Extended UTAUT Model for Mobile Learning Adoption Studies

Chand, Sailesh Saras; Kumar, Bimal Aklesh; Goundar, Munil Shiva; Narayan, Anupriya

doi:10.4018/ijmbl.312570

Cited by 6 publications

(4 citation statements)

References 56 publications

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“…With the wide adoption of mobile devices in recent years, together with the continued enhancement of hardware and software devices, m-learning has become a popular learning approach. The widespread use of mobile devices in the teaching and learning process indicates a bright future (Chand et al, 2022).…”

Section: Literature Review 21 Mobile Learningmentioning

confidence: 99%

Continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory

2023

Front. Psychol.

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IntroductionThis study examines the factors that predict Chinese students’ continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory.MethodOne hundred seventy undergraduates have participated in the survey and the structural equation modeling is conducted to assess the validity of the integrated model and hypotheses.ResultsThe findings show that instructor support can significantly predict autonomy, competence and relatedness. Autonomy and competence are positively related to perceived ease of use and continuance intention to use mobile learning for English acquisition. Relatedness significantly correlates with perceived ease of use but is not directly related to learners’ continuance intention. The relationship between perceived ease of use and continuance intention to adopt mobile learning is positive and significant. In addition, the results show that instructor support, autonomy, relatedness, competence and perceived ease of use can predict Chinese students’ continuance intention to use m-learning for second language acquisition, with 70.5% of the total variance in continuance intention being explained by these five variables.ConclusionThese results thus empirically support the integrated model, which can be used as a theoretical framework in future studies on mobile learning in higher education contexts. Moreover, the results of this study have a number of practical implications for universities and instructors.

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Section: Literature Review 21 Mobile Learningmentioning

confidence: 99%

Continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory

2023

Front. Psychol.

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“…For example, in combination with the characteristics of college informatisation teaching, computer anxiety (CA) and self-efficacy (SE)-two external variables-have been introduced on the basis of the integrated technology acceptance model (UTAUT), and an influencing factors model of college teachers' informatisation teaching acceptance has been constructed [35]. Seven new constructs (interaction, self-efficacy, innovation and motivation, satisfaction, attitude, literacy, and readiness, and non-functional needs) were added to the UTAUT model to investigate the adoption of mobile learning [36]. Furthermore, regarding the change of the moderating variable in the UTAUT model, Zhang et al [37] added teaching age as the moderating variable on the basis of the UTAUT model and conducted a study on the influencing factors of primary and secondary school teachers' use of online learning space.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study of the Factors Influencing Teachers' Willingness to Use Generative Artificial Intelligence Based on the UTAUT Model

Zhang,

Wareewanich

2024

Int. J. Interact. Mob. Technol.

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The advancement of wireless communication and mobile computing technologies has paved the way for the extensive application of Artificial Intelligence across diverse sectors, including electronics, automotive, medical, industrial, and educational fields. Employing the Unified Theory of Acceptance and Use of Technology, this study investigates the key determinants influencing preschool teachers’ willingness to utilize Generative Artificial Intelligence (GAI) in Jiangsu Province, China. The research, involving 154 participants, analyzes their inclination towards GAI adoption through four dimensions: performance expectancy, effort expectancy, social influence, and facilitating conditions. Findings reveal that performance expectancy, social influence, and facilitating conditions significantly enhance their willingness to adopt GAI. Additionally, teaching experience and IT proficiency were found to moderate the effects of certain variables on this willingness. This aligns with the broader goal of integrating digital technology into education, which is a vital element of the nexus between digital technology development and innovation.

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“…It identifies factors that influence the acceptance and use of technology. The model has been applied to various contexts, including information systems and technologies (IS/IT) (Khechine, Lakhal and Ndjambou, 2016), mobile learning (Chand et al, 2022), technology adoption in agriculture (Siregar et al, 2022b), information technologies in higher education, and social media apps adoption in SMEs (Abdat, 2020a). While the specific technologies being adopted may vary, the UTAUT model consistently considers factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions.…”

Section: The Unified Theory Of Acceptance and Use Of Technologymentioning

confidence: 99%

Unveiling Technology Acceptance in the Elevator Industry: An Extension of the Unified Theory of Acceptance and Use of Technology Model

Suwankanit,

Bowonchaiyarit,

2024

AJSSS

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This study aimed to examine the factors influencing individuals' behavioral intentions to use hydraulic elevators through Structural Equation Modeling (SEM) analysis and assess their feasibility across five crucial aspects: Economic, Technical, Legal, Operational, and Scheduling. We found that perceived benefits, perceived risks, and social influence significantly impact behavioral intentions. The feasibility analysis results indicate that hydraulic elevators are economically, technically, and operationally viable, with mean scores of 0.68, 5.03, and 4.98, respectively. However, potential legal and scheduling challenges were identified, with mean scores of 0.33 for both aspects. The study's implications suggest addressing individuals' perceptions of benefits, risks, and social influence, as well as informing decision-makers about practical considerations for successful hydraulic lift implementation. Despite limitations, such as self-reported data and cross-sectional survey design, the study provides valuable insights into factors influencing behavioral intentions and feasibility considerations. Future research could explore longitudinal data and additional factors, such as environmental concerns, accessibility, and user experience.

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Extended UTAUT Model for Mobile Learning Adoption Studies

Cited by 6 publications

References 56 publications

Continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory

Continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory

A Study of the Factors Influencing Teachers' Willingness to Use Generative Artificial Intelligence Based on the UTAUT Model

Unveiling Technology Acceptance in the Elevator Industry: An Extension of the Unified Theory of Acceptance and Use of Technology Model

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