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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 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.

This paper systematically evaluates the existing literature of innovations in freight transport, including all modes, to uncover the key research themes and methodologies employed by researchers to study innovations and their implications in this industry. It analyses the role of transport and the impact of innovations during crises, such as COVID-19.

Qualitative and quantitative analysis of the innovations in freight transport unravels the pre-requisites of such endeavours in achieving a resilient and sustainable transport network that effectively and efficiently operates during a crisis. The authors performed keyword co-occurrence network (KCON) analysis and research focus parallelship network (RFPN) analysis using BibExcel and Gephi to determine the major resulting research streams in freight transport.

The RFPN identified five emerging themes: transport operations, technological innovation, transport economics, transport policy and resilience and disaster management. Optimisation and simulation techniques, and more recently, artificial intelligence and machine learning (ML) approaches, have been used to model and solve freight transport problems. Automation innovations have also penetrated freight and supply chains. Information and communication technology (ICT)-based innovations have also been found to be effective in building resilient supply chains.

Given the growth of e-commerce during COVID-19 and the resulting logistics demand, along with the need for transporting food and medical emergency products, the role of automation, optimisation, monitoring systems and risk management in the transport industry has become more salient. Transport companies need to improve their operational efficiency using innovative technologies and data science for informed decision-making.

This paper advises researchers and practitioners involved in freight transport and innovation about main directions and gaps in the field through an integrated approach for evaluating research undertaken in the area. This paper also highlights the role of crisis, e.g. COVID-19, and its impacts on freight transport. Major contributions of this paper are as follows: (1) a qualitative and quantitative, systematic and effective assessment of the literature on freight transport through a network analysis of keywords supplemented by a review of the text of 148 papers; (2) unravelling major research areas; (3) identifying innovations in freight transport and their classification as technological and non-technological and (4) investigating the impact of crises and disruptions in freight transport.

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 paper aims to identify and empirically validate the various factors for adoption, usage and intention to recommend e-pharmacy for purchasing medications by consumers.

Based on constructs from well-established theoretical models, the technology acceptance model, extended unified theory of acceptance and use of technology and self-determination theory, a model was proposed for the study. The model was validated with a sample size of 184 respondents using partial least squares method and factor analysis to establish and validate relationships among the various identified constructs.

The results show that performance expectancy, effort expectancy, social influence and hedonic motivation have a positive co-relation with adoption of e-pharmacy and the intention to recommend. The results depict that gender and educational background have no correlation toward adoption and intention to recommend e-pharmacies for purchasing medicines in India.

This research comes along with a geographic limitation of the sample size. The research was conducted in an urban suburb city of Bengaluru, India.

At an academic level, this research will provide interesting insights for exploring adoption and usage intention of consumers toward e-pharmacy. At a managerial level, this empirically supported study will provide insights into the relationship among the various constructs and the consumers’ motivation toward adoption and usage intention of e-pharmacy.

This research is the first of its form which uses constructs from the technology acceptance model, extended unified theory of acceptance and use of technology and self-determination theory in the online healthcare space to understand consumer usage behavior.

This study aims to examine the mediating role of social influence on the relationship between key predictors of E-pharmacy adoption among young consumers based on the unified theory of adoption and use of technology (UTAUT).

This study employs a quantitative correlational research design. Based on cluster sampling, data was collected from 306 university students from three public universities in southwestern Nigeria. Data was analysed using partial least square structural equation modeling.

The primary determinant driving the adoption of e-pharmacy is performance expectancy. Social influence plays a partial mediating role in linking performance expectancy to e-pharmacy adoption. In contrast, it fully mediates the relationship between effort expectancy, facilitating conditions and the adoption of e-pharmacy services.

This study provides theoretical clarity on recent issues within the UTAUT framework. Findings highlight the complexity of how social factors interact with individual beliefs and external conditions in determining technology acceptance.

Research includes information relevant to access the impact of e-pharmacy services on healthcare accessibility, affordability and quality in developing countries.

The findings extend the adoption of technology literature in healthcare and offer a new understanding of adoption dynamics. The results emphasize the importance of performance expectancy in driving e-pharmacy adoption, providing a clear direction for stakeholders to enhance service quality and user experience of e-pharmacy. Additionally, the mediating effect of social influence highlights the significance of peer recommendations, celebrity endorsements and social media campaigns in shaping consumer adoption of e-pharmacies among young people.

This study aims to provide a meaningful comparison of airports’ performance and better understand the differences observed in the analysed airport performance by presenting a model to analyse the relationship between operational and financial performance and airport characteristics.

This study uses a quantitative analysis approach. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy weight were utilised to analyse 17 airports in three Airports Council International regions: Asia, Europe and North America. Through operational and financial factors, these sample airports identified the most efficiently operated airports from 2016 to 2019.

Overall, Asian airports were superior in operational and financial efficiency. Unlike operating performance, the sample airport’s financial and total performance results show a similar trend. There were no noticeable changes in operational factors. Therefore, differences in financial variables for each airport may affect the total performance.

This study provides insightful implications for airport policymakers to establish a standardised information disclosure foundation for consistent analysis and encourage airports to provide this information.

The adoption of Earnings Before Interest, Taxes, Depreciation, and Amortisation (EBITDA) to debt ratio and EBITDA per passenger, which had previously been underutilised in the previous study as financial factors, demonstrated differences between airports for airport stakeholders. In addition, the study presented a model that facilitates producing more intuitive results using TOPSIS, which was relatively underutilised compared to other methodologies such as date envelopment analysis.

This case study would enable students to understand the concept, process and advantages of mergers and acquisitions as a growth strategy with respect to 1mg. Also, the students would be able to use the threats, opportunities, weaknesses and strengths matrix to map 1mg’s strengths, weaknesses, opportunities and threats.

This case study analyses the transformation journey of 1mg to Tata 1mg, one of the most trusted internet pharmacies in India. This case describes a small start-up that was launched in 2013 and had made many acquisitions since then. This case revolves around Tata Digital’s purchase of 1mg. The case starts out by explaining 1mg’s financial situation and why the company was acquired. This case study focuses on how the integration helped Tata Digital and 1mg realize their respective missions. Furthermore, the case study illustrates the benefits and difficulties of this integration.

This case study is basically aimed at postgraduate management students; it can be used in strategic management and health-care courses. Students can understand the concept of diversification and acquisition with the help of this case study. Students can also gain an insight into the organic and inorganic diversification as a growth strategy.

Teaching notes are available for educators only.

CSS 11: Strategy.