Optimizing the Cash-Cost Nexus in Project-Driven Engineering SMEs: Empirical Evidence from Pixel Controls Pvt. Ltd., Bangalore
Authors: Chennakeshava Reddy K, Mrs. Roopa Ajwal
Abstract: This paper explores the structural interface between upfront project costing frameworks and downstream working capital management (WCM) dynamics in project-driven engineering Small and Medium Enterprises (SMEs). Engineering SMEs exist within a distinctive cash-cost nexus, in which bespoke, contract-based business models produce highly customized component specifications, and unmetered engineering modifications result in a cascading liquidity deficit that traditional, steady-state corporate finance models cannot capture. The paper is based on a diagnostic mixed-methods research design and analyses primary survey data from n=82 cross-functional operational stakeholders and two years of audited financial statements, ledger accounts and component level Bills of Materials (BOM) extracted from the TallyPrime ERP system of Pixel Controls Pvt. Ltd. in Peenya Industrial Area, Bangalore. Significant data silos between engineering execution teams and financial controllers are revealed by statistical analyses, such as One-Sample $t$-Tests, Ordinary Least Squares (OLS) linear regression models, and descriptive Chi-Square tests. Empirical results show a systemic average project cost overrun of 10.74%, which is caused by unreported scope creep and fluctuating raw material prices (such as those of copper and fiber optics). OLS regression analysis reveals that initial cost estimation realism (beta = – 0.302, p = 0.009$) and absolute structural clarity (beta = -0.285, p = 0.005) are the main drivers of downstream variance minimization, despite One-Sample t-Tests confirming that baseline project pricing structures appear superficially transparent (p > 0.05). In addition, contract ambiguities and slow resolution of administrative queries consume significant working capital and push the company’s Days Sales Outstanding (DSO) to 88 days, slowing the velocity of internal cash flow. To address these structural inefficiencies, the study proposes a scalable strategic roadmap based on transition parameters from reactive bookkeeping to automated milestone-driven milestone triggers and real-time project variance dashboards.
A Study On Service Effectiveness and Customer Satisfaction at Sai Suchith Pest Control in The Bangalore Metropolitan Region
Authors: Vishweshwar, Bharath. M
Abstract: The rapid urbanization and infrastructural expansion in modern metropolitan cities have intensified the challenges of pest management in both residential and commercial ecosystems. This empirical study evaluates the service effectiveness levels, operational touchpoints, and long-term customer satisfaction drivers towards Sai Suchith Pest Control operating within the Bangalore metropolitan area—a tech-dense market characterized by high real estate density, specialized corporate infrastructure, and rising consumer health awareness. Utilizing a positivist, mixed-methods framework, primary data was cross-sectionally collected from N=250 active residential and commercial clients across major technology, residential, and industrial hubs using structured questionnaires. The empirical findings indicate that response time agility, treatment efficacy (complete pest elimination), eco-friendly chemical safety protocols, and technician professionalism exert the highest cumulative impact on consumer satisfaction. Inferential statistical testing revealed a strong, positive linear relationship between perceived service reliability and ultimate brand loyalty (r = 0.789, p < 0.05). Multiple linear regression analysis proved that treatment efficacy and response speed are the strongest statistical predictors of customer retention, explaining 69.8% of the variance in overall customer satisfaction. Conversely, unexpected pest recurrence post-treatment and insufficient communication regarding preparation protocols were identified as primary friction points. The paper concludes with actionable operational frameworks for optimizing service schedules, improving post-treatment warranty management, and fostering brand loyalty in competitive urban service sectors.
Data-Driven Decisions – Leveraging HR Analytics for Talent Optimization and Organisational Effectiveness in Financial Services
Authors: Samansiri Sooriyagama
Abstract: The analysis investigates how financial service organisations benefit from HR analytics to achieve superior talent management and enhanced organisational effectiveness. Financial institutions use data insights to strengthen recruitment, maintain employee retention rates, and unlock maximum staff performance while integrating workforce approaches to business purposes. This research assesses HR analytics execution platforms while discovering valuable advantages with implementation hurdles and offering vital implementation strategies. The research targets human resources professionals and decision-makers to assist them with implementing analytics capabilities within their strategic planning to achieve enduring business accomplishments.
Blockchain-Based Secure Data Management For Distributed Applications
Authors: Dr. S. Kumari, Mrs. M. Menaka
Abstract: The advent of distributed applications and cloud computing is posing considerable difficulties in securing the data. Centralized data management systems are prone to failures, breaches of security, and lack of transparency. In this research work, we propose an effective model that provides secure data management using blockchain technology by adopting decentralized storage, smart contracts, and cryptography. In the proposed model, we use the InterPlanetary File System (IPFS) for decentralized storage of data, Ethereum blockchain for managing the metadata of the data immutably, and role-based access control through smart contracts. Our performance evaluation shows that our model provides a data availability rate of 99% when 50% chunk is lost. Our proposed framework gives an improvement in data processing time by 2.54 times and 18.3% improvement in data integrity verification over previous methods.
Blockchain-Driven Intelligent Supply Chain Management For Enhanced Security, Transparency, And Traceability
Authors: Dr.B.Balaji Srinivasan
Abstract: Among the various problems that have persisted in global supply chains include data silos, information asymmetry, and vulnerability to fraud. In this paper, a blockchain-based intelligent management of the supply chain model has been suggested, involving distributed ledger technology, smart contracts, and Internet of Things for real-time tracking. The model features a four-tiered architecture consisting of data ingestion, blockchain network, smart contract automation, and application tiers. Tasks that include registering stakeholders, verifying the authenticity of the goods, transferring ownership, and verifying compliance can be automated through smart contracts. The solution offers the ability to process up to 200 transactions per second with an 18% reduction in gas costs as opposed to conventional solutions. Trace back time reduces from 95 seconds to 8 seconds, and the consumer trust index grows by 70%.
Artificial Intelligence-Driven Decision Making For Modern Management Practices
Authors: Dr R Janani, Mrs S Saranya Jegajothi, Dr Shankar Ganesh, Dr. Vijaya Rani N
Abstract: The incorporation of AI technologies into decision making in organizations is an example of a paradigm change in contemporary management processes. In this paper, we look at how AI-based decision making increases the efficiency of managers during strategic, tactical, and operational decision making. Based on the theories of decision-making theory, dynamic capabilities theory, and socio-technical system theories, we have developed an effective model of decision making where capabilities of artificial intelligence are combined with human judgment in order to maximize organizational results. The methodology of research includes the use of deep reinforcement learning and explainable AI technologies for more transparent and efficient decision making. Quantitative results based on the analysis of datasets from various organizations show that decision making assisted by AI increases decision speed by 58% and strategic efficiency by 41%.
Impact Of AI Adoption On Business Process
Authors: Dr. C.Addlin Pooviga, Dr. B. X. Jonitha Stany Mary
Abstract: Artificial intelligence (AI) is changing the face of business processes, but the nature of this change is still a point of controversy. This paper aims to conduct an evaluation of the effect of AI adoption on business processes by considering the insights revealed by recent research. It was found out that adoption of AI brings about positive results in several dimensions of business processes. Quantitative information showed that tasks' completion rate with assistance of AI amounts to 78%, whereas in case of no assistance, this rate is 54%; moreover, up to 62.5% of efficiency increase is attainable via AI automation. AI fluency, which can be understood as integration of AI into thousands of tasks, is associated with 1.7× sales conversions increase, 1.5× customer satisfaction improvement, 35% productivity increase in software development, and 30% manufacturing throughput increase. However, the path to achieving all those benefits is a process that involves stages from personal productivity to company productivity and is accompanied by certain obstacles, such as high costs of implementation, etc.
Operating, Financial, And Combined Leverage As Determinants Of Profitability: Evidence From Selected BSE-Listed Pharmaceutical Companies (2016–2025)
Authors: Ms. Pooja Arvindbhai Pandya, Dr. Rajesh A. Mulchandani
Abstract: This study investigates how operating, financial, and combined leverage influence the profitability of selected pharmaceutical firms listed on the Bombay Stock Exchange (BSE) for the years 2016 to 2025. Five top pharmaceutical companies were chosen based on their market capitalization, and the research relies on secondary data gathered from their standalone annual reports. The analysis utilized the Degree of Operating Leverage (DOL), Degree of Financial Leverage (DFL), Degree of Combined Leverage (DCL), and Earnings Per Share (EPS). Descriptive statistics, One-Way ANOVA, and Pearson's correlation were applied to explore the relationship between leverage and profitability. Results indicate there is no significant difference in operating, financial, and combined leverage among the selected companies, while EPS shows a significant difference. The study concludes that maintaining an optimal level of leverage is essential.
Quality Evaluation Of Cardamom
Authors: Fida Sherin, Dr. G P Meena, Dr. Santhosh Lal Jat
Abstract: Cardamom (Elettaria cardamomum Maton), known as the “Queen of Spices”, is one of India’s most valuable export commodities because of its unique aroma, flavor and essential oil content. The quality of export-grade cardamom depends on scientific post-harvest processing and comprehensive laboratory quality evaluation. The present study aimed to evaluate the processing operations and quality parameters of export-grade cardamom processed at VEBARO Trading & Export Pvt. Ltd., Wayanad, Kerala. The processing stages included harvesting, washing, grading, drying, polishing, sorting, quality inspection, packaging and storage. Quality evaluation was performed through physicochemical, microbiological and chemical analyses. Parameters such as moisture content, volatile oil, total ash, acid insoluble ash, extraneous matter, microbial load, pesticide residues and heavy metals were assessed according to export quality requirements. The results indicated that scientifically processed cardamom maintained desirable moisture content, high volatile oil retention and acceptable microbiological quality, meeting the prescribed export standards. The study concludes that implementation of Good Manufacturing Practices (GMP), Good Hygiene Practices (GHP) and systematic laboratory quality assurance significantly improves product quality, shelf life and international market acceptance.
Bridging Markets: The Expansion of Korean Business, Innovation and Fashion in India and Its Impact on Global Trade
Authors: Vanshika Choudhary, Kumari Deepika Singh, Prachi Singh, Mary Christina, Sakshi Mahind, Muskaan, Anusha
Abstract: South Korea has become a global leader in technology, business innovation, fashion, and consumer industries. In recent years, Korean businesses and brands have expanded significantly in India through strategic investments, digital technologies, product localization, and strong marketing practices. At the same time, the growing popularity of Korean culture, known as the Korean Wave (Hallyu), has increased the demand for Korean fashion, beauty, skincare, and lifestyle products among Indian consumers. This report explores how Korean businesses have successfully entered and expanded within the Indian market. It examines market expansion strategies, technological innovation, digital consumer engagement, visual branding, and the influence of Korean fashion, beauty, biotechnology, and wellness industries. The report also highlights how these developments have strengthened economic and cultural ties between India and South Korea. Overall, the study shows that innovation, cultural influence, and consumer-focused business strategies have played a major role in the growing success of Korean businesses and their contribution to global trade.
A Study On Employee Engagement at HCL Technologies
Authors: Vanthadupula Nikhil, Dr. G. Radha Krishna Murthy
Abstract: Employee engagement has emerged as a critical factor influencing organizational success in the modern business environment. Organizations increasingly recognize that engaged employees contribute significantly to productivity, innovation, customer satisfaction, and long-term sustainability. This study examines the level of employee engagement among employees of HCL Technologies and identifies the key factors influencing their commitment and involvement in organizational activities. The research adopts a descriptive research design and utilizes primary data collected from 120 employees through a structured questionnaire. The study focuses on variables such as career development, managerial support, communication, workplace relationships, recognition, and job challenges. The findings reveal that employees generally demonstrate positive engagement levels, particularly in areas related to career growth opportunities and organizational support. However, concerns regarding workload management and recognition practices indicate opportunities for improvement. The study concludes that employee engagement can be strengthened through supportive leadership, effective communication, career development initiatives, and a positive work environment.
A Study on Equity Analysis with Reference to the Information Technology Sector at Emkay Global Financial Services Limited, Hyderabad
Authors: Katike Ghousuddin, Professor Dr. S. Narender
Abstract: Equity analysis serves as a cornerstone in the investment decision-making process, enabling investors to evaluate both the financial health and market valuation of publicly traded firms. The Information Technology (IT) sector in India has witnessed remarkable expansion, driven by rapid technological innovation, widespread digital transformation initiatives, and a surge in global demand for software and technology-enabled services. This research concentrates on conducting an equity analysis of chosen IT enterprises, with specific reference to Emkay Global Financial Services Limited, located in Hyderabad. The investigation assesses the financial performance, profitability trajectory, earnings capacity, and competitive market standing of the selected IT firms through the application of fundamental analysis methodologies. The primary goal of this study is to equip investors with the knowledge required to make well-informed investment choices by examining key financial metrics such as Earnings Per Share (EPS), Price-to-Earnings Ratio (P/E), Net Profit Margin, Return on Equity (ROE), and Dividend Yield. The results demonstrate that India's IT sector continues to present substantial growth prospects and remains a compelling destination for long-term capital allocation.
Comparative Performance Analysis of Private and Public Sector Mutual Funds: Evidence from IIFL Ltd., Hyderabad
Authors: Mohammad Ameer Sohail, Professor Dr. S. Narender
Abstract: Mutual funds have emerged as one of the most accessible and systematically managed investment instruments in India, particularly for retail investors seeking professionally guided market participation. This article examines the comparative performance of three private sector mutual funds — ABSL Frontline Equity Fund, Axis Long Term Equity Fund, and ICICI Prudential Balanced Advantage Fund — against three public sector counterparts: LIC MF Balanced Advantage Fund, SBI Exchange Traded Fund Sensex, and UTI Nifty ETF. Performance evaluation is conducted using six quantitative metrics: standard deviation, alpha, beta, R-squared, Sharpe ratio, and expense ratio. The empirical findings indicate that while SBI ETF Sensex delivered the highest one-year return of 10.72%, ICICI Prudential Balanced Advantage Fund demonstrated the most favourable risk-adjusted profile with the lowest standard deviation (13.86%) and the highest alpha (1.25). The study concludes that neither sector categorically outperforms the other, and investment decisions should be grounded in individual risk tolerance, investment horizon, and return expectations.
A Study on Supply Chain Management in Fmcg Industey at Marico
Authors: Askani Shanker, Associate Professor Dr. Sivaji Jinka
Abstract: Supply Chain Management (SCM) plays a vital role in the Fast-Moving Consumer Goods (FMCG) industry, where products must reach customers quickly, efficiently, and at low cost. This study focuses on the supply chain management practices followed by Marico Limited, one of India's leading FMCG companies known for brands such as Parachute, Saffola, Hair & Care, Livon, Set Wet, and Nihar Naturals. Marico has built an extensive supply chain network that includes procurement, manufacturing, warehousing, transportation, distribution, and retail operations across India and international markets. The main objective of this study is to understand how Marico manages the flow of raw materials, production, inventory, and product distribution to ensure timely delivery and customer satisfaction. The study also examines the company's use of modern technologies, demand forecasting, supplier relationship management, and digital solutions to improve supply chain efficiency. Marico has increasingly invested in analytics, digital planning, responsible sourcing, and agile distribution to improve responsiveness and resilience The research highlights that an effective supply chain helps Marico reduce operational costs, improve inventory management, maintain product quality, and respond quickly to changing consumer demand. The company has also strengthened sustainable sourcing, supplier partnerships, and traceability, making its supply chain more reliable and environmentally responsible. The distribution network of Marico is one of its major strengths. The company serves millions of consumers through a large network of distributors, wholesalers, retailers, supermarkets, and online platforms. Effective transportation and logistics systems enable the company to deliver products quickly and efficiently across urban and rural markets. Warehousing facilities are strategically located to reduce transportation costs and improve delivery speed. Technology plays a significant role in Marico's supply chain operations. The company uses Enterprise Resource Planning (ERP) systems, supply chain analytics, automation, and digital tracking systems to monitor product movement and improve decision-making. These technologies help increase visibility, enhance operational efficiency, and reduce delays in the supply chain. The study also highlights the challenges faced by Marico, including fluctuations in raw material prices, changing consumer preferences, transportation disruptions, and increasing competition in the FMCG industry. To overcome these challenges, the company continuously improves its supply chain processes through innovation, supplier collaboration, risk management, and sustainability initiatives.
School-Based Environmental Governance and Plastic Pollution Reduction: An Integrated Framework for Sustainability Education and Ecological Citizenship in Australia
Authors: Dr. Muhammad Imran Ashraf
Abstract: Purpose: This study examines how Australian schools contribute to environmental governance by reducing plastic pollution through sustainability education and fostering ecological citizenship. It investigates the institutional, educational, and behavioral mechanisms through which schools support long-term environmental sustainability. Design/methodology/approach: A qualitative research design was employed using documentary analysis of Australian government policies, educational frameworks, sustainability reports, and peer-reviewed literature. Data were analyzed through thematic analysis guided by Environmental Governance Theory, Institutional Theory, Social Learning Theory, and Ecological Citizenship Theory. Findings: Six interrelated themes emerged: (1) plastic pollution as an environmental governance challenge; (2) schools as strategic governance institutions; (3) experiential environmental education as a driver of behavioral transformation; (4) institutional culture as a facilitator of sustainable practices; (5) ecological citizenship as an outcome of sustainability education; and (6) institutional and resource barriers affecting implementation. The findings indicate that schools strengthen environmental governance by integrating sustainability into leadership, curriculum, operational practices, and community partnerships. Practical implications: The study recommends stronger policy integration between environmental and educational sectors, increased investment in sustainability leadership and teacher capacity, and enhanced collaboration among schools, governments, industry, and communities to support long-term behavioral change. Originality/value: The study develops an integrated conceptual framework linking environmental governance, institutional processes, social learning, and ecological citizenship. It re-conceptualizes schools as strategic environmental governance institutions that translate public policy into sustainable behaviors and contribute to the achievement of the United Nations Sustainable Development Goals.
A Study on Currency Derivatives at ICICI Bank
Authors: Rajulapati Subramanyam, Professor Dr. K. Pushpa Latha
Abstract: The present study focused on analysing currency derivatives with specific reference to selected currency pairs, examining their trading behaviour, open interest dynamics, and market depth. The research aimed to evaluate how different indicators such as volume, price movement, and order book structure influence trading decisions and risk-return characteristics. By analysing market data for a specified expiry period, the study provided insights into the functioning of currency derivatives and the factors affecting their performance. The findings revealed that liquidity and market participation play a significant role in determining the efficiency of currency derivative contracts. Highly traded contracts exhibited stable price behaviour and offered better trading opportunities, while low-liquidity contracts were associated with higher risks and inefficiencies. The analysis of open interest further highlighted its importance in understanding market sentiment, indicating whether positions were being accumulated or liquidated. Additionally, the study emphasized the relevance of order book analysis in identifying short-term trading signals. The study concluded that a comprehensive analytical approach combining multiple indicators is essential for effective decision-making in currency derivatives trading. It also highlighted the need for investors to focus on liquidity, market trends, and risk management while engaging in derivative markets. The research contributes to the existing body of knowledge by providing practical insights and a structured analytical framework for understanding currency derivatives, thereby aiding both academic research and real-world trading applications.
International Capital Inflow and Economic Growth in Nigeria
Authors: Christian FridayAgbo, Fredrick Onyebuchi Asogwa, Nicholas Attamah
Abstract: This study examined the impact of international capital inflow on economic growth in Nigeria using time series quarterly data from 1991Q1 to 2023Q4. The specific objectives of the study were to evaluate the impact of international capital inflow on economic growth; determine the direction of causality between international capital inflow and economic growth and to determine the nature of the relationship between international capital inflow and economic growth in Nigeria. The study carried out pre-estimation tests to check for the order of integration and the existence of a long run relationship among the variables. Classical Multiple Regression Model and the Autoregressive Distributed Lag (ARDL) model were applied. The Classical Multiple Regression Model was adopted because of its Best Linear unbiased Estimator (BLUE) property while ARDL was used to capture both long run and short run dynamics. The variables used in the study were the growth of the Real Gross Domestic Product (RGDPgr), Foreign Direct Investment (FDI), Exchange Rate (EXR), Remittances, Trade Openness (TOP)), and the Gross Fixed Capital Formation (GFCF). The findings of the study showed a significant negative impact of the Foreign Direct Investment (FDI), Gross Fixed Capital Formation (GFCF) on economic growth in Nigeria. The result also showed that Exchange Rate (EXR), Remittances (REM), and Trade Openness (TOP) had positive significant impact on economic growth in Nigeria. The result of the ARDL indicated that remittances and gross fixed capital formation had negative and insignificant impact on economic growth in Nigeria while Trade Openness (TOP) had significant positive relationship with economic growth both in the short run and the long run. The result of the co-integration test indicated a long run relationship between international capital inflow and economic growth in Nigeria. The study also found no evidence of direction of causality relationship between capital inflow and economic growth. The study therefore recommends the need to consciously improve the business environment such as insurgence, kidnapping and other related security threat to enable capital inflow contribute more to economic growth in Nigeria. The study also recommends that government should develop Nigeria’s local financial markets for easy inflow of capital.
A Study on Employee Perception of API Quality in Radison Pharma Pvt Ltd
Authors: Penmetsa Indu Naga Kanyaka Durga, Associate Professor Dr. Vellala Subramanya Ramamurty
Abstract: To guarantee the safety, effectiveness, and dependability of medications, the pharmaceutical business mostly depends on the quality of Active Pharmaceutical Ingredients (APIs). This study, "A Study on Radison Employees on API Quality for Sales," looks at how employees at Radison Labs Pvt. Ltd. perceive the effect of API quality on sales success and client trust. Radison Labs is an Indian bulk drug manufacturing company that produces APIs and intermediates with a heavy emphasis on technical know-how, regulatory compliance, and quality standards. The study's main goal is to examine how API quality affects long-term business relationships, sales growth, and customer confidence. Employee perceptions of quality assurance procedures, sourcing dependability, regulatory compliance, product consistency, and their role in market competitiveness are also assessed in this study. Employees involved in production, quality control, marketing, and sales were given a standardized questionnaire to complete in order to gather data for the study.
Evaluating the Performance of Government Health Insurance Schemes in Karnataka: A Comparative Approach
Authors: Associate Professor Dr. Ramapriya H D
Abstract: The government is committed to provide „Health for all‟ and adequate financing is critical to ensure it. Universal Health Coverage (UHC) which has subsequently replaced the “Health for All” agenda defines “ensuring that all people can use the promote, preventive, curative and rehabilitative health services they need, of sufficient quality to be effective, while also ensuring that the use of these services does not expose the user to financial hardship.. The present study is engaged in a detailed understanding of existing government health insurance schemes of Karnataka state. An empirical study is being endeavoured to capture the perceptions on government health insurance schemes. . Many rural and economically disadvantaged communities in Karnataka struggle with access to formal banking services, which affects their ability to register for and utilize health insurance benefits. The lack of awareness and financial literacy further limits their participation in such schemes. This gap affects the effective utilization of health insurance schemes, as financial inclusion is crucial for accessing and benefiting from such programs.
Study on Change & Organization Development with Reference to Cipla LTD.
Authors: Podugu Srilekha, Assistant Professor Dr. P. Girija Sri
Abstract: This study investigated employee perceptions toward organizational change and development within Cipla Ltd, a prominent pharmaceutical organization. The research addressed a pressing managerial concern regarding limited empirical understanding of employee attitudes during continuous organizational transformation. It pursued objectives encompassing perception assessment, factor analysis, and examination of relationships with job satisfaction. The study additionally sought to generate practical suggestions supporting HR managers within the organization. A quantitative research design guided the investigation, employing a structured, closed-ended questionnaire administered to one hundred employees. Purposive sampling ensured respondent relevance to the research objectives concerning change and development perceptions. Key variables examined included communication, employee involvement, leadership support, training adequacy, and resource availability. Statistical tools, including frequency analysis, regression, and analysis of variance, facilitated rigorous empirical examination of collected data. Findings revealed generally favourable, though variable, employee perceptions toward organizational change and development at Cipla Ltd. A significant relationship emerged between change management practices and employee job satisfaction, alongside noTable 4. variation across job levels. These findings carried important implications for strategic HR practice, emphasising communication, leadership development, and resource allocation. The study ultimately contributed valuable, context-specific insights supporting evidence-based organizational decision-making within pharmaceutical enterprises.
A Study on Sales and Service at Harsha Toyota
Authors: Bavandlaplly Kalyan, Associate Professor Dr. Vellala Subrahmanya Ramamurty
Abstract: The study had examined sales and service in a dealership environment with particular emphasis on customer perception and customer intention. It had been undertaken in recognition of the fact that modern customers had evaluated organisations not solely through product quality, but through the combined influence of sales communication, service responsiveness, and postpurchase support. The research had adopted a quantitative design and had collected primary data from 100 customers through a structured questionnaire. Secondary information had been drawn from journals, books, internet sources, and organisational material in order to support the conceptual framework and contextual interpretation of the findings. The analysis had shown that customers had generally perceived the sales process favourably, especially in relation to the clarity of explanation, prompt response, and professionalism of the showroom environment. After-sales service had also been viewed positively in terms of timeliness, complaint handling, and transparency. The results had further demonstrated that customer perception had maintained a significant positive relationship with customer intention, indicating that favourable experiences had tended to encourage repeat purchase intention and recommendation behaviour. The statistical analysis had therefore supported the contention that customer-facing practices had shaped behavioural outcomes in meaningful ways.
Effect of Digital Marketing Strategies on Consumer Purchasing Behavior in the Home Appliance Market
Authors: Assistant Professor Dr. P Subburaj, Assistant Professor K. Valarmathi, Assistant Professor J. Jabasteen
Abstract: This paper examines how digital marketing strategies directly influence consumer purchasing behavior in India's at-home appliance market. The main research question investigates how digital channels shape customer preferences and influence buying decisions. The goal is to identify specific mechanisms—such as targeted campaigns, influencer participation, and online demonstrations—that encourage online consumer engagement in this sector. The analysis combines insights from relevant literature, secondary data, and case studies to show how digital engagement has shifted consumer focus from traditional media to interactive online touchpoints. Notably, brands like LG and Samsung have utilized digital-first initiatives and partnerships to boost engagement and conversion rates, as illustrated by LG’s smart refrigerator launch and Samsung’s targeted YouTube collaborations. These examples demonstrate the increasing significance of digital marketing in informing, persuading, and converting consumers through product demonstrations, comparisons, and reviews, ultimately making digital channels a key part of purchase decisions in the home appliance market.
Igbonisation: Portable Institutionalism And Networked Resilience: The Institutional Political Economy Of Igbo Enterprise
Authors: Christian Osita Godson, Ebubechukwu Osita Godson
Abstract: How can durable economic coordination emerge where formal state capacity is weak, uneven, or distrusted? This article develops portable institutionalism through an historically informed analysis of Igbo socioeconomic institutions in southeastern Nigeria and their reproduction across locations. It conceptualises Igbonisation as the bounded empirical process through which apprenticeship, associational governance, relational finance, reputation, and informal dispute resolution travel with participants and are reconstructed in new commercial settings. The argument bridges micro-level accounts of indigenous entrepreneurship and macro-level theories of state capacity by identifying a meso-institutional architecture that lowers transaction costs, circulates skills and capital, and supports enterprise regeneration. The article further introduces networked resilience to explain how mutually reinforcing human, social, institutional, and reputational resources absorb shocks and reproduce productive capacity. The framework neither romanticises informality nor treats relational governance as a substitute for capable states: exclusion, hierarchy, regulatory evasion, and limits to technological upgrading define its boundary conditions. Portable institutionalism contributes to institutional economics by showing how institutions can be territorially mobile, socially enforced, and developmentally consequential beyond the formal organisations of the state.
Transforming Finance with AI: Understanding the Influencing Factors
Authors: Associate Professor Dr. Manisha Kaushal Arora
Abstract: The banking industry is undergoing a transformation thanks to artificial intelligence (AI), which is changing operational environments and conventional procedures. This study looks at the main variables affecting AI's ability to propel efficiency, innovation, and customer-focused financial services solutions. AI helps organisations make better decisions, identify fraud, and maximise risk management by automating repetitive operations and enabling predictive analytics. However, organisational, cultural, technological, and regulatory factors influence its adoption. AI's influence in finance is made possible by technological developments like big data analytics, machine learning, and natural language processing. These developments provide improved portfolio management, individualised financial services, and algorithmic trading. The efficiency of AI integration is determined by organisational elements, including as leadership commitment, investment in AI infrastructure, and staff skill development, which are equally significant. Regulatory frameworks that prioritise accountability, transparency, and data privacy have a big impact on the adoption of AI. The pace and extent of AI deployment are being shaped by ethical issues, which include resolving algorithmic bias and guaranteeing justice. These factors are increasingly becoming essential to legal requirements. Furthermore, public acceptability is important since worries about data security, employment displacement, and ethical quandaries frequently erode faith in AI systems. AI presents financial institutions with revolutionary prospects in spite of these obstacles. It lowers expenses, improves client interaction, and stimulates creativity. Its potential is further highlighted by emerging trends like the emergence of finance companies and the integration of AI with blockchain. In order to fully realise AI's promise, this article emphasises how crucial it is to solve obstacles while seizing possibilities. To successfully manage AI-driven transformation, financial institutions need to take a strategic approach to investment, regulatory compliance, and stakeholder engagement. The finance industry can promote innovation, drive sustainable growth, and create a more robust and inclusive financial ecosystem by comprehending the elements affecting AI adoption.
Towards a Frictionless Architecture of Hybrid Work Ecosystem: A Multidimensional Narrative Amalgamation of Spatial, Technological, Strategic and Experiential Transitions
Authors: Assistant Professor Pushpa Prajapati, Professor Dr. Shweta Bhatia
Abstract: Transitions in spatial, technological, organisational strategies and employer-employee experiences have disintegrated the organisational systems post pandemic. This narrative review synthesises extant literature to highlight frictions caused by the factors of hybrid work flexibility and focuses on building a fluid hybrid work ecosystem that can overcome the headless chicken zig-zag shuttling and firefighting instincts. A narrative review approach was necessary to capture radically fragmented and diversified nuances of hybrid work and provide a single integrated seamless architecture of a hybrid work ecosystem, not synthesised previously. This narrative review was based on wide verifiable reference base as against a narrow exhaustive literature through replicable search protocol or formal quality appraisal. Targeted searches identified peer-reviewed journals and practitioner gray literature published between 2019 to 2026 reporting accelerated implementation and adoption of hybrid work. A prominent indication of the review is that hybrid work frictions originate from multidimensional causes. Resource allocation and availability at multi-locations, technological gaps, unclear policies and psychological burdens compound and produce a cumulative friction on both the employer and the employee and when addressed in isolation fails to provide flawless hybrid work ecosystem. These findings do corroborate with gray literature reports on practitioner scale. The findings are highly illustrative and provide a conceptual thematisation which should not be treated as an evidence map but used as a precursory guide for future systematic and empirical studies. This study is the first of its kind to integrate the fragmentated and diverging aspects of various hybrid work frictions, proposing an integrated hybrid work ecosystem that balances boundary spanning, technology, organisational reforms and psychological obstructions that informs sustainable organisational designs and future interdisciplinary research.
The Impact Of Marketing Strategies On Consumer Purchase Behaviour In The Automobile Industry
Authors: Nandan Pari, Rachana D
Abstract: The automobile industry has become highly competitive with increasing use of digital marketing and customer relationship management strategies. This study examines which marketing approaches work best for automobile dealerships, specifically focusing on selected automobile dealerships in Karnataka. A survey was conducted among 60 respondents using a 30-item questionnaire covering three areas: traditional marketing impact, digital marketing effectiveness, and CRM performance. Results show that customers generally agree all three strategies work (scores above 4 out of 5). However, regression analysis revealed that CRM effectiveness is the strongest predictor of purchase intention (β = 1.038, p < 0.001). Age significantly affects digital marketing influence, with younger customers (18-35 years) scoring 4.80 compared to seniors scoring 2.10. Females value CRM more than males (4.58 vs 4.07). The study recommends focusing on CRM improvements, age-based marketing segmentation, and reducing celebrity endorsements which scored lowest (3.40).
A Comprehensive Analysis of Hashtag Usage as A Digital Branding and Customer Engagement
Authors: Karthik N B, Dr. Suma
Abstract: The rapid growth of social media platforms has, kind of, changed the way businesses talk to customers and build their brand identity. In that whole digital marketing space, hashtags have become this solid mechanism for boosting brand visibility, getting more audience interaction, and even making online communities feel more alive. This study looks at how hashtag usage works as a kind of digital branding and customer engagement tool inside the healthy food industry. Here the research tries to understand how strategic hashtag implementation can affect brand awareness, audience interaction, how easy the content is to discover, and whether customers keep coming back. For this, a mixed-method approach is used, meaning social media content analysis, engagement metrics evaluation, and also a check on consumer perception all included. The findings suggest is that branded hashtags, industry-specific ones, and those tied to campaigns do matter, quite a lot. They appear to contribute to higher reach, stronger engagement rates, and better brand recognition overall. The study also shows that when a healthy food brand uses hashtags well, it can connect with health-conscious consumers more effectively. It also helps spread product awareness, and it encourages user-generated content, which feels like free momentum. Overall, the research points out that hashtag strategies shouldn’t be treated like something isolated. Instead, they should be integrated into broader digital marketing frameworks if the goal is to maximize customer engagement and gain a competitive edge in the healthy food sector.
A Study on the Financial Performance of Prarthana Engineering Pvt. Ltd.: An Analysis of Profitability, Liquidity, and Financial Stability
Authors: Akshay Kumar H M, Rachana D
Abstract: This study examines the financial performance of Prarthana Engineering Pvt. Ltd., a small-scale engineering firm in Bangalore, focusing on profitability, liquidity, and financial stability as perceived by its employees. A structured questionnaire with 30 Likert-scale items was administered to 60 employees across different departments, experience levels, and age groups. The collected data were analysed using SPSS version 20. Descriptive statistics, reliability analysis (Cronbach’s alpha), independent t-tests, one-way ANOVA, chi-square tests, Pearson correlation, and linear regression were performed. The results indicate that employees generally agree that the company has sound profitability (mean 3.92), liquidity (mean 3.86), and financial stability (mean 3.95). The questionnaire demonstrated excellent reliability (α = 0.997). Significant differences were observed across departments, experience levels, and age groups, with senior management and experienced employees rating financial health much more positively than their junior counterparts. Profitability emerged as the strongest predictor of overall financial stability (R² = 0.926, p < 0.001). The study recommends improved internal financial communication, especially for new hires and operational staff.
An Analysis of Promotional Strategies and Customer Engagement in the Digital Era
Authors: Mallikarjuna M B, Ms. Rachana. D
Abstract: In the contemporary business environment, organizations increasingly rely on promotional strategies to attract customers, strengthen brand awareness, and improve customer engagement. The rapid expansion of digital technologies and social media platforms has transformed the manner in which businesses communicate with consumers. Traditional promotional methods are now complemented by digital marketing techniques such as social media campaigns, influencer collaborations, content marketing, and community-based engagement initiatives. Customer engagement has emerged as a critical factor influencing organizational success. Engaged customers are more likely to develop positive perceptions toward brands, participate in promotional activities, recommend products and services to others, and remain loyal over time. As a result, businesses continuously invest in promotional strategies that enhance customer interaction and strengthen brand relationships. This study examines the influence of promotional strategies on customer engagement and brand perception. The research explores how promotional activities affect customer behavior, purchasing decisions, satisfaction levels, and loyalty. The findings indicate that effective promotional efforts positively contribute to customer engagement and strengthen overall brand image. The study highlights the growing importance of digital promotion in maintaining competitiveness and fostering long-term customer relationships.
Management Of Financial Services In India: A Study Of Banking, Insurance And Mutual Fund Services
Authors: Ms. Bindu Tripathi, Khilendra Kumar
Abstract: Artificial Intelligence (AI) has emerged as a transformative technology in the financial services industry, significantly influencing banking, insurance, and investment management. In India, the rapid adoption of digital financial services, supported by government initiatives, technological advancements, and changing customer expectations, has accelerated the integration of AI into financial operations. This research paper examines the role of Artificial Intelligence in enhancing the efficiency, accuracy, and customer-centricity of financial services in India. The study aims to analyze the impact of AI on operational performance, risk management, fraud detection, customer service, and investment decision-making while identifying the opportunities and challenges associated with its implementation. The research is based on a descriptive and analytical approach using secondary data collected from reliable sources, including reports published by the Reserve Bank of India (RBI), Securities and Exchange Board of India (SEBI), Insurance Regulatory and Development Authority of India (IRDAI), annual reports of financial institutions, and recent peer-reviewed journal articles. The collected data were analyzed through comparative analysis, trend analysis, and interpretation of published statistics to evaluate the growing adoption of AI in the Indian financial sector. The findings indicate that AI has substantially improved operational efficiency, customer experience, fraud prevention, credit assessment, and personalized financial services. However, issues related to data privacy, cybersecurity, regulatory compliance, ethical concerns, and the shortage of skilled professionals continue to pose significant challenges. The study concludes that Artificial Intelligence will remain a key driver of innovation and sustainable growth in India's financial services industry. Effective governance, continuous technological investment, and a balanced regulatory framework are essential to maximize AI's benefits while ensuring transparency, security, and consumer trust.
A Study of Human Resource Management in Banking with Special Reference of Employee Training at State Bank of India
Authors: Dr. Sadaf Khan, Associate Professor, Shivam Mishra.
Abstract: Human Resource Management (HRM) plays a vital role in enhancing employee performance and organisational effectiveness in the banking sector. Among various HRM practices, employee training is a key factor in improving employees' knowledge, skills, and competencies to meet the dynamic demands of the banking industry. This study examines the employee training practices at State Bank of India and evaluates their impact on employee performance, job satisfaction, and organisational productivity. The research focuses on the objectives, methods, effectiveness, and challenges of training programmes implemented by the bank. The study is based on both primary and secondary data collected through questionnaires, interviews, annual reports, journals, and other relevant sources. The findings indicate that well-structured training programmes significantly improve employees' technical skills, customer service quality, and adaptability to technological changes. The study concludes that continuous training and development are essential for maintaining employee competence, increasing operational efficiency, and ensuring sustainable growth in the banking sector.
An Intelligent Web-Based Missing Person Identification System Using Optimized Face Recognition
Authors: Assistant Professor T.Balanandhi, E. Pravalika
Abstract: The increasing number of missing and unrecognized persons has become a major social and public safety concern worldwide. Traditional methods of identifying missing individuals rely heavily on manual investigations, newspaper advertisements, police records, and public notifications, which are often time-consuming and inefficient. Recent advancements in artificial intelligence and computer vision have enabled the development of intelligent face recognition systems capable of automatically identifying individuals with high accuracy. This paper presents an intelligent web-based missing person identification system that integrates an optimized face recognition algorithm with the Django web framework to facilitate rapid identification of missing and unrecognized persons. The proposed system utilizes Histogram of Oriented Gradients (HOG) for face detection, dlib facial landmark extraction, 128-dimensional face encoding, and Euclidean distance-based face matching to compare uploaded images against a centralized database of known individuals. The web application incorporates Google Authentication, SQLite database management, secure user registration, anonymous reporting, and automated alert generation to improve accessibility and public participation. The system maintains separate databases for known and unknown individuals while allowing authorized users to upload missing person information and register unidentified persons through an intuitive web interface. Experimental evaluation demonstrates that the optimized face recognition model effectively identifies matching facial images while rejecting blurred, duplicate, fake, and non-face images, thereby improving the reliability of the identification process. The integration of artificial intelligence, face recognition, and web technologies provides an efficient, secure, and scalable solution for assisting law enforcement agencies and the general public in locating missing persons while reducing the time and effort required for manual investigations. The proposed framework offers significant potential for deployment in public safety applications and future integration with real-time surveillance systems for continuous missing person identification.
An Intelligent Framework for Cellular Signal Identification Using Extreme Learning Machine
Authors: Assistant Professor CH.Sravan Kumar, G.Niharika
Abstract: Automatic identification of wireless communication signals is a fundamental capability of intelligent radio systems, enabling efficient spectrum utilization, interference management, network monitoring, and electronic surveillance. Conventional automatic signal identification (ASI) techniques, including likelihood-based and feature-based methods, often experience limitations associated with high computational complexity, sensitivity to channel variations, and reduced robustness under practical deployment conditions. Recent advances in machine learning have demonstrated significant potential for improving signal identification accuracy while reducing computational overhead. This study presents an Extreme Learning Machine (ELM)-based framework for the automatic identification of cellular signals using real Power Spectral Density (PSD) measurements collected over the air. The proposed methodology processes PSD measurements belonging to Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), and Long-Term Evolution (LTE) networks by transforming PSD measurements into two-dimensional binary images through a PSD image-mapping process. Following preprocessing and image standardization, the generated signal images are supplied to an ELM classifier that utilizes randomly initialized hidden-layer parameters and analytically determined output weights to achieve rapid model training. The proposed model is evaluated using two independent datasets, DS1 and DS2, where DS1 is employed for hyperparameter optimization and DS2 is used to evaluate robustness and generalization capability. Experimental results demonstrate that the proposed ELM framework achieves high identification accuracy while significantly reducing training complexity compared with existing neural network approaches. These findings indicate that the proposed model provides an efficient and reliable solution for intelligent wireless communication systems requiring fast and accurate cellular signal identification.
Deep Learning-Based Automated Pill Identification Using Image Preprocessing
Authors: Assistant Professor M.Anitha, G. Lahari
Abstract: Accurate pill identification is essential for reducing medication dispensing errors and improving patient safety in healthcare environments. The increasing number of prescription and over-the-counter medications has made manual pill identification a challenging and time-consuming task for pharmacists and healthcare professionals. Similarities in pill shape, color, size, and imprint often contribute to medication errors, potentially leading to serious health consequences. This paper presents an intelligent deep learning framework for automated pill identification that integrates Convolutional Neural Networks (CNNs) with image preprocessing techniques to accurately recognize pharmaceutical pills. The proposed framework employs the Pillbox dataset, image augmentation using the Keras Image Data Generator, OpenCV for image preprocessing, HSV-based color segmentation, Paddle OCR for imprint recognition, and feature extraction based on pill shape, color, and imprint characteristics. Two CNN architectures are developed for pill detection and pill classification using ReLU activation and Softmax classification layers. The feature extraction module further enhances recognition accuracy by comparing extracted characteristics with a reference database. Experimental evaluation demonstrates that the proposed CNN model achieves 98.9% training accuracy, 98.9% validation accuracy, and 97.5% testing accuracy, while integrating image preprocessing improves the overall identification accuracy to 97.9%. The proposed framework provides a scalable, accurate, and efficient solution for automated medication identification and has significant potential for deployment in pharmacies, hospitals, and intelligent healthcare systems to minimize medication dispensing errors and improve pharmaceutical safety.
Machine Learning Approaches for Early Detection of Voice Disorders: A Comprehensive Review
Authors: Assistant Professor Pradhyumna Yambar, G. Triveni
Abstract: The human voice is generated through a highly coordinated vocal production mechanism that enables individuals to regulate pitch, loudness, and speech quality. Various using machine learning techniques. The survey examines widely used voice disorder databases, commonly adopted feature extraction methods, and various ML algorithms employed for detecting and classifying pathological voice conditions. Furthermore, the strengths, limitations, and performance of different computational approaches are discussed to provide a better understanding of current developments in this field. The review highlights the growing significance of intelligent voice analysis systems and identifies future research directions for developing accurate, reliable, and efficient automated voice disorder detection frameworks.
A Review of Personalized Medicine Recommendation Systems
Authors: S.Akhila, G. Shirisha
Abstract: The extraction of meaningful and valuable medical knowledge from large-scale healthcare data has become a major research focus for supporting clinical decision-making. Among these advancements, personalized medicine recommendation has emerged as a significant area of study, aiming to identify the most appropriate medications for individual patients based on their specific health conditions. Such recommendation systems assist healthcare professionals in making informed prescribing decisions while minimizing the risk of medication-related complications, thereby attracting considerable attention from the research community. This survey presents a comprehensive review of personalized medicine recommendation by first defining the problem and its fundamental objectives. It then systematically categorizes and analyzes recent recommendation approaches according to four major perspectives: multi-disease medicine recommendation, medicine recommendation using combination patterns, recommendation enhanced with additional medical knowledge, and recommendation driven by patient feedback. Furthermore, the survey discusses commonly adopted evaluation methodologies and performance metrics used to assess these recommendation models. Finally, it highlights the major challenges in personalized medicine recommendation and outlines promising future research directions and emerging development trends in this rapidly evolving field.
A Comparative Investigation of Text Similarity Techniques for Human-Written and AI-Generated Scientific
Authors: Assistant Professor S.Venkateswara Rao, G. Varalaxmi
Abstract: Significantly transformed the way scientific documents are generated, summarized, and paraphrased. Modern AI systems are capable of producing highly fluent and contextually meaningful scientific abstracts that often resemble human-written content,Natural Language Processing (NLP), plagiarism detection, academic integrity, and automated content verification. text similarity methods for evaluating A large dataset consisting of approximately 36,500 scientific abstracts is categorized into three groups to facilitate detailed similarity analysis. The proposed evaluation framework incorporates multiple categories of similarity techniques, including lexical similarity methods, character-based algorithms, statistical text representation approaches, semantic embedding models, and transformer-based language models. Traditional , Dice Coefficient, Smith-Waterman Algorithm, Levenshtein Distance, and TF-IDF are evaluated alongside modern embedding techniques including Word2Vec, FastText, GloVe, and BERT. To measure the effectiveness of these approaches, statistical indicators Experimental analysis demonstrates that contextual embedding models consistently achieve superior semantic similarity performance compared with conventional lexical methods. The findings also indicate that AI-generated abstracts preserve semantic meaning more effectively than lexical structure, highlighting the importance of contextual language models in scientific text evaluation. The proposed comparative analysis provides valuable insights for plagiarism detection systems, AI content verification, scientific publishing, and future NLP applications involving automated text similarity assessment.
AI-Driven Vision Assistance for the Blind Using Real-Time Object Detection
Authors: Assistant Professor S.Srinivas, G. Triveni
Abstract: Vision impairment significantly affects an individual's ability to move independently and safely in unfamiliar environments. Conventional mobility aids, such as white canes and guide dogs, provide valuable assistance but are limited in delivering detailed information about surrounding objects. To address these limitations, VisionAssist is proposed as a smartphone-based intelligent assistance application that employs deep learning techniques for real-time object detection. The system analyzes live camera input to recognize surrounding objects and delivers immediate audio feedback through text-to-speech technology, enabling users to better understand their environment. Designed to operate on smartphones without requiring specialized hardware, the application offers broad accessibility and customizable settings to accommodate individual user preferences. By combining real-time object recognition with voice-guided assistance, the proposed system enhances mobility, improves situational awareness, and promotes greater independence and confidence among visually impaired users.
An Intelligent CNN-Based Framework for Crop Pest Classification in Sustainable Agriculture
Authors: Dr.B.Sai Venkata Krishna, G. Kavitha
Abstract: One of the main industries promoting both global food security and economic expansion is agriculture. However, because they significantly lower crop output and quality, insect infestations remain a danger to agricultural productivity. As a result, accurate insect pest identification is crucial to avoiding needless pesticide use and putting prompt, efficient pest control measures into place. Conventional pest detection methods are labor-intensive, time-consuming, and less successful for extensive agricultural monitoring because they mostly rely on manual inspection and handcrafted feature extraction. Recent developments in deep learning have made it possible for automated image analysis systems to accurately identify intricate visual patterns. Convolutional neural networks (CNNs) are used in this study's deep learning-based architecture for autonomous agricultural pest classification. First, several methods for identifying pests are analyzed to determine their benefits and drawbacks. The suggested approach uses CNN architectures and transfer learning to categorize insect pests gathered from various agricultural datasets. The classification model is trained and assessed using three benchmark datasets: Xie1, Xie2, and NBAIR. The Xie1 and Xie2 databases have 24 and 40 insect categories, respectively, whereas the NBAIR collection has photos of 40 insect classes gathered from various crops. In-depth experiments assess classification performance by contrasting the suggested CNN model with numerous well-known deep learning architectures. According to experimental results, the suggested framework maintains remarkable precision, recall, and F1-score across all datasets while achieving high classification accuracy. By improving precision agricultural decision-making, reducing crop losses, and early pest detection, the study shows how CNN-based image classification is useful for intelligent pest identification and how it may help sustainable agriculture.
A Machine Learning Approach for Early Heart Disease Detection and Prediction
Authors: Assistant Professor S.Venkateswara Rao, G.Priyanka vanditha
Abstract: Improving patient survival and decreasing healthcare costs requires early diagnosis and precise prediction of cardiac events, because heart disease remains one of the top causes of death globally. There are a lot of machine learning algorithms that may help doctors spot cardiac problems, but they all have their limits when it comes to dealing with datasets that are different. Concerns with computational efficiency, false classification rates, feature selection, and forecast accuracy are common with current classification methods. In order to tackle these problems, this research introduces a hybrid machine learning classification framework that combines the strengths of SVM, Decision Tree J48, ANN, and Hidden Markov Model (HMM). In order to determine which qualities are most important for classification, the suggested approach uses two feature selection methods: Correlation-Based Feature Selection (CFS) and Gain Ratio in conjunction with the Ranker search method. A layered processing technique based on Naïve Bayes is used to integrate the best classification models for better prediction, depending on the algorithms' comparative performance. To begin, several feature subsets are used to assess the effectiveness of various classification methods. To improve prediction accuracy and overall QoS, the most efficient algorithms are then included into the proposed hybrid architecture. A better prediction performance compared to current methodologies is achieved by the suggested hybrid classification strategy, according to experimental study. This makes it a useful decision-support tool for healthcare applications and detection of heart disease.
Machine Learning-Based Prediction of Smartphone Addiction Using User Behavioral Patterns
Authors: Assistant Professor Pradhyumna Yambar, I.Lakshmi Prasanna
Abstract: The rapid usage of smartphones has altered contemporary lives by boosting communication, education, entertainment, and professional activities. In spite of these benefits, smartphone addiction, low productivity, damaged social connections, and other mental and physical health issues are common outcomes of excessive use, which has become a major public health concern. Early identification of people showing addictive smartphone use habits is thus vital for implementing timely prevention measures and individualised therapies. Advances in Machine Learning (ML) have made it possible to analyse behavioural data and accurately identify addiction tendencies using computational methodologies. Using demographic, behavioural, and psychological data gathered from a structured survey, this study introduces a smart machine learning framework for the prediction of smartphone addiction. There are 21 predictive indicators in the dataset that describe things like demographics, smartphone use, and mental health issues including anxiety, depression, and stress. Data is preprocessed by filling in missing values, encoding labels, and normalising features before model building begins in order to maximise learning efficiency. Multiple machine learning algorithms, namely Decision Tree, Random Forest, Logistic Regression, Multi-Layer Perceptron (MLP), and Adaptive Moment Estimation (Adam), are employed to construct predictive models capable of identifying individuals at risk of smartphone addiction. Common performance indicators, like as classification accuracy, are used to assess the trained models. Addiction may be predicted by behavioural variables such as the amount of time spent using a smartphone each day, how often one checks their notifications, how they use applications, their stress levels, their age, and their gender, according to the findings of an experiment. By allowing early addiction identification and encouraging healthy smartphone use patterns, the suggested architecture demonstrates how machine learning may assist healthcare providers, educators, and application developers.
Deep Learning-Driven Automated Bank Cheque Verification Using Image Processing Techniques
Authors: Assistant Professor P.Premchand Goud, Jakkula Supriya
Abstract: Even though digital banking services are widely used, bank cheques are still crucial for financial transactions. Traditional methods of verifying checks, on the other hand, rely heavily on human intervention, which may be laborious, error-prone, and even fraudulent. This study proposes a framework for automated bank cheque verification that uses deep learning and image processing to overcome these obstacles and increase the trustworthiness and efficiency of cheque authentication. By use of picture preprocessing and segmentation, the suggested method retrieves crucial information from checks, such as the account number, legal amount, courtesy amount, IFSC code, and the signature of the account holder. While a Convolutional Neural Network (CNN) is used for handwritten numerical character identification, Optical Character identification (OCR) is used for machine-printed text recognition. Signature verification and classification are handled by Support Vector Machines (SVMs), while feature extraction is handled by Scale Invariant Feature Transforms (SIFTs). By automating the verification process, the suggested architecture drastically cuts down on human participation while still adhering to the CTS-2010 criteria for cheque verification. Results from experiments show that the suggested technique works, with CNN model accuracy reaching 99.14% for handwritten digit recognition, OCR accuracy reaching 97.7% for machine-printed text recognition, and SIFT-SVM accuracy reaching 98.10% for signature verification. Based on these results, the suggested framework is a safe, efficient, and accurate way to verify bank checks automatically, which speeds up processing and improves the authenticity of the checks.
Automatic Speech Disorder Detection Using Voice Signals and Machine Learning Techniques
Authors: Assistant Professor K.Rajkumar, J.Akshaya
Abstract: When a person has trouble communicating, it may have a negative impact on their social life, academic performance, and mental health, all of which contribute to a worse quality of life overall. Stuttering, vocal tremor, and dysarthria are three of the most common speech disorders; these conditions have different acoustic features and need to be properly diagnosed in order to provide effective therapeutic treatment. The assessment method is subjective, time-consuming, and reliant on clinical skill in conventional diagnostic processes since it mostly relies on perceptual evaluation undertaken by speech-language pathologists. This has led to a rise in the need for sophisticated AI-powered diagnostic tools that can accurately and consistently categorise speech disorders. Opportunities for the development of automated diagnostic frameworks that analyse complicated audio patterns with higher accuracy have arisen due to recent breakthroughs in digital voice processing and Machine Learning (ML). This research introduces a system that uses machine learning to automatically categorise speech problems based on audio signal analysis. The suggested method augments model generalisability and increases dataset variety by combining real-world voice recordings with synthetically produced speech signals produced by MATLAB-based mathematical modelling, thus overcoming the barrier of limited clinical speech datasets. Important acoustic parameters, such as fundamental frequency (F0), formant frequencies (F1 and F2), and signal augmentation and normalisation are performed on speech recordings during preprocessing in order to extract them. These properties characterise normal and disordered speech patterns, respectively. After that, several supervised machine learning algorithms, such as Gradient Boosting, Support Vector Machine (SVM), and Random Forest, are trained on the retrieved characteristics to categorise speech samples into various disorders. Traditional regression-based performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²) are used to assess the built models, in conjunction with an 80% training and 20% testing approach. Under the specified experimental circumstances, Random Forest and Gradient Boosting outperform Support Vector Machine in classification performance, with an overall accuracy of 62.50%, according to the experimental study. Based on the results, ensemble learning approaches outperform traditional classifiers when it comes to complicated pathological speech patterns. Objective speech disorder diagnosis, early clinical intervention, and enhanced telemedicine-based speech assessment systems for improved patient care are all possible outcomes of the suggested framework's integration of machine learning, acoustic signal processing, and synthetic data generation.
Predicting ICU Hospital Stay Duration Through Explainable Machine Learning Models
Authors: Assistant Professor P.Rama Krishna, J.Akhila
Abstract: Clinical decision-making, patient flow management, and hospital resource utilisation may all be greatly enhanced with accurate ICU length of stay (LOS) prediction. Healthcare providers may improve operational efficiency, offer prompt treatment planning, and optimise bed allocation with accurate patient stay expectations. This research introduces a machine learning architecture that can be easily explained. Its purpose is to use data taken from EHRs inside CIS to forecast the length of time a patient will spend in the intensive care unit (ICU) when they are admitted. Using actual hospital data, the suggested approach uses supervised machine learning models to divide intensive care unit patients into two groups: those with a short length of stay (LOS) and those with a long LOS. A number of performance metrics are used to evaluate the accuracy, specificity, sensitivity, precision, recall, F1-score, and associated classification measures in order to guarantee a trustworthy model assessment. By differentiating between intensive care unit stays that were brief and those that were lengthy, XGBoost showed the best predictive performance among the machine learning algorithms that were tested, with an area under the curve (AUC) of 98%. In order to make prediction outputs more transparent and interpretable, the framework uses Explainable Artificial Intelligence (XAI). This helps healthcare practitioners understand what elements are impacting the model's predictions. Clinicians, hospital administrators, and resource management teams may rely on the suggested framework's reliable decision-support features, which enhance the functionality of hospital Clinical Information Systems via the combination of accurate prediction and model explainability. In order to promote educated clinical decision-making and efficient healthcare resource planning, the experimental findings show that the suggested method effectively predicts the length of stay in the intensive care unit.
Smart Machine Learning Framework for Enhancing Safety and Sustainability in Railway and Powerline Networks
Authors: Assistant Professor M.Pradeepthi, J. Divya
Abstract: Ensuring the safety, reliability, and sustainability of modern railway and powerline systems has become increasingly important due to the rapid growth of transportation networks and energy infrastructure. Continuous monitoring of operational conditions, timely fault detection, accurate current load prediction, and efficient signal management are essential for preventing failures, reducing maintenance costs, and improving overall system performance. Recent advancements in Machine Learning (ML) have enabled intelligent predictive models that can analyze large volumes of operational data and support automated decision-making for critical infrastructure. This paper presents a machine learning-based framework for improving the safety and sustainability of railway and powerline systems through predictive analytics and intelligent monitoring. The proposed methodology utilizes Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Gradient Boosting (GB), Reinforcement Learning (RL), and Time Series Analysis (TSA) to perform safety diagnostics, current load forecasting, and railway signal efficiency optimization. Synthetic datasets representing railway operational parameters and powerline conditions are employed to train and evaluate the predictive models. Comprehensive experimental analysis is conducted using multiple performance measures, including Accuracy, Precision, Recall, F1-score, and confusion matrix-based evaluation. The results demonstrate that ensemble-based machine learning techniques provide superior predictive performance for infrastructure monitoring while enabling reliable fault detection and operational forecasting. The proposed framework offers an intelligent decision-support solution that enhances infrastructure safety, optimizes maintenance planning, improves energy efficiency, and contributes to the sustainable operation of railway and powerline systems.
A Predictive Machine Learning Approach for Digital Payment Platform Adoption Among Retail Vendors
Authors: Assistant Professor Dr.B.Sai Venkata Krishna, J. Swapna
Abstract: The rapid advancement of digital technologies has significantly transformed the retail sector by accelerating the adoption of digital payment platforms. Digital payment solutions provide enhanced transaction speed, improved security, operational efficiency, and greater convenience for both merchants and consumers. Despite these advantages, the adoption of digital payment platforms among small and medium-sized retail vendors remains inconsistent due to several technological, organizational, and behavioral factors. Understanding these influencing factors is essential for financial institutions, payment service providers, policymakers, and retailers seeking to promote a cashless economy. Recent developments in Machine Learning (ML) have enabled more effective analysis of complex adoption patterns by identifying hidden relationships within large datasets and generating reliable predictive models.. The predictive performance of the proposed framework is evaluated using Accuracy, Precision, Recall, and F1-Score metrics. Experimental analysis demonstrates that the Support Vector Machine achieves an overall accuracy of 56.67%, precision of 59.38%, recall of 66.67%, and an F1-score of 66.25%, indicating moderate predictive capability for digital payment adoption. The findings further reveal that perceived usefulness, social influence, and compatibility are the most significant factors encouraging adoption, whereas perceived ease of use and technical support exhibit relatively lower influence. The proposed framework provides valuable insights for improving digital payment adoption strategies and supports policymakers, financial institutions, and platform providers in developing more effective initiatives that encourage wider acceptance of digital payment technologies among retail vendors.
An Edge AI Framework for Real-Time Mobile Phone Detection and Identity Recognition
Authors: Assistant Professor G.Chandram, Manasa Sai
Abstract: The unauthorized use of mobile phones within restricted environments such as examination halls, government offices, hospitals, research laboratories, and other secure facilities presents serious concerns related to security, privacy, operational efficiency, and regulatory compliance. Conventional mobile phone detection techniques primarily rely on monitoring radio frequency or network signals, making them ineffective when devices operate in airplane mode, remain disconnected from communication networks, or employ signal suppression mechanisms. To overcome these limitations, this paper presents Phone Patrol, an intelligent image-based surveillance framework developed for detecting unauthorized mobile phone usage in mobile-free environments. The proposed system integrates You Only Look Once version 5 (YOLOv5) for real-time mobile phone detection and You Only Look Once version 9 (YOLOv9) for face recognition, enabling accurate identification of individuals violating mobile usage policies. The framework is deployed on a Raspberry Pi 5, providing a compact and cost-effective Internet of Things (IoT) solution capable of processing live video streams and automatically issuing SMS notifications through the Twilio API whenever a violation is detected. Experimental evaluation demonstrates excellent detection performance, with the YOLOv5 model achieving a mean Average Precision (mAP50) of 0.992, confirming its capability to accurately recognize mobile phone usage under real-time operating conditions. Although the current implementation is intended as a prototype and requires further optimization for large-scale deployment, the proposed framework demonstrates the effectiveness of combining deep learning, edge computing, and automated notification services to enforce mobile-free policies. The developed system provides a reliable, network-independent surveillance solution that strengthens security, improves policy compliance, and supports intelligent monitoring in restricted environments.
An Intelligent Hybrid Recommendation Framework for Personalized Job Matching Using LinkedIn User Profiles
Authors: S.Venkateswara Rao, K. Mounika
Abstract: The rapid growth of online professional networking platforms has transformed modern recruitment by enabling organizations and job seekers to connect through data-driven digital ecosystems. Among these platforms, LinkedIn has become one of the most widely used professional networks, providing users with opportunities to showcase their educational qualifications, technical skills, work experience, certifications, and career interests. However, the enormous volume of available job postings often makes it difficult for users to identify employment opportunities that accurately match their professional profiles. Conventional search-based recruitment methods generally require considerable manual effort and frequently produce recommendations that are either irrelevant or insufficiently personalized. Consequently, there is an increasing demand for intelligent job recommendation systems capable of delivering accurate, personalized, and unbiased career suggestions through advanced recommendation algorithms. This study presents an intelligent hybrid job recommendation framework that utilizes LinkedIn user profiles to generate personalized employment recommendations. The proposed system combines Content-Based Filtering and Collaborative Filtering to exploit both user-specific profile information and collective behavioral patterns. The content-based module analyzes attributes such as educational background, professional skills, work experience, job preferences, and profile characteristics to identify employment opportunities with similar requirements. Simultaneously, the collaborative filtering component predicts user preferences by learning interactions among users and job postings through matrix factorization and optimization techniques. To further improve recommendation quality, neural network-based content representation and TensorFlow-based optimization are incorporated for effective feature learning and personalized ranking. The recommendation framework is evaluated using a diverse LinkedIn job recommendation dataset containing multiple job domains and user profiles. Performance analysis is conducted using standard evaluation metrics, including Precision, Recall, and F1-Score, to compare the effectiveness of collaborative filtering and content-based recommendation approaches. Experimental results demonstrate that the content-based filtering approach consistently achieves higher precision and recall than collaborative filtering across different job categories, indicating its superior capability for generating personalized recommendations. Furthermore, the application of dimensionality reduction techniques significantly decreases computational complexity and training time, making the proposed framework more suitable for real-time online recommendation services. The developed system demonstrates the effectiveness of integrating hybrid recommendation techniques with machine learning to provide intelligent, scalable, and personalized job recommendations, thereby improving the recruitment process for both job seekers and employers while supporting fair and efficient career matching.
An Intelligent Deep Learning Framework for Sarcasm Detection in News Headlines
Authors: G.Sudheer Kumar, K. Sindhu
Abstract: Sarcasm is a sophisticated form of linguistic expression in which the intended meaning differs from the literal interpretation of the text, making automatic sentiment analysis a challenging task for conventional Natural Language Processing (NLP) systems. News headlines frequently employ sarcastic language to attract readers, convey criticism, or present humorous perspectives, thereby increasing the complexity of accurate text classification. This paper presents an intelligent sarcasm detection framework that integrates Machine Learning (ML) and Deep Learning (DL) techniques to improve the automatic identification of sarcastic news headlines. The proposed methodology utilizes comprehensive text preprocessing followed by feature representation using CountVectorizer and Word2Vec embeddings. Multiple learning models, including AdaBoost, Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (BiLSTM), and a Hybrid CNN–BiLSTM architecture, are trained and comparatively evaluated using the Kaggle Sarcasm Detection News Headlines Dataset. To enhance model transparency, Local Interpretable Model-Agnostic Explanations (LIME) is incorporated for interpreting prediction outcomes, while t-distributed Stochastic Neighbor Embedding (t-SNE) is employed for feature visualization. Experimental analysis demonstrates that the RNN model delivers the highest overall performance, achieving an accuracy of 79.88% together with an F1-score of 0.76, indicating its superior capability for identifying contextual patterns associated with sarcastic language. The proposed framework offers an effective and explainable solution for sarcasm detection and can support numerous natural language understanding applications, including sentiment analysis, opinion mining, intelligent recommendation systems, and social media analytics.
A Secure Blockchain-Based Framework for Counterfeit Product Detection and Authentication Using QR Code Verification
Authors: Assistant Professor B.Naresh, K. Pravalika
Abstract: The increasing circulation of counterfeit products has become a major challenge for manufacturers, retailers, regulatory authorities, and consumers worldwide. Counterfeit goods not only cause significant financial losses and damage brand reputation but also introduce serious risks to consumer health and safety. Conventional product authentication techniques, including serial numbers, holograms, and barcode-based verification, are increasingly vulnerable to duplication and tampering due to advancements in counterfeiting technologies. Consequently, there is a growing demand for secure, transparent, and tamper-resistant authentication mechanisms capable of ensuring product authenticity throughout the supply chain. The emergence of blockchain technology provides a decentralized and immutable infrastructure that enables trusted product traceability while preventing unauthorized modifications to product information. Combined with Quick Response (QR) codes, blockchain offers an efficient solution for real-time product verification and counterfeit detection. This study proposes a secure blockchain-based framework for counterfeit product detection and authentication using QR code verification. The proposed system enables manufacturers to register product information on a blockchain network, where each product is assigned a unique digital identity through smart contracts. A corresponding QR code is generated and attached to the product, allowing consumers to verify authenticity by simply scanning the code using a mobile device. During verification, the scanned information is compared with the immutable blockchain records to determine whether the product is genuine or counterfeit. In addition to product authentication, the framework incorporates customer reviews and ratings to assist administrators in identifying suspicious products and monitoring fraudulent activities. The decentralized architecture ensures transparency, traceability, and data integrity across every stage of the product lifecycle while reducing opportunities for counterfeit manipulation. The proposed framework is validated by comparing its computational performance with existing anti-counterfeiting approaches, including LWEM, Blowfish, AES, and RSA. Experimental evaluation demonstrates that the proposed blockchain-based solution achieves lower overall processing time while maintaining secure and reliable product authentication. The integration of blockchain, smart contracts, and QR code verification significantly improves counterfeit detection efficiency and enhances consumer confidence by providing a transparent mechanism for verifying product authenticity. The proposed framework offers a scalable and practical solution for securing supply chains, protecting brand integrity, and strengthening trust between manufacturers, retailers, and consumers in modern digital commerce.
A Hybrid Machine Learning Approach for Forecasting Cryptocurrency Market Prices
Authors: S.Srinivas, K. Srimukhi
Abstract: The rapid expansion of the cryptocurrency market has created significant interest in developing intelligent forecasting models capable of predicting future price movements with improved accuracy. Among various digital assets, Bitcoin exhibits substantial price volatility because of its decentralized nature, market sentiment, macroeconomic conditions, trading activity, and blockchain-related indicators. These highly dynamic characteristics make cryptocurrency price prediction a challenging task for conventional statistical techniques. This paper presents a machine learning-based framework for forecasting Bitcoin prices by integrating Random Forest Regression, Long Short-Term Memory (LSTM) networks, and a Bagging technique to capture both nonlinear relationships and temporal dependencies within historical market data. The proposed methodology utilizes a comprehensive dataset covering the period from March 31, 2015, to April 1, 2023, consisting of 47 explanatory variables categorized into multiple market-related groups. Before model development, extensive data preprocessing, feature preparation, and normalization are performed to improve learning efficiency and prediction reliability. Model performance is evaluated using standard regression metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy (DA). Experimental analysis demonstrates that the proposed machine learning framework effectively captures complex cryptocurrency market behavior and provides reliable forecasting performance for Bitcoin price prediction. The study highlights the potential of combining ensemble learning and deep learning techniques to support intelligent financial decision-making, cryptocurrency market analysis, investment planning, and risk management applications.
A Deep Learning-Based Framework for Real-Time Violence Detection in Surveillance Videos
Authors: Assistant Professor Dr.B.Nageshwar Rao, K. Sushmitha
Abstract: The increasing demand for intelligent surveillance systems has accelerated the adoption of artificial intelligence and deep learning technologies for improving public safety and crime prevention. monitoring, making them susceptible to delayed responses, operator fatigue, and human error, particularly in crowded public environments. Detecting violent incidents in real time remains a significant challenge because aggressive activities often occur unexpectedly and require immediate intervention. automated video analysis systems capable of identifying complex human activities with high accuracy, thereby supporting proactive surveillance and timely emergency response. Lightweight convolutional neural network architectures and temporal sequence learning models have further in real-world surveillance environments. This study presents an intelligent videos by integrating MobileNet and The proposed framework is evaluated using standard classification metrics including Accuracy, Precision, Recall, and F1-Score. Experimental results demonstrate that the integrated MobileNet–BiLSTM model achieves an overall classification accuracy of approximately 96%, with balanced precision and recall for both violent and non-violent activity recognition. The lightweight architecture significantly reduces computational complexity while maintaining high prediction performance, making the framework suitable for deployment on resource-constrained surveillance devices. The developed system enables rapid identification of violent incidents, supports faster emergency response by law enforcement agencies, and provides a scalable solution for intelligent public safety monitoring in transportation hubs, educational institutions, commercial facilities, and other high-risk public environments.
A Machine Learning-Based Framework for Automatic Speech Emotion Recognition
Authors: Assistant Professor S.Venkateswara Rao, K. Vaishnavi
Abstract: Human speech conveys not only linguistic information but also valuable emotional cues that reflect an individual's psychological and behavioral state. Accurately identifying these emotions is becoming increasingly important for developing intelligent human–computer interaction systems capable of delivering natural, adaptive, and personalized communication experiences. Conventional speech emotion recognition methods often depend on handcrafted acoustic features and traditional classification techniques, which may struggle to capture the complex temporal and spectral characteristics of emotional speech. Recent advances in machine learning and deep learning have significantly improved the capability of automated systems to analyze speech signals and recognize emotional patterns with greater accuracy, enabling their application in domains such as virtual assistants, customer support, healthcare, education, and mental health monitoring. The effectiveness demonstrates that the CNN model achieves the highest recognition performance among the evaluated approaches, outperforming both SVM and LSTM in emotion classification accuracy while effectively learning complex acoustic representations from speech data. The integration of advanced preprocessing, discriminative feature extraction, and deep learning-based classification contributes to reliable and efficient emotion recognition across multiple emotional categories. The proposed framework provides a scalable solution for intelligent speech analysis and offers significant potential for enhancing emotion-aware applications in modern human–computer interaction systems by enabling machines to interpret and respond to human emotions more naturally and effectively.
An Automated Wire Rope Inspection System Based on Image Processing and Anomaly Detection
Authors: Assistant Professor G.Sudheer Kumar, Deepthi Reddy Kethireddy
Abstract: Wire ropes are essential load-bearing components in construction machinery, elevators, cranes, and other heavy industrial systems, where their structural integrity directly influences operational safety and reliability. Detecting defects such as wire breakage at an early stage is critical for preventing equipment failure and minimizing the risk of severe accidents. However, conventional inspection procedures are predominantly performed through manual visual examination, making the inspection process dependent on operator experience, environmental conditions, and available working time. These limitations often lead to inconsistent inspection quality and reduce the reliability of damage assessment. This paper presents an image processing-based framework for automated wire rope damage detection that minimizes dependence on human expertise while providing a practical and efficient inspection solution. The proposed methodology investigates two complementary image analysis techniques: an Autoencoder-based anomaly detection model and a Gabor filter-based feature extraction approach for identifying wire breakage and other surface abnormalities. The Autoencoder is trained exclusively using normal wire rope images to learn the visual characteristics of undamaged ropes, allowing abnormal regions to be identified through reconstruction error analysis. Additionally, a Convolutional Autoencoder (CAE) is evaluated to examine its capability for anomaly reconstruction, while connectivity analysis combined with Otsu thresholding is employed to improve the detection of wear-related defects. The Gabor filter method exploits local frequency characteristics of wire rope textures to identify minute wire breakage with high sensitivity. Experimental evaluation demonstrates that the Autoencoder-based approach is effective for identifying wear regions through connectivity analysis, whereas the Gabor filter successfully detects fine wire breakage that is difficult to identify using reconstruction-based methods alone. The proposed framework provides a practical, cost-effective, and image-based inspection strategy that supports safer and more reliable wire rope monitoring in construction environments.
A Machine Learning-Based Regression Framework for Accurate Groundwater Level Prediction
Authors: Assistant Professor S. Venkateswara Rao, K. Bhavitha
Abstract: Groundwater serves as one of the most important freshwater resources for domestic consumption, agriculture, industrial activities, and ecological sustainability. Rapid urbanization, increasing population growth, irregular rainfall patterns, and climate change have significantly influenced groundwater availability, making variables, limiting their predictive capability in dynamic groundwater systems. Recent developments in Machine Learning (ML) provide powerful data-driven techniques capable of discovering hidden patterns from historical hydrogeological observations, enabling more reliable groundwater forecasting for sustainable resource management. The proposed approach utilizes historical groundwater observations together with geographical attributes such as latitude, longitude, and water depth to develop an accurate predictive model. The complete framework consists of data collection, preprocessing, feature engineering, regression model development, hyperparameter optimization, and performance evaluation. During preprocessing, missing values, duplicate records, and inconsistencies are removed while the dataset is normalized to improve model stability. The optimized regression geographical variables and groundwater levels, enabling accurate prediction of future groundwater conditions. The effectiveness of the proposed framework is evaluated using standard regression performance metrics, Experimental analysis demonstrates that the developed model successfully captures groundwater variation patterns while providing reliable prediction accuracy. The reported results indicate a Mean Squared Error (MSE) of approximately 0.025 and an R² value of about 0.85, demonstrating superior predictive capability compared with conventional groundwater estimation approaches. The proposed machine learning framework offers an efficient and scalable solution for groundwater monitoring, environmental planning, and sustainable water resource management, assisting policymakers and resource managers in making informed decisions for long-term groundwater conservation.
An Android-Based Intelligent Safety System for Live Location Tracking and Emergency Support
Authors: Assistant Professor Y.Vasudha, K .Chaithanya
Abstract: Personal safety has become an increasingly important concern due to the rising number of emergencies, accidents, and security threats encountered in everyday life. Rapid communication of a user's location during critical situations plays a significant role in minimizing response time and enabling timely assistance from family members, emergency responders, or law enforcement agencies. Although smartphones provide numerous communication capabilities, many existing safety applications either lack integrated emergency response features or require multiple manual interactions before an alert can be transmitted. Such limitations may delay rescue operations during situations where immediate action is essential. This paper presents an Android-based intelligent emergency assistance system that combines real-time GPS location monitoring, Google Maps integration, emergency contact management, and instant alert communication within a unified mobile application. The proposed system enables users to register securely, maintain predefined emergency contacts, continuously monitor their location, and transmit emergency notifications containing real-time geographic coordinates through SMS and phone communication whenever assistance is required. The application further supports nearby assistance services by utilizing location-based mapping technologies to improve emergency response efficiency. Experimental implementation demonstrates that the developed Android application provides reliable real-time location sharing, rapid emergency notification, and user-friendly operation while enhancing personal safety and reducing emergency response delays. The proposed framework offers a practical and cost-effective mobile safety solution suitable for everyday use and emergency management applications.
A Machine Learning-Based Framework for Early Software Bug Prediction
Authors: Assistant Professor G.Preethi, K . Arundhathi
Abstract: The rapid growth of software applications across industrial, commercial, and scientific domains has significantly increased the demand for reliable and high-quality software systems. , identifying defects during the early stages of development has become an essential requirement for reducing maintenance costs, improving software reliability, and enhancing overall product quality. Traditional software testing approaches primarily depend on manual inspection and extensive debugging procedures, which are often time-consuming, resource-intensive, and incapable of detecting all potential defects before software deployment. Recent advancements in Machine Learning (ML) have introduced intelligent data-driven techniques capable of analyzing historical software metrics to predict defect-prone modules, thereby supporting proactive quality assurance throughout the software development life cycle. This study presents an intelligent proposed framework utilizes software engineering metrics collected from the JM1 repository to train multiple supervised machine learning models for identifying defective software modules. The framework incorporates comprehensive data preprocessing, including label encoding, one-hot encoding, feature scaling, and dataset standardization before model training. Seven supervised learning algorithms, enabling objective evaluation of each classifier's predictive capability. The proposed framework is evaluated using widely accepted performance measures including Accuracy, Precision, Recall, F1-Score, and Root Mean Square Error (RMSE). Experimental analysis demonstrates that the Random Forest classifier provides the highest overall prediction performance, achieving approximately 81% classification accuracy together with superior precision, recall, F1-score, and the lowest RMSE among the evaluated models. methodology for software defect prediction. The developed framework offers a reliable, reducing maintenance effort, and supporting more efficient software engineering practices.
Deep Learning-Driven Real-Time Spam Message Detection in Chat Applications Using Long Short-Term Memory Networks
Authors: Assistant Professor M.Swathi, Kolipaka Aishwarya
Abstract: The rapid growth of instant messaging platforms has significantly improved digital communication while simultaneously increasing the spread of unsolicited and malicious messages. Spam messages not only interrupt user interactions but also expose individuals to phishing attacks, malware, financial scams, and privacy risks. and struggle to capture contextual relationships within sequential text, limiting their effectiveness against evolving spam patterns. To overcome these limitations, this research presents a real-time spam message detection sequence padding, and word embedding to transform raw chat messages into structured numerical representations suitable for deep learning. The LSTM network is trained using a labelled SMS spam dataset and optimized with the Adam optimizer and binary cross-entropy loss function. To improve model generalization and reduce overfitting, dropout regularization and early stopping mechanisms are incorporated during training. The trained model is integrated into a chat application where every outgoing message is automatically analyzed before delivery. Experimental evaluation demonstrates that the proposed framework effectively distinguishes spam from legitimate messages while maintaining high prediction accuracy and reliable real-time performance. The system successfully combines intelligent spam filtering with standard messaging functionalities such as individual conversations, group chats, multimedia sharing, and user authentication, providing a secure and user-friendly communication environment.
An Intelligent Machine Learning Framework for Automated Online News Article Classifications
Authors: Assistant Professor S.Venkateswara Rao, Kolipaka Divya
Abstract: The rapid expansion of online news platforms has resulted in an enormous volume of digital articles being published every day. Organizing these articles into meaningful categories is essential for improving information retrieval, recommendation systems, and content management. This research presents a machine learning-based framework for automatic news article classification using count vectorization before being transformed into numerical feature vectors. Two supervised outperforming the Naïve Bayes classifier, which records an accuracy of 76%. The proposed framework demonstrates that traditional machine learning algorithms combined with effective text preprocessing techniques provide reliable and efficient performance for automated news categorization.
Deep Learning-Based Emergency Siren Recognition for Intelligent Traffic Signal Prioritization Using Convolutional Neural Networks
Authors: Assistant Professor S P.Premchand Goud, Pooja Kurikala
Abstract: The rapid expansion of urban transportation networks has increased traffic congestion, making it difficult for emergency vehicles to reach their destinations without delay. Ensuring uninterrupted movement for ambulances, police vehicles, and fire engines is essential for improving emergency response efficiency and public safety. This study presents an intelligent siren recognition framework that employs Mel-Frequency Cepstral Coefficients (MFCCs) for extracting discriminative audio features and a Convolutional Neural Network (CNN) for multi-class classification of emergency vehicle sounds. Prior to feature extraction, audio recordings undergo preprocessing and noise suppression to minimize the influence of environmental disturbances commonly found in city traffic. The CNN model learns the unique spectral and temporal characteristics of ambulance, police, and fire truck sirens, enabling accurate classification with high reliability. Once an emergency siren is detected, the system can support adaptive traffic signal control by assigning signal priority to the approaching emergency vehicle, thereby reducing waiting time at intersections. Experimental evaluation demonstrates that the proposed approach achieves classification accuracy exceeding 90%, confirming its suitability for practical intelligent transportation applications. The framework provides an efficient and scalable solution for smart cities and can be further enhanced through integration with real-time traffic monitoring systems and Internet of Things (IoT) infrastructure.
An Intelligent Machine Learning Framework for Early Hypertension Risk Prediction Using Lifestyle and Clinical Health IndicatorS
Authors: Assistant Professor M.Sai Manasa, L.srivani
Abstract: Hypertension is one of the leading chronic diseases responsible for cardiovascular disorders and premature mortality across the world. Detecting individuals who are at risk during the early stages can significantly reduce severe health complications through timely medical intervention and lifestyle modification. This study presents a machine learning-based framework for predicting hypertension by analyzing demographic, clinical, and lifestyle-related attributes collected from a publicly available dataset. The proposed work investigates the performance of Decision Tree, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Random Forest classifiers. To improve predictive capability, are incorporated along with appropriate preprocessing and cross-validation strategies. The experimental findings indicate that feature optimization considerably enhances classification performance by reducing redundant information and improving model generalization. Among the evaluated algorithms, Gradient Boosting and XGBoost demonstrate superior prediction capability, while the hybrid learning approach further improves robustness and classification facilitating personalized hypertension management through data-driven decision-making.
Marketing in EdTech and Online Learning Platforms
Authors: Associate Professor Dr Anjali Gupta
Abstract: This chapter examines the ethical, behavioural, and strategic dimensions of marketing in EdTech and online learning platforms. Drawing on peer-reviewed literature, it addresses two central themes: (1) the ethical imperatives of data privacy, consumer trust, and responsible advertising unique to the EdTech sector; and (2) the social-native marketing behaviours of Gen Z and Generation Alpha — the primary consumers of contemporary digital education. The chapter introduces two original frameworks: the CREST Ethical Marketing Model, grounded in Privacy-by-Design principles and empirical trust research, and the SCROLL Framework for social-first EdTech marketing strategy. Findings confirm that transparency in data handling is a significant predictor of student trust; that influencer credibility outperforms follower count in driving enrolment decisions; and that effective EdTech marketing for Generation Alpha requires simultaneous engagement of both the child learner and the Millennial parent gatekeeper. The chapter concludes with recommendations for two additional topics — AI personalisation marketing and Global South EdTech strategy — each representing significant gaps in the current literature.
Impact of Employee Engagement on Organizational Performance: A Study with Reference to Mahindra & Mahindra Ltd
Authors: Assistant Professor Ms. Tanveer Fatma, Harsh Sharma
Abstract: Employee engagement has turned into one of the most discussed ideas in human resource management over the last two decades, mainly because organisations have started realising that engaged employees are directly connected to better productivity, lower attrition and stronger financial outcomes. This paper is an attempt to study the relationship between employee engagement and organisational performance by taking Mahindra & Mahindra Ltd., one of India's largest automobile and farm equipment manufacturers, as the unit of analysis. The study relies on secondary data collected from the company's integrated annual reports, HR trade publications, company communication and available academic literature, since conducting a large primary survey inside the organisation was not practically possible within the scope of this paper. It looks closely at Mahindra's engagement philosophy, popularly known as 'Rise', along with its learning and development initiatives, diversity practices, recognition programmes and CSR involvement, and tries to connect these practices to the company's recent business performance. A conceptual framework mapping Mahindra's HR practices against established engagement models (Gallup Q12 and Kahn's dimensions of engagement) is presented, along with a proposed survey instrument that future researchers could use to test the relationship empirically inside the organisation. The broad conclusion drawn is that Mahindra's engagement practices are fairly comprehensive and directionally consistent with the record financial and operational performance the company has posted in recent years, although a controlled primary study would be required before any causal claim can be made with confidence.
A Study on Global Trends in E-Business Sustainability and Customer Attitude and Preferences
Authors: Dr. Sonam Arvind Singh, Dr. Kunal Patil
Abstract: Customers are increasingly aware of the rising volume of online purchases and expect sustainable, environmentally friendly business practices. Despite growing interest in the topic, there is limited research on the state, development, and structure of consumer behavior and sustainability within e-commerce scholarship. This study addresses that gap by examining the intellectual, conceptual, and social structure of research on consumer behavior and sustainability in e-commerce. A bibliometric analysis was conducted on data drawn from 104 articles indexed in Scopus. The findings show the topic is closely tied to city logistics, big data analytics, customer engagement, the circular economy, online services, and omnichannel retail, and that it spans multiple research approaches and cross-cutting themes. By identifying trends and proposing future research directions, this study contributes to the broader field of sustainability research.
Impact of Artificial Intelligence on Human Resource Management
Authors: Dr. Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Rohit Lodhi, Toheed khan, Pankaj
Abstract: Artificial Intelligence is the most disruptive technology in Human Resource Management in the last decade. This research paper examines how AI is changing traditional HR practices and creating new opportunities for strategic HR.The main objective is to analyze the impact of AI on 4 core HR functions: Recruitment, Training & Development, Performance Management, and Employee Engagement. The study also identifies challenges and suggests solutions for AI adoption in Indian organizations.A descriptive research methodology was used. Primary data was collected from 100 HR professionals and employees working in IT, BPO and Manufacturing companies in Bhopal and Raisen, Madhya Pradesh through structured questionnaire. Secondary data was taken from SHRM, Deloitte, PwC reports and academic journals. Key findings: 72% organizations use AI in recruitment which reduced time-to-hire by 45%. 65% employees reported improved satisfaction due to AI chatbots. Predictive analytics helped 41% companies in reducing attrition. Major challenges are Data Privacy 38%, Lack of Skilled HR 29%, and Algorithm Bias 21%. The paper concludes that AI is not replacing HR professionals but is transforming their role. Future HR will require both emotional intelligence and data analytics skills. Recommendations include HR upskilling, ethical AI policies, and phased AI implementation.
Financial Risk Management
Authors: Dr. Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Aaditya Soni
Abstract: Financial Risk Management (FRM) is the process of identifying, analysing, assessing, and controlling financial risks that may affect an organisation's performance and financial stability. It aims to minimise potential losses arising from market fluctuations, credit defaults, operational failures, liquidity shortages, and other uncertainties while maximising opportunities for sustainable growth. Effective financial risk management involves the use of various tools and techniques, such as risk assessment, diversification, hedging, Value at Risk (VaR), stress testing, and regulatory compliance. By implementing a structured risk management framework, organisations can improve decision-making, protect assets, enhance stakeholder confidence, and ensure long-term financial resilience in an increasingly dynamic global business environment.
AI, Financial App Engagement, and Consumer Financial Well-Being: Emerging Perspectives
Authors: Assistant Professor Dr. Ekta Aggarwal
Abstract: The convergence of artificial intelligence (AI) and mobile financial applications is reshaping personal finance. This paper synthesizes theoretical and empirical perspectives on how AI-driven financial apps shape consumer engagement and financial well-being. Drawing on an integrative review spanning technology acceptance, behavioral economics, consumer psychology, and financial services research, we develop a conceptual framework mapping the pathways through which AI capabilities—personalized recommendations, conversational interfaces, predictive analytics, and automated advice—influence engagement behaviors and downstream financial outcomes. Three mechanisms emerge as central to how AI-powered financial apps affect well-being: information personalization that reduces cognitive load and improves decision quality; behavioral nudging that redirects spending and saving patterns; and capability-building that strengthens self-efficacy and financial literacy. At the same time, algorithmic opacity, data privacy vulnerabilities, over-reliance on automated advice, and engagement-maximizing design pose significant risks to consumer welfare. The paper advances five research propositions and concludes with implications for app designers, regulators, and consumer advocates, arguing that realizing AI's potential in personal finance requires subordinating engagement metrics to well-being outcomes, underpinned by ethical governance and evidence-based regulation.
HR Retention Policies in the Gig Economy: An Empirical Study of Zomato Delivery Partners in Delhi NCR
Authors: Professor (Dr.) Manisha V. Nain
Abstract: Purpose The gig economy has reshaped urban employment in India, but the openness that makes platform work attractive also drives high workforce turnover. This study examines how three human resource (HR) retention strategies — incentives, insurance and safety benefits, and flexibility in working hours — relate to the commitment and work frequency of food-delivery partners, using Zomato as the focal case. Design and methodology A descriptive research design was adopted. Primary data were collected through a structured, closed-ended questionnaire of fifteen items administered to 100 Zomato delivery partners across urban and semi-urban areas of Delhi NCR, selected by convenience sampling. Responses were analysed using percentage (frequency-distribution) analysis and presented through tables and charts. Findings Flexibility was the most valued and most consistently experienced benefit, with 75% of partners reporting high or moderate flexibility; 70% reported insurance coverage, and 65% were satisfied with incentives. A 65% commitment rate and 70% overall satisfaction indicate that the retention framework is moderately effective but incomplete: a 30% insurance gap and dissatisfaction with earnings among a significant minority remain material attrition risks. All three hypotheses — that incentives, insurance, and flexibility each strengthen commitment, work frequency, and retention respectively — were supported by the descriptive evidence. Implications The study offers platform managers an evidence-based, segmented retention agenda and contributes India-specific, firm-level evidence to the gig-work literature. Because the sample is small and non-representative, the findings are indicative rather than generalisable.
Artificial Intelligence in Banking and Financial Services: Opportunities and Challenges
Authors: Ms. Trupti Patle Assistant Professor, Lucky Chouksey
Abstract: Artificial Intelligence (AI) is becoming an important part of the banking and financial sector. Many banks and financial institutions are using AI technology to improve their services and provide better customer support. AI helps banks in online banking, fraud detection, digital payments, customer service, and data management. The use of AI is increasing because it saves time, reduces human effort, and improves work efficiency. The main objective of this research paper is to study the role of Artificial Intelligence in banking and financial services. The paper also aims to understand the benefits, applications, challenges, and future scope of AI in the banking sector. This study is based on secondary data collected from journals, research papers, articles, websites, and online sources. Different information related to AI and banking has been studied and analyzed for this research paper. The findings of the study show that AI helps banks improve customer satisfaction, increase productivity, reduce fraud, and provide faster services. AI also supports better decision-making and improves overall banking operations. However, some challenges such as cyber security risks, data privacy concerns, high implementation cost, and lack of technical knowledge are also present. The study concludes that Artificial Intelligence has a strong future in banking and financial services. Proper use of AI can help banks achieve better growth and improved customer experience.
Smart Financial Management Systems For Enhancing Corporate Resilience And Profitability
Authors: Dr. N. Kousalya, Dr. V. Renuka, Mrs. S. Anuvisalakshi, Dr. D. Jansi
Abstract: Given the increasing level of uncertainty in economic environment, coupled with the fast-evolving technological advancements, there is a need for intelligent financial management systems that are resilient enough yet profitable at the same time. In this paper, a framework that uses Artificial Intelligence (AI), real-time analytics and multi-criteria decision making is presented to improve organizational financial resilience and profitability. Through various case studies and empirical findings from leading financial firms, a smart financial management system is proposed in this paper, which will incorporate predictive forecasting, automated treasury functions and adaptive risk management. Based on quantitative analysis of various organizational settings, AI-driven financial management system helps to increase decision-making process by 70%, decrease transaction cost by 30% and increase financial resilience scores by 40%.
The Impact Of Artificial Intelligence On Leadership, Innovation, And Organizational Transformation
Authors: Praneeth. M, Dr. Venkata HRD
Abstract: Integration of Artificial Intelligence (AI) within organizational settings has been transforming leadership practices, innovating new approaches, and revolutionizing organizations in general. The present paper reviews the various effects of the use of AI in today's organizations from a number of perspectives in order to develop a comprehensive methodology for AI integration which is to cover leadership, innovations, and organizational change aspects. It will be shown that the effect of AI on decision-making accuracy in data-intensive environments can be up to 28-35% while structured employee training programs help in fostering innovative work behaviors. In addition, some crucial challenges such as governance concerns, ambiguous roles of humans and AI during their cooperation, and the necessity for constant upskilling have been identified. Overall, the successful organization transformation in the time of AI depends on the balance of technological and human components.
Behavioural Biases And Investment Decisions In The Stock Market
Authors: Mr. Surya C L, Dr.S. Krishnakumar, Dr. S. Parthiban
Abstract: Conventional finance theories posit that investors act rationally on the basis of the available information. However, research evidence shows that the psychological elements have a systematic effect on the investment decision-making process and lead to market anomalies. This study explores the influence of behavioural biases like overconfidence, herding, loss aversion, and anchoring on individual investment decision in equity markets. The methodology proposed in this study is a quantitative approach utilizing structural equation modelling (SEM). Survey data collected from 380 retail investors will be used for the analysis. It will be found out that the behavioral biases affect the investment decisions, directly and indirectly through risk perception, and financial literacy mitigates the impact of these biases on investment decisions.
Artificial Intelligence And Education: Bridging Ancient Educational Wisdom With Modern Technological Innovation
Authors: Chandrakala M
Abstract: Education has undergone remarkable transformations from the ancient Gurukula system to the contemporary digital classroom. Ancient civilizations such as India, China, and Greece regarded education as a means of cultivating moral values, discipline, wisdom, and holistic personality development. In contrast, modern education increasingly emphasizes technological competence, standardized assessments, and career-oriented learning. The emergence of Artificial Intelligence (AI) has further revolutionized educational practices by offering personalized learning experiences, intelligent tutoring systems, automated assessment, and improved accessibility. However, alongside these advancements, AI has introduced significant challenges, including academic dishonesty, excessive dependence on technology, erosion of critical thinking, data privacy concerns, algorithmic bias, and the widening digital divide. This paper explores the evolution of educational philosophies from ancient civilizations to the AIdriven learning environment of the twenty-first century. It critically examines both the opportunities and challenges associated with AI in education while emphasizing the continued importance of human teachers in nurturing ethical values, creativity, emotional intelligence, and social responsibility. The study argues that the future of education should integrate technological innovation with the timeless principles of value-based learning, thereby creating an educational system that develops intellectually competent, morally responsible, and socially conscious citizens.
Digital Transformation And It’s Impact On Competitive Advantage In Modern Enterprise
Authors: Dr. R. Ramki, Dr. Rajidi Rammohan Reddy
Abstract: Digital transformation has become a critical strategic requirement for today's firms aiming at gaining sustainable competitive advantage in the digitized economy. The current research paper explores the complex connection between digital transformation projects and competitive advantage, bringing together the existing empirical literature on the subject matter based on recent studies in manufacturing, finance, and SME industries. The research methodology is a mixed methods design that involves both the quantitative examination of the enterprise level data and the configurational analysis aimed at uncovering the ways digital competence is translated into competitive advantage. It is found that digital transformation is critical for improving enterprise competitiveness via many means such as operational efficiency, innovation competence, and strategic management accounting implementation. Moreover, management features, digital competence, and cyber security competence are found to be the important moderating factors of the successful implementation of digital transformation projects.
Women Participation in Agriculture: Evidence from the Aspirational District of Dhalai, Northeast India
Authors: Research Scholar Taniya Saha, Dr. Arobindo Mahato
Abstract: The tribal women are one of the most significant contributors to the state’s total agricultural production. For the people of Dhalai district, it is the primary occupation, as well as for other similar districts in Tripura, where agriculture is the main source of subsistence. Tribal women handle all kinds of agricultural activities, ranging from preparation of the land for planting, sowing or transplanting, intercultural, harvesting and post-harvesting activities, animal husbandry, and home gardens. Despite their great contribution to the region’s net output and rural economy, tribal women are underrepresented in agricultural production due to several gender-specific factors, including ownership, credit, extension, technology, and participation in decision-making processes. This study aims to assess the socio-economic status of tribal women involved in agricultural activities with a focus on their participation in the process, access to credit and resources, and problems encountered while performing them. The article will adopt a descriptive and analytical study design to address the hypothesis, primarily based on primary data collected from personal interviews/household survey and secondary data gathered from various government reports, census data, and published literature. It is hypothesized that such factors as education levels, access to credit, training, and other institutional support play a significant role in determining the extent of women’s involvement, productivity levels, and overall socio-economic development. In addition, the research aims to generate sufficient quantitative and qualitative evidence to bridge the existing gap between gender and agriculture, offering policymakers the opportunity to intervene and influence several areas of tribal women’s livelihood, essential for achieving the Sustainable Development Goals.
Artificial Intelligence in Banking and Financial Services: An Analytical Study of Adoption, Impact and Challenges in the Indian Banking Sector
Authors: Dr. Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Bhumi Hedau, Muskan Batham
Abstract: Artificial Intelligence (AI) has emerged as one of the most transformative forces reshaping the global banking and financial services industry, and India has become one of its most dynamic testing grounds. This paper analytically examines the adoption of AI in the Indian banking sector, its impact on operational efficiency, customer service, fraud management and financial inclusion, and the principal challenges accompanying its diffusion. The study adopts a descriptive and analytical research design based entirely on secondary data drawn from the Reserve Bank of India (RBI) publications, including the FREE-AI Committee Report (2025), RBI Annual Reports (2022- 23 to 2025-26), the RBI Payment Systems Report, National Payments Corporation of India (NPCI) statistics, industry reports and peer-reviewed academic literature. Trend analysis, percentage analysis and comparative tabulation are used to interpret the data. The findings indicate that AI adoption in Indian banking has grown rapidly but unevenly: larger and better-capitalised banks, particularly private sector banks, lead adoption, while customer support, credit underwriting, sales and cybersecurity constitute the dominant use cases. The analysis further shows a steep decline in the number of card, internet and digital payment fraud cases between 2023-24 and 2025-26, coinciding with the expansion of AI-enabled transaction monitoring, even as the value of advances-related frauds remains a concern. The paper concludes that AI can make Indian banking more inclusive, efficient and secure, provided banks invest in data governance, explainability, talent and ethical safeguards consistent with the RBI's FREE-AI framework, and offers recommendations for banks, regulators and researchers.
Impact of Training and Development Programs on Employee Performance: A Study of Human Resource Development Practices
Authors: Dr. Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Khushi Patil, Rashi Deshmukh
Abstract: Human Resource Development (HRD) is a fundamental aspect of Human Resource Management (HRM) that focuses on enhancing employees’ knowledge, skills, competencies, and overall performance. In the current competitive business environment, organizations increasingly recognize employees as strategic assets whose capabilities contribute significantly to organizational effectiveness, productivity, and sustainable competitive advantage. The rapid growth of technology, globalization, and changing business requirements has increased the need for skilled, motivated, and adaptable employees. As a result, training and development programs have become essential tools for improving employee capabilities and achieving organizational objectives. The present study examines the relationship between Human Resource Development practices, particularly training and development initiatives, and employee performance. It focuses on key HRD practices such as skill development, career advancement opportunities, performance evaluation, continuous learning systems, and organizational support for employee growth. The study aims to understand how effective HRD practices influence employee productivity, motivation, job satisfaction, work efficiency, and organizational commitment. A descriptive research design is adopted for the study. Primary data is proposed to be collected through structured questionnaires from employees of selected organizations, while secondary data is obtained from books, research journals, articles, and organizational reports. Statistical techniques such as percentage analysis, descriptive statistics, mean score analysis, correlation analysis, and regression analysis are used to evaluate the impact of HRD practices on employee performance. The study is expected to demonstrate that well-designed HRD initiatives have a positive influence on employee performance and organizational effectiveness. It also highlights the challenges involved in implementing HRD programs, including financial constraints, inadequate management support, and ineffective training evaluation systems. Furthermore, the research emphasizes the importance of human resource planning in ensuring that organizations possess the right workforce with appropriate skills and competencies to achieve long-term goals. The findings of the study provide valuable insights for organizations seeking to enhance employee potential, improve performance, and gain sustainable competitive advantage through effective Human Resource Development practices.
Artificial Intelligence in Recruitment and Selection: A Systematic Literature Review of Opportunities, Challenges, and Ethical Concerns
Authors: Dr. Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Kratika Jain
Abstract: Artificial Intelligence (AI) has significantly transformed Human Resource Management (HRM), particularly in recruitment and selection processes. This study aims to systematically review existing literature to examine the role of AI in recruitment, focusing on its applications, benefits, challenges, and ethical implications. A systematic literature review (SLR) methodology was adopted, analyzing peer-reviewed articles published between 2015 and 2025 from databases such as Google Scholar and Scopus. The findings reveal that AI enhances recruitment efficiency by automating resume screening, improving candidate-job fit through predictive analytics, and reducing time and cost. Additionally, AI-driven tools such as chatbots and virtual assistants improve candidate experience. However, challenges such as algorithmic bias, lack of transparency, data privacy concerns, and reduced human interaction remain critical issues. The study concludes that while AI offers substantial advantages in recruitment, its ethical implementation and integration with human judgment are essential for effective and fair hiring practices. The paper also identifies research gaps and suggests directions for future research in AI-driven HRM.
Impact of Digital Banking Services on Customer Satisfaction: A Study of Indian Banks
Authors: Bindu Tripati Assistant Professor, Shraddha Vishwakarma
Abstract: Digital banking has transformed the Indian banking sector by providing customers with fast, secure, and convenient financial services through mobile banking, internet banking, UPI, ATMs, and digital payment applications. This research paper examines the impact of digital banking services on customer satisfaction among users of Indian banks. The study aims to analyse customer awareness, usage patterns, satisfaction level, and the major challenges faced while using digital banking services. The research is based on both primary and secondary data. Primary data is collected through a structured questionnaire from 100 respondents, while secondary data is collected from journals, books, RBI reports, and research articles. The findings indicate that most customers prefer digital banking because of convenience, speed, and 24×7 availability. However, issues such as cybersecurity threats, internet connectivity, and lack of digital literacy continue to affect customer experience. The study concludes that digital banking has significantly improved customer satisfaction, but banks must continuously strengthen security measures, improve customer awareness, and provide user-friendly digital services to increase customer trust and adoption.
Financial Markets and Institutions: A Study of the Indian Financial System
Authors: Bindu Tripati Assistant Professor, Lata Kushwaha
Abstract: Financial markets and institutions are the foundation of every modern economy, facilitating the efficient allocation of financial resources from savers to borrowers. They promote capital formation, economic development, financial inclusion, and investment opportunities while ensuring liquidity and risk management. In India, financial markets have undergone significant transformation due to economic liberalization, technological advancements, regulatory reforms, and digital financial services. This study examines the structure, functions, major participants, regulatory framework, challenges, and future prospects of financial markets and institutions in India. The research is based on secondary data collected from reports of the Reserve Bank of India (RBI), the Securities and Exchange Board of India (SEBI), the National Stock Exchange (NSE), the Bombay Stock Exchange (BSE), journals, books, and government publications. The study concludes that India's financial system has become more transparent, efficient, and technology-driven, although challenges such as cyber risk, market volatility, financial literacy, and regulatory compliance remain.
Cyber Security Challenges in Digital Banking
Authors: Dr.Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Jitendra Meena, Mohit Mali, Jay Thakur
Abstract: The rise of digital banking has radically changed how banking services are accessed and delivered, with increased reach, efficacy, and client convenience. But this digital transformation has also brought with it a set of sophisticated security issues that present significant risks for consumers and for financial organizations as well. This systematic review provides an insight into the ever transforming threat scenario with digital banking along with the key threats, which include Phishing, Identity Theft (ID Theft), Malware and SIM swap fraud. A leitmotif of the literature is the greater sophistication of the cyber-threat and the increased sense of insecurity among users, with the asphyxiating effect these have on trust as well as on the take-up of digital technologies. According to the review, institutions are struggling to secure increasingly connected systems, in particular the interbank payment infrastructures, which are being targeted on an almost daily basis by cyber adversaries. In turn, banks have implemented technological barriers, such as multi-factor authorization, real-time fraud detection, and cyber incident response labs. However, these measures work provided they are updated continuously in response to new threats. Concluding remarks This overview has emphasized the importance of a number of 'lines of defense' consisting of technological change, user learning, regulatory compliance and a proactive management of e-security risk. Future research can also examine the potential impact of advanced technologies such as AI, block chain and behavioral biometrics in enhancing Cybersecurity systems to make digital banking ecosystems more sustainable and resilient.
Effect of Carbon Fibre Content on The Mechanical Properties and Wear Resistance of 3D Printed Pla-Based Composites Fabricated Using Iq200 Fdm Printer
Authors: Deepak Dawar, Om Prakash Sondhiya
Abstract: The increasing demand for lightweight, high-strength, and environmentally sustainable materials has accelerated the adoption of additive manufacturing technologies in engineering applications. Among various additive manufacturing techniques, Fused Deposition Modelling (FDM) has gained significant attention due to its cost-effectiveness, design flexibility, and capability to fabricate complex geometries. Polylactic Acid (PLA) is one of the most extensively used thermoplastic materials in FDM because of its biodegradability, low processing temperature, and excellent dimensional stability. However, the relatively low mechanical strength and poor wear resistance of neat PLA restrict its utilization in structural and tribological applications. To overcome these limitations, carbon fibre reinforcement has emerged as a promising approach for improving the performance of PLA-based composites. Carbon fibres possess high specific strength, excellent stiffness, low density, and superior wear resistance, making them suitable reinforcements for polymer matrices. The present research investigates the influence of varying carbon fibre content on the mechanical behaviour and tribological performance of 3D printed PLA composites fabricated using an IQ200 FDM printer. PLA composites containing 0 wt.%, 5 wt.%, 10 wt.% and 15 wt.% carbon fibre are considered for investigation. Mechanical characterization includes tensile strength, flexural strength, impact strength, and hardness evaluation according to ASTM standards. Wear behaviour is analysed using a pin-on-disc tribometer under varying loading conditions. Scanning Electron Microscopy (SEM) is proposed to examine fracture surfaces and wear tracks for understanding failure mechanisms and fibre-matrix interactions. Furthermore, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modelling are incorporated to establish predictive relationships between carbon fibre content, printing parameters, and performance responses. The anticipated outcomes include enhanced tensile strength, increased stiffness, reduced wear rate, and improved dimensional stability. The findings are expected to contribute toward the development of advanced lightweight composite materials for automotive, aerospace, biomedical, and industrial applications.
Digital Marketing Strategies for Small and Medium Enterprises (SMEs): An Empirical Analysis of Adoption, Performance, and Sustainable Growth
Authors: Md Ashif Karim Assistant Professor, Anshul Dangi, Satyendra Rajpoot
Abstract: The Small and Medium Enterprises are the backbone of the present day economy because of their role in innovation, job creation, and socio-economic development. But the rapid development of Information and Communication Technology (ICT) along with the changing consumer behavior has pushed SMEs to abandon the traditional marketing strategies and adopt digital marketing practices. Digital marketing practices come with many benefits like increased market coverage, reduced costs, audience targeting, and real time customer communication but SMEs face some challenges in adopting these practices fully owing to structural deficiencies, technical knowledge limitations, and financial constraints. This is a research paper on the adoption and implementation of digital marketing practices by SMEs. In consideration of RBV and TOE theoretical frameworks, this research utilizes quantitative research design involving collection of empirical evidence from 280 SMEs owner-managers, marketers and executives across different business sectors. In the course of empirical evidence data analysis, statistical tools including SPSS were applied to carry out Cronbach’s alpha reliability test, descriptive analysis, Pearson correlation and multiple linear regressions to test the five hypotheses developed. The results show that SMM, SEO, content marketing and PPC advertising have statistically significant positive influence on SME overall business performance in terms of financial performance, brand visibility and customer retention. SMM proved to have the highest influence as an individual factor driving customer acquisition while SEO proved to have the highest influence on digital sustainability and organic brand equity. Financial constraints, lack of digital skills and inadequate strategic alignment were some of the main barriers inhibiting effective digital conversion. The results of empirical evidence have guided the development of a conceptual model and recommendations for SMEs leaders, policy makers and industry practitioners on how to enhance their digital skills to ensure sustainable enterprise growth.
Impact of FinTech Solutions on Small-Scale Entrepreneurship: Evidence from Kondotty Municipality, Kerala
Authors: Nafeesathul Huda M
Abstract: Financial technology (FinTech) has transformed the financial ecosystem by improving access to digital financial services, enhancing operational efficiency, and promoting financial inclusion among small-scale entrepreneurs. This study examines the impact of FinTech solutions on small-scale entrepreneurship in Kondotty Municipality, Kerala. The objectives were to assess the adoption of FinTech tools, examine their influence on financial business operations, and evaluate their impact on entrepreneurial efficiency and growth. A descriptive research design was adopted using both primary and secondary data. Primary data were collected through a structured questionnaire administered to 60 small-scale entrepreneurs selected through purposive sampling. Statistical techniques including percentage analysis, one-way ANOVA, correlation analysis and regression analysis were used. The findings indicate that digital payment platforms, online banking, and accounting software significantly improve business efficiency, transaction speed, and financial management. However, the ANOVA results reveal no significant relationship between age and FinTech adoption (F = 0.458, p = .712). The study concludes that FinTech solutions positively influence operational efficiency and customer satisfaction among small-scale entrepreneurs. The findings have implications for policymakers, financial institutions, and entrepreneurship development agencies in promoting digital financial inclusion and technology-driven business growth.
A Study on Digital Financial Services and Customer Adoption: A Case Study of Bajaj Finance
Authors: Ms. Krishna Goswami Assistant Professor , Anil Yadav
Abstract: Digital Financial Services (DFS) have transformed the financial sector by providing customers with convenient, fast, and technology-driven financial solutions. This study examines customer adoption of digital financial services with a specific focus on Bajaj Finance. The research explores the factors influencing customers' acceptance and continued use of digital platforms, including perceived ease of use, security, trust, convenience, service quality, and digital awareness. The study also evaluates the impact of Bajaj Finance's digital initiatives, such as mobile applications, online loan processing, digital payment options, and customer support services, on customer satisfaction and loyalty. A descriptive research design using primary and secondary data can be employed to analyze customer perceptions and adoption behavior. The findings indicate that convenience, quick service delivery, and user-friendly digital platforms significantly encourage customer adoption, while concerns regarding cybersecurity and data privacy remain important challenges. The study concludes that continuous technological innovation, enhanced security measures, and customer education are essential for increasing the adoption of digital financial services and strengthening customer relationships. The research provides valuable insights for financial institutions seeking to improve digital service delivery and customer engagement in the rapidly evolving fintech environment.
Impact of Artificial Intelligence on Consumer Buying Behavior: An Empirical Investigation of Personalization, Algorithmic Decision-Making, and Purchase Intent
Authors: Md. Ashif Karim Assistant Professor, Kunal Rathore, Tanuja Bhakne
Abstract: The development and adoption of Artificial Intelligence (AI) technology in the digital commerce arena have radically transformed the consumer decision-making process in today’s era. The touchpoints such as Personalized Recommendation Engine, Conversational Chatbots, Predictive Analytics, Visual Search, and Automated Pricing Algorithms have come into play to influence the consumers at every step of their purchase process. Although there is extensive use of such AI interventions in various retail and service businesses, there still remains a gap in the empirical knowledge regarding the effect of AI touchpoints in affecting consumer’s cognition, affective evaluation, and purchase behavior. This empirical research aims to explore the structural effect of Artificial Intelligence touchpoints on consumer purchase behavior in pre-purchase, post-purchase evaluation of alternatives, purchase decisions, and customer journey. Building on the theories of Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT) and Stimulus-Organism-Response (S-O-R) model, this paper proposes a quantitative cross-sectional research approach based on primary survey data collected from a stratified sample of 320 respondents actively using AI enabled e-commerce platforms. Statistical analyses were performed using IBM SPSS Statistics (Version 28.0) including reliability analysis using Cronbach’s Alpha coefficient, descriptive statistics, Pearson bivariate correlation and Multiple Linear Regression models Recommendation algorithms became the driving force behind impulse buying and basket growth, while conversational artificial intelligence made a substantial contribution to the effectiveness of the pre-purchase assessment stage. On the other hand, the privacy concerns and lack of transparency were recognized as moderating factors that undermine consumers' trust. In light of these results, an integrated management framework is suggested together with recommendations for marketers and developers.
Development of a Polycarbonate UV-Protective and Rust-Preventive Coating System for Solar Panel Structural Materials: A Systematic Approach to Testing and Validation
Authors: Mitesh Patel, Om Prakash Sondhiya
Abstract: Due to their constant exposure to harsh external conditions, solar panel structural materials are extremely susceptible to air corrosion, thermal stress, and UV radiation-induced deterioration. The mechanical robustness and durability of solar mounting infrastructure are seriously jeopardized by these environmental variables. The invention, extensive testing, and validation of a novel polycarbonate-based UV-protective and rust-preventive coating system designed especially for solar panel structural supports are the main objectives of this work. The coating offers a strong, dual-action defense mechanism against extended solar exposure and moisture-driven oxidation by incorporating sophisticated corrosion-inhibiting nano-agents and specific organic UV absorbers into a durable polycarbonate matrix. Performance was assessed using a thorough, methodical testing strategy that included surface morphology evaluations, neutral salt spray corrosion assays, cross-cut adhesion strength measures, and accelerated weathering experiments. According to experimental results, the coated substrates show remarkable resistance to UV-induced yellowing and gloss retention while retaining their structural integrity and high barrier qualities against dampness and chloride ions. Additionally, a significant improvement in corrosion resistance was proven by electrochemical impedance spectroscopy (EIS) and extended exposure to salt fog, which demonstrated minimal degradation and no rust development over extensive operational cycles. The developed polycarbonate coating system's dependability and industrial potential to greatly increase the service life, lower maintenance costs, and guarantee structural safety of solar panel installations in a variety of harsh climates are highlighted by this methodical validation framework.
Algorithmic Bias In Recruitment: Evaluating The Fairness And Diversity Impact Of AI-Driven Resume Screeners And Video Interview Analysis
Authors: Ms. Kajal Yadav, Kavita Ahirwar
Abstract: This research paper focuses on the mechanics, implications, and demographic effects of bias in contemporary human resources recruitment processes. As more companies integrate AI technology to improve the efficiency of recruiting large quantities of applicants, automated technologies, such as Natural Language Processing (NLP)-based ATS and Computer Vision/Audio-based AVI, have changed hiring processes significantly. Though the vendors of these tools claim them to be unbiased tools for overcoming human cognitive biases in evaluation, numerous empirical studies show that these technologies tend to perpetuate and exacerbate the historical employment biases. Through the use of an empirical research design, the study analyses applicant evaluation outputs (N=450 recruitment profiles interactions in Fortune 500 hiring processes) using traditional metrics of algorithmic fairness, which include Disparate Impact Ratio (DIR) based on EEOC's Four-Fifths rule, Demographic Parity, and Equalized Odds. The study uses systematic analysis of two main AI hiring pipelines: semantic resume screeners based on BERT and LLM embeddings, as well as affective/vocal AI video interview analyzers. The results show that the use of NLP-based resume parsers displays severe forms of proxy discrimination by punishing applicants belonging to minority demographic groups through implicit semantic associations with geographical zip code, university tier classification, and employment gap expressions. Moreover, facial expression analysis and vocal intonation detection during video interviews have shown substantial variance in systematic errors, which disadvantage non-native speakers, individuals with neurodevelopmental conditions, and racial minorities owing to the training data set distribution norms. Quantitative assessment indicates that the use of an unchecked AI screening system produces Disparate Impact Ratio below 0.72 for protected groups, which is inadequate according to the legal regulations. On the other hand, using pre-processing techniques, adversarial training of subnetworks, and Human-in-the-loop auditing results in Disparate Impact Ratio of 0.88.
Social Media Influencer Marketing And Brand Loyalty: An Empirical Study On The Impact Of Influencer Marketing On Consumer Loyalty
Authors: Dr.Sadaf Khan, Kajal Yadav, Hariom Patidar, Raj Thakur, Mausam Bisen
Abstract: Social media has transformed the way businesses communicate with consumers and build long-term relationships with their target audiences. Among the various digital marketing strategies, social media influencer marketing has emerged as one of the most effective approaches for increasing brand awareness, consumer engagement, and brand loyalty. Influencers act as opinion leaders who use their credibility, expertise, and relationships with followers to influence consumer attitudes and purchasing decisions. This research paper examines the relationship between social media influencer marketing and brand loyalty, focusing on how influencer credibility, authenticity, engagement, and trust affect consumers’ emotional attachment and commitment toward brands. The study explores the role of influencers in shaping consumer perceptions and examines whether influencer-generated content contributes to stronger brand relationships. A conceptual framework is developed based on theories such as the Source Credibility Theory and Social Influence Theory. The research adopts a quantitative approach using survey methodology to analyze consumer responses toward influencer marketing activities. The findings suggest that influencer authenticity, perceived trustworthiness, and audience engagement significantly influence brand loyalty. Consumers are more likely to develop positive attitudes toward brands promoted by influencers they perceive as genuine and reliable. The study highlights the importance of selecting appropriate influencers who align with brand values and consumer expectations. It provides valuable insights for marketers seeking to improve digital marketing strategies through influencer partnerships. The research concludes that social media influencer marketing is not only an effective promotional tool but also a strategic mechanism for developing sustainable customer relationships and long-term brand loyalty.
Women Entrepreneurs as Catalysts for Sustainable Economic Growth in India: Government Initiatives, Challenges, and Contributions to the Sustainable Development Goals
Authors: Assistant Professor M. Hemarani, Assistant Professor V.Megala
Abstract: Women entrepreneurship has become a vital driver of inclusive economic growth, innovation, employment generation, and sustainable development in India. The increasing participation of women in entrepreneurial activities has strengthened the Micro, Small and Medium Enterprises (MSME) sector, promoted financial inclusion, and enhanced socio-economic empowerment. This review paper examines the role of women entrepreneurs in fostering sustainable economic growth, analyses major government initiatives supporting women-led enterprises, identifies key challenges, and explores their contribution to achieving the United Nations Sustainable Development Goals (SDGs). The study adopts a descriptive research design based entirely on secondary data collected from government reports, policy documents, international organizations, and peer-reviewed literature. The findings indicate that government initiatives such as Startup India, Stand-Up India, Pradhan Mantri Mudra Yojana (PMMY), Skill India, Digital India, and the Deendayal Antyodaya Yojana–National Rural Livelihoods Mission (DAY-NRLM) have significantly improved women's access to finance, entrepreneurial skills, digital technologies, and market opportunities. Women entrepreneurs have contributed substantially to employment generation, MSME development, innovation, rural development, financial inclusion, and sustainable business practices. However, persistent challenges, including limited access to finance, gender discrimination, inadequate digital literacy, infrastructure constraints, market barriers, and work–life balance issues, continue to impede their entrepreneurial growth. The study further demonstrates that women entrepreneurship contributes directly to the achievement of SDGs 1 (No Poverty), 5 (Gender Equality), 8 (Decent Work and Economic Growth), 9 (Industry, Innovation and Infrastructure), 10 (Reduced Inequalities), and 13 (Climate Action). The paper concludes that strengthening policy support, expanding financial inclusion, enhancing digital and entrepreneurial skills, promoting innovation, and improving institutional support are essential for accelerating women-led enterprise development and achieving sustainable economic growth in India.
Employee Job Satisfaction: A Study on Factors Influencing Workplace Satisfaction
Authors: Dr. Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Anudeep Sharma
Abstract: Established links to productivity, employee retention, and organizational performance. This paper reviews existing literature to examine the key factors that shape workplace satisfaction, including compensation and benefits, work environment, leadership style, work-life balance, career development, job security, and interpersonal relationships. The review draws on established motivational frameworks, particularly Herzberg's Two-Factor (Motivation-Hygiene) Theory, to explain why some workplace elements primarily prevent dissatisfaction while others actively generate satisfaction and engagement. The paper finds that job satisfaction is multidimensional and shaped by both organizational-level factors (pay, policies, environment, leadership) and individual-level factors (age, personality, education, and work experience). It concludes that organizations seeking to improve employee satisfaction must address hygiene factors to prevent dissatisfaction while simultaneously investing in motivators such as recognition, autonomy, and growth opportunities to build genuine engagement. Directions for future research, including sector-specific and cross-cultural studies, are also identified.
International Finance And Global Market
Authors: Dr. Sadaf Khan, Miss Kajal Yadav, Balveer Kushwah
Abstract: This research paper focuses on the mechanics, implications, and demographic effects of bias in contemporary human resources recruitment processes. As more companies integrate AI technology to improve the efficiency of recruiting large quantities of applicants, automated technologies, such as Natural Language Processing (NLP)-based ATS and Computer Vision/Audio-based AVI, have changed hiring processes significantly. Though the vendors of these tools claim them to be unbiased tools for overcoming human cognitive biases in evaluation, numerous empirical studies show that these technologies tend to perpetuate and exacerbate the historical employment biases. Through the use of an empirical research design, the study analyses applicant evaluation outputs (N=450 recruitment profiles interactions in Fortune 500 hiring processes) using traditional metrics of algorithmic fairness, which include Disparate Impact Ratio (DIR) based on EEOC's Four-Fifths rule, Demographic Parity, and Equalized Odds. The study uses systematic analysis of two main AI hiring pipelines: semantic resume screeners based on BERT and LLM embeddings, as well as affective/vocal AI video interview analyzers. The results show that the use of NLP-based resume parsers displays severe forms of proxy discrimination by punishing applicants belonging to minority demographic groups through implicit semantic associations with geographical zip code, university tier classification, and employment gap expressions. Moreover, facial expression analysis and vocal intonation detection during video interviews have shown substantial variance in systematic errors, which disadvantage non-native speakers, individuals with neurodevelopmental conditions, and racial minorities owing to the training data set distribution norms. Quantitative assessment indicates that the use of an unchecked AI screening system produces Disparate Impact Ratio below 0.72 for protected groups, which is inadequate according to the legal regulations. On the other hand, using pre-processing techniques, adversarial training of subnetworks, and Human-in-the-loop auditing results in Disparate Impact Ratio of 0.88.
The Impact of Flexible Work Arrangements on Employee Retention in the Post-Pandemic Corporate Ecosystem: An Empirical Investigation
Authors: Mehazabeen Anjum Mansoori Assistant Professor, Vikas Sahu, Abhishek Sahu
Abstract: Within the current post-pandemic corporate environment, the conventional approach to inflexible five-day working weeks has undergone a complete metamorphosis. In response to fast-paced technological development, changing worker attitudes and socio-economic dynamics, flexible work arrangements have moved beyond the boundaries of innovative benefits into the very foundation of human resource management strategy. This empirical study explores the multi-faceted influence of flexible work arrangements on the retention rate of employees within medium and large-scale enterprises. Based on the theories of social exchange, job demands-resources, and self-determination, this study seeks to understand the influence of multiple dimensions of flexibility on organizational commitment, job satisfaction, employee autonomy, work-life balance and the subsequent voluntary turnover intentions. By applying a quantitative and descriptive research methodology, data were collected using a structured cross-sectional survey among N = 380 full-time corporate employees in the technology, financial services, health care administration, and professional business services industries. Inferential statistics such as the Pearson correlation, multiple linear regression models, and two-way Analysis of Variance (ANOVA) show a significant negative association between structured flexibility and voluntary turnover intentions. In particular, the predictors of sustained organizational commitment were schedule autonomy and location flexibility, with the help of less burnout and job satisfaction. However, there are some key boundary conditions, since unstructured flexibility without policy boundaries causes more work-life boundaries crossing and thus increases psychological exhaustion and decreases the effectiveness of retention strategy. In conclusion, the paper offers strategic recommendations to the HR managers, suggesting that it is necessary to develop a personalized framework for flexibility management in order to maintain employees' retention.
The Impact of Continuous Performance Feedback on Employee Productivity
Authors: Krishna Goswami Assistant Professor, Boby Rajak
Abstract: The contemporary dynamic business environment has seen a growing perception that the conventional annual performance appraisals are outdated, inflexible, and inadequate for fostering organizational growth and development. This research paper examines the practical effects of continuous performance feedback systems on the productivity of employees in modern organizations. The research study is designed using a combination of quantitative survey (N= 350) and qualitative interview approach to study the effects of continuous and constructive performance interactions on the individual work productivity of the employees. he empirical observations indicate the presence of a significant positive relationship between the frequency and quality of feedback and observed increases in the productivity of employees. In particular, companies which shifted from conducting annual performance appraisals to having a continuous feedback process on a weekly or bi-weekly basis observed an improvement of 24.8% in achieving KPIs, a decrease of 31.2% in time delays for projects, and an increase of 19.5% in job satisfaction. The data shows that continuous feedback removes the psychological ambiguity associated with unclear role perceptions, allows for establishing dynamic goal congruence by utilizing OKRs, and provides for increased intrinsic motivation through timely recognition. Moreover, the structural equation modeling shows that psychological safety and feedback literacy of managers act as important moderating factors. This paper shows that replacing episodic evaluations with continuous feedback models changes performance management from reactive, back-looking administrative tool to proactive, forward-looking strategic approach for human capital optimization. The recommendations for leadership development, continuous feedback culture creation, and digital feedback architecture implementation are given below.
Employee Well-being and Organizational Performance in the Post-Pandemic Workplace: An Empirical Analysis of Hybrid Work Models, Psychological Safety, and Engagement
Authors: Pankaj Kumar Patel, Vishal Chidar, Aditiya Kumar Chidar
Abstract: The global shift triggered by the COVID-19 pandemic permanently altered the organizational landscape, elevating employee well-being from a secondary human resource perk to a core strategic imperative. As hybrid and remote work models become institutionalized, organizations face unprecedented challenges in maintaining employee mental health, physical vitality, and work-life balance while striving for sustainable organizational performance. Objective: This study empirically examines the relationship between employee well-being—encompassing psychological, physical, and digital/social dimensions—and multidimensional organizational performance in the post-pandemic workplace. Additionally, it evaluates the mediating role of work engagement and the moderating influence of psychological safety. Methods: A cross-sectional quantitative research design was executed across IT, financial services, and healthcare sectors (N = 384). Primary data was collected via structured Likert-scale questionnaires and analyzed using Descriptive Statistics, Pearson Correlation, and Multiple Linear Regression modeling. Results: Regression analysis revealed that psychological well-being (β = 0.382, p < 0.001), work-life flexibility (β = 0.294, p < 0.001), and physical health resources (β = 0.186, p = 0.002) serve as significant positive predictors of organizational performance (R² = 0.548, F(3, 380) = 153.42, p < 0.001). Work engagement partially mediated the relationship between well-being initiatives and overall productivity. Conclusion: Investing in holistic well-being frameworks directly correlates with enhanced organizational agility, reduced turnover intentions, and higher operational efficiency. Organizations must transition from reactive wellness policies to structurally integrated, human-centric work designs.
Impact of Awareness and Implementation of Diversity and Inclusion Policies for Organizational Development
Authors: Dr.R.Radhamani, Research Scholars Ms.S.Sridevi
Abstract: Diversity is a fact. Inclusion is a choice. Belonging is an outcome."— Arthur Chan This study deals with the impact of awareness and implementation of diversity and inclusion policies support for the development of organization. This study carries the steps taken by the organization to create awareness and implementation of diversity and inclusion policies of IT sector employees. From this evocative analysis, Diversity and Inclusion (D&I) policies are formal frameworks used by organizations to ensure that people from all backgrounds—regardless of race, gender, sexual orientation, physical ability, age, or neuro diversity—have equal access to opportunities and feel valued within the workplace. In the modern corporate world, Diversity and Inclusion (D&I) policies have evolved from human resources compliance checklists into core business strategies. Organizations that prioritize D&I outperform their competitors, attract top talent, and foster environments of high innovation. Those who feel like outsiders are less likely to feel engaged with and committed to the organizations they work for. Through this study the researcher how the diversity and inclusion policies and awareness supported for the organization development
AI-Driven Smart Human Resource Management Systems: Transforming Recruitment, Talent Acquisition And Employee Performance Through Intelligent HR Analytics
Authors: Dr. Sadaf Khan, Associate Professor, Kajal Yadav, Assistant Professor, Ira Sahani, Khushi
Abstract: Artificial Intelligence (AI) has become one of the most influential technologies transforming Human Resource Management (HRM). Organizations are increasingly adopting AI-powered solutions to improve recruitment, talent acquisition, employee selection, and performance management. Traditional HR processes often involve lengthy recruitment cycles, manual resume screening, and subjective decision-making, which can reduce efficiency and increase hiring costs. AI addresses these challenges by automating repetitive tasks, analyzing large volumes of candidate data, and providing predictive insights that support better HR decisions. This study examines the role of AI in modern HR practices with a particular focus on recruitment, talent acquisition, and employee performance management. The study proposes the SMART-HR Framework, an original conceptual model integrating intelligent recruitment, AI-based analytics, employee development, and ethical decision-making. A descriptive research methodology is suggested using survey responses from HR professionals to understand the adoption and effectiveness of AI technologies in HR functions. The findings indicate that AI has the potential to reduce recruitment time, improve the quality of hiring decisions, enhance employee performance monitoring, and support data-driven workforce planning. However, challenges such as algorithmic bias, privacy concerns, and the need for human oversight remain important considerations. The study concludes that AI should complement, rather than replace, human judgment in HR management. Organizations adopting AI responsibly can improve operational efficiency, employee satisfaction, and overall organizational performance.
Emotional Intelligence as a Mediator in the Relationship Between Cognitive Style and Leadership Effectiveness: Insights from Multinational Corporations
Authors: Research Scholar Sonal Sharma, Dr. Anjoo Chauhan
Abstract: Cognitive style and emotional intelligence (EI) are increasingly recognised as complementary, rather than competing, determinants of managerial success, yet the mechanism through which cognitive preferences translate into leadership outcomes remains under-theorised, particularly in multinational corporations (MNCs) where leaders must reconcile analytical rigour with cross-cultural sensitivity. This paper synthesises secondary evidence drawn from peer-reviewed journals, systematic reviews, and industry reports to examine whether emotional intelligence mediates the relationship between cognitive styleconceptualised through the Herrmann Brain Dominance Instrument (HBDI) whole-brain modeland leadership effectiveness in MNC settings. Using a structured narrative-review methodology informed by PRISMA principles, findings from more than two decades of empirical and conceptual literature are integrated to propose a testable conceptual model in which analytical, practical, relational, and experimental thinking preferences influence leadership effectiveness both directly and indirectly through the four EI competencies of self-awareness, self-management, social awareness, and relationship management. The synthesis suggests that whole-brained leadersthose who integrate cognitive and affective processing rather than relying on a single dominant quadrantconsistently emerge as more effective across the reviewed studies, and that EI substantially, though not universally, mediates this relationship. The paper contributes a consolidated theoretical framework and a set of falsifiable hypotheses for future primary research in the Indian and global MNC context, and offers implications for leadership development, expatriate selection, and cross-cultural training design.
Green Human Resource Management And Organizational Performance In The Educational Sector: A Conceptual Review And Framework
Authors: Abhishek Kumar, Jitendra Singh
Abstract: Purpose: Educational institutions are increasingly expected to demonstrate environmental responsibility alongside academic excellence, yet the human resource mechanisms through which this responsibility is actually embedded into institutional life remain under-examined. This paper conceptually examines how Green Human Resource Management (Green HRM) practices influence organizational performance in the educational sector, and proposes an integrated framework linking Green HRM practice bundles to institutional performance outcomes through employee green behaviour. Design/Methodology/Approach: The paper adopts a conceptual, literature-based approach, synthesizing peer-reviewed research on Green HRM, the Ability-Motivation-Opportunity (AMO) framework, the Resource-Based View, and Social Exchange Theory, with particular attention to studies situated in higher education institutions. Findings: The review indicates that Green HRM practices – green recruitment and selection, green training and development, green performance management, green compensation, and green employee involvement – enhance organizational performance in educational institutions primarily by shaping employee green behaviour, which in turn improves environmental performance, staff engagement and retention, institutional reputation, and social responsibility outcomes. Top management support and green organizational culture emerge consistently as boundary conditions that strengthen or weaken this relationship. Practical Implications: The proposed framework offers educational leaders and HR administrators a structured basis for designing, sequencing, and evaluating Green HRM interventions, rather than adopting green practices in an ad hoc or symbolic manner. Originality/Value: While Green HRM has been extensively studied in manufacturing and corporate contexts, its conceptualization within educational institutions – as employers of knowledge workers with a dual mandate to model and teach sustainability – remains comparatively limited. This paper contributes an integrated, AMO-grounded conceptual framework tailored specifically to the educational sector.
Decoding Generation Z Consumer Behavior: The Impact of Influencer Marketing on Purchase Intentions and Brand Trust
Authors: Bindu Tripathi Pandey Assistant Professor, Neetesh Dangi, Suhani Thagele
Abstract: In this study, structural effects of influencer marketing on consumer behavior of Generation Z (1997–2012) have been analyzed, particularly the effect of digital opinion leaders in shaping brand perceptions and purchase intentions. Born in the digital age, Generation Z consumers show increased levels of skepticism towards conventional forms of commercial advertising, depending mostly on social media sites like Instagram, TikTok, and YouTube for recommendations and discoveries. Using a descriptive and analytical research design based on the Stimulus-Organism-Response (S-O-R) model, Source Credibility Theory, and Parasocial Interaction Theory, this paper analyses the major factors affecting influencer persuasion. Primary data collection was done using a structured closed-ended questionnaire distributed among 384 Generation Z consumers using purposive sampling. Data were analyzed using IBM SPSS Statistics employing descriptive statistics, reliability test using Cronbach’s alpha, Pearson’s bivariate correlation, and multiple linear regression analysis. The empirical results indicate that the influencer’s credibility, authenticity perception, and relational empathy have a statistically significant positive impact on the purchase intention of Gen Z consumers (R² = 0.614, p < 0.001). Moreover, the micro-influencers exhibited higher levels of engagement and trust conversion than macro- and celebrity influencers, owing to their parasocial connection and perceived expertise. On the other hand, commercialization and concealment of sponsorship deals were found to severely undermine the level of brand trust. Thus, the study suggests that authenticity and interactivity become essential mediators in transforming online visibility into purchase intention. Drawing on the conclusions from the research, this article presents a set of recommendations for brand managers on how to effectively select influencers, choose content type, and build transparent communication for the Gen Z audience.
Effect of Performance Appraisal System on Employee Satisfaction
Authors: Dr. Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Mr. Nitesh Meena, Ranjan Meena
Abstract: Performance appraisal systems play an important role in managing employee performance, providing feedback, identifying development needs, and improving organizational effectiveness. A fair and transparent appraisal system can influence employee satisfaction by improving motivation, recognition, and career growth opportunities. This research study examines the relationship between performance appraisal practices and employee satisfaction. The study focuses on understanding employees’ perceptions of appraisal fairness, feedback mechanisms, rewards, and promotion opportunities. Primary data can be collected through questionnaires from employees, and statistical tools can be used to analyze the impact of appraisal systems on satisfaction levels.
A Study on the Role of Artificial Intelligence in the Loan Disbursement Process in Banks
Authors: Dr. Sadaf Khan Associate Professor, Kajal Yadav Assistant Professor, Smita Pandey
Abstract: Artificial Intelligence (AI) is transforming the banking industry by improving the speed, accuracy, and efficiency of financial services. One of the most significant areas of transformation is the loan disbursement process, where AI helps automate customer verification, document processing, credit assessment, fraud detection, and decision-making. This research study examines the role of Artificial Intelligence in enhancing the loan disbursement process in banks and its impact on operational efficiency and customer satisfaction. The study aims to understand how AI technologies are integrated into various stages of loan processing, compare traditional and AI-based loan disbursement methods, and identify the benefits and challenges associated with AI adoption in the banking sector. The research is based on secondary data collected from books, research articles, banking reports, journals, and official publications related to AI and digital banking. The findings indicate that AI significantly reduces loan processing time, minimizes human errors, improves credit risk assessment, enhances fraud detection, and provides a better customer experience. However, challenges such as data privacy, cybersecurity risks, implementation costs, regulatory compliance, and the need for skilled professionals remain important considerations for banks. The study concludes that Artificial Intelligence has become a key driver of digital transformation in the banking industry. Banks that effectively implement AI in their loan disbursement processes can achieve greater operational efficiency, faster decision-making, improved risk management, and higher customer satisfaction. The research also suggests that continuous investment in AI technologies, employee training, and robust regulatory frameworks will support the sustainable growth of AI-enabled banking services.
Impact of Performance-Based Compensation on Employee Motivation and Organizational Performance
Authors: Abhishek Malviya, Sameer, Neha kadwe
Abstract: The performance-based compensation system has become more common than the traditional fixed salary system within contemporary Human Resource Management (HRM), with the basis being the link between financial motivation and employee outputs to improve performance of both the employee and organization at large. In this empirical research paper, I explore the intricate relationship between the performance-based compensation system, the multi-dimensional employee motivation (both intrinsic and extrinsic motivation), and the effectiveness of the organization in the modern corporate setting. The empirical data used in the study was collected through a quantitative cross-sectional research approach by using a well-structured pre-tested survey instrument with a Likert scale of 5 for data collection among a stratified random sample of 384 employees and mid-level managers in the information technology, banking and manufacturing industries. The analysis was done statistically using SPSS and SmartPLS software. It is shown empirically that there is a statistically significant positive correlation between performance-based compensation and organizational performance (β = 0.482, p < 0.001). Besides, the empirical results suggest that extrinsic motivation acts as an important mediator between incentive structures and task productivity in the short term. On the other hand, continued dependence on monetary incentives, without other forms of recognition, has been seen to reduce intrinsic motivation in the long run. Empirical testing of the hypotheses suggests that pay fairness, goal clarity, and feedback timeliness are critical moderators that improve the effects of compensation. It is concluded from the study that performance-based compensation is a powerful driver of institutional performance if it is done through transparency. Thus, it is recommended to the executive management and human resources managers to design compensation structures that integrate both monetary and non-monetary motivators and KPIs.
Impact of Digital Payment Systems on Online Consumer Buying Behavior
Authors: Md Ashif Karim Assistant Professor, Leeladhar Choure, Vicky Pawar, Shubham Rai
Abstract: The accelerated developments in FinTech and digitization have resulted in a dramatic shift in modern retail trends as consumer purchases have moved from being predominantly made in cash through physical means to becoming entirely digitalized transactions. The current research paper presents an empirical and theoretical analysis of the effect of digital payment mechanisms like Unified Payment Interface (UPI), mobile wallets, buy now pay later (BNPL) schemes, credit/debit cards, and Internet banking on online consumer purchase behaviors. Using descriptive and analytical research design, the current study explores the influence of major characteristics of digital payment interface, including perceived ease of use, transaction time, perceived security, promotional offers (cash-backs and discounts), and deferred payment options on psychological drivers of purchases like impulsive buying, reduced cart abandonment, loyalty towards brands, and total spend. The data was collected via the use of a structured questionnaire distributed to a sample size of N=450 individuals that actively use online shopping in metropolitan and semi-urban locations, with secondary data from financial databases and e-commerce institutions. The quantitative methods that were used to test five major hypotheses include exploratory factor analysis (EFA), multiple linear regressions, and structural equation modeling (SEM). From the empirical results, it becomes clear that perceived ease of use and instant payment are strongly correlated with the impulsive online buying process, which helps reduce cognitive costs related to expenditure. Robust security measures and smooth payment procedures are able to significantly minimize the incidence of abandoned shopping carts in e-commerce. On the other hand, delayed payment methods such as BNPL have an evident predisposition to increasing the amount of purchases made by young consumers, although there is an issue of over-indebtedness.
Artificial intelligence in India – one of the Key Driver of Indian Economy & Development
Authors: Bhaskar Banerjee
Abstract: Artificial intelligence is the development of computer systems able to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. This abstract examines core concepts, real-world uses, and ongoing societal challenges in the context of Indian Economy and Inclusive Developments.
The Impact of Artificial Intelligence on Banking
Authors: Miss Kajal Yadav Assistant Professor, Sandeep Kumar Verma
Abstract: Artificial Intelligence (AI) is transforming the banking industry by improving operational efficiency, enhancing customer experience, and strengthening risk management. AI-powered technologies such as machine learning, natural language processing, robotic process automation, and predictive analytics enable banks to automate routine tasks, detect fraudulent transactions, assess creditworthiness, and provide personalized financial services. AI-driven chatbots and virtual assistants offer 24/7 customer support, while advanced data analytics help banks make informed decisions and improve regulatory compliance. Despite its benefits, AI adoption also presents challenges, including data privacy concerns, cybersecurity risks, ethical issues, algorithmic bias, and the need for skilled professionals. As financial institutions continue to invest in AI technologies, it is essential to establish robust governance frameworks and ensure responsible AI implementation. This study examines the impact of Artificial Intelligence on the banking sector, highlighting its applications, benefits, challenges, and future prospects. The findings suggest that AI has become a key driver of innovation and competitiveness in modern banking, enabling financial institutions to deliver secure, efficient, and customer-centric services.
The Impact of Continuous Performance Feedback on Employee Productivity
Authors: Krishna Goswami Assistant Professor, Boby Rajak
Abstract: The contemporary dynamic business environment has seen a growing perception that the conventional annual performance appraisals are outdated, inflexible, and inadequate for fostering organizational growth and development. This research paper examines the practical effects of continuous performance feedback systems on the productivity of employees in modern organizations. The research study is designed using a combination of quantitative survey (N= 350) and qualitative interview approach to study the effects of continuous and constructive performance interactions on the individual work productivity of the employees. he empirical observations indicate the presence of a significant positive relationship between the frequency and quality of feedback and observed increases in the productivity of employees. In particular, companies which shifted from conducting annual performance appraisals to having a continuous feedback process on a weekly or bi-weekly basis observed an improvement of 24.8% in achieving KPIs, a decrease of 31.2% in time delays for projects, and an increase of 19.5% in job satisfaction. The data shows that continuous feedback removes the psychological ambiguity associated with unclear role perceptions, allows for establishing dynamic goal congruence by utilizing OKRs, and provides for increased intrinsic motivation through timely recognition. Moreover, the structural equation modeling shows that psychological safety and feedback literacy of managers act as important moderating factors. This paper shows that replacing episodic evaluations with continuous feedback models changes performance management from reactive, back-looking administrative tool to proactive, forward-looking strategic approach for human capital optimization. The recommendations for leadership development, continuous feedback culture creation, and digital feedback architecture implementation are given below.
The Impact Of Artificial Intelligence On Banking And Financial Services: Opportunities, Challenges, And Future Directions
Authors: Dr. Sadaf Khan, Kajal Yadav, Kartikey Sirohiya, Bharat Lal Napit
Abstract: Artificial Intelligence (AI) has emerged as a transformative technology in the banking and financial services sector, revolutionizing the way financial institutions operate and interact with customers. AI-driven technologies such as machine learning, natural language processing, predictive analytics, and robotic process automation are enhancing operational efficiency, improving customer experiences, strengthening fraud detection mechanisms, and supporting data-driven financial decision-making. As banks increasingly adopt digital transformation strategies, AI plays a vital role in delivering personalized financial services, automating routine processes, and managing financial risks more effectively. This study aims to examine the impact of Artificial Intelligence on banking and financial services by exploring its opportunities, challenges, and future directions. The research adopts a descriptive research design and utilizes primary data collected through a structured questionnaire administered to 100 respondents, including banking professionals and customers. Convenience sampling has been employed, while the collected data are analyzed using descriptive statistical techniques through MS Excel and SPSS. The expected findings indicate that AI significantly improves service quality, enhances customer satisfaction, accelerates decision-making, and reduces operational costs and fraudulent activities. However, challenges such as data privacy concerns, cybersecurity risks, algorithmic bias, regulatory compliance, and the need for skilled professionals continue to influence AI implementation. The study concludes that Artificial Intelligence should be viewed as a strategic enabler that complements human expertise rather than replacing it entirely. By integrating AI responsibly and ethically, financial institutions can achieve sustainable growth, improve customer trust, and maintain competitiveness in the rapidly evolving digital financial ecosystem.
Recruitment in Indian Organisations
Authors: Dr. Sadaf Khan Associate Professor, Ms. Kajal Yadav Assistant Professor, Shivangi, Muskan Patel
Abstract: Recruitment is the foundation of an organisation's human resource strategy, as it determines the quality of talent that enters and eventually shapes the culture and performance of a company. In the Indian context, recruitment practices have undergone a significant transformation over the last two decades, moving from traditional newspaper advertisements and walk-in interviews to technology-driven processes involving job portals, social media, Applicant Tracking Systems (ATS) and Artificial Intelligence (AI). This research paper examines the concept, process, sources and evolving trends of recruitment in Indian organisations. It further explores the factors, legal framework and challenges faced by HR departments, such as skill mismatch, high attrition, regional diversity and the pressure of digital transformation, while also highlighting best practices adopted by leading Indian companies and offering a comparative view of public and private sector recruitment. The study is based on secondary data collected from books, journals, company reports and reliable online sources, supported by illustrative charts and diagrams to aid interpretation. The findings suggest that Indian organisations are increasingly adopting a blended approach that combines technology with human judgement to build an efficient, inclusive and cost-effective recruitment process. The paper concludes with practical suggestions for improving recruitment effectiveness in the Indian corporate environment.
Impact of Artificial Intelligence-Driven HR Practices on Employee Retention, Productivity, and Workplace Wellbeing in Modern Organizations
Authors: Ms. Bindu Tripathi Assistant Professor, Rupali Rane
Abstract: The rapid integration of Artificial Intelligence (AI) into Human Resource Management (HRM) has fundamentally transformed traditional organizational operations, recruitment processes, performance management, and employee relations. This research paper investigates the impact of AI-driven HR practices on employee retention, overall productivity, and workplace wellbeing within modern corporate environments. Utilizing a quantitative research design, primary data was collected from corporate and IT professionals using structured questionnaires, alongside secondary data from recent academic literature and Scopus-indexed journals. The findings indicate that while predictive analytics and automated systems significantly enhance administrative efficiency and talent acquisition, they also introduce challenges concerning employee anxiety, data privacy concerns, and a reduction in personalized human interactions. The study concludes that organizations must strike a strategic balance between technological automation and empathetic human leadership to foster a healthy, productive, and sustainable work culture. Practical implications and future research scopes are also discussed.
Tradable Returns Versus Model Returns: Pricing Frictions, Volatility Risk, and the Case for Long-Dated Equity Options
Authors: Abhinav Yadav
Abstract: Long-dated call options, marketed as Long-Term Equity Anticipation Securities (LEAPS), have been promoted to long-horizon investors as a low-cost way to multiply the long-run return of the stock market. The evidence offered for this claim rests on a particular measurement choice: prices are generated with the Black-Scholes-Merton model under fixed volatility, interest rate, and dividend assumptions, so every reported gain or loss is a change in a theoretical model price rather than a price a trader could actually have obtained. This paper argues that the central open question is not whether long-dated calls amplify index returns, which is almost mechanical, but whether the amplification survives once the pricing is made realistic: traded bid-ask quotes, stochastic volatility, volatility risk premia, transaction costs, and liquidity. The research is organized into three connected studies. The first constructs a realistic backtest of rolling two-year SPY call positions using market prices from 1996 through 2025, comparing results against the fixed-parameter model benchmark to isolate the model error. The second estimates how much of the long-run return is compensation for bearing volatility risk rather than equity risk, using a straddle-based pricing kernel under stochastic volatility. The third evaluates whether protective put structures, often recommended as insurance against expiration risk, retain their value once the cost of the hedge is priced at market levels. Together, the three studies address a gap that is both empirical and conceptual: the existing case for LEAPS has never been examined under the pricing assumptions under which the instruments are actually traded. The findings suggest that the fourfold amplification claim overstates realized returns by a wide margin once transaction costs and market pricing are incorporated, and that a substantial portion of the surviving premium compensates investors for bearing volatility risk rather than equity risk.
Circular Economy, Environmental Pollution And Financial Sustainability
Authors: Dr. Nisha
Abstract: Rising environmental pollution and resource depletion have increased interest in transitioning to a circular economy (CE). Circular economy policies aim to decouple economic growth from resource use by promoting efficiency, waste reduction, and sustainable production. While their environmental benefits are well studied, less is known about their combined impact on environmental quality and financial sustainability. This study addresses this gap through a qualitative analysis of recent circular economy regulations. The research reviews key policies and literature (2018–2026), focusing on instruments such as Extended Producer Responsibility, eco-design, green procurement, and sustainable finance. Findings show that effective CE regulations reduce waste, emissions, and resource use while enhancing financial sustainability through cost savings, innovation, and improved competitiveness. However, outcomes vary across countries due to differences in governance, infrastructure, and financial capacity, with developing economies facing greater implementation challenges. The study proposes a framework linking circular economy regulation to environmental performance and financial sustainability, highlighting the roles of innovation, resource efficiency, and policy effectiveness. It offers insights for policymakers and stakeholders aiming to support sustainable and resilient economic systems.
A Study On The Effectiveness Of Solar Energy Utilization And Consumer Satisfaction
Authors: K. Vasumitha, Dr.P. Vinodhini
Abstract: The increasing demand for energy has contributed to the growing adoption of solar energy among households, businesses, and agricultural users. Solar energy helps consumers reduce electricity costs and provides environmental benefits by reducing carbon emissions and dependence on fossil fuels. However, the effectiveness of solar energy utilization depends on consumer awareness, proper installation, regular maintenance, government support, and customer satisfaction. This study examines the effectiveness of solar energy utilization among consumers and identifies the factors influencing customer satisfaction. The study adopted a descriptive research design and collected primary data from 87 customers through a structured questionnaire. Secondary data were collected from research journals, books, government reports, and other relevant sources. Convenience sampling was used to select the respondents based on their accessibility and willingness to participate. The collected data were analysed using appropriate statistical tools. The findings indicate that most respondents are satisfied with their solar energy systems and appreciate the reduction in electricity expenses. Consumers also recognize the environmental benefits of solar energy and consider it a worthwhile investment. Statistical analysis revealed that customer satisfaction does not differ significantly based on gender or age, indicating relatively similar perceptions across demographic groups. The study concludes that greater consumer awareness, proper installation, effective after-sales support, regular maintenance, and increased government incentives can encourage wider adoption of solar energy. The findings provide useful insights for policymakers, solar-energy companies, and researchers seeking to promote sustainable and environmentally friendly energy solutions.
A Study On Marketing Strategies and Customer Engagement
Authors: Vimal Raj S., Dr. P. Vinodhini
Abstract: Marketing strategies are crucial in helping organisations attract customers, increase brand awareness, and strengthen customer relationships. In today's competitive business landscape, organisations need to adopt innovative marketing approaches to boost customer engagement and achieve sustainable growth. This study investigates the marketing strategies employed by Global Techno Solutions and their impact on customer engagement. The research focuses on elements such as digital marketing, social media marketing, customer relationship management, promotional activities, customer satisfaction, and customer loyalty. Data were collected from 100 respondents using a structured questionnaire. Percentage analysis, One-Way ANOVA, and Correlation Analysis were employed to interpret the data. The findings suggest that effective marketing strategies significantly enhance customer awareness, satisfaction, trust, and long-term engagement. The study concludes that organisations should continually strengthen their digital presence, improve customer communication, and implement customer-centric marketing practices to attain a sustained competitive advantage.
DOI: http://doi.org/10.5281/zenodo.21884616
Awareness and Utilization of Health Insurance Schemes in Kerala
Authors: Research Scholar Merin Titty D Cunha, Assistant Professor Dr. Jisha S Kumar
Abstract: Health insurance is becoming more accepted as an important way to ensure everyone has access to healthcare, lower the risk of spending too much on health issues, and help build better, lasting healthcare systems.Even though there are many public and private health insurance options in India, there are still big differences in how well people know about these schemes and how often they use them, especially in rural areas.This study looks at how aware people are of health insurance, how often they use it, and how their views on risk affect whether they get insurance in Kerala.The research gathered information from 50 rural families in Annallur Village, Thrissur District, through a detailed survey.The study uses Partial Least Squares Structural Equation Modelling (PLS-SEM) to understand the links between awareness of health insurance, how people see risk, and how much they use insurance. The results show that people have a moderate level of awareness, but a large number of households are not insured. The study found that seeing health risks as a real concern helps people decide to buy insurance. It emphasizes the need for better awareness, financial knowledge, and focused policies to increase insurance use. These findings add to the growing body of knowledge on health insurance adoption and offer useful guidance for policymakers, insurance providers, and public health leaders working to make healthcare more affordable and sustainable in Kerala.
A Study On Order Fulfillment Process And Customer Experience In E-Commerce Enterprise
Authors: Udhaya Sarathy. S, Dr.P.Vinodhini
Abstract: Order fulfillment has become a central determinant of customer satisfaction in the e-commerce industry, as customers increasingly judge an online retailer not by its product catalogue alone but by how accurately, safely, and quickly their orders are processed and delivered. This study examines the order fulfillment process and its relationship with customer experience within the e-commerce operations of a retail and lifestyle-products organization. Using a descriptive research design, primary data were collected from 62 employees engaged in order processing, inventory management, warehousing, logistics, and customer support functions through a structured questionnaire. The study evaluates perceptions of packaging quality, inventory management, order tracking, warehouse operations, order accuracy, inter-departmental communication, complaint handling, technological support, dispatch timeliness, and delivery performance. The findings indicate that while packaging quality, order accuracy, and departmental communication are rated highly, inventory management and stock visibility emerge as the areas most in need of improvement. Fast delivery is identified as the single most influential factor shaping customer satisfaction, followed by customer service and product quality. The study concludes with practical recommendations, including the adoption of advanced inventory management systems, real-time order tracking, and stronger logistics coordination, to strengthen operational efficiency and enhance customer experience in competitive e-commerce environments.
A Study on Analysis of Freight Forwarding operations
Authors: Vismaya VS, Dr.P.Vinodhini
Abstract: Freight forwarding plays a vital role in facilitating the movement of goods through transportation, documentation, customs clearance, cargo handling, shipment tracking, and delivery. The present study analyses freight forwarding operations with particular emphasis on operational practices, transportation preferences, customs clearance, documentation, digital technologies, and service satisfaction. The study adopts a descriptive research design based on both primary and secondary data. Primary data were collected through a structured questionnaire, and 72 valid responses were considered for analysis. Percentage analysis was used to present descriptive findings, while the Chi-square test and One-Way ANOVA were employed for hypothesis testing. The findings indicate that 66.67% of respondents prefer sea transportation, while 80.56% prefer Full Container Load (FCL) shipments and 72.22% are engaged in both import and export activities. Furthermore, 87.50% of respondents were satisfied or highly satisfied with the customs clearance process, while 76.39% agreed or strongly agreed that online documentation facilitates error detection and shipment tracking. The statistical analysis revealed significant associations between the nature of business and terms and conditions, the nature of business and online documentation, and exporting time and terms and conditions. The study emphasizes the importance of digital documentation, real-time shipment tracking, employee training, effective coordination, and contingency planning for improving the efficiency and reliability of freight forwarding operations.
A Study On Customer Data Analysis for Improving Banking Services with Special Reference to Indian Overseas Bank
Authors: Mounikha E. I, Dr. P.Vinodhini
Abstract: Customer data has become one of the most valuable assets for banks in understanding customer needs, preferences, and behaviour and in improving the quality of financial services. This study examines how customer data analysis can be used to improve banking services, with special reference to Indian Overseas Bank (IOB). The study is based on practical exposure gained during an internship at IOB, where activities such as customer account opening, KYC verification support, and customer-data updation highlighted the importance of maintaining accurate and updated customer information in banking operations. Primary data were collected from 100 customers of Indian Overseas Bank using a structured questionnaire covering demographic characteristics, banking-service usage, digital banking adoption, and customer satisfaction. The collected data were analysed using percentage analysis and the Chi-Square test. The findings indicate that a majority of customers hold savings accounts and actively use digital banking services such as mobile banking, internet banking, and UPI. The results also indicate a generally positive level of customer satisfaction with the banking services provided by IOB. The Chi-Square analysis further identified significant associations among selected customer-related variables, indicating differences in customer perceptions across categories. The study concludes that effective customer data analysis enables banks to understand customer behaviour, personalize financial products and services, strengthen customer relationships, and support data-driven decision-making. The study recommends strengthening customer-data management systems, improving digital banking awareness, and using customer analytics continuously to enhance service quality and customer satisfaction.
A Review on Functional Shrikhand Enriched with Cymbopogon Citratus and Nigella Sativa: Physicochemical Properties, Microbial Quality and Antioxidant Potential
Authors: Sankaraiah R, Arunkumar U, Narendra P, Adaikala Raj G, P. Gnana Suriya, Assistant Professor Dr. G. Adaikala Raj
Abstract: Shrikhand is a traditional fermented dairy dessert widely consumed in India and prepared from strained curd (chakka) blended with sugar and flavoring agents. In recent years, the incorporation of herbal and plant-based ingredients into dairy products has gained attention due to their potential health benefits. Functional shrikhand enriched with Cymbopogon citratus (lemongrass) and Nigella sativa (black cumin) has emerged as a promising value-added dairy product with enhanced nutritional and therapeutic properties. The addition of lemongrass and black cumin significantly influences the physicochemical characteristics of shrikhand, including pH, titratable acidity, moisture, fat, protein and total solids. Lemongrass contains bioactive compounds such as citral, flavonoids and phenolic acids, which may slightly reduce pH and increase acidity due to their natural organic components. Similarly, Nigella sativa seeds are rich in essential oils, proteins and minerals that contribute to improved nutritional quality and flavor. These ingredients also influence the texture and stability of shrikhand by modifying moisture retention and total solids. Microbial quality is an important factor in fermented dairy products. Studies have shown that herbal additives such as lemongrass and Nigella sativa possess natural antimicrobial compounds that may inhibit the growth of spoilage microorganisms and pathogenic bacteria. This contributes to improved shelf life and microbial safety of the product when prepared under hygienic conditions. In addition, both ingredients are well known for their antioxidant properties. The phenolic compounds and flavonoids present in lemongrass and the bioactive component thymoquinone in Nigella sativa enhance the antioxidant potential of shrikhand. Antioxidant activity is commonly evaluated using DPPH and ABTS radical scavenging assays, which demonstrate the ability of functional shrikhand to neutralize free radicals. Therefore, the incorporation of these herbal ingredients can improve the nutritional, functional and health-promoting properties of shrikhand.
A Study On Sales and Customer Relationship Management in The Two-Wheeler Industry
Authors: Yuvaraj. V, Dr.P. Vinodhini
Abstract: This study concentrates on the sales performance and Customer Relationship Management (CRM) practices of KTM and Husqvarna dealerships. The two-wheeler industry in India has become highly competitive, making customer satisfaction and long-term relationship management essential for business success. The primary aim is to examine how CRM strategies, including customer communication, after-sales service, complaint handling, digital engagement, and loyalty initiatives, influence customer satisfaction and retention. The study adopts a descriptive research design and utilises both primary and secondary data. Primary data are collected through structured customer questionnaires, while secondary data are obtained from journals, company reports, websites, and published research articles. The collected data are analysed using percentage analysis, tables, and charts to ascertain customer perceptions and satisfaction levels. The findings suggest that effective CRM practices significantly enhance customer trust, improve service quality, strengthen brand loyalty, and encourage repeat purchases. Moreover, efficient after-sales support and personalised customer interactions are crucial for maintaining long-term customer relationships. The study concludes that KTM and Husqvarna can achieve sustainable growth and gain a competitive advantage by continually improving their CRM strategies, adopting advanced digital communication tools, and delivering superior customer service.
A Study On The Adoption Of AI Voice And Text Agents And Their Impact On Business Operations
Authors: Aravindh Pranav.v, Dr. P. Vinodhini
Abstract: Artificial intelligence is changing the way businesses communicate with customers, particularly through AI voice and text agents used for customer support. These agents can respond quickly, operate continuously, handle large numbers of conversations, and support businesses across channels such as WhatsApp, websites, in-app chat, and voice calls. At the same time, customer acceptance depends on familiarity, comprehension, trust, tone, and the availability of human support. This study examines the adoption of AI voice and text agents and their perceived impact on business operations from the customer perspective, using the Karta AI internship context as its grounding. A descriptive, quantitative and cross-sectional research design was used. Primary data were collected through a structured questionnaire from 30 respondents who had interacted with AI voice or text-based customer support agents, using convenience sampling. Secondary information was obtained from academic literature, industry reports and company publications. The collected data were analysed using frequency and percentage analysis, Chi-Square tests and One-Way ANOVA. The findings show that 70% of respondents were aged 18–25, 60% stated that AI understood their queries correctly, and 56.67% supported increased use of AI agents in customer support. WhatsApp bots were the most commonly used channel at 36.67%. Financial/payment disputes, complex technical issues and emotionally sensitive complaints were the situations in which customers most preferred human support. Robotic or impersonal responses and difficulty reaching a human agent were the most frequently reported challenges. Statistical analysis found a significant relationship between familiarity with AI and willingness to see greater AI use in the future (p = 0.022), while no significant relationship was found between frequency of AI use and query comprehension (p = 0.823) or between age group and future outlook (p = 0.220). The study concludes that AI adoption is broadly welcomed, but long-term effectiveness depends on better comprehension, more human-like interaction, transparent escalation and a balanced human-AI support model.
Impact of Credit Risk Management on the Financial Performance of Commercial Banks
Authors: Pankaj Kumar Patel, Neha Prajapati, Bharti Kushwaha, Sunil Kumar Meena
Abstract: Management of credit risk is a key factor that contributes towards financial stability and sustainability in the commercial banking industry in the international business environment. The current study seeks to analyze the effects of credit risk management on the financial performance of commercial banks in a ten year period starting in 2014 up to 2023. Using econometric modeling approach, the financial performance is measured using Return on Asset (ROA) and Return on Equity (ROE) and credit risk management using Non-Performing Loan Ratio (NPLR), Capital Adequacy Ratio (CAR), Provision Coverage Ratio (PCR), and Loan-to-Deposit Ratio (LDR). Secondary data was collected from the audited annual financial statement of 15 leading commercial banks as well as central bank macro-economic bulletins to arrive at 150 panel observations. Panel data regression analysis, namely the Fixed Effect Model chosen using the Hausman specification test, was used among other diagnostics such as tests for multicollinearity, heteroscedasticity and autocorrelation. From the analysis, it is clear that there exists a statistically significant negative relationship between NPL ratio and bank profitability in the form of ROA and ROE. This means that decline in the asset quality leads to a reduction in the earnings due to loan loss provision and loss in the capital. On the other hand, there exists a statistically significant positive relationship between the capital adequacy ratio and provision coverage ratio and bank performance, which clearly shows the significance of adequate capital cushion and loan provision to minimize risk of defaults. Loan deposit ratio has shown a non-linear relationship as an intermediate loan deposit ratio increases profitability whereas extreme loan deposit ratios increase the risk of defaults.
A Study On Operational Efficiency in Courier and Cargo Management
Authors: S. Sangeetha, Dr. P. Vinodhini
Abstract: The courier and cargo industry plays a vital role in supporting business operations and meeting the growing demand for fast and reliable delivery services. This study examines the operational efficiency of UM EXPRESS Courier and Cargo Services by analysing key operational activities such as shipment processing, transportation, route planning, delivery performance, technology usage, and customer service. A descriptive research design was adopted, and data were collected from 88 respondents (30 employees and 70 customers) using a structured questionnaire, along with secondary data from company records and published sources. The collected data were analyzed using percentage analysis, ranking methods, weighted average method, and graphical representation. The findings reveal that the company's overall operational efficiency is satisfactory, with transportation delays identified as the major operational challenge. The study concludes by suggesting improvements in transportation management, technology adoption, and process coordination to enhance service quality, customer satisfaction, and overall operational performance.
The Sustainable Theory of Wealth: Bridging the Macro-Micro Paradox of Ultra-High Net Worth Capital
Authors: Shreyasee Das
Abstract: Modern economic systems evaluate wealth through a macro-aggregative framework, measuring capital accumulation via net worth, asset valuation, and market capitalization. However, this traditional framework creates a severe operational discrepancy between macro-level asset ownership and micro-level subjective utility flow. Ultra-high net worth individuals (UHNWIs) frequently maintain balance sheets running into billions, yet experience a personal consumption and experiential flow bounded by physiological, cognitive, and temporal limits—often representing less than 0.0001% of total holdings. This paper introduces the Sustainable Theory of Wealth (STW), introducing a formal Conscious Capital Matrix Model that transitions capital deployment from fear-driven asset hoarding to flow-optimized, high-utility allocation. By decoupling security perception from total nominal equity, STW establishes a dual-tier model of wealth optimization that harmonizes micro-level subjective well-being with macro-level socioeconomic sustainability and dynamic policy incentives.
To The Study of Bibliometric Analysis for Block Chain Technology in Banking Sector
Authors: Dr. Shobha Bajetha, Dr. Abhimanyu Kumar
Abstract: Bibliometric analysis is used across a wide range of disciplines to provide insights into research productivity, the influence of specific authors or institutions, and the overall growth of scientific knowledge. It helps identify leading scholars, influential works, and collaborative networks, as well as forecast future research directions. This study conducts a comprehensive bibliometric analysis of scholarly research using 1,286 articles from the Dimensions database. The analysis explores four key dimensions: citation networks, co-authorship patterns, bibliographic coupling by institutions, and co-citation of references. The citation network reveals 85 sources in 11 clusters, highlighting prominent themes such as technological forecasting, social sustainability, and international business research. Co-authorship analysis identifies 73 authors, with the largest connected network featuring 31 authors across six clusters, emphasizing collaborative dynamics. Bibliographic coupling maps 224 institutions, with central nodes like Malaviya National Institute of Technology and Symbiosis International University. Finally, co-citation analysis identifies thematic clusters around journals such as Journal of Business Research and Scientometrics, offering insights into intellectual structures. This study provides a detailed map of academic interactions, shedding light on influential works, contributors, and institutional collaborations.
