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Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

The study aims to discern the primary obstacles confronted by the implementing agencies in their efforts to foster financial inclusion through the “Pradhan Mantri MUDRA Yojana” (PMMY).

To collect primary data, a semi-structured questionnaire was developed. Around 120 loan officers from the implementing agencies (Scheduled Commercial Banks (SCBs), Regional Rural Banks (RRBs), Small Finance Banks (SFBs), Non-Banking Financial Companies (NBFCs) and Micro- Finance Institutions (MFIs)) of Haryana were randomly selected to fulfill the objectives. To categorize the perceived problems into discrete factors, the “factor analysis” technique was employed. The scales were then regressed on factors linked to the demographic characteristics of the loan officers to validate the hypotheses.

The study highlighted the primary obstacles impeding the advancement of financial inclusion, which encompass a range of factors. These include challenges in management, infrastructure, politics, finance and technology. Furthermore, the study established the association of the explanatory variables, namely gender, age, educational qualification, location and experience of the officers, with the extracted constraints. Notably, the experience of loan officers emerged as the most influential variable contributing to the promotion of financial inclusion through the scheme.

The current body of literature lacks any empirical investigation focusing on the perspectives of the implementing agencies regarding the challenges they encounter in advancing FI. Given the significance of FI in India, where access to formal financial services remains a critical issue, this research adds value by addressing the gaps in understanding the problems encountered.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0462

The study aims to enhance the detection and classification of conjunctival eye diseases' severity through the development of ConjunctiveNet, an innovative deep learning framework. This model incorporates advanced preprocessing techniques and utilizes a modified Otsu’s method for improved image segmentation, aiming to improve diagnostic accuracy and efficiency in healthcare settings.

ConjunctiveNet employs a convolutional neural network (CNN) enhanced through transfer learning. The methodology integrates rescaling, normalization, Gaussian blur filtering and contrast-limited adaptive histogram equalization (CLAHE) for preprocessing. The segmentation employs a novel modified Otsu’s method. The framework’s effectiveness is compared against five pretrained CNN architectures including AlexNet, ResNet-50, ResNet-152, VGG-19 and DenseNet-201.

The study finds that ConjunctiveNet significantly outperforms existing models in accuracy for detecting various severity stages of conjunctival eye conditions. The model demonstrated superior performance in classifying four distinct severity stages – initial, moderate, high, severe and a healthy stage – offering a reliable tool for enhancing screening and diagnosis processes in ophthalmology.

ConjunctiveNet represents a significant advancement in the automated diagnosis of eye diseases, particularly conjunctivitis. Its originality lies in the integration of modified Otsu’s method for segmentation and its comprehensive preprocessing approach, which collectively enhance its diagnostic capabilities. This framework offers substantial value to the field by improving the accuracy and efficiency of conjunctival disease severity classification, thus aiding in better healthcare delivery.

The present paper aims to conduct a comprehensive scientific mapping of exchange rate forecasting, highlighting trends, developments, and methodological changes. This will provide research scholars, researchers, and policymakers valuable insights to facilitate predictions.

The researchers performed a bibliometric analysis of exchange rate forecasting using a scientific search method on the Scopus database from 1991 to 2022. They applied a web interface program called Biblioshiny, part of the Bibliometric package built in R by Aria and Cuccurullo (2017). VOSviewer was used to identify the most influential journals, authors, countries, articles, citations, and co-citations from 1,602 documents.

The conceptual and intellectual framework of the papers under review provided an in-depth and comprehensive analysis of the domain. The research outcomes provided essential information on the subject matter, highlighting the need for further investigation. The study’s findings demonstrate the evolution of the theory of forecasting exchange rates reflecting continuous developments in the methodologies applied to forecast the exchange rates.

The scientific mapping of the present study’s bibliometric analysis is limited to the Scopus database because of its comprehensive coverage of high-quality journals and predefined structures compatible with Bibliometrix software.

The study provides considerable insight into forecasting exchange rates and their interlinkages. By outlining the social and intellectual structure of the field, it directs upcoming scholars about the relevant topics, contexts and potential collaborations emerging in this field. The study also reveals the critical concerns in the area and leads to potential research opportunities.

The study sheds light on emerging trends and approaches to forecasting exchange rates and will provide valuable information and insights to policymakers, economists, investors and decision-makers in the financial sector. It will contribute to prioritising research in overlooked areas and support the formulation of effective policies.

This study contributes significantly by bringing together disparate literature on exchange rate forecasting, highlighting important journals, authors and documents, and examining the recent studies on the foreign exchange rate.