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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.

This paper argues that the closures will cause regressive rather than progressive modern slavery shifts as the necessity of survival prevails over addressing modern slavery risks within supply chains.

In the spring of 2020, global clothing retailers were advised or ordered to close physical stores due to lockdown measures of the COVID-19 pandemic and many supply chains temporarily halted production. This paper explains how pre-pandemic modern slavery advancements will be detrimentally affected as a result of societal lockdowns and apparel retail closures around the world.

Two consequences of lockdowns are highlighted, which will have negative implications on modern slavery progress. These are the exploitation of vulnerable people, which includes higher exploitation of those already involved in modern slavery and increased risk of exploitation for those susceptible to being drawn (back) into modern slavery and; the need for repetition of previous work completed by external stakeholders or in some cases, a better alternative.

The pandemic itself causes friction between immediate response solutions and long-term modern slavery goals.

In response to modern slavery drivers, governments may need to fill governance gaps, to control the power of corporations and to reconsider migration regulation.

The COVID-19 lockdowns and subsequent supply chain disruptions were unforeseen. This paper argues that there are significant negative effects on the developments in modern slavery eradication made in the past 10 years. As businesses struggled for basic survival, the apparel manufacturing sector has been detrimentally affected as upstream labourers are now at higher risk from the increased likelihood of modern slavery violations.

This research aims to study the recent changes in consumer behaviour and purchase pattern during the Covid-19 pandemic. Covid-19 pandemic has forced consumers to stockpile, which has its own consequences. The article proposes the importance of “minimalism in consumption” to avoid greed in consumer behaviour.

The data are collected from consumers across India using an online survey during the first lockdown from March 2020 to May 2020. A simple random sampling technique is used for data collection, and the collected data are analysed using SPSS version 26.

The study states that there will be a shift in the purchase pattern of the consumers if lockdowns are imposed in the future or during any other crisis. However, at present, consumers have developed a stockpiling mentality fearing the unavailability of essentials.

Pandemic has stimulated a drastic change in consumer behaviour, which is a situational effect. Each crisis affects consumer behaviour in a different way. In this research, we have considered only fear, greed and anxiety in the light of Covid-19. On the other hand, the research intends to draw realistic conclusions based on consumers' experiences during the lockdown.

The study proposes solutions that will help marketers frame exclusive strategies for a future crisis. Analysing the change in consumer behaviour and the shift in purchase patterns will emphasize the importance of market research to know consumer expectations during a crisis situation in order to cater to their new demands.

Consumers who stockpile should realize the unavailability of goods to other consumers who are in need. They also have to understand the importance of “minimalism in consumption” during a crisis.

The data are collected during the most taxing crisis, the Covid-19 pandemic. Data are collected at the peak time of the first wave of Covid-19 in India, during a major shift in consumers' behaviour and purchase pattern. The article brings to the larger consciousness and also preaches a life lesson to all consumers to execute their responsibilities in consumption without over-demands and expectations.

This study investigates the determinants of teleworking before and during COVID-19 in Canada. It explores the extent of telework adoption across industrial sectors, as well as the long-term impacts of large-scale adoption of teleworking on urban travel demand and the dwindling demand for commercial real estate in downtowns.

Using data from a survey of business establishments, this study employs data visualization techniques to illustrate how telework adoption evolved during the early stages of the COVID-19 lockdown. The study also estimates Logit models to explore the determinants of telework before and during the pandemic using a subsample from the Canadian labor force survey.

The study found that telework adoption reached a peak in March and April of 2020 when almost 40% of the workers in Canada were teleworking. Only 12% of employees reported teleworking before the pandemic. The adoption of teleworking was far more pronounced amongst firms that use information and communication technologies (ICTs) extensively. Teleworking appears to be far more frequent among university-educated (knowledge economy) workers.

Knowledge economy and highly educated workers, who switched to teleworking during the pandemic at higher rates than workers with less education, are more frequently employed in offices located in the urban core or downtowns. The drastic decline in commuting to the urban core via public transit and record low occupancy levels in downtown office towers suggests that, even if telework prevailed at levels much lower than those observed during March and April of 2020, landlords holding large portfolios of commercial real estate must prepare to cope with the lower demand for commercial real estate. This is especially significant when commercial leases come up for renewal in the next few years. In addition, governments struggling to address traffic congestion by spending hundreds of billions of dollars on transport and transit infrastructure might want to promote teleworking as a means of reducing travel demands and costly infrastructure expenditures.

Teleworking has partially eroded the boundaries between work and home by enabling millions to continue being productive while working from home. However, teleworking would require new norms and rules to maintain a work-life balance. This change requires workers and employers to cooperate.

To the best of the authors’ knowledge, this is the first study that explores the extent of telework adoption during COVID-19 in Canada and the determinants of telework adoption, presenting both employee and firm-level perspectives using Canada-wide representative data.

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.

Upon completion of this case study, students will be able to identify the key changes in the marketing environment affecting the industry, demonstrate the elements of the marketing mix in the fast-food industry, illustrate the crucial elements of customer value-driven marketing strategy, critique relevant marketing strategies that are crucial for business development and formulate effective market expansion strategies for Al-Chef Cafetaria to achieve sustainable competitive advantage in the VUCA world.

The Al-Chef Cafeteria, established by Ali Arif, one of the partners, became one of the most happening quick service restaurants (QSRs) in Patna. However, the outbreak of the COVID-19 pandemic disrupted the once-thriving fast-food market in the city as the government imposed lockdowns to restrict the onslaught of the pandemic. The relentless waves of the pandemic in the subsequent months severely impacted India and worsened the economic challenges. Consumer behaviour towards outdoor eateries, especially QSRs, became uncertain, which led to the exit of several smaller players in the industry. In June 2021, because of an uncertain future, Arif was forced to contemplate different business trajectories for survival and growth. Arif’s resilience was highlighted, as he endeavoured to revive his dream cafe. To start the café, Arif had quit a stable job in the Middle East. His journey mirrored the broader narrative of businesses navigating uncharted waters as the cafe transformed from a flourishing enterprise to one reeling from adversity and looking forward to undergoing a strategist lens for revival. Against an uncertain business landscape and wavering consumer sentiment, Arif grappled with the question of whether a return to normalcy was possible or if a new-normal system would emerge. This case study highlighted the challenges and uncertainties faced by the Al-Chef Cafeteria post-pandemic and the strategies needed to rewire the previous business model to chart a new growth trajectory.

This case is suitable for postgraduate-level marketing management or sales management (business development) courses in any of the following programmes: MBA programme, PG diploma in marketing management/PG diploma in hospitality and tourism management/PG diploma in sales management/PG diploma in food and beverage service/PG diploma in service management, part-time diploma programmes in management and executive programmes in management.

Teaching notes are available for educators only.

CSS 8: Marketing.

In 2014, real estate investment trust (REIT) emerged as a new alternative investment option in India. This research aims to give an empirical authentication of the Indian REITs performance from April 2019 to July 2022 across a range of investment variables.

Using monthly total returns in Indian Rupee, risk-adjusted Indian REIT performance and investment portfolio characteristics are examined. Indian REITs' potential in a diversified multi-asset portfolio is analysed using the mean-variance analysis, asset allocation diagram and efficient frontier.

During April 2019–July 2022, Indian REITs provided a lower return than stocks but outperformed bonds despite coronavirus disease 2019 (COVID-19) lockdowns, which hurt the traditional working from office concept. The study also examined REIT allocation to an Indian mixed-asset portfolio and the benefits of a diversified portfolio.

Indian REITs provide a liquid, transparent alternative to direct property for investors seeking exposure to Indian real estate markets. Indian REITs gave real estate companies an extra funding source and investors an alternate asset. This paper explores Indian REITs' potential opportunities, given that domestic and foreign investors' demand for transparent property investment in India. The analysis found a positive early performance despite a challenging environment.

This paper offers the first empirical performance validation of Indian REITs as a way to obtain exposure to commercial property in India and the REITs' role in a diversified asset portfolio. The authors' study improves investors' decision-making abilities by providing empirically validated, valuable and practicable property investing insights.

Transport shapes the health of urban populations. It can support healthy behaviours such as participation in regular physical activity and access to community connection. Transport systems can also have major negative impacts on health. For example, through air pollution from fossil fuel-based modes of travel, the risk of injury and death from transport related collisions, and in the way sedentary modes of travelling can contribute to less physically active lifestyles.

This chapter considers the long-term impact of the pandemic on a series of well-researched transport-related health outcomes. It first describes the established connections between transport and health. It then considers the future implications of three potential pandemic-induced shifts: the increased uptake of working from home (WFH); decreased usage of public transport and increased interest in walking and cycling in the local neighbourhood. The impacts of these shifts on the transport-health nexus are then discussed, revealing both positive and negative outcomes. The authors conclude by providing policy recommendations to mitigate possible negative outcomes and strengthen the positive consequences into the future.

• After working through the case and assignment questions, students will be able to do the following:• Identify the difference between core competencies and dynamic capabilities and how they make a difference in a crisis towards building an agile organisation.• Evaluate the support through a collaborative, temporary, trans-organisational system to local manufacturers to achieve agility and sustainability.• Realise the importance of clear expectations exchange and communication between partners to enhance collaboration, even in temporary structures in increasing agility and sustainability.

The COVID-19 pandemic and subsequent lockdowns created a crisis for South Africa and the President, Cyril Ramaphosa called for urgent collaboration between business, labour and government to meet the demand for locally produced Personal Protective Equipment (PPE) and medical devices. The case illustrates this response and collaboration between government, labour and business through a temporary newly formed structure, called Business for South Africa (B4SA). Ross Boyd, Head of the innovation work stream of B4SA which housed the local manufacturing partnership (LMP) was considering his dilemma of how to be agile in sustaining their support to the South African manufacturers even though the LMP was a temporary structure. The local manufacturers had to repurpose their production lines to produce local PPE and medical devices. How could the LMP support the South African manufacturers to sustain their agility in building capabilities during COVID-19? At the same time, Ahmed Dhai, the Group Executive of Operations of Kingsgate Clothing, which was benefiting from the support of the LMP, was reflecting on his leadership in taking decisions to repurpose production and increase capacity during COVID-19. Dhai was considering his dilemma of being agile during and beyond the COVID-19 pandemic. The case features several decisions taken by Kingsgate and offers students the opportunity to evaluate these decisions given the fluctuations in supply and demand of PPE and the leadership that Dhai demonstrated in how he communicated and dealt with his staff during the pandemic. Students could also give recommendations to Ross Boyd and Ahmed Dhai on how they could lead their organisations to be more agile during and beyond COVID-19.

The case study is suitable for MBA or MPhil level on Strategy courses. The case would also find good application in Organisational Behaviour and Leadership courses on Masters level and Executive Education programmes.

Teaching notes are available for educators only.

CSS 7: Management Science.

The study provides medium-term estimates of recovery paths for Indian economy using a dynamic factor (DF)-based approach that employs data on high-frequency indicators à la Bhadury, Ghosh, and Kumar (2020). The DFs are used to analyze the post-pandemic recovery and convergence with its pre-COVID-19 trend for India between March 2021 and March 2022. A broad sectoral assessment of the impact of COVID-19 is also conducted. In addition, forward-looking measures based on stock returns are used to analyze the transmission of additional banking sector risks to the real sectors by constructing daily delta conditional value-at-risk (CoVaR) estimates. Our estimates based on the DFs suggest that the aggregate economic activities may catch up to the estimated pre-COVID trend by March 2021 predominantly driven by the growth in services sector. The industrial sector and consumer goods sector continue to show moderate signs of recovery. Our CoVaR estimates corroborate these findings. Banking sector transmission risk is among the lowest for services such as healthcare and information technology (IT), for both the lockdown period between March 25 and June 8, 2020, and for the latter months. The transmission risk continues to remain high for metal, oil and gas, and capital goods sector. Broadly, the evidence on forward-looking banking sector risk transmission for major sectors is in alignment with our finding on their recovery based on DF models, after easing of COVID-19 lockdown.