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 alone, but whether systems are perceived as intelligent, trustworthy, augmentative, and organizationally integrable within complex environments. This paper introduces the Artificial Intelligence Acceptance Model (AIM), a next-generation adoption framework purpose-built for intelligent, autonomous, and data-intensive technologies. AIM replaces classical TAM logic with four AI-era determinants: System Intelligence (SI), Trustworthiness (TW), Human Augmentation Value (HAV), and Integration Readiness (IR). These constructs shape Value-in-Use (VIU), which subsequently 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 influencing AI adoption outcomes in real organizational settings. 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. The findings support AIM as a structurally coherent, empirically grounded framework for understanding AI adoption beyond the assumptions of classical technology acceptance theories. Version 3 Update Statement Version 3 introduces a further refined and standardized visual representation of the AIM framework to enhance graphical precision, structural symmetry, and publication readiness. Minor editorial adjustments were made to the figure caption to ensure alignment between the conceptual model and its structural depiction. No modifications were made to the theoretical framework, constructs, hypotheses, measurement model, analytical procedures, or empirical results. This update strictly concerns visual optimization and formatting refinement. Keywords: Artificial Intelligence Acceptance Model (AIM); AI Adoption; ERP Systems; System Intelligence; Trustworthiness; Human Augmentation; Integration Readiness; Blockchain Maturity; PLS-SEM.