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The purpose of this paper is to develop a multistakeholder scale for assessing an excellent human resource (HR) function to demonstrate the extent to which it is focused on creating value from the perspective of its key internal and external stakeholders.

At the first stage, an in-depth literature review was conducted to extract the best practices for an excellent HR function. Then, to test the validation of the developed framework, it was sent to HR academics and practitioners in different countries. The survey responses were analyzed using the methods of the structural equation modeling (SEM) and confirmatory factorial analysis.

This study proposes an excellent HR multistakeholder assessment scale consisting of ten criteria based on the perceptions of internal and external HR stakeholders.

This study suggests a framework for assessing overall HR excellence based on the perceptions of key internal and external HR stakeholders. In addition, it is recommended that future researchers empirically test the developed scale across various industries and firm sizes.

HR managers, by using this framework, could continuously assess their HR excellence and compare their HR excellence with other companies’ HR excellence in the industry and then plan for continuous improvement in different HR areas to improve their stakeholders’ experiences.

This paper identifies the enablers and results of an excellent HR department and designs a multistakeholder feedback scale to better understand key internal and external HR stakeholders’ perceptions.

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

This paper aims to explore the pivotal role of human resources (HR) in fostering employee engagement and well-being in a workplace transformed by digitalization. It addresses how HR can integrate meaningful work with enhanced well-being to create a more fulfilling and productive workplace.

The analysis is grounded in a synthesis of recent surveys, academic research and thought leadership in HR, providing a strategic framework for enhancing employee engagement in the digital age.

The research reveals that employees crave not only professional growth but also meaningful work and genuine connections, which are integral to their well-being. It suggests that HR strategies should align employee roles with organizational purpose and personal values and use technology to break down barriers to collaboration to increase trust and purpose-driven culture.

By combining insights from various fields, this paper contributes a unique perspective on the transformation of HR into a strategic force that aligns business objectives with employee well-being and the need for a purposeful work life.

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.

The research examines the role of Smart HRM 4.0 in developing dynamic capabilities and its impact on human resources and organizational performance.

The authors used a grounded theory approach and conducted interviews of 39 senior HR managers from IT, ITeS, consulting, services and E-commerce companies through a semi-structured questionnaire. The authors analyzed the interview data with NVivo 8.0 to identify the themes related to the dynamic capabilities to Smart 4.0 HR practices.

The study provides a conceptual framework for organizational performance using dynamic capabilities built due to Smart HRM 4.0 practices. Organizations use Smart HRM 4.0 to develop dynamic capabilities: building learning and knowledge sharing capability and integration, reconfiguration capabilities. Further, the dynamic capabilities contribute to HR and organizational performance.

This study divulges the role of Smart HRM 4.0 practices in developing dynamic capabilities in Indian firms. The study provides an appealing insight into the structural link between Smart HRM 4.0 and dynamic capabilities, which are yet to be explored. This study extends the Smart HRM 4.0 and dynamic capabilities concepts for senior HR professionals and contributes to human resource management and organizational performance literature.

HR analytics is a process for systematic computational analysis of data or statistics. It discovers, interprets and communicates significant patterns in data to enable evidence-based HR research and uses analytical insights to help organizations achieve their strategic objectives. However, its adoption and utilization among HR professionals remain a subject of concern. This study aims to determine the reasons that facilitate or inhibit the acceptance of HR analytics among HR professionals in the banking, financial services and insurance (BFSI) sector.

A sample of 387 HR professionals in BFSI firms across India was collected through non-probabilistic purposive sampling. Structural equation modeling was applied to analyze the association between predetermined variables. In addition, the predictive relevance of “Data Availability” was analyzed using hierarchical regression.

The results revealed that data availability, hedonic motivation and performance expectancy positively influenced behavioral intention (BI). In contrast, effort expectancy, social influence and habit had an insignificant effect on BI. Also, facilitating conditions (FCs), habit, BI achieved a variance of 60% in HR analytics use. The use behavior of HR analytics was significantly influenced by FCs and BIs.

This study focuses on insights into the elements that influence HR analytics adoption, revealing additional light on success drivers and grey areas for failed adoption.

This research adds to the body of knowledge by identifying factors that hinder the adoption of HR analytics in Indian organizations and signifies the relevance of easy accessibility and availability of data for technology adoption.

This paper aims to identify the relationships among critical success factors (CSFs) for lean six sigma (LSS) implementation in higher education institutions (HEIs).

An extensive literature review was conducted to design the survey instrument, which the authors later administered in Mexican public HEIs to identify the existing relationships among the CSFs and their impact on the benefits obtained from implementing LSS projects. The data were empirically and statistically validated using exploratory and confirmatory factor analysis. Additionally, the authors applied the structural equation modeling (SEM) technique on SPSS Amos to validate the nine hypotheses supporting the research.

The results suggest that the success of LSS projects in HEIs is highly bound to a serious commitment from top management and several interrelated factors.

The main limitations of the study are that the research is cross-sectional in nature and regional in focus. Namely, the data used to validate the structural model were gathered from a small representative subset of the study population – i.e. Mexican public HEIs – and at a specific point in time.

The results reported here represent a reference framework for HEIs worldwide that wish to continuously improve their processes through LSS improvement projects.

This study proposes a statistically validated model using the SEM technique that depicts the relationships among LSS CSFs in HEIs.

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.

The purpose of this study is to examine the rostering practices and work experiences of medical scientists at four health services in the Australian public healthcare sector. There are over 16,000 medical scientists (AIHW, 2019) in Australia responsible for carrying out pathology testing to help save the lives of thousands of patients every day. However, there are systemic shortages of medical scientists largely due to erratic rostering practices and workload issues. The purpose of this paper is to integrate evidence-based human resource management (EBHRM), the LAMP model and HR analytics to enhance line manager decision-making on rostering to support the wellbeing of medical scientists.

Using a qualitative methodological approach, the authors conducted 21 semi-structured interviews with managers/directors and nine focus groups with 53 medical scientists, making a total 74 participants from four large public hospitals in Australia.

Across four health services, manual systems of rostering and management decisions do not meet the requirements of the enterprise agreement (EA) and impact negatively on the wellbeing of medical scientists in pathology services. The authors found no evidence of the systematic approach of the organisations and line managers to implement the LAMP model to understand the root causes of rostering challenges and negative impact on employees. Moreover, there was no evidence of sophisticated use of HR analytics or EBHRM to support line managers' decision-making regarding mitigation of rostering related challenges such as absenteeism and employee turnover.

The authors contribute to HRM theory by integrating EBHRM, the LAMP model (Boudreau and Ramstad, 2007) and HR analytics to inform line management decision-making. The authors advance understandings of how EBHRM incorporating the LAMP model and HR analytics can provide a systematic and robust process for line managers to make informed decisions underpinned by data.

The purpose of this paper is to provide a comprehensive framework that identifies thematic clusters and their interconnections within Global Talent Management (GTM), global careers and talent management (TM).

In this paper, this study conducted a co-citation analysis using bibliographic data to unveil the intellectual connections and relationships among thematic articles related to GTM sourced from the Web of Science.

This review highlights three key research themes: experiences working abroad, TM approaches and the complex nature of GTM as a living system.

The main limitation of this research is the sample itself. Content analysis based on the co-citation method resulted in some more recent releases being omitted.

The practical implications of the paper include providing a structured framework for understanding the complexities of GTM.

Research into the academic literature in this area is divided into various clusters, empirically demonstrating how GTM and global mobility are intertwined, revealing the need for us to more thoroughly comprehend the social ramifications of GTM practices and activities and the need to further analyse the influencing social aspects in a GTM strategy, like diversity, increased mobility and virtual reality.

The analysis revealed the emergence of three distinct thematic groups: (1) global work experiences, (2) TM approaches and (3) GTM.