The Role of Green HRM in Promoting Green Innovation: Mediating Effects of Corporate Environmental Strategy and Green Work Climate, and the Moderating Role of Artificial Intelligence

Abstract

This study aimed to discover how green human resource management (GHRM) practices influence green innovation, with a focus on the mediating roles of corporate environmental strategy and a green work climate and the moderating effect of artificial intelligence (AI). A quantitative method was used in this study. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypothesized model. The findings indicate that GHRM positively influences green innovation and that the enhanced effect of artificial intelligence can serve as a major determinant of innovative outcomes. This study suggests that implementing GHRM can have a positive impact on environmental performance and organizational sustainability. This study significantly promotes green innovation and enhances overall organizational sustainability. GHRM practices focus on integrating environmental concerns into HR functions and promoting a culture of environmental responsibility among employees.

1. Introduction

Green human resource management is increasingly global, owing to the growing need for sustainable practices and frameworks within organizations [1]. Indeed, it is a modern approach to human resource management (HRM) that emphasizes incorporating environmental sustainability into all aspects of HR practices. Initiating innovation is a vital strategy for achieving sustainable improvement, and it aligns strongly with Sustainable Development Goal (SDG) 9, which focuses on industry, innovation, and infrastructure. Countries that can adopt environmental policies that focus on these progressions through energy efficiency and new technologies will significantly enhance sustainability [2].

Positive green work climate perceptions provide the necessary atmosphere for employees to be encouraged by the strategic values and mission of a company [3]. By implementing advanced and modern technologies, banks can achieve a higher rate of green initiatives and foster a green climate that delivers sustainable value. It has been reported that green human resource management (GHRM) has a direct impact on the sustainability and overall performance of organizations [4]. This suggests that providing empirical evidence from Palestine, as a small economy in the Middle East, can contribute to the existing literature because of the scarcity of such examinations, yielding a regional organizational dynamic understanding [5]. Furthermore, this research argues that having artificial intelligence (AI) as a resource can enhance the engagement of employees while encouraging innovation, which can yield positive organizational outcomes, such as efficiency and green capabilities [6].

The integration of artificial intelligence (AI) with organizational performance strategies offers a significant pathway for banks to enhance their competitiveness. However, an important research gap concerns how HR managers’ observations of AI impact its implementation and performance in green HRM activities designed to increase environmental sustainability in the banking sector [7]. In the same context, corporate environmental strategy and green work climate perceptions are recognized as important variables that can influence positive outcomes due to the deep level of implementation in the workplace and in the workforce [8].

As illustrated in the proposed model (see Figure 1), the interaction between GHRM practices, corporate environmental strategy, green work climate perceptions, and AI plays a major role in determining the extent to which green innovation can manifest itself within the company and among its members [9]. This research embeds the premise of the Resource-Based View (RBV) model [10] as the theoretical setting that explains and supports current arguments. Under this premise, the GHRM practices embedded in the strategies of the firm and the climate of the organization that is aligned with these strategies can significantly improve the innovation of the firm from a sustainability perspective.

2. Theoretical Background and Hypothesis Development

2.1. Resource-Based View (RBV) Model

The Resource-Based View (RBV) model [11] suggests that a firm’s ability to sustainably obtain a competitive advantage can be determined by its resources. The higher the value, rarity, inimitableness, and non-substitutable (VRIN) of the resource, the greater the advantage and competitive edge. RBV entails all aspects of organizational resources, including human resources, monetary assets, and other instruments such as AI, which can be a critical resource contributing to the firm’s performance, innovativeness, and sustainability [4]. Furthermore, GHRM and its effectiveness can be improved when the organization’s environment is supportive of such initiatives. This can amplify the processes of decision-making and innovation among members of the organization through green strategies and the management of the organization [12,13]. RBV provides the necessary setting for the research hypotheses, which entail human capital, organizational climate, strategies, and assets (i.e., AI), and are aligned with green goals and structures, and the notion that competitiveness and improved performance can be gained by emphasizing the internal resources of the firm [9,11]. When AI is combined with the Resource-Based View (RBV) and its VRIN (Valuable, Rare, Inimitable, Non-substitutable) criteria, it can create a significant competitive advantage for organizations. AI-driven insights can become valuable, rare, and inimitable, particularly when combined with human expertise and dynamic capabilities. This interaction allows HR to move beyond transactional tasks and become a strategic partner, driving organizational performance and sustainability [14]. Thus, applying this model to the current study can provide a better understanding of sustainability measures and innovation in this area in the underexplored banking sector of Palestine.

2.2. GHRM and Green Innovation

GHRM practices and aspects include recruitment, training, performance appraisal, and compensation, and employee engagement focused on sustainability. On this premise, the position of human capital is a key resource that aligns employee behavior with the organization’s green objectives [4]. As members within the organization are directed and empowered towards sustainable initiatives, the green competency of the firm grows, which yields a competitive advantage for the firm by fostering greater green innovation and activities. As a resource, this is difficult to imitate, and thus, the competitive edge of the firm can become more solid in the market [12]. In the current study, GHRM is regarded as an internal resource that is highly valuable, as it can directly impact outcomes such as green innovation by fostering a climate of responsibility and creativity towards sustainable and green initiatives. When recruiting, training, appraisals, and compensations are meticulously aligned under the green premise of GHRM, staff members are more likely to engage in green behaviors that address current challenges and provide new solutions (i.e., green innovation) [5]. The RBV model provides a framework for adjusting the climate of an organization towards a positive attitude toward sustainability. This can amplify innovative outcomes, as it requires support and encouragement for employees to take green action [15].

Through using the premises of the RBV model, this study shows how the banking industry of Palestine can move towards sustainable competitive advantages by embedding GHRM practices and directing the strategies of the firm through green policies that incorporate all levels of managing assets of the organization (i.e., human resources) [5,11,13]. In particular, an adequate system for human resources (GHRM) and other elements included in the current model (see Figure 1) that entail the technical resources available to the firm are expected to yield positive long-term outcomes, such as green innovation [13]. It can be argued that, by implementing GHRM policies and guidelines, the workforce is steered towards green activities, which can directly impact the extent to which members can or will show innovativeness in the sustainability domain. This is because GHRM and its dimensions have been rigorously planned to enhance green initiatives among staff members. These dimensions are operationalized as follows:

Green recruitment can ensure that employees who are attracted to a firm possess green values and have environmental awareness. This is further in line with the overall competencies needed to align new members with the organization’s goals [16]. Green training falls under the same logic as employees being provided with skills and knowledge that are designed to enhance their ability to act sustainably in their routine operations [17]. The appraisal of green performance consists of the assessment of the staff regarding their initiatives in the green domain. In turn, this can increase their tendency to participate and contribute to such initiatives. Such performance can be rewarded by monetary and nonmonetary means to further encourage embracing green activities [16]. Green participation is the last dimension of GHRM, in which members of the organization are directed and motivated to take actions that are influential in the decision-making process of the firm, which can strengthen their sense of responsibility and contribution to sustainable development and achieving goals. Within the context of the current study, it is hypothesized that these dimensions are significant in determining green innovation in an organizational setting, as they align and enhance the usage of resources of the firm under the RBV model [17]. With higher participation from employees, managers are more likely to have a variety of solutions as staff members share their ideas, yielding positive outcomes for the firm (e.g., green innovation and competitiveness) [5,11].

In this respect, each dimension of GHRM contributes to the manifestation of green innovation, such as green training and recruitment, ensuring that employees have a set of skills and/or competence in the environment and sustainability, which is further combined with various initiatives that enable participation and promote taking actions through incentives. This establishes a workplace that centralizes sustainability and increases competitive advantages [16]. This workplace is then capable of integrating GHRM policies and practices within the company’s framework, aligning strategies with the principles of RBV [9]. Accordingly, we propose the following hypothesis:

H1. 

Green human resource management (GHRM) practices positively influence green innovation in the banking sector.

2.3. Mediating Effect of Corporate Environmental Strategy

This study includes corporate environmental strategy (CES) as a factor that indirectly influences the linkage between GHRM practices and green innovation (GI). Using the premises of the RBV model, a sustained competitive advantage can be obtained when an organization establishes strategic objectives that efficiently use the resources and capabilities of the firm [5,9], CES can act as a critical resource for ensuring that the internal efforts of an organization are aligned with its goals and vision while maintaining and/or enhancing performance in all aspects of the organization (i.e., sustainability). This entails policies and guidelines for GHRM, which should be embedded in the strategic planning of the firm to achieve specific results. CES defines the approach and how such practices can be integrated into the processes of the firm, making green aspects (e.g., GI) a vital goal [18]. When an organization’s operations are infused with green objectives, they further amplify the incentives for green innovation and engage in green behavior [4]. This shows that CES can better align GHRM practices with the specified outcomes of the firm (i.e., GI). The efforts of the firm through CES and under the premises of the RBV model are complementary, as they establish a structure within the firm and enable the development and execution of green initiatives. Thus, it acts as a foundation for prioritizing the usage of resources and their allocation while encouraging the staff to act green through feedback and support [4,12].

CES entails all practices of the firm, which implies that through adequate strategic planning, GHRM practices can thrive and foster an environment in which green solutions are highly encouraged. Actions such as energy efficiency, waste management, and green product development are emphasized, and employees are provided with training, motivation, and support in such efforts, furthering green outcomes (i.e., GI) [12]. This leads to a synergistic effect within the workplace that steers employees towards innovation, thus improving the overall innovative prowess of the organization [19]. In other words, an adequate CES can embed various practices (e.g., GHRM) in a comprehensive manner that ensures that no effort is isolated and that all actions are aligned with strategic objectives. Employees can perceive this alignment, which encourages them to develop a sense of responsibility that motivates them to contribute to the firm’s green goals. This can be demonstrated through ideas, thoughts, solutions, technologies, and new green methods to address existing issues [9]. Thus, corporate environmental strategy acts as a mediator by enhancing the impact of GHRM practices on green innovation, creating a clear pathway for employees to translate their environmental awareness into innovative solutions. This leads to the following hypothesis.

H2. 

Corporate Environmental Strategy mediates the relationship between GHRM practices and green innovation.

2.4. Mediating Effect of Green Work Climate Perceptions

In addition to what was mentioned, green work climate perceptions (GWC) are also included in the current model (see Figure 1) as a mediating factor that can better explain the linkage between GHRM practices and the manifestation of GI as an outcome. Under the premises of the RBV model, the current research argues that GWC is an intangible resource that can shape the extent to which an organization can effectively utilize its capabilities (e.g., GHRM) to reach positive outcomes (i.e., innovation) [11,20]. GWC can be described as a shared sense of perception, attitudes, and norms among staff members regarding their workplace, which entails practices and organizational values [4]. GWC can constitute the bedrock that provides support for the alignment of sustainability goals and actions undertaken by the firm. Thus, GWC can better implement GHRM by fostering an atmosphere in which employees are encouraged towards environmental awareness and eco-friendly initiatives [1].

Referring to the RBV model, the role of GWC as an intangible resource is highly valuable because it can promote collaborative measures through open communication. Specifically, in the context of the current research, environmental consciousness can be promoted in the climate of a firm to boost the impact of GHRM policies on innovative means that encompass green measures [19]. Employees are more likely to contribute to the innovation of green actions when they are provided with support and an encouraging workplace [21]. Thus, when a supportive GWC is in place, it can effectively guide employees towards adopting environmentally friendly practices outlined in GHRM policies and initiatives, which can lead to a significant positive impact on the organization’s overall green performance, often referred to as green innovation (GI) [22].

GWC, when aligned with the strategic goals of the firm (GHRM in this case), can drive innovation, as it increases the likelihood of employees adopting similar strategies that are further encouraged by rewards and training [23]. Thus, the supportive climate of a firm is a driver of action among employees through a shared sense of values, achievements, and recognition that is guided by training, appraisals, and participation. This leads to a better translation of intangible resources into tangible ones, such as GI in the current context [16].

The facilitation of GHRM practices and the effectiveness of these implementations can be achieved through an OC, where an understanding of the vitality of green actions persists and grows as employees are equipped with tools and support that lead them towards participating in green behaviors [9,24]. GWC can establish a sense of group belonging, ownership, and shared value, which can lead to positive competitiveness among employees in developing innovative green ideas [25]. By fostering a conducive green work climate perception, companies can transform GHRM practices into strategic assets, reinforcing the RBV perspective that internal resources and capabilities are pivotal to achieving sustainable innovation outcomes. This leads to the following hypothesis.

H3. 

Green work climate perceptions mediate the relationship between GHRM practices and green innovation.

2.5. Moderating Effect of Artificial Intelligence

The current study also examined the moderating (enhancing) effect of artificial intelligence (AI) on the linkage between GHRM and GI in the banking sector. Under the premise of the RBV model as a unifying model in the context of this research, it can be stated that AI is a vital resource for organizations to advance their technological prowess through tailored solutions that can tackle challenges while providing tools for employees to be directed towards green and innovative solutions [11]. When modern tools such as AI are used along with GHRM practices, the management of the firm can expect better results, as new solutions can be developed through innovative means that also encourage employees to participate in these developments [26]. Thus, it can be argued that AI can act as a strengthening factor for driving innovative outcomes such as GI through adequate practices (e.g., GHRM). AI tools, particularly when used in a specific manner (such as specified language models designed for banking) [27,28]. These AI technologies can significantly enhance organizational capabilities and resource effectiveness, aligned with the RBV perspective. The use of technologies such as AI is also beneficial for GHRM practices, as the systems that are specifically designed for businesses (specific language models or domain-specific modeling) can enhance processes and practices, such as recruitment, and can aid in identifying individuals who share the same values as the organization. Training platforms enriched by AI systems can personalize learning for each employee, leading to increased knowledge and skills that can contribute to the green efforts of the firm [29]. Through the customization of practices (i.e., GHRM) via an adequate implementation of AI, innovation as an outcome can be expected, as the actions and processes of the firm can become more fluid and effective [30]. Similarly, the performance of employees can be assessed and evaluated by AI systems specifically designed to provide real-time feedback while highlighting the aspects and areas where improvements are needed. This leads to an optimized version of GHRM that can foster a culture of innovation, further encouraging innovative solutions among staff [31].

In addition to what was noted, AI can act as a major player in analyzing environmental data and generating insights to enhance and direct new strategies for the organization. This shows that leveraging AI can be a great resource for companies to introduce and/or enhance data analysis, machine learning, and predictive analysis that can guide management in their decision-making processes [31,32]. This enables the firm to develop a proactive strategy entailing green solutions while providing staff with modern tools for green innovation [33]. As a result, innovation becomes centric, making AI a complementary resource for amplifying GHRM efforts as these practices become tailored and targeted [34]. Considering the premises of the RBV model, AI can act as an enhancer of GHRM practices, further increasing the rarity, inimitability, and non-substitutability (VRIN) of resources, yielding competitive edges for the organization and improving its outcomes [35]. The integration of AI as a resource with human resource management practices makes the asset rarer and more valuable [36]. The automation of various processes, predictive capabilities, and the identification of opportunities for growth through advanced analytics can act as drivers of green innovation, which may have been overlooked without the aid of such advanced tools [9]. This study argues that, by introducing AI (Figure 1), the synergistic effect among other practices (i.e., GHRM) can be improved, thus optimizing the allocation of resources that are designed to yield GI. Accordingly, the following hypothesis was developed:

H4. 

Artificial intelligence positively moderates the relationship between GHRM practices and green innovation, such that the relationship is stronger at higher levels of AI usage.

3. Materials and Methods

3.1. Approach and Sampling

To test the research hypotheses, a combination of purposive and stratified sampling techniques was employed to ensure that the sample adequately represented the different hierarchical levels within the banking sector. According to Neyman [37], stratified sampling enhances external validity by capturing the heterogeneity of subgroups within a population, whereas purposive sampling ensures the inclusion of participants with relevant knowledge and roles. In this study, purposive sampling was first used to target executive staff, middle managers, and senior managers who are directly involved in strategic decision-making and sustainability-related initiatives. This was followed by stratified sampling based on job positions to ensure proportional representation from each level within the organizational hierarchy. The final sample consisted of 298 executive staff members (83.4%), 42 middle managers (11.8%), and 17 senior managers (4.8%), reflecting the actual distribution of roles within the participating institutions.

An a priori power analysis was conducted using G*Power software version 3.1.9.7, which indicated that a minimum sample size of 111 was required (power = 0.85; effect size = 0.30; α = 0.05). However, for complex PLS-SEM models, a larger sample size was targeted. Ultimately, 357 valid responses were collected from four commercial banks in Palestine, using a cross-sectional design. These banks represent broader details of Palestine’s banking sector. Therefore, we selected only four commercial banks in Palestine. The first author administered the questionnaires directly, using language proficiency and existing professional networks to facilitate access and encourage participation. Ethical research practices were strictly followed: no personal or sensitive data were collected; anonymity and confidentiality were guaranteed; participants were informed of their right to withdraw at any time; and the purpose and scope of the study were communicated. These procedures ensured data quality and minimized potential bias in participant responses. The respondents’ profiles are presented in

Table 1. Demographic characteristics of the respondents.

Variables	Options	Frequency	Percentage (%)
Gender	Male	235	65.8
	Female	122	34.2
Age	Less than 28 years	49	13.7
	28–40 years	275	77.0
	Above 40 years	33	9.3
Education	Undergraduate degree	307	86.0
	Postgraduate degree	50	14.0
Years of Experience in the Banking Sector	Less than 4 years	48	13.5
	4–10 years	250	70.0
	Above 10 years	59	16.5
Position	Executive staff	298	83.4
	Middle Management	42	11.8
	Senior Management	17	4.8
Total		357	100

.

3.2. Measurements

All constructs in this study were modeled as reflective, in line with the theoretical assumptions and measurement model guidelines for latent variable modeling [38]. To measure green human resource management (GHRM), the validated scale developed by Dumont [39] was adopted, which includes five key dimensions: green recruitment, green training, green performance appraisal, green compensation, and green participation. Each item is rated on a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”).

Green innovation (GI) was assessed using a four-item scale adapted from Sobaih [40]. While the original GI construct often comprises two subdimensions, green product innovation and green process innovation [41], this study focused solely on general green innovation performance as a unidimensional construct based on the selected four items. As such, the naming of the construct was maintained as “Green Innovation,” consistent with prior research using similar unidimensional operationalizations [40].

The corporate environmental strategy (CES) construct was measured using an 11-item scale adopted from Das [42], which was originally developed by Ramus and Steger [43]. This scale captures the extent to which environmental sustainability is integrated into an organization’s strategic planning, policy development, and operational alignment. The version adapted by Das [42] provides updated and contextually relevant wording suitable for emerging market environments, particularly in linking CES to broader organizational practices such as GHRM.

The green work climate (GWC) was measured using eight items adapted from Norton [44], consisting of two subdimensions: organizational climate and coworker climate, each measured with four items. Although these subdimensions were acknowledged in the scale development, they were aggregated into a single higher-order construct of GWC in this study, consistent with approaches in prior research that focused on overall green climate perception in the workplace. This approach was justified by the theoretical alignment and empirical evidence of strong intercorrelations between the subdimensions.

Finally, artificial intelligence (AI) was measured using a seven-item scale adapted from Wijayati [45], focusing on AI adoption and its role in supporting HRM functions, sustainability decisions, and operational efficiencies. This scale captures the perceived integration of AI tools into organizational practices related to strategic innovation and green capabilities.

In addition, three control variables—age, gender, and work experience—were included in the model based on their potential influence on innovation-related behaviors, consistent with previous studies [39]. Appendix A provides a full list of scale items, sources, and coding.

3.3. Data Screening

Before proceeding with statistical analyses, the dataset was thoroughly screened to ensure accuracy and validity, in line with established guidelines in social science research [46,47]. Missing data were assessed, and incomplete responses were not found. Univariate outliers were examined using a boxplot analysis for each variable, and no extreme values were detected. Multivariate outlier detection was conducted using the Mahalanobis distance, with cases exceeding the chi-square threshold at p < 0.001 identified and removed to preserve the robustness of the parameter estimates [48]. In addition, Cook’s distance values were calculated, and all fell below the recommended cutoff of 1.0, indicating the absence of influential observations [49]. Although normality is not a strict assumption in PLS-SEM, skewness, kurtosis, and the Shapiro–Wilk test were used to assess the distribution of the data. The results showed mild non-normality but were within acceptable ranges for structural modeling purposes. These screening procedures ensured that the dataset was clean, reliable, and suitable for multivariate analysis.

3.4. Common Method Bias

To mitigate concerns related to common method bias (CMB), both procedural and statistical remedies have been employed under established recommendations in social science research [50]. Procedurally, anonymity and confidentiality were assured, participation was voluntary, and the questionnaire items were designed to reduce evaluation apprehension and avoid item-priming effects. To statistically assess the presence of CMB, Harman’s single-factor test was conducted by entering all the measurement items into an unrotated exploratory factor analysis. The results showed that the first factor accounted for less than 37% of the total variance, suggesting that CMB is not a significant concern. Additionally, the inner variance inflation factor (VIF) values were examined, with all values falling below the conservative threshold of 3.3 [51], indicating the absence of multicollinearity and minimal risk of CMB. These combined approaches provide reasonable assurance that the findings are not substantially affected by the common method variance.

4. Analysis and Results

Data were analyzed using SmartPLS software version 4, employing Partial Least Squares Structural Equation Modeling (PLS-SEM), a robust statistical technique suited for analyzing complex models with latent constructs, particularly when the data distribution is non-normal and the sample size is relatively moderate [52]. PLS-SEM was chosen because of its suitability for predictive modeling and its ability to handle multiple mediators and a moderator within a single-model framework. To ensure the reliability of the path coefficient estimates and significance testing, a non-parametric bootstrapping procedure with 5000 resamples was performed, as recommended by [38]. This approach enhances the robustness of the statistical inference by generating bias-corrected confidence intervals for all model estimates. The analysis was conducted in two stages: an assessment of the measurement model to ensure construct validity and reliability, and an evaluation of the structural model to test the hypothesized relationships.

4.1. Measurement Model Assessment

The measurement model was assessed to evaluate the internal consistency, reliability, convergent validity, and discriminant validity of all constructs. As shown in

Table 2. Measurement of the model assessment.

Construct	Items	Outer Loadings	VIF	CA	CR	AVE
GHRM				0.957	0.966	0.824
	GHRM1	0.910	2.755
	GHRM2	0.909	2.806
	GHRM3	0.923	2.183
	GHRM4	0.935	2.200
	GHRM5	0.875	1.750
	GHRM6	0.894	1.225
AI				0.854	0.888	0.533
	AI1	0.780	2.138
	AI2	0.765	2.219
	AI3	0.703	1.778
	AI4	0.629	1.535
	AI5	0.766	1.953
	AI6	0.720	1.725
	AI7	0.736	1.806
CES				0.938	0.947	0.621
	CES1	0.785	2.933
	CES2	0.691	2.262
	CES3	0.818	2.281
	CES4	0.838	3.190
	CES5	0.832	2.416
	CES6	0.803	2.412
	CES7	0.839	2.709
	CES8	0.793	2.753
	CES9	0.752	2.481
	CES10	0.788	2.309
	CES11	0.714	2.320
GWC				0.919	0.934	0.639
	GWC1	0.771	2.207
	GWC2	0.811	2.524
	GWC3	0.801	2.385
	GWC4	0.816	2.817
	GWC5	0.817	2.650
	GWC6	0.842	2.865
	GWC7	0.741	2.055
	GWC8	0.789	2.270
GI				0.893	0.926	0.758
	GI1	0.871	2.510
	GI2	0.816	1.949
	GI3	0.917	2.428
	GI4	0.876	2.524

Note(s): Green human resource management (GHRM), artificial intelligence (AI), corporate environmental strategy (CES), green work climate (GWC), and green innovation (GI).

, internal consistency was evaluated using Cronbach’s alpha (CA) and composite reliability (CR), with threshold values above 0.70 considered acceptable [38,53]. The CR values for all constructs were satisfactory and reported individually as follows: GHRM = 0.966, AI = 0.888, CES = 0.947, GWC = 0.934, and GI = 0.926, indicating high internal consistency across all constructs.

The average variance extracted (AVE) was also computed to assess convergent validity, with values above 0.50 considered acceptable [52]. While the AVE for AI was relatively low (0.533), it still exceeded the minimum threshold, reflecting acceptable convergence. All other constructs had AVE values above 0.60, indicating strong convergent validity. Discriminant validity was assessed using the heterotrait–monotrait ratio (HTMT), with all values remaining below the conservative threshold of 0.85 [54], thereby confirming the discriminant validity of the constructs. The HTMT values are listed in

Table 3. Heterotrait–monotrait ratio (HTMT) values.

Construct	AI	CES	GHRM	GI	GWC
AI	0
CES	0.588	0
GHRM	0.647	0.648	0
GI	0.424	0.649	0.728	0
GWC	0.532	0.621	0.546	0.683	0

Note(s): Green human resource management (GHRM), artificial intelligence (AI), corporate environmental strategy (CES), green work climate (GWC), and green innovation (GI).

.

4.2. Structural Model Assessment

Before testing the mediating and moderating effects, the structural model was assessed by examining the direct relationships between the independent variable (GHRM), mediators (CES and GWC), and dependent variable (GI). Path coefficients, t-statistics, and significance levels were evaluated using a bootstrapping procedure with 5000 resamples, following the recommendations of [38]. The results show that GHRM has a strong and significant positive effect on CES (β = 0.617, t = 16.398, p < 0.001), indicating that green HRM practices substantially contribute to the formation of corporate environmental strategies. GHRM also significantly influenced GWC (β = 0.516, t = 11.932, p < 0.001), suggesting that green-oriented HR policies foster work climates that support environmental initiatives. Both mediators demonstrated significant effects on green innovation: CES → GI (β = 0.368, t = 8.410, p < 0.001) and GWC → GI (β = 0.102, t = 2.658, p < 0.01). These findings confirm the conceptual soundness of the model and establish the conditions necessary to proceed with the mediation analysis, as advocated for by Baron and Kenny [55] and Zhao [56]. Figure 2 presents the direct path results, while

Table 4. Hypothesis testing results.

Relationships	Path Coefficient	t-Statistics	Cis		p-Values	Decision
			2.5%	97.5%
Direct Effect
H1: GHRM → GI	0.231	7.491	[0.168, 0.289]		0.000	Supported
GHRM → CES	0.617	16.398	[0.538, 0.686]		0.000	Supported
GHRM → GWC	0.516	11.932	[0.429, 0.598]		0.000	Supported
CES → GI	0.368	8.410	[0.281, 0.452]		0.000	Supported
GWC → GI	0.102	2.658	[0.027, 0.176]		0.008	Supported
Mediation Effect
H2: GHRM → CES → GI	0.227	7.047	[0.167, 0.292]		0.000	Supported
H3: GHRM → GWC → GI	0.053	2.540	[0.014, 0.094]		0.008	Supported
Moderation Effect
H4: GHRM × AI → GI	0.060	2.788	[0.026, 0.112]		0.005	Supported
Control Variables
Gender → GI	−0.171	2.805	[−0.291, −0.049]		0.005	Supported
Age → GI	0.005	0.104	[−0.086, 0.103]		0.917	Not Supported
Experience → GI	0.016	0.324	[−0.086, 0.109]		0.746	Not Supported

Note(s): Green human resource management (GHRM), artificial intelligence (AI), corporate environmental strategy (CES), green work climate (GWC), and green innovation (GI).

details the outcomes of the direct, indirect, moderation, and control effects, with decisions for each hypothesis indicated.

Path coefficients explain the correlation between the dependent and independent variables. Hypotheses H1, H2, H3, and H4 are supported because they have p-values less than 0.05. Hypothesis H1 states that green HRM positively influences green innovation (p = 0.000). Similarly, H2 indicated that CES mediates the relationship between green HRM and GI (p = 0.000). Correspondingly, GWC positively mediates the relationship between green HRM and GI (p = 0.008). Finally, the results of Hypothesis H4 suggest that AI positively moderates the relationship between green HRM and GI, with a p-value of 0.005.

4.3. Predictive Power of the Structural Model

To assess the explanatory and predictive power of the structural model, key model fit statistics were evaluated, including the coefficient of determination (R²), predictive relevance (Q²), and effect size (f²), following Hair [38]. The R² values indicated that the model explained a substantial portion of the variance in the endogenous constructs: R² = 0.266 for GWC, R² = 0.381 for CES, and R² = 0.725 for GI, suggesting moderate to substantial explanatory power [57]. Predictive relevance was assessed using Stone–Geisser Q² values obtained via the blindfolding procedure. The results showed Q² = 0.261 for GWC, Q² = 0.374 for CES, and Q² = 0.585 for GI, with all exceeding the zero threshold and thus confirming the model’s strong predictive accuracy [58]. Additionally, effect sizes (f²) were calculated to determine the impact of each exogenous construct on its respective endogenous variables. The results revealed a small effect of GHRM on GI (f² = 0.108), a moderate effect of GHRM on GWC (f² = 0.363), and a large effect of GHRM on CES (f² = 0.616), providing further support for the model’s robustness [59]. Collectively, these findings suggest that the proposed model possesses both explanatory strength and predictive utility in capturing the dynamics among green HRM practices, strategic and perceptual mediators, and innovation outcomes.

4.4. Discussion

These findings support H1, indicating a significant direct relationship between GHRM and GI (β = 0.231, t = 7.491, p < 0.001). This suggests that green HRM practices such as green recruitment, environmental training, performance appraisal, and participative initiatives play a critical role in fostering environmentally innovative outcomes [1]. When integrated into organizational routines, these practices promote a workplace conducive to sustainable innovation [4]. In the Palestinian banking sector, this implies that increased engagement in green HRM can significantly enhance employee commitment to environmental performance and eco-innovation.

The mediating effect of CES further supports H2, showing that corporate environmental strategy significantly transmits the impact of GHRM on GI (β = 0.226, t = 7.047, p < 0.001). This finding suggests that strategic environmental alignment amplifies the effectiveness of GHRM in producing innovative outcomes. Drawing on the resource-based view (RBV), this highlights the strategic value of aligning HRM resources with broader environmental goals [60]. For banks in Palestine, adopting robust environmental strategies enables a cohesive organizational approach, helping to convert internal HRM capabilities into green innovations that provide competitive advantages [19].

Support for H3 was also evident in the mediating role of GWC (β = 0.053, t = 2.540, p < 0.01), indicating that a supportive environmental work climate enhances the influence of GHRM on green innovation. GWC has been identified as a key contextual factor shaping employees’ attitudes and behaviors related to sustainability [22]. When green HRM is embedded within a climate that values environmental goals, employees are more likely to engage in green initiatives, further enhancing organizational innovation capacity [23]. This underscores the importance of banks cultivating a green culture that reinforces HRM efforts.

Finally, the moderating role of AI, as proposed in H4, is supported (β = 0.060, t = 2.788, p < 0.01). The results suggest that the positive impact of GHRM on GI is stronger when organizations leverage artificial intelligence technologies. AI enhances GHRM’s effectiveness by enabling data-driven decisions, personalized green training, and predictive HR analytics, thereby supporting innovation aligned with environmental sustainability [29,61]. In the Palestinian banking sector, AI not only facilitates operational efficiency but also reinforces green strategies, making the HRM–innovation relationship more robust and impactful.

5. Conclusions

Based on the presented statistical findings that supported the research hypotheses, the positive influence of GHRM on the innovation performance of banks in the Middle East region, and particularly in Palestine, cannot be neglected. Green human resource management (GHRM) plays a crucial role in green innovation by creating an environment in which employees are motivated and equipped to develop environmentally friendly solutions. Through practices such as incorporating sustainability into recruitment, providing green training, and aligning performance evaluations with environmental goals, GHRM can indirectly influence innovation by encouraging a positive green work climate and corporate environmental strategies. However, implementing AI in green HRM can lead to significant cost benefits through increased efficiency, reduced resource consumption, and improved employee engagement, contributing to a more sustainable and cost-effective organization. AI can identify individual employees’ needs and provide customized training programs, leading to increased employee engagement and reduced training costs. This suggests a complex interplay between GHRM, corporate environmental strategy, green work climate, and AI, in which each element works together to promote sustainable innovation within an organization. Moreover, employees are more likely to engage in green behaviors and innovative solutions when they are provided with a green work climate that not only supports green actions but also shares and develops values that are beneficial to the staff, both personally and professionally. By actively incorporating sustainable practices into HR practices, companies can create an environment that encourages employees to develop and implement environmentally friendly innovations, with AI further facilitating this process through data analysis and efficient resource allocation.

5.1. Theoretical Implications

Given the underexplored nature of GHRM in the Palestinian banking context, these findings provide practical insights for resource-based strategic alignment in a resource-constrained environment, where value, rarity, inimitability, and non-substitutable (VRIN) resources can determine innovative outcomes. GHRM practices can be further enhanced through the adaptation of new technologies, such as AI, which can be a VRIN resource that boosts all aspects of actions and processes within a bank. It can become more valuable by improving employee performance, engagement, and overall organizational outcomes. AI-powered tools can personalize training programs, optimize recruitment processes, and provide data-driven insights for better decision making, making the workforce more effective. AI-driven insights into GHRM are rare because they are specific to an organization’s unique context and data. The ability to analyze vast amounts of data and identify unique patterns can provide a competitive advantage that is difficult for others to replicate. The manner in which an organization integrates AI into its GHRM practices, including specific algorithms, data analysis techniques, and the organizational culture surrounding these practices, can be difficult to imitate. This creates a competitive advantage, which is difficult for competitors to replicate. While some HR practices can easily be substituted, the combination of AI-driven insights, GHRM practices, and a company’s specific organizational context can be difficult to replace with alternative solutions. By aligning HR practices with organizational goals and leveraging AI-driven insights, organizations can achieve better overall performance, including financial performance, operational efficiency, and employee satisfaction. The RBV framework can be used to understand how GHRM practices, by developing dynamic capabilities, can lead to improved environmental performance, enhanced competitiveness, and sustainability for an organization.

5.2. Practical Implications

As noted, managerial (practical) implications can be derived from the current findings that can be beneficial for managers in the banking sector of Palestine, as well as neighboring countries. Green values can be embedded in the recruitment process of a firm, enabling the HR department to have a more suitable candidate pool. This, combined with training and development programs as well as rewards and incentives based on individual green performance, can yield a higher rate of participation and further integrate these elements into the company’s atmosphere. A culture of sustainable actions and behaviors can be cultivated through these actions, which provide employees with personal and professional paths for improvement and, in turn, translate these actions into long-lasting outcomes for the company as a unit (i.e., green innovation). In addition, having the strategic vision of the organization aligned with green goals will create a sense of prioritization, which further solidifies the practices and guidelines of GHRM across the company. This strategic orientation towards green objectives ensures that there is a commitment to sustainability that is then communicated to employees and motivates them in terms of taking green actions. Furthermore, a green work climate perception in its optimum form creates a workplace that is positive and supportive, and it encourages participation in an array of organizational initiatives that foster cooperation, communication, and green solution findings that contribute to the overall green efforts of the company. This further improves the relationships between staff members and managers, which improves the application of innovative ideas. AI tools can also be used in GHRM.

Table 5. Specific GHRM practices and AI tools.

S. No	AI Technologies	GHRM Practices
1	Virtual Training	Training and Development
2	Chatbot Nina	Answers employee queries
3	Jobs Intelligence Maestro	Recruitment
4	Digital HRM system	Overall HR practices
5	LTI Safe Radius	Employee health protection

lists some AI tools and GHRM practices. AI can automate tasks, provide data-driven insights, and encourage innovation in sustainability initiatives, whereas policymakers can leverage AI-driven data to refine environmental regulations and promote sustainable practices. Lastly, by leveraging technology (i.e., AI), banking managers in Palestine can significantly improve their workplaces and achieve their desired results (e.g., GI). AI tools designed specifically to meet the needs of a specific bank can act as major elements in determining how effective actions and processes are and to what extent they are aligned with GHRM guidelines and policies. This further ensures that the performance outcomes are long-term and benefit the company by separating its resources and services from those of other competitors.

6. Limitations and Future Research

Similar to other studies, the current research was limited due to several restraining factors that hindered the investigation process. As the data were gathered in a cross-sectional manner, causal inferences were derived from the current findings. To effectively address cross-cultural comparisons in future studies, researchers should prioritize cultural sensitivity, utilize diverse methodologies, and foster collaboration. This involves actively seeking knowledge about different cultures, employing techniques such as translation and back-translation, and engaging researchers from various backgrounds. However, the nature of such an approach limits the dynamic essence of included constructs over time. This can be overcome in future studies through the deployment of a longitudinal approach, which can examine changes over time and provide a more informed conclusion. Future longitudinal research on the role of green human resource management and the use of artificial intelligence (AI) will be conducted over 5–10 years to observe trends and changes, with a focus on tracking the long-term impacts of these technologies and practices on various organizational outcomes. To address a longitudinal design, researchers should carefully plan their studies, focusing on defining clear objectives, selecting appropriate study designs, and establishing robust data collection methods. The sampling techniques deployed in this research (i.e., purposive and stratified) provided a sufficient basis for this research. However, such approaches can limit the generalizability of the findings to other sectors and/or regions [62]. A larger sample size, with a wider range of institutions, can enhance the external validity of this issue. Furthermore, the context of Palestine and the status of GHRM are limited in the existing literature, which constrains the ability of this research in terms of the theoretical framework. While the contributions of this research have been stated, it is important to note that a more comprehensive body of research specific to Palestine and extending the region of the Middle East can provide a better understanding of the interplay of the factors examined in this model. This can also include socioeconomic, political, and other organizational factors in the region. This study focused only on Palestine. This is also a limitation of the present study. Therefore, future research should consider more countries to achieve better results. This study was conducted using a quantitative method. Therefore, future research should be conducted using qualitative methods. Finally, as AI models rapidly improve, their relationship and impact on various aspects of organizational innovation and performance should be examined to provide a deeper understanding of their effectiveness.