Search results

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

Asset pricing literature is facing a credibility crisis, given debates associated with publication biases, data mining, replicability, crowding and lack of theoretic foundations. This study proposes that emerging markets (EMs), with their specific socioeconomic characteristics and lesser financial integration with developed markets (DMs), might provide useful arguments to the debate and improve our understanding of asset pricing. Beyond out-of-sample evidence for DM findings, EM research can unveil regional-specific factors overlooked by mainstream literature and investigating reasons behind differences can reveal epistemological elements for proposing and testing theories, and clarifying causal mechanisms.

The present work conducts an EM-focused systematic review, by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a transparent and replicable procedure, to gather a representative collection of papers and using bibliometric methods to analyze bibliographic data.

Besides summarizing and contextualizing scientific production, revealing most important themes and actors, we confirm a clear bias toward DM and identify low scientific collaboration among EM. Additionally, we assess to which extent current studies fulfills EM research potential.

There is a home bias in literature, with most studies focusing on DM. This systematic review is exclusive to EM literature and suggests how EM research can improve literature credibility.

The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

This paper investigates the impact of microfinance on poverty gap which is the shortfall in income or consumption expenditures below $1.90, $3.20 and $5.50 per day. The paper’s primary goal is to investigate how microloans have impacted the severity of poverty and influenced the cost of poverty eradication in Latin America, empirically evaluate these effects and offer appropriate policy recommendations.

This paper used panel data for 13 Latin American countries from world bank spanning the period 2001–2019 and Fully Modified Ordinary Least Squares model for heterogeneous cointegrated panels. This study used Gross Loan Portfolio per active borrowers, gross domestic product per capita, Gini index, Inflation and Unemployment rate as independent variables and poverty gaps as dependent variables.

Poverty gaps narrow as the loan per borrower increases, and the degree of effect differs with the poverty line, with the magnitude increasing as the poverty line falls, underscoring microloans as an effective tool in closing poverty gaps and lowering the cost of poverty eradication. Growth of GDP per capita is helpful reducing the poverty gap, especially for the less poor of the poor. Inflation and unemployment have no to little impact on the severe poverty gaps, but they start to matter when the poverty line is $5.5 per day. Finally, income distribution inequality widens the poverty gap regardless of the poverty line used.

This study suggests several implications. For example, Latin American nations need to embrace tangible policies that encourage economic growth while reducing inequalities in income distribution to effectively eradicate poverty. More supportive environment is necessary to increase the effectiveness of microfinance operations, particularly for the poorest populations. Microfinance institutions need to set less stringent conditions for loan accessibility and repayment schedules that are commensurate with different levels of poverty. Finally, strengthening microfinance as a strategic policy to gradually close poverty gaps and reduce the cost of poverty eradication.

This study aims to assess the pitting resistance of austenitic stainless steel welded joints fusion zone (FZ) with high density of inclusions before and after surface treatment, including potentiostatic pulse technique (PPT) and pickling.

The potentiodynamic polarization tests and critical pitting temperature tests were carried out for estimating pitting resistance. The PPT and pickling were performed as surface treatment. Scanning electron microscope (SEM) and energy dispersive spectrometer were used for characterize the microstructure and elemental distribution. Electron back-scattered diffraction (EBSD) was used to assess the portion of phases and morphology of grains.

The weld metal exhibits a higher degree of alloying compared to the base metal, and it contains d-phase and sulfur-containing inclusions. Sulfur-containing inclusions serve as initiation sites for pitting, and they diminish the pitting resistance of weld metal. Both PPT and pickling can remove sulfur-containing inclusions, but PPT causes localized dissolution of the weld metal matrix around the inclusions, while pickling does not. Because of the high density of inclusions, certain pits initiated by PPT are significantly deeper, which makes the formation of stable pitting easier. Because of the high density of inclusions, certain pits initiated by the PPT are deeper. This characteristic facilitates the progression of these initial defects into fully developed, stable pits.

Analysis of pitting initiation in shielded metal arc welding FZ with PPT and ex situ SEM tracking observation. Explanation of why the PPT surface treatment is not able to enhance the pitting resistance of stainless steel with a high inclusion density.

This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.

The purpose of this study is to conduct a systematic content review and bibliometric analysis of the current research trends, core concepts and knowledge mapping on the topic Islamic Banking and Finance (IBF) during Covid-19. Apart from highlighting the contributions of prolific authors, prominent institutions and countries, a comprehensive review of a significant number of documents using co-citation and co-word analysis is carried out for the science mapping.

A data set of 125 papers was collected published in Scopus database during the period December, 2019 and January 5th, 2023. Yearly publications, most-cited papers and authors, active sources, affiliations and countries are highlighted with descriptive analysis. Knowledge structure of the topic was mapped with investigating the social, intellectual and conceptual structures of IBF research. Content analysis is carried out to uncover the underlying research clusters that shape the scientific knowledge structure of studies.

A diverse group of authors and institutions contribute to the growing body of knowledge on the topic. IBF is adopting new paradigms and frameworks to integrate FinTech, crowd funding and Islamic social finance to provide sustainable solutions in both crisis and normal periods. The research on IBF is classified in to three themes: “financial markets in Covid-19,” “modeling risk and market regimes” and “FinTech and Islamic social finance.”

This study collects data only from Scopus database. Future studies must include research articles from other databases such as, Web of Sciences.

This study highlights research gaps in the existing literature and provides directions for future research.

This study conducts a systematic literature review (SLR) on social inclusion within upstream supply chains, targeting a notable literature gap in modern SCM discourse. By delving into this critical, yet underexamined, domain, this study spotlights the pressing need to incorporate social inclusion practices, particularly as global supply chains face increased scrutiny over their social ramifications. It examines social inclusion’s intricacies, offering practical insights for industry professionals to adopt, so that trustworthy social inclusion practices can proliferate across their upstream supply chains, thereby making a substantial contribution to both theoretical understanding and practical application.

Employing five search queries across two leading academic databases, this investigation reviewed 86 articles that examined social issues related to social inclusion in the upstream supply chain. Via content analysis, this study aims to answer essential research questions and employs statistical bibliometric analyses to investigate the collected data further.

This study’s findings establish a definition of social inclusion within the upstream supply chain and present a conceptual framework delineating levers and indicators for evaluating such practices. Through rigorous analysis, it becomes apparent that mechanisms such as supplier compliance, collaboration and development are crucial for promoting social inclusion; however, their importance differs at various levels of suppliers in multi-tiered supply chains. Furthermore, a methodological matrix is introduced for assessing social inclusion practices’ efficacy, equipping practitioners with a roadmap for developing and executing strategies that extend social inclusion efforts throughout the supply chain, as well as emphasising these levers through monitoring, assessment and application of six specified indicators.

This study contributes to the dialogue surrounding upstream supply chain management by spotlighting social inclusion practices, addressing the literature gap in comprehending how social inclusion dynamics operate within upstream supply chains and outlining a distinct direction for forthcoming research. By highlighting the pressing importance of enhancing social inclusion practices, this study not only enriches the theoretical landscape but also lays the groundwork for subsequent empirical studies aimed at deciphering the complexities and practical hurdles associated with the efficient execution of these practices.

This study explores digital transformation in the tea supply chain within developing economies, with a focus on smallholder tea producers in Sri Lanka. Tea is one of the most widely consumed beverages in the world. Among the tea producers, smallholder tea producers account for a substantial portion of total tea production in several countries. Mobile phones play a significant role in providing smallholder producers with access to crucial agricultural information, markets and financial services.

This study adopts a deductive approach, analysing mobile phone ownership, literacy, experience and perception among smallholder tea producers. The chi-squared test of independence and hierarchical clustering methods were used to test the hypotheses and address the research questions.

The study identifies four clusters of smallholder tea producers as Basic Tech Adopters, Digital Laggards, Skeptical Feature Phone Users and Tech-savvy Adopters based on their characteristics towards mobile-based technologies. Approximately 75% of the surveyed sample, which included both tech-savvy and basic-tech adopters, showed a positive attitude toward adopting mobile-based agricultural technologies.

The study suggests developing targeted strategies and policies to enhance the productivity of the smallholder tea production process in developing economies. The study highlights the importance of awareness, access, affordability and availability when implementing digital services for businesses at the base of the pyramid, such as tea smallholdings in developing economies.

The present study aims to address the lack of data-driven empirical studies on the use of mobile phones in smallholder settings. The findings of this study enable the enhancement of entrepreneurship within the tea production supply chain, especially, within stakeholders who deliver digital transformation support services.

Several farm safety net strategies are available to farmers as a source of financial protection against losses due to price instability, government policies, weather fluctuations and global market changes. Producers can employ these strategies combining crop insurance policies with countercyclical policies for several crops and production areas; however, less is known about the efficiency of these strategies in enhancing profit and reducing its variability. In this study, we examine the efficiency of these strategies at minimizing inter crop year farm profit variability.

We utilized relative mean of profit and coefficient of variation, to compare counterfactually calculated farm safety net strategies for a sample of 28,615 observations across 2,486 farms and four dryland crops (corn, soybean, sorghum and wheat) in Kansas spanning nine crop years (2014–2022). A no farm safety net strategy is used as the benchmark for every alternative strategy to ascertain whether a policy customization is statistically different from a no farm safety case.

The general pattern of the results suggests that program combination strategies that have a high-profit enhancement potential necessarily have low profit risk for dryland wheat and sorghum production. On the contrary, such a connection is absent for dryland corn and soybeans production. Low-cost farm safety net strategies that enhance corn and soybeans profits do not necessarily lower profit risks.

This paper is one of the first to use a large sample of actual farm-level observations to evaluate how combinations of safety net programs offered under the Title I (PLC, ARCCO and ARCIC) and XI (FCIP) of the U.S. Farm Bill rank in terms of profit level enhancement and profit risk reduction.