INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Mapping Review onAI, Employee Experience, and Work Performance:A  
BibliometricAnalysis  
Rimpy Singhwal, Ipshita Bansal  
Department of management studies, Bhagat Phool Singh Mahila Vishwavidyalaya, Sonipat, Haryana,  
India  
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500113  
Received: 01 May 2026; Accepted: 06 May 2026; Published: 06 June 2026  
ABSTRACT  
This bibliometric review examines the evolving relationship between artificial intelligence (AI), employee  
experience (EX), and work performance (WP) in organisational contexts from 2000 to 2025. As AI continues to  
transform human resource management practices, increasing scholarly attention has been directed toward  
understanding its impact on employee-centric outcomes and organisational effectiveness. The study analyses  
373 peer-reviewed articles indexed in the Web of Science database, selected through the PRISMA screening  
process. Bibliometric and science mapping techniques were employed using Biblioshiny and VOSviewer to  
evaluate publication trends, leading authors and institutions, geographic contributions, keyword co-occurrence,  
and collaboration networks. The findings reveal a significant surge in research output after 2020, indicating  
heightened academic and practical interest in AI-driven HRM. Key themes identified include AI-enabled  
decision-making, employee engagement, performance enhancement, and organisational productivity. More  
recent studies increasingly focus on ethical considerations, transparency, employee well-being, and inclusivity  
in AI applications. This study offers a novel contribution by integrating AI, employee experience, and work  
performance into a single analytical framework, an area that has received limited systematic exploration. By  
mapping thematic evolution over 25 years, it highlights a clear shift toward human-centred and sustainability-  
oriented perspectives in AI research. Additionally, the study uncovers collaboration patterns and conceptual  
developments, providing valuable insights for future research directions in the field.  
Keywords: Artificial Intelligence (AI), Employee Experience (EX), Work Performance (WP), Human Resource  
Management (HRM), Bibliometric Review.  
INTRODUCTION  
The contemporary workplace is undergoing a profound transformation driven by rapid advances in Artificial  
Intelligence (AI), which are reshaping how work is designed, performed, and evaluated across organisational  
contexts (Tambe et al., 2019). Unlike earlier waves of digitalisation that focused primarily on automation and  
efficiency, AI technologies increasingly interact with human judgment, decision-making, and emotional labour,  
thereby influencing the very experience of work itself (Budhwar et al., 2023). As organisations adopt machine  
learning, people analytics, and generative AI tools, employees are no longer passive recipients of technology but  
active participants in human-machine systems that redefine roles and expectations (Bessen, 2019).  
This shift has elevated employee experience from a peripheral concern to a strategic priority, particularly in  
technology-intensive work environments (Malik et al., 2023). Employee experience reflects how individuals  
perceive, interpret, and emotionally respond to their interactions with organisational systems, including AI-  
enabled processes (Braganza et al., 2021). When AI systems are integrated into performance management,  
recruitment, or workflow coordination, they shape not only outcomes but also employees’ sense of fairness,  
autonomy, and trust (Hughes et al., 2019). These experiential dimensions are increasingly recognised as critical  
determinants of sustainable work performance in AI-mediated contexts (Cheng et al., 2022).  
Page 1451  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
At the same time, work performance remains a central organisational concern driving AI adoption, as firms seek  
to enhance productivity, accuracy, and decision quality (Ramachandran et al., 2022). Empirical studies suggest  
that AI can support employees by automating routine tasks and providing data-driven insights, thereby enhancing  
performance at both individual and team levels (Tong et al., 2021). However, performance gains are neither  
automatic nor uniform; they depend heavily on how employees perceive and adapt to AI systems within their  
work environment (Man Tang et al., 2022).  
Theoretical Perspectives Informing the Nexus  
Several theoretical lenses help explain the complex relationship between AI, employee experience, and  
performance. Complementarity theory emphasises that AI contributes positively to performance when it aligns  
with human skills and task requirements, reinforcing employees’sense of usefulness and competence (Man Tang  
et al., 2022). Role theory further explains how AI alters role expectations and responsibilities, influencing role  
clarity and emotional responses at work (Arslan et al., 2022).  
Psychological contract theory highlights how AI adoption reshapes perceived obligations between employees  
and organisations, affecting trust, commitment, and engagement (Braganza et al., 2021). From a socio-technical  
systems perspective, AI outcomes are understood as products of interaction between technology, organisational  
structures, and human agency rather than technology alone (Bag et al., 2021).  
Insights from Prior Research  
Existing research on artificial intelligence and work performance has largely emphasised efficiency, productivity,  
and task optimisation, presenting AI as a tool that augments human capability and improves decision quality  
(Dixon et al., 2021; Tong et al., 2021). While these studies demonstrate measurable performance gains, they also  
indicate that outcomes depend on how AI systems are introduced and perceived by employees, particularly in  
contexts where transparency and autonomy are limited (Cheng et al., 2022).  
In parallel, research on employee experience highlights the emotional and psychological implications of AI  
adoption, documenting both positive outcomes, such as enhanced engagement and negative responses, including  
anxiety, burnout, and perceptions of surveillance (Kong et al., 2021; Giermindl et al., 2022). Comparative  
evidence increasingly suggests that employee experience mediates the relationship between AI adoption and  
work performance, yet prior reviews often examine these dynamics in isolation, limiting theoretical integration  
and cumulative insight (Malik et al., 2023; Panda et al., 2025).  
Why a Review of the Nexus of Employee Experience, Work Performance, and AI Is Necessary  
Although research on artificial intelligence in the workplace has expanded rapidly, the literature remains  
conceptually fragmented across disciplines and research traditions (Vrontis et al., 2023). Studies in human  
resource management predominantly emphasise AI applications and functional efficiency, often giving limited  
attention to how these technologies are experienced by employees in their day-to-day work (Arora et al., 2024).  
In contrast, employee experience research foregrounds engagement, trust, and well-being but frequently treats  
AI as a contextual backdrop rather than a central explanatory mechanism (Özmen & Gökhan, 2024).  
While existing systematic reviews have advanced understanding of AI-related workplace outcomes, their  
qualitative orientation constrains insight into broader research patterns and intellectual linkages (Pereira et al.,  
2023). Bibliometric approaches offer a complementary perspective by mapping influential contributions and  
thematic evolution; however, prior analyses rarely integrate employee experience and work performance within  
a unified framework, despite evidence that experiential responses critically shape performance in AI-enabled  
environments (Mathushan et al., 2023; Cheng et al., 2022; Soulami et al., 2024).  
Scope and Focus of the Present Review  
This bibliometric review examines research addressing the interconnected relationship between artificial  
intelligence, employee experience, and work performance across organisational contexts. It includes conceptual  
and empirical studies that explore AI-enabled HRM systems, human–AI interaction, employee perceptions of  
Page 1452  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
intelligent technologies, and performance-related outcomes at individual and collective levels (Arslan et al.,  
2022; Qamar et al., 2021). Rather than treating technology, experience, and performance as isolated constructs,  
the review adopts an integrative perspective that reflects the realities of AI-mediated work environments.  
By mapping publication trends, citation relationships, and thematic development, the review seeks to identify  
influential contributions and emerging research trajectories shaping this field (Kaushal et al., 2023). Particular  
attention is given to how experiential dimensions such as engagement, trust, and fairness are linked to  
performance outcomes in AI-enabled settings, as well as to variations in how work performance is conceptualised  
across studies (Braganza et al., 2021; Ramachandran et al., 2022; Arora et al., 2024).  
Research Gap and Contribution  
Although the literature on AI in the workplace has grown rapidly, a lack of integrated understanding remains  
regarding how employee experience and work performance are jointly shaped by AI adoption (Arora et al.,  
2024). Existing bibliometric reviews focus predominantly on technological applications or HR functions,  
offering limited insight into experiential and performance-related dynamics (Kaushal et al., 2023). Moreover,  
theoretical perspectives are often applied selectively, hindering cumulative theory building (Soulami et al.,  
2024).  
This study addresses these gaps by providing a bibliometric synthesis of research at the nexus of employee  
experience, work performance, and artificial intelligence by exploring the following questions to gain a deeper  
understanding of the connections between them.  
Q1. What are the Keyword Co-occurrence Network, Co-authorship Network, seminal trends, and highly cited  
works that characterise the scholarly discourse on artificial intelligence, employee experience, and work  
performance?  
Q2. Which prolific authors, institutions, countries, and publication outlets have made the most significant  
contributions to advancing knowledge in this domain?  
RESEARCH METHODOLOGY  
This study employs a comprehensive bibliometric review to map and analyse the academic discourse on artificial  
intelligence, employee experience, and work performance. The Preferred Reporting Items for Systematic  
Reviews and Meta-Analyses (PRISMA) protocol was employed for the identification, screening, and selection  
of relevant publications and to ensure transparency and replicability. Bibliometric analysis was subsequently  
conducted using VOSviewer, which facilitates co-authorship, co-occurrence, citation analysis, clustering, and  
visualisation, and Biblioshiny (an R package with a Bibliometric interface), which assists researchers in mapping  
collaborations, themes, and significant works via interactive bibliometric networks to combine descriptive and  
science mapping techniques.  
Data Sources  
A prominent and influential bibliographic database, Web of Science (WoS), was chosen for data extraction  
(Mathushan et al., 2023). Data extraction was performed on September 29, 2025, on the bases of year (2000-  
2025) with the assistance of a search string from the database by using these keywords: - (“Artificial Intelligence”  
OR “AI”) AND (“Human Resource Management” OR “HRM” OR “Human Resource” OR “Talent  
Management” OR “Employee Management”) AND (“Employee Experience” OR “Employee Engagement” OR  
“Workplace Experience” OR “Employee satisfaction”) AND (“Work Performance” OR “Job Performance” OR  
“Employee Performance” OR “Task Performance”). The base of the years of paper selection, 2000-2025, was  
chosen because papers related to this theme were not available before the year 2000 on the WOS database. For  
inclusion and exclusion criteria, we utilised the PRISMA model.  
Page 1453  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Data Filtering and Screening  
The Figure 1 PRISMA approach is a recognised and rigorous methodology designed to systematically identify,  
select, and evaluate pertinent literature, therefore enhancing the accuracy and reproducibility of the review  
process (Al Naqbi et al., 2024). The model is usually illustrated using the PRISMA flow diagram, which outlines  
four primary stages: Identification, Screening, Eligibility, and Inclusion. During phase 1 of identification, 1,417  
records were obtained from the Web of Science database. After removing 458 entries that were outside the chosen  
time period, 959 records remained for screening. 942 reports were considered for further retrieval after removing  
17 reports based on document type (only articles and review papers were considered). Book chapters, books,  
notes, editorials, and book series were excluded. Only English language studies were considered. After removing  
569 documents based on subject area, the final review contained 373 studies, which served as the foundational  
dataset for interpretation and analysis.  
Source: https://www.prisma-statement.org/prisma-2020-flow-diagram  
Figure 1: PRISMA Flow Diagram  
Analysis and findings  
This study employs descriptive analysis to examine publishing and citation trends, as well as the leading authors,  
journals, institutions, and nations. Scientific mapping is utilised to investigate co-authorship networks, keyword  
co-occurrence, thematic structures, and patterns of collaboration.  
Descriptive Analysis  
Table 1 shows an overview of the bibliometric dataset from 2000 to 2025. A total of 330 records were retrieved  
from 111 sources, with an annual growth rate of 22.72%. The documents are 1.71 years old on average, and each  
publication receives around 31.75 citations. In terms of content, the dataset contains 778 Keywords Plus and  
1126 author keywords provided by 1,025 authors. 23 authors created single-authored works, and a single author  
wrote 24 documents. Each document had an average of 3.58 co-authors, with international collaborations for  
47.88%. Articles (296) dominated the collection, with reviews (34) following closely behind.  
Page 1454  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Table 1: Overview of data.  
Description  
Result  
Main Information About Data  
Timespan  
2000:2025  
111  
Sources (Journals)  
Documents  
330  
Annual Growth Rate %  
Document Average Age  
Average citations per doc  
References  
22.72  
1.71  
31.75  
21538  
Document Contents  
Keywords Plus (ID)  
Author's Keywords (DE)  
Authors  
778  
1126  
Authors  
1025  
23  
Authors of single-authored docs  
Authors Collaboration  
Single-authored docs  
Co-Authors per Doc  
International co-authorships %  
Document Types  
Article  
24  
3.58  
47.88  
238  
58  
30  
4
article; early access  
Review  
review; early access  
Source: - Biblioshiny  
Figure 2 shows the annual scientific production trend from 2000 to 2025. There is no publication between 2000  
to 2002. From 2002, Research output remained quite low and constant until roughly 2018. A substantial boom  
occurred beginning in 2020, with publications peaking dramatically in 2024 and 2025, showing increased  
Page 1455  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
scholarly attention to the topic during this time period. Overall, the trend shows an exponential increase in  
scientific contributions, notably in recent years.  
Source: Biblioshiny  
Figure 2: Publication Trend Year-wise  
Figure 3 presents the use of Bradford's Law to identify the key journals that contribute to the field. The  
distribution reveals that a small number of journals account for the majority of publications, known as "core  
sources," while the remaining journals contribute fewer articles in a dispersed way. Journals such as Personnel  
Review, International Journal of Human Resource Management, Human Resource Management, and Human  
Resource Management Review stand out as the most productive, publishing a disproportionately large number  
of publications compared to others. This emphasises the concentration of information transmission in a few  
significant sources, in line with Bradford's Law, which emphasises the uneven distribution of scientific output  
across journals.  
Source: Biblioshiny  
Figure 3: Core Sources by Bradford’s Law in the AI in HRM Domain  
The top 15 highly cited journals are listed in Table 2, with the Human Resource Management Review (1,436  
citations) and the International Journal of Human Resource Management (1,010 citations) standing out as the  
most influential due to their key roles and ongoing contributions to the field of human resource management  
research. With 862 citations, Technological Forecasting and Social Change ranks third, emphasising the  
importance of social and technological perspectives in the field. The Human Resource Management Journal and  
Page 1456  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
the International Journal of Manpower, with 542 and 525 citations respectively, are additional notable sources  
that demonstrate significant scholarly impact. Despite having only one publication each, California Management  
Review and Strategic Management Journal are among the most cited sources, illustrating the substantial  
influence of their individual articles. Overall, the data reveal that most human resource management research is  
published in specialised HR and management journals, with a few multidisciplinary publications receiving  
significant citations.  
Table 2: Top 15 Highly Cited Sources  
No.  
Sources  
Total Citation  
Total  
Publications  
1
Human Resource Management Review  
International Journal of Human Resource Management  
Technological Forecasting and Social Change  
Human Resource Management Journal  
International Journal of Manpower  
International Journal of Contemporary Hospitality Management  
California Management Review  
1436  
1010  
862  
542  
525  
471  
468  
405  
244  
243  
241  
239  
238  
206  
191  
14  
16  
12  
8
2
3
4
5
7
6
7
7
1
8
Human Resource Management  
14  
8
9
Journal of Business Research  
10  
11  
12  
13  
14  
15  
Journal of Hospitality Marketing & Management  
Journal of Innovation & Knowledge  
Technology In Society  
3
9
7
Management Decision  
9
Strategic Management Journal  
1
Management Science  
2
Source: Author’s Own Work  
Table 3 displays the top 10 most prolific authors based on their publications in this field. Malik A is the leading  
author with 16 publications and 917 citations, making him the most significant in this area. Budhwar P follows  
with 12 publications and 871 citations, showing ongoing academic effort. Authors such as Prikshat V and Kumar  
S contribute with 6 articles each. Chowdhury S, Dutta D, and Nguyen M each have 6 publications, while Pereira  
V, Patel P, and Varma A each have 4. Pereira V has the highest number of citations (995) among them. Overall,  
the data indicate that, despite differences in publication frequency, productivity, and citation impact are key  
indicators of research importance in this field.  
Table 3: Top 10 Most prolific Authors as per Documents published  
NO.  
1
Author  
Total Citation  
Total Publication  
Malik A  
917  
871  
198  
92  
16  
12  
6
2
Budhwar P  
Prikshat V  
Kumar S  
3
4
6
Page 1457  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
5
Chowdhury S  
Dutta D  
691  
81  
5
5
5
4
4
4
6
7
Nguyen M  
Pereira V  
Patel P  
28  
8
995  
72  
9
10  
Varma A  
315  
Source: Author’s Own Work  
Figure 4 illustrates the publishing trends of the five top universities from 2000 to 2025, showing that research  
productivity increased significantly after 2020. Aston University and the Indian Institute of Management (IIM)  
system both exhibit significant research activity in recent years, with the latter showing the fastest increase and  
becoming the most prolific contributor by 2025. While the University System of Ohio displays a more modest  
and stable pattern of output, the University of Newcastle and the University System of Georgia show constant  
rising patterns, showing consistent intellectual effort. Overall, the chart shows a noticeable increase in  
institutional research output after 2020, indicating growing scholarly interest and cooperation in the area.  
Source: Biblioshiny  
Figure 4: Affiliations based on production over time  
Table 4 presents the total citations and research productivity of various countries in AI research within HRM.  
France has the highest total citations (1391) and a strong average citation rate (86.9) across 120 papers, despite  
its modest output, highlighting its significant influence in the field. The United States and the United Kingdom  
follow, with 1371 and 1350 citations, respectively. Their consistent contributions are evident in their impressive  
publication counts (500 and 284). Cyprus, with a small research output, produces high-quality work, as shown  
by its notable citation impact (average of 252 per article) from only 12 publications. In contrast, China and India,  
with more publications (325 and 143, respectively), have lower average citations, indicating a broader but less  
impactful publication trend. Finland, Sweden, and Switzerland demonstrate strong average citation rates (79.6,  
76.0, and 70.7), reflecting high-quality academic output despite fewer publications. Overall, the data show that  
research quantity and citation impact vary significantly between countries, with some exerting greater influence  
despite fewer publications.  
Page 1458  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Table 4: Top 15 Most Cited Countries  
Country  
Total Citation  
Average Article  
Citations  
Total Publications  
France  
1391  
1371  
1350  
944  
940  
756  
571  
522  
398  
284  
222  
212  
209  
152  
124  
86.90  
24.90  
46.60  
35.00  
18.80  
252.00  
31.70  
30.70  
79.60  
35.50  
11.70  
70.70  
52.20  
76.00  
20.70  
120  
500  
284  
193  
325  
12  
USA  
United Kingdom  
Australia  
China  
Cyprus  
Germany  
Italy  
95  
114  
27  
Finland  
Netherlands  
India  
71  
143  
28  
Switzerland  
Canada  
41  
Sweden  
29  
Malaysia  
Source: Author’s own work  
42  
Table 5 highlights the rapid growth and scholarly significance by showcasing the top 15 highly cited works on  
AI and HRM. Tambe et al. (2019) and Vrontis et al. (2022) list important challenges and frameworks in AI-HRM  
integration, following Bag et al. (2021), the most referenced article, which focuses on AI adoption and  
sustainability. Recent research by Budhwar et al. (2023) and Chowdhury et al. (2023) indicates a growing interest  
in generative AI and how it affects HR procedures. Overall, the table highlights the dominance of top HR  
publications and the growing scholarly interest in HRM research powered by AI.  
Table 5: Top 15 Highly Cited Documents  
Rank  
1
Title  
Author  
Journal  
Total  
Citation  
Role of institutional pressures and  
resources in the adoption of big  
data analytics-powered artificial  
intelligence, sustainable  
Bag et al., (2021)  
Technological Forecasting  
and Social Change  
488  
manufacturing practices, and  
circular economy capabilities  
Page 1459  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
2
3
Artificial Intelligence in Human  
Resources Management:  
Challenges and a Path Forward  
Tambe et al.,  
(2019)  
California Management  
Review  
468  
450  
Artificial intelligence, robotics,  
advanced technologies and human  
resource management: a  
Vrontis et al.,  
(2022)  
The International Journal of  
Human Resource  
Management  
systematic review  
4
5
6
7
Unlocking the value of artificial  
intelligence in human resource  
management through AI capability  
framework  
Chowdhury et al.,  
(2023)  
Human Resource  
Management Review  
312  
292  
208  
206  
Human resource management in  
the age of generative artificial  
intelligence: Perspectives and  
research directions on ChatGPT  
Budhwar et al.,  
(2023)  
Human Resource  
Management Journal  
A systematic literature review on  
the impact of artificial intelligence  
on workplace outcomes: A multi-  
process perspective  
Pereira et al.,  
(2023)  
Human Resource  
Management Review  
The Janus face of artificial  
intelligence feedback: Deployment  
versus disclosure effects on  
employee performance  
Tong et al., (2021) Strategic Management  
Journal  
8
Influences of artificial intelligence  
(AI) awareness on career  
competency and job burnout  
Kong et al.,  
(2021)  
International Journal of  
Contemporary Hospitality  
Management  
204  
189  
9
The Robot Revolution: Managerial Dixon et al.,  
Management Science  
and Employment Consequences  
for Firms  
(2021)  
10  
Productive employment and  
decent work: The impact of AI  
adoption on psychological  
contracts, job engagement and  
employee trust  
Braganza et al.,  
(2021)  
Journal of Business Research 186  
11  
The adoption of artificial  
intelligence in employee  
recruitment: The influence of  
contextual factors  
Pan et al., (2022)  
Artificial intelligence and  
international HRM  
178  
12  
13  
Impact of artificial intelligence on  
employees working in industry 4.0  
led organizations  
Malik et al.,  
(2022)  
International Journal of  
Manpower  
161  
154  
150  
Emotional intelligence or artificial  
intelligence–an employee  
perspective  
Prentice et al.,  
(2020)  
Journal of Hospitality  
Marketing & Management  
14  
When conscientious employees  
meet intelligent machines: An  
Tang et al., (2022)  
Academy of Management  
Journal  
Page 1460  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
integrative approach inspired by  
complementarity theory and role  
theory  
15  
The dark sides of people analytics:  
reviewing the perils for  
Giermindl et al.,  
(2022)  
European Journal of  
Information Systems  
133  
organisations and employees  
Source: Author's own work  
Scientific Mapping  
Figure 5 illustrates a co-occurrence network visualization that presents the conceptual framework and new  
research topics linking human resource management and artificial intelligence. Human resource management  
and artificial intelligence are the two largest and most central nodes, indicating their frequent co-occurrence and  
dominant roles, signifying that AI-driven techniques are increasingly influencing HRM research and practices.  
Digital transformation, talent management, technology adoption, and machine learning are closely related terms  
that describe how HR operations incorporate new technology. The human and ethical aspects of AI in the  
workplace appear to be gaining more attention, as seen by emerging terms like employee experience, AI ethics,  
and emotional intelligence. The colour gradient (from blue to yellow) shows how research has evolved. The  
yellow-green nodes represent more recent topics (2024–2025), indicating that scholars are now more interested  
in employee empowerment, AI literacy, and strategic HR transformation. This map demonstrates that AI–HRM  
research is constantly evolving and involves many different fields. It also shows how it is shifting from a focus  
on technology to a focus on people and strategy.  
Source: VOSviewer  
Figure 5: The co-occurrence network of keywords in the domain of AI in HRM  
Figure 6 illustrates the co-citation network of authors within the research area linking artificial intelligence (AI)  
and human resource management (HRM). The connections indicate the frequency with which two authors are  
cited together, reflecting their mutual influence and shared topics that connect them. The nodes represent the  
authors. The network displays three main categories of scholarly concepts. Well-known writers such as Malik  
Page 1461  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
A., Tambe P., Chowdhury S., and Pereira V. belong to the green cluster, which focuses on the potential  
applications of AI in HRM and evolving organisations. The red cluster, including individuals like Brynjolfsson  
E and Davenport T H, explores the technological and economic impacts of AI and digital innovation on  
organisations. The blue cluster highlights significant contributions to performance evaluation, management, and  
innovation that advance AI–HRM research, featuring Podsakoff P M, Fornell C, and Teece D J. The connections  
among these clusters illustrate the growing integration of technological, strategic, and managerial perspectives,  
demonstrating how AI-HRM research draws upon a diverse range of important yet interconnected scholarly  
work.  
Source: VOSviewer  
Figure 6: Co-Citation Network of Authors  
Figure 7 shows that, based on topical importance and development, this thematic map illustrates the conceptual  
framework of research at the intersection of artificial intelligence and human resource management. The upper-  
right quadrant (Motor Themes) features highly developed and significant themes, such as generative artificial  
intelligence (GenAI), HR analytics, human resource management, and artificial intelligence. These areas are  
advancing progress in the field. The lower-right quadrant (Basic Themes), which underpins the AI-HRM study,  
encompasses core yet less developed themes, including job happiness, emotional intelligence, employee  
performance, and employee engagement. The upper-left quadrant (Niche Themes) contains specialised but less  
interconnected fields such as algorithmic HRM, algorithmic bias, discrimination, technology adoption, and  
digital transformation, indicating focused but limited links with mainstream research. The Niche topics and  
fading themes are where knowledge is exchanged. Lastly, the lower-left quadrant (Emerging or Declining  
Themes) features AI identity, resilience, and work performance—either emerging areas or topics receiving less  
scholarly attention. Overall, the map reveals that AI and HRM are the most prominent and evolving concerns,  
reflecting growing academic interest in integrating ethical, technological, and human-centred aspects within the  
HR framework.  
Page 1462  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Source: Biblioshiny  
Figure 7: The Thematic Map according to development degree and relevance degree  
Figure 8 shows a map illustrating how different countries are shaded in various shades of blue, with darker tones  
indicating higher levels of academic collaboration and research output. The strength and variety of these  
international connections are reflected by the density and direction of the lines linking countries, which represent  
co-authorship links or collaborative research efforts. The chart clearly demonstrates that countries with strong  
international ties and key collaboration hubs include the US, UK, China, and Australia. Because they frequently  
collaborate with a diverse range of countries across continents, these nations are vital to the development of  
international research. Other main contributors include Europe (Germany, France, Italy), Japan, Canada, and  
India, suggesting that high-income countries mainly dominate international research networks. This map  
highlights the importance of international cooperation in scholarly work. Collaborative research not only  
enhances the visibility and impact of scientific efforts but also promotes innovation, capacity building, and  
knowledge sharing. Since areas with lower engagement (shown in grey or light blue) often struggle to connect  
with international research networks, these patterns may reveal disparities in research funding and infrastructure.  
Overall, this figure emphasises the interconnectedness of modern scientific research and the growing importance  
of cross-border academic collaborations.  
Page 1463  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Source: Biblioshiny  
Figure 8: The Country Collaboration Map  
Figure 9 shows a network visualisation that depicts patterns of institutional collaboration between universities  
and higher education systems. The links between each node, representing a university or university system,  
indicate co-authorship or academic collaboration relationships. The colour-coded clusters represent various  
regional affiliations or cooperative groupings. Strong UK-Australian interactions are shown in the orange cluster,  
which is centred on Aston University, the University of London, and the University of Reading. Indian  
management institutions are expanding globally, as evidenced by the red cluster, which is headed by the Indian  
Institute of Management (IIM system) and displays active connections with Asian and worldwide universities.  
The University System of Georgia, Texas A&M University System, and University System of Ohio dominate  
the blue cluster, which reflects significant regional collaboration in the US. This implies that public university  
systems in the United States have a strong domestic academic network. The University of Illinois System and  
Pennsylvania State University are among the smaller but clearly identifiable collaborative organisations that  
make up the purple and pink clusters; these could be specialised research partnerships. Hong Kong Polytechnic  
University and other isolated nodes point to a lack of collaborative connections within this network.  
Source: Biblioshiny  
Figure 9: Collaboration network of universities  
Figure 10: The co-authorship network illustrates the collaborative links between researchers in a specific field.  
Each node represents an author, while the connecting lines show joint publications. The size of a node indicates  
Page 1464  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
how often an author collaborates or publishes. The network reveals several important clusters for collaboration.  
Among them, Ashish Malik and Pawan Budhwar stand out as key figures, showing strong connections with  
scholars such as Mai Nguyen, Parth Patel, and Arup Varma. Their links clearly demonstrate efforts to build and  
maintain international research collaborations. The Vijay Pereira group is a capable team likely interested in  
organisational or human resource management topics, based on connections to authors such as Bamber, Mustafa  
Ozbilgin, and Qijie Xiao, which suggests the presence of other active, cross-disciplinary research networks. The  
colour gradient depicts the publishing timeline, with more recent partnerships (in yellow) involving emerging  
academics, such as Nguyen Mai and Ebesam Abdullah Alzeby, and older partnerships (in blue and green) with  
Budhwar and Pereira. Centred on Budhwar and Malik, the network exemplifies a vibrant, cohesive academic  
community that promotes collaborative research across disciplines.  
Source: VOSviewer  
Figure 10: Co-authorship network analysis  
Figure 11 illustrates the intellectual connections between authors through a bibliographic coupling visualisation  
based on references shared across their works. Each node represents an author, and the lines connecting them  
indicate the extent of overlap in their cited references, reflecting conceptual or thematic closeness in research.  
As the most prominent author, Ashish Malik's work is linked to many other scholars throughout clusters and  
frequently shares references, as shown by the map. A dense network of closely related researchers, including  
Greg Bamber, Soumyadeb Chowdhury, Arup Varma, Geoffrey Wood, and Mustafa Özbilgin, surrounds Malik,  
demonstrating a strong intellectual foundation in organisational studies and human resource management.  
Recent links, highlighted in yellow-green tones, connect emerging contributors such as Nguyen Mai, Ritika  
Gugnal, and Ebesam Abdullah Alzeby, positioned on the periphery and indicating rising interest in the core ideas  
of the field. Meanwhile, clusters including David De Cremer, Michael Campion, and Boone Caroline represent  
different but interconnected research subdomains, possibly focusing on performance, behaviour, and leadership.  
Page 1465  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
Source: VOSviewer  
Figure 11: Bibliographic Coupling Analysis  
Limitations and Future Research Directions  
This bibliometric review provides a structured overview of research at the intersection of artificial intelligence,  
employee experience, and work performance; however, certain limitations should be acknowledged. The  
analysis is based exclusively on publications indexed in the Web of Science database and restricted to English-  
language journal articles and review papers. While this ensures data quality and consistency, it may exclude  
relevant studies published in other databases, languages, or formats, thereby limiting the comprehensiveness of  
the review. In addition, bibliometric methods rely on citation patterns and keyword relationships, which capture  
the intellectual structure of the field but do not account for the contextual depth or methodological rigour of  
individual studies. Consequently, nuanced organisational and employee-level dynamics associated with AI  
adoption may not be fully reflected.  
These limitations also offer directions for future research. Subsequent studies could extend this work by  
incorporating multiple databases and complementary review methods, such as systematic reviews or meta-  
analyses, to deepen analytical insight. Greater emphasis on empirical and longitudinal research is needed to  
examine how employee experience mediates or shapes work performance in AI-enabled environments over time.  
Future scholarship would also benefit from stronger theoretical integration, particularly through human-centred,  
ethical, and socio-technical perspectives, to inform sustainable and inclusive AI adoption in organisational  
contexts.  
CONCLUSION AND FINDINGS  
This bibliometric review offers a structured synthesis of research examining the relationship between artificial  
intelligence, employee experience, and work performance. The analysis reveals a pronounced growth in  
scholarly output after 2020, reflecting increasing academic and managerial interest in AI-driven HRM and  
workplace transformation. Findings highlight the dominance of HRM and management journals, the influence  
of a small group of prolific authors and institutions and the emergence of collaborative international research  
networks. Thematic mapping indicates a gradual shift from technology-focused discussions toward human-  
centred, ethical, and experiential perspectives, with employee experience increasingly positioned as a key  
mechanism linking AI adoption to performance outcomes. At the same time, the field remains conceptually  
fragmented, with limited theoretical integration across studies. By consolidating existing knowledge and  
Page 1466  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
identifying emerging themes, this review provides a clear foundation for future research aimed at developing  
sustainable, inclusive, and performance-oriented AI practices in organisational contexts.  
BIBLIOGRAPHY  
1. Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative  
artificial  
intelligence:  
A
comprehensive  
literature  
review.  
Sustainability,  
16(3),  
1166.  
https://doi.org/10.3390/su16031166  
2. Arora, M., Gupta, J., Mittal, A., & Prakash, A. (2024). A bibliometric review of artificial intelligence  
technologies in human resource management: An overview of research trends. Global Knowledge,  
Memory and Communication. https://doi.org/10.1108/GKMC-04-2024-0237  
3. Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2022). Artificial intelligence and human workers  
interaction at team level: A conceptual assessment of the challenges and potential HRM strategies.  
International Journal of Manpower, 43(1), 75–88. https://doi.org/10.1108/IJM-01-2021-0052  
4. Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and  
resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing  
practices and circular economy capabilities. Technological Forecasting and Social Change, 163,  
120420.https://doi.org/10.1016/j.techfore.2020.120420  
5. Bessen, J. (2019). AI and jobs: The role of demand (No. w24235). National Bureau of Economic  
Research. https://doi.org/10.3386/w24235  
6. Bhandari, A. (2024). Demystifying Organization Success: A Bibliometric Analysis and Future Research  
Agenda. FIIB Business Review, 15(1), 36-58. https://doi.org/10.1177/23197145231216861  
7. Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2021). Productive employment and decent work: The  
impact of AI adoption on psychological contracts, job engagement and employee trust. Journal of  
business research, 131, 485-494.https://doi.org/10.1016/j.jbusres.2020.08.018  
8. Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., ... & Varma, A. (2023).  
Human resource management in the age of generative artificial intelligence: Perspectives and research  
directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659.10.1111/1748-  
8583.12524  
9. Cheng, K. T., Chang, K., & Tai, H. W. (2022). AI boosts performance but affects employee emotions.  
Information Resources Management Journal, 35(1), 1–18. https://doi.org/10.4018/irmj.314220  
10. Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong,  
L. (2023). Unlocking the value of artificial intelligence in human resource management through AI  
capability  
framework. Human  
resource  
management  
review, 33(1),  
100899.https://doi.org/10.1016/j.hrmr.2022.100899  
11. Dastane, O., & Haba, H. F. (2023). The Landscape of Digital Natives Research: A Bibliometric and  
Science Mapping Analysis. FIIB Business Review, 0(0). https://doi.org/10.1177/23197145221137960  
12. Dixon, J., Hong, B., & Wu, L. (2021). The robot revolution: Managerial and employment consequences  
for firms. Management Science, 67(9), 5586–5605. https://doi.org/10.1287/mnsc.2020.3812  
13. Erixon, F. (2018). Recipe: The economic benefits of globalization for business and consumers.  
https://ecipe.org/publications/the-economic-benefits-of-globalization  
14. Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). The dark sides of  
people analytics: reviewing the perils for organisations and employees. European Journal of Information  
Systems, 31(3), 410-435.10.1080/0960085X.2021.1927213  
15. Grewal, D., Benoit, S., Noble, S. M., Guha, A., Ahlbom, C. P., & Nordfält, J. (2023). Leveraging in-store  
technology and AI: Increasing customer and employee efficiency and enhancing their experiences.  
Journal of Retailing, 99(4), 487–504. https://doi.org/10.1016/j.jretai.2023.10.002  
16. Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). Artificial intelligence, employee engagement,  
fairness, and job outcomes. In Managing Technology and Middle- and Low-skilled Employees (The  
Changing Context of Managing People) (pp. 61–68). Emerald Publishing. https://doi.org/10.1108/978-  
1-78973-077-720191005  
17. Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018). A conceptual artificial intelligence application  
framework  
in  
human  
resource  
management.  
ICEB  
2018  
Proceedings  
(pp.  
91).  
https://aisel.aisnet.org/iceb2018/9  
Page 1467  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026  
18. Kaushal, N., Kaurav, R. P. S., & Sivathanu, B. (2023). Artificial intelligence and HRM: Identifying future  
research agenda using systematic literature review and bibliometric analysis. Management Review  
Quarterly, 73, 455–493. https://doi.org/10.1007/s11301-021-00249-2  
19. Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence  
(AI) awareness on career competency and job burnout. International Journal of Contemporary  
Hospitality Management, 33(2), 717-734.https://doi.org/10.1108/IJCHM-07-2020-0789  
20. Malik, A., Budhwar, P., Mohan, H., & NR, S. (2023). Employee experience—the missing link for  
engaging employees: Insights from an MNE's AI-based HR ecosystem. Human Resource Management,  
62(1), 97–115. https://doi.org/10.1002/hrm.22133  
21. Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2022). Impact of artificial intelligence on employees  
working in industry 4.0 led organizations. International Journal of Manpower, 43(2), 334-  
354.https://doi.org/10.1108/IJM-03-2021-0173  
22. Man Tang, P., Koopman, J., McClean, S. T., Zhang, J. H., Li, C. H., De Cremer, D., ... & Ng, C. T. S.  
(2022). When conscientious employees meet intelligent machines: An integrative approach inspired by  
complementarity  
theory and role theory. Academy of  
Management  
journal, 65(3), 1019-  
1054.10.5465/amj.2020.1516  
23. Mathushan, P., Gamage, A. S., & Wachissara, V. (2023). Human resource management and artificial  
intelligence: A bibliometric exploration. Journal of Business Research and Insights (Former Vidyodaya  
Journal of Management), 9(1). https://doi.org/10.31357/vjm.v9iI.6370  
24. Özmen, Ö. M., & Gökhan, S. (2024). Examining studies on the relationship between employee  
experience and digital transformation through bibliometric analysis. International Social Sciences  
Studies Journal, 10(8). https://doi.org/10.5281/zenodo.13383676  
25. Panda, G., Aggarwal, S., Kaswan, M. S., & Dhillon, K. (2025). Artificial intelligence in agile human  
resource practices: Systematic literature review and bibliometric analysis. International Journal of Lean  
Six Sigma, 16(4), 918–945. https://doi.org/10.1108/IJLSS-07-2024-0159  
26. Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2023). The adoption of artificial intelligence in employee  
recruitment: The influence of contextual factors. In Artificial intelligence and international HRM (pp.  
60-82). Routledge 10.1080/09585192.2021.1879206  
27. Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on the  
impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource  
Management Review, 33(1), 100857.https://doi.org/10.1016/j.hrmr.2021.100857  
28. Prentice, C., Dominique Lopes, S., & Wang, X. (2020). Emotional intelligence or artificial intelligence–  
an  
employee  
perspective. Journal  
of  
Hospitality  
Marketing  
&
Management, 29(4),  
377-  
403.10.1080/19368623.2019.1647124  
29. Qamar, Y., Agrawal, R. K., Samad, T. A., & Jabbour, C. J. C. (2021). When technology meets people:  
The interplay of artificial intelligence and human resource management. Journal of Enterprise  
Information Management, 34(5), 1339–1370. https://doi.org/10.1108/JEIM-11-2020-0436  
30. Ramachandran, K. K., Mary, A. A. S., Hawladar, S., Asokk, D., Bhaskar, B., & Pitroda, J. R. (2022).  
Machine learning and role of artificial intelligence in optimizing work performance and employee  
behavior. Materials Today: Proceedings, 51, 2327–2331. https://doi.org/10.1016/j.matpr.2021.11.544  
31. Smith, A., & Anderson, J. (2020). AI, robotics, and the future of jobs. Pew Research Center.  
32. Soulami, M., Benchekroun, S., & Galiulina, A. (2024). Exploring how AI adoption in the workplace  
affects employees: A bibliometric and systematic review. Frontiers in Artificial Intelligence, 7, 1473872.  
https://doi.org/10.3389/frai.2024.1473872  
33. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management:  
Challenges  
and  
a
path  
forward. California  
management  
review, 61(4),  
15-  
42.https://doi.org/10.1177/0008125619867910  
34. Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback:  
Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9),  
1600–1631. https://doi.org/10.1002/smj.3322  
35. Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2023). Artificial  
intelligence, robotics, advanced technologies and human resource management: a systematic  
review. Artificial  
intelligence  
and  
international  
HRM,  
172-  
201.https://doi.org/10.1080/09585192.2020.1871398  
Page 1468  
www.rsisinternational.org