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Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in this is the growing expectation of the human resource (HR) function to understand how workforce analytics informs the business and fuels success. This paper aims to explore how the HR function can achieve this.

The evolution of the “Future of HR” and how it is moving from “descriptive and diagnostic” to “prescriptive and predictive.”

According to KPMG’s 2019 Future of HR survey: 37 per cent of respondents feel “very confident” about HR’s actual ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60 per cent say they plan to invest in predictive analytics. Among those who have invested in AI to date, 88 per cent call the investment worthwhile, with analytics listed as a main priority (33 per cent). Despite data’s remarkable ability to deliver news insights and enhance decision-making, 20 per cent of HR believe analytics will be a primary HR initiative for them over the next one to two years, and only 12 per cent cite analytics as a top management concern.

Taking a page from meeting customer needs, innovative technologies such as AI and the cloud, data analytics can give an organization the potential to gather infinitely greater amounts of information about customers.

Today’s workforce analytics focuses mostly on what happened and why. For instance, you might have tools for identifying areas of high turnover and diagnosing the reasons. But thanks to advancements in technology and data analytics capabilities, HR is better-positioned to be the predictive engine required for the organization’s success.

There has never been a better time for HR to create greater strategic value, as the potential for meaningful workforce insights and analytics comes within reach. Even advancements in cloud-based systems for human capital management are coming packaged with analytics and visualization capabilities, enabling HR leaders to integrate people data with other data sources, such as customer relationship management, for a full view of the business.

This paper will be of value to HR leaders and practitioners who wish to use predictive analytics and emerging technology to drive performance improvement and gain the insights about their workforces.

According to the significant growth of literature and continued adoption of people analytics in practice, it has been promised that people analytics will inform evidence-based decision-making and improve business outcomes. However, existing people analytics literature remains underdeveloped in understanding whether and how such promises have been realized. Accordingly, this study aims to investigate the current reality of people analytics and uncover the debates and challenges that are emerging as a result of its adoption.

This study conducts a systematic literature review of peer-reviewed articles focused on people analytics published in the Association of Business School (ABS) ranked journals between 2011 and 2021.

The review illustrates and critically evaluates several emerging debates and issues faced by people analytics, including inconsistency among the concept and definition of people analytics, people analytics ownership, ethical and privacy concerns of using people analytics, missing evidence of people analytics impact and readiness to perform people analytics.

This review presents a comprehensive research agenda demonstrating the need for collaboration between scholars and practitioners to successfully align the promise and the current reality of people analytics.

This systematic review is distinct from existing reviews in three ways. First, this review synthesizes and critically evaluates the significant growth of peer-reviewed articles focused on people analytics published in ABS ranked journals between 2011 and 2021. Second, the study adopts a thematic analysis and coding process to identify the emerging themes in the existing people analytics literature, ensuring the comprehensiveness of the review. Third, this study focused and expanded upon the debates and issues evolving within the emerging field of people analytics and offers an updated agenda for the future of people analytics research.

This paper aims to contribute to the literature on human resources (HR) digitalization, specifically on HR analytics, disentangling the concept of analytics applied to HR and explaining the factors that hinder companies from moving to analytics. Therefore, the central research questions addressed in this study are: what does HR analytics encompass? What impedes the adoption of analytics in HR within organizations?

The authors performed a comprehensive literature review on analytics as applied in HR. The authors relied on two of the major multidisciplinary publication databases (i.e. Scopus and WoS). A total of 64 manuscripts from 2010 to 2019 were content analyzed.

The results reveal that there is an ongoing confusion on HR analytics conceptualization. Yet, it seems that there is an emerging consensus on what HR analytics encompasses. The authors have identified 14 different barriers for HR analytics adoption grouped into four categories, namely, data and models, software and technology, people and management. Grounding on them the authors propose a set of 14 key factors to help to successfully adopt HR Analytics in companies.

This paper brings clarity over the conceptualization of HR analytics by offering a comprehensive definition. Additionally, it facilitates business and HR leaders in making informed decisions on adopting and implementing HR analytics. Moreover, it assists HR researchers in positioning their paper more explicitly in current debates and encouraging them to develop some future avenues of research departing from some questions posed.

In this chapter, we apply diffusion of innovation theory and the theory of management fashion to examine the diffusion trajectory of human resource (HR) analytics in a U.S. context. We focus on the role mass media plays in influencing the diffusion process and address two research questions. First, does the mass media on HR analytics make observable the positive outcomes of HR analytics and is this related to increasing HR analytics adoption over time? Second, does the mass media on HR analytics show evidence of management trendsetting rhetoric?

We analyze published popular trade, business press, and peer-reviewed academic articles over a decade using a big data discourse analytical technique, natural language processing.

We find preliminary evidence that suggests that although the media has broadcasted positive outcomes of HR analytics, adoption has tailed off. In concert with the tailing off of HR analytic adoptions, the media appears to be recasting HR analytics as solving newer problems such as managing talent. Whether this shift makes a difference has yet to be determined.

Business press appears to influence the adoption process, both by broadcasting positive outcomes and through creating management fashion trendsetting rhetoric.

To promote the use of HR analytics, academic institutions and the HR profession need to train HR professionals in the use and benefits of HR analytics.

We lay the groundwork to improve our understanding of the role media plays in influencing how new HRM practices spread across organizations. We introduce the application of an emerging big data analytic technique, natural language processing, to analyze published media on HR analytics.

Drawing on human capital theory and the human capital resources framework, this study explores the knowledge, skills, abilities and other characteristics (KSAOs) required by the emerging role of human resource (HR) analysts. This study aims to systematically identify the key KSAOs and develop a competency model for HR Analysts amid the growing digitalization of work.

Adopting best practices for competency modeling set out by Campion et al. (2011), this study first analyzes 110 HR analyst job advertisements collected from five countries: Australia, Canada, Ireland, the United Kingdom and the USA. Second a thematic analysis of 12 in-depth semistructured interviews with HR analytics professionals from Canada and Ireland is then conducted to develop a novel competency model for HR Analysts.

This study adds to the developing and fast-growing field of HR analytics literature by offering evidence supporting a set of six distinct competencies required by HR Analysts including: consulting, technical knowledge, data fluency and data analysis, HR and business acumen, research and discovery and storytelling and communication.

The research findings have several practical implications, specifically in recruitment and selection, HR development and HR system alignment.

This study contributes to the evolving HR analytics literature in two ways. First, the study links the role of HR Analysts to human capital theory and the human capital resource framework. Second, it offers a timely and empirically driven competency model for the emerging role of HR Analysts.

Shows how workforce analytics can help human resource (HR) management specialists to be more effective in the digital era.

Draws on the author’s experience, plus a review of the literature.

Reveals how analytics have transformed ways of managing a diversified workforce and helped to put HR at the center of organizational decision-making.

Claims that the predictive power of analytics can help to make HR more effective.

Highlights how analytics can help to fit the right people to the right jobs, ensure they receive the right training and the right pay and stay loyal to the organization.

Reveals that the power of analytics can be used to make virtually all HR functions more effective.

Driven by the rapidly accelerating pace of technology-enabled developments within human resource management (HRM), human resource (HR) analytics is infiltrating the research and business agenda. As one of the first in its field, the purpose of this paper is to explore what the future of HR analytics might look like.

Using a sample of 20 practitioners of HR analytics, based in 11 large Dutch organizations, the authors investigated what the application, value, structure, and system support of HR analytics might look like in 2025.

The findings suggest that, by 2025, HR analytics will have become an established discipline, will have a proven impact on business outcomes, and will have a strong influence in operational and strategic decision making. Furthermore, the development of HR analytics will be characterized by integration, with data and IT infrastructure integrated across disciplines and even across organizational boundaries. Moreover, the HR analytics function may very well be subsumed in a central analytics function – transcending individual disciplines such as marketing, finance, and HRM.

The results of the research imply that HR analytics, as a separate function, department, or team, may very well cease to exist, even before it reaches maturity.

Empirical research on HR analytics is scarce, and studies on scenarios, values, and structures of expected developments in HR analytics are non-existent. This research intends to contribute to a better understanding of the development of HR analytics, to facilitate business and HR leaders in taking informed decisions on investing in the further development of the HR analytics discipline. Such investments may lead to an enhanced HR analytics capability within organizations, and cultivate the fact-based and data-driven culture that many organizations and leaders try to pursue.

The growth of the global labor force and business analytics has significantly impacted human resource management (HRM). Human resource (HR) analytics is an emerging field that creates value for employees and organizations. By examining the existing studies on HR analytics, the paper systematically reviews the literature to identify active research areas and establish a roadmap for future studies in HR analytics.

A portfolio of 503 articles collected from the Scopus database was reviewed. The study has adopted a Latent Dirichlet allocation (LDA) topic modeling approach to identify significant themes in the literature.

The HR analytics research domain is classified into four categories: HR functions, statistical techniques, organizational outcomes and employee characteristics. The study has also developed a framework for organizations adopting HR analytics. Linking HR with blockchain technology, explainable artificial intelligence and Metaverse are the areas identified for future researchers.

The framework will assist practitioners in identifying statistical techniques for optimizing various HR functions. The paper discovers that by implementing HR analytics, HR managers and business partners can run reports, make dashboards and visualizations and make evidence-based decision-making.

The previous studies have not applied any machine learning techniques to identify the topics in the extant literature. The paper has applied machine learning tools, making the review more robust and providing an exhaustive understanding of the domain.

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.

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

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

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

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

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