Assessing AI readiness and adoption for marketing optimization in Bangkok's SMEs: An application of structural equation modeling (SEM)
Artificial intelligence (AI) has emerged as an important strategic tool for improving marketing performance and enhancing the competitiveness of small and medium-sized enterprises (SMEs). Despite its growing adoption, limited empirical evidence explains how different dimensions of organizational readiness influence AI usage in SMEs, particularly in emerging economies. This study investigates the effects of Technological Infrastructure Readiness (TIR), Human Resource Readiness (HRR), and Organizational Culture and Change Management Readiness (OCCMR) on AI Usage Behavior, with Behavioral Intention serving as a mediating variable. The proposed research framework integrates the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Technology–Organization–Environment (TOE) framework, the Resource-Based View (RBV), and Organizational Readiness Theory. Data were collected from 402 SME owners and managers in Bangkok, Thailand, and analyzed using covariance-based structural equation modeling (CB-SEM). The measurement model demonstrated satisfactory reliability and validity, while the structural model exhibited an excellent fit (χ²/df = 1.91, CFI = .983, TLI = .978, RMSEA = .047). The results indicate that Technological Infrastructure Readiness, Human Resource Readiness, and Organizational Culture and Change Management Readiness significantly influence Behavioral Intention. In addition, Technological Infrastructure Readiness, Organizational Culture and Change Management Readiness, and Behavioral Intention have significant positive effects on AI Usage Behavior, whereas the direct effect of Human Resource Readiness on AI Usage Behavior is not significant. Behavioral Intention is the strongest predictor of AI Usage Behavior (β = .591, p < .001) and significantly mediates the relationships between organizational readiness and AI adoption.