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Purpose: The study aims to analyse and discuss the effect of COVID-19 on businesses. The chapter discusses the various machine learning (ML) tools and techniques, which can help in better decision making by businesses in the present world.

Need for the Study: COVID-19 has increased the role of VUCA elements in the business environment, and there is a need to address the challenges faced by businesses in such environment. ML and artificial learning can help businesses in facing such challenges.

Methodology: The focus and approach of the chapter are in the context of using artificial intelligence (AI) and ML techniques for decision making during the COVID-19 pandemic in a VUCA business environment.

Findings: The key findings and their implications emphasise the importance of understanding and implementing AI and ML techniques in business strategies during times of crisis.

Practical Implications: The chapter’s content is in the context of using AI and ML techniques during the COVID-19 pandemic and in a VUCA business environment.

Introduction: A company or organisation must resolve various problems in the business environment for better operation in any corporate environment. Such issues are traditionally handled in multiple ways. A small sector unit with many employees encounters this corporate issue, for example, the handicraft sector. The impact of handicraft issues and their intensity, speed, and regularity is growing in our system.

Purpose: This chapter studies how small businesses might succeed in the handcraft industry in a volatile, uncertain, complex, and ambiguous (VUCA) environment. There is a lack of proper knowledge of how the VUCA affects business proficiency in the Indian handicraft sector. A novel business strategy for the handicraft sector, like other business proficiency called best practices in handicraft business in a VUCA environment, will be presented along with a discussion about VUCA environments. This considers both the individual influences of each particular word and the overall impact of VUCA.

Methodology: The study included a thorough literature analysis for three learning areas: performance improvement, including VUCA, and the leadership incorporation of risk and quality. Awareness in the trade will be examined in further sections, as the mastery of VUCA is achieved with various traditional and digital management ideas.

Findings: The research defined a new unorganised firm concept to maintain and succeed in a high VUCA environment in the handicraft sector, identifying 18 important success characteristics through a comprehensive literature review. The authors proposed a conceptual framework for fusing quality management to attain proficiency in the handicraft sector VUCA environment.

Purpose: The COVID-19 pandemic has been a good example of a Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) world. Higher educational institutions (HEIs) have faced a massive hit because the jobs in this industry have become unexpected. Considering the most valuable assets ‘Teachers’ crunched in the VUCA crisis, the study intends to determine if personal harmony (PH) and organisational citizenship behaviour (OCB) would enhance teachers’ job satisfaction (JS).

Design/methodology/approach: Data are collected from the teachers of Indian HEIs and teachers who have experienced the impact of the COVID-19 catastrophe (VUCA). Considering the pandemic restrictions, data have been collected through an online survey (N = 364).

Practical Implications: PH is an individual’s internal quality and attribute that cannot be developed on force or situational need. Even in an uncertain situation, teachers have tried their best to contribute through professional service. Hence, people who possess PH contribute their best even though unsatisfied with their jobs.

Originality/value: This study has focused on finding the relationship between two different variables, PH and OCB (which has not been explored in Asian countries, majorly in India, where it has a vast cultural diversity and structure influencing the educational policies) that hinders the factors influencing JS, where these two variables are highly influenced by hygiene factors such as values, culture, ethical standards, personal belief, leadership styles, and fair treatment showcased by the organisations/institutions.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

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.