A Systematic Literature Review of Artificial Intelligence Advancements in Green Human Resource Management

Abstract

In response to the growing need for environmental stewardship, Green Human Resource Management (GHRM) has emerged to incorporate sustainability in organizational practices, with Artificial Intelligence (AI) offering transformative potential. However, a comprehensive synthesis of the intersection between AI and GHRM is notably absent, prompting this review to systematically map the existing knowledge base and identify key trends. To bride this research gap, a systematic literature review was undertaken following the PRISMA framework, employing content and bibliometric analysis on 53 relevant articles published between 2018 and 2025. The analysis revealed five primary research themes, highlighting AI’s significant role in enhancing green recruitment, training, and performance management, while also underscoring critical challenges related to ethical considerations and organizational readiness. This review offers a structured synthesis of the AI-GHRM landscape, concluding with key interpretations that guide future research toward areas including adaptive systems, big data analytics, and the development of robust ethical frameworks, thereby serving as a valuable resource for advancing sustainable HRM practices. Moreover, this study directly contributes to the United Nations Sustainable Development Goals (SDGs), particularly SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production), through emphasizing the strategic significance of AI-enabled Green HRM in fostering organizational sustainability.

1. Introduction

Human resources hold a strategic position in shaping organizational performance and have consequently become a pivotal source of competitive advantage, with Human Resource Management (HRM) shifting from a primarily administrative focus to a strategic role that stimulates innovation, learning, and commitment [1,2,3,4]. Alongside this strategic evolution, global stakeholders are intensifying their calls for environmental stewardship, with organizations increasingly facing expectations to generate economic value while proactively minimizing their ecological footprint, thereby catalyzing a global shift toward greener practices [5,6]. As environmental concerns continue to intensify worldwide, Green Human Resource Management (GHRM) has evolved, which is the systematic integration of environmental principles into HR policies to reduce a company’s ecological footprint while simultaneously sustaining high performance [6,7]. Therefore, GHRM’s potential to effectively balance economic, social, and environmental goals positions it as an integral lever for corporate sustainability [8].

The definition of GHRM has evolved, progressively reshaping traditional HR practices by embedding environmental sustainability deeply into organizational strategies. Initial definitions focused on systematically integrating environmental considerations into HRM through initiatives like training and goal alignment [9], aligning traditional HR functions with green objectives to foster environmental responsibility [10], and providing frameworks that bridged environmental management with HRM strategies [11]. Building on this foundation, subsequent work emphasized reengineering HR functions to cultivate an eco-conscious workforce engaging in voluntary pro-environmental behaviors beyond formal job requirements [12,13]. This definition of GHRM continued to mature, moving beyond mere regulatory compliance towards the strategic deployment of employees’ voluntary actions for environmental protection [14,15]. Further refinement by [16,17] described GHRM as a comprehensive transformation embedding environmental objectives into every facet of HRM, commencing from job design and recruitment to performance appraisal and compensation, aiming to create an engaged workforce within an environmentally sensitive culture. Furthermore, Ref. [18] reinforced the comprehensive scope of GHRM, emphasizing its role in recruitment and training to develop human capital committed to environmental objectives, thereby directly linking personnel development to the organization’s broader sustainability agenda and environmental performance. Therefore, this study considers GHRM’s evolution as the systematic application of environmentally friendly HR policies and procedures designed to enhance organizational efficiency and productivity while simultaneously fostering environmental sustainability [6,7,19]. Beyond the definition, the practical application of GHRM yields demonstrable advantages, with evidence confirming that firms embedding practices like green recruitment and training, and eco-linked incentives report tangible benefits such as higher job satisfaction, stronger engagement, and measurable reductions in resource consumption [20,21].

Concurrently, Artificial Intelligence (AI) and allied digital innovations, including Internet of Things (IoT) solutions, big data analytics, 5G networks, and cloud computing, are transforming HRM by automating routine tasks, mining talent analytics, and enabling predictive decision-making [22,23]. AI, which is computer systems capable of performing cognitive functions such as learning and problem-solving, has transformed HRM by automating routine tasks and enabling predictive decision-making [24,25,26]. Building on these foundational capabilities, practical evidence from multinationals further substantiates these claims; for instance, Refs. [27,28,29] report that companies like PepsiCo and Google have achieved demonstrably shorter hiring cycles and lower administrative costs after deploying AI-enabled platforms. Consequently, recent studies such as [30] assert that these improvements in digital efficiency inherently minimize the environmental footprint by decreasing travel, paper waste, and energy consumption, thereby establishing a clear and compelling synergy with GHRM’s ecological focus.

Building on this inherent synergy, a growing body of empirical evidence explicitly demonstrates the synergistic potential of AI-enabled GHRM. Specifically, AI-driven recruitment tools, exemplified by platforms like HireVue and Pymetrics, are shown to expand candidate pools while filtering for green values, provide adaptive e-learning platforms (i.e., Coursera for Business, Degreed) that effectively support the delivery of low-carbon training initiatives, while AI-supported analytics facilitate the linkage of incentives to real-time environmental metrics [30,31]. Beyond these individual applications, studies spanning diverse sectors, including manufacturing, healthcare, and hospitality, consistently indicate that the thoughtful combination of AI capabilities with GHRM practices enhances firms’ environmental performance while simultaneously driving green innovation and boosting financial returns [32,33,34]. Therefore, the literature suggests that incorporating AI into GHRM facilitates optimization and innovation. Studies indicate that this is achieved by leveraging predictive modeling, data-driven analytics, and automated-driven solutions, which are shown to help advance sustainability goals, reduce energy consumption, minimize waste, and boost operational efficiency [30].

Despite the increasing recognition of the potential benefits associated with integrating AI into GHRM, a comprehensive systematic literature review explicitly addressing this intersection remains absent in the extant body of knowledge. Existing studies are predominantly empirical or conceptual in nature, lacking a structured synthesis of the field’s thematic development, research gaps, and future directions. For instance, Ref. [35] analyzed the potential gains of AI in enhancing the effectiveness of GHRM processes, while [36] investigated how AI enhances GHRM to promote eco-friendly practices and achieve environmental sustainability in organizations. Similarly, Ref. [37] investigated the influence of digital transformation technologies and GHRM on the adoption of Green Supply Chain Management operational practices and the subsequent effects on environmental and economic performance. Furthermore, Ref. [38] explored how AI can be applied to various HRM functions to advance environmental sustainability, while [39] identified the potential synergies and benefits of integrating technological innovation in GHRM practices. Moreover, Ref. [40] investigated the application of AI to improve GHRM for better organizational sustainability, highlighting the benefits, challenges, and managerial implications of such integration. However, none of these contributions adopt a systematic review approach, nor do they comprehensively map the breadth of AI technologies within the GHRM domain.

In parallel, several literature reviews have synthesized advancements in GHRM; nevertheless, these works do not engage with AI-specific integrations. For example, Benevene and Buonomo [41] conducted an evidence-based systematic review of GHRM practices and outcomes; however, their analysis remained confined to conventional HR interventions and excluded technological enablers such as AI. Similarly, Ref. [42] examined the interconnections between environmental management, HRM, and GHRM, yet their review overlooked digital or AI-driven innovations within HR practices. Furthermore, other reviews, such as those by [5,8], provide valuable insights on sustainable HRM and digital transformation, respectively, but do not directly address the incorporation of AI within GHRM practices. Within this context, a systematic literature review serves as a critical function by organizing and consolidating this dispersed knowledge. While earlier reviews on GHRM [5,8,41,42] or AI in HRM [35,38,40] have advanced their respective domains independently, this study uniquely integrates both perspectives to uncover their convergence, offering a holistic synthesis that addresses an important conceptual and empirical gap between technological innovation and sustainable HRM. To date, no comprehensive review has mapped how AI applications specifically advance GHRM practices, their sectoral adoption, or their performance outcomes. Moreover, none of the existing reviews employ both content and bibliometric analyses to provide an integrated overview of this emerging domain. The absence of a focused and systematic synthesis in the AI–GHRM domain emphasizes the relevance and novelty of the present review. Therefore, this review aims to address this gap by systematically synthesizing and mapping the current state of knowledge of this critical nexus. Specifically, the overarching research question guiding this systematic review is: “What is the current state of scholarly knowledge on the integration of artificial intelligence AI into GHRM practices, and what are the key trends, applications, and driving factors shaping this interdisciplinary domain?”. To address the research question, this review explores the following objectives: (1) Identify and categorize the range of AI technologies utilized in GHRM, (2) Examine the application of AI-enabled GHRM across different industrial sectors, (3) Analyze the incorporation of AI applications into GHRM practices, (4) Evaluate the role of AI-driven GHRM in performance management, and (5) Assess the key factors shaping the implementation of the AI-GHRM nexus. Finally, by mapping the dominant themes and critically identifying under-explored areas, this review provides a structured and evidence-based roadmap that directly addresses the need for clear future research directions, helping to guide the field’s evolution in a more cohesive and impactful manner.

The remainder of this paper is structured as follows: Section 2 outlines the research methodology adopted for the systematic literature review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. In Section 3, a comprehensive content analysis is presented, synthesizing key findings from diverse studies through a thematic analysis approach that identifies the emergent research gaps, thereby establishing a foundation for future academic research. Section 4 utilizes bibliometric analysis to identify research trends, geographic distribution, interdisciplinary collaboration, and additional publication metrics. Finally, Section 5 provides the conclusion, discusses theoretical implications, outlines an agenda for future research, and acknowledges the study’s limitations.

2. Materials and Methods

This study adopts a systematic literature review approach, guided by the PRISMA methodology, to examine the integration of AI within GHRM. This approach ensures a rigorous and comprehensive synthesis of the existing scholarly literature, facilitating the identification of key trends and critical knowledge gaps within this evolving domain. PRISMA provides a robust methodological backbone to systematically organize and consolidate dispersed knowledge. The systematic review process adhered to the PRISMA 2020 guidelines for conducting systematic reviews, with the completed PRISMA 2020 checklist included in the Supplementary Material (Tables S1 and S2) to ensure reporting transparency and methodological consistency. This systematic approach ensures a transparent and reproducible review process, crucial for establishing the current research landscape in this emerging interdisciplinary domain. Additionally, PRISMA maintains methodological precision in identifying and analyzing relevant literature, thereby laying a strong foundation for future theoretical and empirical work in this critical intersection of AI and GHRM [43], guiding the systematic review process. It begins with literature retrieval from the Scopus database, encompassing English-language articles, conference papers, and book chapters. The subsequent step, literature screening, involves a meticulous duplicate check and a preliminary assessment of titles, keywords, and abstracts for relevance and eligibility. These initial phases collectively establish the final dataset of articles for subsequent in-depth content and bibliometric analysis.

• Literature retrieval—The initial step of this systematic literature review necessitates the selection of a precise search string to effectively capture publications relevant to the integration of AI advancements and GHRM. Literature retrieval was conducted using the Scopus database, which serves as the primary database due to its comprehensive and widely recognized multidisciplinary coverage of peer-reviewed literature across science, technology, engineering, social sciences, and management disciplines [44]. The literature search was conducted using the Scopus database, which was selected as the primary data source owing to its broad multidisciplinary scope and comprehensive coverage of peer-reviewed journals in the domains of engineering, management, and social sciences. Scopus encompasses nearly all journals indexed in Web of Science and includes a wider range of conference proceedings and regional publications relevant to emerging topics such as AI–HRM integration [45,46]. This methodological choice ensures comprehensive coverage of the interdisciplinary dimensions of AI–GHRM research, thereby minimizing potential publication bias or omission. The initial literature search using the specified search string was conducted on 08-03-2025 and the following search string was employed, based on the article title, abstract and keyword: TITLE-ABS-KEY ((“Artificial intelligence” OR “AI” OR “machine learning” OR “Generative artificial intelligence” OR “Generative AI” OR “Predictive analytics” OR “Deep learning” OR “Natural language processing” OR “NLP” OR “Cognitive computing” OR “Intelligent systems” OR “Business intelligence” OR “BI” OR “AI-powered” OR “Autonomous decision” OR “intelligent automation” OR “robotic process automation” OR “RPA” OR “data mining” OR “predictive modeling” OR “HR analytics” OR “people analytics” OR “digital HR” OR “smart HR” OR “analytics” OR “big data” OR “chatbots” OR “conversational AI”) AND (“green human resource” OR “green HRM” OR “green HR” OR “GHRM” OR “environmental human resource” OR “environmental HRM” OR “sustainable HRM” OR “eco-friendly HRM” OR “sustainable human resource” OR “sustainable HR” OR “eco HRM” OR “green recruitment” OR “green talent management” OR “ sustainable recruitment” OR “ sustainable talent management” OR “Eco-HR”)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “ch”)) AND (LIMIT-TO (LANGUAGE, “English”)). Subsequently, the retrieved papers were systematically filtered based on clearly defined inclusion criteria to maintain the validity and quality of the selected dataset. Our inclusion criteria were specifically defined as follows: first, with regard to document type, the review considered only journal articles, conference proceedings, and book chapters that had reached their final publication stage. This criterion was applied to guarantee the inclusion of high-quality, peer-reviewed scholarly contributions that offer both methodological rigor and substantive academic value, thereby enhancing the reliability and credibility of the review’s findings. Second, only publications written in English were considered, as this facilitated consistency in analysis. Based on these inclusion criteria, the initial search retrieved 81 documents covering the period 2016–2025. This time range reflects the coverage of the Scopus database during the retrieval process rather than a pre-defined selection by the authors.
  To ensure a comprehensive and inclusive analysis, this study employed a broad data collection strategy. Rather than confining the review to pre-determined variables, the objective was to capture a wide spectrum of insights at the nexus of AL and GHRM. Accordingly, the inclusion criteria were designed with flexibility to accommodate diverse study designs and data types, thereby incorporating a rich variety of perspectives and contextual settings.
  The flexible nature of the data collection, guided by broad inclusion criteria rather than strict variable constraints, resulted in a complete dataset, thereby eliminating the necessity of inferring or making assumptions about missing information. This rigorous approach strengthens the study’s validity, ensuring that the study’s outcomes are both representative and aligned with the stated research aims.
• Literature screening—This step employs analyzing existing research on techniques, methodologies, and tools related to the integration of AI advancements and GHRM. The research methodology was thoroughly structured, gaining inspiration from established approaches as demonstrated in [44,47,48,49]. As mentioned in the literature retrieval step, 81 articles were initially included according to the inclusion and exclusion criteria. To ensure the reliability of the coding process, two independent researchers undertook the screening process by evaluating the titles, abstracts, and keywords and conducted open coding and constant comparison separately, followed by a consensus discussion to reconcile any minor discrepancies. Intercoder reliability was therefore established through mutual agreement rather than through statistical measures, in line with qualitative research standards for thematic synthesis. To facilitate this process and enhance screening efficiency, Rayyan, an AI-assisted platform specifically designed for systematic reviews, was utilized. This tool enabled the automatic identification and removal of duplicate entries while also supporting the structured organization of references throughout the review process [50]. Subsequently, a duplicate check was conducted on Rayyan, which revealed no duplicate entries, thus maintaining the initial count of 81 articles. Following this, a preliminary screening for relevance and eligibility was performed based on the authors’ effort of screening the titles, keywords, and abstracts, which resulted in the exclusion of articles deemed irrelevant to the focal topic. After applying the relevance and quality screening, it was observed that the first studies addressing the integration of AI and GHRM appeared in 2018. Consequently, the final dataset comprised 53 studies published between 2018 and 2025, representing the actual emergence of scholarly work in this domain, as illustrated in Figure 1. This timeframe corresponds with the discernible development of scholarly literature addressing the intersection of AI and GHRM. Prior to 2018, the research output in this specific area was limited. In alignment with the PRISMA framework, which assisted in the identification of studies relevant to the review topic, a complementary quality appraisal process guided by the Joanna Briggs Institute (JBI) Critical Appraisal Checklist systematically evaluated the methodological soundness of the included studies. The assessment examined the clarity and relevance of each study’s research question, the suitability of inclusion criteria, and the adequacy of the search strategy and data sources. It also considered the appropriateness of study appraisal methods, procedures to minimize data extraction errors, and the techniques used to synthesize findings. Furthermore, the evaluation reviewed whether potential publication bias was addressed, whether conclusions and recommendations were supported by the presented evidence, and whether directions for future research were clearly articulated.
  While a formal tool for assessing bias was not employed, each study included in the review was carefully examined by the reviewers with respect to its methodological rigor, transparency in data reporting, and consistency with the stated research objectives. This evaluative process was supported using Rayyan, which facilitated a structured and consistent screening workflow, thereby contributing to the systematic and impartial selection of studies. All assessments were performed manually to ensure precision and critical appraisal during the entire course of this systematic review.
  To maintain methodological integrity and ensure the reliability of the reviewed studies, all studies that met the inclusion criteria underwent a structured quality assessment procedure. Each article was independently reviewed by two evaluators according to three key dimensions, (1) methodological rigor: including clarity of design, data source validity, and transparency of analytical procedures; (2) relevance to the research objectives: measured by the degree of alignment between AI applications and Green HRM constructs; and (3) reporting quality: assessed based on the completeness of results, acknowledgment of limitations, and replicability of findings. Articles that demonstrated low clarity in design or insufficient methodological detail were flagged for discussion between reviewers, and inclusion decisions were reached through consensus. While no numerical scoring scale was applied, this qualitative approach ensured the consistency, credibility, and transparency of the final dataset.
  In addition, the results were synthesized descriptively, a methodological choice necessitated by the heterogeneity of the included studies, which precluded statistical aggregation through meta-analysis. This synthesis process focused on abstracting and summarizing key data related to AI technologies in GHRM. Subsequently, identified patterns, thematic trends, and recurring themes were consolidated to offer an integrative interpretation of the results, aligning with the study’s exploratory purpose. Formal techniques to evaluate bias resulting from unreported or selectively reported findings were not employed in this synthesis; nonetheless, steps were taken to minimize the possible impact of such reporting biases. This involved incorporating a wide array of studies and favoring those with clear methodologies and thorough data reporting. The intention behind this approach was to decrease the chance of omitting pertinent findings while upholding the synthesis’s dependability.
  In the absence of a meta-analysis, sensitivity analyses aimed at evaluating the robustness of the synthesized findings were not conducted. Such quantitative robustness testing was not required, given the study’s exploratory nature and its reliance on a descriptive synthesis methodology. Rigor was instead established by focusing on the consistency and reliability of the evidence base. This was accomplished through a systematic appraisal of each study’s quality and relevance during the selection phase, thereby ensuring that the synthesized findings accurately represent the diversity and significance of the included literature.
  Since statistical pooling was not performed, formal techniques for assessing heterogeneity, such as subgroup analysis or meta-regression, were deemed inapplicable. Instead of these quantitative techniques, variability among the studies was addressed through a descriptive approach. This involved qualitatively identifying and discussing thematic differences, patterns, and trends observed across the findings. This method allowed the analysis to preserve the contextual richness of the individual studies while systematically acknowledging the diversity in their designs and methodologies.
  Given the study’s descriptive approach, a formal assessment of certainty in the evidence was not conducted. The synthesis focused on identifying patterns and themes, not on quantitatively rating outcome confidence. However, the reliability of the findings was supported by prioritizing studies with robust methodologies and transparent reporting during the selection process. The inclusion criteria were designed to filter for high-quality, relevant literature, thereby ensuring the credibility of the synthesized conclusions.
  To enhance clarity and facilitate cross-study comparison, the findings from individual studies were systematically summarized and presented in a tabular format. These tables were structured to categorize the literature according to key characteristics, including the year of publication, primary research focus, methodological approach, and principal outcomes. Furthermore, visual representations were developed where appropriate to provide a consolidated summary of emergent themes aimed at improving the overall interpretability of the synthesized results.
  The publication trend of the included articles in the review concerning the integration of AI advancements and GHRM, categorized by year of publication, is depicted in Figure 2. The graph reveals a noticeable increase in publications starting from 2022, reaching a significant peak in 2024. This observed rise highlights the increasing academic focus on exploring the intersection of AI and GHRM. However, a sharp decline in the number of publications is observed in 2025, with a substantial reduction in published works. This decrease may be attributed to the review period concluding in the early part of 2025, hence, it is anticipated that the publication count will rise as more research is released throughout the year. Additionally, the fluctuations in publication numbers during the preceding years suggest the relative novelty of this interdisciplinary area within scholarly communication, resulting in a more limited initial exploration of the topic.
• Content analysis—The step comprises a detailed examination of the selected articles to identify and synthesize contemporary trends, patterns, and themes pertinent to the integration of AI advancements and GHRM. This was accomplished through a systematic organization of the extracted information from each included study. Specifically, content analysis was employed to explore the instances of the intersection between AI advancements and GHRM. The articles were then categorized according to a structured framework of themes and sub-themes. This analytical methodology facilitated the classification and comprehensive analysis of the available information, which enables the derivation of robust conclusions from the synthesized literature regarding the convergence of AI advancements with GHRM.
• Bibliometric analysis—The concluding step of this review incorporates bibliometric techniques and descriptive statistics to evaluate the impact and influence of the research. This approach served to gauge the significance of individual scholars and journals, as well as to discern emerging trends and patterns within the field of study. Specifically, the analysis encompasses the examination of research topic textual data, keywords, citation counts, co-authorship networks across countries, and collaborative patterns among authors, thereby illuminating interdisciplinary collaborations. Furthermore, the analysis extends to identify predominant publication sources and assess the citation impact of the reviewed articles.

Figure 1. PRISMA framework (Literature screening).

Figure 1. PRISMA framework (Literature screening).

Figure 2. Chronological growth of published articles (2018–2025).

Figure 2. Chronological growth of published articles (2018–2025).

Following this methodological overview, the paper presents detailed content and bibliometric analysis, which is conducted in accordance with the outlined steps. This sequencing prioritizes establishing a deep thematic understanding of the literature through content analysis (Section 3), which is then comprehensively complemented by a robust contextual overview provided by the bibliometric analysis (Section 4).

3. Results of the Content Analysis

The literature demonstrates a clear and growing scholarly interest in the convergence of AI and GHRM, with research addressing a variety of aspects within this domain. To identify research gaps and provide a synthesized overview of the current state of knowledge, this section provides a detailed content analysis of the selected articles. This analysis was performed through a robust inductive thematic coding process. Starting with open coding, key concepts and recurring patterns were identified directly from the data within each of the 53 selected articles. These initial codes were then iteratively grouped and refined through constant comparison, leading to the emergence of the primary themes and subsequent sub-themes, ensuring that the categorization accurately reflects the dominant discussions in the literature. The 53 selected articles were systematically organized into five primary themes, including AI technologies utilized in GHRM, AI-GHRM in different sectors, AI application in GHRM practices, the role of AI-GHRM in performance management, and factors impacting the adoption of AI in GHRM. Additionally, several sub-themes were defined within each of these main themes to further refine the analysis and capture beneficial insights.

3.1. GHRM Related Technologies

This section presents a comprehensive analysis of the technological advancements that are impacting GHRM. To provide a synthesized overview of the current state of knowledge, this section systematically organizes the selected articles into three primary technology-focused sub-themes: AI technologies, Big Data, and Integrated Intelligent & Connected Technologies. Each of these sub-themes is detailed in the following subsections, with their corresponding literature presented in Table 1, Table 2, and Table 3, respectively. While these technological categories are inherently interconnected, their distinction as separate sub-themes is justified by the unique focus and primary contribution observed in the extant literature. “AI technologies utilized in GHRM” emphasizes the application of specific AI tools and algorithms; “Big Data in GHRM” highlights the role of large datasets and their analytics as a distinct enabler; and “Integrated Intelligent & Connected Technologies in GHRM” focuses on the synergistic interplay and holistic systems formed by combinations of various advanced technologies. This categorization allows for a clearer understanding of distinct research streams within the technological landscape of GHRM.

3.1.1. AI Technologies Utilized in GHRM

This sub-section analyzes research articles that specifically explore the application of AI technologies within the context of GHRM. It investigates a range of AI-driven tools and techniques, detailing their implementation and their significant impact on various GHRM practices. A detailed summary of these studies is provided in Table 1.

Table 1. AI Technologies in GHRM.

Theme	Authors	Focus
AI	[30]	Provides real-world examples of AI-driven HR tools (i.e., Pymetrics, HireVue, Coursera for Business, Degreed, Glint, Peakon, PayScale) and illustrates how these technologies support eco-friendly HR practices by streamlining operations, reducing resource consumption, and enhancing decision-making.
	[31]	Shows that AI moderates the relationships among green HRM, green knowledge management, and sustainable performance, suggesting that AI enhances the effectiveness of green HR strategies in driving sustainable outcomes.
	[32]	Uses AI and high levels of technological competence to see positive environmental performance outcomes from their Green HRM practices.
	[33]	Demonstrates that AI serves as a crucial enabler by influencing employees’ innovative work behavior and supporting data-driven decision-making within green HR practices.
	[35]	Analyzes the use of AI in GHRM and its potential benefits through secondary data, providing insights on how technology can transform HR practices to be more sustainable.
	[36]	Investigates how AI can be used to improve various aspects of GHRM practices.
	[38]	Discovers the complexities of the application of AI algorithms in HR analytics and the potential impact it will have on HR practices.
	[40]	Discovers how AI can be leveraged to enhance organizational sustainability through its integration with GHRM practices, in addition to the challenges of this integration.
	[51]	Uses AI to improve GHRM practices including green recruitment, green training and development, and other GHRM activities
	[52]	Examines how AI personalize HRM practices to better fit the needs of individual employees, which can further improve engagement and performance
	[53]	Explores the various ways AI can be leveraged to enhance e-recruitment processes, support GHRM practices, promote corporate university sustainability, and ultimately improve organizational performance
	[54]	Specifies how AI can be leveraged to improve the efficiency, effectiveness, and sustainability of the recruitment process
	[55]	Demonstrates the integration of AI technologies into HR functions to transition corporate HR into a green, resource-efficient, and sustainable system.
	[56]	Highlights the role of AI in streamlining HR processes by automating routine tasks and offering data-driven insights for decision-making, AI tools reduce employee workload and foster a more engaging and innovative work environment, ultimately contributing to higher employee engagement levels.
	[57]	Details how emerging AI technologies, including machine learning, neural networks, IoT, and chatbots, are leveraged in IR 5.0 to support various green HRM functions, from talent acquisition and training personalization to performance management and employee empowerment.
	[58]	Highlights how AI can help organizations navigate the challenges posed by the pandemic and develop a sustainable work-from-home culture.
	[59]	Emphasizes how HR analytics and AI are applied as decision-support tools. It shows that by leveraging descriptive, diagnostic, predictive, and prescriptive analytics, organizations can gain valuable insights into HR processes, such as compensation, retention, and emotional labor, that support sustainable HRM practices.
	[60]	Shows that AI has a direct positive effect on employees’ innovative work behavior, and moderates the relationship between green hard talent management and innovative work behavior which indicates that AI can either enhance or dampen the influence of traditional green HR practices, highlighting its critical role as a technological enabler.
	[61]	Offers propositions on how AI can reinforce GHRM and drive sustainability, providing insights for further exploration and development of AI-enabled HRM strategies.
	[62]	Proposes the development and use of an AI-powered talent intelligence platform that leverages AI-based talent analytics to enhance people management and drive sustainable competitive advantage and sustainable talent management.
	[63]	Explores the various ways AI can be leveraged to enhance GHRM practices, promote employee behavior, and ultimately improve organizational performance.
	[64]	Determines how AI can be leveraged to enhance green training and development, promote employee’s green behavior, and ultimately contribute to environmental sustainability within organizations.

Table 1. AI Technologies in GHRM.

Theme	Authors	Focus
AI	[30]	Provides real-world examples of AI-driven HR tools (i.e., Pymetrics, HireVue, Coursera for Business, Degreed, Glint, Peakon, PayScale) and illustrates how these technologies support eco-friendly HR practices by streamlining operations, reducing resource consumption, and enhancing decision-making.
	[31]	Shows that AI moderates the relationships among green HRM, green knowledge management, and sustainable performance, suggesting that AI enhances the effectiveness of green HR strategies in driving sustainable outcomes.
	[32]	Uses AI and high levels of technological competence to see positive environmental performance outcomes from their Green HRM practices.
	[33]	Demonstrates that AI serves as a crucial enabler by influencing employees’ innovative work behavior and supporting data-driven decision-making within green HR practices.
	[35]	Analyzes the use of AI in GHRM and its potential benefits through secondary data, providing insights on how technology can transform HR practices to be more sustainable.
	[36]	Investigates how AI can be used to improve various aspects of GHRM practices.
	[38]	Discovers the complexities of the application of AI algorithms in HR analytics and the potential impact it will have on HR practices.
	[40]	Discovers how AI can be leveraged to enhance organizational sustainability through its integration with GHRM practices, in addition to the challenges of this integration.
	[51]	Uses AI to improve GHRM practices including green recruitment, green training and development, and other GHRM activities
	[52]	Examines how AI personalize HRM practices to better fit the needs of individual employees, which can further improve engagement and performance
	[53]	Explores the various ways AI can be leveraged to enhance e-recruitment processes, support GHRM practices, promote corporate university sustainability, and ultimately improve organizational performance
	[54]	Specifies how AI can be leveraged to improve the efficiency, effectiveness, and sustainability of the recruitment process
	[55]	Demonstrates the integration of AI technologies into HR functions to transition corporate HR into a green, resource-efficient, and sustainable system.
	[56]	Highlights the role of AI in streamlining HR processes by automating routine tasks and offering data-driven insights for decision-making, AI tools reduce employee workload and foster a more engaging and innovative work environment, ultimately contributing to higher employee engagement levels.
	[57]	Details how emerging AI technologies, including machine learning, neural networks, IoT, and chatbots, are leveraged in IR 5.0 to support various green HRM functions, from talent acquisition and training personalization to performance management and employee empowerment.
	[58]	Highlights how AI can help organizations navigate the challenges posed by the pandemic and develop a sustainable work-from-home culture.
	[59]	Emphasizes how HR analytics and AI are applied as decision-support tools. It shows that by leveraging descriptive, diagnostic, predictive, and prescriptive analytics, organizations can gain valuable insights into HR processes, such as compensation, retention, and emotional labor, that support sustainable HRM practices.
	[60]	Shows that AI has a direct positive effect on employees’ innovative work behavior, and moderates the relationship between green hard talent management and innovative work behavior which indicates that AI can either enhance or dampen the influence of traditional green HR practices, highlighting its critical role as a technological enabler.
	[61]	Offers propositions on how AI can reinforce GHRM and drive sustainability, providing insights for further exploration and development of AI-enabled HRM strategies.
	[62]	Proposes the development and use of an AI-powered talent intelligence platform that leverages AI-based talent analytics to enhance people management and drive sustainable competitive advantage and sustainable talent management.
	[63]	Explores the various ways AI can be leveraged to enhance GHRM practices, promote employee behavior, and ultimately improve organizational performance.
	[64]	Determines how AI can be leveraged to enhance green training and development, promote employee’s green behavior, and ultimately contribute to environmental sustainability within organizations.

The body of literature reviewed suggests that integrating AI into GHRM can lead to significant changes in organizational practices, with demonstrable effects on sustainability and operational efficiency. Scholarly investigations, as detailed Table 1, highlight AI’s capacity to enhance a spectrum of GHRM functions. AI-driven tools are instrumental in enhancing green recruitment and training processes, thereby fostering a workforce equipped to advance environmental stewardship [51,53]. Moreover, AI facilitates the personalization of HRM practices, which elevates employee engagement and performance, as well as aligns individual contributions with organizational sustainability goals. The deployment of AI extends to strategic HR functions, where it aids in data-driven decision-making, augments HR analytics, and supports the transition towards resource-efficient systems [33,56]. Remarkably, AI’s moderating role in the relationship between green HRM and sustainable performance highlights its potential to amplify the effectiveness of green HR strategies. Despite existing challenges, Masood et al. [40] suggests the predominant evidence that AI functions as a crucial catalyst, fostering innovative work behavior, advancing corporate sustainability initiatives, and empowering organizations to address the multifaceted complexities of contemporary environmental issues and disruptive events, including the recent pandemic. Ultimately, the strategic application of AI in GHRM is essential for promoting sustainable competitive advantage and ensuring that HR practices support ecological balance and contribute to long-term organizational resilience and success.

3.1.2. Big Data in GHRM

Building on the broader scope of AI, this sub-section focuses on the pivotal role of Big Data within GHRM. The literature highlights how big data analytics, often an integral component of AI frameworks, act as a crucial enabler for enhancing GHRM practices and achieving sustainable outcomes. The key studies focusing on Big Data’s role are outlined in Table 2.

Table 2. Big Data in GHRM.

Theme	Authors	Focus
Big Data	[65]	Identifies the role of big data analytics in moderating the relationships between GHRM and green innovation, showcasing the practical application of AI tools (big data analytics) to enhance the understanding of HR and performance outcomes.
	[66]	Explores whether the use of big data analytics enhances the positive effects of green innovation on achieving circular economy objectives.
	[67]	Details the role of AI-driven big data analytics in moderating the impact of green HRM on CEP. It emphasizes that big data analytics (BDA) not only enables data integration and decision-making but also strengthens the positive effects of sustainable HR practices on environmental performance.
	[68]	Explains that big data analytics is assimilated through acceptance, routinization, and assimilation processes. Establishes the role of big data technologies in optimizing internal processes by integrating them with green HRM practices, thereby recommending a combined approach to improve sustainable capabilities and firm performance.
	[69]	States that big data analytics capability acts as a mediator, enabling organizations to effectively translate GHRMP into green competitive advantage through data-driven insights and decision-making.
	[70]	Focuses on big data analytics through exploring its direct impact on sustainability-related factors, highlighting its strategic role in achieving sustainability goals, and providing empirical evidence within a specific industry context.
	[71]	Highlights the role of Big Data and digital transformation in enhancing firms’ sustainability when integrated with GHRM.
	[72]	Positions big data as a critical technological antecedent that, when integrated with green HRM practices, fosters green innovation and thereby supports sustainable performance.
	[73]	Highlights big data analytics as a crucial HRM technology, illustrating how it enhances and regulates the influence of green HRM practices on organizational performance.
	[74]	Reveals that among various dimensions of big data analytics utilization, only BDA assimilation significantly moderates the relationship between GHRM and LSR which highlights the critical role of data analytics processes in enhancing the outcomes of GHRM practices.
	[75]	Moderates the positive effects of GHRM on green service production and environmental performance, demonstrating that effective data assimilation is essential for harnessing the full potential of SHRM practices in challenging environments.

Table 2. Big Data in GHRM.

Theme	Authors	Focus
Big Data	[65]	Identifies the role of big data analytics in moderating the relationships between GHRM and green innovation, showcasing the practical application of AI tools (big data analytics) to enhance the understanding of HR and performance outcomes.
	[66]	Explores whether the use of big data analytics enhances the positive effects of green innovation on achieving circular economy objectives.
	[67]	Details the role of AI-driven big data analytics in moderating the impact of green HRM on CEP. It emphasizes that big data analytics (BDA) not only enables data integration and decision-making but also strengthens the positive effects of sustainable HR practices on environmental performance.
	[68]	Explains that big data analytics is assimilated through acceptance, routinization, and assimilation processes. Establishes the role of big data technologies in optimizing internal processes by integrating them with green HRM practices, thereby recommending a combined approach to improve sustainable capabilities and firm performance.
	[69]	States that big data analytics capability acts as a mediator, enabling organizations to effectively translate GHRMP into green competitive advantage through data-driven insights and decision-making.
	[70]	Focuses on big data analytics through exploring its direct impact on sustainability-related factors, highlighting its strategic role in achieving sustainability goals, and providing empirical evidence within a specific industry context.
	[71]	Highlights the role of Big Data and digital transformation in enhancing firms’ sustainability when integrated with GHRM.
	[72]	Positions big data as a critical technological antecedent that, when integrated with green HRM practices, fosters green innovation and thereby supports sustainable performance.
	[73]	Highlights big data analytics as a crucial HRM technology, illustrating how it enhances and regulates the influence of green HRM practices on organizational performance.
	[74]	Reveals that among various dimensions of big data analytics utilization, only BDA assimilation significantly moderates the relationship between GHRM and LSR which highlights the critical role of data analytics processes in enhancing the outcomes of GHRM practices.
	[75]	Moderates the positive effects of GHRM on green service production and environmental performance, demonstrating that effective data assimilation is essential for harnessing the full potential of SHRM practices in challenging environments.

The integration of Big Data and GHRM is an essential development for enhancing organizational sustainability and performance. The research clearly shows that Big Data analytics plays a key role in how GHRM practices influence organizational success, including improvements in green innovation, progress towards circular economy goals, and better overall sustainable performance (see Table 2). Essentially, Big Data’s value lies in its ability to convert GHRM efforts into real environmental and competitive advantages through the provision of data-driven insights and improved decision-making capabilities [69,75]. Furthermore, incorporating Big Data analytics into HRM systems enhances internal processes, which creates a synergy between technological capabilities and green HR practices to boost sustainability [71]. The strategic application of Big Data facilitates the attainment of sustainability goals, positioning it as a key technological enabler of green innovation and enhanced corporate sustainability performance.

3.1.3. Integrated Intelligent & Connected Technologies in GHRM

Beyond individual AI applications, this sub-section explores the synergistic integration of intelligent and connected technologies, such as STARA (Smart Technology, Artificial Intelligence, Robotics, and Algorithms), IoT (Internet of Things), Machine Learning, NLP (Natural Language Processing), ANN (Artificial Neural Networks), and RPA (Robotic Process Automation) within GHRM. This theme examines how these combined technologies drive HR enhancement and contribute to environmental sustainability. Table 3 presents the literature focusing on these integrated technological systems.

Table 3. Integrated Intelligent & Connected Technologies in GHRM.

Theme	Authors	Focus
Integrated Intelligent & Connected Technologies	[34]	Relates to STARA by exploring its predictive role in environmental sustainability, its impact on GHRM programs, and its interplay with GHRM in promoting sustainability outcomes
	[76]	Explores how the adoption of green talent management practices and the development of leader competence in STARA technologies (smart technology, artificial intelligence, robotics, and algorithms) affect employee turnover intention.
	[77]	Examines the impact of STARA capabilities on GHRM, green supply chain management practices, and sustainable performance.
	[78]	Reveals the impact of STARA on green performance through examining the mediating roles of GHRM and employees’ green commitment, and the moderating role of green psychological climate.
	[79]	Uses IoT to enhance data collection, analysis, and decision-making in HRM to improve employee management and organizational growth.
	[80]	Examines the impact of the IoT on strategic HRM, and how it impacts on sustainable HR growth.
	[81]	Integrates IoT for real-time data acquisition with fuzzy theory–based algorithms to perform adaptive optimization and expert system analysis. This integration supports intelligent performance evaluation and decision-making in hotel GHRM, allowing for a more precise and adaptable evaluation of the hotel’s eco-friendly HR practices.
	[82]	Uses machine learning to evaluate STP that promote organizational growth and ensure the attainment of sustainable HRM objectives.
	[83]	Highlights the role of machine learning as a core technological tool that facilitates the efficient training of a digitally competent and environmentally responsible workforce, essential for a carbon–neutral future digital economy.
	[84]	Combines structured frameworks (ontologies) with context-aware machine learning models to enable detailed comparative analyses, offering a systematic way to understand how various components of green HRM affect sustainability outcomes.
	[85]	Uses NLP (Natural Language Processing) techniques to analyze online job vacancies and profiles to identify green skills gaps, showcasing the application of AI technology in HR analytics.
	[86]	Details the application of various AI methods, such as NLP (TF-IDF), and multi-objective optimization (NSGA-II), to analyze HR data, predict turnover, and optimize resource usage, thus showcasing the technical tools that support sustainable HR practices.
	[87]	Employs ANN modeling to forecast key variables such as waste management and resource consumption, thus, the paper exemplifies the practical use of advanced AI technologies to drive HR optimization and environmental cost management.
	[88]	Emphasizes RPA as a pivotal AI technology, discussing its various positioning approaches (conservative, efficiency improving, strategic), and illustrates how software robots are leveraged to streamline HR processes and support sustainable practices across the organization.
	[89]	Details the application of various AI technologies, including Applicant Tracking Systems (ATS), social, mobile, analytical, and cloud (SMAC) technologies, and big data analytics for career planning, rewarding, and training employees. These enhance digital HRM, demonstrating that these tools contribute to process optimization and sustainable HR practices.
	[90]	Integrates advanced analytical methodologies and AI to promote HR excellence by improving decision-making processes, increasing worker efficiency, and developing sustainable HR practices
	[91]	Emphasizes the contribution of diverse digital technologies, robotic systems, AI, big data, and IT in transforming SHRM, fostering a human- and environment-centered approach that advances social, economic, and environmental sustainability.
	[92]	Investigates how digital HR technology can moderate the relationship between GHRM practices and environmental performance.
	[93]	Provides insights into how technology and GHRM can be used to support environmental sustainability
	[94]	Examines the potential of computer networks and green computing to enhance HR analytics by enabling efficient data management and analysis while promoting sustainable practices.

Table 3. Integrated Intelligent & Connected Technologies in GHRM.

Theme	Authors	Focus
Integrated Intelligent & Connected Technologies	[34]	Relates to STARA by exploring its predictive role in environmental sustainability, its impact on GHRM programs, and its interplay with GHRM in promoting sustainability outcomes
	[76]	Explores how the adoption of green talent management practices and the development of leader competence in STARA technologies (smart technology, artificial intelligence, robotics, and algorithms) affect employee turnover intention.
	[77]	Examines the impact of STARA capabilities on GHRM, green supply chain management practices, and sustainable performance.
	[78]	Reveals the impact of STARA on green performance through examining the mediating roles of GHRM and employees’ green commitment, and the moderating role of green psychological climate.
	[79]	Uses IoT to enhance data collection, analysis, and decision-making in HRM to improve employee management and organizational growth.
	[80]	Examines the impact of the IoT on strategic HRM, and how it impacts on sustainable HR growth.
	[81]	Integrates IoT for real-time data acquisition with fuzzy theory–based algorithms to perform adaptive optimization and expert system analysis. This integration supports intelligent performance evaluation and decision-making in hotel GHRM, allowing for a more precise and adaptable evaluation of the hotel’s eco-friendly HR practices.
	[82]	Uses machine learning to evaluate STP that promote organizational growth and ensure the attainment of sustainable HRM objectives.
	[83]	Highlights the role of machine learning as a core technological tool that facilitates the efficient training of a digitally competent and environmentally responsible workforce, essential for a carbon–neutral future digital economy.
	[84]	Combines structured frameworks (ontologies) with context-aware machine learning models to enable detailed comparative analyses, offering a systematic way to understand how various components of green HRM affect sustainability outcomes.
	[85]	Uses NLP (Natural Language Processing) techniques to analyze online job vacancies and profiles to identify green skills gaps, showcasing the application of AI technology in HR analytics.
	[86]	Details the application of various AI methods, such as NLP (TF-IDF), and multi-objective optimization (NSGA-II), to analyze HR data, predict turnover, and optimize resource usage, thus showcasing the technical tools that support sustainable HR practices.
	[87]	Employs ANN modeling to forecast key variables such as waste management and resource consumption, thus, the paper exemplifies the practical use of advanced AI technologies to drive HR optimization and environmental cost management.
	[88]	Emphasizes RPA as a pivotal AI technology, discussing its various positioning approaches (conservative, efficiency improving, strategic), and illustrates how software robots are leveraged to streamline HR processes and support sustainable practices across the organization.
	[89]	Details the application of various AI technologies, including Applicant Tracking Systems (ATS), social, mobile, analytical, and cloud (SMAC) technologies, and big data analytics for career planning, rewarding, and training employees. These enhance digital HRM, demonstrating that these tools contribute to process optimization and sustainable HR practices.
	[90]	Integrates advanced analytical methodologies and AI to promote HR excellence by improving decision-making processes, increasing worker efficiency, and developing sustainable HR practices
	[91]	Emphasizes the contribution of diverse digital technologies, robotic systems, AI, big data, and IT in transforming SHRM, fostering a human- and environment-centered approach that advances social, economic, and environmental sustainability.
	[92]	Investigates how digital HR technology can moderate the relationship between GHRM practices and environmental performance.
	[93]	Provides insights into how technology and GHRM can be used to support environmental sustainability
	[94]	Examines the potential of computer networks and green computing to enhance HR analytics by enabling efficient data management and analysis while promoting sustainable practices.

The integration of intelligent and connected technologies into GHRM is reshaping organizational approaches to sustainability and human capital management. Research by Ogbeibu et al. [34] and Shen [81] indicates a strong emphasis on the role of emerging technologies, such as STARA and IoT, in driving both environmental sustainability and HR effectiveness (see Table 3). Studies highlight the impact of these technologies on various GHRM functions, from green talent management practices and employee turnover intention to the optimization of green supply chain management and overall green performance. The application of IoT facilitates enhanced data collection and analysis for improved employee management and organizational growth, while machine learning plays a crucial role in training a digitally competent and environmentally responsible workforce [79]. Furthermore, AI technologies, including NLP and ANN, are being leveraged for tasks such as identifying green skills gaps, predicting employee turnover, optimizing resource usage, and forecasting environmental impact variables, which demonstrate the capacity of these tools to support sustainable HR practices and environmental cost management [85,87]. The broader trend highlights the potential of these integrated technologies to streamline HR processes, enhance decision-making, increase worker efficiency, and ultimately foster a more human- and environment-oriented approach to sustainable HRM.

3.2. AI-GHRM in Different Sectors

This section explores the studies that illustrate the application of AI-driven GHRM across manufacturing and service sectors which reveal common trends and sector-specific variations, as detailed in Table 4 and Table 5.

3.2.1. AI-GHRM in Manufacturing Sectors

The manufacturing sector emerges as a prominent area for the application of AI-driven GHRM, with research consistently highlighting the integration of advanced technologies to enhance sustainability and operational outcomes. Studies in this domain explore how green talent management, STARA capabilities, and big data analytics are leveraged to improve environmental performance, achieve green competitive advantage, and contribute to a more sustainable future within manufacturing organizations, including those in the textile, automotive, and petroleum industries. The key literature focusing on these applications in the textile, automotive, and petroleum industries is detailed in Table 4.

Table 4. AI-GHRM (Manufacturing Sectors).

Theme	Author	Focus
Manufacturing (Textile, Automotive, Petroleum)	[31]	Provides evidence that the integration of AI with green HRM practices, along with effective knowledge management and innovation, leads to enhanced sustainable performance, reinforcing the role of AI in driving environmental and organizational benefits.
	[32]	Investigates the relationship between Green HRM practices and environmental performance in manufacturing organizations
	[34]	Implements organizational STARA capabilities and GHRM practices within manufacturing firms to boost sustainability performance and promote a more environmentally responsible and sustainable future.
	[63]	Relates to the manufacturing sector as it explores how AI can be leveraged to enhance GHRM practices, improve employee behavior, and ultimately optimize organizational performance
	[66]	Investigates the impact of GHRM on circular economy performance within the textile sector.
	[67]	Provides empirical evidence from SMEs in the Pakistani textile industry, illustrating how green HRM practices, supported by AI-driven big data analytics and a data-driven culture, enhance circular economy performance in this resource-intensive manufacturing sector.
	[69]	Provides insights into the key factors that contribute to achieving a green competitive advantage and improved environmental performance.
	[70]	Examines the sector’s unique sustainability challenges, the utilization of BDA in manufacturing operations, the potential for achieving competitive advantage through big data analytics, and its direct influence on environmental performance.
	[71]	Relates to manufacturing by exploring how GHRM can contribute to a more sustainable, efficient, and competitive manufacturing sector when introduced along with big data and digital transformation.
	[76]	Conducts a study in the manufacturing sector of Nigeria which aims to investigate how green talent management, leader competence in STARA technologies, and digital task interdependence collectively influence employee turnover intention.
	[77]	Adopts GHRM practices and leverages Big Data Analytics Capability (BDAC), to enhance the sustainability performance in manufacturing companies and contributes to a more environmentally friendly and sustainable future.
	[79]	Investigates the growing importance of IoT in global manufacturing organizations and its potential to enhance organizational growth in the digital workplace environment
	[84]	Fills a literature gap by focusing on the petroleum sector in India, providing sector-specific insights into how green HRM practices with machine learning influence environmental outcomes and organizational performance.
	[93]	Offers insights into how the industry can harness emerging technologies and GHRM practices to attain sustainable organizational performance and foster a more sustainable future.

Table 4. AI-GHRM (Manufacturing Sectors).

Theme	Author	Focus
Manufacturing (Textile, Automotive, Petroleum)	[31]	Provides evidence that the integration of AI with green HRM practices, along with effective knowledge management and innovation, leads to enhanced sustainable performance, reinforcing the role of AI in driving environmental and organizational benefits.
	[32]	Investigates the relationship between Green HRM practices and environmental performance in manufacturing organizations
	[34]	Implements organizational STARA capabilities and GHRM practices within manufacturing firms to boost sustainability performance and promote a more environmentally responsible and sustainable future.
	[63]	Relates to the manufacturing sector as it explores how AI can be leveraged to enhance GHRM practices, improve employee behavior, and ultimately optimize organizational performance
	[66]	Investigates the impact of GHRM on circular economy performance within the textile sector.
	[67]	Provides empirical evidence from SMEs in the Pakistani textile industry, illustrating how green HRM practices, supported by AI-driven big data analytics and a data-driven culture, enhance circular economy performance in this resource-intensive manufacturing sector.
	[69]	Provides insights into the key factors that contribute to achieving a green competitive advantage and improved environmental performance.
	[70]	Examines the sector’s unique sustainability challenges, the utilization of BDA in manufacturing operations, the potential for achieving competitive advantage through big data analytics, and its direct influence on environmental performance.
	[71]	Relates to manufacturing by exploring how GHRM can contribute to a more sustainable, efficient, and competitive manufacturing sector when introduced along with big data and digital transformation.
	[76]	Conducts a study in the manufacturing sector of Nigeria which aims to investigate how green talent management, leader competence in STARA technologies, and digital task interdependence collectively influence employee turnover intention.
	[77]	Adopts GHRM practices and leverages Big Data Analytics Capability (BDAC), to enhance the sustainability performance in manufacturing companies and contributes to a more environmentally friendly and sustainable future.
	[79]	Investigates the growing importance of IoT in global manufacturing organizations and its potential to enhance organizational growth in the digital workplace environment
	[84]	Fills a literature gap by focusing on the petroleum sector in India, providing sector-specific insights into how green HRM practices with machine learning influence environmental outcomes and organizational performance.
	[93]	Offers insights into how the industry can harness emerging technologies and GHRM practices to attain sustainable organizational performance and foster a more sustainable future.

The manufacturing sector, which encompasses diverse industries such as textile, automotive, and petroleum, is undergoing a significant transformation through the integration of Green HRM practices and advanced technologies (as shown in Table 4). Research consistently emphasizes the crucial role of GHRM in driving sustainability within manufacturing, with studies exploring its impact on environmental performance, green innovation, and circular economy objectives [31,66,67]. A key focus is the adoption of technologies including AI, Big Data analytics, and STARA to enhance GHRM effectiveness and achieve sustainable outcomes. These technologies are leveraged to optimize various aspects of manufacturing operations, from talent management and employee behavior to production processes and supply chain management [76]. Furthermore, studies highlight the importance of contextual factors, such as organizational capabilities and a data-driven culture, in facilitating the successful implementation of GHRM and technology integration [67]. The overarching trend indicates a growing recognition that strategic GHRM, coupled with technological advancements, is essential for manufacturing organizations to achieve a green competitive advantage, improve environmental performance, and contribute to a more sustainable future.

3.2.2. AI-GHRM in Service Sectors

The service sector, encompassing a diverse array of industries such as healthcare, education, hospitality, and IT, also demonstrates significant engagement with AI-GHRM applications. Research in this area illustrates how AI and digital HR technologies are tailored to address sector-specific challenges, fostering innovative work behavior among employees, enhancing resource utilization, and supporting environmental sustainability initiatives across various service-oriented businesses. A summary of these studies across the healthcare, education, hospitality, and IT sectors is provided in Table 5.

Table 5. AI-GHRM (Service Sectors).

Theme	Author	Focus
Service (Healthcare, Educational, Hospitality, IT/Digital, Banking, Social Care, Logistics)	[33]	Provides insights into how green talent management, coupled with ethical leadership and AI, can foster innovative work behavior and thereby deliver a competitive edge in a highly regulated and innovation-driven sector like the pharmaceutical industry.
	[53]	Focuses on corporate universities and provides valuable guidance to improve organizational performance through understanding the impact of e-recruitment and AI technologies
	[59]	Demonstrates the sector-specific application of HR analytics and AI. It underscores the need for technology adoption tailored to the unique challenges and opportunities within healthcare, contributing to sustainable HRM in this industry.
	[60]	Illustrates how green talent management practices, when integrated with transformational leadership and AI, contribute to fostering innovative work behavior among academic staff, thereby supporting sustainable competitive advantage in the higher education sector.
	[74]	Provides insights into how GHRM practices are leveraged to promote socially responsible logistics.
	[75]	Offers insights by illustrating how the integration of GHRM practices and big data management helps healthcare organizations overcome operational challenges and achieve environmental sustainability during emergencies.
	[78]	Examines how the hospitality industry’s GHRM practices and employees’ green commitment can help implement STARA effectively to achieve better environmental performance.
	[80]	Relates to the IT sector as it explores how IoT can be leveraged to enhance strategic HRM practices and promote sustainable HR growth
	[81]	Focuses specifically on the hospitality sector by constructing a performance evaluation framework tailored to hotel green HR practices, illustrating how green HR practices can be transformed using digital technologies to support environmental sustainability and service quality
	[83]	Provides a detailed overview of how young workers are being trained and how machine learning is used within the digital industries of BRICS countries, highlighting sector-specific dynamics and challenges for achieving carbon neutrality.
	[87]	Highlights the unique challenges and opportunities for integrating AI into HR management to foster environmental awareness within the social care sector.
	[92]	Presents insights for banks that are looking to improve their environmental performance through GHRM and digital HR technology

Table 5. AI-GHRM (Service Sectors).

Theme	Author	Focus
Service (Healthcare, Educational, Hospitality, IT/Digital, Banking, Social Care, Logistics)	[33]	Provides insights into how green talent management, coupled with ethical leadership and AI, can foster innovative work behavior and thereby deliver a competitive edge in a highly regulated and innovation-driven sector like the pharmaceutical industry.
	[53]	Focuses on corporate universities and provides valuable guidance to improve organizational performance through understanding the impact of e-recruitment and AI technologies
	[59]	Demonstrates the sector-specific application of HR analytics and AI. It underscores the need for technology adoption tailored to the unique challenges and opportunities within healthcare, contributing to sustainable HRM in this industry.
	[60]	Illustrates how green talent management practices, when integrated with transformational leadership and AI, contribute to fostering innovative work behavior among academic staff, thereby supporting sustainable competitive advantage in the higher education sector.
	[74]	Provides insights into how GHRM practices are leveraged to promote socially responsible logistics.
	[75]	Offers insights by illustrating how the integration of GHRM practices and big data management helps healthcare organizations overcome operational challenges and achieve environmental sustainability during emergencies.
	[78]	Examines how the hospitality industry’s GHRM practices and employees’ green commitment can help implement STARA effectively to achieve better environmental performance.
	[80]	Relates to the IT sector as it explores how IoT can be leveraged to enhance strategic HRM practices and promote sustainable HR growth
	[81]	Focuses specifically on the hospitality sector by constructing a performance evaluation framework tailored to hotel green HR practices, illustrating how green HR practices can be transformed using digital technologies to support environmental sustainability and service quality
	[83]	Provides a detailed overview of how young workers are being trained and how machine learning is used within the digital industries of BRICS countries, highlighting sector-specific dynamics and challenges for achieving carbon neutrality.
	[87]	Highlights the unique challenges and opportunities for integrating AI into HR management to foster environmental awareness within the social care sector.
	[92]	Presents insights for banks that are looking to improve their environmental performance through GHRM and digital HR technology

The service sector, which encompasses diverse areas such as healthcare, education, hospitality, IT/digital, banking, social care, and logistics, is increasingly leveraging GHRM practices and technology to address unique sustainability challenges and enhance organizational performance (Table 5). Research highlights the importance of integrating GHRM with various technological advancements, including AI, HR analytics, and IoT, to foster innovative work behavior, improve operational efficiency, and achieve environmental sustainability within service-oriented organizations. Studies emphasize the sector-specific applications of these integrated approaches, which demonstrate their relevance in improving talent management, training, and performance evaluation across different service industries [33,83]. Furthermore, the literature highlights the role of GHRM in promoting socially responsible practices within sectors, such as logistics, and the potential of digital HR technology to enhance environmental performance in banking [74,92]. The overarching trend indicates a growing recognition that tailored GHRM strategies, coupled with appropriate technological tools, are essential for service organizations to navigate the complexities of their respective industries while contributing to a more sustainable future.

3.3. AI Application in GHRM Practices

The following section synthesizes the key findings from the reviewed literature, highlighting the multifaceted applications of AI in GHRM practices, and outlines avenues for future research to address the identified gaps, as shown in

Table 6. AI in GHRM Practices.

Theme	Authors	Focus
Talent Management	[33]	Investigates how green talent management impacts employees’ innovative work behavior, while also examining how ethical leadership and AI moderate this relationship. This illustrates how traditional HR practices can be transformed through AI-driven approaches to support sustainability.
	[51]	Evaluates GHRM practices, such as talent management, and emphasizes the significance, potential challenges, and the advantages of artificial intelligence adoption.
	[60]	Examines both green hard and soft talent management as emerging green HRM practices. It demonstrates that these practices, along with AI integration, significantly influence employees’ innovative work behavior, thereby positioning green talent management as a key approach for SHRM.
	[62]	Explores how AI can be integrated into various talent management interventions, including recruitment, skill mapping, career management, employee retention, and compensation, to build a sustainable talent management architecture for organizational competitive advantage.
	[76]	Investigates how green talent management, STARA competence, and digital task interdependence influence employee turnover intention. The findings reveal that both green hard and soft talent management, along with leader STARA competence, increase turnover intention.
	[85]	Provides a blueprint for identifying and addressing green skills gaps using NLP techniques, informing strategic HR practices for workforce readiness in the green transition.
Recruitment	[53]	Understands how e-recruitment and AI technologies impact organizational performance and sustainability initiatives within the framework of global HRM and corporate universities
	[54]	Explores the implementation of AI in green recruitment through the Zoho recruitment system.
	[57]	Explains how IR 5.0, characterized by AI with human-like interaction, facilitates remote recruitment, online training via simulation, and other digital HR processes, reducing human contact and lowering carbon emissions, resulting in more efficient, environmentally aligned processes.
	[86]	Proposes a systematic approach that leverages AI-driven insights to optimize the recruitment and retention of top personnel, integrating HR practices with sustainability goals to reduce environmental impact.
	[91]	Illustrates how AI-driven tools (i.e., e-recruitment, e-learning, cloud computing, and HR data analytics) are integrated into HR practices to support sustainable human resource management and promote environmentally friendly operations.
Training and Development	[63]	Examines the influence of green training and development propelled by AI on the environmental behavior of employees in SMEs.
	[65]	Develops a research framework based on GHRM practices and Green Digital Learning Orientation (GDLO) that helps organizations foster green innovation. It illustrates how HR strategies, when combined with digital learning initiatives, can be used to address environmental challenges and enhance workforce capabilities.
	[83]	Examines how the balanced and harmonious training of young personnel is integrated with machine learning to develop digital and green competencies, contributing to the formation of a carbon–neutral digital economy.
Performance, Compensation & Reward	[30]	Highlights how AI and data analytics are integrated into HR processes to promote eco-engagement and environmentally responsible practices within green HRM (i.e., recruitment, performance management, compensation management, employee discipline management, and employee retention).
	[32]	Investigates the relationship between GHRM practices, such as recruitment and selection, training, and reward. and environmental performance in organizations, with a focus on the moderating role of artificial intelligence and technological competence.
	[36]	Explores the use of AI in various GHRM practices (i.e., training, recruitment, and rewards) to enhance efficiency and employee productivity.
	[52]	Examines the interrelations between AI-Driven Sustainable Human Resource Management (HRM), Employee Engagement, Employee Performance, and Conscientiousness Personality
	[55]	Explores how AI and machine learning can be applied to traditional HR processes, such as candidate screening, recruitment, training, performance appraisal, compensation, and career development, to reduce resource use and foster an environmentally stable, sustainable HR approach.
	[79]	Investigates the impact of Internet of Things (IoT) technology on Human Resource Management (HRM) practices in the digital workplace. It explores how IoT can enhance HR functions, improve employee management, and contribute to organizational growth through data-driven decision-making and people analytics.
	[84]	Employs machine learning to evaluate how GHRM practices, specifically training programs, employee input into environmental policy, and open lines of communication regarding sustainability objectives, translate into pro-environmental behaviors and influence overall organizational green performance.
	[88]	Details how RPA is deployed to automate core HR functions, such as recruitment, training, performance evaluation, and administrative tasks, thereby reducing manual workload, lowering resource consumption, and aligning HR operations with sustainability goals.
	[89]	Examines how AI and digital HRM practices can be leveraged to enhance sustainable HRM. The study discusses integrating AI into all HR practices (recruitment, remote work management, compensation and benefits, etc.) to stabilize resource consumption and support sustainable business practices.

.

The literature demonstrates the expanding role for AI in GHRM practices, with applications spanning across various HR functions to enhance sustainability and organizational effectiveness (Table 6). Specifically, findings from [62,85,86] reveal that AI is being extensively integrated into talent management, where it is utilized to identify green skills gaps through techniques like NLP, enhance recruitment and retention strategies, enhance skill mapping and career progression, and ultimately contribute to building a sustainable talent architecture for competitive advantage. Furthermore, several studies including [33,60,76] demonstrate AI’s influence on employee outcomes within talent management, impacting turnover intention and fostering innovative work behavior, often moderated by factors like ethical leadership. In the domain of recruitment, AI-driven tools, including e-recruitment systems and HR data analytics, are being leveraged to streamline processes, improve organizational performance, and support sustainability initiatives by reducing manual effort and carbon emissions, aligning with the principles of Industry 5.0 [53,54,57,91]. Within Training and Development, AI and machine learning are instrumental in advancing green training initiatives, fostering the development of essential digital and green competencies necessary for a carbon-neutral economy, and enabling Green Digital Learning Orientations (GDLO) that drive green innovation among employees [63,70,83]. Lastly, in performance management, compensation, and rewards systems, AI is applied to optimize traditional HR processes, reduce resource consumption, enhance efficiency and employee productivity, and integrate sustainability goals into performance evaluations and reward systems [32,36,55,89]. This integration extends to examining the interplay between AI-driven sustainable HRM, employee engagement, and performance, and employing machine learning, Robotic Process Automation (RPA), and the Internet of Things (IoT) to automate functions, translate GHRM practices into pro-environmental behaviors, and improve overall organizational green performance [30,52,79,84,88]. Collectively, these studies highlight the transformative potential of AI in operationalizing GHRM practices, driving organizational efficiency and ecological sustainability outcomes.

3.4. Role of AI-GHRM in Performance Management

This section discusses how the integration of AI influences GHRM’s role in performance management, encompassing its effects on environmental, employee, and organizational performance as shown in

Table 7. AI-GHRM in Performance Management.

Theme	Authors	Focus
Environmental Performance and Sustainability	[30]	Explores how AI-powered analytics and automation in performance management enable real-time monitoring, objective evaluation, and incentive alignment with environmental sustainability goals, thereby enhancing overall green performance through the enhancement of energy consumption and waste reduction.
	[32]	Explores how Green HRM practices can help organizations to reduce their environmental impact and improve their environmental performance.
	[34]	Specifies how organizational STARA capability and GHRM programs predict environmental sustainability
	[36]	Reviews and discusses the integration of AI in GHRM practices to promote ethical behavior and environmental sustainability.
	[38]	Analyzes the application of AI in GHRM to analyze and measure organizations’ environmental impact, thus detecting areas for improvement, and implementing sustainable practices.
	[65]	Emphasizes that effective GHRM practices, along with digital technologies, focused on sustainability, can lead to improved environmental and economic outcomes.
	[67]	Demonstrates that GHRM practices have a significant positive impact on circular economy performance, with green innovation acting as a mediator between GHRM and CEP, thereby linking sustainable HR practices with improved environmental and operational outcomes.
	[68]	Demonstrates that the integration of big data analytics with green HRM and green supply chain management practices drives sustainable capabilities that, in turn, enhance overall firm sustainable performance.
	[69]	Investigates the relationship between GHRM practices, big data analytics capability, green competitive advantage, and environmental performance, with the moderating effect of managerial environmental concern.
	[72]	Develops a comprehensive path model showing that green HRM practices, together with big data analytics, drive green innovation, which in turn enhances sustainable organizational performance.
	[75]	Demonstrates that big data management significantly drives green service production like green procurement, service design, and service practices, which improves environmental performance during healthcare emergencies.
	[86]	Highlights practical advantages of AI-GHRM including lower employee turnover, increased job satisfaction, and significant savings in energy consumption and carbon emissions, thereby linking HR performance with positive, sustainable outcomes.
	[89]	Investigates the impact of AI and digital HRM on minimizing resource consumption, linking these practices to broader sustainable development outcomes such as economic growth, social inclusion, and environmental protection.
	[91]	Details how AI applications facilitate real-time performance monitoring, predictive analytics, and objective evaluation, thereby strengthening green performance management and aligning HR outcomes with sustainability targets.
	[92]	Investigate how GHRM affects commercial banks’ environmental performance. It also examines the moderating effect of digital HR technology.
	[93]	Investigates the role of new technologies and the implementation of GHRM practices in achieving sustainable organizational performance.
Employee Outcomes	[33]	Shows that green talent management significantly boosts innovative work behavior, with ethical leadership enhancing and AI influencing this relationship
	[51]	Improve employee performance through adopting AI, leading to a more engaged, productive, and sustainable workforce.
	[52]	Explores how AI-Driven Sustainable HRM helps organizations to improve employee engagement and performance.
	[54]	Examines the influence of incorporating AI into GHRM on the performance of organizations, and it explicitly studies how employee behavior acts as a mediator in this relationship.
	[56]	Demonstrates that integrating sustainable HRM with AI technology significantly improves employee well-being and engagement, reducing workload and enhancing decision-making support, which ultimately boosts productivity, enhances organizational performance. and aligns with SDG 3 objectives
	[57]	Highlights that IR 5.0 enables real-time monitoring of employees’ green performance, facilitating transparent performance assessments, timely rewards, and corrective actions that enhance overall organizational sustainability
	[63]	Investigates the impact of AI-driven green training and development on employees’ environmental knowledge and their green behavior in the workplace.
	[76]	Examines the effects of green talent management, leader STARA competence, and digital task interdependence on employee turnover intention.
	[78]	Investigate the impact of STARA on green performance, specifically examining the mediating roles of GHRM and employees’ green commitment, and the moderating role of green psychological climate (GPC)
Organizational Growth and Operational Efficiency	[31]	Examines how GHRM positively impacts organizational performance through the mediating roles of green knowledge management and green innovation, with artificial intelligence moderating these relationships to enhance overall performance.
	[53]	Explores how e-recruitment and AI can support corporate university programs to enhance organizational performance, as well as develop a skilled workforce.
	[71]	Analyzes the GHRM literature within the manufacturing sector, using bibliometric analysis to uncover the structure of existing research, identify current trends and emerging topics, and offer valuable insights for enhancing organizational performance.
	[73]	Demonstrates that big data analytics significantly enhances the positive effects of GHRM practices on organizational performance by supporting faster decision-making and efficient resource utilization.
	[79]	Investigates the growing importance of IoT in global organizations and its potential to enhance organizational growth in the digital workplace environment
	[80]	Explores the potential of the IoT in strategic HRM practices and its impact on HR growth
	[81]	Proposes a performance evaluation model for hotel green HR that uses fuzzy decision techniques to assess and optimize hotel GHRM performance and aims to enhance evaluation accuracy and efficiency, leading to improved economic benefits and environmental stewardship.
	[82]	Evaluates Sustainable Training Practices (STP) that promote organizational growth and ensure the attainment of proper HRM objectives.
	[87]	Shows that the application of AI improves predictive analytics and decision-making in HRM, leading to cost reductions and enhanced operational performance, thereby contributing to overall organizational performance.
	[88]	Demonstrates that RPA implementations lead to significant labor cost savings, improved process efficiency, and enhanced productivity, which in turn contribute to both economic and environmental dimensions of sustainable HRM by optimizing resource allocation and reducing waste.

.

The literature indicates that AI plays a significant role in enhancing GHRM’s contribution to performance management across environmental, employee, and organizational outcomes (Table 7). A significant finding is the clear link between AI-enabled GHRM practices and enhanced environmental and sustainable performance. Studies such as [32,38,67,86,89,92,93], demonstrate that leveraging AI, in conjunction with digital HR technologies, big data analytics, and automation (such as RPA), allows organizations to effectively reduce their environmental impact, improve their environmental performance metrics, measure and analyze their environmental footprint, and align their operational outcomes with sustainability targets, contributing positively to areas like circular economy performance and resource minimization. Beyond environmental metrics, AI-GHRM significantly influences employee outcomes. Multiple studies [33,51,52,56,57,64], indicate that integrating AI into GHRM practices, from green talent management to green training and performance monitoring, enhances employee performance, engagement, and well-being, and fosters pro-environmental knowledge and behavior, thereby contributing to a more productive and sustainably oriented workforce. Furthermore, the application of AI within GHRM is shown to reinforce organizational growth and operational efficiency. This is because AI contributes to cost reductions, enhanced productivity, and overall organizational performance by improving predictive analytics, decision-making, process efficiency, and resource utilization, through mediating factors like green knowledge management and green innovation [31,73,87,88]. Collectively, these findings highlight the transformative potential of AI in enabling GHRM to effectively measure, monitor, and improve performance across environmental, employee, and operational spheres, positioning it as a critical driver for achieving sustainable organizational performance.

3.5. Factors Impacting the Adoption of AI in GHRM

This section provides an overview of the factors affecting the adoption of AI in GHRM, including both the challenges and opportunities associated with its implementation, as outlined in

Table 8. Factors Impacting AI Adoption in GHRM.

Theme	Authors	Focus
Organizational Readiness	[30]	Outlines key challenges (i.e., resistance to change, loss of human touch, data quality, privacy concerns, high costs, and skill gaps) as well as the benefits of integrating AI into HRM, providing insights into factors that impact the successful adoption of green HRM practices.
	[40]	Explores the integration of AI and GHRM and its potential to enhance organizational sustainability, as well as the challenges of this integration.
	[57]	Examines key factors influencing the adoption of IR 5.0 in green HRM, such as cost, data privacy, technical readiness, and top management support, illustrating both barriers and opportunities for implementing AI-driven HR practices effectively.
	[59]	Emphasizes that successful technology adoption in HRM hinges on proper application rather than acquisition. It underscores the importance of collaboration between HR and IT, addresses employee resistance and concerns, and highlights the need for transparent, user-friendly systems to effectively integrate HR analytics and AI into sustainable HR practices.
	[67]	Reveals that a data-driven culture is necessary to moderate the relationship between green HRM practices and circular economy performance, indicating that organizational cultural factors play a key role in the successful AI adoption in green HRM.
	[74]	By testing multiple dimensions of BDA (acceptance, adoption, and assimilation), the research demonstrates that not all facets equally impact the GHRM–LSR. The absence of moderation effects for BDA acceptance or adoption (with support only for assimilation) points to nuanced challenges in the effective integration of big data analytics in GHRM.
	[83]	Identifies key risks (i.e., quality and price risks) and prospects (i.e., unified public–private standards) that affect the systemic adoption of AI-enhanced training for green human resources.
	[88]	Identifies key adoption challenges related to RPA, such as resistance to change, ensuring effective communication and education, selecting appropriate KPIs, and addressing data quality and ethical issues.
	[89]	Highlights that many SHRM departments are cautious about AI and digital HRM due to data integration issues, technological barriers, and remote work complexities. The research emphasizes the barriers and influencing factors that affect the successful adoption of AI-driven sustainable HRM practices.
	[91]	Discusses challenges such as managing large datasets, data privacy, cost, ethical and legal constraints, and technical complexities, rapid decision-making needs, and evolving industrial demands.
Streamlined Operations	[35]	Analyzes the emergence of AI in GHRM processes and the potential benefits of AI in achieving operational efficiency and sustainability.
	[51]	Aims to help organizations make informed decisions about AI adoption and ensure that AI is used to enhance GHRM practices and achieve organizational goals
	[54]	Explores how AI can be implemented in green recruitment to achieve sustainability, efficiency, and modernization in organizations.
	[58]	Identifies the effect of an AI-enabled work-from-home culture on strengthening GHRM practices and promoting sustainable development within organizations.
	[65]	Highlights that the effective use of AI-supported data analytics can overcome challenges related to HRM implementation in sustainability contexts.
	[66]	Illustrates the impact of GHRM on circular economy performance, along with the mediator role of green innovation and the moderator roles of big data analytics and data-driven culture
	[69]	Implements GHRM practices and big data analytics capability, so organizations can improve their environmental performance, which leads to improved operational efficiency and market positioning.
	[70]	Highlights the role of big data analytics in fostering green innovation, competitive advantage, and environmental performance to combat sustainability issues and to boost the adoption of Big data analytics.
	[73]	Speeds up decision-making, cuts down on time, and ensures resources are used effectively, leading to improved operational and strategic outcomes.
	[77]	Considers the influence of organizational STARA capabilities on GHRM practices, green supply chain management practices, and their direct impact on sustainable performance outcomes.
	[90]	Explores the transformative capabilities of advanced analytical methodologies and AI technology to improve the sustainability and effectiveness of HR management practices.
	[94]	Reveals the opportunities of using computer networks and green computing systems while implementing information and communication technology for effective HR analytics.
Ethical Considerations	[33]	Identifies ethical leadership and AI as key regulatory factors that influence how green talent management translates into innovative work behavior.
	[62]	Addresses the complex interconnections between AI, talent management, and ethics by aiming to break myths and biases in AI-driven sustainable talent management.
	[86]	Examines the challenges hindering AI-GHRM implementation, such as integration difficulties and ethical issues, emphasizing the necessity for continuous adaptation and the establishment of ethical frameworks to ensure responsible AI use in GHRM, aligning operational success with environmental responsibility.

.

The adoption of AI in GHRM is influenced by a complex interplay of factors, encompassing both drivers and barriers that shape its effective implementation and impact on organizational outcomes (Table 8). A significant body of work [30,40,57,59,89,91], highlights the challenges related to organizational readiness, including technological barriers such as data integration issues, data quality concerns, and managing large datasets, alongside non-technical hurdles like high costs, skill gaps, employee resistance to change, and the perceived loss of the human touch in HR processes. Henceforth, other studies [57,59,67] argued that successfully navigating these challenges requires factors such as top management support, effective communication, collaboration between HR and IT, and the cultivation of a data-driven organizational culture, which is identified as crucial for leveraging the benefits of AI and big data analytics in driving sustainable outcomes. Conversely, the literature emphasizes the opportunities for streamlined operations that advocate AI adoption. AI and advanced analytics are recognized for their potential to enable more informed decision-making, enhance efficiency, modernize HR processes, particularly in recruitment, optimize resource utilization, speed up strategic outcomes, and ultimately improve both environmental performance and market positioning [35,51,54,69,70]. Additionally, refs. [70,77] discussed that these opportunities are often linked to leveraging organizational capabilities like STARA and fostering green innovation. Furthermore, the successful and responsible adoption of AI in GHRM is largely linked to ethical considerations. Studies explicitly address the need to mitigate biases in AI-driven processes, develop robust ethical frameworks for AI use in GHRM, and recognize ethical leadership as a moderating factor influencing the integration of AI with sustainable talent management practices [33,62,86]. Therefore, the adoption of AI in GHRM is not merely a technical implementation but a complex organizational transformation influenced by readiness, driven by operational and strategic opportunities, and bounded by essential ethical considerations.

Figure 3 presents a conceptual framework that synthesizes findings at the intersection of AI and GHRM, offering a comprehensive visualization and categorization of the reviewed literature. The framework features a bubble diagram with a central dark green circle connected to two tiers: the first tier, shown in light green, represents the core thematic domains, while the second tier, in white, depicts specific research focuses within each domain. The central node represents the overarching theme of “AI Advancements in GHRM,” which integrates the interrelated mechanisms through which AI contributes to sustainable HR outcomes. The first tier encompasses the main themes, “AI application in GHRM Practices,” “Factors impacting the adoption of AI in GHRM,” “AI-GHRM in different sectors,” “GHRM-related Technologies,” and “The role of AI-GHRM in performance management”. These themes interact through enabling mechanisms such as data-driven decision-making, process automation, and digital capability development, which collectively influence green behavior and organizational performance.

The second-tier details specific areas of focus, for example, “AI application in GHRM Practices” expands into sub-themes such as “Recruitment,” “Training and Development,” “Talent Management,” and “Performance, Compensation & Reward.” The varying circle sizes reflect the relative prominence of each topic, indicating where scholarly attention has been concentrated and where research remains limited. Together, these connections reveal not only the breadth of AI applications but also the causal pathways and boundary conditions, including organizational readiness, technological maturity, and ethical governance, that shape their effectiveness. Hence, the framework moves beyond a thematic listing to illustrate an integrated understanding of how AI mechanisms operate within diverse GHRM contexts and where future studies can further validate these relationships. While this review offers a comprehensive synthesis of the AI–GHRM literature, the quality and robustness of evidence across studies remain uneven. A significant portion of the reviewed work relies on conceptual or cross-sectional designs, limiting the ability to infer causal relationships or measure long-term sustainability outcomes. Only a minority of studies employ longitudinal or experimental methods capable of validating the direct environmental or organizational impacts of AI-driven GHRM practices. Furthermore, data heterogeneity and varying measurement scales restrict comparability across sectors and geographic contexts. These methodological constraints highlight the need for future research adopting more rigorous designs to strengthen the empirical foundation of this emerging field.

It is important to note that a cross-comparison of the reviewed studies reveals both convergent and divergent perspectives regarding the integration of AI into GHRM practices. While most studies agree that AI facilitates the efficiency and sustainability of HR processes, significant variation exists in the degree and scope of this impact. For example, empirical evidence from manufacturing and service sectors supports AI’s role in improving environmental performance and green innovation, yet conceptual works often idealize its transformative potential without addressing contextual limitations such as data quality or organizational readiness. Furthermore, theoretical contributions remain fragmented as few studies adopt comprehensive frameworks linking AI-driven decision systems with green behavioral outcomes or sustainability performance indicators. Methodologically, the literature is dominated by cross-sectional and conceptual approaches that limit causal inference. Moreover, longitudinal studies and mixed-method designs remain scarce, constraining the generalizability of results. Finally, the limited exploration of boundary conditions, such as cultural context, firm size, and technological maturity, suggests the need for more nuanced, multi-level research designs to validate and extend the theoretical propositions currently available.

4. Results of the Bibliometric Analysis

The following section presents an in-depth bibliometric analysis and provides a detailed examination of the publication trends, conceptual patterns, prominent contributors, and scholarly networks characterizing the intersection of AI and GHRM. VOSviewer (version 1.6.20), a specialized tool for scientific mapping, was utilized, and the analysis draws on the curated dataset of 53 peer-reviewed articles published between 2018 and 2025. The analysis applied the association strength normalization method to identify distinct thematic clusters. The aim is to uncover the intellectual structure of the field and its developmental trajectory. This comprehensive bibliometric overview serves as a foundational reference for scholars aiming to navigate the evolving AI-GHRM research landscape and to inform future investigative efforts.

4.1. Co-Occurrence Map (Text Data)

To explore the most prominent and recurring concepts within the intersection of AI and GHRM, a text data analysis was conducted on the 53 articles included in this review, the results of which are visualized in Figure 4. This analysis employed a co-occurrence mapping technique, focusing on the words and phrases directly extracted from the titles and abstracts of the selected papers. This approach provides a “bottom-up” view, revealing the most prominent and recurring concepts and themes as they are organically expressed and discussed within the content of the papers, which can include emergent topics not yet formalized as keywords. The goal was to identify the semantic relationships between key terms and identify those directly associated with the domain of AI-GHRM. From an initial pool of 1552 extracted terms, only 44 satisfied the inclusion requirement of a minimum of 10 occurrences. Given the relatively limited corpus of 53 articles, a full counting method was utilized to ensure comprehensive term representation.

Subsequently, VOSviewer was employed to calculate the relevance score for each term. To ensure a focused and meaningful visualization, VOSviewer’s default settings were utilized to apply a minimum occurrence threshold of 10, filtering out infrequent terms that might represent peripheral discussions or noise, and managing complexity for clearer representation of the field’s core intellectual structure. From the remaining terms, the top 60% by relevance score were then selected for network visualization, yielding 26 terms for Figure 4. This specific percentage-based selection, inherent to VOSviewer’s relevance score functionality, was chosen to balance comprehensive thematic coverage with clarity of representation, prioritizing terms most central to the discussion. As noted by [95], higher relevance scores signify more specific topics within the textual data, while lower scores denote a wider thematic scope. This systematic selection process, guided by VOSviewer’s established methodology helps to identify the core concepts that drive the discussion on AI and GHRM, as well as provide a structured overview of the thematic landscape within the reviewed literature.

The VOSviewer network visualization (Figure 4) reveals three distinct thematic clusters. Our interpretation of these clusters is grounded in the co-occurrence of their constituent terms and is supported by the findings from the content analysis in Section 3.

The red cluster was interpreted as representing “Practical Applications and Outcomes.” This rationale is based on the strong linkage between its core terms: ‘GHRM practice’ signifies the concrete implementation of policies, ‘Big data analytic’ represents the primary tool for measuring and optimizing these practices, and ‘Green innovation’ is a key tangible result. This data-driven grouping provides empirical support for the themes discussed in our content analysis (Section 3.3 and Section 3.4), where the application of GHRM practices and their impact on performance and innovation were identified as major research streams.

The centrally positioned cluster was interpreted as “Core GHRM Technologies and Frameworks.” This is because it connects “Green human resource management” directly with specific technologies like “STARA” within the “organization” and “HRM” context. This structure aligns with the focus of Section 3.1, which detailed the specific technologies being integrated into GHRM frameworks.

Finally, the blue cluster was interpreted as the “Broader Business and Digital Context.” The inclusion of terms like “Internet,” “Business,” and “Development” indicates that the research connects AI-GHRM initiatives to wider trends in digital transformation and overall business strategy, a context explored throughout the introduction and discussion sections of the reviewed articles. The prominence of terms, reflected by node size, and the strength of connections, indicated by line thickness, collectively illustrates the frequency and relevance of concepts, as well as their degree of co-occurrence. Therefore, the network visualization map provides a comprehensive overview of the complex intersection between AI and GHRM, which highlights the growing body of literature that seeks to understand and enhance this critical intersection.

Table 9. Top Ten Terms (Occurrences).

Rank	Term	Occurrences	Relevance Score
1	Organization	56	0.3498
2	HRM	48	0.3859
3	Technology	44	0.4709
4	Green human resource management	35	0.5508
5	Relationship	31	0.5141
6	Development	30	0.5589
7	Process	29	0.5605
8	Green innovation	23	2.0078
9	GHRM practice	21	0.9196
10	sustainable performance	21	1.4186

outlines the top 10 most frequent terms associated with AI and GHRM, depicting a domain where organizational dynamics, technological adoption, and sustainability initiatives collectively influence the evolution of modern human resource management. Foremost, the emphasis on “Organization” highlights a foundational understanding that AI-driven GHRM practices are not implemented in isolation, but rather within a complex framework of corporate structures and strategic goals. This awareness seamlessly transitions to the recognition of “HRM” as a critical component, acknowledging that traditional HRM principles remain pivotal, even as AI and sustainability are integrated. The prominence of “Technology” further illuminates the transformative power of technological advancements, acting as a catalyst for evolving AI and GHRM strategies. As such, “Green human resource management” itself emerges as a central theme, highlighting a dedicated focus on sustainable HR practices that align with broader environmental objectives. The exploration of “Relationship” suggests a clear understanding of the interconnected factors influencing AI and GHRM, which indicates that successful implementation hinges on a holistic perspective. Moreover, the term “Development” implies a dynamic field where continuous evolution and adaptation are essential. The focus on “Process” emphasizes the importance of streamlined and efficient HR workflows, which are vital for effective AI and GHRM integration. Remarkably, “Green innovation” stands out as a critical outcome, which demonstrates the potential of the intersection of AI and GHRM to drive environmentally conscious advancements. This is further highlighted by “GHRM practice”, which emphasizes the practical application of these strategies within organizational settings. Finally, the “Sustainable performance” reinforces the literature’s commitment to linking AI and GHRM with long-term organizational and environmental sustainability.

Figure 5 presents a VOSviewer overlay visualization based on text data extracted from titles and abstracts of the 53 selected publications. The map illustrates the temporal evolution of recurring concepts in the AI–GHRM literature. The color of each node represents the average publication year of the documents containing that term, with blue signifying earlier research attention and yellow denoting more recent academic focus. Early publications (in blue tones) emphasized general digital transformation themes such as “internet,” “organization,” “technology,” and “development,” reflecting a foundational interest in embedding AI within broader organizational and technological frameworks. These terms, while central, were relatively generic and exploratory, suggesting a nascent field aligning AI capabilities with HRM structures.

The thematic focus shifted in mid-phase publications (green hues) toward more specialized concepts such as “green human resource management,” “integration,” “HRM,” and “STARA,” indicating a transition toward conceptual consolidation of AI-enabled GHRM practices. These terms represent a more targeted examination of how AI technologies are being aligned with sustainability objectives in human capital management.

Notably, recent publications (shown in yellow) have begun to emphasize application-oriented and impact-focused topics, such as “big data analytic,” “green innovation,” “sustainable performance,” and “GHRM practice.” This indicates a trend toward evaluating the performance implications, innovation potential, and operationalization of AI within green HRM practices.

Overall, the map demonstrates a temporal progression from abstract and systemic themes to applied, performance-oriented constructs. This supports the view that AI-GHRM is evolving into a mature interdisciplinary research area where theoretical discussions are giving way to evidence-based inquiry into AI-driven sustainability outcomes.

Therefore, the analysis of these terms, their occurrences, and relevance scores reveals a landscape where organizational context, technological integration, and sustainable practices converge to shape modern human resource management. This comprehensive overview highlights the core concepts driving discussions at the intersection of AI and GHRM, providing a structured understanding of the thematic landscape within the reviewed literature.

4.2. Co-Occurrence Map (Keywords)

In contrast to the text data analysis, this co-occurrence map based on keywords was constructed using the keywords associated with each article, specifically including both author keywords (terms chosen by the researchers themselves to characterize their work) and index keywords (controlled vocabulary terms assigned by bibliographic databases for information retrieval). This provides a “top-down” view, reflecting the more formalized categorization of research within the discipline. Therefore, a keyword analysis was conducted on the 53 articles included in this review, which identified the most pertinent and recurrent terms, as depicted in Figure 6. Out of 329 identified keywords, 18 satisfied the minimum occurrence threshold of five. The analysis encompassed all keywords, including both author-provided and indexed terms. Author keywords refer to terms chosen by researchers to describe their studies, whereas index keywords are standardized vocabulary terms assigned by indexers to facilitate information retrieval and subject classification in bibliographic databases [96].

The keyword network visualization (Figure 6) reveals three distinct thematic clusters, whose interpretations are grounded in a contrast between foundational concepts, strategic goals, and technological enablers.

The red cluster represents “Foundational HR Functions,” as its keywords include “Human resource management” and “Resource allocation.” These terms represent the established, core processes into which sustainability must be integrated, forming the basis of GHRM theory. In contrast, the green cluster can be interpreted as “Strategic Green Initiatives.” It combines the goal (“Green innovation”) with the methodology (“Big data analytics”) and the specific framework (“Green human resource management”). This structure mirrors the findings in our content analysis (Section 3.1) showing the use of specific technologies to achieve green objectives. Finally, the blue cluster focuses on the “Integration of AI for Macro-Level Sustainability.” This interpretation is based on its linkage of the primary technology (“Artificial intelligence”) with the ultimate macro-level objectives (“Sustainable development” and “Environmental sustainability”). This empirically validates the paper’s core premise that AI is viewed as a key enabler for achieving broader sustainability goals through GHRM.

Table 10. Top Ten Keywords (Occurrences).

Rank	Keyword	Occurrences	Total Link Strength
1	Artificial intelligence	17	54
2	Green human resource management	16	63
3	Human resource management	14	75
4	Human resources management	13	64
5	Sustainable development	12	50
6	Resource allocation	10	58
7	Human resource management practices	8	43
8	Big data analytics	7	19
9	Environmental management	7	38
10	Green hrm	7	19

presents the 10 most frequent keywords on the intersection of AI and GHRM, along with their respective occurrence counts and total link strength, where link strength indicates the number of publications in which two keywords co-occur [97]. “Artificial intelligence” emerges as the most prominent keyword, indicating its central role to shape and enhance GHRM practices. This is closely followed by “Green human resource management” which highlight the specific focus on GHRM practices within the reviewed literature. The significance of traditional HR functions is highlighted by “Human resource management” and “Human resources management”, hence illustrating the foundational role of established HR principles in the context of AI integration. “Sustainable development” further emphasizes the broader environmental and social goals that drive the adoption of AI-enhanced GHRM strategies. Moreover, “Resource allocation” indicates a focus on the strategic management of resources to enhance both HR processes and sustainability outcomes. The term “Human resource management practices” reflects the practical execution of HR strategies, emphasizing the adoption of AI-driven tools and approaches within real-world organizational contexts. The use of data-driven approaches is emphasized by “Big data analytics”, which reveal its role in informing and enhancing GHRM decisions. Additionally, “Environmental management” reflects the literature’s concern with the ecological impact of organizational activities and the role of GHRM in mitigating these effects. Finally, “Green hrm” reinforces the specific focus on the integration of green practices within human resource management.

Figure 7 presents an overlay visualization of keyword co-occurrence using VOSviewer, mapping the temporal evolution of research themes within the AI–GHRM literature. As shown, early literature primarily concentrated on foundational themes such as “human resources management,” “resource allocation,” “natural resources management,” and “information management,” all shaded in blue. These keywords suggest an initial focus on embedding sustainability into general HRM processes and broader environmental management paradigms.

The central green cluster, comprising terms like “artificial intelligence,” “green human resource management,” “sustainable development,” and “green innovation”, represents the thematic core of the literature during the mid-phase, where integration of AI into sustainability-focused HRM gained conceptual and empirical traction.

Importantly, yellow keywords such as “human resource,” “big data analytics,” “green HRM,” and “sustainability” signify a recent surge of interest in the operationalization and practical applications of AI and data-driven tools in green HRM. This shift indicates a growing emphasis on performance-centric, technology-enabled, and metrics-driven approaches to sustainability in human capital management.

Overall, this temporal landscape reveals a thematic evolution where the literature has progressed from traditional resource and HRM concerns to a more technology-intensive and outcome-oriented discourse in recent years. This maturation underscores the increasing scholarly focus on how AI technologies such as big data analytics and automation are being leveraged to enhance environmental performance, green innovation, and organizational sustainability within HRM contexts.

To gain a more comprehensive understanding of the keyword landscape, VOSviewer analyses were performed for both author and index keywords. The author keyword map was generated with a minimum occurrence threshold of five and resulted in a focused network visualization comprising six keywords (Figure 8). In a similar manner, the index keyword map, also produced with a minimum occurrence threshold of five, yielded a more extensive network of twelve keywords (Figure 9). A comparative analysis of these maps reveals both alignment and divergence in the conceptual structuring of the AI-GHRM research domain. The author keyword map presents a streamlined representation, which emphasizes the core concepts of “Artificial intelligence”, “Green human resource management”, and “Big data analytics” within the context of “Green innovation” and “Environmental sustainability”, which reflect the authors’ conceptualization of their work. On the other hand, the index keyword map was generated with a minimum occurrence threshold of five and resulted in a total of 12 keywords, as illustrated in Figure 9. In contrast to the author keywords, the index keyword map demonstrates a more granular and interconnected network, which incorporates broader HR management terminology such as “Human resource management” and “Human resources management prac” alongside resource-oriented terms such as “Resource allocation” and “Information management”. This expanded scope is driven by controlled vocabulary indexing, which highlights the field’s connection to established HR principles and practices, while still maintaining the centrality of AI and green initiatives. Notably, “Environmental sustainability” is featured in both maps, underscoring its enduring significance. However, the presence of “Human resource” and the stronger linkages between “Artificial intelligence” and “Sustainable development” in the index keyword map indicate a more cohesive understanding of the technological and societal dimensions underlying the convergence of AI and GHRM.

4.3. Co-Occurrence Map (Country of Co-Authorship)

A geographical distribution analysis of the 53 articles was conducted based on co-authorship by country. From a total of 34 countries with relevant publications, a threshold, 3 was applied as the minimum inclusion threshold, leading to 10 nations that have actively engaged in co-authorship on publications within the selected dataset. Consequently, the network visualization map (Figure 10) depicts the co-authorship relationships among these ten countries.

The country co-authorship map depicts global collaboration trends in research exploring the intersection between AI and GHRM. A prominent collaborative network emerges with India serving as a central hub, strongly connected to Malaysia, the United States, the United Arab Emirates, and Saudi Arabia, suggesting significant research activity and established partnerships in this cluster. Another distinct collaborative cluster links the United Kingdom and Pakistan, both showing connections to Malaysia and the United States. Furthermore, China demonstrates strong ties with Pakistan and the United Kingdom, and is notably linked to Turkey, indicating diverse collaborative paths. While certain strong regional or established alliances are evident, the interconnectedness of these nations suggests an evolving global network. This structure implies that international collaboration in this field is characterized by specific country alliances, which are likely influenced by geographic proximity or established research relationships, while gradually expanding to include more diverse contributions. In addition, the top countries with robust link strength on the network visualization map are summarized in

Table 11. Top Ten Countries (Link Strength).

Rank	Country	Documents	Citations	Total Link Strength
1	United Arab Emirates	3	368	3
2	Pakistan	11	174	16
3	United Kingdom	5	172	5
4	China	11	138	11
5	United States	5	117	7
6	Malaysia	10	110	12
7	India	17	103	4
8	Turkey	3	77	1
9	Saudi Arabia	3	30	5
10	Poland	3	0	0

below.

The analysis of country co-authorship identifies countries with the highest frequency of publication within our sample of 53 articles. It is crucial to interpret these findings with caution; in a nascent field, these figures reflect early trends rather than established global leadership. Within this context, the United Arab Emirates, Pakistan, and the United Kingdom are frequent contributors, with their associated publications showing high citation counts within this dataset. This suggests their work has been frequently referenced by other articles included in this review. This suggests that researchers in these countries are producing impactful studies that resonate with the academic community. China also exhibits a strong research output, but its collaborative ties appear to be less extensive compared to other nations. The United States demonstrates a solid contribution to the field, with its research also gaining significant recognition through citations. Furthermore, Malaysia stands out for its strong collaborative connections, which suggests a high level of international partnership in relation to the intersection of AI and GHRM research endeavors. In contrast, India shows a relatively different pattern of influence as measured by citations, while demonstrating a considerable volume of research. This reflects potential variations in research focus or dissemination strategies. Notably, Turkey and Saudi Arabia also contribute to the research output, indicating a broader geographical spread of interest in the field. Poland, while having publications, currently shows no collaborative links within this specific network. In essence, while the research on the intersection of AI and GHRM has a global scope, evaluating international contributions requires considering both the volume of publications and their citation impact. The bibliometric analysis also reveals a clear regional imbalance, with most publications originating from Asia, particularly China and India, followed by Europe. This concentration reflects broader economic and policy drivers, such as government-led digital transformation initiatives and sustainability mandates, while regions including Africa and South America remain underrepresented. This uneven distribution suggests not only disparities in research funding and digital readiness but also missed opportunities to contextualize AI–GHRM within developing economies where sustainability challenges are most acute. Hence, this reveals complex differences in scholarly influence among nations influenced by varying research priorities, publication strategies, and global trends.

4.4. Data Analysis on Authorship

A more comprehensive insight into the academic landscape of AI in GHRM emerges through the analysis of the most influential authors and their collaborative networks.

Table 12. Top 5 Authors (Documents Produced and Citations).

Rank	Author	Documents	Citations	Total Link Strength	H-Index
1	Gaskin, J.	2	95	3	48
2	Ogbeibu, S.	2	95	3	18
3	Pereira, V.	2	48	2	52
4	Khan, W.	2	36	0	7
5	Nisar, Q.A.	2	36	0	33

presents the top five authors based on their publication output and citation impact within the field of AI in GHRM. The “total link strength” metric indicates the degree of collaborative or co-occurrence relationships between these authors [97]. As indicated, Gaskin, J., and Ogbeibu, S., emerge as the most highly cited authors within our sample, each having published two documents with a total of 95 citations. This high citation count, relative to other authors in the dataset, suggests their work has been frequently referenced by other scholars within this specific body of literature.

The substantial citation count is primarily attributed to two key publications that we worked on in collaboration: first, “Green talent management and turnover intention: the roles of leader STARA competence and digital task interdependence” which explores the combined impact of green talent management, leader STARA competence, and digital task interdependence on employee turnover in Nigerian manufacturing industry [76]; and second, “Demystifying the roles of organizational smart technology, artificial intelligence, robotics and algorithms capability: A strategy for green human resource management and environmental sustainability” which investigates the predictive relationship between organizational STARA capability, GHRM, and environmental sustainability within the same sector [34]. In contrast, Pereira, V., demonstrates a citation count of 48 with a total link strength of 2, while Khan, W., and Nisar, Q.A., with 36 citations each.

4.5. Data Analysis on Sources

The 53 papers selected for analysis were distributed across 46 different sources.

Table 13. Top 5 Sources (Documents Produced and Citations).

Rank	Source	Type	Documents	Citations	Total Link Strength
1	Journal of Cleaner Production	Journal Article	2	484	0
2	Discover Sustainability	Journal Article	2	14	0
3	2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT 2023)	Conference Proceedings	2	4	0
4	E3S Web of Conferences	Conference Proceedings	2	4	0
5	Studies in Systems, Decision and Control (SSDC)	Book Series	2	3	0

presents the top five sources based on their publication output and citation impact within the field of AI in GHRM. Notably, the “Journal of Cleaner Production” stands out as a prominent source, having published two articles with a substantial 484 citations. This indicates that while the journal’s publication volume is relatively similar to other sources in the top five, its articles have had a significantly greater influence on subsequent research within this domain. The Journal of Cleaner Production’s significant citation count is largely attributed to two highly influential articles. The first is titled “Role of big data analytics in developing sustainable capabilities” with 364 citations [68] and the second journal article is “Big data analytics as a roadmap towards green innovation, competitive advantage and environmental performance” with 120 citations [70]. In contrast, “Discover Sustainability”, despite also publishing two articles, has garnered 14 citations, highlighting a considerable difference in citation impact. Furthermore, Table 13 reveals that the remaining sources are conference proceedings or book series, each with two publications and low citation counts (4, 4, and 3, respectively). This suggests a trend where journal articles, particularly those in established publications like the Journal of Cleaner Production, tend to have a greater impact and visibility within the AI in GHRM field.

4.6. Data Analysis on Citation Impact of Reviewed Papers

While citation analysis is a useful proxy for impact, the findings below must be interpreted with significant caution. In a small and emerging field, citation patterns can be preliminary and may not represent long-term scholarly influence. The following analysis simply identifies the most cited papers within the elected dataset to highlight work that has been foundational to the subsequent articles in this review. Figure 11 presents the 10 most influential studies in the field of AI in GHRM, identified based on their citation counts. Citation analysis uses citation counts to measure the impact of research, providing insights into the significance of scholarly contributions. The frequency with which a research paper is cited serves as a key metric in citation analysis, which helps to determine the impact of scholarly work and broader academic trends [98]. A significant disparity in citation counts is evident, with the work by [68] having accrued 364 citations, a number that significantly surpasses other papers in our sample. This high count indicates that this particular paper has been frequently referenced by the other authors whose work is included in this review, suggesting it has been foundational to the subsequent discourse within this body of literature.

Their study explores the impact of big data integration, green supply chain management, and GHRM on sustainable capabilities and firm performance, revealing the pivotal role of GHRM in facilitating big data adoption and strengthening the relationship between green supply chain practices and sustainable performance. Next, Ref. [70] ranks second with 120 citations, examining how green innovation, GHRM, and competitive advantage mediate—and how organizational commitment and corporate green image moderate—the relationship between big data analytics and environmental performance in China’s manufacturing industry.

To verify the reliability of the bibliometric results, robustness tests were performed by modifying key analytical parameters. A time-window sensitivity test was applied by excluding publications from 2025 to account for possible indexing delays or incomplete metadata. The resulting visualization showed consistent cluster groupings and network densities, indicating that the bibliometric patterns observed are stable across parameter variations and time-window adjustments. This reinforces the reliability of the bibliometric results and confirms that the emerging trends identified remain robust.

5. Conclusions, Implications, and Future Research

This paper conducted a systematic literature review, guided by the PRISMA framework, to synthesize the current state of knowledge at the intersection of AI and GHRM. In directly addressing our core research question, we established that AI is not merely a tool for efficiency but a transformative enabler that fundamentally reshapes GHRM practices and outcomes. From an initial search of the Scopus database, a final corpus of 53 scholarly articles published between 2018 and 2025 was selected for in-depth analysis. Employing a dual-method approach, the study utilized inductive content analysis to identify dominant research themes and a comprehensive bibliometric analysis to map the field’s intellectual structure. The thematic analysis successfully distilled the literature into five primary themes, encompassing the specific technologies deployed, sector-specific applications, integration into core GHRM practices, impact on performance management, and key factors influencing adoption. Concurrently, the bibliometric analysis visualized research trends, keyword co-occurrence, international collaboration networks, and the most impactful publications, providing a quantitative overview of the scholarly landscape. The subsequent sections elaborate on the theoretical implications of the results, propose a structured agenda for future research, and recognize the study’s limitations.

5.1. Theoretical Implications

This systematic literature review offers several significant theoretical implications for the fields of human resource management, information systems, and sustainability. While the intersection of AI and GHRM is still emerging, the synthesis of existing literature allows for the extension of existing theories, while also laying the groundwork for new theoretical development. Specifically, this review reveals that the integration of AI extends foundational management theories by introducing a new, powerful variable that reshapes traditional relationships. For instance, the results imply an expansion of the Resource-Based View (RBV), wherein GHRM practices, traditionally regarded as organizational resources, contribute to achieving sustainable competitive advantage, as discussed in Section 3.4 [99]. This synthesis shows that AI acts as a critical dynamic capability that moderates this relationship, with technologies like big data analytics enabling organizations to translate GHRM initiatives into a green competitive advantage through superior data-driven insights and decision-making [60,62]. Thus, AI-powered tools are not merely resources themselves but are capabilities that allow a firm to more effectively deploy its human and green resources to achieve enhanced environmental and firm performance.

Furthermore, this review in Section 3.3 indicates that AI extends the Ability–Motivation–Opportunity (AMO) model in a green context [100,101]. AI technologies directly enhance employees ‘Ability’ to perform green tasks through adaptive e-learning platforms and intelligent training systems that foster digital and green competencies. They foster ‘Motivation’ by enabling transparent, real-time monitoring of green performance and facilitating the alignment of rewards with environmental metrics. Finally, AI creates ‘Opportunity’ by automating routine tasks, which allows employees to focus on more innovative environmental initiatives, and by providing data-driven insights that empower green decision-making.

Beyond extending existing models, the synthesis of literature also challenges the deeply human-centric assumptions of many traditional HRM theories. The implementation of AI in core HR functions like recruitment, performance evaluation, and training introduces a non-human element that redefines HR processes. The challenges identified across the literature, such as algorithmic bias, data privacy concerns, high implementation costs, and the “loss of human touch” (Section 3.5), necessitate a modification of HRM models to formally account for human–AI interaction [30,57,62]. Existing theories must evolve to address complex questions of fairness, transparency, and agency when strategic and operational decisions are co-created by human managers and intelligent systems. Building on the theoretical implications outlined earlier, the study’s findings also carry significant practical relevance for organizations seeking to operationalize sustainable HR strategies through AI. For practitioners, translating AI–GHRM integration from theory to practice requires a phased, context-sensitive approach. Organizations should begin by auditing existing HR processes to identify areas where AI can reduce environmental impact, such as digital recruitment or paperless training. Pilot programs can then test the feasibility of AI-enabled initiatives before scaling them. Moreover, management should prioritize employee readiness, ethical oversight, and transparent communication to mitigate resistance and ensure responsible AI use. Aligning these practices with sustainability reporting frameworks, such as the UN SDGs or ESG metrics, can also strengthen organizational accountability and stakeholder trust. From a policy perspective, the findings underscore the necessity of establishing robust ethical and regulatory frameworks to ensure the responsible application of AI in HR functions while fostering sustainability-oriented innovation. By linking theoretical insights with practical implications, this review lays the groundwork for future empirical studies to explore how AI-enabled HR systems can drive the shift toward sustainable and human-centered organizational models.

5.2. Research Gaps and Future Directions

The analysis of GHRM-related technologies highlights the need for future research to explore the potential of emerging AI paradigms to address current limitations and unlock new avenues for sustainable HRM practices, thereby expanding the toolkit available to GHRM practitioners and researchers. Hence, future research on AI technologies ought to focus on developing robust frameworks and metrics to evaluate their competitive ROI and conducting comparative studies across diverse organizational contexts.

In the realm of Big Data within GHRM, existing research emphasizes the need for more in-depth exploration of how distinct types of Big Data analytics can be effectively utilized to tackle specific challenges faced by GHRM. This includes examining the differential impacts of various analytical techniques (i.e., predictive, prescriptive) to provide more nuanced guidance on their strategic deployment. Furthermore, research should consider the influence of contextual factors, such as organizational culture and industry dynamics, on the successful implementation and effectiveness of Big Data-supported GHRM initiatives.

Furthermore, the analysis of integrated intelligent and connected technologies emphasizes the importance of adopting a system-level perspective in future research. This involves moving beyond the study of individual technologies to investigate their complex interactions and the emergent properties of holistic GHRM ecosystems. In-depth examination of the human-technology interface is crucial, requiring exploration of how employees experience and interact with these integrated systems, and the resulting socio-psychological and ergonomic implications. The development of adaptive GHRM systems that leverage real-time data and AI to dynamically adjust HR practices also represents a significant avenue for future inquiry.

Sector-specific analyses further delineate avenues for future inquiry, acknowledging that the optimal application of AI and GHRM may vary across industries. In manufacturing, research should delve into industry-specific challenges and opportunities, such as addressing skills gaps and workforce displacement arising from increased automation, and optimizing resource efficiency within manufacturing processes. It should also examine the supply chain implications of GHRM practices, exploring how these practices can influence the environmental performance of suppliers and customers. Within the service sector, the literature identifies the need to explore the dynamic interplay between GHRM, technology, and service innovation, investigating how technology can augment, rather than replace, human interaction in service encounters. Additionally, future research should focus on the challenges of measuring service productivity and quality in technology-rich settings, developing robust metrics that capture both efficiency and customer experience outcomes, and examining the future of work in the service sector, particularly in light of increasing digitalization and automation.

Across GHRM practices, future research is crucial for developing and refining robust strategies to effectively navigate the challenges of AI adaptation and ensure its positive long-term impact. Specifically, this includes employing longitudinal studies to track the effects of AI-driven GHRM practices on organizational sustainability metrics over extended periods, thereby validating the efficacy of implementation strategies. Moreover, strategies for addressing the critical ethical implications of AI in GHRM are paramount; consequently, future research must guide the development of proactive governance frameworks that ensure responsible and equitable implementation, explicitly considering employee perspectives on acceptance, fairness, and concerns around algorithmic bias and data privacy, as these are identified as significant barriers to adoption. Beyond individual functional strategies, a holistic strategic approach is also needed to examine the interconnectedness of AI-driven GHRM functions (i.e., recruitment, training, performance management) and their cumulative impact on overall GHRM effectiveness, thereby ensuring integrated strategic planning across HR functions. Finally, future investigations must critically assess strategies for enhancing the organizational context, which encompasses fostering a supportive culture, securing strong leadership buy-in, and developing employee readiness and digital skill sets, as these factors are fundamental to overcoming initial hurdles and ensuring the successful adoption and sustained effectiveness of AI in GHRM.

In terms of the role of AI-GHRM in performance management, studies should investigate which specific AI techniques are most effective for distinct green performance management tasks, such as predicting environmental impact from HR data, and explore the psychological and behavioral mechanisms through which AI-powered performance systems influence employee outcomes with environmental goals. The development of robust, AI-specific measurement frameworks is crucial for accurately quantifying the unique contribution of AI to both micro-level employee green performance and macro-level organizational sustainability metrics. Future research should construct more sophisticated models to capture the dynamic interactions among various AI applications in performance management, thereby offering a deeper understanding of how AI can be strategically harnessed to achieve sustainable performance outcomes.

Furthermore, regarding the factors impacting the adoption of AI in GHRM, research is needed to understand the interdependence and relative importance of various organizational readiness factors, from data infrastructure limitations to organizational culture, and to quantify the link between overcoming adoption barriers (i.e., cost, resistance to change) and realizing sustainable benefits (i.e., improved efficiency, reduced resource consumption). The paper also calls for practical guidance and empirical work on how to effectively implement and govern AI ethically within GHRM practices, addressing concerns such as data privacy and algorithmic bias. Future research should adopt longitudinal perspectives to capture the dynamic evolution of adoption challenges and enablers and conduct comparative studies across diverse industries and geographical regions to understand how contextual factors shape the adoption landscape of AI in GHRM.

While this review provides a comprehensive synthesis, its findings should be contextualized within several methodological limitations that shape its scope and depth. The boundaries of this review were primarily defined by the reliance on the Scopus database and the resulting sample of 53 articles. This limited corpus, while representative of a nascent research domain, naturally constrains the generalizability of our findings. Consequently, the conclusions, particularly from the bibliometric analysis, should be interpreted as a preliminary snapshot of the field’s structure rather than a definitive measure of influence. The nascent character of the literature also informed our analytical approach. The heterogeneity of the studies made a quantitative meta-analysis impractical, necessitating the use of a descriptive synthesis, which precludes statistical validation or the establishment of causal inferences. Likewise, no formal risk-of-bias assessment was conducted, restricting the capacity to systematically evaluate the evidence according to the methodological rigor of the included studies. Finally, the temporal scope, with its early 2025 cut-off, further defines the boundaries of this snapshot of a rapidly evolving field.

Accordingly, the insights derived from this review provide several concrete and practical recommendations for practitioners and managers seeking to incorporate AI into their GHRM strategies. The analysis of adoption factors (Section 3.5) strongly indicates that organizational readiness, not the technology itself, is the primary predictor of success; therefore, managers should treat a thorough readiness assessment focusing on data infrastructure, digital skill gaps, and leadership support as a critical first step before committing to large-scale AI investments. Following this assessment, a strategic approach to implementation should prioritize well-documented applications that offer a clear return on investment. The literature consistently highlights the effectiveness of AI in streamlining green recruitment and enhancing green training (Section 3.3), making these areas ideal starting points for practitioners to build momentum. Crucially, and in parallel with any implementation, establishing a robust ethical governance framework is essential. As concerns regarding algorithmic bias and data privacy were identified as significant barriers to employee acceptance (Section 3.5), proactive governance is vital for building the trust required for a successful and equitable integration.