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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.

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.

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.

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

This study aims to model tourist activities in a network and explore the properties of the network. Such network enables the author to explain and quantify how tourist activities are connected in determining tourist consumption as well as the organization of destination supply.

The author developed a network formation mechanism to create edges between nodes based on the joint probability of a pair of activities undertaken by tourists at a destination. By adjusting network sparsity, the author created an ensemble of four topologically similar networks for empirical testing. The author used tourist activity data of Hong Kong inbound tourists to test the network model.

The author found a robust hub–periphery topological structure of the tourist activity network. In addition, the network is featured by high clustering, short diameter and positive correlations between four node centralities, namely, degree, closeness, betweenness and eigenvector centralities. The author also generated the k-cores of the networks to further unravel the structure of hub nodes. The author found that the k-cores are dominated by tourist activities related to shopping or sightseeing, suggesting the high complementarity of these activities.

This study provides a different lens through which tourist consumption can be understood from a macroscopic angle by examining network topology and from a microscopic angle by examining node centralities.

To the best of the author’s knowledge, this is the first study attempting to model tourist activity and consumption in a network and explore the properties of the network. Not only has this study provided a new real-world network for network research, but it has also suggested an innovative modeling approach for tourist behavior research.

The purpose of this paper is to examine the adoption of Islamic finance products (murabaha, musharakah, mudarabah, salam, ijara, istisna and Qard Hassan) by small and medium-sized enterprises (SMEs) in Cameroon, a non-Islamic Sub-Saharan African country.

It used primary data collected from a cross-section of 1,358 SMEs in eight regions of Cameroon using self-administered structured questionnaires. To facilitate the analyses and interpretation, these products are grouped into four groups based on certain characteristics. A multivariate probit model is estimated to take into account the interaction between these different Islamic finance products.

This study revealed that the desire to comply with Sharia law, awareness, attitude and intention were critical determinants of the decision to adopt Islamic finance products by Cameroonian SMEs. The least influential factors were perceived behavioral control, subjective norms, enterprise characteristics (size, age and location) and socio-demographic characteristics of the entrepreneur (gender, age and marital status). The extension of the multivariate approach permitted us to compute for predicted probabilities which revealed that there exists a synergy effect between the different Islamic finance products. That is, Cameroonian SMEs combine different Islamic finance products at the same time based on their needs. This is especially the case between the partnership-based products (musharakah and mudarabah) and manufacture/rent products (istisna and ijara).

Policymakers are encouraged to develop stakeholder-oriented strategies to promote effective consumer education in Islamic finance products which will boost awareness. Also, Islamic finance institutions should endeavor to develop innovative financial products that are Sharia-compliant and economically beneficial to the individual and business needs of SMEs. Moreover, policymakers and management of Islamic finance institutions should ensure the putting in place of effective governance structures to guide Islamic finance operations. Finally, policymakers should endeavor to take into account the possible synergy between the different Islamic finance products in their quest to develop this activity.

To the best of the authors’ knowledge, this is the first study that analyses the adoption of different Islamic finance products while taking into account the possible synergy that exists between these products.

The authors provide a general framework of behavior under asymmetric information and develop indices of diligence, objectivity and quality by an analyst and analyst firm about a studied firm, and relate them to the accuracy of its forecasts. The authors test the associations of these indices with time.

The test of Public Information versus Non-Public Information Models provides the index of diligence, which equals one minus the p-value of the Hausman Specification Test of Ordinary Least Squares (OLS) versus Two Stage Least Squares (2SLS). The test of Objectivity versus Non-Objectivity Models provides the index of objectivity, which equals the p-value of the Wald Test of zero coefficients versus non-zero coefficients in 2SLS regression of the earnings forecast residual. The exponent of the negative of the standard deviation of the residuals of the analyst forecast regression equation provides the index of analytical quality. Each index asymptotically equals the Bayesian ex post probability, by the analyst and analyst firm about the studied firm, of the relevant behavior.

The authors find that ex post accuracy is a statistically and economically significant increasing function of the product of the indices of diligence, objectivity and quality by the analyst and analyst firm about the studied firm, which asymptotically equals the Bayesian ex post joint probability of diligence, objectivity and quality. The authors find that diligence, objectivity, quality and accuracy did not improve with time.

There has been no previous work done on the systematic and objective characterization and joint analysis of diligence, objectivity and quality of analyst forecasts by an analyst and analyst firm for a studied firm, and their relation with accuracy. This paper puts together the frontiers of various disciplines.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

The purpose of this paper is to investigate the effect of corruption level in steering the business–government relations (BGRs) in developing countries. It also examines the moderating effect of firm size.

Using robust tobit and probit models, this study tests the response behavior of 9787 firms from 23 African countries to their government's policy and regulations and the direct effect of corruption control level in their response decisions. The authors also perform several other additional analyses to ensure the robustness of the findings, including change analysis, two-stage model and recursive bivariate model.

The result shows that corruption level is among the significant factors that drive BGRs exponentially. The finding points out that, there is a strong alliance of business and government in more corrupt countries. Moreover, the impact of corruption level exacerbates when the firm is bigger.

Managers should focus more on activities that create long-term sustainable advantage. Valuable time of the senior managers should not waste on negotiating government policies to earn a short term advantages.

It is evident that legal and transparent government alliances can lead to economic rent for firms. However, it is important to note that any alliance based on corruption and illegality is short-lived and ultimately detrimental to long-term prosperity. Therefore, it is crucial for firms to prioritize ethical business practices and build relationships with governments that prioritize transparency and accountability.

Given the detrimental impact of corruption on economic progress, it is crucial for Africa policy-makers to prioritize reforms aimed at reducing its adverse effect. By implementing ethical and transparent business practices, countries can attract more investment and promote economic growth.

This study contributes to the existing literature on the passive form of political connectivity/activity and to what extend corruption level affect the political activities of firms.

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

This paper investigates how gender, ethnicity, and network membership interact to influence how small and medium-sized enterprise (SME) owner-managers become aware of finance support programmes developed by government policy and/or support schemes advanced by the banking industry.

Drawing on expectation states theory (EST), we develop eight sets of hypotheses and employ the UK SME Finance Monitor data to test them using bivariate probit regression analysis.

In general, network membership increases awareness, but more so for government programmes. We also find no differences between female and male owner-managers when in networks. However, we identify in-network and out-network differences by ethnicity, with minority females seemingly better off than minority males.

Business networks are better for disseminating government programmes than industry-led programmes. For native White women, network membership can enhance policy awareness advantage further, whilst for minorities, networks significantly offset the big policy awareness deficits minorities inherently face. However, policy and practice need to address intersectional inequalities that remain in access to networks themselves, information access within networks, and the significant out-network deficits in awareness of support programmes afflicting minorities.

This study provides one of the first large-scale empirical examinations of intersectional mechanisms in awareness of government and industry-led enterprise programmes. Our novel and nuanced findings advance our understanding of the ways in which gender and ethnicity interact with network dynamics in entrepreneurship.

This study addresses the reliability of a composite fiber (carbon fibers/epoxy matrix) at microscopic level, with a specific focus on its behavior under compressive stresses. The primary goal is to investigate the factors that influence the reliability of the composite, specifically considering the effects of initial fiber deformation and fiber volume fraction.

The analysis involves a multi-step approach. Initially, micromechanics theory is employed to derive limit state equations that define the stress levels at which the fiber remains within an acceptable range of deformation. To assess the composite's structural reliability, a dedicated code is developed using the Monte Carlo method, incorporating random variables.

Results highlight the significance of initial fiber deformation and volume fraction on the composite's reliability. They indicate that the level of initial deformation of the fibers plays a crucial role in determining the composite reliability. A fiber with 0.5% initial deformation exhibits the ability to endure up to 28% additional stress compared to a fiber with 1% initial deformation. Conversely, a higher fiber volume fraction contributes positively to the composite's reliability. A composite with 60% fiber content and 0.5% initial deformation can support up to 40% additional stress compared to a composite containing 40% fibers with the same deformation.

The study's originality lies in its comprehensive exploration of the factors affecting the reliability of carbon fiber-epoxy matrix composites under compressive stresses. The integration of micromechanics theory and the Monte Carlo method for structural reliability analysis contributes to a thorough understanding of the composite's behavior. The findings shed light on the critical roles played by initial fiber deformation and fiber volume fraction in determining the overall reliability of the composite. Additionally, the study underscores the importance of careful fiber placement during the manufacturing process and emphasizes the role of volume fraction in ensuring the final product's reliability.