Mobile learning acceptance and use in higher education during social distancing circumstances: an expansion and customization of UTAUT2

Sitar‐Tăut, Dan‐Andrei; Mican, Daniel

doi:10.1108/oir-01-2021-0017

Cited by 61 publications

(65 citation statements)

References 48 publications

(98 reference statements)

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“…These assumptions hold true especially in light of the rapid, erratic, and sudden outbreak of COVID-19. While the role of technology in supporting people in ordinary conditions is widely recognized, it is unclear the extent to which technology is capable to respond to their needs in unusual circumstances and extreme conditions such as COVID-19 [8].…”

Section: Introductionmentioning

confidence: 99%

Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

et al. 2021

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In higher education learning, e-learning systems have become renowned tools worldwide. The evident importance of e-learning in higher education has resulted in a prenominal increase in the number of e-learning systems delivering various forms of services, especially when traditional education (face-to-face) was suddenly forced to move online due to the COVID-19 outbreak. Accordingly, assessing e-learning systems is pivotal in the interest of effective use and successful implementation. By relying on the related literature review, an extensive model is developed by integrating the information system success model (ISSM) and the technology acceptance model (TAM) to illustrate key factors that influence the success of e-learning systems. Based on the proposed model, theory-based hypotheses are tested through structural equation modeling employing empirical data gathered through a survey questionnaire of 537 students from three private universities in Jordan. The findings demonstrate that quality factors, including instructor, technical system, support service, educational systems, and course content quality, have a direct positive influence on students’ satisfaction, perceived usefulness, and system use. Moreover, self-regulated learning negatively affects students’ satisfaction, perceived usefulness, and system use. Students’ satisfaction, perceived usefulness, and system use are key predictors of their academic performance. These findings provide e-learning stakeholders with important implications that guarantee the effective, successful use of e-learning that positively affects students’ learning.

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Section: Introductionmentioning

confidence: 99%

Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

et al. 2021

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“…This complicates the process of conducting a consistent analysis of instructor adoption. Modified versions of UTAUT2 have often eliminated moderating factors rendering a transparent comparison of studies unlikely (Gunasinghe et al , 2019; Mittal et al , 2021; Sitar-Taut and Mican, 2021), similar to studies using the related TAM model (Gibson et al , 2008; Rienties et al , 2016; Teo, 2010; Yuen and Ma, 2002).…”

Section: Suggestions For Further Research and Conclusionmentioning

confidence: 99%

Technology acceptance by university lecturers: a reflection on the future of online and hybrid teaching

Weilage

Stumpfegger

2022

OTH

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Purpose Most research using extended unified theory of acceptance and use of technology (UTAUT2) and other technology acceptance models (TAM) are quantitative studies. This leaves room for interpretation when they are applied to university lecturers’ acceptance of online teaching because the models were originally created for the consumer perspective. This study aims to bridge this gap by integrating existing (quantitative) research with (qualitative) reasoning. Design/methodology/approach This study reflects online and hybrid teaching acceptance by reviewing exemplary existing research using UTAUT2 as the conceptual framework. Findings UTAUT2 TAMs use a broad range of criteria that do not immediately agree with university lecturers’ acceptance of online teaching. This study finds that existing research results are inconclusive and attempts to link criteria when suitable. Performance expectancy should not only encompass individual attitudes and skills but also the nature of the subject taught. Social influence is driven by recognition and student evaluations. Hedonic motivation best fits the elsewhere well-researched concept of intrinsic motivation. This study suggests that universities choose their online teaching technology wisely, promote its ease of use and offer training as well as continuous support to lecturers, especially when addressing future uncertainties. Originality/value This study explains the implications of using TAMs for research of higher education online teaching. Potential reasons and arguments for the inconclusiveness of the studies reviewed are discussed, and measures for university policy and communication improvement are suggested.

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“…Crompton and Burke (2018) found that 74% were undergraduate students and 54% took place in a formal education context. Thus, scholars have recently highlighted the importance of M-learning acceptance and use of applications in the context of higher education (Sitar-Taut and Mican, 2021;Yip et al, 2021). Prior studies argued that M-learning offers a number of benefits, such as enhancing learners' skills, providing opportunities to learn new things, enriching the learning experience, facilitating information sharing and communication, offering study helps to learners enhance confidence and learners' achievements and reducing cost (Crompton and Burke, 2018;Gureyev et al, 2020;Hamidi and Chavoshi, 2018;Reychav et al, 2015;Tan et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying the time continuum model of motivation to explain how major factors affect mobile learning motivation: a comparison of SEM and fsQCA

Fan

Ndavi

Huang

et al. 2022

OIR

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PurposeStudying mobile learning – the use of electronic devices (i.e. cellphone and tablets) to engage in learning across multiple contexts via connection to peers, media, experts and the larger world is a relatively new academic enterprise. This study analyzes the influencing factors of mobile learning (M-learning) motivation based on the time continuum model of motivation (TCMM).Design/methodology/approachThe study uses structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to verify relationships between mobile learning motivation, attitude, need, stimulation, emotion, ability and reinforcement. Justification for the use of both methods lies in the complementarity relationships that existed between the variables and research methodologies. The sample contains 560 mobile learners' feedback.FindingsResults show that attitude, need, emotion, ability and reinforcement are important factors to enhance mobile learning motivation, while stimulation is not.Practical implicationsThis work highlights the importance of training for app designers on how to design an M-learning App with high learning motivation by paying prior attention to learning content, teaching team and online learning communities.Originality/valueThis study proposes three precise solutions (scholars, managers and practitioners) to improve learning motivation based on the categorization of mobile learners.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0226.

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Mobile learning acceptance and use in higher education during social distancing circumstances: an expansion and customization of UTAUT2

Cited by 61 publications

References 48 publications

Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach

Technology acceptance by university lecturers: a reflection on the future of online and hybrid teaching

Applying the time continuum model of motivation to explain how major factors affect mobile learning motivation: a comparison of SEM and fsQCA

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