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The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

Consumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from businesses. Hence, in this research the authors aim to explore the effect of the complainant's social characteristics and the complaint's social and content characteristics on the likelihood of receiving a response to a grievance from the business on social media.

The authors build a conceptual model and then empirically test it to explore the effect of the complainant's characteristics and the complaint's characteristics on the likelihood of response from a business on social media. The authors use data of consumer grievances received by an Indian airline operator on Twitter during two time periods – the first corresponding to lockdown during Covid-19 pandemic, and the second corresponding to the resumption of business as usual following these lockdowns. The authors use logistic regression and the hazard rate model to model the likelihood of response and the response delay, respectively, for social media customer grievances.

Complainants with high social influence are not more likely to get a response for their grievances on social media. While tagging other individuals and business accounts in a social media complaint has negative effect on the likelihood of business response in both the time periods, the effect of tagging regulatory bodies on the likelihood of response was negative only in the Covid-19 lockdown period. The readability and valence of a complaint were found to positively affect the likelihood of response to a social media grievance. However, the effect of valence was significant only in lockdown period.

This research offers insights on what elicits responses from a service provider to consumers' grievances on social media platforms. The extant literature is a plenty on how firms should be engaging consumers on online media and how online communities should be built, but scanty on grievance redressal on social media. This research is, therefore, likely to be useful to service providers who are inclined to improve their grievance handling mechanisms, as well as, to regulatory authorities and ombudsmen.

During the COVID-19 pandemic micro-, small- and medium-sized businesses were hit hard. This was specially so in the restaurant business where physical lockdown and social distancing norms were challenging. Thus, small business performance was a concern (Akpan, Udoh, & Adebisi, 2020). This case study addressed this theoretical aspect. Zhang, Gerlowski, & Acs (2021) had highlighted the challenges of business continuance and enterprise sustenance, during the COVID-19 pandemic. This case study provided insights regarding how these aspects of business continuance and enterprise sustenance were addressed by “Café Tato”. Bhattacharyya and Thakre (2021) had deliberated regarding how firms through strategic initiatives and tactical responses were required to manage COVID-19 pandemic and economic lockdown. Café Tato case study was in line with these recommendations for firms. Barbieri et al. (2020) and Reardon et al. (2021) had outlined steps regarding maintaining business operations reliance given a business crisis situation like during the COVID-19 pandemic. Café Tato case study described this. Café Tato case study accommodated perspectives regarding business crisis situation, business continuance, enterprise sustenance, operations reliance, organizational strategic versus tactical initiatives and finally small business performance.

The teaching case study was written based upon primary data collected from the owners of Café Tato. Furthermore, secondary data was used for building the case.

Café Tato was the most popular and the oldest tea cafés in Goa, India. It had a presence in Panjim and Margaon in Goa. On 24 March 2020 India went into an economic and physical lockdown because of COVID-19 pandemic related crisis. One week down the line on 31 March 2020, Mr Pranav Dhuri (hereby referred as Pranav), one of the owners of Café Tato, was contemplating how to ensure business continuance once there was semi-normalcy restored. What would the initiatives that need to be undertaken was critical for success of Tato the legendary café in Goa was playing in the mind of Pranav.

This teaching case study could be used in the class for strategy planning and implementation in the course of strategic management. This case study could apply for teaching both bachelor’s and master’s students of business management. The case could also be taught to management students for helping them understand how small business firms, while confronting challenges of COVID-19, planned and implemented strategies in the course of strategic management. This study helped strategic management students understand the importance of organization culture in any business as it directly influenced the profitability and sustenance of business. The Dhuri family had all along laid stress on keeping the culture alive by taking various steps at critical times in improvising and maintaining the standards. The study also helped to understand the importance of good leadership and its direct impact on the employees in a crisis situation (COVID-19) work environment and ensure business sustenance.

The purpose of this paper is to identify the themes that emerged from retail supply chain (RSC) literature during the coronavirus disease 2019 (COVID-19) pandemic that inform future mitigation and recovery strategies.

This study analyses contributions in the RSC literature using four databases: Emerald, Elsevier (Science Direct), Wiley and Taylor & Francis. The systematic review approach resulted in identifying 74 articles covering 2020 to 2022.

Four themes emerged from the RSC literature on COVID-19. The first theme highlighted the factors that exacerbated the effects of the COVID-19 pandemic on the RSC. The second theme focussed on the types of disruptions that occurred in the RSC during the pandemic. The third theme demonstrated the recovery strategies used to reduce the impact of COVID-19 on the RSC. The fourth theme identified proposed mitigation strategies for the RSC post-COVID-19 outbreak.

The study provides a deeper understanding of how RSC managers could successfully reduce the effects of the COVID-19 pandemic by dealing with interruptions. Based on the reviewed studies and the four themes that evolved from RSC literature on COVID-19 throughout 2020–2022, 11 key RSC strategies and lessons have been recommended to decision-makers in the retail industry.

This is the first study to identify the themes that emerged from RSC literature during the COVID-19 pandemic to inform future mitigation and recovery strategies. The resulting themes add to the existing body of knowledge and establish the need for further research into other sectors that might be affected by future pandemics.

This research aims to study the students' perspectives on synchronous and asynchronous learning during the COVID-19 Pandemic. Both synchronous and asynchronous learning approaches used in online education have positive and negative outcomes. Hence, the aim is to study online education's positive and negative consequences, reflecting sync and async approaches. This research followed a mixed research approach. The key stakeholders of this research are the Indian educational institutions and students.

This research collected data from the students undergoing synchronous and asynchronous learning amidst the COVID-19 Pandemic. The data were collected (N = 655) from various students taking online classes during the pandemic. A questionnaire survey was distributed to the students through online platforms to collect the data. In this research, the authors have collected data using simple random sampling, and the same has been used for data analysis using SPSS version 26. The collected data were exposed to a factor analysis using a principal component analysis technique to reduce the vast dimensions.

The study findings show that synchronous learning is sometimes stressful, placing more responsibility on students mainly because of the increased screen time. At the same time, asynchronous learning allows the students to self-explore and research the topics assigned to them. Students also felt that asynchronous activities create a burden because of many written assignments to be submitted within a short period. Overall, the COVID-19 pandemic has been challenging for the students and the teachers. However, teachers have helped students to learn through digital platforms. The majority of the respondents opined that technological disruptions and death in the family circle had been significant reasons for not concentrating during online classes. However, the combination of synchronous and asynchronous learning has led to a balanced education.

Higher education has undergone multiple transformations in a short period (from March 2020, 2021 and beyond). Educational institutions underwent a rapid transition in remote teaching and learning in the initial stages. As time progressed, educational institutions did course navigation where they relooked into their course plans, syllabus and brought a structural change to match the pandemic requirements. Meanwhile, educational institutions slowly equipped themselves with infrastructure facilities to bring academic integrity. At present, educational institutions are ready to face the new normality without disrupting services to society.

Educational institutions create intellectual capital, which is important for the development of the economy. In the light of COVID-19, there are new methods and approaches newly introduced or old methods and approaches, which are reimplemented, and these approaches always work for the benefit of the student community.

The authors collected data during the COVID-19 pandemic; it helped capture the students' experience about synchronous and asynchronous learning. Students and faculty members are newly exposed to synchronous and asynchronous learning, and hence, it is essential to determine the outcome that will help many stakeholders.

The purpose of this paper is to examine the role of digital supply chain (DSC) technologies in automotive supply chain resilience (SCR) practices to improve the supply chain performance (SC-Perf.) objectives of companies operating in the automotive industry. This study also compares the results of the associated SC-Perf objectives before and after the COVID-19 pandemic outbreak lockdown situation.

The authors undertook in-depth empirical research using a questionnaire survey to explore the performance of automotive supply chains. The sample for this study consisted of practitioners from supply chain entities such as automotive original equipment manufacturers, Tier-1 component manufacturers and lead logistics providers in Asia-Pacific (AP) emerging markets. Research questions, framework and hypotheses were developed using the literature review.

The research outcome from analysis of the data the authors collected from an emerging market context, specifically the automotive sector, emphasizes the role of DSC technologies and encourages the firm’s SCR practices which, in turn, supports the SC-Perf objectives. The DSC technologies competency moderates the SCR and SC-Perf objectives relation, and the moderation effect is higher for post-COVID-19 pandemic outbreak lockdown situation than at prior state.

The scope of the study is restricted to the automotive firms in the AP region. The data were collected from a representative sample of the population through a questionnaire survey. The small size of the sample incurs a certain level of subjectivity.

This research provides practical insights for practitioners and academicians on DSC technologies’ influence in SCR practices to improve the firm’s SC-Perf. This research shares the literature insights on use of DSC technologies across the sector to allow the automotive firm to reassess the existing operational practices.

The paper adds insights on introducing or implementing DSC technologies across AP automotive firms to increase the operations’ performance by improving SCR practices and sustainability.

This paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach.

The present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation.

The findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week.

The implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain.

The novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

Corona Virus Disease 2019 (COVID-19) is a deadly virus named after severe acute respiratory syndrome coronavirus 2; it affects the respiratory system of the human and sometimes leads to death. The COVID-19 mainly attacks the person with previous lung diseases; the major cause of lung diseases is the exposure to nitrogen dioxide (NO2) for a longer duration. NO2 is a gaseous air pollutant caused as an outcome of the vehicles, industrial smoke and other combustion processes. Exposure of NO2 for long-term leads to the risk of respiratory and cardiovascular diseases and sometimes leads to fatality. This paper aims to analyze the NO2 level impact in India during pre- and post-COVID-19 lockdown. The study also examines the relationship between the fatality rate of humans because of exposure to NO2 and COVID-19.

Spatial analysis has been conducted in India based on the mortality rate caused by the COVID-19 using the data obtained through Internet of Medical things. Meanwhile, the mortality rate because of the exposure of NO2 has been conducted in India to analyze the relationship. Further, NO2 level assessment is carried out using Copernicus Sentinel-5P satellite data. Moreover, aerosol optical depth analysis has been carried out based on NASA’s Earth Observing System data.

The results indicate that NO2 level has dropped 20-year low because of the COVID-19 lockdown. The results also determine that the mortality rate because of long-time exposure to NO2 is higher than COVID-19 and the mortality rate because of COVID-19 may be a circumlocutory effect owing to the inhalation of NO2.

Using the proposed approach, the COVID-19 spread can be identified by knowing the air pollution in major cities. The research also identifies that COVID-19 may have an effect because of the inhalation of NO2, which can severe the COVID-19 in the human body.

Purpose: The purpose of the chapter lies in identifying the challenges that female employees in the South African hospitality industry will face in the context of the economic impact of the COVID-19 lockdown.

Design: With this aim, we developed a questionnaire that has been filled by 101 hospitality professionals in order to explore the work security and perceived opportunities of people working in hospitality in South Africa.

Findings: Our findings confirm that South African women feel more uncertainty about their occupational future and are more afraid that they will not find a job within the tourism sector, even though it is one of the sectors where female employment is higher.

Research Limitations: The study was limited by the lack of literature regarding impact on gender disparities due to lockdown and the relative short period of time during which the data were collected. We recommend that future research should measure long-term effects of COVID-19 pandemic on this issue.

Practical/Social Implications: In order to alleviate the damage done by the post-COVID-19 lockdown on the South African tourist and hospitality industries policies addressing the accessibility of female workers to the job market need to be urgently developed and implemented.

Originality: Ours is the first research ever focussing on the gender inequalities in the tourism labour market in South Africa.