Traditional to digital: human resource management transformation

The debate on “Man and Technology” or “Man versus Technology” has persisted for centuries (Zavyalova et al., 2022). With the rise of Industry 4.0, attention has shifted to its impact on the digital transformation of human resource processes within organizations (Strohmeier, 2014). The digitization of HRM is a crucial component of an organization’s overall digital transformation (Febrianti and Jufri, 2022). Although often used interchangeably, “digitization,” “digitalization,” “digital transformation” and “digital disruption” have distinct meanings (Nadkarni and Prügl, 2021; Joel et al., 2024). Strohmeier (2020) explains that digitization and digitalization are process-oriented, whereas digital transformation and digital disruption are outcome-oriented (Maršíková and Mazurchenko, 2019). Furthermore, digitization is purely technical, while the other concepts encompass both technical and socio-technical dimensions.

Digital transformation is a deliberate strategic choice made by organizations to gain a competitive edge, whereas digital disruption is an external force requiring adaptation for business sustainability (Febrianti and Jufri, 2022). In HRM, digital transformation involves leveraging digitalization to enhance HR strategies and create organizational value (Strohmeier, 2020). HR analytics play a crucial role by enabling evidence-based decision-making. It proactively integrates digital technology into HR planning while ensuring business continuity (Poulose et al., 2024). Deloitte predicts that HRM will increasingly rely on generative AI, autonomous HR systems, workforce intelligence and digital identity management (Stacho et al., 2023). This study explores emerging research trends, including key journals, keywords, top authors and collaborations. Additionally, this investigation facilitated the systematic identification of the advancement of the research area. The study then systematically investigated the topic’s development by classifying the literature into theory, context, characteristics and methodology using the TCCM framework.

The integration of TCCM with bibliometric analysis using VoSviewer offers key insights for future research. This study explores major contributors, research focus areas in “digital transformation of human resource management” and prevalent theories, contexts and methodologies. It covers the topic overview, methodology, results, discussion, conclusion and future research recommendations.

The integration of HRM with digitalization began with the development of the human resource information system (HRIS) (Murugesan et al., 2023). which aims to reduce costs, improve productivity and gain a competitive edge (Hmoud and Várallyai, 2020) by linking HR functions like recruitment, performance management and training with information management systems (Prokopenko et al., 2023). It is a techno-social concept involving computational systems (Theres and Strohmeier, 2023) and collaborative processes supporting employee well-being (Muzanenhamo and Rankhumise, 2023).

An AI-integrated compensation management system assesses employee engagement and compensation equity (Ghosh et al., 2022). It streamlines performance appraisals by analyzing input-output, identifying improvement areas and predicting performance (Štaffenová and Kucharčíková, 2021). AI highlights adverse performance disclosure’s negative impact (Vardarlier, 2019). Neurolinguistic AI captures emotions (Kirilmaz, 2020), enhances engagement, and is cost-effective (Shukla et al., 2023). AI bots assist employees (Kaminska and Borzillo, 2018) with generational differences in enterprise social network adoption. This study references five systematic reviews (Votto et al., 2021; Kane et al., 2015).

The goal of a systematic literature review (SLR) is to comprehensively analyze all published research to date (Shukla et al., 2023). This study employs bibliometric and TCCM analysis to conduct an SLR and identify research gaps (Bindra et al., 2023). Bibliometric analysis, a quantitative method, evaluates academic publications to track authorship, publishing trends and knowledge dissemination (Donthu et al., 2021; Alsharif et al., 2020). It helps assess research performance and intellectual structures (Gaviria-Marin et al., 2018; Sharma et al., 2020), but qualitative analysis is needed for a complete research evaluation.

The research questions aid in understanding the main reasons for performing an SLR study and guide the subsequent research procedure (Bhardwaj et al., 2023; Sharma et al., 2020). Consequently, the study formulated the following research questions to focus on reviewing the digital transformation of the human resource management domain:

Once the research questions were defined, the next essential step in the (SLR was identifying and collecting relevant literature (Bindra et al., 2022). The search process involved selecting appropriate keywords – “human resource management,” “digitalization” and “artificial intelligence” and the lexicons of similar words – across titles, keywords and abstracts. Boolean operators such as “AND,” “OR” and “LIMIT” ensured precise results. Articles were limited to English-language journal publications in the business, management and accounting collection. The review team established inclusion and exclusion criteria through discussions. Literature was retrieved from Scopus, Web of Science (WoS) and Business Source Complete, along with journal homepages to ensure comprehensive coverage.

A structured screening process was used to identify relevant literature on the digital transformation of HRM, following previous SLR studies (Kirilmaz, 2020). The initial search yielded 1,863 articles using three keywords and related lexicons. After applying inclusion and exclusion criteria, 546 articles remained (Calderon-Monge and Ribeiro-Soriano, 2024). Abstract and in-depth screening did not reduce this number. To ensure a comprehensive perspective, all selected articles were retained for further analysis. Consequently, the SLR was conducted on the final dataset of 546 articles, ensuring a thorough exploration of the subject domain.

This paper aims to analyze research performance in the subject domain since its inception, providing a comprehensive review. It highlights keywords, impactful journals, sources, trends and TCCM-based categorizations, identifying methodologies and future research opportunities.

The chronological publishing patterns of the digital transformation of HRM are presented in Figure 1, indicating that 2015 was the year when the concept of the digital transformation of HRM emerged in the literature.

Figure 1 clearly illustrates that the number of publications remained negligible until 2018, averaging only five per year. However, since 2019, there has been a noticeable increase in the number of publications, as indicated by the upward trend. According to a McKinsey survey (McKinsey, 2022, 2024), the global AI adoption trend experienced a significant spike in 2018, with the percentage of surveyed organizations using AI rising from 20% in 2017 to 47% in 2018. This surge is also reflected in the increased number of scholarly research works on the topic post-2018.

The COVID-19 pandemic (2019–2020) further accelerated digitalization, particularly the adoption of Industry 4.0 technologies, including artificial intelligence (AI), due to the need for physical distancing. While the growth rate remained relatively stable from 2018 to 2023, a significant increase was observed in 2024, with the percentage of AI-adopting organizations rising to 72%. An example of this rapid adoption is ChatGPT, launched on November 30, 2022, which quickly captured the interest of both researchers and industry professionals. Despite initial apprehensions, businesses are now rapidly integrating generative AI (Gen AI) into various business functions.

The McKinsey survey highlights an S-shaped growth path for Industry 4.0. Until 2018, the industry was in the learning phase, characterized by trial and error. The adoption phase followed from 2018 to 2020, marked by gradual but steady growth. Post-2020, the new normal emerged as the optimization phase, focusing on cost efficiency and rapid technological advancements across business functions.

Table 1 showcases the top ten sources/journals with the highest citations. The identified and selected literature was published in 23 different sources, which were SSCI and Scopus indexed.

Figure 2 graphically represents the data on the influence and scope of several journals on the topic of HRM, as determined by their total number of citations and articles. The International Journal of Human Resource Management and Human Resource Management Review are notable for their substantial citation counts per article, which indicate their considerable impact. On the other hand, publications such as the Asia Pacific Journal of Human Resource Management and Personnel Review have somewhat fewer average citations per article, indicating that they have less impact compared to others.

The VOSviewer image (Figure 3) is a network visualization that represents the relationships and connections between various terms related to HRM and technology. Each node (circle) represents a term, and the size of the node indicates the frequency or significance of the term in the context of the analyzed literature. The lines (edges) connecting the nodes represent co-occurrences or relationships between the terms (Table 2). The colors indicate clusters ^[1] of terms that are closely related (Table 3).

The frequent usage and prominent placement of concepts such as “artificial intelligence” and “machine learning” suggest their increasing significance in HRM. The terms “digitization,” “digitalization,” “information systems” and “information management” indicate a significant emphasis on the digitalization of HR operations. The concepts of “employee engagement,” “employee performance” and “personnel training” underscore the focus on leveraging technology to improve operations connected to employees. The use of terminology such as “efficiency” and “people analytics” highlights the objective of enhancing HR efficiency by using data-driven insights.

The VOSviewer visualization effectively maps the interconnections between various technological and HRM-related terms, highlighting key areas of focus and the integration of advanced technologies in HR practices. It provides a comprehensive overview of the current trends and themes in HRM research, emphasizing the significant role of AI, machine learning and digital transformation in shaping the future of human resource management.

The TCCM framework is a systematic approach used to categorize and analyze literature in various fields. The literature on the digital transformation of HRM has developed in a phased manner. Table 4 highlights the categorization of the literature in the two phases: the first phase (2015–2020) and the second phase (2021 onwards).

Between 2011 and 2021, RBV and the dynamic capability model evolved, with RBV reaching conceptual maturity (D’Oria et al., 2021). This phase saw RBV applied to HRM’s role in firm performance and technology adoption. Neo-institutional theory analyzed institutional pressure’s role in organizational change (Hwang, 2023), while institutional and contingency theories were integrated (Tiwari et al., 2024).

Industries like technology, manufacturing, healthcare, finance, retail and education have embraced HR digitalization. A McKinsey (2022) survey found that AI’s early adoption (pre-2018) benefited risk and manufacturing, while within 2–3 years, its bottom-line impact expanded to marketing, sales, corporate finance, and supply chain. Key HR trends included HRIS, automation, and analytics, evaluated through surveys and econometric models. Deloitte (2024) categorized AI-enabled HRM maturity into four stages, based on a six-month study involving multiple surveys and interviews with HR technology leaders. Their framework assessed strategy, organizational readiness, AI and automation governance, adaptability, solution architecture and delivery.

• Stage 1 – Inexperienced and exploring: When industries were trying to comprehend the potential of AI and its potential to automate HR processes and explore its various possibilities.

• Stage 2 – Opportunistic and experimenting: Organizations are ready to experiment with AI and automation of the HR processes to build their strategies, structures and capabilities.

• Stage 3 – Structured and scaling: Organizations develop vision and strategies to embrace AI and automation processes.

• Stage 4: Agile and innovating: Industries have governance structures, advanced capabilities, sound technology to innovate, and are agile for automation.

Most industries were at stage 1 (30%) and stage 2 (45%) of automation during pre-2021. From 2021 onwards, industries are moving toward stage 3 and stage 4. Industries which digitalization for increasing the efficiency of other core functions such as supply chain management, marketing and finance were quicker to adopt AI-enabled HRM.

The second phase emphasized the hospitality, telecommunications, the public sector, energy and utilities, logistics and transportation and nonprofit organizations in addition to the sectors which transformed their processes between 2015 and 2020.

The evolution of theories such as the technology acceptance model (TAM), sociotechnical systems theory, diffusion of innovations theory, unified theory of acceptance and use of technology (UTAUT) and media richness theory has significantly contributed to digitalization. TAM and its extensions, including UTAUT and UTAUT2, are widely used to analyze digital adoption at both individual and organizational levels, incorporating socio-psychological perspectives and focusing on individuals’ willingness to embrace technology. In socio-technological systems, governance, project management and business integration form the social component, while functionality, interoperability and usability define the technological aspect. Early adoption stages (1 and 2) rely on individual intention. For instance, Alam et al. (2022) applied UTAUT to AI-based talent acquisition in Bangladesh, while Islam et al. (2022) examined AI adoption in manufacturing and services. Rogers’ (1962) diffusion of innovations theory classifies adopters into innovators, early adopters, early majority, late majority and laggards. Amoako et al. (2023) used TAM and diffusion of innovation to study E-HRM adoption in public enterprises in an emerging nation.

Emphasis shifted to the application of machine learning in improving employee engagement and performance within HRM through cloud-based solutions, including AI and machine learning, mobile accessibility, employee self-service, integration, customization and real-time updates to the employees.

The bibliometric analysis highlights a key challenge: while research interest in HRM digitalization is rapidly growing (144 publications in 2023 and 170 in 2024), fundamental questions regarding its role remain unresolved. Research has transitioned from basic automation (Phase 1) to complex human-AI interaction (Phase 2), yet gaps persist in understanding how AI and digitalization can complement rather than replace HR functions. The most cited papers emphasize technical implementation (e.g. Tambe et al., 2019 – 518 citations and Vrontis et al., 2023 – 452 citations) rather than ethical or strategic concerns. Only 19 out of 546 papers address ethical considerations, often highlighting AI-related challenges (Tambe et al., 2019; Giermindl et al., 2022) or appearing in systematic reviews (Mori et al., 2024; Hunkenschroer and Luetge, 2022), primarily focusing on AI-driven recruitment transparency.

AI and technology-enabled HRM strategies are examined in 20 studies, covering digital transformation (Ruiz et al., 2024), AI applications in compensation (Marler, 2024), digitalization of enterprise resource planning (Yue, 2024), supply chain HR integration (Song et al., 2021) and green HRM (Ogbeibu et al., 2024).

Theoretical advancements have progressed from traditional competitive strategy theories to more techno-management-oriented perspectives post-2021, including the technology acceptance model (TAM), sociotechnical systems theory, diffusion of innovations theory, unified theory of acceptance and use of technology (UTAUT) and media richness theory.

Since 2021, HR digitalization research has expanded beyond automation and human resource management information systems to incorporate advanced technologies such as AI and machine learning into HR functions. While initial studies predominantly emerged from developed nations like the UK, USA, Australia and France, recent years have seen increased contributions from researchers in China, India, Vietnam, Morocco and Turkey (see Figure 4).

AI-powered talent databases, utilizing intelligent algorithms, help align corporate talent needs with job credentials. AI surpasses human resume screening by leveraging cloud computing for precise candidate selection. Additionally, AI facilitates online interviews, accommodating candidates across various locations and schedules, thus optimizing HR operations.

However, AI-driven recruitment has both advantages and limitations. While digitization enhances accessibility, it also raises concerns regarding employee privacy and business confidentiality. AI relies on big data analytics for candidate evaluation but may overlook individual work preferences, potentially leading to dissatisfaction and increased turnover.

Digital transformation is revolutionizing compensation and performance management by enabling data-driven decision-making and real-time feedback. The integration of human–machine collaboration helps organizations move beyond traditional performance assessments and improve HR utilization. Moreover, digitalization reduces biases associated with subjective evaluations and enhances the timeliness and accuracy of performance data. HR departments can leverage data visualization tools to better analyze complex projects and identify key business performance indicators.

As digitalization progresses, organizations must prioritize competencies such as AI proficiency, automation, data analytics, adaptability and creativity. AI is expected to handle routine administrative and supervisory tasks, yet decision-making will continue to rely on human expertise. Consequently, adaptable thinking and problem-solving skills will gain greater importance, leading to evolving job-specific skill requirements.

Some employees face job displacement due to skill obsolescence, resulting in potential technical unemployment. The automation of procedural tasks poses challenges for mid-level employees, necessitating either role transitions or upskilling for advanced positions. Those who continuously update their skills and adapt to technological advancements will remain employable in the evolving digital workforce.

Since 2021, researchers have increasingly engaged in the digitalization of HR processes. Research methodologies have evolved from traditional surveys and qualitative interviews to include case studies, observational research, ethnography and mixed-method studies, allowing for a more comprehensive understanding of HR digital transformation.

The ongoing digital transformation in HRM leverages technology to enhance HR operations and improve organizational efficiency. As part of this evolution, HR practices are integrating cutting-edge technologies such as cloud computing, big data, machine learning and AI. These advancements are set to revolutionize the HRM industry imminently. AI-powered tools will refine recruitment by automating resume screening, matching candidates to job roles and conducting initial interviews through chatbots. Machine learning algorithms will facilitate proactive HR strategies by predicting employee attrition, identifying potential leaders and suggesting customized development plans. In workforce planning, employee engagement and performance monitoring, HR departments will increasingly depend on big data analytics to make well-informed decisions. Advanced analytics will generate real-time insights into employee behavior and performance, enabling timely support and intervention.

Technology will personalize onboarding, training and career development, aligning with individual requirements. Cloud-based HR systems with self-service portals will empower employees to manage HR functions like leave applications, benefits enrollment and performance evaluations independently. As remote work expands, digital workplaces will enhance collaboration, communication and project management across distributed teams. Virtual reality (VR) and augmented reality (AR) will enrich remote training and virtual team-building, fostering immersive learning experiences. Robotic process automation (RPA) will streamline repetitive HR tasks such as payroll processing, compliance reporting, and benefit administration, freeing HR professionals for strategic priorities. Additionally, blockchain and smart contracts will bolster security and efficiency in onboarding, contract management and payroll, reducing administrative workload while ensuring greater transparency.

The integration of wearable technology and mobile applications will facilitate the implementation of comprehensive wellness programs. These programs will monitor and support individuals’ physical, mental and emotional well-being. However, digitalization must address the challenge of technostress – anxiety, tension and stress that arise due to individuals feeling overwhelmed by new technology. Digitalization and AI can pose a threat if they replace human activities within organizations instead of complementing human expertise. Notably, our bibliometric analysis identified only three studies focusing on the issue of technostress.

Table 5 outlines several potential directions for future research. AI-powered platforms will offer personalized mental health support, including virtual counseling and stress management resources. Robotic technology has the capacity to assume the role of caregivers for patients, but a critical question remains: Can robots genuinely replicate human nurturing, emotional care and empathy? A robot assigned to patient care may track and transmit real-time updates on a patient’s physical and mental status to a centralized system. However, capturing images of patients in vulnerable moments could constitute a significant violation of their rights to dignity and a decent livelihood.

AI technologies will contribute to identifying and mitigating biases in recruitment, performance evaluations and promotions, thereby fostering more diverse and inclusive workplaces. Data analytics will play a crucial role in designing and monitoring organizational policies that promote diversity, equity and inclusion. Future investments in human resource management will prioritize e-learning platforms that offer employees personalized and on-demand learning opportunities. Gamification is emerging as a preferred tool for learning and development in higher education and corporate training, yet the impact of its various features on learners’ performance remains underexplored. AI will facilitate skill gap analysis and recommend targeted training programs to ensure workforce competitiveness in an ever-evolving job market. However, AI also has the potential to replicate and exacerbate biases inherent in traditional recruitment practices. According to the Deloitte (2024) survey, while 75% of business leaders anticipate that GenAI will shape talent strategies in the next two years, only 23% are currently investing in it.

To comply with data protection regulations and establish robust cybersecurity safeguards, organizations must implement stringent cybersecurity measures. Successful digital transformation necessitates effective change management strategies to overcome resistance and ensure seamless adoption of new technologies. Continuous training and communication will be paramount in this process. Despite automation and AI’s potential to enhance efficiency, preserving the human element in human resource interactions remains crucial. Digitalization has fostered an agile working culture that demands a resilient and adaptable workforce with diverse skill sets. Employees unable to quickly adjust to these changes risk becoming obsolete and losing their jobs (Holbeche, 2018). Consequently, digitalization could exacerbate workforce disparities and pose a threat to equality, inclusivity and diversity (Kirchschläger, 2021).

Maintaining a balance between technology, empathy, and personal connection is essential for fostering employee engagement and satisfaction. AI-enabled human resource management (HRM) has the potential to revolutionize workforce management by enhancing productivity, improving decision-making and creating a more engaging and supportive work environment. However, this transformation presents challenges that must be addressed to unlock AI’s full potential. Lütge (2019) emphasizes the urgent need for global regulations and policies on digital ethics. Kirchschläger (2021) advocates for the establishment of a global supervisory and monitoring institution for data-based systems, similar to the International Atomic Energy Agency (IAEA). This proposed International Data-Based Systems Agency (DSA) would serve as the primary intergovernmental forum for scientific and technical cooperation on digital transformation. Integrated within or affiliated with the United Nations, it would promote the secure and ethical use of data-based systems while advancing international peace, security, human rights, and the United Nations’ Sustainable Development Goals.

As technological advancements continue to shape the world, the future of human resource management will depend on its ability to integrate innovation while preserving fundamental human connections and ensuring employee well-being.