Scientometric Study of Digital Transformation and Human Resources: Collaborations, Opportunities, and Future Research Directions

Abstract

While the importance of understanding digital transformation from a Human Resources (HR) perspective is widely acknowledged, a comprehensive analysis of the scholarly landscape remains limited. This study examines the scientific production on digital transformation and HR within the Web of Science database, analyzing bibliometric indicators of production, visibility, impact, and collaboration. The results reveal that digital transformation is a recent, relevant, and transdisciplinary construct that significantly influences talent management strategies and necessitates new skill sets within HR functions. This impact requires organizations to proactively develop digital skills, facilitating a regulated and controlled transition during the implementation of digital strategies. Specifically, organizations must focus on fostering employee adaptability and promoting work–life balance in this rapidly changing environment. Building on these findings, future research could benefit from employing mixed-methods approaches, combining quantitative bibliometric analysis with qualitative case studies to explore the contextual factors influencing the complex relationship between digital transformation and HR, such as organizational culture and leadership styles.

1. Introduction

Digital transformation (DT) is a multifaceted phenomenon posing significant challenges for organizations across various sectors. Characterized by research contributions from diverse fields, including mathematics, engineering, computer science, social sciences, economics, and behavioral sciences (Li, 2022).

For the purposes of this publication, we will focus on the approach to DT from the perspective of human resources, highlighting the role of individuals in the technological changes occurring in the workplace.

Within this framework, digital transformation fosters performance through strategic innovations that generate value (Hess et al., 2016). These strategic innovations enable organizations to respond to continuously evolving global demands (Matt et al., 2015; Hess et al., 2016; Correani et al., 2020).

In the contemporary business landscape characterized by uncertainty and volatility, organizations confront the critical imperative of adapting to ensure sustained competitiveness, a phenomenon widely recognized as ‘digital Darwinism’. This concept elucidates the rapid technological evolution and the consequent pressure exerted on organizations to innovate or risk obsolescence (Kreutzer et al., 2017; Li, 2022).

Digital transformation constitutes an organizational change engendered by technology, yielding economic and social repercussions, thereby stimulating innovation and advancing the development of products with enhanced added value (Vial, 2019; Tomaszewski, 2021; Hanelt et al., 2021).

The technological transformations propelling DT are frequently catalyzed by economic and social shifts that cultivate innovation and facilitate technology transfer (Vial, 2019; Hanelt et al., 2021). Within this transformative process, organizational culture constitutes a significant factor. Through the implementation of novel technologies, agility is enhanced, and processes are optimized (Rogers, 2016; Kudyba, 2020). Furthermore, organizational change necessitates that entities adapt their structures and foster a culture that underpins these transformations (Kane, 2019).

This nexus between DT and culture underscores the necessity for individuals possessing the requisite technological competencies (Kane, 2019; Soto-Acosta, 2020; Nadkarni & Prügl, 2020; Ellström et al., 2022). These competencies empower organizations to coordinate activities effectively, leverage resources, and develop innovative products and services (Routley et al., 2013).

Considering that competencies constitute an inherent attribute of individuals, examining the perceptions and responses of individuals regarding digital transformation is paramount (Teichert, 2019; Martínez-Peláez et al., 2023). This includes leaders and managers, who encounter the challenge of developing strategies that are congruent with the evolving digital landscape (Berghaus & Back, 2016). Organizations that architect flexible structures to realize their strategy are optimally positioned to adapt to digital transformation (Teece, 2010; Verhoef et al., 2021).

From a practical standpoint, a primary consequence involves the automation of repetitive tasks, thereby necessitating a greater emphasis on human capabilities such as creativity, empathy, judgment, intuition, interpersonal sensitivity, and problem-solving skills (Soto-Acosta, 2020).

Digital transformation encompasses a fundamental transformation in the mechanisms through which companies generate value, propelled by a cultural transformation in alignment with the organizational strategy, with the objective of fostering agility within the organizational structure to respond effectively and efficiently to evolving environmental dynamics (Rogers, 2016).

In summary, the following authors (

Table 1. Leading authors in DT research.

Author	Main Findings
(Vial, 2019)	Digital transformation (DT) has become recognized as a strategic phenomenon, wherein technology constitutes only a component of a complex process enabling organizations to respond to potentially instantaneous environmental shifts, thereby generating alterations in value creation. This necessitates modifications to the organizational structure, organizational culture, leadership, and the competencies of personnel.
(Hanelt et al., 2021)	Ambiguity persists regarding the precise definition and scope of DT, although a clear correlation exists between DT and organizational change. These authors posit that DT is initiated by the integration of digital technology, thereby facilitating the evolution towards flexible organizational structures.
(Hess et al., 2016)	Strategy is paramount to the success of DT, thereby necessitating the engagement of managers. Managers are tasked with overseeing the application of DT strategies through the analysis of technology utilization, alterations in value creation, structural modifications, and financial considerations. The authors assert that the initial phases of DT implementation are characterized by a significant degree of uncertainty.
(Verhoef et al., 2021)	Digital transformation (DT) is not an intrinsic objective but rather a multidisciplinary phenomenon occurring in response to changes induced by technology, encompassing three distinct stages: typification, digitization, and digital transformation. Each stage necessitates specific conditions with respect to digital resources, organization, structure, and metrics. Organizations that undertake DT enhance agility, establish networks, and facilitate information management.
(Nadkarni & Prügl, 2020)	Digital transformation (DT) is a phenomenon underpinned by diverse theoretical foundations, for which there is limited quantitative empirical evidence. DT is enabled by technology but not determined by it. It is a construct comprising two primary dimensions: technology and actor. Furthermore, each dimension consists of several sub-dimensions. Within the technology dimension: change environment, technological capacity, value creation and capture, and market environment. Within the actor dimension: transformative leadership, managerial and organizational skills, culture, and work environment.
(Ogrean & Herciu, 2021)	Digital transformation (DT) encompasses the domain of digital technologies and their applications. Consequently, DT is not an ultimate goal but rather a means to attain digital economies, foster economic growth, enhance the quality of life, promote sustainable development, and ensure sustainability.

) have made significant contributions to the study of DT:

There is a consensus that DT is as much about people as it is about technology (Erceg & Zoranović, 2022; He et al., 2023) in a change process that requires teams to move beyond habitual practices (Teece et al., 2016).

The change process demands a significant effort from the entire company, making the reformulation of a business strategy fundamental for success (Sebastian et al., 2017; Erceg & Zoranović, 2022).

The growing body of literature on digital transformation (DT) highlights the significant interest in its impact (Vial, 2019; Baiyere et al., 2020; Broz et al., 2020; Chawla & Goyal, 2022).

The objective of this review is to map the academic landscape, to provide insights into the challenges and opportunities, and to identify future directions for both researchers and practitioners in the field of digital transformation and human resource management.

To this end, this article addresses the following research questions:

• What are the key publication trends in the field of digital transformation and human resource management?
• What are the most influential studies on digital transformation and human resource management based on citation analysis, and what are their main contributions and theoretical findings?
• Which countries and institutions host the leading researchers in this field?
• In which research networks do the main authors participate in?
• Which scientific journals contribute most significantly to the body of knowledge on this topic?
• What are the promising future research avenues for digital transformation and human resources?
• What are the practical implications of current research on digital transformation and human resource management for HR professionals and organizational leaders when informing their strategies, decision-making, and implementation efforts?

This study not only maps the scientific landscape of this interdisciplinary area but also enables academics to identify and pursue future lines of research.

2. Materials and Methods

This research employs a bibliometric analysis (Shang et al., 2015; Wang et al., 2020; Hernández-Perlines et al., 2023) to assess the quality, impact, and trends of scholarly output on the intersection of digital transformation (DT) and human resources (HR). Bibliometric analysis is a well-established method for evaluating scientific research through quantitative indicators (Nalimov & Mul’chenko, 1971; Mingers & Leydesdorff, 2015; Araya-Castillo et al., 2022).

Data were retrieved from the Web of Science Core Collection (WoS) database, encompassing the SSCI, ESCI, SCI, BKCI-SSH, A&HCI, CPCI-SSH, BKCI-S, and CPCI-S indexes. The study focuses its search on the online database of the Web of Science (WoS), which is one of the most recognized and accepted platforms by the scientific community and by evaluation and research agencies.

The following search string was used, searching within the title, abstract, and author keywords (TS): TS = (“Digital Transformation*”) AND TS = (“human resources” OR “people management” OR “personnel management” OR “human capital”).

Within the realm of academic research on digital transformation, the application of the truncation symbol (“”) to the term “Digital Transformation” is deemed essential to ensure the comprehensiveness of the search. This practice enables the inclusion of diverse terminological variations, thereby optimizing the identification of pertinent studies addressing the core concept of digital transformation (Hood & Wilson, 2001).

Furthermore, when exploring the intersection of digital transformation and human capital management, the term “human resources” emerges as a central component. The search strategy is expanded by incorporating synonyms such as “people management”, “personnel management”, and “human capital”, linked by the Boolean operator “OR”. This measure is warranted by the terminological variations found in the literature and the necessity to cover diverse conceptual perspectives within employee management (Hood & Wilson, 2001).

Lastly, within the context of bibliometric and scientometric studies, limiting the search to the title, abstract, and author keywords (TS) fields is grounded in their capacity to provide a succinct representation of the core content of the articles. These fields are of primary importance for indexing in academic databases, thereby ensuring a greater likelihood of identifying the literature most relevant to the research subject.

The search, conducted on 5 February 2024, encompassed the period from 1975 to 2023. This initial search yielded 279 records. Subsequently, the dataset was refined to include only peer-reviewed articles published in indexed journals up to 31 December 2023, to ensure a consistent timeframe for analysis. This refinement excluded 8 articles under review, 2 book chapters, 1 editorial, 1 meeting abstract, and 1 retraction. The final dataset comprised 266 documents, which have collectively garnered 1938 citations.

Following retrieval, a two-stage filtering process was applied. First, duplicates were removed. Second, articles focusing on DT outside the realm of HR (e.g., computer science, mathematics, business sciences) and opinion pieces were excluded. This process resulted in a final sample of 266 scientific articles.

While WoS is a widely used database, it is important to acknowledge its inherent limitations. The database exhibits a known bias towards English-language publications and may underrepresent regional journals with lower impact factors. Furthermore, coverage of social science disciplines in WoS may be less comprehensive compared to natural and exact sciences. These limitations should be considered when interpreting the findings of this study.

The analysis proceeded in three stages. First, descriptive statistics were generated to map key concepts and their frequency of occurrence. Second, cluster analysis was performed to identify thematic areas within the research landscape (Bornmann & Marx, 2013; Araya-Castillo et al., 2021; Barrueto-Mercado et al., 2024). Finally, a social network analysis, utilizing graph theory, was conducted to visualize and analyze collaboration patterns among authors and institutions. This analysis was performed using VOSviewer software (version 1.6.20).

3. Results

This section presents the key findings of bibliometric analysis, organized according to the research objectives. These findings provide valuable insights for the design of digital transformation (DT) strategies tailored to the specific needs of human resource management (HRM).

Figure 1 illustrates the temporal distribution of publications on the intersection of DT and HRM. The first publications in this area appeared in 2019. The dataset comprises 266 articles, accumulating a total of 1938 citations. The highest number of publications (111) occurred in 2023, indicating a recent surge in research interest. An exponential growth trend is evident, closely approximated by the equation y = e^0.5185x, with a strong fit (R² = 0.9838). (See Figure 1).

Figure 2 illustrates that the number of citations per year for the concepts of “Digital Transformation” and “Human Resources” is incipient in 2019, followed by a sustained and exponential growth until 2023, reaching 1160 citations.

Table 2. General citation structure.

Number of Appointments	Number of Items	Items (%)
More than 100	1	0.38%
Between 50 and 100	7	2.63%
Between 10 and 50	47	17.67%
Between 1 and 10	132	49.62%
0 appointments	79	29.70%
Total	266	100%

Source: Web of Science Data (2024).

presents the distribution of citations across the 266 articles in the dataset, which collectively received 1938 citations. A substantial portion of the articles (79, representing 29.7%) received no citations. Of the cited articles, the majority (132, or 49.62%) received between 1 and 10 citations. Forty-seven articles (17.67%) received between 11 and 50 citations. Seven articles (2.63%) received between 51 and 100 citations, and one article (0.38%) received more than 100 citations.

While several authors have contributed significantly to the field, Suzanna El Massah and Mahmoud Mohieldin (2020) stand out with respect to the Hirsch index (Bornmann & Marx, 2013). Their article, published in Ecological Economics (Q1), has garnered 114 citations, representing 5.88% of the total citations for this body of research. This influential work explores the contribution of technology to the development of human capabilities, focusing on skills and competencies within a social-organizational context.

The second most cited article, with 82 citations (4.23% of the total), was authored by Francesco Caputo et al. (2019) and published in Management Decision (Q3). This study examines the impact of the digital revolution on business innovation, emphasizing the crucial role of soft skills and the use of big data in enhancing business performance. The authors highlight the importance of soft skills, such as creativity, effective communication, and problem-solving, in leveraging the opportunities presented by digital technology.

Table 3. Most influential articles.

R	Authors	Year	Title	Magazine	TC
1	El Massah, S., and Mohieldin, M.	2020	Digital transformation and localizing the Sustainable Development Goals (SDGs)	Ecological Economics	114
2	Caputo, F., Cillo, V., Candelo, E., and Yipeng, L.	2019	Innovating through digital revolution The role of soft skills and Big Data in increasing firm performance	Management Decision	82
3	Trenerry, B., Chng, S., Wang, Y., Suhaila, Z. S., Lim, S. S., Lu, H. Y., and Oh, P. H.	2021	Preparing Workplaces for Digital Transformation: An Integrative Review and Framework of Multi-Level Factors.	Frontiers in Psychology	67
4	Chen, C. L., Lin, Y. C., Chen, W. H., Chao, C. F., and Pandia, H.	2021	Role of Government to Enhance Digital Transformation in Small Service Business	Sustainability	64
5	Vuksanović Herceg, I., Kuč, V., Mijušković, V. M., and Herceg, T.	2020	Challenges and Driving Forces for Industry 4.0 Implementation.	Sustainability	61
6	Malik, N., Tripathi, S. N., Kar, A. K., and Gupta, S	2022	Impact of artificial intelligence on employees working in Industry 4.0 led organizations	International Journal of Manpower	55
7	Ivančić, L., Vukšić, V. B., and Spremić, M.	2019	Mastering the Digital Transformation Process: Business Practices and Lessons Learned	Technology Innovation Management Review	51
8	Huong, T. T. L., and Thanh, T. T.	2022	Is digitalization a driver to enhance environmental performance? An empirical investigation of European countries	Sustainable Production and Consumption	50
9	Flechsig, C., Anslinger, F., and Lasch, R.	2022	Robotic Process Automation in purchasing and supply management: A multiple case study on potentials, barriers, and implementation.	Journal of Purchasing and Supply Management	40
10	Blanka, C., Krumay, B., and Rueckel, D.	2022	The interplay of digital transformation and employee competency: A design science approach.	Technological Forecasting and Social Change	38

Note: R = Rank; TC = Total Citations. Source: Authors’ elaboration.

presents the 10 most influential articles, ranked by total citations. These 10 articles collectively account for 32.1% of all citations, indicating a high concentration of influence within this field.

• Author Productivity and Influence:

Table 4. Most influential authors in digital transformation and human resources.

R	Authors	Institution	TP-TD	TC-TD	(%)	HA	TP-A	TC-A	T25
1	Massah, Suzanna El	Zayed University	2	121	6.24%	8	18	292	1
2	Mohieldin, Mahmoud	Cairo University	1	114	5.88%	6	15	212	1
3	Candelo, Elena	University of Turin	1	82	4.23%	12	28	716	1
4	Caputo, Francesco	University of Naples Federico II	1	82	4.23%	22	47	1.097	1
5	Cillo, Valentina	Link Campus University	1	82	4.23%	14	30	1.128	1
6	Liu, Yipeng	Zhejiang University of Technology	1	82	4.23%	32	134	2.993	1
7	Chng, Samuel	Singapore University of Technology & Design	1	68	3.51%	8	24	397	1
8	Lim, Sun Sun	Singapore Management University	1	68	3.51%	15	44	722	1
9	Lu, Han Yu	Jinan University	1	68	3.51%	8	26	222	1
10	Peng Ho Oh	Australian Catholic University	1	68	3.51%	1	2	68	1

Note: R = Author Rank; TP-TD = Total Publications in the Dataset; TC-TD = Total Citations in the Dataset; HA = Author h-index; TP-A = Total Publications; TC-A = Total Citations; T25 = Number of Top 25 Publications. Source: Authors’ elaboration based on Web of Science data (2024).

presents the 10 most influential authors. Suzanna El Massah (Zayed University) is the most influential author, with two publications garnering 121 citations (6.24% of the total). Notably, one of these articles also ranks among the top 25 most influential articles based on h-index. Mahmoud Mohieldin (Cairo University), who co-authored one article with El Massah, is the second most influential author, with 114 citations for that single publication.

Table 5. The most productive authors in digital transformation and human resources.

R	Authors	University	TP-SR	TC-SR	PC-SR	Tt (%)	HA	TP-A	TC-A
1	Hang Nguyen	Thai Nguyen University	3	5	1.67	1.13%	-	4	5
2	Massah, Suzanna El	Zayed University	2	121	60.50	0.75%	8	18	292
3	Herceg, Iva Vuksanovic	University of Belgrade	2	83	41.50	0.75%	2	5	84
4	Zemtsov, Stepan P.	Russian Presidential Academy of National Economy & Public Administration	2	62	31.00	0.75%	10	33	378
5	Jiang, Kangqi	Shantou University	2	33	16.50	0.75%	8	13	170
6	Gilch, Phyllis Messalina	University of Bayreuth	2	29	14.50	0.75%	1	2	29
7	Li, Xin	Shanghai University of Finance & Economics	2	23	11.50	0.75%	6	9	201
8	Rivza, Peteris	Latvia University of Life Sciences & Technologies	2	23	11.50	0.75%	4	35	99
9	Xu, Qiong	Central South University	2	23	11.50	0.75%	7	12	220
10	Rivza, Baiba	Latvia University of Life Sciences & Technologies	2	23	11.50	0.75%	7	100	197

Note: R = Author Rank; HA = Author h-index; TP-A = Total Publications; TC-A = Total Citations. Source: Authors’ elaboration based on Web of Science data (2024).

presents the most productive authors, defined as those with two or more publications. Suzanna El Massah leads with a PC-SR of 60.50 and a TC-A of 292. Iva Vuksanovic Herceg follows with a PC-SR of 41.50 and a TC-A of 84. Hang Nguyen has a PC-SR of 1.67 and a TC-A of 5.

• Journal Productivity and Influence:

Table 6. Journals publishing research on digital transformation and human resources.

R	Magazines	N	Tt (%)	TC-TD	PC-TD	H-TD	FI 5Y	Q
1	Sustainability	25	9.40%	304	12.16	10	4.0	Q2
2	Frontiers in Psychology	5	1.88%	86	17.20	3	4.3	Q1
3	Finance Research Letters	4	1.50%	41	10.25	2	8.9	Q1
4	Corporate Social Responsibility and Environmental Management	3	1.13%	25	8.33	1	10.6	Q1
5	Financial and Credit Activity Problems of Theory and Practice	3	1.13%	15	5.00	2	0.7	Q3
6	IEEE Access	3	1.13%	6	2.00	2	4.1	Q2
7	Journal of Chinese Human Resources Management	3	1.13%	2	0.67	1	1.6	Q4
8	Quality Access to Success	3	1.13%	17	5.67	2	0.5	Q4
9	Scientific Papers series Management Economic Engineering in Agriculture and Rural Development	3	1.13%	8	2.67	2	0.9	Q3
10	Systems	3	1.13%	2	0.67	1	2.5	Q2

Note: R = Journal Rank; N = Number of Publications in Dataset; PC-TD = Average Citations per Publication in Dataset; H-TD = h-index in Dataset; TC-TD = Total Citations in Dataset; FI 5Y = 5-Year Impact Factor; Q = Journal Quartile. Source: Authors’ elaboration based on Web of Science data (2024).

presents the 10 most productive journals, defined as those publishing 10 or more articles on this topic. These journals collectively published 55 articles, representing 20.7% of the total publications, and received a total of 506 citations, averaging 9.2 citations per article. The h-index for this journal set is 11. “Sustainability” is the most productive journal, with 25 publications, and also the most influential, with 304 citations. However, “Frontiers in Psychology” has the highest average citation rate per article (17.2). “Corporate Social Responsibility and Environmental Management” has the highest 5-year impact factor (10.6).

• Institutional Productivity and Influence:

Table 7. Author affiliations.

R	Institutions	Country	NP	TT (%)	TC-TD	PC-TD	H-TD
1	Ministry of Education Science of Ukraine	Ukraine	12	4.51%	35	2.9	4
2	University of Zagreb	Croatia	6	2.26%	160	26.7	4
3	Egyptian Knowledge Bank	Egypt	5	1.88%	136	27.2	4
4	Plekhanov Russian University of Economics	Russia	5	1.88%	9	1.8	2
5	Jinan University	China	4	1.50%	48	12.0	3
6	Thai Nguyen University	Vietnam	4	1.50%	1	0.3	1
7	Vietnam National University Ho Chi Minh City	Vietnam	4	1.50%	8	2.0	2
8	National Economics University Vietnam	Vietnam	3	1.13%	61	20.3	2
9	University of Foscari Venice	Italy	3	1.13%	44	14.67	3
10	Shanghai University of Finance Economics	China	3	1.13%	25	8.3	1

Note: R = Institution Rank; TC-TD = Total Citations in Dataset; PC-TD = Average Citations per Publication in Dataset. Source: Authors’ elaboration based on Web of Science data (2024).

presents the 10 most productive institutions, defined as those with three or more publications in the dataset. These institutions account for 18.42% of the total publications, indicating a relatively low concentration of institutional output. The Ministry of Education and Science of Ukraine is the most productive, with 12 publications. However, the University of Zagreb is the most influential in terms of total citations, with 160. The Egyptian Knowledge Bank has the highest average number of citations per publication (27.2).

• Collaboration Network Analysis:

Table 8. Clusters of Co-Authorship.

Cluster 1	Cluster 2	Cluster 3	Cluster 4
Atlantic Science and Technology Academic Press (atlantic sci & technol acad pr)	K.G. Razumovsky Moscow State University	Moscow Automobile and Road Construction (moscow automobile & rd constru)	Donetsk National Technical University
Autonomous Nonprofit Org Publishing	Lomonosov Moscow State University (lomonosov moscow state univ)	Moscow Humanitarian Economic University	Kuban State Agrarian University
Bauman Moscow State Technical University	Moscow Institute of Aviation Technology	Moscow State Technical University of Civil Aviation	Plekhanov Russian University
Capital University Economics and Business	Moscow Polytech University	Moscow State University Civil Engineer	State University Management
Financial University under the Government of the Russian Federation	Russian State Social University	Plekhanov Russian University Economics (plekhanov russian univ econ)	Vladivostok State University Economics
HSE University	Russian University Cooperative	Ulyanovsk State University
I.M. Sechenov First Moscow State Medical University	St Petersburg State University Economics
Ministry Finance of the Russian Federation	Voronezh State University
Moscow Metropolitan Governance
National University of Science and Technology MISiS
North Eastern Federal University
University Sumatera Utara

Source: Authors’ elaboration based on Web of Science data (2024), using VOSviewer software.

presents the collaborative network among institutions that published at least one article during the observation period. Of the 492 institutions identified, 31 (6.3%) have co-authored publications, forming four distinct clusters. Cluster 1 exhibits the highest degree of collaboration, with 12 institutions, while Cluster 4 has the lowest, with 5 institutions.

Figure 3 presents a network visualization of institutional co-authorship, with each of the four clusters represented by a distinct color. In Cluster 1 (red), all member institutions have 12 co-authorships. Russian State Social University has the highest number of co-authorships (13) in Cluster 2 (green). In Cluster 3 (blue), Plekhanov Russian University of Economics leads with six co-authorships. Finally, in Cluster 4 (yellow), State University of Management has the highest number of co-authorships (4).

• Geographic Distribution of Research:

Table 9. Countries/regions of author affiliation.

R	Countries/Regions	NP	Tt (%)	TC-TD	PC-TD	h-TD
1	People’s Republic of China (peoples r china)	49	18,421%	325	6.63	11
2	Russia (russia)	30	11,278%	131	4.37	5
3	Vietnam (vietnam)	18	6,767%	100	5.56	4
4	Italy (italy)	15	5,639%	171	11.40	6
5	Ukraine (ukraine)	14	5,263%	41	2.93	5
6	Germany (germany)	12	4,511%	138	11.50	6
7	Romania (romania)	11	4,135%	76	6.91	6
8	Spain (spain)	9	3,383%	65	7.22	5
9	Brazil (brazil)	8	3,008%	60	7.50	3
10	Portugal (portugal)	8	3,008%	45	5.63	2

Note: R = Country Rank; NP = Number of Publications; TC-TD = Total Citations in Dataset; PC-TD = Average Citations per Publication in Dataset. Source: Authors’ elaboration.

presents the 10 most productive countries, defined as those with more than eight publications in the dataset. These 10 countries account for 60.15% of the total publications across 65 countries. Collectively, they have an h-index of 19, with an average of 6.53 citations per publication and a total of 1.044 citations (53.9% of all citations). China is the most productive country (49 publications) and the most influential in terms of total citations (325), also exhibiting the highest h-index (11). However, Germany has the highest average number of citations per publication (11.5).

Table 10. Clusters of country co-authorship.

Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5	Cluster 6	Cluster 7	Cluster 8	Cluster 9
India	Denmark	Chile	Brazil	Australia	Estonia	Egypt	Iraq	People's Republic of China
Indonesia	Hungary	Czech Republic	Canada	Croatia	Finland	France	Russia	Singapore
Latvia	Iceland	Italy	Iran	England	Germany	Slovenia	Spain	South Korea
Iithuania	Ireland	Japan	Netherlands	Poland	Sweden	United Arab Emirates	Turkey
Malaysia	Romania	Scotland	New Zealand	Ukraine	Switzerland
Pakistan	Serbia	Thailand	Portugal
Saudi Arabia	Slovakia	United States
Taiwan
Vietnam

Authors’ elaboration.

presents a co-authorship network analysis, identifying countries with at least one publication co-authored with another country. The analysis includes 56 countries grouped into nine clusters. Cluster 1 exhibits the highest degree of collaboration, comprising nine countries. Conversely, Cluster 9 has the lowest, with only three countries. Cluster 4 also shows a relatively low level of collaboration, with five countries. Clusters 2 and 3, as well as Clusters 5 and 6, and Clusters 7 and 8, each have the same number of countries.

Figure 4 presents a network visualization of co-authorship among countries, with each cluster represented by a distinct color. In Cluster 1 (red), Indonesia has the highest number of co-authorships (8) and exhibits connections with most other clusters. Romania leads Cluster 2 (green) with eight co-authorships. Italy is the most collaborative country in Cluster 3 (blue) with nine co-authorships. Canada leads Cluster 4 (yellow) with six co-authorships. In Cluster 5 (purple), England and Ukraine are the most collaborative, each with four co-authorships. Germany leads Cluster 6 (light blue) with nine co-authorships. France is the most collaborative in Cluster 7 (orange) with five co-authorships. Russia leads Cluster 8 (brown) with nine co-authorships. Finally, China dominates Cluster 9 (light green) with 12 co-authorships.

• Keyword Analysis:

Table 11. Keyword co-occurrence clusters.

Cluster	Key Words
Cluster 1	Automation; Change Management; COVID-19; Digitalization; Digitization; Firm Performance; Human Resource Management; Industry 4.0-SMES-Sustainability.
Cluster 2	Artificial Intelligence; China; Digital Technologies-Digitalisation; Human Capital; Innovation; Labour Market; Russia.
Cluster 3	Barriers; Digital Economy; Digital Maturity-Digital Technology; Human Resources; Human Resources Management.
Cluster 4	0; Higher Education-Industry 4; Skills; Technology.
Cluster 5	Big Data; Human Resource; Sustainable Development; Vietnam.
Cluster 6	Digital Transformation; Technological Innovation.

Source: Web of Science data (2024).

presents the clusters of keywords based on their co-occurrence frequency. In Cluster 1 (red), “Digitalization” is the most frequent keyword (17 occurrences). In Cluster 2 (green), “Human Capital” is the most frequent (20 occurrences). “Human Resources” is the most frequent keyword in Cluster 3 (blue) with 14 occurrences. In Cluster 4 (yellow), the keyword “0” is the most frequent with 16 occurrences. This requires further investigation to understand its meaning in this context. In Cluster 5 (purple), “big data” is the most frequent keyword (9 occurrences). Finally, “digital transformation” is the most frequent keyword in Cluster 6 (light blue) with 32 occurrences.

Figure 5 presents a visualization of keyword co-occurrence, based on 977 keywords, of which 35 occur at least four times. The visualization comprises six clusters: Cluster 1 (red) contains 10 keywords; Cluster 2 (green) contains eight keywords; Cluster 3 (blue) contains six keywords; Cluster 4 (yellow) contains five keywords; Cluster 5 (purple) contains four keywords; and Cluster 6 (light blue) contains two keywords.

Table 12. Most frequent author keywords.

N°	Key Words	Occurrence
1	Digital Transformation	125
2	Human Capital	39
3	Digitalization	22
4	Digital Economy	22
5	0	20
6	Industry 4	15
7	Artificial Intelligence	15
8	Human Resources	13
9	Innovation	9
10	Industry 4.0	9

Source: Web of Science data (2024).

presents the 10 most frequently used author keywords. “Digital Transformation” appears most frequently (125 occurrences), followed by “Human Capital” and “Digitalization”. These three keywords highlight the key themes of this research area: human capital development and the processes of digitalization and digital transformation.

4. Discussion and Conclusions

Regarding the limitations of this study, it is acknowledged that bibliometrics, with its inherent focus on quantitative metrics, often overlooks qualitative contributions and nuanced interpretations of research quality. Consequently, future research may benefit from exploring systematic reviews of the literature, which would facilitate an objective and systematic integration of empirical study results to determine the current state of knowledge within a specific research domain (Cabrerizo et al., 2010).

Furthermore, the Web of Science (WoS) has historically demonstrated a pronounced focus and robust coverage in the domains of natural sciences, engineering, and biomedical research. This has resulted in the underrepresentation of the social sciences and humanities (SSH) within WoS coverage. Notably, certain disciplines within the humanities rely heavily on formats such as books and book chapters, which are not as comprehensively indexed by WoS. Similarly, the breadth and depth of thematic coverage for journals in SSH may be comparatively limited within this database (Garfield, 2002). Additionally, WoS coverage exhibits a clear dominance of English-language journals, which are overrepresented at the expense of other languages. This linguistic bias may disadvantage research produced in non-English speaking regions and distort the representation of global research output (Ammon, 2001). Moreover, both WoS and Scopus present accuracy issues concerning the assignment of corresponding authorship. Inaccurately indexed author names or affiliations can result in the erroneous attribution of publications and citations, potentially skewing metrics such as the h-index or institutional rankings. Finally, a systematic increase in the number of citations over time is observed due to the expanding length of reference lists. Consequently, recently published works may accumulate a higher number of citations simply due to the increased volume of publications and citations, rather than an inherently greater impact compared to older works (Leydesdorff & Ivanova, 2021).

The concentration of citations among a limited number of authors may inadvertently exclude valuable perspectives. Therefore, it is essential to promote the dissemination of less visible works through increased methodological rigor, content specificity, or publication in lower-impact journals, as suggested by Chawla and Goyal (2022). Furthermore, given this concentration of influence, the integration of junior researchers and the incorporation of less explored geographical contexts are crucial. The significant influence of certain institutions raises questions about their collaboration networks and resource availability, warranting further investigation. Similarly, the prominent role of certain countries necessitates a deeper analysis of the impact of public policies on digital transformation (DT) research.

This research delves into the intersection of DT and human resources (HR), revealing significant theoretical and practical contributions to the field. Although the observed exponential growth in publications is encouraging, it does not guarantee practical application within organizations, particularly in developing regions. This growth aligns with previous findings that highlight the positive impacts of DT on profits, cost reduction, and efficiency (Hanelt et al., 2021; Tomaszewski, 2021). This study reveals limited participation by Spanish-speaking authors in DT and HR research, suggesting a need for increased theoretical and practical development in Spanish-speaking contexts. This research contributes by summarizing existing theoretical and practical advancements, enabling an analysis of DT challenges within the social, economic, and political context of these regions. The incorporation of data from additional databases could provide a more comprehensive representation of Spanish-language research on DT.

From a theoretical perspective, this study analyzes the development and evolution of the specialized literature. In practical terms, it highlights the strategic role of digital competencies as key facilitators in DT processes. This approach enables the understanding of how the implementation of change management plans, aimed at aligning organizational culture with strategic objectives of innovation and agility, is directly influenced by these competencies.

The analysis of publication trends reveals a growing interest in the role of soft skills within DT-driven change processes, as well as the importance of digital competencies for successful adoption of new technologies. The most influential studies provide practical guidelines to facilitate these processes, emphasizing the relevance of individual, group, and organizational factors from a systemic perspective, and identifying existing research gaps related to employee-related factors. Among the most prominent journals identified in this study are Sustainability, Frontiers in Psychology, and Finance Research Letters.

In the global context, countries with higher scientific production in this area tend to have strategies that link public policies with technology-based productive efficiency, reflected in higher R&D spending. These countries, characterized by their focus on innovation and the production of high value-added goods and services, perceive DT as a competitive advantage. Scientific collaboration networks, predominantly among industrialized countries, reflect this concentration of efforts and resources.

The findings of this research have significant practical implications for organizations. Firstly, the identification of the most relevant digital competencies enables the design of specific training programs for employees, optimizing the DT process. These plans seek to align organizational culture with the strategic objectives of innovation and agility, essential elements in the current organizational context. Secondly, this study highlights the need to develop a validated psychometric instrument to measure the development of digital competencies in Spanish-speaking contexts, overcoming the limitations of existing qualitative assessments.

Finally, the literature review provides guidance for articulating DT processes with a focus on aligning personnel competencies with organizational strategy. Additionally, it emphasizes the importance of public policies that promote research and knowledge transfer, driving efficiency and the retraining of human capabilities in both the public and private sectors. It is recommended that future research explore the impact of DT on various dimensions of work–life quality, such as wage compensation, occupational safety and health, interpersonal relationships, work climate, workload, and technostress (Wang et al., 2023). Furthermore, the findings of this study should be interpreted considering the temporal framework of its analysis, given the dynamic nature of this field.

Organizations can utilize the findings of this study to identify the most relevant digital competencies for their needs and to design specific training programs to develop these competencies in their employees. The findings of this study should be interpreted in consideration of the analyzed timeframe, given that the field of digital transformation and human resources is in constant evolution.