Paper Type
Complete Research Paper
Description
Technology and mobile devices have been successfully integrated in peoples´ everyday activities. Educational institutions around the world are increasing their interest to create mobile learning (ML) environments considering the advantage of connectivity, situated learning, individualized learning, social interactivity, portability, affordability and more widely ubiquity. Even with the fast development of ML environments. There is however a lack of understanding about the factors that influnce ML adoption. This paper proposes a framework for ML adoption integrating a modified Unified Theory of Acceptance and Use of Technology (UTAUT) with constructs from the Expectation-Confirmation Theory (ECT). Since the goal for education is learning, this research will include individual attributes such as learning styles (LS) and experience to understand how they moderate ML adoption and actual use. For this reason, the framework brings together the adoption theory for initial use and the constructs of continuance intention for actual and habitual use as an outcome of learning. The framework is divided in two stages, acceptance and actual use. The purpose of this paper is to test the first stage: ML acceptance through the structural equation modeling statistical techniqu. The data was collected from students that already are experiencing ML. Findings demonstrate that performance and effort expectation constructs are significant predictors of ML and there is some influnce of LS and experience as moderators for ML adoption. The practical implication in educational services is to incorporate LS influnce when designing strategies for learning enhanced by mobile devices.
TECHNOLOGY ACCEPTANCE AND ACTUAL USE WITH MOBILE LERANING: FIRST STAGE FOR STUDYING THE INFLUENCE OF LEARNING STYLES ON THE BEHAVIORAL INTENTION
Technology and mobile devices have been successfully integrated in peoples´ everyday activities. Educational institutions around the world are increasing their interest to create mobile learning (ML) environments considering the advantage of connectivity, situated learning, individualized learning, social interactivity, portability, affordability and more widely ubiquity. Even with the fast development of ML environments. There is however a lack of understanding about the factors that influnce ML adoption. This paper proposes a framework for ML adoption integrating a modified Unified Theory of Acceptance and Use of Technology (UTAUT) with constructs from the Expectation-Confirmation Theory (ECT). Since the goal for education is learning, this research will include individual attributes such as learning styles (LS) and experience to understand how they moderate ML adoption and actual use. For this reason, the framework brings together the adoption theory for initial use and the constructs of continuance intention for actual and habitual use as an outcome of learning. The framework is divided in two stages, acceptance and actual use. The purpose of this paper is to test the first stage: ML acceptance through the structural equation modeling statistical techniqu. The data was collected from students that already are experiencing ML. Findings demonstrate that performance and effort expectation constructs are significant predictors of ML and there is some influnce of LS and experience as moderators for ML adoption. The practical implication in educational services is to incorporate LS influnce when designing strategies for learning enhanced by mobile devices.