Name
Abstract | ||
|---|---|---|
Purpose - Based on the literature on technology readiness, online learning readiness, and mobile computer anxiety, the purpose of this paper is to develop and validate a mobile learning readiness (MLR) scale which can be used to assess individuals' readiness to embrace m-learning systems.Design/methodology/approach - Based on previous literature, this study conceptualizes the construct of MLR and generates an initial 55-item MLR scale. A total of 319 responses are collected from a three-month internet-based survey. Based on the sample data, this study provides an empirical validation of the MLR construct and its underlying dimensionality, and develops a generic MLR scale with desirable psychometric properties, including reliability, content validity, criterion-related validity, convergent validity, discriminant validity, and nomological validity.Findings - This study develops and validates a 19-item MLR scale with three dimensions (i. e. m-learning self-efficacy, optimism, and self-directed learning). A tentative norm of the MLR scale is presented, and the scale's theoretical and practical applications are also discussed.Originality/value - This study is a pioneering effort to develop and validate a MLR scale. The results of this study are helpful to researchers in building m-learning theories and to educators in assessing and promoting individuals' acceptance of m-learning systems. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1108/IntR-10-2014-0241 | INTERNET RESEARCH |
Keywords | Field | DocType |
M-learning readiness, Mobile computer anxiety, Online learning readiness, Technology readiness | Mobile computing,Technology readiness,Discriminant validity,Computer science,Nomological network,Optimism,Artificial intelligence,Content validity,Machine learning,Marketing,Convergent validity,The Internet | Journal |
Volume | Issue | ISSN |
26 | 1 | 1066-2243 |
Citations | PageRank | References |
7 | 0.43 | 36 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsin-Hui Lin | 1 | 767 | 30.86 |
| Shin-jeng Lin | 2 | 96 | 28.69 |
| Ching-Hsuan Yeh | 3 | 31 | 2.08 |
| Yi-Shun Wang | 4 | 1798 | 70.08 |