Determinants of university students’ attitudes towards smart devices in the smart campus environment

Smart devices usage in the smart campus environment

In the ongoing digital transformation of the education sector, the IoT is playing an increasingly crucial role in smart campus development. It drives the growth of the “Internet of Education Things”, which involves integrating diverse smart devices into education (Kassab et al., 2020). By integrating advanced technologies across campuses, the IoT breaks down information silos among devices, establishing an efficient service system, seamless connectivity, and intelligent decision-making mechanisms (Asgharinezhad et al., 2024). This comprehensively elevates the quality of campus smart services.

In higher education settings within smart campuses, smart devices are widely utilized to create personalized and smart learning environments for students. Leveraging data collection and intelligent analysis, these devices adapt students’ learning behaviors to meet their individual needs. Refer to Badshah et al. (2024) for a summary of IoT smart devices in education. Specifically, the widespread use of personal smart devices, such as smartphones, tablets, and laptops, enables students to access a broad range of online learning resources (Al-Adwan, 2020). Moreover, these devices are integrated with LMS, facilitating streamlined interactions between teachers and students through course-related activities, such as learning, assignment submission, and assessment (Al-Mamary et al., 2024). In the physical campus environment, smart devices also enhance teaching and learning. Smart whiteboards enrich instruction by displaying multimedia content and promoting interactive learning, thereby increasing engagement (Alturki et al., 2021). Radio frequency identification devices automate attendance tracking, improving efficiency and accuracy. Surveillance cameras support campus safety and enable lecture recordings for post-class review. Smart audio systems improve classroom interaction, whereas virtual reality (VR) and augmented reality (AR) technologies immerse students in experiential learning, helping them better understand complex concepts (Faqih and Jaradat, 2021).

However, the widespread adoption of smart devices represents a double-edged sword. On the one hand, some studies have suggested that the excessive use of personal mobile devices can negatively affect learning outcomes. For example, Yao and Wang (2023) observed that excessive use of personal mobile devices can lead to information overload and technostress, which in turn can negatively impact students’ sleep quality and academic performance. On the other hand, the evolution of mobile learning applications has prompted a revaluation of the role personal mobile devices play in learning. Lin et al. (2021) conducted a large-scale study that revealed the positive, direct impacts of using mobile learning on academic performance and the adverse effects of using entertainment applications. This suggests that, in a smart learning environment, the proper use of smart devices as learning aids can lead to positive learning outcomes, a finding supported by other studies (Lee et al., 2022; Hossain et al., 2020, 2019). Regarding devices in the campus physical environment, research has consistently affirmed their positive influence on learning. For instance, Wang et al. (2022) revealed that students’ individual perceptions of classroom process quality were significantly related to their engagement in the smart classroom environment. Lu et al. (2021) observed that students’ positive perceptions of smart environments improve classmate interaction, motivation, and learning strategies, thereby fostering higher-order thinking. Similarly, Asad et al. (2024) highlighted that IoT-based smart laboratories promote academic performance through interaction, creativity, motivation, and hands-on learning.

Theoretical foundation

The integration of emerging educational technologies poses challenges for educational institutions in terms of technology acceptance and utilization. In recent years, several theoretical frameworks, including TAM, the theory of planned behavior (TPB), and the unified theory of acceptance and use of technology (UTAUT), have been widely used to study technology adoption in education. Among these, TAM is one of the most widely applied and influential models. Two constructs lie at its core: perceived ease of use (PEU) and perceived usefulness (PU) (Davis, 1989). If users believe that technology is easy to use, they will be more willing to continue using it in the future. On the other hand, if users believe that technology is useful for work, their attitude and intention to continue using it will strengthen. Moreover, many researchers have extended TAM by introducing external variables to gain a more context-specific understanding of technology adoption.

Unlike previous research that has focused on the adoption of a single technology, this study emphasizes how the core inherent characteristics of the smart campus ecosystem influence students’ attitudes. This research perspective has guided our theoretical foundation. Compared to TAM, the TPB and UTAUT models explain user behavior through external factors, such as subjective norms, perceived behavior control, social influence, and facilitating conditions. Therefore, these models were not chosen for this study. First, because these models incorporate many external social factors, they may shift the focus of the study toward the organizational level (Hu, 2022). Second, social factors are difficult to assess in advance, as the different functions of smart devices in the smart campus environment are often not fully known in advance (Lagstedt et al., 2020). Moreover, neither the TPB nor the UTAUT model directly addresses the core design attributes of a system. To maintain the simplicity of the model and emphasize the core inherent characteristics of the technology, as well as to keep the focus on a learning-center perspective, this study adopts TAM as the primary theoretical foundation.

Since its introduction in 1989, TAM has been extensively validated across different technologies and user groups, demonstrating its robustness and broad applicability. Particularly in the educational field, TAM has become the dominant model for studying the acceptance of educational technologies (Al-Mamary, 2022). Additionally, the TAM framework is widely used for research on IoT-based devices in education, particularly in guiding studies on smart campus technologies.

To better understand how the core inherent characteristics of smart devices influence students’ attitudes toward using them in the smart campus environment, this study extends TAM by incorporating TTF and PRT. TTF posits that when the characteristics of technology align well with task demands, users’ acceptance and usage intentions will significantly increase (Przegalinska et al., 2025). By analyzing the interaction between tasks (learning) and technology, the actual efficacy of the technology can be measured more effectively. Specifically, this study introduces two variables based on TTF: LS and PI, which are considered as key predictors of PU and PEU. Additionally, drawing on the PRT and considering the extensive data collection in smart campus environment, IS is selected as a direct predictor of PU and ATT. Figure 1 shows the research model of this study.

Learning support (LS)

The concept of LS was first proposed by Sewart (1993), and it was initially referred to as a series of informational, resource-based, personnel, and facility-based services provided by educational institutions to guide, assist, and promote personalized and self-directed learning among students, ultimately aiding learners in achieving their educational goals. Its original intention was to address issues such as decreased student motivation and insufficient engagement in distance learning environments (Tait, 2003).

However, with the rapid development of digital technologies, the scope and context of LS have undergone marked changes. LS is no longer confined to distance education but has been deeply integrated into higher education, becoming a key component in driving educational transformation (Wei et al., 2021). In the context of a smart campus, the integration of digital technologies provides students with more comprehensive and personalized LS. Smart devices, both personal and within smart learning environments, create convenient and diverse learning conditions for students, promoting the development of self-directed and personalized learning capabilities (Zhang et al., 2022) and enabling students not only to acquire knowledge but also to become the creators of knowledge.

To better understand the factors determining students’ attitudes toward the use of smart devices in smart campus environment, we incorporate LS into the research model and combine it with TTF. According to TTF, the characteristics of technology closely parallel task requirements, which aligns well with LS in educational technology contexts. In this context, LS refers to the degree to which the use of smart devices in the smart campus environment fits the requirements of students’ personalized learning and self-directed learning tasks. Specifically, it encompasses the provision of ubiquitous learning environments, as well as customized learning resources and feedback facilitated by smart devices in smart campus environment.

Currently, most research on LS has focused on e-learning, with existing studies showing that LS has a significant positive impact on students’ engagement (He et al., 2019), learning performance (Wongwatkit et al., 2020), and system satisfaction (Zhao et al., 2022). However, research on LS in the context of blended online–offline learning environments within smart campuses is relatively limited. Notably, LS serves as a key manifestation of alignment between task (learning activities) and technology, studies across various educational technologies in higher education, such as e-learning spaces (Wang et al., 2024a), video-based learning platforms (Pal and Patra, 2021), MOOCs (Kim & Song, 2022), and educational robots (Suhail et al., 2024), have demonstrated that task-technology alignment is crucial for technology adoption, as it enhancing both PU and PEU. Specifically, if students believe that smart devices can effectively support their self-direct and customized learning tasks, they are likely to perceive these devices as offering higher value in terms of enhancing learning efficiency and outcomes while reducing the cognitive load and effort required when using these devices for learning, thereby increasing their PU and PEU of the devices. The following hypotheses are thus proposed:

H1: LS has a positive effect on PU

H2: LS has a positive effect on PEU

Perceived interactivity (PI)

In an IoT-empowered smart campus environment, using smart devices to enhance collaboration and interactivity during learning is a prominent feature (Haleem et al., 2022c). Existing education research has subdivided interaction into learner–learner and learner–instructor interactions (Pan et al., 2023). Smart devices create favorable conditions for students to deeply engage in classroom activities, effectively enhancing interaction and communication between students, classmates, and teachers (Cheung et al., 2021). For instance, mobile learning applications on personal smart devices facilitate better communication between students and teachers, substantially improving the frequency and quality of interaction between both parties (Criollo-C et al., 2021). Moreover, multimedia tools in smart classrooms, such as screen sharing, digital whiteboards, and virtual discussion platforms, enhance the learning atmosphere, making classroom learning more engaging and interactive (Bilotta et al., 2021).

Knowledge resources acquisition and feedback are only one part of learning, and interaction and collaboration with instructors and classmates are equally vital components of the learning process (Sun et al., 2022). The interactive features of smart devices provide strong support for the successful completion of learning tasks. Therefore, based on TTF, PI can be defined as the degree of alignment between the technology characteristics and the task requirements. Specifically, in this context, PI refers to students’ perception of how smart devices enhance their interaction with instructors and classmates in the smart campus environment.

In the field of education, numerous studies have already established a link between interactivity and learning performance (Sun and Wu, 2016; Oyarzun et al., 2018). Some empirical research has also confirmed that, from the perspective of learner–learner and learner–instructor interactions, PI has a significant positive impact on PU and PEU of educational technologies (Girish et al., 2022). Similar to research on LS, that on PI has focused on online education, whereas studies on blended learning environments, which combine both online and offline learning, are relatively scarce. Specifically, when students perceive that the high-quality interaction facilitated by smart devices during learning, they tend to believe that these devices can improve learning performance and enhance learning efficiency, thus increasing their evaluation of PU. Meanwhile, the enhanced interactivity allows students more easily solve the problems associated with the use of the devices, reducing the cognitive load when using the smart devices, and thereby improving their PEU. Therefore, the following hypotheses are proposed:

H3: PI positively influences PU

H4: PI positively influences PEU

Information security (IS)

Ongoing security concerns in smart learning environments make it crucial to study how students perceive the risks associated with learning in such environments (Jiang et al., 2022). Risk perception is widely regarded as one of the major barriers to user adoption of innovative technologies. Numerous studies have identified information security as a key factor affecting the use of IoT devices (Jaspers and Pearson, 2022). In smart campus environment, data form the basis for services, and many IoT-based smart campus services rely on the collection and analysis of increasingly specific personal information (e.g., trajectories, learning documents, and biometric data) (Jurcut et al., 2020). The pervasiveness of sensing technologies that handle large amounts of personal sensitive data increases the risk of privacy breaches significantly, thereby hindering the effective application of smart campus technologies (Bates and Friday, 2017). Meanwhile, studies have shown that IoT systems in smart campuses face various attacks and threats, as well as many security vulnerabilities (Zhang et al., 2022). If these data are leaked, not only are users’ privacy severely infringed, but their personal and financial security are also threatened (Gill et al., 2021). In university environments, unauthorized access or hacking could alter or leak sensitive information, such as exam papers, student grades, and academic papers, thereby damaging the institution’s academic integrity, affecting students’ academic careers, and tarnishing the institution’s reputation (Rajab and Eydgahi, 2019). Moreover, information security incidents may also have negative psychological effects on students and faculty, leading to anxiety, fear, and distrust (Aqeel et al., 2022).

The role of IS in the success of smart campuses is further underscored by PRT, which suggests that how individuals perceive potential risks associated with technology markedly influences their attitudes and behaviors toward adoption (Li, 2025). In the context of smart campus environment, IS refers to the degree to which students perceive that the smart campus system can protect their information from unauthorized access. Some scholars have argued that the success of future IoT-based smart campus applications depends on users’ perceptions of privacy, security, and trust (Sneesl et al., 2022a). Previous studies in various contexts have indicated that perceived information risk negatively influences not only users’ attitudes regarding technology use but also PU due to its association with negative consequences and uncertainty (Al-Adwan et al., 2023; Samadzad et al., 2023). Conversely, users who exhibit lower aversion to potential costs and losses are more likely to adopt a given technology; meanwhile, those who perceive lower risks in adoption tend to view the technology as more useful and form more positive attitudes toward it. Based on this, the following hypotheses are proposed:

H5: IS positively affects PU

H6: IS positively affects ATT

Perceived ease of use (PEU)

According to Davis (1989), PEU refers to the degree to which an individual believes that using a particular technology or system requires minimal physical and mental effort. In this study, PEU refers to university students’ subjective perception of the ease with which they can operate smart devices within the smart campus environment for learning-related activities. According to the TAM, PEU is a direct predictor of both PU and attitude toward using technology. Similarly, when students perceive smart devices in the smart campus environment as easy to use, they are more likely to enhance their perception of the effectiveness of the devices in improving learning efficiency and outcomes while also developing a more positive emotional and cognitive response to their use within the smart campus environment. This relationship has been widely validated in recent studies on educational technology adoption (Tan et al., 2023; Ma et al., 2024; Sawiji, 2024). Accordingly, the following hypotheses are proposed:

H7: PEU positively influences PU

H8: PEU positively influences ATT

Perceived usefulness (PU)

PU refers to the degree to which an individual believes that a particular technology can enhance job performance, particularly in terms of increasing efficiency and effectiveness (Davis, 1989). In the original TAM framework, PU is considered as a key antecedent of both attitude and behavior intention to use technology. Numerous studies have shown that PU positively influences the student’ attitudes and intentions regarding the use of educational technologies, including various tools and platforms within smart campuses (Zhang et al., 2023; Wang et al., 2024b). Specifically, when students perceive a technology as capable of improving their efficiency, they are more likely to regard it as useful and demonstrate a more positive attitude and intention to adopt it (Dhingra and Mudgal, 2019). In this study, PU is defined as university students’ perception of the effectiveness of using smart devices in the smart campus environment. As students recognize tangible benefits, such as increased learning efficiency or improved academic results, their cognitive and affective evaluation of using smart devices tends to become increasingly favorable, thereby reinforcing a positive attitude toward using them. Based on this, the following hypothesis is proposed:

H9: PU has a positive effect on ATT