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The food supply chain has faced many challenges due to its complex and complicated nature. Blockchain technology is one of the mechanisms used to improve agri-food supply chain processes by evolving organization capabilities. A study is being conducted to scrutinize the adoption of blockchain technology in the agri-food supply chain through the lens of the operational capability approach. It further makes an attempt to identify the capabilities of blockchain to improve supply chain processes.

The qualitative research method with semi-structured interviews was used to gather information from experts and professionals in the food supply chain and blockchain technology. The authors have adopted a systematic approach of coding using open, axial and selective methods to depict and identify the themes that represent the blockchain-enabled agri-food supply chain. The data were collected from 32 interviews of selected participants.

The result shows five critical areas where blockchain can come up to enhance the agri-food supply chain performance by providing traceability, transparency, information security, transactions, and trust and quality. Further, the study reveals that blockchain will provide safety, lower the cost of transactions and can create trust among users to communicate within the whole supply chain without the intervention of a third party. This study demonstrated that the capabilities need to be considered when introducing technology into the practice.

The study implies thought-provoking implications for bridging the theory-practice gap by examining the empirical data to demonstrate how the operational capabilities of blockchain technology further strengthen the agri-food supply chain. Additionally, this study provides some suggestions for utilizing the results and proposes a framework to understand more about blockchain use cases in the agri-food supply chain as well as extend the application of blockchain using an operational capability approach for future academic researchers in this area.

This study presented some more important managerial implications which reveal that the majority of organisations were in the initial stages of adoption process of blockchain technology. Further, the positive influence of managers and IT experts can help the information technology companies (IT) and stakeholders for developing and promoting blockchain solutions in the agri-food supply chain. The important implication of blockchain enabled agri-food supply chain is to maintain information security and incresae supply chain performance.

The study shows the operational capabilities of agri-food supply chain using blockchain technology. Blockchain can contribute in enhancing the agri-food supply chain to increase traceability and transparency and helps to reduce the risk of disruptions.

Blockchain technology can overcome many complicated problems related to confidentiality, integrity and availability of fast and secure distributed systems in the agri-food supply chain. In emerging economies like India, blockchain application in the agri-food supply chain is still new, and their adoption is underdeveloped. This paper aims to investigate the drivers of blockchain technology adoption and their effect on the behavioral intention of stakeholders in adopting blockchain technology among various stakeholders in the agri-food supply chain. The study also develops a framework to enhance understanding of blockchain adoption in the agri-food supply chain as well as the stakeholders' motivation in seeking blockchain solutions.

Considering the most significant aspects of blockchain adoption in the agri-food supply chain, this study attempts to develop an adoption model by using the extended unified theory of acceptance and technology model with interfirm trust and transparency as additional factors. Data was collected from a sample of 200 stakeholders in the North Indian state of Punjab. The empirical analysis was carried out using structural equation modeling in Smart PLS3.

The findings supported the developed framework and the results of SEM indicate that all the paths are supported. In particular, the findings of the study reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, interfirm trust and transparency are the drivers of blockchain adoption and have a significant impact on the behavioral intention of stakeholders. Cumulatively, the results positively impact the performance of agri-food supply chain. From this study, it is found that the adoption of blockchain technology in agri-food supply chain enhances their performance.

The originality of the study lies in the developed framework, technology adoption will help them focus in the right direction by eliminating manual methods and converting the agri-food supply chain into a digitalization system.

Dual-rotor wind turbines (DRWTs) are a novel type of wind turbines that can capture more power than their single-rotor counterparts. Because their surrounding flow fields are complex, evaluating a DRWT design requires accurate predictive simulations, which incur high computational costs. Currently, there does not exist a design optimization framework for DRWTs. Since the design optimization of DRWTs requires numerous model evaluations, the purpose of this paper is to identify computationally efficient design approaches.

Several algorithms are compared for the design optimization of DRWTs. The algorithms vary widely in approaches and include a direct derivative-free method, as well as three surrogate-based optimization methods, two approximation-based approaches and one variable-fidelity approach with coarse discretization low-fidelity models.

The proposed variable-fidelity method required significantly lower computational cost than the derivative-free and approximation-based methods. Large computational savings come from using the time-consuming high-fidelity simulations sparingly and performing the majority of the design space search using the fast variable-fidelity models.

Due the complex simulations and the large number of designable parameters, the design of DRWTs require the use of numerical optimization algorithms. This work presents a novel and efficient design optimization framework for DRWTs using computationally intensive simulations and variable-fidelity optimization techniques.

An improvement in the energy efficiency of wind turbines can be achieved using dual rotors. Because of complex flow physics, the design of dual-rotor wind turbines (DRWTs) requires repetitive evaluations of computationally expensive partial differential equation (PDE) simulation models. Approaches for solving design optimization of DRWTs constrained by PDE simulations are investigated. The purpose of this study is to determine design optimization algorithms which can find optimal designs at a low computational cost.

Several optimization approaches and algorithms are compared and contrasted for the design of DRWTs. More specifically, parametric sweeps, direct optimization using pattern search, surrogate-based optimization (SBO) using approximation-based models and SBO using kriging interpolation models with infill criteria are investigated for the DRWT design problem.

The approaches are applied to two example design cases where the DRWT fluid flow is simulated using the Reynolds-averaged Navier−Stokes (RANS) equations with a two-equation turbulence model on an axisymmetric computational grid. The main rotor geometry is kept fixed and the secondary rotor characteristics, using up to three variables, are optimized. The results show that the automated numerical optimization techniques were able to accurately find the optimal designs at a low cost. In particular, SBO algorithm with infill criteria configured for design space exploitation required the least computational cost. The widely adopted parametric sweep approach required more model evaluations than the optimization algorithms, as well as not being able to accurately find the optimal designs.

For low-dimensional PDE-constrained design of DRWTs, automated optimization algorithms are essential to find accurately and efficiently the optimal designs. More specifically, surrogate-based approaches seem to offer a computationally efficient way of solving such problems.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

In the last 10 years, India has amended its laws dealing with sexual offences against women with the changes ranging from increasing terms of imprisonment for the offence of rape to state-funded compensation schemes for women and child victims. In this regard, challenges persist for the agencies of the criminal justice system in India especially the courts to realise the vision of restorative justice as these forums have to navigate the relevant statutory provisions and binding precedents. This chapter seeks to analyse the challenges faced by courts in proper reintegration of victims and offenders of sexual offences, the institutional responses of the courts and suggests reforms to the criminal justice system in India in consonance with the principles of restorative justice acknowledged in the restorative justice movement in the international discourse.

The learning outcomes of this case study are as follows: to understand the concept of social commerce and how it is different from e-commerce business, to discuss the unique features of Meesho’s social commerce model, to understand concepts of entrepreneurship (e.g. addressing the gap through business, pivoting), to understand the dynamics of online grocery market and e-commerce market and to apply business strategy concepts to make recommendations.

This case study presents Meesho, an organization in social commerce in India. Meesho was founded by Indian Institute of Technology graduates Vidit Aatrey and Sanjeev Barnwal in the year 2015 to help the small business owners with online selling. It was initially launched as an app that connected local retailers to the customers. Owing to low customer interest and low profit margins, they pivoted the business to a reseller app that facilitated the individuals and small retailers to resell the wholesalers’ products (unbranded and long-tail products) to the customers on social media channels. However, the tough competition from other start-ups in social commerce and retail giants such as Amazon and Flipkart who targeted the same customers impacted their growth. After receiving a funding of US$300m, the founders were considering if they should enter the e-commerce market and directly compete with giants such as Amazon and Flipkart or extend the product line to the online groceries market and compete with dominant players such as BigBasket and Blinkit. Through this case study, the students could be provided an opportunity to evaluate a situation, apply the strategic management concepts and make a recommendation on the strategic plan.

The case study can be taught in the business and strategy courses at the graduate and postgraduate levels in business schools. It is also suitable for the entrepreneurship course with focus on e-commerce start-up and sustainability, which is also taught at the MBA level. This case study can also be used in executive development programs for abovementioned courses.

Teaching notes are available for educators only.

CSS 11: Strategy.

By combining the notion of prospect theory with advanced machine learning algorithms, this study aims to predict whether financial institutions (FIs) adopt a reactive stance when they perceive climate change as a risk, consequently leading to the adoption of environmental, social and governance (ESG) practices to avoid this risk. Prospect theory assumes that decision-makers react quickly when decisions are framed as a risk or threat rather than as an opportunity.

We used a sample of 168 FIs across 27 countries and seven regions over the period 2003–2020. To conduct our empirical investigation, we compared the prediction accuracy of various machine learning algorithms.

Our findings suggest that out of 12 machine learning algorithms, AdaBoost, Gradient Boosting and XGBoost have the most precision in predicting whether FIs react to climate change risk in adopting ESG practices. This study also tested the overall climate change risk and risks associated with physical, opportunity and regulatory shocks of climate change. We observed that risks associated with physical and regulatory shocks significantly impact the adoption of ESG practices, supporting prospect theory predictions.

The insights of this study provide important implications for policymakers. Specifically, policymakers must take into account the risk posed by climate change in the corporate decision-making process, as it directly influences a firm’s adoption of corporate actions (ESG practices).

To the best of our knowledge, this is the first study to investigate the firm-level climate change risk and adoption of ESG practices from a prospect theory perspective using novel machine learning algorithms.

The purpose of this paper is to examine the impact of remittance inflows (remittances) on electricity consumption and electric power losses in Jamaica.

The authors use annual data from 1976 to 2014 and apply vector error correction modelling, Granger causality testing and impulse response analysis.

First, the authors find that there is co-integration between remittances and the energy variables, namely electricity consumption and electric power losses. Second, short-run Granger causality exists between the energy variables and remittances. This causality is bidirectional between the energy variables and positive changes in remittances, but it is unidirectional running from the energy variables to negative movements in remittances. Third, the authors find that in the long-run remittances have a negative relationship with electric power losses and a positive relationship with the consumption of electricity.

Findings from this paper will help to elucidate the relationship between electricity consumption, and electric power losses, and remittances.

The problem of electric power losses is acute in Jamaica and it is mostly due to theft. At the same time, Jamaica receives significant remittances. Social policy could have a role to encourage the use of remittances to help stem the theft of electricity.

This is the first study that examines the relationships between remittances, electricity consumption and electric power losses.