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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.

This paper aims to investigate the use of Industry 4.0 (I4.0) technology and its barriers in human resourcemanagement (HRM) for Smart HR 4.0 and its impact on HR performance.

The research has been conducted using the grounded theory approach. Semi-structured interviews were conducted with 122 senior HR officers of national and multi-national companies in India after the extensive literature review. NVivo 8.0 software was used for the analysis of the interview data.

I4.0 technology is used for HRM functions by HR professionals. It is revealed that Smart HR 4.0 that emerged from the I4.0 technology has leveraged the HR performance. It is also found that usage barriers, traditional barriers and risk barriers affect the use of I4.0 technology in HRM.

A model is developed using the grounded theory approach for HR managers to understand the impact of I4.0 on HRM. This study reveals the barriers affecting the use of I4.0 technology in HRM. It also provides the model for HR performance that emerged through the use of I4.0 technology in HR and Smart HR 4.0. The research delivered key insights for the HR professionals, marketers of HR technology and technology developers.

Human resource (HR) transformation research has not studied the role of digital HR technology and HR role in the context of Indian organisations. To address the gap, the current research aims to investigate the impact of HR role and digital HR technology on successful HR transformation. Further, the research shall investigate the mediating role of various HR roles (i.e. administrative, employee champion, change agent and strategic partner role) on digital HR technology and business outcomes.

The research used a post-positivist methodology using survey method. Data has been collected from 918 executives representing several sectors of Fortune 500 Indian companies. Validated instrument has been used and the collected data are analysed using AMOS and structural equation modelling.

HR transformation using Digital human resource technology (HRT) can significantly enhance business outcome of fortune 500 companies of India if it is mediated by different HR role (strategic, employee champion, change agent and administrative expert). The result also proved that just implementation and adaption of the Digital HRT may not guarantee HR Transformation unless HR optimise the specific role as per the need of the hour.

HR transformation research has not studied the role of digital HR technology and HR role in the context of fortune 500 Indian organisations.

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.

Digital technology has had a massive impact on business management procedures, driving modifications. The goal of this study is to figure out how human resource management (HRM) in businesses should change as digital technology develops. Via social, mobile, analytic and cloud technology, businesses can monitor and ensure that preconceptions and assumptions inside the organization drive acceptable behaviour. The outcomes of this study reveal that information transformation has influenced jobs growth, labour supply and the composition of corporate talent. Management and control in management of human resources (HR) should give way to empowerment. The goal of the current review is to examine current technological trends in HRM-related enterprises. Peer-reviewed articles from databases like EBSCO, Emerald and Sage publications provide the foundation of the review. Since there are few academic articles on the subject of the study, which covers technology in HRM and cloud in HRM, references from organizational reports have also been incorporated. Utilizing digital technologies in HR will increase organizational performance through decisions involving talent, forecasting workforce requirements and maximizing talent through planning and development. Informed decision-making will also allow HR to assist an organization in achieving corporate objectives. It also makes it possible to manage staff members through hiring, training, job happiness, productivity and task assignment based on qualifications. Additionally, it aids in determining the cause of attrition and locating high-value individuals who are departing.

The purpose of this study was to develop and empirically examine a model of cloud-based human resource information systems (HRIS) adoption by small businesses based on the technology–organization–environment model (Tornatzky & Fleischer, 1990).

This study utilized a survey of 41 small- to medium-sized enterprises in the northeastern United States to examine what HR functions were being supported by cloud-based HRIS and the relationship between three technology factors, three organizational factors, and three environmental factors, and their relationship with the adoption of cloud-based HRIS.

Findings indicated that small businesses are most likely to implement cloud-based HRIS to support day-to-day HR operations. In addition, the findings indicated that top management support (positive), vendor support (positive), and anticipated growth (negative) were each related to organizational adoption of cloud-based HRIS.

The study illustrates how the adoption of a cloud-based HRIS is motivated by different factors than those underlying the adoption of other types of information systems. Executives and small business owners will need to adapt their strategy when considering cloud-based HRIS compared to other types of systems.

Given that small- to medium-sized organizations are the backbone of most global economies, findings from this study can help support society by helping these businesses better understand how to best consider the factors that will support the implementation of cloud-based HRIS.

This chapter represents one of the first to empirically validate a model of the factors affecting adoption of cloud-based HRIS by small businesses.

Three decades of academic and professional discourse on HR technologies (HRTs) have produced continued disagreement over construct definitions and research streams that are highly fragmented. These realities suggest that greater consistency in meanings is sorely needed if we are to integrate and upgrade knowledge in this area. This chapter draws on the findings of a systematic research review to properly define the content domains of human resource information systems (HRIS), virtual human resources (virtual HR), electronic human resource management (e-HRM), and business-to-employee (B2E) systems. An integrative synthesis was performed on 242 system-level writings that appeared in the literature from 1983 to 2017. The weight of the evidence strongly supports treating HRIS, virtual HR, e-HRM, and B2E systems as independent, complimentary constructs. While the first three comprise a firm’s HRT system, the fourth construct is more appropriately positioned in the business-collaborative system. The sample was further evaluated with an analytic framework to detect patterns of practice in research designs. This revealed that much more attention has been focused on system actions and outcomes than on attitudes and system characteristics. Different units of analysis were well represented aside from trans-organizational studies. Finally, a case is made for better contextualizing HRT research by recognizing differences in assimilation stage, functional penetration, and collective proficiency. These factors are rarely mentioned, let alone studied, raising additional concerns about measurement error. Detailed suggestions are offered on ways to incorporate them. Together, these materials should promote more sophisticated and generalizable assessments of technology, improving our ability to understand its impacts.

The purpose of this article is to examine all the studies carried out within the scope of e-HRM and smart HRM, grouped according to the sub-functions of technical and HRM. The use of technology in HRM has started since the mid-1990s. However, this study focused on the articles published after 2014 in order to keep up to date. Any search strategy should allow for the completeness of the search to be evaluated. The terms “electronic-HRM”, “AI and HRM”, “Industry 4.0 and HRM”, “Society 5.0 and HRM”, “Human Resource Information Systems” and “Digital Technologies and HRM” “Human-robot interaction” has been questioned in IEEE Xplore, ALM digital library, Emerald Insight, SpringerLink, and Science Direct. The Web of Science and Scopus were also queried to double-check the findings and find other relevant articles in lesser-known libraries. Google Scholar was also used for forward and backward searches. These online databases have been chosen because they present the most important peer-reviewed full-text journals, conference proceedings, book chapters. Then, the references of each article were reviewed for additional articles on digital technologies and HRM. Each subsequent article is then reviewed for additional reference.

A total of 5,580 articles have been reviewed. Duplicate items have been removed. The titles and abstracts of 3,500 articles have been scanned to identify potential articles. The full-text evaluation of 2,554 was based on compliance with the inclusion criteria. In addition, 2,458 studies have been excluded. In total, 96 studies have been selected for data extraction. Additionally, questionnaires and reviews have been used to provide comprehensive research on e-HRM and smart HRM. The search terms used are expected to cover most, if not all, of the studies involving e-HRM and smart HRM.

The study carried out in this article is qualitative research. In the article, which methods are used and what has changed in e-HRM and smart HRM are examined. In particular, it has been thought about what can happen with the inclusion of human-machine interaction, AI, chatbots, industry 4.0 and information systems in HRM. Unlike previous studies, this review takes HRM from a broader perspective and groups it by topic, both by technical and HR functions. In addition, the reviewed articles provide brief information about the AI technologies used. In particular, criteria were taken into account according to the field, type and subject of the articles.

This study has the distinction of being the first in the literature in terms of examining all the studies carried out within the scope of e-HRM and smart HRM and grouped according to the sub-functions of technical and HRM in line with its purpose. The article focuses specifically on research published after 2014. It is expected to contribute to the literature in terms of collecting all studies in a single article. Other contributions of this article can be summarized in four main articles: 1) it presents a summary of previous research by grouping the studies on e-HRM and smart HRM according to the interests of researchers. 2) It saves time for the reader as it provides a brief explanation of the studies on the subject. 3) Instead of explaining in detail the general details analyzed in other articles, it offers a practical perspective by focusing on the type, subject and field of the article. 4) With the digitalization of HRM, new, up-to-date research and techniques are introduced.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.