The Strategic-Behavioral Analysis: Factors Influencing AI-Powered Learning Platform Adoption in Thai Higher Education

Abstract

Aim/Purpose This study investigates how users' behavioral intention to adopt AI-powered learning platforms in Thailand is influenced by both traditional acceptance factors and organizational strategic capabilities, addressing the limited understanding of adoption determinants in rapidly evolving educational AI ecosystems.Background While substantial research has examined behavioral factors affecting technology adoption, less attention has been given to how users' perceptions of organizational strategic capabilities and trust in AI affect their adoption decisions. This study addresses this gap by integrating the UTAUT2 framework, dynamic capabilities theory, and trust perspectives into a unified model specifically contextualized to Thai educational settings. Methodology Data were collected from 1,368 Thai users (63.9% students, 14.0% teach-ers/faculty) through online and offline questionnaires. PLS-SEM was employed to test the proposed structural model, with multi-group analysis examining differences across user experience levels, occupations, and usage frequencies. Findings All hypothesized relationships were supported, with the integrated model explaining 63.4% of the variance in behavioral intention. Strategic capabilities significantly influenced both traditional adoption factors and direct adoption intention. Trust in AI demonstrated dual pathways of influence: direct effects and enhanced perceptions of platform adaptability. Contribution This study extends existing adoption theories by demonstrating how strategic capabilities (sensing, seizing, reconfiguring) influence both direct adoption intention and traditional acceptance factors. It establishes a novel dual pathway of trust influence and develops a comprehensive framework that bridges individual-level behavior with organizational-level strategic readiness.Recommendations for Practitioners Educational institutions should prioritize user-centered design (emphasizing ease of use and social integration) and demonstrate strategic adaptability through transparent roadmaps and responsive feature development. Building trust through ethical governance frameworks and clear data practices is essential for sustainable adoption. Impact on Society Understanding the complex interplay can help institutions implement AI technologies more effectively. This potentially addresses educational inequities, supports linguistic diversity, and scales quality learning experiences across diverse contexts. Future Research Future research should explore cultural and institutional moderators of strategic capability influence, examine longitudinal adoption patterns as AI technologies evolve, and investigate how strategic capability perceptions form across different user demographics and educational contexts. Cross-cultural validation of the integrated model across countries with vary ing philosophies would enhance generalizability.