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The changing roles of the United Nations (UN) and national institutions have made addressing climate change a critical concern for many multinational enterprises’ (MNEs) survival and growth. This chapter discusses how such institutions, which vary in their nature and characteristics, shape firm strategies for climate change adaptation. Exploring different versions of institutional theory, the chapter demonstrates how and why institutional characteristics affect typical patterns of firm ownership, governance, and capabilities. These, in turn, influence companies’ internationalisation and climate-change strategies. Climate change poses challenges to how we understand firms’ strategic decisions from both an international business (IB) (HQ–subsidiary relations) and global value chains (GVC) (buyer–supplier relations) perspective. However, climate change also provides opportunities for companies to gain competitive advantages – if firms can reconfigure and adapt faster than their competitors. Existing IB and GVC research tends to downplay the importance of climate change strategies and the ways in which coherent or dysfunctional institutions affect firms’ reconfiguration and adaptation strategies in a globally dispersed network of value creation. This chapter presents a perspective on the institutional conditions that affect firms’ climate change strategies regarding ownership, location, and internalisation (OLI), and GVCs, with ‘investment’ and ‘emerging standards’ playing a significant role. The authors illustrate the discussion using several examples from the Global South (i.e. Bangladesh) and the Global North (i.e. Denmark, Sweden, and Germany) with a special emphasis on the garment industry. The aim is to encourage future research to examine how a ‘business systems’, or varieties of capitalism, institutional perspective can complement the analysis of sustainability and climate change strategies in IB and GVC studies.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

Manufacturing in Mauritius is mostly export-oriented. Any supply chain (SC) failure or resilience deficit may result in cancellation of orders and loss of customers, market share and revenue and reduce capability to compete globally. Addressing this challenge is complex, although digital technologies and artificial intelligence (AI) models can improve resilience by assisting decision-making and mitigate risks, thus infusing greater predictability across the SC.

Supply chains are facing increasing disruptions and uncertainties owing to extreme weather events, the war in Ukraine, market volatility and the ongoing COVID-19 pandemic, among other factors. Manufacturing industries and their supply chains essentially create thousands of jobs that enable economic growth and sustain export capability. In addition, they need to maintain or increase both productivity and efficiency and recover quickly from unforeseen or unexpected challenges – that is they need to be resilient. Transformation initiatives, whether in production or supply chain management (SCM), are never easy. Process changes not supported by data or hurried human decisions can sometimes have unintended consequences, mainly adverse. However, in times of greater uncertainty (war and pandemic), setbacks can have greater consequences on the business. Manufacturers are already apprehensive and report slowing exports as recession concerns have caused consumers and businesses to pull back on spending. There is therefore a need to reduce uncertainty and augment resilience by unlocking and synthesising insights that emanate from the power of data analytics, AI and machine learning to improve the resilience efficiency balance.

This chapter will discuss the opportunities arising from the adoption and implementation of digital technologies and AI in SCM, leading to better value creation, less greenhouse gas emissions and resilience. The hurdles that enterprises are facing to integrate AI in their logistics and SCs will also be highlighted. This work comments on initiatives that uphold the objectives of SDG 8 – decent work and economic growth, SDG 9 – industry, innovation & infrastructure and SDG 13 – climate action.

Introduction: This study investigates the COVID-19 pandemic’s effects on the sustainability of the supply chain. It investigates how modern supply networks and procedures were equipped for such a catastrophe, and the pandemic’s effects on the environment, highlighting the significance of studying resilience and sustainability concurrently.

Purpose: The study acknowledges the importance of environmental sustainability for businesses and the need to examine trends in organisational, customer, policy, and distribution networks.

Need for the Study: The COVID-19 pandemic has significantly impacted supply chains. This study aims to provide insight into the long-term repercussions of the crisis and the importance of incorporating environmental considerations.

Methodology: The study uses a mixed-methods approach to evaluate the effects of the COVID-19 pandemic on supply networks and environmental sustainability indices. Data from industry reports, governmental publications, polls, and qualitative research techniques have been gathered.

Findings: The results of this study advance our understanding of how to preserve supply chains in the wake of the COVID-19 pandemic. It highlights the need for enhanced resilience and sustainability measures, expose the flaws and weaknesses of contemporary supply networks, and uncover developing patterns and tactics in customer behaviour, policy frameworks, distribution networks, and supply chain management.

Practical Implications: The COVID-19 pandemic has provided businesses, decision makers, and researchers with guidance on handling its potential and challenges – increasing the supply chain’s resistance to future interruptions, incorporating environmentally friendly practises, developing policies to support resilient and sustainable supply chains, adapting to changing consumer tastes, increasing effectiveness, and minimising the environmental impact of distribution networks.

Sustainability has become the topic of the era, and the world is moving toward a circular economy. As part of it, companies are also integrating sustainability in supply chain management. Therefore, this study conducts a technology-empowered systematic literature review to examine the state of the literature on the technologies in the sustainable supply chain. The review reveals that limited studies have been conducted in the area. There is still scope for the researchers to explore new innovative technologies, many clusters in author collaboration, and involve more sectors in the study. Future studies can enrich the field by employing other methodologies like meta-analysis review.

Managing diversity requires an understanding of culture. The majority of businesses have realised that competitiveness no longer stems from formal organisational structures but rather from the mindsets, competencies, and functioning of individuals who create, develop, and support the organisation and who frequently come from different cultural backgrounds. Understanding the essence of a culture, its components, variations, and how all these things effect the business and the managerial process is very beneficial for international managers. Businesses in global value chains need cross-cultural management practices to obtain a competitive advantage. Global value chains make it possible to benefit from the comparative advantages of other nations, and without cross-cultural management, these multinational corporations would be unable to carry on with their business operations. Although there are many explanations on global value chains in the literature, there is no study on the effect of different cultures in value chain management and how different cultures can be managed in global value chains.

In this chapter, the definition of the concept of culture, which is comprehensive and crucial in managing differences, will be depicted. Then, the concept of cross-cultural management will be emphasised and what cross-cultural management means and why and to what extent it is important will be explained. In addition, the impact of cross-cultural management in the inclusive global value chain will be discussed, emphasising the value chain analysis, how it emerged, its basic concepts, and its importance in the international context.

Purpose: This chapter examines the effect of COVID-19 on the stock market volatility (SMV) in the Colombo Stock Exchange (CSE), Sri Lanka.

Need for the Study: The study is necessary to understand investor behaviour, market efficiency, and risk management strategies during a global crisis.

Methodology: Utilising daily All Share Price Index (ASPI) data from 2 January 2018 to 31 August 2021, the data are divided into subsamples corresponding to the pre-pandemic period, the pandemic period, and distinct waves of the pandemic. The impact of the pandemic is investigated using the Mann–Whitney U test, the Kruskal–Wallis test, and the Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model.

Findings: The pandemic considerably affected CSE – the Mann–Whitney U test produced different market returns during the pre-COVID and COVID eras. The Kruskal–Wallis test improved performance during COVID-19 but did not continue to do so across COVID-19 waves. The EGARCH model detected increased volatility and risk during the first wave, but the second and third waves outperformed the first. COVID-19 had a minimal overall effect on CSE market results. GARCH and Autoregressive Conditional Heteroskedasticity (ARCH) models identified long-term variance memory and volatility clustering. The News Impact Curve (NIC) showed that negative news had a more significant impact on market return volatility than positive news, even if the asymmetric term was not statistically significant.

Practical Implications: This study offers significant insight into how Sri Lanka’s SMV is affected by COVID-19. The findings help create efficient mitigation strategies to mitigate the negative consequences of future events.