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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.

The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives.

Conceptual review of the current state and meaning of HR analytics. Using the HR analytics cycle as a framework, the authors describe a seven-step process for building evidence-based and ethical HR analytics capabilities.

HR analytics is a nascent discipline and there are a multitude of monikers and competing definitions. With few exceptions, these definitions lack emphasis on evidence-based practice (i.e. the use of scientific research findings in adopting HR practices), ethical practice (i.e. ethically gathering and using HR data and insights) and the role of broader HR research and experimentation. More importantly, there are no practical models or frameworks available to help guide HR leaders and practitioners in doing HR analytics work.

The HR analytics cycle encompasses a broader range of HR analytics practices and data sources including HR research and experimentation in the context of social, behavioral and organizational science.

This paper introduces the HR analytics cycle as a practical seven-step approach for making HR analytics work in organizations.

Despite the growth and adoption of human resource (HR) analytics, it remains unknown whether HR analytics can impact organizational performance. As such, this study aims to address this important issue by understanding why, how and when HR analytics leads to increased organizational performance and uncover the mechanisms through which this increased performance occurs.

Using data collected from 155 Irish organizations, structural equation modeling was performed to test the chain mediation model linking HR technology, HR analytics, evidence-based management (EBM) and organizational performance.

The study's findings support the proposed chain model, suggesting that access to HR technology enables HR analytics which facilitates EBM, which in turn enhances organizational performance.

This research contributes significantly to the HR analytics and EBM literature. First, the study extends our understanding of why and how HR analytics leads to higher organizational performance. Second, the authors identify that access to HR technology enables and is an antecedent of HR analytics. Finally, empirical evidence is offered to support EBM and its impact on organizational performance.

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

This study aims to explore how human resource (HR) practitioners conceive of their practice, reveal challenges they grapple with in daily work and generate a conceptual framework of HR praxis.

The paper is based on interviews with HR practitioners in Sweden and a review of articles that examine aspects of HR practitioners' work.

The HR practitioners' work is fragmented and reactive, filled with meetings and affords few opportunities to work undisturbed. Operational tasks are prioritised over strategic work, and their work sometimes involves tasks that are not HR's responsibility. The nature of HR practitioners' daily work mimics the work of their main “customer”, i.e. managers within the organisations.

The HR practitioners were working mainly in the service of managers, which suggests that they have an internal focus. Consistent with current, prescriptive HR discourse, HR practitioners should adopt a multi-stakeholder perspective of human resource management (HRM) and a more external focus that is necessary to contribute to wider, organisational effectiveness. The findings could enrich what is taught in higher education by providing students with an account of the reality of HR practitioners' daily work.

The study provides a situated account of the daily work of HR practitioners, which is largely absent from the literature.

The purpose of this paper is to explore the key ingredients that people analytics teams require to contribute to organizational performance. As the information that is currently available is fragmented, it is difficult for organizations to understand what it takes to execute people analytics successfully.

To identify the key ingredients, a narrative literature review was conducted using both traditional people analytics and broader business intelligence literature. The findings were summarized in the People Analytics Effectiveness Wheel.

The People Analytics Effectiveness Wheel identifies four categories of ingredients that a people analytics team requires to be effective. These are enabling resources, products, stakeholder management and governance structure. Under each category, multiple sub-themes are discussed, such as data and infrastructure; senior management support; and knowledge, skills, abilities and other characteristics (KSAOs) (enablers).

Many organizations are still trying to set up their people analytics teams, and many others are struggling to improve decision-making by using people analytics. For these companies, this paper provides a comprehensive overview of the current literature and describes what it takes to contribute to organizational performance using people analytics.

This paper is designed to provide organizations and researchers with a comprehensive understanding of what it takes to execute people analytics successfully. By using the People Analytics Effectiveness Wheel as a guideline, scholars are now better equipped to research the processes that are required for the ingredients to be truly effective.

This research analyzed the existing academic and grey literature concerning the technologies and practices of people analytics (PA), to understand how ethical considerations are being discussed by researchers, industry experts and practitioners, and to identify gaps, priorities and recommendations for ethical practice.

An iterative “scoping review” method was used to capture and synthesize relevant academic and grey literature. This is suited to emerging areas of innovation where formal research lags behind evidence from professional or technical sources.

Although the grey literature contains a growing stream of publications aimed at helping PA practitioners to “be ethical,” overall, research on ethical issues in PA is still at an early stage. Optimistic and technocentric perspectives dominate the PA discourse, although key themes seen in the wider literature on digital/data ethics are also evident. Risks and recommendations for PA projects concerned transparency and diverse stakeholder inclusion, respecting privacy rights, fair and proportionate use of data, fostering a systemic culture of ethical practice, delivering benefits for employees, including ethical outcomes in business models, ensuring legal compliance and using ethical charters.

This research adds to current debates over the future of work and employment in a digitized, algorithm-driven society.

The research provides an accessible summary of the risks, opportunities, trade-offs and regulatory issues for PA, as well as a framework for integrating ethical strategies and practices.

By using a scoping methodology to surface and analyze diverse literatures, this study fills a gap in existing knowledge on ethical aspects of PA. The findings can inform future academic research, organizations using or considering PA products, professional associations developing relevant guidelines and policymakers adapting regulations. It is also timely, given the increase in digital monitoring of employees working from home during the Covid-19 pandemic.

In this article we aim to understand how the network formed by fitness tracking devices and associated apps as a subset of the broader health-related Internet of things is capable of spreading information.

The authors used a combination of a content analysis, network analysis, community detection and simulation. A sample of 922 health-related apps (including manufacturers' apps and developers) were collected through snowball sampling after an initial content analysis from a Google search for fitness tracking devices.

The network of fitness apps is disassortative with high-degree nodes connecting to low-degree nodes, follow a power-law degree distribution and present with low community structure. Information spreads faster through the network than an artificial small-world network and fastest when nodes with high degree centrality are the seeds.

This capability to spread information holds implications for both intended and unintended data sharing.

The analysis confirms and supports evidence of widespread mobility of data between fitness and health apps that were initially reported in earlier work and in addition provides evidence for the dynamic diffusion capability of the network based on its structure. The structure of the network enables the duality of the purpose of data sharing.

The purpose of this paper is to identify the variables that influence corporate real estate (CRE) decision-making and gauge their relative importance to each other, thereby understanding the consequent challenges/implications for CRE managers (CREM’s).

Interviews were undertaken with experienced CREM’s using the causal network elicitation technique to create decision networks for the variables they considered for the specifically defined scenario: dealing with surplus property from a change of business strategy. These networks illustrate the complexity of the mental representations required for the realignment of the CRE portfolio. The key variables are more extensive than alignment theory suggests, namely, financial stakeholders. Additional variables identified include risk, lease accounting, costs, financial analysis, business metrics and motivational drivers. The latter indicates the importance of self-esteem and peer recognition for CREM’s and financial benefits for the C-suite. Accordingly strategy alignment needs to incorporate CRE both in terms of strategy creation and implementation.

These networks illustrate the complexity of the mental representations required for the realignment of the CRE portfolio. The key variables are more extensive than alignment theory suggests, namely, financial stakeholders. Additional variables identified include risk, lease accounting, costs, financial analysis, business metrics and motivational drivers. The latter indicates the importance of self-esteem and peer recognition for CREM’s and financial benefits for the C-suite. Accordingly, strategy alignment needs to incorporate CRE both in terms of strategy creation and implementation.

This research appears to be the first that looks in detail at the mental representations used by decision-makers while making CRE decisions.

This study aims to examine the effect of human resource management (HRM) in mitigating negative effects of Lean management and Six Sigma (LM&SS) on employee well-being in health care. The authors subdivide well-being into three components: happiness, trust and health.

This is a cross-sectional, multisite survey study in internal service units of hospitals. Data analyzed using multivariate regression come from a sample of 1,886 survey respondents (42 units, N = 218 supervisors, N = 1,668 employees) in eight Dutch academic hospitals that have implemented LM&SS.

The present study findings show no or weak effects of LM&SS on the happiness and health component of employee well-being. In addition, the authors found a significant but weak direct positive effect (ß = 0.07) of the LM&SS bundle on the trusting relationships component of well-being. Therefore, moderating effects of HRM practices on the relationship between LM&SS and employee well-being seem less relevant because an existing relationship between LM&SS and employee well-being is a prerequisite for moderation (Hayes, 2009). There were unexpected side effects. Inspired by research that discusses direct effects of HRM on employee well-being, the authors tested this relationship and found that HRM has a direct positive effect on trust and happiness of employees in health care. For the health component of well-being, the present results show a weak negative effect of HRM.

This study results in a cautiously optimistic view about LM&SS in health care, provided that it is applied in a targeted manner (to improve the performance of their processes) and that HRM is strategically aligned with the goals of LM&SS to improve employees’ happiness and trusting relationships.

Unique features of the study are the focus on the consequences for employees’ well-being related to LM&SS in health care, the role of HRM in regard to this relationship and the participation of all eight Dutch academic hospitals in this research.