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Integrating digital technologies such as artificial intelligence and blockchain make the agri-food supply chain (ASCM) transparent, resilient and flexible. However, its adoption is quite complex due to various anticipated barriers. So, the presented research purposes to explore and investigate the barriers.

This study employs hybrid approach including Best-Worst Method (BWM) and Graph Theoretic Approach (GTA). Data were collected from industry experts employed in the agri-food sector and analyzed by means of standard operating procedures.

GTA results show that Technological barriers have the highest barrier intensity. Moreover, BWM results show that “Increased operational complexity” is the topmost barrier to adopting blockchain in ASCM. “Lack of interoperability” ranks second among the identified barriers.

The results benefit the managers, practitioners and researchers to understand the anticipated barriers so that necessary strategies can be developed, and organizations can become more resilient, agile, transparent and traceable.

The presented work is the first to develop a mathematical model and assess the industry’s eagerness to adopt blockchain in ASCM. The proposed framework will greatly benefit the stakeholders working in agri-food sector.

This chapter investigates pandemic impact in a variety of industries, including food, travel, education and pharmaceuticals, considering elements such as isolation, emotions and social influences, which can lead to panic buying. The goal of this research is to ascertain how COVID-19 influences the buying decisions of customers. Additionally, the study aims to identify consumer consumption trends for a spectrum of products and services, including fast-moving consumer goods (FMCGs), entertainment, pharmaceuticals, travel and tourism. A comprehensive review of different research papers is done to conclude. The papers considered are from 2020 to 2022. Different keywords are used to search the relevant papers such as ‘pandemic’, ‘COVID-19’, ‘behaviour’, ‘impulsive’, etc. TCCM framework has been applied while reviewing the articles. During the isolation, consumer behaviour moved to panic buying and stockpiling, favouring organic basics, and encouraging e-commerce, as well as economic nationalism favouring made-in-India products. This study helps in knowing the reasons for change in consumers' behaviour for different products and services due to unforeseeable situations like COVID-19 and can find possible ways to deal with them. Business owners learn about changing consumer purchasing behaviours and how to modify products. The government can change policies to improve medical tourism and social protection.

This study aims to examine the intricate relationships in the making of a box office through a stakeholder lens that considers the influence of filmmakers and theatres on moviegoers' intention to watch a movie at the theatre.

Employing covariance-based structural equation modelling (CB-SEM), this study analyses survey data on cinema-going experience collected from 673 moviegoers in digital era of a new normal.

The findings elucidate that movie branding, movie genre and theatre preference positively influence moviegoers' intention to watch a movie at the theatre. Furthermore, the study unveils that theatre preference is swayed by an array of personal and social factors, including control belief and social companion. Intriguingly, promotional elements, both commercial and non-commercial, were found to influence movie branding, yet not the genre when predicting theatre attendance intentions.

Amid the burgeoning alternatives for watching movies (e.g. cable television and online streaming platforms), this article offers a contemporary exploration of the variables that motivate audiences to partake in the cinema-going experience, thereby serving as a proxy to decipher the factors that drive a movie's box-office success in digital era.

Unlike prior studies relying on archival data, the present study collects and uses survey data to develop a novel stakeholder theory-based marketing framework for the box office and moviegoers. The study also provides seminal insights on the box office and moviegoers in the digital era of a new normal.

Stringent environmental regulations and the need for a robust supply chain (SC) network have necessitated organizations to adopt circular economy (CE) practices. With proven impact of CE practices on SC activities, digital technologies are prompting organizations to digitalize SC networks. Yet, the correlation between SC digitalization and CE practices has been less examined. This study aims to identify and evaluate, the critical success factors (CSFs) necessitating SC digitalization and strategies helping in SC digitalization.

An extensive literature review was performed to identify CSFs and strategies for SC 4.0 (SC4.0), and for finalization, experts’ input was obtained with the Delphi approach. An integrated Fermatean fuzzy set – analytic hierarchy process – decision-making trial and evaluation laboratory – combined compromise solution technique was used to evaluate CSFs and strategies.

Smart work environment, performance monitoring and data reliability and relevance were identified as the top three important CSFs for SC digitalization. Enhancement of analytical capability, data-driven process optimization and development of an integrated digital platform were identified as potential SC4.0 transition strategies.

This study helps SC practitioners better understand the CSFs and strategies for the SC4.0 transition. Furthermore, this study explores the integration of CE principles within these digital strategies, emphasizing how sustainability practices can be embedded in the SC4.0 framework to foster a more resilient and environmentally conscious electronics SC in India.

To the best of the authors’ knowledge, this work is the first to analyze CSFs for SC4.0 in the Indian electronics industry.

Gas metal arc-based directed energy deposition (GMA-DED) process experiences residual stress (RS) developed due to heat accumulation during successive layer deposition as a significant challenge. To address that, monitoring of transient temperature distribution concerning time is a critical input. Finite element analysis (FEA) is considered a decisive engineering tool in quantifying temperature and RS in all manufacturing processes. However, computational time and prediction accuracy has always been a matter of concern for FEA-based prediction of responses in the GMA-DED process. Therefore, this study aims to investigate the effect of finite element mesh variations on the developed RS in the GMA-DED process.

The variation in the element shape functions, i.e. linear- and quadratic-interpolation elements, has been used to model a single-track 10-layered thin-walled component in Ansys parametric design language. Two cases have been proposed in this study: Case 1 has been meshed with the linear-interpolation elements and Case 2 has been meshed with the combination of linear- and quadratic-interpolation elements. Furthermore, the modelled responses are authenticated with the experimental results measured through the data acquisition system for temperature and RS.

A good agreement of temperature and RS profile has been observed between predicted and experimental values. Considering similar parameters, Case 1 produced an average error of 4.13%, whereas Case 2 produced an average error of 23.45% in temperature prediction. Besides, comparing the longitudinal stress in the transverse direction for Cases 1 and 2 produced an error of 8.282% and 12.796%, respectively.

To avoid the costly and time-taking experimental approach, the experts have suggested the utilization of numerical methods in the design optimization of engineering problems. The FEA approach, however, is a subtle tool, still, it faces high computational cost and low accuracy based on the choice of selected element technology. This research can serve as a basis for the choice of element technology which can predict better responses in the thermo-mechanical modelling of the GMA-DED process.

The primary objectives of the case study are to get the participants exposed to the issues of working capital which even profitable companies face on a day-to-day basis; give the participants an understanding of how to balance the, at times, conflicting objectives of increasing profits and sales through favorable credit terms; and expose them to the impact of increase in inventory levels and average collection period on margins in a period of slow growth. They will also learn about the concept of factoring and its uses.

The case study is about a group of companies engaged in education, steel fabrication and oil businesses owned by a single proprietor. The company was based in Fatehnagar which was part of Hyderabad district in the state of Telangana, India, and the case study traces the origins of the group from 1960s to 2021. The group was invested the surplus cash flows from the oil business to initiate and expand other businesses during this period. The economic downturn due to the COVID-19 pandemic had hit the company, particularly its oldest business – Noble Chemical Agency. The oil business was facing issues related to its growth and profitability, and the uncertainty around COVID-19-related restrictions had only augmented the fears of the management. The case study looks at issues and the dilemma which the owner of the company faced. The case study highlights various issues related to working capital management, especially related to receivables management and inventory levels faced by businesses during the slow-growth phase. It demonstrates how working capital management issues, if not resolved in time, can lead to insolvency of even a successful company with a sound business model.

The case study is meant for teaching in postgraduate management programs (Master of Business Administration and Postgraduate Diploma in Management) in the following courses: corporate finance/financial management course in the first year (the case study should be taught towards the end of the course); and management accounting courses in first year (the case study should be positioned in the middle of these courses). The case study can also be used to highlight issues related to working capital and small business management in a Management Development Programme (MDP) course for “Finance fundamentals for non-finance executives”.

Teaching notes are available for educators only.

CSS 1: Accounting and finance.

The COVID-19 pandemic has transformed consumer buying behavior across the world. COVID-19 crisis brought a behavioral change in consumers' attitudes toward health, financial and social well-being. The current research work highlights the factors influencing consumer buying behavior during the COVID-19 pandemic considering saving and safety perspectives.

This study attempts to understand the gap in buying behavior with reference to saving and safety. Survey-based study was conducted during the second phase of COVID-19, and the respondents were those who lived in highly affected COVID cities in India. Exploratory factor analysis and multiple regression analysis were carried out for testing the hypotheses.

Seven factors became the prominent factors in consumer buying patterns during the pandemic. Consumers in the times of COVID-19 pandemic spend only on essential items as compared to nice-to-have and non-essential items.

Respondents considered in the research were millennials aged 25–40. The current research is limited to specific geographic location.

The study assessed how savings and safety influence consumer buying behavior. The 2S framework model for consumer buying behavior during pandemic has been developed. The findings of the study provides a road map to the companies, policy makers, managers and consumers in understanding the consumer buying behavior during pandemic.

The current research work observe the changes in the behavioral patterns of consumers in the context of 2S framework, i.e. saving and safety. This study offer novel contribution as there is no available literature that examined the saving and safety aspects together for consumer buying behavior during crisis.

The study aims to investigate the impact of board gender diversity (BGD) on firm performance (FP) by testing two hypotheses – the existence of a positive relationship between BGD and FP, and the moderating role of a critical mass of female directors on FP. The study also explores whether the association varies across different industries.

The authors collect data using Bloomberg and CMIE Prowess, from the Bombay Stock Exchange (BSE) 500 index for the period 2008–2018 and employ a robust statistical methodology (Dynamic Panel Data Model).

A critical mass of female directors positively moderates and strengthens the relationship between BGD and FP. The study fails to find evidence of a direct association between BGD and FP. The study also finds evidence of industry effects.

Though we use a very robust statistical methodology, any modifications in the methodology or choice of a different methodology are likely to change the results. Moreover, some of the findings are statistically significant at the 10% level.

The findings of our study hold particular significance for emerging economies like India where regulatory initiatives aim to enhance gender diversity within boardrooms.

The study contributes to the critical mass literature by examining the association between a critical mass of female directors as a moderating variable of BGD and FP. Further, the study also identifies those industries which show a positive association between FP and BGD.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

The current study investigates the role of consumer ethnocentrism (CE) in the context of locally produced organic food. This research work further extends the extended theory of planned behavior (TPB) model by examining the mediating effect of extended TPB variables (ATT, SN, PBC, PV) between CE and PI for locally produced organic food.

Data were obtained from the visitors of two shopping malls situated in a large, heavily populated city of India using survey method. Further, two-step approach was applied to analyze the hypothesized model.

Findings indicate that CE is a substantial determinant of PI for locally produced organic food. Moreover, extended TPB mediates the relation between CE and PI for locally produced organic food.

Post Covid-19, market size of organic food is rapidly growing in India. In this regard, this study presents useful implications for the marketers of organic food for gaining better consumer insights to further develop appropriate marketing strategies.

CE has been found to be a useful predictor of their food attitudes. However, studies, investigating the role of CE in the context of sustainable food consumption, are scant. Moreover, studies exploring the mediating effect of extended TPB variables are also very limited.