Smart Devices as U-Learning Tools: Key Factors Influencing Users’ Intention

Abstract: There was a lack of knowledge about the user’s acceptance of smart devices as ubiquitous learning (u-learning) tools at higher education institutions in Sweden. As the mobile technology grows, the demand for mobile devices, particularly smart devices increases as well. With the increase in the usage of smart devices, the higher education institutions provide mobile learning platforms to attract more customers in the competitive industry of education. Thus, understanding the key factors from the perspectives of end-users is important for the institutions to survive in the competitive market. This study explores and explains Behavioral and Continuance intentions of students regarding the acceptance and usage of smart devices (Smartphones and Personal Digital Assistants or PDA) as u-learning tools. Key factors related to the users’ intentions to accept and continue using smart devices as u-learning tools were identified and hypothesized in the Swedish context. Ten hypotheses were suggested based on TAM, UTAUT, and ECT. To achieve the aim and objective of this study, a quantitative approach was chosen, and a survey strategy based on purposive and convenience sampling techniques were used. A web-based questionnaire on five-points Likert Scale was designed to collect the required data. 115 (96 valid) students answered the questionnaire. The collected data were used to conduct statistical operations in SPSS. Five hypotheses were supported, and the other five were not. The findings suggest that Performance Expectancy, Perceived Mobility value, Confirmation, and Satisfaction positively influence both Behavioral and Continuance Intentions of students to accept and continue using smart devices as u-learning tools. According to the findings, Confirmation and Satisfaction from ECT can be included as separate constructs in UTAUT and UTAUT2. Higher education institutions planning to have (and those that already have) learning platforms, compatible with smart devices, can benefit from the findings. Higher education institutions can also design their u-learning platforms according to the Performance Expectancy, Perceived Mobility value, Confirmation, and Satisfaction of the students.