TY - JOUR
T1 - Artificial intelligence applied to potential assessment and talent identification in an organisational context
AU - França, Tiago Jacob Fernandes
AU - São Mamede, José Henrique Pereira
AU - Barroso, João Manuel Pereira
AU - Santos, Vítor Manuel Pereira Duarte dos
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
França, T. J. F., São Mamede, J. H. P., Barroso, J. M. P., & Santos, V. M. P. D. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4), 1-25. [e14694]. https://doi.org/10.1016/j.heliyon.2023.e14694
PY - 2023/4/1
Y1 - 2023/4/1
N2 - Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.
AB - Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.
KW - Artificial intelligence
KW - Human resources
KW - Potential assessment
KW - Talent management
KW - Next-gen HR
UR - http://www.scopus.com/inward/record.url?scp=85150830181&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000998145400001
U2 - 10.1016/j.heliyon.2023.e14694
DO - 10.1016/j.heliyon.2023.e14694
M3 - Article
SN - 2405-8440
VL - 9
SP - 1
EP - 25
JO - Heliyon
JF - Heliyon
IS - 4
M1 - e14694
ER -