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In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.

This chapter highlights how implementing circular economy principles can help companies working with sustainability to move from a reductionist and waste management approach to marketing competitive circular value propositions that intentionally design out waste (e.g. emissions and pollution) by rethinking, reinventing and redesigning the value chain. Schijvens, a Dutch family-owned corporate fashion textile company, acts as a case for exemplifying successful implementation of circular economy principles as a marketing strategy in a sector that struggles with finding solutions to the ethical challenges of producing and marketing textile fashion. The textile industry has, for many years, been accused of production that is based on environmentally harmful processes and conditions that are not socially fair. Circular economy principles provide a range of suggestions to address the ethical challenges occurring from covering the human needs of having clothes to wear. Yet, implementing circular economy principles is not a panacea. It is not only a question of delivering a technological quick fix but also a question of managing the new processes and human mindset guiding the actions in the value chain. This chapter, therefore, outlines reasons for a different perspective on the traditional linear value chain and related implications managers face when undertaking a journey from sustainability based on a reductionist approach to a closed-loop approach. It is argued that implementing circular economy principles by pro-actively managing the value chain processes based on eco-centric dynamic capabilities can provide even more radical changes than the incremental reductionist approach often associated with being a green sustainable company.

This study developed a new analytical model to quantify the influence of business intelligence (BI) adoption on bank performance. An in-depth review of academic literature revealed a significant research gap exists in investigating BI's performance impacts, especially in the under-studied Indian banking context. Additionally, customer relationship management (CRM) was incorporated as a moderating variable given banks' large customer databases.

A survey was administered to 413 employees across leading Indian banks to collect empirical data for evaluating the conceptual model. Relationships between variables were analysed using partial least squares structural equation modelling (PLS-SEM). This technique is well-suited for theory building with smaller sample sizes and non-normal data.

Statistical analysis supported the hypothesised positive effect of BI adoption on bank performance dimensions including growth, internal processes, customer satisfaction, and finances. Furthermore, while CRM did not significantly moderate this relationship, its inclusion represents an incremental contribution to the limited academic literature on BI in Indian banking.

The model provides a quantitative basis for strategies leveraging BI's performance benefits across the variables studied. Moreover, the literature review revealed an important knowledge gap and established a testable framework advancing BI theory in the Indian banking context. Significant future research potential exists through model replication, expansion, and empirical verification.

This research thoroughly reviewed existing academic literature to develop a novel testable model absent in prior studies. It provides a robust conceptual foundation and rationale for ongoing scholarly investigation of BI's deployment and organisational impacts.

In 2023, the Leadership Pulse project will have been running for two decades. Since the inception of the program, we have engaged thousands of leaders around the world to quickly learn from them via short pulse surveys conducted multiple times per year. This chapter is the first overall discussion of what we learned during the last 20 years about leader energy, energy flow, predictors of energy, and outcomes of energy, which have been focused on individual and firm-level performance. Over the years, we learned that leaders are not immune to personal energy challenges; in fact, we find that their energy is continually tested by extreme demands within and outside their organizations. Also, we learned that there are solutions for helping leaders manage their energy better, and these do not have to be expensive, outsourced programs. In this chapter, we review key findings from the data and hope to help leaders continue learning to help themselves, their employees, customers, and the organizations they work in overall. I also will review three different interventions that we found to help leaders and employees work and stay at their best and enhance overall organizational goals and outcomes.