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The recent pandemic caused by coronavirus disease 2019 (COVID-19) has significantly impacted the operational performances of pharmaceutical supply chains (SCs), especially in emerging economies that are critically vulnerable due to their inadequate resources. Finding the possible barriers that continue to impede the sustainable performance of SCs in the post-COVID-19 era has become essential. This study aims to investigate and analyze the barriers to achieving sustainability in the pharmaceutical SC of an emerging economy in a bid to help decision-makers recognize the most influential barriers.

To achieve the goals, two decision-making tools are integrated to analyze the most critical barriers: interpretive structural modeling (ISM) and the matrix of cross-impact multiplications applied to classification (MICMAC). In contrast to other multi-criteria decision-making (MCDM) approaches, ISM develops a hierarchical decision tool for decision-makers and cluster analysis of the barriers using the MICMAC method based on their driving and dependency powers.

The findings reveal that the major barriers are in a four-level hierarchical relationship where “Insufficient SC strategic plans to ensure agility during crisis” acts as the most critical barrier, followed by “Poor information structure among SC contributors,” and “Inadequate risk management policy under pandemic.” Finally, the MICMAC analysis validates the findings from the ISM approach.

This study provides meaningful insights into barriers to achieving sustainability in pharmaceutical SCs in the post-COVID-19 era. The study can help pharmaceutical SC practitioners to better understand what can go wrong in post-COVID-19, and develop actionable strategies to ensure sustainability and resilience in practitioners' SCs.

Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction.

In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results.

The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task.

The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.

Following the COVID-19 pandemic, the upward trend of organic food (OF) consumption is no longer restrained to western markets but has also extended to emerging markets in different parts of the world. Still, extent post-pandemic literature has devoted little attention to the factors shaping consumers' intent to purchase organic food in developing markets. The current research empirically investigates the antecedents of consumers' attitude and intent to purchase organic food in an emerging market context (i.e. Morocco). This research also explores the differences between OF consumers' manifest attitudes prior to and following the COVID-19 pandemic.

To achieve the study's objectives, data were collected from 1,060 Moroccan respondents using online self-administered questionnaires in two different eras: prior to the COVID-19 pandemic with a total of 441 valid responses and following the pandemic with 619 valid responses. Confirmatory factor analysis (CFA) was employed to validate the data, and a partial least squares (PLS) estimation was used to test the hypotheses.

First, attitude towards OF has a significant impact on consumers' purchase intention; at the same time, it is influenced by subjective norms, organic labeling, perceived behavioral control, health consciousness, and environmental concern. This study's findings also suggest subjective norms, organic labeling, perceived behavioral control, health consciousness, environmental concern, age, and income as the key determinants of consumers' intention to purchase OF. Second, the IPMA analysis suggests that while health consciousness, organic labeling and perceived behavioral control are the most important constructs influencing attitude towards OF, consumers' purchase intention is mostly influenced by health consciousness, attitude towards OF and age. Finally, the PLS-Multigroup Analysis conveyed few discrepancies in the results when comparing the two eras (i.e. subjective norms and age had more significant impacts on consumers' purchase intent following the COVID-19 pandemic).

This study provides organic food retailers and practitioners with a deeper understanding of the key aspects shaping consumers' intent to purchase organic products in emerging markets. The comparative analysis will also provide important insights on how to shape consumers' attitudes and purchase intentions in a new-normal marked by the COVID-19 pandemic.

This study's findings contribute to the growing literature on consumers' behavior in the organic food industry, particularly in developing countries where research is still narrow. This study's study is the first of its kind to compare consumers' intention to purchase organic food before and following the COVID-19 pandemic.

This study examines the performance effect of working capital for a large sample of Indian manufacturing firms in light of supply chain disruption, i.e. the COVID-19 pandemic.

This study is based on secondary data collected from the Prowess database on Indian manufacturing firms listed on the Bombay Stock Exchange (BSE) 500. Panel data regression analyses are used to estimate all models. Moreover, this study has employed robust standard errors to consider for heteroscedasticity concerns.

The results challenge the current notion of working capital investment and reveal that higher working capital has a positive and significant impact on firm performance. Further, it highlights that Indian manufacturing firms suffered financially post-COVID-19 as they significantly lack the working capital to run day-to-day operations.

This research contributes to the scant literature by examining the association between working capital financing and firm performance in light of the COVID-19 pandemic, representing typical developing economies like India.

As a result of the COVID-19 pandemic, many countries have implemented preventive measures that may negatively affect the physical and mental health of older adults. This study aims to investigate the changes in life satisfaction, depressive symptoms and quality-of-life (QoL) of older adults during the post-COVID-19 pandemic in Oman.

A prospective four-week follow-up study was conducted in one health-care center in Muscat, Oman. Convenience sampling was used, and the recruitment was from November 2021 to October 2022, with 38 participants aged 60–81, of whom 36 completed the study. The study evaluated changes in psychological well-being over time and analyzed gender differences.

The study revealed a positive impact of the preventive measures on the QoL (F = 3.08, p = 0.032) and a reduction in depressive symptoms (F = 3.09, p = 0.045). However, there was no significant change in life satisfaction (F = 1.90, p = 0.165). In particular, older women reported decreased depression (F = 5.31, p = 0.006), while older men reported improved QoL (F = 4.27, p = 0.025).

Few studies have reported on the well-being issues of older adults in Oman during the pandemic. Thus, the findings of this study can contribute to the international understanding of the impact of pandemics on the well-being of older adults in Oman and help health-care professionals tailor health-care programs for older adults in the community setting.

This study provides valuable insights into the well-being of older adults in Oman during the pandemic. Understanding the effects of long-term health hazards is crucial for developing community-level intervention plans and strategies for older adults in the community setting.

The purpose of this paper is to study the interlinkages between the cryptocurrency and stock market by characterizing their connectedness starting from January 1, 2018 to December 31, 2021.

The author employs a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach.

The pandemic shocks appear to have influences on the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that each cryptocurrency and stock have a heterogeneous role, conditional on their internal characteristics and external shocks. In particular, Bitcoin and Binance Coin are reported as the net receiver of shocks, while the role of Ethereum shifts from receivers to transmitters. As for the stock market, the US stock market stays persistent as net transmitters of shocks, while the Asian stock market (including Hong Kong and Shanghai) are the two consistent net receivers. During the COVID-19 pandemic shock, pairwise connectedness reveals that cryptocurrencies can explain the volatility of the stock markets with the impact most severe at the beginning of 2020.

Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets.

The author is the first to investigate the interlinkages between the cryptocurrency and the stock market and assess the influences of uncertain events like the COVID-19 health crisis on the dynamic interlinkages among these two markets. The author employs the TVP-VAR combined with an extended joint connectedness approach.

This paper aims to study the interlinkages between cryptocurrency and the stock market by characterizing their connectedness and the effects of the COVID-19 crisis on their relations.

The author employs a quantile vector autoregression (QVAR) to identify the connectedness of nine indicators from January 1, 2018, to December 31, 2021, in an effort to examine the relationships between cryptocurrency and stock markets.

The results demonstrate that the pandemic shocks appear to have influences on the system-wide dynamic connectedness. Dynamic net total directional connectedness implies that Bitcoin (BTC) is a net short-duration shock transmitter during the sample. BTC is a long-duration net receiver of shocks during the 2018–2020 period and turns into a long-duration net transmitter of shocks in late 2021. Ethereum is a net shock transmitter in both durations. Binance turns into a net short-duration shock transmitter during the COVID-19 outbreak before receiving net shocks in 2021. The stock market in different areas plays various roles in the short run and long run. During the COVID-19 pandemic shock, pairwise connectedness reveals that cryptocurrencies can explain the volatility of the stock markets with the most severe impact at the beginning of 2020.

Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets.

The author is the first to investigate the interlinkages between the cryptocurrency and the stock market and assess the influences of uncertain events like the COVID-19 health crisis on the dynamic interlinkages between these two markets.

本學術論文擬透過找出加密貨幣與股票市場兩者相互關聯之特徵,來探討這個聯繫;文章亦擬探究2019冠狀病毒病全球大流行對這相互關聯的影響。

作者以分量向量自我迴歸法、來找出2018年1月1日至2021年12月31日期間九個指標的關聯,藉此探討加密貨幣與股票市場之間的關係。

研究結果顯示,全球大流行的驚愕,似對全系統動態關聯產生了影響。動態總淨值定向關聯暗示了就我們的樣本而言,比特幣是一個純短期衝擊發送器。比特幣在2018年至 2020年期間是一個衝擊的長期純接收器,並進而於2021年年底成為一個衝擊的長期純發送器。以太坊則為短期以及長期之純衝擊發送器。幣安在2019冠狀病毒病爆發期間,在2021年接收純衝擊前、成為一個純短期衝擊發送器。位於不同地區的股票市場,無論在短期抑或長期而言均扮演各種不同的角色。在2019冠狀病毒病全球大流行的驚愕期間,成對的關聯顯示了加密貨幣可以以2020年年初最嚴重的影響去解釋和說明股票市場的波動。

研究結果使我們能深入認識有關的市場之間不同情緒和看法的蔓延所帶來的影響的主要先例,這些知識、亦能幫助決策者制定適當的政策,以減少有關的市場的弱點,並把這些市場間的風險和不確定性的散播減到最低。

作者是首位研究加密貨幣與股票市場之間的相互關聯的學者,亦是首位學者、去評估像2019冠狀病毒病健康危機的不確定事件,會如何影響有關的兩個市場之間的動態相互關聯。

This study aims to explore the effects of the COVID-19 outbreak on exports of goods and services, logistics performance, environmental management system (ISO 14001) certification and quality management system (ISO 9001) certification in top affected Asian countries of India, Iran, Indonesia, Philippines, Bangladesh and Pakistan.

A novel grey relational analysis models’ approach is used to examine the inter-relationship between COVID-19 economic growth and environmental performance. Moreover, the authors applied a conservative (maximin) model to investigate which countries have the least intensifying affected among all of the top affected COVID-19 Asian countries based on the SS degree of grey relation values. The data used in this study was collected from multiple databases during 2020 for analysis.

Results indicate that the severity of COVID-19 shows a strong negative association and influence of COVID-19 on the exportation of goods and services, logistics performance, ISO 9001 and ISO 14001 certifications in all the six highly affected countries during a pandemic outbreak. Although the adverse effects of COVID-19 in exporting countries persisted until December 31, 2020, their magnitude decreased over time in Indonesia and Pakistan. During the COVID-19 outbreak, Pakistan showed comparatively better performance among the six top highly affected Asian countries due to its smart locked down strategy and prevents its economy from severe damages. While India and Iran export drastically go down due to a rapid increase in the number of COVID-19 cases and deaths.

The research findings produce much-required policy suggestions for leaders, world agencies and governments to take corrective measures on an emergent basis to prevent the economies from more damages and improve their logistics, environmental and quality performance during the pandemic of COVID-19.

This study develops a framework and investigates the intensifying effects of COVID-19 effects on economic growth, logistics performance, environmental performance and quality production processes.

The recent outbreak of the coronavirus disease 2019 (COVID-19) pandemic has severely disrupted the operations of global supply chains (SCs) providing an opportunity for decision-makers to rethink and tune their existing strategies. To tackle the impacts caused by such a pandemic, this study aims to examine the contextual relations among the factors influencing supply chain viability (SCV) for achieving long-term Sustainable Development Goals (SDGs).

A decision-aid approach by integrating Pareto analysis, grey theory and total interpretive structural modeling (TISM) has been propounded. The proposed approach examines contextual relations among the factors for SCV for small and medium-sized enterprises (SMEs) in an emerging economy. This study contributes to theory, methodology and practice by exploring a new research problem in the context of the SCV and COVID-19 pandemic and by developing a new decision-aid approach.

The findings reveal that the creation of SC digital twin and transformation of SCs to supply networks would help the policymakers to deal with the “new normal.” Also, SC crowdfunding and policy development for health protocols are critical driving factors influencing SCV.

This research work is perhaps one of few initial attempts to advance the theoretical and practical understanding of SCV and to achieve SDGs in industries following a pandemic risk.