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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.

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.

Little is known about what emotionally un(intelligent) CEOs really say to their close collaborators within the boardroom. Would the rhetoric content differ between an emotionally intelligent and an emotionally unintelligent CEO, especially during a crisis? This chapter aims to answer this question.

40 CEOs of large corporations were asked to deliver a verbal address to their board members in reaction to a vignette describing a critical situation for the company. Participants were provided with the Schutte self-report emotional intelligence (EI) test. The verbal content of CEOs' closed-door discourses was analyzed using Cognitive-Discursive Analysis (CDA) and, subsequently, Geometric Data Analysis (GDA).

The results revealed that CEOs with low EI tend to evoke unpleasant emotions, talk about competition, and often blame some – or all – of the board members for their (poor) actions in comparison to CEOs with high or medium EI. In contrast, CEOs with high EI tend to use terms in relation to decision or realization and appear to be more cooperative than those with lower EI and were also ready to make decisions on behalf of team.

Previous research has mainly focused on CEOs' public speeches. But the content of CEOs' speeches within the boardroom might noticeably differ from what they would say in a public address. The results of our exploratory study can serve CEOs as a basis toward improving their closed-door rhetoric during a crisis.

It would be interesting to enlarge the size of our population in order to strengthen our statistical analyses as well as explore other cultural and linguistic environments and other channels through which emotions can be expressed (e.g., human face, gesture, vocal tone).

This chapter explores how industrial PhD students are engaged in authentic leadership processes while coping with challenges through self-leadership. The authors illustrate how self-leadership can be a helpful approach to managing the leading-and-being-led dilemma. They argue that self-leadership is a process of goal achievement in collaboration with key stakeholders and, therefore, an important aspect of authentic leadership. The authors identify four aspects of self-leadership that influence authenticity: roles, resources, relations and results. Kringelum, Mortensen and Holmgren call for research into the emergence of self-leadership and authentic leadership, the leadership capabilities required and the double-sidedness and dilemmas inherent in such emergences across different contexts.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

This chapter addresses the issue of arbitration in tourism from the perspective of litigation. International tourism requires two or more legal systems to solve a given problem, which creates great complexity. To diminish this effect, organizations have been encouraged to use arbitration instead of courts, and thus, it is necessary to measure its effect on them. The analysis used the Biplot methodology, a multivariate technique in the context of reduced dimensionality. The results obtained indicated that tourism demand and supply were willing to pay for arbitration, but the latter erroneously considered the former unwilling to pay. This chapter suggests that tourism companies can raise the price of their products to increase their profit.

Purpose: Innovation is a multi-dimensional phenomenon influenced at the organisational level by internal and external factors that can determine how innovative an organisation can be, determining a firm’s business performance. This chapter measures and predicts how innovative a company can be, considering key internal factors using modern data analytics/science.

Need for Study: The increasing challenge of modern business operations is affected by how quickly, sustainably, effectively, and efficiently companies can innovate to mitigate the dynamic challenges of current business environments and evolving customer needs. The ability to predict, measure, and manage innovation becomes necessary to ensure that businesses are fit for purpose.

Methodology: A model was designed following the study hypotheses and statistically tested. A historical data sample from the OECD global industry dataset for eight years was used for the analysis. The ordinary least square method was used to test for model fit. Also, in machine learning engineering, predictive analysis using the multivariate linear regression analysis method was carried out.

Findings: The results support the hypotheses that an organisation’s capacity to be innovative can be measured and predicted, and it is influenced by a good number of internal factors or independent variables at various degrees.

Practical Implications: Managers must understand how to measure and predict innovation metrics to manage innovation better, ultimately leading to better business outcomes and performance. Also proposed are new measurement matrices for innovation management: innovation capacity (I_C), business innovation value (BIV), innovation creation factor (ICF), and a practical data-driven innovation management and prediction system.

Purpose: Nikoletta Taylor and Close, (2022) asserts that a skill gap analysis can be used to determine the lacking skill and carry out recruitment based on that gap. It goes without saying that for a student, that would be their first employment, and with internships, there is no way to experience all aspects of the working world.

Need for This Study: When it comes to the recruiter’s expectations and the applicant’s skills, specifically among students, a gap still exists, leading to a need for practical exposure for applicants.

Study Design/Methodology/Approach: A quantitative study was adopted. A questionnaire was prepared with constructs and variables from the review and a total of 403 responses was collected from reputed institutions in Tamil Nadu.

Findings: This study reveals that skill development is needed; it is found that experiential learning through the gamification process is a suitable learning strategy. The experiential learning factors supporting skill development are self-efficacy, outcome expectation, interest, choice actions, and performance, followed by review, feedback, and continuous improvement.

Originality/Value: Learning all these factors through the gamification process would give students practical exposure to real-time scenarios of the corporate world. The proposed model gives students experience through gamification and learning activities.