Research Prospects and Trends in Sustainable HRM using Bibliometric and Clustering Approach
Amita Yadav
Research Scholar
Management Studies
J.C. Bose University of Science and Technology,
YMCA (Formerly YMCA UST) 6,
NH-19, Sector 6, Faridabad, Haryana 121006, India
amitayadav2911@gmail.com
amitayadav@jcboseust.ac.in
Corresponding author
ORCID iD: 0009-0008-8274-9029
Prof. Ashutosh Nigam
Professor,
Department of Management Studies
J.C. Bose University of Science and Technology,
YMCA (Formerly YMCA UST) 6,
NH-19, Sector 6, Faridabad, Haryana 121006, India
drashutoshnigam@gmail.com
drashutoshnigam@jcboseust.ac.in
Abstract
Purpose
The purpose of this study is to analyse the existing research on sustainable human resource management (HRM) and to identify significant contributors, research themes, clusters, techniques, and contextual factors.
Design/ Methodology/ Approach
A systematic literature review using PRISMA was carried out, looking at 218 papers from the Scopus and Web of Science databases that were published between 2014 and 2025. Bibliometric, network, and cluster analysis were performed using a variety of analytical software packages, including R studio, Biblioshiny, Vosviewer, and Python.
Findings
The results of the study indicate that although scholars have shown interest in the topic, the field's level of understanding is still quite low. India is the most productive country in the world. The "Journal of Cleaner Production" is the leading contributing journal with 12 published papers and 1190 citations altogether. With seven papers, Piwowar-Sulej K is the most prolific author. The Australian Catholic University of Australia is the most significant organization. This study also found four clusters using K-means clustering namely, Sustainability in HRM for environmental responsibility, Mixed influences of sustainable HRM, Technology-driven HRM and sustainability, and Advancing strategic HRM for sustainable organizations. Thematic analysis is used to define future research themes.
Originality
The paper contributes by providing an in-depth examination of sustainable HRM from two reliable databases. For those new to the sustainable HRM space, it offers a helpful starting point and insightful perspectives from people who have already worked in the subject. The Davies Bouldin Score is used to confirm the study's findings, which are the first of their kind to use the K-means elbow and silhouette techniques to identify the optimal number of clusters.
Keywords: CSR, Green HRM, K-means clustering, Silhouette, Sustainable HRM, Technology.
Introduction
Human Resource Management (HRM) has undergone profound transformations over the past few decades, influenced by globalization, technological advancements, shifting workforce demographics, and the growing emphasis on sustainability (Kramar, 2022; Shao et al., 2024). The integration of artificial intelligence (AI), and digital HRM solutions has redefined traditional HR functions, making them more data-driven, strategic, and evidence-based (Donald et al., 2020; Gomes et al., 2023). Simultaneously, organizations are recognizing HRM’s role in fostering employee well-being, enhancing organizational resilience, and contributing to long-term sustainability (Donald et al., 2020; Nayal et al., 2023). These evolving dynamics have led to an expanding body of research dedicated to HRM’s impact on business performance, workforce development, and corporate responsibility. The concept of sustainability in HRM has evolved alongside broader discussions on corporate social responsibility (CSR) and sustainable development. Traditional HRM practices focused on efficiency and performance optimization (Kramar, 2014). However, the rising awareness of environmental issues, social justice, and stakeholder inclusivity has driven the transition toward sustainable HRM (Lopez-Cabrales & Valle-Cabrera, 2020). It encompasses a holistic approach that integrates employee engagement, diversity management, and environmental responsibility (Banga & Gobind, 2025).
Sustainable HRM seeks to balance economic, social, and environmental objectives while maintaining organizational competitiveness (Aust et al., 2020; Ehnert et al., 2016; Kramar, 2014). Unlike conventional HRM, which focuses primarily on maximizing employee productivity and organizational profitability, sustainable HRM aligns human capital with sustainable development goals (Brewster & Brookes, 2024). In particular, the fundamental goal is to create long-term value for organizations, employees, and society (Jiang et al., 2024). Sustainable HRM consists of multiple dimensions that address various aspects of workforce management while adhering to sustainability principles. These dimensions can be broadly categorized into economic, social, environmental, and common good HRM practices (Aust et al., 2020; Jerónimo et al., 2020). Economic sustainability ensures that HR policies contribute to long-term business success while fostering employee growth. This includes strategies such as talent retention, leadership development, and performance management systems that align with sustainable corporate objectives (Genari & Macke, 2022). Sustainable compensation structures, career development initiatives, and financial incentives further reinforce economic resilience (Nakra & Kashyap, 2025). Social sustainability focuses on employee well-being, diversity, and ethical labor practices. Organizations implementing SHRM prioritize fair wages, safe working conditions, work-life balance, and inclusion (Jia et al., 2023; Su et al., 2023). Energy conservation, sustainable transportation, and paperless procedures are all encouraged by environmental sustainability in HRM (R. Chaudhary, 2018). Green training initiatives raise workers' understanding of environmental issues (Jabbour & Renwick, 2018). Digital HRM and remote work are essential for lowering HR's carbon footprint (Aragão & Jabbour, 2017). Embedding sustainability in HR strategy enhances business reputation (Lu et al., 2023). In order to promote moral leadership and sustainable employment practices that improve work-life balance and human dignity, the Common Good HRM strategy places a strong emphasis on social welfare and ethical corporate conduct (Aust et al., 2020; Järlström et al., 2024).
Businesses that incorporate sustainability into HRM are better equipped to handle market upheavals, regulatory changes, and economic uncertainties (Mushtaq & Akhtar, 2024). AI, big data analytics, and automation are driving the digital transformation of HRM, which improves engagement, training, and hiring (Mishra & Pathak, 2024). But it's crucial to strike a balance between technology and moral HRM (Eger & Žižka, 2024). With a focus on moral decision-making, corporate social responsibility, and employee empowerment, leadership is essential to the advancement of sustainable HRM (Ahmad & Fatima, 2023). It is essential to spend money on leadership development that is in line with sustainability objectives (Brewster & Brookes, 2024). The absence of established measurements makes it difficult to measure the impact of sustainable HRM (Jerónimo et al., 2020). Frameworks for evaluating its efficacy should be developed in future studies (Piwowar-Sulej et al., 2024). A growing body of bibliometric research highlights the evolution of SHRM as a field of study. Recent analyses have mapped the intellectual structure of sustainable HRM, identifying key themes, influential scholars, and emerging research trajectories (Malik & Singh, 2024). Bibliometric studies have revealed an increasing focus on sustainability-driven HRM strategies, employee well-being, and corporate social responsibility. These studies provide valuable insights into the theoretical underpinnings and practical applications of SHRM, shaping future research directions and policy developments (Chaudhary et al., 2025).
Rationale of the study
Academic interest in sustainable HRM has grown, but the bibliometric research that has been done so far has only provided partial answers. Furthermore, previous studies have not completely mapped sustainable HRM research trends using sophisticated bibliometric techniques like topic analysis and K-means clustering. The identified research gaps are presented in Table 1.
Table 1 Research Gaps
|
Research Gap |
Contribution of This Study |
Reference |
|
Overreliance on Scopus, ignoring Web of Science, limiting the comprehensiveness of bibliometric insights. |
Integrates both Scopus and Web of Science for a more holistic bibliometric analysis of sustainable HRM research. |
(Faisal, 2023; Piwowar-Sulej K., 2020) |
|
Special focus on the sustainability-HRM nexus, but lacks comprehensive bibliometric insights. |
Addresses an exhaustive knowledge gap on sustainable HRM |
(Abu-Mahfouz et al., 2023) |
|
Limited mapping of sustainable HRM knowledge due to the evolving nature of the field. |
Systematically analyzes sustainable HRM themes, enhancing the existing literature. |
(Anlesinya, 2020) |
Source: Authors own creation
To bridge these gaps, the research is designed to address following key questions:
RQ1: What is the current publication trend with respect to sustainable HRM Practices?
RQ2: Which countries are leading the publication in the field?
RQ3: Which are the most productive journals, and authors in this area?
RQ4: What is the optimal number of clusters in the field?
RQ5: What are the different emerging themes pertaining to sustainable HRM Practices?
Research Methodology
Article Identification and Screening
The coverage of the database is important in showing the growth and intellectual framework of previous literature. To identify relevant literature on Sustainable HRM Practices, the authors assessed Scopus and Web of Science databases. A comprehensive search string was formulated using multiple keywords such as "sustainable HRM," "sustainable human resource management," and "sustainable HRM practices," combined with Boolean operators like "AND" and "OR." Articles from 2014 to 2025 were targeted, excluding those before 2014 due to insufficient publications. The search focused on papers within the fields of "business, management, and accounting" or "humanities multidisciplinary," and found 710 articles initially. Peer-reviewed journal articles published in English were considered. To refine the selection, conference papers, book chapters, editorials, non-English content, and papers outside the business/management categories were excluded. Following these inclusion and exclusion criteria, 218 articles that met the defined standards were compiled for review.
Fig. 1 PRISMA flow diagram for Systematic Literature Review
Source: Authors own criteria from Scopus Database
Software
The current study presents a comprehensive assessment of the field using bibliometric analysis. The open-source bibliometric analysis tools Bibliometrix R package (Cuccurullo and Aria, 2017) and Vosviewer (Eck and Waltman, 2009) were used by the authors as they are frequently used for bibliometric analysis and provide a better visual representation over other available tools. Also, the above stated tools and software’s are easily assessable. Additionally, this research makes use of Python, and SciPy library for K-means clustering to make the clusters more reliable and authentic.
Bibliometric Analysis and Discussion
Summary statistics
The dataset includes 218 documents from 112 journals spanning from 2014–2025, revealing a dynamic yet declining field with a -1.81% annual growth rate. Despite this, the average citation counts of 30.43 reflects high academic impact. The research is recent (average age 3.92 years) and diverse, with 722 Author Keywords and 541 Keywords Plus. High collaboration is evident with only 6.4% are single-authored, with 3.32 co-authors per paper and 16.97% featuring international partnerships.
Trend Analysis
The depicted Fig. 2 reveals the unsteady annual scientific production in sustainable HRM from 2014 to 2025. After a slow start and decline in 2015, steady growth emerged between 2016 and 2019. A significant rise occurred post-2020, likely driven by global sustainability goals. Peaks in 2022 and 2024 signal increased interest although fluctuations suggest the field hasn’t achieved stable growth, reflecting evolving academic attention.
Fig. 2 Year- wise publication between the time span of 2014- 2025
Source: Authors own creation
Top Productive Journals
Journal of Cleaner Production leads with 12 articles and 1,190 citations, indicating top influence. Employee Relations follows with 11 papers but only 199 citations. Human Resource Management Review 9 papers and 734 citations and International Journal of Human Resource Management 7 papers, 902 citations show strong impact. Other contributors include emerging sources like Asia-Pacific Journal of Business Administration with 4 papers and 127 citations. Bradford’s Law (Brookes, 1977) categorizes journals into core (Zone I), relevant (Zone II), and peripheral (Zone III). Fig. 3 shows Zone I includes 10 core journals (9.3%) contributing 33.5% of papers, while Zones II consists of 31 journals (28.7%) publishing 74 papers (33.9%) and Zone III contribute nearly equal papers but from many more journals. This underscores the value of focusing on core sources for impactful sustainable HRM research.
Fig. 3 Bradford’s law
Source: Authors own creation
Most Relevant Authors
The analysis of authorship in sustainable HRM research reveals key contributors shaping the field through both collaborative and individual efforts. Piwowar-Sulej K emerges as the most prolific author with seven publications, though a lower fractionalized score indicates frequent co-authorship. In contrast, Mariappanadar S, with six papers and a higher fractionalized score, demonstrates a more dominant authorship role. This trend continues with other notable contributors like Jabbour C, Kramar R, Edgar F, and Chaudhary R, each bringing distinct thematic focuses such as green HRM, strategic HRM, and CSR. Supporting Lotka’s Law, the data in Fig. 4 shows that 89.9% of authors have published only one paper, while very few have multiple contributions. This confirms that sustainable HRM research is largely shaped by a small group of highly productive scholars.
Fig. 4 Lotka’s Law
Source: Authors own creation
Geographic Distribution of Articles
The geographic analysis of corresponding authors in shows Europe leading with 22.5% of research output, notably from the UK, Italy, and Spain. Asia follows with 19.8%, driven by India (13.8%) and China (6%). Developed countries contribute 32.1%, marked by higher international collaborations, especially France (40%) and Italy (36.4%). In contrast, developing countries like India, China, and Brazil account for 22.6%, with a stronger focus on single-country publications. Oceania, led by Australia (6%), and North and South America also contribute significantly. The data suggests a gradual shift toward balanced global contributions, with emerging economies becoming increasingly active in sustainable HRM research.
Science Mapping
Keyword Analysis
The keyword analysis in Fig. 5, conducted using VOSviewer, identified 24 keywords with a minimum of five occurrences. "Sustainable human resource management" appeared seventy-five times, followed by "sustainability" thirty-five times and "human resource management" nineteen times. Other prominent keywords include "green HRM," "corporate social responsibility," "job satisfaction," "employee engagement," and "stakeholder theory." The strong network links show how sustainable HRM intersects with corporate governance, employee well-being, and environmental sustainability, highlighting modern role of HRM.
Fig. 5 Network Visualization of all keywords
Source: Authors own creation
K- means Clustering
K-means clustering is a fundamental unsupervised learning algorithm widely used to categorize datasets into a predefined number of clusters (k). It is particularly effective in partitioning data based on similarity, where each data point is assigned to the nearest centroid, thereby forming clusters. The effectiveness of K-means largely depends on the initial placement of centroids, as different initial positions can lead to varying results. Therefore, a strategic selection of centroids, ensuring they are as far apart as possible, helps in obtaining optimal clusters (Lloyd, 1982). However, choosing an appropriate value for k remains a challenge. To address this, researchers employ validation techniques such as the Elbow Method and the Silhouette Analysis (Annapurna, 2016).
Elbow Method
Elbow Method is a graphical technique used to determine the optimal number of clusters by evaluating the within-cluster sum of squares (WCSS) (Thorndike, 1953). The procedure involves running K-means clustering for different values of k and plotting WCSS against k. The point at which the WCSS curve experiences a sharp bend (the "elbow") represents the ideal number of clusters. This happens because, initially, increasing k significantly reduces WCSS, but after a certain point, the rate of decrease slows down, indicating that adding more clusters no longer provides substantial improvement (Annapurna, 2016). In the Fig. 6, the WCSS graph shows a steep decline until K=4 and then stabilizes, it suggests that four clusters optimally represent the data distribution. However, the Elbow Method relies on visual interpretation, which can sometimes be subjective, necessitating supplementary validation methods (Kodinariya & Makwana, 2013).
Fig. 6 Elbow Method
Source: Authors own creation
Silhouette Method
The Silhouette Method evaluates clustering quality by measuring how similar each point is to its own cluster compared to other clusters (Rousseeuw, 1987). The silhouette coefficient ranges from -1 to 1, where values close to 1 indicate well-clustered points, values near 0 suggest overlapping clusters, and negative values imply misclassification (Kaufman & Rousseeuw, 1990). The optimal number of clusters corresponds to the k value that yields the highest average silhouette score. Fig. 7, reveals K=4 where the silhouette coefficient is 0.369904949 which is highest among all the tested k in the range of two to nine. In particular, both elbow and silhouette methods suggest the same number of clusters (K=4) providing an optimal balance between minimizing intra-cluster variance and maximizing inter-cluster separation, reinforcing the model’s reliability.
Fig. 7 Silhouette Method
Source: Authors own creation
Davies-Bouldin Index
DBI was introduced by Davies and Bouldin (1979), assesses clustering quality by evaluating compactness and separation. Lower DBI values indicate distinct clusters, while higher values suggest poor separation (Rousseeuw, 1987). The DBI score of 0.905 for k = 4 signifies a well-structured clustering solution. Other k values, ranging from 2 to 9, show DBI values above 1, suggesting suboptimal clustering. The elbow and silhouette methods also support k = 4, confirming its optimality. The agreement among these techniques demonstrates clustering stability, as highlighted by Tan in his 2018 book, "Introduction to Data Mining."
Fig. 8 Davies-Bouldin Index
Source: Authors own creation
Implementing K-means in Python
Python offers open source, cross-platform, object-oriented programming, dynamic typing, ease of learning, and a wealth of supporting libraries for statistics, mathematics, and visualization. Python modules such as matplotlib, Python-Sklearn, Scipy (Open-Source Library of Scientific Tools), and Numpy (Scientific Computing Tools for Python—Numpy) are utilized (Annapurna, 2016). It provides the silhouette score function from sklearn.metrics to validate the results and the K-means class from sklearn.cluster, which enables smooth clustering (Soobramoney, J. et al, 2025).
Cluster Size Distribution and PCA Visualization
Cluster size distribution is essential for assessing clustering effectiveness. Ideally, clusters should be well-balanced. Large clusters may indicate overgeneralization, while small ones could represent noise or outliers. The cluster distribution in Fig. 9, based on keywords from 218 documents, shows Cluster 2 as the largest (78 data points, 35.78%), representing a broad thematic area. Cluster 0 follows with 55 data points (25.22%) and Cluster 3 with 53 data points (24.31%), both showing a balanced distribution. Cluster 1 is the smallest, with 32 data points (14.68%), potentially reflecting niche topics. The PCA visualization in Fig. 10 provides a two-dimensional view of clustering, illustrating distinct thematic groups. Cluster 0 (dark blue) is dense and concentrated, indicating strong similarity, while Cluster 1 (teal) shows more variance. Cluster 2 (green) covers a wide range of topics, and Cluster 3 (light green) has moderate dispersion with some overlap. The red crosses represent centroids, indicating cluster cohesion and thematic diversity, with overlapping regions suggesting shared keywords between clusters. Thus, the formed clusters are named and discussed in detail.
Fig. 9 Distribution of Cluster Size
Source: Authors own creation
Fig. 10 PCA Cluster Visualization
Source: Authors own creation
Cluster 0: Sustainability in HRM for environmental responsibility (Dark Blue)
This cluster delves into the integration of sustainability principles within HRM, widely recognized as Green HRM. Research in this domain highlights the implementation of environmentally conscious HR practices, including green recruitment initiatives, sustainability-centered employee training programs, and performance management systems that align with ecological objectives (Cosenza et al., 2024; Kramar, 2022; Liang et al., 2024). The growing interconnection between Green HRM, corporate social responsibility, and organizational performance underscores the pivotal role of stakeholder’s theory in enhancing employees and corporate reputation (Song et al., 2023; Stahl et al., 2020). Empirical findings suggest that organizations that actively integrate Green HRM practices benefit from enhanced brand equity, increased employee commitment, and a notable reduction in environmental impact (Markey et al., 2016; Ntsiful, 2025).
Cluster 1: Mixed influences of sustainable HRM (Teal)
This cluster examines HRM strategies designed to foster employee well-being (Diaz-Carrion et al., 2020; Qamar et al., 2024), engagement, and resilience. Employee engagement and well-being play fundamental role in influencing workplace productivity, retention, and overall job satisfaction (Donald et al., 2020). The Job Demands-Resources (JD-R) model provides a structured framework for balancing workplace demands with HR policies that enhance employee engagement and job performance (Bakker & Demerouti, 2017; Lu et al., 2023). Empirical studies indicate that organizations that prioritize employee engagement and resilience experience elevated performance outcomes(Lu et al., 2023). Sustainable HRM policies, such as leadership development initiatives and resilience-building strategies, are instrumental in enabling organizations to navigate crises while maintaining operational stability (Ahmad & Fatima, 2023; W. Schaufeli, 2021).
Cluster 2: Technology-Driven HRM and Sustainability (Green)
The transformative impact of technology on HRM, particularly in the areas of AI-driven recruitment, HR analytics, and the use of digital platforms to enhance employee engagement is highlighted in this cluster (Gomes et al., 2023, 2025; Maley, 2024; Shao et al., 2024). The integration of artificial intelligence and big data analytics in HR decision-making processes has garnered significant academic and industry attention, as machine learning optimizes performance (Agarwal et al., 2022; Gamage et al., 2024). Person-Organization (P-O) Fit and Ability-Motivation-Opportunity (AMO) theories have the potential to improve overall performance (Jia, X., Hou, Y., 2024). The widespread adoption of HR technology has revolutionized traditional workforce management by driving efficiency, facilitating data-driven decision-making, and enabling personalized career development programs (Donald et al., 2020; Nayal et al., 2023; Sharma et al., 2022).
Cluster 3: Advancing Strategic HRM for Sustainable Organizations (Light Green)
This cluster explores the strategic dimensions of HRM, emphasizing talent management, leadership development, and performance optimization as essential drivers of long-term organizational sustainability. Research underscores the necessity of aligning HRM with corporate strategy to establish a sustainable competitive advantage in dynamic business environments (Cheng et al., 2024; Lu et al., 2023; Piwowar-Sulej, 2021; Piwowar-Sulej et al., 2024). Studies highlight the importance of structured leadership development programs, succession planning mechanisms, and competency-based HR models in ensuring organizational agility and resilience (Mariappanadar, 2020; Poon & Law, 2022). Furthermore, talent retention, learning and employee motivation emerge as pivotal themes within strategic HRM, with empirical findings supporting the effectiveness of career development pathways and performance-linked engagement initiatives (Bhatti M.A., 2024; Guerci et al., 2019; Nakra & Kashyap, 2024).
Thematic Map
The thematic map in Fig. 11, based on Callon’s density and centrality, categorizes research themes in sustainable HRM. Motor themes (top-right quadrant), such as "performance," "human resource management," and "impact," are highly developed and relevant, central to the field. Niche themes (top-left), like "environmental sustainability" and "sustainable HRM," are specialized but less connected to the broader HRM discourse. Basic themes (bottom-right), including "HRM," "innovation," and "firm performance," are foundational and widely relevant but not deeply explored. Emerging or declining themes (bottom-left), such as "organizational justice," "turnover," and "stress intensification," represent topics with either diminishing or minimal research momentum. The thematic map highlights opportunities for further exploration and expansion within sustainable HRM.
Fig. 11 Thematic Map
Source: Authors own creation
Conclusion
Sustainable human resource management is a strategic imperative for organizations aiming to balance social, economic, and environmental goals. By integrating sustainability into HRM processes, businesses can enhance employee engagement, well-being, and long-term organizational sustainability. Based on a review of 218 papers from Scopus and Web of Science, the study highlights key themes, influential authors, and emerging trends in sustainable HRM, demonstrating the field’s growing scholarly interest. Through K-means clustering, supported by elbow, silhouette, and Davis-Bouldin indices, four distinct clusters were identified. However, gaps remain in global representation and comprehensive database coverage. Future research should explore sustainable HRM’s practical applications across diverse regions and industries to ensure integration of sustainability into HRM.
Implications of the Study
Cluster 1 highlights the critical role of sustainable HRM in fostering workplace well-being through enhanced employee engagement and promoting diversity and inclusion by aligning HRM practices with regulatory standards. The surge in publications since 2020 reflects the rising interest in sustainability-focused HRM frameworks that support the United Nations Sustainable Development Goals by 2030. A growing international co-authorship rate (16.97%) signals global collaboration, enabling firms to adapt HRM strategies to diverse regulatory environments while supporting accountability and responsiveness to evolving labor and environmental standards in organizational practices.
Proposed Research directions
Future research should integrate niche and motor themes to develop a holistic sustainable HRM framework. Deeper exploration is needed into how environmental sustainability and development practices influence performance and HRM strategies. Studies can also bridge basic themes with advanced ones by examining the link between HR innovation and sustainable HRM (Kramar, 2014). Addressing emerging themes like stress intensification and organizational justice may offer insights into balancing performance with employee well-being. Additionally, examining the role of resource allocation across diverse organizations and applying methods like MAP-DP, DBSCAN, or hierarchical clustering is recommended.
Limitations of the study
This research has limitations, including a narrow focus on "sustainable HRM," potentially overlooking relevant studies using different terms. Bibliometric analysis tools like R and VOSviewer have inherent drawbacks, such as database indexing reliance and exclusion of non-English publications. Citation-based analysis may also miss conceptual advancements. Additionally, sustainability research is constantly evolving, and the dataset may not reflect emerging trends. Future research should incorporate mixed methods, broader search terms, and diverse data sources for a more comprehensive view of sustainable HRM.
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Abbreviations:
AI Artificial Intelligence
AMO Ability Motivation Opportunity theory
CSR Corporate Social Responsibility
JD-R Job Demands– Resources theory
HR Human resource
HRM Human resource management
P-O Person-Organization Fit
SHRM- Sustainable Human resource management
Declarations
Funding: This study did not receive funding from any organization be it public for-profit sector, commercial entity, or funding agency as it was literature review paper.
Conflict of Interests: The authors affirm that there are no conflicts of interest associated with the publication of this work. Furthermore, we affirm that ethical considerations, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, duplicate publication and/or submission, and redundancy, have been meticulously addressed and adhered to throughout the research process.
Availability of data and materials: Data taken from Scopus and Web of Science, Used easily available software’s like R studio, Biblioshiny, Vosviewer, and Python.
Acknowledgements:We gratefully acknowledge the support and assistance provided by the experts of the field and the institution (JC Bose University of Science &Technology, YMCA Faridabad) during the course of this research. Special thanks to Mr. Abhijeet, Engineer, WAPCOS Ltd., India for guiding the authors on Python.
