Determinants of university students’ attitudes towards smart devices in the smart campus environment
Bibliographic Data
| ID | 22230804 |
|---|---|
| Authors | Bingling Wei (0000-0003-2222-7516, Shenzhen Technology University), Yuefan Zhuo (Shenzhen Technology University, corresponding author), Huiqi Zeng (Shenzhen Technology University), Huijia Hong (Shenzhen Technology University), Hang Liu (0000-0002-0801-6897, South China Agricultural University, corresponding author) |
| Year | 2025 |
| Volume | 12 |
| Issue | 1 |
| Publication date | 2025-09-30 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Humanities and Social Sciences Communications (JOURNAL) |
| Journal identifiers | ISSN: 2662-9992 • E-ISSN: 2662-9992 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1057/s41599-025-05853-1 |
| OpenAlex | W4414662229 |
| Language | EN |
| Citations received | 2 |
| References cited | 96 |
Considering the rapid advancement of information and communication technology, smart campuses represent a significant evolution of digital campus, integrating intelligent technologies to enhance educational quality, resource allocation, and management efficiency. However, the factors influencing university students’ comprehensive experiences and perceptions of smart devices usage within the broader, integrated smart campus environment remain insufficiently understood. Addressing this gap, the present study integrates the technology acceptance model with the task-technology fit theory and perceived risk theory to investigate the factors influencing university students’ attitudes toward using smart devices (ATT) in the smart campus environment. The results from a sample of 428 Chinese university students obtained using partial least squares structural equation modeling reveal that perceived usefulness (PU), perceived ease of use (PEU), and information security significantly influence students’ ATT, whereas learning support and perceived interactivity exert indirect effects on ATT through PU and PEU. This study deepens the understanding of technology acceptance in highly technology-integrated smart campus environments, advances human-centered research on the smart campus, and provides practical insights to facilitate the effective implementation and broader adoption of smart devices on a smart campus, thereby supporting the sustainable development of higher education
Interactivity · Perception · Risk perception · Smart device · Smart environment · Structural equation modeling · Technology Acceptance Model · Unified theory of acceptance and use of technology · Usability · Education and Learning Interventions · Impact of Technology on Adolescents · Technostress in Professional Settings
Understanding the role of digital technologies in education
Partial least squares structural equation modeling (PLS-SEM)
Examining the key influencing factors on college students’ higher-order thinking skills in the smart classroom environment
Acceptance of artificial intelligence among pre-service teachers
A primer on partial least squares structural equation modeling (PLS-SEM)
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When to use and how to report the results of PLS-SEM
Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology
Strategic framework and global trends of national smart education policies
Exploring the impact of intelligent learning tools on students’ independent learning abilities
The effects of the expectation confirmation model (ECM) and the technology acceptance model (TAM) on learning management systems (LMS) in sub-saharan Africa
Predicting the actual use of m-learning systems
Acceptance of educational use of the Internet of Things (IoT) in the context of individual innovativeness and ICT competency of pre-service teachers
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Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism
Revisiting the relationship between smartphone use and academic performance
Teacher beliefs, classroom process quality, and student engagement in the smart classroom learning environment
Assessment of cognitive, behavioral, and affective learning outcomes in massive open online courses
A model for assessing student satisfaction with smart classroom environment in higher education
Smart campus—A sketch
Developing the rotational synchronous teaching (RST) model
Smart campus communication, Internet of Things, and data governance
Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education
Remote learning via video conferencing technologies
Are students ready for robots in higher education? Examining the adoption of robots by integrating UTAUT2 and TTF using a hybrid SEM-ANN approach
Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |