Besides, response cost was found to have a significant negative effect on students’ behavioral intention. The results indicated that perceived severity, perceived vulnerability, self-efficacy, response efficacy, subjective norm, attitude, and perceived behavioral control have a significant positive impact on students’ behavioral intention to use smartwatches for educational purposes. Through the use of machine learning classification algorithms, the proposed model has been validated using data collected via an online survey from 511 university students. Therefore, this research aims to propose a theoretical research model through the integration of the theory of planned behavior (TPB) and protection motivation theory (PMT) to understand the students’ behavioral intention to use smartwatches in learning activities. However, there is a scarcity of knowledge regarding the determinants affecting the adoption of these wearables in education. The emergence of wearable technologies, including smartwatches, has received a considerable attention from scholars across several sectors. The results of this paper will offer valuable directions for mobile learning designers and developers to better promote mobile learning application utilization in universities. The results of machine learning predictive algorithms showed that constructs of perceived enjoyment, perceived ease of use, perceived usefulness, effectiveness, efficiency, behavioural intention to use and utilization could predict the acceptance of mobile learning within accuracy rate of 87%. The research findings found that RandomForest and IBK algorithms are the best two algorithms in predicting the main determinants of mobile learning acceptance as comparison with other machine learning algorithms with an accuracy of 81.3%. Machine learning algorithms were employed to analyze the hypothesized relationships among the constructs in the proposed model. Therefore, this study seeks to understand the main factors influencing the acceptance of mobile learning applications by proposing a hybrid model by combining the TAM with new constructs of TUT model. However, the acceptance of mobile learning system among university students is limited. Mobile learning applications are increasing popularity among learners due to their benefits and effectiveness. Mobile learning applications could play a crucial role during this pandemic. The global spread of COVID-19 has motivated many universities to adopt online distance learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems. The results revealed that "social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use" are the strongest and most important predictors in the intention of and students towards E-learning systems. To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. A theoretical framework was developed based on the technology acceptance model (TAM). The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. The research findings are believed to provide future directions for stickers developers to better promote stickers in educational activities.Į-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The results pointed out that IBk and RandomForest classifiers have performed better than the other classifiers in predicting the actual use of stickers with an accuracy of 78.57%. A novel approach was employed to analyze the hypothesized relationships among the constructs in the research model through the use of machine learning algorithms. A questionnaire survey was circulated to collect data from 372 university students who have been engaged in a “Group Talk” in WhatsApp. Thus, this research aims to empirically examine the determinants affecting the acceptance of WhatsApp stickers through a proposed theoretical model by integrating the technology acceptance model (TAM) with the uses and gratifications theory (U&G). However, the acceptance of stickers by university students is still in short supply. WhatsApp stickers are gaining popularity among university students due to their pervasiveness, specifically in educational WhatsApp groups.
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