The purpose of this chapter is to critically review the Korean Official Development Assistance (ODA) policy in terms of its context, actors, structures, and values so as to find…
Abstract
The purpose of this chapter is to critically review the Korean Official Development Assistance (ODA) policy in terms of its context, actors, structures, and values so as to find how these characteristics are reflected in the education ODA of Korea. Previous studies, documents of Korean Government, and ODA-related statistics are reviewed. Self-confident in being transformed from a poor recipient country to a DAC donor, Korean government emphasizes the so-called Korean ODA Model in sharing its economic development knowledge and experiences with the developing countries. Despite the coordination effort by Prime Minister’s Office, government ministries tend to pursue its own ODA policies, while the finance ministry and the ministry of foreign affairs play major roles. As a result Korean ODA is economy-oriented, fragmented, and uncoordinated in planning and implementation. This study has found that such characteristics of Korean ODA are also reflected in the education ODA. For instance, TVET and higher education are the priority, while basic education is neglected, and the education ministry has its own ODA policies and programs. Outside studies on Korean ODA policy is rather scarce, furthermore, critical reviews other than policy advocacy are hard to find, particularly in English. This study will be a good start for further ones to understand the Korean ODA policy including education.
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Mahmoud Abdelrahman, Danial Hemmings and Aziz Jaafar
This paper empirically examines how tax haven use affects classification shifting by public and private UK firms.
Abstract
Purpose
This paper empirically examines how tax haven use affects classification shifting by public and private UK firms.
Design/methodology/approach
The authors conduct multivariate regression analyses of classification shifting on proxies of tax haven use for a broad sample of UK non-financial public and private firms from 2010 to 2018. An array of additional tests is conducted to ensure the robustness of the findings.
Findings
Firms using tax havens engage in more classification shifting relative to those that do not. The result is concentrated for public firms. While private firms’ classification shifting is generally pronounced, it appears unaffected by tax haven use. The findings suggest that the use of tax havens facilitates public firms’ classification shifting due to the lower institutional environment quality of these jurisdictions. In addition, classification shifting may be a less costly earnings management device for public firms using tax havens due to their political sensitivity.
Practical implications
The study highlights the need for regulatory intervention to constrain classification shifting, especially when firms use tax havens. It also calls for further scrutiny by auditors and financial analysts on the classification of income statement items.
Originality/value
While prior research focuses on accrual and real earnings management by public firms, this study investigates the consequences of using tax havens on classification shifting, a largely underexplored but heavily exploited earnings management strategy. Differences between public and private firms are also tested. Overall, this study offers an advanced understanding of how a firm’s institutional and political environments influence its financial reporting.
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Hoi-Lam Ma, Zhengxu Wang, S.H. Chung and Felix T.S. Chan
The purpose of this paper is to study the impacts of time segment modeling approach for berth allocation and quay crane (QC) assignment on container terminal operations efficiency.
Abstract
Purpose
The purpose of this paper is to study the impacts of time segment modeling approach for berth allocation and quay crane (QC) assignment on container terminal operations efficiency.
Design/methodology/approach
The authors model the small time segment modeling approach, based on minutes, which can be a minute, 15 min, etc. Moreover, the authors divided the problem into three sub-problems and proposed a novel three-level genetic algorithm (3LGA) with QC shifting heuristics to deal with the problem. The objective function here is to minimize the total service time by using different time segments for comparison and analysis.
Findings
First, the study shows that by reducing the time segment, the complexity of the problem increases dramatically. Traditional meta-heuristic, such as genetic algorithm, simulated annealing, etc., becomes not very promising. Second, the proposed 3LGA with QC shifting heuristics outperforms the traditional ones. In addition, by using a smaller time segment, the idling time of berth and QC can be reduced significantly. This greatly benefits the container terminal operations efficiency, and customer service level.
Practical implications
Nowadays, transshipment becomes the main business to many container terminals, especially in Southeast Asia (e.g. Hong Kong and Singapore). In these terminals, vessel arrivals are usually very frequent with small handling volume and very short staying time, e.g. 1.5 h. Therefore, a traditional hourly based modeling approach may cause significant berth and QC idling, and consequently cannot meet their practical needs. In this connection, a small time segment modeling approach is requested by industrial practitioners.
Originality/value
In the existing literature, berth allocation and QC assignment are usually in an hourly based approach. However, such modeling induces much idling time and consequently causes low utilization and poor service quality level. Therefore, a novel small time segment modeling approach is proposed with a novel optimization algorithm.
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Chaorui Huang, Song-Man Wu, Hoi Lam Ma and Sai Ho Chung
Considering the financial service providers’ (FSPs) information asymmetry in evaluating the supplier and their distinct quit probabilities, we want to examine the supplier’s…
Abstract
Purpose
Considering the financial service providers’ (FSPs) information asymmetry in evaluating the supplier and their distinct quit probabilities, we want to examine the supplier’s preference of the financing schemes if both the bank and the online platform exist and how the buyer sets the contract terms in the two financing schemes.
Design/methodology/approach
We establish a Stackelberg game model to capture the interactions among three parties, i.e. a supplier, a capital-sufficient buyer and an FSP (either a bank or an online platform), within a first-time contract.
Findings
In the non-FSPs’ quit case, the buyer’s profit is higher under the bank loan scenario, while the supplier’s profit performs adversely. The supply chain’s profit is heavily dependent on the buyer’s profit difference between the two financing schemes. Moreover, we find that the supplier borrows the money to exactly cover the production cost. The equilibrium solutions of the FSPs’ quit case and of the capital-sufficient supplier’s case are also derived.
Originality/value
First, we assign different risk profiles to different FSPs in our setting so that modeling a previously ignored but practically significant problem. Second, we innovatively take the FSP’s quit probability into account in our model. Third, we elucidate how these factors can influence the relative efficiency of the two types of financing schemes and the settings of the contract, which further complements and extends the current SCF research.
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Abdelrahman E.E. Eltoukhy, Felix T.S. Chan, S.H. Chung, Ben Niu and X.P. Wang
The purpose of this paper is twofold. First, to propose an operational model for aircraft maintenance routing problem (AMRP) rather than tactical models that are commonly used in…
Abstract
Purpose
The purpose of this paper is twofold. First, to propose an operational model for aircraft maintenance routing problem (AMRP) rather than tactical models that are commonly used in the literature. Second, to develop a fast and responsive solution method in order to cope with the frequent changes experienced in the airline industry.
Design/methodology/approach
Two important operational considerations were considered, simultaneously. First one is the maximum flying hours, and second one is the man-power availability. On the other hand, ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA) approaches were proposed to solve the model, and the upper bound was calculated to be the criteria to assess the performance of each meta-heuristic. After attempting to solve the model by these meta-heuristics, the authors noticed further improvement chances in terms of solution quality and computational time. Therefore, a new solution algorithm was proposed, and its performance was validated based on 12 real data from the EgyptAir carrier. Also, the model and experiments were extended to test the effect of the operational considerations on the profit.
Findings
The computational results showed that the proposed solution algorithm outperforms other meta-heuristics in finding a better solution in much less time, whereas the operational considerations improve the profitability of the existing model.
Research limitations/implications
The authors focused on some operational considerations rather than tactical considerations that are commonly used in the literature. One advantage of this is that it improves the profitability of the existing models. On the other hand, identifying future research opportunities should help academic researchers to develop new models and improve the performance of the existing models.
Practical implications
The experiment results showed that the proposed model and solution methods are scalable and can thus be adopted by the airline industry at large.
Originality/value
In the literature, AMRP models were cast with approximated assumption regarding the maintenance issue, while neglecting the man-power availability consideration. However, in this paper, the authors attempted to relax that maintenance assumption, and consider the man-power availability constraints. Since the result showed that these considerations improve the profitability by 5.63 percent in the largest case. The proposed operational considerations are hence significant. Also, the authors utilized ACO, SA, and GA to solve the model for the first time, and developed a new solution algorithm. The value and significance of the new algorithm appeared as follow. First, the solution quality was improved since the average improvement ratio over ACO, SA, and GA goes up to 8.30, 4.45, and 4.00 percent, respectively. Second, the computational time was significantly improved since it does not go beyond 3 seconds in all the 12 real cases, which is considered much lesser compared to ACO, SA, and GA.
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Yu-Cheng Chou, Yi-Hua Fan, Madoka Nakajima and Yi-Lin Liao
The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs).
Abstract
Purpose
The purpose of this paper is to present the use of artificial immune systems (AISs) to solve constrained design optimization problems for active magnetic bearings (AMBs).
Design/methodology/approach
This research applies the AIS approach, more specifically, a representative clonal selection-based AIS called CLONALG, to the single-objective structural design optimization of AMBs. In addition, when compared with a genetic algorithm (GA) developed in the previous work, the CLONALG fails to produce best solutions when a nearly zero feasible ratio occurs in an AMB design problem. Therefore, an AIS called ARISCO (AIS for constrained optimization) is proposed to address the above issue.
Findings
A total of six AMB design cases are solved by the GA, CLONALG, and ARISCO. Based on the simulation results, in terms of solution quality, the ARISCO is shown to have better overall performance than the CLONALG and GA. In particular, when solving a problem with a nearly zero feasible ratio, the ARISCO and GA perform equally and both outperform the CLONALG.
Originality/value
In summary, the contributions of this paper include: this research applies the AIS approach, more precisely, the CLONALG, to the single-objective structural design optimization of AMBs; the ARISCO overall produces better AMB designs than the CLONALG and a GA developed in the previous work; in situations where a nearly zero feasible ratio occurs, the ARISCO and GA perform equally, and they both outperform the CLONALG.
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Kathirvel Kalaiselvi, Ill-Min Chung, Seung-Hyun Kim and Mayakrishnan Prabakaran
The purpose of this paper is to investigate the inhibitive performance of Coreopsis tinctoria (C. tinctoria) plant extract for the corrosion of mild steel in 0.5 M H2SO4.
Abstract
Purpose
The purpose of this paper is to investigate the inhibitive performance of Coreopsis tinctoria (C. tinctoria) plant extract for the corrosion of mild steel in 0.5 M H2SO4.
Design/methodology/approach
The inhibition efficiency was studied by weight loss, electrochemical measurements and the surface analysis was done by Raman, scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM-EDS) and atomic absorption spectroscopy (AAS) analysis.
Findings
Maximum inhibition efficiency of C. tinctoria in 0.5 M H2SO4 on mild steel is 80.62 per cent (500 ppm) at 303 ± 1K. The adsorption of the C. tinctoria on the mild steel surface in 0.5 M H2SO4 was found to obey Langmuir adsorption isotherm. Temperature studies were carried out and the significant parameters, such as change in enthalpy (ΔH°), change in entropy (ΔS°) and change in free energy (ΔG°ads) and heat of adsorption (Qads), were calculated. The productive layer formed on the mild steel surface in 0.5 M H2SO4 were confirmed by the Raman spectral analysis.
Originality/value
This paper provides information on the inhibitive properties of C. tinctoria plant extract which is found to be a good corrosion inhibitor for mild steel in 0.5 M H2SO4.
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Waqar Ahmed Khan, S.H. Chung, Muhammad Usman Awan and Xin Wen
The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its…
Abstract
Purpose
The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its generalization performance and convergence rate (learning speed); to identify new research directions that will help researchers to design new, simple and efficient algorithms and users to implement optimal designed FNNs for solving complex problems; and to explore the wide applications of the reviewed FNN algorithms in solving real-world management, engineering and health sciences problems and demonstrate the advantages of these algorithms in enhancing decision making for practical operations.
Design/methodology/approach
The FNN has gained much popularity during the last three decades. Therefore, the authors have focused on algorithms proposed during the last three decades. The selected databases were searched with popular keywords: “generalization performance,” “learning rate,” “overfitting” and “fixed and cascade architecture.” Combinations of the keywords were also used to get more relevant results. Duplicated articles in the databases, non-English language, and matched keywords but out of scope, were discarded.
Findings
The authors studied a total of 80 articles and classified them into six categories according to the nature of the algorithms proposed in these articles which aimed at improving the generalization performance and convergence rate of FNNs. To review and discuss all the six categories would result in the paper being too long. Therefore, the authors further divided the six categories into two parts (i.e. Part I and Part II). The current paper, Part I, investigates two categories that focus on learning algorithms (i.e. gradient learning algorithms for network training and gradient-free learning algorithms). Furthermore, the remaining four categories which mainly explore optimization techniques are reviewed in Part II (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80 articles into 38 and 42 for Part I and Part II, respectively. After discussing the FNN algorithms with their technical merits and limitations, along with real-world management, engineering and health sciences applications for each individual category, the authors suggest seven (three in Part I and other four in Part II) new future directions which can contribute to strengthening the literature.
Research limitations/implications
The FNN contributions are numerous and cannot be covered in a single study. The authors remain focused on learning algorithms and optimization techniques, along with their application to real-world problems, proposing to improve the generalization performance and convergence rate of FNNs with the characteristics of computing optimal hyperparameters, connection weights, hidden units, selecting an appropriate network architecture rather than trial and error approaches and avoiding overfitting.
Practical implications
This study will help researchers and practitioners to deeply understand the existing algorithms merits of FNNs with limitations, research gaps, application areas and changes in research studies in the last three decades. Moreover, the user, after having in-depth knowledge by understanding the applications of algorithms in the real world, may apply appropriate FNN algorithms to get optimal results in the shortest possible time, with less effort, for their specific application area problems.
Originality/value
The existing literature surveys are limited in scope due to comparative study of the algorithms, studying algorithms application areas and focusing on specific techniques. This implies that the existing surveys are focused on studying some specific algorithms or their applications (e.g. pruning algorithms, constructive algorithms, etc.). In this work, the authors propose a comprehensive review of different categories, along with their real-world applications, that may affect FNN generalization performance and convergence rate. This makes the classification scheme novel and significant.
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Marta M. Vidal Suárez and Esteban García‐Canal
In this paper we analyze the influence of transaction costs on the stock market reaction to global alliance formation. In particular, we analyze to what extent the stock market…
Abstract
In this paper we analyze the influence of transaction costs on the stock market reaction to global alliance formation. In particular, we analyze to what extent the stock market reacts negatively to the presence of attributes that increase motivation or coordination costs. We adopt a relational framework, analyzing the direct impact of these attributes not only on transaction costs but also on the potential synergies of the alliance and the incentives to invest in the relationship. Our results show that the stock market reacts negatively to transaction costs only in connection with free riding hazards.
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Amélie Charles, Rey Dang and Etienne Redor
Numerous empirical studies have been conducted to analyze the impact of board gender diversity (BGD) on firm performance without being able to establish a clear relationship. In…
Abstract
Numerous empirical studies have been conducted to analyze the impact of board gender diversity (BGD) on firm performance without being able to establish a clear relationship. In this paper, we reassess the relationship between BGD and firm performance by using a quantile regression approach. Our results indicate that BGD matters only across a subset of the firm performance distribution. Moreover, when the possible endogeneity of the relationship between BGD and firm performance is taken into account, there are some conditions under which a positive and significant relationship is observed for the eight lowest quantiles.