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Open Access
Article
Publication date: 14 April 2023

Alebachew Destaw Belay, Wuletaw Mekuria Kebede and Sisay Yehuala Golla

This study aims to examine determinants of farmers’ use of climate-smart agricultural practices, specifically improved crop varieties, intercropping, improved livestock breeds and…

2212

Abstract

Purpose

This study aims to examine determinants of farmers’ use of climate-smart agricultural practices, specifically improved crop varieties, intercropping, improved livestock breeds and rainwater harvesting in Wadla district, northeast Ethiopia.

Design/methodology/approach

A cross-sectional household survey was used. A structured interview schedule for respondent households and checklists for key informants and focus group discussants were used. This study used both descriptive statistics and a multivariate probit econometric model to analyze the collected data. The model was used to compute factors influencing the use of climate-smart agricultural practices in the study area.

Findings

The results revealed that households adopted selected practices. The likelihood of farmers’ decisions to use improved crop varieties, intercropping, improved livestock breeds and rainwater harvesting was 85%, 52%, 69% and 59%, respectively. The joint probability of using these climate-smart agricultural practices was 23.7%. The model results confirmed that sex, level of education, livestock holding, access to credit, farm distance, market distance and training were significant factors that affected the use of climate-smart agricultural practices in the study area.

Originality/value

The present study used the most selected locally practiced interventions for climate-smart agriculture.

Details

International Journal of Climate Change Strategies and Management, vol. 15 no. 5
Type: Research Article
ISSN: 1756-8692

Keywords

Open Access
Article
Publication date: 21 December 2022

GyeHong Kim

This paper shows a new methodology for evaluating the value and sensitivity of autocall knock-in type equity-linked securities. While the existing evaluation methods, Monte Carlo…

467

Abstract

This paper shows a new methodology for evaluating the value and sensitivity of autocall knock-in type equity-linked securities. While the existing evaluation methods, Monte Carlo simulation and finite difference method, have limitations in underestimating the knock-in effect, which is one of the important characteristics of this type, this paper presents a precise joint probability formula for multiple autocall chances and knock-in events. Based on this, the calculation results obtained by utilizing numerical and Monte Carlo integration are presented and compared with those of existing models. The results of the proposed model show notable improvements in terms of accuracy and calculation time.

Details

Journal of Derivatives and Quantitative Studies: 선물연구, vol. 31 no. 1
Type: Research Article
ISSN: 1229-988X

Keywords

Open Access
Article
Publication date: 3 July 2023

Hung T. Nguyen

This paper aims to offer a tutorial/introduction to new statistics arising from the theory of optimal transport to empirical researchers in econometrics and machine learning.

Abstract

Purpose

This paper aims to offer a tutorial/introduction to new statistics arising from the theory of optimal transport to empirical researchers in econometrics and machine learning.

Design/methodology/approach

Presenting in a tutorial/survey lecture style to help practitioners with the theoretical material.

Findings

The tutorial survey of some main statistical tools (arising from optimal transport theory) should help practitioners to understand the theoretical background in order to conduct empirical research meaningfully.

Originality/value

This study is an original presentation useful for new comers to the field.

Details

Asian Journal of Economics and Banking, vol. 7 no. 2
Type: Research Article
ISSN: 2615-9821

Keywords

Open Access
Article
Publication date: 22 April 2022

Roxana Gómez-Valle and Nathalie Holvoet

This paper explores the relationship between married women's intrahousehold decision-making participation and marital gender roles, next to factors suggested in the household…

1688

Abstract

Purpose

This paper explores the relationship between married women's intrahousehold decision-making participation and marital gender roles, next to factors suggested in the household bargaining literature. Additionally, the authors investigate whether women's employment carries the same importance for decision-making participation as contributions to household incomes.

Design/methodology/approach

Using 2011/2012 Nicaraguan Demographic and Health Survey (DHS), the authors estimate multinomial logistic regressions for eight decision-making domains, analyzing three levels of decision-making: wife-dominant or sole decisions, joint decision-making (with the partner) and decision-making by someone else. The authors create an additive index for measuring internalized marital gender roles.

Findings

Women's intrahousehold decision-making participation is explained differently depending on the decision-making area and level of participation. Women with a better relative position vis-à-vis partners and not following patriarchal gender roles are more likely to make decisions jointly with their partners, but not alone. Women's age and educational level are the strongest predictors in the analysis. Women's employment reduces their decision-making participation in children's disciplining and daily cooking-related decisions.

Research limitations/implications

It focuses on married women only, while marital status might be a determinant of decision-making itself and left out the contribution of unearned incomes.

Practical implications

Interventions aimed at increasing women's intrahousehold decision-making participation should not only focus on economic endowments but also comprehend the gendered dynamics governing intrahousehold allocation.

Originality/value

The study incorporates quantitative measures of marital gender roles in the study of intrahousehold decision-making. It also contributes to the literature with insights from contexts where women's involvement in employment increased against a background of patriarchal gender roles.

Details

Fulbright Review of Economics and Policy, vol. 2 no. 1
Type: Research Article
ISSN: 2635-0173

Keywords

Open Access
Article
Publication date: 7 September 2021

Ming Qi, Jiawei Zhang, Jing Xiao, Pei Wang, Danyang Shi and Amuji Bridget Nnenna

In this paper the interconnectedness among financial institutions and the level of systemic risks of four types of Chinese financial institutions are investigated.

2149

Abstract

Purpose

In this paper the interconnectedness among financial institutions and the level of systemic risks of four types of Chinese financial institutions are investigated.

Design/methodology/approach

By the means of RAS algorithm, the interconnection among financial institutions are illustrated. Different methods, including Linear Granger, Systemic impact index (SII), vulnerability index (VI), CoVaR, and MES are used to measure the systemic risk exposures across different institutions.

Findings

The results illustrate that big banks are more interconnected and hold the biggest scales of inter-bank transactions in the financial network. The institutions which have larger size tend to have more connection with others. Insurance and security companies contribute more to the systemic risk where as other institutions, such as trusts, financial companies, etc. may bring about severe loss and endanger the financial system as a whole.

Practical implications

Since other institutions with low levels of regulation may bring about higher extreme loss and suffer the whole system, it deserves more attention by regulators considering the contagion of potential risks in the financial system.

Originality/value

This study builds a valuable contribution by examine the systemic risks from the perspectives of both interconnection and tail risk measures. Furthermore; Four types financial institutions are investigated in this paper.

Details

Kybernetes, vol. 51 no. 13
Type: Research Article
ISSN: 0368-492X

Keywords

Open Access
Article
Publication date: 3 February 2020

Kai Zheng, Xianjun Yang, Yilei Wang, Yingjie Wu and Xianghan Zheng

The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.

Abstract

Purpose

The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.

Design/methodology/approach

Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods.

Findings

The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M.

Originality/value

This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

Details

International Journal of Crowd Science, vol. 4 no. 1
Type: Research Article
ISSN: 2398-7294

Keywords

Open Access
Article
Publication date: 4 December 2020

Sudip Patra and Partha Ghose

The current paper is a brief review of the emerging field of quantum-like modelling in game theory. This paper aims to explore several quantum games, which are superior compared…

Abstract

Purpose

The current paper is a brief review of the emerging field of quantum-like modelling in game theory. This paper aims to explore several quantum games, which are superior compared to their classical counterparts, which means either they give rise to superior Nash equilibria or they make the game fairer. For example, quantum Prisoners Dilemma generates Pareto superior outcomes as compared to defection outcome in the famous classical case. Again, a quantum-like version of cards game can make the game fairer, increasing the chance of winning of players who are disadvantaged in the classical case. This paper explores all the virtues of simple quantum games, also highlighting some findings of the authors as regards Prisoners Dilemma game.

Design/methodology/approach

As this is a general review paper, the authors have not demonstrated any specific mathematical method, rather explored the well-known quantum probability framework, used for designing quantum games. They have a short appendix which explores basic structure of Hilbert space representation of human decision-making.

Findings

Along with the review of the extant literature, the authors have also highlighted some new findings for quantum Prisoners Dilemma game. Specifically, they have shown in the earlier studies (which are referred to here) that a pure quantum entanglement set up is not needed for designing better games, even a weaker condition, which is classical entanglement is sufficient for producing Pareto improved outcomes.

Research limitations/implications

Theoretical research, with findings and implications for future game designs, it has been argued that it is not always needed to have true quantum entanglement for superior Nash Equilibria.

Originality/value

The main purpose here is to raise awareness mainly in the social science community about the possible applications of quantum-like game theory paradigm. The findings related to Prisoners Dilemma game are, however, original.

Open Access
Article
Publication date: 3 September 2021

Ahmad Reza Talaee Malmiri, Roxana Norouzi Isfahani, Ahmad BahooToroody and Mohammad Mahdi Abaei

Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a…

1418

Abstract

Purpose

Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

Design/methodology/approach

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

Findings

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

Originality/value

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

Details

Journal of Tourism Futures, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2055-5911

Keywords

Open Access
Article
Publication date: 10 December 2019

Javier Serrano and Rafael Myro

This paper aims to analyse the relevance of management and productivity in the behaviour of firms in international trade.

2394

Abstract

Purpose

This paper aims to analyse the relevance of management and productivity in the behaviour of firms in international trade.

Design/methodology/approach

Using a survey of Spanish manufacturing firms, the authors use a management quality index to serve as a proxy for the good management practice of the firm.

Findings

The results demonstrate that exporter and multinationals firms are more productive and better managed than domestic firms. Furthermore, in the periods in which switcher firms decide to export or to invest abroad, they are better managed but are not more productive than in the rest of the periods. Finally, results indicate that regardless of its positive relationship with productivity, management also has a direct impact on the firm’s probability of exporting and involving in foreign direct investment.

Originality/value

This paper aims to reconcile the recent international trade literature, which focusses on the role of productivity heterogeneity in international trade, with the international business literature, concentrated on depicting the key management practices that impact internationalization.

Details

Applied Economic Analysis, vol. 28 no. 82
Type: Research Article
ISSN: 2632-7627

Keywords

Open Access
Article
Publication date: 6 March 2017

Zhuoxuan Jiang, Chunyan Miao and Xiaoming Li

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by…

2108

Abstract

Purpose

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Design/methodology/approach

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

Findings

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Research limitations/implications

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

Practical implications

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

Originality/value

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

Details

International Journal of Crowd Science, vol. 1 no. 1
Type: Research Article
ISSN: 2398-7294

Keywords

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