Journal of Research Administration

Abstract

The rapid integration of Artificial Intelligence (AI) and automation in the aviation sector presents a critical challenge for Human Resource management: aligning workforce capabilities with technological demands while mitigating displacement anxiety. This study assessed reskilling needs and employee adaptation strategies within the Nigerian aviation industry, focusing on the interplay between technical competence, psychological acceptance, and organizational support. Employing a mixed-methods design, data were collected from 250 aviation professionals across three airlines and two international airports using stratified random sampling for quantitative surveys (n=250) and purposive sampling for semi-structured interviews (n=15). The theoretical framework integrated the Technology Acceptance Model (TAM) and Human Capital Theory to examine how reskilling quality, perceived usefulness, and organizational support influence multidimensional adaptation. Quantitative analysis revealed a significant hybrid skills deficit: while emotional intelligence scored high (M=3.9), AI tool proficiency remained critically low (M=2.1). Multiple regression analysis demonstrated that Reskilling Training Quality was the strongest predictor of employee adaptation (β=0.38, p<.001), followed by Perceived Usefulness (β=0.28) and Organizational Support (β=0.25), collectively explaining 62% of variance in adaptation outcomes. Qualitative thematic analysis identified fear of job displacement, inadequate practical training, and leadership communication gaps as primary barriers to successful transition. The study concludes that AI adoption in aviation is fundamentally a human systems challenge requiring role-specific reskilling, transparent co-design processes, and career-integrated learning pathways. Seven strategic recommendations are proposed, including establishing national upskilling funds, embedding AI competencies in promotion criteria, and deploying psychological safety metrics.