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Financial stress, financial literacy, and financial insecurity in India's informal sector during COVID-19
The lockdowns and restrictions imposed to control COVID-19 have made life miserable for people, especially those involved in informal economic activities. The pandemic induced financial hardships, caused financial anxiety and financial stress among informal sector participants. This study aimed to measure and analyze the financial stress and financial insecurity of one of the important informal sector elements (street vendors) in India. Street vendors in Bangalore were interviewed in this descriptive research through personal interaction and telephonic interviews. The collected primary data were processed using SPSS statistical package. The results have indicated that the pandemic inflicted financial stress on street vendors irrespective of their gender, marital status, age, education, monthly income, and type of product dealt. Financial stress levels varied depending on the number of dependents of street vendors and their business nature. Financial literacy differed according to street vendors' marital status. A person becomes extremely sensitive and cautious in personal finance matters on getting married. Financial stress and financial literacy correlated negatively. 89.5% of street vendors perceived that they had financial insecurity in the future due to this pandemic. The results indicated that financial stress and financial literacy did not affect financial insecurity perceptions of street vendors. The predictors of financial insecurity have been marital status and the number of dependents of the street vendors (r2: 16.6%). However, marital status alone impacted the 6% variance in financial insecurity. This study concluded that the pandemic caused financial stress and financial insecurity among street vendors, but not financial stress and financial literacy. Thangaraj Ravikumar, Mali Sriram, S Girish, R Anuradha, M Gnanendra, 2022. -
Relationship between financial stress and financial well-being of micro and small business owners: Evidence from India
Micro and small businesses financially suffered due to COVID-19 in India. This financial suffering created financial stress among them and deteriorated their financial well-being. However, micro and small business owners exhibit financial resilience by bouncing back to regular business activities through their hope, optimism, and selfefficacy, which are the components of positive psychological capital. This study analyzes the relationship between financial stress and financial well-being of micro and small firm owners keeping financial resilience as a mediator and positive psychological capital as a moderator in the mediation. This descriptive analysis employed a survey method to collect primary data using the interview method. The interview method was used as most micro and small business owners are comfortable with interaction rather than filling out the questionnaires due to the language barrier. The sample size is 384 respondents, as per Krejcie and Morgan's formula. The mean scores indicate a moderate degree of financial stress (2.354), financial resilience (2.623), and financial well-being (2.637). The level of financial stress differs based on the respondents' gender. Financial stress is more among female business owners (2.504) than their male counterparts (2.265). Further, business owners who earn more have a higher level of financial resilience (2.985), psychological capital (2.951), and financial well-being (2.711). Financial stress significantly impacts financial well-being (28.4%). Financial resilience has a partial mediation effect (65%) on financial stress and financial well-being. Finally, psychological capital moderates indirect relationships among financial stress, financial resilience, and financial well-being. Thangaraj Ravikumar, Mali Sriram, Nagalingam Kannan, Issac Elias, Vinita Seshadri, 2022. -
Behavioural drivers of access-based consumption among millennial and generation Z in India
The world of consumerism is very dynamic, and technology driven changes in the field of consumerism are unavoidable especially among new generation customers millennial and generation Z. The customers, especially in urban areas, gradually move from ownership-based consumption to access-based consumption. The purpose of this study is to explore the behavioural drivers of new generation customers towards access-based consumption. The study is descriptive in nature and employed a survey method for data collection. The drivers identified are tested through a quantitative study and the primary data are collected using online questionnaires. The study has also analysed the impact of behavioural drivers on current usage of access-based consumption as well as on willingness to use access-based consumption in the future. The study has found that sustainability is the only driver that significantly motivates access-based consumption in Indian urban areas. Copyright 2022 Inderscience Enterprises Ltd. -
Digital financial literacy among adults in India: measurement and validation
The ongoing COVID-19 pandemic has considerably promoted the usage of Digital Financial Services (DFS) in India. Therefore, exploring the various determinants influencing the DFS users is crucial for the DFS providers to understand their customers better. This study aims to identify, measure, and validate the determinants of Digital Financial Literacy (DFL) from the Indian adults who use Digital Financial Services. A sample of 384 adult DFS users from India was surveyed using a self-administered questionnaire in 2021. A multidimensional scale was developed to measure the Digital Financial Literacy in this study. The results exhibit that Digital Knowledge, Financial Knowledge, Knowledge of DFS, Awareness of Digital Finance Risk, Digital Finance Risk Control, Knowledge of Customer Right, Product Suitability, Product Quality, Gendered Social Norm, Practical Application of Knowledge and Skill, Self-determination to use the Knowledge and Skill and Decision Making are the determinants of DFL among the adults in India. Further, the users of DFS without DFL will face numerous challenges such as inability to com-plete the transaction, financial loss and privacy breach, etc. Hence, the study concludes that DFL is prerequisite to use DFS effectively. 2022 The Author(s). This open access article is distributed under a Creative. -
Impact of digital payments on economic growth: Evidence from India
In recent years, economic transactions are carried out through electronic or online or cashless means all over the world especially in developed countries and developing countries like India. As a result of increased digital means of payment has brought down usage of cash transactions in the economy. Digital transactions have the features of speed, less cost, and comfort. A well functioning digital payment system has much relevance on overall economic activity, monetary policy, and financial stability of a country. This study tries to verify the impact of digital payments on the economic growth of India. The economic growth is measured through a proxy real Gross Domestic Product. Digital payments are measured using Real Time Gross Settlement (RTGS), Clearing Corporation of India Ltd (CCIL) operated systems, paper clearing, retail electronic clearing, Card payments, and Prepaid Payment Instruments (PPIs). Data for digital payments and real GDP are collected from the year 2011 to 2019. Ordinary Least Square Regression, Auto-Regressive Distributed Lag (ADRL) co-integration approach and ARDL Bounds test are employed for the analysis. The study results reveal that digital payments impact economic growth significantly in the short run. But, digital payments dont impact economic growth in the long-run. BEIESP. -
A comprehensive literature review on financial inclusion /
Asian Journal Of Research In Banking And Financial, Vol.7, Issue 8, pp.119-133, ISSN: 2249-7323. -
An Empirical Examination of the Factors of Big Data Analytics Implementation in Supply Chain Management and Logistics
Numerous companies have effectively exploited Big Data Analytics (BDA) potential to enhance their effectiveness in the Big Data period. Given that big data application in logistics and supply chain management (SCM) is nevertheless in its early stages, assessments of BDA could differ from various viewpoints, producing certain difficulties in comprehending the significance and potential of big data. Based on past research on BDA and SCM, this work examines the factors that influence organizations' willingness to implement BDA in their everyday activities. This research divides potential elements into 4 groups: technical, firm, ecological, and supply chain issues. A framework consisting of direct factors like technical, firm, and mediators was presented based on the technology diffusion hypothesis. The experimental findings demonstrated that anticipated advantages and high-level management assistance might have a considerable impact on intended adoption. Furthermore, ecological variables like competitive adoption, administration legislation, and supply chain connection can greatly alter the direct connections between influencing causes and intended adoption. 2023 IEEE. -
Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment
Machine learning has emerged as formidable instrument in realm of malware detection exhibiting capacity to dynamically adapt to ever-shifting topography of digital hazards. This study presents an exhaustive comparative analysis of four intricate machine learning algorithms namely XGBoost Classifier, K-Nearest Neighbors (KNN) Classifier, Binomial Logistic Regression and Random Forest with primary objective of assessing their effectiveness in domain of malware detection. Conventional signature-based detection methodologies have struggled to synchronize with rapid mutations exhibited by malware variants. In sharp contrast machine learning algorithms proffer data-centric approach adept at unraveling intricate data patterns thereby enabling identification of both well-known and hitherto uncharted threats. To meticulously appraise efficacy of these machine learning models we employ stringent set of evaluation metrics. Precision, recall, F1 Score, testing accuracy and training accuracy are meticulously scrutinized to ascertain distinctive strengths and frailties of these algorithms. By providing comparative analysis of machine learning algorithms within milieu of malware detection this research engenders significant contribution to ongoing endeavor of fortifying cybersecurity. Resultant analysis elucidates that each algorithm possesses its unique competencies. XGBoost Classifier showcases remarkable precision (Benign files: 99%, Malicious files: 99%), recall (Benign files: 97%, Malicious files: 99%) and F1 Score (Benign files: 98%, Malicious files: 99%) implying its aptitude for precise malware identification. KNN Classifier excels in discerning benign software exhibiting precision (Benign files: 90%) and recall (Benign files: 91%) to mitigate likelihood of erroneous positives. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A characterization of star-perfect graphs
Motivated by Berge perfect graphs, we define star-perfect graphs and characterize them. For a finite simple graph G(V, E), let (Formula presented.) denote the minimum number of induced stars contained in G such that the union of their vertex sets is V(G), and let (Formula presented.) denote the maximum number of vertices in G such that no two of them are contained in the same induced star of G. We call a graph G star-perfect if (Formula presented.), for every induced subgraph H of G. A graph G is star-perfect if and only if G is (Formula presented.) -free, for every (Formula presented.). A bipartite graph G is star-perfect if and only if every induced cycle in G is of length (Formula presented.). The minimum parameter (Formula presented.) and the maximum parameter (Formula presented.) have been extensively studied in various contexts. 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. -
Effect of Heat Treatment on Fatigue Characteristics of En8 Steel
Fatigue failure is an important factor in most of the engineering applications, especially in steel materials, and among the steel materials, it is an important phenomena in medium carbon steels like EN8, which is very commonly used in components like shaft, gears etc., since it is prone to fatigue failure. Hence, without changing the composition, an attempt is made to enhance the fatigue strength by different heat treatment techniques. In this study, the investigation is carried out on heat treatment of EN8 steel material. Various kinds of heat treatment techniques like quench and temper, normalizing and annealing are performed on EN8 steel. After exposure to the heat treatment, the EN 8 steel material specimens are machined as per the ASTM standards and are subjected to RR MOORE test and SN-curves are plotted from the obtained results; the obtained results from the fatigue tests are further analyzed with the help of ANSYS software. Fatigue life and Factor of Safety (FOS) comparisons for EN 8 steel material is made with the structural steel material and it is found from the comparisons, that the heat treatment process enhances the fatigue strength and endurance limit. Published under licence by IOP Publishing Ltd. -
Bibliometric Analysis of AI Research in Sustainable Smart Cities
Smart cities have the potential to improve city-wide governance, environmental sustainability, sustainable transportation, and economic growth. Urban areas may find these advantages useful in their pursuit of SDG-11 objectives. A key component of smart city architecture is the addition of artificial intelligence (AI) and other smart technology into urban areas. The Artificial Neural Network (ANN) is a major machine learning approach. A number of review studies have already been published, reflecting the substantial interest in artificial neural networks (ANN) for smart city applications. In the past, researchers have shown an interest in studying structural monitoring applications, transportation systems, cybersecurity, and the Internet of Things (IoT). But knowledge about how ANN can help Smart Cities achieve SDG-11 is limited. This paper provides a systematic bibliometric analysis of present research trends on artificial neural networks for smart cities, with an emphasis on SDG-11. The research employed a keyword-based search to obtain 131 papers for content analysis and 743 papers for descriptive analysis. Both the amount of interest in the topic and the tendency for related topics to cluster have increased exponentially, according to the findings. Urbanization, Transportation, and Eco-friendly were identified as the main topics of this study. Specifically, this evaluation focuses on particular SDG-11 issues and provides insights on research trends and thematic importance. 2025 Saravanan Krishnan, A. Jose Anand and Raghvendra Kumar. -
Secured Health Insurance Management
Many Low- and Middle-Income Countries (LMICs) have expanded their healthcare coverage over the past decade thanks to reforms and investments motivated by Universal Health Coverage (UHC). UHC strives to guarantee that all individuals have access to high-quality healthcare, protecting them from public health hazards and financial hardship caused by the need to treat sick family members. With UHC as its end objective, this study examines health insurances function as a policy instrument to address health funding. Here, researchers study the laws to ensure that all Indians have access to health care and how technology facilitates quicker participation in health insurance programs. The data was collected between August and October of 2022. The study was designed as a cross-sectional case study: (i) the research on the effects of UHC, (ii) documents about Indias health insurance systems (HIS), and (iii) a discussion of the benefits and challenges of using MedStrat, a homegrown digital Health Insurance Management System (HIMS), to run health insurance programs across different states in India. Data from research and document evaluations, as well as health insurance statistics, were triangulated with modern technology adoption models to determine (i) factors that influence the rate at which digital insurance plans are adopted, (ii) the effect of technology on increasing peoples access to health insurance; and (iii) the potential for the digital insurance intervention to be scaled further. Digital insurance administration systems can increase insurance enrolment, especially among low-income households. There are three enabling contexts for digital insurance plan adoption: supportive regulation, public-private partnerships, and ongoing stakeholder contact and education. There are three essential requirements for digital health insurance programs to be widely adopted in India and other similar situations. (i) user-friendliness; (ii) an established network for digital insurance policies; and (iii) confidence, which may be shown through measures like encrypted data storage, complete audit trails, and built-in fraud protection. Our results prove that digital health technologies hold great promise for achieving UHC in LMICs. 2024 Scrivener Publishing LLC. -
Impact of Childhood Trauma on Psychological Distress and Personality Pathology in Young Adults
Adulthood is a time of change, thus stressful. A predetermining factor to this is a provision for a safe environment during the crucial years of life (childhood). Children make meanings of everything and are more dynamic in the early developmental years. It is a basis for their overall development and defines their coping mechanisms during adulthood. Therefore, if they develop faulty meanings of themselves, others, and the world at large, it can alter their abilities to function during adulthood. It is fundamental to understand the psychological well-being and personality traits in adulthood by this very nature of traumatic experiences in childhood. This paper is a conceptual framework discussing a three-tier model to retrospectively understand the impact of childhood trauma on psychological distress and personality pathology in adulthood. This paper suggests future research to focus on developing intervention and prevention models for young adults (childhood trauma survivors) on positive parenting practices. The Electrochemical Society -
Perceived Reality of Self and Others with Two Childhood Trauma Survivors - An Idiographic Case Study
Impacts of childhood trauma can be crucial in understanding personality traits and psychological distress. However, it could be hard to predict if these individuals develop posttraumatic stress or growth. Several quantitative research studies have concluded the connections between childhood trauma and psychopathology or maladaptive personality traits. Various researchers have discovered the negative consequences of early childhood trauma and its long-term effects which may be rudimentary in understanding the causation of life-long psychological and medical deficiencies. This has been very elementary in understanding trait patterns and psychopathology for outcome generalizability and implementing prevention and intervention models. However, these studies still fail to spotlight the importance of the lived experiences of trauma survivors. Nevertheless, the present study is an idiographic single-case study research design used in the exploration of the lived experiences and perceived reality of self and others with two childhood trauma survivors. The Electrochemical Society -
A Family of Mexican Hat Wavelet Stieltjes Transform for Unbounded Non-decreasing Functions
In the present article, we examine the characteristics of the Mexican hat wavelet Stieltjes transform (MHWST) for a specific set of functions belonging to one of the sub-class of bounded variation functions. The subset comprises functions that are unbounded and non-decreasing. Further, a unified approach is applied to establish a uniqueness theorem and subsequently derive a representation theorem for the MHWST. The Author(s), under exclusive licence to The National Academy of Sciences, India 2024. -
Integral Transforms andGeneralized Quotient Space ontheTorus
In this chapter, we discuss one of the recent generalization of Schwartz distributions that has significantly influenced the expansion of various mathematical disciplines. Here, we study the space of generalized quotient on the torus. Different integral transforms are investigated on the space of generalized quotients on the torus BS?(Td). The space BS?(Td) is made of both distributions as well as space of hyperfunctions on the torus. Further, by introducing the relation between the Fourier and other integral transforms, the conditional theorems are proved for generalized quotients on tours. Moreover, we study the convergence structure of delta-convergence on the generalized quotient space, and an inversion theorem is proved. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Impact of helpful reviews on customer purchase intention with special reference to mobile phone reviews
Technological advances in the digital space have provided renewed impetus to businesses. Costly, labor-intensive marketing campaigns have been replaced by digital marketing. However, along with benefits, the increasing sophistication and exponential growth of e-commerce businesses have also introduced new challenges. The large number of similar product offerings and the high volume of reviews have created a technology-induced hurdle for consumers that can impair their thought processes. Often, users will only scan the top few reviews to arrive at a decision. In the current setup, older reviews that accumulate votes over time are found at the top of the helpful review list, in contrast to fresh entrants. The current study proposes placing reviews in appropriate positions in the helpful review list using statistical and scientifically derived helpfulness scores. The study utilized a sample of consumer goods (specifically, mobile phones) and re-ranked reviews based on their expected score. Amazon.in provided the initial review dataset. Random Forest and gradient-boosting regression techniques were used to predict review helpfulness. An Elaboration Likelihood Model was used to explore the impact of central and peripheral cues on review helpfulness. The gradient-boosting regression was the best-performing method of predicting review helpfulness, and the reviews were re-ranked. The re-ranked reviews were tested for helpfulness vis-a-vis the initial ranking of reviews using the survey method. The result indicated that the proposed re-ranking of reviews was more helpful to end users and helped mitigate uncertainty in decisions. The study utilized the Information Acceptance Model to assess the influence of electronic word of mouth on purchase intention. 2023 Conscientia Beam. All Rights Reserved. -
A Meta-Analysis on the Determinants of Online Product Reviews with Moderating Effect of Product Type
The technological advances in digital space have provided a renewed impetus for business to expand their footprint across digital modes. The growth of the internet and the ease of its access to the masses has encouraged many businesses to go online. Online e-commerce platforms make it easy to search, locate and place orders. Technology-assisted supply chains and fast delivery mechanisms ensure that users don't have to go elsewhere to fulfill their needs. To earn loyalty and customer satisfaction, e-commerce platforms have evolved into a sophisticated recommender system. It has evolved from just an informational source to a participative mode where users can share their experiences about their purchases. Customer values other user experiences more than the information provided by the seller. The presence of many conflicting and contradicting reviews can make the task of making rational decisions difficult for many users. Many studies were performed to understand what constitutes a review helpful and came up with different or mixed outcomes. The present study reviews the factors that influence online customer reviews helpful. Meta-analysis was performed to reconcile the mixed findings of different factors of online review helpfulness. The meta-analysis found that with the moderating effect of product type, factors like review length, readability, rating, reputation, and expertise positively correlate with helpfulness. Further, the customer finds moderate reviews more helpful in terms of polarity. Meta-analysis has a mix of findings for the selected data points in the study. The mixed findings include product type (search, experience, or other) and helpfulness measurement criteria. 2022 Kavita Rawat and Sunita Kumar. This is an open access article licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. -
Quantum Computing: Navigating The Technological Landscape for Future Advancements
Quantum Computing represents a transformative paradigm in information processing, leveraging principles of quantum mechanics to enable computations that transcend the limitations of classical computing. This research paper explores the cutting-edge technologies employed in Quantum Computing, examining the key components that facilitate quantum information processing.The purpose of this study is to provide a comprehensive exploration of the state-of-the-art technologies in Quantum Computing, laying the groundwork for future advancements and applications in this rapidly evolving field.The methodology employed in this study integrates three analytical approaches: sentiment analysis, topic modeling, and thematic analysis. Sentiment analysis is utilized to discern and quantify emotional tones within the content. Topic modeling is applied to identify latent themes and patterns within the data, revealing underlying structures. Thematic analysis, on the other hand, involves a systematic identification and exploration of recurrent themes to provide a nuanced understanding of the subject matter. This tripartite methodology ensures a comprehensive examination of the data, facilitating a robust and multifaceted analysis of quantum computing technologies. 2024 IEEE. -
The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations
The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a fine-grained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) and (ii) silver lining (SL). To provide a more comprehensive understanding, both orientations are further sub-categorized into intrinsic and extrinsic, with three degrees of severity - (i) mild, (ii) moderate, and (iii) alarming. We also meticulously categorize hallucination into six types: (i) acronym ambiguity, (ii) numeric nuisance, (iii) generated golem, (iv) virtual voice, (v) geographic erratum, and (vi) time wrap. Furthermore, we curate HallucInation eLiciTation (), a publicly available dataset comprising of 75,000 samples generated using 15 contemporary LLMs along with human annotations for the aforementioned categories. Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI). Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess and measure which LLM is more vulnerable towards hallucination. We firmly believe that HVI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making. In conclusion, we propose two solution strategies for mitigating hallucinations. 2023 Association for Computational Linguistics.