1 Introduction
In the contemporary landscape of organizational management, the emergence of Human Resources (HR) Analytics represents a significant trend towards data-driven decision-making within HR practices (Margherita
2022). Technological advancements have made an unprecedented volume of data accessible for analysis. The potential to extract profound insights and improve decision-making based on this data is promising (Thakral et al.
2023). In addition, it has the potential to better link HR practices to business results, as well as to an organization’s strategic planning process (Suri and Lakhanpal
2022; van der Togt and Rasmussen
2017). HR Analytics is receiving significant attention as it promises companies a competitive advantage and rational decision-making (Ferrar and Green
2021). A corresponding definition of HR Analytics is: “An HR practice enabled by information technology that uses descriptive, visual and statistical analyses of data related to HR processes, human capital, organizational performance, and external economic benchmarks to establish business impact and to enable data-driven decision-making” (Marler and Boudreau
2017, p. 15). Reasons for the use of HR Analytics are more statistically-methodically oriented than theoretical. This includes a shift towards evidence-based management (McCartney and Fu
2022a), emphasizing the use of empirical evidence and data to support management decisions, as well as the necessary strengthening of organizational capabilities (Minbaeva
2017) through statistical analysis methods, such as evaluating the effectiveness of HR strategies, talent management quality, or adaptability to changing workforce requirements. Additionally, it underscores the essential resource-based view perspective (Samson and Bhanugopan
2022), which emphasizes that an organization’s resources and capabilities are fundamental to its competitive advantage and necessitates analyzing how resources in the human resources domain can contribute.
While HR Analytics holds promise in enhancing organizational effectiveness through insights derived from data analysis, its actual contribution remains a subject of ongoing theoretical inquiry. At its core, HR Analytics draws from a range of theoretical frameworks, including Human Capital Theory (Minbaeva
2017), and Work and Organizational Psychology (Ontrup et al.
2024; Oswald et al.
2020). Human Capital Theory posits that employees’ skills and knowledge are crucial assets influencing organizational performance. Investments in employee development and management are presumed to yield long-term benefits. Similarly, Work and Organizational Psychology provides insights into employee behavior and motivation, suggesting avenues for improving performance and engagement. Yet, the application of psychological theories within HR Analytics is still evolving, and the extent to which they can drive meaningful organizational change remains unclear.
For addressing research questions, specific theoretical approaches are needed for understanding the relevant phenomenon at hand. For instance, Ontrup et al. (
2022) argued that proactivity should be considered as a psychological construct when seeking to enhance organizational performance through data-driven means. However, the number of potentially relevant constructs is extensive, given that organizational performance is influenced by numerous factors, and there exist various theoretical approaches to predict organizational performance.
In reviews of research on HR Analytics, theoretical approaches are not typically emphasized; instead, classifications are proposed (e.g., Bonilla-Chaves and Palos-Sánchez
2023). Margherita (
2022) categorizes HR Analytics research into enablers, applications, and value, while Thakral et al. (
2023) further breaks them down into HR functions, statistical techniques, organizational outcomes, and employee characteristics. The application fields of HR Analytics are broad (Ontrup et al.
2024) and include: (1) performance and compensation management, (2) employee deployment and planning, (3) recruitment and onboarding, (4) health promotion and health early warning systems, (5) employee retention, (6) personnel and management development, and (7) workplace and work design.
Overall, while it is important to remember that HR Analytics is an emerging innovation with as-yet unknown consequences, current research tends to cast it in a positive light (Tursunbayeva et al.
2021) and the dark side of HR Analytics is predominantly overlooked (Giermindl et al.
2022). Accordingly, the literature identifies various advantages and potential: HR Analytics leads to better real time data availability and can enable executives to make faster, better informed decisions (Guenole et al.
2017). The focus on data and the use of analytics in the decision making process also offers the opportunity to enhance organizational ethics through reducing human bias (Tursunbayeva et al.
2021). Nevertheless, potential ethical and legal risks that may arise in the collection and analysis of data need to be addressed (Edwards et al.
2022).
Although the topic is currently receiving significant attention, the data situation is much less clear in both practice and research (Edwards et al.
2022; Yoon
2021). This raises the question of the degree of implementation and the actual benefits of HR Analytics. With regard to the level of implementation of HR analytics, a distinction is often made between three maturity categories: Reporting (retrospective, descriptive), diagnosis (causal) and forecasting (predictive). With regard to the benefits of HR Analytics from the employer’s perspective, despite the high hopes placed in HR Analytics, there are also increasingly critical voices (Giermindl et al.
2022; McCartney and Fu
2022b). A central weakness is a lack of high-quality empirical studies (Edwards et al.
2022; McCartney and Fu
2022a). With regard to existing models and findings from international research, it can be stated that these are still very limited (Marler and Boudreau
2017; McCartney and Fu
2022b; Peeters et al.
2020). This also applies to German-speaking countries. For example, little is known about the prevalence of HR Analytics in German organizations. Rather vague statements are made, indicating that primarily larger organizations are already implementing HR Analytics (Hammermann et al.
2022). In Switzerland, although an initial study on the prevalence of HR Analytics called the HR Tech Survey was conducted, the results were not published scientifically. Against this background, an explorative and descriptive approach was chosen for the study to better understand the level of implementation and the benefits of HR Analytics in Switzerland. Therefore, the research questions are:
1.
What is the level of implementation of HR Analytics?
2.
What are the benefits of HR Analytics?
To answer these two questions, we conducted a mixed method study.
2 Goals of the study
2.1 Objective
The objective of this study is to determine the current state of HR Analytics in Switzerland. The study serves to create a factual basis and, in line with the basic idea of HR Analytics, provides evidence-based findings on the maturity of HR Analytics in Swiss companies. The present study aimed to determine the current implementation status of HR Analytics and to gain more comprehensive insights into the state of development.
2.2 Methods
The study included a survey and interviews. The survey was conducted in December 2022 and focused on establishing the following key aspects:
1.
For the first research question, areas of application, purpose, data sources, and critical prerequisites.
2.
For the second research question, relevance and target achievements related to HR Analytics
Concerning the areas of application of HR Analytics, various HR processes were queried using yes/no questions (7 possible applications were included, e.g., recruitment/onboarding). The application areas were queried with two time references: today and in the future (2–5 years). The purpose of HR Analytics was queried using three maturity categories: reporting (retrospective, descriptive), diagnostic (causal), and forecasting (predictive). With regard to the data sources, eight different aspects were queried (e.g., survey data). In the queries about the prerequisites for HR Analytics, ten aspects were assessed on a 4-point scale, ranging from “not given at all” to “fully given” (e.g., high data quality). Regarding the relevance and target achievement of seven different objectives related to HR Analytics were evaluated (e.g., increase productivity).
The sample recruitment was conducted through social media. As a result, companies that have positive attitudes towards HR Analytics or are already using HR Analytics were more likely to be addressed. The survey was conducted in German, French and English. The participants were asked for their informed consent. If interested, respondents were sent a results report, otherwise there were no further incentives. One hundred and thirty-three companies participated in the survey, and the results generally refer to this sample size of N = 133. A few results are based on partial samples, as specific questions were only asked if the preceding question was answered affirmatively (e.g., asking about the benefits of HR Analytics for a specific objective only if that objective is pursued by the company). With regard to the size of the company, the responses come predominantly from large companies. Of the companies surveyed, 85% have more than 250 employees. The majority of participating companies come from the finance and insurance sector, as well as logistics, transport and the public sector (50%). Most of the participants are HR representatives (67%). Half of the companies surveyed have their own HR Analytics function, which in the majority of cases (90%) is located in HR. In terms of resources, about half of the companies have at least 1 FTE (full time equivalent) available for HR Analytics (regardless of whether there is a dedicated analytics function or not).
Based on the quantitative study, 12 interviews were carried out with HR Analytics experts from the quantitative survey in January and February 2023. The aim of the interviews was to further explore the most important findings of the study with regard to the level of implementation and the benefits of HR Analytics, The experts interviewed represent seven distinct business sectors. The vast majority works for a large company. It was a semi-structured interview with an average duration of one hour. The interview guide was characterized by a framework of questions that still left room for situational inquiries and flanking questions (Flick
2022). The questions were related to the level of implementation and measures to improve the implementation status as well as potentially value adding topics and ways to optimize the impact of HR Analytics.
Based on the seven steps of content analysis according to Kuckartz (
2014), we analyzed 12 interviews. The aim of this specific form of qualitative content analysis was to filter out certain aspects from the material in a structured way (Mayring
2015). This method of analysis is suitable for the study of the data material at hand, because the categories can be developed through an inductive approach (Kuckartz
2014). One interviewer coded the 12 interviews using multiple coding, resulting in a first version of a category system. A second member of the research team individually coded the 12 interviews to refine the coding scheme and to generate additional codes. In a final meeting, interviews that still did not fit into the system due to irregularities or unusual features were discussed and ultimately coded. This coding process resulted in a category system consisting of two themes and 12 subthemes in line with the two research questions.
5 Practical implications
Work and Organizational Psychology can make relevant contributions to the establishment of HR analytics. In this section, against the background of the study results we discuss the possibilities for optimizing HR a\nalytics by work and organizational psychologists.
One of the most immediate barriers to realizing the full potential of HR Analytics in organizations is the lack of analytical and interpretive skills. In addition, the ability to graphically present results and translate abstract data and models into practical measures are core competencies in demand. Here, the work and organizational psychologist can provide these skills as well as a broad range of statistical and methodical competencies. Work and organizational psychologists should also have the competence to initiate and accompany implementation measures and thus act as multipliers for the findings from HR Analytics. In addition to managers, HR consultants and HR business partners may also need support in interpreting the data and deriving measures. In general, it is advisable to combine insights from Industrial and Organizational Psychology, Human Resources, and Organizational Behavior to successfully implement HR Analytics.
Further, data analysts, HR professionals, and decision makers need a more interdisciplinary way of working together to make sure tangible measures follow the analysis (Ferrar and Green
2021). Interdisciplinary collaboration and stakeholder management are key success factors for the successful introduction of HR analytics. Work and organizational psychologists with skills in empathy, storytelling, and stakeholder management should be able to make a special contribution here to building bridges to the line and the decision makers: Intertwiners that build bridges between hierarchies and organizational units are of great practical importance for successful HR analytics.
Finally, HR Analytics is seen by respondents as having great potential to improve the employee experience of HR processes and products. This represents an opportunity for HR and work and organizational psychologists in particular to optimize the customer experience with insights from HR Analytics and methods of human-centered design, and to add more perceived value to organizations in general (Ferrar and Green
2021).
Overall, there is still a great need for more substantial and scientific research in the field of HR Analytics. In particular, a better understanding of how disruptive technologies such as HR Analytics can be integrated with traditional HRM practices and how they can be better aligned with overall business goals should be a focus of further research (Edwards et al.
2022; Margherita
2022; Thakral et al.
2023).
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