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This research aims to explore the role of banks in supporting women agriculture entrepreneurs (WAEs) to contribute towards achieving the Sustainable Development Goals (SDGs). It focusses on the experiences of women entrepreneurs in the agriculture sector, recognising their vital role in driving economic growth and achieving the SDGs.

The study utilises the role congruity theory and the feminist agri-food systems model as its theoretical framework. Qualitative data from 35 WAEs and 7 bank managers (BMs) responsible for agricultural financial services and business development are collected and thematically analysed to achieve the research objectives.

Although BMs claim they offer specialised financial products with dedicated support teams, WAEs express scepticism due to fears of unfavourable deals and excessive requirements. WAEs need more understanding of SDGs but recognise their substantial contributions. BMs acknowledge the need to enhance efforts, improve communication of offers and integrate SDGs across all business operations beyond agriculture and women-centric initiatives.

Banks must prioritise gender sensitivity and inclusivity for WAEs, offering tailored financial products and flexible loan structures. Microfinance and strategic marketing can enhance outreach. WAEs benefit from forming associations, accessing support networks, collaborating with banks, government agencies, non-governmental organisations and agricultural associations for mentoring and networking, and achieving the SDGs and sustainable agriculture.

The study connects WAEs and banks in achieving SDGs.

Regulation is critical for sustainable microfinance sector growth. Under this premise, the study aims to examine the different regulatory noncompliance (RNC) practices prevalent in the operations of microfinance institutions (MFIs) at the ground level.

Both the quantitative and qualitative (observations, interviews and focus group discussions) techniques are used to extract the findings.

The study highlights the different RNC practices exercised by the loan officers at the field level in their microfinance loan disbursements.

This study is based on the primary data collected from microfinance clients. The arguments put forth for the RNC practices are extracted from direct personal interviews with the loan officers and the clients. The role of various dilemmas/circumstances of the loan officers and the beneficiaries that implicate the MFIs in RNC is highlighted.

Digital credit is spreading rapidly across Sub-Saharan Africa and holds potential for financial inclusion and female financial autonomy. Women in developing economies have long been targeted by microfinance institutions due to the women’s reliability and positive spillover effects. Yet, adoption rates for digital financial innovations remain moderate among rural women in Sub-Saharan Africa. The authors explore whether female preferences for digital and conventional credit differ from males.

The authors conduct a Discrete Choice Experiment with 420 smallholder farmers in central Madagascar, one of the region's poorest countries, to assess preferences for selected digital and conventional credit attributes.

Results of the mixed logit model and the comparison of the willingness-to-pay via Poe-test suggest high general demand for both credit forms. The demand of female respondents is higher than that of males, suggesting that they might be underserved. This holds for both credit forms. However, differences in willingness to pay for the credit attributes are mostly not statistically significant, indicating that designing gender-specific services may not be advisable.

This article is believed to be the first to assess and compare gendered willingness to pay for digital and conventional credit. The study’s findings give valuable insights to decision-makers in development politics as well as the fintech industry.

This study aims to propose a conceptual framework to examine the impact of corporate social responsibility (CSR) and green organizational culture (GOC) on green innovation with the moderating role of environmental, social and governance (ESG) disclosure in the Malaysian manufacturing industry.

The study is based on primary data to be collected from 204 manufacturing enterprises of consumers, products and services sector through a questionnaire that incorporates the five-point Likert scale. The exploratory factor analysis is proposed to be performed using SPSS 24.0 and confirmatory factor analysis is suggested to be conducted using AMOS.21 software to explore the factors and reliability of the items and to confirm the factorial structure of pertinent variables, respectively. Furthermore, partial least square structural equation modeling is proposed to investigate relationships between constructs and latent variables.

The proposed framework suggests that the comprehensive adoption of CSR and GOC with the moderating role of ESG disclosure has a significant and positive impact on green innovation.

This study provides insights into formulating strategies for enhancing green innovation and serves as a valuable resource for stakeholders for sustainable development in manufacturing enterprises.

To the best of the authors’ knowledge, regarding originality, this is the first attempt at conceptualizing the integrated framework of CSR, GOC, green innovation and ESG disclosure for collective examination that is likely to extend the existing literature. Furthermore, this study extends stakeholders and resource-based view theory by proving their utility in the perspective of CSR, GOC, green innovation and ESG disclosure to achieve environmental sustainability.

The purpose of this study is to consolidate the existing literature on small retail and develop a conceptual framework using thematic analysis.

The relevant set of 224 articles has been obtained from the Scopus database by applying the PRISMA framework. Bibliometric analysis has been performed using Biblioshiny in Bibliometrix and VOSviewer.

Four major themes have been identified within the conceptual structure of the small retail domain, and a conceptual framework has been developed using the interlinkages within the themes. The intellectual structure of the domain has been explored using citation analysis, co-citation analysis and bibliographic coupling. Future research directions are also identified and documented based on the thematic analysis and overall consolidation of the literature.

This is perhaps one of the first attempts to consolidate the published literature on small retail using bibliometric analysis.

The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without change.

A novel inverse data envelopment analysis (DEA) approach with flexible measures is proposed in this research to assess inputs (outputs) and flexible measures when outputs (inputs) are perturbed on condition that the relative efficiency scores remain unchanged. Furthermore, flexible inverse DEA approaches proposed in this study are used for a numerical example from the literature and an application of Iranian banking industry to clarify and validate them.

The findings show that including flexible measures into the investigation effects on the changes of performance measures estimated and leads to more reasonable achievements.

The traditional inverse DEA models usually investigate the changes of some determinate input-output factors for the changes of other given input-output indicators assuming that the efficiency values are preserved. However, there are situations that the changes of performance measures should be tackled while some measures, called flexible measures, can play either input or output roles. Accordingly, inverse DEA optimization models with flexible measures are rendered in this paper to address these issues.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

This study explores whether targeted microcredit can effectively alleviate households’ multidimensional relative poverty (MdRP) in rural China in the new era following the poverty elimination campaign and discusses it from a gendered perspective.

This study applies a fixed-effects model, propensity score matching (PSM) and two-stage instrumental variable method to two-period panel data collected from 611 households in rural western China in 2018 and 2021 to explore the effects, mechanisms and heterogenous performance of targeted microcredit on households’ MdRP in the new era.

(i) Targeted microcredit can alleviate MdRP among rural households in the new era, mainly by reducing income and opportunity inequality. (ii) Targeted microcredit can promote women’s empowerment, mainly by enhancing their social participation, thereby helping alleviate households’ MdRP. The effect of the targeted microcredit on MdRP is more significant in medium-educated women households and non-left-behind women households. (iii) The MdRP alleviation effect is stronger in villages with a high degree of digitalization.

Learn from the experience of targeted microcredit. Accurately identify poor groups and integrate loan design into financial health and women empowerment. Particularly, pay attention to less-educated and left-behind women households and strengthen coordination between targeted microcredit and digital village strategies.

This study clarifies the effect of targeted microcredit on women’s empowerment and households’ MdRP alleviation in the new era. It also explores its various effects on households with different female characteristics and regional digitalization levels, providing ideas for optimizing microcredit.

Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.

The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.

The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.

The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.