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1 – 10 of 844Mohd Irfan and Anup Kumar Sharma
A progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior…
Abstract
Purpose
A progressive hybrid censoring scheme (PHCS) becomes impractical for ensuring dependable outcomes when there is a low likelihood of encountering a small number of failures prior to the predetermined terminal time T. The generalized progressive hybrid censoring scheme (GPHCS) efficiently addresses to overcome the limitation of the PHCS.
Design/methodology/approach
In this article, estimation of model parameter, survival and hazard rate of the Unit-Lindley distribution (ULD), when sample comes from the GPHCS, have been taken into account. The maximum likelihood estimator has been derived using Newton–Raphson iterative procedures. Approximate confidence intervals of the model parameter and their arbitrary functions are established by the Fisher information matrix. Bayesian estimation procedures have been derived using Metropolis–Hastings algorithm under squared error loss function. Convergence of Markov chain Monte Carlo (MCMC) samples has been examined. Various optimality criteria have been considered. An extensive Monte Carlo simulation analysis has been shown to compare and validating of the proposed estimation techniques.
Findings
The Bayesian MCMC approach to estimate the model parameters and reliability characteristics of the generalized progressive hybrid censored data of ULD is recommended. The authors anticipate that health data analysts and reliability professionals will get benefit from the findings and approaches presented in this study.
Originality/value
The ULD has a broad range of practical utility, making it a problem to estimate the model parameters as well as reliability characteristics and the significance of the GPHCS also encourage the authors to consider the present estimation problem because it has not previously been discussed in the literature.
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Chon Van Le and Uyen Hoang Pham
This paper aims mainly at introducing applied statisticians and econometricians to the current research methodology with non-Euclidean data sets. Specifically, it provides the…
Abstract
Purpose
This paper aims mainly at introducing applied statisticians and econometricians to the current research methodology with non-Euclidean data sets. Specifically, it provides the basis and rationale for statistics in Wasserstein space, where the metric on probability measures is taken as a Wasserstein metric arising from optimal transport theory.
Design/methodology/approach
The authors spell out the basis and rationale for using Wasserstein metrics on the data space of (random) probability measures.
Findings
In elaborating the new statistical analysis of non-Euclidean data sets, the paper illustrates the generalization of traditional aspects of statistical inference following Frechet's program.
Originality/value
Besides the elaboration of research methodology for a new data analysis, the paper discusses the applications of Wasserstein metrics to the robustness of financial risk measures.
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Matias G. Enz, Salomée Ruel, George A. Zsidisin, Paula Penagos, Jill Bernard Bracy and Sebastian Jarzębowski
This research aims to analyse the perceptions of practitioners in three regions regarding the challenges faced by their firms during the pandemic, considered a black-swan event…
Abstract
Purpose
This research aims to analyse the perceptions of practitioners in three regions regarding the challenges faced by their firms during the pandemic, considered a black-swan event. It examines the strategies implemented to mitigate and recover from risks, evaluates the effectiveness of these strategies and assesses the difficulties encountered in their implementation.
Design/methodology/approach
In the summer of 2022, an online survey was conducted among supply chain (SC) practitioners in France, Poland and the St. Louis, Missouri region of the USA. The survey aimed to understand the impact of COVID-19 on their firms and the SC strategies employed to sustain operations. These regions were selected due to their varying levels of SC development, including infrastructure, economic resources and expertise. Moreover, they exhibited different responses in safeguarding the well-being of their citizens during the pandemic.
Findings
The study reveals consistent perceptions among practitioners from the three regions regarding the impact of COVID-19 on SCs. Their actions to enhance SC resilience primarily relied on strengthening collaborative efforts within their firms and SCs, thus validating the tenets of the relational view.
Originality/value
COVID-19 is (hopefully) our black-swan pandemic occurrence during our lifetime. Nevertheless, the lessons learned from it can inform future SC risk management practices, particularly in dealing with rare crises. During times of crisis, leveraging existing SC structures may prove more effective and efficient than developing new ones. These findings underscore the significance of relationships in ensuring SC resilience.
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Augustine Senanu Komla Kukah, De-Graft Owusu-Manu, Edward Badu, David J. Edwards and Eric Asamoah
Public-private partnership (PPP) power projects are associated with varying risk factors. This paper aims to develop a fuzzy quantitative risk allocation model (QRAM) to guide…
Abstract
Purpose
Public-private partnership (PPP) power projects are associated with varying risk factors. This paper aims to develop a fuzzy quantitative risk allocation model (QRAM) to guide decision-making on risk allocation in PPP power projects in Ghana.
Design/methodology/approach
A total of 67 risk factors and 9 risk allocation criteria were established from literature and ranked in a two-round Delphi survey using questionnaires. The fuzzy synthetic evaluation method was used in developing the risk allocation model.
Findings
The model’s output variable is the risk allocation proportions between the public body and private body based on their capability to manage the risk factors. Out of the 37 critical risk factors, the public sector was allocated 12 risk factors with proportions = 50%, while the private sector was allocated 25 risk factors with proportions = 50%.
Originality/value
To the best of the authors’ knowledge, this research presents the first attempt in Ghana at endeavouring to develop a QRAM for PPP power projects. There is confidence in the model to efficiently allocate risks emanating from PPP power projects.
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Seyed Mojtaba Taghavi, Vahidreza Ghezavati, Hadi Mohammadi Bidhandi and Seyed Mohammad Javad Mirzapour Al-e-Hashem
This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of…
Abstract
Purpose
This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.
Design/methodology/approach
The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.
Findings
In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.
Originality/value
This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.
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Saida Dammak and Manel Jmal Ep Derbel
The present work aimed to present the perception of Tunisian professionals towards companies engaged in social responsibility practices and describe the tax evasion strategies of…
Abstract
Purpose
The present work aimed to present the perception of Tunisian professionals towards companies engaged in social responsibility practices and describe the tax evasion strategies of socially responsible Tunisian companies following the coronavirus disease 2019 (COVID-19) pandemic (COVID-19) shock.
Design/methodology/approach
A survey was sent to 119 Tunisian tax administration auditors. Data analysis methods principal component analysis (PCA) and regression analysis were used. The data were collected through a questionnaire after the general containment of Tunisia from September 2020 to February 2021. These quantitative data were analysed using processing software (STATA).
Findings
Professionals of the tax authorities, particularly those in charge of the audit mission, aim for corporate profitability from the perspective of stakeholders that seek to integrate ethics and social responsibility into companies and consider employee morale a top priority. The results show that highly ethical and socially responsible professionals are far from practising aggressive strategies. Thus, an auditor from the tax administration is far from engaging in social responsibility to justify fraudulent acts. During the COVID-19 period, the role of these professionals was to prevent and detect fraud in the tax sector to fight corruption and investigate taxes based on sound regulations.
Research limitations/implications
The results are consistent with optimal taxation theory, which postulates that a tax system should be chosen to maximise a social welfare function subject to a set of constraints. Professionals seek to make taxation much simpler for taxpayers by providing advice and consultation to manage tax obligations. The minimisation of tax or the play of tax values requires expertise in the field to respect legal constraints. Therefore, these professionals play a crucial role in tax collection, as the professionals' advice and suggestions can influence taxpayers' decision-making.
Practical implications
In recent years, academic researchers, policy makers and the public have become increasingly interested in corporate tax evasion behaviour. At the same time, companies are under increasing pressure to integrate CSR into the companies' decision-making processes, which has led to increased academic interest in CSR. Opportunistic tax minimisation reduces state resources and funds needed for government programmes to improve the social welfare of the entire community. This study represents an overriding concern not only for legal and tax authorities and companies, but also for shareholders and stakeholders.
Originality/value
The authors' study contributes to the existing literature by determining the state of play on corporate social responsibility (CSR) practices amongst Tunisian tax authorities' professionals. In Tunisia, an executive of the tax authorities in charge of the verification mission is required to verify the proper application of the accounting and tax legislation in force, follow up on tax control operations on declared taxes and validate the sincerity of the accounts. This study focussed on the tax evasion of companies engaged in social responsibility practices according to the judgements of Tunisian tax authorities' auditors during the global COVID-19 pandemic.
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The purpose of this article is to make a contribution to the existing knowledge by using the unique cross-jurisdiction data drawn from the FCA’s REP-CRIM submissions to explore…
Abstract
Purpose
The purpose of this article is to make a contribution to the existing knowledge by using the unique cross-jurisdiction data drawn from the FCA’s REP-CRIM submissions to explore dynamics behind firms’ perceptions on financial crime. Capturing firm’s sentiment is notoriously challenging, and any relevant regulatory data is usually not available in the public domain. A recent exception is the UK Financial Conduct Authority’s (FCA’s) financial crime data return (REP-CRIM) submissions which include the cross-country regulatory data on the UK financial institutions’ perceptions of jurisdiction risk. Despite a broad literature with respect to financial crime, there exists an important gap in the existing knowledge with respect to factors that are associated with the perceptions of firms with respect to jurisdiction risk, which this article aims to close.
Design/methodology/approach
Using cross-country regulatory data on the UK financial institutions’ perceptions of jurisdiction risk, this study empirically determines that perceptions of jurisdiction risk is significantly and positively associated with anti-money laundering and countering the financing of terrorism (AML/CFT) framework, as well as with tax burden on business and institutional and legal risk in the case of 165 jurisdictions.
Findings
The findings lend support to the proposition that unsystematic efforts and too much publicity may ascertain the high-risk image of a jurisdiction, deterring cross-border business. Policy implications that emerge from the study also add to the case for strengthening institutional and legal frameworks, as well as relieving the tax burden on doing business.
Research limitations/implications
Findings of the present study should be interpreted with caution, as the dependent variable used in the present study reflects UK firms’ perceptions of jurisdiction risk, which may depend on various factors such as different risk appetites and the countries in which firms carry out business, and not necessarily the actual level of risks based on financial crime statistics. For example, a jurisdiction which may indeed be considered high risk, would not necessarily be ranking high on the FCA’s list of UK firms’ jurisdiction risk perceptions due to few firms operating in that particular country. As a result, the list could differ from the Financial Action Task Force’s black and grey lists. Findings based on the regulatory data on the UK financial institutions’ perceptions of jurisdiction risk should be considered preliminary in nature, given that they are based on a single year cross sectional data. As global and country-level AML/CFT efforts continue to intensify and as more regulatory data becomes publicly available, it would be imperative to bring further empirical evidence to bear on the question of whether financial crime perceptions are likely to be more pronounced for jurisdictions where AML/CFT efforts are more intensified. Likewise, from a policy standpoint, it would be equally important to explore further the role that institutional and legal risk, as well as tax burden on businesses, play in shaping firms’ perceptions of jurisdiction risk.
Practical implications
Findings lend support to the proposition that unsystematic efforts and too much publicity may ascertain the high-risk image of a jurisdiction, deterring cross-border business. Therefore, rather than waiting for more data to be made available by other financial regulators, which could lead to a more conclusive evidence in the future, on balance, the findings of this study add to the case for carefully designing and systematically implementing AML/CFT measures in a less publicized manner. Findings lend support to the theoretical postulation that disorderly efforts and undue publicity regarding AML/CFT efforts serve to ascertain the high-risk image of a jurisdiction, which could deter cross-border business and could be detrimental to how firms undertake due diligence. They also suggest that disorderly implementation of AML/CFT measures may hinder access to formal financial service and jeopardize authorities’ ability to trace the movement of funds, which may also add to negative perceptions of jurisdiction risk.
Social implications
Findings are in line with the theoretical expectations that perceptions of jurisdiction risk would be expected to be higher in countries with inadequate disclosure rules, lax regulation and opacity jurisdiction. Likewise, results are aligned with the expectations that tax burden on business would be expected to be in a positive relationship with jurisdiction risk, as it would increase the likelihood of tax evasion, which incentivizes financial crime. Therefore, policy implications that emerge from the study also add to the case for strengthening institutional and legal frameworks and relieving the tax burden on doing business as part of efforts to improve the international image of jurisdictions with respect to financial crime risks.
Originality/value
Using the cross-country regulatory data on the UK financial institutions’ perceptions of jurisdiction risk, this study has empirically determined that perceptions of jurisdiction risk is significantly and positively associated with AML/CFT framework, as well as with tax burden on business and institutional and legal risk. These findings have implications from a policy standpoint.
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Faheem Ur Rehman, Md. Monirul Islam and Kazi Sohag
China's Belt and Road Initiative (BRI) is the most ambitious investment strategy for infrastructural development belonging to the significant potential for stimulating regional…
Abstract
Purpose
China's Belt and Road Initiative (BRI) is the most ambitious investment strategy for infrastructural development belonging to the significant potential for stimulating regional economic growth in Asia, Europe and Africa. This study aims to investigate the impact of infrastructure on spurring inward foreign direct investment (FDI) within the purview of human capital, GDP per capita, foreign aid, trade, domestic investment, population and institutional quality in BRI countries.
Design/methodology/approach
In doing so, the authors analyze panel data from 2000 to 2019 within the framework of the system generalized method of movement (GMM) approach for 66 BRI countries from Europe, Asia, Africa and the Middle East.
Findings
The investigated results demonstrate that aggregate and disaggregate infrastructure indices, e.g. transport, telecommunications, financial and energy infrastructures, are the driving forces in attracting foreign direct investment (FDI) in the BRI countries. In addition, control variables (i.e. institutional quality, human capital, trade, domestic investment, foreign aid and GDP per capita) play an essential role in spurring FDI inflows.
Originality/value
The authors’ study uniquely investigates both the pre- (2000–2012) and post- (2013–2019) BRI scenarios using the aggregate and disaggregate infrastructural components from the perspectives of full and clustered sample regions, such as Asia, Europe, Africa and the Middle East. The study provides several policy implications.
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Ashish Trivedi, Amit Tyagi, Ouissal Chichi, Sanjeev Kumar and Vibha Trivedi
This study aims to provide a scientific framework for the selection of suitable substation technology in an electrical power distribution network.
Abstract
Purpose
This study aims to provide a scientific framework for the selection of suitable substation technology in an electrical power distribution network.
Design/methodology/approach
The present paper focuses on adopting an integrated multi-criteria decision-making approach using the Delphi method, analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The AHP is used to ascertain the criteria weights, and the TOPSIS is used for choosing the most fitting technology among choices of air-insulated substation, gas-insulated substation (GIS) and hybrid substation, to guarantee educated and supported choice.
Findings
The results reveal that the GIS is the most preferred technology by area experts, considering all the criteria and their relative preferences.
Practical implications
The current research has implications for public and private organizations responsible for the management of electricity in India, particularly the distribution system as the choice of substations is an essential component that has a strong impact on the smooth functioning and performance of the energy distribution in the country. The implementation of the chosen technology not only reduces economic losses but also contributes to the reduction of power outages, minimization of energy losses and improvement of the reliability, security, stability and quality of supply of the electrical networks.
Social implications
The study explores the impact of substation technology installation in terms of its economic and environmental challenges. It emphasizes the need for proper installation checks to avoid long-term environmental hazards. Further, it reports that the economic benefits should not come at the cost of ecological degradation.
Originality/value
The present study is the first to provide a decision support framework for the selection of substation technologies using the hybrid AHP-TOPSIS approach. It also provides a cost–benefit analysis with short-term and long-term horizons. It further pinpoints the environmental issues with the installation of substation technology.
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Wei-Zhen Wang, Hong-Mei Xiao and Yuan Fang
Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing…
Abstract
Purpose
Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.
Design/methodology/approach
The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.
Findings
The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.
Originality/value
The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.
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