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This paper aims to expand the literature on financial and operational performance by analyzing the effects of undergoing through Six Sigma training.

The effects of implementing Six Sigma trainings is analyzed for 108 Fortune 500 companies. The authors estimate long-term stock returns and 14 financial ratios of Six Sigma companies, both pre- and post-adoption periods. Furthermore, The authors match the 108 companies by size and industry to 108 non-Six Sigma companies also within the Fortune 500.

Looking at long-term stock returns, the evidence shows that Six Sigma firms need at least four years before they start to outperform the controlling sample. Furthermore, looking at operational performance, unlike prior reported results, the authors find supporting, and more importantly, persisting statistical evidence that Six Sigma firms are less liquid and have a negative growth in staff levels in comparison to the matching firms.

The findings of this suggest that if Six Sigma provides any value to the company, it comes at the expense of overloaded staff levels, as evidenced by the fact that Six Sigma firms have less growth in staff levels than the matching firms.

It is one of the first paper to thoroughly investigate the effects on both financial performance and operational performance of spending, sometimes billions of dollars, in Six Sigma training.

Disruptive digital technological innovation has the potential to dramatically alter the corporate landscape as we know it. The authors explore this premise by examining both industry practices and their conceptual bases in the digital age. The authors then describe cases and trends in the three main mediums of digital innovation – artificial intelligence, fintech, and blockchain. The authors focus on how these innovative technologies can impact the firms by creating values as part of corporate strategy, and by changing the way employees work. However, the impacts will likely go well beyond business and finance, and are likely to be adopted by healthcare, non-government organizations, and governments as well.

This chapter examines the patterns influencing the trajectory of fintech enterprises. With the looming challenge of climate change, the financial realm's responsibility in mitigating climate risks has surged into focus. This chapter investigates fintech enterprises' response to climate-related corporate social responsibility in six main domains: (1) climate risk assessment tools, (2) green bonds and sustainable investment tools, (3) ESG integration, (4) carbon trading and carbon credits, (5) sustainable banking, and (6) DeFi and climate initiatives. It also investigates how fintech firms recognize the impact of climate change within their official declarations and efforts to amplify consciousness about climate-related concerns. This chapter assesses climate-linked terminology and expressions using quantitative and qualitative approaches, illuminating these firms' dedication to assimilating climate risk within their operational blueprints.

In this chapter, the utilization of Artificial Intelligence (AI) and Machine Learning (ML) tools, improved by human expertise, offers a detailed investigation into their transformative effects on Corporate Social Responsibility (CSR), Environmental, Social, and Governance (ESG) standards, and sustainability. Initial insights and structural foundations were developed using AI, specifically utilizing ChatGPT-4, with the author significantly enhancing the content through in-depth academic research and analysis. This synergistic approach provides a holistic view of AI and ML’s role in promoting ethical business practices, improving sustainability reporting efficiency, and facilitating informed decision-making within CSR and ESG frameworks. Incorporating extensively verified and expanded real-world examples, the work delves into the practical applications of these technologies and addresses the ethical considerations they raise. This collaboration highlights the evolving role of AI in research and emphasizes the critical importance of integrating AI-generated insights with scholarly research to drive forward sustainable and socially responsible corporate strategies and policies.

This chapter provides a discussion on some issues in blockchain finance that regulators are concerned about – an area which bitcoin promoters have remained silent about. Blockchain technology in finance has several benefits for financial intermediation in the financial system; notwithstanding, several issues persist which if addressed can make the adoption of blockchain technology in finance easier and accepted by regulators. The blockchain issues discussed in this chapter are relevant for recent debates in blockchain finance.

In 1971, the patent for the Automated Teller Machine was awarded to David Wetzel. While possibly not the first application of financial technology, since 1971 time, the innovation in the financial industry has grown beyond expectations. However, most studies in innovation ignore the financial sector altogether. In this study, the authors investigate financial technology firms and innovation. After identifying firms that are considered financial technology, the authors collect innovation outcomes such as patents and data breaches associated with those firms. The authors show that patent activity has enjoyed modest growth year over year; however, firms still have challenges to overcome such as market risk and data security. This study serves as a perspective on financial technology.

The authors analyze the expansion of Big Data and artificial intelligence technologies from the perspective of economic theory. The authors argue that these technologies can be viewed from three perspectives: (1) as an intangible asset; (2) as a search and matching technology; and (3) as a forecasting technology. These points of view shed light on how new technologies are likely to affect matching between firms and consumers, productivity growth, price discrimination, competition, inequality among firms, and inequality among workers.

The purpose of this chapter is to demonstrate how blockchain technology – which permits the Internet-based exchange of value (digital assets) – enables supply chain finance banks to overcome the challenges they face when attempting to create win–win transactions for supply chain participants. Traditionally, buyers and suppliers linked together in a supply chain have conflicting objectives as manifested by a zero-sum payoff structure. Suppliers want their invoices to be paid quickly in order to reduce their need for working capital. In contrast, buyers want to delay payment of invoices as long as possible in order to reduce their need for working capital. In other words, suppliers want a short cash conversion cycle; buyers want a long cash conversion cycle. This conflict is eliminated by the insertion of a financial intermediary (supply chain finance bank) between the buyer and the supplier. The bank eliminates the conflict by: (1) using its balance sheet to decouple the cash conversion cycles of the buyer and supplier; and (2) providing cheaper financing to impatient suppliers and reluctant buyers (since the bank has a higher credit rating than both the supplier and the buyer).

One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.