Povilas Lastauskas and Julius Stakėnas
What would have been the hypothetical effect of monetary policy shocks had a country never joined the euro area, in cases where we know that the country in question actually did…
Abstract
What would have been the hypothetical effect of monetary policy shocks had a country never joined the euro area, in cases where we know that the country in question actually did join the euro area? It is one thing to investigate the impact of joining a monetary union, but quite another to examine two things at once: joining the union and experiencing actual monetary policy shocks. The authors propose a methodology that combines synthetic control ideas with the impulse response functions to uncover dynamic response paths for treated and untreated units, controlling for common unobserved factors. Focusing on the largest euro area countries, Germany, France, and Italy, the authors find that an unexpected rise in interest rates depresses inflation and significantly appreciates exchange rate, whereas gross domestic product (GDP) fluctuations are less successfully controlled when a country belongs to the monetary union than would have been the case under the independent monetary policy. Importantly, Italy turns out to be the overall beneficiary, since all three channels – price, GDP, and exchange rate – deliver the desired results. The authors also find that stabilizing an economy within a union requires somewhat smaller policy changes than attempting to stabilize it individually, and therefore provides more policy space.
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Alessandro Rebucci, Jonathan S. Hartley and Daniel Jiménez
This chapter conducts an event study of 30 quantitative easing (QE) announcements made by 21 central banks on daily government bond yields and bilateral US dollar exchange rates…
Abstract
This chapter conducts an event study of 30 quantitative easing (QE) announcements made by 21 central banks on daily government bond yields and bilateral US dollar exchange rates in March and April 2020, in the midst of the global financial turmoil triggered by the COVID-19 outbreak. The chapter also investigates the transmission of innovations to long-term interest rates in a standard GVAR model estimated with quarterly pre-COVID-19 data. The authors find that QE has not lost effectiveness in advanced economies and that its international transmission is consistent with the working of long-run uncovered interest rate parity and a large dollar shortage shock during the COVID-19 period. In emerging markets, the QE impact on bond yields is much stronger and its transmission to exchange rates is qualitatively different than in advanced economies. The GVAR evidence that the authors report illustrates the Fed’s pivotal role in the global transmission of long-term interest rate shocks, but also the ample scope for country-specific interventions to affect local financial market conditions, even after controlling for common factors and spillovers from other countries. The GVAR evidence also shows that QE interventions can have sizable real effects on output driven by a very persistent impact on long-term interest rates.
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Mohamed Aboelmaged, Saadat M. Alhashmi, Gharib Hashem, Mohamed Battour, Ifzal Ahmad and Imran Ali
The literature on knowledge management in sustainable supply chain (KMSSC) has witnessed significant growth in the past two decades. However, a scientometric review that…
Abstract
Purpose
The literature on knowledge management in sustainable supply chain (KMSSC) has witnessed significant growth in the past two decades. However, a scientometric review that consolidates the primary trends and clusters within this topic has been notably absent. This paper aims to scrutinize recent advancements and identify the intellectual underpinnings of KMSSC research conducted between 2002 and 2022.
Design/methodology/approach
The present review employs a scientometric analysis approach via visualization maps of prolific contributions, co-citation, co-occurrence and thematic networks to examine a total of 114 articles and conference papers on KMSSC.
Findings
Emerging research frontiers and hotspots are revealed and a state-of-the-art framework of KMSSC research structure is developed.
Practical implications
The review provides significant implications that guide KMSSC research and better inform sustainability decisions in the supply chain context.
Originality/value
To the best of the authors' knowledge, this is the first review to thoroughly synthesize the intersected domain of KMSSC using scientometric analysis.
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Gharib Hashem and Mohamed Aboelmaged
The rapid global changes we are witnessing pose a pressing challenge that necessitates reevaluating conventional supply chain practices. Consequently, the integration of digital…
Abstract
Purpose
The rapid global changes we are witnessing pose a pressing challenge that necessitates reevaluating conventional supply chain practices. Consequently, the integration of digital technologies into supply chain operations, often referred to as digital supply chain (DSC), has emerged as a strategic shift that aims to empower organizations to proactively seize new opportunities rather than being caught off guard by unforeseen disruptions arising from economic volatility, global pandemics and regional conflicts. Thus, this study embraces a knowledge-centric approach to explore the direct and indirect impact of knowledge management, innovation and learning capabilities on DSC adoption in an emerging economy context. Furthermore, it aims to shed light on the moderating role of environmental dynamism in this intricate interplay.
Design/methodology/approach
Employing a cross-sectional survey, the research data were collected from 354 managers representing Egyptian manufacturing and service firms utilizing a structured questionnaire. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).
Findings
The results unveiled that knowledge management capability (KMC) has the highest path coefficient value among all endogenous variables. It accounts for a significant portion of the variance in innovation and learning capabilities, which play pivotal roles in adopting DSC. Notably, learning capability appears to exert a more powerful influence on DSC adoption than innovation capability through direct and mediating effects. Furthermore, the findings underscore the moderating effect of environmental dynamism on the relationship between learning capability and DSC adoption. However, this moderating role is not observed in the link between innovation capability and DSC adoption.
Practical implications
There is a growing trend among firms to adopt DSC in response to significant environmental shifts. This study offers valuable insights for managers and policymakers, providing them with a deeper understanding of the DSC adoption process. The study’s findings assist in identifying crucial factors that boost DSC adoption and offer guidance on successfully leveraging digital technologies for managing supply chain practices. Moreover, the study offers stimulating directions for future DSC research directions.
Originality/value
The study contributes to the existing literature by expanding our understanding of the adoption of DSC by utilizing knowledge, innovation and learning capabilities within the context of emerging economies.
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Ming Kong, Jiti Gao and Xueyan Zhao
This chapter re-examines the determinants of health care expenditure (HCE), using a panel of 32 Organization for Economic Cooperation and Development (OECD) countries from 1990 to…
Abstract
This chapter re-examines the determinants of health care expenditure (HCE), using a panel of 32 Organization for Economic Cooperation and Development (OECD) countries from 1990 to 2012. In particular, a panel semiparametric technique (i.e., a partially linear model) is employed, with cross-sectional dependence allowed. Beside the study of coefficients, this chapter investigates the trending functions of HCE. After the common and individual trends of HCE are estimated via semiparametric methods, the authors calibrate them with polynomial specifications, leading to out-of-sample forecasting. The validities of the calibration are tested as well. Contrary to those studies that do not take into account time series properties, our finding suggests that medical care is not a luxury commodity. Other determinants, such as public financing, and the supply of doctors, are all positively related to HCE. Moreover, the calibrated trending models perform well in out-of-sample forecasting.
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Mohammed Elastal, Mohammad H Allaymoun and Tasnim Khaled Elbastawisy
This chapter proposes a model for discovering suspicious financial operations such as money laundering. To achieve this, the authors reviewed research papers on money laundering…
Abstract
This chapter proposes a model for discovering suspicious financial operations such as money laundering. To achieve this, the authors reviewed research papers on money laundering and financial institutions’ cases and problems, especially those related to financial transfers. They also collected primary data through face-to-face semi-structured interviews with financial companies’ owners and experts in financial transfers to identify hypotheses that help discover suspicious transfers. The chapter discusses the six big data analysis cycle phases from problem discovery to model deployment to identify suspicious transfers. The chapter uses hypothetical data and models to discuss the results and focuses on exchange companies willing to analyze financial operations. The chapter proposes tools that exchange companies can use to monitor and prevent suspicious transfers including data visualization and machine learning algorithms.
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Shafeeq Ahmed Ali, Mohammad H. Allaymoun, Ahmad Yahia Mustafa Al Astal and Rehab Saleh
This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter…
Abstract
This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.
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Essaki Raj R. and Sundaramoorthy Sridhar
This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads.
Abstract
Purpose
This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads.
Design/methodology/approach
Taking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance.
Findings
The experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function.
Originality/value
The important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance.
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Michael K. Fung and Arnold C. S. Cheng
Using a sample of developed and developing nations (including China and Hong Kong), this study examines the financial market and housing wealth effects on consumption. Housing…
Abstract
Using a sample of developed and developing nations (including China and Hong Kong), this study examines the financial market and housing wealth effects on consumption. Housing performs the dual functions as both a commodity providing a flow of housing services and an investment providing a flow of capital income. With an empirical framework based on the permanent income hypothesis, this study's findings suggest that a rise in housing price has both a positive wealth effect and a negative price effect on consumption. While the positive wealth effect is caused by an increase in capital income from housing investment, the negative price effect is caused by an increase in the cost of consuming housing services. Moreover, the sensitivity of consumption to unanticipated changes in housing price is related to the level of financial and institutional development.