Теоретичні та прикладні питання економіки. Збірник наукових праць.

Випуск 2 (49)

UDC 331.1:004.8

JEL M12, D83

ORCID ID: 0000-0003-4889-1247

ORCID ID: 0009-0009-2710-3396

DOI https://doi.org/10.17721/tppe.2024.49.20

Galyna Chornous,

doctor of economics, professor, Taras Shevchenko National University of Kyiv, Kyiv

galyna.chornous@knu.ua

Viktoriia Myronets,

student, Taras Shevchenko National University of Kyiv, Kyiv

myronetsviktoria@knu.ua

ARTIFICIAL INTELLIGENCE IN HRM PROCESSES: GENERAL FRAMEWORK AND APPLICATION FOR PSYCHOLOGICAL CLIMATE MONITORING

The article considers the potential for the widespread use of artificial intelligence (AI) to address current challenges in the labor market and improve the efficiency of human resource management (HRM). It analyzes modern approaches to integrating relevant methods and tools into HRM practices. The study explores how AI can complement existing HR processes, creating a more data-driven and adaptive framework for decision-making. Companies are actively seeking ways to incorporate innovative solutions, so the results of this study can be seen as a step towards combining current HRM practices with AI's transformative potential to support effective management decisions. The paper presents powerful AI capabilities: mechanical AI, thinking AI, and feeling AI. Mechanical AI is best suited for automating tasks such as job creation, enrolment in training courses, reporting, monitoring career development, and performance evaluation. This type of AI significantly reduces administrative burdens, allowing HR professionals to focus on strategic decision-making and employee engagement. Thinking AI excels in analyzing CVs, learning outcomes, productivity, and career forecasting. Feeling AI focuses on analyzing applicant behavior, personalizing training programs, providing psychological support, and monitoring the workplace's psychological climate. To address challenges in emotional analysis, the article presents a conceptual model for sentiment analysis of data on the emotional state and satisfaction of employees. The paper includes specific recommendations for implementation and demonstrates the model's application on Glassdoor Job Reviews data using Python programming language. This approach illustrates how AI can enhance employee well-being while aligning HR practices with organizational goals.

Keywords: Human resource management, feeling AI, sentiment analysis, modeling, decision-making, company

Full Text: PDF

DOI: https://doi.org/10.17721/tppe.2024.49.20