From TAM to AIM: Reengineering Technology Acceptance in the AI Era – The Case of ERP Systems

The Technology Acceptance Model (TAM) played a foundational role in explaining early information-system adoption, where perceived usefulness and ease of use shaped user intention. In the AI era, however, such constructs have become baseline assumptions: modern enterprise technologies are inherently usable, integrated, and embedded within familiar digital workflows. What determines adoption today is not simplicity or utility, but whether systems are perceived as intelligent, trustworthy, augmentative, and organizationally integrable within complex enterprise environments. This paper introduces the Artificial Intelligence Model (AIM), a next-generation adoption framework purpose-built for intelligent, autonomous, and data-intensive technologies. AIM replaces classical TAM logic with four contemporary determinants of AI-era adoption: System Intelligence (SI), Trustworthiness (TW), Human Augmentation Value (HAV), and Integration Readiness (IR). These constructs shape Value-in-Use (VIU), which—together with IR—drives Adoption Intention (INT), Actual Use (AU), and Continuance Intention (CI). In addition, AIM incorporates key contextual moderators—Blockchain Maturity (BCM), Regulatory Support (RSC), Digital Literacy (DLIT), and Job Replacement Anxiety (JRA)—capturing the technological, regulatory, and human contingencies that influence AI adoption outcomes in real organizational settings. Version 2 of this record introduces a refined and clarified visual representation of the AIM framework and an updated figure caption to ensure full alignment between the conceptual model, hypothesized paths, and empirical testing. No changes were made to the theoretical foundation, constructs, or empirical results; rather, this version enhances conceptual clarity, structural consistency, and reusability of the model for future empirical applications. By shifting from TAM’s usability-driven logic to AIM’s intelligence-centric, trust-centric, and augmentation-oriented perspective, this study offers a theoretically innovative and practically relevant framework suited to AI-enabled ERP ecosystems spanning HR, finance, supply chain, healthcare, education, government, and private industry. Methodological Note: The model was empirically validated using Partial Least Squares Structural Equation Modeling (PLS-SEM) based on data collected from 356 ERP professionals across multiple sectors. Keywords: Artificial Intelligence Model (AIM); AI Adoption; ERP Systems; System Intelligence; Trustworthiness; Human Augmentation; Integration Readiness; Blockchain Maturity; PLS-SEM.