MODEL FACTORS OF TECHNOLOGICAL, ORGANIZATIONAL, AND ENVIRONMENTAL INFLUENCES ON ARTIFICIAL INTELLIGENCE ADOPTION AFFECTING ECONOMIC PERFORMANCE AND WORK EFFICIENCY FOR SMES IN THE METROPOLITAN AREA
Keywords:
Technology, Organization,, Environment,, Artificial Intelligence Adoption, Economic Capability and Operational PerformanceAbstract
This study aimed to 1) examine the significance of technological, organizational, and environmental factors, along with the adoption of artificial intelligence (AI), on the economic capability and operational performance of small and medium-sized enterprises (SMEs) in the metropolitan area; 2) analyze the direct relationships among these factors and how they influence AI adoption and subsequent outcomes; and 3) provide recommendations for effective AI implementation. A mixed-methods approach was employed. Quantitative data were collected from 320 SMEs, selected using purposive sampling at the researcher's discretion. Structured questionnaires were used, using the data were analyzed using confirmatory factor analysis and structural equation modeling. Additionally, qualitative data were obtained through interviews with 30 key informants. The results indicated that, among technological latent factors, cost was the most influential observed variable. AI adoption had the strongest positive impact on both economic capability and operational performance. The qualitative findings reinforced this by highlighting the practical use of AI tools such as chatbots, product recommendation systems, and customer behavior analytics, which help reduce repetitive work and operational costs. Based on these findings, the study recommends that SMEs invest strategically in AI technologies to improve productivity, enhance efficiency, and ensure long-term competitiveness in an increasingly digital business environment.
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