Evaluating Students’ Acceptance Intention of Augmented Reality in Automation Systems Using the Technology Acceptance Model

Abstract

This study aims to evaluate the factors influencing the acceptance of augmented reality among students within an automation system. The researchers of this study adopted the technology acceptance model (TAM) and gathered data to examine the path relationships among key variables: perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention to use, all of which impact system acceptance. A survey was administered to students to gauge their perspectives on integrating augmented reality into the automation system. The study sample consisted of 41 students enrolled in the automation system course where augmented reality was utilized in teaching. Utilizing the partial least square approach, the hypothesis model was analyzed. The results revealed a lack of significant correlation between perceived ease of use and perceived usefulness in relation to augmented reality usage. However, perceived ease of use positively influences students’ behavioral intention to use, while perceived usefulness significantly affects behavioral intention to use through augmented reality. Notably, perceived usefulness exhibits a significant relationship with both behavioral intention to use and attitude toward use in the context of augmented reality usage. Moreover, perceived ease of use positively affects attitude toward use for students within the context of automation systems with augmented reality. Furthermore, no significant relationships were observed between attitude toward use and behavioral intention to use, nor between perceived ease of use and perceived usefulness regarding augmented reality usage. This study offers valuable insights for educators in the field of engineering and technology who are engaged in training and sustainability development programs.

Towards a Refined Heuristic Evaluation: Incorporating Hierarchical Analysis for Weighted Usability Assessment

“…The comparison between user perception-based methods like the SUS [48] and technology acceptance model [26] and expert-driven heuristic evaluations [14,25,27] reveals a comprehensive understanding of usability. Expert evaluations offer a broader perspective by incorporating detailed analysis and guiding questions about the system, surpassing the subjective beliefs often captured by user-centric surveys [18].…”

“…Performing heuristic evaluations offers numerous advantages beyond enhancing the technology acceptance [26], including cost-effectiveness, as it requires minimal time and fewer users compared to traditional user testing [27,28]. Additionally, it demands less extensive planning, involves fewer personnel, and entails a streamlined analysis process.…”

Towards a Refined Heuristic Evaluation: Incorporating Hierarchical Analysis for Weighted Usability Assessment

“…Comparing user perception-based methods like the SUS [53] and the technology acceptance model [31] with expert-driven heuristic evaluations [13,30,32], we find that expert evaluations offer a broader perspective by incorporating detailed analyses and guiding questions about the system. This depth of insight is instrumental in addressing nuanced usability components that non-expert assessments might overlook [19].…”

“…Performing heuristic evaluations offers numerous advantages beyond enhancing technology acceptance [31], including cost-effectiveness, as it requires minimal time and fewer users compared to traditional user testing [32,33]. Additionally, it demands less extensive planning, involves fewer people, and entails a streamlined analysis process.…”

Integrando IA en los Nuevos Paradigmas de las Ciencias Económicas y Gestión Empresarial

“…La inteligencia artificial ha transformado la predicción de flujos de efectivo y el análisis de riesgo, proporcionando herramientas avanzadas que permiten a las empresas anticipar futuras condiciones financieras y evaluar riesgos con mayor precisión (Shyr et al, 2024).…”