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Human resource analytics (HRA) is a practice that is emerging within the human resources function. This chapter aims to provide an overview of the knowledge that currently exists about HRA and to identify facilitators and restraints of using HRA. Based on both emerging trends in the literature and in-depth interviews with key practitioners in the field, the authors deduce recommendations for organisations to effectively employ HRA. The analysis shows that HRA is ready to add value towards organisational effectiveness, although barriers exist in realising its potential for the same. Facilitators of HRA include the right set of competencies, with relationship building identified as especially important.

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.

The purpose of this paper is to examine the evolution of the concept of human resource analytics (HRA) and propose an operational framework demonstrating the sources generating data for HRA, as well as the impact of HRA on multiple levels in the organization.

A review of literature was conducted to present the existing body of knowledge and build upon for the development of an operational framework for successful implementation of HRA as a human resources (HR) process.

Building upon the existing literature, this paper presents an operational HRA framework, positioning HRA as an analytical process through integrating advanced statistical methodology. HRA presents a tool to obtain evidence-based analytical results for improving people-related performance, operational effectiveness, and ultimately the impact of the business strategy. By using HR big data, HRA impacts multiple organizational levels, from individual employees to HR functions and the organizational strategy.

While research on data analytics has recently flourished across various management fields, this has not been the case for the broader field of HR. This is especially a growing concern as the lack of understanding of the basics and fundamentals of people analytics in the field of HR may delay the effective implementation and operationalization of HRA and present additional barriers impacting on-going HR activities, as well as HR’s role as a strategic business partner. HR practice may greatly benefit from gaining an understanding of HRA and the multi-levels of impact it may have on the organization.

This paper explores various concepts related to HRA by examining terms such as “HR metrics” vs “HR” and “HR big data” vs “big data.” Furthermore, the comprehensive HRA operational framework presented in this paper provides HR professionals and researchers with a better understanding of HRA in the age of data analytics and artificial intelligence.

Human resources analytics (HRA) can potentially create value and provide a competitive advantage; however, whether and how HRA creates this value has been sparsely explored in scholarly literature. Hence, the purpose of this study is to provide a process-oriented framework for value creation from HRA use by exploring the underlying mechanisms, complementary resources and outcomes.

The study used a qualitative research design as the research question was exploratory. A total of 26 in-depth expert interviews with different organizations were conducted. These interviews were transcribed and coded for emerging themes, which were placed in a temporal sequence of occurrence to derive a process understanding of value creation from HRA. Additionally, validation tests were conducted.

The thematic analysis using NVivo provided qualitative evidence of the value-creating potential of HRA. Further, it unraveled the process of value creation from HRA in the form of problem construction, insight generation, the buy-in of stakeholders and solution implementation. This process resulted in various human resource management (HRM) and organizational outcomes. The analysis also highlighted the significance of three complementary resources, namely data quality, analytical competency and business knowledge.

This study offers guidance for HR executives and business managers to assess the conditions under which HRA can add business value to organizations.

The paper is novel as this is among the first studies to provide evidence of value creation from HRA and identify the underlying mechanism, which has been highlighted as a gap in the literature. Based on resource-based theory and its complementarities perspective, the study makes a valuable contribution to the nascent HRA literature.

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.

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.

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.

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.

Based on resource-based and dynamic capabilities theorizing, this study explores how human resource analytics (HRA) can improve human resource management (HRM) performance and organizational performance, with creative problem-solving capability (CPSC) as an underlying mediator for creating value from HRA. It also explores how data quality and HRA personnel expertise act as moderators in this relationship.

Hypotheses are tested in an empirical study including 191 firms using partial least square structural equation modeling technique.

The findings confirm the direct and indirect effect of HRA use and maturity on HRM and organizational performance, as well as the mediating role of CPSC. HRA personnel expertise was found to moderate the relationship between HRA and CPSC, data quality being an important factor.

The findings contribute to the sparse evidence of value creation from HRA use/maturity on HRM and organizational outcomes, providing a theoretical logic of resource-based view and dynamic capabilities view based on the underlying causal mechanism through which HRA creates value. The study identified complementary capabilities which when combined with HRA use/maturity and CPSC result in value creation.

This study aims to investigate the relationship between the adoption of human resource (HR) analytics and managerial decision-making (DM), with attitude toward artificial intelligence (AI) as a potential moderator.

This study was conducted in three phases. In Phase I, a comprehensive scale to measure the “Adoption of HR analytics” was conceptualized and developed. In Phase II, the scale was validated and operationalized. Finally, in Phase III, a survey of 377 managers was conducted, and a conceptual model was validated using structural equation modeling.

This study reveals that the adoption of HR analytics (HRA) and a positive attitude toward AI significantly influence DM. The findings suggest that the structural factors play the most important role in the adoption of HRA, followed by individual factors, value and system support.

These findings hold valuable implications for managers seeking integration of HRA and AI within organizational systems and processes. HR practitioners can evaluate their organization’s readiness for HRA, enabling them to build a future-proof workforce with the necessary skills. It can help managers make the adoption of AI-enabled HRA a reality. The study also helps to remove inhibitions and concerns of HR managers and employees related to AI.

This paper addresses the methodological, practical knowledge and evidence gap in the area of adoption of HRA and DM. It sheds light on the “future of work” in HR, highlighting a potential shift toward human-AI collaboration.

The purpose of this paper is to determine which constructs (factors) will significantly predict and influence the intention to complete a health risk assessment (HRA) in a hospital employee population.

The authors used a cross-sectional design using the theory of planned behavior to design a questionnaire to determine the variables associated with intention to complete an HRA. From a sample of those who completed (n=17) and those who did not complete (n=16) the HRA, the authors used elicitation inquiry to determine the leading factors associated with the intention to complete an HRA. The authors used the responses from this inquiry to develop a questionnaire for a hospital population (n=1,550). A total of 503 hospital employees completed and returned this questionnaire. Using the returned questionnaire data, the authors used logistic regression analysis to determine the best fit model for predicting intention to complete an HRA.

The predictive model was statistically significant at the p < 0.001 level. Discriminant analysis correctly verified the predictive model classified intenders and non-intenders the majority (84 percent) of the time. These study results indicated that perceived behavioral control factors such as having time to complete the HRA, confidence in completing the HRA and trust that the information divulged in the HRA would be kept confidential had the strongest influence (OR=5.39) in predicting participation in taking an HRA.

Potential limitations of this study include; response and selection bias, homogeneity for age and sex and generalizability. These results help to identify key behavioral-related factors predicting hospital employee participation to complete an HRA.

Administrators of worksite health promotion programs can systematically explore means of addressing identified participation barriers for the purpose of increasing overall HRA participation success beyond financial incentives.

The HRA has become a widely accepted assessment tool used to help mitigate the rise in chronic disease. However, HRA completion rates are reported to be low to moderate with very limited research focussed on factors predicting HRA participation.

The purpose of this paper is to provide an overview and history of human resource accounting (HRA) with the objective of promoting both continued academic research and organizational applications. The history of HRA illustrates how academic research can generate improvement in management systems. The paper defines HRA and suggests implications of measuring human capital for financial reporting and managerial uses. Recent Swedish‐based HRA applications with respect to measuring human assets and intellectual capital, including the Skandia Navigator, illustrate how intellectual history and developments in business schools can influence business history.

While training is seen to be of high importance in ensuring adequate human reliability, both in human reliability theory and practice, the question of what “adequate training” actually entails is rarely addressed in this field. Many techniques of human reliability assessment, for example, utilize a “performance‐shaping factor” called “training” to help to quantify human error probabilities. However, these techniques do not specify in a useful way how it is decided whether training is good or poor. Similarly, the training analyst working within what are termed “mission‐critical applications” also utilizes quantitative techniques to assist in the definition of training dimensions which contribute to training effectiveness. Such dimensions are essential to ensure that the performance shaping factor of training is effective when the cognitive and motor skills are transferred to the operational environment. Explores the approaches which are commonly used both by the training analyst and by the human reliability practitioner, and indicates where the sharing of approaches and data may provide valuable insights into the relationship between effective training and human reliability.