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The purpose of this paper is to acquaint the readers with the insights regarding the interventions of microalgal technology for production of metabolites and functional ingredients from microalgae for food and nutraceutical application and exploration of microalgae biomass for food application.

Various information databases such as journals, library catalogues and professional websites were used to collect information pertaining to application of microalgae in food and nutraceutical sector. Systematic review was made with recent studies covering the vital aspects of art of microalgae cultivation for metabolite production, functional ingredients from microalgae, market scenario and utilisation of microalgae biomass for the valorisation of the food products. Key points have been discussed after every section to highlight the practical implications to make this review more insightful for the readers.

Microalgal technology provides sustainable solution for its application in food and nutraceutical sector. The heart of metabolite production lies in the optimisation of cultivation conditions of microalgae. Wide array of functional components are obtained from microalgae. Microalgae offer an alternative source for omega-3 fatty acids. Microalgae is widely exploited for production of pigments, namely, ß-carotene, astaxanthin, lutein, phycocyanin and chlorophyll, that have important implication as natural colourants and nutraceuticals in food. Larger diversity of sterols found in microalgae confers bioactivity. Microalgae is finding its place in market shelves as nutraceuticals where its functional ingredients are in the form of powder, tablets, extract and beverages and in innovative products such as microalgae protein and fat, culinary algae oil and butter. Sprulina and Chlorella are popular choice for the supplementation of food products with microalgae biomass.

This is a comprehensive review that highlights the application of microalgal technology for the development of healthy food products and presents holistic intervention in food and nutraceutical sector.

The purpose of this study is to investigate the relationship between role overload (RO) and the work–family interface (work–life conflict and work–life enrichment) among bank employees and the moderating effects of personal life characteristics and commitments on this relationship. It aimed to bring out the importance of contextual factors in individual's interactions across various roles.

A structured questionnaire based on validated instruments was designed and administered to 279 employees from the banking sector in India. The instrument was adapted to the local language to ensure ease of comprehension.

RO was positively correlated with both work interference with personal life (WIPL) and personal life interference with work (PLIW), and negatively correlated with work–personal life enrichment (WPLE). Gender, number of children and age of the youngest child significantly moderated the relationship between RO and WIPL.

This study was limited by the use of self-reported data and its cross-sectional nature. Future studies will need to include a larger sample with people from across the workplace hierarchy.

This paper provides valuable insight into the influence of personal life characteristics and commitments on RO and the work–family interface.

The banking sector is among the top 10 most stressful workplaces in India due to high work pressure and the threat of competition. These working conditions make it important to understand employee perceptions of RO and its impact on the work–family interface.

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.

This paper aims to study the underpricing phenomenon of initial public offerings (IPOs) of 355 Indian companies issued from 2007 to 2019. The research question this paper empirically examines is whether Indian corporate executives deliberately underprice IPOs from its fair value to attract investors, thereby causing an abnormal spike in the prices on the listing day. The findings of this study challenge a commonly held notion of leaving money on the table by IPO issuing companies. Of the overall average listing day returns of 17%, the deliberate premarket underpricing component is found to be mere 5.3%, while the remaining price fluctuation is, inter alia, a result of market momentum along with the unmet demands of impatient investors.

Following Koop and Li (2001), this study uses Stochastic frontier model (SFM) to study a routine anomaly of disparity between the primary market price (i.e. IPO issue price) and the secondary market price (listing price). The jump in the issue price observed on a listing day is decomposed into deliberate premarket underpricing component that reflects the extent of managerial manipulation and the after-market misvaluation component attributable to information asymmetry and prevailing market volatility.

This paper uses SFM to bifurcate initial returns into deliberate underpricing by managers and after-market mispricing by noise traders. This study finds that a significant part of the initial return is explained through after-market mispricing. This study finds that average initial returns are 17%, deliberate premarket underpricing is 5.3% and after-market mispricing averages 11.9%.

This study can isolate underpricing done at the premarket by estimating a systematic one-sided error term that measures the maximum predicted issue price deviation from the offered price. Consequentially, the disaggregation of initial returns may be especially informative for retail investors in planning their exit strategy from an IPO by separating the strength of the firm's fundamentals and its causal relationship with the initial returns. Substantial proportion of after-market mispricing implies that future research should focus on factors causing after-market mispricing. As underlying causes are identified, tailor-made policy responses can be formulated to benefit investors.

This paper has empirically validated that initial return is a mix of both components, i.e. deliberate underpricing and aftermarket mispricing. This disaggregation of initial returns can prove helpful for investors in planning their exit strategy. This study can help investors to become more aware of the importance of the fundamentals of the firm and its causal relation with the initial returns. This information in turn can help reduce the information asymmetry amongst investors and help them lessen the costs of adverse selection.

A large number of research studies on IPO pricing find overwhelming evidence of underpricing in public issues. This research attempts to decompose the extent of underpricing into deliberate underpricing and after-market mispricing, thereby supplementing the existing literature on the IPO pricing puzzle. To the best of the authors’ knowledge, this study is the first contribution to the literature on initial return decomposition for the Indian capital markets.

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 regulatory design of Indian stock market provides us with the opportunity to disaggregate initial returns into two categories, i.e. voluntary premarket underpricing and post market mispricing. This study explores the impact of investor attention on the disaggregated short-run returns and long-run performance of initial public offerings (IPOs).

The study employs regression techniques on the sample of IPOs listed from 2005 to 2019. It measures investor attention with the help of the Google Search Volume Index (GSVI) extracted from Google Trends. Along with GSVI, the subscription rate is used as a proxy to measure investor attention.

The empirical results suggest a positive and significant relationship between initial returns and investor attention, thus validating the attention theory for Indian IPOs. Furthermore, when the returns are analysed for a more extended period using buy-and-hold abnormal returns (BHARs), it was found that price reversal holds in the long run.

This study highlights the importance of information diffusion in the market. It emphasizes the behavioural tendency of the investors in the pre-market, which reduces the market efficiency. Hence, along with fundamentals, investor attention also plays an essential role in deciding the returns for an IPO.

According to the best of the authors’ knowledge, this is one of the first studies that has attempted to explore the influence of investor attention and its interplay with underpricing and long-run performance for IPOs of Indian markets.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.