An integrated model of acceptance for Pervasive learning from students` perspective

Abstract

The technological innovation and advancements have entirely transformed our daily life activities. Smart phones and wireless communication devices have reshaped society. New learning techniques, such as Pervasive learning, Mobile learning and Electronic learning have been developed through ICT innovation. P-learning uses a variety of devices embedded in the surrounding environment that make the user aware of its context in a way to provide an atmosphere that enhances learning, the system supports learning at anywhere, anytime, with any device and in any format while the user is on the move. Indeed, P-learning saves time and reduces expenses, such as travel and printed learning materials, in an affordable way of learning. Information Systems and Information Technology research provide the facility to assess the level of users’ intention to accept and use new technology, the objective of this research is to identify the factors influencing students to accept P-learning. In this research an integrated model is proposed and validated based on the technology acceptance model (TAM) and others factors from the information systems acceptance research and theories. This research uses mixed methodology where the researcher combines both qualitative and quantitative approach to get the desired research aims. According to proposed conceptual model the acceptance of P-learning was determined by eight factors i.e. perceived usefulness (PU), perceived ease of use (PEOU), compatibility (CMP), self-efficacy (SE), subjective norms (SN), complexity (CP), context awareness (CA) and ubiquity (UB). The model was tested on a sample of 380 online learning students of Pakistan. Using structural equation modelling with Analysis of Moment Structures (AMOS) software, data analysis showed that 12 out of 15 hypothetical paths were significant. The UB followed by CA were most influential factors on dependent variable. Overall, 58.7% was explained by the PU, PEOU, CA, and UB on behavioural intention to use P-learning. However, PEOU, CMP, SE, SN, CA, and UB was explained 42.2% of the variance in the PU and SE, SN, CP, CA, and UB explained 31.8% of the variance in the PEOU. However, the hypothesized relationships between the CMP and PU, CP and PEOU, and the CA and PEOU were found to be insignificant. SE and SN were found to be more influential factors of the PEOU than the PU. Besides many theoretical and methodological contributions for academicians, this research seizes many significant practical implementations for researchers, policy makers, technology consultants, information system developers, and designers.