Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India

Children and Youth Services Review, Journal Year: 2020, Volume and Issue: 119, P. 105535 - 105535

Published: Oct. 1, 2020

Language: Английский

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Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Interactive Learning Environments, Journal Year: 2020, Volume and Issue: 31(3), P. 1293 - 1308

Published: Oct. 14, 2020

This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The has made use Google Meet© as an educational social platform in private higher education institutes. data obtained from were analyzed by using partial least squares structural equation modeling (PLS-SEM) machine learning algorithms. main hypotheses this are related Meet rise s various types fear. During Coronavirus pandemic, due family lockdown situation, failure losing relationships most common threat that may face students teachers/educators. These fears connected with two important factors within TAM theory, which are: perceived ease (PEOU) usefulness (PU), another external factor TAM, is subjective norm (SN). results revealed both analysis techniques have successfully provided support all hypothesized research model. More interesting, J48 classifier performed better than other classifiers predicting dependent variable cases.

Language: Английский

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The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services

Computers in Human Behavior, Journal Year: 2021, Volume and Issue: 127, P. 107026 - 107026

Published: Sept. 17, 2021

Language: Английский

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The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model

Technology in Society, Journal Year: 2020, Volume and Issue: 63, P. 101430 - 101430

Published: Oct. 12, 2020

Language: Английский

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Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

JMIR Medical Education, Journal Year: 2021, Volume and Issue: 7(1), P. e24032 - e24032

Published: Jan. 14, 2021

Background Mobile learning has become an essential instruction platform in many schools, colleges, universities, and various other educational institutions across the globe, as a result of COVID-19 pandemic crisis. The resulting severe, pandemic-related circumstances have disrupted physical face-to-face contact teaching practices, thereby requiring students to actively use mobile technologies for learning. offer viable web-based platforms that are accessible teachers learners worldwide. Objective This study investigated purposes United Arab Emirates higher education institutions. Methods An extended technology acceptance model theory planned behavior were proposed analyze university students’ adoption accessing course materials, searching web information related their disciplines, sharing knowledge, submitting assignments during pandemic. We collected total 1880 questionnaires from different universities Emirates. Partial least squares-structural equation modeling machine algorithms used assess research model, which was based on data gathered student survey. Results Based our results, each hypothesized relationship within supported by analysis results. It should also be noted J48 classifier (89.37% accuracy) typically performed better than classifiers when it came prediction dependent variable. Conclusions Our revealed could considerably benefit adopting remote systems tools However, value such lessened because emotions experience, including fear poor grades, stress family circumstances, sadness loss friends. Accordingly, these issues can only resolved evaluating

Language: Английский

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Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model

Technology in Society, Journal Year: 2021, Volume and Issue: 64, P. 101515 - 101515

Published: Jan. 4, 2021

Language: Английский

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The Emergence of Service Robots at Restaurants: Integrating Trust, Perceived Risk, and Satisfaction

Sustainability, Journal Year: 2021, Volume and Issue: 13(8), P. 4431 - 4431

Published: April 15, 2021

As various types of robots increasingly influence consumers’ service experiences, companies need to consider how be competitive in this new artificial intelligence and automation business environment. However, only limited studies have investigated the factors involved consumer behaviors toward robot restaurant visitors their impacts. This study integrates trust, perceived risk, satisfaction with well-known Technology Acceptance Model’s (TAM) original constructs (perceived usefulness, ease use, acceptance) setting. A scenario-based online survey was performed on 338 respondents. Structural equational modeling shows direct impact PU usefulness) indirect PEOU use) revisit intention restaurants. Trust significantly increases a robot, increased trust decreases risk as well satisfaction. Further, intention. The provides useful information for hospitality marketers acknowledge consumers accept better understand key drivers

Language: Английский

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Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism

Computers in Human Behavior, Journal Year: 2023, Volume and Issue: 154, P. 108097 - 108097

Published: Dec. 27, 2023

Language: Английский

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Healthcare providers’ acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model

PLoS ONE, Journal Year: 2021, Volume and Issue: 16(4), P. e0250220 - e0250220

Published: April 22, 2021

Background In almost all lower and middle-income countries, the healthcare system is structured in customary model of in-person or face to care. With current global COVID-19 pandemics, usual health care service has been significantly altered many aspects. Given fragile high number immunocompromised populations lower-middle income economic impacts are anticipated be worse. such scenarios, technological solutions like, Telemedicine which defined as delivery remotely using telecommunication technologies for exchange medical information, diagnosis, consultation treatment critical. The aim this study was assess providers’ acceptance preferred modality telemedicine factors thereof among professionals working Ethiopia. Methods A multi-centric online survey conducted via social media platforms telegram channels, Facebook groups/pages email during Jul 1- Sep 21, 2020. questionnaire adopted from previously validated low setting. Internal consistency items assessed Cronbach alpha (α), composite reliability (CR) average variance extracted (AVE) evaluate both discriminant convergent validity constructs. extent relationship variables were evaluated by Structural equation modeling (SEM) SPSS Amos version 23. Results From expected 423 responses, 319 (75.4%) participants responded data collection period. majority male (78.1%), age <30 (76.8%) had less than five years work experience (78.1%). structural result confirmed hypothesis “self-efficacy a significant positive effect on effort expectancy” with standardized coefficient estimate (β) 0.76 p-value <0.001. also indicated that self-efficacy, expectancy, performance facilitating conditions influence have direct user’s attitude toward telemedicine. User’s behavioral intention use influenced expectancy attitude. ruled out does not directly squared multiple correlations (r 2 ) value 57.1% 63.6% explained model. Conclusion This found predictors professionals’ Attitude systems highly self-efficacy conditions. mediators predicting users’ addition, mHealth approach most opens an opportunity integrate post pandemic services low-income countries.

Language: Английский

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Modelling electric vehicle purchase intention among generation Y consumers in Malaysia

Research in Transportation Business & Management, Journal Year: 2022, Volume and Issue: 43, P. 100784 - 100784

Published: Jan. 17, 2022

Language: Английский

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