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 multiperspective approach and have overlooked the impact of IoT on the technology acceptance model 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 the 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 United Arab Emirates.
Methods: A questionnaire was distributed to students across various colleges and universities in the United Arab Emirates, 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 to evaluate the significance and performance of each factor affecting IoT adoption.
Results: The study, involving 463 participants, identifies 2 primary levels at which factors influence the intention to adopt IoT technologies. Initial influences include technology optimism (TOP), innovation, and learning motivation, crucial for application engagement. Advanced influences stem from technology acceptance model constructs, particularly perceived ease of use (PE) and perceived usefulness (PU), which directly enhance adoption intentions. Detailed statistical analysis using partial least squares–SEM reveals significant relationships: TOP and innovativeness impact PE (β=.412, P=.04; β=.608, P=.002, respectively), and PU significantly influences TOP (β=.381, P=.04), innovativeness (β=.557, P=.003), and learning motivation (β=.752, P<.001). These results support our hypotheses (H1, H2, H3, H4, and H5). Further, the intention to use IoT is significantly affected by PE and usefulness (β=.619, P<.001; β=.598, P<.001, respectively). ANN modeling enhances these findings, showing superior predictive power (R2=89.7%) compared to partial least squares–SEM (R2=86.3%), indicating a more effective identification of nonlinear associations. Importance-performance map analysis corroborates these results, demonstrating the importance and performance of PU as most critical, followed by technology innovativeness and optimism, in shaping behavioral intentions to use IoT.
Conclusions: This research contributes methodologically by leveraging deep ANN architecture to explore nonlinear 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.
doi:10.2196/58377
Keywords
Introduction
Background
The use of Internet of Things (IoT) applications has been implemented in medical education projects []. IoT has the ability to completely transform the educational landscape by offering a more adaptable and quantifiable educational system that unites teachers and students under a single technological roof []. IoT innovation plays a significant role in transforming training at all levels, from school and college to university education []. Everyone, including students, instructors, and college campuses, can benefit from this innovation. Educators and administrators can leverage the power of IoT to connect people with devices and data, enabling them to gain valuable insights that have not been previously used in education. The traditional human-centered educational system has been transformed into an IoT-based one [-] using IoT. IoT has been leveraged to change the conventional personalized schooling system to an IoT-based system [-]. 
Figure 1 illustrates a cohort of university students actively engaging with IoT technology within an academic context. These individuals are adorned with sophisticated wearable devices, including smart glasses and smartwatches, which project information into their visual field, indicative of a digital and augmented educational milieu. The students are depicted with a palpable focus, using a portable electronic apparatus, likely for the manipulation of or interaction with the data overlay provided by their IoT devices.
The IoT has the potential to transform institutional practices and enhance learning capabilities across various levels and domains. University lecturers, students, and support staff can leverage large IoT platforms successfully. However, there is room for improvement in the utilization of IoT technology across different educational institutions. Researchers, scholars, and students can collaborate to develop IoT systems, devices, applications, and services, leading to the evolution of the educational environment as an increasingly dynamic and globally relevant subject. Better IoT deployments in colleges and universities have significantly improved positively to the creation of efficient and useful educational resources [,].
This research aims to explore how IoT technology is adopted and used by educators and students in universities across the Middle East. By integrating the technology acceptance model (TAM) and additional external factors, the study seeks to evaluate the effectiveness of IoT as an educational tool from the perspectives of both students and educators. Using the TAM as a metric, along with external variables, the research intends to identify and analyze the factors that influence the acceptance and use of IoT in medical education. The analysis uses a hybrid framework that combines structural equation modeling (SEM) and artificial neural networks (ANN) to examine how intrinsic motivational factors and perceived technological attributes affect IoT adoption. The SEM-ANN approach was specifically chosen to leverage the strengths of both methodologies [,]. SEM is highly effective in assessing the relationships between observed and latent variables, providing clarity on both the direct and indirect effects within the hypothesized model []. This method allows for robust statistical analysis capabilities, making it ideal for hypothesis testing and understanding the structural relationships among the theoretical constructs []. Conversely, ANN is used for its superior ability to model complex nonlinear relationships between variables, which are often not adequately captured by traditional linear models like SEM. ANN’s data-driven nature allows it to directly learn and adapt to these relationships from the data, thereby enhancing the model’s predictive accuracy and robustness. By integrating SEM with ANN, the study not only validates the theoretical framework through SEM’s rigorous statistical analysis but also enhances the predictive power and generalization capabilities of the model with ANN’s computational intelligence. This hybrid approach is particularly effective in exploring deeper, nonlinear interactions within the data, offering a more comprehensive understanding of the factors influencing IoT adoption. Given the complexities of modern datasets, which often exhibit nonlinear and digital behaviors among variables, this methodological integration is well-suited to achieving the research objectives and provides a justified, robust approach for the study.
The proposed hypotheses are: (1) intrinsic motivational factors like technology optimism (TOP), innovation, and learning motivation (LMT) significantly impact students’ intention to adopt IoT; (2) perceived ease of use (PE) and perceived usefulness (PU), fundamental components of the TAM, strongly predict IoT adoption intentions among students in the United Arab Emirates.
This paper is organized as follows: “Literature Review” section delves into the existing research related to the adoption of IoT technologies, highlighting key theories and previous findings that set the groundwork for this study. Section “Methodology” details the research design, sampling methods, data collection procedures, and analytical techniques used to investigate the hypotheses. Section “Findings” presents the results of the data analysis, offering quantitative insights into the factors influencing IoT adoption. Section “Discussion” interprets the findings in the context of the existing literature, discussing the implications for theory and practice. Finally, the section “Conclusions” summarizes the study’s main contributions, outlines its limitations, and suggests directions for future research. This structure is designed to provide a clear and logical progression through the topics covered, facilitating a comprehensive understanding of the study’s scope and conclusions.
Related Work
The review of existing literature on the topic of IoT has explored both practical and theoretical aspects, suggesting a correlation between IoT and other factors such as self-efficacy, technology utilization, motivation, security, privacy, training, and more [-], indicating a relationship between the IoT and other elements including self-reliance, technological use, inspiration, safety, privacy, schooling, and beyond [-]. Likewise, research has examined the impact of IoT in conjunction with TAM and other external factors, and IoT with the help of TAM and external factors [,].
As IoT applications become more intricate, they can significantly impact learning. The difficulty is the IoT technology’s quick development, which requires diverse skills ranging from developing IoT applications to incorporating devices into management systems that analyze device-generated data [,]. Previous research has highlighted the significance of IoT in addressing challenges students encounter when using modern IoT apps and gadgets. Potential solutions include focusing on computational thinking education, assisting students in solving challenges, and providing clear instructions and training to facilitate the integration of new students with IoT devices and encourage training in it.
A different approach was taken in another study, where a workshop was offered to address the importance of IoT. The results showed that students found the workshop highly satisfactory for learning about IoT, improving problem-solving skills, and enhancing problem-solving capabilities, while also finding it enjoyable [-].
Several outside variables, including drive, contentment, ease of use, effectiveness, involvement, and interest, have been investigated to explore the relationship between IoT and students’ attitudes. The most significant factors are motivation and enjoyment regarding IoT technology, which are essential for its acceptance among students. While satisfaction and performance indirectly affect the application of IoT, greater contentment levels are not proportional to higher interactions [,]. Recent studies have used qualitative and quantitative methods to examine the uptake of IoT and have identified favorable attitudes, ease of use, contentment, affordability, basic knowledge, security, and privacy as crucial factors that influence its adoption. Apart from motivation and enjoyment, training and experience are also crucial in uptaking IoT devices, and educational training workshops can significantly improve the learning curve, digital learning, real-life applications, and problem-solving skills [,,].
Previous research has explored the various benefits of implementing IoT technologies in educational settings, including the ability for teachers to gain insight into students’ performance and knowledge levels, as well as the potential for improved teaching quality. As a result, the use of IoT has the potential to greatly impact learning environments, leading to a more advanced educational landscape. This could also lead to changes in how we interact and collaborate as a society, as IoT technology continues to connect us in new ways [,]. While previous studies have investigated students’ attitudes toward IoT from different angles [,,], there has been limited research on the importance of IoT technology from the perspective of teachers’ knowledge of the significance of IoT innovation from the educational viewpoint of instructors and how it impacts pedagogy in the classroom. Therefore, this study aims to address this gap by developing a model that combines both students’ and teachers’ attitudes to examine the effectiveness and efficiency of IoT in educational environments. By creating a framework that incorporates the viewpoints of learners and educators, this research intends to close this imbalance by analyzing the usefulness and efficacy of IoT in classrooms.
Developing Hypotheses and Theoretical Framework
The study paradigm shown in 
Figure 2 describes how the inspiration for learning, technological exuberance, and technological advancements influence learners’ perceptions of the PU and ease of use (PE) of IoT devices. Such concepts have not yet been looked into in relation to IoT devices and applicability. Although earlier research has examined how these factors may affect IoT adoption plans [], their individual effects on students’ attitudes toward this technology have not been analyzed before.
LMT is a crucial factor impacting students’ behavior in conventional and digital educational settings. It is an outside variable of the suggested approach and can be influenced by multiple factors, including the learning environment, expectations, and social values. Research has shown that LMT significantly impacts students’ academic achievements []. Students must therefore be driven to acquire knowledge in order to achieve the suggested intended goals [,]. Highly motivated learners can achieve their learning goals spontaneously and willingly. Social cognitivism emphasizes the significance of requirements, conducts, and ideologies in education.
Researchers are investigating the influence of TOP and innovation on students’ adoption of IoT in the education sector. The study aims to understand students’ attitudes and behavior toward technology by using 3 theoretical concepts: inventiveness, technological exuberance, and learning incentive. The researchers have combined the (TAM) constructs with the aforementioned variables to accomplish their study’s objectives. Several researchers have used the TAM model to evaluate the effectiveness of IoT technology and its applications [,]. The model explains why people accept technology based on their attitudes and beliefs and how these beliefs impact their behavioral intention toward technology use [,]. It is an established concept that supports embracing technology in various settings and strongly links it to technological progress [-]. The TAM model can forecast variables like technology elation and inventiveness that influence how well a certain technology is received [,]. TOP refers to users’ favorable perception of technology, while technology innovativeness (TIN) refers to users’ willingness to adopt technology early and lead to its use.
The PE and PU constructs of TAM are closely related to TOP and TIN. In the academic setting, students’ perceptions of technology can be influenced by their peers and instructors. If the student’s immediate academic circle has a positive view of a particular technology, the student will likely also develop a favorable opinion. Similarly, tech-savvy students tend to have a positive self-perception. Early on in the acceptance of technology, students are usually eager to pioneer using advanced technologies [-].
Furthermore, a student’s optimism about technology is linked to their level of involvement in guiding its use. Similarly, technological optimism can significantly impact a student’s attitude. IoT acceptance is greater for learners who are enthusiastic about experimenting with novel innovations. Technological innovators infrequently perceive new innovations as being challenging or outside their comprehension. Individuals are more inclined to regret not having the freedom to play around with novel technologies []. As a result, a number of hypotheses are put forth:
- H1: TOP affects positively the PE.
- H2: TOP affects positively the PU.
- H3: TIN affects positively the PE.
- H4: TIN affects positively the PU.
- H5: LMT affects positively the PU.
- H6: PE affects positively the intention to use Internet of Things (INT).
- H7: PU affects positively the INT.
By evaluating the association between TAM and its connected factors, this research seeks to add to a collection of current work. It has been said that it is important to measure these variables and analyze their relationship to TAM to assess the efficiency of IoT in classrooms. Past research and literature have mostly focused on preservice teachers, and there is a need to conduct more studies involving in-service teachers to enhance the practicality of the TAM model [,]. Thus, this study investigates the correlation between teachers’ levels of TAM and their attitudes toward IoT acceptance when working with teachers from different fields.
Methods
Data Collection
The data collection for this study was carried out between January 20 and March 20, 2023, throughout the academic year 2023‐2024’s winter semester, at educational institutions in the United Arab Emirates. The research team used web-based surveys to collect data, with 500 questionnaires randomly distributed. Of these, 463 surveys were answered, resulting in a response rate of 93%. Some questionnaires were rejected due to missing values. Since a few surveys had no responses, they were discarded, leaving 769 usable questionnaires, which is considered an appropriate acceptable sample size according to Krejcie and Morgan []. Although the sample size exceeded the minimum requirements, the research team used SEM SmartPLS (version 3.2.7; SmartPLS GmbH) and SPSS Statistics (version 23; IBM Corp) to evaluate the hypotheses and confirm the relationship between variables. It is important to note that the hypotheses were based on previous theories related to IoT, which formed the foundation of this study.
Ethical Considerations
Ethical considerations were meticulously adhered to throughout the research process. All procedures involving human participants were approved by the institutional review board of the host universities in the United Arab Emirates (#RAREC00065), ensuring compliance with ethical standards. Informed consent was obtained from all participants before data collection, and they were informed of their rights to withdraw from the study at any time. Privacy and confidentiality were strictly maintained, with all data being anonymized and securely stored to prevent unauthorized access. Participants were not compensated for their participation, as the study involved minimal risk and was conducted as part of educational activities within the institutions involved.
Student’s Personal Information
The demographic data of the respondents (N=463) are presented in 
Table 1. The data showed that 70% (n=325) of participants were female and 30% (n=138) were male. In terms of age, 42% (n=193) of participants were between 18 and 29 years, 35% (n=163) were between 30 and 39 years, 21% (n=99) were between 40 and 49 years, and 2% (n=8) were between 50 and 59 years. Regarding education, 80% (n=372) of participants held a bachelor's degree, 14% (n=62) held a master's degree, and 6% (n=29) held a doctoral degree. To obtain participants' willingness to participate, the research team used a purposive sampling approach. The participants came from various universities, academic levels, and programs relevant to this research. SPSS Statistics was used to analyze the demographic data.
| Category | Value, n (%) | |
| Sex | ||
| 325 (70) | ||
| 138 (30) | ||
| Age (years) | ||
| 193 (42) | ||
| 163 (35) | ||
| 99 (21) | ||
| 8 (2) | ||
| Educational qualification | ||
| 372 (80) | ||
| 62 (14) | ||
| 29 (6) | ||
Study Instrument
This research proposed a survey instrument for validating the hypotheses. To assess the 6 constructs of the questionnaire, an additional 18 questions were included in the survey. The origins and histories of these constructs are presented in Table 2. To make the research more relevant, the researchers modified the questions from previous studies.
| Constructs | Items | Definition | Instrument | Sources |
| TINa |
| The term “technology innovativeness” describes a user’s perception that they are at the forefront of technology use. Users who are pioneers in adopting new technologies typically do not view them as complicated or difficult to comprehend. Such users may feel a sense of regret if they miss the chance to experiment with new technologies. |
| [] |
| TOPc |
| A person’s readiness to use technology is known as technological optimism. |
| [,] |
| LMTd |
| The concept of learning motivation is used to measure the behavioral intention to use technology. Motivation learning is composed of 4 key components, which are attention, relevance, confidence, and satisfaction. These components have been identified in previous studies [,]. |
| [,] |
| PEe |
| The TAMf was introduced by Davis [] as a means of assessing the effectiveness and acceptance of technology. The model includes the concept of PE, which refers to the user’s perception of how effortless it is to use the technology. |
| [] |
| PUg |
| Usefulness refers to PU that the users of technology may see. |
| [] |
| INTh |
| An individual’s view of what others think about a certain behavior is known as a behavioral intention to use. |
| [] |
aTIN: technology innovativeness.
bIoT: Internet of Things.
cTOP: technology optimism.
dLMT: learning motivation.
ePE: perceived ease of use.
fTAM: technology acceptance model.
gPU: perceived usefulness.
hINT: intention to use Internet of Things.
Survey Structure
The questionnaire survey given to the students has 3 sections. Three components make up the survey that is provided to learners: (1) personal data is the focus of the first section, (2) the general question related to the “Intention to Use IoT” is the second section, and (3) 15 items that deal with TIN, TOP, LMT, PE, and PU is present in the third section.
A 5-point Likert scale was used to assess the 18 items, with response options ranging from 1=strongly disagree to 2=disagree, 3=neutral, 4=agree, and 5=strongly agree.
Results
Data Analysis
In this study, the gathered data were analyzed using SmartPLS (version 3.2.7) software through the partial least squares–structural equation modeling (PLS-SEM) technique [-]. The evaluation consisted of 2 stages: the measurement model and the structural model [,]. PLS-SEM was selected for this research after considering several factors.
The selection of PLS-SEM for this study was based on several reasons. First, PLS-SEM is preferred when the study aims to build on an existing theory []. Second, PLS-SEM is effective in handling complex models in exploratory research. Third, PLS-SEM analyzes the entire model as a single entity rather than dividing it into components []. Finally, PLS-SEM allows for the simultaneous analysis of structural and measurement models, leading to more accurate results [].
Convergent Validity
Hair et al [] suggested that to assess the measurement model, it is important to examine the construct reliability (including Cronbach ɑ and composite reliability) and validity (including convergent and discriminant validity). Table 3 shows that the Cronbach ɑ readings are greater than the suggested criterion of 0.7, and vary from 0.797 to 0.858 [], indicating good construct reliability. Similarly, the proposed criterion is also greater than the composite reliability numbers, which vary from 0.735 to 0.858 []. Factor loading and average-variance extracted (AVE) analysis are required to evaluate convergent validity []. The factor loading values in Table 3 are higher than the recommended threshold of 0.7, and the AVE readings are greater than the suggested limit of 0.5, spanning from 0.556 to 0.712. These results suggest that there is convergent validity.
a, CRb, Dijkstra-Henseler’s ρ ≥0.70, and AVEc >0.5).| Constructs and items | Factor loading | CA | CR | AVE | |
| TINd | 0.856 | 0.824 | 0.608 | ||
| 0.723 | |||||
| 0.873 | |||||
| 0.858 | |||||
| TOPe | 0.842 | 0.858 | 0.701 | ||
| 0.804 | |||||
| 0.816 | |||||
| 0.801 | |||||
| LMTf | 0.815 | 0.851 | 0.712 | ||
| 0.765 | |||||
| 0.844 | |||||
| 0.758 | |||||
| PEg | 0.797 | 0.735 | 0.662 | ||
| 0.858 | |||||
| 0.825 | |||||
| 0.758 | |||||
| PUh | 0.858 | 0.853 | 0.556 | ||
| 0.801 | |||||
| 0.829 | |||||
| 0.732 | |||||
| INTi | 0.825 | 0.843 | 0.612 | ||
| 0.812 | |||||
| 0.721 | |||||
| 0.749 | |||||
aCA: Cronbach ɑ.
bCR: composite reliability.
cAVE: average-variance extracted.
dTIN: technology innovativeness.
eTOP: technology optimism.
fLMT: learning motivation.
gPE: perceived ease of use.
hPU: perceived usefulness.
iINT: intention to use Internet of Things.
Discriminant Validity
To assess discriminant validity, 2 criteria were recommended: the Heterotrait-Monotrait ratio and the Fornell-Larcker criterion []. According to Table 4, the Fornell-Larcker criterion is met, as the AVE and its square root for each construct exceed its correlation with other constructs []. Table 5 displays the Heterotrait-Monotrait ratio results, which demonstrate that each construct’s value is below the threshold value of 0.85 []. This suggests that discriminant validity exists, and the measurement model’s reliability and validity were confirmed without any issues. As a result, the collected data can be used for analyzing the structural model.
| TINa | TOPb | LMTc | PEd | PUe | INTf | |
| TIN | 0.864g | —h | — | — | — | — |
| TOP | 0.675 | 0.883g | — | — | — | — |
| LMT | 0.182 | 0.263 | 0.723g | — | — | — |
| PE | 0.664 | 0.245 | 0.236 | 0.861g | — | — |
| PU | 0.664 | 0.283 | 0.373 | 0.313 | 0.812g | — |
| INT | 0.540 | 0.573 | 0.275 | 0.407 | 0.286 | 0.890g |
aTIN: technology innovativeness.
bTOP: technology optimism.
cLMT: learning motivation.
dPE: perceived ease of use.
ePU: perceived usefulness.
fINT: intention to use Internet of Things.
gThese values represent the square root of the average variance extracted for each construct, according to the Fornell-Larcker criterion. They are placed diagonally to demonstrate discriminant validity. A construct should share more variance with its indicators than with other constructs (off-diagonal correlations).
hNot applicable.
| TINa | TOPb | LMTc | PEd | PUe | INTf | |
| TIN | —g | 0.355 | 0.473 | 0.113 | 0.741 | 0.336 |
| TOP | 0.355 | — | 0.406 | 0.512 | 0.579 | 0.512 |
| LMT | 0.473 | 0.406 | — | 0.702 | 0.559 | 0.021 |
| PE | 0.113 | 0.512 | 0.702 | — | 0.328 | 0.363 |
| PU | 0.741 | 0.579 | 0.559 | 0.328 | — | 0.486 |
| INT | 0.336 | 0.512 | 0.021 | 0.363 | 0.486 | — |
aTIN: technology innovativeness.
bTOP: technology optimism.
cLMT: learning motivation.
dPE: perceived ease of use.
ePU: perceived usefulness.
fINT: intention to use Internet of Things.
gNot applicable.
Hypotheses Testing Using PLS-SEM
To assess whether the theoretical constructs of the structural model are interconnected, the study used Smart PLS with maximum likelihood estimation to create a structural equation model []. The proposed hypotheses were then analyzed using this model. The results indicated a high level of predictive power for the model, with 86.3% of the variance in INT being accounted for, as shown in 
Figure 3 and Table 6.
Table 7 provides information on the β values, t values, and P values of all developed hypotheses based on the findings produced using the PLS-SEM technique. The researchers have confirmed each hypothesis. The empirical data supported H1, H2, H3, H4, H5, H6, and H7 following the data analysis hypotheses. This study demonstrates that TOP and TIN have a noteworthy influence on PE with respective regression coefficients (β=.412, P=.04), and (β=.608, P=.002), supporting H1 and H3. Moreover, the results indicate that PU is significantly impacted by TOP (β=.381, P=.04), TIN (β=.557, P=.003), and LMT (β=.752, P<.001), supporting hypotheses H2, H4, and H5, respectively. Finally, the study reveals that the relationship between PE and PU significantly affects INT with respective regression coefficients (β=.619, P<.001) and (β=.598, P<.001), supporting H6 and H7.
| Construct | R2 | Results |
| PEa | 0.692 | High |
| PUb | 0.731 | High |
| INTc | 0.863 | High |
aPE: perceived ease of use.
bPU: perceived usefulness.
cINT: intention to use Internet of Things.
| H | Relationship | Path | t test (df) | P value | Direction | Decision |
| H1 | TOPa -> PEb | 0.412 | 5.552 (461) | .048 | Positive | Supported |
| H2 | TOP -> PUc | 0.381 | 4.843 (461) | .04 | Positive | Supported |
| H3 | TINd -> PE | 0.608 | 10.247 (461) | .002 | Positive | Supported |
| H4 | TIN -> PU | 0.557 | 9.358 (461) | .003 | Positive | Supported |
| H5 | LMTe -> PU | 0.752 | 14.450 (461) | <.001 | Positive | Supported |
| H6 | PE -> INTf | 0.619 | 16.753 (461) | <.001 | Positive | Supported |
| H7 | PU -> INT | 0.598 | 14.195 (461) | <.001 | Positive | Supported |
aTOP: technology optimism.
bPE: perceived ease of use.
cPU: perceived usefulness.
dTIN: technology innovativeness.
eLMT: learning motivation.
fINT: intention to use Internet of Things.
ANN Results
The predictors identified during the PLS-SEM analysis are further investigated through ANN analysis using SPSS software. The PLS-SEM analysis identified TIN, TOP, LMT, PE, and PU as critical factors; therefore, ANN analysis also considers these 3 factors only. The structure of the ANN model is based on behavioral intention as an output neuron and TIN, TOP, LMT, PE, and PU as input neurons (
Figures 4-). ANN model supported deep learning in all the output neuron modes through its 2-hidden layer deep structure [,]. The researcher applied the activation function of the sigmoid function to hidden neurons, as well as output neurons, keeping the values of input and output neurons between [0, 1]; this allowed the researcher to obtain better performance from the research model [,]. He also applied the 10-fold cross-validation method to training and testing data in the ratio of 80:20 to ensure that there is no overfitting in the ANN model []. The researcher evaluated the root mean square of error (RMSE) to test the neural network model for accuracy. The training data showed an RMSE value of 0.1388 for the ANN model while the testing data showed an RMSE value of 0.1439. The training and testing data showed only slight variance in the RMSE values and SD values (ie, 0.0043 and 0.0096). Hence, we can infer that using the ANN model enhances the accuracy of the research model.
Sensitivity Analysis
The researcher estimated the value of normalized importance by comparing each predictor’s average value with the maximum mean value depicted among all predictors stated in percentage form. The values of normalized importance and mean importance computed for the predictors involved in ANN modeling are recorded in Table 8. This table also depicts the outcomes of the sensitivity analysis which identifies the predictor of PU to have the most significant impact on behavioral intention; the second most significant impact was imposed by TOP while the least impact was imposed by PE. ANN application was additionally assessed for its accuracy and performance by computing the goodness-of-fit for authentication and validation of the application. The ANN application uses goodness-of-fit just as PLS-SEM analysis uses R2 [,]. The predictive powers of both the applications are compared where ANN analysis outperforms the other one with a predictive power of (R2=89.7%) against the PLS-SEM predictive power of (R2=86.3%). Hence, ANN analysis can explain endogenous constructs more effectively than PLS-SEM. Moreover, ANN analysis is based on deep learning and has the potential to better identify nonlinear associations among constructs which results in variances in the predictive powers of the ANN and PLS-SEM methods.
| Importance | Normalized importance (%) | |
| TINa | 0.335 | 83.1 |
| TOPb | 0.463 | 94.8 |
| LMTc | 0.361 | 77.5 |
| PEd | 0.118 | 19.2 |
| PUe | 0.539 | 100 |
aTIN: technology innovativeness.
bTOP: technology optimism.
cLMT: learning motivation.
dPE: perceived ease of use.
ePU: perceived usefulness.
Importance-Performance Map Analysis
This study has used importance-performance map analysis (IPMA) as an advanced approach in PLS-SEM, which used behavioral intention as the main variable. As suggested by Ringle and Sarstedt [], a better interpretation of the results of PLS-SEM is possible by using IPMA. A substitute way to only test the path coefficients (ie, importance measure), the average value of the latent constructs, and their indicators (ie, performance measure) are also included in the IPMA []. According to IPMA, the total effects reflect the predecessor factors’ importance in developing the target factor (ie, behavioral intention), while the average of latent constructs’ values is a reflection of their performance. The IPMA findings are reported in 
Figure 7. The estimation of the importance and performance of the 5 factors (ie, TIN, TOP, LMT, PE, and PU) has been shown in this table. According to the findings, the PU has been reported to have the largest values in terms of both importance and performance measures. Furthermore, it can be clearly seen that TIN has the second largest values in terms of both importance and performance measures. The third largest value was reported in the case of TOP in terms of the importance measure; however, it has the smallest value on the performance measure. Relatively, the opposite scenario was reported in the case of PE, as it had the lowest value on the importance measure.
Discussion
Principal Findings
The main findings of this study indicate that the intention to adopt IoT technologies is positively influenced by both intrinsic motivational factors and TAM constructs. Specifically, TOP, innovation, and LMT play crucial roles at the initial level of influencing IoT adoption. At a secondary level, the perceived ease of use and PU, core elements of the TAM, directly enhance the INT technologies. Further analysis using ANNs and IPMA highlighted PU as a particularly significant predictor of IoT use intentions.
Based on these results, it is evident that technology features are critical in shaping users’ PE and PU toward IoT applications and tools. The positive influence of TOP, innovation, and LMT is reflected across all model variables, suggesting that well-designed technology features can significantly boost IoT acceptance. The TAM framework effectively provides a user-friendly experience that meets users’ value expectations and fosters positive emotional responses, which in turn, positively impacts their INT. Additionally, the significant positive impact of technology features on TOP boosts users’ trust in the system’s quality, further influencing their willingness to engage with IoT systems. The social aspect of technology use, where familiarity influences adoption, also plays a pivotal role in the INT features. Technology innovation contributes significantly to perceived ease and usefulness, enhancing trust and satisfaction during the user experience, thus fostering a conducive environment for IoT adoption.
The confirmation of hypotheses H1-H5 supports the proposed conceptual model and the proposed hypotheses [-]. Previous research has also produced results that are consistent with these findings. However, other studies have shown that the lack of sufficient security and privacy are major challenges that may hinder the deployment of IoT in education [,], and to reduce these obstacles, future efforts to implement IoT in education must consider these factors. Although IoT has not been widely adopted in resource-limited countries, scholars need to examine the factors affecting its adoption to enable effective deployment. Therefore, scholars need to investigate the use and adoption of these technologies in other domains.
Managerial Implications
The adoption of IoT in educational settings demands that administrators and educational leaders ensure their faculty is proficient in using IoT technologies effectively in the classroom. This includes competencies in handling relevant technology tools, understanding pedagogical integration, and applying these technologies within various teaching scenarios. To support this, institutions should offer targeted professional development that focuses on both the technical and educational aspects of IoT. Additionally, management should consider the infrastructure upgrades necessary to support IoT technologies, such as improved wireless networks and enhanced security measures to protect student data [,].
Practical Implications
The practical applications of this study highlight the necessity for educational curricula to evolve alongside technological advancements. Institutions offering programs in computer science and engineering should integrate IoT courses to prepare students for the demands of the workforce, which increasingly relies on IoT technologies. Moreover, schools should align their IoT strategies with real-world applications, providing students with hands-on opportunities to work with IoT in context. This could include partnerships with IoT companies or practical projects that allow students to solve real problems using IoT solutions [,].
Theoretical Implications
This study contributes to the academic understanding of technology adoption by confirming the significant role of intrinsic motivational factors and TAM constructs in the adoption of IoT technologies. By highlighting the dual influence of personal motivation and perceived technological attributes, this research extends existing models of technology acceptance. Furthermore, the use of advanced analytical methods such as ANN and IPMA provides a deeper insight into the nonlinear relationships among the constructs, offering a nuanced perspective that can inform future research in technology adoption theories. This could encourage scholars to explore how different educational contexts or cultural backgrounds influence the adoption and effective use of emerging technologies like IoT [,].
Conclusions
IoT technology has fundamentally altered the tech and business industries, laying the groundwork for the creation of intelligent societies and advancing social and economic development. The IoT has advanced quickly and significantly. The research team developed the PE and PU frameworks for Arab customers using path estimation and modeling of structural equations depending on their responses to analyze individuals’ acceptance of IoT. In addition to additional factors including technology exuberance, advancements in technology, and academic drive, the research additionally looked at the effects of TAM components on these factors. These factors and TAM components were found to be directly related to the study. According to the research, the incentive to learn, technological advancement, and technological positiveness all had a substantial influence on PE and PU. While earlier research has demonstrated that TAM components affect the motivation to use IoT, this research has investigated how other external factors, including social factors like LMT, can affect these constructs. The study used a conceptual model to examine users’ attitudes toward IoT adoption, with 2 levels of analysis. The first level focused on social attitudes, particularly the impact of the incentive to learn as a standalone factor. The second stage looked at how personal traits influenced technological positivism and inventiveness, influencing customer needs and IoT interactions. Subsequent research could look at how individual characteristics affect incentives to learn and look at extra technological aspects that affect IoT uptake. Subsequent research could improve the assessment and give more insight into the value of IoT by including mediating factors between personal characteristics and technological attributes. The theoretical framework could also be used with cutting-edge technology like metaverse systems and artificial intelligence.
Data Availability
The datasets generated or analyzed during this study are available from the corresponding author upon reasonable request.
Authors' Contributions
K Alhumaid wrote the manuscript and conducted the formal analysis. SS validated the results and reviewed and edited the manuscript. MK developed the software and created the visualizations. K Ayoubi contributed to the investigation and validation. All authors reviewed and approved the final manuscript.
Conflicts of Interest
None declared.
References
- Ali M, Bilal HSM, Razzaq MA, et al. IoTFLiP: IoT-based flipped learning platform for medical education. Digital Commun Netw. Aug 2017;3(3):188-194. [CrossRef]
- Almaiah M, Alfaisal R, Salloum S, et al. Integrating teachers’ TPACK levels and students’ learning motivation, technology innovativeness, and optimism in an IoT acceptance model. Electronics (Basel). 2022;11(19):3197. [CrossRef]
- Al-Adwan AS, Al-Debei MM. The determinants of Gen Z’s metaverse adoption decisions in higher education: integrating UTAUT2 with personal innovativeness in IT. Educ Inf Technol. Apr 2024;29(6):7413-7445. [CrossRef]
- Banica L, Burtescu E, Enescu F. The impact of internet-of-things in higher education. Sci Bull Sci. 2017;16(1):53-59. URL: http://economic.upit.ro/repec/pdf/2017_1_6.pdf [Accessed 2025-03-23]
- Gul S, Asif MA, Ahmad S, et al. A survey on role of internet of things in education. Int J Comput Sci Netw Secur. 2017;17(5):159-165. URL: https://www.researchgate.net/profile/Muhammad-Yasir-5/publication/345896193_A_Survey_on_Role_of_Internet_of_Things_in_Education/links/5fb11b8b92851cf24cd29ba7/A-Survey-on-Role-of-Internet-of-Things-in-Education.pdf [Accessed 2025-03-23]
- Wakefield KJ. How the Internet of Things is transforming manufacturing. Forbes. Jul 1, 2014. URL: https://www.forbes.com/sites/ptc/2014/07/01/how-the-internet-of-things-is-transforming-manufacturing/ [Accessed 2025-03-26]
- Aldowah H, Ul Rehman S, Ghazal S, Naufal Umar I. Internet of Things in higher education: a study on future learning. J Phys: Conf Ser. Sep 21, 2017;892(1):012017. [CrossRef]
- Ning H, Hu S. Technology classification, industry, and education for future Internet of Things. Int J Commun. Sep 2012;25(9):1230-1241. [CrossRef]
- Kurdi BA, Alshurideh A, Nuseir M, Aburayya A, Salloum SA. The effects of subjective norm on the intention to use social media networks: an exploratory study using PLS-SEM and machine learning approach. In: Advanced Machine Learning Technologies and Applications (AMLTA 2021). 2021:581-592. [CrossRef]
- Almarzouqi A, Aburayya A, Salloum SA. Determinants predicting the electronic medical record adoption in healthcare: a SEM-artificial neural network approach. PLoS One. 2022;17(8):e0272735. [CrossRef] [Medline]
- Almarzouqi A, Aburayya A, Salloum SA. Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis. Inform Med Unlocked. 2022;28:100859. [CrossRef]
- Alhumaid K, Alnazzawi N, Akour I, Khasoneh OA, Alfaisal R, Salloum S. An integrated model for the usage and acceptance of stickers in WhatsApp through SEM-ANN approach. Int J Data Netw Sci. 2022;6(4):1261-1272. [CrossRef]
- Malhotra S, Kumar A, Dutta R. Effect of integrating IoT courses at the freshman level on learning attitude and behaviour in the classroom. Educ Inf Technol. May 2021;26(3):2607-2621. [CrossRef]
- Lai YH, Chen SY, Lai CF, Chang YC, Su YS. Study on enhancing AIoT computational thinking skills by plot image-based VR. Interact Learn Environ. Apr 3, 2021;29(3):482-495. [CrossRef]
- Mavroudi A, Divitini M, Gianni F, Mora S, Kvittem DR. Designing IoT applications in lower secondary schools. Presented at: 2018 IEEE Global Engineering Education Conference (EDUCON); Apr 17-20, 2018; Santa Cruz de Tenerife, Spain.
- Yamamoto T, Horita T. Analysis of teacher training students’ understanding on learning objectives in elementary school programming classes using IoT materials. Presented at: Society for Information Technology & Teacher Education International Conference; Apr 6-10, 2020; New Orleans, LA, United States.
- Akbar MA, Rashid MM, Embong AH. Technology based learning system in Internet of Things (IoT) education. Presented at: 2018 7th International Conference on Computer and Communication Engineering (ICCCE); Sep 19-20, 2018; Kuala Lumpur. [CrossRef]
- Lee S, Cho K. A study on the structural relationship between IoT usage and life satisfaction among university students. J Internet Things Converg. 2021;7(2):55-63. URL: https://koreascience.kr/article/JAKO202120461983908.page [Accessed 2025-03-26]
- Glaroudis D, Iossifides A, Spyropoulou N, Zaharakis ID. Investigating secondary students’ stance on IoT driven educational activities. Presented at: European Conference on Ambient Intelligence; Nov 12-14, 2018; Larnaca, Cyprus.
- Yildiz Durak H. Modeling of relations between K-12 teachers’ TPACK levels and their technology integration self-efficacy, technology literacy levels, attitudes toward technology and usage objectives of social networks. Interact Learn Environ. Oct 3, 2021;29(7):1136-1162. [CrossRef]
- Yang J, Wang Q, Wang J, Huang M, Ma Y. A study of K-12 teachers’ TPACK on the technology acceptance of E-schoolbag. Interact Learn Environ. Oct 3, 2021;29(7):1062-1075. [CrossRef]
- Pastor-Vargas R, Tobarra L, Robles-Gómez A, Martin S, Hernández R, Cano J. A WoT platform for supporting full-cycle IoT solutions from edge to cloud infrastructures: a practical case. Sensors (Basel). Jul 5, 2020;20(13):3770. [CrossRef] [Medline]
- Akbari M, Rezvani A, Shahriari E, Zúñiga MÁ, Pouladian H. Acceptance of 5 G technology: mediation role of trust and concentration. J Eng Technol Manag. Jul 2020;57:101585. [CrossRef]
- Seiler L, Kuhnel M, Honal A, Ifenthaler D. Mobile learning analytics: potenziale für lernen und lehren am beispiel hochschule. In: Handbuch Mobile Learning. Springer; 2018:585-608.
- Majeed A, Ali M. How internet-of-things (iot) making the university campuses smart? QA higher education (QAHE) perspective. Presented at: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC); Jan 8-10, 2018; Las Vegas, NV, United States. [CrossRef]
- Singh UG, Msibi A. Students’ perceptions towards IoTs adoption: a case study. J Phys: Conf Ser. Jan 1, 2021;1714(1):012044. [CrossRef]
- Liu Y, Gan Y, Song Y, Liu J. What influences the perceived trust of a voice-enabled smart home system: an empirical study. Sensors (Basel). Mar 13, 2021;21(6):2037. [CrossRef] [Medline]
- Hampton NZ, Mason E. Learning disabilities, gender, sources of efficacy, self-efficacy beliefs, and academic achievement in high school students. J Sch Psychol. Mar 2003;41(2):101-112. [CrossRef]
- McCown RR, Driscoll M, Roop P. Facilitating student motivation. In: Educational Psychology: A Learning-Centered Approach to Classroom Practice. Allyn and Bacon; 1996:278-309.
- Stipek D, Feiler R, Daniels D, Milburn S. Effects of different instructional approaches on young children’s achievement and motivation. Child Dev. Feb 1995;66(1):209. [CrossRef]
- Sırakaya M, Sırakaya DA. Augmented reality in STEM education: a systematic review. Interact Learn Environ. 2020:1-14. URL: https://www.tandfonline.com/doi/abs/10.1080/10494820.2020.1722713 [Accessed 2025-03-26]
- Alsyouf A, Lutfi A, Alsubahi N, et al. The use of a technology acceptance model (TAM) to predict patients’ usage of a personal health record system: the role of security, privacy, and usability. Int J Environ Res Public Health. Jan 11, 2023;20(2):1347. [CrossRef] [Medline]
- Esteban-Millat I, Martínez-López FJ, Pujol-Jover M, Gázquez-Abad JC, Alegret A. An extension of the technology acceptance model for online learning environments. Interact Learn Environ. Oct 3, 2018;26(7):895-910. [CrossRef]
- Huang HM, Liaw SS, Lai CM. Exploring learner acceptance of the use of virtual reality in medical education: a case study of desktop and projection-based display systems. Interact Learn Environ. Jan 2, 2016;24(1):3-19. [CrossRef]
- Parasuraman A. Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. J Serv Res. 2000;2(4):307-320. [CrossRef]
- Al-Adwan AS. Investigating the drivers and barriers to MOOCs adoption: the perspective of TAM. Educ Inf Technol. Nov 2020;25(6):5771-5795. [CrossRef]
- Alsyouf A, Lutfi A, Al-Bsheish M, et al. Exposure detection applications acceptance: the case of COVID-19. Int J Environ Res Public Health. Jun 14, 2022;19(12):7307. [CrossRef] [Medline]
- Chin WW. The partial least squares approach to structural equation modeling. In: Modern Methods for Business Research. Vol 295. Psychology Press; 1998:295-336.
- Alhumaid K, Rizk RY, Snášel V, Abdel-Kader RF. Predicting the intention to use audi and video teaching styles: an empirical study with PLS-SEM and machine learning models. Presented at: International Conference on Advanced Machine Learning Technologies and Applications; May 5-7, 2022; Cairo, Egypt. [CrossRef]
- Ngafeeson MN, Sun J. E-Book acceptance among undergraduate students: a look at the moderating role of technology innovativeness. Int J Web-Based Learn Teach Technol. 2015;10(3):36-51. [CrossRef]
- Li CH, Wu JJ. The structural relationships between optimism and innovative behavior: understanding potential antecedents and mediating effects. Creat Res J. Apr 2011;23(2):119-128. [CrossRef]
- Ziyae B, Mobaraki MH, Saeediyoun M. The effects of psychological capital on organizational performance with an emphasis on the mediatory role of innovation in information technology: a case study of the branches of agriculture bank in Tehran. Innov Manag J. 2015;3(4):77-100. URL: https://www.nowavari.ir/article_14739_en.html?lang=en [Accessed 2025-03-26]
- Issham I, Siti NA, Nizuwan A. Accessing innovativeness of distance learners toward their readiness in embracing technology. African J Bus Manag. 2011;5(33):12768-12776. [CrossRef]
- Al-Adwan AS, Li N, Al-Adwan A, Abbasi GA, Albelbisi NA, Habibi A. Extending the Technology Acceptance Model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Educ Inf Technol (Dordr). Apr 28, 2023:1-33. [CrossRef] [Medline]
- Karahanna E, Straub DW, Chervany NL. Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q. Jun 1999;23(2):183. [CrossRef]
- Kabakci Yurdakul I, Odabasi HF, Kilicer K, Coklar AN, Birinci G, Kurt AA. The development, validity and reliability of TPACK-deep: a technological pedagogical content knowledge scale. Comput Educ. Apr 2012;58(3):964-977. [CrossRef]
- Schmidt DA, Baran E, Thompson AD, Mishra P, Koehler MJ, Shin TS. Technological pedagogical content knowledge (TPACK) the development and validation of an assessment instrument for preservice teachers. J Res Technol Educ. 2009;42(2):123-149. [CrossRef]
- Krejcie RV, Morgan DW. Determining sample size for research activities. Educ Psychol Meas. Sep 1970;30(3):607-610. [CrossRef]
- Chung N, Han H, Joun Y. Tourists’ intention to visit a destination: the role of augmented reality (AR) application for a heritage site. Comput Human Behav. Sep 2015;50:588-599. [CrossRef]
- Huang HMM, Rauch U, Liaw SSS. Investigating learners’ attitudes toward virtual reality learning environments: based on a constructivist approach. Comput Educ. Nov 2010;55(3):1171-1182. [CrossRef]
- Huang HM, Liaw SS. An analysis of learners’ intentions toward virtual reality learning based on constructivist and technology acceptance approaches. Int Rev Res Open Distrib Learn. 2018;19(1):1. [CrossRef]
- Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. Sep 1989;13(3):319. [CrossRef]
- Fishbein M, Ajzen I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley; 1977.
- Ringle CM, Wende S, Becker JM. SmartPLS 3. SmartPLS; 2015.
- Mouzaek E, Marzouqi A, Alaali N, Salloum S, Aburayya A, Suson O. An empirical investigation of the impact of service quality dimensions on guests satisfaction: a case study of Dubai hotels. J Contemp Issues Bus Gov. Apr 2, 2021;27(3):1187. [CrossRef]
- Salloum SA, Mhamdi C, Kurdi BA, Shaalan K. Factors affecting the adoption and meaningful use of social media: a structural equation modeling approach. Int J Inf Technol Lang Stud. 2018;2(3):96-109.
- Hair J, Hollingsworth CL, Randolph AB, Chong AYL. An updated and expanded assessment of PLS-SEM in information systems research. Ind Manag Data Syst. Apr 10, 2017;117(3):442-458. [CrossRef]
- Salloum SA, Al-Emran M, Habes M, Alghizzawi M, Ghani MA, Shaalan K. What impacts the acceptance of E-learning through social media? An empirical study. Recent Adv Technol Accept Model Theor. 2021:419-431. [CrossRef]
- MacKenzie SB, Podsakoff PM, Jarvis CB. The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. J Appl Psychol. Jul 2005;90(4):710-730. [CrossRef] [Medline]
- Goodhue DL, Lewis W, Thompson R. Does PLS have advantages for small sample size or non-normal data? MIS Q. 2012;36(3):981. [CrossRef]
- Barclay D, Higgins C, Thompson R. The partial least squares (PLS) approach to casual modeling: personal computer adoption and use as an illustration. Technol Stud. 1995. URL: https://www.scirp.org/reference/referencespapers?referenceid=2800017 [Accessed 2025-03-23]
- Nunnally JC, Bernstein IH. Psychometric Theory. McGraw Hill; 1994.
- Kline RB. Principles and Practice of Structural Equation Modeling. Guilford Publications; 2015.
- Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. Feb 1981;18(1):39-50. [CrossRef]
- Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. Jan 2015;43(1):115-135. [CrossRef]
- Almaiah MA, Alfaisal R, Salloum SA, et al. Examining the impact of artificial intelligence and social and computer anxiety in E-learning settings: students’ perceptions at the university level. Electronics (Basel). 2022;11(22):3662. [CrossRef]
- Lee VH, Hew JJ, Leong LY, Tan GWH, Ooi KB. Wearable payment: a deep learning-based dual-stage SEM-ANN analysis. Expert Syst Appl. Nov 2020;157:113477. [CrossRef]
- Elareshi M, Habes M, Youssef E, Salloum SA, Alfaisal R, Ziani A. SEM-ANN-based approach to understanding students’ academic-performance adoption of YouTube for learning during Covid. Heliyon. Apr 2022;8(4):e09236. [CrossRef] [Medline]
- Liébana-Cabanillas F, Marinkovic V, de Luna IR, Kalinic Z. Predicting the determinants of mobile payment acceptance: a hybrid SEM-neural network approach. Technol Forecast Soc Change. Apr 2018;129:117-130. [CrossRef]
- Aburayya A, Salloum SA, Alderbashi KY, et al. SEM-machine learning-based model for perusing the adoption of metaverse in higher education in UAE. Int J Data Netw Sci. 2023;7(2):667-676. [CrossRef]
- Sharma SK, Sharma M. Examining the role of trust and quality dimensions in the actual usage of mobile banking services: an empirical investigation. Int J Inf Manage. Feb 2019;44:65-75. [CrossRef]
- Leong LY, Hew TS, Ooi KB, Lee VH, Hew JJ. A hybrid SEM-neural network analysis of social media addiction. Expert Syst Appl. Nov 2019;133:296-316. [CrossRef]
- Salloum SA, AlAhbabi NMN, Habes M, Aburayya A, Akour I. Predicting the intention to use social media sites: a Hybrid SEM - machine learning approach. Adv Intell Syst Comput. 2021;1339:324-334. [CrossRef]
- Ringle CM, Sarstedt M. Gain more insight from your PLS-SEM results: the importance-performance map analysis. Ind Manage Data Syst. 2016. [CrossRef]
- Zaidi MFA. The IoT readiness of SMEs in Malaysia: are they worthwhile for investigation? Presented at: International Conference on International Business, Marketing and Humanities (ICIBMAH 2017); Aug 26-27, 2017; Alor Setar, Malaysia.
- Cadenas GA, Cantú EA, Lynn N, Spence T, Ruth A. A programmatic intervention to promote entrepreneurial self-efficacy, critical behavior, and technology readiness among underrepresented college students. J Vocat Behav. Feb 2020;116:103350. [CrossRef]
- Wang Y, Liu C, Tu YF. Factors affecting the adoption of AI-based applications in higher education. Educ Technol Soc. 2021;24(3):116-129. URL: https://www.jstor.org/stable/27032860 [Accessed 2025-03-25]
- Chweya R, Ajibade SSM, Buba AK, et al. IoT and big data technologies: opportunities and challenges for higher learning. Int J Recent Technol Eng (IJRTE). 2020;9(2):909-913. [CrossRef]
- Rajamohan K, Rangasamy S, Manoj CN, Mary L, Sabu R, Jose SS. Internet of things (IoT) as a game changer to the education sector. In: Advanced IoT Technologies and Applications in the Industry 40 Digital Economy. CRC Press; 2024:251-274. [CrossRef]
- Abraham S. Using internet of things (IoT) as a platform to enhance interest in electrical and computer engineering. Presented at: 2016 ASEE Annual Conference & Exposition; Jun 26-29, 2016; New Orleans, Louisiana. [CrossRef]
- Almodaires A. Technology-supported reflection: towards bridging the gap between theory and practice in teacher education. University of Twente; 2009. URL: https://research.utwente.nl/en/publications/technology-supported-reflection-towards-bridging-the-gap-between--2 [Accessed 2025-03-26]
- Marikyan M, Papagiannidis P. Unified Theory of Acceptance and Use of Technology: a review. In: TheoryHub Book. 2021.
- Syed H, Malik AN. Comparative study of effect of culture on technology adoption in Pakistan and USA. Bus Manag Rev. 2014;5(1):42. URL: https://cberuk.com/cdn/conference_proceedings/rogeimra14_6.pdf [Accessed 2025-03-26]
Abbreviations
| ANN: artificial neural network |
| AVE: average-variance extracted |
| INT: intention to use Internet of Things |
| IoT: Internet of Things |
| IPMA: importance-performance map analysis |
| LMT: learning motivation |
| PE: perceived ease of use |
| PLS-SEM: partial least squares–structural equation modeling |
| PU: perceived usefulness |
| RMSE: root mean square of error |
| SEM: structural equation modeling |
| TAM: technology acceptance model |
| TIN: technology innovativeness |
| TOP: technology optimism |
Edited by Andre Kushniruk; submitted 14.03.24; peer-reviewed by Abdalwali Lutfi, Ahmad Samed Al-Adwan, Azizollah Arbabisarjou, Ghulam Farid, Raghad Alfaisal; final revised version received 01.09.24; accepted 15.01.25; published 10.04.25.
Copyright© Khadija Alhumaid, Kevin Ayoubi, Maha Khalifa, Said Salloum. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 10.4.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be included.