Trustworthiness as a Mediator Between User Perceptions and AI Adoption Intention in ADNOC’s Supply Chain

Abstract

Objective:
This study aims to develop and validate a structural model that examines trustworthiness as a mediating variable between user perceptions specifically Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) and the intention to adopt AI technologies in ADNOC’s supply chain management.

Research Method:

A conceptual model was formulated based on the Technology Acceptance Model (TAM) and relevant literature. Primary data were collected through a structured questionnaire survey administered to 328 employees of the ADNOC supply chain department. The model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS software.

Findings:
The results confirmed that all constructs exhibited strong internal consistency, convergent validity, and discriminant validity. Structural model analysis showed that PEOU and PU significantly influence Trust, which in turn strongly predicts Behavioral Intention to Use (BIU). The model demonstrated moderate to strong explanatory power and substantial predictive relevance, highlighting trust as a critical mediator in AI adoption.

Originality:
This study extends the traditional TAM by incorporating trustworthiness as a mediator, offering a novel perspective on AI adoption in high-stakes, public sector environments. The validated model provides both theoretical contributions and practical insights for guiding successful AI implementation in complex organizational settings like ADNOC.