This study explores the influence of green dynamic capabilities (GDC) and green knowledge sharing (GKS) on circular economy practices (CEP) and enhances the outcomes of hotel sustainable performance (HSP) within hotels by employing a natural resource-based view theory and a triple bottom line framework.
A quantitative method was applied in this study in order to achieve research objectives.
The finding supports the assertion that GKS is a significant influence and a necessary condition of CEP, whereas GDC is important but not essential. Next, GDC, GKS and CEP are vital determinants as well as necessary conditions of HSP. The findings demonstrate that CEP mediates the link between GKS, GDC and HSP. Furthermore, this study indicates green employee behavior (GEB) positively and significantly moderates the relationship between CEP and HSP.
This study contributes to methodological development by weaving the PLS-SEM and necessary condition analysis (NCA) methods together, which offers a holistic view of complex sustainable hotel management exchange issues. It also provides practical strategies for encouraging sustainability within the hospitality industry, particularly in developing economies.
1. Introduction
Over the past few decades, there has been a rising concern about sustainability issues across most business sectors, including the tourism and hospitality industry (Kumar et al., 2024). There are global challenges that should be taken into consideration, such as climate change, greenhouse gas emissions, waste management, land pollution and water pollution (Zeng et al., 2024). One of the approaches that is used in order to tackle these problems, which has recently received much attention, is the circular economy (CE).
CE provides a clear shift in terms of resource management, climate change and other environmental issues (Gatell and Avella, 2024). The Ellen McArthur Foundation explains CE as restorative and regenerative, maintaining products and materials at maximum utility (Ellen McArthur Foundation, 2017). CE offers a strategic response to global resource demands and climate change (Whalen, 2019), while aligning with UN Sustainable Development Goals in tourism and hospitality. The industry significantly contributes to the green economy (Lockstone-Binney et al., 2022). It promotes zero waste through closed-loop material flows, contrasting with linear consumption patterns (Julião et al., 2019). Nonetheless, many hotels remain conspicuous in their overall low efforts in implementing conventional CEP, particularly in areas of resource-heavy activity or the drivers that are inclusive of energy, heating, cooling and water (Abdel-Maksoud et al., 2016).
This limited acceptance of CEP raises the question of how organizational mechanisms enable hotels to embed sustainability into resource-intensive operations. A key factor in this regard is the development of green dynamic capabilities (GDC), defined as the ability to sense, seize and reconfigure environmental knowledge to drive sustainability-oriented innovation (Yuan and Cao, 2022). Yet, the hospitality industry struggles to leverage such capabilities effectively, which partly explains its limited CE adoption. Evidence suggests that a certain level of GDC is necessary for hotels to translate sustainability knowledge into improved performance outcomes (Janjua et al., 2024).
Moreover, without effective mechanisms for diffusing sustainability knowledge across organizational levels, even firms with environmental capabilities may fail to control their ecological footprint. In this regard, green knowledge sharing (GKS) becomes critical, as it facilitates the dissemination of both explicit and tacit knowledge necessary to operationalize CE practices and enhance hotel sustainability performance (Sun et al., 2025). Beyond simple information exchange, GKS functions as a collective learning process that enables employees to adapt strategies to context-specific sustainability challenges, thereby embedding CE principles into daily operations (Chen et al., 2018). Sharing best practices helps embed closed-loop systems and reduces environmental impact, driving innovation and competitive advantage (Rubel et al., 2021).
Understanding the key drivers behind the successful adoption of CEP is essential, as these practices have significant potential to mitigate environmental impacts in the hospitality industry (Zaki, 2024). A particularly important element in this regard is green employee behavior (GEB), which reflects employees’ environmentally conscious attitudes and their active participation in sustainability initiatives (Yeşiltaş et al., 2022). The extent to which employees engage in green behaviors can substantially influence how effectively CE measures are implemented and sustained, thereby shaping the overall environmental performance of hotels.
Despite growing attention to sustainability and CEP, several critical gaps remain in the hospitality literature. As most of the prior studies focus on manufacturing or tech-intensive industries (Jabbour et al., 2019), leaving service sectors like hotels underexplored despite their unique resource demands and reliance on frontline employees. Importantly, while GDC and GKS are recognized as essential for sustainability innovation, their role in driving CEP in hotels and translating environmental knowledge into performance outcomes remains largely untested. As GKS has been studied primarily in technology firms (Lin et al., 2017; Sahoo et al., 2023), with minimal insight into how knowledge flows in hotel service environments support CEP. Finally, this study tests the moderating role of GEB in linking capabilities and knowledge sharing to CEP and HSP, which is underexplored.
Hence, the primary objective of this study is to investigate how GDC and GKS influence HSP through the adoption of CEP, with GEB as a moderating factor. To address the objective and fill the identified gaps, the study poses the following research questions.
How do GDC and GKS practices influence the overall HSP?
How does CEP mediate the relationship between GDC, GKS and HSP?
How does GEB moderate the relationship between GDC, GKS, CEP and HSP?
Consequently, this study makes several contributions to the literature. Firstly, it advances theoretical understanding by evaluating the direct impact of GDC and GKS on CEP and HSP. Secondly, it contributes by examining the mediating role of CEP between GDC, GKS and HSP. Thirdly, this research seeks to determine whether GEB has any moderating influence on the relationships between GDC, GKS, CEP and HSP. Fourthly, it offers a methodological contribution by integrating NCA and PLS-SEM methodologies to identify key drivers of HSP.
Hence, practically, the study provides actionable guidance for hotel managers and industry stakeholders by demonstrating how systematically fostering GDC, promoting GKS and leveraging GEB can enhance the adoption of CEP and improve overall HSP. These insights offer concrete strategies to reduce resource consumption, waste and emissions while boosting operational efficiency and competitive advantage. Thus, the research aligns with SDGs 8, 12 and 13, focusing on economic growth, responsible consumption and climate action, while offering insights applicable to service industries and emerging economies.
2. Literature review
Given the importance of sustainability in hospitality and the significance of green dynamic capabilities, green knowledge sharing and behavioral factors, it is thus essential to examine the interrelationships among these constructs. The literature review section outlines the theoretical foundations of this study, particularly the natural resource-based view (NRBV) and the triple bottom line (TBL) frameworks. Also, it synthesizes the key concepts and prior research relevant to this study, including GDC, GKS, CEP, GEB and HSP within the hospitality industry, and based on earlier studies, it develops hypotheses among the constructs.
2.1 The natural resource-based view theory (NRBV)
The resource-based view, introduced by Mahoney and Pandian (1992), integrates traditional strategy, organizational economics and industrial organization perspectives. NRBV argues firms must leverage environmental resources for sustainable advantage (Hart, 1995). Building on this, NRBV outlines three pathways to achieve such advantage: sustainable development, product stewardship and pollution prevention (Aboelmaged et al., 2018). These are the foundation for sustainable practices that conserve natural resources and promote energy efficiency (Bornay-Barrachina et al., 2023). From this, it is the current study’s contention that GDC and GKS, along with CEP, jointly enhance HSP. This current study provides hotel managers and policymakers with valuable knowledge to construct sustainability in the hospitality industry, closes gaps found in the literature and proposes strengthening existing practices.
2.2 Triple bottom line (TBL)
Elkington (1994) introduced the concept of social, environmental and economic dimensions of corporate performance. The term triple bottom line was subsequently articulated and elaborated in his 1997 work, Cannibals with Forks (Elkington, 1997a, b). TBL explains the incorporation of the environmental, social and economic lines (Elkington, 1997a, b). Alhaddi (2015) also emphasized the importance of combining environmental, social and economic variables in regard to determining an organization’s performance. The TBL framework and the CE form a more complex and dynamic relationship. It adheres to the CE’s principles of zero waste and maximum resource production (Pereira et al., 2021). For example, along with the economic aspect, several large hotel companies have made explicit strategic decisions about reducing, reusing and recycling (Murtaza et al., 2024). Furthermore, the European Union, CE action strategy and farm-to-fork plan considerably improve resource management and waste streams (Bux et al., 2023). TBL enables a holistic analysis of hotel sustainability in environmental, social and economic dimensions. Facilitating NRBV’s focus on internal capabilities, TBL quantifies the long-term impacts of CEP on society and profitability and orients theory to global SDGs and industry applicability.
2.3 Green dynamic capabilities and hotel sustainable performance
Dynamic capabilities are widely recognized for positively influencing organizational performance (Eikelenboom and de Jong, 2019). Protogerou et al. (2012) empirically linked firm competencies to enhanced profitability, highlighting the strategic value of such capabilities. Recent research extends this view by connecting green dynamic capabilities with sustainability outcomes in hotels, emphasizing quality management, human resource practices and sustainable performance (Pereira-Moliner et al., 2021). GDC enable organizations to adapt swiftly to evolving social, economic and environmental demands by constantly modifying their functional competencies (Saleem et al., 2024). Exploratory studies also reveal the presence of a positive connection between sustainability-oriented dynamic capabilities and companies’ unique sustainability features. Based on this evidence, we hypothesize that:
Green dynamic capabilities significantly and positively impact hotel sustainable performance.
2.4 Green knowledge sharing and hotel sustainable performance
GKS enhances HSP by promoting the exchange of eco-friendly practices among employees, leading to innovation and better environmental outcomes (Andoh et al., 2025). Socially responsible human resource management supports GKS and increases employees’ environmental commitment (Rubel et al., 2023). Knowledge sharing is a strategic capability that drives innovation and competitive advantage (Yoo and Reimann, 2017) and is critical for sustainability across industries (Pantouvakis et al., 2017). Sustainable value creation depends on the ability of an organization to effectively generate, transfer and execute intellectual knowledge. Effective knowledge management systems improve organizational performance and reliability (Doda, 2017). Thus, hotels that foster GKS develop distinctive capabilities to sustain long-term sustainability. Hence, the following hypothesis is suggested:
Green knowledge sharing significantly and positively impacts hotel sustainable performance.
2.5 Green dynamic capabilities and circular economy practices
GDC enable firms to integrate and reconfigure resources to address environmental challenges and foster sustainable innovation (Abbas, 2024). In hotels, GDC support continuous learning and eco-innovation to meet evolving sustainability demands (Al-Romeedy, 2024). These capabilities facilitate CEP, such as waste reduction, resource reuse and green procurement, which lower environmental impact and operational costs (Xu et al., 2025). A core aspect of GDC is green human resource management, which promotes eco-friendly behavior and employee engagement in sustainability (Elshaer et al., 2024). Despite resource constraints, effective management allows hotels to implement CEP successfully, enhancing sustainability and reputation (Khalil et al., 2024). Thus, we hypothesize:
Green dynamic capabilities considerably and positively influence circular economy practices.
2.6 Green knowledge sharing and circular economy practices
GKS is a strategic asset that enhances sustainable development by facilitating the exchange of environmentally friendly ideas to reduce harm (Abbas and Khan, 2023). It plays a key role in improving circular business models and engaging stakeholders in knowledge exchange that shapes business strategy (Atiku, 2020). Greater GKS supports sustainable CEP by fostering leadership development and overcoming knowledge transfer barriers (Guerreschi et al., 2023). Within hospitality, GKS reflects the extent to which employees share green knowledge, enabling effective CEP implementation. Accordingly, we hypothesize:
Green knowledge sharing positively and significantly affects circular economy practices.
2.7 Circular economy practices and hotel sustainable performance
CEP significantly enhances hotel sustainability by addressing key environmental challenges such as food waste, water use and energy consumption (Bux and Amicarelli, 2023). Despite concerns about guest comfort, rising consumer demand for eco-friendly options drives hotels toward sustainable practices (Rahman et al., 2016). Globally, hotels increasingly align economic, social and environmental goals within structured frameworks (Franzoni and Avellino, 2019). Empirical evidence confirms CEP’s positive impact on hotel performance. Meirun et al. (2024) emphasized green creativity and empowered staff in Southeast Asia, while Costa et al. (2024) highlighted Portugal’s hotel sector adopting R-principles (reduce, reuse and recycle). Collectively, these studies validate CEP’s global applicability, supporting the basis for the following hypothesis.
Circular economy practices positively and significantly affect hotel sustainable performance.
2.8 The mediating role of circular economy practices
CEP has gained global attention for addressing environmental challenges via resource efficiency, waste reduction and closed-loop systems (Manoharan et al., 2023). The hospitality sector increasingly adopts CE principles, driven by rising commitment to circularity and sustainable practices (Julião et al., 2019). Consumer environmental concern remains a key driver of these green initiatives (Solakis et al., 2022). As a result, eco-control systems reduce pollutants, enhance CEP and mediate the link to economic performance (Laguir et al., 2024). Industry 4.0 technologies also boost sustainable outcomes through CE (Cuevas-Pichardo et al., 2025). Grounded in NRBV, CEP operationalizes green capabilities like GDC and GKS into measurable performance by implementing efficiency, waste minimization and closed-loop practices (Shukla, 2025), thereby validating its essential mediating role. Thus, the following hypothesis is proposed:
The impact of green dynamic capabilities on hotel sustainable performance is mediated by circular economy practices.
The impact of green knowledge sharing on hotel sustainable performance is mediated by circular economy practices.
2.9 The moderating role of green employee behavior
GEB encompasses voluntary, routine actions such as waste reduction, recycling and energy saving that minimize environmental harm (Al-Swidi et al., 2021). Encouraging eco-friendly behavior is vital for reducing hotels’ negative impacts (Dixon et al., 2013). Management’s environmental commitment fosters proactive employee behavior (Karatepe et al., 2022), promoting sustainability across hotel operations (Patwary et al., 2024). Green HR practices also strengthen employees’ ecological responsibility, improving environmental performance (Loor-Zambrano et al., 2022). GEB may enhance or limit organizational sustainability outcomes (Norton et al., 2015) and has been shown to moderate the green innovation–performance link (Li et al., 2023). Grounded in social exchange theory, this study explores GEB as a moderator, reflecting its crucial but often underexplored role in realizing green capabilities and CEP. Therefore, the following hypotheses are proposed.
Green employee behavior strengthens the effect of green dynamic capabilities on hotel sustainable performance.
Green employee behavior strengthens the effect of green knowledge sharing on hotel sustainable performance.
Green employee behavior strengthens the effect of circular economy practices on hotel sustainable performance.
3. Research methodology
3.1 Constructs and items
It is indicated in the study model that the constructs of all items were derived from the pre-existing scales, which are shown in Figure 1. Five items were obtained from Janjua et al. (2024) to measure GDC, whereas five items were modified from Ma et al. (2022) in order to evaluate GKS. Six items were utilized in order to assess CEP, which were derived from Khan et al. (2023). Four items were utilized in order to assess GEB, which were adapted from Tian et al. (2020). Six items, which were taken from Janjua et al. (2024) to assess the HSP.
The diagram starts on the left with two circles arranged vertically: “Green dynamic capabilities” at the top left and “Green knowledge sharing” at the bottom left. In the center, there is a circle labeled “Circular economy practices” enclosed within a dotted square. At the top right, a circle is labeled “Green employee behavior,” and at the far right, a circle is labeled “Hotel sustainable performance.” Arrows connecting the circles are as follows: A right-pointing arrow labeled “H 1” from “Green dynamic capabilities” points to “Hotel sustainable performance.” A right-pointing arrow labeled “H 2” from “Green knowledge sharing” points to “Hotel sustainable performance.” A right-pointing downward diagonal arrow labeled “H 3” from “Green dynamic capabilities” points to “Circular economy practices.” A right-pointing upward diagonal arrow labeled “H 4” from “Green knowledge sharing” points to “Circular economy practices.” A right-pointing arrow labeled “H 5” from “Circular economy practices” points to “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 a” from “Green employee behavior” points to the arrow between “Green dynamic capabilities” and “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 b” from “Green employee behavior” points to the arrow between “Circular economy practices” and “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 c” from “Green employee behavior” points to the arrow between “Green knowledge sharing” and “Hotel sustainable performance.”Conceptual model. Source(s): Developed by authors
The diagram starts on the left with two circles arranged vertically: “Green dynamic capabilities” at the top left and “Green knowledge sharing” at the bottom left. In the center, there is a circle labeled “Circular economy practices” enclosed within a dotted square. At the top right, a circle is labeled “Green employee behavior,” and at the far right, a circle is labeled “Hotel sustainable performance.” Arrows connecting the circles are as follows: A right-pointing arrow labeled “H 1” from “Green dynamic capabilities” points to “Hotel sustainable performance.” A right-pointing arrow labeled “H 2” from “Green knowledge sharing” points to “Hotel sustainable performance.” A right-pointing downward diagonal arrow labeled “H 3” from “Green dynamic capabilities” points to “Circular economy practices.” A right-pointing upward diagonal arrow labeled “H 4” from “Green knowledge sharing” points to “Circular economy practices.” A right-pointing arrow labeled “H 5” from “Circular economy practices” points to “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 a” from “Green employee behavior” points to the arrow between “Green dynamic capabilities” and “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 b” from “Green employee behavior” points to the arrow between “Circular economy practices” and “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 c” from “Green employee behavior” points to the arrow between “Green knowledge sharing” and “Hotel sustainable performance.”Conceptual model. Source(s): Developed by authors
3.2 Measurement development
Data were collected over seven months (Aug 2023–Feb 2024) using a self-administered questionnaire designed from established literature. Following Brislin’s (1970) translation method, the instrument was translated into Urdu from English and back-translated to ensure linguistic accuracy. A panel of five experts (two hotel directors and three hospitality professors) assessed content validity. Pilot testing confirmed reliability and validity, demonstrating strong internal consistency of the measures. Variables were measured using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The measures used are exhibited in Appendix.
Using purposive sampling, 500 questionnaires were distributed to general operations, facilities managers and supervisors from hotels in Islamabad, Rawalpindi, Lahore, Karachi and Gilgit Baltistan. These cities were strategically chosen to capture diverse hospitality contexts, urban centers with high tourist influx and eco-tourism regions, reflecting Pakistan’s major hotel industry hubs, including luxury and mid-range hotels. This approach enhances external validity by covering operational variability. As recommended by G*Power (Faul et al., 2007), a minimum sample size of 119 was required. Of 255 collected responses, 232 valid responses were retained after excluding 23 due to missing or inconsistent data (Fayyaz et al., 2025) (see Table 1).
Demographics
| Frequency | Percentage | |
|---|---|---|
| Gender | ||
| Male | 154 | 66.4 |
| Female | 78 | 33.6 |
| Marital status | ||
| Single | 101 | 43.5 |
| Married | 113 | 48.7 |
| Widow | 6 | 2.6 |
| Divorced | 12 | 5.2 |
| Age (years) | ||
| 18–21 years old | 11 | 4.7 |
| 22–25 years old | 46 | 19.8 |
| 26–29 years old | 82 | 35.3 |
| 30–33 years old | 58 | 25.0 |
| 34 years above | 35 | 15.1 |
| Experience in hotel industry | ||
| 0–2 years | 32 | 13.8 |
| >2–5 years | 61 | 26.3 |
| >5–8 years | 73 | 31.5 |
| >8–10 years | 31 | 13.4 |
| >10–15 years | 35 | 15.1 |
| Education level | ||
| Degree/bachelor | 113 | 48.7 |
| Masters and above | 97 | 41.8 |
| Diploma | 22 | 9.5 |
| Job title | ||
| General manager | 67 | 28.9 |
| Operation manager | 63 | 27.2 |
| Facilities manager | 48 | 20.7 |
| Supervisor | 54 | 23.3 |
3.3 Results and analysis
This study employed SmartPLS version 4.0.9.7 to conduct PLS-SEM alongside NCA. PLS-SEM is widely recognized as a valuable technique for testing complex theoretical models by simultaneously evaluating both measurement and structural components (Hair et al., 2019). In addition to this, NCA was applied to assess the relative importance of each variable and to validate the results obtained from PLS-SEM. More specifically, NCA was instrumental in uncovering factors deemed critical for the consequence. This method identifies variables that are necessary conditions to achieve the desired outcome (Dul, 2016).
It is essential to distinguish NCA from fuzzy-set qualitative comparative analysis (fsQCA). While fsQCA explores combinations of causal conditions sufficient or necessary for outcomes through set theory and qualitative logic, NCA strictly focuses on identifying single necessary conditions and their threshold levels required for an outcome (Mondal et al., 2024). In this way, NCA offers a complementary lens to PLS-SEM by highlighting bottlenecks and essential prerequisites for producing robust and reliable outcomes (Hussain, 2025).
4. Results
4.1 Measurement model
The scholars emphasize that item loadings should exceed the 0.70 threshold criteria (Hair et al., 2019). However, indicator loadings that range from 0.40 to 0.70 can be deemed acceptable if the value of the average variance extracted (AVE) is greater than 0.50 (Hair et al., 2019). We found that the defined standards are achieved (see Table 2). Moreover, the scholars note that composite reliability (CR-rho_a and rho_c) and Cronbach’s α values must exceed 0.70 in order to establish internal consistency (Hair et al., 2019). The CR values specifically ranged from 0.750 to 0.936, whereas the Cronbach’s α values ranged from 0.702 to 0.916. Thus, the current study establishes an adequate internal consistency. Furthermore, Table 3 demonstrated that the HTMT value falls below the threshold of 0.85. Therefore, the current study has no discriminant validity issue exist.
Reliability and validity of the measurement model
| Outer loadings | Cronbach’s alpha | CR-rho_a | CR-rho_c | AVE | ||
|---|---|---|---|---|---|---|
| Green dynamic capability | GDC1 | 0.807 | 0.885 | 0.886 | 0.916 | 0.687 |
| GDC2 | 0.839 | |||||
| GDC3 | 0.871 | |||||
| GDC4 | 0.863 | |||||
| GDC5 | 0.758 | |||||
| Green knowledge sharing | GKS1 | 0.895 | 0.902 | 0.906 | 0.928 | 0.720 |
| GKS2 | 0.916 | |||||
| GKS3 | 0.834 | |||||
| GKS4 | 0.821 | |||||
| GKS5 | 0.767 | |||||
| Circular economy practices | CEP1 | 0.792 | 0.914 | 0.917 | 0.934 | 0.703 |
| CEP2 | 0.816 | |||||
| CEP3 | 0.852 | |||||
| CEP4 | 0.911 | |||||
| CEP5 | 0.898 | |||||
| CEP6 | 0.752 | |||||
| Green employee behavior | GEB1 | 0.619 | 0.702 | 0.750 | 0.810 | 0.518 |
| GEB2 | 0.739 | |||||
| GEB3 | 0.701 | |||||
| GEB4 | 0.808 | |||||
| Hotel sustainable performance | HSP1 | 0.801 | 0.916 | 0.917 | 0.934 | 0.704 |
| HSP2 | 0.870 | |||||
| HSP3 | 0.853 | |||||
| HSP4 | 0.840 | |||||
| HSP5 | 0.794 | |||||
| HSP6 | 0.873 |
4.2 Structural model
We initially assessed the and values, which indicate the predictive competence of the endogenous variables within the sample (Hair et al., 2019). The values for CEP and HSP were 0.294 and 0.331, respectively, whereas the values of CEP and HSP were 0.279 and 0.25, respectively. Both results indicate moderate predictive power. Hence, the model is satisfactory.
Moreover, we compared PLS-RMSE with the LM-RMSE values next by following the suggested criteria by Shmueli et al. (2019). According to the defined benchmark, if PLS-RMSE gives lower values than LM-RMSE, it indicates a goodness of model fit. The current study shows a better fit, which is shown in Table 4.
PLS predict
| Construct | Items | PLS-SEM_RMSE | LM_RMSE | (PLS-SEM_RMSE) – (LM_RMSE) |
|---|---|---|---|---|
| HSP | HSP1 | 0.948 | 0.952 | −0.004 |
| HSP2 | 1.188 | 1.192 | −0.005 | |
| HSP3 | 1.029 | 1.058 | −0.029 | |
| HSP4 | 1.143 | 1.167 | −0.024 | |
| HSP5 | 1.117 | 1.171 | −0.055 | |
| HSP6 | 1.226 | 1.214 | 0.013 |
Finally, we performed bootstrapping in order to assess the association between the constructs. The findings revealed that there is a significant impact of GDC on HSP, which was indicated by a β-value of 0.252, a t-value of 3.898 and being statistically significant (p < 0.000). We also found with a β-value of 0.201, a t-value of 2.822, and (p < 0.005) that GKS has an impact on HSP. It is worth mentioning that there is a strong impact of GDC on CEP, which was indicated by a β-value of 0.207, a t-value of 3.192, and being significant (p < 0.05). Hence, H1, H2 and H3 were accepted. We also observed a positive impact of GKS on CEP, which was indicated by a β-value of 0.430, a t-value of 7.314, and a p < 0.001 value. H5 was also evaluated with the CEP impact on HSP, which obtained a β-value of 0.197, a t-value of 3.149 and a p < 0.05 value. As a result, H4 and H5 were validated and accepted, which is shown in Table 5 and Figure 2.
Hypothesis test results (bootstrapping)
| Hypothetical path | β-value | t-value | p-value | Decision |
|---|---|---|---|---|
| H1: GDC → HSP | 0.252 | 3.898 | 0.000 | Accepted |
| H2: GKS → HSP | 0.201 | 2.822 | 0.002 | Accepted |
| H3: GDC → CEP | 0.207 | 3.192 | 0.001 | Accepted |
| H4: GKS → CEP | 0.430 | 7.314 | 0.000 | Accepted |
| H5: CEP → HSP | 0.197 | 3.149 | 0.001 | Accepted |
| Mediation analysis | ||||
| H6a: GDC → CEP → HSP | 0.041 | 2.133 | 0.016 | Accepted (partial mediation) |
| H6b: GKS → CEP → HSP | 0.085 | 2.823 | 0.002 | Accepted (partial mediation) |
| Moderation analysis | ||||
| H7a: GEB x GDC → HSP | −0.154 | 3.175 | 0.001 | Rejected |
| H7b: GEB x GKS → HSP | −0.092 | 1.309 | 0.095 | Rejected |
| H7c: GEB x CEP → HSP | 0.173 | 2.738 | 0.003 | Accepted |
The diagram starts on the left with two circles arranged vertically: “Green dynamic capabilities” at the top left and “Green knowledge sharing” at the bottom left. In the center, there is a circle labeled “Circular economy practices” enclosed within a dotted square. It has the labels “H 6 a (beta equals 0.041, p less than 0.05)” and “H 6 b (beta equals 0.085, p less than 0.05)” written on the top. At the top right, a circle is labeled “Green employee behavior,” and at the far right, a circle is labeled “Hotel sustainable performance.” Arrows connecting the circles are as follows: A right-pointing arrow labeled “H 1 (beta equals 0.252, p less than 0.001)” from “Green dynamic capabilities” points to “Hotel sustainable performance.” A right-pointing arrow labeled “H 2 (beta equals 0.201, p less than 0.05)” from “Green knowledge sharing” points to “Hotel sustainable performance.” A right-pointing downward diagonal arrow labeled “H 3 (beta equals 0.207, p less than 0.05)” from “Green dynamic capabilities” points to “Circular economy practices.” A right-pointing upward diagonal arrow labeled “H 4 (beta equals 0.430, p less than 0.001)” from “Green knowledge sharing” points to “Circular economy practices.” A right-pointing arrow labeled “H 5 (β equals 0.197, p less than 0.05)” from “Circular economy practices” points to “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 a (beta equals negative 0.154, p less than 0.001)” from “Green employee behavior” points to the arrow between “Green dynamic capabilities” and “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 b (beta equals negative 0.092, p less than 0.05)” from “Green employee behavior” points to the arrow between “Circular economy practices” and “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 c (beta equals 0.173, p less than 0.05)” from “Green employee behavior” points to the arrow between “Green knowledge sharing” and “Hotel sustainable performance.”Results-based model. Source(s): Developed by authors
The diagram starts on the left with two circles arranged vertically: “Green dynamic capabilities” at the top left and “Green knowledge sharing” at the bottom left. In the center, there is a circle labeled “Circular economy practices” enclosed within a dotted square. It has the labels “H 6 a (beta equals 0.041, p less than 0.05)” and “H 6 b (beta equals 0.085, p less than 0.05)” written on the top. At the top right, a circle is labeled “Green employee behavior,” and at the far right, a circle is labeled “Hotel sustainable performance.” Arrows connecting the circles are as follows: A right-pointing arrow labeled “H 1 (beta equals 0.252, p less than 0.001)” from “Green dynamic capabilities” points to “Hotel sustainable performance.” A right-pointing arrow labeled “H 2 (beta equals 0.201, p less than 0.05)” from “Green knowledge sharing” points to “Hotel sustainable performance.” A right-pointing downward diagonal arrow labeled “H 3 (beta equals 0.207, p less than 0.05)” from “Green dynamic capabilities” points to “Circular economy practices.” A right-pointing upward diagonal arrow labeled “H 4 (beta equals 0.430, p less than 0.001)” from “Green knowledge sharing” points to “Circular economy practices.” A right-pointing arrow labeled “H 5 (β equals 0.197, p less than 0.05)” from “Circular economy practices” points to “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 a (beta equals negative 0.154, p less than 0.001)” from “Green employee behavior” points to the arrow between “Green dynamic capabilities” and “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 b (beta equals negative 0.092, p less than 0.05)” from “Green employee behavior” points to the arrow between “Circular economy practices” and “Hotel sustainable performance.” A downward dashed arrow labeled “H 7 c (beta equals 0.173, p less than 0.05)” from “Green employee behavior” points to the arrow between “Green knowledge sharing” and “Hotel sustainable performance.”Results-based model. Source(s): Developed by authors
4.3 Mediation and moderation test
This also established the mediating role of CEP. First, the study established that the indirect effect of GDC on HSP via CEP (β = 0.041, t-value of 2.133, p < 0.05). Similarly, GKS showed a significant indirect effect on HSP through CEP (β = 0.085, t-value of 2.823, p < 0.05). These results indicate CEP partial mediation in the link between GDC, GKS and HSP, supporting H6a and H6b (see Table 5).
Additionally, this study tested the moderating role of GEB. GEB negatively moderated the GDC to HSP link (β = −0.154, p < 0.05). Moreover, this study also found a negative and insignificant moderating effect of GEB in the link between GKS and HSP (β = −0.092, p > 0.05). Hence, H7a and H7b were rejected. However, GEB positively moderated the association between CEP and HSP (β = 0.173, p < 0.05). Thus, H7c was accepted. Overall, findings suggest that an increase in GEB strengthens the link between ECP and HSP (see Table 5).
4.4 NCA analysis
Necessary factors for achieving CEP and HSP were identified using the NCA method (Dul et al., 2023). NCA logically determines essential conditions required for desired outcomes. This approach offers a refined understanding of critical elements essential for effective performance within the studied domain. Dul et al. (2023) suggested that NCA is assessed by effect size (d ranging from 0 to 1) and significance level (p < 0.01).
Table 6 identifies key determinants that influence outcomes through the NCA method. GDC showed no effect on CEP (d = 0.000, p = 1.000), whereas GKS demonstrated a moderate effect and was identified as a necessary condition (d = 0.131, p = 0.000), confirming GKS as essential for CEP. For HSP, GDC (d = 0.153, p = 0.000) and CEP (d = 0.165, p = 0.000) showed a moderate effect, whereas GKS had a small effect (d = 0.070, p = 0.001); hence, all three constructs are necessary conditions for HSP. Bottleneck analysis (Table 7) revealed that achieving 60% CEP requires GKS ≥1.293, GDC is 0.000; for 60% HSP, GDC ≥7.328, CEP ≥1.724 and GKS ≥1.293. Scatter plots in Figure 3 visually validated these statistical relationships, reinforcing NCA’s robustness. For further details, refer to Table 8 for an exhaustive comparison between PLS-SEM and NCA outcomes.
NCA effect sizes
| Determinants | Outcomes | Original effect size (d) | Permutation p-value |
|---|---|---|---|
| GDC | CEP | 0.000 | 1.000 |
| GKS | 0.131 | 0.000 | |
| GDC | HSP | 0.153 | 0.000 |
| GKS | 0.070 | 0.001 | |
| CEP | 0.165 | 0.000 |
Bottleneck table (percentages)
| CEP | GKS | GDC | HSP | CEP | GDC | GKS |
|---|---|---|---|---|---|---|
| Bottleneck for CEP (%) | Bottleneck for HSP (%) | |||||
| 0 | 0.000 | 0.000 | 0 | 0.000 | 0.000 | 0.000 |
| 10 | 0.000 | 0.000 | 10 | 0.431 | 0.000 | 0.000 |
| 20 | 1.293 | 0.000 | 20 | 0.431 | 0.000 | 0.000 |
| 30 | 1.293 | 0.000 | 30 | 1.724 | 0.000 | 1.293 |
| 40 | 1.293 | 0.000 | 40 | 1.724 | 1.724 | 1.293 |
| 50 | 1.293 | 0.000 | 50 | 1.724 | 3.017 | 1.293 |
| 60 | 1.293 | 0.000 | 60 | 1.724 | 7.328 | 1.293 |
| 70 | 13.793 | 0.000 | 70 | 1.724 | 11.207 | 1.293 |
| 80 | 13.793 | 0.000 | 80 | 13.793 | 11.207 | 1.293 |
| 90 | 24.138 | 0.000 | 90 | 20.69 | 11.21 | 10.35 |
| 100 | 24.138 | 0.000 | 100 | 37.50 | 11.21 | 25.00 |
The figure shows five scatter and area plots combined in a single plot area, arranged in three rows. The details of each plot are as follows: The first scatter and area plot is titled “N C A ceiling line chart.” The vertical axis is labeled “C E P” with values ranging from negative 2.384 to 1.628 in increments of 0.2 till 1.416, and the final value of 1.628. The horizontal axis is labeled “G D C” with values ranging from negative 2.594 to 1.806 in increments of 0.4. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The complete background of the plot is in yellow. Multiple points for “Observations” are scattered across the plot area. Some of the points are (negative 0347, 0.127), (1.065, negative 0.662), (1.924, negative 1.489), (1.428, 1.32). The second scatter and area plot is titled “N C A ceiling line chart.” The vertical axis is labeled “C E P” with the same range as the first plot, and the horizontal axis is labeled “G K S” with values ranging from -2.024 to 1.376 in increments of 0.2. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The area for “C E-F D H” lies below the point at (negative 2.024, negative 1.856), increases in a stepwise manner passing (negative 1.85, 0.135), (negative 1.28, 0.96), and ends at (negative 0.747, 1.63). The area for “C R-F D H” lies between the steps of “C E-F D H,” and the line with a positive slope, spanning from (negative 2.03, 1.05) and (negative 0.826, 1.638). Multiple points for “Observations” are scattered across the plot area. Some of the points are (negative 1.46, negative 1.69), (1.39, 1.30), (0.95, negative 1.17), (negative 156, negative 353). The third scatter and area plot is titled “N C A ceiling line chart.” The horizontal axis is labeled “C E P” with values ranging from negative 2.384 to 1.628 in increments of 0.2 till 1.416, and the final value of 1.628. The vertical axis is labeled “H S P” with values ranging from negative 1.985 to 1.603 in increments of 0.2 till 1.415 and the final value of 1.603. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The area for “C E-F D H” lies below the point at (negative 2.391, negative 1.653), increases in a stepwise manner passing (negative 2.066, negative 1.11), (negative 1.89, 0.844), (1.226, 1.166), (negative 0.884, 1.452), and ends at (negative 0.237, 1.627). The area for “C R-F D H” lies between the steps of “C E-F D H,” and the line with a positive slope, spanning from (negative 2.398, negative 0.981) and (negative 0.618, 1.636). Multiple points for “Observations” are scattered across the plot area. Some of the points are (negative 1.579, negative 1.266), (negative 0.887, 0.927), (0.957, 1.314), (0.9, negative 0.861). The fourth scatter and area plot is titled “N C A ceiling line chart.” The horizontal axis is labeled “G C D” with values ranging from negative 2.594 to 1.806 in increments of 0.4. The vertical axis is labeled “H S P” with the same range as the third plot. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The area for “C E-F D H” lies below the point at (negative 2.594, negative 0.766), increases in a stepwise manner passing (negative 2.172, negative 0.481), (negative 1.947, 0.027), (1.518, 0.498), and ends at (negative 1.334, 1.611). The area for “C R-F D H” lies between the steps of “C E-F D H,” and the line with a positive slope, spanning from (negative 2.437, negative 0.677) and (negative 1.327, 1.175). Multiple points for “Observations” are scattered across the plot area. Some of the points are (negative 2.138, negative 1.291), (negative 1.281, 0.943), (negative 0.156, negative 0.196), (1.404, 1.29), (1.111, negative 0.953). The fifth scatter and area plot is titled “N C A ceiling line chart.” The horizontal axis is labeled “G K S” with values ranging from negative 2.024 to 1.576 in increments of 0.2. The vertical axis is labeled “H S P” with values ranging from negative 1.985 to 1.603 in increments of 0.2 till 1.415 and the final value of 1.603. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The area for “C E-F D H” lies below the point at (negative 2.019, negative 1.114), increases in a stepwise manner passing (negative 1.837, 0.93), (negative 1.632, 1.142), (1.249, 1.1476), and ends at (negative 0.675, 1.609). The area for “C R-F D H” lies between the steps of “C E-F D H,” and the line with a positive slope, spanning from (negative 2.029, negative 0.001) and (negative 0.992, 1.603). Multiple points for “Observations” are scattered across the plot area. Some of the points are (1.842, negative 1.697), (negative 1.258, 0.658), (1.469, 1.286), (1.249, negative 1.455) and (negative 0.054, negative 0.198). Note: All numerical values are approximated.Scatter plots. Source(s): Developed by authors
The figure shows five scatter and area plots combined in a single plot area, arranged in three rows. The details of each plot are as follows: The first scatter and area plot is titled “N C A ceiling line chart.” The vertical axis is labeled “C E P” with values ranging from negative 2.384 to 1.628 in increments of 0.2 till 1.416, and the final value of 1.628. The horizontal axis is labeled “G D C” with values ranging from negative 2.594 to 1.806 in increments of 0.4. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The complete background of the plot is in yellow. Multiple points for “Observations” are scattered across the plot area. Some of the points are (negative 0347, 0.127), (1.065, negative 0.662), (1.924, negative 1.489), (1.428, 1.32). The second scatter and area plot is titled “N C A ceiling line chart.” The vertical axis is labeled “C E P” with the same range as the first plot, and the horizontal axis is labeled “G K S” with values ranging from -2.024 to 1.376 in increments of 0.2. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The area for “C E-F D H” lies below the point at (negative 2.024, negative 1.856), increases in a stepwise manner passing (negative 1.85, 0.135), (negative 1.28, 0.96), and ends at (negative 0.747, 1.63). The area for “C R-F D H” lies between the steps of “C E-F D H,” and the line with a positive slope, spanning from (negative 2.03, 1.05) and (negative 0.826, 1.638). Multiple points for “Observations” are scattered across the plot area. Some of the points are (negative 1.46, negative 1.69), (1.39, 1.30), (0.95, negative 1.17), (negative 156, negative 353). The third scatter and area plot is titled “N C A ceiling line chart.” The horizontal axis is labeled “C E P” with values ranging from negative 2.384 to 1.628 in increments of 0.2 till 1.416, and the final value of 1.628. The vertical axis is labeled “H S P” with values ranging from negative 1.985 to 1.603 in increments of 0.2 till 1.415 and the final value of 1.603. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The area for “C E-F D H” lies below the point at (negative 2.391, negative 1.653), increases in a stepwise manner passing (negative 2.066, negative 1.11), (negative 1.89, 0.844), (1.226, 1.166), (negative 0.884, 1.452), and ends at (negative 0.237, 1.627). The area for “C R-F D H” lies between the steps of “C E-F D H,” and the line with a positive slope, spanning from (negative 2.398, negative 0.981) and (negative 0.618, 1.636). Multiple points for “Observations” are scattered across the plot area. Some of the points are (negative 1.579, negative 1.266), (negative 0.887, 0.927), (0.957, 1.314), (0.9, negative 0.861). The fourth scatter and area plot is titled “N C A ceiling line chart.” The horizontal axis is labeled “G C D” with values ranging from negative 2.594 to 1.806 in increments of 0.4. The vertical axis is labeled “H S P” with the same range as the third plot. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The area for “C E-F D H” lies below the point at (negative 2.594, negative 0.766), increases in a stepwise manner passing (negative 2.172, negative 0.481), (negative 1.947, 0.027), (1.518, 0.498), and ends at (negative 1.334, 1.611). The area for “C R-F D H” lies between the steps of “C E-F D H,” and the line with a positive slope, spanning from (negative 2.437, negative 0.677) and (negative 1.327, 1.175). Multiple points for “Observations” are scattered across the plot area. Some of the points are (negative 2.138, negative 1.291), (negative 1.281, 0.943), (negative 0.156, negative 0.196), (1.404, 1.29), (1.111, negative 0.953). The fifth scatter and area plot is titled “N C A ceiling line chart.” The horizontal axis is labeled “G K S” with values ranging from negative 2.024 to 1.576 in increments of 0.2. The vertical axis is labeled “H S P” with values ranging from negative 1.985 to 1.603 in increments of 0.2 till 1.415 and the final value of 1.603. The legend below the chart indicates “C R-F D H” in grey, “C E-F D H” in yellow, and “Observations” in blue. The area for “C E-F D H” lies below the point at (negative 2.019, negative 1.114), increases in a stepwise manner passing (negative 1.837, 0.93), (negative 1.632, 1.142), (1.249, 1.1476), and ends at (negative 0.675, 1.609). The area for “C R-F D H” lies between the steps of “C E-F D H,” and the line with a positive slope, spanning from (negative 2.029, negative 0.001) and (negative 0.992, 1.603). Multiple points for “Observations” are scattered across the plot area. Some of the points are (1.842, negative 1.697), (negative 1.258, 0.658), (1.469, 1.286), (1.249, negative 1.455) and (negative 0.054, negative 0.198). Note: All numerical values are approximated.Scatter plots. Source(s): Developed by authors
Hypothesis results (PLS-SEM) vs effect sizes (NCA)
| Relationship/variable | PLS-SEM hypothesis results | NCA effect size and p-value | Comparison and insight |
|---|---|---|---|
| GDC → HSP | β = 0.252, p < 0.01 | Effect size = 0.153, p < 0.01 | PLS-SEM confirms a strong and statistically significant relationship, indicating that GDC enhances HSP. Moreover, the NCA result shows a reasonable effect size of GDC, suggesting it is necessary for achieving HSP |
| GKS → HSP | β = 0.201, p < 0.01 | Effect size = 0.070, p < 0.01 | GKS has a positive effect on HSP in PLS-SEM. Moreover, NCA shows that GKS has a small effect on HSP, meaning that GKS alone is not a strict requirement for HSP but plays a supporting role |
| GDC → CEP | β = 0.207, p < 0.01 | Effect size = 0.000, p > 0.01 | While PLS-SEM shows a significant path. Whereas the NCA reveals that GDC is not a necessary condition for CEP. This suggests that GDC can influence CEP, but it is not a necessary condition to achieve CEP |
| GKS → CEP | β = 0.430, p < 0.01 | Effect size = 0.131, p < 0.01 | GKS exhibits the strongest impact on CEP through PLS-SEM, and NCA confirms that it is a necessary condition for CEP. To maximize CEP, organizations must fully develop GKS |
| CEP → HSP | β = 0.197, p < 0.01 | Effect size = 0.165, p < 0.01 | CEP significantly affects HSP in PLS-SEM, and NCA reinforces that CEP is a necessary condition for maximizing HSP. To optimize HSP, the CEP must be fully implemented |
5. Discussion and conclusion
The findings affirm the significance of GDC and GKS in regard to enhancing HSP, which aligns with the prior studies that emphasize the role of eco-innovation and environmental strategies in order to promote organizational sustainability (Tyagi et al., 2022). Our results therefore reinforce that GDC and GKS are significantly linked with HSP, which therefore validate H1 and H2. These results support the argument that GDC is critical for long-term sustainability in the hospitality industry (Amaranti et al., 2019). Furthermore, GKS facilitates the dissemination of green innovations and best practices, which enable organizations to adopt more sustainable operations.
The recent studies further elaborated on the role of absorptive capacity in the formation of GDC, which emphasizes that the ability to absorb and combine external knowledge is key to driving green (Yousaf, 2021). This suggests that fostering a knowledge-sharing culture within hotels can significantly enhance their capacity for innovation, which leads to improved sustainability outcomes. This study confirms that both GDC and GKS positively influence CEP, which supports H3 and H4. We also found that H5, CEP plays an important role concerning improving HSP, which corroborates with the study by Salvador et al. (2020). The integration of CEP, which involves waste reduction, efficient resource use and sustainable procurement, helps hotels reduce environmental impact while maintaining competitiveness.
Our findings indicate that CEP significantly mediates the relationship between GDC, GKS and HSP. GDC and GKS exhibit stronger effects on HSP when mediated by CEP. This study aligned with the previous research of Cuevas-Pichardo et al. (2025) and Laguir et al. (2024) that explained the mediating role of CEP. This reinforces the idea that a circular business model can more effectively address environmental challenges than traditional linear models by promoting closed-loop systems (Ghisellini et al., 2016). Moreover, strategically implementing CEP can drive innovation in hotel operations, leading to better guest experiences and a stronger market position.
This study also sought to extend the analysis of GEB as a moderator in the relationships between GDC, GKS, CEP and HSP. The results reveal that GEB positively moderates the CEP and HSP link (H7c), indicating that employee involvement in sustainability practices enhances the benefits derived from CE initiatives. This finding aligns with Li et al. (2023), who demonstrated GEB’s moderating role in fostering sustainable operations. They argue that employees’ pro-environmental behavior improves the effectiveness of green initiatives. However, no support was found for H7a and H7b, suggesting that GEB does not moderate the effect of GDC and GKS on HSP. This implies that while employee behavior is vital for CEP’s success. However, GEB’s moderating role between GDC, GKS and HSP has remained limited.
Additionally, NCA results confirmed the significant influence of GKS on CEP, demonstrating reasonable effect sizes and statistical relevance. The analysis also highlighted the roles of GKS, GDC and CEP in advancing sustainable practices and improving HSP. Bottleneck analysis specified minimum thresholds for these variables, reinforced by scatter plots guiding sustainable development targets. These insights underscore the necessity of integrating key green capabilities to drive HSP.
5.1 Conclusion
This study investigates the influence of GDC and GKS on HSP, with CEP as a mediator and GEB as a moderator. By integrating the NRBV and TBL frameworks, the research offers a holistic perspective on how organizational capabilities and employee behavior interact to drive sustainability in the hospitality industry. Findings confirm that GDC and GKS significantly enhance both CEP and HSP, while CEP plays a partial mediating role. GEB positively moderates the link between CEP and HSP, reinforcing the importance of employee engagement in sustainability outcomes. The application of both PLS-SEM and NCA enriches the methodological depth, identifying both significant and essential factors for sustainable performance.
Overall, this study contributes to theory by integrating dynamic capabilities, circular practices and behavioral factors into a unified framework and provides actionable guidance for hotel managers aiming to improve sustainability performance through capability building and employee engagement.
5.2 Theoretical contribution
This study contributes to sustainability research in hospitality by applying NRBV and TBL frameworks to examine how GDC and GKS drive HSP. This study established that GDC and GKS positively influence CEP and HSP. Moreover, the integration of CEP as a mediator shows that GDC and GKS translate into performance outcomes in service-intensive industry. Thus, the current study offers a strong theoretical framework through the foundational perspective on how green initiatives assist in environmentally friendly strategies, such as CEP and foster sustainability within the hospitality industry. Moreover, this study also contributes to the hospitality literature by establishing the moderating role of GEB. Thus, it reveals that GEB amplifies CEP’s effect on HSP, while it does not increase GDC and GKS’s direct effect on HSP. This indicates the need to refine how behavioral factors integrate into dynamic capability theory.
This study also contributes methodologically by confirming the combined use of the PLS-SEM and NCA, which significantly determines associations between constructs based on sufficiency and necessity logics. The findings indicate that GKS is both a significant influence and a necessary condition for CEP. Moreover, GKS, GDC and CEP significantly influence HSP and are a necessary condition.
5.3 Practical implications
This study offers insights for hotel practitioners and policymakers to improve environmental performance. Hotels should invest in developing green capabilities like eco-innovation and knowledge-sharing culture to enable the successful adoption of CEP, driving resource efficiency and waste reduction. Second, the mediating role of CEP emphasizes the importance of embedding circular principles into hotel operations. Managers should align processes with circular economy objectives for environmental and economic value. Third, the moderating effect of GEB highlights employees’ role in sustainability goals. Hotels should implement training and incentives to foster environmentally responsible behavior. A sustainability-oriented culture enhances the impact of internal capabilities and circular initiatives. Fourth, integrate GKS, GDC and CEP targets into performance appraisal, recognize “green champions” and incorporate green problem solving into supervisor training programs. Lastly, beyond generic awareness campaigns, hotels should embed sustainability into the design of work itself. This entails job redesigning where learning is tied to CEP tasks, for instance, how to sort waste or check water reuse systems. Use clear signs at workstations to show the green way to do each task. These steps will strengthen CEP, improve hotel sustainable performance and support sustainable development goals (SDG 8, 12 and 13).
5.4 Limitations
This study is subject to several limitations that can guide future research. Firstly, data from five Pakistani cities may limit the generalizability to other locations or foreign hotel chains operating with different cultural or regulatory conditions. The external validity can be maximized through more inclusive cross-regional studies. Second, response biases like social desirability were eliminated using expert-validated questionnaires and anonymous surveys, although future research can use objective variables like third-party audits to enhance reliability.
Third, the cross-sectional approach limits causal inference; longitudinal studies would be in a better position to identify dynamic effects. Fourth, external factors like policy or technological changes were not considered, which may influence hotel sustainability. Additionally, the nonsignificant moderation by GEB suggests the presence of latent moderators, while the partial mediation by CEP indicates that other mediators and moderators, such as environmental concern, global warming consciousness or other relevant variables, should be investigated.
The supplementary material for this article can be found online
