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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 purpose of this study is to explore the mechanism of digitalized transformation in organizations’ human resource management (HRM). This study summarizes three basic factors driving the digital transformation process in China: level of perception, level of application and speed of transformation.

This study analyzes the strategic transformation process of HRM in Haier, Hisense and Chambroad to explore the human resource digital transformation mechanism in Chinese enterprises.

The results of this study show that three HR value chain models can be constructed based on how well HRM deals with business: the efficiency-oriented HRM value chain, quasi-business-oriented HRM value chain and business-oriented HRM value chain. The basic factors – level of perception, level of application and speed of transformation – are observed in the entire HRM digital transformation process.

This study provides theoretical and empirical insights for enterprises to explore the value of digital technology in HRM and facilitate the digital transformation of HRM.

This study investigates the psychological adoption of technology in relation to employees' mental beliefs about using technology in their workplace, because it is necessary to investigate the direct and indirect effects of information systems (IS) on employees' work-related results that underpin creativity and engagement.

Using a cross-sectional design, data were collected from 153 human resource (HR) employees who used human resource information systems (HRIS) in small- and medium-sized enterprises (SMEs) in Malaysia.

The results show that effective acceptance and adoption of an HRIS enables HR employees and management in SMEs to be creative, balanced and engaged. Facilitating conditions and task-technology fit positively affect the behavioral intention to accept and adopt an HRIS. Additionally, organizational citizenship behavior moderates the relationship between the behavioral intention to accept and adopt an HRIS and employee creativity.

This study significantly advances the fields of human resource management and IS by elucidating the factors influencing employees' adoption of technology. In an effort to address a research gap in existing research, it introduces a unified theory of acceptance and use of technology, which precedes the psychological adoption process by individuals. Furthermore, it offers both empirical and theoretical insights into the interplay between technology adoption factors and their subsequent impact on work-related outcomes.

Smart HR 4.0 is a new concept characterized by adopting innovative technologies of Industry 4.0 (I4.0) in the HR domain. This study attempts to identify the key factors of Smart HR 4.0 to foster organizational innovation ambidexterity.

Based on review of literature and survey from expert opinions by using the Delphi method, 12 factors were found most suitable for this study. Further, the fuzzy-TISM technique was used to establish contextual relationships and develop a hierarchical model on the identified factors. Subsequently, the MICMAC analysis was applied to classify these factors according to their driving and dependence power.

This study framed a conceptual hierarchical model of Smart HR 4.0 and established contextual relationships among identified factors. Result shows that smart organic structure, industry–institute interface, IT-enabled system and ambidextrous leadership are important factors as they have the highest driving power. Further, knowledge management, learning culture and psychological empowerment are the linkage factors having both driving as well as dependency power in the whole system.

This study can guide the managers in smoothly implementing these practices to manage their human capital amidst digital disruption, ensuring innovation competitiveness of the firm. The structural hierarchical framework of Smart HR 4.0 may serve as a blueprint for HR professionals and business leaders to attain organizational innovation ambidexterity in the current wave of digital disruptions (Industry 4.0).

This study provides a holistic model of smart HR 4.0 integrating innovation ambidexterity in I4.0.

The purpose of our study was two-fold: (1) to examine the effect of perceived value derived from perceived benefits and sacrifices in the adoption of artificial intelligence (AI) in talent acquisition and (2) to investigate the moderating role of human resource (HR) readiness in the association between perceived value and AI adoption intention.

A structured questionnaire was administered to 198 talent acquisition executives and HR professionals of Indian IT companies based on a purposive sampling technique. Partial least squares structural equation modeling (PLS-SEM) was used on the Smart PLS 2.0 platform to analyse the data and test the model.

Results revealed that perceived benefits and sacrifices significantly predict perceived value which significantly affects the HR professional’s AI adoption intention. The study further found that HR readiness moderates the link between perceived value and the intention of HR professionals to adopt AI in the talent acquisition process in the Indian IT industry.

IT companies are advised to continuously monitor and evaluate the performance of AI tools to ensure that they are meeting the recruitment process needs to leverage AI’s benefits in talent acquisition. This study seeks to provide the impetus for a planned AI adoption in talent acquisition.

This research provides ample evidence for the existing technology adoption theories. It explored the predictors of adoption by validating the value-based adoption model in the Indian context. It provides valuable insights into the practice of acquiring talents in the IT sector using artificial intelligence.

This paper aims to propose a user adoption model of human resource information system (HRIS) in the Jordanian public sector by integrating the task technology fit (TTF) model and the unified theory of acceptance and usage of technology (UTAUT).

Using a quantitative approach, survey data were collected using an online survey from employees working in four different public organizations in Jordan, and structural equation modelling has been used to validate the research model.

The study found that among the constructs of the UTAUT model performance expectancy, social influence and facilitating condition have a significant effect on users’ behavioural intention to adopt HRIS. Furthermore, the results also reveal that effort expectancy has an insignificant effect on adoption behaviour. The findings also show that all TTF hypotheses were supported by the data collected. Both task characteristics and technology characteristics have a significant effect on the TTF construct, which further determines users’ adoption behaviour.

These findings contribute to the extant academic literature and have practical implications, improving the understanding of the HRIS adoption and use in public sector organizations.

Based on the JD-R model and process-focused HRM perspective, this research paper aims to investigate the processes underlying the relationship between AI-enabled HR analytics and employee well-being outcomes (resilience) that received less attention in the AI-driven HRM literature. Specifically, this study aims to examine the indirect effect between AI-enabled HR analytics and employee resilience via job crafting, moderated by HRM system strength to highlight the contextual stimulus of AI-enabled HR analytics.

The authors adopted a time-lagged research design (one-month interval) to test the proposed hypotheses. The authors used two-wave surveys to collect data from 175 full-time hotel employees in China.

The findings indicated that employees' perceptions of AI-enabled HR analytics enhance their resilience. This study also found the mediation role of job crafting in the mentioned relationship. Moreover, the positive effects of AI-enabled HR analytics on employee resilience amplify in the presence of a strong HRM system.

Organizations that aim to utilize AI-enabled HR analytics to achieve organizational missions should also dedicate attention to its associated employee well-being outcomes.

This study enriched the literature with regard to AI-driven HRM in that it identifies the mediating role of job crafting and the moderating role of HRM system strength in the relationship between AI-enabled HR analytics and employee resilience.

This study is dedicated to critically analysing research addressing human resource management (HRM) and the adoption of artificial intelligence (AI) with the purpose of driving development in the field of human resources (HR) at the strategic and managerial level.

A systematic literature review (SLR) was conducted using the Scopus database, which gathered 61 articles between 2002 and 2022. The SLR process has the potential to, in addition to generating knowledge and theories, support and guide policy development and practice in many disciplines.

The results of this study allowed the author to identify three main conclusions: (a) there are four thematic clusters – (i) Strategic HR and AI, (ii) Recruitment and AI, (iii) Training and AI and (iv) Future of work; (b) there is a growing academic interest in studying the implementation of AI to develop the HR sector and (c) the application of AI stands out in the strategic HR and AI cluster as a means of achieving profit maximisation and the overall development of the organisation.

This study is the first SLR to present a strategic and managerial view on AI applications associated with specific HRM dimensions. The study is also the first SLR to identify key trends in the literature, drivers and obstacles to the development of AI in HRM and then place them within the landscape of positive and negative approaches in a framework. Also, as a contribution, the study has practical implications for HR managers and practitioners in adopting AI as a decision support in the area's processes.

This study aims to explore the emergence of the human resource (HR) analyst role. The job posts on LinkedIn display the industry demand and skills required by the organizations. This study identifies the different knowledge, skills and abilities (KSA) required for an HR analyst role in different stages of professional growth (i.e. entry-level, middle-senior level and top-level) across different industries/sectors as applicable to the crisis.

A total of 80 job posts were extracted from LinkedIn. Details such as industry, job levels, qualifications, job experience, job functions, job descriptions (JDs) and job skills (JS) were collected. Further, 30 videos were extracted from YouTube and converted into text. Text analysis was conducted using NVivo software to analyze JDs, JS and job functions. Using NVivo, word frequency, word cloud, word tree and treemap were created to visualize the data. Finally, ten in-depth interviews were conducted with senior HRA managers based in India to understand the essential competencies required for the HR analyst role and the strategies to develop them.

The findings indicate that not only technical skills are needed, but business and communication skills are particularly important for all job levels during a crisis. The JD word cloud showed words, such as data, business, support and management, and the word tree depicted HR data and change agents as important words with many related sentences as branches. General JS included analytical, communication, problem-solving and management. Technical JS were the most widely used and included structure query language, system applications & products in data processing, human capital management, TABLEAU, management information system and PYTHON. Strategies to develop these competencies included case studies, live projects, internships on HR analytics (HRAs) assignments and mentoring by senior HRA professionals.

The sample used was small, as the study included 80 job posts available on LinkedIn restricted to India. The study was restricted to qualitative approach and text analytics was used. Survey methods and a quantitative approach can be used to collect data from HR recruiters, job holders and senior leaders to understand the role of HRAs in the job market and then these variables can be tested empirically.

Based on the McCartney et al.’s (2020) competency model for the HR Analyst role, this study has explored the KSA framework using data visualization techniques and used text analytics to analyze LinkedIn job posts for different levels, videos from YouTube and in-depth interviews. It also mapped the KSA for the HR analyst role to the various stages of crisis system management given by Mitroff (2005). The use of social media analytics, such as analyzing LinkedIn data and YouTube videos, are highlighted.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.