<![CDATA[This study empirically investigates the factors influencing the adoption of mobile-based artificial intelligence (AI) services among manufacturing small and medium enterprises (SMEs) in Tanzania, developing and testing an integrated framework that combines the Mobile Services Acceptance Model (MSAM) with Innovation Diffusion Theory (IDT) and context-specific factors relevant to emerging economies. A mixed-methods research design was employed. Quantitative data were collected from 412 manufacturing SMEs across eight regions of Tanzania using a structured survey instrument. Qualitative data were gathered through semi-structured interviews with 28 SME owners and managers. Structural equation modeling was used to test hypothesized relationships, while thematic analysis was applied to qualitative data. The results reveal that perceived usefulness (β = 0.314, p < 0.001), perceived ease of use (β = 0.287, p < 0.001), compatibility (β = 0.256, p < 0.001), top management support (β = 0.298, p < 0.001), and vendor support quality (β = 0.243, p < 0.001) are the strongest direct predictors of mobile-based AI adoption intention. Infrastructure availability (β = 0.189, p < 0.01) and cost considerations (β = -0.172, p < 0.01) significantly influence adoption. Trust (β = 0.208, p < 0.01) and observability (β = 0.195, p < 0.01) also demonstrate significant effects. Power distance significantly moderates the relationship between top management support and adoption (β = -0.142, p < 0.05). Qualitative findings reveal that trialability, compatibility with existing workflows, and peer influence through business networks emerge as critical determinants. The study focuses on manufacturing SMEs in Tanzania, which may limit generalizability to other sectors or national contexts. The cross-sectional design captures adoption intentions rather than actual sustained usage. The findings provide actionable guidance for SME managers making AI adoption decisions, inform policymakers developing supportive infrastructure and capacity-building interventions, and assist technology vendors in designing solutions appropriate for the Tanzanian market context. This study makes the first empirical contribution to understanding mobile-based AI adoption among manufacturing SMEs in Tanzania, integrating multiple theoretical perspectives with context-specific factors to develop and test a comprehensive framework validated through mixed-methods research.]]>
