literature review of hr practices

HR analytics-as-practice: a systematic literature review

Journal of Organizational Effectiveness: People and Performance

ISSN : 2051-6614

Article publication date: 21 December 2023

Human resource analytics (HRA) is an HR activity that companies and academics increasingly pay attention to. Existing literature conceptualises HRA mostly from an objectivist perspective, which limits understanding of actual HRA activities in the complex organisational environment. This paper therefore draws on the practice-based approach, using a novel framework to conceptualise HRA-as-practice.

Design/methodology/approach

The authors conducted a systematic literature review of 100 academic and practitioner-oriented publications to analyse existing HRA literature in relation to practice theory, using the “HRA-as-practice” frame.

The authors identify the main practices involved in HRA, by whom and how these practices are enacted, and reveal three topics in nomological network of HRA-as-practice: HRA technology, HRA outcomes and HRA hindrances and facilitators, which the authors suggest might actualize enactment of HRA practices.

Practical implications

The authors offer HR function and HR professionals a basic ground to evaluate HRA as a highly contextual activity that can potentially generate business value and increase HR impact when seen as a complex interaction between HRA practices, HRA practitioners and HRA praxis. The findings also help HR practitioners understand multiple factors that influence the practice of HRA.

Originality/value

This systematic review differs from the previous reviews in two ways. First, it analyses both academic and practitioner-oriented publications. Second, it provides a novel theoretical contribution by conceptualising HRA-as-practice and comprehensively compiling scattered topics and themes related to HRA.

Human resource analytics
HR analytics
Practice theory
HR practices
HR practitioners

Espegren, Y. and Hugosson, M. (2023), "HR analytics-as-practice: a systematic literature review", Journal of Organizational Effectiveness: People and Performance , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JOEPP-11-2022-0345

Emerald Publishing Limited

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

1. Introduction

Human resource analytics (HRA) is a human resource (HR) activity that has recently attracted growing interest among companies and public organisations. HRA has broadly been seen as the collection, analysis and reporting of data to inform people-related decisions and improve individual and organisational outcomes ( Fernandez and Gallardo-Gallardo, 2021 ).

The interest in HRA has evolved mainly through HR management (HRM) experts and business consultants, who portray HRA as creating numerous advantages for organisations and the HR profession. These advantages include, e.g. improved rigour of HR decisions, increased credibility and strategic value of the HR function, enhanced competitive advantage and business success that can be achieved through a better understanding of the organisational workforce ( Davenport et al. , 2010 ; Huselid, 2018 ; Deloitte, 2021 ).

However, despite the enhanced popularity of the topic, the academic field of HRA is still reported as nascent, the research, though growing, is rather scarce, lacking a unifying understanding of HRA practice, possibly due to the continuously increasing development and evolution of the HRA scope ( van den Heuvel and Bondarouk, 2017 ; Margherita, 2022 ). HRA literature remains scattered, with too few consistent frameworks, and its current state can be described as “wild, wild west” ( Levenson and Fink, 2017 ).

In an attempt to grasp the scope of HRA and reflect on its early development, several literature reviews have been conducted in the field ( Marler and Boudreau, 2017 ; Tursunbayeva et al. , 2018 , 2022 ; Ben-Gal, 2019 ; Fernandez and Gallardo-Gallardo, 2021 ; Margherita, 2022 ; Qamar and Samad, 2022 ; McCartney and Fu, 2022 ; Jiang and Akdere, 2022 ; Giermindl et al. , 2022 ). Despite the difference in purposes and research questions, these literature reviews are similar in their understanding of HRA from an objectivist perspective, namely, as something – process or tool – that organisations have or lack. This view is, however, useful but somewhat deficient and fragmented in its depiction of the complexity of organisational reality and focuses more on what should be done to succeed with HRA rather than on the actual activities within organisations. Thus, as also indicated by Jiang and Akdere (2022) , there is a clear need for theoretical development in the field as it stands at the present.

The purpose of this paper is therefore to draw on the alternative perspective originated in the practice-based ontology and reconceptualise HRA using a new framework of HRM-as-practice. This conceptualisation has three dimensions: HRA practices, HRA praxis and HRA practitioners.

HRM-as-practice framework was proposed by Björkman et al. (2014) as a holistic and more dynamic approach to understand and innovatively examine the general practice of HRM. The framework addresses intersections between the three components of general HRM practice – practices, praxis and practitioners – by asking, “ What” general practices does HRM involve? “ Who” are the HRM practitioners? “H ow” do HRM practitioners enact HRM practices? Applying this approach, we intend to answer the following research questions: (1) What practices constitute HRA? (2) How are these practices enacted in organisations? (3) What actors are involved in the enactment of the practices? And, finally, (4) What connects HRA practices, their enactment and HRA practitioners into a coherent model of HRA-as-practice?

To answer the research questions, we conducted a literature assessment of academic and practitioner-oriented articles. Considering the general lack of empirical academic research in the field ( Edwards et al. , 2022 ), this methodological approach allows for a wider coverage of material for the analysis, particularly with our special focus on the practice of HRA.

This study thus provides an overview of the HRA field from a practice perspective and aims to contribute to the theoretical understanding of HRA by constructing it as a coherent and holistic concept. Moreover, it also compiles a nomological network of HRA-as-practice, adding more recent and ample findings to the topics and concepts that exist around the practice of HRA and influence and are influenced by it. Practically, it offers HR functions and HR professionals an overview of how HRA operates as a practice within organisations. It also provides a basis for evaluating the conditions enabling and moderating HRA as a possible solution to generate business value and increase HR impact due to a better understanding of the important features of HRA and what is implied for its useful enactment.

The paper is structured as follows: first, the practice theory is briefly introduced, followed by a description of the methodology. Next, the results of the literature review are presented and discussed.

2. Theoretical approach: HRA-as-practice

For our analysis, we have chosen to elaborate on a framework proposed by Björkman et al. (2014) and their concept of HRM-as-practice, which in turn leans on the strategy-as-practice discourse ( Whittington, 2006 ; Jarzabkowski et al. , 2007 ). By arguing that organisational phenomena do not exist until they are enacted in practice, practice theory particularly aims to overcome the structure-agency dualism, implying that both individual doings and structural influences only acquire meaning when they manifest themselves in practice ( Nicolini, 2012 ). These ideas emerged from seminal works in sociology (e.g. Giddens, 1984 ) underpinning all practice theories and have lately been used for studying different areas of business administration and management, such as organisational learning and knowing ( Tsoukas, 1996 ), strategy ( Whittington, 1996 ), technology ( Orlikowski, 2000 ), accounting ( Ahrens and Chapman, 2006 ) and HRM ( Björkman et al. , 2014 ).

Central to the ideas of strategy-as-practice are three interrelated concepts suggesting that studying social practice should focus on practices, praxis , practitioners and intersections between them ( Whittington, 2006 ; Jarzabkowski et al. , 2007 ). Integrating these three concepts allows for a coherent depiction and understanding of a social phenomenon and how it evolves. The conceptualisation is ontologically rooted in the duality identified by Giddens (1984) , when structures and actors simultaneously influence and are influenced by each other.

The HRM-as-practice draws on these ideas and adapts the concept to the HRM field. The conceptualisation involves the three basic categories, contextually defining them as HRM practices, HRM praxis and HRM practitioners ( Björkman et al. , 2014 ). The “three Ps” are thus in line with the general practice theories, seen as inseparable, interconnected and difficult to distinguish since practices are “pertinent” when enacted by practitioners in a certain context ( Whittington, 2006 ). The model calls for an extended assessment of what links these elements in their intersections, constituting the practice of HRM as it is enacted. According to the underlying theory, particular practices are entangled with other practices, creating the problem of making clear distinctions between them. In other words, the practice theory tradition emphasises that practices are entangled in bundles, embodied in practitioners and enacted by them ( Jarzabkowski et al ., 2016 ). Thus, the distinction used in this article is basically an exercise of abstraction from a wider context of practices. It is an analytical choice intended as a necessary simplification for a better understanding of the practice of HRA.

The definition of practices varies in practice literature depending on the field of application. Björkman et al. (2014) understand practices as tools, norms, processes and procedures and traditionally exemplify HRM practices as HRM routines and techniques that ensure implementation of HRM policies, e.g. high-performance HRM practices. Since there is no traditional agreement on HRA practices due to the current evolution and development of such activities, we choose to depict them in line with the more broad understanding in the strategy-as-practice tradition as something that is “done” ( Whittington, 2006 ), the supposed or routinised activities that can be “diverse and variable” and combined and adjusted depending on their utilisation in a particular context ( Jarzabkowski et al. , 2007 ). In other words, HRA practices are seen as recognised abstract general activities that principally construct HRA.

Enacted HRA practices are situated activities, which we call HRA praxis . Jarzabkowski et al. (2007) discuss praxis as the flow of actual activities that are situated, socially accomplished and consequential. In line with this definition, we understand HRA praxis as an actual activity, representing how people “go about things” when they perform HRA practices. If HRA practices are more generally recognised patterns and principles about what is included in HRA work, HRA praxis is the actual work, representing how the abstract activities are enacted in real situations.

HRA practitioners are the actors who enact, construct and reconstruct HRA practices through their actual activities. The practitioners are seen as the “prime movers” who perform the actual work ( Whittington, 2006 ). In this study, we understand HRA practitioners as those who are directly involved in the enactment of HRA practices. The strategy-as-practice approach, although recognising both internal and external actors who impact strategy-making, often tends to focus on the individual actors and their agency. Instead, Björkman et al. (2014) follow HRM tradition, distinguishing between individual and collective actors. In this exploratory study, we adhere to a broader perspective and are interested in revealing all possible practitioners involved in HRA, both individual and collective, to get a holistic picture.

Finally, we are also interested in how the above-mentioned “three Ps”, HRA practices, HRA praxis and HRA practitioners, are actualised and connected into one whole practice of HRA. According to Björkman et al. ’s (2014) model, the three Ps are de facto entangled, and their interconnections are of prime importance for the model because HRM-as-practice manifests itself in these intersections. Björkman et al. (2014) , although stressing importance of the interconnections, neither provide any detailed descriptions of what exactly happened there nor introduce any defined entities that influence and are influenced by the practice of HRA and its elements. Instead, Björkman et al. (2014) suggest several potentially interesting research questions that arise from the intersections without being rigid guidelines and might be modified by future HRM research. Based on the elusive and, to a certain degree, obscure nature of the interconnections, we are interested in revealing what topics and concepts in the nomological network of HRA-as-practice might actualise intersections between HRA practices, HRA praxis and HRA practitioners and integrate them into a coherent whole.

3. Methodology

A systematic literature review has been conducted to analyse relevant HRA literature. The literature review process used here followed the four steps: (1) developing a review protocol; (2) searching for the literature; (3) selecting the studies for review; and (4) summarising the evidence ( Boell and Cecez-Kecmanovic, 2015 ). A PRISMA flow diagram was used to document the search and selection of the studies for the review ( Moher et al. , 2009 ). A summary of this process is depicted in Figure 1 .

According to the review protocol, three databases were searched to identify articles for the review: Scopus, Web of Science and Business Source Complete. Scopus and Web of Science were chosen as the two most well-known interdisciplinary research databases with a wide coverage of academic articles ( Chadegani et al. , 2013 ). Scopus and Web of Science each provide access to more than 80 million records in more than 21,000 journals. The database Business Source Complete, as one of the leading databases in the field of business and management studies and with access to more than 4,000 high-profile journals, was chosen as a complementary source.

Although HRA is found to be the most frequently used term for the studied phenomena ( Margherita, 2022 ), it is still not recognised as an exclusive search term in all academic disciplines ( Edwards et al. , 2022 ). The existing literature reports numerous synonyms of HRA ( Marler and Boudreau, 2017 ; Fernandez and Gallardo-Gallardo, 2021 ). In line with this condition, the following terms were applied when searching for source titles, abstracts and keywords: HR analytics, human resource analytics, talent analytics, workforce analytics, people analytics, human capital analytics and employee analytics. The usage of the different names is explained by the emergent nature of the HRA phenomenon ( Margherita, 2022 ). This paper disregards the potential semantic differences among the different labels and uses HRA as a common term for all the synonyms. The three databases were searched in November 2021 for publications published between 2010 and 2021. The starting year was chosen based on the previously reported observation that the number of HRA articles noticeably increased after 2010 and were almost non-existent before that date ( Marler and Boudreau, 2017 ).

The search was limited by type of publication, language and subject area. We included articles from journals written in English in the field of business and management. This led to the identification of 301 publications. After removing duplicates, 202 records remained. A screening process of abstracts at this point resulted in the exclusion of a further 83 records for the following reasons: the content was not relevant for the HRA topic (45); the content was of questionable quality because it was published in a journal or by a publisher listed in Beall’s list of potentially predatory journals and publishers (21); the article was an editorial (7), a book review (3), a summary of other studies (3), an internal university publication (2), an executive interview (1), or a review of conferences (1). After identification and selection, all the 119 full-text articles were read. An additional 11 were excluded based on their content because the focus was on other topics and HRA was only marginally mentioned in the text. Eight literature review articles were also excluded from the analyses because of the prime interest in empirical findings in connection to HRA. This resulted in a final total of 100 articles, which formed the basis for our analysis. A full list of articles is included in Appendix .

Among the 100 articles selected for analysis, it is possible to clearly distinguish between two broad groups. The first one comprises 53 traditional academic articles. The other group comprises 47 articles, mostly aimed at the practitioner audience. They are shorter than academic publications, do not necessarily deploy scientific methods, often have a viewpoint character or are case studies and are often published by practitioners in trade journals. We labelled these articles “practitioner-oriented”. The reason we have included practitioner-oriented publications is because they are directly related to the topic of HRA-as-practice and cover relevant contextual details as regards HRA usage, experiences of HRA practitioners and cases of successful HRA implementation. They also reflect the ideas of actors involved in HRA more directly and have been published with less delay than the academic articles, thus potentially mirroring important HRA developments before the topic appeared in traditional academic journals.

4. Analysis

The analysis started with identifying content in each examined article consistent with the three main categories of the HRA-as-practice framework – HRA practices, HRA practitioners and HRA praxis. We assessed the articles to reveal what fits under these categories, namely what practices are discussed as a part of HRA, how they are enacted and what actors are involved. We also paid attention to other interesting topics and concepts covered in the articles in connection to the three main categories. The analysis revealed three broad topics, i.e. HRA technology, HRA outcomes and HRA hindrances and facilitators, which we call topics in the nomological network of HRA-as-practice. We discuss them below, after the main categories. These topics and their content are of particular interest for addressing the question of what connects the “three Ps” in a nomological network of HR-as-practice and what creates coherence in understanding of practice of HRA. In the analysis, the content of the articles was also synthesised into several subcategories under the three main and three related nomological categories. A complete overview of the categories and subcategories with examples can be found in Table 1 .

The results regarding the number of articles discussing the basic categories – HRA practices, HRA practitioners and HRA praxis – and additional related topics – HRA technology, HRA outcomes, and HRA hindrances and facilitators – are illustrated in Table 2 . It clearly shows that all articles analysed in one way or another address practices involved in HRA and HRA outcomes. However, the findings show that not all reviewed papers deal with HRA practitioners, HRA technology, and HRA hindrances and facilitators. Interestingly, even in the practitioner-oriented group, not all papers address these categories. It often occurs in either technical papers that focus on providing a certain statistical method for HRA or publications of a promotional character that treat companies as competing actors in the market.

HRA praxis was found to be the category addressed least in the reviewed literature. Only 44 out of the 100 articles addressed the question of how HRA practices are enacted. This number is even lower for articles within the academic group, where only 19 out of 53 articles address HRA praxis.

4.1 HRA practices

In the analysis, we aimed for a wide coverage of possible HRA practices discussed in the literature with a focus on general activities, something that is done by practitioners. We could identify several sub-practices that are seen to construct HRA. To categorise the content of the articles as a HRA sub-practice, we were looking for what is done in the organisations when they say to be involved in HRA, e.g. activities such as data collection and extraction, producing different types of analyses and reporting of results.

The multiple HRA sub-practices extracted from the analysed literature have been synthesised into four separate but related groups. The first group includes HRA practices linked to data usage , such as data management and governance. The category includes practices connected to both HR and other business data and data from external sources, such as market or industry data (e.g. Jacobus, 2015 ; Hamilton and Sodeman, 2020 ). Practices of constructing and following different measures, also called metrics or indicators, that might be relevant for HR and business strategy are included (e.g. Brown, 2020 ; Buttner and Tullar, 2018 ).

The second group includes HRA sub-practices linked to data analysis . The examined literature suggests the application of different statistical analyses at different levels of sophistication, distinguishing between descriptive (e.g. Jones and Sturtevant, 2016 ), predictive (e.g. Brandt and Herzberg, 2020 ), occasionally prescriptive (e.g. Rasmussen and Ulrich, 2015 ) and even autonomous analytics, such as in the context of autonomous algorithms (e.g. Gal et al. , 2020 ). Much attention was found to be paid to the practice of prediction: predicting valuable HR and organisational outcomes, such as employee retention or individual and organisational performance (e.g. Zuo and Zhao, 2021 ; Speer, 2021 ).

The third group includes practices related to producing data-based insights . Insight generation is mentioned by almost all reviewed articles as the central practice of HRA (e.g. Ames, 2014 ; Dahlbom et al. , 2020 ). Insight generation is seen to include visualisation (e.g. Andersen, 2017 ), storytelling (e.g. Welbourne, 2015 ) and communication of results produced by data analysis (e.g. Lipkin, 2015 ). It is these practices that are argued to be of great importance for successful HRA users' buy-in.

Finally, the fourth group includes HRA practices of decision support . Since improved HR and business decisions are assumed to be the goal of HRA, most of the analysed publications discuss sub-practices that pay particular attention to evidence-based (e.g. Hirsch et al. , 2015 ), user-tailored (e.g. DiClaudio, 2019 ), action-oriented (e.g. Jörden et al. , 2022 ) and often strategy-driven (e.g. Minbaeva, 2018 ) decisions.

4.1.1 HRA practitioners

The analysis revealed two broader groups of HRA practitioners: HRA producers and HRA users. HRA producers are the practitioners who are directly involved in the everyday activities of producing HRA, such as managing and collecting data, producing analyses, visualising results and communicating insights. HRA consumers are the practitioners who use HRA results as a basis for decision-making. HRA producers and HRA users represent both individual and collective actors, e.g. various individual professionals, groups, teams, departments, organisations and even the whole HR profession.

The most discussed HRA producers are HRA teams and their members, sometimes also called HR analysts . Such groups often include specialists from different functional and organisational areas: HR, IT and data science. There is no consensus regarding the exact organisational position where such teams are placed; placement both inside and outside HR departments is possible (e.g. Peeters et al. , 2020 ; Van den Heuvel and Bondarouk, 2017 ). Together with discussing the organisational belonging of HRA teams, the analysed articles also focus on the competences, knowledge and skills of team members and how these connect to the different areas linked to HRA. The role of an HR analyst, for example, is still in development, but several articles discuss the required competences, which are said to include technical and data knowledge, ability regarding statistical analysis, visualisation and communication and business and HR knowledge (e.g. Kryscynski et al. , 2018 ; McIver et al. , 2018 ; Minbaeva, 2018 ; Van der Togt and Rasmussen, 2017 ; Feinzig, 2015 ). A competency model already exists for the emerging role of HR analysts ( McCartney et al. , 2020 ). “HR analyst” is the label most frequently used to depict HRA producers. However, Gal et al. (2020) suggest another title, that of “algorithmists” or auditors of algorithms, named after the algorithmic technology they are supposed to apply in their HRA work.

Other categories of HRA practitioners as producers are also discussed in some of the analysed articles, such as IT and management consultants, researchers and academics and external experts. These types of practitioners, though represented by external actors, have direct influence on HRA making within organisations. IT and management consultants, for instance, commodify and sell similar technical solutions accompanied by business models and processes to several organisations, popularising HRA and making it a HR “best practice” ( Angrave et al. , 2016 ). Researchers and academics, when involved in organisational HRA projects, might directly contribute with their theoretical knowledge and rigorous social science research methods to complement and modify different HRA practices and their enactment ( Simón and Ferreiro, 2018 ). Similarly, external experts might influence HRA activities in organisations by using their expert knowledge in, e.g. the artificial intelligence area, legal and ethical requirements and diversity and inclusion questions ( Hamilton and Davison, 2022 ).

Not surprisingly, however, the most common category of HRA practitioners discussed in the assessed literature is HR departments and HR professionals. Interestingly, HR departments and HR professionals such as HR managers, HR business partners and HR specialists are considered both producers and consumers of HRA. Many articles suggest that HR professionals, especially HR managers responsible for HR decisions, are important users of HRA (e.g. Levenson, 2018 ; Nicolaescu et.al ., 2020 ; Pessach et al. , 2020 ). Some articles argue that HR professionals are the ones who also should produce HRA (e.g. Boudreau and Cascio, 2017 ; Howes, 2014 ; Vargas et al. , 2018 ). Other publications do not support this idea, arguing that traditional HR professionals and the whole HR profession generally lack the appropriate analytical skills and business acumen, which makes them incapable of producing HRA ( Angrave et al. , 2016 ).

Another group of HRA users who also attract a good deal of attention, apart from HR professionals, are top managers, including CEOs ( Shet et al. , 2021 ), line managers ( Nicolaescu et al ., 2020 ) and other types of managers and business professionals ( Barrette, 2015 ).

As a complement to the analysis, it is important to note that some of the analysed articles address broader groups of actors that have some connection to the general topic of HRA, including external actors or stakeholders such as the general public, key opinion leaders, customers, suppliers and regulatory agencies (e.g. Hamilton and Sodeman, 2020 ; Belizón and Kieran, 2022 ). Although these actors are seen as only indirectly involved in the enactment of HRA practices in organisational settings, they are important for understanding the topic of HRA, especially from a multi-level institutional perspective. For instance, such institutional actors are involved in forming general public opinion and building legitimacy of HRA practices, influencing other actors, e.g. organisations, organisational leaders and HR professionals, in their decisions regarding HRA usage ( Belizón and Kieran, 2022 ).

Employees are another interesting group mentioned in some of the articles ( Khan and Tang, 2016 ; Giermindl et al. , 2022 ). While their role is certainly worth considering as an important aspect, especially in connection to the ethical and legal requirements regarding HR data ownership, the analysed literature is still limited in addressing the employee perspective and the employees' role in the enactment of HRA practices.

4.1.2 HRA praxis

It has been more difficult to identify HRA praxis in the reviewed articles in comparison to the more stable and defined concepts of HRA practices and HRA practitioners. This might partly be explained by the elusive character of praxis, grounded as it is in actual activities, which is, thus, challenging to capture in the text, especially in non-empirical articles. We therefore based our analysis on the definition of praxis as actually situated activities when practices are enacted in context. In other words, from the assessed literature, we attempted to extract what exactly happens in organisations when HRA practices are “done” by practitioners and how abstract practices of data usage, analysis, insight generation and decision support are unfolded in the context. We further attributed such individual situated activities to a broader category in order to provide a general picture of HRA praxis. We, however, acknowledge that such operationalisation might inevitably limit the scope of multiple unique manifestations of HRA praxis that potentially exist in real life. We assume, although, that it is reasonable due to the nature of this study.

Based on this assumption, we attributed the HRA praxis described in the analysed literature either to a process of multiple steps or a particular mechanism that HRA practitioners use to enact HRA practices.

As the analysed literature indicates, a detailed process is often described as a set of logical and sequential steps that usually begin with question formulation and then move on to extracting or collecting data, building models and measures, conducting analysis, dissimilating results, acting on results and evaluating actions (e.g. Garvin, 2013 ; Green, 2017 ; McIver et al. , 2018 ; McCartney et al. , 2020 ). The reviewed articles often contextualise these steps and provide rich descriptions of how, for example, questions for HRA are or should be formulated, analysis conducted and results acted upon. These steps are often seen to intertwine with HRA practices, which point to the interconnection of the two elements. Indeed, an abstract HRA practice, for example, data usage, is translated into praxis by its enactment in a process step of extracting relevant data from the database for a given question at hand.

To exemplify a possible HRA process in a typical firm, Hamilton and Sodeman (2020) , for instance, illustrate several steps that happen in sequence: understanding firms value chain, determining significant questions and locations of data, coordinating with stakeholders, analysing data, screening for ethical concerns, making assessments for changes together with stakeholders, and, finally, implementing change together with line managers. Another example of possible HRA praxis in the form of a process is provided by McIver et al. (2018) , who describe five iterative steps: prioritise issues with the greatest potential for organisational outcomes, decide on either a data-driven or theory-driven approach, prepare and validate data, apply multiple methods of analysis and finally transform insights into actions.

HRA praxis has also been described within the literature as comprising different mechanisms for the enactment of HRA practices, such as customisation ( Jörden et al. , 2022 ), alignment to decision makers' perceptions of business reality ( Ellmer and Reichel, 2021 ), building of relationships and networks ( Collins, 2015 ), establishment of HRA’s credibility and legitimacy ( Hirsch et al. , 2015 ), exercise of strategic commitment ( Belizón and Kieran, 2022 ), demonstration of ethical ( Gal et al. , 2020 ) and legal compliance ( Hamilton and Davison, 2022 ) and encouragement of employee involvement and protection of their benefits ( Lipkin, 2015 ).

Ellmer and Reichel (2021) , for example, describe how HRA practitioners produce HRA outputs by aligning to the decision-makers’ perception of business reality. Such alignment to the final users' needs includes speaking the language of numbers, customising dashboards and boundary spanning. Thus, in this case, HRA practices are enacted through using certain numbers, particularly financial indicators, which is a common language for decision-making managers, adapting figures and diagrams for the visualisation of HRA results, and establishing relationships across diverse functional departments. Another example of HRA mechanisms is suggested by Belizón and Kieran (2022) , who argue that HRA enactment happens through the legitimacy establishment process, where strategic commitment, data infrastructure decisions and focus on HRA projects explain how HRA unfolds in practice. In this case, HRA praxis is made evident via HR practitioners' commitment to HR and business strategy, decisions on HRA data storage, whether inside HR function or as part of a companywide data warehouse, and focus on small-scaled HRA projects.

We again acknowledge that it is naturally impossible to identify all mechanisms that might be used by HRA practitioners to enact HRA practices in reality, as they are context-dependent and individual in every situation. Thus, the mechanisms extracted from the analysed literature represent only a few examples of HRA praxis described in the articles. Presenting them, however, provides an indication of how HRA practices are enacted in real life. For example, it is feasible to assume that, e.g. the practice of producing data-based insights can be enacted by the mechanism of aligning to the final users' needs. HRA producers can engage in meetings and talks with their HRA users, in this case, business managers. This allows them to better understand their managers' needs and produce insights accordingly.

4.1.3 Topics in the nomological network of HRA-as-practice

Along with identifying what practices constitute HRA and how and by whom they are enacted, this study is also interested in understanding how these three elements are connected in a coherent model and what topics exist around them in a nomological network. The analysis of the reviewed articles has revealed three topics that are widely discussed in the assessed literature. We have labelled them: HRA technology, HRM outcomes and HRA hindrances and facilitators. These categories and their content have a very clear connection to HRA practices, their implementation and their development, but they do not fall directly under any of the three main categories in Björkman et al. ’s (2014) model. We see it as reasonable and natural to bring forward these entities and conditions as clear candidates for the topics in the nomological network of HRA-as-practice. Mapping these topics and their content in the network of HRA-as-practice creates coherence between the main categories, helping to understand the practice of HRA holistically.

Accordingly, HRA technology is discussed in almost all reviewed articles, which is not surprising because the phenomenon of HRA is often linked to technology and is enabled by it. The depth of the discussions regarding HRA technology varies, however. Some articles mention technology in general terms (e.g. Vargas et al ., 2018 ; Karwehl and Kauffeld, 2021 ; Andersen, 2017 ), and some focus on one type of technology, such as artificial intelligence (e.g. Gal et al. , 2020 ; Roberts, 2017 ).

We divided HRA technology into three subcategories: general technology, HRA tools and HRA techniques. Articles dealing with general technology discuss topics of automation ( Van den Heuvel and Bondarouk, 2017 ), computerisation ( Murphy, 2016 ), cloud technology ( Feinzig, 2015 ), social media ( Leonardi and Contractor, 2018 ), big data ( Wang and Cotton, 2018 ), robotics ( Jones, 2015 ), artificial intelligence ( Hamilton and Davison, 2022 ), algorithms ( Gal et al. , 2020 ), facial recognition ( Hamilton and Sodeman, 2020 ), as well as the internet of things, biometric technology, sensors and wearables ( Holwerda, 2021 ). HRA tools include data storage and management tools, such as different organisational information systems, databases and data warehouses, with a particular focus on HR information systems as an important source of HR data (e.g. Dahlbom et al. , 2020 ; McCartney et al. , 2020 ). Another example covers tools that can carry out different data analyses or perform statistical calculations, such as Excel, SPSS, R, Stata or Python ( King, 2016 ; Ryan, 2021 ), and those examining the reporting and visualisation tools, such as dashboards and PowerPoint ( Buttner and Tullar, 2018 ; Welbourne, 2014 ). Lunsford and Phillips (2018) identify more than 300 different HRA tools and provide a detailed list of the most popular tools used by a broad range of organisations. The articles dealing with HRA techniques are focused on carrying out different statistical descriptive, predictive and prescriptive analyses, such as benchmarking ( Jones, 2015 ), data mining ( Rombaut and Guerry, 2018 ), sentiment analyses ( Gelbard et al. , 2018 ), machine learning ( Yuan et al. , 2021 ) and mathematical modelling ( Pessach et al. , 2020 ). There are thus clear links from general HRA technology, tools and techniques both to HRA practices, HRA praxis and also to HRA practitioners.

The next topic that all the articles address is HRA outcomes . We divided them into two broad groups: business benefits and HR-related outcomes. Articles dealing with business benefits focus on issues, such as improved firm performance ( Larsson and Edwards, 2022 ), revenue and ROI ( Holwerda, 2021 ), time and cost savings ( Hickman et al. , 2021 ), effectiveness ( Levenson, 2018 ), efficiency ( Zuo and Zhao, 2021 ), competitive advantage ( DiClaudio, 2019 ), increased productivity ( Lal, 2015 ), reduced uncertainty ( Frederiksen, 2017 ), facilitation of strategic change ( Hamilton and Sodeman, 2020 ) and effective strategy execution ( Levenson, 2018 ). Articles dealing with HR-related outcomes examine phenomena such as HR impact and strategic influence ( King, 2016 ), operational effectiveness of HR function ( Walford-Wright and Scott-Jackson, 2018 ), improved HR processes, such as recruitment ( Staney, 2014 ) and assessment ( Lam and Hawkes, 2017 ), employee learning ( Hicks, 2018 ), credibility and the professional legitimacy of HR ( Belizón and Kieran, 2022 ), increased individual job performance of HR professionals ( Kryscynski et al. , 2018 ), accuracy, fairness and employee commitment ( Sharma and Sharma, 2017 ), a just workplace ( Hamilton and Davison, 2022 ), and effective HRM ( Hamilton and Davison, 2022 ). One of the HRA outcomes that is commonly discussed in both groups is an improved decision-making process and better overall decisions, either business- or HR-related. This is the most frequently mentioned outcome in the reviewed literature. Better decisions are decisions that are data- and evidence-based, objective, strategic and effective (e.g. Boudreau and Cascio, 2017 ; Lunsford and Phillips, 2018 ).

An interesting observation is that all articles, in one way or another, sometimes with conditions, mention the positive outcomes of HRA, either as potential or as actually achieved. The only exception is Jörden et al. (2022), who suggest HRA has a negative impact on the HR profession because of the differences in identities and logics between managers and HRA practitioners. In sum, different HRA outcomes can clearly be linked to all main categories of the HRA-as-practice model and particularly to the idea that HRA-as-practice is a continuously evolving entity.

The final topic that is revealed from our review is HRA hindrances and facilitators . It would have been possible to discuss these two groups separately. For the purposes of this paper, however, we chose to join them together in one category because not only are they opposite sides of the same factor, but often the lack of a facilitating factor constitutes an actual hindrance. We attribute HRA hindrances and facilitators to the following subgroups: individual, technological, organisational and environmental. Individual factors related to HRA practitioners include the display (or otherwise) of different skills such as analytical and statistical ( Diclaudio, 2019 ), HR professional ( Jones, 2014 ), business knowledge and understanding ( Dahlbom et al. , 2020 ), as well as the ability to communicate ( Welbourne, 2015 ) and build relationships ( Lam and Hawkes, 2017 ). HRA users' buy-in and trust ( Lam and Hawkes, 2017 ), employees' buy-in ( Lipkin, 2015 ) and attitudes and mindsets ( Rasmussen and Ulrich, 2015 ) are also named among individual HRA hindrances and facilitators. Technological factors mentioned in the literature are either linked to data availability and quality or infrastructure and IT systems (e.g. Dahlbom et al. , 2020 ; Leonardi and Contractor, 2018 ). Organisational factors include the “right” organisational structure ( Angrave et al. , 2016 ) and analytical culture ( Ellmer and Reichel, 2021 ), resource allocation ( Simón and Ferreiro, 2018 ), operational processes ( Howes, 2014 ) and leadership support ( Hamilton and Sodeman, 2020 ). Environmental factors mentioned in the literature are privacy ( Gelbard et al. , 2018 ), ethical and legal concerns ( Hamilton and Davison, 2022 ) and the gap between academia and industry ( Rombaut and Guerry, 2018 ). The content of the topic HRA hindrances and facilitators is clearly linked to the activities of “doing” HRA. They constitute the contextual basis for action. Hindrances and facilitators are also clearly linked to the main practices of HRA and the conditions that are assumed by them. Finally, some of the features are also related to the individual characteristics of the practitioners, indicating that they might theoretically serve to integrate the main categories of our practice model, helping to constitute HRA-as-practice.

5. Discussion and future research

The analysis section has mirrored the content of the reviewed articles and constructed the current HRA-as-practice as depicted in Figure 2 . This frame and underlying theory for analysis imply, for analytical purposes, a possible separation between content belonging to the main categories describing HRA practices, HRA praxis and HRA practitioners. But it is clear from our analysis that there are many juxtapositions between them. For example, the HRA practice of data analysis is also a sequential step in a process that depicts HRA praxis. Another example is that the HRA practice of insight generation is revealed only when a HR analyst uses her analytical and communication skills in a certain visualisation activity, which is part of the HRA praxis of aligning to the final users' needs. Such observations are in line with the initial theoretical standpoint about the inseparability of practices, praxis and practitioners ( Jarzabkowski et al. , 2016 ).

According to the departure point for our analysis, the three major elements – HRA practices, HRA practitioners and HRA praxis – are inseparable in real life and thus together create a coherent whole of the practice of HRA. Our analysis has also revealed three closely related topics: HRA technology, HRA outcomes and HRA hindrances and facilitators, which are clearly linked to HRA-as-practice as a whole. These topics, together with the concept of HRA-as-practice represent the nomological network and enhance understanding of the underlying structure of the HRA field. Although HRA technology, outcomes and hindrances and facilitators were previously widely discussed in the existing literature and even categorised as HRA enablers and moderators (e.g. Marler and Boudreau, 2017 ), we tried to compile them in a nomological network of HRA, linking them to the enacted HRA practices. We have also expanded the existing categorisation of these topics by adding more recent and ample findings to their content. We suggest that these topics influence and are influenced by the combined concept of HRA-as-practice. They might also actualise the intersections between the main elements of the model and its components. Although our findings clearly show the importance of the revealed topics for HRA practices enactment, the more precise effects and relationships between them and the main categories of the HRA-as-practice framework are to be discussed in future empirical and theoretical investigations. Tentatively though, we have placed the three related topics outside the framework of HRA-as-practice in a nomological network where they are mostly illustrative for how they might influence and be influenced by the “inseparable” practice of HRA.

The topic of HRA technology covers different tools and techniques, such as HR and other organisational information systems, software for data collection, analysis and visualisation. It is found to be closely related to the practice of HRA. The proximity of technology to the concept of practice is widely discussed in the literature. Björkman et al. (2014) , for instance, in their original model of HRM-as-practice place tools including, presumably, HRM technology under the category of HRM practices. Our analysis shows that technology plays rather a different role than just simply constituting one or several HRA practices, as we understand them as abstract ideas of what is included in HRA. Technology in our suggested model has relationship not only to HRA practices, but rather actualises all constituent elements of the HRA-as-practice concept, including praxis and practitioners, by enabling abstract practices to be enacted by HRA practitioners. For instance, enactment of data analysis practice requires technology in the form of computerisation (general technology) and the application of some statistical analyses, such as regression analysis (HRA technique), using some statistical tool for data analysis, such as Excel or SPSS (HRA tool). The availability of certain HRA technology can also influence the practice of HRA with all its constituent parts: what HRA practices can be chosen, how they can be enacted, and by which practitioner. For instance, the availability of an integrated database storing data on an individual level provides the possibility for predictive analyses enacted in a set of sequential steps by a HRA practitioner with statistical skills. Conversely, HRA technology can be, in its turn, influenced by the practice of HRA. Namely, the availability of the HRA team with mixed competences, high organisational legitimacy and strategy-driven assignments at hand might influence the choice of technology to be used. Understanding HRA technology as actualising HRA practice and as influencing and being influenced by it is also in line with the idea of technology-in-practice proposed by Orlikowski (2000) , where she argues that technology is not just an artefact but manifests itself only when it is used in practice, thus converting abstract ideas of practices into evident praxis in a given situation.

HRA outcomes are another important topic in the nomological network of HRA-as-practice. As with HRA technology, HRA outcomes might also actualise the intersections between the main categories of the framework. We suggest that HRA practitioners, both aggregate and individual HRA producers and HRA consumers, are involved in the enactment of particular HRA practices depending on the potential outcomes they are seeking to achieve, thus making HRA outcomes an important component of joining practitioners, praxis and practices together. For example, a HR analyst (HRA producer) is guided by improved decision-making (HR-related outcome) when she is involved in relationship-building activities (HRA praxis) for generating data-driven insights (HRA practice). Alternatively, a line manager (HRA user) is guided by time and cost savings (business benefit) when engaging in the exercise of strategic commitment (HRA praxis) for the HRA practice of making evidence-based decisions. We also see that HRA outcomes influence and are influenced by the practice of HRA. For instance, the expected HRA outcome of improved HR reporting influences the choice of HRA practices such as visualisation of existing personnel records enacted via customisation for the line manager’s needs. On the other hand, the need for action-oriented decision support enacted via exercising of strategic commitment by the HR director might influence the choice of an expected HRA outcome, such as effective strategy execution. We also see that depending on what role HRA practitioners play, it impacts what HRA practices they draw upon and how they enact those, e.g. HR producers, such as HRA analytical teams and individual analysts, who are guided by different HRA outcomes and thus draw upon HRA practices involving data governance, statistical analyses and the generation of data insights, while HRA consumers who use such results are guided by other potential HRA outcomes and are mostly involved in the HRA practices of making evidence-based decisions.

And, finally, the actualisation of HRA-as-practice depends on HRA hindrances and facilitators. One example is that a HR analyst (HRA practitioner) involved in HR data analysis (HRA practice) by engaging in the process of sequential steps, from question formulation to dissemination of results (HRA praxis), might be facilitated by an analytical organisational culture but hindered by a lack of competence of various kinds. In line with Björkman et al. (2014) , we also assume that depending on who HRA practitioners are, it might influence how they enact HRA practices in a context and what hinders and facilitates their activities. HR analysts with a statistical background might enact practices involving sophisticated predictive data analysis in a contextual process of interrelated steps, unlike a more traditional HR practitioner, who might be inclined towards the visualisation of descriptive HR-related data through the mechanism of alignment to the final user’s needs. The praxis of these different practitioners is also potentially hindered and facilitated by different factors, e.g. specialists in statistics might be hindered by a lack of communicational skills and HR knowledge, while more traditional HR practitioners experience a lack of competence when it comes to technological and analytical skills. We suggest that this question might be an interesting and fruitful area for further empirical research.

Overall evidence from the analysed articles supports the practice perspective by clearly indicating that HRA practices have meaning only when they are implemented by practitioners. This emphasises the importance of the context in which HRA practices are enacted by practitioners. However, our study clearly shows that the context of HRA is not much elaborated in the current HRA literature. Only a few articles provide information that can be used to understand HRA praxis, namely, how HRA practices are enacted in a given context. While all the reviewed articles address HRA practices, and most of them also mention HRA practitioners, HRA praxis is only discussed in less than half of the studies. This result might be seen as a sign that the implementation of HRA is lagging in comparison to the creation of general ideas on the practices that should form the basis for any actual work. In line with the previous research (e.g. Margherita, 2022 ; Marler and Boudreau, 2017 ), we found a low number of empirical papers in our material, especially qualitative papers, with most of the studies covering conceptual research. It also goes hand in hand because studying HRA praxis in its context requires the application of qualitative methodology, such as observations and interviews. To understand how HRA practices are enacted by HRA practitioners and what contextual factors are at play, researchers must closely observe what practitioners do, say and how they interact with the environment, other actors and other things. Beneficial for HRA praxis studies would also be longitudinal approaches since the practice is under development and currently being implemented by organisations (e.g. Belizón and Kieran, 2022 ), and HRA praxis manifests itself in a certain contextual process of sequential steps and mechanisms, such as, for example, alignment to users' needs, which is naturally processual.

The important role of context is also widely supported in the broader HRM literature, suggesting a contextual approach to HRM ( Paauwe and Farndale, 2017 ). The results of this study, however, also reveal the lack of macro-contextual considerations in the existing literature, with only a few articles covering either geographical or industrial contexts, such as, for instance, the public sector in different countries. The shortage of contextual approaches to the practice of HRA is evident in the limited discussions about multiple factors influencing organisations, their HRM work in general, and consequently the shaping of HRA practices. For instance, legal requirements regarding data protection and ownership and labour union involvement might influence what HRA practices are implemented in different countries and how they are enacted by the practitioners ( Hamilton and Davison, 2022 ). The contextual macro-level praxis of HRA might also be impacted by different cultural norms; for example, the application of HRA practices in different areas, such as employee control, individual financial performance, or organisational health and wellbeing, might be more or less congruent with a certain national culture. Additionally, the lack of studies covering HRA in the public sector opens the possibility for future research to understand if HRA practices and their contextual enactment vary in business firms versus public sector organisations.

Lastly, studying HRA-as-practice in a given context is seen to have the advantages of close cooperation with HRA practitioners in different types of organisations. We strongly believe that our practice-based approach to HRA, possibly combined with some participative form of research, e.g. the so-called “engaged scholarship” ( Van de Ven, 2007 ), might generate a deeper understanding of HRA practical aspects, e.g. what activities are prioritised by HRA practitioners and why, while at the same time generating value for the practitioners in their everyday work of how different HRA practices and their enactment can solve practical problems they address. Our proposed model for HRA-as-practice benefits future research by providing a possible guideline for empirical investigations, namely, suggesting areas to cover and their potential content: HRA practices, HRA practitioners, HRA praxis and several entities actualising intersections between them, HRA technology, HRA outcomes and HRA hindrances and facilitators.

6. Conclusion

This study conceptualises HRA from a practice-based perspective by identifying what practices are included in HRA, by whom and how they are enacted and what connects them in a coherent model of HRA-as-practice. Moreover, it compiles the nomological network of HRA-as-practice, revealing what factors exist in proximity to the practice of HRA and how they influence and are influenced by it.

Summarising the results of the analysis, we suggest that HRA involves four groups of HRA practices linked to: data usage, data analysis, data-based insights and decision support. Regarding HRA practitioners, a general conclusion is that HR professionals are seen from two perspectives. They are viewed either as producers or consumers of HRA or as both. The analysis shows that most HRA practitioners are seen to be members of HRA teams, whose composition often includes non-traditional HR professionals such as data analysts and specialists in IT, statistical analysis, visualisation and communication. Findings suggest that, based on the nature of the HRA practices and competencies needed to enact them, the role of traditional HR professionals as a relevant category of HRA producers might be questioned. The study also shows that the issue of HRA praxis is the least addressed in the reviewed literature. In our analysis, HRA praxis is attributable to either contextualised processes addressing relevant problems or to certain mechanisms that HRA practitioners use to enact HRA practices.

Based on our results, we suggest that HRA practices, HRA practitioners and HRA praxis are closely interrelated and intersect. Together, they form the practice of HRA actualised by HRA technology, HRA outcomes and HRA hindrances and facilitators that influence and are influenced by it. HRA is, thus, a bundle of four types of practices, associated with data usage, data analysis, data-based insights and decision support, enacted by HRA producers and HRA users via engaging in a process of interrelated steps driven by different contextual mechanisms and actualised by HRA technology, HRA outcomes and HRA hindrances and facilitators.

Besides the theoretical contribution of conceptualising HRA-as-practice, this study contributes to HR practical work by providing a description of HRA and enabling a deeper understanding of the HRA field and how different HRA concepts are linked together. It offers HR function and HR professionals a basic ground to evaluate HRA as a potential solution to generate business value and increase HR impact by providing a holistic model, the constituent parts of which indicate the complex and highly contextual character of HRA. The model suggests that the success of HRA depends not only on the standard HRA practices that generate value as soon as they are implemented in an organisation but rather on the complex context of how and by whom such practices are enacted and actualised. HR departments and HR professionals will benefit by taking into considerations factors such as HRA technology, HRA potential outcomes and diverse HRA hindrances and facilitators that might influence the context in which HRA practices are enacted. It might potentially facilitate relevant measures when one or several named factors seem inadequate or problematic. Depending on what potential outcomes HR practitioners expect from engaging in HRA impacts, what particular practices they should implement and develop. When relevant HRA practices are chosen, their enactment is actualised by the appropriate HRA technology but can be hindered or facilitated by several factors and conditions, which are also context-dependent and require close attention in every individual situation. This means that HRA enactment in practice is highly contextual and providing a single recipe for success is problematic. However, understanding the complex contextual character of the practice of HRA might provide a useful tool for how HR professionals can work with HRA in their own individual situations.

PRISMA flow diagram

HRA-as-practice framework

Categories and subcategories in analysis

	Examples

data usage	internal HR- and other business data (e.g. ) external market and industry data (e.g. ) data management and governance (e.g. ; ; ) constructing metrics and indicators (e.g. ; )
data analysis	descriptive (e.g. ) predictive (e.g. ) prescriptive (e.g. ) autonomous (e.g. ., 2020)
data-based insights	visualisation (e.g. ) storytelling (e.g. ) communication of results (e.g. )
decision support	evidence-based (e.g. ., 2015) user tailored (e.g. ) action oriented (e.g. , 2022) strategy driven (e.g. )

HRA producers	HR professionals (e.g. ) HR analysts (e.g. ., 2020) external IT and management consultants (e.g. ) academics (e.g. ) internal and external experts (e.g. )
HRA users	HR professionals (e.g. ) top managers (e.g. ., 2021) line managers (e.g. , 2020) business professionals (e.g. )

HRA processes	a set of sequential steps such as formulating question, collecting data, building models, analysing data, reporting results, evaluating actions (e.g. ; ; ., 2018; ., 2020)
HRA mechanisms	customisation (e.g. , 2022) alignment to decision makers' perceptions of business reality (e.g. ) building of relationships and networks (e.g. ) establishment of HRA’s credibility and legitimacy (e.g. ., 2015) exercise of strategic commitment (e.g. ) demonstration of ethical and legal compliance (e.g. ) encouragement of employee involvement (e.g. )

general technology	automation (e.g. ) computerisation (e.g. ) cloud technology (e.g. ) social media (e.g. ) big data (e.g. ) robotics (e.g. ) artificial intelligence (e.g. ) algorithms (e.g. ., 2020) facial recognition (e.g. ) Internet of Things, biometric technology, sensors and wearables (e.g. )
HRA tools	HR- and other organisational IS (e.g. ., 2020; ., 2020) statistical soft: Excel, SPSS, R, Stata, Python (e.g. ; ) reporting and visualisation tools (e.g. ; )
HRA techniques	benchmarking (e.g. ) data mining (e.g. ) sentiment analyses (e.g. ., 2018) machine learning (e.g. ., 2021) mathematical modelling (e.g. ., 2020)

business benefits	improved business decisions (e.g. ) improved firm performance (e.g. ) revenue and ROI (e.g. time and cost savings (e.g. ., 2021) effectiveness (e.g. ) efficiency (e.g. ) competitive advantage (e.g. ) increased productivity (e.g. ) reduced uncertainty (e.g. ) facilitation of strategic change (e.g. ) effective strategy execution (e.g. )
HR-related outcomes	Improved HR decisions (e.g. ) HR impact and strategic influence (e.g. ) operational effectiveness of HR function (e.g. ) improved HR processes (e.g. ; ) credibility and the professional legitimacy of HR (e.g. ) increased individual job performance of HR professionals (e.g. ., 2018) accuracy, fairness and employee commitment (e.g. ) just workplace (e.g. ) effective HRM (e.g. )

individual	analytical and statistical skills (e.g. ) HR professional knowledge (e.g. ) business acumen (e.g. ., 2020) communication skills (e.g. ) relationships (e.g. ) managerial buy-in and trust (e.g. ) employees' buy-in (e.g. ) attitudes and mindsets (e.g. )
technological	data availability and quality (e.g. ., 2020) infrastructure and IT systems (e.g. )
organisational	organisational structure (e.g. ., 2016) organisational culture (e.g. ) resource allocation (e.g. ) operational processes (e.g. leadership support (e.g. )
environmental	privacy (e.g. ., 2018) ethical and legal concerns (e.g. ) gap between academia and industry (e.g. )

Authors' own creation

	Academic articles	Practitioner-oriented articles	Total

HRA practices	53	47	100
HRA practitioners	37	40	77
HRA praxis	19	25	44

HRA technology	43	37	80
HRA outcomes	53	47	100
HRA hindrances and facilitators	42	36	78

Source(s): Authors' own creation

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Acknowledgements

Since submission of this article, the following author have updated their affiliations: Mårten Hugosson is at the Inland School of Business and Social Sciences; Department of Organisation, Leadership and Management.

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Employee perceptions of HR practices: A critical review and future directions

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Assessing the Effects of Workplace Contextual Factors on Turnover Intention: Evidence from U.S. Federal Employees

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Published: 17 June 2024

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This study delves into the crucial topic of turnover intention among U.S. Federal Employees, shedding light on the workplace factors that play a pivotal role in mitigating this issue. Through the utilization of Ordinary Least Squares (OLS) regression analyses, the study meticulously examines various workplace contextual factors and their impact on turnover intention. The standout finding of the study underscores the paramount importance of opportunities for growth and development (Factor 4) in reducing turnover intention. This emphasizes the significance of investing in employees’ professional advancement and creating avenues for them to expand their skills and progress in their careers. Moreover, the study brings to the forefront additional factors that contribute to decreased turnover intention, such as longer tenure, higher salary, gender (with females exhibiting lower turnover intention), and age. These insights not only provide a nuanced understanding of the dynamics at play but also offer actionable strategies for organizations to address turnover concerns effectively. By identifying these key factors, the study equips HR professionals and decision-makers with valuable guidance for implementing tailored HRM practices aimed at curbing turnover and fostering a more stable and satisfied workforce within the U.S. Federal sector. Ultimately, these findings have the potential to drive tangible improvements in employee retention and organizational performance.

The Impacts of High Employee Turnover in the UAE Hospitality Industry

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Introduction

Employee turnover imposes significant costs on organizations. Upon an employee’s resignation, the organization must navigate a protracted process involving recruiting, hiring, and training a replacement—an undertaking accompanied by substantial tangible expenses (Moynihan & Landuyt, 2008 ; Tziner & Birati, 1996 ). In addition to the quantifiable costs linked to this replacement cycle, voluntary turnover brings about intangible costs including loss of knowledge, decreased customer satisfaction, disruptions in organizational performance, and diminished morale among remaining employees (Dess & Shaw, 2001 ; Felps et al., 2009 ; Hausknecht et al., 2009 ). Considering these multifaceted costs, it is understandable that scholars and practitioners have given particular attention to comprehending the reasons behind employee departures since the early 20th century (Hom et al., 2017 ).

Since the pioneering work of March and Simon in 1958, researchers have strived to predict voluntary turnover using various models. A recent meta-analysis of studies on voluntary turnover studies (Holtom et al., 2008 ) show that more than 50 broad antecedents have been recognized as scientifically valuable predictors of voluntary turnover. While factors such as personality, job satisfaction, job characteristics, and job stress have traditionally been extensively studied in turnover research, the organizational context, including aspects such as organizational culture and employees’ working environment, has only recently garnered recognition from researchers (Holtom et al., 2008 ; Hom et al., 2017 ). The term “context” can be defined as “stimuli and phenomena that surround and exist in the environment external to the individual” (Mowday & Sutton, 1993 ).

Given the pivotal role of context in studies of organizational behavior, it is noteworthy that researchers have not adequately delved into the impact of contextual factors on voluntary turnover. Unlike existing turnover studies, which mainly focus on specific contextual factors like organizational justice (Hemdi & Nasurdin, 2007 ), trust (Jabeen & Isakovic, 2018 ), and organizational culture (Campbell & Im, 2016 ), this study undertakes a more comprehensive examination and endeavors to address a significant gap in the literature by examining the combined influence of various workplace characteristics on turnover intention. Through a comprehensive review of workplace contextual factors and their effects on turnover intention, this study aims to reduce undesired voluntary turnover by gaining a nuanced understanding of the broader organizational context.

Theoretical Backgrounds

Turnover is defined as the extent of individual movement across the membership boundary of a social system (Price, 1977 , P. 3). When an employee decides to cross the membership boundary of an organization, it is termed voluntary turnover, whereas involuntary turnover occurs when an employer makes this decision, such as through firing or layoffs. This study specifically centers on voluntary turnover, with turnover intention serving as a proxy for this phenomenon. Turnover intention refers to an individual’s thoughts about leaving their current organization. Although turnover intention may not always lead to actual turnover, research suggests a substantial relationship between the two (Park & Shaw, 2013 ), with turnover intention frequently regarded as an alternative measure for actual turnover (Price, 2001 ). Indeed, turnover intention has emerged as a widely acknowledged proxy for actual voluntary turnover in studies conducted in both the public and private sectors (Bertelli, 2007 ; Caillier, 2011 ; Cho & Lewis, 2012 ; Cohen et al., 2016 ; Moynihan & Landuyt, 2008 ; Pitts et al., 2011 ).

Determinants of Voluntary Turnover

The cost of replacing a departing employee is estimated to be approximately twice their annual salary (Allen et al., 2010 ). This significant expense has motivated researchers’ efforts over the past century to understand the reasons behind employee departures (Hom et al., 2017 ). Resulting in over 1,500 academic studies, these investigations have identified 50 broad antecedents of voluntary turnover (Holtom et al., 2008 ). Determinants of voluntary turnover are typically categorized into external environmental, work-related organizational, and individual characteristic factors (Mobley et al., 1979 ). External environmental factors encompass perceived alternatives (such as job availability) and the unemployment rate (e.g., Anderson & Milkovich, 1980 ; Carsten & Spector, 1987 ; Fields, 1976 ; Lee et al., 2017 ). Research indicates a positive correlation between employees’ turnover and turnover intention with job availability and a negative correlation with the unemployment rate (Peters et al., 1981 ). Work-related organizational factors include various aspects like job satisfaction (Medina, 2012 ), development and growth opportunities (Weng et al., 2010 ), promotion (Yücel, 2012 ), pay (Irvine & Evans, 1995 ), perceived autonomy (Gillet et al., 2013 ), goal clarity (Davis & Stazyk, 2015 ), and job stress (Arshadi & Hayavi, 2013 ). In general, turnover or turnover intention tends to decrease when employees experience high job satisfaction, ample development and growth opportunities, increased promotion prospects, better compensation, greater autonomy, clearly defined goals, or minimal job stress (Griffeth et al., 2000 ). Individual characteristic factors encompass demographic variables such as gender (Sabharwal, 2015 ), education level (Lambert et al., 2001 ), tenure (Um & Harrison, 1998 ), race (Jones & Harter, 2004 ), age (Emiroğlu et al., 2015 ), and marital status (Kim et al., 2012 ). Typically, turnover or turnover intention tends to decrease when employees are female, less educated, long-tenured, white, older, or married (Griffeth et al., 2000 ).

Remarkably, the exploration of job turnover in public sector settings did not receive significant attention until the new millennium (Lee & Jimenez, 2011 ). Determinants of turnover for public employees have been identified across various areas, including job characteristics (Kim, 2012 ), human resource management practices (Tinti et al., 2017 ), person-organizational fit (Ballinger et al., 2016 ), and public service motivation (Bright, 2007 ).

Contextual Factor Studies

The organizational context has only recently garnered attention from turnover researchers, despite its significant impact on the occurrence and interpretation of organizational behavior (Rubenstein et al., 2018 , p. 38). Traditionally, turnover researchers have focused on examining the effects of selected contextual factors on voluntary turnover, considering both the organizational context level and the person-context interface (Holtom et al., 2008 ; Rubenstein et al., 2018 ). Factors at the organizational context level that have captured turnover researchers’ attention include organizational support (Amarneh et al., 2021 ), engagement aggregated (Harter et al., 2003 ), organizational citizenship behavior (Mallick et al., 2014 ), organizational size (Guan et al., 2014 ), and diversity level (Choi, 2013 ). Generally, researchers have found that turnover or turnover intention decreases when employees experience high organizational support, exhibit high engagement, engage in organizational citizenship behaviors, work in larger organizations, and experience lower levels of diversity.

Person-context interface factors encompass elements such as offered rewards (Miao et al., 2013 ), organizational justice (George & Wallio, 2017 ), trust (Ellickson & Logsdon, 2001 ), and organizational culture (Egan, 2008 ). Researchers generally found that employees’ turnover or turnover intention decreases when employees are satisfied with rewards, experience justice and trust in their organization, or when a positive organizational culture (e.g., high-performing or learning culture) prevails.

Workplace and Demographic Factors

The study employed the Merit Principles Survey 2016 Data, providing a comprehensive perspective on various workplace aspects. Utilizing exploratory factor analysis, we grouped 20 workplace variables into four distinct factors. Further elaboration on the methodology of the factor analysis will be provided in the subsequent methods section. In this section, our focus shifts to proposing hypotheses centered around each of the identified factors and demographic variables, which will serve as focal points in our regression analyses.

Workplace Factors

Happy and innovative working climate (factor 1).

Studies suggest that when employees feel valued, their inclination to leave decreases (Mancuso et al., 2010 ). For example, expatriate educators are more inclined to stay in their current positions when they experience a positive work environment, characterized by recognition from both peers and management (Odland & Ruzicka, 2009 ). An innovative work environment encourages employees to explore fresh ideas, nurturing a heightened sense of psychological empowerment and job satisfaction (Hsu & Chen, 2017 ). As a result, an innovative work environment has been linked to a reduction in turnover intention (Yeun, 2014 ).

H1: Employees working in happy and innovative working climate tend to have a low level of turnover intention.

Feeling Valued and Trusted (Factor 2)

Employees who perceive low support are less likely to feel valued, and this has been linked to higher turnover, particularly in the retail sector (Shanock & Eisenberger, 2006 ). The sense of not feeling valued is identified as a significant factor contributing to voluntary turnover among U.S. child welfare employees (Nittoli, 2003 ). Conversely, when employees feel trusted, they experience higher levels of autonomy, fostering a desire to remain in their current organizations (Dirks & Skarlicki, 2004 ).

H2: When employees feel valued and trusted, they tend to have a low level of turnover intention.

Coworker Support and the Spirit of Camaraderie (Factor 3)

The relationship with coworkers and their support play a crucial role in determining organizational departure (Feeley, 2000 ). Employees who maintain positive relationships with coworkers and receive support from them exhibit lower turnover intentions (Young, 2015 ). Furthermore, a strong sense of camaraderie also diminishes turnover intentions (Lopes Morrison, 2005 ).

H3: When the spirit of camaraderie exists in an organization and employees receive support from coworker, they tend to have a low level of turnover intention.

Opportunities for Growth and Development (Factor 4)

Employees often depart organizations in pursuit of better growth and development opportunities (Quarles, 1994 ). For instance, the availability of career advancement prospects within current organizations fosters organizational commitment, potentially reducing turnover intentions among employees of public accounting firms (Nouri & Parker, 2013 ).

H4: When employees are satisfied with opportunities for growth and development, they tend to have a low level of turnover intention.

Demographic Factors

In this study, various demographic factors were included in regression analyses, revealing that turnover intention generally decreases due to certain demographic factors. Employees with longer tenure (Griffeth et al., 2000 ), a managerial status (Farris, 1969 ), higher salary (Sturman & Trevor, 2001 ), an older age (van Hooft et al., 2021 ), union membership or a teleworker status (Peters et al., 2004 ) typically exhibit lower levels of turnover intention. Conversely, turnover intention tends to increase among female employees (Karatepe et al., 2008 ), racial minorities (Xue, 2015 ), or those with higher levels of education (Choi, 2006 ).

H5-1: Employees with longer tenure show a lower level of turnover intention than employees with shorter tenure.

H5-2: Employees with higher managerial status show a lower level of turnover intention than employees with lower managerial status or non-managerial status.

H5-3: Employees with higher salary show a lower level of turnover intention than employees with lower salary.

H5-4: Employees with older age show a lower level of turnover intention than employees with younger age.

H5-5: Employees with a labor union membership show a lower level of turnover intention than employees without a labor union membership.

H5-6: Teleworkers show a lower level of turnover intention than non-teleworker.

H5-7: Female employees show a higher level of turnover intention than male counterparts.

H5-8: Employees with racial minority status show a higher level of turnover intention than employees with racial majority status.

H5-9: Employees with more education show a higher level of turnover intention than employees with less education.

This study employed ordinary least squares (OLS) regression analyses to investigate the influence of various workplace characteristics on turnover intention. Before conducting the OLS regression, exploratory factor analysis was utilized to uncover underlying factors among the 20 workplace variables. The data for this study were obtained from the Merit System Protection Board (MSPB)’s 2016 Merit Principles Survey (MPS) dataset ‘path 2.’ The sample consisted of 14,473 full-time civilian federal employees from 24 federal agencies, with a response rate of 38.7% for the dataset ‘path 2’ (Merit System Protection Board, 2016 ).

Major Variables

Dependent variable

In this study, turnover intention was used as the dependent variable. While turnover intention may not perfectly align with actual turnover, a strong correlation between the two exists (Belkhir, 2009 ), and turnover intention has remained a key focus in turnover research (Cohen et al., 2016 ). Consequently, turnover intention frequently acts as a proxy measure for actual turnover in public administration literature (Moynihan & Landuyt, 2008 ), as well as in general turnover studies (Griffeth et al., 2000 ). In the 2016 MPS dataset, survey participants were asked to indicate their agreement level regarding the possibility of transitioning to a different occupation or line of work, with responses rated on a scale from 1 (strongly disagree) to 5 (strongly agree).

Independent variables

Various aspects of the workplace for federal employees served as independent variables in this study. Survey participants were asked to express their level of agreement on diverse workplace variables, rated on a scale from 1 (strongly disagree) to 5 (strongly agree). To identify underlying dimensions among the 20 workplace variables, an exploratory factor analysis was conducted. In determining the number of factors, both eigenvalues and an eigenvalue scree plot were considered, as suggested by methodologists (Ferguson & Cox, 1993 ; Hayton et al., 2004 ). While one factor exhibited an eigenvalue greater than one (see Appendix Table 4 ), the eigenvalue scree plot indicated that the slope of the graph did not change significantly after the fourth factor (see Appendix Fig. 1 ). Following the scree test guideline (DeCoster, 1998 ; Yong & Pearce, 2013 ), it was recommended to retain all factors until the slope displayed minimal change. Consequently, this study identified four factors. Further details regarding the factor analysis can be found in Appendix . After the factor analysis, appropriate names were assigned to the extracted factors, acknowledging that the given names may not fully encapsulate the meaning of all component variables in each factor. The four attained factors, along with their component variables, are outlined below. The meanings of the component variables in each factor are detailed in Appendix Table 5 .

Factor 1 (happy and innovative working climate): w9, w10, w11, w12, w14, w16, w17

Factor 2 (feeling valued and trusted): w2, w4, w5, w6, w7, w8

Factor 3 (coworker support and the spirit of camaraderie): w1, w3, w8, w13, w15

Factor 4 (opportunities for growth and development): w19, w20

The internal consistency or reliability of these four factors was evaluated by computing Cronbach’s alpha values. The Cronbach’s alpha values for all four factors fell within the range of 0.85 to 0.95 (refer to Appendix Table 5 ). According to the standards for internal consistency (Cronbach, 1951 ; Nunnally, 1978 ), a set of variables is considered to have satisfactory internal consistency or reliability when the Cronbach’s alpha value exceeds 0.7.

Demographic variables

Nine demographic variables were utilized in this study, with numbers assigned to each variable as follows.

Years with current agency (d2)

1: 3 years or less, 2: 4 years or more,

Supervisory status (d4)

1: non-supervisor, 2: team leader, 3: supervisor, 4: manager, 5: executive

Union membership (d8)

0: non-union membership, 1: dues-paying union membership

Salary level (d10)

1: $74,999 or less, 2: $75,000-$99,999, 3: $100,000-$149,999, 4: $150,000 or more

Racial minority (d11)

0: non-minority, 1: minority

Gender (d12)

0: male, 1: female

Age group (d15)

1: 39 and under, 2: 40 and over

Education level (d16)

1: less than AA degree, 2: AA or BA degree, 3: graduate degree

Teleworker status (d18)

0: non-teleworker, 1: teleworker

Descriptive Statistics and Correlations

Mean values and standard deviations for both workplace and demographic variables are presented in Tables 1 and 2 . Table 1 demonstrates that all correlations between workplace variables were significant and positive ( p <.01), and likewise, all correlations between workplace variables and turnover intention were significant and negative ( p <.01). In essence, turnover intention tends to decrease when employees express agreement or strong agreement with various workplace variables.

On average, federal employees did not demonstrate a high level of turnover intention (mean = 2.33 out of 5). Among the 20 workplace variables, the top five variables with which employees reported a high level of agreement were as follows: “I understand how I contribute to my agency’s mission” (w18, mean = 4.17), “My judgment is trusted and relied on at work” (w6, mean = 3.93), “I feel needed and depended on at work” (w4, mean = 3.88), “I like the quality of relationships I have with my coworkers” (w13, mean = 3.82), and “I feel comfortable being myself at work” (w17, mean = 3.78). It is worth noting that three of these variables (w18, w6, and w4) belong to Factor 2 (Feeling valued and trusted), while none are from Factor 4 (Opportunities for growth and development).

Conversely, the bottom five variables with which employees reported a low level of agreement were: “I am able to share my true thoughts and feelings at work” (w11, mean = 3.38), “I feel encouraged to try new things in my work” (w10, mean = 3.43), “I feel fully appreciated at work” (w9, mean = 3.44), “There is a culture of openness and support for new or different perspectives in my work unit” (w3, mean = 3.45), and “I feel cared about personally at work” (w12, mean = 3.46). Remarkably, four of these variables (w11, w10, w9, and w12) belong to Factor 1 (Happy and innovative working climate).

In Table 2 , it is noted that, on average, survey participants remained in their current agencies for more than 4 years and held positions as team leaders or higher-level managers (mean = 2.25). Moreover, 15% of survey participants were union members, 33% were racial minorities, 42% were female, and 56% had the option to telework. Additionally, the average salary range fell between $75,000 and $150,000, the average age exceeded 40 years, and the typical education level was an associate’s (AA) or bachelor’s (BA) degree.

Workplace Variables, Demographics, and Turnover Intention

OLS regression analyses were conducted to determine which aspects of workplace characteristics contribute to decreased turnover intention and how demographic variables influence turnover intention. Variance inflation factor (VIF) was assessed during regression runs to detect potential multicollinearity issues arising from high correlations among workplace variables (see Table 1 ). Severe multicollinearity in regression analysis can compromise the statistical significance of each independent variable and render the results unreliable (Mansfield & Helms, 1982 ). However, the average VIF was found to be 2.79. According to suggested guidelines (Mansfield & Helms, 1982 ; Miles, 2005 ), multicollinearity is not a concern in regression analysis if the average VIF value is below 10.

The regression analyses began with only demographic variables. As depicted in Model 1 (Table 3 ), turnover intention decreased as salary increased (coefficient: − 0.170, p <.001), as employees aged (coefficient: − 0.094, p <.05), or when employees had the option to telework (coefficient: − 0.057, p <.05). Conversely, turnover intention increased if employees were union members (coefficient: 0.102, p <.01) or belonged to racial minorities (coefficient: 0.343, p <.001).

In Model 2 (Table 3 ), the four workplace variable factors (consisting of 20 variables) were incorporated with the demographic variables from Model 1. Notably, more demographic variables demonstrated significant effects on turnover intention in this model. In addition to the demographic variables that showed significant effects in Model 1, turnover intention decreased with longer tenure at current agencies (coefficient: − 0.096, p <.05), or among female employees (coefficient: − 0.059, p <.05). Conversely, turnover intention increased when employees held higher managerial status (coefficient: 0.051, p <.001). However, telework status and education level did not exhibit a significant effect on turnover intention.

In summary, out of the nine hypotheses regarding the effects of demographic variables, four were supported. Longer tenure (H5-1), higher salary (H5-3), and older age (H5-4) were associated with lower turnover intention, while racial minority status (H5-8) was linked to higher turnover intention. The effects of higher managerial status (H5-2), union membership (H5-5), and being female (H5-7) contradicted the predictions in the hypotheses. Unexpectedly, turnover intention increased when employees held higher managerial status or were union members, but decreased when employees were female. These unexpected findings will be discussed in the last section.

Regarding the effects of workplace variables, Model 2 revealed that three out of seven variables in Factor 1, three out of six variables in Factor 2, one out of five variables in Factor 3, and two out of two variables in Factor 4 had significant and negative effects on turnover intention. In other words, turnover intention significantly decreased when employees agreed or strongly agreed with those workplace variables. Overall, H1 and H2 were partially supported, H3 was not supported, and H4 was supported. Specifically, turnover intention significantly decreased when employees agreed or strongly agreed with Factor 4 (Opportunities for growth and development), whereas turnover intention was not affected by most variables in Factor 3 (Coworker support and the spirit of camaraderie). Among Factor 1 variables, turnover intention significantly decreased when employees agreed or strongly agreed with “I feel fully appreciated at work” (w9), “I feel comfortable talking to my supervisor about the things that matter to me at work” (w14), and “I feel comfortable being myself at work” (w17). For Factor 2 variables, turnover intention significantly decreased when employees agreed or strongly agreed with “My judgment is trusted and relied on at work” (w6), “I feel valued at work” (w7), and “I understand how I contribute to my agency’s mission” (w18).

Managerial Implications and Contributions to the Literature

This study identifies specific workplace factors that significantly influence turnover intention. Factor 4, which pertains to opportunities for growth and development, emerged as the most influential, indicating that investing in employee development can significantly reduce turnover intention. Conversely, Factor 3, related to coworker support and camaraderie, showed the least impact. This suggests that while social support is important, it may not be as influential in retaining employees as opportunities for professional growth. HR managers can use these findings to tailor their strategies and initiatives to address turnover. Emphasizing and enhancing opportunities for growth and development, such as training programs, career advancement paths, and skill-building opportunities, can be particularly effective in reducing turnover intention. Additionally, fostering a positive and innovative working climate (Factor 1) and ensuring employees feel valued and trusted (Factor 2) are also crucial for mitigating turnover intention.

This study also highlights the influence of demographic variables on turnover intention, including tenure, salary, age, race, managerial position, union membership, and gender. Understanding these demographic trends can help managers identify at-risk groups and implement targeted retention strategies. For example, recognizing that employees promoted to higher managerial positions may experience increased turnover intention due to heightened stress can guide the design of leadership development and support programs. In summary, this study underscores the importance of considering diverse workplace factors and demographic variables in understanding and addressing turnover intention. By leveraging these insights, organizations can develop targeted HRM practices that foster employee engagement, satisfaction, and retention.

The findings of this study make a significant contribution to the existing literature on turnover intention and workplace factors. The study fills a gap in the turnover literature by examining a wide array of organizational contextual factors that have not received significant attention previously. Unlike prior studies that often focused on specific factors such as organizational justice or culture, this research considers all contextual factors simultaneously. This holistic approach provides a comprehensive understanding of how various organizational elements interact to influence employee turnover intentions. By identifying which contextual factors have a more pronounced effect on turnover intention, the study offers valuable insights for organizations seeking to implement targeted retention strategies. For example, it highlights the significant impact of opportunities for growth and development (Factor 4) in reducing turnover intention. Understanding the relative importance of different factors can help organizations allocate resources more effectively to address turnover. While acknowledging the need for caution in interpreting findings, the study’s comprehensive approach to examining workplace factors and turnover intention enhances the generalizability of its results. By considering a diverse range of demographic variables and organizational contexts, this study provides insights that can be applicable across various industries and settings.

Discussion and Conclusion

This study aimed to identify workplace factors that could potentially alleviate turnover intention. Initial correlation analyses suggested a general inverse relationship between turnover intention and various workplace factors. However, further examination through Ordinary Least Squares (OLS) regression analyses revealed that turnover intention was primarily influenced by Factor 4 (opportunities for growth and development), while Factor 3 (coworker support and camaraderie) had the least impact. Additional findings from the OLS regression analyses indicated that Factor 1 (a positive and innovative working climate) and Factor 2 (feeling valued and trusted) exerted a partial influence on turnover intention. These results emphasize the nuanced interplay of workplace elements in shaping employee attitudes toward turnover.

This study also delved into the impact of major demographic variables on turnover intention. Consistent with existing literature, employees with longer tenure, higher salaries, or older age exhibited a lower level of turnover intention, while those from racial minority backgrounds displayed a higher level of turnover intention. However, certain findings contradicted initial hypotheses. Contrary to the prediction in hypothesis H5-2, turnover intention increased when employees were promoted to higher managerial positions. Despite the general tendency for managers to stay in their current organizations due to higher investment (Farris, 1969 ), the study suggests that the heightened stress associated with elevated managerial roles, as indicated by Cavanaugh et al. ( 2000 ), may outweigh the perceived value of their investment, potentially leading to resignation. Additionally, turnover intention showed an unexpected increase among union members, contrary to the prediction in hypothesis H5-5. This finding supports an argument that union members typically experience lower job satisfaction than non-members (Hammer & Avgar, 2017 ). Despite the protective measures offered by unions (Freeman & Medoff, 1984 ), this study suggests that the reported lower job satisfaction among union members might contribute to their decision to resign. Contrary to the prediction in hypothesis H5-7, female employees exhibited a lower level of turnover intention than their male counterparts. This unexpected finding may be attributed to the observed higher job satisfaction among female employees, despite typically facing working conditions and rewards inferior to those of their male counterparts, as noted in some studies (e.g., Clark, 1997 ). The implication is that the presence of high job satisfaction among female employees serves as a mitigating factor against turnover intention, influencing their decision to remain in their current roles.

Despite the valuable contributions made by this study, it is essential to approach the interpretation of its findings with caution. Several factors warrant consideration. Firstly, while the focus on a limited number of workplace contextual factors and their impact on turnover intention is helpful, it is important to note that there is no universally agreed-upon set of workplace factors. The definition of workplace contextual factors may vary based on the survey questions posed. Conducting studies across diverse settings is crucial to establish more commonly agreed-upon workplace factors, enhancing the generalizability of findings. Secondly, the applicability of the findings in this study to public employees at different levels of government, such as state and local governments, or in various countries, remains uncertain. Given that the MPS data included only federal employees in the U.S., further research in different settings is necessary before extrapolating the findings to other contexts. Expanding the scope of investigation to include a broader range of participants will contribute to a more comprehensive understanding of the relationships between workplace contextual factors and turnover intention. Overall, this study advances our understanding of turnover intention by shedding light on the nuanced interplay of workplace elements and their impact on employee attitudes. Its findings offer actionable insights for HR practitioners and provide a foundation for future research in this field.

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Appendix. Factor Analysis of Work Environments

Scree plot of eigenvalues after factor analysis

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Human Resource Practices and Policies: A Literature Review
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A Systematic Review of Human Resource Management Systems and Their
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HR Analytics and the Implementation Imperative
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Managing Careers: Strategies and Best Practices in HR
5. Regularly Review and Update Career Management Practices: The business environment and workforce needs are constantly changing. Regularly reviewing and updating career management practices ensures that they remain effective and relevant. Solicit feedback from employees and use it to make continuous improvements. Conclusion
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APA's clinical practice guidelines (CPGs) and professional practice guidelines (PPGs) are the result of a complex and lively process of decision-making, interdisciplinary discussion, literature review, public comment, APA approval, and more. Here's a quick rundown of how they come together: Clinical practice guidelines
Ithaca College Will Implement New Policy on Background Checks for
The complete policy may be found in policy 2.49 of the Ithaca College Policy manual.The Office of Human Resources will be responsible for coordinating the process of conducting background checks, through which the college will obtain criminal history record information. ... and after review will make a recommendation to the hiring supervisor as ...
(PDF) Employee perceptions of HR practices: A critical review and
Employee perceptions of HR practices: A critical review and future directions, The International Journal of Human Resource Management, 31:1, 128-173, DOI: 10.1080/09585192.2019.1674360

HR analytics-as-practice: a systematic literature review

Design/methodology/approach

Practical implications

Originality/value

1. Introduction

2. Theoretical approach: HRA-as-practice

3. Methodology

4. Analysis

4.1 HRA practices

4.1.1 HRA practitioners

4.1.2 HRA praxis

4.1.3 Topics in the nomological network of HRA-as-practice

5. Discussion and future research

6. Conclusion

Appendix Articles included for analysis

Acknowledgements

Corresponding author

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Employee perceptions of HR practices: A critical review and future directions

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Assessing the Effects of Workplace Contextual Factors on Turnover Intention: Evidence from U.S. Federal Employees

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The Impacts of High Employee Turnover in the UAE Hospitality Industry

Introduction

Theoretical Backgrounds

Determinants of Voluntary Turnover

Contextual Factor Studies

Workplace and Demographic Factors

Workplace Factors

Feeling Valued and Trusted (Factor 2)

Coworker Support and the Spirit of Camaraderie (Factor 3)

Opportunities for Growth and Development (Factor 4)

Demographic Factors

Major Variables

Descriptive Statistics and Correlations

Workplace Variables, Demographics, and Turnover Intention

Managerial Implications and Contributions to the Literature

Discussion and Conclusion

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Corresponding author

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Informed Consent

Conflict of Interest

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Appendix. Factor Analysis of Work Environments

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Ithaca College Will Implement New Policy on Background Checks for Employees

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