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Understanding ERP Adoption in Agribusiness: An Empirical Study Using Extended Technology Acceptance Model PLS-SEM and IPMA

Jul 2026 · International Seminar on Intelligent Technology and Its Applications · pp. 310-315 · 0 citations · 18 references

Abstract

Adoption of Enterprise Resource Planning (ERP) in agriculture firms is influenced by a complex interaction of organizational, human, and technology factors. Even though SAP-based ERP systems are widely used in Indonesian agricultural businesses, there is still a significant disconnect between system deployment and real use. Using an Extended Technology Acceptance Model (Extended TAM) that takes into account three organizational external factors training, management support, and organizational size this study investigates the factors that influence ERP system adoption at an agribusiness company in Pasuruan, East Java. The study concurrently evaluates the measurement model and the structural correlations among constructs using Partial Least Squares Structural Equation Modeling (PLS-SEM) using a sample of 46 active ERP users. To determine managerially actionable priorities, Importance-Performance Map Analysis (IPMA) is also utilized. Perceived Ease of Use (PEOU) significantly influences both Attitude (ATU) and Perceived Usefulness (PU), while Attitude (ATU) is the strongest predictor of ERP System Adoption. PEOU is also significantly increased by training, and PU is significantly improved by management support. However, adoption behavior was not significantly predicted by organizational size. According to IPMA data, ATU deserves ongoing support as the main adoption driver (Quadrant I), while PEOU is the highest-priority intervention target (Quadrant IV: high importance, low performance). Notably, Training did not significantly enhance Perceived Usefulness, and Management Support operated exclusively through an indirect pathway via Perceived Usefulness rather than directly influencing Attitude or adoption behavior. These deviations constitute theoretically meaningful boundary conditions, indicating that the TAM antecedent structure functions differently in resource-constrained agribusiness organizations. This study thus contributes a contextually calibrated variant of Extended TAM for agribusiness settings in emerging economies, alongside practical guidance through usercentered interface improvements, targeted training redesign, and sustained managerial engagement.

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