The Role of Government Support in Adopting Green Supply Chain Management: The Influence of Green Market Orientation and Employee Environmental Commitment in Libya

Abstract

This study examines the role of government support (GS) in facilitating green supply chain management (GSCM) practices in Libya. It focuses on the moderating effect of green market orientation (GMO) and the mediating role of employee environmental commitment (EEC). The study explores how government policies drive sustainable supply chain practices, considering the influence of employees’ environmental awareness and market orientation. This study uses empirical data from various Libyan industries and applies structural equation modeling (SEM) to analyze relationships. The results show that GS positively impacts EEC, enhancing the adoption of GSCM practices. Furthermore, GMO strengthens the relationship between EEC and GSCM implementation. These findings highlight the importance of government support in fostering environmental sustainability within supply chains. This study contributes to the literature on sustainable supply chain management by providing empirical evidence from a developing country, offering valuable insights for policymakers and business leaders in Libya and similar regions.

1. Introduction

The global emphasis on sustainability has reshaped industries, requiring organizations to balance economic growth with environmental conservation. Climate change, resource depletion, and pollution have intensified—pressuring industries to adopt sustainable practices [1,2]. Given their extensive resource consumption and environmental impact, manufacturing and supply chains play a key role in achieving environmental objectives among these sectors. This highlights the need for strategies such as green supply chain management (GSCM), which integrates environmental considerations into supply chain processes to enhance sustainability while maintaining competitiveness [3].

Libya faces unique sustainability challenges as a resource-rich nation heavily reliant on oil and gas. While these industries are crucial to the economy, they contribute significantly to pollution and unsustainable extraction practices [2]. Addressing these issues requires transformative approaches that balance economic and environmental sustainability. GSCM offers a framework that embeds sustainability into procurement, production, distribution, and waste management activities [1]. However, the adoption of GSCM in Libya remains underexplored, and this study aims to bridge this gap by examining the role of government support, employee environmental commitment, and green market orientation in driving sustainable practices.

1.1. The Innovative Aspects of the Study

While previous research has examined GSCM in various contexts, this study introduces several innovative contributions:

• Integration of Multiple Influential Factors: Unlike the existing studies that focus solely on regulatory or organizational aspects, this research integrates government support (GS), employee environmental commitment (EEC), and green market orientation (GMO) into a single framework, providing a comprehensive understanding of GSCM adoption.
• Focus on a Developing Economy Context: The study highlights Libya’s unique challenges and opportunities in adopting GSCM, addressing gaps in the literature concerning sustainability transitions in resource-dependent economies.
• Advanced Analytical Approach: By employing structural equation modeling (SEM), the study offers a robust statistical analysis of the relationships between GS, EEC, GMO, and GSCM adoption, providing empirical evidence on how these factors interact.
• Practical Implications for Policymakers and Businesses: The research offers actionable insights for Libyan policymakers and industry leaders, outlining strategies to enhance GSCM adoption through targeted government interventions and market-driven sustainability initiatives.

1.2. The Significance of Green Supply Chain Management (GSCM)

GSCM has emerged as a key response to the environmental challenges posed by traditional supply chain practices. It integrates sustainability into operations, offering benefits such as cost savings, improved efficiency, and enhanced corporate reputation [4,5]. However, in developing economies like Libya, GSCM adoption faces obstacles, including limited stakeholder awareness, weak infrastructure, and regulatory challenges [2,3]. Despite these issues, GSCM presents substantial environmental and economic advantages, helping Libyan industries align with global sustainability standards.

1.3. The Role of Government Support (GS)

Government support is essential for advancing industry sustainability initiatives. Policymakers can promote sustainable practices by implementing regulations, offering financial incentives, and enforcing environmental policies [3,6]. In Libya, institutional barriers and resource constraints make GS particularly crucial in overcoming resistance to change and facilitating GSCM adoption [2]. Effective GS can address financial and technical challenges by promoting clean technologies, subsidizing sustainability initiatives, and ensuring regulatory compliance [1].

1.4. The Role of Employee Environmental Commitment (EEC)

Employee environmental commitment (EEC) is key in promoting sustainability within organizations. EEC reflects employees’ dedication to supporting environmental initiatives, positively influencing GSCM adoption. Research indicates that engaged employees enhance environmental performance [7].

In Libya, where environmental awareness is still developing, understanding the EEC’s role in GSCM adoption is essential. This study examines how employee commitment influences sustainability decisions in organizations, offering insights for policymakers and business leaders.

1.5. The Role of Green Market Orientation (GMO)

Green market orientation (GMO) is a proactive approach to integrating environmental values into market strategies. By fostering eco-friendly product development and sustainable marketing, GMO enhances corporate reputation and innovation capacity [4,8]. GMO strengthens the link between EEC and GSCM adoption, amplifying the impact of consumer-driven environmental initiatives.

This research investigates the role of GMO in enhancing GSCM practices in Libya. Libyan firms can better respond to environmental demands and ensure long-term sustainability by adopting a market-oriented approach.

2. Theoretical Framework and Hypotheses Development

This study’s theoretical framework is based on institutional theory, which underscores the impact of external pressures, such as government support, in shaping organizational behavior [9]. Additionally, the study employs the dynamic capabilities framework to highlight the internal competencies necessary for organizations to adapt to environmental challenges and generate sustainable value [10]. Institutional pressures, including regulatory mandates and market expectations, are crucial in driving firms toward green supply chain adoption, emphasizing the significance of both external and internal forces in sustainability transitions [11]. Recent studies further support this notion, highlighting how regulatory frameworks and market-driven sustainability expectations shape organizational behavior and green supply chain adoption [12].

This research investigates the relationship between GS, EEC, and GMO in facilitating GSCM adoption in Libya. It contributes to the knowledge of GSCM within resource-dependent economies by identifying critical drivers and barriers to sustainability. Moreover, it provides policymakers and industry leaders with insights to enhance Libya’s environmental and economic resilience.

2.1. Employee Environmental Commitment (EEC)

EEC is a fundamental factor in motivating organizations to embrace sustainable practices. Employees with pro-environmental attitudes tend to adopt workplace behaviors that align with environmental standards, encouraging companies to integrate sustainability into their operations. Studies indicate that environmentally committed employees actively participate in green initiatives, strengthening organizational sustainability [13].

Incorporating sustainability into an organization’s core values necessitates a cultural transformation in which employees develop a personal commitment to environmental outcomes. This transformation enables companies to comply with regulations while proactively addressing sustainability challenges, improving performance, and meeting employee expectations [14,15].

2.2. Green Market Orientation (GMO)

GMO reflects a firm’s commitment to integrating environmental considerations into its marketing strategies by developing eco-friendly products and responding to consumer demand for sustainability [16]. Companies with a strong GMO are more likely to implement GSCM practices, gaining a competitive advantage through sustainable value propositions [17].

Additionally, GMO is a moderating factor in the relationship between EEC and GSCM adoption. As employee commitment to environmental issues increases, firms with a well-established GMO is more likely to incorporate green practices into their supply chains [18]. Aligning sustainability strategies with market demands further facilitates the implementation of GSCM [16].

2.3. Government Support (GS)

Government support is a key determinant in fostering GSCM adoption. Various governmental initiatives, including regulatory frameworks, financial incentives, and policies, are essential in encouraging businesses to adopt sustainable practices [19]. Financial subsidies, tax benefits, and research funding serve as mechanisms to mitigate the costs associated with sustainability initiatives [20]. Consequently, GS is crucial in promoting GSCM adoption, particularly in regions facing financial and infrastructural limitations.

2.4. Hypotheses Development

Building on the theoretical framework, the study proposes the following hypotheses:

H1. 

Government support (GS) positively affects employee environmental commitment (EEC) [14].

H2. 

Employee environmental commitment (EEC) positively affects green supply chain management (GSCM) [13].

H3. 

Government support (GS) positively affects green supply chain management (GSCM) [21].

H4. 

Employee environmental commitment (EEC) mediates the relationship between government support (GS) and green supply chain management (GSCM) [3].

H5. 

Green market orientation (GMO) moderates the relationship between employee environmental commitment (EEC) and green supply chain management (GSCM) [16].

2.5. Research Contribution

This study integrates institutional and capability-based perspectives to explain how external pressures (GS) interact with internal competencies (EEC and GMO) to drive GSCM adoption. Focusing on Libya, a developing economy with sustainability challenges, provides practical insights for policymakers and business leaders to enhance sustainable industrial practices.

H4 indicates that (GS) indirectly enhances (GSCM) through (EEC). Government initiatives, such as environmental regulations, financial incentives, and sustainability programs, increase employees’ environmental awareness and encourage eco-friendly behaviors. This, in turn, promotes the adoption of green supply chain strategies, reinforcing the impact of government support on (GSCM), as highlighted by the dashed line in Figure 1.

3. Method

3.1. Sampling and Data Collection

This study targeted professionals in Libya’s production sector, utilizing a digital questionnaire distributed over four months. Respondents’ perceptions were measured using a seven-point Likert scale, ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”). Of the 281 distributed questionnaires, 249 valid responses were retained after rigorous screening for completeness and inattentiveness. The STDEV.P formula in Excel was applied to identify and remove 32 inattentive responses. According to [22], the final sample size exceeded the minimum requirement, which should be at least ten times the number of arrows pointing to a latent variable in the PLS path model.

A simple random sampling technique ensured representation and a balanced cross-section of managers, administrative staff, and engineers in Libya’s production sector. As noted by [23], simple random sampling minimizes bias in the selection process, enhances the generalizability of findings, and maintains a low margin of error, as highlighted by [24].

Data analysis was performed using IBM SPSS Statistics 27.0.1.0 and SmartPLS 4.1.0.9. SPSS provided demographic insights by analyzing gender, age, education, occupation, and experience. Meanwhile, SmartPLS facilitated structural equation modeling (SEM) to explore relationships among variables and validate the measurement model.

PLS-SEM was chosen over CB-SEM and other methods for its suitability for exploratory research, ability to handle complex models, and flexibility with small-to-medium sample sizes. Unlike CB-SEM, which requires large samples and assumes data normality, PLS-SEM is robust with non-normally distributed data, making it ideal given the variability in respondents’ perceptions. It prioritizes predictive accuracy by maximizing explained variance (R²) and employs bootstrapping for hypothesis testing [22]. Additionally, PLS-SEM effectively manages complex relationships, including direct, indirect, and moderating effects, which are central to this study [22]. While it lacks traditional model fit indices and may slightly overestimate path coefficients, these limitations are mitigated by assessing R² and effect sizes. Given this study’s focus on predicting GSCM adoption in Libya and identifying key influencing factors, PLS-SEM is the most appropriate method for deriving meaningful insights.

3.2. Measures

This study employed a questionnaire to collect data and analyze the relationships outlined in the proposed model. The measurement scales were adapted from relevant previous research, with minor modifications to certain items to better align with the study’s objectives.

• Government support was measured using six items adapted from [3] to assess this construct comprehensively.
• Employee environmental commitment was evaluated through six items adopted from [25].
• Green market orientation was assessed using six items from [26], with adjustments to ensure they aligned more closely with this study’s specific goals.
• Green supply chain management was measured using six items adapted from [27].

These refined scales provided a structured and precise assessment of the key variables in this research.

4. Results

4.1. Demographics of Respondents

The respondents’ demographic profiles were analyzed using IBM SPSS Statistics 27.0.1.0. These profiles, categorized by demographic variables such as gender, age, education level, occupation, and years of experience, provide a detailed understanding of the sample characteristics.

Table 1. Demographic characteristics.

Items	Frequency (N = 249)	(%)
Gender
Male	216	86.7
Female	33	13.3
Age
20–30 years	23	9.2
31–40 years	139	55.8
41–50 years	79	31.7
50 or more years	8	3.2
Qualification
High Diploma	26	10.4
Bachelor	149	59.8
Master	56	22.5
PhD	18	7.2
Years of Experience
Less than 5 years	32	12.9
5–9 years	68	27.3
10 to 14 years	64	25.7
15–19 years	52	20.9
20 years or more	33	13.3
Job
Department manager	60	24.1
Administrative Staff	98	39.4
Engineer	66	26.5
General Manager	25	10

provides the complete details of the demographic characteristics of the respondents who participated in this study.

4.2. Exploratory Factor Analysis (EFA)

The Kaiser–Meyer–Olkin (KMO) [28] measure of sampling adequacy is 0.937, indicating that the data are highly suitable for factor analysis. A value above 0.9 is considered “marvelous”, reflecting strong correlations among variables.

Bartlett’s Test of Sphericity yielded a chi-square value of 3861.097 with 276 degrees of freedom. The test was statistically significant (p < 0.001). This confirms that the correlation matrix is not an identity matrix, thereby validating the use of factor analysis.

4.3. Communalities

Communalities indicate the proportion of variance in each variable that the extracted factors can explain. A commonality value above 0.5 is often considered acceptable in factor analysis, as the extracted components retain significant variance [29]. However, several studies suggest using a higher threshold, such as 0.6, to ensure more substantial factor loadings and more reliable measurement constructs [30,31]. A threshold of 0.6 enhances the robustness of the factor structure, reducing the likelihood of weak or unstable factor loadings and ensuring that items contribute meaningfully to their respective constructs.

In this study, all the items exhibit high extraction values, ranging from 0.617 (EEC_2) to 0.785 (GSCM_2), demonstrating strong alignment with the factor structure and substantial retained variance.

• Government Support (GS): GS_1 through GS_6 have extraction values between 0.678 and 0.732, indicating their substantial contribution to the factor.
• Employee Environmental Commitment (EEC): Items show extraction values between 0.617 (EEC_2) and 0.741 (EEC_1), reflecting their good fit within the model.
• Green Supply Chain Management (GSCM): Items display extraction values between 0.661 (GSCM_5) and 0.715 (GSCM_6), emphasizing their alignment with the factor structure.
• Green Market Orientation (GMO): Extraction values range from 0.648 (GMO_3) to 0.705 (GMO_5), further confirming their strong association with the components.

This study follows the established methodological guidelines by adopting 0.6 as the threshold, ensuring a more rigorous and reliable factor analysis approach.

4.4. Common Method Bias

This research also applied the common method bias using Harman’s single-factor approach. The variance extracted using one factor is 36.565%, below 50%, indicating no common method bias in this study [30].

4.5. Measurement Model

Table 2. Reliability and validity analysis.

Construct	Items	Loading	Alpha	CR	AVE
			>0.7	>0.7	>0.5
GS	GS_1	0.853	0.914	0.933	0.699
	GS_2	0.837
	GS_3	0.829
	GS_4	0.846
	GS_5	0.826
	GS_6	0.827
EEC	EEC_1	0.853	0.908	0.929	0.684
	EEC_2	0.786
	EEC_3	0.804
	EEC_4	0.832
	EEC_5	0.833
	EEC_6	0.854
GMO	GMO_1	0.788	0.900	0.922	0.663
	GMO_2	0.836
	GMO_3	0.840
	GMO_4	0.797
	GMO_5	0.838
	GMO_6	0.784
GSCM	GSCM_1	0.846	0.914	0.933	0.700
	GSCM_2	0.833
	GSCM_3	0.849
	GSCM_4	0.840
	GSCM_5	0.816
	GSCM_6	0.838

overviews the reliability and validity metrics for four constructs: GS, EEC, GMO, and GSCM. The results demonstrate strong reliability across all the constructs, as indicated by Cronbach’s Alpha (α) and the Composite Reliability (CR) values exceeding the recommended threshold of 0.7 [29,32], indicating high internal consistency and stability of the scale. Additionally, the average variance extracted (AVE) values are all above 0.5, signifying satisfactory convergent validity. Item loadings for each construct exceed 0.7, confirming that the individual items adequately represent their respective constructs.

Table 3. Discriminant validity for zero order construct (Fornel–Larcker and HTMT) [30].

Constructs	1	2	3	4
1. EEC	0.827	0.237	0.548	0.586
2. GMO	0.208	0.814	0.172	0.190
3. GS	0.503	−0.159	0.836	0.666
4. GSCM	0.535	−0.183	0.611	0.837

Note: Values on the diagonal (italicized) represent the square root of the average variance extracted, while the off-diagonals are correlations.

presents the discriminant validity assessment of the constructs using the Fornell–Larcker criterion and the Heterotrait–Monotrait (HTMT) ratio. The square root of the AVE for each construct, shown along the diagonal, is higher than the correlations between constructs represented by the off-diagonal values. This result satisfies the Fornell–Larcker criterion, indicating that each construct is empirically distinct. Furthermore, the HTMT ratios were below the conservative threshold of 0.85 [33].

Figure 2 provides a graphical representation of the assessment of the measurement model. This visual representation supports the validity assessment by illustrating the relationships among the constructs.

4.6. Structural Model

After confirming the reliability and validity of the measurement model, the structural model was assessed to test the hypotheses. The analysis was performed in SmartPLS version 4.1.0.9 using the bootstrap method with 5000 resamples at a two-tailed significance level of 0.05. This technique provides robust estimates of path coefficients, enabling validation of the hypothesized relationships.

4.6.1. Hypotheses Testing: Direct Effect

The hypothesis testing analysis, which includes direct, mediation, and interaction effects, provided significant insights into the relationships among the study variables.

For the direct effects, the findings provide strong support for the proposed hypotheses:

• H1, which examined the relationship between government support (GS) and employee environmental commitment (EEC), was confirmed with a beta value (β) of 0.503, t-value of 6.969, and p-value of 0.000, indicating a strong and statistically significant positive relationship.
• H2 established that EEC positively influences green supply chain management (GSCM), with β = 0.518, t = 5.155, and p = 0.000, further validating the hypothesis.
• H3, which assessed the impact of GS on GSCM, was also supported, with β = 0.285, t = 2.674, and p = 0.008, demonstrating a significant positive association.

These findings highlight the crucial role of government support in shaping employee environmental awareness and promoting sustainable supply chain practices.

Table 4. Direct effect.

Hypothesis	Direct	Std.	Std.	T	p	Decision
	Relationships	Beta	Error	Values	Values
H1	GS → EEC	0.503	0.072	6.969	0.000	Accepted
H3	GS → GSCM	0.285	0.107	2.674	0.008	Accepted
H2	EEC → GSCM	0.518	0.100	5.155	0.000	Accepted

presents the model’s direct effects, illustrating the relationships between the key constructs. The results provide statistical evidence supporting the hypothesized paths.

4.6.2. Hypotheses Testing: Indirect Effect (Mediation)

The mediation analysis offered further insights. H4, which examines the mediating role of EEC in the relationship between GS and GSCM, was supported with an indirect effect (β = 0.260, t = 4.136, p = 0.000). This finding confirms that EEC plays a significant role in mediating the relationship, underscoring its importance in linking GS to GSCM.

Table 5. Hypotheses testing mediation effects.

Hypothesis	Indirect/Mediation	Std.	Std.	T	p	Decision
	Effect	Beta	Error	Values	Values
H4	GS → EEC → GSCM	0.260	0.063	4.136	0.000	Accepted

presents the hypotheses testing for mediation effects, demonstrating the indirect relationships between the variables and their statistical significance.

Figure 3 provides a graphical representation of the structural model, illustrating the relationships between the constructs and their estimated path coefficients.

4.6.3. Hypotheses Testing: Interaction Effect

The interaction effect analysis provided additional insights into the moderating role of green market orientation (GMO) in the relationship between EEC and GSCM.

H5 was supported (β = 0.172, t = 3.795, p = 0.000), confirming that GMO significantly moderates this relationship, thereby amplifying the impact of EEC on GSCM.

Table 6. Interaction effect.

Hypothesis	Interaction	Std.	Std.	T	p	Decision
	Effect	Beta	Error	Values	Values
H5	GMO × EEC → GSCM	0.172	0.045	3.795	0.000	Accepted

presents the interaction effect, highlighting the moderating influence between the key constructs.

Figure 4 illustrates the graphical representation of the interactions between GMO × EEC leading to GSCM, highlighting the moderating effects within the model.

4.6.4. Coefficient of Determination (R²)

The R² value, also known as the coefficient of determination, evaluates the predictive power of structural models. It shows the proportion of variance in an endogenous construct explained by its predictors. R² values are interpreted as follows: 0.25 suggests a weak explanatory power, 0.50 represents a moderate level, and 0.75 indicates a substantial predictive capability [34]. As presented in

Table 7. R² values.

Constructs	R-Square
EEC.	0.253
GSCM	0.526

, the R² values for the endogenous variables indicate the extent to which the exogenous constructs explain the variance in the dependent variables.

4.6.5. Effect Size (F²)

The effect size (F²) measures the relative contribution of each exogenous construct to the explained variance (R²) of an endogenous variable. SmartPLS reports these values in the Consistent PLS Algorithm output. According to Cohen’s (2013) guidelines [35], the effect sizes are interpreted as follows:

• 0.02: Small effect;
• 0.15: Medium effect;
• 0.35: Large effect.

Table 8. F² values.

	F2
EEC ≥ GSCM	0.300
GS ≥ EEC	0.338
GS ≥ GSCM	0.101
GMO × EEC ≥ GSCM	0.102

Note: GS = “government support”, EEC = “employee environmental commitment”, GSCM = “green supply chain management”, GMO = “green market orientation”.

presents the F² values for each exogenous construct, illustrating their effect sizes on the respective endogenous constructs.

Figure 5 visually represents R² and F², showcasing the model’s explanatory power and effect sizes.

5. Discussion

The findings of this study contribute to the expanding body of literature investigating the roles of government support (GS), green market orientation (GMO), employee environmental commitment (EEC), and green supply chain management (GSCM) in promoting organizational sustainability. This study employs advanced statistical techniques and contemporary models to highlight how these factors collectively drive green practices and give organizations a competitive advantage.

5.1. Theoretical Implications

This research highlights the significant influence of GS on both EEC and GSCM, confirming prior findings that institutional support is pivotal for environmental initiatives [36]. Government policies, financial incentives, and strategic frameworks encourage organizations to prioritize environmental concerns, thereby advancing GSCM practices. The R² value for EEC (0.253) suggests that GS has weak predictive power over EEC, while the R² for GSCM (0.526) indicates a moderate predictive relationship, demonstrating the nuanced role of government support.

The significant positive impact of EEC on GSCM aligns with the existing research, emphasizing that employees with strong environmental commitment are more likely to support and actively implement GSCM practices within their organizations, fostering a culture of sustainability [37].

Moreover, GMO was identified as a crucial moderating variable that strengthens the relationship between EEC and GSCM. This finding aligns with strategic orientation theories, emphasizing the importance of market-driven strategies in integrating environmental values into organizational processes [38]. Despite its F² value of 0.102 (small effect), the moderating role of GMO underscores its strategic importance.

These theoretical insights can be observed in real-world applications. While institutional support influences GSCM adoption, its effectiveness varies across industries. For example, governmental efforts in Libya have yet to drive corporate sustainability fully. A review of the available literature and industry reports suggests that no company in Libya has fully implemented sustainability principles. However, according to some participants in this study from Arabian Gulf Oil Company, the company has made notable, yet partial, progress in integrating environmental sustainability into its corporate strategy. They reported that the company has invested in energy-efficient technologies, reduced carbon emissions, and explored eco-friendly alternatives. However, they also acknowledged that the adoption of GSCM remains limited. Initiatives such as employee training on sustainable operations and green marketing efforts contributed to improved environmental performance and enhanced market competitiveness.

5.2. Managerial Implications

The practical implications of this study are significant for policymakers and organizational leaders striving to achieve sustainability goals. Government entities are encouraged to develop policies and provide incentives that raise environmental awareness among firms and consumers. On the other hand, firms should integrate GMO strategies into their operations to enhance the efficiency and effectiveness of GSCM practices. The mediating role of the EEC in linking GS and GSCM underscores the need for organizations to align their practices with consumer values, thereby maximizing the impact of governmental support.

The findings also reveal the following F² values: GS → EEC (0.338, medium effect), EEC → GSCM (0.300, medium effect), and GS → GSCM (0.101, small effect). These values indicate that while GS significantly affects EEC, its direct impact on GSCM is less pronounced. This insight suggests that focusing on employee commitments could amplify the benefits of governmental interventions, thereby enhancing environmental performance and stakeholder trust.

5.3. Research Contributions

This study contributes to the theoretical frameworks of stakeholder and dynamic capabilities theory by providing empirical evidence on the relationships between GS, GMO, EEC, and GSCM. Through the application of SmartPLS 4.1, the study ensures robust structural equation modeling, enhancing the reliability and validity of the findings. The validated model strengthens the empirical foundation for future research and provides a methodological benchmark for analyzing sustainability practices in similar contexts.

By focusing on the Libyan context, this study addresses a critical gap in the literature by exploring the challenges and opportunities associated with adopting green practices in emerging markets. The findings offer valuable insights for policymakers and industry leaders, highlighting key drivers and barriers to GSCM adoption, which can inform future sustainability strategies in developing economies.

6. Conclusions

This study provides empirical evidence on the role of government support (GS), green market orientation (GMO), and employee environmental commitment (EEC) in driving green supply chain management (GSCM) adoption. The results indicate that GS positively influences EEC, which enhances the implementation of GSCM practices. Furthermore, GMO strengthens the relationship between EEC and GSCM, highlighting the importance of a market-driven approach to sustainability.

The findings underline the need for policymakers to design supportive regulatory frameworks and provide financial incentives that encourage firms to integrate sustainable practices. Based on the results, it is recommended that governments implement more robust environmental policies and offer incentives that foster the adoption of green practices, especially in emerging markets.

Businesses should also focus on strengthening employee engagement in environmental initiatives and adopting market-driven green strategies to enhance their competitive advantage. Specifically, firms can enhance green logistics management by investing in eco-friendly transportation, optimizing resource efficiency, and implementing circular economy principles. Additionally, from a marketing perspective, companies should promote sustainable practices and green products in their marketing strategies to attract environmentally conscious consumers.

6.1. Research Limitations

Despite its contributions, this study has certain limitations that should be acknowledged. First, the research focuses specifically on the construction sector in Libya, which may limit the generalizability of the findings to other industries or regions. Future research could expand the scope to include different sectors and cross-country comparisons to provide a broader perspective on GSCM adoption.

Second, the study relies on survey-based data collection, which may introduce potential biases such as social desirability bias or self-reporting inaccuracies. Future studies could employ mixed-method approaches, incorporating qualitative insights from interviews or case studies to enhance the depth of analysis.

Third, while structural equation modeling (SEM) provides robust insights into the relationships among variables, it does not capture the dynamic evolution of GSCM adoption over time. Longitudinal studies could be conducted to examine how these relationships change with evolving government policies and market conditions.

6.2. Prospects for Future Research

Future research should explore additional factors that may influence GSCM adoption, such as technological innovation, corporate culture, and international environmental standards. Moreover, investigating the role of circular economy practices within GSCM could provide valuable insights into achieving long-term sustainability goals.

Furthermore, based on the research results, organizations should explore using green marketing as a key strategy to reinforce their commitment to sustainability. Effective marketing campaigns highlighting eco-friendly initiatives can improve brand image and customer loyalty, creating a positive feedback loop that drives more sustainable practices.

Additionally, studies could examine the effectiveness of specific government policies and incentive programs in promoting sustainable supply chain practices. Comparative studies between Libya and other developing economies could offer a richer understanding of the contextual differences in GSCM implementation.

Future studies can further contribute to advancing sustainable supply chain management and supporting the global transition toward environmentally responsible business practices by addressing these limitations and exploring new research avenues.