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Previous research assumes that technologies 4.0, particularly big data, may be highly relevant for organizations to increase human resources (HR) communication strategies, but the research provides little or no evidence on whether and how these tools are applied in employees and labor market relations. This study intends to offer a first insight on the adoption of data-driven HR/talent management approach, contributing to the ongoing debate on the Industry 4.0. This study aims to investigate the use of 4.0 technologies in HR and talent management functions, focusing also on the adoption of big data analytics for internal and recruitment communication.

The analysis of the literature enables to define the research questions and an exploratory web survey was carried out through a structured questionnaire. The analysis unit of the empirical survey includes the communication and marketing managers of 90 organizations in Italy, examined in the Mediobanca Report on the “Main Italian Companies.”

Findings highlight a lack of the use of 4.0 technologies and big data analytics in employee and labor market relations and reveal some sectoral differences in the adoption of 4.0 technologies. Moreover, the study points out that the development of HR analytics is hampered by short-term perspective, data quality problems and the lack of analytics skills.

Due to the exploratory research design and the circumscribed sample from a single country (Italy), further cross-national evidence is needed. This study provides digital communication managers with useful insights to improve the data-driven HR/talent management approach, which is a strategic asset for ensuring a sustainable competitive advantage and optimizing business performance.

The study offers an overview about the use of big data analytics in internal and recruitment communications. Considering the alignment between Italian and European trends in the use of big data and in the adoption of HR analytics, the study can provide insights also for other European organization.

This paper aims to propose a research model with eight constructs, i.e. BDA leadership, BDA talent quality, BDA security quality, BDA privacy quality, innovation, financial performance, market performance and customer satisfaction.

The research model focuses on whether (1) Big Data Analytics (BDA) leadership influences BDA talent quality, (2) BDA talent quality influences BDA security quality, (3) BDA talent quality influences BDA privacy quality, (4) BDA talent quality influences Innovation and (5) innovation influences a firm's performance (financial, market and customer satisfaction). An instrument was designed and administered electronically to a diverse set of employees (N = 188) in various organizations in the USA. Collected data were analyzed through a partial least square structural equation modeling.

Results showed that leadership significantly and positively affects BDA talent quality, which, in turn, significantly and positively impacts security quality, privacy quality and innovation. Moreover, innovation significantly and positively impacts firm performance. The theoretical and practical implications of the findings are discussed. Recommendations for future research are provided.

The study provides empirical evidence that leadership significantly and positively impacts BDA talent quality. BDA talent quality, in turn, positively impacts security quality, privacy quality and innovation. This is important, as these are all critical factors for organizations that collect and use big data. Finally, the study demonstrates that innovation significantly and positively impacts financial performance, market performance and customer satisfaction. The originality of the research results makes them a valuable addition to the literature on big data analytics. They provide new insights into the factors that drive organizational success in this rapidly evolving field.

In the context of a corporate case study, this paper aims to propose and test a talent analytics model that enables organisations to optimise their employer value propositions and branding strategies in a comparative manner.

After the conceptual framework was translated into a quantitative model, data were collected via online self-completed questionnaires and analysed with statistical techniques.

Revealing misalignments with the employment preferences of Gen Z, the model’s capability to optimise employer value propositions and branding strategies is effectively demonstrated.

Providing an actionable framework for corporate and academic readers, this paper has scope to serve as a guide for scholarly or practitioner talent analytics projects focused on attraction.

Focused exclusively on attraction, to the best of the author’s knowledge, this is the first paper to apply an in-depth talent analytics model which prescribes employer branding as a mediator variable.

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 research paper aims to analyse the scholarly literature on advancing HR analytics as an intervention for attrition, a problem that lingers on organisational performance. This study aspires to provide an in-depth literature review and critically assess the knowledge gaps in HR analytics and attritions within organisational performance.

The review analyses the corpus of 196 research articles published in ostensible journals between 2011 and 2023. To identify research gaps and provide valuable insights, this study synthesises relevant studies using School of thought (S), Context (C), Methodology (M), Triggers (T), Barriers (B), Facilitators (F) and Outcomes (O) (SCM-TBFO framework). This study employs the R programming language to conduct a systematic literature review in accordance with the “preferred reporting items for systematic reviews and meta-analysis” (PRISMA) guidelines.

The emerging discipline of HR analytics encompasses the potential to manage attrition and drive organisational performance enhancements effectively. The study of SCM-TBFO encompasses a multidimensional approach, incorporating diverse perspectives and analysing its complex aspects compared to various approaches. The School of thought includes the human capital theory, expectancy theory and resource-based view. The varied research contexts entail the USA, United Kingdom, China, France, Italy and India. Further, the methodologies adopted in the studies are artificial neural networking (ANN), regression, structure equation modelling (SEM) case studies and other theoretical studies. HR analytics and attrition triggers are data mining decision systems, forecasting for firm performance and employee satisfaction. The barriers include leadership styles, cultural adaptability and lack of analytic skills, data security and organisational orientation. The facilitators were categorised into data and technology-related facilitators, human resource policies and organisational growth and performance-related facilitators. The study's primary outcomes are technology adoption, effective HR policies, HR strategies, employee satisfaction, career and organisational expansion and growth.

The primary goal of the literature review is to provide a comprehensive overview of the current state of HR analytics and its impact on organisational performance, particularly in relation to attrition. Further, the study suggests that attrition, a critical organisational concern, can be effectively managed by strategically utilising HR analytics and empowering data-driven interventions that optimise performance and enhance overall organisational outcomes.

This study aims to provide an in-depth understanding of big data analytics (BDA) in human resource management (HRM). The emergence of digital technology and the availability of large volume, high velocity and a great variety of data has forced the HRM to adopt the BDA in managing the workforce.

This paper evaluates the past, present and future trends of HRM through the bibliometric analysis of citation, co-citation and co-word analysis.

Findings from the analysis present significant research clusters that imply the knowledge structure and mapping of research streams in HRM. Challenges in BDA application and firm performances appear in all three bibliometric analyses, indicating this subject’s past, current and future trends in HRM.

Implications on the HRM landscape include fostering a data-driven culture in the workplace to reap the potential benefits of BDA. Firms must strategically adapt BDA as a change management initiative to transform the traditional way of managing the workforce toward adapting BDA as analytical tool in HRM decision-making.

This study presents past, present and future trends in BDA knowledge structure in human resources management.

Human resource analytics (HRA) has developed as a new business trend and challenge, stressing the strategic relevance of human resource management (HRM) to senior management executives. HRA is a process that uses statistical techniques, to link HR practices to organizational performance. The purpose of this study is to carry out recent development in HRA, bibliometric analysis and content analysis to present a comprehensive account of HRA to fill the gap in the evolution and status of its research.

The study is based on the recent advances in HRA in terms of it evolution and advancement by analyzing and drawing conclusions 480 articles retrieved from the Web of Science (WoS) database from 2003 to March 2022. The methodology is divided into four steps: data collection, analysis, visualization and interpretation. The study performed a rigorous bibliometric assessment of HRA using the bibliometric R-package and VOS viewer.

The findings based on the literature survey, and bibliometric analysis, reveal the path-breaking articles, the prominent authors, most contributing institutions and countries that have contributed to the HRA scholarship. The results show that the number of publications has significantly increased from 2015 onwards, reaching a maximum of 101 journals in 2021. The USA, China, India, Canada and the United Kingdom were the most productive countries in terms of the total number of publications. Human Resource Management Journal, Human Resource Management, International Journal of Manpower, and Journal of Organizational Effectiveness-People and Performance are the top four academic outlets in the field of HRA. Additionally, the study identifies four clusters of HRA research and the knowledge gaps in HRA scholarship.

The present study is based on the articles retrieved from the WoS. The study underpins HRA research to understand the trends and presents a structured account. However, the study is not free from limitations. It is recommended that future research could be undertaken by combining WoS and Scopus databases to have a more detailed and comprehensive view. This study indicates that the field is still in its infancy stage. Hence, there is a need for more arduous research on the topic to help develop a better understanding of this field.

The findings of knowledge clusters will drive future researchers to augment the field. The evolution of the four clusters and their subsequent development will fill the gaps in the literature. This study enriches the HRA literature and the findings of this study may assist academicians, researchers and managers in furthering their research in the identified research clusters

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 study aims to identify the enablers of human resource (HR) digitalization and HR analytics. This paper also aims to build a relationship map using interpretive structural modeling.

A systematic literature review is used to identify the key enablers of HR digitalization and HR analytics. Ten expert opinions have been taken from the key officials of IT firms located in New Delhi North Central Region.

This study is focused on the enablers of HR analytics. It is found that change management (CM) in the organization is the key enabler of implementing HR digitalization and analytics in an organization. However, other elements like learning culture, training and development, E-learning management and HR transformation (HRT) play a vital role in implementing HR analytics. It is also found that implementing artificial intelligence for HR practices is the ultimate goal for every organization.

Management teams in IT firms should focus on the continuous learning process in the organization. The CM should be expedited for digitalization and adoption of HR analytics. Managers must go through the ramification of HRT, which possesses diligence in HR analytics and artificial intelligence.

This study explicitly talks about the enablers of HR digitalization and HR analytics. It also explores the relationship between the enablers. This study also describes the driving and dependence power of all the enablers.

This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises.

A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises.

Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation.

The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry’s radical innovation.

This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry’s radical innovation by considering the mediating role of three dimensions of business intelligence.