Artificial Intelligence adoption in Ethiopia Airlines

Abstract

The global economy's expansion has intensified project management challenges, necessitating the integration of Artificial Intelligence to automate repetitive tasks. Despite AI's benefits, there remains a significant research gap regarding its implementation and adoption, particularly in organizational and managerial contexts. This study investigates AI adoption in project management at Ethiopian Airlines, employing the Diffusion of Innovation (DOI) and Technology Acceptance Model (TAM) as theoretical frameworks. The research employs both descriptive and explanatory research designs, incorporating qualitative methods. Multiple linear regression, along with correlation analysis, was used to analyze the relationships between independent variables (compatibility, complexity, management support, relative advantage, and perceived usefulness) and the dependent variable (decision of adoption). The study surveyed 156 individuals from Ethiopian Airlines' infrastructure, engineering, and IT divisions, yielding 142 usable responses (91.03% response rate). Correlation and multiple linear regression analyses revealed that compatibility, management support, relative advantage, and perceived usefulness are key determinants of AI adoption, while complexity does not significantly affect adoption decisions. The findings suggest significant AI usage among respondents, with growth potential and rapid adoption anticipated within two years. However, barriers such as cost, time, and AI solution immaturity must be addressed for broader adoption. This research provides valuable insights for Ethiopian Airlines and other organizations seeking to leverage AI for improved project outcomes and competitive advantage in the global economy.