Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam

The factors that influence students' attitudes towards mobile learning (ML) is an intriguing topic. Though previous studies have examined the determinants influencing the acceptance of ML, few studies have investigated the influences of different cross-cultural factors on ML acceptance in higher education. When students from various countries participate in class, it is very important to address and improve the learning experiences of cross-cultural students. To elucidate this issue, this study proposes a conceptual model and theoretical framework through the theory of planned behavior (TPB) by incorporating diverse constructs to examine the influences of cross-cultural students' attitudes toward ML. Moreover, the following issues must be addressed: (1) examining cross-cultural ML problems within cross-cultural perspectives in a culturally sensitive manner, and (2) identifying the similarities and differences in cross-cultural students' behavioral intentions (BI) when influenced by attitude (ATT), subjective norm (SN), and perceived behavior control (PBC). Data were collected using an online survey from 947 respondents in Taiwan, Vietnam, Indonesia, and China. Our results show that BI towards the adoption of ML was influenced by ATT, SN, and PBC among the Taiwanese, Chinese, Indonesian, and Vietnamese undergraduate students. In addition, PBC was a significant predictor for students in Taiwan and Vietnam but was not a significant predictor for students in China and Indonesia. On the contrary, SN was significant in China and Indonesia but not in Taiwan and Vietnam. These findings showed a weaker relationship with SN in both China and Indonesia. Overall, our proposed model has reached an acceptable level. As a result, 82.3% and 81.2% acceptance levels were found for Taiwan and Vietnam, respectively, indicating that these students exhibit PBC-orientated distinguishing characteristics. This implies that students in Taiwan and Vietnam seem to be have more confidence in their ability to accept and perform a specific task for ML. Meanwhile, it is interesting to note that the 77.3% and 80.2% acceptance levels for China and Indonesia, respectively, indicate that these students also have SN-orientated distinguishing characteristics. The findings imply that students tend to follow other students’ decisions to use or not use ML. These findings are expected to facilitate decision makers and service providers in formulating appropriate strategies to improve the uptake of ML activities. Furthermore, these findings can help us understand the issues facing ML adoption in different cultural settings and contribute to the design and adequate provisions of ML programs.