Source-linked AI summary
Extension of Technology Acceptance Model by using System Usability Scale to assess behavioral intention to use e-learning
Anastasia Revythi, Nikolaos Tselios
TL;DR
The study examines what shapes students’ acceptance and intended use of learning management systems. It applies a modified TAM framework to e-class and finds that several usability, social, access, and self-efficacy factors significantly relate to behavioral intention. The findings offer guidance for LMS development while remaining bounded by the sampled population and system.
Problem
The study examines how students accept learning management systems and which factors affect their behavioral intention to use them.
Method
The study assesses LMS acceptance with a modified Technology Acceptance Model and analyzes the relationships among its factors.
Results
Behavioral intention to use e-class is greatly affected by social influence, system access, perceived usefulness, self-efficacy, and perceived ease of use.
Takeaways & Limitations
LMS developers and educators can use these findings and usability evaluations to compare systems and guide redevelopment and optimization.
Takeaways & Limitations
The sample comprised a single student population, limiting representativeness across other departments, universities, and learning management systems.
Abstract
from arXiv · showhide
This study examines the acceptance of technology and behavioral intention to use learning management systems (LMS). In specific, the aim of this research is to examine whether students ultimately accept and use educational learning systems such as e-class and the impact of behavioral intention on their decision to use them. An extended version of technology acceptance model has been proposed and used by employing the System Usability Scale to measure perceived ease of use. 345 university students participated in the study and the data analysis was based on partial least squares method. The results were confirmed in most of the research hypotheses. In particular, social norm, system access and self-efficacy significantly affect behavioral intention to use. As a result, it is suggested that e-learning developers and stakeholders should focus on these factors to increase acceptance and effectiveness of learning management systems.
Introduction
The study addresses how users accept information systems and how perceived usefulness and ease of use shape intention to use them. It motivates examining these factors to understand and improve system design.
- Technology acceptance is defined as users’ willingness to use technologies and tools developed to support them.
- Users’ intention to use a system is primarily affected by perceived usefulness and perceived ease of use.
- Researchers develop techniques to identify factors influencing information-system acceptance and predict system success.
Conclusions
The modified TAM analysis found that behavioral intention to use e-class is associated with social influence, system access, perceived usefulness, self-efficacy, and perceived ease of use. The findings also identify practical usability implications and limit generalization beyond the studied population and system.
- Conclusions: Self-efficacy significantly affects both perceived ease of use and behavioral intention, but not attitudes or perceived usefulness.
- Conclusions: Perceived usefulness has statistically significant effects on both attitude towards e-class and behavioral intention to use.
- Conclusions: Social norm significantly affects behavioral intention, attitude, perceived ease of use, and perceived usefulness.
- Conclusions: System access has a significant mediation effect across behavioral intention, attitude, perceived ease of use, and perceived usefulness.
- Conclusions: Behavioral intention to use e-class is greatly affected by social influence, system access, perceived usefulness, self-efficacy, and perceived ease of use.
- Conclusions: Perceived ease of use was affected by e-learning self-efficacy, social norm, system accessibility, and academic year.
- Conclusions: Usability evaluation tools can quickly compare systems and provide feedback for redevelopment and optimization.
- Limitations: The sample represented a single student population, so other departments, universities, and learning management systems should be investigated.