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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.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

We form portfolios based on return and liquidity and examine the effects of liquidity and other risk factors on asset pricing in the Chinese stock market. Our results show that the past loser-and-illiquid stock portfolios tend to outperform the past winner-and-liquid stock portfolios in the 1–12 months holding period. The excess return is significantly associated with the market-wide liquidity factor even when we control the three Fama-French and momentum factors. Cross-sectionally, the liquidity beta significantly affects the excess return even with control of other risk betas and other traditional liquidity proxies.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

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.

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.

Multinational enterprises (MNEs) own and control technological resources and capabilities that make them critical actors in accelerating the transition toward net zero. Even beyond the energy sector, stakeholders are putting increasing pressure on MNEs to reduce the carbon intensity of their operations, that is, to improve their carbon performance. While there is unambiguous evidence that national climate policy is a critical catalyst for long-term carbon performance improvements, there is limited research on how MNEs’ carbon strategies react to climate policies. This chapter reviews the concepts, drivers, and strategies connected to carbon performance in the broader sustainability and management literature to clarify potential complementarities to international business (IB). The authors then highlight how MNEs will face increasing institutional complexity along two dimensions: (1) the structural diversity of institutional environments and (2) institutional dynamism, primarily reflected by public policy. The proposed conceptual framework maps these two dimensions to national and subnational levels, and the authors present two data sources that allow the quantitative analysis of country differences in the diversity and dynamism of national climate policy. The authors conclude that there are ample opportunities for IB researchers to explore MNEs’ strategic reactions to climate policy and to inform policymakers about the consequences of national climate policy in the global economy.

This chapter analyzes the efficiency levels of a circular economy (CE) with an emphasis on transaction costs. It examines the governance aspect of CE activities in comparison to the predominant linear value creation. Extant CE research in business studies tends to be descriptive and lacks a theoretical foundation, particularly in understanding CE management. Transaction cost theory explains efficiency in economic organizing, lending itself to the study of arrangements that maximize resource efficiency at continued economic virtue. The conceptualization proposes that CE transaction costs are greater than those within the linear economy (LE), primarily due to the uncertainties about reciprocal dependencies, looping material complexities, exchanging novel information, and increased contracting efforts. Geographically bounded and institutionally homogeneous CE initiatives may curb these rising costs. By bringing efficiency concerns into CE analysis, the chapter demonstrates the applicability of transaction cost theory and highlights CE relevance to international business by pointing out spatial choice implications.

The current chapter proposes a theoretical framework to assess the sustainability of economic growth in the Gulf Cooperation Council (GCC) States. The authors integrate insights from endogenous growth models and consider the unique socioeconomic characteristics of the GCC region to provide a comprehensive and tailored approach to understanding the determinants of economic growth and formulating effective policy measures to foster sustainable development and growth. This chapter highlights the environmental challenges faced by GCC; based on this, the authors suggested indicators to construct a theoretical framework (Economic Growth, Climatic Indicators, Energy Indicators, Social Indicators, and Economic Resources Indicators). The authors propose that policymakers and researchers in GCC States should take these factors into account when devising policies or conducting research aimed at fostering sustainable economic growth. Overall, this chapter presents significant insights for policymakers, researchers, and stakeholders involved in promoting the sustainable economic advancement of the GCC States.