leadership development as a long-term institutional investment. Public healthcare organizations should embed digital leadership competencies within executive development frameworks, emphasizing strategic alignment, adaptive decision-making, and coordinated transformation efforts. Sustainable performance improvements are unlikely to emerge from technology deployment alone without leadership capable of orchestrating digital initiatives in alignment with organizational objectives.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond\u003c/b\u003e, the significant relationship between Digital Leadership and Knowledge Integration highlights the necessity of strengthening institutional knowledge-sharing infrastructures. Policymakers and senior administrators should prioritize the development of structured knowledge integration mechanisms, including cross-departmental coordination routines, collaborative digital platforms, and formal learning processes. Investments in digital infrastructure must be accompanied by governance mechanisms that ensure information is systematically shared, interpreted, and embedded into operational practices.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThird\u003c/b\u003e, the mediating role of Knowledge Integration indicates that sustainable performance gains are partially driven by structured knowledge processes. This suggests that digital transformation policies should extend beyond hardware and software acquisition toward organizational design reforms that enhance coordination, documentation, and institutional memory. In public-sector healthcare systems, where regulatory compliance and service quality are closely intertwined, integrated knowledge processes can reinforce operational efficiency, stakeholder trust, and environmental responsibility simultaneously.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFourth\u003c/b\u003e, the findings imply that economic, environmental, and social performance objectives need not be treated as competing priorities. When supported by effective leadership and coordinated knowledge processes, digital transformation can contribute to balanced and integrated sustainability outcomes. Policymakers should therefore adopt holistic digital governance strategies that align technological investment, leadership capability development, and knowledge management systems within a coherent institutional framework.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFinally\u003c/b\u003e, for policymakers in the Middle East and comparable centralized governance systems, the study provides empirical evidence that structured leadership development combined with institutionalized knowledge integration can serve as foundational pillars for sustainable digital transformation in public healthcare. The framework proposed in this study offers a scalable model for other governmental institutions seeking to translate digital transformation initiatives into measurable and sustained organizational performance outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec58\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Limitations:\u003c/h2\u003e \u003cp\u003eDespite the methodological rigor and robustness of the structural model, several limitations should be acknowledged.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst\u003c/b\u003e, the study is context-specific. Data were collected from a single public-sector healthcare organization within a centralized governance system. While this setting provides valuable insight into digital transformation within highly regulated environments, institutional characteristics may influence the strength of leadership\u0026ndash;knowledge\u0026ndash;performance relationships. Replication across different governance structures, including decentralized or hybrid systems, would enhance external validity and boundary condition specification.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond\u003c/b\u003e, although the model demonstrates high explanatory power, the research design remains bounded to a focused set of constructs derived from Dynamic Capabilities and Absorptive Capacity perspectives. Other organizational and institutional factors\u0026mdash;such as ethical climate, organizational culture, political dynamics, or public service motivation\u0026mdash;were not incorporated. Future studies may expand the framework to integrate additional contextual moderators or complementary mechanisms influencing sustainable organizational performance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThird\u003c/b\u003e, the study adopts a cross-sectional survey design based on self-reported perceptions. While statistical procedures, including bootstrapping and predictive assessments, strengthen internal reliability, cross-sectional data limit strong causal inference. Longitudinal or multi-wave research designs would allow for deeper examination of temporal dynamics and capability development processes over time.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFourth\u003c/b\u003e, although structured measures were adapted from validated prior scales, reliance on perceptual data may introduce common method considerations. Future research could incorporate multi-source data, objective performance indicators, or archival sustainability metrics to triangulate findings and further strengthen empirical robustness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec59\" class=\"Section2\"\u003e \u003ch2\u003e6.5 Directions for Future Research:\u003c/h2\u003e \u003cp\u003eBuilding on the identified limitations, several promising avenues for future research emerge.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst\u003c/b\u003e, longitudinal research designs would allow scholars to examine how digital leadership capabilities develop over time and how knowledge integration processes evolve as organizational routines mature. Such temporal investigations would strengthen causal inference and provide deeper insight into capability formation and sustainability trajectories within public-sector institutions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond\u003c/b\u003e, replication across diverse institutional environments is essential. Future studies may test the proposed framework in decentralized healthcare systems, education sectors, social services, and private-sector organizations to assess contextual stability and governance boundary conditions. Comparative cross-national research would further clarify how regulatory structures, public accountability mechanisms, and institutional logics shape leadership\u0026ndash;knowledge\u0026ndash;performance relationships.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThird\u003c/b\u003e, future research may extend the framework by incorporating additional behavioral and institutional conditioning variables. Recent public management scholarship highlights the importance of integrity climate, organizational citizenship behavior (OCB), and public service motivation in shaping performance dynamics (Saputra et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Integrating such constructs as moderators or complementary mediators would deepen understanding of how digital leadership capabilities become operationalized at the employee and team levels. Multi-level modeling approaches could further clarify how leadership-driven knowledge integration cascades across hierarchical layers within public healthcare systems.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFourth\u003c/b\u003e, scholars may explore potential asymmetries in sustainability dynamics by examining how knowledge integration differentially influences economic, environmental, and social performance under varying sectoral or regulatory conditions. Such investigations would contribute to ongoing debates regarding reinforcing versus trade-off logics in sustainability research and may reveal whether integrated knowledge processes reduce or amplify sustainability tensions in public institutions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFinally\u003c/b\u003e, given the rapid advancement of emerging digital technologies, future studies should investigate how developments in artificial intelligence, predictive analytics, and advanced data infrastructures reshape digital leadership practices and knowledge integration mechanisms. Understanding how evolving technological ecosystems interact with leadership agility and organizational capabilities will be critical for explaining the next phase of sustainable digital transformation in public-sector governance contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with institutional research standards at Cairo University, Egypt. As this research involved a non-clinical, survey-based design with voluntary participation and no sensitive personal data, formal ethical approval was not required. The exemption is consistent with the applicable institutional research practices at Cairo University. All procedures involving human participants were performed in line with internationally recognized ethical standards, including the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants prior to their participation in the study. Participation was voluntary, and respondents were assured of anonymity and confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdner R, Helfat CE. Corporate effects and dynamic managerial capabilities. Strateg Manag J. 2003;24(10):1011\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/smj.331\u003c/span\u003e\u003cspan address=\"10.1002/smj.331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlavi M, Leidner DE. Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Q. 2001;25(1):107\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3250961\u003c/span\u003e\u003cspan address=\"10.2307/3250961\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlboliteeh M, Alrashidi MS, Alrashedi N, Gonzales A, Mostoles RJ, Pasay-an E, Dator WL. Knowledge Management and Sustainability Performance of Hospital Organisations: The Healthcare Managers\u0026rsquo; Perspective. Sustainability. 2023;15(1):203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.3390/su15010203\u003c/span\u003e\u003cspan address=\".10.3390/su15010203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAudia N, Putera RE, Aromatica D, Bisri MBF. (2021). Capabilities of the city government in combating COVID-19 in the city of Padang. \u003cem\u003eE3S Web of Conferences, 331\u003c/em\u003e, 01009. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1051/e3sconf/202133101009\u003c/span\u003e\u003cspan address=\"10.1051/e3sconf/202133101009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwan U, Shamim S, Khan Z, Zia NU, Shariq SM, Khan MN. Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technol Forecast Soc Chang. 2021;168:120766. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.techfore.2021.120766\u003c/span\u003e\u003cspan address=\"10.1016/j.techfore.2021.120766\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal P, DesJardine MR. Business sustainability: It is about time. Strategic Organ. 2014;12(1):70\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1476127013520265\u003c/span\u003e\u003cspan address=\"10.1177/1476127013520265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenitez J, Henseler J, Castillo A, Schuberth F. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information Management. 2020;57(2):103168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.im.2019.103168\u003c/span\u003e\u003cspan address=\"10.1016/j.im.2019.103168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorah K, Ghosh B. Integrative knowledge processes and the triple bottom line performance. J Bus Ethics. 2022;178(2):327\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10551-020-04579-3\u003c/span\u003e\u003cspan address=\"10.1007/s10551-020-04579-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunner TJJ, Schuster T, Lehmann C. Leadership\u0026rsquo;s long arm: The positive influence of digital leadership on managing technology-driven change over a strengthened service innovation capacity. Front Psychol. 2023;14:988808. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2023.988808\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2023.988808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheah J, Amran A, Yahya S. Internal oriented resources and social enterprises\u0026rsquo; performance: How can social enterprises help themselves before helping others? J Clean Prod. 2019;211:607\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2018.11.203\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2018.11.203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Jiang C, Peng L, Zhao S, Chen C. Digital infrastructure construction and urban industrial chain resilience: Evidence from the Broadband China strategy. Sustainable Cities Soc. 2025;121:106228. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2025.106228\u003c/span\u003e\u003cspan address=\"10.1016/j.scs.2025.106228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen WM, Levinthal DA. Absorptive capacity: A new perspective on learning and innovation. Adm Sci Q. 1990;35(1):128\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2393553\u003c/span\u003e\u003cspan address=\"10.2307/2393553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Giudice M, Scuotto V, Garcia-Perez A, Petruzzelli AM. Shifting wealth II in Chinese economy: The effect of the horizontal technology spillover for SMEs for international growth. Technol Forecast Soc Chang. 2019;145:307\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.techfore.2018.03.013\u003c/span\u003e\u003cspan address=\"10.1016/j.techfore.2018.03.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEberl J, Drews P. Digital leadership\u0026mdash;Mountain or molehill? A literature review. Business Information Syst Engineering. 2021;63(3):353\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12599-020-00680-0\u003c/span\u003e\u003cspan address=\"10.1007/s12599-020-00680-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEikelenboom M, de Jong G. The impact of dynamic capabilities on the sustainability performance of SMEs. J Clean Prod. 2019;235:1360\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2019.07.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2019.07.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElkington J. Cannibals with forks: The triple bottom line of 21st century business. Oxford, UK: Capstone Publishing; 1997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFebriantina S, Rohzisky A, Rivalen M. Digital leadership: Challenges and opportunities in the age of technology. Doktrin: Jurnal Dunia Ilmu Hukum dan Politik. 2025;3(1):200\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59581/doktrin.v3i1.4537\u003c/span\u003e\u003cspan address=\"10.59581/doktrin.v3i1.4537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelin T, Foss NJ, Heimeriks KH, Madsen TL. Microfoundations of routines and capabilities: Individuals, processes, and structure. J Manage Stud. 2012;49(8):1351\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1467-6486.2012.01052.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-6486.2012.01052.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGold AH, Malhotra A, Segars AH. Knowledge management: An organizational capabilities perspective. J Manage Inform Syst. 2001;18(1):185\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07421222.2001.11045669\u003c/span\u003e\u003cspan address=\"10.1080/07421222.2001.11045669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrant RM. Toward a knowledge-based theory of the firm. Strateg Manag J. 1996;17(S2):109\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/smj.4250171110\u003c/span\u003e\u003cspan address=\"10.1002/smj.4250171110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuandalini I. Sustainability through digital transformation: A systematic literature review for research guidance. J Bus Res. 2022;148:456\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2022.05.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2022.05.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo X, Chen X. The impact of digital transformation on manufacturing-enterprise innovation: Empirical evidence from China. Sustainability. 2023;15(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su15043124\u003c/span\u003e\u003cspan address=\"10.3390/su15043124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Article 3124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHahn T, Figge F, Pinkse J, Preuss L. Trade-offs in corporate sustainability: You can\u0026rsquo;t have your cake and eat it. Bus Strategy Environ. 2010;19(4):217\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/bse.674\u003c/span\u003e\u003cspan address=\"10.1002/bse.674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF, Hult GTM, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). 3rd ed. Thousand Oaks, CA: Sage; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF, Ringle CM, Gudergan SP, Fischer A, Nitzl C, Menictas C. Partial least squares structural equation modeling-based discrete choice modeling: An illustration in modeling retailer choice. Bus Res. 2019;12(1):115\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40685-018-0072-4\u003c/span\u003e\u003cspan address=\"10.1007/s40685-018-0072-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanelt A, Bohnsack R, Marz D, Antunes Marante C. A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. J Manage Stud. 2021;58(5):1159\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/joms.12639\u003c/span\u003e\u003cspan address=\"10.1111/joms.12639\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart SL. A natural-resource-based view of the firm. Acad Manage Rev. 1995;20(4):986\u0026ndash;1014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5465/amr.1995.9512280033\u003c/span\u003e\u003cspan address=\"10.5465/amr.1995.9512280033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart SL, Dowell G. A natural-resource-based view of the firm: Fifteen years after. J Manag. 2011;37(5):1464\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0149206310390219\u003c/span\u003e\u003cspan address=\"10.1177/0149206310390219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelfat CE, Martin JA. Dynamic managerial capabilities: Review and assessment of managerial impact on strategic change. J Manag. 2015;41(5):1281\u0026ndash;312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0149206314561301\u003c/span\u003e\u003cspan address=\"10.1177/0149206314561301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenseler J. Partial least squares path modeling: Quo vadis? Quality Quantity. 2018;52(1):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11135-018-0689-6\u003c/span\u003e\u003cspan address=\"10.1007/s11135-018-0689-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalil S, Ghadiri SM. Balancing exploration and exploitation: How green knowledge integration drives supplier performance in sustainable supply chains. Logistics. 2025;9(4):176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/logistics9040176\u003c/span\u003e\u003cspan address=\"10.3390/logistics9040176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKludacz-Alessandri M, Hawrysz L, Żak K, Zhang W. The impact of digital transformational leadership on digital intensity among primary healthcare entities: A moderated mediation model. BMC Health Serv Res. 2025;25(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 117. https://doi.org/10.1186/s12913-025-12283-x\u003c/span\u003e\u003cspan address=\"Article 117. 10.1186/s12913-025-12283-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKock N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int J e-Collaboration. 2015;11(4):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4018/ijec.2015100101\u003c/span\u003e\u003cspan address=\"10.4018/ijec.2015100101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraus S, Jones P, Kailer N, Weinmann A, Chaparro-Banegas N, Roig-Tierno N. Digital transformation: An overview of the current state of the art of research. J Bus Res. 2022;138:889\u0026ndash;902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2021.09.051\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2021.09.051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrejcie RV, Morgan DW. Determining sample size for research activities. Educ Psychol Meas. 1970;30(3):607\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/001316447003000308\u003c/span\u003e\u003cspan address=\"10.1177/001316447003000308\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Wei X. The mediating role of knowledge integration in digital transformation for sustainability. Technol Forecast Soc Chang. 2022;175:121390. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.techfore.2021.121390\u003c/span\u003e\u003cspan address=\"10.1016/j.techfore.2021.121390\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLundin M, Nordstr\u0026ouml;m Skans O, Zetterberg P. Leadership experiences within civil organizations and candidacy in public elections: Causal evidence from a quasi-experimental approach. Polit Behav. 2016;38(2):433\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11109-015-9320-x\u003c/span\u003e\u003cspan address=\"10.1007/s11109-015-9320-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLundin M, Nordstr\u0026ouml;m Skans O, Zetterberg P. Leadership experiences, labor market entry, and early career trajectories. J Hum Resour. 2021;56(2):480\u0026ndash;511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3368/jhr.56.2.0617-8866R3\u003c/span\u003e\u003cspan address=\"10.3368/jhr.56.2.0617-8866R3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuthans F. Positive organizational behavior: Developing and managing psychological strengths. Acad Manage Perspect. 2002;16(1):57\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5465/ame.2002.6640181\u003c/span\u003e\u003cspan address=\"10.5465/ame.2002.6640181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaletic M, Maletic D, Dahlgaard JJ, Dahlgaard-Park SM, Gomišcek B. Do corporate sustainability practices enhance organizational economic performance? Int J Qual Service Sci. 2015;7(2\u0026ndash;3):184\u0026ndash;200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/IJQSS-02-2015-0025\u003c/span\u003e\u003cspan address=\"10.1108/IJQSS-02-2015-0025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatarazzo M, Penco L, Profumo G, Quaglia R. Digital transformation and customer value creation in made in Italy SMEs: A dynamic capabilities perspective. J Bus Res. 2021;123:642\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2020.10.033\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2020.10.033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMergel I, Edelmann N, Haug N. Defining digital transformation: Results from expert interviews. Government Inform Q. 2019;36(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 101385. https://doi.org/10.1016/j.giq.2019.06.002\u003c/span\u003e\u003cspan address=\"Article 101385. 10.1016/j.giq.2019.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMollah MA, Ibrahim A, Masud A, Chowdhury MS. How does digital leadership boost competitive performance? The role of digital culture, affective commitment, and strategic agility. Heliyon. 2024;10(23):e40839. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e40839\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e40839\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNonaka I, Toyama R. The knowledge-creating company. Harvard Business Rev. 2007;85(7\u0026ndash;8):162\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNuryadin R, Sobandi A, Santoso B. Digital Leadership in the Public Sector: Systematic literature review. Jurnal Ilmu Administrasi: Media Pengembangan Ilmu dan Praktek Administrasi. 2023;20(1):90\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31113/jia.v20i1.934\u003c/span\u003e\u003cspan address=\"10.31113/jia.v20i1.934\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkhuysen GA, Eisenhardt KM. Integrating knowledge in groups: How formal interventions enable flexibility. Organ Sci. 2002;13(4):370\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1287/orsc.13.4.370.2947\u003c/span\u003e\u003cspan address=\"10.1287/orsc.13.4.370.2947\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey J, Majumdarr S, Hassan Y, Benuyenah V. Role of digital leadership capability in shaping IT innovation: A digital agility perspective. J Global Inform Manage. 2023;31(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 333168. https://doi.org/10.4018/JGIM.333168\u003c/span\u003e\u003cspan address=\"Article 333168. 10.4018/JGIM.333168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorter ME, Kramer MR. Creating shared value. Harvard Business Rev. 2011;89(1\u0026ndash;2):62\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen L, Deng S, Men L, Boudouaia A. (2025). A study on factors shaping innovative work behavior and service innovation performance in government sectors: Role of digital leadership and dynamic capabilities. Humanities and Social Sciences Communications, 12, Article 1076. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1057/s41599-025-05378-7\u003c/span\u003e\u003cspan address=\"10.1057/s41599-025-05378-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuvio A, Rosenblatt Z, Hertz-Lazarowitz R. Entrepreneurial leadership vision in nonprofit vs. for-profit organizations. Leadersh Q. 2010;21(1):144\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.leaqua.2009.10.011\u003c/span\u003e\u003cspan address=\"10.1016/j.leaqua.2009.10.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacav\u0026eacute;m A, de Bem Machado A, dos Santos JR, Palma-Moreira A, Belchior-Rocha H, Au-Yong-Oliveira M. Leading in the Digital Age: The Role of Leadership in Organizational Digital Transformation. Administrative Sci. 2025;15(2):43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/admsci15020043\u003c/span\u003e\u003cspan address=\"10.3390/admsci15020043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafitri C, Valentina TR, Wicaksono A. (2021). Forms of government collaboration at the local level in handling the COVID-19 disaster. \u003cem\u003eE3S Web of Conferences, 331\u003c/em\u003e, 02003. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1051/e3sconf/202133102003\u003c/span\u003e\u003cspan address=\"10.1051/e3sconf/202133102003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamoili S, L\u0026oacute;pez Cobo M, G\u0026oacute;mez E, De Prato G, Martinez-Plumed F. AI watch: Defining artificial intelligence. European Commission; 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2760/382730\u003c/span\u003e\u003cspan address=\"10.2760/382730\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Joint Research Centre.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantoro G, Ferraris A, Adrodegari F. Knowledge integration and sustainability in innovative organizations. J Knowl Manage. 2023;27(1):45\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/JKM-09-2022-0694\u003c/span\u003e\u003cspan address=\"10.1108/JKM-09-2022-0694\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaputra N, Putera RE, Zetra A, Azwar A, Valentina TR, Mulia RA. Capacity building for organizational performance: A systematic review, conceptual framework, and future research directions. Cogent Business Management. 2024;11(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23311975.2024.2434966\u003c/span\u003e\u003cspan address=\"10.1080/23311975.2024.2434966\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaputra N, Putera RE, Zetra A, Azwar A, Valentina TR, Mulia RA. Enhancing public management through integrity and organizational citizenship behavior: A systematic review. Int J Public Adm. 2026;1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01900692.2026.2614578\u003c/span\u003e\u003cspan address=\"10.1080/01900692.2026.2614578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunders M, Lewis P, Thornhill A. Research methods for business students. 8th ed. Pearson Education; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimion Ludușanu D-G, Fertu D-I, Tinică G, Gavrilescu M. Integrated Quality and Environmental Management in Healthcare: Impacts, Implementation, and Future Directions Toward Sustainability. Sustainability. 2025;17(11):5156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su17115156\u003c/span\u003e\u003cspan address=\"10.3390/su17115156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutanto EM, Asbari M, Purwanto A, Santoso PB. (2024). Digital leadership and innovation capability: The mediating role of knowledge integration. \u003cem\u003eKnowledge Management Research\u003c/em\u003e \u0026amp; \u003cem\u003ePractice\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14778238.2023.2293656\u003c/span\u003e\u003cspan address=\"10.1080/14778238.2023.2293656\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSyamsir S, Saputra N, Mulia RA. Leadership agility in a VUCA world: A systematic review, conceptual insights, and research directions. Cogent Business Management. 2025;12(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23311975.2025.2482022\u003c/span\u003e\u003cspan address=\"10.1080/23311975.2025.2482022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeece DJ. Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strateg Manag J. 2007;28(13):1319\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/smj.640\u003c/span\u003e\u003cspan address=\"10.1002/smj.640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeece DJ. Business models and dynamic capabilities. Long Range Plann. 2018;51(1):40\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lrp.2017.06.007\u003c/span\u003e\u003cspan address=\"10.1016/j.lrp.2017.06.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeece DJ, Pisano G, Shuen A. Dynamic Capabilities and Strategic Management. Strateg Manag J. 1997;18(7):509\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.jstor.org/stable/3088148\u003c/span\u003e\u003cspan address=\"http://www.jstor.org/stable/3088148\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTemelkova M. Model of the relation digital leadership\u0026ndash;digital entrepreneurship in the realities of Industry 4.0. Int J Inform Theor Appl. 2019;26(4):324\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTigre S, Curado C, Henriques PL. Digital leadership: A systematic literature review and future research agenda. Small Bus Econ. 2023;61(3):1023\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11187-023-00723-1\u003c/span\u003e\u003cspan address=\"10.1007/s11187-023-00723-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValentina TR, Putera RE, Salsabila L. Collaborative governance in handling the waste crisis: A systematic literature review. Int J Sustainable Dev Plann. 2025;20(2):761\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18280/ijsdp.200225\u003c/span\u003e\u003cspan address=\"10.18280/ijsdp.200225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Roekel H, Branderhorst M, Tummers L, Meijer A. Digital transformation leadership: A public value-centered measurement scale. Government Inform Q. 2025;42(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 102091. https://doi.org/10.1016/j.giq.2025.102091\u003c/span\u003e\u003cspan address=\"Article 102091. 10.1016/j.giq.2025.102091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVial G. Understanding digital transformation: A review and a research agenda. J Strateg Inf Syst. 2019;28(2):118\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jsis.2019.01.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jsis.2019.01.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Chen L, Jia F, Luo Y, Zhang Z. The relationship between supply chain integration and sustainability performance: a meta-analysis. Int J Logistics Res Appl. 2024;27(8):1388\u0026ndash;409. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13675567.2022.2144812\u003c/span\u003e\u003cspan address=\"10.1080/13675567.2022.2144812\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang T, Lin X, Sheng F. Digital leadership and exploratory innovation: From the dual perspectives of strategic orientation and organizational culture. Front Psychol. 2022;13:902693. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2022.902693\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2022.902693\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatson RT, Boudreau M-C, Chen AJ. Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Q. 2010;34(1):23\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/20721413\u003c/span\u003e\u003cspan address=\"10.2307/20721413\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong CWY. Leveraging environmental information integration to enable environmental management capability and performance. J Supply Chain Manage. 2013;49(2):114\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jscm.12005\u003c/span\u003e\u003cspan address=\"10.1111/jscm.12005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Mamun A, A., Salameh AA. Leadership, capability and performance: A study among private higher education institutions in Indonesia. Heliyon. 2023;9(1):e13026. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2023.e13026\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e13026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahoor N, Gerged AM. Relational capital, environmental knowledge integration, and environmental performance of small and medium enterprises in emerging markets. Bus Strategy Environ. 2021;30(8):3789\u0026ndash;803. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/bse.2840\u003c/span\u003e\u003cspan address=\"10.1002/bse.2840\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahra SA, George G. Absorptive capacity: A review, reconceptualization, and extension. Acad Manage Rev. 2002;27(2):185\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5465/amr.2002.6587995\u003c/span\u003e\u003cspan address=\"10.5465/amr.2002.6587995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital leadership, Knowledge integration, Sustainable Organizational performance, Dynamic capabilities, Absorptive Capacity, Public sector, Abu Dhabi","lastPublishedDoi":"10.21203/rs.3.rs-9106807/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9106807/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmid accelerating digital transformation, public healthcare organizations face mounting pressure to achieve sustainable organizational performance (SOP) that balances economic efficiency, social responsibility, and environmental dimensions of the triple-bottom-line framework. Although digital leadership has attracted increasing scholarly attention, prior research has predominantly emphasized technological adoption and short-term operational outcomes, offering limited insight into the internal organizational mechanisms through which digital leadership translates into sustained triple-bottom-line performance particularly within public-sector healthcare systems.\u003c/p\u003e \u003cp\u003eDrawing on Dynamic Capabilities Theory and Absorptive Capacity Theory, this study develops and tests a mechanism-based model in which knowledge integration mediates the relationship between digital leadership and sustainable organizational performance. Digital leadership is conceptualized as an integrated strategic capability that enables organizations to mobilize digital resources and orchestrate knowledge processes effectively. Sustainable organizational performance was operationalized as an overall composite construct reflecting the integrated economic, social, and environmental dimensions of the triple-bottom-line framework.\u003c/p\u003e \u003cp\u003eData were collected from 359 managers and professionals at the Abu Dhabi Department of Health using a proportionate stratified random sampling technique. The proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM).\u003c/p\u003e \u003cp\u003eThe results indicate that digital leadership has a significant positive direct effect on sustainable organizational performance (β\u0026thinsp;=\u0026thinsp;0.682, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a strong positive effect on knowledge integration (β\u0026thinsp;=\u0026thinsp;0.897, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Knowledge integration also exerts a significant positive effect on sustainable organizational performance (β\u0026thinsp;=\u0026thinsp;0.231, p\u0026thinsp;=\u0026thinsp;0.001). Mediation analysis confirms a complementary partial mediating role of knowledge integration (indirect β\u0026thinsp;=\u0026thinsp;0.207, p\u0026thinsp;=\u0026thinsp;0.001). The model demonstrates substantial explanatory power (R\u0026sup2; \u0026asymp; 0.80) and positive out-of-sample predictive relevance, indicating robust structural validity.\u003c/p\u003e \u003cp\u003eThese findings suggest that sustainable performance in public healthcare organizations depends not only on digital transformation initiatives themselves but on leadership-driven knowledge integration mechanisms that translate strategic digital orientation into coordinated institutional action. By offering a capability-based explanation of how digital leadership contributes to sustainable organizational performance, this study advances digital leadership scholarship beyond efficiency-centered perspectives and provides empirical evidence from an underexplored governmental healthcare context in the Arab region.\u003c/p\u003e","manuscriptTitle":"Digital Leadership Driving Sustainability through Knowledge Integration in Public Healthcare Organizations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 10:47:32","doi":"10.21203/rs.3.rs-9106807/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:29:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T02:44:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155611488582489077287990637057336617695","date":"2026-04-29T22:39:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T12:34:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147229590832160008159253066954903540047","date":"2026-04-26T04:50:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32036252891915001019178712384322780965","date":"2026-04-23T20:03:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30768662431024322941943750023225245184","date":"2026-04-23T16:19:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46841993191331885853050234186873194242","date":"2026-04-23T16:05:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105000304850839454105640811359011881590","date":"2026-04-21T19:34:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T11:58:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-27T12:19:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T16:52:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-03-24T16:47:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6eab046f-e090-461f-bcdf-42ca7c7ee0b2","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:29:54+00:00","index":61,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T02:44:11+00:00","index":60,"fulltext":""},{"type":"reviewerAgreed","content":"155611488582489077287990637057336617695","date":"2026-04-29T22:39:12+00:00","index":59,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T12:34:47+00:00","index":58,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T10:47:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 10:47:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9106807","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9106807","identity":"rs-9106807","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}