ABSTRACT
Purpose: The impact of the COVID-19 pandemic provides the scope to conduct online classes in the university teaching methods. This study aims to investigate the impact of technology self-efficacy on students' behavioral intention on the effectiveness of online learning. Design/methodology/approach: This study was conducted with 323 university students using the online survey platform. Data analysis was acquired by implementing the partial least squares technique to obtain the results. Findings: The findings revealed that the COVID-19 pandemic affects technology self-efficacy. Technology self-efficacy has a significance on perceived usefulness (PU) and ease of use, which influences students' behavioral intention to use online learning effectively. The results identified that user innovativeness facilitated the relationship between PU and behavioral intention to use online learning efficiency. Originality/value: This study has a significant insight into the higher educational institutes and academia that lessons from the impact of the COVID-19 pandemic on technology self-efficacy toward online learning effectiveness. © 2023, Emerald Publishing Limited.
ABSTRACT
PurposeThe study aims to identify the extent to which industry 4.0 (IR4.0) adoption impacts the sustainable manufacturing (SM) performance of the manufacturing industry, focusing on the comparative analysis between developed and developing economies amid coronavirus disease 2019 (COVID-19) pandemic.Design/methodology/approachThe study proposes a conceptual model formed on seminal theories and literature using the cross-sectional design. For data collection, a purposive sampling method is used where 154 Malaysian (developing) and Australian (developed) manufacturing firms' data were collected. Partial least square-based structural equation modeling is employed to test the hypothesis and proposed research model.FindingsThis study finds that adoption of IR4.0 technologies does not directly influence the sustainability performance of the manufacturing industry, but rather the trajectories of SM (efficiency, flexibility, automation and big data and granularity) fully mediate the relationship between IR4.0 adoption and sustainability manufacturing performance. The comparative analysis between Australia and Malaysia shows no significant difference in the relationships or the framework;hence, the differences between developed and developing countries are not significant in this mechanism.Originality/valueThe study contributes to the insights of the managers regarding COVID-19 and the implementation of IR4.0 in the SM domain. The policymakers would further get better insights since the study pays attention to sustainable development goal, industry, innovation, infrastructure and responsible production.
ABSTRACT
The most recent COVID-19 pandemic has posed a risk to the world economy that has never been seen before. Therefore, the welcoming attitude of the residents in tourist destinations has become a concern for post-COVID-19 tourism recovery. There seem to be many issues concerning the interactions of hosts and tourists as Covid-19 fear exists. Thus, this study aims to examine the role of place attachment and host tourists' attractions on the welcoming attitude of the residents in Langkawi, Malaysia. The researchers conducted a quantitative method and cross-sectional approach in this study. Researchers distributed 600 questionnaires to the respondents in Langkawi, Malaysia, and 461 usable questionnaires were returned and proceeded for further analysis. This study used structural equation modelling to use Smart PLS version 3 software. In structural equation modelling, the measurement and structural model of the study were reported. The study found that place attachment and host tourists interaction play a significant role in maximizing residents' welcoming attitude. The practitioners and academicians will be benefited from the outcome study while exploring tourism recovery strategies and post-Covid tourist arrival. © 2022 Editura Universitatii din Oradea. All rights reserved.
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The tourism industry remains profoundly uncertain due to the post-COVID-19 pandemic. The pandemic continues to hit hard in the global tourism and tourism events. The local tourism industry is assisting with relaxing the blow, governments have taken an amazing prompt move to re-establish and re-initiate the sector while supporting occupations and tourism businesses. Many countries are now developing measures to construct a stronger tourism economy post COVID-19 pandemic. These include incorporating planning that intends to help the sustainable recuperation of the travel and tourism events industry, promoting the digital transition and move to a greener tourism system, and rethinking the tourism and tourism events for the future. Digital transformation is changing the way people live, travel and work. It has opened up new opportunities for tourism events businesses to compete in global markets. This chapter aims to explore the rethinking of tourism and tourism events framework with the COVID-19 pandemic, the importance of technology acceleration and its impact on tourism events. This study uses a method of research to synthesize existing literature, and concepts that are being explained in the context of digital transformation in the tourism events industry, and future research direction with the ongoing uncertainty of the COVID-19 pandemic. This chapter offers several recommendations for rethinking the development of the tourism model, global value ecosystems, digital acceleration trends in tourism events and identifies a number of key policy considerations to foster technology acceptance and use in the tourism and tourism events industry. © 2022 selection and editorial matter, Azizul Hassan;individual chapters, the contributors.
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This study examined the effect of ubiquitous connectivity, service quality, system quality, perceived usefulness, perceived ease of use, and perceived enjoyment on the intention and adoption of mobile shopping among consumers in Malaysia. A total of 316 respondents were collected from consumers in Malaysia using the online platform. The findings revealed that ubiquitous connectivity, perceived usefulness, perceived ease of use, and perceived enjoyment had a significant positive effect on the behavioral intention to adopt mobile shopping whereas service quality and system quality contributed insignificant impact on consumers’ intention to adopt mobile shopping. The results identified that consumers’ behavioral intention exhibited higher significant impact on the adoption of mobile shopping during the COVID-19 lockdown. The findings further revealed that intention to adopt mobile shopping mediated the association between ubiquitous connectivity, perceived usefulness, ease of use, and enjoyment on the adoption of mobile shopping. The current study contributed significant theoretical and practical implications for marketers and mobile service providers to better promote the adoption of mobile shopping consumers in Malaysia through the implementation of an effective strategy. © The Author(s) 2022.
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Diagnosis with medical images has soared to new heights and play massive roles in assisting radiologists to detect and analyse medical conditions. Computer-Aided Diagnosis systems are successfully used to detect tuberculosis, pneumonia, etc. from CXR images. CNNs have been adopted by many studies and achieved laudable results in the field of medical image diagnosis, having attained state-of-art performance by training on labeled data. This paper aims to propose an Ensemble model using a combination of deep CNN architectures, which are Xception, InceptionResnetV2, VGG19, DenseNet-201, and NasNetLarge, using image processing and artificial intelligence algorithms to quickly and accurately identify COVID-19 and other coronary diseases from X-Rays to stop the rapid transmission of the virus. We have used classifiers for the Xception model, VGG19, and InceptionResnet model and compiled a CXR dataset from various open datasets. Since the dataset lacked 1000 viral pneumonia images, we used image augmentation and focal loss to compensate for the unbalanced data and to introduce more variation. After implementing the focal loss function, we got better results. Moreover, we implemented transfer learning using ImageNet weights. Finally, we obtained a training accuracy of 92% to 94% across all models. Our accuracy of the Ensemble Model was 96.25%.
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Since December 2019, Novel coronavirus disease has been shown an extensive impact on social, mental, personal, and economic fields throughout the world. In this pandemic situation, people are worried and interested to know what is going on in the upcoming days. Therefore, it is very important to provide relevant information about how many people are affected and will infect in near future. Moreover, they need to know how to spread different symptoms and prevention steps of this disease. Hence, we developed an informative and prediction-based web portal named COVID-19: Update, Forecast and Assistant which provides real-Time information on COVID-19 cases in Bangladesh and worldwide. In this model, we also provide a machine learning-based short-Term forecasting web tool that is used to predict infectious and fatality cases in an upcoming couple of days. Also, we provide precaution steps against coronavirus, emergency contacts of testing, and treatment centers for individuals. © 2021 IEEE.