Abstract
Purpose
This study explores the rationale behind the utilisation of human resource (HR) analytics in public sector organisations. The existing HR analytics literature exhibits limited empirical evidence and has predominantly focused on the business context of private firms. In addressing this gap, the study seeks to answer the following research question: What reasons for the adoption of HR analytics can be identified in public sector organisations?
Design/methodology/approach
The study employs a qualitative approach drawing on the empirical data collected from three public administrations in Sweden – national, regional and local. A total of 51 in-depth interviews are conducted with HR and other organisational practitioners engaged in HR analytics practices.
Findings
Drawing on the institutional legitimacy perspective, this paper suggests that public sector organisations adopt HR analytics to secure cognitive, socio-political and technological legitimacy, stemming from explanations rooted in economic rationality. This encompasses organisational and HR-related outcomes achieved through data management and analysis, driven by the personal interests of specific individuals.
Research limitations/implications
This study contributes to ongoing debates about the adoption of HR analytics in diverse contextual settings. Future research is needed in other organisational contexts, including various national and international settings.
Practical implications
The results of this study offer practical insights for HR practitioners in public sector organisations seeking to adopt HR analytics to enhance organisational and HR legitimacy.
Originality/value
This study contributes to the HR analytics literature by providing empirical evidence from the public sector. Furthermore, it advocates for a synthesis of economic rationality with legitimacy gains and individual interests to elucidate the rationale behind the adoption of HR analytics.
Keywords
Citation
Espegren, Y. (2024), "Reasons for HR analytics adoption in public sector organisations: evidence from Swedish public administrations", Personnel Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/PR-03-2024-0219
Publisher
:Emerald Publishing Limited
Copyright © 2024, Yanina Espegren
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Rapid changes in technology development, such as the ever-increasing speed of computer processing, the availability of multiple data, and improved methods of data analysis, have had a profound impact on organisations and their operations, including human resource management (HRM) (Tursunbayeva et al., 2022). Human resource (HR) analytics, an HR practice that involves technology-driven statistical analysis of various data to make more objective, rational, and effective employee-related decisions, has generated significant interest from organisations and has been a topic of discussion among HR practitioners and academics for over a decade (Margherita, 2022).
HR analytics has been recognised as an important area for HR investment (Ulrich and Dulebohn, 2015) and labelled as a “game changer” for the future of HR (Van der Togt and Rasmussen, 2017). Consequently, there is a significant increase in the number of organisations adopting it (McCartney et al., 2021; McCartney and Fu, 2022). According to Deloitte (2018), 84% of organisations identified HR analytics as the second most important HR trend, and Orgvue (2019) indicated that 89% claimed to be already utilising HR analytics. Although public sector organisations being reported to lag behind their private counterparts (Boston Consulting Group, 2016), HR analytics is now incorporated into their strategic plans as well (EY, 2022).
While practical interest in HR analytics is growing, empirical evidence on its use remains limited (Qamar and Samad, 2022; Edwards et al., 2022), particularly in the context of public sector organisations, where research is significantly less developed compared to the private sector. Existing literature, which is predominantly conceptual, focuses largely on private firms, outlining potential business-related benefits of HR analytics, such as enhanced profitability and return on investment (Chalutz Ben-Gal, 2019), competitive advantage (Minbaeva, 2018), and improved alignment with business strategy (Levenson, 2018). Furthermore, studies highlight various organisational and technical challenges associated with HR analytics adoption and implementation, including a lack of analytical skills among practitioners, issues with data quality and availability, high costs of technology, limited managerial support, and legal and ethical concerns (Fernandez and Gallardo-Gallardo, 2021; Giermindl et al., 2022). There is also widespread critique of HR analytics as potentially being a management fad (Angrave et al., 2016). Against this backdrop—and given the single bottom-line focus of much existing literature, along with the general lack of empirical studies within the public sector—it remains unclear what drives public sector organisations to adopt HR analytics.
This topic is particularly compelling given that public sector organisations might logically be less inclined to adopt HR analytics compared to their private sector counterparts. Public sector organisations are often seen as less advanced in their HRM practices (Vanhala and Stavrou, 2013), less digitally mature (OECD, 2020), and less driven by profit maximisation and market competition (Cunningham, 2016)—all key factors that typically support HR analytics adoption. Additionally, they operate under strict budget constraints and face pressures from competing values, such as democratic accountability and societal welfare (Fredriksson and Pallas, 2016). Consequently, the public sector, including its HRM functions, diverges from the private sector in significant ways (Boyne, 2002; Boyne et al., 1999), which likely affects the types of HR practices adopted and the motivations behind their adoption. It has also been argued that managerial practices effective in business environments may not necessarily apply to other types of organisations (Blom et al., 2020).
In the literature, numerous calls have been made for more empirical research on HR analytics in general, particularly within distinct contexts such as the public sector (Belizón and Kieran, 2022; Qamar and Samad, 2022; Angrave et al., 2016). The public sector is considered unique and essential (Knies et al., 2018), yet empirical studies focussing on HR analytics in this context are nearly absent despite growing practical interest. These factors underscore the importance of examining HR analytics within public sector organisations, as this represents a significant gap in the current research. The aim of this paper, therefore, is to enhance our understanding of the rationale behind the utilisation of HR analytics in the public sector. This exploratory qualitative study of three Swedish public administrations was conducted to address the following research question: What reasons for the adoption of HR analytics can be identified in public sector organisations?
The existing HR analytics literature, mostly centred on private business firms, suggests competitive advantage and financial profit as key reasons for HR analytics adoption. However, public sector organisations, less influenced by competitive business markets, prioritise perceived appropriateness and acceptance within their social context (Rainey and Cook, 2014), indicating that legitimacy considerations may also be at play. Drawing on the institutional legitimacy perspective, this paper suggests that public sector organisations adopt HR analytics to ensure cognitive, socio-political, and technological legitimacy derived from economic rationality explanations driven by the personal interests of particular individuals.
The contribution of this paper is twofold. First, it advances the existing literature by proposing a legitimacy-based framework for understanding the multilevel reasons behind HR analytics adoption. This model integrates economic rationality, individual interests, and organisational legitimacy into a cohesive structure. Second, the paper adds empirical insights to HR analytics literature by offering a detailed contextual analysis and evidence on why public sector organisations are motivated to adopt HR analytics. Additionally, the study seeks to enrich the broader debate on public sector digitalisation by elucidating why public sector organisations utilise data in their HR operations. This study is one of the first empirical explorations of HR analytics within a public sector context.
The paper is structured as follows: the theoretical background is presented with an emphasis on the institutional legitimacy approach, followed by a review of current HR analytics literature. This is followed by a description of the research methodology and the introduction of the studied organisations and participants. Subsequently, the findings are presented and discussed, culminating in the conclusion.
Theoretical background and literature review
This section reviews existing literature on the reasons for adopting HR analytics, focussing on the perspectives of institutional legitimacy and economic rationality. These two approaches offer distinct yet complementary insights on why public sector organisations might implement HR analytics, each with unique merits for understanding this phenomenon.
The institutional legitimacy approach emphasises the importance of social acceptance and appropriateness within specific contexts. It suggests that organisations adopt HR practices, including HR analytics, not solely for efficiency but also to align with external expectations and norms. This perspective is particularly relevant for public sector organisations, where aligning with social norms and stakeholder expectations is critical. It highlights motivations for adopting HR analytics beyond economic gains, offering a nuanced understanding of public organisations’ challenges.
Conversely, the economic rationality approach posits that organisations adopt HR analytics primarily to enhance performance and efficiency. The literature largely focuses on the tangible benefits of data-driven decision-making, such as improved organisational performance, increased operational efficiency, and enhanced employee well-being. While this approach provides a robust framework for understanding the benefits of HR analytics, it often emphasises competitive pressures and profit motivations, which may not align with the fundamental objectives of public sector organisations.
Both the institutional legitimacy and economic rationality perspectives offer valuable insights into HR analytics adoption motivations in public sector organisations. The former underscores the necessity of aligning HR practices with social norms and stakeholder demands, while the latter highlights practical benefits applicable to public sector contexts, albeit differently from the private sector. Together, these approaches provide a comprehensive understanding of HR analytics adoption, illustrating that public sector organisations are influenced by a combination of legitimacy concerns and economic imperatives. Recognising the interplay between these dimensions can enhance theoretical discourse and offer practical insights for HR leaders, guiding them toward a more informed and holistic adoption of HR analytics that addresses their operational realities and stakeholder expectations.
Institutional legitimacy
While empirical research on the reasons behind organisations adopting HR analytics is still limited, the broader HRM literature suggests a perspective grounded in institutional theory (Paauwe and Boselie, 2005). Organisations adopt HR practices not only for the sake of economic rationality per se, such as efficiency, but also to conform to institutionalised myths in the organisational environment, ensuring organisational legitimacy (Meyer and Rowan, 1977). Legitimacy is a fundamental concept of institutional theory that influences how organisations behave (Deephouse et al., 2018). It is generally defined as the extent to which an organisation is perceived as appropriate for its social context (Suchman, 1995).
The concept of legitimacy has been widely used in the general HRM literature to explain the adoption of different HR practices (Lewis et al., 2019), as well as to study the credibility of HR function and the HR profession (Heizmann and Fox, 2019). In relation to HR analytics, Belizón and Kieran (2022) propose the application of an entrepreneurial legitimacy approach based on the work of Aldrich and Fiol (1994). This approach is particularly fitting for studying the reasons for the adoption of HR analytics, as HR analytics represents a new technology-driven innovation in the early stages of diffusion (Marler and Boudreau, 2017; Coolen et al., 2023). Aldrich and Fiol (1994) suggest two forms of legitimacy: cognitive and socio-political. Belizón and Kieran (2022) supplement this multifaceted view with technological legitimacy, as technology plays an important enabling role in leveraging HR analytics.
Cognitive legitimacy refers to the spread of knowledge regarding new activities (Aldrich and Fiol, 1994). Understanding the content of HR analytics, its benefits, and what it can do for HR and organisation constitutes a significant aspect of organisational legitimation through HR analytics. Although all new activities typically begin with low cognitive legitimacy, HR analytics presents a particularly challenging case due to its HR context. The HR function has gained notoriety for its lack of analytical skills, data literacy, technological proficiency, comprehension of overall organisational operations, and understanding of how these elements relate to HR’s potential contribution—all essential components of the complex cognitive construct of HR analytics (Angrave et al., 2016). Consequently, the degree to which organisations perceive their legitimacy through HR analytics depends on their ability to comprehend and effectively utilise HR analytics in practical settings (Belizón and Kieran, 2022).
Socio-political legitimacy denotes the collective acceptance of an organisation, its operations, and practices as appropriate and just by various actors, including key stakeholders within a specific institutional context and the general public (Aldrich and Fiol, 1994). Public organisations are surrounded by diverse external stakeholders, including politicians who set political priorities and agendas for organisations to fulfil, regulatory and controlling authorities, the media, other organisations, and the general public, who are the primary consumers of public services. These stakeholders often influence the decisions and actions of public organisations, including matters related to public employment and HR operations. Furthermore, HR analytics involves multiple internal stakeholders such as HR practitioners, senior leadership, managers at various levels, HR data users, and even employees. Both internal and external stakeholders, whether consciously or unconsciously, demand quantifiable measures regarding the state of organisational workforce, HR policies and operations, and their impact on organisational and societal value for citizens.
Finally, technological legitimacy concerns the availability of technology to achieve specific organisational aims (Belizón and Kieran, 2022). Technology is advancing rapidly and is now being applied in various areas of human activities. Digitalisation, the use of digital technologies and data, is generally viewed as a modern positive trend in society due to its promises of efficiency and effectiveness gains (Fernandez and Gallardo-Gallardo, 2021). Organisations that implement different technological solutions are perceived as up-to-date and progressive. HR analytics is a technology-driven HR innovation that acknowledges the importance of information technology to collect, store, make accessible data from different organisational informational systems, produce reports and analysis, and visualise them using dashboards and scorecards (Marler and Boudreau, 2017).
In summary, building upon the aforementioned institutional perspective, this study proposes that HR analytics, serving as a mechanism for ensuring legitimacy, is grounded in the fundamental principle of economic rationality widely endorsed within the organisational context. The existing HR analytics literature extensively explores the economic rationality rationale for adopting HR analytics, which will now be reviewed.
Economic rationality
The majority of existing HR analytics literature advocates for economic rationality, suggesting the potential for analytics to improve organisational performance and economic efficiency (e.g. Huselid, 2018; Tursunbayeva et al., 2018). Often, these reasons are not explicitly discussed but are rather implied as potential outcomes and opportunities for organisations and HR (Marler and Boudreau, 2017). HR analytics is believed to influence organisational performance through enhanced decision-making facilitated by data and their analyses (Rasmussen and Ulrich, 2015), aimed at either business-related or HR-related organisational outcomes (Espegren and Hugosson, 2023). These reasons for HR analytics, i.e. data analysis, business-, and HR-related outcomes are summarised below.
Different types of data and their analysis encompass descriptive, predictive, and prescriptive analytics (Margherita, 2022). Descriptive analytics aims to delineate what happened in the past, including traditional HR reporting. Predictive analytics anticipates what might occur in the future, utilising historical data to forecast HR developments such as future employee attitudes, behaviour, and performance (Giermindl et al., 2022). Prescriptive analytics seeks to offer recommendations for managerial decisions on what should be done, relying on even more advanced analyses of existing data, such as computer simulations and machine learning algorithms (Gal et al., 2020).
Business-related organisational outcomes encompass improved financial performance, increased competitive advantage, and enhanced strategy execution. The potential to ensure HR impact on business and improve financial outcomes, such as sales, revenue, profit, and return on investment (Chalutz Ben-Gal, 2019; Fernandez and Gallardo-Gallardo, 2021), is among the proposed reasons for companies' interest in HR analytics. For instance, Simón and Ferreiro (2018) found in their study of a fashion retailer that HR analytics is utilised to increase store productivity, measured by levels of sales and revenues. Similarly, Schiemann et al. (2017) describe how HR analytics in a fast-food restaurant chain can impact all financial measures, including reduced costs and increased profit. Another often-suggested reason for the adoption of HR analytics is the potential to gain competitive advantage (Minbaeva, 2018; Huselid, 2018) through the analysis of HR-related data. Davenport et al. (2010) suggest that using analytics in talent management, specifically to identify and manage high-performing employees, might constitute a long-term competitive advantage. Effective business strategy execution is another frequently discussed business-related outcome in the literature (Levenson and Fink, 2017; McIver et al., 2018). For instance, Levenson (2018) argues that HR analytics should be adopted to address critical business issues and strategically important areas rather than focussing solely on incremental improvements in operational outcomes.
While business-related reasoning predominates, some studies also suggest HR-related outcomes, such as the improvement of HR operational work and employee benefits. HR analytics, in practice, can be applied to various HR operational activities, including recruitment and selection, workforce planning, training and development, performance appraisal, and employee retention (Chalutz Ben-Gal, 2019; Giermindl et al., 2022). It has the potential to advance these activities and contribute to more effective and efficient HR functions. For example, Brandt and Herzberg (2020) suggest an increased efficiency of employee recruitment by applying HR analytics to predict job application success through the analysis of data from CVs and cover letters. Another illustration is provided by Frederiksen (2017), who outlines several ways to predict employee turnover using data from job satisfaction surveys. HR-related reasons for HR analytics also assume the potential to generate employee-related value (Margherita, 2022; Tursunbayeva et al., 2018). In a review of existing research, Giermindl et al. (2022) highlight the possibilities of offering health benefits to employees and improving their job experience, making them happier and more productive. For instance, high-performing employees might be easily identified and offered better career opportunities (Huselid, 2018), which is not only valuable for the organisation but also for employees and their individual development. HR analytics may also impact employee health, for example, by analysing health data from their wearable devices or monitoring their computer work routines to suggest recommendations for healthier behaviour (Gal et al., 2020).
Methodology
To comprehend the rationale behind the adoption of HR analytics in public sector organisations, a qualitative methodology was employed as an appropriate means to explore the “why” of a particular phenomenon (Eisenhardt, 1989). The data were collected through semi-structured interviews with representatives from three public sector organisations in Sweden and analysed using the Gioia methodology (Gioia et al., 2013).
Research context
This study is conducted in the Swedish public sector context. Sweden serves as a suitable geographical setting due to its one of the largest public sectors with high government spending, employing almost 30% of the country’s workforce (SCB, 2024). Furthermore, Sweden scores lowest on the Digital Government Index, reflecting the limited adoption of digital technologies and data in public organisations (OECD, 2020), making it an interesting context to explore the reasons for the adoption of HR analytics despite the challenging technological prerequisites.
To ensure representativeness of the public sector context, this study’s data was collected from three organisations, each representing a distinct level within the Swedish public sector: national (state authorities), regional (regions), and local (municipalities). Despite their structural differences, these three levels share a common characteristic—they are governed by democratic elections and, unlike private organisations, prioritise citizens' service and safety over profit, as they are not bound by the same requirements for capital returns as private entities (Cunningham, 2016). State authorities operate nationwide and manage tasks delegated by the national government, including areas such as defence, higher education, and tax collection. The state authority included in this study is specifically responsible for long-term infrastructure planning for road and rail traffic, shipping, and aviation, as well as for building and maintaining national roads and railways. Regions are responsible for services within larger geographic areas, primarily focussing on healthcare and public transportation (Swedish Association of Local Authorities and Regions, 2024). The regional administration studied here represents one of Sweden’s largest geographic regions, overseeing healthcare services (e.g. hospitals, primary care, dental care), regional development, public transportation, and cultural activities. Local municipalities represent the lowest level of government and are primarily responsible for community services. The local administration included in this study is a mid-sized Swedish municipality, overseeing social services such as elderly care and special needs support, as well as managing primary and secondary education, including preschools and elementary schools.
The selection of these organisations was guided by several criteria. First, each organisation needed to represent the Swedish public sector at a different level—national, regional, and local—to provide a comprehensive perspective on public sector operations. Second, the selected organisations had to have a relatively large workforce to ensure the availability of sufficient employee-related data for an adequate HR analytics foundation. While varying in size, each organisation has a substantial number of employees, with the national Authority employing over 10,000, the Region 53,000, and the local Municipality 5,000. Additionally, all three organisations have complex structures with multiple organisational units, contributing to the substantial volume of personnel data collected and stored—key prerequisites for effective HR analytics (Fernandez and Gallardo-Gallardo, 2021). Third, the organisations selected had to have initiated the digitalisation of their HR functions and actively engage with basic HR analytics practices, such as utilising numerical data and conducting elementary analyses, as well as showing an ambition to expand HR analytics in their operations. All three organisations demonstrated a clear interest in HR analytics, initiating various developmental processes aimed at enhancing the availability, use, and analysis of HR data. Current practices include data-driven personnel reporting through customised dashboards, basic descriptive analysis of HR metrics, benchmarking with peer organisations, and the use of data visualisation tools. However, during data collection, it was evident that HR analytics maturity within these organisations remains in its early stages, with a primary focus on descriptive analytics that relies on administrative HR data and focuses on past events. Nevertheless, there is a stated ambition to develop these capabilities further. Lastly, given the vast scope of the Swedish public sector, encompassing many organisations across various subsectors, this study specifically focuses on public administrations—entities directly connected to government functions at the national, regional, and local levels and responsible for implementing political decisions. This focus provides a certain degree of operational homogeneity, as individual smaller entities such as hospitals, schools, or elderly care homes are not considered as standalone entities but rather as integral units within larger public administrations that govern and oversee these institutions.
Interviews
A total of 51 semi-structured interviews (Brinkmann and Kvale, 2018) were conducted with various organisational representatives. An overview of the participating organisations and study respondents is presented in Table 1.
The interviews were conducted with both HR and non-HR practitioners engaged in HR analytics practices. HR practitioners encompassed both generalists and specialists from various organisational levels, including HR head offices and HR units. This group comprised HR directors, HR managers, HR partners, HR strategists, and HR controllers. HR practitioners were targeted because the process of HR analytics development in all three studied organisations was initiated by the HR departments, thus making them the most knowledgeable informants and “knowledgeable agents” (Gioia et al., 2013) to provide insights into the reasons for its adoption in their respective organisations.
Additionally, non-HR practitioners were also interviewed, such as representatives from IT, finance, and organisational line managers. This step was taken to ensure internal validity and triangulate the collected data. Non-HR respondents were included due to their direct involvement in HR analytics practices, either as producers (e.g. IT specialists) or users (e.g. line managers).
Two guiding criteria were applied when selecting informants. Firstly, interviewees were required to have direct involvement in organisational practices linked to HR analytics, such as the strategic or operational usage of HR data and their analysis. Secondly, interviewees were chosen to represent different organisational levels, central and operational units, and various functions (HR and non-HR) to ensure a holistic understanding of the organisational reasons for the adoption of HR analytics.
Although there were no formally established operational HR analytics departments and teams in the studied organisations, HR directors and practitioners closely involved in HR analytics practices were selected as key informants. Other respondents were then contacted through them. Moreover, the snowball sampling technique was used, with previously interviewed respondents suggesting suitable interviewees. Relevant permission for the research project was obtained from the Swedish Ethical Review Authority (reference number 2021-00651). All study participants were provided information about the project, and informed consent was obtained from each of them.
All interviews were conducted in Swedish, lasted between one and a half to two hours, were digitally recorded, and were further transcribed verbatim. All quotations were translated from Swedish to English by the author of this study. The interviews broadly focused on the current state of organisational strategic and operational work with HR-related data and their analysis, encompassing the reasons for HR analytics adoption, as well as organisational expectations and aspirations for the future. The interview guide covered general topics about respondents' responsibilities, organisational and HR strategy, HR data and measurements, and HR analysis and reporting. The interviews allowed for flexibility, and respondents were encouraged to discuss matters they found most relevant and important, raising topics as they wished. Questions were open-ended and modified appropriately during the interviewing process. To ensure the credibility of respondents’ answers, a critical incident technique (Butterfield et al., 2005) was applied. Interviewees were asked to describe their experiences and provide specific illustrative examples, rather than relying on theoretical concepts from the existing academic literature, which remains confusing and scattered (Levenson and Fink, 2017).
Data analysis
The interview transcripts were uploaded into NVivo14 software and analysed using the Gioia methodology (Gioia et al., 2013), one of the most frequently employed qualitative data analysis methods in business and management research (Gehman et al., 2018). The primary characteristic of this method is the development of a data structure, combining 1st-order terms, 2nd-order themes, and aggregate dimensions to construct a theoretical model and make sense of the findings.
The first phase of the analysis commenced during the interview process to ensure the collection of relevant information necessary to address the research question and adjust data collection accordingly. In the second stage of the analysis, the interview transcripts were meticulously read and reread to gain a thorough understanding of the data. Initial coding, based on the informants’ terms, was then conducted. More than 100 initial codes were generated, continuously refined, compared, separated, and merged.
Seeking similarities and differences between the 1st-order codes and moving back and forth between the interview transcripts and the literature, the number of codes was subsequently reduced to eight 2nd-order themes. These themes were further distilled into three aggregate dimensions, forming the data structure used to interpret the findings. In the final stage of the analysis, a theoretical explanation was proposed for the identified reasons for the adoption of HR analytics in public sector organisations.
Findings
In the empirical data, several reasons for the adoption of HR analytics were identified, which were categorised into three general groups reflecting different types of explanations: economic rationality, institutional legitimacy, and individual reasons. The results are graphically represented in the theoretical model depicted in Figure 1, illustrating the three levels of reasoning. When taken together, these reasons are suggested to explain the rationale behind HR analytics adoption.
Economic rationality arguments were found to predominate organisational explanations for HR analytics. Therefore, they are centrally placed in the model in Figure 1. Better data management and data analysis, identified as two reasons for HR analytics adoption, are not only interconnected, as visualised by the thicker arrow (i.e. analyses are not possible without data), but also linked to various HR-related and organisational outcomes, as depicted by the thinner arrows. For example, simple descriptive sick leave data disaggregated to the department level might be utilised to prioritise HR activities aimed at improving employee health and performance. Concurrently, providing sick leave data in real-time is a prerequisite for more effective work planning and scheduling, consequently improving organisational operational and financial performance. Similarly, HR-related outcomes are also tied to organisational outcomes, as represented by the thicker arrow between them. All HR activities, in order to deliver value, need to be aligned with broader organisational outcomes, such as organisational efficiency and effectiveness.
While economic rationality is found to dominate organisations’ explanations for the adoption of HR analytics, it serves as an intermediary step ideologically justifying institutional legitimacy. It means that organisations adopt HR analytics to build legitimacy by drawing on the economic rationality of data-driven HR practices. Economic rationality and institutional legitimacy are thus not mutually exclusive; rather, they complement each other, with economic rationality serving as a necessary institutionalised frame of reference used to gain legitimacy as an informed, recognised, and technologically modern public organisation. Additionally, it has been found that the individual interests of certain practitioners appear to affect adoption decisions. Below, these findings are presented, illustrated with quotations from the interview transcripts. For additional illustrative examples, refer to Table 2.
Economic rationality
First, the results for the identified reasons falling under the level of economic rationality are presented. These include data management, data analysis, organisational, and HR-related outcomes.
Many respondents highlighted data management as a crucial reason. HR analytics is perceived as a tool to systematically handle the numerous data collected and stored in organisations, primarily in different HR information systems such as payroll systems, recruitment systems, and employee attitude measurement systems. According to all respondents, their organisations possess a wealth of potentially useful HR data that, if organised systematically, could provide an opportunity for further analysis. A common understanding among all respondents is that establishing effective data management is a significant reason for all work with HR data and its subsequent analysis. In addition to the systematic approach to HR data, considerable attention was given to the need for linking together different functional data, such as financial, operational, and HR. The willingness to engage in HR analytics was often explained by the need to have easier access to different data sources, enabling a more holistic view of the organisation or a particular organisational unit, rather than relying solely on personnel-related numbers, which are not considered sufficient by themselves. For instance, in the Region, sick absence data from the HR information system alone was deemed insufficient to provide a full picture of the current situation in a hospital. Combining it with financial data about the costs of such absence, along with relevant patient health data, was considered essential. An HR developer from the Region reflected on this issue:
A great deal of work is done in silos. HR analytics is an attempt to work much more broadly. The idea is that we would bridge data silos from different organisational functions and units.
The need for data analysis is another frequently mentioned reason for the adoption of HR analytics by our respondents. It is closely linked to the need for data management because the availability of systematised and easily accessible data is seen as a prerequisite for further analysis. The types of possible analyses range from the very basic, such as more effective reporting of descriptive HR metrics and benchmarking, to more sophisticated predictions of employee behaviour and organisational futures. The most frequently mentioned reason for adopting HR analytics was the need for improved HR reporting that is more efficient and effective. Instead of dedicating much time and resources to manually extracting relevant data, the respondents see numerous advantages in the automated reporting process, with the possibility to break down information to relevant organisational levels, such as units, departments, and even teams. Other types of descriptive analyses are also seen as reasons for HR analytics adoption. Many respondents discussed HR measurement and benchmarking. Calculating different HR measures grounded in data is often seen as a relevant goal for HR analytics. A data strategist from the Region described a situation when myths and rumours about personnel numbers commonly arose among different organisational actors. For instance, during the COVID pandemic, it was widely believed that almost all nurses within the health care sector were quitting their jobs, contributing to anxiety and negative attitudes not only among the employees but also among managers. The adoption of HR analytics, with its ability to reveal actual and relevant measures of staff turnover grounded in numbers and available in real-time, helped address such questions. It turned out that most of the nurses who quit were at the age of 60, which means that they were about to retire soon in any case. The most valuable group of nurses in their 25–40 years was not as much affected.
While the respondents mostly discussed simple descriptive analyses when explaining why their organisations are interested in HR analytics, some of them also mentioned the desire for more sophisticated analyses beyond mere descriptions. This aligns with the current state of HR analytics in the studied organisations, where descriptive analyses are already implemented for HR reporting and visualisation of HR metrics. However, more advanced analyses, such as predictions, are also seen as the ultimate goal of HR analytics efforts and an aspiration for the future. An HR manager from a healthcare unit in the Region expressed this desire for more advanced analysis:
Most of the time, the manager already knows how many people were sick last month. It's like yesterday's news for the managers. They would rather know how many will be away next week, next summer, next year.
At the same time, some respondents do not entirely endorse the value of more advanced HR analysis for their organisations. According to an HR partner from the Authority,
Predictions might not be valuable for us because our organisation is constantly changing. There are new political decisions all the time. In such a dynamic context, it is challenging to predict because there are numerous other parameters that can change. It might be intriguing, but the question is whether it's exciting for a government authority like us? Or whether it's more appealing for different types of companies, I don't know.
Leveraging HR data and analysis is seen as an intermediate step towards more general goals. Most of the participants discussed HR-related outcomes as a final reason for why their organisations adopt HR analytics. They see the potential to apply HR analytics to increase the effectiveness of HR operational and strategic work, including identifying areas for HR actions and following up on the effects of such actions. For example, according to a work environment specialist in the Region, following up on HR policies is a relevant task for HR analytics:
We have reduced the number of employees per manager, implemented smaller work groups to create a better working environment. It is such a question that you would like to evaluate.
HR analytics is believed to contribute to many HR functional areas, such as competence supply, e.g. recruitment and retention, workforce planning, employee motivation and performance, reducing sick absence and employee turnover, work environment, and managing different types of employees, e.g. contractors. One of the most prominent reasons for HR analytics adoption is the willingness to address competence supply issues. Participating organisations experience challenges in attracting and retaining personnel. The Region and the Municipality were mostly concerned about the health care sector, e.g. nurses and care personnel, while the Authority observed a shortage of technical specialists. In addition to assisting organisations in attracting and recruiting new employees, HR analytics also has the potential to enhance managing existing competence. According to a digitalisation expert in the Authority,
We would like to use [HR analytics] to do a competence mapping and see what competences we have, which ones are available, how we can prioritise … I think today you can do it in a completely different way.
The desire to achieve HR-related outcomes can be a self-sufficient reason for HR analytics adoption, but often the respondents discussed it in connection with more general organisational outcomes, such as the improvement of financial measures, the development and execution of organisational strategies, the need for managerial control, and the assessment of reaching overall organisational goals. These reasons for HR analytics adoption are closely related to the general HR willingness to impact main operational activities, rather than focussing only on HR-related areas. The respondents say that HR contribution to the main operations is the important goal for HR analytics. According to an HR manager from the Region:
It is self-evident that we want to know if personnel feel well. But if we feel very well according to the survey, does it affect sick absence, does it affect how many people apply for internal positions? But, most importantly, does it affect the service, the number of errors we make, how our hospital patients are treated? It is this connection we would like to see.
It is interesting that despite the absence of profit gain expectations in the studied organisations, the respondents mentioned potential financial outcomes as a reason to use HR analytics. Improving stuffing efficiency, reducing costs, and effective use of budgets were named as relevant areas for HR analytics to add value. This is how one line manager from the Authority described it with the example of sick absence:
[HR analytics] is used to reduce costs. We have clear goals for sick absence numbers. We made an action plan based on them because we saw that there is a financial gain in reducing sick absence.
However, the potential financial gains are viewed from a different perspective than reported in the existing literature focused on the business context, which emphasises financial measures such as profit, sales, and return on investment. The participants see financial reasons as part of a larger perspective of being tax-financed public services. Another interesting finding is that the respondents also mentioned HR analytics' potential for assessing success in reaching organisational goals. Often, these goals are based on strategic directions outlined in overall organisational plans, constantly monitored by politicians and top management. The goals requiring attention from HR analytics are linked to the HR area; for instance, sustainability, diversity, and becoming an attractive employer were frequently mentioned as relevant goals. For example, according to an HR specialist in the Region, the achievement of the attractive employer goal can be assessed by measuring and analysing indicators such as the number of applicants per position, the number of returning employees who once left the organisation but returned, and the number of internal recruitments, among others.
Institutional legitimacy
While economic rationality reasoning emerges as a crucial theme, it alone does not suffice. Economic rationality is found to constitute a fundamental principle upon which legitimacy rests. The findings uncover evidence supporting the institutional legitimacy explanation, revealing distinct themes related to cognitive, socio-political, and technological legitimacy, as described below.
Cognitive legitimacy pertains to the understanding of what HR analytics entails and how it contributes to organisational value. While many respondents acknowledged the general utility of HR analytics as a tool for making HR more evidence-based, replacing gut feelings with facts, a considerable number expressed uncertainty about its specifics and how they can derive concrete benefits from it. The somewhat ambiguous responses regarding the reasons for adoption were attributed to this uncertainty about the content of HR analytics. A data strategist, responsible for implementing an HR analytics project in the Region, articulated this frustration:
The initial discussions were somewhat vague: “We want to know everything. We need to understand how everything is connected and so on.” It has been incredibly frustrating for me that the need for [HR analytics] isn't clearly perceived.
The strategist further elucidated that the comprehension of the reasons behind HR analytics adoption and the scope of its application evolved during the implementation process. Identifying fundamental prerequisites, such as the absence of various data, prompted initial efforts to make them available. This initial phase laid the foundation for viewing HR analytics as a technical solution for data accessibility. The understanding then expanded to encompass HR metrics, reporting, visualisation, and the establishment of potential prerequisites for future statistical analyses. Thus, the adoption of HR analytics unfolds concurrently with the construction of its content, contributing to its cognitive legitimacy.
In addition to cognitive legitimacy, it was identified that HR analytics is adopted to gain acceptance for various organisational activities, including HR activities, from multiple stakeholders both within and outside the organisation, thereby acquiring socio-political legitimacy. Respondents revealed that HR analytics assists in meeting the expectations of diverse organisational stakeholders, including national and local politicians, media representatives, organisational leaders, managers, and practitioners in various functional areas, predominantly within HR and finance across multiple organisational units with diverse operational activities. At the core of HR analytics are aggregated numbers, data, and statistical analyses, widely recognised in general managerial professions as a legitimate foundation to claim verifiable facts for the broader public, underpin decisions, and assess organisational success. Respondents explicitly acknowledged the benefits of HR analytics in aligning with political agendas and addressing requests from politicians, thus legitimising their organisational existence and the effectiveness of personnel policies. The HR director in the Municipality highlighted that the local political majority formulated a plan for the local administration, with specific points directly addressing the HR department, such as efficient staffing, reduction of sick absence among elderly care personnel, implementation of trust-based leadership, and becoming an attractive employer. The HR director further emphasised that HR analytics has the potential to address these points, providing accounts to report back to politicians and demonstrating the legitimacy of HR operations in the Municipality.
Another crucial stakeholder with a constant interest in the activities of public organisations and their fulfilment of welfare missions is the media. HR analytics is perceived as a tool to inform the media, gaining legitimacy among the wider public and citizens by disseminating trustworthy and reliable information. According to an HR manager from an organisational unit in the Authority,
Public organisations can expect a certain level of scrutiny, both from politicians and evaluating authorities, as well as from the media and citizens. Transparency about one's resources, costs, work environment, and personnel issues, in general, is what citizens anticipate from a government employer.
While gaining legitimacy among external stakeholders, such as politicians, media, and the general public is a significant driver for leveraging HR analytics, another prevalent reason discussed by the respondents is to support internal stakeholders, such as managers in organisational units in their decision-making activities. Providing timely and easily accessible HR data and their analysis is deemed to be an important aim for HR analytics. It is perceived as an instrument to gain internal legitimacy among line managers, both in central functions and organisational units, as well as among other functional practitioners. To secure such legitimacy, HR analytics is argued to align with the diverse needs of complex organisational units and departments that may require tailored support based on their daily operations. Furthermore, HR analytics can be utilised to technically support personalised data availability on different organisational levels, such as units, divisions, departments, and teams. An HR director in an organisational unit within the Region succinctly summarises it as follows:
[HR analytics] has been about simplifying for managers and assisting their various tasks.
Finally, technological legitimacy was identified as a reason why organisations adopt HR analytics. The interviewees discussed HR analytics as a technological innovation capable of positively influencing their organisational legitimacy, especially in the HR domain. According to an HR partner in the Authority:
For our organisation, it is imperative to be a modern technological authority. It should be integrated into all aspects of our operations, including our personnel work and our employees.
By developing technical solutions for data availability, analysis, automated HR reporting, and more effective visualisation, public organisations contribute to technological legitimacy, particularly for the HR function, which traditionally has not been viewed as technologically advanced compared to other functions. Several ways in which organisations are establishing technological legitimacy through HR analytics were identified. The study’s respondents predominantly discussed the significance of creating technological prerequisites for various types of HR analytics. These prerequisites include building an integrated data repository for storage and retrieval, encompassing different functional data, ensuring the feasibility of statistical analyses, implementing automated HR reporting tailored to the specific needs of various organisational users, and visualising HR data and measurements using easily navigable platforms. The importance of more effective technology-enabled HR reporting and visualisation is also recognised as a contribution to the technological legitimacy of HR analytics.
Individual reasoning
The adoption of HR analytics, as explained by the respondents, is often driven by the interest and initiative of a select few individuals, particularly those with an educational background or experience in utilising numerical and analytical methods in their professional roles. While many HR practitioners are perceived as more inclined towards the “soft” aspects of their field and may lack technical and analytical skills, there are individuals in the studied organisations with the expertise and knowledge to advocate for the integration of numerical and evidence-based approaches in HR. Typically, these individuals originate from diverse educational backgrounds, such as technical and natural sciences rather than humanities or social sciences, or have experience working with numbers in fields such as IT, finance, and reporting. These driving individuals possess a keen interest in promoting the use of numbers, coupled with a relevant background and proficiency in mathematical, statistical, technical, and analytical skills together with the personal characteristics inclined towards data analysis. According to an HR manager from the Authority responsible for initiating HR data and analytics efforts, having an individual with extensive experience in technical system administration and a self-driven interest in new technology proved crucial to the initiative’s success:
We initiated [HR analytics] much thanks to Y [HR system administrator]. She is the driving force behind almost every aspect. Her extensive experience with [HR system], coupled with a keen interest in new technology, is noteworthy, and she is essentially self-taught.
Discussion
This exploratory study aims to identify the rationale behind the adoption of HR analytics in public sector organisations. It reveals reasons linked to institutional legitimacy derived from economic rationality arguments influenced by the individual interests of certain individuals.
The findings regarding economic rationality conform with the existing HR analytics literature, though the empirical literature in the field is limited and generally focussing on business companies (Qamar and Samad, 2022; Edwards et al., 2022). Surprisingly, a juxtaposition of the rational explanations for the adoption of HR analytics in public organisations with those reported in the literature reveals that economic reasoning, often suggested in business research, is also perceived significant in the public sector. Multiple organisational outcomes, including HR-related outcomes such as efficiency, effectiveness, and cost reduction through improved management and analysis of HR-related data, are found to be relevant for public organisations as well. This is despite the previously reported differences among public organisations and their HR practices from private firms (Boyne et al., 1999) and not directly given applicability of private sector solutions to the public sector (Blom et al., 2020).
Economic rationality arguments revealed by this study are also partly consistent with the competitive advantage explanations reported in the business literature (e.g. Minbaeva, 2018). HR analytics applied to HR practices, such as workforce planning, employee recruitment, and retention, is believed to potentially assist in gaining a competitive advantage through a unique combination of organisational workforce, ensuring the right person is in the right place at the right time. Although public organisations are less sensitive to market competition and profit maximisation, they are still influenced by the labour market and financial factors. For instance, public organisations compete both with each other and with private companies for certain types of workers, such as health care and technical personnel. Financial gains driving HR analytics adoption are also perceived relevant by public sector organisations, although not to increase profit and shareholders’ value, but rather to account for the efficient usage of taxpayers’ money.
The suggested explanation for the relevance of economic reasons, even for public sector organisations is based on the institutional legitimacy perspective (Meyer and Rowan, 1977; Aldrich and Fiol, 1994; Belizón and Kieran, 2022). Economic rationality, understood as a widely spread institutionalised norm, permeates society at large, particularly organisational environments, including general managerial and HR contexts. It is not always consciously perceived in organisations by different professionals who adhere to rationalised models and 'myths' as conceptions of the appropriate way of doing things (Fu et al., 2023). HR analytics is seen as one such rational societal norm and is believed to provide numerous positive outcomes through the management and analysis of HR data. This might also explain why most of the HR analytics literature focuses only on reasons linked to economic rationality, such as the potential to improve organisational performance through better decision-making enabled by quantitative data and their analysis.
It was also found that there was a temptation among public sector organisations to proclaim economic rationality as the key reason for the adoption of HR analytics because such rational explanations are widely accepted in organisational and managerial realities which is in line with the institutional legitimacy perspective (Deephouse et al., 2018). Supported by the usage of “objective” HR numbers, measurements, and analysis, they create the illusion of appropriateness and provide the right to exist and continue using taxpayers' money to deliver efficient and effective value for the citizens. Legitimacy thus arises from adherence to the prevailing social norms and beliefs widely disseminated in society. HR analytics stems from the societal norm of economic rationality, which is based on certain assumptions regarding the superiority of analytics over human decision-making and its capacity to predict and modify human behaviour, thereby leading to economically rational organisational decisions (Giermindl et al., 2022). Rationality stands as one of the primary “maxims” in today’s organisational landscape, particularly evident in the public sector with its continual pursuit of efficiency, smarter use of public funds, and developing new working practices. The significance of quantifiable data, statistical analysis, metrics, graphs, and trend curves may never have been as pronounced as it is now. HR analytics, by its very nature, is found to be viewed as a tool that facilitates increased measurement and quantitative analysis to ensure economic rationality, believed to enhance the acceptance and appropriateness of the organisations and their HR operations. Organisations that make rational, data-driven decisions, including those related to HR and personnel policies, are regarded as legitimate and acceptable actors in the broader social context, both by governing politicians and the general public. Such organisations are perceived as transparent, as they substantiate their decisions with “objective” numbers and analysis, contributing to legitimacy, albeit this may sometimes be merely an illusion of rationality.
It was found that institutional legitimacy – cognitive, socio-political, and technological, had the capacity to explain the ultimate reason for HR analytics adoption. Thus, the revealed rational economic explanations, such as the need for data management and analysis to achieve organisational and HR-related outcomes, constitute an intermediate step towards gaining legitimacy. This is also consistent with the earlier studies on the adoption of other HR practices (Lewis et al., 2019). Thus, organisations adopt HR analytics practices because they are grounded in a rational evidence-based approach that is widely accepted as part of the prevailing norm of economic quantitative rationality supported by technology to gain legitimacy and acceptance.
Though this study supports institutional legitimacy explanations, surprisingly, it does not explicitly reveal traditional institutional forces – coercive, mimetic, and normative - that are sometimes argued to explain the adoption of HR practices by late adopters such as public sector organisations (Paauwe and Boselie, 2005; Coolen et al., 2023). The absence of traditional institutional forces in the findings might result from the studied context of Swedish public administrations. In Sweden, there is no legislation that coerces organisations to conduct statistical analyses of their HR-related data. On the contrary, the EU General Data Protection Regulation (GDPR) imposes limitations on how employees’ personal data may be processed and transferred. The absence of mimetic forces identified by the study might be due to the lack of organisations to imitate. Most public organisations in Scandinavia face general challenges with digitalisation, and even private firms were previously reported to either not leverage HR analytics or be reluctant to disclose successes for the preservation of competitive advantage (Holwerda, 2021). Normative influences also appear to be weak. The HR profession has been widely criticised for lacking analytical skills (Angrave et al., 2016), indicating that HR professionals are unlikely to establish institutional professional norms related to numerical data analysis in the near future due to inadequate educational training. This is particularly true for the Swedish context, where university education for HR professionals primarily focuses on less quantitative disciplines such as work science, pedagogy, sociology, and psychology, with limited emphasis on mathematics, statistics, and management. Those who choose HRM majors prefer working with people over numbers, further contributing to the limited engagement with analytics within the HR profession in general (Cayrat and Boxall, 2022).
Another interesting and counterintuitive finding is that employee-related benefits were not identified as a potential reason for HR analytics adoption, despite the pro-social nature of the public sector and some previous research (Gal et al., 2020). For instance, Giermindl et al. (2022) mention better work experience throughout the employee job cycle, including personal career development and health benefits. Huang et al. (2023) argue that HR analytics can facilitate personalised HRM tailored to individual employee needs. A possible explanation for these missing results could be linked to the generally low level of HR analytics maturity in public sector organisations. Facilitating individualised HR practices requires relatively sophisticated technical solutions, such as Artificial Intelligence and algorithmic technology, which are not yet integrated into HR in public sector organisations.
Finally, the results of this study also indicate the importance of individuals’ interests as a reason for organisations to adopt HR analytics. In summary, individuals’ interests in numbers, data, analysis, and data-driven HR decision making precedes both the potential realisation of organisational, including HR-related benefits through data management and analysis, and the gains in legitimacy. Individual factors for HR analytics adoption were earlier studied by Vargas et al. (2018), who found that self-efficacy, positive attitudes, and social influence impact individuals in making adoption decisions. The study confirms that this is a fruitful subject for further investigation due to the importance of individual practitioners with technical and analytical interests, even for the adoption of HR analytics at the organisational level.
Theoretical and practical implications
The study enhances existing HR analytics literature by providing empirical evidence from the underexplored context of public sector organisations, thereby addressing a critical gap, as most prior research has primarily focused on private sector firms. By examining organisations that operate under distinct values and constraints divergent from those in the more widely studied private sector, this research contributes to a more comprehensive understanding of HR analytics adoption.
Furthermore, the findings advance theoretical knowledge by proposing a legitimacy framework that elucidates the multilevel structure of reasons for HR analytics adoption. This framework integrates three key dimensions—economic rationality, individual interests, and organisational legitimacy—into a unified model. This integration illustrates the complex interplay of these factors and offers a nuanced perspective on how public sector organisations justify their adoption of HR analytics. This contrasts with existing literature, where the drivers of HR analytics adoption are primarily examined through the lens of economic rationality, focussing on performance improvement and financial profit, or through the personal characteristics of practitioners, such as analytical skills and technical knowledge. Legitimacy-based motivations for HR analytics adoption have received less attention, likely due to the initial stage of HR analytics implementation, where economic rationality and competitive mechanisms dominate the discourse. By integrating these three dimensions into a single framework, based on evidence from a context where organisations prioritise public service over profit maximisation, this study introduces a novel approach to understanding HR analytics adoption.
Additionally, by combining these dimensions into a cohesive model, the study not only enriches theoretical understanding but also provides practical insights for public sector practitioners. These insights can guide HR leaders in developing strategies that align with the unique challenges and opportunities present in public sector contexts, ultimately promoting a more data-driven approach to HRM.
Moreover, the study has significant practical implications for public sector organisations interested in implementing HR analytics, emphasising the importance of addressing legitimacy concerns alongside economic and individual motivations to ensure successful adoption and integration of analytics practices. To ensure legitimacy, these organisations would benefit from focussing on three main areas. First, clearly defining the objectives of HR analytics, gaining a cognitive understanding of whether it is for better reporting, visualisation of relevant HR metrics, understanding past events, predicting future HR trends, or linking HR to operational and economic areas. This clarity helps in communicating the value of HR analytics to stakeholders. Second, gaining acceptance among multiple stakeholders, including political governance, by understanding what politicians, the wider society, and media are interested in at the moment. For example, addressing the “hot” topic of nurse attrition during the COVID-19 pandemic or the disproportionate allocation of personnel among different geographical units. Additionally, aligning to the needs of internal users, like local HR and line managers in typically large organisational units, by providing relevant HR data and analysis that can support their functions. Third, ensuring integration with the ongoing HR digitalisation movement in the public sector. This involves leveraging technological advancements for HR functions and ensuring that data collected through various technological solutions (such as recruitment, competence profiling, scheduling, performance evaluation, and employee surveys) can be aggregated, visualised, and analysed in conjunction with data from existing HRIS and other organisational IS.
The study shows that legitimacy is acquired through a focus on economic rationality—a language universally understood by professionals and managers across all types of organisations. By emphasising the organisational and HR-related outcomes that can be achieved through HR analytics, organisations can secure better alignment of HR analytics initiatives with their overall goals. Highlighting the tangible benefits, such as improved decision-making and operational efficiency, can reinforce the value of HR analytics. Moreover, the successful implementation of HR analytics depends on the availability of high-quality data across organisational functions, particularly HR data used for various analyses. Ensuring that accurate and comprehensive data is accessible is a prerequisite for achieving the desired outcomes from HR analytics. Finally, it is crucial to foster the interest and motivation of individuals involved in HR analytics development by providing organisational opportunities for such engagement. This involves not only enhancing analytical, statistical, and quantitative competencies but also nurturing the motivation of those who already possess an interest and positive attitudes towards HR analytics.
Limitations and future research
While several reasons for HR analytics adoption were identified, this study is not without limitations. The first limitation is the small number of participating organisations. However, this limitation is mitigated by in-depth analysis, which includes respondents from various organisational roles—both HR and non-HR practitioners—and by strategically selecting participating organisations that represent different levels of the public sector: national, regional, and local.
The second limitation is the focus on a single national context, which may hinder the extrapolation of findings to international settings with potentially different institutional conditions. It is recommend that future research involve public sector organisations from other countries to advantageously compare different national contexts.
Although this study is one of the first attempts to explore the topic within a limited number of public sector organisations in their particular local context, there is a clear need for large-scale studies to be conducted in the future with more comprehensive samples, such as at the country level. This presupposes however that more public sector organisations start leveraging HR analytics in their operations. There is also a great need for more qualitative in-depth studies aimed at understanding not only the reasons for HR analytics adoption but also the evolution of the implementation process over time. Longitudinal studies of the HR analytics implementation process have the potential to reveal if the stated reasons for its adoption change at different stages of implementation and whether these reasons are ultimately realised, along with their specific results, such as achieving organisational legitimacy in practice.
Conclusion
This paper explores the rationale behind the adoption of HR analytics in public sector organisations, employing an in-depth qualitative study of three Swedish public administrations. The findings suggest an institutional legitimacy explanation for the adoption of HR analytics. Driven by the interests of specific individuals, public sector organisations adopt HR analytics to ensure cognitive, socio-political, and technological legitimacy through improved HR data management and analyses, aiming to impact organisational and HR-related outcomes. The study found some evidence regarding the reasons why public sector organisations are willing to utilise HR analytics, but there is an obvious need for more empirical research on whether these reasons are realised in practice in the form of measurable HR, organisational, and employee benefits.
Figures
Organisations and respondents
Authority | Region | Municipality | |
---|---|---|---|
Size (number of employees) | 10,000 | 53,000 | 5,000 |
Number of respondents/interviews | 22/26 | 19/22 | 6/3 |
HR-/non-HR practitioners | 15/7 | 13/6 | 6/- |
Female/Male | 13/9 | 14/5 | 5/1 |
Source(s): Author’s own creation
Illustrative quotations
Reasoning | Illustrative quotations |
---|---|
Economic rationality | |
Data management | We knew that we were sitting on a lot of data and that it needed to be structured and managed technically (HR manager, Authority) |
Data analysis | You don’t want just to look back, you also want to look forward with other methods, for example, statistical analyses about why things happen and what will happen in the future so we can predict different actions (HR strategist, Region) |
HR-related outcomes | We would like to have a tool to support us in our competence supply and staff planning. So, there is a need for HR analytics to become such tool (HR manager, Authority) |
Organisational outcomes | We should generally ensure that we are cost-effective, we should not cost taxpayers more than is necessary, so there is the efficiency idea in everything we do (HR partner, Authority) |
Institutional legitimacy | |
Cognitive legitimacy | You don’t really grasp the gain with the whole HR analytics project. But I think the challenge with the working group is that not everyone dares to say that they don’t really understand it (IT specialist, Region) |
Socio-political legitimacy | We are a politically controlled organisation. [Politicians] set a strategic plan for what we need to achieve. This is what we want to measure and analyse in HR (HR director, Municipality) |
Technological legitimacy | From HR analytics we would like to have an effective technical solution when you get a quick confirmation, so you know that you’re doing the right thing (HR manager, Authority) |
Individual reasoning | |
Individual interest | Everything started because of X [data analyst]. She managed to convince everybody; she impressed the managers that this is super important (IT specialist, Region) |
Source(s): Author’s own creation
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Acknowledgements
The author thanks all participants who took part in the study and extends gratitude to Mårten Hugosson for his assistance with data collection.