This study aimed to investigate factors affecting behavioral intention and use behavior of technologies of Thai people under the COVID-19 circumstances. 390 respondents were participated in our survey as sample size for statistical analysis. The authors utilized PLS-SEM assessment for testing the research hypotheses. Descriptive analysis revealed that Thai people in the quarantine period or work from home had suffered from a moderate to high levels of anxiety or stress. This has made Thai people increasingly use online and mobile technology or programs compared to the past. The study revealed four key factors that had significant and positive effects on the intention of users in using online technology including performance expectancy, effort expectancy, trust, and perceived risk. In addition, it indicated that behavioral intention positively affected the actual use behavior of technologies during quarantine time. The authors expect that policymakers or strategists could be used to manage the use of online and mobile technology for people, especially during the tough time.

Understanding the probabilistic or statistical behavior of air concentrations is necessary for the effective management of air pollution, such as PM2.5. Failure to consider the appropriateness of the model can lead to making inferences that are not supported by scientific evidence. The main focus of this article is to find the best statistical distribution in fitting PM2.5 concentrations in the periods of February–June 2018 and February–June 2019 (the periods without COVID-19) and in the period of February–June 2020 (the period with COVID-19) in Jakarta, Indonesia. This article considers making an assessment of the performance of both generalized distributions (e.g., generalized gamma, generalized extreme value, and generalized log-logistic [GLL]) and classical distributions (such as lognormal [LN], gamma, Weibull, log-logistic, and Gumbel) in modeling daily concentrations of PM2.5 in the period of February–June 2020, or the period during which the COVID-19 pandemic is present, in Jakarta. For comparison purposes, this study also analyzed PM2.5 concentrations in the periods of February–June 2018 and February–June 2019. The comparative evaluation of the models of each period of data uses graphical analyses and goodness-of-fit statistics. The results of applications indicate that the generalized distributions fit the data better than do the classical distributions. Particularly, compared with the classical distributions, including the LN model, the GLL distribution is the most appropriate model in fitting PM2.5 concentrations in the periods without and during the period with COVID-19 in Jakarta, Indonesia.

The COVID-19 outbreak is a black swan event that has uncovered the delicateness of global supply chains and business architecture. Underpinned by the agency theory and institutional theory, a proposition for business continuity in the highly regulated pharma industry is presented in this paper. A cross-sectional quantitative study was carried out on a sample of 102 pharma supply chain executives in Malaysia. The primary data were gathered by administering a self-administered questionnaire and analyzed using the partial least squares structural equation modelling (PLS-SEM). The result reveals that supply chain orientation directly influences supply chain resilience. Also, introducing collaborative regulation as a mediator in this relationship shows partial mediation. The notion of collaborative regulation as a behavioral governance mechanism is relatively new, thus, presenting interesting opportunities for further exploration of the subject matter.

The purpose of this descriptive-explanatory study is to examine the relationship between critical thinking and creativity and the performance of Micro, Small, and Medium-sized businesses (MSMEs) in Indonesia during the coronavirus disease 2019 (COVID-19) pandemic. In this study, a questionnaire instrument was used, which was given online using Online Survey Forms, to collect information from a sample of 108 business managers who were selected at random to participate. Using the SmartPLS software, an analysis was performed once the data had been collected. The results indicate a substantial impact on the performance of MSMEs in Indonesia was found to be associated with critical thinking and innovation, according to the findings.

The COVID-19 outbreak has illustrated the wide range of issues that supply networks confront when they are subjected to major interruptions. The supply networks for vaccines are no exception. To get out of this pandemic, it's critical to identify and address problems with the COVID-19 vaccine supply chain (VSC). This work identifies 13 challenges and prioritizes those. The findings provide stakeholders and government policymakers with realistic advice for developing a better VSC. This paper proposes a methodology based on fuzzy analytical hierarchy process (Fuzzy-AHP) with the use of triangular fuzzy numbers for prioritizing VSC barriers. It has been found that the impact of poor health worker training facilities becomes maximum with the highest weightage. Moreover, the managerial implication of the results is also provided, which will be useful for VSC sectors to take suitable decisions to overcome these obstacles.

This research was conducted to examine the extent to which market uncertainty can encourage market players, especially SMEs, to exploit innovation and environmentally friendly orientation to improve their performance. From a supply chain perspective, market uncertainty, which in this study is proxied by the Covid-19 pandemic, has great potential to reduce performance and disrupt production and distribution lines as well as consumer demand. This encourages affected SMEs, such as SMEs that focus on providing tourism products, such as fashion and merchandise, to maintain their performance with product innovation, and minimize the use of non-environmentally friendly products. The object of research is Small and Medium Enterprise (SME) producing tourism souvenirs in Yogyakarta, Indonesia. Using the analysis technique of Structural Equation Modeling (SEM) with 150 respondents, the findings indicate that market uncertainty serves as a catalyst for SMEs to maintain performance through marketing innovation and product reorientation. Specifically, the results show that there is a positive and significant influence between innovation and green orientation on SME performance, and the mediating effect of market uncertainty to increase marketing innovation and environmentally friendly orientation. These findings theoretically contribute to explaining the relationship between supply chain management in the context of market uncertainty. In practical terms, this study confirms the need for support by stakeholders to support limited domestic tourism, according to health protocols, as well as digitalization of marketing for tourism SMEs.

The performance of sustainable supply chain management today, especially for palm oil, continues to experience a drastic decline from the social, economic, and environmental perspectives. Both the supply and demand sides are undergoing severe disruption due to the COVID-19 pandemic. To survive the COVID-19 situation and afterward, the palm oil industry needs to focus on priority indicators for immediate improvement. For that reason, our study aims to determine the primary indicators used to assess the performance of sustainable supply chain management to improve the palm oil industry's performance immediately. The F-AHP method is used to rank which indicators are focused on the COVID-19 situation and thereafter. The findings of this study designate that there are three main indicators, namely from the economic side (adaptability), the social side (improving employee health and safety), and the environmental side (sustainable supplier management). This finding is beneficial for the industry and for supply chain actors such as suppliers, customers, and the government in taking attitudes and setting policies related to sustainable supply chain management in the face of pandemic.

The objective of this study was to examine the role of green Human Resources Management (HRM) in the green supply chain (SC) and firm performance. The relationships between green HRM, green SC, lockdown, and firm performance were examined. In addition to this, the mediating role of green SC and the moderating role of lockdown was examined. The population of the study was based on the food industry of Bahrain and various companies were selected for data collection. Therefore, data were collected from the food supply companies in Bahrain. A questionnaire was used for data collection in which cluster sampling was applied. The findings of the study highlighted that green HRM has major importance for food supply companies. It has a positive role in promoting the performance of food supply companies in Bahrain. Furthermore, green SC also plays a vital contribution to the performance of food supply companies. However, COVID-19 has a negative role in firm performance. The situation of lockdown due to COVID-19 has a negative effect on the performance of these companies.

The slowdown in globalization preceded by the 2008–2009 financial crisis and accompanied by stagnation in cross-border flows of goods and investments ignited the scientific debate about globalization shutting down. This paper assesses globalization on the basis of the observed flows of investment, data, population and trade for the period of 1998–2018. To do so, we propose the Index of Globalization (IG) that allowed us to highlight the key trends in the global economic development. Our assessments show that there was a sustained decline in the trade and investment components of globalization. The information flow demonstrates a slowdown in developed economies, but its growth rate remains stably high. The IG dynamics indicates a significant decrease in times of crises. In contrast to the 2008–2009 financial crisis and the outbreak of SARS in 2003, the COVID-19 pandemic has already caused a more considerable reduction in international flows of factors of production and global GDP. The changes in society behavior adopted during the COVID-19 spread are forecast to have long-term consequences. In terms of globalization, the pandemic has positively affected the dynamics of the information flow by increasing volumes of Internet traffic and intensifying the use of communications. The research results demonstrate that in the case of a significant slowdown in the trade, investment and population flows, an increase in the information flow will not have a substantial impact on globalization processes and will be unable to break the overall trend towards a decline in the Index of Globalization.

The COVID-19 pandemic has led to different health outcomes, including long COVID (LCo) and mental health (MH) disorders, impacting millions globally. To enable early healthcare diagnosis, including the prediction of MH conditions and LCo, various research studies have utilized machine learning (ML) techniques. However, there is still a gap in understanding the mental health of recovered COVID-19 patients with long COVID using ML techniques. This study aims to bridge this gap by developing and evaluating ML models, including support vector machine, multilayer perceptron (MLP), k-nearest neighbor, gradient boosting, voting classifier, and extreme gradient boosting, tailored for mental health and long COVID datasets from recovered COVID-19 patients. Additionally, feature selection methods, e.g., Recursive Feature Elimination (RFE) and Extra Trees (ET), and optimized models with hyper-parameter tuning will be employed. Our experiments utilize the dataset of recovered COVID-19 patients. Among these ML models, the MLP with ET-based features achieved the highest accuracy and AUC scores in this dataset, with 1.00 and 0.97 ± 0.02, respectively. The research reveals the high prevalence and risk factors of mental health disorders and long COVID from the dataset. These findings will contribute to personalized healthcare strategies for individuals navigating the complexities of post-COVID-19 recovery, integrating machine learning insights into mental health and long COVID support.