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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.

Open innovation has attracted the attention of experts and business entities for the sustainable survivability of firms, especially in the post-COVID-19 era. The food and beverage industry has been facing sustainable survivability problems. It is important to identify and evaluate the factors of open innovation from the perspectives of the food and beverage industry. This study serves that purpose by identifying and evaluating the factors of open innovation in the post-COVID-19 era with a special reference to Pakistan's economy.

The present study integrates the Fuzzy Delphi Method (FDM), Interpretive Structural Modelling (ISM) and Matrice d’ Impacts Croises Multiplication Applique a Classement (MICMAC) methods to analyze the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan. Firstly, based on an extensive literature review of the most relevant studies, the factors affecting open innovation have been identified and finalized using FDM and experts' opinions. Secondly, the hierarchical framework has also been prepared by implementing the ISM approach. Thirdly, the MICMAC approach was employed to evaluate the factors to examine the driving and dependence powers of the factors of open innovation adoption.

The study identified 17 factors of open innovation adoption in Pakistan's food and beverage industry and 16 factors were finalized using FDM. The ISM-MICMAC matrix unveiled that awareness seminars and training, along with a lack of executive commitments, were strong factors with high driving power, but these factors proved to be weakly dependent powers regarding the other factors. Moreover, a lack of innovation strategy, R&D and non-supportive organizational culture exhibited low driving power but strong dependent power.

The findings of the study could help firms and business entities understand the driving and dependent factors involved in open innovation for the sustainable survivability of the food and beverage industry. The study provides strong reasons to believe that an open innovation strategy, along with stakeholder collaboration, the adoption of rules and regulations and managerial commitment, could stimulate open innovation. Moreover, governments should promote the business sector, especially the food and beverage industry, to facilitate the sector while also providing awareness seminars and training, creating environments conducive to reducing innovation costs.

Some previous studies have analyzed the factors involved in green innovation from the perspective of the manufacturing industry and environmental protection. The present study is a pioneer study to examine the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan from the perspective of the post-COVID-19 era. For this purpose, the present study uses an integrated Fuzzy Delphi-ISM-MICMAC approach for the analysis.

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.

Service-learning as a pedagogical avenue to apply students’ knowledge and skills, relate such knowledge and skills to communal needs, and prompt students’ civic participation has been emphasized in higher education for decades. It has been highlighted as one of the major roles, if not the most major, of universities in modern times, transitioning such universities toward becoming more socially vibrant, responsible, and connected institutions rather than being isolated think tanks explicit to a specific audience. Nonetheless, service-learning might seem neither a very spontaneous learning process for some theoretical courses (e.g., language, civilization, communication, and math courses) nor a very practical approach to implement during unprecedented times of lockdown and physical distancing such as that of the COVID-19. This chapter presents and analyzes the case study of a Lebanese higher education institution, in emphasizing service-learning through the “Learning in the Workplace and Community” (LiWC) approach across various theoretical and practice-based courses prior to and during the COVID-19 pandemic. This chapter asserts that prior to the pandemic, service-learning enabled universities to equip their students to become agents of social, economic, and technological change and development. On the other hand, the chapter argues that during and post-COVID-19, service-learning will empower universities to play a more critical and vital role in preparing their students to become agents of support, resilience, agility, and sustainable growth within a global era of social, economic, technological, and healthcare chaos and change. In other words, service-learning will equip students to become avant-garde of certainty in such an uncertain, complex world.

COVID-19 pandemic has impacted global human lives, killing millions and attacking the real economy to its core. United Nations has called for global solidarity to overcome this unprecedented disaster. Having said that, zakat has been utilized to respond to this ongoing suffering in Muslim countries. This chapter explore zakat utilization for COVID-19 under the shariah perspective and discuss the role of zakat to respond to the pandemic in Muslim countries. A qualitative methodology including conceptual and content analysis is applied to conduct this study. The findings show that most sharia scholars agreed to utilize zakat for the COVID-19 pandemic and endorse advance zakat payment. Moreover, zakat has been disbursed mainly to fulfil basic needs, support emergency health services, sustain education activities, recover economic impact and maintain dakwah programme. The global zakat institutions have also started up the international initiative to respond to this pandemic. This study contributes to the academia on showing how zakat is a conceivable and reliable instrument to give immediate response to this pandemic in Muslim countries. The best practices found in this study are worthy for zakat institutions to continue their programme under this pandemic and be resilient for the next global scale of disasters.

This paper aims to examine tourists' behavioral changes in response to health crises, this study examines the individual's uncertainty and adaptability to the challenges using behavioral coping strategies.

The study combines the theory of planned behavior (TPB) and protection motivation theory. Using the PLS-SEM technique, this study examines the relationship between the destination's competitive profits and travel intention of Iranian tourists in the post-Covid-19 pandemic.

The social-support coping (Instrumental) does not incorporate tourists' adaptive behaviors. Vulnerable vaccination significantly affects the extremeness of an individual's problem-focused coping, which affects tourist's adaptive behaviors in crisis time, indicating the effectiveness of the Covid-19 vaccination on travel intention.

The findings may assist tourism authorities and planners develop unique tourism products and services based on tourist behavior following the health crises.

This study contributes to development of the TPB method, indicating that visa exemption and competitive profits of a destination would motivate travel intention existing inefficacy of local government and its negative background, reshaping and thus influencing changing behavior.