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In an era where sustainability and digital transformation are becoming indispensable pillars of successful business operations, this chapter explores the potent synergy between these two paradigms. As businesses strive to align their operations with Environmental, Social, and Governance (ESG) goals, digital transformation emerges as a powerful enabler. This chapter delves into how digital technologies are not only revolutionizing traditional business models but are also paving the way toward more sustainable practices. From data-driven decision-making to improved resource management, this chapter discusses the diverse ways in which digital transformation contributes to sustainability. It also offers an in-depth analysis of real-world case studies, illustrating how businesses have successfully integrated digital transformation in their pursuit of sustainability. Recognizing the potential roadblocks, this chapter also addresses the challenges businesses may face in this journey, including cybersecurity risks, data privacy issues, and the need for technological literacy. It further presents strategies to navigate these challenges and underscores the importance of preparedness in managing potential risks. Finally, this chapter ventures into the future of digital transformation, evaluating current trends and predictions, and their potential impact on sustainable business practices.

The rapid advancement of Education Technology (EdTech) offers promising opportunities for educational institutions to integrate sustainable business practices into their operations and curriculum. The integration of EdTech into sustainability education has emerged as a powerful tool to promote environmental awareness, foster sustainable behavior, and address the pressing challenges of climate change and resource depletion. This chapter explores the growing significance of EdTech in sustainability education, analyzing its potential to cultivate a generation of environmentally conscious and responsible global citizens. It also aims at identifying and examining the most prominent emerging EdTech tools specifically designed to promote sustainability in educational settings. Furthermore, it aims to comprehend the institutional elements that have successfully incorporated and expanded the utilization of EdTech tools to promote enduring business practices. Additionally, the chapter addresses the challenges and obstacles faced by educational institutions in adopting and implementing these technologies and propose strategies to overcome these barriers.

One of the most marginalized communities in India is the Lesbian, Gay, Bisexual and Transgender (LGBT) community which commonly experiences discrimination. Many studies have countered that the LGBT community faces high discrimination in the banking and financing industry. As a result, this study concentrates on this marginalized community and its acceptance and continuation habit regarding mobile wallets. Consequently, this study has considered continuance intentions as a response to confirm the progress of the mobile-wallet industry. Also, this study tried to study the relationship between behavioral intention (BI) and continuous intention (CI) which is seriously lacks in the library of literature. The research operationalized the stimulus–organism–response (SOR) framework for the conceptual model and surveyed 100 self-proclaimed members of the LGBT community in India. The analysis has been done using the partial least structure (PLS). The findings demonstrate that variables like perceived trust (PT) directly influence the BI. On the other hand, variables like perceived ease of use (PEoU), social influence (SI), and satisfaction (S) doesn’t influence BI of the LGBT Community. The main outcome was a favorable association between BI and CI. It will help the stakeholders to understand how important this new market avenue is and how it can be explored. To ensure safe and secure transactions, a group think tank composed of important parties (financial institutions, mobile-wallet providers, the government, security specialists, etc.) should make recommendations. Mobile-wallet providers will attain benefit from this study’s understanding of user categories and ability to tailor their service offers as per the community.

This chapter contributes to the scholarship of teaching and learning in extracurricular enterprise and entrepreneurship education. It draws on research from two annual ‘Business Challenge Weeks’ (BCW) held at Oxford Brookes University in 2021 and 2022, in which teams of postgraduate students from three faculties worked on external client projects, supported by an academic mentor. It presents and discusses findings derived from a survey and interviews conducted after the second of these years. The chapter takes a transdisciplinary perspective, after Budwig and Alexander (2020), Piaget (1972) and Klein et al. (2001) and explores the relationship between this and the enterprise and entrepreneurship development pipeline set out by QAA (2018). It analyses the experiences of the three main participating groups engaged in the challenge weeks – students, external clients and academic mentors – and explores the organising challenges inherent in multiparty pedagogical initiatives. The chapter contributes to knowledge in this area by revealing and reflecting on the motivations and expectations of the three participant groups, the roles they played during the week and the outcomes they reported. It also expands understanding of transdisciplinary enterprise pedagogy.

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.

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.