Through an international business risk management lens, the widespread and catalytic implications of the 2020 COVID-19 pandemic on the supply chains (SCs) of fashion multinational corporations (MNC) are analyzed to contribute to existing research on supply chain management (SCM). While a movement towards agile, networked supply chain models had been in consideration for many firms prior to the outbreak, the pandemic highlights issues inherent in supply chains that employ concentrated production. We examined the current state of fashion supply chains, risks that have arisen historically and recently, and existing risk mitigation methods. We found that while lean supply chain management is primarily favored for its cost and waste reduction advantages, the structure is limited by the lack of supply chain transparency that results as well as the increasing demand volatility observed even before the COVID-19 outbreak. Although this problem might exist in the agile supply chain, agile supply chains combat this by focusing on enhancing communication and buyer-supplier relationships to improve information exchange. However, this structure also entails an associated increase in inventory and inventory costs. The COVID-19 pandemic has caused supply and demand disruptions which have resonating effects on supply chain activities and management, indicating a need to build flexibility to mitigate epidemic and demand risks. To address this, several strategies that firms can adopt to control for such risks are outlined and key areas for further research are identified which consider parties both upstream and downstream of the fashion supply chain.

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After the outbreak of COVID-19, schools heavily depend on e-learning technologies and tools to shift from in-person class to online. This review article analyzes the changes of technology evolution and technology adoption of e-learning in pre- and post-COVID-19 based on the Technology System Evaluation Theory (TSET) and technology adoption of e-learning based on the Unified Theory of Acceptance and Use of Technology (UTAUT). We intend to explore the interaction of technology evolution and technology adoption in the different focus of e-learning technology in the two stages and the particularity and heterogeneity of the UTAUT model. The results indicate that (1) The moderating results of technology evolution are proposed and evaluated under the UTAUT model before the COVID-19 outbreak. Studies after the COVID-19 pandemic paid more attention to technology efficiency rather than effectiveness; (2) Research on e-learning focuses on the infrastructure to reach more users after the outbreak of COVID-19 because e-learning is the only way to continue education; (3) COVID-19 fear moderates the relationship between the external factors and the behavior intention of e-learning users. The lack of financial support on technology evolution will directly weaken the implementation of new technology. Social Isolation offers more opportunities for students to engage in e-learning. Meanwhile, it slows down the implementation of e-learning because of out-to-date hardware and software. This article offers an enhanced understanding of the interaction of technology evolution and technology adoption under unexpected environments and provides practical insights into how to promote new technology in a way that users will accept and use easily. This study can be tested and extended by empirical research in the future.

Due to the lockdown regulations worldwide during the COVID-19 pandemic, the global aviation industry has been severely hit. This study focuses on the volatility estimation of stock indexes in the Chinese Airport Shipping Set (ASS) at industry-enterprise levels and identifies possible business behavior that may cause fluctuating differences. Depending on the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, text mining method and Word Cloud Views, results show that (1) the holistic volatility of Airport Shipping Set Index (ASSI) increases relative to the pre-COVID period; (2) volatility of airport stocks has crucial differences, while the volatility of shipping stocks is similar; (3) there are different responses to the pandemic between Shenzhen Airport and Shanghai Airport shown in their semiannual financial reports. Compared to the latter, the former had a more positive attitude and took various measures to mitigate risks, providing evidence of the volatility differences between firms.

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