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…
Abstract
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.
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Sadia Samar Ali, Rajbir Kaur and Jose Antonio Marmolejo Saucedo
The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data…
Abstract
Purpose
The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA), based on the previous study of grey relational decision-making model, and it considers the advantages of the decision-making schemes and the subjective preferences of decision makers.
Design/methodology/approach
First of all, through AHP, the preference of each index is analyzed and the index weight is determined. Second, the DEA model is adopted to obtain the index weight from the perspective of the most beneficial to each scheme and objectively reflect the advantages of different schemes. Then, assign the comprehensive weights to each index of the grey relational decision-making model of three-parameter interval grey number, and calculate the grey relation degree of each scheme to rank the schemes.
Findings
The effectiveness of the model is proved by an example of carrier aircraft selection.
Practical implications
The applicability of this model is analyzed by taking carrier aircraft selection as an example. In fact, this model can also be widely used in agriculture, industry, economy, society and other fields.
Originality/value
In this paper, the combination of AHP and DEA is used to determine the index weight. Based on which, the grey relation degree under the three-parameter interval grey number is calculated. It intended the application space of the grey relational decision-making model.
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Francesco Moscone, Veronica Vinciotti and Elisa Tosetti
This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and…
Abstract
This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.
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Junfei Chu, Jie Wu, Qingyuan Zhu and Jiasen Sun
Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the…
Abstract
Purpose
Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view.
Design/methodology/approach
To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model.
Findings
The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency.
Research limitations/implications
The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds.
Practical implications
The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice.
Originality/value
This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments.
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S. Maryam Masoumik and Salwa Hanim Abdul-Rashid
In the current highly competitive market, most organizations are moving away from reactive and compliance-based to proactive environmental management. This proactive approach to…
Abstract
In the current highly competitive market, most organizations are moving away from reactive and compliance-based to proactive environmental management. This proactive approach to environmental management calls for taking a strategic approach to adoption of green practices beyond an organization’s internal borders. In this respect, incorporating green practices into a firm’s supply chain has attracted interest of operations management scholar and practitioners. The influence of external pressures on the adoption of green supply chain management (GSCM) practices has been established in the literature. This research posits that the adoption of GSCM practices is also driven by a firm’s internal strategic factors including its key resources and competitive strategy. It also suggests that these direct effects are further mediated by the green strategies (GSs) adopted in companies. Theoretically, these relationships are supported by combining the institutional theory with the natural resource-based view. A structural equation modeling is applied to formulate and analyze the relationships and the mediating effect using a survey data collected from 139 ISO14001-certified manufacturers in Malaysia. The results verified the mediating effect of GS adoption on the relationship between internal and external strategic factors, and GSCM practices. This research has made an original contribution to knowledge by bridging the fields of strategic environmental management and GSCM.
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Mahtab Kouhizadeh, Qingyun Zhu, Lojain Alkhuzaim and Joseph Sarkis
Overconsumption of resources has become a global issue. To deal with resource depletion and mitigate these impending crises, the circular economy (CE) holds some promise. A wide…
Abstract
Overconsumption of resources has become a global issue. To deal with resource depletion and mitigate these impending crises, the circular economy (CE) holds some promise. A wide range of performance measurements for CE have emerged over the years. However, with increasing complexity of supply chains, appropriate and potentially new performance measurements are needed for effective CE management. Blockchain is an innovative technology that may advance CE development. This chapter provides an overview of the potential linkages between blockchain technology and CE from sustainability perspectives – the specific focus will be on the performance measurement of reverse logistics activities. One of the main findings indicates that both blockchain and CE performance measurements – especially reverse logistics processes – are still evolving in both theory and practical developments. Future directions with a critical analysis including research and theoretical applications will conclude this chapter.
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Francis J. Yammarino, Minyoung Cheong, Jayoung Kim and Chou-Yu Tsai
For many of the current leadership theories, models, and approaches, the answer to the question posed in the title, “Is leadership more than ‘I like my boss’?,” is “no,” as there…
Abstract
For many of the current leadership theories, models, and approaches, the answer to the question posed in the title, “Is leadership more than ‘I like my boss’?,” is “no,” as there appears to be a hierarchy of leadership concepts with Liking of the leader as the primary dimension or general factor foundation. There are then secondary dimensions or specific sub-factors of liking of Relationship Leadership and Task Leadership; and subsequently, tertiary dimensions or actual sub-sub-factors that comprise the numerous leadership views as well as their operationalizations (e.g., via surveys). There are, however, some leadership views that go beyond simply liking of the leader and liking of relationship leadership and task leadership. For these, which involve explicit levels of analysis formulations, often beyond the leader, or are multi-level in nature, the answer to the title question is “yes.” We clarify and discuss these various “no” and “yes” leadership views and implications of our work for future research and personnel and human resources management practice.
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Siti Aisjah and Sri Palupi Prabandari
Small and medium enterprises (SMEs) are expected to be more creative and innovative to survive in the business competition and to make their businesses environmentally friendly…
Abstract
Small and medium enterprises (SMEs) are expected to be more creative and innovative to survive in the business competition and to make their businesses environmentally friendly, to develop global supply chain strategies, and to make innovations in products and business processes to become indispensable. This study discusses the effect of green supply chain integration (GSCI) and environmental uncertainty on performance through the moderation of green innovation. Structural equation modeling and maximum likelihood estimation were used to analyze a sample of 130 SMEs in East Java, Indonesia. The result shows that GSCI and environmental uncertainty significantly affect performance, and green innovation significantly moderates the effect. This research found that SME’s performance is influenced by GSCI concept and green innovation application as well as SME’s understanding about recent and future environmental uncertainties; this fits the market demand.
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Bingzi Jin, Xiaojie Xu and Yun Zhang
For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one of essential edible oil, peanut oil’s price swings are…
Abstract
Purpose
For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one of essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale price index for the period of January 1, 2010 to January 10, 2020 is used to address this specific forecasting challenge for the Chinese market.
Design/methodology/approach
The nonlinear auto-regressive neural network (NAR-NN) model is the forecasting method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, are assessed to build the final specification.
Findings
With training, validation and testing root mean square errors of 5.89, 4.96 and 5.57, respectively, the final model produces reliable and accurate forecasts. Here, this paper demonstrates the applicability of the NAR-NN approach for commodity price predictions.
Originality/value
On the one hand, the findings may be used as independent technical price movement predictions. Conversely, they may be included in forecast combinations with forecasts derived from other models to form viewpoints of commodity price patterns for policy research.