Construction and psychometric analysis of teachers mobile learning acceptance questionnaire

Abstract

Purpose The mobile technology-pedagogy nexus has been instrumental in shifting the focus of information and communications technology in education from e-learning to mobile learning (ML). Learning with mobile technologies is more accessible, flexible, situated, personal, collaborative and lifelong. The use of technologies for educational purposes is the primary focus of higher education institutes. The successful implementation of ML rests on its acceptance by higher education teachers. It is, therefore, pertinent to determine the factors that impact higher education teachers’ acceptance of ML. This study aims to identify these factors and develop a reliable and valid instrument to measure higher education teachers’ acceptance of ML. Design/methodology/approach A sequential exploratory research design, a type of mixed method research was used for the study. A sound conceptual framework and rigorous scale development process provided the background for data collection and validation. Probability proportionate to size sampling technique was used to gather data from 212 higher education teachers from 42 higher education institutes. Teachers gave their responses on five-point Likert type items. The responses obtained were subjected to exploratory factor analysis, which provided a nine-factor solution. The factors were further validated through confirmatory factor analysis. Findings Teachers’ mobile learning (ML) acceptance questionnaire (TMLAQ), a 32-item questionnaire was developed to measure acceptance of ML among higher education teachers. A detailed literature review, interviews and focus group discussions with teachers facilitated the identification of nine constructs or antecedents of ML acceptance. These constructs were named as: perceived usefulness, ease of use, self-enhancement, constructivist belief (CB), technological barriers, attitude and behavioral intention. The scale possesses sound psychometric properties such as reliability, face validity, content validity and construct validity. Practical implications This instrument can serve as an authentic, valid and reliable measure of higher education teachers’ ML acceptance. It can be used by organizations to assess teachers’ perceptions and aid in the successful ML implementation. Originality/value There is a lack of measurement instrument that caters to wide angle view of teachers’ perception toward ML in the Indian context. This comprehensive scale will bridge this gap. Two new research constructs: CB and self-enhancement were found to be crucial from the teachers’ point of view. These constructs have not been explored in previous technology acceptance studies. To the best of the knowledge, such a comprehensive study has not been undertaken yet in the Indian context. This study can serve as a model for conducting similar kinds of studies in other developing nations.