Research on the investment value of China's medical sector in the context of COVID-19

COVID-19 has slowed the growth of, the global economy, which has certain practical significance. Consequently, this study seeks to analyze the investment opportunities in the medical sector before and after the COVID-19 outbreak. In this study, the Markowitz mean–variance (MV) model, capital asset pricing model (CAPM), and correlation models are constructed based on the principle of Markowitz MV and correlation analysis. Simultaneously, statistical analysis is used to verify the analysis, and the MATLAB statistical tool is used to build the model. The results show that the actual expected yield of China's medical sector is significantly higher than that calculated by the CAPM before and after the pandemic, and that the investment value of the medical sector is undervalued by the market. From the perspective of risk, China's medical sector has a stable systemic risk premium. Based on the above analysis, when building investment portfolios in the post-pandemic era, investors should appropriately allocate stocks in the medical and pharmaceutical sectors to improve the portfolio income and diversify the investment risk. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

The spatial and temporal resilience of the tourism and outdoor recreation industries in the United States throughout the COVID-19 pandemic

Accurately quantifying industry resilience is essential to devising effective recovery strategies. Previous research into industry resilience has either quantified the concept with single metrics aggregated across large geographies (e.g., visitation) or used metrics comparing the relative concentration of an industry within a region to the national average (e.g., location quotients). The former set of metrics prohibits spatially targeted recovery efforts while the latter fails during national crises. We propose the measurement of tourism and outdoor recreation industry resilience to COVID-19 based on growth rates in employment, wages, and establishments using publicly accessible time-series data on all counties in the United States. We use these indicators to characterize the spatio-temporal patterns of industry resilience across the country. The indicators can serve as a useful reference for diagnosing and monitoring industry resilience as well as developing targeted policies, programs, and promotion efforts that facilitate more localized response efforts. © 2022 Elsevier Ltd

A study on the changes in the ICT industry after the COVID-19 pandemic

Purpose: The aim of this paper is to explore the changes in the ICT and global value chains (GVCs) after the COVID-19 pandemic. Design/methodology/approach: This study compared the difference between Korea' domestic ICT industries, ICT imports and ICT exports before and after the COVID-19 outbreak by using trade data of ICT products and national economic indicators, and presents growth strategy for the ICT industry in the post-COVID 19 era. For this purpose, this study determined the causalities between Korea's imports/exports of ICT products and composite Indexes before and after COVID-19, and derived implications in the ICT industry environment after the COVID-19 pandemic. Findings: Analysis results showed the following changes in Korea's ICT industry in the post-COVID-19 world. (1) Non-face-to-face and contact-free technologies related sectors in the ICT industry, such as the semiconductor sector, have grown exponentially;(2) as the USA has grown as the new key player, the causal relationship with China, a key player of the GVC in the pre-COVID-19 era, disappeared;and (3) the GVC of the ICT industry is not a rigid one-way vertical structure, but is changing to a flexible structure influenced by cooperation and competition between countries. Originality/value: The results indicate that it is essential to constantly develop new ICT sectors that make use of non-face-to-face and contact-free technologies in the post-COVID-19 era, and the main strategies in response to the changed GVC would be taking the initiative by securing source technologies and expanding through cooperation with other GVCs and resource sharing. © 2022, Emerald Publishing Limited.

The COVID-19 Pandemic and Factors Influencing the Destination Choice of International Visitors to Vietnam

The Association of Southeast Asian Nations (ASEAN) is an attractive tourist destination with diverse and unique experiences, in which Vietnam is considered one of the most famous destinations in this region. Quality evaluations and strategies for attracting international tourists are being thoroughly researched. However, the COVID-19 pandemic has had the most significant impact on the tourism industry, which has suffered greatly. Therefore, the recovery and expansion of international tourism necessitate the employment of tourism-related businesses and service sector workers. Extensive research must be conducted to identify solutions and new directions to recover the international tourist market's growth as quickly as possible. This study identifies the factors that influence the destination of international visitors visiting Vietnam after the COVID-19 pandemic by modifying and evaluating the scales of the theoretical model. Using the convenience sampling technique, data were collected through interviews with 208 international visitors, with 29 observed variables. Using SPSS 22.0, five factors influencing international visitors' decisions to visit Vietnam were revealed: tourist motivation, tourist attitude, destination image, social media, and environmental quality. Finally, the authors provide policy recommendations to enhance the allure and viability of Vietnam's tourism following the effects of the COVID-19 pandemic. This study's outcome is intended to establish the importance of the many variables influencing the choice of destination for international visitors. © 2022 by the authors.

New Normal Remote Communication for Collaboration

Remote communication is not new for the architecture, engineering and construction (AEC) industry and academia. Organisations started using what was common, called "conference rooms” with sophisticated technological equipment prepared for "conference calls” when face-to-face meetings were not possible, and the industries culture and work practices were rooted in face-to-face meetings. This was current practice until the beginning of 2020, with the emergence of the global COVID-19 pandemic. The pandemic forced people to have safe distances between them, to be isolated for long periods of time, and several restrictions to travel not being possible to meet face-to-face. This situation rapidly created a new need to find ways to communicate as alternatives to traditional face-to-face meetings, for "conference call rooms” anywhere and accessible at any time by everyone. The extended duration of the pandemic made organisations adapt to that new normal and remarkable new opportunities arose in a new way. This article explores recent situations in academia and industry that can highlight potential guidance towards the new normal in remote communication for learning–teaching and the AEC industry sectors. In conclusion, appropriate use of these electronic processes provides opportunities to significantly improve remote communication in future. It is expected that the number of opportunities to develop international relationships and partnerships can be boosted to another level of accessibility. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Supply chain accountability, COVID-19, and violations of workers' rights in the global clothing supply chain

Purpose: This study aims to contribute to the debate on the efficacy of softer regulations to prevent violations of workers' rights in the global clothing supply chain. Design/methodology/approach: This study draws on value trap and adverse incorporations as a theoretical lens to understand the reasons behind the continued violations of workers' rights. The empirical findings are based on an analysis of 24 semi-structured interviews with workers and owners. Extensive documentary evidence to track the plight of workers in Bangladeshi clothing factories during the pandemic. Findings: The study demonstrates how imbalances in supply chain relationships allow retailers to take advantage of the pandemic. The authors find that some retailers worsened the working conditions by cancelling orders, demanding discounts on old orders and forcing suppliers to agree to a lower price for new orders. Large brands and retailers' responses to the COVID-19 pandemic remind us that softer regulations, such as third-party audits, are likely to be ineffective given the power imbalance at the heart of the supply chain. Practical implications: The study presents a case for regulatory frameworks and intense stakeholder activism to encourage large retailers and brands to behave responsibly. This is especially important when a supply chain is value-trapped and workers are adversely incorporated and unprotected. Originality/value: Drawing on studies on adverse incorporations, value-trapped supply chains and the plight of workers during the COVID-19 pandemic, the study offers a broader understanding of the continued violation of workers' rights and the efficacy of softer regulations. © 2023, Emerald Publishing Limited.

Single Minute Exchange Die (SMED): A sustainable and well-timed approach for Bangladeshi garments industry

Bangladesh is the second largest Ready-Made Garments (RMG) exporting country after China. The cost of cotton and other raw materials, labor cost, and subsidiary cost increased much in post COVID-19 with the comparison of pre-Covid-19 times, but from the prospect of buyer's price is not increasing that much. In this context, our study focused on the RMG's very first time extensive Quick Changeover (QCO) process to minimize cost reduction as well as wastage and time using Single Minute Exchange Die (SMED). Initially, concentrated on the learning period to make acknowledge the changing phase of one style to another. At the same time, tried to figure out the overall weekly performance before and after implementing QCO on the floors, efficiency, before and after implementing QCO hit rate and time consumption, and wastages. According to the case study, floor one had the best average weekly performance, action achieved percentage, and efficiency performance of 57%, 48%, and 46%, respectively, among the five, analyzed floors. From the investigated five floors, the third one had the lowest weekly performance, percentage of actions completed, and efficiency, at 52%, 40%, and 34%, respectively. In the case of hit styles, floor two and floor five both achieved 83% after QCO apply in the floors. During the QCO, the highest production loss on floor one was the alarming sign which was 21,940 pieces and on floor three loss production was the lowest 2605 pieces after QCO implementation. © 2022 The Authors

The effect of a hotel's star-rating-based expectations of safety from the pandemic on during-stay experiences

As a result of the COVID-19 pandemic, safety is one of the top priorities for travellers when choosing a hotel. This work examines the effect of customers' pre-stay expectations of a hotel about its safety-focused services, shaped through its official star-rating, on the during-stay confirmation of those expectations, satisfaction, and revisit intentions. A cross-sectional research design is used spanning temporally from the pre-stay to the during-stay phases. The pre-stay phase was the peak COVID-19 period in India (June–July 2021) to stimulate the safety concerns in the travellers planning their travel, while the during-stay phase was when the planned travel was undertaken with the traveller staying at the planned hotel (October 2021–January 2022). Data were collected from 452 customers and the results supported the proposed model. Further, the star-rating, as a signal for safety-focused services, was found to have a serial effect on revisit intentions, through the pre-stay expectations of safety services, and the during-stay confirmation of expectations and satisfaction. © 2022 Elsevier Ltd

Impact of Covid-19 on shipbuilding and shipping industry

Covid-19 has been affecting the world for more than two years. The maritime sector in general has been hit hard by the pandemic as well. Since most of the world trade is carried out by sea transportation, the sector has been suffering deeply. The supply and demand chain were broken due to preventive measures such as lock-downs, travel restrictions etc. have been imposed the governments. Passenger transportation by sea was affected as well. As a result of all these the demand for ships and in turn for new buildings was dipped. Some of the shipyards got into trouble completing their ongoing projects due to financial difficulties even bankrupted. This paper deals with the problems that were surfaced during pandemic in maritime industry and for possible remedies to get out of it. Along with the global review of the impact of the pandemic, the effects on Turkish shipbuilding was also taken into consideration. © 2023 the Author(s).

The effects of COVID-19 pandemic on the UK construction industry and the process of future-proofing business

Purpose: COVID-19 was officially declared as a worldwide pandemic by the World Health Organisation on 11th March 2020, before the UK was put into lockdown on the 23rd March 2020. Organisations had to reconsider their policies and procedures to allow their businesses to continue. This paper aims to focus on the effects of COVID-19 that the UK construction sector has had to undertake to enable businesses while employees had to adhere to COVID-19 lockdown rules. In addition, how the sector can positively continue once normality has returned within the industry. In doing so, this paper understands the historical issues within the construction sector and has had an effect during COVID-19. Design/methodology/approach: A qualitative research methodology approach was taken to help obtain live information. In total, 19 semi-structured interviews from 15 organisations related to the construction sector were conducted to collect data. This information was evaluated using thematic analysis to arrive at the results, inferences and recommendations to the sector. Findings: This research has revealed that companies have had to adopt a three-stage process to overcome a new dimensional challenge of COVID-19. These include: 1. Making quick decisions during the first stage of the pandemic. 2. Producing new policies and procedures to restart businesses enabling staff to return to the workplace safely. 3. Implementing methods to future-proof organisations against any potential pandemics. To help organisations future-proof their business five C's are recommended. Originality/value: This paper provides a rich insight into the understanding and awareness of the effects of COVID-19 and the changes that the construction sector has had to undertake to adhere to the lockdown rules while remaining productive. This research contributes towards informing policymakers on some of the lessons learned during the management of the COVID-19 pandemic from a construction sector perspective. © 2022, Emerald Publishing Limited.

Assessment of the Bankruptcy Risk in the Hotel Industry as a Condition of the COVID-19 Crisis Using Time-Delay Neural Networks

In this paper we demonstrate a new conceptual framework in the application of multilayer perceptron (MLP) artificial neural networks (ANNs) to bankruptcy risk prediction using different time-delay neural network (TDNN) models to assess Altman's EM Z″-score risk zones of firms for a sample of 100 companies operating in the hotel industry in the Republic of Serbia. Hence, the accuracies of 9580 forecasting ANNs trained for the period 2016 to 2021 are analyzed, and the impact of various input parameters of different ANN models on their forecasting accuracy is investigated, including Altman's bankruptcy risk indicators, market and internal nonfinancial indicators, the lengths of the learning periods of the ANNs and of their input parameters, and the K-means clusters of risk zones. Based on this research, 11 stability indicators (SIs) for the years under analysis are formulated, which represent the generalization capabilities of ANN models, i.e., differences in the generalization errors between the preceding period and the year for which zone assessment is given;these are seen as a consequence of structural changes at the industry level that occurred during the relevant year. SIs are validated through comparison with the relative strength index (RSI) for descriptive indicators of Altman's model, and high correlation is found. Special focus is placed on the identification of the stability in 2020 in order to assess the impact of the COVID-19 crisis during that year. It is established that despite the fact that the development of bankruptcy risk in the hotel industry in the Republic of Serbia is a highly volatile process, the largest changes in the analyzed period occurred in 2020, i.e., the potential applications of ANNs for forecasting zones in 2020 are limited. © 2022 by the authors.

Firm-Specific Determinants of Firm Performance in the Hospitality Sector in India

The hospitality sector has been one of the worst-hit industries due to the onset of the COVID-19 pandemic, followed by nationwide lockdowns and curfews. Further, other factors, including the Russia–Ukraine war, commodity price rise, and recession, have acted as hurdles in the slow recovery process. Policy experts at different forums have advocated for proactive and robust measures by the government to reduce adverse impacts during these unprecedented times. To design such measures, determining the firm-specific factors that significantly impact their profitability is essential. In this context, this study tries to understand firm-specific factors that affect the hospitality sector's performance in India. It also explores whether the firm-specific characteristics have changed over time due to changes in political regimes and differ between private and publicly listed companies. Using a sample of 440 public and private hospitality firms for 11 years (2010–2020) and after controlling for unobserved heterogeneity using firm fixed effects, we tested the relationship between firm characteristics and performance. The estimation results demonstrate that the net asset turnover, liquidity, foreign earnings intensity, and age have significant, positive impacts on profitability. In contrast, solvency and size have negatively impacted firm performance. Further, we found differences in the magnitudes of coefficients for private and publicly listed companies. The findings provide important implications for managers and regulators to stimulate new solutions to overcome the ongoing difficult period. © 2022 by the authors.

Factors influencing tourists' nightlife experience in Belgrade

Purpose: This study aims to understand the impact that the experience environment has on the nightlife experience, as well as to identify the factors from the nightlifescape that most influence the tourists' experience in Belgrade. Additionally, this study seeks to discover whether these factors changed after the outbreak of the COVID-19 pandemic. Design/methodology/approach: To achieve the study's objectives, 679 tourist reviews were collected from one of the most popular travel platforms, TripAdvisor, and analyzed using RapidMiner, the popular software for data/text mining. Findings: The perception of the physical aspects of the experience environment, the presence of other tourists and the feeling of acceptance are identified as the key factors that influence tourists' nightlife experience. This study also found that certain factors from the social and sensory environment, such as staff, the presence of other people, the atmosphere and music, had a positive impact on the tourist experience and their intention to recommend the nightlife experience in Belgrade. Moreover, it was discovered that the COVID-19 pandemic did not provoke changes in the main factors influencing tourists' nightlife experience. Originality/value: The perception of tourists about Belgrade during the night contributes to the growing body of tourism literature on destination image. Focusing solely on the perception of tourists about Belgrade during the night, this study adds a temporal determinant to the destination image, which can be considered as a valuable add on to the current knowledge in the field. © 2022, Emerald Publishing Limited.

Methodological framework for a deeper understanding of airline profit cycles in the context of disruptive exogenous impacts

This paper combines the k-means clustering method in combination with PCA and the system dynamic modeling approach to derive a better insight into the behavior of airline profitability during the time span of 1995 until 2020. The model includes various explanatory variables that capture different aspects of airline economic and operational metrics, whose fluctuations may affect the airline profitability. By forecasting these exogenous variables, the system dynamic model is used to predict airline profitability through 2025 and answer the question of whether the US airline industry will return to its pre-COVID 19 pandemic state. The latter research question can be agreed with, as the effect of introducing a fourth dimension derived from Principal Component Analysis (PCA) to sufficiently cover the variation within the dataset during the years of COVID-19 pandemic diminishes towards the end of the forecast period. Furthermore, the key measures from PCA imply that under the assumption of continuous growth and a non-exogenous shock, future years will not cluster in past years. The six different clusters from 2019 to 2025 showed how the system stays in a certain state for a few years and then drifts further to a new state. There are only a few variables that change to transfer from one cluster to the next. © 2022 The Authors

Enhancing sustainable supply chain performance by adopting sustainable lean six sigma-Industry 4.0 practices

PurposeThe Sustainable Lean Six Sigma (SLSS) adoption approach, advancements in Internet technologies and the use of Industry4.0 technologies has resulted in faster customer need fulfilment. The Industry4.0 technologies have resulted in a new paradigm where strategic and operational decisions are in favour of profitability and long-term viability. The purpose of this study is to identify Industry4.0-SLSS practices and sustainable supply chain performance metrics, as well as to develop a framework for decision-makers and managers to make supply chains more sustainable.Design/methodology/approachThe 33 Industry4.0-SLSS practices and 24 performance metrics associated with the sustainable supply chain are shortlisted based on extensive literature review and expert opinion. The Pythagorean Fuzzy Analytical Hierarchy Process (PF-AHP) approach is used to evaluate the weights of Industry4.0-SLSS practices after collecting expert panel opinions. The Weighted Aggregated Sum Product Assessment (WASPAS) methodology used these weights to rank performance metrics.FindingsAccording to the results of PF-AHP, "Product development competencies (PDC)" are first in the class of major criteria, followed by "Advanced technological competencies (ATC)" second, "Organisational management competencies (OMC)" third, "Personnel and sustainable competencies (PSC)" fourth and "Soft Computing competencies (SCC)" fifth. The performance metric "Frequency of NPD" was ranked first by the WASPAS method.Research limitations/implicationsThe proposed paradigm helps practitioners to comprehend Industry4.0 technology and SLSS practices well. The identified practices have the potential to boost the sustainability and supply chain's performance. Organizational effectiveness will benefit from practices that promote a sustainable supply chain and the use of developing technology. Managers can evaluate performance using performance metrics that have been prioritized.Originality/valueThe present study is one of the unique attempts to establish a framework for enhancing the performance of the sustainable supply chain. The idea of establishing Industry4.0-SLSS practices and performance measures is the authors' original contribution.

Losing talent due to COVID-19: The roles of anger and fear on industry turnover intentions

Early in the COVID-19 pandemic, the US hospitality industry workforce experienced significant job loss via furloughs and job eliminations. Over a year later, the American hospitality industry is now facing a labor shortage. However, there is a dearth of literature explaining why the hospitality industry's response due to a mega-event, like the pandemic, can motivate employees to leave the hospitality industry. Instead, theory and research have primarily focused on organizations as the focal point for understanding turnover, while neglecting the industry. Using the affect theory of social exchange, this paper examined how anger and fear related to job status changes (i.e., being furloughed or laid-off) due to the pandemic, influence intentions to leave the industry. Study 1 used a survey of management-level employees, whereas Study 2 used an experiment to test the proposed model. Both studies showed that employees who lost their job due to the pandemic felt more anger and fear than those still employed. However, mediation analyses revealed anger, but not fear, as the primary driver of industry turnover intentions. These results highlight a potentially problematic trend. Should skilled hospitality workers switch industries due to job loss amidst an industry-wide negative event, it may become difficult for hospitality businesses to find qualified employees once the industry recovers and rehiring begins. © 2022 The Authors

Predicting Credit Ratings using Deep Learning Models - An Analysis of the Indian IT Industry

Due to the complexity of transactions and the availability of Big Data, many banks and financial institutions are reviewing their business models. Various tasks get involved in determining the credit worthiness like working with spreadsheets, manually gathering data from customers and corporations, etc. In this research paper, we aim to automate and analyze the credit ratings of the Information and technology industry in India. Various Deep-Learning models are incorporated to predict the credit rankings from highest to lowest separately for each company to find the best fit Margin, inventory valuation, etc., are the parameters that contribute to the credit rating predictions. The data collected for the study spans between the years FY-2015 to FY-2020. As per the research been carried out with efficiencies of different Deep Learning models been tested and compared, MLP gained the highest efficiency for predicting the same. This research contributes to identifying how we can predict the ratings for several IT companies in India based on their Financial risk, Business risk, Industrial risk, and Macroeconomic environment using various neural network models for better accuracy. Also it helps us understand the significance of Artificial Neural Networks in credit rating predictions using unstructured and real time Financial data consisting the influence of COVID-19 in Indian IT industry.