The DOI matches its Crossref record; 52 of the 54 references it cites were found in a registry or an index.
A study of Artificial Intelligence impacts on Human Resource Digitalization in Industry 4.0
Open accessAbstract
Artificial Intelligence (AI) has opened up tremendous opportunities in the workplace through robotics innovation, which envelops both AI and the Internet of Things (IoT). Precision, Efficiency, and Flexibility are considered the potential benefits of Industry 4.0. The implementation of Industry 4.0 requires a lot of changes, including the Human Resource (HR) function. In Industry 4.0, the HR capability is more critical and gives an upper hand to the organization. The HR capability should be more cautious and adaptable to adjust to the difficulties and requirements. We study the contributions of AI in HR digitalization and practices in Industry 4.0. 271 HR experts working in Information Technology (IT), Manufacturing, and administration are selected to participate in this review focusing on five AI applications in HR capability and three elements of HR readiness. The information collected was examined utilizing the Statistical Package for Social Sciences (SPSS) tool and Analysis of Moment Structures (AMOS). The results uncovered that hierarchical organization examination is a fundamental part of acquiring sustainable development. Adaptability and human asset capability are upheld by each of the five components of AI application areas of HR. Well-being and Safety improvement were viewed as vital components under the AI application in HR.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Digital technology enables more effective collection and analysis of employee data, providing deeper insights for better decision making.”
From Digital Transformation in Human Resource Management: A Systematic Literature Review (2023) · cites this paper as Murugesan et al., 2023
In the citing paper
“Research by Murugesan, et al. (2023) shows that manufacturing companies using data analytics in HR management can increase employee productivity through more data-driven decisions.”
From Digital Transformation in Human Resource Management: A Systematic Literature Review (2023) · cites this paper as Murugesan et al., 2023
Citations
Cites
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,882
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941
Principles and Practice of Structural Equation Modeling
Tabri, N., & Elliott, C. M. (2012) · Canadian Graduate Journal of Sociology and Criminology · doi:10.15353/cgjsc.v1i1.3787 · Cited by 39,094
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
A Review of Scale Development Practices in the Study of Organizations
Hinkin, T. R. (1995) · Journal of Management · doi:10.1177/014920639502100509 · Cited by 3,193
Jourdan, A., & Laffly, D. (2020) · doi:10.1002/9781119720492.ch8 · Cited by 3,170
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609
The Internet of Things—A survey of topics and trends
Whitmore, A., Agarwal, A., & Da Xu, L. (2015) · Information Systems Frontiers · doi:10.1007/s10796-014-9489-2 · Cited by 1,568
Research works printed but not linked: 23
- Bibi, 2016, Impact of human resource management practices on employee retention in telecom sector, J. Human. Soc. Sci., 21, 26
- Chui, 2010, McKinsey Quarterly, 2, 9
- Nazri, 2019, Predicting academician publication performance using decision tree, Int. J. Recent Technol. Eng., 8, 180
- Reddy, 2019, Artificial Intelligence (AI) in learning and development: A conceptual paper, J. Manag. Dev., 38, 34
- Bakeel, 2020, The impact of artificial intelligence on human resources management, J. Manag. Res., 12, 159
- Wang, 2020, An Artificial Intelligence-enabled health and safety management system for industry 4.0, Safety Sci., 124
- Ngai, 2020, Artificial intelligence applications in healthcare: A thematic analysis, J. Health Manag., 22, 220
- Arias, 2021, Chatbots: The future of HR and employee benefits communication, Benef. Quart., 37, 7
- Subramaniam, 2021, Artificial intelligence in workplace ergonomics: A review of current trends and future research directions, J. Occup. Health Psychol., 26, 135
- Zhang, 2021, Intelligent workplace comfort management based on Internet of Things and Artificial Intelligence, IEEE Access, 9, 143659
- Yu, 2020, An intelligent chair system for personalized sitting comfort management, Sensors, 20, 4478
- Ugwu, 2020, Stress detection in the workplace using artificial intelligence and Internet of Things technologies, J. Amb. Intell. Humanized Comput., 11, 89
- Sharma, 2022, Review on digitalisation and artificial intelligence in human resource function of energy sector, Water Energy Int., 65, 38
- Li, 2023, Real-time performance tracking and improvement for employee engagement using artificial intelligence, J. Bus. Res., 149, 675
- Dolan, 2022, Artificial intelligence and human resource management: Advancing theory and research, J. Manag., 48, 59
- Ye, 2021, Analyzing and visualizing organizational networks with deep learning and social network analysis, Inform. Manag., 58
- Lei, 2020, A social media-based approach for organizational network analysis, J. Bus. Res., 112, 1
- Vishwakarma, 2023, Employee engagement and collaboration: an empirical investigation of factors influencing in the age of AI, J. Bus. Res., 151, 666
- Yu, 2023, An intelligent network analysis for organizational collaboration improvement, IEEE Access, 11, 24609
- Naveen, 2020, Impact of wearables and IoT on employee health and wellness, Bus. Today
- A. Barman, K. Das, Internet of Things (IoT) as the Future Smart Solution to HRM-How would wearable IoT bring organisational efficiency, in: International Conference Dec, 2018.
- Yawson, 2019, Organisational agility: A review of the literature, J. Manag. Strat., 10, 33
- Gupta, 2020, Exploring relationship between employees well-being and green IOT using structural equation modeling, Innovation, 29, 2590
Cited by
Sorted by citations
Items 201–218 of 218
Santhanalaxmi, K., & Kavitha, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2541-3.ch007
Xia, Z. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02926-4 · Cited by 1
Reddy, G. K., & M.Rajyalaxmi (2026) · International Conference on Computing Theory and Wireless Communications (ICCTWC) · doi:10.1109/icctwc68241.2026.11583379
Antonyuk, I., Grabovska, O., Sopiga, A., Kovalenko, N., & Kamenetska, Y. (2026) · Multidisciplinary Reviews · doi:10.31893/multirev.2026spe008
Ansari, M. S. A., Jonathan, H., Shaker, H., Farooquie, J. A., & Stakić, A. J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01845-5
Nnaji, I. L., Obi, V. Y.-M., Ekwunife, R. A., Ukeje, I. O., Ndukwe, C., Offiah, G. A., & Ejem, E. N. (2026) · Public Organization Review · doi:10.1007/s11115-026-01035-y
Gestión del talento humano frente a la automatización y la digitalización empresarial.
Casanova-Villalba, C. I., Herrera-Sánchez, M. J., & Casanova-Villalba, L. A. (2026) · Revista de Investigación y Liderazgo · doi:10.63618/omd/revinvlid/v3/n1/49
121Chapter 8 Applications of Workforce Intelligence in Healthcare Sector
Chhabra, D. (2026) · De Gruyter eBooks · doi:10.1515/9783112237687-008
Using Business Intelligence to Improve Organizational Efficiency and Decision-Making
Ramaraj, S., Pillai, A. S., Prafulla, A., Chaudhary, N., Sachan, V. S., & Srinivasan, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0553-0.ch013
Priyadarshini, S., Kuanr, J., Dash, S., Dulla, N., & Panda, S. (2026) · Research Square · doi:10.21203/rs.3.rs-10689652/v1
Singh, B. D., Jindal, P., & Arora, M. (2026) · Evidence-based HRM a Global Forum for Empirical Scholarship · doi:10.1108/ebhrm-09-2025-0398
AI-Driven Sustainable Food Logistics
Verma, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0680-1.ch007
AOURARH, Y., & Elkharraz, A. (2026) · Systems · doi:10.3390/systems14091173 · Cited by 1
Singhal, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9599-9.ch003
Talent management: Challenges and opportunities of the 21st century
Beslagic, M., & Bešlagić, A. (2025) · Poslovna ekonomija · doi:10.5937/poseko27-57249
Utilizing Artificial Intelligence in Human Resources: A Qualitative Study in the Philippines
Caringal‐Go, J. F., Teng‐Calleja, M., Gutib, M. V., Navarro, J. J., & Cabangon, A. D. (2026) · The Journal of Behavioral Science · doi:10.69523/tjbs.2026.295818
Behavioral and Learning Foundations of AI-Driven HR Analytics for Resource Optimization
Christina, J. L., Alamelu, R., Sathya, M., Raja, P., & Blessy, M. I. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9868-6.ch011
Rosário, G., Reis, G., Duarte, R., Pereira, T., Salvadorinho, J. P., & Teixeira, L. C. (2026) · International Journal of Lean Six Sigma · doi:10.1108/ijlss-12-2025-0379
Metadata
- Authors
- Murugesan, U., Subramanian, P., Srivastava, S., & Dwivedi, A.
- Affiliations
- Christ University (IN)
- O. P. Jindal Global University (IN)
- Corresponding author
- Dwivedi, Ashish
- Venue
- Decision Analytics Journal
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 7
- Pages
- 100249
- ISSN
- 2772-6622 (ISSN-L)
- OpenAlex
- W4382173516
- Cited by
- 217
- References
- 54
- Citations by year
- 2026: 57 · 2025: 79 · 2024: 50 · 2023: 31
- FWCI
- 63.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Decision Analytics Journal (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
- Keywords
- artificial intelligence in human resource management, HR digitalization, Industry 4.0, organizational adaptability, human resource capabilities, employee well-being, occupational safety, Internet of Things
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review