Mobile learning acceptance in social distancing during the
            <scp>COVID</scp>
            ‐19 outbreak: The mediation effect of hedonic motivation

Sitar-Tăut, Dan Andrei

doi:10.1002/hbe2.261

Cited by 65 publications

(48 citation statements)

References 77 publications

(150 reference statements)

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“…Ketchen [74] stated that the PLS-SEM is adequate for validating predictive power and estimating significantly complex models. Accordingly, PLS-SEM has been used by many scholars in various fields such as e-commerce [75][76][77][78][79][80][81], information systems [82], e-government [83,84], and educational technology-related research [85][86][87][88]. Corresponding with Anderson and Gerbing [89], data analysis using SmartPLS v.3.3.3 was performed throughout two key analytical stages.…”

Section: Resultsmentioning

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: Resultsmentioning

confidence: 99%

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

et al. 2021

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“…The questionnaire items that extend the list presented in (Sitar-Tăut, in press), are put together after a rigorous TAM literature review, as Table A1 shows. The survey initially contained 49 items, including gender, age, experience and GPA, to be tested as possible moderators.…”

Section: Methodsmentioning

confidence: 99%

“…UTAUT . Our research is based on the first approach, UTAUT, met in a series of studies, such as (García Botero et al , 2018; Hoi, 2020; Sitar-Tăut, in press; Zhang et al , 2020). It is a reference hybrid empirical model developed by (Venkatesh et al , 2003), and the resulting complex research is based on eight relevant preexisting models in this field: the TAM, theory of planned behavior (TPB), combined TAM and TPB (C-TAM-TPB), model of PC utilization (MPCU), theory of reasoned action (TRA), innovation diffusion theory (IDT), social cognitive theory (SCT) and motivational model (MM).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

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

Sitar‐Tăut

Mican

2021

OIR

Self Cite

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PurposeThis paper investigates if the existing degree of students' acceptance and use of mobile or m-learning may face the online shift determined by SARS-CoV-2. Based on the extended unified theory of acceptance and use of technology (UTAUT2), a new comprehensive model, SD-UTAUT (social distancing-UTAUT), is developed to better understand relationships between the original constructs, plus personal innovativeness (PI) and information quality (IQ). It identifies the key factors affecting behavioral intention (BI) and use by examining the influence of revaluated hedonic motivation (HM) and learning value (LV) importance as mediators.Design/methodology/approachThe paper opted for an exploratory study involving 311 learners, using partial least squares structural equation modeling (PLS-SEM).FindingsSD-UTAUT can be a new m-learning model in higher education. It has high predictive power and confirmed 15 out of 16 hypotheses. The most powerful relationship is between performance expectancy (PE) and HM. IQ affected LV the most, since HM the behavioral use (BU). HM impacts the use behavior (UB) more than LV, but habit (HT) affects it the most.Research limitations/implicationsBecause of the pandemic context, output may lack generalizability and reproducibility.Practical implicationsTo improve usage, staff must provide better support, course creators emphasize the objectives and competencies and developers integrate innovation. The joy and pleasure of m-learning use may stimulate the LV through interesting and interactive content, like incorporating gamification.Originality/valueThe model set-up and circumstances are previously unseen. SD-UTAUT confirms ten new hypotheses and introduces the student's grade point average (GPA) as a moderator.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/OIR-01-2021-0017

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“…• "Consumers' cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them" [26] Habit • "The extent to which people tend to perform behaviors automatically because of learning" [26] Therefore, the UTAUT2 model seems appropriate specifically for mHealth technologies as it focuses on individuals and their needs [26,40]. This is visible, for example, in the construct of hedonic motivation, which has been described in some scientific articles as particularly important for consumers of a product or technology [26,41,42].…”

mentioning

confidence: 99%

Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study

Schretzlmaier¹,

Hecker²,

Ammenwerth³

2022

JMIR Hum Factors

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Background In recent years, the use of mobile health (mHealth) apps to manage chronic diseases has increased significantly. Although mHealth apps have many benefits, their acceptance is still low in certain areas and groups. Most mHealth acceptance studies are based on technology acceptance models. In particular, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was developed to predict technology acceptance in a consumer context. However, to date, only a few studies have used the UTAUT2 model to predict mHealth acceptance and confirm its suitability for the health sector. Thus, it is unclear whether the UTAUT2 model is suitable for predicting mHealth acceptance and whether essential variables for a health-related context are missing. Objective This study aims to validate the suitability of UTAUT2 for predicting mHealth acceptance. Methods In this study, diabetes was used as an example as mHealth apps are a significant element of diabetes self-management. In addition, diabetes is one of the most common chronic diseases affecting young and older people worldwide. An explorative literature review and guided interviews with 11 mHealth or technology acceptance experts and 8 mHealth users in Austria and Germany were triangulated to identify all relevant constructs for predicting mHealth acceptance. The interview participants were recruited by purposive sampling until theoretical saturation was reached. Data were analyzed using structured content analysis based on inductive and deductive approaches. Results This study was able to confirm the relevance of all exogenous UTAUT2 constructs. However, it revealed two additional constructs that may also need to be considered to better predict mHealth acceptance: trust and perceived disease threat. Conclusions This study showed that the UTAUT2 model is suitable for predicting mHealth acceptance. However, the model should be extended to include 2 additional constructs for use in the mHealth context.

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Mobile learning acceptance in social distancing during the COVID ‐19 outbreak: The mediation effect of hedonic motivation

Cited by 65 publications

References 77 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

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

Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study

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