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The paper is aimed at examining the mediating role of action and coping plans in reducing intention-behaviour gap as a function of underlying perceived self-efficacy [action self-efficacy (ActS) and coping self-efficacy (CopS)] in organic food (OF) consumption.

A cross-sectional study was designed to examine the moderating role of perceived self-efficacy in the intention–plans–behaviour relationship. To test the theoretical framework, data from 453 valid questionnaires were recruited in Hanoi (Vietnam) and were analysed to test validity and reliability before being evaluated for hypothesis testing by Smart-PLS 4.0.

The results indicate that action and coping plans have significant positive effects on the relationship between intention and behaviour. CopS has a significant positive effect on the two paths of the intention-coping plan-behaviour mediation, whereas action plan (AP) has merely a significant positive link to the intention-AP path.

There are several limitations of the paper, including a small and undiversified-characteristic sample and general OF.

The findings of the study make recommendations for marketers to boost OF consumption in Vietnam.

This is the first study to examine a dual-moderated mediation model in narrowing the intention-behaviour gap, especially in the context of OF consumption. Particularly, the notions of plan and self-efficacy are divided into sub-constructs on the basis of different functions and both paths in the intention-plan-behaviour mediation are investigated. AP and coping plan are served as dual mediators, whereas ActS is regarded as moderator for both paths of intention-AP-behaviour link and CopS is examined as moderator for both paths of intention–coping plan–behaviour relationship, providing a holistic mechanism in translating intention into behaviour.

We study how capital requirements, intended as a measure to ensure security for the financial system, can create moral hazard for banks in dealing with distressed debts.

Over the period spanning from 1993 to 2019, we manually gathered data on 1953 firms, identifying a total of 2,146 distress events, with 804 instances resulting in bankruptcy fillings.

Our analyses at the loan level and the bank level consistently show that loans of distressed firms are much more likely to be extended when the lenders are closer to the capital requirement limit. Exploiting the discontinuity in the predetermined maturity date of loans, we provide causal evidence on the relationship between capital ratios and extension likelihood. Distressed loans that are due just before the report date (end of a quarter) are much more likely to be extended than loans due just after the report date, after controlling for loan and firm characteristics. Additional analyses show that the effects are stronger when external financing is more costly and when the banks are poorly capitalized.

Our paper presents the first causal evidence of capital requirements on lending distortion, contributing to our understanding of the dynamics within the banking sector and providing policy implications for promoting financial stability and regulatory efficacy.

Although training is essential to continuous improvement, scant literature examines post-training facilitators for continuous improvement. The study aims to explore the relationship between training and continuous improvement, the mediating role of self-efficacy and the moderate role of training transfer climate.

This study utilizes the questionnaire survey of 455 Vietnamese employees to test the link between continuous improvement training and continuous improvement, the moderate role of the training transfer climate and the mediating role of self–efficacy.

Research results reveal that training positively influences continuous improvement. Furthermore, self-efficacy fully intervenes in the link between training and continuous improvement. Finally, the training transfer climate positively moderates this link.

Although the link between training and continuous improvement is suspicious, there is scant research on post-training facilitators of continuous improvement applications. To the best of the author's knowledge, this study is one of the first to explore the moderation role of transfer climate and the mediation role of self-efficacy in the relationship between training and continuous improvement.

Financial ratios are often used to classify firms into different clusters of financial performance. This study aims to classify firms using financial ratios with advanced techniques and identify the transition matrix of firms moving clusters during the COVID-19 period.

This study uses compositional data (CoDa) analysis based on existing clustering methods with transformed data by weighted logarithms of financial ratios. The data include 66 listed firms in Vietnam’s food and beverage and fishery sectors over a three-year period from 2019 to 2021, including the COVID-19 period.

These firms can be classified into three clusters of distinctive characteristics, which can serve as benchmarks for solvency and profitability. The results also show the migration from one cluster to another during the COVID-19 pandemic, allowing for the calculation of the transition probability or the transition matrix.

The findings indicate three distinct clusters (good, average and below-average firm performance) that can help financial analysts, accountants, investors and other strategic decision-makers in making informed choices.

Clustering firms with their financial ratios often suffer from various limitations, such as ratio choices, skewed distributions, outliers and redundancy. This study is motivated by a weighted CoDa approach that addresses these issues. This method can be extended to classify firms in multiple sectors or other emerging markets.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

The paper aims to assess the impact of weather-induced shocks on household food consumption in the rural Vietnamese Mekong Delta (VMD) through the case of Long An province and evaluate the effectiveness of widely used coping strategies in mitigating weather-related shock impacts.

The system generalized method of moments (GMM) estimation method is applied to explore information on shock incidence, recovery, and time occurrences. The paper uses a sample of 272 repeated farming households from 5-wave survey data from 2008 to 2016, resulting in 1,360 observations.

The paper confirms the robust negative effect of a natural shock on food consumption. Additionally, using savings proves to be the most potent measure to smooth food consumption. Other favorable coping strategies are “getting assistance from relatives, friends” or “getting assistance from the Government, and non-government organizations (NGOs).” The mitigating effects are also traced in the current analysis.

Using caution when generalizing the results from Long An to the whole VMD is reasonable. The rather limited observations of coping strategies do not allow the authors to analyze any specific strategy.

The proposed approach employs the GMM technique and controls for endogenous coping strategies and thus provides accurate estimates of the effects of weather-related shocks and the mitigation effectiveness in the rural VMD.

Promoted for its inclusivity, agricultural value chain (AVC) financing leverages social capital and mechanisms such as off-take agreements and forward contracts to reduce borrowing and lending costs and risks for both farmers and lending institutions. AVC financing has been defined as the flow of financial products and services to and among the various actors within the AVC to address constraints of production and distribution and fulfill the needs of those involved in the chain by reducing risk and improving efficiency. This paper investigates how farmers' involvement in AVC affects their access to credit.

The authors collected primary data from 400 crop farmers in northern Ghana through a semi-structured questionnaire and analyzed the data, using the multinomial endogenous switching regression model.

Joint participation in AVC increased the amount of formal and informal credit received by 64 and 78%, respectively, compared to nonparticipation. Similarly, participation in AVC horizontal linkage and AVC vertical linkage increased the amount of formal and informal credit received by 40 and 47% and 46 and 74%, respectively, compared to nonparticipation. Irrigation farming, extension visits, knowledge of AVC in the community, access to a storage facility and trust in contract farming significantly influenced farmers' participation in AVC.

The authors’ work offers valuable insights into how different dimensions of value chain participation can impact smallholder farmers' access to credit. This work also underscores the importance of considering both formal and informal credit sources when analyzing the outcomes of value chain participation. The findings could enable formal financial providers to identify, liaise and/or resource informal financial players such as value chain actors to supply both formal and informal credit to farmers in AVCs.