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Accepted for/Published in: JMIR Human Factors

Date Submitted: Mar 14, 2024
Date Accepted: Jan 15, 2025

The final, peer-reviewed published version of this preprint can be found here:

Factors Determining Acceptance of Internet of Things in Medical Education: Mixed Methods Study

Alhumaid K, Ayoubi K, Khalifa M, Salloum S

Factors Determining Acceptance of Internet of Things in Medical Education: Mixed Methods Study

JMIR Hum Factors 2025;12:e58377

DOI: 10.2196/58377

PMID: 40209037

PMCID: 12005465

Factors Determining Acceptance of IoT in Medical Education: A SEM-ANN Approach

  • Khadija Alhumaid; 
  • Kevin Ayoubi; 
  • Maha Khalifa; 
  • Said Salloum

ABSTRACT

Background:

The global increase in the Internet of Things (IoT) adoption has sparked interest in its application within the educational sector, particularly in colleges and universities. Previous studies have often focused on individual attitudes toward IoT without considering a multi-perspective approach and have overlooked the impact of IoT on the Technology Acceptance Model (TAM) outside the educational domain.

Objective:

This study aims to bridge the research gap by investigating the factors influencing IoT adoption in educational settings, thereby enhancing understanding of collaborative learning through technology. It seeks to elucidate how IoT can facilitate learning processes and technology acceptance among college and university students in the UAE.

Methods:

A questionnaire was distributed to students across various colleges and universities in the UAE, garnering 463 participants. The data collected were analyzed using a hybrid approach that integrates Structural Equation Modeling (SEM) and Artificial Neural Network (ANN), along with Importance-Performance Map Analysis (IPMA) to evaluate the significance and performance of each factor affecting IoT adoption.

Results:

The findings reveal that the intention to adopt IoT is positively influenced at two levels. The first level includes factors such as technology optimism, technology innovation, and learning motivation, highlighting their essential role in IoT application. The second level identifies TAM constructs (perceived ease of use and perceived usefulness) as directly and positively influencing IoT adoption intentions. The ANN and IPMA assessments further pinpoint the perceived usefulness construct as a critical predictor of IoT usage intention.

Conclusions:

This research contributes methodologically by leveraging deep artificial neural network (ANN) architecture to explore non-linear relationships among factors influencing IoT adoption in education. The study underscores the importance of both intrinsic motivational factors and perceived technological attributes in fostering IoT adoption, offering insights for educational institutions considering IoT integration into their learning environments.


 Citation

Please cite as:

Alhumaid K, Ayoubi K, Khalifa M, Salloum S

Factors Determining Acceptance of Internet of Things in Medical Education: Mixed Methods Study

JMIR Hum Factors 2025;12:e58377

DOI: 10.2196/58377

PMID: 40209037

PMCID: 12005465

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