Abstract
<p align="left"> </p><p>This study investigates online students’ acceptance of mobile learning and its influence on learning achievement using an information system success and extended technology acceptance model (TAM). Structural equation modeling was used to test the structure of individual, social, and systemic factors influencing mobile learning’s acceptance, and how said acceptance influences learning satisfaction and achievement. Unlike earlier TAM-related research that did not provide a broad view of technological acceptance and its impact on learning activities, the present study’s results highlight the relationship between behavioral intention/learning satisfaction and learning achievement. Additionally, this study tests the theoretical model of successful mobile learning by empirically accepting mobile learning management systems. The findings further imply that students at online universities have started to accept mobile technology as a new learning tool; consequently, its acceptance has influenced their learning achievement both directly and indirectly. These discoveries should facilitate a better understanding of students’ usage of mobile learning systems in higher education, and provide timely guidance for its development and implementation.</p>
Highlights
technology acceptance model (TAM)Researchers have spent many years attempting to develop and test models for predicting technology acceptance
This study investigated online students’ acceptance of mobile learning and its influence on learning achievement (LA) using an Extended-Technology Acceptance Model (eTAM) and information system success (ISS) model
structural equation modeling (SEM) was employed to test the structure of individual, social, and systemic factors influencing the acceptance of mobile learning, and how its acceptance influenced learning satisfaction (LS) and LA
Summary
TAMResearchers have spent many years attempting to develop and test models for predicting technology acceptance. The TAM comprises factors affecting behavioral intentions in technology use, and demonstrates the effects of self-efficacy and outcome expectations (the perceived ease of use and usefulness of a technology [Davis & Venkatesh, 1996]) on attitudes toward technology use. Technology acceptance in this model entails four main factors: perceived ease of use (PEU), perceived usefulness (PU), attitudes toward technology use, and behavioral intention (BI). Among these predictors, PU and PEU are hypothesized to be the fundamental determinants of user acceptance, a notion verified through empirical support (Gibson, Harris, & Colaric, 2008). PU is the extent to which a person believes using a particular technology will enhance his or her job performance, while PEU refers to the degree of simplicity a prospective user expects from a target system (Davis et al, 1989)
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