The Used of UTAUT-2 in Examining The Usage of Mobile-LMS Binus Online Learning

Ikhsan, Ridho Bramulya; Prabowo, Harjanto; Yuniarty, -; Simamora, Bachtiar H.

doi:10.1109/icimtech53080.2021.9535053

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…In addition, users believe that the LCNC platform could help them save time [40]. Previous research discovered an individual's belief in technology would help to enhance optimal performance (performance expectation) [41][42][43][44]. As a result, these studies produce the following hypothesis:…”

Section: Performance Expectationsmentioning

confidence: 99%

Enhancing Business Efficiency through Low-Code/No-Code Technology Adoption: Insights from an Extended UTAUT Model

Abdul Razak,

Phey Ernn,

Yussoff

et al. 2024

J. Hum. Earth Future

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The growing need for new applications and software has driven developers to seek quick development options. As a result, the low-code/no-code technology platform emerges as a potential option, leading to the adoption of low-code and no-code technologies in enterprises becoming a focus of inquiry. This study aims to examine users' behavioral intentions toward the adoption of the low-code/no-code technology platform, considering the increasing need for new applications. The Extended Unified Theory of Acceptance and Use of Technology (UTAUT) model serves as the theoretical framework for understanding the factors influencing individuals’ intentions to adopt low-code or no-code technologies. The study focused on the five key components of the model: Performance Expectation (PE), Effort Expectation (EE), Social Influence (SI), Perceived Risk (PR), and Perceived Cost (PC). Based on the surveys and data analysis techniques, the findings show relationship between these five categories with an individual's Behavioral Intention (BI) to adopt low-code/no-code technologies. Furthermore, the analysis identifies the most significant BI construct. These findings are beneficial to businesses seeking to enhance efficiency and expedite application development processes in response to increasing digital demands. In general, this study contributes to the topic of technology adoption and improves our understanding of the practicality of the Extended UTAUT model. Doi: 10.28991/HEF-2024-05-01-07 Full Text: PDF

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Section: Performance Expectationsmentioning

confidence: 99%

Enhancing Business Efficiency through Low-Code/No-Code Technology Adoption: Insights from an Extended UTAUT Model

Abdul Razak,

Phey Ernn,

Yussoff

et al. 2024

J. Hum. Earth Future

View full text Add to dashboard Cite

show abstract

“…Previous studies used UTAUT and UTAUT-2 models (Alalwan et al, 2017) to help assess user intentions in e-healthcare (Ben Arfi and Hikkerova, 2019), mobile banking (Pham et al, 2020), online shopping behaviour (Matsuoka, 2018), self-service technology, and restaurant industry (Jeon et al, 2011). UTAUT-2 has been utilised in predicting consumers' buying behaviour in a variety of contexts, including advanced driver assistance systems (Jun et al, 2019), IPV6 Adoption (Alghatrifi and Khalid, 2019), Mobile-LMS (Ikhsan et al, 2021), and Travel Mobile Application (Indrawati and Amalia, 2019). The UTAUT-2 model remains a subject of IS-related studies, and new frameworks have been developed by extending the UTAUT-2 model to other theories.…”

Section: Neobanking Adoption Studymentioning

confidence: 99%

Neobanking adoption – An integrated UTAUT-3, perceived risk and recommendation model

Bhatnagr

Rajesh

2023

SAJM

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PurposeThe authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.Design/methodology/approachThe authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.FindingsEmpirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.Research limitations/implicationsLimited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.Originality/valueThe study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.

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“…In previous studies on the Moodle LMS, Ikhsan used the UTAUT Model with the addition of an external variable, User Satisfaction (US) (Ikhsan, Prabowo, Yuniarty, et al, 2021). The US is a variable in the DeLone McLean Model.…”

Section: Introductionmentioning

confidence: 99%

Investigating Students’ Use of the Moodle Platform: A Pilot Study Testing

Zulherman,

Zain,

Sailin

2023

JHETP

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The use of the Moodle platform during the pandemic has dramatically increased, especially in education and learning. There have been numerous studies on user acceptance of learning technology. However, until now, there has been no evaluation research on user satisfaction by transformational leadership as a mediator. This study evaluated user satisfaction, namely students, based on the UTAUT and Delone McLean models. The method used is random sampling as a pilot study of 2 classes at a private Islamic university in Jakarta, Indonesia. In this study, we used ten variables, namely; Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), System Quality (SQ), Information Quality (IQ), Service Quality (SeQ), Transformational Leadership (TL), User Satisfaction (US). The results showed that eight hypotheses were accepted out of 6, so 75% of this pilot study was successful. We concluded that how students act significantly affects how interested they are in using the Moodle platform.

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The Used of UTAUT-2 in Examining The Usage of Mobile-LMS Binus Online Learning

Cited by 8 publications

References 31 publications

Enhancing Business Efficiency through Low-Code/No-Code Technology Adoption: Insights from an Extended UTAUT Model

Enhancing Business Efficiency through Low-Code/No-Code Technology Adoption: Insights from an Extended UTAUT Model

Neobanking adoption – An integrated UTAUT-3, perceived risk and recommendation model

Investigating Students’ Use of the Moodle Platform: A Pilot Study Testing

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