A comparison of Fuzzy DNP and SEM in analyzing novel mobile learning technology acceptances by learners

Abstract

Mobile learning, the learning method which learners can leverage mobile devices to learn everywhere, has become the one of the most potential learning approach as novel mobile devices emerges. However, how the novel mobile learning technology can be accepted by learners was seldom addressed. Meanwhile, due to the unavailability of large number (more than 100) experts for evaluating novel mobile learning devices, the traditional statistical methods, e.g. the structural equation model (SEM), are not appropriate for evaluating the factors influencing the acceptance of novel techniques, a feasible research framework will be very helpful for achieving the evaluation purposes. In this work, the author proposes a novel technology acceptance modeling (TAM) evaluation framework with the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) based Network Process (DNP) to resolve the above mentioned real world problem. The SEM based analytic results based on learners will be introduced for demonstrating the effectiveness of the Fuzzy DNP. An empirical study based on the analysis of factors influencing Taiwanese mobile learners' acceptances of some specific mobile learning software being installed in iPhone will be introduced for demonstrating the feasibility and effectiveness of the proposed method. Based on the analytic results, the Fuzzy DNP based framework can be used for real world technology acceptance analysis of novel mobile learning techniques. Meanwhile, the derived factors which can influence the mobile learning technology acceptances can serve as the basis for educators and marketers for designing and improving the next generation learning devices.