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Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.

This work discusses gender issues related to the video game medium, addressing its production, consumption, and media repercussions. It begins with an overview of the emergence of the video game with the targeting of audiences that focused on sales campaigns to consumers along gendered lines that amplified the dominance of men in the space. The discussion then focuses on numerous ways that the gaming industry as a whole perpetuates a culture of misogyny. Empirical examples are provided of harassment, attacks, and the controversial event known as GamerGate. Subsequently, the complicated history of Brazilian video gaming development is presented to draw parallels with the development of the industry and the market in the United States. Finally, the chapter concludes with suggestions to stimulate new producers, developers, and video game scholars who are committed to building a more aware and diverse community.

This chapter seeks to understand the intersection between eGovernment, social media, and digital inequalities by examining the disparate flow of information during the COVID-19 pandemic. Developed economies are increasingly transitioning to digital interfaces for information dissemination and provision of services. The authors explore the potential of, and challenges facing eGovernment by looking at the use of social media during the COVID-19 pandemic. This chapter employs a case study approach to probe the dynamics of government-initiated efforts at information dissemination through the Center for Disease Control and Prevention’s (CDC) website and social media account on Twitter. The analysis in this chapter uses NodeXL to examine communication roles played by government and non-governmental actors within this slice of the Twittersphere centered around CDC@gov. As the findings demonstrate, non-governmental actors played key roles in the dissemination of public health messaging. The authors analyze these data with an eye to the potential of social media for public health communication and extrapolate that understanding to the use of digital access and social media for the provision of accurate, official information in other circumstances. While the COVID-19 pandemic was a global health crisis, individuals and households face individual or local crises every day. This angle of vision allows the chapter to conclude with recommendations pertaining to government-led information dissemination for the public good during crisis and non-crisis situations alike. In the concluding section, the authors probe the degree to which eGovernment can also address digital inequalities including connectivity, device, and literacy gaps. The authors offer solutions needed for eGovernment initiatives in light of challenges posed by digital inequalities to ensure that digital information sharing and services are accessible to all.

Introduction: Micro, small, and medium enterprises (MSME) have long been viable in the Indian economy. In the case of post-COVID-19, 20–40% of MSMEs in government can be permanently closed. The state should pay special attention to MSMEs for survival (Min, 2023).

Purpose: This chapter provides a framework for MSMEs to study industry challenges in Punjab and to discuss the conceptual framework and road map for future MSMEs in Punjab.

Need for This Study: The COVID-19 pandemic has drastically impacted the variable economic activities within the world. This study is responsible for explaining the different vulnerable sectors related to small- and medium-sized enterprises. On the other hand, this study is an analytical and descriptive research in nature.

Methodology: A mixed method of data collection has been used in this chapter. The data have been collected by floating a questionnaire to the various entrepreneurs of MSMEs. Secondary data have been collected through the Internet.

Findings: Through this research, we could analyse the MSMEs’ conceptual framework, the challenges they face, and the industrial units’ future roadmap.

Practical Implications: This research is mainly considered a clear explanation of current competition and market access challenges that small- and medium-sized enterprises face. This situation is derived due to the COVID-19 pandemic, so many enterprises are trying to find their exit ways. On the other hand, some MSMEs are trying to focus on the online business market to make some profit and to overcome the loss.

This study investigates how firms’ awareness of sustainability affects the revision of their internationalization strategy. Adopting a resource-based view (RBV) approach, the authors argue that sustainable-oriented firms have a higher propensity to de-internationalize (i.e., to go back to their home country) when confronted with the need to relocate foreign manufacturing subsidiaries, as the shortening of value chains would allow the reduction of transportation emissions and enhanced corporate image as green-oriented entities. Furthermore, the authors explore the role exerted by a stringent regulatory setting in the home country on the likelihood of de-internationalization. The empirical test conducted on a sample of relocations performed across European nations in 2002–2014 reveals that multinational enterprises (MNEs) – regardless of their sustainability orientation – have a higher probability to de-internationalize when their home countries have strict institutional contexts in place.

This study focuses on listed and over-the-counter (OTC) companies in the Taiwan Stock Exchange. It found that an increase in the ownership proportion of institutional investors (INs), including foreign investors, investment trusts, and dealers can enhance the informativeness of stock prices. The relationship between these factors follows an inverted U-shaped pattern, indicating that excessively high ownership ratios can actually lead to a decrease in the informativeness of stock prices. Additionally, increasing the ownership proportions of foreign investors and investment trusts can reduce the risk of stock price collapse, while dealers show no significant relationship in this regard. This study also reveals that the technical variable of the price deviation rate is an important explanatory factor for post-collapse returns. It is positively correlated with the magnitude of the price decline after a collapse, meaning that stocks with weaker pre-collapse performance experience larger post-collapse declines. When the data during the 2020 pandemic period are excluded, changes in foreign ownership ratios show a significant positive correlation with postcrash returns in both the long and short term. The significant correlation in the short term may be due to a high proportion of foreign ownership. Any reduction in this could put pressure on stock prices, and retail investors may follow suit and sell-off, using foreign investors as a reference. The significant correlation in the long term might be due to foreign investors themselves possibly also trying to avoid the pressure that their own short-term sell-offs could exert on stock prices. The changes in the ownership ratios of investment trusts and dealers indicate that medium and long-term changes have a significant impact on postcrash returns, while the changes in the major players' ownership show no significant correlation. When data from 2020 are included in the analysis, the significance of all INs decreases.

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.

This chapter is focused on what we can learn from oppressive governance, in this case specifically relating to university governance in terms of vice chancellors and presidents, to the deputy vice chancellor and deputy president and down the ever-growing university hierarchy to deans and heads of schools and their deputies, from a Freirean perspective. Freire wrote at length about how leaders ‘controlled’ education and why they did so, but he also wrote at length about how governments control populations – himself being both a political prisoner and a person in exile to escape persecution. This chapter subsequently examines Freire's ideas around what techniques people employ to control populations and applies them to a higher education setting because the similarities are numerous and the tactics familiar.

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.