This paper conducts a critical review of the influence of health and quality narratives on consumer preference between domestic and foreign Fast Moving Consumer Goods (FMCG) products in India. It is argued that international brands heavily depend on influencer marketing, laboratory tests, and expert-backed reports to present their products as safer or better, in the context of growing globalization and internet penetration. At the same time, the quality of domestic products is being questioned in various narratives which have become popular on digital media platforms. The study through a secondary research methodology based on academic journals, government reports, policy documents, industry publications, and digital discourse analysis on X (formerly Twitter), Instagram, and YouTube. The major focus is on the Indian dairy sector and public debates over the quality of milk and animal feed. The findings suggest that narrative framing indeed has a strong impact on consumer trust and can even change brand preference without there being structural regulatory failures. Therefore, perception management is one of the strategic points of competition in liberalized FMCG markets.

The Goods and Services Tax (GST), introduced in India on 1 July 2017, is one of the country’s biggest indirect tax reforms aimed at creating a single, unified market. It replaced multiple taxes with one system to simplify taxation and improve transparency. Small and Medium Enterprises (SMEs), which play a major role in employment, GDP, and exports, have experienced significant changes under GST. The reform has affected their compliance processes, cost structure, working capital management, supply chains, and use of technology. This study, based on secondary data from government and industry sources, finds that while GST has simplified the tax system and created opportunities for business expansion, SMEs also face challenges such as higher compliance requirements, technical difficulties, and working capital issues. Overall, GST offers growth potential, but further improvements are needed to fully support SMEs.

The rapid integration of Artificial Intelligence (AI) into educational technology (EdTech) has transformed teaching, learning, and educational management across the globe. This study conducts a bibliometric analysis to examine research trends in the intersection of EdTech and Artificial Intelligence. Using bibliometric data extracted from the OpenAlex database, the study analyses publication trends, open access patterns, research topics, and document types. The results reveal a significant increase in research output between 2020 and 2025, with a notable peak in 2025. The findings indicate that AI-driven personalized learning, intelligent tutoring systems, learning analytics, and digital pedagogy are among the most dominant themes in the literature. The study highlights the growing academic interest in AI-enabled educational tools and provides insights into emerging research directions. These findings contribute to understanding the evolving landscape of educational technology research and offer guidance for future scholarly work in this domain.

Stock markets are inherently volatile and influenced by a complex interaction of economic indicators, market information, and investor behaviour, making accurate stock price prediction a persistent challenge for investors and financial analysts. Traditional forecasting approaches largely rely on statistical techniques that assume linear relationships among financial variables, which often fail to capture the nonlinear and dynamic patterns present in modern financial markets. With the rapid advancement of computational technology, machine learning techniques have emerged as powerful tools capable of processing large financial datasets and identifying hidden patterns that influence market movements. The present study investigates the application of machine learning approaches in stock market price prediction using secondary market data obtained from recognized financial databases and stock exchange records. The study adopts a quantitative analytical research design, utilizing key market indicators such as opening price, highest price, lowest price, trading volume, and previous closing price to examine their relationship with stock price movements. Data analysis using correlation and regression techniques reveals a strong positive relationship between historical price indicators and stock closing prices, indicating the relevance of these variables in predictive modeling. The results demonstrate that the analytical model explains a substantial proportion of variation in stock prices, highlighting the capability of machine learning–based approaches to enhance forecasting performance. The study concludes that data-driven machine learning techniques significantly improve stock market prediction accuracy, thereby providing valuable insights for investors, financial analysts, and policymakers in developing more effective investment and risk management strategies.

Higher education has become increasingly dependent on financial resources, and many students worldwide struggle to afford rising tuition costs and other education-related expenses. Traditional student loan systems are often complicated, require strict eligibility criteria, and take a long time to process, making it difficult for many students to access education financing. In recent years, Financial Technology (FinTech) has played an important role in improving access to education financing. FinTech uses digital platforms, artificial intelligence, blockchain technology, and data analytics to create innovative financial services. These technologies are changing the education financing system by introducing alternative funding models, such as peer-to-peer lending, crowdfunding, and income-share agreements. This study examines the role of FinTech innovations in improving access to educational financing and explores the opportunities for students to pursue higher education in the digital era. This study adopts a conceptual research approach using secondary data gathered from academic journals, policy reports, and industry publications. The findings indicate that fintech can improve financial inclusion and make education funding easier to obtain. However, issues such as regulatory challenges, data privacy risks, and digital literacy gaps must be addressed. The study concludes that collaboration between governments, educational institutions, and fintech companies is essential to fully utilize the potential of fintech in expanding access to higher education.

This paper presents a novel Context-Aware Ambient Intelligence Framework implementing a Zero-Interface paradigm for privacy-preserving voice-actuated human-computer interaction. The proposed system, termed "Ghost Assistant," addresses critical limitations in contemporary voice-based intelligent agents by prioritizing local processing, user-controlled activation mechanisms, and complete elimination of traditional graphical user interfaces. Unlike prevalent cloud-dependent assistants (Amazon Alexa, Google Assistant, Apple Siri), our framework operates entirely on edge devices, ensuring data sovereignty and mitigating privacy vulnerabilities inherent in server-based architectures. The system employs a multi-layered architecture encompassing hotkey-triggered activation, real-time speech recognition, natural language understanding, contextual command routing, and synthesized audio feedback, all executed locally without external data transmission. Implementation utilizes Python-based libraries including Speech Recognition for audio-to-text conversion, pyttsx3 for offline text-to-speech synthesis, and custom intent classification algorithms. Experimental validation demonstrates 95% speech recognition accuracy with sub-second response latency (<1s) for local commands, while maintaining minimal computational overhead (3-5% CPU utilization). The framework supports 50+ voice commands spanning application launching, web navigation, intelligent notetaking with real-time dictation, calendar management with natural language date parsing, and optional AI integration through both cloud-based (OpenAI GPT-3.5) and local (Ollama) large language models. System architecture ensures complete user control through explicit activation mechanisms rather than continuous ambient listening, thereby eliminating the "always-on" surveillance concerns associated with commercial alternatives. Performance metrics indicate superior privacy preservation, competitive accuracy rates, and significantly lower resource consumption compared to industry-standard solutions. The modular design enables extensibility through custom command addition and integration with existing IoT ecosystems. This research contributes to the emerging field of ambient intelligence by demonstrating feasibility of sophisticated voice interaction systems that maintain user privacy, operate offline, and require minimal hardware resources, thereby democratizing access to advanced human-computer interaction technologies.

The rapid advancement of digital technologies and automated trading systems has significantly transformed the structure and functioning of global financial markets. Among these developments, algorithmic trading has emerged as a prominent mechanism through which investors execute trades using computer-based programs that follow predefined instructions related to price, timing, and trading volume. The increasing adoption of such automated strategies has raised important questions regarding their influence on stock market efficiency, liquidity, and price discovery. The primary objective of this study is to examine the relationship between algorithmic trading activity and stock market efficiency, and to analyze its impact on market liquidity and the price discovery process. The study adopts a quantitative research design based on secondary data collected from major Indian stock exchanges, focusing on actively traded large-cap companies. Statistical techniques including correlation analysis and multiple regression analysis were employed to evaluate the association between algorithmic trading activity and key market efficiency indicators. The results of the data analysis reveal a strong positive relationship between algorithmic trading and market liquidity, along with a significant contribution to efficient price discovery and reduced bid–ask spreads. The regression results further confirm that algorithmic trading significantly improves overall stock market efficiency. However, the study also indicates that excessive automated trading activity may contribute to short-term market volatility. Overall, the study highlights the growing importance of algorithmic trading in enhancing the efficiency and transparency of modern capital markets.

The rapid digitalization of financial services has transformed the way individuals and organizations conduct transactions, making them faster, more efficient, and globally accessible. However, this transformation has also significantly increased exposure to cybersecurity risks, including data breaches, phishing attacks, malware intrusions, and financial fraud. The research problem addressed in this study is the growing vulnerability of digital financial transactions in the absence of robust cybersecurity frameworks and user awareness. The primary objectives of this study are to examine the relationship between cybersecurity measures and transaction safety, and to analyze the impact of technological security protocols on reducing cyber threats in financial systems. The study adopts a quantitative research design using secondary data collected from financial reports, cybersecurity databases, and industry publications over the period 2018–2024. Statistical tools such as correlation and regression analysis are applied to assess relationships between variables. The findings (hypothetical) suggest that stronger cybersecurity infrastructure, including encryption, multi-factor authentication, and regulatory compliance, significantly reduces financial fraud incidents. The study contributes to existing literature by integrating technological, behavioral, and regulatory perspectives, offering insights for financial institutions, policymakers, and researchers to strengthen digital transaction ecosystems.

The rapid expansion of financial technology (FinTech) has significantly transformed the structure and accessibility of modern capital markets. Digital investment platforms such as robo-advisory systems and mobile trading applications have enabled retail investors to participate in financial markets with greater convenience, lower transaction costs, and improved access to real-time financial information. Despite the increasing adoption of these platforms, empirical evidence regarding their influence on portfolio diversification behaviour remains limited. The present study investigates the relationship between digital investment platform usage and portfolio diversification among retail investors. The study adopts a quantitative research design based on secondary data collected from financial market reports, platform analytics, and investment behaviour studies covering the period 2018–2024. Portfolio allocation data across major Indian companies and multiple asset classes were analysed using correlation and regression techniques. The empirical analysis indicates a strong positive correlation (r = 0.74) between digital platform usage and portfolio diversification. Regression results further reveal that digital investment accessibility and platform-driven advisory features significantly contribute to improved diversification levels among investors. The findings demonstrate that digital investment platforms encourage diversified portfolio construction by providing automated investment recommendations, multi-asset investment options, and simplified portfolio management tools. The study concludes that the integration of FinTech-enabled investment services plays a crucial role in enhancing investment accessibility, portfolio efficiency, and retail investor participation. These insights contribute to the growing literature on digital finance and investment behaviour while offering practical implications for investors, financial institutions, and policymakers.

Customer satisfaction plays a crucial role in the success and growth of retail organizations. The present study focuses on analyzing the level of customer satisfaction towards the services provided by D-Mart retail stores. D-Mart has emerged as one of the leading retail chains in India, known for its wide product range, affordable pricing, and customer-oriented services. The purpose of this research is to evaluate customers’ perceptions regarding various aspects of D-Mart’s services such as product availability, pricing strategy, store layout, staff behaviour, billing process, and overall shopping experience. The study is based on primary and secondary data. Primary data was collected through a structured questionnaire from customers who frequently shop at D-Mart stores. Secondary data was gathered from journals, articles, websites, and previous studies related to customer satisfaction in the retail sector. The collected data was analyzed to understand the factors influencing customer satisfaction and the effectiveness of the services provided by DMart. The findings of the study indicate that competitive pricing, product variety, and efficient service significantly influence customer satisfaction. However, certain aspects such as crowd management and waiting time at billing counters can impact the overall shopping experience. The research highlights the importance of maintaining service quality and improving operational efficiency to enhance customer satisfaction. This study will help in understanding customer expectations in the retail sector and provide useful insights for improving service quality at D-Mart stores.

The rapid advancement of Financial Technology (FinTech) has significantly transformed the accessibility and operational structure of modern financial markets. Digital brokerage platforms, mobile trading applications, and automated advisory systems have reduced traditional entry barriers and enabled a broader base of individual investors to participate in equity markets. Despite the rapid growth of technology-driven financial services, the extent to which FinTech platforms influence retail investor participation in stock markets remains an important subject of academic investigation. This study examines the role of FinTech platforms in expanding retail participation and evaluates how technological features influence individual investment behaviour. The study primarily aims to analyse the relationship between FinTech platform adoption and retail investor participation, as well as to evaluate the impact of platform accessibility, transaction cost efficiency, and digital advisory tools on market engagement. A quantitative research design is adopted using secondary data collected from stock exchange reports, brokerage industry publications, and financial market databases. Statistical techniques including descriptive statistics, correlation analysis, and multiple regression analysis are employed to examine the relationships among the variables. The results of the empirical analysis indicate a strong positive association between FinTech adoption and retail participation in stock markets. The findings further reveal that higher platform accessibility and lower transaction costs significantly encourage individual investors to participate in equity markets. The study concludes that FinTech-driven financial innovation plays a crucial role in improving market inclusivity and expanding retail investor engagement, thereby contributing to the development of more accessible and efficient capital markets.

The retail fashion industry in India has experienced rapid growth with the emergence of affordable fashion brands. Zudio has become a popular choice among customers due to its trendy designs and budget-friendly pricing. Understanding customer satisfaction towards its products and services is important for maintaining competitiveness and improving customer loyalty. The research problem of this study focuses on identifying the factors that influence customer satisfaction with Zudio’s products, service quality, store environment, and pricing strategy. The main objectives of the study are to evaluate customer satisfaction levels, analyze customer perceptions about product quality and pricing, and examine the effectiveness of services offered by Zudio stores. A descriptive research design was used for the study. Primary data were collected through structured questionnaires from customers visiting Zudio stores, while secondary data were gathered from journals, reports, and online sources. Basic statistical tools such as percentage analysis and simple interpretation methods were used to analyze the collected data. The hypothetical findings indicate that most customers are satisfied with Zudio’s affordable pricing, fashionable collections, and store ambience. However, some respondents suggested improvements in product variety and billing efficiency. This study contributes to understanding consumer expectations in the affordable fashion segment and provides insights that may help Zudio enhance its product offerings and service quality to improve overall customer satisfaction.

The rapid evolution of financial technology has significantly transformed traditional lending mechanisms, giving rise to digital lending platforms that provide faster, more accessible, and data-driven credit services. This study examines the impact of digital lending platforms on customer borrowing behavior, focusing on how technological features influence decision-making patterns. The research problem stems from the increasing reliance on digital platforms without a comprehensive understanding of their behavioral implications. The primary objectives of the study are to analyze the relationship between digital platform attributes and borrowing behavior, and to assess the influence of convenience, accessibility, and risk perception on customer decisions. The study adopts a quantitative research design using secondary data from financial reports, industry publications, and fintech databases. Statistical tools such as correlation and regression analysis are employed. The findings indicate that ease of access, quick approval processes, and personalized credit offerings significantly influence borrowing tendencies, often leading to increased borrowing frequency. However, concerns related to over-indebtedness and financial literacy persist. This study contributes to the literature by integrating behavioral finance perspectives with fintech adoption, offering insights for policymakers, financial institutions, and digital lenders in designing responsible lending frameworks.

Insurance plays a crucial role in financial planning by providing risk protection and long-term investment opportunities. Despite the growing importance of insurance products in modern financial markets, awareness and understanding of insurance among individuals remain uneven, particularly in developing economies. Limited financial literacy, misconceptions regarding insurance policies, and inadequate dissemination of information often influence individuals' investment decisions. This study examines the relationship between insurance awareness and investment behavior among individuals and evaluates how awareness levels influence financial decision-making related to insurance products. The study adopts a quantitative research approach using secondary data sources such as reports published by the Insurance Regulatory and Development Authority of India (IRDAI), government publications, and financial sector reports. Analytical tools including correlation and regression analysis are used to examine the relationship between insurance awareness indicators and investment behavior trends. Hypothetical statistical results indicate a positive relationship between insurance awareness and the likelihood of individuals adopting insurance as a financial investment instrument. Higher awareness levels significantly influence investment diversification and long-term financial planning. The study contributes to the literature by highlighting the role of financial awareness in shaping investment decisions related to insurance products. The findings offer valuable insights for policymakers, financial institutions, and insurance companies in designing strategies to improve financial literacy and expand insurance penetration.

Mergers often lead to the integration of advanced digital systems, automation tools, and streamlined processes aimed at improving operational efficiency. This research examines the effects of technological advancements introduced through bank mergers on employee satisfaction and productivity.

The rapid digitalization of financial services has transformed the banking sector, with mobile banking emerging as a primary mode of financial transactions. However, the adoption and usage of mobile banking among senior citizens remain comparatively limited, raising concerns about financial inclusion and digital literacy. This study examines the primary usage patterns, determinants, and barriers influencing mobile banking adoption among senior citizens. The research aims to analyze the relationship between perceived ease of use, trust, and technological awareness with mobile banking usage among older adults. A quantitative research design is adopted using secondary data sources such as published reports, banking statistics, and prior empirical studies. Statistical tools, including correlation and regression analysis, are employed to assess the relationships among variables. The findings (hypothetical) suggest that perceived ease of use and trust significantly influence mobile banking adoption, while technological anxiety and lack of digital literacy act as major barriers. The study contributes to the existing literature by highlighting the behavioral and technological challenges faced by senior citizens and offering insights for policymakers and financial institutions to design inclusive digital banking systems.

In an era where mobile technology permeates every aspect of people’s lives, the question of who controls health data has become both urgent and essential. As of 2024, more than 100,000 health-related mobile apps are available in major app stores, and these applications generate large volumes of personal health data every day. In addition, cybersecurity threats have surged and the number of individuals impacted by healthcare data breaches nearly tripled from 14 million to 45 million between 2018 and 2021. Patients increasingly consider data privacy as a fundamental right and over ninety percent people believe that health app developers should be transparent, offer meaningful consent processes, and allow them to opt out of data usage or sharing. These trends expose a growing gap between the promise of mobile health and the realities of ethical and sustainable practice. Recognizing this, global health leaders and national regulators are emphasizing the need for stronger governance. In 2024, the G20 health ministers affirmed that trusted, secure, interoperable digital health systems supported by regulatory frameworks that respect national context are essential for equitable healthcare and resilience. At the UN World Data Forum, participants called for a global health data governance framework rooted in rights-based, equitable principles, in order to unlock the public value of health data while protecting individuals’ rights. Meanwhile, India’s Ayushman Bharat Digital Mission has significantly advanced sovereign digital health infrastructure and by early 2025, more than 739 million health IDs had been issued, linking approximately 490 million health records. The mission includes a Health Data Management Policy that embeds “privacy and security by design,” consent frameworks, user control rights, and interoperability as core elements. Nonetheless, draft legislation such as DISHA remains pending, highlighting persistent challenges in implementing robust health data protection in practice. Beyond policy, emerging technological models like the Decentralized Health Intelligence Network (DHIN) offer promising alternatives. DHIN combines federated learning, personal health records, and blockchain incentives to ensure individuals retain control over their health data, benefit financially from participation, and safeguard AI development with decentralized safeguards. Drawing on these dynamics, this paper proposes a comprehensive framework that aligns mobile health application design and deployment with three pillars: data sovereignty, ethical governance, and SDG-driven impact. It emphasizes clear role definitions (like data owner, data steward), codified consent and ownership rights, federated system architectures, and interoperability standards tailored to local contexts. It also explores how platforms can integrate mHealth apps into broader digital public infrastructure while preserving individual autonomy and privacy. This study grounds its analysis in real-world data, lived experience, and ethical principles. It demonstrates that privacy and sovereignty are not obstacles to innovation. On the contrary, they serve as foundational enablers that support and guide responsible technological advancement.Properly governed mobile healthcare applications can advance SDG 3 (health), SDG 5 (gender equality), SDG 9 (innovation and infrastructure), and SDG 16 (peace, justice and strong institutions). In summary, when built around user control and accountability, mobile health systems can deliver more trustworthy, sustainable, and inclusive health services empowering individuals while building healthier societies. This research explores how mobile healthcare applications can support the United Nations Sustainable Development Goals, not only by improving health outcomes under SDG 3 but also by reinforcing data sovereignty as a pillar of trust, equity, and sustainability.

This study examines the influence of ethical advertising on consumer psychology in fostering trust, long term brand loyalty and sustainable consumer engagement. In the contemporary digital environment, transparency plays an important role in acting as a catalyst in consumer decision making. Increased access to digital platforms such as google, Instagram, twitter, Facebook, YouTube, etc has enhanced consumer awareness, making unethical practices increasingly unsustainable for businesses. For instance, cases like the Volkswagen emission scandal (2015) and Pepsi’s controversial advertisement (2015) illustrates how digital scrutiny amplifies consumer awareness and erodes brand trust. Consumers increasingly expect brands to connect with them emotionally and psychologically through responsible and value – driven communications, especially in culturally rooted markets like India where familial traditions and generational brand preferences are significant drivers influencing purchasing decisions. Brands like TATA Group, which is considered a trust-based brand in India consumed across generations. Drawing upon key psychological frameworks—including cognitive processing theory, emotional appeal, the Theory of Planned Behaviour, and persuasion principles—this study adopts a secondary research approach to analyse how ethical communication influences consumer decision-making. The findings indicates that transparent and psychologically aligned advertising strengthens trust and engagement, whereas misleading practices contribute to scepticism and weakened brand loyalty at times even damage the brand irreversibly. This study underscores the strategic importance of ethical advertising and the growing role of social media in enhancing accountability and consumer empowerment in the digital marketplace.

Financial literacy has emerged as a critical determinant of effective financial decision-making, particularly in developing economies where retail participation in financial markets is expanding. In India, the Reserve Bank of India (RBI) has undertaken several financial literacy initiatives aimed at enhancing awareness and promoting informed investment behavior among individuals. However, the extent to which these programs influence actual investment decisions remains an area requiring systematic examination. The present study investigates the impact of financial literacy programs, with specific reference to RBI initiatives, on individual investment decisions. The research primarily focuses on understanding whether exposure to financial literacy improves investment awareness, risk perception, and portfolio diversification. A quantitative research design is adopted using secondary data and structured analytical frameworks. Statistical tools such as correlation and regression analysis are employed to examine relationships between financial literacy and investment behavior. The findings suggest that financial literacy programs significantly influence investment decisions by enhancing risk assessment capabilities and encouraging diversified investment choices. The study contributes to existing literature by providing insights into the effectiveness of institutional financial literacy efforts in shaping investor behavior. It also offers policy implications for strengthening financial education frameworks in India.

The way individuals consume entertainment material has changed dramatically due to the quick growth of digital technologies and high-speed internet. Over-the-Top (OTT) platforms are now among the most widely used types of entertainment worldwide. With internet-enabled devices, these platforms enable users to watch films, web series, and television programs at any time and from any location. Binge viewing, which is defined as watching several episodes of a television show or digital content in one sitting, is one of the main behavioral trends resulting from OTT consumption. This study looks into OTT users' binge-watching habits and pinpoints the variables driving this tendency. The impact of binge watching on everyday habits and lifestyle is also examined in the study. The study is based on secondary data gathered from books, journals, and research publications as well as primary data gathered from OTT users via a questionnaire. The results indicate that the main causes of binge watching are convenience, interesting material, and flexible viewing times. Even if over-the-top (OTT) platforms offer amusement and relaxation, excessive binge watching may have a detrimental impact on productivity and sleep habits.

Digital transformation has significantly reshaped the entrepreneurial ecosystem across the globe. The rapid growth of internet technologies, cloud computing, artificial intelligence, and social media platforms has enabled entrepreneurs to establish, manage, and scale businesses more efficiently. This research paper examines the role of digital tools in entrepreneurship, their impact on business growth, marketing, operations, and decision-making processes. It also explores challenges faced by entrepreneurs in adopting digital tools and suggests strategies for effective implementation. The study concludes that digital tools are no longer optional but essential for sustainable entrepreneurial success in the modern competitive environment. Keywords— Digital Entrepreneurship, Artificial Intelligence, Cloud Computing, Innovation

The rapid digitalization of banking services has significantly transformed the financial landscape, enhancing efficiency, accessibility, and customer experience. However, this transformation has also introduced complex cyber security risks and data protection challenges. The increasing reliance on digital platforms exposes banks and customers to cyber threats such as data breaches, phishing, ransomware, and identity theft. The present study investigates the critical issues surrounding cyber security and data protection in digital banking, focusing on the effectiveness of existing security frameworks and the role of technological safeguards. The primary objectives of the study are to examine the relationship between cyber security measures and customer trust, and to analyze the impact of data protection mechanisms on the performance of digital banking systems. The study adopts a quantitative research approach using secondary data collected from reports, regulatory publications, and academic literature over the period 2015–2025. Statistical tools such as correlation and regression analysis are used to evaluate the relationships among variables. The findings suggest that robust cyber security infrastructure significantly enhances customer trust and reduces financial fraud incidents, while inadequate data protection policies negatively affect banking performance. The study contributes to the existing literature by providing a comprehensive analysis of security challenges and proposing a framework for strengthening cyber resilience in digital banking systems.

The rapid adoption of generative artificial intelligence tools such as ChatGPT and Google Gemini has significantly influenced teaching and learning practices in higher education. These tools assist students in completing assignments, generating ideas, summarizing academic content, and organizing information. While generative AI improves efficiency and accessibility, concerns have emerged regarding its potential impact on students’ critical thinking abilities and independent learning. This study explores the perceived effects of reliance on generative AI on college students’ critical thinking skills and examines faculty perspectives on the increasing use of these technologies in academic work. A qualitative exploratory research design was adopted using focus group discussions and student interviews.

Environmental, Social, and Governance (ESG) investing has emerged as a transformative paradigm in global financial markets, driven by increasing awareness of sustainability, ethical governance, and long-term value creation. In the Indian context, ESG investing is gaining momentum due to regulatory initiatives, growing investor consciousness, and corporate responsibility frameworks. However, the adoption of ESG practices remains uneven, and the empirical understanding of its financial implications is still evolving. The present study aims to examine the trend of ESG investing in India and analyze its impact on financial performance and investor decision-making. The research adopts a quantitative approach using secondary data collected from ESG-focused mutual funds and listed companies over a defined study period. Statistical tools such as correlation and regression analysis are employed to test the relationship between ESG scores and financial performance indicators. The findings (hypothetical) suggest a positive and statistically significant relationship between ESG performance and firm profitability, indicating that sustainable practices enhance long-term value. The study contributes to the growing body of literature by providing insights into ESG adoption in emerging markets and offering implications for investors, policymakers, and corporate managers.

The Indian banking sector serves as the primary financial intermediary for Public-Private Partnership (PPP) initiatives in education financing. This paper examines the financial soundness of selected public and private sector banks in India over the period 2015–2023, employing the CAMEL model — Capital Adequacy, Asset Quality, Management Efficiency, Earnings Quality, and Liquidity — as the analytical framework. Using secondary data sourced from Reserve Bank of India annual reports, SEBI disclosures, and individual bank balance sheets, the study evaluates ten banks — five from the public sector and five from the private sector — across five CAMEL parameters. Findings reveal that private sector banks consistently demonstrate superior performance in Capital Adequacy Ratio (CAR), Return on Assets (ROA), and Liquidity Coverage Ratio (LCR), while public sector banks exhibit significantly higher Non-Performing Asset (NPA) ratios, particularly during the 2017–2019 NPA crisis period. The paper extends these findings to assess the PPP readiness of Indian banks as institutional partners for education financing schemes under the National Education Policy 2020. The study concludes with policy recommendations for structuring CAMEL-calibrated PPP agreements in education financing, offering a novel framework linking bank soundness to institutional capacity for educational investment in emerging economies.

The rapid expansion of startups in the education sector, particularly driven by technological innovation, has transformed traditional learning systems. However, despite strong growth potential, education startups encounter significant financial challenges that hinder sustainability and scalability. This study examines the financial constraints faced by startups in the education sector, focusing on funding accessibility, cash flow management, cost structures, and revenue uncertainties. The primary objectives are to analyze the relationship between financial constraints and startup performance and to evaluate the impact of funding limitations on growth prospects. The study adopts a quantitative research approach using secondary data collected from industry reports, startup databases, and financial disclosures over a five-year period (2019–2024). Statistical tools such as correlation and regression analysis are employed to examine relationships between variables. The findings indicate that limited access to external funding, high customer acquisition costs, and delayed revenue realization significantly affect financial stability. Moreover, dependence on venture capital exposes startups to volatility in funding cycles. The study contributes to the existing literature by providing empirical insights into financial bottlenecks specific to education startups, offering practical implications for investors, policymakers, and entrepreneurs in designing sustainable financial strategies.

This study examines real estate as an alternative investment asset in comparison with traditional financial instruments such as equities and bonds. The research evaluates risk-return characteristics, diversification benefits, financing mechanisms, valuation practices, and investor perception toward property investments. The objectives of the study are: (1) to assess real estate’s effectiveness as an alternative asset class, (2) to analyze its risk-return structure relative to traditional investments, (3) to evaluate investor preferences across demographic groups, and (4) to determine diversification potential within investment portfolios. Primary data were collected through structured questionnaires distributed among 102 respondents using simple random sampling. Statistical analysis including mean comparison, percentage analysis, and hypothesis testing was conducted. The findings indicate that real estate demonstrates distinct risk-return characteristics, strong income stability, and long-term capital appreciation potential, thereby reinforcing its strategic role in diversified investment portfolios.

The rapid advancement of digital technology has significantly transformed the education sector worldwide. One of the most significant developments has been the emergence of online learning platforms that function not only as providers of academic content but also as structured education enterprises operating through digital business models. Platforms such as BYJU'S, Unacademy, Coursera, and Udemy have gained popularity by offering flexible learning solutions through subscription-based courses, certification programs, and skill-development modules. In a metropolitan academic environment like Mumbai, the demand for accessible and flexible learning resources has increased among undergraduate and postgraduate students. Students frequently rely on digital learning platforms to supplement classroom education, revise complex subjects, and prepare for examinations. As a result, these platforms have evolved beyond simple educational tools and now operate as structured enterprises combining technology, management strategies, and educational services. This study adopts an empirical approach to examine the academic outcomes associated with the use of online learning platforms among college students in Mumbai. Primary data was collected through structured questionnaires distributed among undergraduate and postgraduate students across different colleges. The study evaluates the influence of online learning platforms on academic performance, conceptual understanding, engagement levels, and learning flexibility. The findings indicate that features such as recorded lectures, interactive quizzes, visual explanations, and self-paced learning contribute positively to academic improvement and student engagement. However, challenges such as digital distractions, excessive screen exposure, and over-dependence on recorded lectures were also identified. Overall, the research concludes that online learning platforms are increasingly becoming an essential component of the modern higher education ecosystem while simultaneously functioning as profitable education enterprises within the digital economy.

Public health systems worldwide face increasing complexity due to demographic transitions, emerging infectious diseases, digital disruption, climate change, and widening inequities. Despite significant policy reforms, fragmentation persists between public health governance, educational institutions, and enterprise sectors. This conceptual paper reconceptualizes public health systems as integrated, multi-sector ecosystems and proposes a Tripartite Integration Framework (TIF) aligning policy, education, and enterprise collaboration. Drawing from systems theory, collaborative governance models, and innovation ecosystem literature, the paper develops a structured conceptual model to address institutional silos and enhance system resilience. The proposed framework identifies governance alignment, workforce transformation, innovation co-production, and accountability mechanisms as foundational pillars. The paper contributes theoretically by bridging public administration, higher education reform, and enterprise innovation within a unified public health systems model. Policy implications and future research directions are discussed to guide global implementation.

The restaurant industry in India has witnessed a major transformation over the past few years. The evolution of Online Food Delivery Services (OFDS) has changed the way how consumers order and enjoy their food, prompting restaurants to implement operational changes. Convenience being the key factor, platforms like Swiggy and Zomato enable customers to order food sitting at their homes or workplace, from a wide variety of food items listed on their apps or websites. This shift has created both opportunities and challenges for the restaurant business. OFD platforms have benefited the restaurant industry by boosting sales and expanding customer reach; however, high commission fees, intense competition, and overdependence also pose challenges for the business. This research paper aims to explore the various impacts OFDS has created on the restaurant businesses in India. The study's findings indicate that OFDS has affected restaurant operations and compelled restaurants to change their operational, marketing and customer retention strategies. The study concluded that restaurants must adopt hybrid business models by integrating online food delivery services with traditional practices to survive in the long run.

Financial literacy plays an important and fundamental role in financial decision-making. Financial inclusion and financial literacy are two important keys for the economic development of any country. Financial literacy is the road map for the success of efforts put forward by government for the financial inclusion in the country. Financial literacy is the awareness, knowledge, attitude, behavior, and skill needed to understand and use financial tools to make informed financial decisions and achieve personal financial well-being. Financial inclusion is an essential driver to accomplish financial development. Financial education can be an effective way to promote financial inclusion. It boosts the understanding of financial and insurance products, encourages responsible financial behaviour and gives people the confidence to take control of their finances. Improving the level of financial literacy is one of the keys to increasing financial inclusion. This paper covers the concept of Financial Literacy and Financial Inclusion, the relationship between the Financial Literacy and Financial Inclusion. The main objective of the study is examine the financial literacy initiatives that are taken for the development of financial inclusion in India.

The rapid integration of artificial intelligence (AI) has structured a new era for financial services, shifting the industry from manual, intuition-driven practices to data-centric, analytical decision-making. This study examines the widening curricular gap between traditional undergraduate finance programs and the evolving expectations of an AI-driven industry. Utilizing a descriptive research design, primary data was collected from 125 respondents. The findings indicate that while traditional subjects like accounting and corporate finance dominate the current curriculum, there is a profound lack of exposure to essential technological tools such as Python, Power BI, and machine learning. An overwhelming 89.6% of respondents agree that current curricula fail to adequately prepare students for the modern financial landscape, highlighting an urgent need for industry-academia alignment and continuous reskilling.