hr analytics research papers

Towards a process-oriented understanding of HR analytics: implementation and application

• Original Paper
• Open access
• Published: 18 August 2022
• Volume 17 , pages 2077–2108, ( 2023 )

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• Felix Wirges   ORCID: orcid.org/0000-0001-9939-6444 1 &
• Anne-Katrin Neyer 1  

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Firms have recognized the opportunities presented by HR analytics; however, it is challenging for HR to convert their available data (sources) into meaningful strategical value. Moreover, research on the implementation and application of HR analytics is still in its infancy. Drawing on the socio-technical system perspective, we examine the implementation and application of HR analytics in firms. Based on a qualitative study with 17 HR analytics experts, we find that a shift to a more process-oriented perspective on HR analytics is needed. More precisely, besides the requirements for the analysis of data, the actual roles in the process of implementing and applying HR analytics need to be defined. In particular, this implies the interaction between the specialist department, the HR business partner and the HR analytics function. From a managerial perspective, we propose a process model for the future implementation and application of HR analytics.

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1 Introduction

Data collection and analysis is an essential part of business process decision-making for a variety of organizations (Côrte-Real et al. 2017 ; George et al. 2014 ). Data-based decision-making takes place in almost every area of an organization; marketing, sales, production and finance are examples of application areas where it is common to make decisions based on analysis and reporting. This is not particularly surprising, as analytics-based decision-making can automate processes and make them more efficient in almost every business area where data can be gathered (Acito and Khatri 2014 ; Earley 2015 ; Ghasemaghaei 2018 ).

However, compared to the extensive use of data in other application areas of the company, its use is rather rare in HR (Tursunbayeva et al. 2018 ). To help to deal with this endeavor, in recent years, the term "HR analytics" has become popular and has increasingly been used in science and practice, often promising nothing less than the revolving of the HR management (Marler and Boudreau 2017 ; van den Heuvel and Bondarouk 2017 ; Huselid 2018 ; McIver et al. 2018 ; Tursunbayeva et al. 2018 ; Greasley and Thomas 2020 ; McCartney and Fu 2022 ). Falletta and Combs ( 2021 ) note that while the amount of data and technology has increased significantly, the use of data to explain organizational issues is not in itself a novel tool. A variety of approaches exist, focusing on a more evidence-based approach to HR management (Lawler and Boudreau 2015 ). This emphasizes that the design of the HR function is per se predestinated for the use of data-based decisions:

First, HR is a business area that, in principle, could generate a large amount of data. Through the ongoing digitization of work processes, the use of mobile devices, the wearing of wearables or the use of company apps, employees generate different types of data. These include e.g. information about locations, communication, personal well-being and many other factors, which are of relevance for HR (Cascio and Montealegre 2016 ).

Second, due to the growing use of information and communication technologies in HR, the term eHRM has become increasingly popular (van den Heuvel and Bondarouk 2017 ). More recently, the use of HR analytics has taken the topic to a new level: The previous use of technology in HR mostly concentrated on operative support or descriptive reporting of key information, such as sick days of the workforce or employee turnover. Through the use of HR analytics, connections and conclusions can now be drawn (a) in each functional area of HR, but also (b) with data from other application areas. Predictive analyses can then be made from these results. However, if we look at the practical application of HR analytics, especially with a focus on the use of predictive analytics, we see a sobering picture. Falletta ( 2014 ) shows in a sample of 220 firms that only 15% place a strategic focus on HR analytics and that, as a rule, they do not carry out any predictive analyses, but only focus on reporting. A survey by Lawler and Boudreau ( 2015 ) published two years later provides similar results. Levenson and Fink ( 2017 ) noted that the term HR analytics has become a catch-all term to describe any handling of data and metrics in HR: “It has come to include anything numerical about talent and HR work. Examples include simple data reports, analyzing data integrated from multiple systems (e.g. performance and compensation), dashboards, making data available “on demand,” and true talent or “predictive” analytics” (Levenson and Fink 2017 :146). More recent definitions of HR analytics emphasize shifting the focus towards a process perspective (Mclver et al. 2018 ): HR analytics is not only understood as a tool where statistical methods are applied and the focus is on key figures, but as a systematic approach (Falletta and Combs 2021 ). Most recently, Falletta and Combs ( 2021 ) define HR analytics as follows: “HR analytics is a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives” (Falletta and Combs 2021 : 3). The two authors highlight that in the application of HR analytics so far, there is too little recognition “of the role of broader HR research and experimentation as part of an overarching HR analytics agenda (i.e. internal HR research or partnership research in the context of social, behavioral and organizational sciences)” (Falletta and Combs 2021 : 54). This goes in line with the evidence-based review by Marler and Boudreau ( 2017 ), which uses an integrative synthesis of published peer-reviewed literature. Their findings emphasize that HR could soon be technically left behind and thus, hint at an issue that has already been discussed for some years: HR must create technological change in order to continue to play an equal role in the company in the future (Shrivastava and Shaw 2003 ; Snell et al. 2002 ; Ulrich 1997 ).

Third, in the wake of the COVID-19 pandemic, it became clear that metrics-based information can be of tremendous importance. Companies were confronted overnight with unprecedented challenges in managing employees in the workplace. The HR function in particular had to ensure within a very short time that the new requirements for remote work, digital collaboration and leadership in teams, and (mental) health issues were met (Kniffin et al. 2020 ). Along the way, previous tasks such as recruiting or HR development had to be transformed into digital solutions. Companies, therefore, raised information about remote work, employee engagement, and well-being to gain a clear picture of the respective needs of employees (Belizón and Kieran 2021 ). The situation created by the pandemic highlights the importance of HR analytics. It essentially offers the possibility of many new types of data sources that can specifically promote the quality of HR analytics (Bryce et al. 2022 ).

Having said this, the pressing issue thus is: why do only a few organizations rely on the (advanced) use of HR analytics, although the circumstances (data generation, software applications, etc.) and the reason (strengthening the strategic role of HR) (Bassi et al. 2012 ) seem to be predestined for an application? Building on this question, we conducted an initial descriptive survey with HR employees and managers to determine the status quo of the implementation of data-based analytics (Wirges et al. 2020 ) (see Table 1 ).

The results of the study showed that working with data plays an important role in HR management. 66% of the interviewees said that data evaluation in HR is included in strategic decision-making. However, it should be noted that these decisions are predominantly based on classic HR controlling (Excel, KPI etc.) or descriptive analyses (the analysis of data related to the past). For example, 96% of the interviewees stated that they use the data obtained for HR controlling. In this study, HR controlling was broadly defined as the simple collection and reporting of individually defined key performance indicators. The use for descriptive analyses is already significantly lower at 32%. Only 5% carry out predictive analyses with the help of the data.

The results of this study reflect a sobering picture regarding the application of HR analytics. This underlines the current state in the literature that the knowledge about the implementation and application of HR analytics is fraught with many challenges and difficulties. For a better understanding of these challenges and to present them in a holistic picture, the aim of this paper is to dive deeper into the implementation and application of HR analytics. To do so, we conducted a qualitative study with HR analytics experts. By applying a socio-technical approach as a theoretical lens, we aim to answer the following research questions: How is the implementation and application of HR analytics shaping up in firms? What challenges and barriers do firms face on their journey towards HR analytics?

2 Theoretical background

To answer our research questions, we apply a socio-technical approach. Socio-technical system theory assumes that new systems can only be successful if both the technical and the social system are considered, analyzed and brought into harmony with each other on an equal footing (Cherns 1976 ). The social system describes the people in an organization and focuses on their needs, relationships and qualifications within the organization. The technical system, on the other hand, often describes novel technological artifacts used to accomplish tasks (Jaffee 2001 ; Mumford 2003 ). The socio-technical paradigm is a holistic view that examines the relationships between the social and technical levels of any system (Trist and Bamforth 1951 ; Coakes 2002 ). Socio-technical design emphasizes the need for an optimal match between the technical and social aspects in terms of the relationship between jobs and people's needs and expectations (Biazzo 2002 ). As discussed earlier, understanding HR analytics from a systematic process perspective has gained importance (e.g. Falletta and Combs 2021 ). In the literature to date, there are initial approaches to linking the socio-technical approach in the context of HR analytics (Belizón and Kieran 2021 ). Nevertheless, this can be classified as rather novel. Thereby, the goal of HR analytics is to enable organizations to achieve their strategic objectives. Since HR analytics comprises more than the introduction of software for personal data analysis, it requires a more holistic approach rather than a traditional IT project approach. Often, projects of this kind fail not because of the technology, but because of a lack of consideration of the mutual interactions of the social and technical system. Maucher et al. ( 2002 ) show that soft factors such as communication, cooperation and the inclusion of informal structures can contribute significantly to the successful implementation and application of IT projects.

Following the socio-technical system theory proposal we analyze the extent to which the technological side, i.e. the artifact needed for the data analysis and the social side, i.e. the organizational structures of a company with a large number of different stakeholders, need to be reflected by HR analytics. We conducted a literature review and classified the challenges we found for the implementation and application of HR analytics within the sociotechnical perspective into the two categories of social and technological. This involved searching for peer-reviewed journal articles that address challenges in the implementation and application of HR analytics. For this purpose, the following search terms were used to identify relevant articles: "HR analytics"; "People analytics"; "Human resource analytics"; "Workforce analytics"; "Data-driven HR" in combination with "challenges"; "difficulties"; and "barriers". The focus in the selection of the respective journal articles was on the thematization of concrete examples of implementation and application difficulties. In doing so, we were able to identify four core areas (see Fig.  1 ) that influence the implementation and application of HR analytics from a systematic process perspective.

Core areas influencing HR analytics

The first of the areas of the technological system we examine involves the data, which represent the elementary cornerstones for carrying out the analyses (Douthitt and Mondore 2014 ; Pape 2016 ). One is the quantitative aspect of analyzing whether or not the necessary HR databases with the associated data sources exist for the use of HR analytics. On the other hand, it must be seen whether the existing data meet the qualitative requirements in order to be able to carry out valid analyses (Jeske and Calvard 2020 ; King 2016 ; Minbaeva 2018 ; Pape 2016 ). Previous research shows the need for the integration of additional data from different areas of the company into the analysis (Marler and Boudreau 2017 ; McIver et al. 2018 ; Rasmussen and Ulrich 2015 ). To do so, interface compatibility plays an important role (Andersen 2017 ; Boudreau and Cascio 2017 ; Douthitt and Mondore 2014 ; Levenson and Fink 2017 ). Angrave et al. ( 2016 ) emphasize this by warning against data silos formed within the individual departments leading to a lack of data exchange. Indeed, the lack of available data has been identified as one of the main obstacles to the successful implementation of analytics, especially in small and medium-sized enterprises (Pape 2016 ).

The second area at the technological level comprises the technology itself i.e. which software and hardware solutions are available to users (Angrave et. al 2016 ; Aral et al. 2012 ; Boudreau and Cascio 2017 ; Douthitt and Mondore 2014 ). A lot of firms still stick to simple spreadsheet programs such as Excel for data analysis (van den Hauvel and Bondarouk 2017 ), even though in recent years the number of other tool providers, offering a wider range of functions for data analysis, has increased. However, so far, the available tools for predictive and prescriptive HR analytics are developed by and aimed at people with analytical skills, not HR business partners (Fernandez and Gallardo-Gallardo 2021 ). This is also shown by the results of our descriptive study, in which we found that the available software solutions are perceived to be too complex. More precisely, software solutions for the application of HR analytics are not tailored to the competencies of the users and, thus, will need to be much more user-friendly (Marler and Boudreau 2017 ). The application of the technology, i.e., software solutions, therefore depends strongly on the respective competencies of the HR business partners or the users of HR analytics in a company.

This assumption simultaneously emphasizes the interconnection of the technological with the social system. A strong social system lays the foundations for the implementation and application of HR analytics. Within the social system, we first focus on the HR business partner as a user of HR analytics (Bassi 2011 ; Mondare et al. 2011 ; Rasmussen and Ulrich 2015 ; Angrave et al. 2016 ). It is generally agreed that one of the main reasons for the low use of HR analytics is the lack of analytical skills (Angrave et al. 2016 ; Marler and Boudreau 2017 ). However, these analytical skills are an elementary prerequisite for performing HR analytics (Andersen 2017 ; Douthitt and Carson 2011 ; Huselid 2018 ; Kryscynski et al. 2018 , Marler and Boudreau 2017 , Minbaeva 2018 , van der Togt and Rasmussen 2017 ). Prior studies have analyzed that individuals working in HR are not primarily interested in operating with key figures, statistical methods or data analysis (Rasmussen and Ulrich 2015 ). Fernandez and Gallardo-Gallardo ( 2021 ) emphasize that the analytical skills needed to apply HR analytics will increase in the future. Thus, a wider range of basic statistical methods in the individual maturity levels of data analysis (reporting, descriptive, predictive, prescriptive) and analytical competencies in data collection and data management are considered important to HR analytics (Levenson 2011 ). Because of the advancing increase in artificial intelligence and its methods such as machine learning, we assume that HR business partners will be required to continuously learn and extend their knowledge (McIver et al. 2018 ). The personnel development measures required for this in turn have a positive effect on the attitude toward HR analytics and increase the individual's self-efficacy (Vargas et al. 2018 ).

The intra-organizational context is also relevant in explaining the influence of the social system on the implementation and application of HR analytics. First, the focus is on the different stakeholders involved in the process (Coco et al. 2011 ; Giuffrida 2014 ; Levenson 2011 ; Rasmussen and Ulrich 2015 ). These can be divided into HR business partners, management, employees and analysis teams (Huselid 2018 ; Peeters Paauwe and van de Voorde 2020 ). In previous research, there are few findings about which stakeholders are involved in the process of HR analytics (Coco et al. 2011 ). However, we argue that our analysis shows they do not look at the respective roles and relationships in the implementation and application of HR analytics in an organization. Second, there are two perspectives that explain how HR analytics should be embedded within the organization, i.e. outsourcing and integration (van den Heuvel and Bondarouk 2017 ). The two different perspectives can be characterized as follows: In outsourcing, the analyses are carried out by experts in the analysis area and not by the actual HR business partners. The HR analytics function operates independently alongside the traditional HR function (Fernandez and Gallardo-Gallardo 2021 ; Rasmussen and Ulrich 2015 ). In the case of integration, an attempt is made to strengthen the competencies of the HR business partner and to perform the analyses within the HR function with the help of HR analytics. Outsourcing HR analytics from the HR function is supposed to align HR analytics more strategically (Rasmussen and Ulrich 2015 ). In contrast, its integration into the HR function (Angrave et al. 2016 ; Bassi 2011 ; Falletta and Combs 2021 ), will enable HR to strengthen its own strategic role. Additionally, it is argued that the expertise of HR business partners in the respective functional areas is needed. If they are not directly integrated into the process, the usefulness of the analysis of HR-specific issues can only be assessed to a limited extent (Andersen 2017 ).

Having presented our framework, we follow Greasley and Thomas ( 2020 ) call for further empirical analysis of analytics projects to conduct research in HR analytics with a focus “on the process of development rather than its outcomes” (Greasley and Thomas 2020 : 506).

3 Methodological approach

In order to gain deeper insights into the implementation and application of HR analytics, we conducted a qualitative study with 17 HR analytics experts from the DACH region. Our study aims to understand the process of implementing and using HR analytics in more detail Therefore, we use a qualitative research approach, which is particularly suitable for investigating topics that have been little empirically researched so far and require a deeper insight into situational conditions. Moreover, qualitative research also lends itself specifically to the representation of organizational processes, as one can derive important information about social interactions and causal relationships from the depth and variety of data obtained (Graebner et al. 2012 ).

Hyde ( 2000 ) notes that the information content in qualitative research is based on the depth of the interviews and even the knowledge of one person, if the rules of qualitative social research are followed, can provide insightful knowledge about complex issues (Hyde 2000 ). Thus, the identification of the experts was the first crucial step in our study. A targeted search was conducted via job-related social networks for job titles that included the competence profile HR analytics, people analytics, workforce analytics or HR executives who mentioned working with data in the HR management in their competence profile. To make sure that only HR analytics experts take part in the qualitative study, the selection of participants study was based on the following criteria:

The interviewees explicitly deal with the topic of HR analytics in their company and already have experience in its implementation and application.

The respective maturity level of the application of HR analytics (reporting, descriptive or predictive) in the company played a subordinate role in order to gather as much experience as possible.

The extent of the professional experience with HR analytics of the interviewees also played a minor role, as many firms are only in the early stages of HR analytics.

Each interview was conducted using a semi-standardized guide (see " Appendix "). The basis for this was the previously deductively formed socio-technical framework with the four categories of data, technologies, personnel deployment and organization. The interviews were then transcribed and analyzed using the atlas.ti software. A total of 200 pages of transcribed interview data were collected. The interviews lasted an average of 45 min. The interviews were conducted and transcribed in German. The results of the qualitative content analysis were translated into English.

Figure  2 illustrates our methodological approach. We proceeded in three steps, which are explained in the following. At the beginning of the interview, it was important to capture the interviewees’ understanding of HR analytics, given that there is no uniform definition of HR analytics. Therefore, the interview partners were asked about their task profile and the current state of application of data-driven decisions in their respective firms. This enabled a classification of the status quo in the subsequent analysis of the interviews (see 1st methodological step). Thereupon, specific questions were asked about the current application of HR analytics. In qualitative research, two general approaches can be distinguished: on the one hand, the frequently used inductive approach, which creates a generalization on the basis of specific observations. On the other hand, there is the deductive approach, which tries to transfer generalizations to a specific case (Hyde 2000 ). We initially have chosen a deductive approach according to Mayring ( 2014 ) and a category system was developed on the basis of the factors that have already been derived in our framework as influencing the implementation and application of HR analytics. In a first step, definitions for the individual categories were assigned and suitable anchor examples and coding rules were determined (see Table 2 ).

Three-step methodological approach

The individual interviews were then analyzed and individual text passages could be assigned to the respective categories on the basis of the predefined rules. Based on this, we structured our analysis along the four categories to identify the problems and challenges mentioned by the interviewees. This process allowed us to identify specific aspects within the theoretically developed framework of existing requirements for the implementation and application. In addition to the four deductive main categories of data, technology, HR business partner and organization, further subcategories were inductively formed in the next step. In contrast to deductive coding, inductive coding is based on the principle of open coding. This means that the respective statements of the interviewees are openly coded in the first step and that more aggregated categories emerge from the raw data through repeated examination and comparisons. Ultimately, this allowed for the formation of further subcategories: database, application area, tools for analysis, tools for the provision of the results, current role model, future role model, management support and added value (see 2nd methodological step). Building on the category system, we then specifically searched for patterns of interaction and process within these categories in order to focus on the systematic process perspective (see 3rd methodological step). We proceeded as follows: The interview material was examined one more time for statements describing interaction processes between individual actors in the context of HR analytics. In coding, we defined interaction as the mutual influence between actors. The focus was on the communication and action processes described by the respondents. For example, the following statement describes the provision of analysis results from the HR analytics function to HR business partners.

So the skillset definitely has to grow and we are also in the process of taking the first step and want to make that available as a service, where now the HR business partner, for example, only has read access (Interview 3).

Upon further coding, additional statements could be found that confirmed this process of providing analysis results. In this way, consistent patterns of interaction between different stakeholders could be derived from the individual statements of the interviewees and presented aggregated in the form of a model. This model we derived represents the status quo of HR analytics from a process-oriented perspective.

4.1 Current state of application of HR analytics

We begin the presentation of our results with a brief description of the current state of application of HR analytics in the respective firms. In doing so, we highlight the general understanding of HR analytics and address the changes within the processes of HR. In the next step, we present the aggregated findings from the respective categories, which are based on our framework data, technology, HR business partner and organization. Table 3 provides an overview of the 17 interviewees.

Our analysis of the current state of application resulted in the majority of respondents still being in the early stages of using HR analytics. Many of the firms are currently in a start-up phase, which is characterized by conducting more in-depth descriptive analyses and answering isolated predictive questions. The analysis of how the interviewees define the term HR analytics is characterized by a uniform understanding. HR analytics is a strategic tool that offers the possibility to steer and influence actions and decisions in the HR context on the basis of analyses. The interviewees emphasized that one does not rely exclusively on these analysis results, but that they should be considered as decision-supporting. Rather, the analyses with the help of HR analytics are intended to stimulate the HR business partners to take a deeper look at topics, as one of the interviewees emphasized:

So, there's also a lot of show and tell in the collaboration with HR staff, rather than them sitting down, doing some calculations themselves and thinking afterwards, okay, that'll get us XY. Yes, it's a people business, and it's also a very emotion- and perception-driven business. And then you can use good and relevant analytics to consistently influence people's perceptions and actions (Interview 9).

4.2 Delimitation HR controlling

The goal of HR analytics is to use the methods applied to HR data to check the effectiveness of HR measures and ultimately identify levers that can contribute to improving processes within HR and also the entire company. In particular, the interviewees underlined a demarcation from classic HR controlling. While HR controlling mainly summarizes key figures on historical data and provides relevant groups with information, analyses with the help of HR analytics aim at looking at these key figures in more detail, deepen them and apply them in future-oriented decision-making processes. Our findings underlined that this process is not solely based on predictive HR analytics. In contrast, the interviewees highlighted the potential of descriptive HR analyses, while at the same time emphasizing that it is difficult to manage the next step towards predictive analyses. Additionally, it is found that it can be difficult to draw a line between HR analytics and HR controlling given that the boundaries are sometimes blurred. For illustration, one of our interview partners emphasized that in some cases HR analytics is understood as the automation of reporting processes.

I think it's a big problem. Because then you are actually leading the absurdity of what this three-pillar model is supposed to achieve. And everything that people analytics is supposed to stand for, namely to really bring a benefit to the business and not just to run some purely administrative evaluations somewhere. Yes, definitely. Unfortunately, that's what many organizations have done (Interview 14).

Based on the initial maturity level in which the interviewees find themselves with regard to the implementation of HR analytics in their firms, it can generally be stated that HR analytics is divided into standardized advanced reporting, which has a descriptive character, and project-related questions, which go deeper in the type of analysis. Table 4 presents the results of the qualitative content analysis in condensed form. These results are based on the coding of the interviews and are explained in more detail below.

4.3 Technological system: data

4.3.1 database.

Our data showed that the data basis is an important challenge for the use of HR analytics. However, it should be noted that firms with a well-functioning HR controlling system have a solid database. These are standard data from the HR management, such as fluctuation figures, sick leave, master data, salaries, etc. One interviewee pointed out that especially firms with little or weak digitization of processes have to struggle with data quality problems. It was pointed out that one of the most important first steps in the implementation of HR analytics should be the creation of an all-encompassing and aggregated HR system, otherwise one is busy with manual data cleansing, especially in the beginning.

If you have an outdated HR system or an old SAP HRM system, you are extremely immobile in the use of the data because you can only get it out with difficulty or not necessarily in the format you would like to have and then you are already back in this operations trap. That is, you come up with a great dashboard, great use case and build it within a week and then you have to download a report every week for the rest of your life, pull it in, maybe clean it up a bit. And these are exactly the pitfalls that you run into (Interview 9).

Our findings showed that firms have a hard time with data integration in particular because HR does not store its data centrally in an enterprise data warehouse, as many firms do out of caution. Consequently, business intelligence topics have also not found access in HR for a long time, which results in a poor-quality database.

4.3.2 Application areas

Our analysis reveals that even though HR analytics is applied in different HR functions, all interviewees emphasized that recruiting can be identified as the most effective area. On the one hand, this is due to the large number of data records generated by applications, which makes it possible to develop valid prediction models compared to other application areas. On the other hand, recruiting is also seen as having the greatest potential for highlighting the added value of HR analytics given that it is a major cost factor for many firms. However, it has also been pointed out that rather “new” topics are suitable for data-based analyses, as the attitude toward these newer topics has not yet solidified in the minds of those involved. One interviewee highlighted that this is specifically the case for diversity, i.e. issues such as equal pay, women's quota, and severely disabled quota, as these are high visibility issues where companies are generally more open to learning more.

Because these are fields that have only been established in this way for a few years. And that's where the knowledge has to be built up. So, there is just less gut feeling or felt gut feeling and therefore more room for such analyses (Interview 1).

When using HR analytics, the potential benefits of the individual questions in the application areas should be considered. For instance, one of the interviewees emphasized that the economic benefit should be kept in mind during the analysis.

I [think we] consider far too little in HR analytics or people analytics, that we align ourselves with the business problems (Interview 6).

For a small company with a large turnover, an analysis of the reasons for turnover can create a relatively large added value, whereas on the other hand a large production group, with an identical turnover has a saving that is not significant, but a potential analysis to improve the ergonomic working conditions on the assembly line can increase productivity.

Whereas the orientation of analyses in the business context is crucial, another interviewee also underlined that especially in the beginning the process of quantifying all possible processes in HR management can contribute to developing a feeling for dealing with numbers and can create an orientation to include analyses in the decision-making process.

So everything that is really purely statistical figures first of all in the personnel area. That's good. To be honest, I think it's also important because it helps you to awaken a bit of an affinity for numbers or a feeling for them. But anything that goes beyond a standard evaluation, I would always tie to a concrete business case (Interview 14).

One interviewee also noted that " you don't want everything you can " (Interview 12). He emphasized that one must consciously look at whether the analyses make sense for the respective industry and really lead to an increase in effectiveness.

4.4 Technological system: technology

4.4.1 tools for analysis.

Besides the data, another technological aspect is the technology itself, i.e. the analysis software used. Here, a largely homogeneous picture emerged among the interviewees. Tools such as Tableau or PowerBI were used for the actual analysis. In rare cases, additional work was done with R or Python. This is mainly the case in firms, which already carry out more advanced analyses. Two firms worked with external analysis tools. One of these is an external software manufacturer that offers a stand-alone HR analytics tool and the other is an integrated analysis function of the HRIS.

Other interviewees were rather critical of the use of external tools, as there is no direct insight into the analysis methods. As an example, tools were cited that offer e.g. speech analyses of interviews that are supposed to predict suitability without providing valid evidence for this. The use of such tools has a counterproductive effect and stirs up fears. Another aspect that has been criticized about external tools is their limited flexibility. An individual adaptation to the circumstances of a company is only possible to a limited extent. Predefined standard use cases may be applicable, but they often cannot be specifically adapted to the particularities of the company structure. This is particularly important in HR management, as it is characterized by a high degree of variance, as the following interviewee noted:

There is a lot of variance in what an organizational structure looks like. Do you have double tops or just single tops, do you have a pyramid or a cell structure? These are all issues that have an extreme influence on the data model that you have to import into such a system. And for the fact that I pay relatively a lot of money for relatively simple dashboards that come out of it, I think that's pretty meager (Interview 9).

4.4.2 Tools for the provision of the results

With regard to the question of the technology to be used, a differentiation must be made between the process of the actual analysis and the provision of the results of this analysis. This is strongly related to the understanding of roles in the HR analytics process. This aspect will be discussed in more detail in the course of the results. The presentation of analysis results is provided to the respective addressees via a tool such as Tableau. Here, the interviewees saw the focus above all on ease of use. The simplicity and instinctive approach were emphasized. One of the interviewees pointed out that it is precisely this simplicity that also empowers and motivates people to work with metrics. Furthermore, the flexibility for visual representations was emphasized, which is particularly important for HR management in order to provide HR business partners with a more comprehensible approach to the subject matter.

When choosing the respective tools, it is necessary to define in advance exactly who plays which role in the HR analytics process, as the following interviewee pointed out:

So, we say, you can introduce the best tool if just this, the use of the tool is not clear. So, if the end-user is not clear where he is going to use this (Interview 3).

4.5 Social system: HR business partner

Another aspect in the investigation of our data was the HR business partner. Here we were able to identify the current role of the HR business partner and the problems associated with it. We were also able to identify the extent to which the understanding of the role must change in the future for the effective application of HR analytics.

4.5.1 Current role model

If we first look at the statements regarding the competencies of the HR business partners, it became clear that the analytical competencies of the HR business partners were predominantly assessed as poor to barely present. In the eyes of the interviewees, the HR business partner is not the one who carries out in-depth analyses. Our analysis showed that there are three main reasons for this.

First, one of the reasons lies in the nature of HR. The background of working in HR is often different from working with data and key figures, so many of the current HR business partners have avoided basic statistical subjects already in their studies and thus have not developed a connection to data-based analyses in the course of their professional life. Secondly, there is a lack of understanding and rejection of working with data in HR. The third aspect lies in the number of operational tasks and lack of time highlighted by the interviewees. Even if HR business partners are willing to build competencies in the area, this often does not happen due to time constraints.

So it's both the skills they have today and the lack of time to build the skills because they have to deal with „Old Work“ every day (Interview 6).

Even in the long term, the interviewees do not see the HR business partner carrying out the analyses with HR analytics. One interviewee cynically noted that a " new species of HR employees must first be born " (Interview 7). Even with advanced competencies in the necessary statistical methods, decentralized performance of analyses by different HR business partners is seen critically. By using different data sets and different methods to conduct the analysis, different people can come to different results: " there were three different people who gave three different results " (Interview 8). The interviewees therefore strongly emphasized the need for a central implementation of HR analytics. This can be described as a vicious circle: administrative tasks continue to dominate the task profile of HR employees and thus there is no time for further training in strategically oriented methods such as HR analytics, which should actually provide relief for operational work. Even in the long term, the interviewees do not see that HR employees will be able to conduct analyses on their own.

4.5.2 Future role model

Our analysis revealed that the HR business partner will have to take on a different role in the future than that of the analyst. The interviewees emphasized that the HR business partner must develop a stronger sense of working with analytics results in the future. Above all, the interviewees highlighted the function as a mediator and consultant between the HR department, the specialist departments and the HR analytics function. In particular, our findings showed that the implementation of HR analytics is still seen as a centralized independent function. Thereby, the task profile will change due to the closer cooperation with the HR analytics function in the sense that there will be a closer exchange between HR management and the specialist departments. In the future, HR business partners will increasingly take on an advisory role based on the analyses carried out by HR analytics. On the one hand, they should record the requirements of the specialist departments with the help of the necessary HR expertise and communicate these to the HR analytics function in a comprehensible way.

They take the requirements from the business department and then translate them into IT. And in my eyes, something like this is also missing in HR, where a business analyst takes the requirements from HR, so to speak, and then makes them available to the data scientist. That role between the different stakeholders that are involved in the process of a data-based analysis (Interview 15). We have said that we do not want this analysis as a service, but we would like to participate in the process because we also want to build up the know-how. Yes, and we work together within this framework. We personally have an interpreter function. That is, there are the data scientists who bring the methodology, who build the tool. On the other hand, there is the specialist department, which would like to know what kind of statements are contained in these free-text comments, and we as People Analytics are the interpreters between both worlds and of course also use this for us to build up the know-how (Interview 3).

Our interviewees highlighted, that the future role model must also change in such a way that the old understanding of HR work changes. Working with data and people must not be mutually exclusive in the minds of HR business partners, but must be thought of as a unit. At the present time, this is not yet possible:

Even if it's not explicitly stated: this caveat, when we, when we talk about people, we shouldn't do it in a quantitative way. In terms of feeling, that's something that resonates very often (Interview 1).

4.6 Social system: organization

4.6.1 management support.

The interviewees outlined that the role of management is a key aspect of the implementation of HR analytics. When introducing HR analytics on the part of HR, it must be ensured that management is also committed to it.

But it is usually not enough if somehow only one, yes, a sub-department head somewhere says I would like to do the whole thing. Then it fails with a sometime at the latest at the point when the whole thing is presented to the board or something else because they do not consider the whole thing so important yet (Interview 14).

Two key aspects could be identified. First, management plays an important role in legitimizing HR analytics. In-depth analyses are often initiated by management and the results are also fed back to management. However, this also clearly limits the implementation of further analysis projects.

But that is the reason why we have the backing, so to speak, for the projects that we then do. You can look at it the other way round and say that we only do the ones where we have the backing. There aren't many of them, it has to be said (Interview 1).

Secondly, one interviewee also pointed out that management's conviction must also be viewed critically. Trend topics such as data-based analyses in particular only deliver added value if they are understood in their entirety and are not just introduced because it is the latest trend.

But currently, it's really still a lot: this is a trend, I have to jump on it. Data Science in general and the whole artificial intelligence topic is so hyped and there are a lot of articles about it, which the management has picked up somewhere and then they have to do something about it, but just not this, this good understanding of what that actually means for such an HR department or where a department actually stands right now (Interview 8).

In addition to management support, the use of HR analytics also requires management to actively demand work with key figures. This means that management must demand more work with data from HR. This must be done alongside strengthening the understanding of working with data. Demanding analytics results from HR business partners thus additionally contributes to making working with HR analytics more natural for HR business partners. At this stage, the additional involvement is seen as another time factor. It should be noted, however, that according to our analysis the roles in the process of conducting HR analytics are not clearly defined, i.e. the management also does not have a clear contact person for the final analysis results.

4.6.2 Added value

One aspect that the interviewees considered particularly difficult to realize in the implementation of HR analytics is the recording of the potential added value respectively the representation of this in monetary key figures. This is particularly difficult because the causal effects of the analyses can only be clearly proven in the rarest of cases. The added value is usually justified, if at all, by time-saving or an increase in employee satisfaction. Another aspect why the added value is not yet captured is the novelty of the topic in the organizations. The interviewees were aware of the need to demonstrate added value, especially to management, but at this stage, they are focusing on conducting analyses. Projects are carried out, which are also approved by the management, since the skepticism is large here, as evidenced by the following statement:

So, the skepticism in this regard is huge. That has to be said very, very clearly. But that is the reason why we have the backing, so to speak, for the projects that we then do (Interview 1).

One interviewee critically noted that the justification and legitimization for the use of HR analytics is flimsy. Data-driven decision-making in HR management is what other corporate functions have had firmly anchored in their structures for many years, and it brings significant benefits there.

Above all, people analytics is a tool to show where inefficiencies are, the alternative to running people analytics is not running it and not knowing what's going on. That's just it, you wouldn't do that in any other area. And in other areas you wouldn't say, do we really need marketing analytics? Of course, you do. And consequently, this retroactive and block position: 'Well, does it really do anything?', I always find a bit flimsy (Interview 9).

To date, none of the interviewees has specifically established a structural process in the sense of tracking the effectiveness of the analysis results and the derived measures.

5 Systematic process perspective

After examining the results of the qualitative analysis, we also carried out an analysis of the findings from a systematic process perspective. Figure  3 shows the process flow of the implementation and application of HR analytics. Our findings reveal that HR analytics in its more structural anchoring and organizational function is not part of the HR department, but operates autonomously alongside the established HR management as a service provider for various internal customers, including the HR department. When considering the implementation of HR analytics, the first thing that stands out is that most firms tend not to involve HR managers directly in the application. Employees who are responsible for HR analytics are mainly not members of the HR department but belong to departments with a statistical background, such as data scientists, business psychologists or sociologists. Depending on the size of the HR analytics team and the given resources of the respective company, manpower and/or the knowledge from departments with data affinity, e.g. Data Science, are used.

Process model of implementation of HR analytics: status-quo

This service character of HR analytics is emphasized by many interviewees and justified, among other things, by the lack of competencies and understanding of numbers on the part of HR managers. The finding of the role of a service provider highlights the critical issue that, in particular in the collaboration with HR, the tasks of HR analytics are not clearly specified. The implications of this missing clarification of responsibilities are twofold. First, it will hinder the successful implementation of tools supporting HR analytics. Secondly, this leads to the added value of the analysis so far not being captured. It is important to define who is ultimately the recipient of the analysis results and who communicates them within the company. The results have shown that the HR analytics function on the one hand directly communicates results to the specific departments or management. The HR management receives these results partly only as information in the form of key figures in a dashboard.

A systematic process for recording the added value of the measures or the implementation of the measures does not exist. A discussion of the results on the part of HR management and the specialist department also often does not take place due to the lack of understanding and access. If we summarize statements regarding existing data, firms with existing HR controlling in particular benefit from historically grown preparatory work. Ultimately, HR analytics at the various levels of maturity often requires the same data as traditional HR controlling. The problems of data management are rather related to the technology factor, as it was pointed out here that especially outdated systems force one to deal more comprehensively with the constant data preparation. The aspect of data in the context of the implementation and application of HR analytics can thus generally be regarded as a factor that takes time but is often given in terms of quantity, insofar as the basic conditions are already present in a company. Angrave et al. ( 2016 ) highlight that the HRIS systems in use do not provide the necessary analysis capabilities. This point should be viewed critically, as our results have shown that in the short to medium term, the HR business partner will not be the user of the analysis either. It is also not expected in the long term that the role of the HR business partner will be sharpened in such a way that it can perform and understand data-based analyses. Rather, interviewees see the HR business partner involved in the sense that HR expertise from an HR analytics perspective is needed for targeted analyses. The technology question in this sense does not arise at all from this point of view of the methodological possibilities, but rather how the analysis results can be visually represented by another tool such as Tableau. The actual analysis, carried out by the HR analytics function, will draw on its methodological knowledge and adept programs with its competencies in the analysis.

6 Discussion

Falletta and Combs ( 2021 ) have noted that despite the interest in the topic of HR analytics, the actual knowledge about it is still in its infancy. This starts with the lack of a common definition and ends with a lack of knowledge about the processes of applying and implementing HR analytics in an intra-organizational context (Falletta and Combs 2021 ; Greasley and Thomas 2020 ). The aim of our research was to examine the implementation and application of HR analytics in firms. By applying a socio-technical approach we have developed a theoretical framework which integrates four areas impacting the implementation and application of HR analytics. This framework guided our qualitative research study, which resulted in a more nuanced understanding of the facets of HR analytics as well as its implementation process. The conclusions we have reached from the results of our study will be discussed as follows: first, we will reflect the results of our qualitative study in light of the socio-technical system perspective. From a practical perspective, we then propose a process model for the future application of HR analytics.

6.1 Theoretical implications

In sum, our results showed that there is a common understanding of the future use of HR analytics. This implies the improvement of HR-related decision-making by using a data-driven approach, which aims at achieving strategic business goals. However, a lot of HR analytics analyses are currently in the early stages and are partly project-based or prototype-based. Thereby, firms still face many challenges. This is in line with the findings of Fernandez and Gallardo-Gallardo ( 2021 ) emphasizing that firms need to overcome organizational barriers with regard to HR analytics. Our study contributes to this important endeavor by examining the barriers of the social and the technical system:

Our first observation relates to the use of available data and technologies for the application of HR analytics. The availability of data and data integration is considered one of the most important factors in the implementation of HR analytics (Halper 2014 ; Pape 2016 ). Our findings confirm this important factor. It should be noted, however, that the interviewees see data integration as a rather operational task, which primarily requires time resources rather than competencies. The provision of the necessary data depends primarily on the level of technologization and the HRIS system used specifically in HR. We did not find any issues regarding the use of external data. However, this may also be due to the fact that many of the respondents are just beginning to explore their options and have not yet conducted analyses that require a deeper data set. It can be noted that the current level of maturity does not meet the demands of some authors who call for a holistic HR analytics function that also includes departments such as finance, production, etc. (Rasmussen and Ulrich 2015 ; Marler and Boudreau 2017 ; McIver et al. 2018 ). The picture is similar for the technology used for analysis: our interviewees have advanced skills, so applying the necessary skills is not a problem. However, it turned out that the issue of technology is much more related to the delivery and visualization of the analytics results rather than to the actual analysis tools. A similar conclusion was also reached by van den Hauvel and Bondarouk ( 2017 ). The authors emphasize that HR analytics goes beyond mere analysis and requires convincing visualization and presentation. (van den Hauvel and Bondarouk 2017 ). Our findings have shown that there is more of a concern with creating awareness that the HR business partner is working with these analytics results and using the dashboards provided (Vargas et al. 2018 ). It might be argued that the previous data and technology challenges were viewed from the perspective of HR business partners as users of HR analytics. However, our study has shown that they are not the actual people who perform advanced analytics using HR analytics. The HR business partner is much more understood as a customer in this process (Jörden et al. 2021 ).

We now turn to the discussion of the results of the social system, i.e. the analysis of the role of the HR business partner and intra-organizational context. Previous research has identified two different approaches to embedding HR analytics in the organization. On the one hand, HR analytics is seen as an independent function that uses the potential of data scientists (Rasmussen and Ulrich 2015 ). On the other hand, it is argued that an integration of HR analytics in HR management is worthwhile (Bassi 2011 ; Falletta and Combs 2021 ).

Our results have shown that the preferred path of the firms in our study is the former and that a new stand-alone HR analytics function is emerging alongside the HR function itself. This may be due to the fact that analytical skills in HR are not sufficient to apply HR analytics independently (Angrave et al. 2016 ; Marler and Boudreau 2017 ). Although our results have shown that HR itself can also be identified as the initiator of HR analytics, the function does not emerge within HR but is carried out by individuals who inherently bring the necessary competencies. This approach can be promising but runs the risk of leaving out HR managers who have been active to date. Ultimately, this can lead to competencies being built up in silos in the long term and not being taught throughout the HR sector. To prevent this, a holistic approach is needed. HR analytics should not be considered as a stand-alone function of the HR value chain but should map it across the entire functions of the HR value chain.

Another important factor that plays a role is the recording of the added value of HR analytics. Our findings have shown that many analytics results are communicated to the respective addressees, e.g. the specific departments, HR or management without an accurate evaluation of the effectiveness or efficiency of these measures afterward. However, one of the core objectives of HR analytics is to improve organizational productivity and employee experience (Tursunbayeva et al. 2018 ). This lack of evaluation also means that HR management's own strategic strengths fall short of expectations and thus the reasons for the legitimacy of HR analytics cannot be optimally communicated to management (Bassi et al. 2012 ). As shown in our status-quo process model (see Fig.  3 ), HR analytics is currently acting autonomously as a service provider. This finding is in line with the observations of Jörden et al. ( 2021 ), who also found in an ethnological study of a people analytics team that HR analytics was “primarily driven and restricted by customer requirements, and as a consequence PA as a specialist professional HR practice was undermined by a lack of managerial commitment to technical quality “(Jörden et al. 2021 : 11). Especially management as a customer is a double-edged sword: our study has shown that the legitimization of HR analytics is a decisive factor in the implementation of HR analytics. On the other hand, Jörden et al. ( 2021 ) see management as a critical factor in this respect, as it can also undermine the possibilities of HR analytics by only carrying out analyses that are demanded by management.

Moreover, we know little about which measures are ultimately implemented by the individual addressees from the derived analysis results (Ellmer and Reichel 2021 ). The lack of evaluation of the derived measures leads to the fact that the seemingly relevant business context of the analyzed issue cannot be clearly evidenced (McIver et al. 2018 ). In the future, HR analytics must have more legitimacy grounds than those of management. This means closer cooperation between firmly integrated functional areas and HR so that HR analytics does not become an end in itself and can bring the promised added value (Rasmussen and Ulrich 2015 ).

In summary, it can be stated that there is a need to better understand HR analytics from a process perspective. This implies to define the different internal stakeholders which are involved in the HR analytics process and to cover their respective ideas and wishes. In line with Ellmer and Reichel ( 2021 ) our findings show that the role of the HR business partner needs to be defined more clearly in the future. Also, the required understanding and affinity for the work with data and data-based analyses can only succeed if the scope of responsibility of the HR department is clearly defined. A sharpening of the role of HR can help to clarify whether HR analytics promises the actual added value, i.e. an increase in HR’s strategic alignment in the organizational context (Greasley and Thomas 2020 ). Still, it is difficult to implement a purely autonomous execution of HR analytics without the involvement of traditional human resource management (Bassi 2011 ). Thus, the targeted communication of the necessary competencies and the definition of the areas of responsibility for HR analytics is a necessary step that firms will have to take to ensure the effective application of HR analytics. In this regard, Falletta and Combs ( 2021 ) don’t position HR analytics as a separate function but argue that it should be located directly in HR management. This contradicts previous research which concluded that HR analytics should be a permanent function (Rasmussen and Ulrich 2015 ; Ulrich and Dulebohn 2015 ).

6.2 Managerial implications

In light of our findings, we argue that in order to strengthen the practical implementation and application of HR analytics the following aspects can be defined as adjusting screws from a process perspective: At a social level, there is a need for a clearer clarification of roles in the intra-organizational process of HR analytics. At the technical level, one needs to be aware that adapted software solutions have to fit the respective competencies of the HR business partners. Based on this we propose a process model for the future application of HR analytics (see Fig.  4 ). Our study has shown that it is not necessary for an HR business partner to have the skills to carry out analyses independently. Rather it is crucial to develop an awareness and speak the language required to understand these analyses and discuss them with the other departments in the next step. A discussion of whether the users of HR analytics have a statistical and business background or rather a social, behavioral and organizational sciences background (Falletta and Combs 2021 ) is not expedient. The following applies here: many solutions lead to the goal; ultimately, it is important that the analyses are based on valid methods and are goal-oriented from the perspective of HR management. In order for an HR business partner to be able to work with these analysis results, more user-friendly software solutions will be needed in the future. Again, it is not a question of carrying out the analysis itself, but rather of providing the necessary information in a targeted manner in order to enter into an exchange with the specialist departments. The role of the HR business partner as a future consultant is seen as particularly important as the previous way of communicating the results of the analyses is not precisely defined: If it is possible for the HR department to make decisions in cooperation with the specialist department on the basis of analysis results, this is also reflected in the communication with management. This in turn has a positive effect on management's attitude toward HR. In the long run, the closer integration of HR analytics and the HR business partners can lead to achieving the actual goal of strengthening the strategic role of HR. For these reasons, we advocate a multidimensional role model for the application of HR analytics.

Supposed process model for the future of implementation of HR analytics

7 Limitations

The study has some limitations. First, the results are based on the conduct of qualitative research. This methodological approach of qualitative research naturally entails some limitations. The results obtained cannot be generalized to a broader population with the same degree of generalizability. However, statistical analysis of the results is also not intended, as the goal of qualitative research should be to gain new insights based on experience and detailed descriptions. We recommend that our proposed model of HR analytics implementation be tested in the future using descriptive and observational studies. Since HR analytics is an interface function between HR and business informatics, one possible approach would be to apply design science research from business informatics. The approach starts from an application-oriented problem, on the basis of which an IT artifact is created and tested in several iterative steps. Future research could target approaches here and investigate different IT artifacts to explore the implementation and application of HR analytics more deeply empirically.

Second, the findings of our study are based on interviews with HR analytics experts. These experts have been chosen according to a clearly defined set of criteria. Given that the implementation and application of HR analytics is still in its infancy in most of the firms, the knowledge base of interviewees is at a beginner or mediocre level. To gain more insights, we recommend to analyze the ongoing development of HR analytics and its structural positioning within organizations once the application and implementation have become more deeply established in firms. This would enable further exploration of the role of the HR business partner as well as identify strategies of how to close the gap between HR analytics and HR function. In addition, we did not examine any other stakeholders involved in the process (e.g., management, HR business partners or specialist departments) as part of this study. In order to get a more holistic picture of the model proposed by us, we recommend conducting further interviews with these groups as well. As a final note, our study was only conducted in German-speaking countries, so the use of HR analytics must be reflected under the applicable data protection aspects.

Third, as mentioned at the beginning, the COVID 19 pandemic has given a new boost to the topic of HR analytics. The sharp increase in the use of digital technologies and the changes in working conditions offer a wide range of new opportunities for analyzing HR issues. Since this study took place precisely during the pandemic and the impact of the pandemic was not a focus, future research should investigate the extent to which the new circumstances influence the implementation and application of HR analytics. In addition, it should be quantitatively investigated to what extent HR analytics could be refined based on the multitude of new types of data sources resulting from the new types of working conditions.

8 Conclusion

Firms have recognized the opportunities presented by HR analytics; however, it is challenging for HR to convert their available data (sources) into meaningful strategical value. Moreover, scant research has explored how the implementation and application of HR analytics is achieved. This study provides one of the first attempts to examine the socio-technical aspects that underline the process of HR analytics. The results of this study contribute to the existing literature by showing that the function of HR analytics needs to be reconsidered. Also, it encourages future research to dive deeper into the variety of contextual and process conditions strengthening or weakening the value of HR analytics.

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1.1 Interview guide

1.1.1 application.

What do you understand by the term HR analytics?

Where does it differ from classic HR controlling?

What concrete added value does the use of HR analytics offer?

Can you quantify the added value using key figures? (e.g. resource savings, quality of recruitment, more transparency, etc.).

If not: How would you describe the added value subjectively?

What has changed in the HR department as a result of using HR analytics? What has changed from an overall organizational perspective?

Can you identify specific areas where data-driven decisions have increased?

What was the cause of this exact area being supported by data analytics? What exactly has changed about the process? How did this decision take place in the past? Have there been any advantages or disadvantages to this?

What problems were/are there in general with the evaluation of HR data?

On the technical/data level

On the evaluation level

What are the data sources for the HR data used?

How is this data systematically collected?

Who has access to this data?

Are there any problems regarding interfaces in data collection?

Which specific questions are answered with the help of the data analysis? Why these in particular?

Which data are necessary for this?

How do you collect and analyze specifically qualitative data?

Do you include external data in the analyses, if so which ones? What difficulties do you encounter in doing so?

1.3 Technology

What software do you currently use to analyze HR data? Dedicated HR analytics software or individual tools in different application areas?

What skills does it take to use this software/tool? Did you already have these skills or were they obtained elsewhere?

What difficulties are encountered with this software/tool? How could these be improved?

1.4 HR business partner

Who has been the primary initiator of HR data analysis to date? Why?

What does the power user of HR data analysis look like? Individual person or a data team?

What are the advantages and disadvantages of this?

What other stakeholders are involved in the process?

At which interfaces do difficulties arise? Which ones?

Has the use of HR analytics changed the collaboration with other departments/areas? In what form? If not, would it have to change from your point of view?

1.5 Organization

What organizational difficulties have arisen in the analysis of HR data? How were/are they dealt with?

What difficulties have arisen in the analysis of HR data on the part of the employees? How was/is this dealt with?

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About this article

Wirges, F., Neyer, AK. Towards a process-oriented understanding of HR analytics: implementation and application. Rev Manag Sci 17 , 2077–2108 (2023). https://doi.org/10.1007/s11846-022-00574-0

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Exploring the Evolution of Human Resource Analytics: A Bibliometric Study

Eithel f. bonilla-chaves.

1 School of Business Administration, Technological Institute of Costa Rica, Cartago 30109, Costa Rica

Pedro R. Palos-Sánchez

2 Department of Financial Economy and Operation Management, Faculty of Economics and Business Sciences, University of Sevilla, 41018 Sevilla, Spain

Associated Data

Not applicable.

The objective of this study is to identify and analyze the most relevant scientific work being undertaken in HR analytics. Additionally, it is to understand the evolution of the conceptual, intellectual, and social structure of this topic in a way that allows the expansion of empirical and conceptual knowledge. Bibliometric analysis was performed using Bibliometrix and Biblioshiny software packages on academic articles indexed on the Scopus and Web of Science (WoS) databases. Search criteria were applied, initially resulting in a total of 331 articles in the period 2008–2022. Finally, after applying exclusion criteria, a total of 218 articles of interest were obtained. The results of this research present the relevant notable topics in HR analytics, providing a quantitative analysis that gives an overview of HR analytics featuring tables, graphs, and maps, as well as identifying the main performance indicators for the production of articles and their citations. The scientific literature on HR analytics is a novel, adaptive area that provides the option to transform traditional HR practices. Through the use of technology, HR analytics can improve HR strategies and organisational performance, as well as people’s experiences.

1. Introduction

In the very competitive environment of the corporate world, it is increasingly important that human resource management (HRM) is performed effectively to achieve corporate success; in this context, strategic HRM (SHRM) is the implementation model employed to manage human resources (HR) along with the activities aimed at allowing the company to achieve its objectives [ 1 ].

This area covers all the major decisions about HR practices, the composition of the group of human capital resources, the specification of required behaviours, and the measurement of the effectiveness of the decisions derived from the various business strategies and/or competitive situations encountered [ 1 ]. The composition of the group of human capital resources is a collective phenomenon and human creation that is based on organizations and information, so organizations transmit information [ 2 ].

This reasoning allows us to propose HR analytics as a novel system to collect, analyze, and present this information from organizations. Using the compendium of definitions made by [ 3 ], They propose that HR analytics is an information- and technology-enabled HR practice that uses descriptive, visual, and statistical analyses of data related to HR processes, human capital, organisational performance, and external economic benchmarks to establish business impact and to enable data-driven decision making.

A variety of terms are used in this subject matter, such as “Workforce analytics”, “Talent analytics”, “People analytics”, “Human Capital analytics”, “Human Resource analytics”, and “HR analytics”. The authors of [ 3 ] indicate in this respect that the most frequently used term in the literature is HR analytics, although this should still be considered to be an emerging term. Likewise, “People analytics” has been identified as another term of much interest that is used frequently; the set of terms mentioned above has therefore been included in the scope and analysis of this study. The work conducted by [ 4 ] defines People analytics as an area of HRM practice, research, and innovation related to the use of information technologies and descriptive and predictive data analysis that employs visualization tools to generate useful information about the dynamics of the workforce, human capital, and individual and team performance that can be used strategically to optimise the effectiveness, efficiency, and results of an organisation, as well as to improve the experience of employees.

The following research questions from this study are presented in Table 1 below:

Research Questions.

This new study seeks to give rise to and suggest new ideas for continued increasing research on this subject matter, in the hope of providing a guide as to the practical application of the adoption and use of HR analytics for evidence-based decision-making at the organisational and individual level, at the same time as supporting the increasingly strategic alignment of HR operations [ 5 ].

To answer these questions, this article has the main objective of identifying and analyzing the scientific literature in the area of HR analytics. Additionally, it seeks to understand the evolution of the conceptual, intellectual, and social structure of this subject in a way that allows the expansion of empirical and conceptual knowledge.

A literature review was therefore carried out by means of bibliometric analysis, consulting the scientific production on HR analytics academic articles indexed in the Web of Science and Scopus databases and analyzing the articles and emerging trends in research published between 2008 and 2022.

This article is organised as follows: Section 1 presents the research topic to be investigated, along with the study’s purpose, objectives, and research questions. Section 2 includes the literature review for the bibliometric analysis. Section 3 explains the scientific methodology used, by means of the Science Mapping Workflow and the Bibliometrix software. This is followed by the analysis of the results and the later discussion of these. Finally, the conclusions are presented, and possible future lines of research are suggested.

2. Literature Review

There exists great and increasing interest in the literature on HR analytics. Exploring the orientation and dynamics of the gradual transformation of this subject is therefore worth conducting by means of reviewing the current state-of-the-art in HR analytics.

Among the studies undertaken to review this development in the academic theory and research on the subject, the research performed by [ 6 ] suggests that HR professionals should pay attention to four key points in HR analytics: (a) HR professionals need to develop a strategic understanding of how people contribute to the success of their organisation; (b) Analytics should be based on a deep understanding of data and the context in which it is collected in order to generate meaningful insight. This allows the generation of significant metrics, which in turn enable the measurement and modelling of the costs and benefits of different HR strategies and methods; (c) These metrics and tools should allow the identification of the key talent segments, those groups of employees whose performance makes the most strategic difference to the business and its performance; (d) Data-based decision-making should be derived after careful empirical analysis is made using advanced statistical and econometric techniques that go beyond the analysis of the correlation between variables used in experiments, such that identification is made of the way that human capital contributes to the organisation’s performance.

The authors of [ 3 ] further explain that People analytics is a term that has arisen from Google, which uses it to describe its data-driven approach to HRM. Google’s success has popularised the concept as a best practice in HRM, given that it is used by the world’s leading companies to improve their competitive advantage as mentioned by [ 5 ]. It is for this reason that Google’s Project Oxygen has been a success story since 2010, as explained by [ 6 ] and referenced by [ 7 ] as a good example of incorporating data analytics into day-to-day decision-making, in a way that has helped to obtain crucial knowledge about people operations. Therefore, we can say that HR analytics enjoys great popularity [ 7 , 8 ]. However, some studies warn of the risks of HR analytics [ 9 ].

It is in this context that [ 7 ] refers at once to both the concepts of People analytics and HR analytics as the use of analytical techniques such as data mining, predictive analytics, and contextual analysis to enable managers to make better workforce-related decisions. Nonetheless, the HR analytics literature remains in a state of constant transformation. The authors of [ 8 ] explain that the use of bibliometric analysis allows an understanding of the evolution of the state-of-the-art of a specific area in the existing literature to be able to discover emerging trends through the performance of articles and journals, patterns of collaboration, research components, and the exploration of intellectual structure. Previous bibliometric analyses of HR analytics by [ 9 , 10 ] conclude that this domain is in an incipient or emerging stage.

Table 2 presents previous reviews related to the topic of this study. As can be seen, this research is focused on articles, early access, and reviews and extends the databases consulted to Web of Science with the 2022 year included.

Previous Literature Overviews.

The authors of [ 19 ] recommend six steps for organisations to take into consideration in promoting HR analytics: (a) The development of an analytics strategy in a way that takes into account current and future needs; (b) The identification of key questions or investment decisions on which to focus; (c) Focussing these questions on future-oriented issues, not past ones; (d) Not settling on the use of the data at hand; (e) Performing data cleansing; (f) Limiting challenges to data validity by means of standardised data definitions and processes in the generation of reports and analyses.

On the other hand, [ 20 ] has elaborated and provided the following five moderating factors for HR analytics: (a) Problem identification: HR professionals must be able to identify organisational problems and ask the right questions; (b) Data infrastructure: HR analytics requires that data that area accessible, accurate and consistent across functions, even including those external data to the organisation; (c) Information technology: This must be appropriate to advanced analysis and focus on data exploration, analysis, and modelling to effectively perform HR analytics; (d) Analytical skills: HR analytics requires professionals with specific skills to prepare the data, perform statistical analysis, and communicate the results in a meaningful and understandable way; (e) Business focus: To implement HR analytics effectively, the business focus must be comprehensive, integrating processes, data, and analytics throughout the organisation.

Despite the progress and efforts made in studying HR analytics, [ 21 , 22 ] reiterate that there remains a shortage of rigorous quantitative and qualitative empirical studies on the results of HR analytics or People analytics. Nonetheless, this study identifies indications that some quantitative empirical studies in HR analytics are beginning to emerge.

3. Materials and Methods

The potential to combine the best available academic evidence with the judgement and experience of practitioners in the true tradition of evidence-based practice can be obtained through the methodology of systematic review [ 14 ]. According to [ 23 ], recognising trends in the analyses of thematic areas is possible by using bibliometrics as an indicator, which can reveal the development of trends in basic structures.

Thus, for this study, bibliometric analysis [ 24 ] was carried out using the general Science Mapping Workflow methodology described by [ 25 ], as shown in Figure 1 . The application and organisation of the bibliometric analysis were carried out by means of the standard workflow consisting of five steps [ 26 ].

Bibliometrix and the Recommended Science Mapping Workflow.

In the data collection stage, information was obtained from the Web of Science (WoS) and Scopus databases. This was performed using a Scientific Mapping Workflow for bibliometric analysis over a 14-year period, between 2008 and 2022. This was performed to complete the systematic review of the literature proposed by [ 27 ], in which the search strategy filters the relevant criteria using the PRISMA methodology [ 28 ]. This methodology details the phases of identification in the databases, the selection of records, and the filtering of elements by the eligibility criteria that have been employed.

As shown in Table 3 , for the databases and search criteria applied, a total of 331 academic articles related to “HR analytics” were identified after applying the PRISMA methodology [ 28 ] to find the documents pertaining to this investigation. The inclusion parameters used in the databases consisted of seven main keywords: “People analytics”, “HR analytics”, “Human Resource analytics”, “Workforce analytics”, “Talent analytics”, “Employee analytics”, and “Human Capital analytics” [ 3 , 4 , 29 ] for the period from the year 2008 to the year 2022 (July).

Search Criteria in the Databases.

The keywords had to appear in the title, abstract, and the keywords themselves of the articles consulted. The search results could only include articles and research reviews. Other selection parameters were also included, such as the incorporation of a filter to include only articles in English, and those that had been published or that had gone through the editorial and/or peer-review process.

The exclusion parameters used to delimit the content of the articles and related documents excluded documents that were not research or scientific review articles. Similarly, articles in languages other than English were excluded. The selected articles had to have a clear relationship with or contribute to the field of study of HR analytics. Likewise, the main objectives and research questions of the articles had to be clearly described and explained.

Once the results of the databases were obtained, the records of each database were exported in the BibTeX plain text file format [ 30 ] to maintain consistency between data sources, to later be able to combine both files into a single file for processing. Both WoS and Scopus databases allow records to be exported directly in the standard BibTeX bibliographic format; however, each database includes the different fields in a different order.

This meant that the databases had to be standardised, starting with the records being converted into a dataframe in R-Studio [ 31 ], then concatenating the records regardless of the database they came from, removing duplicates [ 32 ]. This process eliminated 113 duplicate records from the results obtained from the databases, arriving at a final total of 218 articles. This final result of records in a single database was processed using R statistical software.

Data analysis was made by applying the scientometric methodology for the bibliometric analysis of science mapping using the Bibliometrix software [ 33 ], as other recent work in the field of human resources has been conducted [ 34 , 35 ]. This is supported by the Biblioshiny web interface, also developed by [ 33 ] and available from the Comprehensive R Archive Network (CRAN). The reasons for choosing this software are based on a recent work [ 36 ], which indicates that Bibliometrix contains the most comprehensive and appropriate set of techniques.

This Bibliometrix R software package must be installed and loaded by executing the “library(bibliometrix)” command in R-Studio [ 31 ]. Immediately following this, it is necessary to execute the command “biblioshiny()” and load the Biblioshiny web interface, which provides a graphic visualisation of data and statistics. For the purpose of this study, the graphic information corresponds to HR analytics according to the parameters defined.

4.1. General Summary of the Bibliographic Collection Processed

Subsequently, the analysis and standardisation phase of the Scientific Mapping Workflow procedure was undertaken. Table 4 shows the overview of the research data. It can be highlighted that in the 14-year period that was analyzed, 218 articles were identified as a result of excluding duplicates.

Main Information.

These articles arose from 134 different sources, with an annual average publication rate of three articles per year and an average number of 10.4 citations. Similarly, 9390 articles, 652 keywords, and 45 different authors were referenced. This detail demonstrates how the study of HR analytics is an emerging field and how it manages to maintain or inspire interactions with other topics.

This behaviour can be observed in Figure 2 , which shows that the number of scientific publications on HR analytics begins to increase from the year 2014, some years after what could be considered the starting point of its popularity [ 37 ]. This research explains the six key ways to track, analyze, and use employee data, ranging from establishing simple metrics that monitor the overall health of the organisation to identifying talent shortages and excesses long before these occur.

Annual Scientific Production.

Consecutively over the following years, research in HR analytics showed an average annual growth rate of 1.8%, with an accentuated growth peak between 2016 and 2017. Notable among the publications of the year 2017 are the peer-reviewed article by [ 3 ] and the publication by [ 38 ], which proposes 4 clusters of analytical maturity for companies, with these companies belonging to the innovative disruptive analysis cluster. This cluster commenced using analytics earlier, applying more complex techniques and more advanced applications such as HR analytics, where its use is more common and shows a higher level of analytical maturity.

It can therefore be said that HR analytics research has shown sustained growth since 2017. In the year 2019, there is also notable growth in publications, including an article published by [ 39 ] that details the way a new generation of HR professionals is developing an“HR stack”, which includes other management frameworks to increase HR competencies, among these HR analytics.

An exception can be seen in the decline shown in 2020, at the time of the COVID-19 pandemic. There is also notable growth in publications in the year 2021, which include the publication by [ 40 ] of a literature review of 60 years of research on the relationship between technology and HRM. This explains that in the final proposed time period, from 1997 to 2019, there was increased interest in making better use of the HR data accumulated in HR information systems (HRIS) for business decision-making, with this, therefore, representing the growing field of HR analytics.

Similarly, Figure 2 shows the linear regression of variance with an explanatory effect coefficient of 82.6% for scientific publications per year, representing a positive relationship through the interpretation of [ 41 , 42 ], thus reflecting the validity and accuracy of the research topic.

4.2. Thematic Evolution

The thematic evolution of the keywords related to HR analytics and the most relevant authors on this topic a revisualised in the Sankey diagram [ 43 ] shown in Figure 3 . This indicates the order of magnitude of the various information flows of the quantitative data for the main topics. The indexing of the content represents the redundant visualisation of the quantity of relationships with authors, highlighting the increased connection of the terms “HR analytics” and “Artificial Intelligence”.

Thematic Development.

It could be asserted that consolidation is made of the greater use of term “HR analytics” with respect to other related key terms used in publications on the same subject. Among the authors, it is notable that Steven McCartney together with Na Fu relate to most of the main HR analytics topics, with both of these authors having published very recent articles in HR analytics [ 44 ] and People analytics [ 17 ].

The first article addresses whether HR analytics can increase organisational performance, affirming that access to HR technology is a precursor of HR analytics. The other article provides a systematic review of the literature on People Analytics. Other authors, including Gonen Singer, Dan Avrahami, and Hila Chalutz Ben-Gal, have made use of the term “Artificial Intelligence” together with the term “Machine Learning” for application in HR analytics [ 45 ].

In the study conducted, a comprehensive framework of analysis is proposed that can serve as a support tool for the making of decisions by HR recruiters in real-world environments to improve hiring and placement processes. The prediction approach uses the machine learning model, applying the Variable-Order Bayesian Network model.

4.2.1. Relevant Sources

The most relevant databases were used for the bibliometric analysis. Table 3 shows that there was a greater number of results from Scopus (193) than from WoS (138). Table 5 shows the most relevant scientific sources by the number of articles published on HR analytics. The most relevant scientific journals on the subject of HR analytics were identified in the period analyzed, with an average of two articles published.

Most Relevant Scientific Sources.

At 10 articles each, the Human Resource Management Journal and the Journal of Organizational Effectiveness: People and Performance were the journals that published the most articles on HR analytics, followed by Human Resource Management with eight articles. The journals with the highest number of publications on these topics were journals with a focus on HR.

The 10 most cited journals for the topic of HR analytics are presented in Table 6 , with Human Resource Management being the journal that tops this list with a total of 212 citations. Followed by the International Journal of Human Resource Management and the Academy of Management Journal with 188 and 154 citations, respectively. The journals with the highest number of citations on these topics were journals related to HR and Business, like Harvard Business Review. These represent the most cited journals for the topic of HR analytics.

Most Cited Sources.

The most important journals on the topic of HR analytics can be identified by applying Bradford’s law [ 46 ] as shown in Figure 4 . These core sources are identified in zone 1, the shaded area that includes the following journals: the Human Resource Management Journal and the Journal of Organizational Effectiveness: People and Performance. These journals are at the core [ 47 ] of HR analytics and include the most relevant research on the topic, so they should be given special importance when preparing publications on this subject.

Core Sources.

As shown in Table 7 , the highest impact factor is consistent with Bradford’s law, through the journal Human Resource Management with an h-index [ 48 ] of 8 and 168 citations. This journal started publishing on the topic of HR analytics in 2018. This is followed by the Journal of Organizational Effectiveness: People and Performance, which began publishing on this topic in 2017, and which has an h-index of 7 with 144 citations. These journals have the greatest level of impact of all those publishing on HR analytics.

Journal Impact.

Note: TC: Times Cited, PY_start: Publication start year.

In terms of the increase in publications, the journal Personnel Review stands out, showing exponential growth as seen in Figure 5 . This growth commenced in 2019 and remains on the rise even in the first months of 2022. Included among the HR analytics research contained in this journal are the publications of [ 49 , 50 ].

Growth of Journal Publications.

The article by [ 49 ], “An ROI-based review of HR analytics: Practical implementation tools” conducts a literature review of HR analytics based on ROI (return on investment) and has 22 citations. This article provides the practical application of this quantitative measurement tool for managerial decision-making, as motivated by the limited high-quality research in the field. At the same time, this ROI-based perspective can provide increased opportunities for the practical adoption of HR analytics.

In addition, of note, the article by [ 50 ], “The ethics of people analytics: Risks, opportunities and recommendations” has 10 citations. This article performs a “scoping review” of HR analytics to understand the ethical considerations and recommendations to be taken into account for ethical practice in this matter. These recommendations are (a) Transparency and equity; (b) Legal compliance; (c) Ethical guidelines and statutes; (d) Proportionality and protection; (e) Data rights and consent; (f) Inclusion of data subjects; (g) Skills and people culture; (h) Evaluation; (i) Ethical business models. In contrast, the Harvard Business Review shows a clear decrease in publications, while the Human Resource Management International Digest has begun a sudden reduction in publications.

4.2.2. Relevant Authors

The authors with the most publications on the topic of HR analytics are Caryl Charlene Escolar-Jimenez from the University of Tokyo in Japan, Reggie C. Gustilo from De La Salle University in the Philippines and KichieMatsuzaki from the University of Tokyo in Japan, as shown in Table 8 . These authors have published five articles, with the coincidence that for all three authors, the article with the highest number of citations is “A Neural-Fuzzy Network Approach to Employee Performance Evaluation”, published in 2019 with ten citations.

Relevant Authors.

(*) Other authors grouped under the acronym “NA N”.

This work applied the artificial intelligence technique called “artificial neural networking” using the neuro-fuzzy profiling system to optimise traditional employee performance evaluations. This allows HR departments and decision-makers in organisations to easily identify the strengths and weaknesses of employees for professional promotion, training, and development in achievement, leadership, and behaviour, in contrast to the subjectivity of the traditional system [ 51 ]. The most popular research areas by the authors in HR analytics are computer science, data science, and organizational behaviour.

The scientific output of Hila Chalutz Ben-Gal from the Afeka Tel Aviv Academic College of Engineering in Israel as of 2019 has been continuously focused on the topic of HR analytics, as shown in Figure 6 . In 2019, her first article was “An ROI-based review of HR analytics: Practical implementation tools”.

Scientific Production of Authors over Time. Other authors grouped under the acronym “NA N”.

Similarly, Steven McCartney from Maynooth University in Ireland has been active in the publication of HR analytics articles since 2020. In that same year, he published the article “21st century HR: A competency model for the emerging role of HR Analysts” with five citations, in which he explores the key competencies and KSAOs (knowledge, skills, abilities, and other characteristics) required for the role played by HR Analysts [ 52 ].

The frequency of publications per author in any field of research is known as Lotka’s law [ 53 ]. Table 4 shows that of the 461 authors identified for this study, 86.3%, which are 398 authors, have a publication on HR analytics, as shown in Table 9 . Following the Pareto principle, 10% of the authors wrote two articles and 2.2% contributed three articles. In contrast, there are only four and three authors who published four and five articles, respectively.

Distribution of Scientific Production According to Lotka’s law.

In accordance with [ 54 ], Figure 7 shows that 86.3% of the authors wrote only one article on HR analytics and that only 0.7% of the authors wrote five articles on this. It can therefore be presumed that the majority of the authors have published in the field due to the novelty of the topic.

Scientific Production of Publications.

With an h-index of four, the authors Escolar-Jimenez C., Gustilo R., and Matsuzaki K., who published the article “A Neural-Fuzzy Network Approach to Employee Performance Evaluation”, have the highest impact factor of all HR analytics authors, with this being higher than the average of two as shown in Table 10 .

Impact Factor of the Authors.

Note: TC: Times Cited, NP: Number of publications, PY_start: Publication start year.

These are followed by Boudreau J., GuerryM., and Ben-Gal H. with an h-index of three for the first and two for the latter two authors. For Boudreau J., in addition to the article “An evidence-based review of HR Analytics” published in co-authorship, another publication in 2014 is noteworthy, this being the article “HR strategy: Optimizing risks, optimising rewards” which has 12 citations. This article suggests that in the field of HR, instead of minimising or controlling unwanted results in dealing with risks, a balanced approach to risk-taking is required for the optimisation there of [ 55 ].

Guerry M. and Ben-Gal H. are the authors who come in the middle of the ranking. Noteworthy for Guerry M. among the articles published in co-authorship is the 2018 article, “Predicting voluntary turnover through human resources database analysis”, which has 14 citations. This study determines that by using a priori only available data from reliable HR databases, valuable predictions regarding staff turnover can be generated for use by HR managers to prevent and reduce voluntary turnover more reliably [ 56 ].

The author Ben-Gal H., on the other hand, published the article “An ROI-based review of HR analytics: Practical implementation tools”. For all these authors mentioned, a total of 317 citations are added for articles published related to HR analytics.

The universities to which the authors belong are shown in Table 11 . Notable among these is Bar-Ilan University in Israel, which has ten publications, followed by Tilburg University in the Netherlands and the University of Southern California in the United States of America with eight articles each. In addition, the Copenhagen Business School has six publications, while the remaining universities mentioned presented five articles each.

Affiliations of the Authors.

The scientific production by country shown in Table 12 uses the SCP indicator to show that the USA with 43 articles leads the number of publications on HR analytics by country. It is also the country that shows the highest rate of collaboration with an MCP of four. This is followed by India and the United Kingdom, with 25 and 11 articles published per country, respectively.

Scientific Production by Country.

Note: Freq: Frequency; SCP: Single country publications; MCP: Multiple country publications; MCP Ratio: Multiple country publications ratio.

In similar fashion, the USA maintains the highest number of article citations by country, with 933 representing an average of 21.7% of citations, as can be seen in Table 13 . It is followed by India with 223 citations, the United Kingdom with 204 citations, and the Netherlands with 164 citations. Among the countries mentioned, there are a total of 1524 article citations per country related to HR analytics publications.

Average Number of Article Citations per Country.

Note: TC: Times Cited, AAC: Average Article Citations.

4.2.3. Relevant Articles

The articles with the most citations are presented in Table 14 . The first is the article by [ 6 ] with 147 citations and an average yearly citation rate of 21 times. This study reveals that the development of HR analytics is hampered by the lack of understanding of the analytical thinking of HR professionals and HR analytics teams.

Most Cited Articles.

Note: TC: Times Cited, TCY: Times Cited per year. NA: not assigned.

The article, therefore, suggests that HR professionals should pay attention to improving their skills and knowledge to become “champions” of this new approach, such that HR analytics methods can make HR transcend into having strategic influence at the managerial level in order to benefit the organisation and its employees.

The second most cited article with 122 citations is by [ 57 ]. This article explains that among the domains used to specify where HR investments should be directed, a move should be made to an external-internal approach, in which HR reacts to the challenges of the organisation to participate more fully in the development of strategy and value-adding. The authors propose that HR analytics should be created in a way that focuses on the right problems.

The article by [ 58 ] is in third place with 121 citations and an average annual citation rate of 24.2. The paper presents a case study using HR analytics, which was undertaken using the Smart HR 4.0 analysis methodology to identify employees at risk of attrition. In addition, it promotes linking the concept of Smart HR 4.0 to the digital transformation of HR functions based on a “science of people”.

With 117 citations, the article by [ 37 ] in the Harvard Business Review is the fourth most cited article. This paper reports that leading companies such as Google, Best Buy, P&G, and Sysco use sophisticated data collection and analysis technology to get the most value from their talent. It further includes six key ways to track, analyze, and use employee data.

The article by [ 3 ] providing a peer-reviewed literature review comes in fifth place with 113 citations. In sixth place with 101 citations is the paper by [ 59 ]. In the empirical research these authors conducted, development is made of a model to examine HR analytics practices along with an incentive system that produces greater productivity when the practices are implemented collectively rather than separately. Detailed data on the adoption of HR software are also included.

Finally, [ 60 ] authored the seventh article with 74 citations. This paper uses two case studies to illustrate how HR analytics can deliver value by forming an ongoing part of the management of end-to-end decision-making. Included among the suggestions made are proposals to commence with the business problem, to take HR analytics outside of HR, to remember the “human” side of HR, and to train HR professionals to have an analytical mindset.

The premises, suggestions, and orientation of these articles provide direction as to where the efforts of HR analytics should be focused to transcend beyond research into the subject matter so as to evolve into value-adding practice. At the same time, they emphasize the importance of the role of HR professionals, the transformation towards the use of the correct information, and HR analytics in such a way that these contribute to organisational strategy and decision-making.

Table 15 shows the most cited articles existing in the bibliometric database that have also been cited in the references. Continuing among these is the article by [ 6 ] as the most cited article with 55 citations, followed by those of [ 3 , 37 , 61 ] with 50, 38, and 26 citations, respectively.

Most Cited References.

Note: DOI: Digital Object Identifier. NA: not assigned.

These are followed by the paper [ 62 ] with 25 references. This paper argues that to achieve superior performance and a competitive advantage in companies, HR analytics must be developed as an organisational capacity that is linked to the overall business strategy. This organisational capacity is based on three micro-level categories: individuals, processes, and structure. It further depends on the three dimensions of HR analytics: data quality, analytical competence, and the strategic capacity to act.

The article [ 63 ] following this has 23 citations. This study states that HR having an increasing focus on metrics and analytics can help HR functions to take up a larger participatory role in corporate decision-making and strategy creation. Finally, [ 59 ] authored the seventh article with 74 citations.

4.2.4. Reference Publication Year Spectroscopy (RPYS)

With this method, a chronological profile of a set of articles is created, highlighting the years with the most significant publications [ 61 ] to identify the chronological origins of a discipline. In the time period analyzed, there is an alignment of articles with scientific production as can be seen in Figure 8 , highlighting the relevance of years 2010 and 2016 such that these can be considered, of interest in future research on HR analytics, years that are related to the publications by [ 6 , 37 ].

Reference Publication Year Spectroscopy (RPYS).

On the other hand, in the analysis of the common terms used in the articles shown in Table 16 , in addition to the keywords used to carry out the search for this study, terms were found from the data science area such as “Big Data” and “Artificial Intelligence”, thusdemonstrating that a relationship exists between these terms.

Of these terms, “Big Data” predominates from the time that [ 6 ] mention the growing interest in big data shown in HR analytics. Also significant in this regard is the proposal made by [ 64 ] that a strategic approach to HR is carried out through the analysis of big data to improve company performance.

Similarly, for the term “Artificial Intelligence”, the paper by [ 65 ] reveals that most of the proposed HR analytics models have used artificial intelligence algorithms and methods, demonstrating the rapid development of and the increased interest in applying this technology to the field of HR.

Figure 9 shows the distribution of HR analytics-related themes using the main terms on a map of keywords in the form of a treemap. This represents the most relevant keywords according to the inclusion parameters used in the databases. These are “HR analytics”, “People analytics”, “Workforce analytics”, and “Human Resource analytics”, at 19%, 12%, 6%, and 3% of the total occurrence, respectively.

Word TreeMap.

Additionally, the words “Human Resource Management” and “Analytics” are notable with 6% and 5% of the total occurrence. Similarly, the words “Big Data” and “Artificial Intelligence” are notable with 6% and 4% of the total occurrence respectively. On the other hand, the keyword “Algorithms”, with an occurrence of 1%, shows the lowest prevalence.

Another trend that needs to be analyzed is the behaviour of keywords over time, shown in Figure 10 . It can be observed that in the timelines for each keyword, the term “HR analytics” is above the term “People analytics”, although the curve of this latter term tends towards logistic growth in the period analyzed.

Keyword Growth over Time.

In the same way, the word “Big Data” stands out with regard to the terms from the data science area. This should also be noted since the term shows a trend towards greater growth in HR analytics than the other themes do. In contrast, the words “Human Resource analytics” and “Analytics” are notable in showing a decrease, indicating their use in HR analytics articles has lessened.

4.3. Analysis of Knowledge Structures

According to [ 33 ], three types of general research questions can be answered using bibliometric analysis for scientific mapping to reveal the following:

• The conceptual structure, to examine the research front for a theme or field of research.
• The intellectual structure, to identify the knowledge base of a theme or field of research.
• The social network structure, to discover the production of a particular scientific community.

4.3.1. Conceptual Structure

As shown in Figure 11 , conceptual structure is analysed by means of a co-occurrence network using the Louvain clustering algorithm [ 66 , 67 ]. In this, a series of themes related to the main nodes of “HR Analytics” and “People Analytics” are identified. Within these themes, the terms “Big Data” and “Artificial Intelligence” prevail for the “HR Analytics” node, which also coincides with the relationship between the analyses made of the main keywords.

Co-Occurrence Network.

In correspondence to the previous findings, the different themes of a given domain are observed in the thematic map shown in Figure 12 . Here, centrality represents the degree of relevance of a field of research, and density represents the degree of development of a theme.

Thematic Map.

Notable among the terms in the niche topics quadrant are the terms “neural-networks person-organization fit” and “commerce employment”.

In the terms of the motor quadrant, in addition to the main HR analytics themes, there are terms “productivity dynamics”, “diffusion consequences”, “job-satisfaction system”, “future meta-analysis”, “employee turnover”-“human-resources practices”, and “performance-management”.

The emerging or declining themes quadrant contains only the term “Intelligence and Personality”, “job-performance and leadership”, “employees”, and “human resources neural network”.

Finally, in the basic theme’s quadrant, the main themes of “models human”, “privacy issues”, and “work employee perceptions” appear in this order and degree of density.

The thematic evolution of the theme in the period studied is shown in Figure 13 . The order of magnitude of the various information flows of quantitative data related to the main themes and the indexing of the content over time are shown via the redundant visualisation of the relationships. This reveals that after 2019 the term “Performance”, “Model”, “employee turnover”, and “future” are united with the term “HR analytics”. Additionally, it shows that the term “HR analytics” mostly became consolidated in its usage between 2020 to 2022.

Thematic Evolution.

The Confirmatory Factor Analysis (CFA) approach [ 68 ] was used along with the method of Multiple Correspondence Analysis (MCA) [ 69 ] to determine the dimensions of this study. Figure 14 shows the two dimensions of HR analytics resulting from this analysis.

Factorial Analysis (MCA).

The first dimension (27.16%) seems to indicate the level of analysis of the research studies at the HR Analytics level. On the left-hand side of Figure 14 we can see terms focused on the employee and his or her conditions: “employ turnover”, “job satisfaction”, “human resource practices”, “human”, “employment”, or “workplace”. On the right-hand side of this dimension are more generalist terms such as “science”, “innovation”, “management”, “dynamics”, “performance”, “acceptance”. or “human resource analytics”.

On the other hand, the second dimension (14.03%) represents the level of concrete implementation or specialization of the published research studies. At the bottom, there are words such as “employee”, “human resource”, “employment”, or “business analytics”. On the top of this dimension, words like “behavioral”, “adoption”, “strategic”, “impact”, or “firm” shows the level of specialty of this research works.

The dimensional separation shown in Figure 15 , using a thematic dendrogram, is consistent with the dimensions that have been identified according to [ 70 , 71 ]. The first branch is related to the main HR analytics terms, which in the association have a height of two, while the following sub-branches have a similar height, thus showing that regardless of the theme, the same domain is being discussed. The other branch of “firm performance” and “information system” has a height of approximately 0.5 and a greater distance between terms, thus confirming the separation of dimensions.

Thematic Dendrogram.

Factor analysis identifies the most cited articles as well as those that make the greatest contribution to each cluster. Figure 16 shows the most cited documents, with the number of links between articles for each theme and for each cluster differentiated by colour. The influence of [ 3 , 6 , 58 ] in the HR analytics cluster is very significant. However, for the “Analytics” cluster, the opposite happens with very few publications.

4.3.2. Intellectual Structure

The intellectual structure is analysed through a co-citation network [ 72 ] and a historiographic map [ 73 ]. In the analysis of the co-citation network, the citations of two documents are identified when these are cited by a third document. This is represented graphically as a series of citation occurrences that show a center of gravity as can be seen in the main publications of this study in Figure 17 . The centers of gravity of interest for HR analytics are [ 3 , 6 ]; while for “Analytics” they are [ 59 , 60 ]. These are the most influential and co-cited authors in the time period analyzed.

Co-Citation Network.

Ref. [ 59 ] relates a human capital management (HCM) system to productivity improvement and discuss the advantages and form of implementing an organisational incentive system. On the other hand, present a practical study from which to draw important lessons that show that HR analytics is not a fad in organisational management. The research paper [ 3 ] is one of the first contributions as reviews in HR analytics, as it uses an integrative synthesis of published peer-reviewed literature on Human Resource analytics. Ref. [ 6 ] highlights the role of Big Data in HR and questions the indispensability of HR Analytics in the strategic management of an organisation. The authors point out that the transformative nature of current HR Analytics practices depends largely on managers and HR professionals being fully aware of its advantages and disadvantages.

Analysis of the historiograph map identifies the research routes and the main authors at different times, as can be seen in Figure 18 . In the case of HR analytics, this consolidates into a route with the main authors being [ 59 ], followed by [ 6 , 60 ]. However, it is important to note that HR analytics co-citation relationships in recent years have shorter time periods with ranges of around 1 to 3 years with respect to the first years, representing a good sign with respect to the growth and dynamics of this scientific field.

Historiograph Map.

4.3.3. Social Structure

Social structure is analyzed through an examination of the network of co-authors [ 74 ] and a map of collaboration between countries. Figure 19 shows the collaboration network, representing the analysis undertaken by the network of co-authors, identifying the authors’ relationships in the field of HR analytics. In this respect, two clusters stand out: the first association of authors to mention is that of Escolar-Jimenez C., Gustilo R., and Matsuzaki K.; this is followed by the association between Singer G., Avrahami D., Pessach D., Chalutz B., and Ben-Gal H. These represent the authors and clusters that collaborated the most in the period analyzed.

Collaboration Network.

With respect to the map of collaboration between countries, Figure 20 shows the relationship lines representing the authors and their countries on the world map for the field of HR analytics. It can be noted that relationships of co-authors between countries in HR analytics happen to a greater extent between the continents of America and Asia. Specifically, a higher frequency of these relationships is identified between authors who collaborate from the countries of the USA and China. Moreover, these countries are followed by other European countries, which feature collaborative co-author relationships between Germany and Spain.

Collaboration World Map.

4.4. Notable Themes in HR Analytics

Following [ 75 ] with respect to bibliometric analysis and a review of the literature, some notable topics for HR analytics were identified from a previous series of papers that carried out systematic reviews of the literature (SLRs) in HR analytics. These investigations are summarized in Table 17 .

Summary of notable themes in HR analytics revealed by SLRs.

5. Discussion

The results showed that since 2017, scientific production of HR analytics papers has sustained a notable increase, as can be seen in Figure 2 . This is possibly due to progress in knowledge in the field as well as awareness of the need to take advantage of technology to generate value using HR information in a way that can influence strategy and managerial decision-making to contribute to improving organisational performance.

The bibliometric analysis of HR analytics conducted expands information on research into this scientific field in combining the Scopus and Web of Science (WoS) databases. This paper analyses a database of 218 articles, whereas similar prior works have analyzed a database of 125 articles [ 22 ].

What are the main themes related to HR analytics?

It is notable that scientific production in recent years has increased with respect to the first years of the time periodanalyzed. This emerging field of study was also seen to engage in interactions with terms other than those of the main HR analytics themes that were used for this work. Thus, science terms such as “Big Data” and “Artificial Intelligence” are being employed together with the term “Machine Learning” for applications in HR analytics by researchers.

What are the main scientific journals, authors, and research articles in HR analytics?

The core sources for HR analytics, shown in the shaded area of Figure 4 , are identified by the impact factor of the journals. For HR analytics, the two main scientific journals with the highest impact factor are the journal Human Resource Management, with an h-index of eight, which began publishing on the topic in 2018, and the Journal of Organizational Effectiveness: People and Performance, with an h-index of sevenAND which began publishing on the topic in 2017.

Among the two most cited journals for the topic of HR analytics are the journal Human Resource Management and the International Journal of Human Resource Management, with a total of 212 and 188 citations, respectively. In the growth in journal publications on HR analytics shown in Figure 5 , the journal Personnel Review is notable in showing exponential growth. This growth commenced in 2019 and remains on the rise even in the first months of 2022.

In the scientific production of HR analytics authors over the time period studied as seen in Figure 6 , the authors with the most publications in HR analytics articles are Escolar-Jimenez C., Gustilo G., and Matsuzaki K. These authors have published fivearticles, with the coincidence that for all threeauthors, the article with the highest number of citations is “A Neural-Fuzzy Network Approach to Employee Performance Evaluation”, published in 2019 with ten citations. This article identifies the use of artificial intelligence techniques in contrast to the subjectivity of the traditional system, which suggests new ways to expand the lines of research applied in HR analytics. 86.3% of the authors, that is, 398 of these, have a single publication in HR analytics.

The previously mentioned authors had the highest impact factor among HR analytics authors, with an h-index of four. Also worth mentioning is the author Boudreau J. with an h-index of three. This author stands out among the HR analytics publications for the co-authorship of the article “An evidence-based review of HR Analytics”.

The most cited articles in HR analytics shown in Table 14 are, in the first place, the article by [ 6 ], titled “HR and analytics: Why HR is set to fail the big data challenge”, with 147 citations and an average citation rate per year of 21 times. This is followed by the article by [ 57 ] titled, “Are we there yet?: What’s next for HR?”, with 122 citations and an average rate of citations per year of 15.25 times. These amounts could be considered small compared to other topics. However, for this topic, it is very relevant to know the article by [ 6 ]), as it is also quite influential because it is the most cited reference in Table 14 .

How has the concept of HR analytics developed in recent years?

The co-occurrence network shown in Figure 11 is used to analyze the conceptual structure, demonstrating the prevalence of the main node of “HR Analytics” with the terms of “HR Analytics”, “Big Data”, and “Artificial Intelligence”. Respectively, these have 19%, 6% and 4% of the total occurrence of the keywords in the form of a treemap shown in Figure 9 .

The Confirmatory Factor Analysis shown in Figure 14 identifies the main dimension of this study by the terms “HR Analytics”, “People Analytics”, and “Workforce Analytics”; these together with the terms “Big Data”, “Artificial Intelligence”, and “Human Resource Management” maintain an association that represents 82.64% of the cases in this dimension.

The intellectualstructure is analyzed using the co-citation network shown in Figure 17 and the historiographic map shown in Figure 18 . These identify the important centres of gravity for HR analytics to be [ 3 , 6 ]. In addition, HR analytics co-citation relationships in recent years have shorter time periods with respect to earlier years, now featuring ranges of around 1 to 3 years. This is a good sign of the growth and dynamics of this scientific field.

Analysis of social structure in the field of HR analytics is made through the network of co-authors shown in Figure 19 and the map of collaboration between countries shown in Figure 20 . These highlight the cluster with the strongest association as being that of the authors Escolar-Jimenez C., Gustillo R., and Matsuzaki K. Relationships in HR analytics between co-authors in different countries occur to a greater extent between authors collaborating in the countries of the USA and China.

Scientific production in HR analytics by country is led by the USA with 43 articles. This is also the country showing the highest rate of collaboration with an MCP of four. It is followed by India with twenty-fivearticles and an MCP of threein terms of its collaboration rate.

In the same way, the USA maintains the highest number of article citations per country at 933 citations, representing an average of 21.7% of all citations. It is again followed by India with 223 citations, representing an average of 8.92% of all citations.

What is the focus and vision of future research in HR analytics?

The summary of notable HR analytics themes revealed by the systematic review of the literature (SLR) as shown in Table 16 seeks to give rise to opportunities to promote the closing of gaps in HR analytics. These are proposed to promote progress in the development of research on this subject and to capture recommendations for topics of interest for future exploration.

The authors of [ 17 ] propose the balance of interest approach to explore the theoretical perspective at the individual, team, and organisational level, in order to further extend HR analytics research, which has necessarily concentrated on the application of HR analytics, reinforcing the premise that empirical work iscarried out to demonstrate the theoretical relationship, the antecedents of HR analytics and the general performance of the organisation.

Works such as the benchmark paper by [ 49 ] have explored such topics, indicating that the adoption of HR analytics improves through the incorporation of return on investment (ROI) analysis or an ROI-based framework. This paper further emphasizesthe context in which HR analytics is being adopted and implemented, both in practice and in theory.

The frameworks that describe the adoption of innovation according to [ 3 ] can serve as a basis for understanding the current situation regarding the adoption of HR analytics and its probable future. And likewise, for example, so do the theoretical frameworks that are related to strategic management and organisationalbehaviour.

Furthermore, to understand and contextualise HR analytics as an innovation in HRM, [ 76 ] have used the theory of planned behaviour, the diffusion model of innovation and the technology-organisation-environment framework to subsequently provide a framework for the adoption of HR analytics that identifies five factors influencing this in any organisation, these being technological, organisational, environmental, data governance, and individual factors.

However, the application of HR analytics depends on driving a proactive HR research and analytics agenda in terms of enabling strategic HR decisions. Therefore, it is necessary for an applied researcher with a background in the social, behavioral, and organisational sciences to accurately and ethically interpret the insights derived from HR analytics in the context of individual, group, and organisational behavior [ 78 , 79 ].

Finally, the use of Artificial Intelligence (AI) learning algorithms, allowed [ 21 ] to identify the dangers related to the application of HR analytics. In summary, therefore, we can say that HR analytics is a discipline that uses data and analytical tools to make informed decisions about employee management and organisational performance. Some of the main practical applications of HR analytics are Employee selection and recruitment, helping identify the most suitable candidates for a job using psychometric tests, resume analysis. and structured interviews; Performance evaluation supporting measure employee performance, identify areas for improvement and set clear objectives for skills development and promotion; Talent retention, identifying employees who are most at risk of leaving the organisation and develop strategies to retain them, such as career development programmes and additional benefits; Workforce planning: an organisation forecast future staffing needs, identify skills gaps, and develop plans to address them; Training programme design: planning the skills that employees need and developing training programmes that are effective in meeting those needs.

6. Conclusions

This bibliometric analysis of the scientific literature on HR analytics has made it possible to affirm that the area continues to emerge and to incorporate new terms of interest from the area of data science. At the same time, it is very adaptive due to the need to access personal information through HR information systems and databases to be used in a utilitarian and ethical way by companies for the benefit of the employees themselves as well as organisations.

It, therefore, provides the focus and current state regarding the terms that are most recently used in HR analytics with respect to the search criteria applied to carry out the research into the state-of-the-art of this discipline. Likewise, it emphasizes the value of the current state of scientific production with articles published up to 2022, demonstrating that the field remains dynamic, emerging and trending in accordance with [ 3 , 6 , 61 ].

For organisations, the digital transformation of HR and traditional HR practices with approaches employing technological innovations has made promotion of the use of HR information into a current pressing need to improve the strategies and the performance of organisations themselves, as well as of the people forming part of them. The paper by [ 80 ] seeks to contribute to HR digitisation literature through the adoption of HR analytics.

The benefits for people and organisations can be seen in the usefulness of opting for better performance in the so-called Industry 4.0 (or fourth industrial revolution) by using the information available for decision-making with the application of HR analytics to achieve strategy and business objectives. In addition, HR analytics is postulated as an innovation in HRM, which can accelerate organisational changes, motivating business digitisation in a way always linked to people, forming an intangible value within the very identity and culture of companies.

The incorporation of future research that analyses the adoption and implementation of HR analytics empirically with quantitative studies made using adoption frameworks could further expand knowledge on the subject over and above successful business cases, which allow the analysis of the subject taking into accountorganisational performance itself and its relationship with other variables of interest. This could be either to learn the level of innovation employed or the increase in sales of companies achieved through improving the performance of their employees. Such applications could quantitatively establish these new strategic HR practices for industries at the managerial level and for decision-making based on data, with the novelty of being modern and technological.

Thus, an empirical examination of the adoption of HR analytics could highlight or help expand that understanding, as has been done in similar technology adoption analysis studies [ 81 ].

Within the practical limitations of this research into HR analytics is the acquisition, use, and knowledge of the technology itself, given that other areas of companies remain in processes of digital transformation. Without this being an end in itself, customers and employees themselves push organisations into accelerated updating processes to remain in the market, as a strategy to maintain their own survival [ 82 ]. Another limitation has been to deal with a lot of scattered information limited to specific issues, such as HR Analytics, which does not favour a general overview, although it does favour a description of the situation of scientific research in this specific field.

The field of research into HR Analytics remains of great interest;however, the adaptability of other topics according to their own dynamics sees the body of researchers also evolve in like fashion over time. Similarly, the depth of the subject matter can lead to other turns of research and interests due to aspects related to the main topic, leading this to instead focus on more specific themes, so expanding the subject with terms from the data science area such as “Big Data”, “Artificial Intelligence” [ 83 ], and “Machine Learning” that are currently being taken up in the application of HR analytics.

In the future, more research will be required in the field of HR analytics due to an increasingly technological world that at an organisational level could benefit further from this in its own performance, whether these are large companies or small or medium-sized ones. The breadth of the topic of HR analytics should thusbe investigated more thoroughly in all its aspects and variations, especially with regard to its applications in different areas by researchers and data scientists, as well as from within or as part of corporations themselves. One of the fields within HR Analytics will be the study of telework performance [ 84 ].

The limitations of this bibliometric study are the collection of bibliographic metadata in the Scopus and Web of Science (WoS) databases. This study is limited to these databases.

In short, this research could also be of great interest to academics and professionals who seek to discover the-state-of-the-art of this topic, as well as to expand contributions to knowledge in this scientific field. In this article, bibliometric analysis was employed to identify the main authors contributing their knowledge to the field of HR analytics.

Acknowledgments

We would like to extend our heart-felt thanks to Henry Lizano Mora, computer sciences engineer and IT Director of University of Costa Rica, for his kind help, showing us the capabilities of the tool with which this research has been developed. Wewere inspired by your discussions with him, advanced researchand we appreciate the advice he provided withthe library R code (Bibliometrix). The authors are grateful to Mario Rojas-Sanchezand Garro-Abarca for his published bibliometric analysis and suggestions for improving this methodology.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, methodology, software, formalanalysis, supervision, writing—review and editing, P.R.P.-S.; writing—original draft preparation and visualization, E.F.B.-C. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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The Rise of HR Analytics: Exploring Its Implications from a Developing Country Perspective

Journal of Human Resource Management

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The advent of digitalization and technology has spurred the need for making the systems robust and automated for lesser human intervention. The human resource departments are responsible for managing quite complex tasks such as talent acquisition, performance management, compensation, benefits, and other essential employee-related functions. It is not always easy to manage a huge record of employees manually. Earlier Human resource function was more of a transactional and administrative job. However, with changing roles and job profiles the way of doing things has also changed. As businesses have acknowledged the role of Human Resource Management in leveraging the resources available to help organizations achieve a competitive advantage. HR analytics has become essential for businesses to carry out complex tasks and predict the trend for making future strategies. In the modern era, HR analytics is the buzzword for HR professionals. It helps to figure out the gaps in the performance of individuals and teams and suggest methods to fill them with the usage of Artificial Intelligence or other related technologies. In this study, the focus has been directed toward understanding the role of HR Analytics in transforming Human Resource Functions. Sample of 197 respondents from HR team of different organizations were surveyed to know the benefits, challenges and impact of Transforming Human Resource Management with HR Analytics. It is found that there is a significant impact of Transforming Human Resource Management with HR Analytics on an organization.

Unlocking the power of HR Analytics

Afrasyab Hesami

In today's data-driven world, organizations are increasingly relying on HR analytics to make informed decisions that drive success. HR analytics involves gathering and interpreting data to gain insights into various HR aspects of an organization. By leveraging descriptive, diagnostic, predictive, and prescriptive analysis, HR professionals can make strategic decisions that align with the organization's best interests. This article will explore the different types of HR analytics and delve into the key HR metrics that help measure and improve HR function within an organization.

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Please note you do not have access to teaching notes, human resource analytics: a review and bibliometric analysis.

Personnel Review

ISSN : 0048-3486

Article publication date: 2 February 2021

Issue publication date: 11 March 2022

This paper aims to identify the current research trends and set the future research agenda in the area of human resource (HR) analytics by an extensive review of the existing literature. The paper aims to capture state of the art and develop an exhaustive understanding of the theoretical foundations, concepts and recent developments in the area.

Design/methodology/approach

A portfolio of 125 articles collected from the Scopus database was systematically analyzed using a two-tier method. First, the evolution, current state of the literature and research clusters are identified using bibliometric techniques. Finally, using content analysis, the research clusters are studied to develop the future research agenda.

Based on the bibliometric analysis, network analysis and content analysis techniques, this study provides a comprehensive review of the existing literature. The study also highlights future research themes by identifying knowledge gaps based on content analysis of research clusters.

Research limitations/implications

The evolution and the current state of the HR analytics literature are presented. Some specific research questions are also provided to help future research.

Originality/value

This study enriches the literature of HR analytics by integrating bibliometric analysis and content analysis to develop a more systematic and exhaustive understanding of the research area. The findings of this study may assist fellow researchers in furthering their research in the identified research clusters.

• Literature review
• HR analytics
• Bibliometrics
• Human capital analytics
• Workforce analytics

Acknowledgements

The authors would like to express their sincere gratitude to the two anonymous reviewers and Dr. Chester Spell (associate editor) for their constructive and helpful feedback on the earlier version of the present manuscript.

Qamar, Y. and Samad, T.A. (2022), "Human resource analytics: a review and bibliometric analysis", Personnel Review , Vol. 51 No. 1, pp. 251-283. https://doi.org/10.1108/PR-04-2020-0247

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