Introduction

Over the last couple of years, human resource (HR) analytics has gained momentum in the dynamic landscape of human resource management (HRM) and has emerged as a transformative force due to its power of data-driven decision-making for optimizing workforce management [1,2,3]. This field is becoming pivotal as organizations recognize the benefits of data-driven decision-making in workforce management [4, 5]. Companies are increasingly aware of the strategic importance of their human capital. As a result, HR analytics has become a critical tool for harnessing insights from vast pools of HR data. HR analytics represents the advancement of information technology through the descriptive, visual, and statistical analysis of data related to HR capital, processes, and organizational performance [6, 7]. It systematically collects, analyzes, and interprets data on various aspects of human resources, external economic benchmarks, and organizational outcomes [4]. When implemented efficiently, HR analytics can offer a distinctive advantage to organizations and pave the way for the advancement of HR specialists [8]. By translating HR insights into actionable strategies, companies can position themselves as industry leaders [9]. A key component of HR analytics is predictive analysis, which is used to enhance employee performance by examining historical HR data [4, 10]. Effective utilization of analytics is critical for building high-performing and sustainable organizations [11].

Despite its importance, there have been few bibliometric literature reviews on HR analytics. Existing literature has not sufficiently explored the significant contributors, journals, and countries that shape this field. Previous reviews highlighted issues such as adapting HR analytics, ethical considerations, and the need for ROI-focused empirical research [4, 12]. Therefore, there is a need to conduct a rigorous quantitative bibliometric and systematic review to provide further insights and enhance the current understanding and emerging trends in the field of HR analytics. The previous review was published several years ago and did not include the latest research findings published in recent years [13]. Prior literature has not sufficiently addressed the practical implications and impact of emerging technologies, including machine learning, artificial intelligence, and big data analytics, on HR analytics, even though the field of HR analytics is constantly evolving alongside technological advancements [4, 12, 14]. Therefore, this study aims to address these gaps by conducting a rigorous quantitative bibliometric and systematic review to provide new insights and identify emerging trends in HR analytics.

Our key research question is, “How can a bibliometric and systematic analysis of authorship patterns and publication trends across journals and countries identify current trends and develop a future research agenda in HR analytics?” To answer this research question, this paper aims to conduct a comprehensive bibliometric and systematic analysis of HR analytics, focusing on influential authors, journals, and countries, which will help illuminate current research trends and shape a future research agenda. We will review existing literature to capture state-of-the-art research and develop a theoretical foundation, concepts, and recent developments in HR analytics. Through the utilization of a structured bibliometrics analysis, this study will present a comprehensive overview of HR analytics, highlighting important figures, themes, and areas of interest. We aim to summarize relevant information, identify deficiencies, and contribute to the ongoing conversation about HR analytics empirical basis and scholarly development. Additionally, this study intends to validate and improve existing HR analytics frameworks and models by evaluating impact and prevalence so that these could provide a more solid foundation for future research and real-world applications.

The primary contribution of this study is to offer a thorough and organized synopsis of the academic literature related to HR analytics by identifying important themes, notable contributors, and the evolution of the research focus. The study will also provide practical insights for HR professionals by making the findings applicable and relevant to industry practitioners. The research findings are more appropriate and relevant due to this link between academia and industry.

The rest of the paper is organized as follows: Section Two discusses HR analytics from previous literature, Section Three outlines the methodology, Section Four presents the results and discussion, Section Five addresses the implications of the study, and Section Six concludes the paper.

HR analytics

Human resources are vital to all organizations, contributing skills, knowledge, and labor essential for the functioning, success, and sustainability of any business [15,16,17]. In today’s dynamic business environment, HR departments use advanced data-centric tools for decision-making, known as HR analytics [5, 18, 19]. HR analytics can be defined from various perspectives. It involves applying advanced data mining and business analytics approaches to HR [12]. Specifically, workforce analytics refers to using quantitative data with statistical tools and modeling to extract useful information for evidence-based decision-making. [20]. This systematic process gathers, explores, and presents quantitative and qualitative data to gain deeper insights into human resource challenges and support organization-wide decision-making [21, 22]. To make better decisions, it is necessary to systematically identify and quantify the human drivers of business results. According to Fernandez and Gallardo-Gallardo [23], HR analytics is a logical analysis that aims to forecast and guide company outcomes based on objective business data. It is an interdisciplinary field that aims to improve organizational and individual performance through methodological integration in people-related decision-making [24]. This methodology applies statistical techniques and experimental approaches to understand and evaluate the causal relationship between organizational performance outcomes and human capital decisions intended to influence business strategy and performance [4]. Analytical metrics and measurements pertaining to human processes are the primary focus of HR analytics. It ensures the measurement of investment in human resources and boosts employee productivity. From hiring to onboarding, HR analytics verifies every step of the decision-making process. According to Ciomaga [25], HR analytics is a technique used to strategically impact HR management through statistical models. It encompasses a wide range of modeling methods essential for strategic HR decision-making, including behavioral and predictive modeling, impact analysis, cost–benefit analysis, and ROI analysis [26]. HR management facilitated by IT establishes business impact and enables data-driven decision-making through descriptive, visual, and statistical analysis of data related to HR processes, human capital, organizational performance, and external economic benchmarks [27].

HR analytics involves collecting, transforming, and maintaining essential HR-related data, analyzing the information using business analytics models, and disseminating the results to decision-makers. In HRM literature, HR analytics is seen as a crucial tool for businesses to gain workforce insights and make better decisions [28]. It improves HR practices, such as hiring and managing employees’ skills and talents, by identifying problem areas and implementing data-supported solutions. Additionally, HR analytics integrates HR activities with organizational goals, supporting broader company strategies [4, 12, 23]. As HR analytics has grown and matured, several essential concepts and advancements have emerged. We have identified three significant periods of HR analytics that are involved. Initially, large companies were the driving force behind adopting data and analytics in HR, as they could invest in data gathering and analysis technologies. The Society for Human Resource Management (SHRM) published the first comprehensive guide to HR analytics, providing instructions for implementation in businesses. The rise of cloud computing and data from sources like performance management systems and employee surveys has broadened the application of HR analytics, enabling businesses to analyze information from various sources [29]. The first significant conference dedicated to HR analytics emphasized the importance of data-driven decision-making in HR. HR analytics has become increasingly integrated with overall company strategy, aligning HR activities with organizational goals and adopting a more strategic and data-driven approach. The rapid evolution of new technologies like AI and machine learning has revolutionized HR analytics, allowing for faster, more precise data analysis [30]. Recent literature highlights emerging trends and upcoming themes, including the integration of advanced technologies like big data, machine learning, and AI; strategic decision-making using HR data; enhanced data visualization techniques; and a focus on employee experience and engagement [31, 32]. Future research themes include AI and automation in HR processes, personalization and customization of HR solutions, cross-functional integration with other business functions, and the impact of remote work and hybrid models.

From the above discussion, HR analytics can be defined as an advanced, data-centric approach utilizing statistical tools to collect, process, and interpret data for strategic decisions in workforce management and organizational development.

This comprehensive understanding of HR analytics directly informs our key research question: “How can a bibliometric and systematic analysis of authorship patterns and publication trends across journals and countries identify current research trends and develop a future research agenda in HR analytics?” By clarifying the multifaceted nature of HR analytics, from its definitions and methodologies to technological integrations and ethical considerations, we lay the groundwork for a rigorous bibliometric analysis. Our analysis will track significant contributions from influential authors, journals, and countries, exposing current research trends and providing a comprehensive overview of HR analytics. This analysis will also help identify gaps in the literature, particularly regarding practical implications and emerging technologies.

Methodology

A literature review is a strategic approach to discovering potential research gaps and highlighting the informational limitations of a research issue [33]. Structured literature reviews can address a broad spectrum of literature to provide a detailed and in-depth overview and analysis. This typically involves an iterative cycle of designing relevant search queries, exploring the literature, and conducting evaluations [34, 35]. In this study, we employ the PRISMA technique to examine the field of HR analytics, offering an extensive review of previous research and highlighting significant contributions in the field. The primary objective is to identify notable research clusters and suggest potential future research directions to facilitate the growth and advancement of HR analytics.

Data collection

Designing the search query

To conduct bibliometrics analysis, we first examined our research field, and after establishing the scope of the research, previous literature reviews were consulted to identify suitable keywords for the literature search. Based on that, the following keywords have been identified and used: “HR analytics,” Human resource analytics,” “Workforce analytics,” “People analytics,” “Talent analytics,” and “Human development analytics.”

Data search and preliminary results

Data were collected based on the identified research gap to address the research questions. The data were exclusively extracted from the Scopus database, chosen for its vast coverage of over 20,000 peer-reviewed journals across a wide range of disciplines, including social sciences, science, technology, arts, and humanities [36, 37]. According to Schotten, Meester [37], Scopus stands out as the largest abstract and citation database, ensuring access to a more diverse and comprehensive pool of scholarly resources than other databases like Web of Science. We selected the Scopus database for its citation analysis capabilities, as it provides detailed citation data, which is essential for bibliometric analysis [38]. Citation analysis allows researchers to track citations over time, identify influential works, and measure the impact of specific papers or authors [39]. Scopus offers various citation metrics, such as the h-index and citation counts, which help researchers evaluate the quality of research output [40]. Scopus provides more accurate data and is regularly updated to include new publications and citation data [41]. Scopus is user-friendly, enabling researchers to extract and export data easily for various bibliometric analysis software [42].

We entered a series of search equations into the Scopus database using selected keywords that were analyzed in the query analysis step. Subsequently, an initial search retrieved a total of 376 articles published within the timeframe of 2008 to 2023, shown in Fig. 1.

Fig. 1
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PRISMA model of this study

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Refinements of the results

Ramos‐Rodríguez and Ruíz‐Navarro [43] referred to scientific literature as ‘certified knowledge’ published in peer-reviewed journals. Given the embryonic nature of the research topic, we chose to include eligible conference articles within our study’s scope. Initially, we retrieved 376 articles from the Scopus database, as shown in Fig. 1. After refinement and filtering, we narrowed it down to 102 articles. We saved this bibliographic information in CSV format, including paper titles, author names, collaborations, provinces, citations, terms, and bibliographies.

Bibliometric analysis

Bibliographic analysis is a quantitative and statistical method that allows the display and evaluation of the growing literature in a field of study [44, 45]. This study utilized bibliometric analysis methods to explore the ever-increasing literature in the field of HR analytics research. Bibliometric analysis has gained popularity across various fields of study because it offers a comprehensive view of relevant literature, a feature especially crucial for HR analytics. This rigorous quantitative methodology serves as an effective means to content the ever-expanding body of scholarly research within the field [46]. When the focus is on understanding the extent of research depth, a traditional systematic review of literature can give more insights than bibliometric analysis, but if we consider the range of previous papers, quantitative analysis like bibliometric is better as it captures more paper ranges [32, 47, 48]. The study conducted by Feng, Zhu [49] argued that bibliometric review should not replace traditional methodologies. Nevertheless, the authors highlighted the significant potentiality of bibliometric techniques in complementing and enhancing traditional methods of literature review. Therefore, in order to enrich our study results, we utilized bibliometrics analysis methods for a broader understanding of HR analytic literature review fields.

We used a bibliometric package of R software [50], and VOSviewer [51] for evaluating bibliographic information. We utilized R Studio for bibliometric data analysis because it is a powerful statistical language that allows for extensive customization of analysis [52]. RStudio also offers numerous packages which provide comprehensive tools for data import, analysis, and visualization [53]. VOSviewer is specifically designed for creating, visualizing, and exploring bibliometric maps [54], and it is renowned for its user-friendly interface and its ability to produce clear, insightful visualizations of bibliometric networks such as co-authorship, co-citation, and keyword co-occurrence networks [55].

Result and discussion

Summary statistics

In this study, we analyzed scientific articles in the area of HR analytics from 2008 to 2023, with 102 articles from 66 sources. Table 1 summarizes the results of our bibliometric analysis. The table encapsulates the academic research in HR analytics, revealing an annual growth rate of 9.68% and an average citation per document of 22.38%. The total number of authors in the field of HR analytics is 127, with 19 single-authored documents and an average of 2.4 co-authors per document. This bibliometrics analysis shows that, overall, HR analytics is a growing and vibrant research field.

Table 1 An overview of data
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Annual total production and average total citations per year

Figure 2 represents the annual scientific production and average total citation per year of HR analytics from 2008 to 2023. Although scientific research in HR analytics began in 2008, significant growth in production is evident starting from 2014. Between 2014 and 2023, a total of 99 articles were published, with the peak occurring in 2022, when 24 articles were produced. The average number of citations per year has also increased over time, reaching its highest average of 15.78 citations in 2015. This analysis indicates that HR analytics is an expanding and highly impactful research field.

Fig. 2
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Annual total production and average total citation per year

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Three-field plot

The three-field plot analysis is a bibliometric visualization technique that illustrates the relationship between three variables associated with scholarly literature [45]. In this study, we examine the correlation among authors, their countries, and the most prevalent keywords. Figure 3 depicts the interplay among the most influential authors, their respective countries, and the prevalent keywords in the field of HR analytics spanning from 2008 to 2023. Notably, the chart highlights prominent authors such as McCartney S, Fu N, Guerry M A, Avrahami D, Chalutz Ben Gal H, and Boudreau J, representing the USA, Israel, and Ireland. Additionally, Graph 3 reveals the frequent utilization of keywords such as HR analytics, people analytics, human resource analytics, and big data within the realm of HR analytics. Particularly, HR analytics and people analytics emerge as the most commonly employed keywords, signifying their significance within this domain. This visualization serves as a valuable tool for comprehending the current landscape of HR analytics research, aiding researchers in identifying key scholars and research focal points within the field.

Fig. 3
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Three-field analysis based on authors, author’s countries, and keywords

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Source analysis

Most relevant source

Table 2 presents a summary of the number of articles published in each source. The “Journal of Organizational Effectiveness” emerges with the highest number of articles, totaling 9. The following are closely related “Human Resource Management,” “HRM Journal,” and “International Journal of HRM,” each boasting four articles. Subsequently, “HRM International Digest,” “International Journal of Manpower,” and “International Journal on Emerging Technologies” each contribute three articles. Lastly, “Baltic Journal of Management,” “Decision Support Systems (DSS),” and “RRM Review” present two articles each. This table vividly illustrates the global prevalence of HR analytics, as evidenced by publications from diverse sources worldwide. Researchers can glean valuable insights from this compilation regarding the most pertinent sources in the realm of HR analytics.

Table 2 Most relevant source
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Author influence analysis

Contribution by authors

The bibliometric study conducted for this investigation has revealed 227 authors, as previously outlined in Table 1. Figure 3 showcases the top ten influential authors based on the number of papers they have contributed to the field. In the academic realm of human resource analytics, McCartney S stands out as a prominent contributor, having submitted four separate works to the Scopus database within the specified timeframe. Fu N and Guerry M-A have also made noteworthy contributions to this academic domain, each with three publications. Avrahami D, Boudreau J, Casicio W, Alutz Ben-Gal H, Jain P, Katsamakas E, and Pessach D have each authored two papers, signifying their significant impact on the academic discipline. The graph illustrates that leading contributors in the field of HR analytics originate from diverse geographical regions, underscoring its rapid expansion.

Co-authorship

The co-authorship network analysis is visually represented in a map figure, illustrating the connections among authors from various fields through their published works [56]. His method provides a reliable depiction of authors’ collaborations within the network. In this map, authors are depicted as nodes, with co-authorship links shown as lines or edges connecting them. The size of each author’s node may correspond to the number of works they have contributed to, while the thickness of the edges can indicate the strength of the co-authorship connection, often measured by the number of joint publications. Clusters or groups of authors who collaborate regularly are delineated on the map using different colors. Notably, Boudreau J.W. emerges as a prominent figure due to his extensive participation across several clusters. These clusters represent groups of authors working together to produce scholarly articles. For instance, the red cluster signifies a large network involving Mark A. Huselis, Jia Hui Marler, J. W. Boudreau, P. M. Ramstad, and Dave Ulrich. Similarly, the pink cluster highlights the significant collaboration between Kirkpatrick and Charlwood with other authors across various research papers. The yellow cluster indicates a notable co-authorship network involving Hair. J. G. J, Icek Ajzen, and Sarstedt, M. Additionally, the involvement of Patrick. M. Wright, Haris. J. G & Skter. S stands out in this field. This analysis of the co-authorship network reveals a collaborative research landscape in HR analytics. J.W. Boudreau emerges as a central figure, and the presence of distinct clusters underscores active research collaborations among different groups of scholars (Figs. 4 and 5).

Fig. 4
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Most relevant authors

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Fig. 5
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Co-authorship network of cited author

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Affiliation statistics

As depicted in Table 3, both Vrije Universiteit Brussel and Nova Southeastern University emerge as two of the most influential academic institutions in HR analytics, with seven and five publications, respectively. Noteworthy contributions also come from Copenhagen Business School, Loughborough University, Tel-Aviv University, University of California, and Tilburg University, each boasting four publications in the field. This diversity of contributors underscores the broad research landscape in HR analytics, without any one institution dominating. The collective volume of publications reflects the global emphasis on HR analytics across universities.

Table 3 Most relevant affiliation
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Keyword analysis

Countries scientific production

The scientific production of countries is summarized in a table illustrating the contributions of different countries in the field of HR analysis. Figure 6 reveals that the USA and India lead as the top contributors in HR analytics, with 53 and 41 scientific research publications, respectively. Other prominent contributors include the Netherlands (14), Ireland (12), the UK (12), Australia (10), Germany (10), Israel (10), Belgium (6), and Denmark (6). Additionally, the table highlights the increasing research activity in Ireland, the United Kingdom, Australia, Germany, Israel, Belgium, and Denmark. This suggests a growing recognition of the importance of HR analytics in these countries, prompting increased investment in research within this domain. The dominance of the US and India in HR analytics research can be attributed to several factors, including the presence of leading research institutions, government funding, and a strong emphasis on innovation within these nations.

Fig. 6
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Countries scientific production

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Co-occurrence

Co-occurrence analysis is a technique utilized to identify and analyze patterns of co-occurrence or relationships among terms or items within a dataset [57]. It serves as a valuable tool for comprehending the interrelationships among different terms, keywords, or concepts within a specific field of study. Each node’s size in the network corresponds to the frequency of the keyword’s appearance in the papers, while the edges between nodes indicate the strength of the co-occurrence relationship between the keywords. In Fig. 7, HR analytics, people analytics, algorithms, big data analysis, HR competencies, and the HR profession frequently co-occur in the dataset, resulting in robust links between their respective nodes in the co-occurrence network map. The associated nodes within other clusters also reveal a strong connection among them. HR analytics and people analytics emerge as the most closely associated terms, represented by the red clusters. Human resource analytics, big data, data analysis, and strategic HRM form the most interconnected terms, depicted in a green cluster. Additionally, data management, digital knowledge, HR matrix, and business intelligence are related terms showcased in the blue cluster.

Fig. 7
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Co-occurrence networks

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This co-occurrence network serves as a valuable tool for gaining insights into the current state of HR analytics research. It demonstrates that HR analytics encompasses a broad spectrum of topics and indicates a growing interest in leveraging data analytics to enhance HR decision-making processes.

Bibliographic coupling

Figure 8 illustrates bibliographic coupling in HR analytics, with nodes representing countries and edges indicating connections between them. The size of each node likely corresponds to the number of publications associated with that country, while the thickness of the edges may denote the strength of co-citation connections. Notably, the United States, India, Australia, and the Netherlands emerge as key players in HR analytics research. These countries boast large nodes and dense connections with others, suggesting prolific research output and frequent collaboration. Several clusters of countries exhibit strong interconnections, potentially signifying regional research partnerships or thematic concentrations within HR analytics. For instance, there is a distinct cluster comprising European countries such as Germany, France, Italy, and Spain, alongside another cluster featuring Asian nations like India, China, and Indonesia.

Fig. 8
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Bibliographic coupling by countries

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Cluster analysis

Here, seven clusters have been identified based on Normalized Local Citation Score, impact factor, frequency, certainty levels, present research, and future prospects. The largest cluster comprises J. H. Marler, D. T. Newman, V. Fernandez, Shet, Aral, Van Den Heuval, and U. Gal. The second cluster includes A. Margherita, D. B. Minbaeva, B. Kapoor, C. Royal, and RJW Tijssen. The third cluster features two authors, A. Frederiksen and Sri Harsha B. Notable contributors in the fourth cluster include D. Angrave, Vargas, and A. Sharma. The fifth, sixth, and seventh clusters consist of K.G.S. King, Rathi Meena M, and Rombaut E.

Systematic tabular analysis

The table provides a concise overview of multiple HR analytics review papers, presenting the authors, titles, methodologies, focus areas, levels of analysis, respondent demographics, theoretical frameworks, and key findings. These papers encompass a broad spectrum of topics within HR analytics, touching upon its practical applications, influence on talent management, determinants of adoption, utilization of artificial intelligence, data mining methodologies, and beyond. Employing various research approaches, including empirical studies, non-empirical analyses, qualitative investigations, and case studies, these works delve into the multifaceted dimensions of HR analytics (Tables 4 and 5).

Table 4 Cluster analysis
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Table 5 Systematic tabulations
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Discussion

The bibliometrics analysis focuses on the current trends and practices in the HR analytics fields, where we analyzed 102 Scopus indexed articles. The findings of the study provide valuable insights into the current state and future directions of HR analytics. Through the bibliometrics analysis, the study contributes to understanding the growth, trends, and key players in the fields. The result indicates a notable increase in scientific article production in HR analytics with a 9.68% annual growth rate between 2008 and 2023, expressing the growing interest and recognition of HR analytics as a pivotal area of study. This finding is consistent with the previous research which also indicates that research interest in HR analytics has increased, especially in recent years [4, 11, 12]. This exponential growth of HR analytics research indicates how data-driven decisions are important in HR management and organizational development [58]. The rinsing trend in average total citations per year also highlights the impact and relevance of HR analytics research, indicating the significance of this area to academia [14].

The analysis shows HR analytics is now a global research interest, with contributions from various countries, including European countries, American countries, Asian and African countries, and this result is consistent with previous research [12]. The study indicates that the USA and India emerge as leading contributors, with other countries such as the Netherlands, Ireland, the United Kingdom, and Australia also showing significant research activity in this domain. This geographically dispersed research production underscores the interdisciplinary nature of HR analytics [13]. Collaborative article production emphasizes the interdisciplinary nature of HR analytics and the importance of cross-cultural perspectives in advancing research and practice in the field [19].

High-frequency keywords such as “HR analytics,” “people analytics,” and “big data” emerge as prominent themes, also explored in existing HR analytics literature [14]. These terms highlight the increasing focus on data-driven decision-making in workforce planning, talent acquisition, and performance management [19, 59, 60]. Co-occurrence analysis reflects the multifaceted nature of HR analytics and its integration with advanced data analytics techniques. The analysis of established scholars like S. McCartney, N. Fu, and M-A. Guerry among the most influential authors confirms that the bibliometric analysis has captured prominent figures in the field, and their research focus areas can be further explored to understand their contributions to specific HR analytics themes. The findings of this study have several implications, including the identification of research clusters and future research directions that can guide researchers in exploring emerging trends and addressing critical gaps in the literature. The collaborative research landscape observed in this study emphasizes the importance of knowledge exchange and partnerships among scholars and institutions globally. The co-occurrence network further highlights emerging trends like the growing interest in AI and machine learning applications within HR analytics, guiding future research questions and directions. The study offers a bibliometric examination of HR analytics research, highlighting its growth, trends, and key players. It also provides a valuable resource for researchers, practitioners, and policymakers interested in advancing knowledge and practice in HR analytics by analyzing previous literature. Moving forward, continued collaboration and interdisciplinary approaches will be essential in driving innovation and addressing the complex challenges facing HR analytics in future.

Implications

Theoretical implications

This study contributes theoretically by shedding light on the evolution and current state of HR analytics research. By analyzing publication trends, authorship patterns, and thematic clusters, our findings provide a foundation for theoretical developments in HR analytics scholarship. The papers provide a data-driven decision-making theoretical framework. The exponential growth of HR analytics research suggests a paradigm shift toward data-driven decision-making in HR management, reinforcing the importance of existing theories of evidence-based practice for effective HR strategies. The study highlights that HR analytics is a global and collaborative field of research, indicating the interdisciplinary nature of HR practices. High-frequency keywords like “big data” and “HR analytics” suggest a theoretical shift toward integrating advanced data analytics techniques into HR practice, opening doors for further research on the theoretical underpinnings of big data applications in talent management, performance evaluation, and other HR functions. Researchers can draw upon our insights to refine existing theoretical frameworks, explore new conceptual avenues, and advance theoretical debates within the field. The study strengthens human capital theory and return on investment theory by highlighting that investing in employees yields long-term benefits. Additionally, our identification of influential authors, key sources, and thematic clusters offers valuable inputs for theoretical synthesis and integration, facilitating the development of a more cohesive and comprehensive theoretical framework for HR analytics research. The finding of the study indicates that AI and machine learning are emerging trends in HR which has led to the development of entirely new theoretical frameworks to understand the impact of these technologies on human resource management practices.

Practical implications

From a practical standpoint, our study offers actionable insights for practitioners and policymakers involved in HR analytics. By identifying influential authors, key sources, and thematic clusters, practitioners can stay abreast of the latest developments in HR analytics research and leverage this knowledge to inform evidence-based decision-making in organizational contexts. The finding highlights the importance of HR analytics skills, especially data analysis techniques and interpreting HR data for HR professionals for effective decision-making in HR functions. The study highlights the importance of investing in HR analytics capabilities for leveraging HR analytics to optimize their workforce, identify talent gaps, and measure the effectiveness of HR initiatives. The global research interest trend in the HR analytics area suggests a potential need for developing policies and regulations around data privacy and ethical considerations when using big data in HR practices. Our systematic tabular analysis provides practitioners with a comprehensive overview of methodologies, focus areas, and key findings, enabling them to identify best practices, emerging trends, and potential areas for intervention. Policymakers can also use our findings to inform strategic investments in HR analytics research, foster international collaborations, and promote knowledge exchange across academic and practitioner communities. The study highlights practical guidelines for developing the HR Analytics Ecosystem. The global research landscape of HR analytics suggests a need for fostering an HR analytics ecosystem including the development of educational programs to equip HR professionals with data analysis skills and creating knowledge-sharing platforms for HR practitioners.

Conclusion

This bibliometric analysis offers a comprehensive exploration of HR analytics research trends, author influence, source prominence, and thematic clusters. Our findings highlight the exponential growth of HR analytics research since 2014, the emergence of influential authors and sources, and the prevalence of key thematic areas such as HR analytics methodologies, applications, and impacts. Theoretical implications include the refinement and advancement of theoretical frameworks, while practical implications encompass evidence-based decision-making for practitioners and strategic investments for policymakers. Overall, our study provides a valuable resource for researchers, practitioners, and policymakers seeking to navigate and contribute to the evolving field of HR analytics.

Limitations of the study and future research directions

Despite the fundamental contribution of this study lying in its originality in producing a detailed research map of the HR analytics field for the first time, it is not beyond certain limitations. The present article’s concentration on bibliometric analysis of the HR analytics literature without digging into the articles’ substantive contents is one of its limitations. The research may not provide in-depth insights into the particular theories, frameworks, or methodologies used in those studies, even if its goal is to ascertain the extent of the development and contributions of HR analytics throughout the period. The study emphasizes authorship and collaboration patterns, ignoring the potential influence of non-academic stakeholders like HR practitioners or industry consultants on HR analytics research agendas and practices. Another drawback is its sole dependence on the Scopus database. Despite being a well-known and extensive database, Scopus may not include all relevant papers in HR analytics. The article also does not specifically discuss the uses of HR analytics in certain industries or the contexts in which they are used. The research may overlook the complexity and variances in the use of HR analytics in various organizational contexts and sectors as it concentrates more on the general growth of the field of HR analytics and the contributions of authors, journals, and countries. The study limits the language to English, which ignores publications conducted in other languages, particularly in non-Western regions.

Future research may address the issues raised above and improve our knowledge of HR analytics. First, conducting a systematic review or content analysis of the literature on HR analytics might provide deeper insights into the theories, frameworks, and approaches used in the discipline. This would help gain a more comprehensive understanding of the significance and implications of research on HR analytics. Future researchers can explore the influence of non-academic stakeholders, including HR practitioners, industry consultants, and others, to offer a more comprehensive understanding of the field. To overcome the limitations associated with reliance on a single database, researchers can include additional databases beyond Scopus, such as Web of Science, Google Scholar, and industry-specific databases, to provide more exhaustive coverage of relevant literature. Conducting bibliometric analyses with a focus on sector-specific studies could illuminate how HR analytics is deployed across various industries and organizational settings, revealing distinct challenges and best practices. While our study exclusively scrutinized English-language articles, future investigations could incorporate publications in multiple languages, particularly those from non-Western regions, to provide a more global outlook on HR analytics research. Additionally, exploring the practical applications and real-world impact of HR analytics could bridge the gap between academic inquiry and industry implementation. This endeavor would showcase how theoretical insights are translated into actionable strategies within organizational contexts, thus enriching both academia and industry practice.