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The purpose of this study is to identify the significant demographic and socio-economic characteristics of individuals who are likely to invest in a fraudulent investment scheme. It also quantifies the extent to which financial literacy helps in reducing the odds of investments in such schemes. Based on these findings, it provides policy recommendations to regulators and governments.

This study uses nationally representative data from the “India Assessment of Financial Capability 2018” survey. It further uses logistic regression with a binary outcome variable to assess the individual-level odds of investments in fraudulent investment schemes.

This study concludes that males between 40 and 59 years of age, who are well-educated (are at least graduates), score low in financial literacy, belong to the middle-income group, and SEC A3 households are most vulnerable to victimization by financial fraudulent investment schemes. It finds that financial literacy significantly reduces the odds of investment into fraudulent schemes to the extent of 39.118%.

This study quantifies the extent to which financial literacy helps in reducing the odds of individual investments in a fraudulent investment scheme. As financial literacy has a significant and negative relationship with the likelihood of investment in such schemes, this study provides policy interventions and recommendations to regulators and governments to safeguard the interest of individual investors.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

The discussion about using Chat Generative Pre-Trained Transformer (ChatGPT) by teachers is making notable progress on a daily basis. This research examines the teachers' adoption intention to adopt ChatGPT by focusing on perceived trust and perceived risk. The study seeks to elucidate the impact of these two factors on teachers' adoption intentions towards ChatGPT.

This study was exclusively conducted at private higher educational institutions in Gujarat, India. Data collection was done through a cross-sectional survey design. The proposed conceptual model was examined with the help of structural equation modelling (SEM).

The outcome of the study confirms the significant contribution of perceived usefulness, perceived ease of use, perceived trust, perceived intelligence, perceived anthropomorphism and social influence to teachers' intention to adopt ChatGPT. The findings of the study show that perceived risk exerts a negative moderating effect between perceived usefulness and adoption intention as well as between perceived trust and adoption intention.

This study fills the knowledge gap about teachers’ adoption of ChatGPT at private higher education institutions, thus contributing to the existing literature. Specifically, the distinctive role of key variables like perceived risk and perceived trust helps increase the existing body of knowledge.

Several practical implications are presented on the basis of the conclusions from the outcome of the study that would help increase teachers’ adoption intention of ChatGPT in higher education institutions. These implications include recommendations to promote the integration of ChatGPT in educational set-ups to help teachers leverage its potential benefits into their teaching practices.

This research study goes deeper into the subject than previous research, which mainly focused on the possible advantages and downsides of ChatGPT applications in the field of education. It makes a substantial contribution to our understanding of ChatGPT adoption among teachers for educational purposes by investigating through the lens of perceived risk and perceived trust. The study offers fresh understandings that were previously ignored and brings new perspectives to the body of literature.

This research work aims to study the continuous usage intention of students for digital library systems in higher learning institutions (HLI). Additionally, keeping in mind the lack of focus on academic involvement of students in existing studies, this research work attempts to examine the moderating role of academic involvement in making up the continuous usage intention for digital library systems among students at HLI.

Cross-sectional survey design is used in this study. Target population is students pursuing postgraduate studies and using digital library systems at private universities in the state of Uttarakhand, India. The Stratified random sampling method is used wherein each private university forms one stratum.

The results suggest that the students' continuous usage intention for digital library systems is significantly influenced by dimensions of digital library system quality, mediated by user satisfaction. Further, the level of academic involvement of students exerts moderating impact between dimensions of digital library system quality and user satisfaction; however, it d oes not exist between user satisfaction and their continuous usage intention.

This study contributes to the literature on academic involvement of students by highlighting its importance in the process of continuous usage intention. It also proposes a holistic conceptual framework for continuous usage intention for digital library systems among students and moderating impact of academic involvement. Being cross-sectional survey-based research, this study has certain limitations. Since responses were obtained at a single point in time, variation in respondents' behaviour could not be captured.

The findings of the study can be helpful to the HLI, academic librarians, teachers, digital library systems service provider companies and government regulatory authorities. HLI need to put more effort into procuring subscriptions to reputed publications of e-books, e-research papers, e-magazines, e-reports, etc. for making digital library systems more beneficial for the students. Academic librarians need to be effortful towards building students' satisfaction through constantly upgrading software and systems and active interaction with students. Digital library systems service provider companies can also collaborate to customize their digital resources for different types of academic programs. The government can partnership with private companies for providing digital library systems in HLI.

This research work is the first of its kind in terms of studying the role of academic involvement of students for purpose of evaluating continuous usage intention for digital library systems among students studying in Indian HLI.

The purpose of this paper is to investigate long-run commonality in liquidity using multiple proxies computed from limited order book data of NIFTY50 stocks. The findings indicate the existence of systematic liquidity or commonality on NIFTY50 market and comprising industries.

The sample comprises all intraday transactions corresponding to NIFTY 50 stocks for April 2015. The study runs firm by firm time series regressions to test the concept of long-run commonality, while controlling other effects.

Strong evidence is found in support of long-run commonality across three liquidity measures. On the basis of significance (10%) of long-run commonality beta (β_LR), the strength of long-run commonality is found to be highest in natural resources and infrastructure sector. Portfolios having greater exposure to these sectors will face diversification risk to a great extent.

Knowledge of long-run commonality helps portfolio managers in formulating diversification strategies and reshuffling the portfolio over the period. Commonality risk being non-diversifiable is a policy concern for regulators and central bankers. Its empirical evidence will assist in managing exchange organization and thus preventing market crashes because of sudden liquidity evaporation.

Although there are recent studies documenting commonality in short run, little empirical work has been done on commonality in the long run and in emerging markets such as India. This research contributes to the literature by testing concept of commonality in long-run on NIFTY50 stocks using detailed transaction data from National Stock Exchange.

Extraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.

Extracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.

The proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.

In this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.

Principally, this study aims to test a conceptual framework of the moderating influence of fear of COVID-19 on the following hypothesized relationships (1) quality of work life and bankers' commitment, (2) the mediating spillover effect of job satisfaction in the quality of work life (QWL) and affective commitment relationship.

A quantitative cross-sectional research design is adopted on 318 bankers chosen from four prominent Indian cities. The mediation model is tested through SPSS, PROCESS macro, and AMOS. Conditional process modeling is also administered to test the moderating effect of fear of COVID-19.

The results suggest that the positive effect of QWL on commitment is completely mediated through job satisfaction. Further, the fear induced by COVID-19 negatively moderated the positive direct relation of QWL with commitment and the positive mediating spillover effect of job satisfaction.

The present research is virtually the first to introduce fear of COVID-19 as a psychological construct, to test a moderated mediation model for implications to organizational behavior and human psychology theory and practice. In coalescence of the need satisfaction, spillover, and COR theories, the authors postulate that as spillover between the domains of an individual's life (work, social, financial, personal, and overall life satisfaction) occurs, such effect is calibrated (augmented or attenuated) by the degree of risk/threat/depletion of their resources in the quest for attaining higher valued resources (overall life satisfaction). The moderated mediation mechanism is suggested for replication in other avenues for greater generalizability.

Blockchain technology (BCT) has multiple benefits across industries in varied contexts, but limited organizations have adopted such disruptive innovative technologies in the healthcare industry in India. The research on advancing the understanding of blockchain adoption (BCA) determinants in India's healthcare industry is limited. Thus, the study aims to identify the BCA determinants in the healthcare sector in India. Further, the impact of BCA was examined on organizational performance (OP).

The study utilizes Technology, Organization, and Environment (TOE) framework to investigate the determinants of BCA in the healthcare sector in India. The data were gathered using a seven-point Likert seven-point ranging from “strongly agree” to “strongly disagree” from 272 respondents working in the healthcare industry in India. The relationship within the framework was investigated using structural equation modeling.

The results demonstrate the positive impact of top management support, organizational size, organizational readiness, competitive pressure and government support on BCA in the healthcare sector. On the other hand, compatibility, security and privacy issues do not affect BCA. The results emphasize and validate blockchain’s importance in improving OP in the healthcare sector. Further, the results indicate that non-technological factors are paramount to improving BCA within the healthcare sector. Organizations should invest in employee training and development to ensure their staff have the necessary knowledge and skills to effectively manage BCT.

The model was developed for BCA in the healthcare sector in the Indian context; however, the model applies to other countries with the same business environment. Hence, the model can be further examined in diverse countries to generalize the findings.

The study offers valuable insights into the factors that influence BCA and OP in the healthcare sector. The results of this research can be used to inform policy decisions and guide practitioners toward promoting and facilitating the use of BCT in healthcare organizations.

To the best of the author’s knowledge, the present study is the first of its kind to examine the TOE framework in BCA within the healthcare sector and its implications on OP.

The purpose of this is to classify the social and economic factors which impact the involvement of women in self-help groups (SHGs) for their economic as well as social empowerment.

The research has been conducted in Nainital district of Uttarakhand (India) in 2018. Primary data have been gathered from women respondent only on factors relating to the ownership of asset, housing characteristics and other demographic details. Both SHG and non-SHG women members have been chosen as key informants during the survey. Multi-stage purposive and stratified random sampling has been used for the selection of respondents and SHGs. The logit regression model has been formulated to describe the causes that influence the participation of women in SHGs. Also, an empowerment index has been constructed to measure the effect of SHGs on women empowerment.

The results show that factors including age, education, family type and distance from the market have a significant impact on the participation of women in SHGs. Also, there is a significant difference in both these values which suggests that the value of the empowerment index gets significantly increased after joining the SHGs.

Analytically derived factors have been used to develop an empowerment index. Hence, the present research is valuable for marketing practitioners, entrepreneurs and professionals from the development sector who intend to work amongst SHGs, primarily with women. The paper is valuable for academic researchers in this area so that the limited body of knowledge, on the empowerment index, could be developed.

The present research is unique because the authors did not find work, especially in the context of rural India, in the said area. Factors impacting the participation of women in SHGs along with the impact of participation on empowerment have been explored using the logit regression model, leading to the development of an empowerment index.