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.

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.

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 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.

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.

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.