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The apparel industry of Bangladesh is rethinking lean manufacturing (LM) deployment because of the challenges imposed by the COVID-19 pandemic. Due to COVID-19, LM implementation in the apparel industry has become more difficult. Thus, the purpose of this study is to explore the barriers to implementing LM practices in the apparel industry of Bangladesh in the context of COVID-19 pandemic.

For evaluating the barriers, an integrated framework that combines the Delphi method and fuzzy total interpretive structural modeling (TISM) has been designed. The application of fuzzy TISM has resulted in a structured hierarchical relationship model of the barriers with driving and driven power.

The findings reveal that “lack of synchronization of lean planning with strategic planning”, “lack of proper understanding of lean concept” and “low priority from the top management” are the three top most important barriers of LM implementation in apparel industry.

These findings will help the apparel industry to formulate strategy for implementing the LM practices successfully. The proposed model is expected to contribute to the sustainable development goals (SDGs) such as Responsible Consumption and Production (SDG 12); Decent Work and Economic Growth (SDG 8); Industry, Innovation and Infrastructure (SDG 9) via resilient strategies.

This study is one of few initial efforts to investigate LM implementation barriers during the COVID-19 epidemic in a real-world setting.

In the light of high reliance on digital technology to mitigate the consequences of the coronavirus disease 2019 (COVID-19) pandemic and its containment measures, this study investigates the factors influencing firms' decision to adopt digital technologies during COVID-19 in four Middle East and Northern African (MENA) countries, namely, Egypt, Jordan, Morocco and Tunisia.

The study used the International Labour Organization (ILO)/Economic Research Forum (ERF) COVID-19 - MENA Monitor Enterprise Survey (CMMENT), comprising 5,480 firms, surveyed during 2020–2021. The empirical model is estimated using the linear probability model (LPM) to address the problem of unobserved heterogeneity between firms, countries, and time.

The results revealed that firm characteristics, such as firm size and foreign ownership, encourage digital transformation in the business sector. Moreover, firms that face challenges during the pandemic, comply with the containment measures, and receive government assistance are more likely to adopt digital solutions. Furthermore, the results indicated that firms operating in services sector have a higher likelihood to adopt digital technology. Disaggregating the total sample into several sub-samples, the results are robust across countries and technology types, supporting the initial hypothesis that COVID-19 encourages digital transformation in the MENA region.

The study has numerous contributions. First, to the best of the authors' knowledge, this is the sole study that uses micro data collected during the COVID-19 to examine the factors influencing firms' decision to adopt and invest in digital solutions in the MENA countries. Second, the paper employs the LPM estimator to address the issue of unobserved heterogeneity between firms, countries and time. Finally, the paper offers some practical recommendations for accelerating digital transformation in MENA region.

During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational activities. However, it remains unclear how social media usage influences nonpharmaceutical preventive behavior of individuals in response to the pandemic. This paper aims to explore the impacts of social media on COVID-19 preventive behaviors based on the theoretical lens of empowerment.

In this paper, survey data has been collected from 739 social media users in China to conduct structural equation modeling (SEM) analysis.

The results indicate that social media empowers individuals in terms of knowledge seeking, knowledge sharing, socializing and entertainment to promote preventive behaviors at the individual level by increasing each person's perception of collective efficacy and social cohesion. Meanwhile, social cohesion negatively impacts the relationship between collective efficacy and individual preventive behavior.

This study provides insights regarding the role of social media in crisis response and examines the role of collective beliefs in the influencing mechanism of social media. The results presented herein can be used to guide government agencies seeking to control the COVID-19 pandemic.

The purpose of this research is to segment Pakistani consumers based on their insights and preference towards mango quality and safety attributes and to demographically characterise each segment so that effective value chain (VC) development strategies may be developed.

Factor and cluster analysis were applied to data collected from 400 mango buyers randomly selected from seven different cities in Pakistan.

This empirical study identified four distinct segments of mango consumers in Pakistan. The diversity of consumers' perception was mainly based on four factors: food safety, price and marketing, physical quality and shelf life. Based on their demographic characteristics and preferences for product attributes, the identified consumer segments can be classified as “traditional consumers,” “safety-conscious consumers,” price and market-sensitive consumers,” quality-sensitive consumers” and “perfect consumers.” This knowledge is useful to develop mango VCs that target specific consumer segments based on their chain level abilities and aspirations.

This study offers actors of mango VCs an enhanced understanding of consumer segments based on their perceived preferences towards the quality and safety attributes of mangoes, which would help VC actors design and develop business strategies that deliver value specific to each consumer segments. Findings of this study could also be useful to policy makers to design extension strategies that promote food safety and quality standards in mango VCs. Moreover, this study presents yet another case of consumer segmentation study for VC development in developing countries, adding a stepping stone towards a general VC theory development.

The findings stimulate the momentum of the growing literature, particularly in the developing countries on consumer insights and behaviour toward fruit quality and safety attributes of mangoes.

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

This study aims to focus on the impact of multi-level knowledge sharing between and within organizations on the risk control of rural inclusive finance. The paper presents a synergistic risk control system integrating external and internal factors for rural inclusive finance by constructing different knowledge-sharing platforms in an environment, which is full of many uncertainties.

This study is based on survey methods. To achieve the research objectives, the authors adopt a single case study approach. For data collection, the authors apply a wide variety of methods such as semi-structured interviews, field visits, second-hand databases and official websites.

The results emphasize that using multi-level knowledge sharing such as the inter- and intra-organizational level, can facilitate the risk control of rural inclusive finance during the post-COVID-19 era. Furthermore, it is also noted that achieving knowledge sharing at different levels by building diverse knowledge-sharing platforms can promote the risk control of rural inclusive finance from the individual-organization level to the chain level of multi-organization collaboration, which contributes to the formation of symbiotic risk control ecology.

The authors have formed the “Chinese wisdom” to deal with inclusive financial risks and to promote in-depth development in relation to the “last mile” practice of inclusive finance, which means the final and the most important phase of a project. The conclusions contribute to enriching the outcomes regarding the risk control of rural inclusive finance, provide experiences to its sustainable development and offer a reference to other countries with their risk control of rural inclusive finance.

Drawing on the knowledge-sharing approach, this study creatively resolves the persistent problems in the risk control of rural inclusive finance, which forms a powerful supplement to the extant literature. Meanwhile, the paper combines the two contextual factors of the post-COVID-19 era and emerging economies, which can be deemed as a novel attempt.

The purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management.

This paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile.

The proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management.

What sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.

In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.

This study aims to explore the gap between research and practice on supply chain risks due to COVID-19 by exploring the changes in global emphasis on supply chain risk research.

This work designed a research framework to compare the research of supply chain risks before and during the COVID-19 pandemic based on machining learning and text clustering and using the relevant publications of the web of science database.

The results show that scholars' attention to supply chain crisis has increased in the wake of the COVID-19 outbreak, but there are differences among countries. The United Kingdom, India, Australia, the USA and Italy have greatly increased their emphasis on risk research, while the supply chain risk research growth rate in other countries, including China, has been lower than the global level. Compared with the pre-pandemic period, the research of business finance, telecommunications, agricultural economics policy, business and public environmental occupational health increased significantly during the pandemic. The hotspots of supply chain risk research have changed significantly during the pandemic, focusing on routing problem, organizational performance, food supply chain, dual-channel supply chain, resilient supplier selection, medical service and machine learning.

This study has limitations in using a single database.

This work compared the changes in global and various countries' supply chain risk research before and during the pandemic. On the one hand, it helps to judge the degree of response of scholars to the global supply chain risk brought about by COVID-19. On the other hand, it is beneficial for supply chain practitioners and policymakers to gain an in-depth understanding of the relationship between the COVID-19 pandemic and supply chain risk, which might provide insights into not only addressing the supply chain risk but also the recovery of the supply chain.

The initial exploration of the changing extent of supply chain risk research in the context of COVID-19 provided in this paper is a unique and earlier attempt that extends the findings of the existing literature. Secondly, this research provides a feasible analysis strategy for supply chain risk research, which provides a direction and paradigm for exploring more effective supply chain research to meet the challenges of COVID-19.

Although recent literature has examined diverse measures adopted by SMEs to navigate the COVID-19 turbulence, there is a shortage of evidence on how crisis-time strategy creation behaviour and digitalization activities increase (1) sales and (2) cash flow. Thus, predicated on a novel strategy creation perspective, this inquiry aims to investigate the crisis behaviour, sales and cash flow performance of 528 SMEs in Morocco.

Novel links between (1) aggregate wage cuts, (2) variable operating hours, (3) deferred payment to suppliers, (4) deferred payment to tax authorities and (5) sales performance are developed and tested. A further link between sales performance and cash flow is also examined and the analysis is conducted using a non-linear structural equation modelling technique.

While there is a significant association between strategy creation behaviours and sales performance, only variable operating hours have a positive effect. Also, sales performance increases cash flow and this relationship is substantially strengthened by e-commerce digitalization and innovation.

Theoretically, to the best of the authors’ knowledge, this is one of the first inquiries to espouse the strategy creation view to explain SMEs' crisis-time behaviour and digitalization. For practical purposes, to supplement Moroccan SMEs' propensity to seek tax deferrals, it is argued that debt and equity support measures are also needed to boost sales performance and cash flow.