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Despite talent management’s (TM) importance for improving organizations' competitiveness and resilience, the pandemic highlighted the weakness of organizational-level TM strategies. Therefore, the objective of this study is to investigate the moderating impact of HR analytics on the relationship between TM and its individual outcomes (talent motivation and quality of hires) and subsequently, their impact on organizational outcomes (talent retention).

The structural equation modeling (SEM) technique was used to analyze 219 online questionnaires administered to HR managers from European companies.

A positive relationship exists between TM activities and talent motivation as well as the quality of hires. Furthermore, HR analytics positively moderates these relationships. Finally, talent motivation and the quality of hires are positively related to talent retention.

This study offers several contributions to theory, as it analyzes TM from an individual perspective and provides further empirical confirmation of the potential benefits of HR analytics and additional grounding to the contingency theory.

Our results will allow practitioners to better orient their HR investments, with positive effects for their organizations and their employees.

This study demonstrates that HR analytics can help organizations adopt a human-centric approach to TM, thus increasing the chances for talents to fully express their potential.

This study takes a step forward toward considering TM outcomes from an individual perspective, responding to new generations' need to pay more attention to their individualities. HR analytics can be a suitable tool to do so, as it can provide insights and suggestions based on the actual organizational context, making TM a more data-driven process.

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.

The purpose of this study is to provide an overview of artificial intelligence (AI) in the talent management sphere. The study seeks to contribute to the body of knowledge with respect to human resource management and AI by conducting a literature review on the integration of AI in talent management, synthesising existing approaches and frameworks, as well as emphasising potential benefits.

The study adopts desk research, computational literature review (CLR) and uses topic modelling [with bidirectional encoder representations from transformers (BERTopic)] to throw light on the diffusion of AI in talent management.

The study’s main finding is that the area of AI in talent management is on the verge of gradual development and is in tandem with the growth of AI. We deduced that there is a link between talent management practices (planning, recruitment, compensation and rewards, performance management, employee empowerment, employee engagement and organisational culture) and AI. Though there are some known fears with regards to using the innovation, the benefits outweigh the demerits.

The current study has some limitations. The scope and size of the sample are the primary limitations of this study. No form of qualitative analytics was used in this study; as a result, the information obtained was limited. The study provides a snapshot of AI in talent management and contributes to the lack of literature in the joint fields. Also, the study provides practitioners and experts an overview of where to target investments and resources if need be.

The originality of this study comes from the combination of CLR methods and the use topic modelling with BERTopic which has not been used by previous reviews. In addition, the salient machine learning algorithms are identified in the study, which other studies have not identified.

This research aims to explore whether or how organizations adopt people analytics (PA), its value and potential socio-technical factors that can enable or hinder PA projects by disrupting and reshaping human resource management. We do this by focusing on the Italian context.

We conduct a scoping review of data collected between 2018 and 2022 via Google Alerts (GA), a content change detection and notification service that is gaining popularity in scholarly research.

Our findings suggest that the diffusion of PA applications in Italy, especially those of a descriptive nature, is growing. Most of the existing PA applications are positioned in a positive technocratic light, envisioning the value of PA for both employees and organizations. The value for the latter appears to be direct, while the value for employees is realized through organizational initiatives. The findings also suggest that while enablers can vary between PA application types, the barriers, especially technological and environmental, are generic for both descriptive and predictive/prescriptive PA applications.

Theoretically, we propose a framework for analyzing PA applications, their values, enablers and barriers. Methodologically, we present and describe in detail a novel approach, drawing on GA that can be used to study PA in specific contexts. Practically, our study serves as a helpful point of reference for managers planning or implementing PA in Italy, for benchmarking PA in Italy over time and for comparative international studies.

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.

The objective of this study is to analyse the integration of technology in Human Resources Management (HRM) with a special focus on Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT) and Big Data.

This study aims to contribute to the understanding of these trends by conducting a thorough bibliometric analysis using the Scopus database, encompassing research on HRM and Technology from 1991 to 2022. By employing citation analysis, co-citation analysis and co-word analysis, the study uncovers key patterns and trends in the field.

The findings indicate that AI, Big Data and ML are the focal points of research when exploring the intersection of Technology and HRM. These technologies offer promising prospects for enhancing Human Resource processes, such as Talent Acquisition, Performance Management and Employee Engagement.

In our study, we showcase the practical implications that offer guidance for HR researchers and professionals, enabling them to make informed decisions regarding the adoption and implementation of Information Technology.

This research can provide valuable insights to HR managers on the use of cutting-edge technology in HRM. It aims to enhance the manager’s awareness of how technology-enabled HRM can improve HR performance.

This study adds to the existing body of knowledge on how Modern Technology empowers HRM. It also proposes a conceptual framework for the use of Modern Technology along with Strategic Management and Knowledge Management to improve Human Resource Performance.

The purpose of this study is to explore the diffusion of digital innovation for talent management in Italian learning organizations.

It has been implemented a qualitative methodology to collect data, interviewing 16 experts; a mix method analysis was applied to explore thematic categories and to analyze co-occurrences by a quantitative approach analysis using T-Lab software.

There are some relevant points to underline: digital technologies are meant as a support to human resource management (HRM), and there is often the reference to digital gamification or gamified processes implemented for talent management procedures. Learning is a central element both for employees’ point of view and for HR specialists who feel the need for a major and more specific training on digital technologies.

The limited size and composition of the sample put restrictions on the generalizability of results. The explorative nature of the study provides an in-depth consideration of digital innovation in learning organization, representing a first starting point for future quantitative investigations. From a practical point of view, this study emphasizes a learning organization culture as an essential attitude set to attract, select and retain top talents.

From a practical point of view, this study emphasizes a learning organization culture as an essential attitude set to attract, select and retain top talents.

Giving space and voice to HR and information and communication technologies experts has provided insights regarding the digitalization process in HRM in Italy, in particular, digital learning has been told as a necessary element for the competitiveness of the workforce.

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.

The research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education.

As businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis.

After revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made.

Very limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice.

The study explores the factors affecting the use of smart human resource management 4.0 (SHRM 4.0) practices and its effect on dynamic capabilities and, consequently, on organizational performance.

The authors used socio-technical and dynamic capabilities theory to propose the notable research model. The authors explored the factors driving the use of SHRM 4.0 practices and their contribution to organizational performance through the development of dynamic capabilities. The authors collected data from 383 senior HR managers using a structured questionnaire, and PLS-SEM was used to analyze the data.

The results show that socio-technical factors such as top management support, HR readiness, competitive pressure, technology readiness and perceived usefulness influence the use of SHRM 4.0 practices, whereas security and privacy concerns negatively influence them. Furthermore, the authors also found the use of SHRM 4.0 practices influencing the dynamic capacities (build (learning), integration and reconfiguration) and, subsequently, its impact on organizational performance.

Its novelty lies in developing a model using dynamic capabilities and socio-technical theory to explore how SHRM 4.0 practices influence organizational performance through dynamic capabilities. This study extends the literature on SHRM 4.0 practices, HR technology use, HR and dynamic capabilities by contributing to socio-technical theory and dynamic capabilities and expanding the scope of these theories in the area of HRM. It provides crucial insights into HR and top managers to benchmark SHRM 4.0 practices for improved organizational performance.