Perceived Usefulness of AI Across Digital Marketing Stages: An Empirical Study of Adoption Intention in Saudi Retail SMEs

The landscape of digital marketing is transformed by Artificial Intelligence (AI) technologies that ‎improved personalization, efficiency, and customer engagement. However, research on AI ‎adoption is limited, especially in Saudi retail small and medium-sized enterprises (SMEs). This ‎study addresses this gap by examining how the perceived usefulness of AI in different digital ‎marketing stages influences adoption intention. Drawing on the Technology Acceptance Model ‎‎(TAM) and the RACE framework (Plan, Reach, Act, Convert, Engage), five hypotheses were ‎developed to test the effects of perceived usefulness on adoption intention across these stages. Data ‎were collected through surveys from 450 decision-makers in Saudi retail SMEs. Structural ‎Equation Modeling (SmartPLS) was used for analysis. The results support four hypotheses. ‎Perceived usefulness in the Plan, Reach, Convert, and Engage stages positively influenced ‎adoption intention (H1, H2, H4, H5). The Act stage showed no significant effect (H3). These ‎findings highlight that AI’s perceived value differs across marketing stages. The study contributes ‎to theory by extending TAM in a multi-stage marketing context. It shows that adoption intention is ‎shaped by how useful AI is perceived across customer journey stages. Practically, the results guide ‎managers to focus AI investments in stages where perceived usefulness drives stronger adoption, ‎especially Reach, Convert, and Engage. For policymakers, the findings emphasize the need for ‎targeted support and training to enhance AI readiness in Saudi SMEs‎.