Extension of Technology Acceptance Model by using System Usability Scale to assess behavioral intention to use e-learning (1704.06127v5)

Abstract: 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.

• The paper extends the Technology Acceptance Model (TAM) by integrating the System Usability Scale (SUS) to analyze behavioral intention to use e-learning platforms in a university student sample.
• Key findings reveal self-efficacy, social norms, perceived usefulness, and system access significantly influence the intention to use e-learning technologies.
• The research implies that enhancing factors like usability, self-efficacy, social influence, and accessibility are crucial for improving e-learning system design and adoption.

Extension of Technology Acceptance Model Utilizing System Usability Scale in E-Learning Contexts

This paper investigates the acceptance of e-learning technology by extending the Technology Acceptance Model (TAM) through integration with the System Usability Scale (SUS), focusing on behavioral intention to use Learning Management Systems (LMS). The paper encompasses a sample size of 345 university students, employing partial least squares method for data analysis to understand the nuances of LMS acceptance.

Key Findings

1. Self-Efficacy: The paper identifies self-efficacy as a pivotal factor significantly influencing both perceived ease of use and behavioral intention to utilize LMS tools, corroborating findings from prior research, including Park (2009) and the foundational TAM theory by Davis and Venkatesh (2000).
2. Perceived Usefulness: Statistical analysis confirms perceived usefulness affects both the attitude towards and the intention to use LMS, aligning partially with Park (2009), with geographic variations between Greece and Korea offering insightful implications on user familiarity with internet technologies affecting educational tech usage.
3. Social Norms Influence: The impact of social norms was statistically significant on behavioral intention, attitude, perceived ease of use, and perceived usefulness. This affirms the essential role of social influence echoed in previous inquiries, emphasizing cultural aspects in technology adoption.
4. System Access: As a determinant of usage intention, the role of system access was underscored due to less developed technological infrastructure in Greece compared to Korea, where access commonly impacts perceptions of ease and usefulness of e-learning platforms.
5. Academic Year Influence: The students' academic year was found to significantly affect perceived ease of use, delineating how accumulated experience with LMS technology mediates user attitudes and subsequent use.

The findings suggest a firm validation of the extended TAM using SUS, with statistically significant evidence confirming the interaction between self-efficacy, social norms, perceived usefulness, and system access on the behavioral intention to use LMS technologies. The role of user attitude as a direct mediator between intention and actual use was similarly verified.

Implications and Future Directions

The research underscores the importance for developers and educators in optimizing LMS design by accentuating factors such as self-efficacy, social influence, and system accessibility to bolster technology acceptance in educational settings. Practically, these insights advocate for enhanced usability evaluations, enabling swift assessment of LMS platforms' educational impact, thereby facilitating iterative design enhancements.

Further exploration is suggested to encompass diverse educational contexts via broader demographic participants beyond a singular Greek university setting, allowing for comparative assessments across different LMS technologies. Integrating findings with broader personality assessments (e.g., Big Five Personality Test) could offer more personalized design approaches.

The pursuit of deeper cognitive understanding and information processing strategies will remain instrumental in shaping effective e-learning architectures that promote learner engagement and knowledge acquisition, consequently reinforcing the pedagogical viability of these digital systems.