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1 – 10 of 16Gouda Abdel Khalek and Amany Rizk
This paper aims to obtain a recent estimate of the cost of precautionary foreign reserve accumulation that emerging market and developing economies (EMDEs) had to endure to…
Abstract
Purpose
This paper aims to obtain a recent estimate of the cost of precautionary foreign reserve accumulation that emerging market and developing economies (EMDEs) had to endure to protect themselves against the risks of financial globalization. In addition, the study estimates the cost of excess reserves in emerging market economies (EMEs) using various reserve adequacy indicators that reflect potential sources of foreign exchange drains and vulnerability in EMEs' balance of payments.
Design/methodology/approach
This paper begins by explaining the accumulation of foreign reserves in EMDEs as a self-protection strategy against the risks of financial globalization. Next, it sheds light on the different types of economic costs of foreign reserve accumulation. Finally, it estimates the cost of foreign reserve accumulation in EMEs during the period (1990–2018) and in EMDEs during the period (1990–2015) due to data availability.
Findings
Results indicate that the cost of accumulating foreign reserves as a self-protection strategy in EMDEs and EMEs' was huge compared to their development financing needs. Applying various reserve adequacy measures demonstrates that many of the EMEs were holding inadequate precautionary reserves in 2018. Actually, this reflects the significant increase in external short term debt that many of the EMEs have witnessed since the eruption of the global financial crisis (2008). Thus increasing reserves in EMEs with weak reserve buffers and higher external debt is critical as they are more vulnerable to external shocks and capital flow reversals. Also given the estimated huge costs of accumulating foreign reserves, EMDEs should accompany it by other complementary self-protection policies and liquidity management policies to free up resources for productive investment.
Originality/value
The study contributes to the literature by estimating the cost of precautionary foreign reserve accumulation imposed on EMDEs during an extended period of time that covers a decade after the onset of the global financial crisis. Also to the authors' knowledge, this is the first study that estimates the cost of excess reserves in EMEs using various reserve adequacy indicators including the International Monetary Fund (IMF) assessing reserve adequacy (ARA) approach.
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Christian Nnaemeka Egwim, Hafiz Alaka, Youlu Pan, Habeeb Balogun, Saheed Ajayi, Abdul Hye and Oluwapelumi Oluwaseun Egunjobi
The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning…
Abstract
Purpose
The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.
Design/methodology/approach
For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.
Findings
Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM2.5 concentration level than bagging and boosting ensemble models.
Research limitations/implications
A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.
Practical implications
The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system
Originality/value
This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.
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Tachia Chin, Yi Shi, Rosa Palladino and Francesca Faggioni
Cross-cultural cognitive paradoxes have frequently broken the existing boundaries of knowledge and stimulated demands for knowledge creation (KC), and such paradoxes have…
Abstract
Purpose
Cross-cultural cognitive paradoxes have frequently broken the existing boundaries of knowledge and stimulated demands for knowledge creation (KC), and such paradoxes have triggered and will continue to trigger novel risks in the context of international business (IB). Given the nascency of relevant issues, this study aims to develop a more comprehensive understanding of KC across cultures by proposing a Yin-Yang dialectical systems theory of KC as micro-foundation to more systematically frame the risk/paradox-resolving mechanism elicited by cultural collisions.
Design/methodology/approach
This paper is conceptual in nature. The authors first critically review the literature to lay a broad theoretical foundation. Integrating the philosophy- and praxis-based views, the authors reposition knowledge as a Yin-Yang dialectical system of knowing, with yin representing the tacit while yang represents the explicit. Next, the authors justify the underling logic of realising KC through a contradiction-resolving process. On this basis, the authors draw upon the Yijing’s Later Heaven Sequence (LHS) as the source domain of a heuristic metaphor to reconceptualise KC as a dynamic capability in the IB context.
Findings
Using the LHS paradigm to metaphorically map the intricate patterns of interaction and interconnectivity among the involved individuals, organisations and all related stakeholders, this research identifies and theorises the overall dynamic capability of KC in the IB context, which comprises five sets of processes: contradiction, conflict, communication, compromise and conversion.
Practical implications
This research highlights that KC is simultaneously activated and constrained by human actions as well as by the socially constructed context in which it emerges, which helps individuals, organisations and policy makers more clearly frame the novel risks induced by cross-cultural cognitive conflicts in the IB context.
Originality/value
The authors synthesise Yin-Yang dialectics with the approach of collective phronesis, proposing a novel, praxis-oriented Yin-Yang dialectical systems theory of KC. It provides a deeper understanding of the epistemological paradox inherent in all knowledge, thus enabling KC to be rationalised by a sounder logical reasoning. By fusing the macro and micro perspectives on KC, the authors also enrich existing theory and future theory building in the domain of knowledge management.
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Anindita Bhattacharjee, Dolly Gaur and Kanishka Gupta
India is not geographically close to either Russia or Ukraine. However, India's trade relations with them make it vulnerable to the consequences of the war between these…
Abstract
Purpose
India is not geographically close to either Russia or Ukraine. However, India's trade relations with them make it vulnerable to the consequences of the war between these countries. Thus, the present study aims to examine the impact of the Russia–Ukraine war on various sectoral indices of the Indian economy.
Design/methodology/approach
Event study methodology has been used in this study for analysis. The date of the war announcement is the event day. The sample studied includes ten sectors of the Indian economy listed on the National Stock Exchange (NSE). Results correspond to the period of −167 days to +20 days of the announcement of the war, i.e. from June 25, 2021, to March 28, 2022.
Findings
Almost all the sample sectors earned significantly positive abnormal returns in the post-event period. The metal industry has led this group by showcasing the highest abnormal returns. Though Indian sectors made overall positive returns, the market soon corrected itself and abnormal returns were wiped out.
Practical implications
These results can benefit portfolio managers, analysts, investors and policymakers in hedging risks and selecting suitable investments during increased global uncertainty. The study's conclusions help policymakers establish an institutional and supervisory framework that will make it easier to spot systematic risks and reduce them by putting countercyclical measures in place.
Originality/value
India has no geographical proximity or trade relations with Russia or Ukraine, as strong as any other European country. However, Russia has remained a strong ally to India in the trade of defense equipment. Similar is the case with Ukraine, a significant global partner for India. Thus, the impact of conflict between these two countries has not been limited to Europe only but has also engulfed related economies. Hence, the present study is one of the first attempts to examine the burns sustained by the Indian economy due to this war.
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This exploratory study discusses the policy learning process of the development of disaster risk reduction (DRR) policy.
Abstract
Purpose
This exploratory study discusses the policy learning process of the development of disaster risk reduction (DRR) policy.
Design/methodology/approach
The paper discusses how DRR has and has not developed in Thailand through the two major disasters: the 2004 Indian Ocean Tsunami and the 2011 Great Flood. The information was collected by documentary analysis to gain a historical and critical understanding of the development of the system and policy of DRR in Thailand. Additionally, key stakeholders' interviews were undertaken to supplement the analysis.
Findings
The paper demonstrates that Thailand's DRR development has been “reactive” rather than “proactive”, being largely directed by global DRR actors.
Research limitations/implications
Being a small-scale study, the sample size was small. The analysis and argument would be consolidated with an increase in the number of interviews.
Practical implications
The model can help deconstruct which dimension of the learning process a government has/has not achieved well.
Originality/value
The application of the “restrictive-expansive policy learning” model, which identifies different dimensions of policy learning, reveals that the Thai government's policy learning was of a mixed nature.
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Annie Singla and Rajat Agrawal
This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right…
Abstract
Purpose
This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.
Design/methodology/approach
iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.
Findings
The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.
Originality/value
iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.
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Mehmet Kursat Oksuz and Sule Itir Satoglu
Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response…
Abstract
Purpose
Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.
Design/methodology/approach
This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.
Findings
Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.
Originality/value
This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.
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Raktim Ghosh, Bhaskar Bagchi and Susmita Chatterjee
The paper tries to analyse empirically the impact of India's economic policy uncertainty (EPU) index on different macro-economic variables of India, like import, export, interest…
Abstract
Purpose
The paper tries to analyse empirically the impact of India's economic policy uncertainty (EPU) index on different macro-economic variables of India, like import, export, interest rate, exchange rate, inflation rate and stock market during pre-COVID-19 and COVID-19 era.
Design/methodology/approach
Although there exist several works where relationship and volatility among the stock markets and macro-economic indicators during the COVID-19 pandemic have been estimated, but till now none of the studies examined the effect of EPU index on different macro-economic variables in the Indian context along with the stock market due to the outbreak of COVID-19 pandemic. This is considered a noteworthy gap and hence opens up a new dimension for examination. To get a clear picture, monthly data from January, 2012 to September, 2021 have been considered where January, 2012–February, 2020 is taken as the pre-COVID-19 period and March, 2020–September, 2021 as COVID-19 period. All the data are converted into log natural. The authors applied DCC-GARCH model to investigate the impact of EPU index on volatility of selected variables over the study period across a multivariate framework and Markov regime-switching model to examine the switching over of the variables.
Findings
The results of dynamic conditional correlation - multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model indicates the presence of volatility in the dependent variables arising out of economic policy uncertainty considering the segmentation of the study period into pre-COVID-19 and COVID-19. The results of Markov regime-switching model show the variables make a significant move from low-volatility regime to high-volatility regime due to the presence of COVID-19.
Research limitations/implications
It can be implied that impact of EPU in terms of volatility on the Indian Stock Market will lead to unfavourable investment conditions for the prospective investors. Even, the different macro-economic variables are to suffer from the volatility arising out of EPU across a long time horizon as confirmed from the DCC-MGARCH model.
Originality/value
The study is original in nature. It adds superior values from the new and significant findings from the study empirically. Application of DCC-MGARCH model and Markov regime switching model makes the study an innovative one in terms of methodology and findings.
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David M. Herold, Lorenzo Bruno Prataviera and Katarzyna Nowicka
During the supply chain disruptions caused by COVID-19, logistics service providers (LSPs) have invested heavily in innovations to enhance their supply chain resilience…
Abstract
Purpose
During the supply chain disruptions caused by COVID-19, logistics service providers (LSPs) have invested heavily in innovations to enhance their supply chain resilience capabilities. However, only little attention has been given so far to the nature of these innovative capabilities, in particular to what extent LSPs were able to repurpose capabilities to build supply chain resilience. In response, using the concept of exaptation, this study identifies to what extent LSPs have discovered and utilized latent functions to build supply chain resilience capabilities during a disruptive event of high impact and low probability.
Design/methodology/approach
This conceptual paper uses a theory building approach to advance the literature on supply chain resilience by delineating the relationship between exaptation and supply chain resilience capabilities in the context of COVID-19. To do so, we propose two frameworks: (1) to clarify the role of exaptation for supply chain resilience capabilities and (2) to depict four different exaptation dimensions for the supply chain resilience capabilities of LSPs.
Findings
We illustrate how LSPs have repurposed original functions into new products or services to build their supply chain resilience capabilities and combine the two critical concepts of exploitation and exploration capabilities to identify four exaptation dimensions in the context of LSPs, namely impeded exaptation, configurative exaptation, transformative exaptation and ambidextrous exaptation.
Originality/value
As one of the first studies linking exaptation and supply chain resilience, the framework and subsequent categorization advance the understanding of how LSPs can build exapt-driven supply chain resilience capabilities and synthesize the current literature to offer conceptual clarity regarding the varied implications and outcomes linked to the repurposing of capabilities.
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João J.M. Ferreira and Ana Joana C. Fernandes
This study reviews the literature on collaborative consumption (CC), depicting the main theoretical lineages of the CC approach while leveraging the findings to suggest promising…
Abstract
Purpose
This study reviews the literature on collaborative consumption (CC), depicting the main theoretical lineages of the CC approach while leveraging the findings to suggest promising paths for advancing the literature.
Design/methodology/approach
This review is based on a bibliometric approach. The strict research protocol employed led to the inclusion of 249 articles in the descriptive and bibliometric analyses. The co-citation analysis led to the inclusion of 50 co-cited articles in the content analysis.
Findings
The descriptive analysis depicts the research profile on CC in terms of main features, yearly evolution of publications and citations, most influential articles and most influential journals. The systematization of the co-citation analysis led to the identification of three complementary theoretical lineages of research on CC: (1) theoretical roots of CC, (2) drivers of CC and (3) the sharing economy: consequences/outcomes. An integrative framework of research on CC schematizing the main theoretical lineages identified is proposed. Based on the critical gaps identified in the literature in CC, an agenda for future research is suggested.
Originality/value
Despite the burgeoning interest in the CC approach, the literature has yet to fully grasp the CC concept's real implications. This study portrays a comprehensive review of the literature on CC; an integrative framework of the main theoretical lineages of research on CC is proposed, and an agenda for future research is suggested based on the critical gaps identified and implications for literature, policy and practice are stated.
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