Driving Sustainability Performance in Hotels Through Green Digital Leadership and Circular Economy: The Moderating Role of Hotel Green Efficacy

Abstract

This study examines the role of green digital transformational leadership (GDTL) in enhancing sustainability performance in the hotel industry through the mediating mechanism of circular economy (CE) practices and the moderating effects of otel green efficacy (HGE). Grounded in the dynamic capabilities theory, natural resource-based view (NRBV) theory, and social exchange theory, a novel conceptual model that bridges digital innovation, ecological stewardship, and organizational psychology was proposed. The study adopted a quantitative approach and used a self-administered questionnaire survey to collect data from 402 employees across green-certified hotels in Sharm El-Sheikh, Egypt. Participants were recruited using a stratified sampling method to ensure sectoral representation. Data analysis techniques included performing partial least squares structural equation modeling (PLS-SEM) using Smart PLS 3.0. Key findings reveal that GDTL directly influences the three key aspects of sustainability performance in hotels, including environmental, economic, and social aspects. Likewise, CE practices significantly mediate the linkage between GDTL and hotel sustainability performance. Notably, HGE strengthens the GDTL-CE relationship, underscoring the critical role of employee empowerment in translating leadership vision into regenerative practices. These results add to the growing literature on sustainable leadership by revealing how digital tools like AI, blockchain, and closed-loop systems can synergize to support economic growth and conserve natural resources.

1. Introduction

The hotel industry has always played a fundamental role in driving economic growth and creating millions of job opportunities around the world. However, the ongoing growth of the hospitality industry has increasingly threatened environmental sustainability. With approximately 68% of international travelers nowadays preferring to stay at eco-certified accommodation facilities [1], the hotel industry seeks to align its operations with eco-friendly initiatives and sustainable goals [2]. Although the hospitality sector contributes 1% of global carbon emissions and consumes about 15–20% of commercial energy [3,4], its ecological footprint goes further to include significant strain on water resources and the depletion of ecosystems, particularly in certain regions in developing economies that mainly depend on tourism and hospitality [5,6].

Despite the growing awareness and need for more sustainable hospitality operations, achieving system-wide sustainability in the hotel industry remains elusive. This can be attributed to various administrative barriers, including fragmented leadership approaches, underdeveloped operational frameworks, and limited organizational confidence in green innovation [7,8,9]. In this context, green digital transformational leadership (GDTL) has emerged as a promising paradigm that integrates environmental stewardship with digital innovation. By leveraging advanced technologies such as AI-driven analytics, IoT-enabled resource optimization, and blockchain-enabled transparency in supply chains, GDTL has the potential to reshape leadership as a strategic driver of eco-friendly operations [10,11,12]. In the same vein, circular economy (CE) practices—such as closed-loop systems, resource regeneration, and waste-to-value initiatives—appeared to be promising techniques enabling GDTL to transform into improved sustainability outcomes [13,14]. Another relevant concept to this context is the organizational green efficacy which refers to an organization’s confidence in its ability to implement and maintain green practices [7,9,15]. Together, these factors can potentially lead to satisfactory outcomes in relation to the sustainability performance of hotels.

This study addresses a critical gap in sustainable leadership literature by interrogating the underexplored nexus between green digital transformational leadership (GDTL), circular economy (CE) practices, and sustainability outcomes in hospitality service-oriented sectors. While prior research [11,16] has validated GDTL’s role in manufacturing settings—where CE practices like remanufacturing and industrial symbiosis are well documented—the service sector’s unique dynamics remain overlooked. For instance, hotels face challenges such as intangible service delivery, high customer–employee interaction, and perishable resource flows (e.g., food waste, and energy-intensive amenities), which demand distinct CE strategies compared to material-centric manufacturing. Recent studies [17,18] highlight this disparity, noting that hospitality’s reliance on linear “take-make-dispose” models persists due to fragmented leadership frameworks and a lack of sector-specific CE operationalization. By contrasting manufacturing’s closed-loop systems with hospitality’s service heterogeneity, this study pioneers a contextualized understanding of GDTL’s role in bridging digital innovation (e.g., AI, IoT) and regenerative practices—a gap exacerbated by the absence of empirical evidence from developing economies like Egypt. Drawing on dynamic capabilities theory, the natural resource-based view (NRBV), and social exchange theory, the present study seeks to address these gaps and investigate these variables in a conceptual framework [19,20,21].

Moreover, the significance of this research is underscored by Egypt’s tourism-dependent economy, where hospitality contributes 12% of GDP and faces existential threats from climate change, water scarcity, and energy inefficiency [22]. The ecological footprint of the hotel sector in Egypt—accounting for 15–20% of national commercial energy use [23]—demands immediate alignment with the UN Sustainable Development Goals (SDGs), notably SDG 12 (Responsible Consumption) and SDG 13 (Climate Action). Failure to adopt GDTL and CE practices risks exacerbating resource depletion in a region already grappling with water stress [5] and threatens Egypt’s competitiveness as 68% of global travelers now prefer eco-certified accommodations [1]. Specifically, Egypt currently hosts 183 green-certified hotels, offering around 58,000 rooms across 17 domestic tourist destinations. In this context, Sharm El Sheikh accounts for 42.6% of these certified properties, with 78 hotels holding green certification. These certifications are awarded through the Green Star Hotel (GSH) program, Egypt’s official sustainability certification initiative, administered by the Egyptian Hotel Association (EHA) under the oversight of the Ministry of Tourism and Antiquities [24].

This study pursues a triple analytical focus to advance a scholarly and practical understanding of sustainability in the hospitality sector. First, within the environmental dimension, this study empirically evaluates how green digital transformational leadership (GDTL) operationalizes circular economy (CE) practices—such as IoT-enabled water recycling systems and AI-optimized energy management—to mitigate carbon emissions and resource depletion. By investigating the efficacy of digital–ecological synergies, this objective examines the mechanisms through which CE transitions decouple hotel operations from linear “take-make-waste” paradigms. Second, under the economic dimension, this study rigorously quantifies the fiscal implications of CE adoption, including cost savings from waste-to-value initiatives (e.g., repurposing organic waste into bioenergy) and revenue growth through circular procurement strategies (e.g., sourcing refurbished furnishings). This analysis extends beyond anecdotal claims, offering empirical evidence for how CE-driven resource efficiency enhances financial resilience while aligning global sustainability benchmarks. Third, within the social dimension, this study investigates the sociocultural dynamics of hotel green efficacy (HGE), assessing its role in fostering community engagement (e.g., local partnerships for material recovery) and advancing equitable labor practices (e.g., upskilling employees in green–digital competencies). By situating HGE as a mediator of leadership and grassroots sustainability behaviors, these objective bridges organizational strategy with human-centric outcomes, addressing calls for inclusive transitions in service industries. Collectively, these objectives offer a holistic framework to dissect how digital leadership, regenerative economics, and workforce empowerment converge to redefine sustainability in resource-intensive sectors.

This study is structured as follows: Section 2 synthesizes literature on GDTL, CE, and HGE to develop hypotheses; Section 3 details the PLS-SEM methodology; Section 4 presents empirical results; Section 5 discusses theoretical and practical implications; and concludes the limitations and future research directions.

2. Literature and Hypotheses

2.1. Green Digital Transformational Leadership (GDTL)

Green digital transformational leadership (GDTL) is an essential paradigm that simultaneously addresses the dual requirements of digital innovation and environmental sustainability [16]. Green transformational leadership (GTL) was found to enhance sustainability performance through green work engagement [9]. However, GTL alone does not serve well in organizations with limited resources. For example, Ref. [13] found that GTL affects green HR practices and claimed that external enablers such as policy alignment and technological infrastructure are necessary to leverage sustainability performance. In the same vein, the variability demonstrated by leadership calls for a more integrative leadership model that deals with the gap between ecological vision and technological execution. The transition from GTL to GDTL addresses this gap by incorporating digital fluency with the sustainability demands [16].

The theoretical underpinning of GDTL rests on its capacity to normalize the transformational leadership’s motivational principles and digital and ecological competencies [16]. GDTL is built upon the stimuli–organism–response (SOR) framework, which explains how environmental factors impact human behavior [25] and is grounded in pressures from external forces (e.g., regulatory mandates or stakeholders’ demands) reforming digital tools (e.g., AI, IoT) and human capital to support organizational flexibility to encounter such pressures [12,26]. Furthermore, this coincides with the natural resource-based view (NRBV), particularly eco-innovation and resource efficiency, whereby GDTL is a strategic asset supporting competitive advantage [19,20,27,28].

Green digital transformational leadership (GDTL) is conceptualized through the lens of dynamic capabilities theory [20], which posits that organizational agility hinges on the adaptive reconfiguration of resources. In the hospitality domain, GDTL manifests as leaders’ capacity to integrate digital tools (e.g., AI-driven energy analytics, IoT-enabled waste tracking) with ecological stewardship, fostering resilience against sustainability challenges [29]. For instance, IoT sensors in Egyptian hotels reduced water consumption by 30% by aligning leadership vision with real-time resource data [17], exemplifying GDTL’s role in operationalizing adaptive strategies.

GDTL has already been implemented in manufacturing settings, such as closed-loop systems in which automotive suppliers save 15–20% of material costs in remanufacturing [11]. Pharmaceutical companies have also employed IoT sensors to monitor solvent usage, which reduces 30–40% of toxic waste and meets EU eco-innovation standards [30]. Yet, these applications also demonstrate how circular economic principles have been operationalized in different sectors through GDTL. For example, AI enables hospitality to reduce energy consumption by 25% thanks to energy powered by AI [6]. Meanwhile, blockchain ensures traceability in the sustainable sourcing of goods and align leadership vision with operational execution as proved by a pilot performed by the Radisson Hotel Group [31].

Nevertheless, there are contradictions in the implementation of GDTL [32]. The persistent challenge of the digital paradox is that the environmental costs of technologies such as blockchain exceed CE gains [33]. This calls for an ethical framework for GDTL [16]. Moreover, cultural resistance complicates adoption, particularly in hierarchical organizations, where GDTL adoption through the top–down approach may hinder sustainability initiatives [30]. These challenges provide scope for the boundary conditions of GDTL, where its effectiveness relies on such contextual factors as leadership, institutional support, resource availability, and cultural adaptability [34].

2.2. Green Digital Transformational Leadership and Sustainability Performance

The need to find leadership models that allow high levels of economic growth while at the same time reconciling it with planetary boundaries is “a rare coin”. There has always been an intensified examination of such leadership models that can enable systemic sustainability transitions across the world [35]. Green digital transformational leadership (GDTL) as a next-generation leadership intervention, falls at the crossroads of the Fourth Industrial Revolution and climate urgency as an attempt to reimagine conventional leadership by infusing digital innovation and green ethics in organizational strategies [36]. GDTL integrates transformational leadership with leaders’ competency in green innovation and digital technologies, becoming a cornerstone of the UN Sustainable Development Goals [37,38]. An example of such GDTL leaders is those who employ artificial intelligence (AI) to optimize energy consumption in manufacturing and blockchain to improve and sustain the supply chain [39,40]. The duality between technology use and sustainability represents one of the fundamental debates in sustainability discourses [41]. How businesses can maintain sustainability in the presence of digitalization has been an “unanswered question” for a long time. GDTL can be the answer to this dilemma as it takes advantage of technologies such as data centers and blockchain networks, which are one of the most energy-intensive [42], and reframes them as instruments for circularity, such as using IoT sensors to monitor resource flows in closed loops [43].

The theoretical basis of GDTL stems from the Ellen MacArthur Foundation’s circular economy (CE) framework [44], which encourages the decoupling of economic growth from resource depletion using regenerative design [45]. The alignment of organizations’ digital tools with CE principles allows organizations to go beyond incremental efforts towards sustainability [46]. For instance, predictive maintenance algorithms reduced waste by 30–50% in automotive manufacturing [47]. However, such potential depends on developing a green digital mindset among employees, which transforms sustainability from compliance to a shared value [38]. Empirical studies indicate that leaders of firms with GDTL practices demonstrate 25% higher employee engagement in sustainability initiatives [48].

Nevertheless, GDTL has an uneven impact on SP across sectors and geography. For instance, digital twins are applied in heavy industries like automotive manufacturing to create simulations of circular production, thereby implementing material use at 5% lower levels [49]. On the contrary, SMEs in developing countries do not possess adequate funds to purchase advanced tools, thus opting for limited innovation, such as cloud-based sustainability dashboards [50]. Additionally, as GDTL allows for reporting that enhances corporate reputation, other frameworks often omit social equity metrics such as fair wages in circular supply chains [51]. An example of this gap is displayed with the textile industry, where progress in recycled fabrics reveals persistent labor rights violations in recycling facilities [52]. Based on this literature, GDTL is seen as a transformative power for sustainability, balancing ecological and digital innovation. Therefore, the following hypothesis can be posited:

H1: 

Green digital transformational leadership significantly influences sustainability performance (H1a → economic performance (EP), H1b → environmental performance (EVP), H1c → social performance (SP)).

2.3. Green Digital Transformational Leadership and the Circular Economy

Circular economy (CE) practices are framed by the natural resource-based view [19,53], which positions eco-innovation as a strategic asset for competitive advantage. CE adoption in hotels—such as blockchain-tracked ethical sourcing [31] and closed-loop water recycling systems—transforms linear resource flows into regenerative cycles. A 2024 study demonstrated that hotels adopting CE-aligned procurement achieved 25% cost savings while enhancing brand reputation [54], underscoring the NRBV’s relevance in service industries. Circular economy has evolved because of the global shift from linear “take-make-waste” economic models to regenerative circular systems as a leverage to boost sustainability [55]. At the same time, green digital transformational leadership (GDTL) emerged as a leadership paradigm that can operationalize the CE principle by blending digital innovation and environmental stewardship [16]. Transformational leadership’s visionary tenet interconnects with digital competencies, such as AI-driven resource optimization and blockchain-based supply chain transparency, to create an organizational culture where sustainability is beyond compliance and becomes a shared value [38,56]. Leaders of GDTL embrace a ‘green digital’ mindset and align daily operations with regenerative practices, which are significant to waste reduction and a closed-loop system. Empirical studies demonstrate that GDTL-driven firms can reduce material waste by up to 40% by incentivizing product redesign for disassembly in textile, automotive, and other product sectors [11,57,58].

To realize CE, it is necessary to harmonize technological, cultural, and strategic shifts, which are rooted domains in GDTL [59]. In other words, initiatives for GDTL support driving individual employee goals towards organizational sustainability targets to increase engagement in CE practices such as energy recovery and renewable material adoption [60,61]. Industry 4.0 technologies increase GDTL’s footprint further by extending product lifecycles of the automotive industry by up to 30–50%, through AI predictive maintenance and accounting for circular supply chains by rolling out blockchain, thereby mitigating the risks associated with greenwashing [6,12]. Cross-sector collaborations, such as textile manufacturer–recycler partnerships under GDTL guidance, have increased recycled fabric use by 25%, closing material loops and boosting resale revenues [62].

Opportunities and disparities are also seen in sector-specific applications. GDTL makes it easy for heavy industries to create smart factories that run on digital twins to simulate circular production by reducing raw material use by 35% [63]. On the other hand, small and medium enterprises (SMEs) from emerging economies have structural barriers. For example, Bangladeshi SMEs use fintech innovations to obtain green financing for biodegradable packaging, while limited resources often hinder the adoption of advanced technology [13,17]. CE success was apparent in many examples, e.g., water recycling systems utilizing IoT that have helped recycle the wastewater of hospitality sectors [64] and the reduction of single-use plastic by 50% [54]. Similarly, customers pressured hotels to use energy-efficient workflows and renewable materials, enhancing the hotels’ sustainability performance to become 15–25% higher than before [65].

CE adoption has many universal challenges. For example, being digital, the CE represented by GDTL is facilitated through technologies such as blockchain and AI [66]. These technologies’ environmental footprints, such as energy-intensive data centers, could offset any improvements in sustainability from GDTL if not powered by renewables [12,67]. Another challenge is the substantial investment needed to implement and take advantage of GDTL and CE linkage [68]. That is why SMEs and developing economies still rely on manual waste management and a fragmented GDTL_CL policy [9]. Also, many additional CE frameworks are missing social elements, such as fair labor practices in recycling systems [69]. That causes a complex measurement. For example, some hotels mostly focus on cost savings under sloppy regulations, whereas CE compliance policies work properly in other hotels [7,70]. In similar vein, AI-optimized HVAC systems in Sharm El-Sheikh hotels reduced energy costs by 25% [17], while blockchain-enabled supply chains ensured 85% traceability of sustainable materials in Radisson Group properties [31]. These examples illustrate how digital leadership translates strategic vision into CE operationalization. This work contextualizes GDTL as a trigger for CE, where economic growth and global concern for sustainability are interrelated. Accordingly, this study assumes the following hypothesis:

H2: 

Green digital transformational leadership significantly influences circular economy.

2.4. Circular Economy and Sustainability Performance

Switching to a circular economy (CE) from a linear “take make waste” economic model is a great step to move towards physical sustainability performance (SP), which is multidimensional (environmental stewardship, economic resilience, and social equity) [71]. Furthermore, CE principles such as resource efficiency, waste reduction, and closed-loop concepts align with global sustainability agendas like the United Nations Sustainable Development Goals [72]. Nevertheless, CE and SP links are complex and context-dependent, shaped by sector-specific challenges, technological enablers, and institutional frameworks [73]. CE is conceived of as a regenerative system that decouples economic growth from resource depletion by allowing for reuse and remanufacturing [60], but implementation is heterogeneous across industries and regions [74]. For example, CE is used in smart factories along with AI-driven predictive maintenance for manufacturing sectors to achieve 30–50% material waste reduction [58], whereas SMEs in developing economies adopt grassroots innovations of cloud-based sustainability dashboards as they cannot afford the high cost of advanced technologies [69].

Even though the social component of SP, i.e., equitable labor practices and community engagement, is widely overlooked [75], CE initiatives in Bangladesh have raised worker safety in recycling facilities [76]. However, wage gaps continued underlying the need for an integrated framework that balances technological progress with social equity [69]. It is evident that water recycling systems in Indian hotels decreased consumption by 30% [64], and upcycled furnishings generated 20% revenue growth in luxury resorts (Sailesh, 2024), affirming CE’s alignment with the triple bottom line. This understanding lays the foundation for the following hypothesis.

H3: 

Circular economy has a significant impact on sustainability performance (H3a → economic performance (EP), H3b → environmental performance (EVP), H3c → social performance (SP)).

2.5. Circular Economy as a Mediator

Leadership smooths the path from sustainable business models by harmonizing digital innovation with ecological stewardship [77]. GDTL uses this combination of digital tools to operate CE practices, achieving substantial environmental, economic, and social improvement [16]. For example, the IoT digitalized water recycling in hotels’ ecosystems and reduced after consumption rates by 30% [19,78]. Also, SME firms had realized measurable SP gains through CE-mediated digital adoption [79].

The contradictions of the GDTL-CE-SP nexus are abundant. Despite the digital tools enabling transparency and efficiency, their environmental costs, such as the energy demands of blockchain and data centers, risk offsetting CE’s benefits [80]. This contradiction brings to light the need for context-sensitive technological adoption frameworks that aim to balance ethical values and those of technology [81].

Moreover, sectoral dynamics further complicate this relationship. Under such service industries as tourism, GDTL-supported initiatives like zero-waste hospitality programs can improve community engagement and brand loyalty [16]. In contrast, heavy industries often prioritize technological scalability over social equity, illuminating tensions between efficiency and inclusiveness [82]. Such contrasts highlight the importance of refining mediation models to explain sector differences in terms of barriers and opportunities. Thereby, the following hypothesis can be posited:

H4: 

Circular economy mediates the relationship between green digital transformational leadership and sustainability performance ((H4a → economic performance (EP), H4b → environmental performance (EVP), H4c → social performance (SP)).

2.6. The Moderating Effect of Green Efficacy

Leadership is vitally required for transition to a circular economy (CE), a regenerative system prioritizing resource efficiency, waste reduction, and closed-loop production [38]. It harmonizes sustainability with digital innovation to make this regenerative system the first choice [83]. Green digital transformational leadership (GDTL) is a key enabler of this transition by combining the vision of transformational leadership and abilities in green practices and digital technologies [12,16]. Nevertheless, the effectiveness of GDTL in improving CE depends on contextual factors, including employees’ green efficacy (GE) [18]. GE refers to employees’ confidence in performing actions for environmental goals through individual and collective approaches [84]. This interplay between leadership, technology, and employee empowerment reflects the psychological and organizational dynamics that drive sustainable transformation [85].

Green efficacy (GE), rooted in social cognitive theory (Bandura, 1986), reflects employees’ confidence in executing green practices. This construct bridges leadership intent and grassroots action, as evidenced by gamified HGE training programs in Pakistani hotels, which increased staff participation in composting initiatives by 40% [18]. Bandura’s triadic reciprocity model—encompassing personal, behavioral, and environmental factors—explains how HGE empowers employees to overcome cultural resistance to CE transitions.

GDTL stands to redefine leadership in creating green digital cultures that promote sustainability in making decisions [86]. This type of leadership leverages advanced technologies—such as AI-driven predictive maintenance [87], IoT-enabled resource tracking and traceability [88], as well as blockchain for the transparency of the supply chain [89]—to optimize the flows of resources and work through closed-loop systems [6]. Firms following GDTL principles are 40 percent more likely to adopt circular business models, like product-as-a-service systems and remanufacturing, than those adopting conventional leadership practices [90]. This alignment between digital tools and environmental ethics leads to developing a green digital mindset among employees, which helps organizations more effectively practice CE [16].

Therefore, GDTL stimulates stakeholder collaboration and utilizes digital enablers that extend product lifecycles [91]. However, the effectiveness of such strategies depends on employees’ Green Efficacy, bridging the gap between the intent of the leadership and the actual practice [92]. Rooted in social cognitive theory [93], GE empowers employees to innovate eco-friendly products or optimize energy-efficient processes that strengthen CE outcomes [94,95].

Moreover, GE plays a moderating role in the influence of the IoT on digital supply chain transparency and the sourcing of circular materials, where workforce participation is critical [39]. High GE improves manufacturing to attract employee involvement in design-for-disassembly to join remanufacturing initiatives [96].

Based on this literature, GDTL’s ability to drive CE adoption hinges on fostering green efficacy—a dynamic interplay of leadership vision, digital enablement, and employee empowerment [18]. Addressing the role GE will be critical to realizing the full potential of the tripartite framework of GDTL, CE, and sustainable performance. In prior research, employees with high GE in Egyptian resorts effectively educated guests on towel reuse programs, increasing participation by 35% [17]. This underscores HGE’s role in harmonizing leadership directives with stakeholder engagement. Hence, this study assumes that green efficacy can moderate the relationship between green digital transformational leadership and circular economy.

H5: 

Green Efficacy moderates the relationship between Green Digital Transformational Leadership and Circular Economy.

Based on the above literature and hypotheses, this study proposes a conceptual model (Figure 1) that synthesizes the linkages between GDTL (dynamic capabilities theory), which drives CE practices (NRBV) through digital tools (e.g., AI, blockchain); CE practices, which enhance sustainability performance across environmental, economic, and social dimensions; and GE (social cognitive theory), which moderates this relationship, empowering employees to execute CE initiatives.

3. Methods

The research design employed in this study is depicted in Figure 1 and was conceptualized based on a comprehensive review of the relevant literature. The proposed model comprises one independent variable—digital transformational leadership (GDTL)—alongside a mediating variable: circular economy (CE). The model further incorporates three dependent variables (economic performance (EP), environmental performance (EVP), and social performance (SP)) and one moderating variable (the hotel green efficacy (HGE)). The current study adopted a quantitative research approach to enhance the generalizability of the findings and ensure methodological rigor. Structural equation modeling (SEM) was employed to facilitate robust statistical analysis, enable accurate hypothesis testing, and align with the overarching research objectives [97].

3.1. Sampling and Procedure

The target population of this study consisted of employees from various high-ranking hotels located in Sharm El Sheikh, Egypt. Data were collected between February and April 2025, following a structured process designed to ensure data validity and reliability. Firstly, the research team contacted hotel human resource managers, clearly explaining the study’s goals, significance, and confidentiality measures to secure their consent and collaboration in distributing the survey. Upon approval, the survey link was shared with participating employees, who could complete the questionnaire at their convenience within the designated period. The survey had a letter describing the research objectives, a consent form, and all relevant information for participants.

Before the main data collection, a pilot test was conducted to evaluate and refine the survey instrument. Thirteen participants, all either graduates of tourism and hospitality faculties or enrolled in postgraduate programs, participated in this process. Their feedback enhanced the items’ clarity, refined the questions’ wording, and ensured alignment with the study’s objectives. Additionally, four professors specializing in hotel management reviewed the survey to assess its construct validity.

A total of 402 employees completed the survey, with all responses deemed valid, as mandatory response options were enabled. The sample included 208 males (51.7%) and 194 females (48.3%). Most participants fell within the 18-to-29-year-old age group (74.6%), followed by those aged 30 to 39 (10.7%). This signaled that roughly three-quarters of the participants were below the age of 30, signifying youthful demographic information within the organization. Regarding educational level, 236 respondents (71.1%) held a bachelor’s degree, while 41 (10.2%) possessed a secondary school qualification. The high proportion of bachelor’s degree employees reflected a well-educated labor force, which may impact organizational policies related to development and training.

3.2. Measurement

This study employed well-established scales borrowed from prior research to measure its constructs (see Appendix A). The survey instrument was structured into two sections: the first section contained items assessing green digital transformational leadership (GDTL), sustainability performance (i.e., economic performance (EP), environmental performance (EVP), and social performance (SP)), circular economy (CE), and hotel green efficacy (HGE); the second section collected demographic information. GDTL was measured using a 6-item scale from [98]. Twelve items were used to gauge sustainability performance from [99], with four items for each dimension: EP, EVP, and SP. A 5-item scale was borrowed from [100] assess CE. Finally, HGE scale measured six items adopted from [101]. All survey questions were evaluated on a 5-point Likert scale except for demographic data.

3.3. Tests of Common Method Variance

This study conducted Harman’s single factor test to assess the potential presence of common method bias (CMB). The analysis revealed that a single factor explained 43.838% of the total variance, below 50%, meaning that CMB was not a significant issue [102]. Additionally,

Table 1. Reliability validity.

Diemsnions and Variables	Λ	[VIF]	μ	σ	SK	KU
A. Digital transformational leadership (GDTL) (α=0.918, CR = 0.936, AVE = 0.711)
GDTL_1	0.851	3.109	3.231	1.349	−0.464	−1.077
GDTL_2	0.835	2.588	3.398	1.226	−0.371	−1.015
GDTL_3	0.889	3.687	3.517	1.057	−0.262	−0.757
GDTL_4	0.827	2.749	3.366	1.237	−0.254	−0.888
GDTL_5	0.868	3.181	3.331	1.118	−0.217	−0.650
GDTL_6	0.783	2.099	3.366	1.089	−0.357	−0.662
B. Economic performance (EP) (α=0.848, CR = 0.898, AVE = 0.689)
EP_1	0.778	1.588	3.580	1.345	−0.652	−0.729
EP_2	0.881	2.503	2.928	1.311	−0.274	−1.284
EP_3	0.861	2.251	3.142	1.280	−0.303	−1.078
EP_4	0.794	1.784	3.368	1.132	−0.376	−0.548
C. Environmental performance (EVP) (α=0.860, CR = 0.905, AVE = 0.704)
EVP_1	0.830	2.086	3.211	1.244	−0.290	−0.844
EVP_2	0.836	2.102	3.266	1.201	−0.342	−0.689
EVP_3	0.872	2.378	3.313	1.182	−0.427	−0.635
EVP_4	0.817	1.881	3.430	1.377	−0.628	−0.836
D. Social performance (SP) (α=0.824, CR = 0.884, AVE = 0.657)
SP_1	0.729	1.435	3.381	1.337	−0.602	−0.857
SP_2	0.797	1.820	3.570	1.046	−0.593	−0.022
SP_3	0.852	2.061	3.537	1.209	−0.620	−0.542
SP_4	0.858	2.055	3.532	1.211	−0.499	−0.773
E. Circular economy (CE) (α=0.851, CR = 0.894, AVE = 0.629)
CE_1	0.814	2.077	3.537	1.186	−0.504	−0.588
CE_2	0.818	2.063	3.301	1.260	−0.222	−0.941
CE_3	0.732	1.606	3.403	1.201	−0.312	−0.882
CE_4	0.835	2.132	3.520	1.236	−0.524	−0.740
CE_5	0.760	1.725	3.488	1.295	−0.637	−0.787
F. Hotel green efficacy (HGE) (α=0.885, CR = 0.911, AVE = 0.630)
HGE_1	0.766	2.026	3.488	1.297	−0.514	−0.920
HGE_2	0.786	2.122	3.465	1.342	−0.545	−0.913
HGE_3	0.804	2.397	3.408	1.274	−0.502	−0.741
HGE_4	0.790	2.419	3.488	1.222	−0.635	−0.405
HGE_5	0.808	2.794	3.560	1.224	−0.717	−0.372
HGE_6	0.809	2.628	3.662	1.198	−0.853	−0.058

Note: SK = skewness; KU = kurtosis; μ: mean; σ: standard deviation (α = 0.824, CR = 0.884, AVE = 0.657).

demonstrates the absence of multicollinearity, as all variance inflation factor (VIF) values ranged from 1.435 to 3.687, below 5.0 [103]. Furthermore, kurtosis and skewness tests were performed to assess the normality of the data. As shown in Table 1, the kurtosis (ranging from −1.284 to −0.022) and skewness values (ranging from −0.853 to −0.217) for all items were below the recommended thresholds of 2.1 and 7.1, respectively [104], indicating that non-normality was not a concern.

3.4. Data Analysis

Partial least squares structural equation modeling (PLS-SEM) was operated to analyze the data using SmartPLS 3, and for descriptive statistics, SPSS v 22 was used. PLS-SEM passes a two-stage analytical process. The first stage evaluated the measurement scale’s reliability, internal consistency, and convergent validity. The second is to estimate the structural model by examining the structural relationships among the constructs of the study model.

4. Results

4.1. Reliability and Validity

Ref. [103] suggested some criteria to evaluate the “convergent validity” (CV) in PLS-SEM measurement, including “factor loadings (λ) (varying between 0.729 to 0.889, coefficient alpha (α) (varying from 0.824 to 0.918), and construct reliability (CR) (ranging from 0.884 → 0.936)”. The recommended threshold for these indicators is ≥0.70, while the average variance extracted (AVE) (varying from 0.629 → 0.711) should be ≥0.50. As presented in Table 1, the measurement model meets all the necessary conditions for adequate CV, thereby validating the consistency of the internal model—ensuring the reliability of responses to items associated with the same construct.

Additionally,

Table 2. Discriminant validity (Fornell–Larcker criterion).

	CE	EP	EVP	GDTL	HGE	SP
Circular economy (CE)	0.793
Economic performance (EP)	0.703	0.830
Environmental performance (EVP)	0.684	0.764	0.839
Green digital transformational leadership (GDTL)	0.567	0.623	0.644	0.843
Hotel green efficacy (HGE)	0.593	0.650	0.577	0.600	0.794
Social performance (SP)	0.676	0.673	0.519	0.528	0.625	0.811

proves that AVE scores should exceed the subsequent squared inter-dimension correlations, therefore approving the discriminant validity (DV) [105]. Additionally, some research recommended looking at the HTMT test to validate the DV.

Table 3. Discriminant validity (heterotrait–monotrait ratio (HTMT)).

	CE	EP	EVP	GDTL	HGE	SP
Circular economy (CE)
Economic performance (EP)	0.822
Environmental performance (EVP)	0.795	0.898
Green digital transformational leadership (GDTL)	0.631	0.701	0.728
Hotel green efficacy (HGE)	0.656	0.745	0.645	0.652
Social performance (SP)	0.806	0.801	0.619	0.605	0.722

also demonstrates that the DV is adequate as HTMTs are <0.90 [106].

4.2. Structural Model and Testing Hypotheses

The structural model was validated by measuring beta coefficients (β), R², and Q². R2 should be equal to 0.10 or greater, β must be significant, and the Q² results must be >0.0 [103]. As depicted in

Table 4. Hypothesis results.

Hypothesis	β		t	p	F2	Results
Direct effects
H1a: GDTL → EP	0.331		4.796	0.000	0.172	✔
H1b: GDTL → EVP	0.377		5.230	0.000	0.222	✔
H1c: GDTL → SP	0.213		3.139	0.002	0.060	✔
H2: GDTL → CE	0.378		7.072	0.000	0.153	✔
H3a: CE → EP	0.515		8.903	0.000	0.417	✔
H3b: CE → EVP	0.471		7.259	0.000	0.346	✔
H3c: CE → SP	0.555		8.425	0.000	0.408	✔
Indirect mediating effect
H4a: GDTL → CE → EP	0.195		6.194	0.000		✔
H4b: GDTL → CE → EVP	0.178		5.217	0.000		✔
H4c: GDTL → CE → SP	0.210		5.589	0.000		✔
Indirect mediating effect
H5: GDTL × HGE → CE	0.152		2.435	0.015		✔
Circular economy	R2	0.443	Q2	0.255
Economic performance	R2	0.568	Q2	0.367
Environmental performance	R2	0.565	Q2	0.373
Social performance	R2	0.488	Q2	0.300

Note: green digital transformational leadership = GDTL; economic performance = EP; environmental performance = EVP; social performance = SP; circular economy = CE; hotel green efficacy = HGE; ✔ = supported; β = path coefficients.

, the R² values indicate that the structural model explains a substantial portion of the variance in the endogenous constructs: 44.3% for CE, 56.8% for EP, 56.5% for EVP, and 48.8% for SP. Additionally, all constructs’ Q² values exceed zero, confirming the model’s predictive relevance.

Further, the goodness of fit (GoF) for models operating the PLS-SEM technique was evaluated using the formula proposed by [107]:

$$\mathrm{GoF}=\sqrt{{AVE}_{avy}\times {R^2}_{avy}}$$

The GoF of the current study is large because the result of this equation is greater than 0.36 that that of [107].

Hypothesis testing was conducted based on the measurement and structural models analysis results, which are summarized in Table 4.

Figure 1, and Table 4 presents the results of hypotheses testing. All direct effect hypotheses (H1a–H3c and H2) were supported, as indicated by significant path coefficients (β), t-values exceeding the critical value of 1.96, and p-values below 0.05. Specifically, GDTL has a significant impact on EP (β = 0.331, t = 4.796, p < 0.000), EVP (β = 0.377, t = 5.230, p < 0.000), SP (β = 0.213, t = 3.139, p = 0.002), and CE practices (β = 0.378, t = 7.072, p < 0.000). These paths’ effect sizes (f²) ranged from small to medium, with the strongest effect observed on EVP (f² = 0.222). Further, CE positively impact on EP (β = 0.515, t = 8.903, p < 0.000), EVP (β = 0.471, t = 7.259, p < 0.000), and SP (β = 0.555, t = 8.425, p < 0.000), with high effect sizes, especially for EP (f² = 0.417) and SP (f² = 0.408).

Regarding mediation effects, as seen in Figure 2, all indirect paths (H4a–H4c) were also supported. GDTL impacted EP (β = 0.195, t = 6.194), EVP (β = 0.178, t = 5.217), and SP (β = 0.210, t = 5.589) via the mediating role of CE with significant indirect effects (p < 0.001). Moreover, the moderating role (H5) was supported, as shown in Figure 3; the HGE strengthened the impact of GDTL --> CE (β = 0.152, t = 2.435, p = 0.015).

5. Discussion and Implications

5.1. Findings and Theoretical Contributions

Achieving system-wide sustainability in the hotel industry is still vague due to various managerial barriers that remain unexplored, particularly in the service industry settings. Such barriers include such problems as fragmented leadership approaches, underdeveloped operational frameworks, and limited organizational confidence in green innovation [7,8,9]. Additionally, the unique characteristic of the hospitality industry magnifies the difficulty of integrating sustainability across all functions. These features include the dynamic and people-intensive nature of the hotel industry, service delivery is highly variable, resource usage is continuous, and customer expectations are rapidly changing [17,18].

To that end, the findings of this study provide significant theoretical insights into the growing literature on sustainability leadership within the hospitality sector. The empirical analysis confirms that green digital transformational leadership (GDTL) plays a pivotal role in enhancing hotel sustainability performance across its three major dimensions: environmental, economic, and social. This direct effect concurs with the findings of earlier studies in industrial settings (e.g., [16,96]). Even so, the current study provides new theoretical contribution by extending the application of GDTL to service-based industries, like the hospitality industry, where employee–customer interactions, operational heterogeneity, and resource intensity necessitate adaptive and inclusive leadership models. In service-focused settings like hotels, traditional sustainability models are often less effective as they lack the flexibility and inclusiveness needed for the complex environment of the hotel industry [13]. Green digital transformational leadership (GDTL) helps tackle this challenge by encouraging innovation, teamwork across departments, and the use of digital tools. This makes GDTL important in understanding how to lead sustainability efforts more effectively in the hospitality sector.

Moreover, this study demonstrates that GDTL, such as AI and Blockchain, significantly promotes the adoption of circular economy (CE) practices. For example, AI-enabled systems can optimize energy usage, automate sustainable supply chain decisions, and enable predictive maintenance. Likewise, blockchain ensures transparency and traceability in green procurement and waste management. The findings also showed that circular economy mediates the positive effect of GDTL on sustainability performance in hotel settings. This mediating mechanism provides strong support to conceptual arguments in prior literature that calls for an integrated leadership model that leverages technological innovation to address ecological challenges [12,13]. By empirically validating CE as a fundamental operational pathway through which GDTL influences hotel sustainability performance, this study provides a new perspective that highlights the interconnectedness between leadership behaviors, process innovation, and sustainable development in hotels. In this context, this study extends the findings of prior studies (e.g., [11,57]) which discussed the integration of CE principles with advanced technologies such as AI and blockchain in industrial applications. Hence, this study provides empirical evidence that supports similar benefits of this integration in the hospitality domain, including enhanced energy efficiency and waste reduction through AI-enabled systems.

Another novel contribution of this study is inherent in the validation of hotel green efficacy (HGE) as a primary moderation variable on the linkage between GDTL and CE. Rooted in Bandura’s [93] social cognitive theory, green efficacy reflects employees’ collective confidence in executing environmentally responsible initiatives and practices. This study proves that HGE strengthens the influence of GDTL on CE implementation, reinforcing the theoretical proposition that employee empowerment and psychological readiness are essential pillars for turning strategic leadership into operational practices. This finding confirms the results of previous studies, including [18,84], and draws attention to the role of employee-level dynamics in sustainability transformations. The moderating effect of HGE also helps explain previously mixed findings regarding the success of digital sustainability initiatives in low-resource or culturally resistant environments [30,32]. This suggests that employee confidence and engagement are fundamental to the success of green transformation strategies alongside the availability of technological infrastructure and leadership intent [92,94,95]. This insight is particularly valuable in the context of hospitality, where service delivery relies heavily on human resources. Employees who feel capable, motivated, and supported tend to embrace new technologies and sustainability processes. This helps enhance the effectiveness of leadership-driven change. In this context, HGE is a psychological mechanism supporting the success of green digital leadership efforts.

Lastly, the integrated framework of this study draws on the dynamic capabilities theory, natural resource-based view (NRBV), and social exchange theory, which provides a multidimensional perspective that incorporates strategic leadership, digital innovation, and green behavior. Dynamic capabilities theory explains how GDTL enables hotels to reconfigure digital and human resources in response to sustainability challenges [20]. The NRBV conceptualizes GDTL as a valuable intangible asset contributing to eco-innovation [19,27], while social exchange theory elucidates the mechanisms through which environmentally committed leaders inspire reciprocal green behaviors among employees [108]. The amalgamation of multiple theoretical frameworks in one empirical model represents a significant advancement in the growing literature on sustainability, especially within service-oriented sectors like the hotel industry.

The integration of these three theoretical perspectives enables a more holistic understanding of how leadership, technology, organizational culture, and employee efficacy interact to shape sustainability outcomes. It also illustrates the interdependence between strategy, structure, and behavior in driving environmental, social, and economic performance within a complex service context. The ability of this study to synthesize these dimensions into a unified empirical model is a notable contribution to the literature, and it provides a foundation for future research on sustainable leadership in service-based industries other than the hospitality industry.

5.2. Practical Implications

The findings of this research offer several implications for hotel managers, sustainability officers, and policymakers to support environmental stewardship in the tourism and hospitality industry. Most notably, the study highlights that cultivating green digital transformational leadership is not only a managerial style but also a strategic necessity. Hotels that are seeking to align with global sustainability goals are encouraged to prioritize leadership development programs that integrate digital fluency with ecological values [109]. Such programs should emphasize visionary thinking, stakeholder engagement, ethical decision-making, and competence in employing smart technologies like IoT, AI, and blockchain. To do so, hotels can undertake several practices such as designing customized green digital leadership training programs that include real-world cases from the hospitality sector (e.g., AI-driven energy savings), collaborate with academic institutions or sustainability certification bodies to establish a certification track for managers, establish advisory panels including tech vendors and sustainability experts to align hotel initiatives with hotel sustainability goals, and incorporate hotel sustainability KPIs into leadership evaluation.

The strong mediating role of circular economy (CE) practices underscores that sustainability strategies need to be operationalized through tangible systems such as closed-loop resource cycles, renewable energy adoption, and traceable green procurement. Hotel operators can leverage CE to improve their environmental and social footprint, reduce long-term costs, and enhance reputational value. Existing empirical evidence confirmed cost reductions and revenue gains from CE initiatives [54,62]. Hotels can accomplish that by introducing systems for water recycling, energy recapture, and organic waste composting to minimize resource consumption and environmental impact. Hotels also can prioritize purchasing products and materials that are recyclable, reusable, or made from recycled content. Practices such as linen reuse, food waste tracking software, or refillable bathroom amenities help hotels reduce both waste and costs. Most importantly, hotels need to educate guests about the hotel’s circular initiatives through in-room messaging, digital apps, or interactive sustainability dashboards to encourage guest participation in reuse and recycling activities.

The finding also confirmed that hotel green efficacy (HGE) moderates the GDTL–CE relationship, further emphasizing the importance of fostering employee commitment and psychological empowerment. In this context, human resource departments should incorporate green behavior metrics in performance appraisals, create eco-champions or green teams, and encourage sustainability innovation. A workforce that believes in its ability to contribute meaningfully to sustainability outcomes is more likely to translate leadership vision into actions and outcomes. To that end, hotels are encouraged to provide training workshops for employees focused on environmental literacy and digital tools for sustainability, adopt recognition systems such as “Green Innovator Certificate” or team-based rewards for meeting sustainability goals, and reinforce the belief in employees’ actions by sharing outcomes of employees’ sustainability efforts such as reductions in energy use or waste.

Finally, this study offers some implications for government and industry regulators. On this note, official bodies are encouraged to create supportive ecosystems for the implementation of GDTL and CE. This can be accomplished by providing green financing incentives such as tax credits, green loans, and innovation grants to hotels that invest in digital sustainability technologies. It is strongly advised that exchanging experiences and knowledge-sharing initiatives such as hosting forums, workshops, and digital hubs be supported, where hotels can access best practices, policy updates, toolkits, and case studies related to GDTL and CE implementation. Official bodies are also encouraged to support collaborations between universities, training institutes, and hotel associations to co-develop GDTL-focused educational curricula training programs on CE effectiveness in tourism and hospitality settings. Lastly, official bodies can develop and publish guidelines or competency schemes that outline the essential attributes of green digital leadership in hospitality and align them with national strategies and sustainable tourism policies.

5.3. Limitations and Future Research

This study has some limitations to be acknowledged. First, the research was conducted in a single geographical context, green-certified hotels in Sharm El-Sheikh, Egypt. While these settings highly represent the Middle East and North African (MENA) tourism and hospitality sector, this may limit the generalizability of the study findings to other regions or non-certified hotels. Thus, future studies could extend this model to broader geographical contexts or uncertified hotels in developing economies. Second, using a cross-sectional survey design restricts the ability to establish causal relationships among the examined variables. Although the structural model demonstrates strong explanatory power, longitudinal or experimental designs can capture the dynamic nature of leadership development, technological adoption, and sustainability performance over an extended period.

Third, the dependence on self-reported data from employees may cause biases such as social desirability, given the normative appeal of sustainability practices in the hospitality industry. Triangulating self-reports with objective performance indicators (e.g., energy usage data, waste audits) could improve the validity of future studies. Moreover, this study did not consider some factors that may significantly influence the effectiveness of GDTL and CE implementation, such as organizational size, financial resources, or digital maturity. These factors could serve as important moderators or control variables in future research. Additional work is also needed to examine the feasibility or the environmental gains from digital technologies compared to their ecological costs, such as the energy consumption associated with data centers or blockchain infrastructures [110]. Lastly, future studies can provide a more holistic view of the drivers and barriers to sustainable transformation in the hospitality industry by expanding the model and including other relevant variables such as green organizational culture, stakeholder pressure, customer environmental expectations, or institutional support.