Startups play a vital role in accelerating India’s economic development by contributing to job creation, innovation, capital formation, and regional growth. They support the expansion of new industries, enhance productivity, and encourage competition, which ultimately strengthens the country’s GDP and overall economic structure. The rise of startups in sectors such as fintech, e-commerce, edtech, agritech, and healthcare has created new market opportunities, increased digital adoption, and attracted both domestic and foreign investments. By promoting entrepreneurship and supporting small-scale innovation, startups also contribute to balanced regional development and reduce dependence on traditional employment sectors. This research paper focuses specifically on the contribution of startups to India’s economic development. The study will use both primary and secondary data collection methods. Primary data will be collected through surveys and questionnaires from Young Entrepreneurs, Students , and other relevant respondents . Secondary data will be gathered from government reports, startup databases, research journals, and publications from organizations such as DPIIT, NASSCOM, and RBI. The paper will also evaluate the strengths and weaknesses of startups in the context of economic development. Key strengths include employment generation, innovation-driven growth, increased foreign investment, and the development of new business ecosystems. However, startups also face weaknesses such as high failure rates, funding challenges, regulatory barriers, and limited access to skilled resources in certain regions.

Analyzing primary survey data of gig workers and consumers and secondary data from NITI Aayog the paper delineates how income growth, business expansion, and unemployment correlate with patterns of gig employment. Our two-sample t-test reveals that higher income leads to significant consumption on complement services based on platform, while business scale increases demand for gig workers. But they also find evidence of income volatility, skill underutilization, and high job-switching intentions, which suggest a weakness in overall long-term employment stability. Income insecurity and limited social protection are a big focus for human wellbeing while the gig model promotes flexible labor allocation and rapid platform growth from a business perspective. Policy support, skill alignment, income stabilization mechanisms and will provide sustainable development of gig economy.

Market volatility is a reality in contemporary economic environments, and it affects the way managers process information, make risk assessments, and make strategic decisions. This paper investigates the effect of market volatility on managerial decision-making, using the behavioral economics approach, and it highlights the importance of psychological factors, emotions, and cognitive limitations in managerial decision-making. The paper investigates how factors such as uncertainty, loss aversion, overconfidence, herding, and risk perception influence decision-making during market instability. The study also investigates how managers strike a balance between short-term survival and long-term organizational success when faced with changes in demand, prices, or financial conditions. The study uses both theoretical and practical approaches to identify the patterns of decision-making under pressure and to point out the behavioral aspects that can be both helpful and harmful to business success. The results of the study are expected to provide insights into the human aspects of strategic decision-making and to provide frameworks for improving decision quality in dynamic market environments.

Cloud-based platforms have greatly simplified the training and deployment of machine learning models. Yet, the ease of use of these platforms has come at the cost of limited visibility into the operational mechanisms of the system. This research seeks to address the issue of balancing ease of use with the need to have a better insight into the operation of the systems. In this research, we have carried out a structured comparative evaluation of the ease of use of the Amazon SageMaker platform, as well as an enhanced version of the platform with better observability. The ease of use of the two configurations was evaluated based on four different parameters, namely the granularity of the logs, the ease of access of the metrics, the ability to respond to alerts, as well as the ease of diagnosis of faults. Through the analysis of the configurations, the enhanced version of the platform was found to have better ease of use without a proportional increase in the cost of operation.

The ADHD awareness ecosystem in India is undergoing a transformative shift, yet significant gaps remain in recognition, diagnosis, and structured support systems. Our research aims to identify systemic gaps in ADHD awareness while addressing challenges faced by awareness-driven startups and existing remedial frameworks. The study focuses on understanding lack of awareness, social stigma, misconceptions, shortage of proper facilities, and privacy concerns within society. It also proposes practical solutions to strengthen ADHD screening, early identification, and access to appropriate treatment in India. The research is descriptive in nature and is based on primary data collected through structured questionnaires and interviews from 107 respondents in the KDMC region. A purposive sampling technique was employed for the proposed research. Collected data were analyzed using both quantitative and qualitative methods. The current study also suggests an innovative model to enhance ADHD awareness, improve screening mechanisms, and promote accessible treatment support systems.

The HR system had gone through multiple stages of evolution as it first started as fully manual process then it developed itself and easied E-documentation process by introducing tools like hris erp, etc reducing workload and improving efficiency among the employees. Even with such advancement in HR technologies companies still require more use of HR intelligence system. Our study help to understand the advantages of HR technologies and also understanding the limitation where we can adopt more technology This study aims to analyze how information system operates systematically within the human resource department of an organization. It also tries to understand how intelligent HR systems optimize the use of various resources such as human capital, financial resources, and technological tools. Additionally, the study focuses on analyzing the processes, policies, and structured framework involved in organizational development systems.

This study looks at how AI-assisted decision-making affects human judgment. It mainly focuses on people’s awareness of AI, how much they trust it, how much they depend on it, and the ethical concerns they may have. The research is based on primary data collected from 100 respondents through a structured questionnaire, along with information from secondary sources. The findings show that although AI reduces effort and improves analysis, most users still prefer to check or verify the suggestions given by AI. Some common concerns include over-dependence on AI, reduced critical thinking, data privacy issues, and bias in systems.

The use of cloud computing is now the primary infrastructure model available to students, beginning developers and other educational institutions. Amazon Web Services (AWS) created the AWS Free Tier to motivate cloud adoption. Since this Cloud Service (CS) offers limited and free access to core CS, it still presents a problem for new users who struggle to comprehend their AWS billing and the real-time costs involved in starting to use a CS. The purpose of this study is to investigate and evaluate the ideas of billing interpretability and cost visibility for AWS Free Tier. In this scenario, I will recommend a five-stage Cost Interpretability Framework focusing on each user's configuration of services, how those services were used (measured), how those services were viewed displayed on the dashboard, and the various effects of gap-to-gain between each of these factors on a user's behaviour, learning, and experimentation with AWS Free Tier services. This research shows that even if users stay within their AWS Free Tier limits (use limits), there are still interpretability (cognitive) factors related to costs and usage that affect experimentation and behavioural trends. In order to better help students and beginners in embracing and learning how to use Cloud Services, suggestions are made to enhance the dashboard's user interface clarity.

Sustainability means meeting our present needs without compromising the ability of future generations to meet theirs. It is build on components such as: • Social Sustainability • Economic Sustainability • Environmental Sustainability Let’s take view of sustainability in terms of fashion industry. In fashion industry to keep up with the trend, the brands keep on manufacturing the goods neglecting factors such as social, economic and environment leading to disbalance of the nature. Therefore it is important to keep balance in all the aspects of sustainability. Sustainability in Fashion industry, especially, in bags and accessories sustainability focuses on reducing environmental impact while maintaining ethical and profitable production. Digital platforms plays a crucial role in today’s world in maintaining balance in sustainability, in fashion industry. Use of technology in the processes of sustainability management may help the fashion industry to develop a new, responsible and circular economy. Through this research, it is possible to suggest changes that would change the fashion industry in long run and therefore significantly decrease the negative impact of fashion on the environment, increase the ethical aspects of production, and make the fashion consumption and production processes more sustainable. Implementing the suggested changes would help reduce the negative environmental consequences associated with fashion production, such as excessive water consumption, chemical pollution, textile waste, and greenhouse gas emissions. In addition, it also looks at how the use of technology can enhance ethical standards, including fair labour conditions and supply chain management. Besides, providing brands and manufacturers with information about new technologies in sustainable fashion, this study helps consumers to make better and more environmentally friendly decisions. Overall, this research demonstrates that through evidence-based recommendations and coordinated efforts among policymakers, brands, manufacturers, and consumers, the fashion industry can move toward a more sustainable, ethical, and environmentally responsible future.

This paper assesses the usefulness of an integrated Systematic Investment Plan-Systematic Withdrawal Plan (SIP-SWP) as a model of long-term wealth development and long-term retirement wealth generation in both equity and debt mutual funds. The study is founded on primary survey data and the secondary historical data on NAV data from 2010–2025. Return performance, withdrawal sustainability and investor behaviour were measured using statistical tests that included CAGR, standard deviation, t-test, chi-square test and Monte Carlo simulation. The results indicate that equity funds perform better in promoting accumulation of wealth because of their greater return potential whereas debt funds would be more stable during the withdrawal period. There was a strong correlation between the expectations of returns and fund preference which shows that the view of investors affects the asset allocation decisions. The analysis endorses the idea of balanced equity–debt allocation and the orderly SIP-to-SWP transition plan to enhance the sustainability of retirement corpus. The proposed model is an AI- assisted and zero-brokerage retirement tool that helps plan asset allocation and inflation-adjusted retirement income.

The rapid expansion of the Indian startup ecosystem has resulted in escalating valuations driven primarily by elevated revenue growth, unreasonable capital inflows, higher margins and expectations of long-term market dominance. However, several high-growth startups have experienced valuation corrections and investor losses despite reporting strong top-line expansion. This study examines valuations from various perspectives in the Indian startups using an integrated framework combining Steady-State Value (SSV), Future Value Creation (FVC), capital cycle (outlined by Edward Chancellor) perception, liquidity and much more. The research demonstrates that growth does not inherently translate into value creation unless growth is backed by execution and incremental returns exceed the cost of capital in order to justify high valuations. By analyzing the interaction between capital allocation efficiency, competitive intensity, capital dilution, operating leverage, terminal value assumptions, pricing power, future growth prospects, guidance and industry size, the study explains how valuation inflation during expansionary phases can lead to PE de-rating and investor wealth erosion.The study is meant to simplify the process of valuing high growth startups for the larger public through theoretical concepts with significant real word explanations. The findings highlight the need for disciplined valuation anchored in economic indicators rather than narrative-driven growth expectations and story formations.

In recent year digital technologies have changed the way financial services are delivered across the world. Many underserved communities, especially rural, low-income, and less-educated people, still face difficulties in accessing financial services due to illiteracy and lack of awareness. This study aims to examine how Fintech solutions promote financial inclusion and empower underserved communities. The research study is based on secondary data collected from research articles, reports, and journals. The analysis shows that Fintech improves accessibility, reduces transaction costs, increases financial awareness, and enhances financial participation. Fintech services such as mobile banking, digital payments and online lending platforms have made financial services more accessible, affordable and convenient for people who were previously excluded from the formal financial system. It also shows how digital technology helps reduce financial inequality and allows more people to participate in economic activities. The study shows that Fintech plays an important role in achieving inclusive economic growth.

As artificial intelligence( AI ) is reshaping the global economy, transitioning youth from digital consumers to AI- driven entrepreneurs is no longer an option but a critical requirement to thrive in ever evolving AI landscape. However, while AI literacy is increasing there remains lack of data on whether the youth are strategically prepared to lead AI- driven entrepreneurial ventures. To assess this, a quantitative study was conducted among youth aged 17-25, via Google Forms, based on their proficiency in technology, perception towards automated systems, and the risk-taking and innovative mindset required for AI-driven business environments. The results indicated that while the youth possess foundational knowledge regarding AI and a positive outlook towards integration of AI into entrepreneurship, there was a significant need for specialized skill-training programs, ethical frameworks, and experiential learning opportunities provided by educational and governmental institutions to enhance their preparedness for futuristic AI-driven entrepreneurial ecosystems.

Climate change has intensified the need for effective mechanisms to reduce greenhouse gas emissions while maintaining economic efficiency. Carbon credit trading emerged as a market-based solution under international climate frameworks, enabling the allocation and exchange of emission allowances through structured regulatory systems. This study examines the concept, functioning, and economic relevance of carbon credit trading with particular emphasis on its evolution as a potential financial asset class. The research follows a descriptive and comparative design based on secondary data collected from official reports and market databases. Carbon credit price trends over a five-year period (2021–2025) were analysed across selected global Emissions Trading Systems, including the European Union Emissions Trading System, Canada Federal Output-Based Pricing System, California Cap-and-Trade Program, China National Emissions Trading Scheme, Germany National Emissions Trading System, Korea Emissions Trading Scheme, New Zealand Emissions Trading Scheme, Switzerland Emissions Trading System, Tokyo Cap-and-Trade Program, and the UK Emissions Trading Scheme. Year-wise price comparison was conducted to examine market behaviour, demand–supply interaction, and price volatility. The findings reveal significant variations in carbon credit prices across different markets, reflecting differences in regulatory frameworks, policy stringency, and market maturity. European markets demonstrated comparatively higher price levels, while certain Asian markets maintained lower and more stable price bands. The observed price fluctuations, structured trading platforms, and measurable economic value indicate that carbon credits possess characteristics similar to tradable financial assets.

Startups and MSMEs are very important for local economy because they create jobs and help areas develop, especially growing urban regions like KDMC area. However new business generally faces many problems in the beginning. They work in uncertain conditions and deal with different type of risk such as financial problem, operational issue and government regulations. If they do not have proper risk protection it can affect their stability and long-term growth. This research helps in understanding how financial protection methods especially insurance and risk management practices help startups in KDMC region. This research will use both primary data (like survey) and secondary data like (reports & article). This study aims to measure the level of awareness of insurance product among new startups, identify their challenges and determine how coverage against risk helps business continuity. The findings of study will show why proper risk management is important for startups and how it helps them grow in stable and sustainable way.

Health Insurance in India are generally confusing and slow. Many people do not understand the terms and conditions in the policy coverage, and it takes long time for claim approval. Hospitals and Insurance Companies also faces issues like fraud and counterfeit bills, excessive paperwork. This study aims at how a centralized digital system can connect policyholders, hospitals and insurance companies in one single platform. The aim is to simplify claim process, and make it fast and clear for everyone. The study is based on survey results about claim types; users experience and their satisfaction levels. The digital platform helps users to view all the details of their policy, upload their documents online, apply for claim easily, and track their claim process. This reduces the excessive paperwork, improves communication between stakeholders, and saves time. This study concludes that digital integration can build trust among stakeholders, reduces stress and reliable health insurance claim system.

As digital technology becomes more common in government, there is a growing demand for Civic-tech solutions that can actually fix real-world problems. This paper looks at the Layered Authentication and Voting Integrity System (LAVIS), a new B2G (Business to Government ) Gov-tech startup model designed to create a highly secure, offline-first electoral process. Finding the right balance between human- centric design and sustainable innovation, LAVIS uses a five-step authentication process: a physical KYC check, temporary RFID smart cards, live facial recognition, biometric scanning, and a deactivated cryptographic QR audit trail. Because the system tries to eliminate current gaps and loopholes. Offline database structure segregated into state, constituency, and booth levels ( SDB - CDB - PBD) , it essentially neutralizes remote cyber threats and mass data tampering. This study explores the technical viability of LAVIS and ensures it is legally compliance . Ultimately, it shows how digitally synergized startups can eliminate voter impersonation, cut long-term costs, and help restore public faith in the democratic process.

This study looks at the role and impact of AI-powered predictive consumer behavior models in the kalyan- dombivli municipal corporation KDMC area it explores how businesses use AI technology to analyze consumer data predict buying habits and offer personalized services in a more digital marketplace as e-commerce platforms digital payment systems and data-driven marketing strategies grow rapidly consumer decisions have become more tech-focused this shift creates a need to understand how predictive models are adopted and how effective they are at the regional level the study aims to clarify the concept and functioning of AI-powered predictive consumer behavior models check the awareness and use of AI tools among businesses in the KDMC area evaluate how AI supports marketing and decision-making and identify the challenges and limits of predicting consumer behavior through AI technology this research uses a mixed-method approach it collects primary data from 64 respondents including professionals business owners and consumers in the KDMC area it also looks at secondary data from academic literature and industry sources the findings show that businesses increasingly use AI tools for product research recommendations and purchase support especially among younger consumers and working professionals however while AI improves customer engagement and marketing success concerns about data privacy transparency and trust in algorithms significantly affect how consumers accept AI systems the research further shows that AI-powered predictive models improve business competitiveness and help in making informed decisions yet challenges like limited knowledge of technology infrastructure issues and ethical concerns prevent full implementation the study concludes that successfully adopting AI-powered predictive consumer behavior models in KDMC requires better transparency responsible data practices and stronger consumer trust it also calls for strategic adjustments to fit local market needs this topic is important because artificial intelligence is quickly changing consumer behavior and business practices in the digital economy however little research has looked at its effects at the municipal level especially in emerging urban markets like KDMC this study was chosen to provide a unique perspective by focusing on a local market rather than broader national or global trends by examining region-specific consumer behavior patterns levels of technology adoption and implementation challenges the research offers new insights that set it apart from existing studies enhancing its relevance and originality

The rapid adoption of Artificial Intelligence (AI) by startup platforms has significantly transformed digital services through automation, data analytics, and personalized user experiences. While these advancements improve efficiency and decision-making, they also raise important ethical concerns related to data privacy, transparency, fairness, consent, and accountability. This study examines the data ethics practices implemented by AI-driven startups and evaluates user awareness and perceptions regarding these practices. It explores how startups collect, process, and safeguard personal information while adhering to ethical standards and regulatory frameworks. Using a structured questionnaire, the research analyzes levels of user trust, concerns about potential data misuse, and perceptions of algorithmic fairness. The findings aim to identify gaps in current data governance systems and provide recommendations for strengthening responsible AI practices. The study contributes to the broader discussion on ethical AI by emphasizing the importance of robust data governance in building user trust and ensuring sustainable growth in the startup ecosystem.

This study explores the mental health challenges faced by employees working in remote startup teams. With the growing adoption of remote work models in startup environments, concerns regarding employee wellbeing, work-life balance, and organizational support have become increasingly significant. The primary objectives of the study were to analyze mental health challenges in remote startup teams, examine the impact of remote work on work-life balance, study the relationship between organizational support and employee wellbeing, and suggest strategies to improve mental health in such environments. The research adopted a descriptive methodology, and primary data was collected through a structured questionnaire administered to employees working in remote startup settings. The collected data was analyzed using percentage method and presented in tabular and graphical forms for interpretation. The findings reveal that a considerable proportion of respondents experience stress, burnout, sleep disturbances, and feelings of isolation. A majority reported difficulty disconnecting from work, indicating poor work-life balance, while many also perceived limited organizational support and discomfort in discussing mental health concerns. The study concludes that although remote work offers flexibility and productivity benefits, it also contributes to psychological strain in startup environments. Practical measures such as structured work schedules, right to disconnect policies, improved communication, and greater mental health awareness are recommended to enhance employee wellbeing and ensure sustainable organizational growth.

Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern business operations, particularly in the accounting profession. AI enables automation of repetitive tasks, improves accuracy, enhances data analysis, and supports decision-making processes in financial management. The integration of AI tools in accounting systems helps organisations reduce human error, increase efficiency, and improve financial reporting. This research study examines the role of Artificial Intelligence in the accounting system and its impact on accounting professionals and organisations. The study also explores how accountants and finance professionals perceive AI adoption in their work environment. A survey of 30 respondents in accounting and finance roles was conducted to understand their awareness, use, and impact of AI technologies in accounting. The findings suggest that AI significantly improves efficiency in accounting operations, enhances financial accuracy, and helps accountants focus on strategic decision-making rather than routine tasks. However, concerns about job displacement, technological adaptation, and skill development remain. The study highlights that the future of accounting will involve collaboration between human accountants and AI-powered technologies.

Digital wallets have become an important part of modern payment systems, especially among young adults. A digital wallet is a mobile application that allows users to store money and make payments using smartphones. This study focuses on the adoption of digital wallets and the digital payment behaviour of young adults. Young people prefer digital wallets because they are easy to use, save time, and reduce the need to carry cash. The study examines different factors that influence the use of digital wallets, such as convenience, speed, availability of smartphones, internet access, and attractive offers like cashback and discounts. It also considers security features such as passwords, fingerprint locks, and face recognition, which increase user trust in digital payments. At the same time, the study highlights some challenges, including fear of online fraud, data privacy issues, and technical problems like poor network connectivity. Understanding digital payment behaviour is important for banks, businesses, and service providers to improve digital wallet services. The study shows that young adults are the main users of digital wallets due to their familiarity with technology. Overall, the adoption of digital wallets is increasing, and they are expected to play a major role in future cashless payment systems

Innovation is a fundamental driver of economic growth and competitive advantage, particularly for new ventures that rely on novel technologies and creative business models to establish themselves in the market. However, rapid innovation often brings significant risks and ethical challenges, including concerns related to data privacy, consumer protection, environmental sustainability, social inequality, and regulatory compliance. These challenges highlight the need for a responsible approach to innovation that balances technological progress with ethical considerations and effective risk management. This study examines the concept of responsible innovation and its relevance to new ventures operating in uncertain and highly competitive environments. It seeks to analyze the types of risks associated with innovative activities and to identify the ethical issues that arise during the development and commercialization of new products and services. The study also explores the relationship between risk management and ethical responsibility, emphasizing how ethical decision-making can reduce potential harm and enhance long-term business sustainability. Furthermore, the research focuses on the role of entrepreneurs and stakeholders in promoting responsible innovation practices. It considers the influence of regulatory frameworks and public policies in guiding ethical behavior and ensuring accountability in innovative ventures. By integrating ethical principles into risk management strategies, new ventures can improve trust, protect stakeholders, and achieve sustainable growth. The findings of this study suggest that responsible innovation is not merely a moral obligation but also a strategic necessity for long-term success. New ventures that adopt responsible innovation practices are better positioned to manage uncertainty, minimize negative social and environmental impacts, and build strong reputations in the marketplace. The study concludes that embedding ethics and risk awareness into the innovation process is essential for creating resilient, trustworthy, and sustainable entrepreneurial ecosystems.

Artificial Intelligence (AI) is rapidly transforming financial systems across the globe, particularly in emerging markets where traditional risk assessment methods often fail due to data limitations and market volatility. This study examines the role of AI-based financial risk prediction models in improving decision-making, enhancing credit evaluation, and minimizing investment uncertainty in developing economies. The research explores how machine learning algorithms, predictive analytics, and big data integration contribute to identifying potential financial risks such as credit defaults, market fluctuations, and fraud detection. Emerging markets often face challenges such as inconsistent financial records, lack of transparency, and dynamic regulatory environments, making AI-driven solutions highly relevant. The study highlights the effectiveness of AI in improving forecasting accuracy, reducing human bias, and enabling proactive risk management. However, it also considers limitations such as data privacy concerns and technological barriers. The findings suggest that AI-based risk prediction systems can significantly strengthen financial stability and support sustainable economic growth in emerging markets.