THE POTENTIAL OF ARTIFICIAL INTELLIGENCE (AI) FOR DEVELOPING AN INTEGRATED EMPLOYEE PLACEMENT MODEL BASED ON PERSON-JOB (P-J), PERSON-ORGANIZATION (P-O), AND PERSON-ENVIRONMENT (P-E) FIT: A SYSTEMATIC LITERATURE REVIEW

Abstract

The integration of artificial intelligence (AI) into human resource management has transformed employee placement practices, yet the systematic examination of AI applications across the person-job (P-J), person-organization (P-O), and person-environment (P-E) fit dimensions remains fragmented. This systematic literature review synthesizes 61 peer-reviewed articles (2015-2025) from the Scopus database that examine how AI technologies facilitate matching across three critical fit dimensions. Analysis reveals P-E Fit Theory as the dominant framework (34.4%), with quantitative methodologies prevailing (80-85%) and structural equation modeling preferred (40%). Geographically, Asian countries, particularly China (27.9%) and Taiwan (9.8%), lead research contributions. Findings demonstrate that AI applications enhance recruitment efficiency by approximately 30% while improving matching accuracy through machine learning algorithms that analyze multidimensional candidate profiles. However, challenges persist regarding algorithmic bias, transparency, and explainable AI systems. This review proposes an Augmented Person-Environment Fit Theory that incorporates person-technology fit as an essential dimension in digital workplace contexts, offering practical implications for organizations implementing AI-enhanced placement systems and identifying critical research gaps, including in the tourism industry.