International Journal of LAW, Arts and Humanities
Study on the Rise of Mobile Marketing and App Promotion
The rapid proliferation of smartphones and internet connectivity has fundamentally transformed the landscape of marketing, giving rise to mobile marketing and app-based promotional strategies as dominant channels for consumer engagement. This paper examines the rise of mobile marketing and app promotion, analysing adoption trends, consumer behaviour, effectiveness metrics, and strategic frameworks adopted by businesses across sectors. Drawing on primary data collected from 150 respondents and secondary data from industry reports published between 2019 and 2024, this study investigates how mobile applications have become pivotal touchpoints in the consumer journey—from awareness through purchase to post-sale loyalty. Findings reveal that personalised push notifications, in-app advertising, and social media integration are the most effective mobile promotional tools, while data privacy concerns, ad fatigue, and device fragmentation remain critical barriers. The study concludes that organisations that integrate mobile marketing with data analytics, AI-driven personalisation, and omnichannel consistency achieve measurably superior customer acquisition and retention outcomes. Actionable recommendations are proposed for marketers navigating the mobile-first digital economy.
Devarapaga Keshavulu¹, Maram Sathish², Gadapa Rajini³ Ms. G. Ganesh⁵
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
A STUDY ON ROLE OF TRAINING AND DEVELOPMENT IN EMPLOYEE RETENTION
Employee retention is one of the most pressing challenges faced by modern organizations across all industries. Training and development (T&D) programs play a pivotal role in enhancing employee competencies, motivation, and organizational commitment, directly influencing retention outcomes. This study investigates the relationship between structured training and development initiatives and employee retention levels within select organizations in Hyderabad, Telangana. Primary data was collected from 120 employees across diverse sectors using a structured questionnaire. Secondary data was sourced from academic journals, HR reports, and published research. Statistical tools including percentage analysis, chisquare test, correlation analysis, and weighted average method were employed to analyze the data. Findings reveal a significant positive relationship between effective T&D practices and employee retention, with career development opportunities, skill enhancement programs, and on-the-job training emerging as the most influential retention factors. The study recommends a strategic, employee-centric approach to training design and implementation to reduce attrition and build organizational resilience.
Employee retention, training and development, attrition, skill enhancement, organizational commitment, human resource management, career development, job satisfaction.
Burri Yashwanth¹, B Prudviraj2 , Durgam Vyshnavi3 , Kola Sandeep Kumar4 , Ms. S. Ramya5
A Study on Financial Institutions of Financing Small Scale Industrial at Axis Bank
Small-scale industries (SSIs) form the backbone of India's industrial economy, contributing substantially to GDP, employment generation, and export earnings. Access to timely and adequate institutional finance remains the most critical constraint limiting SSI growth and modernization. This paper examines the role of Axis Bank—one of India's leading private sector banks—in financing small-scale industrial units in Hyderabad, Telangana. Using primary data collected through structured interviews with 50 SSI entrepreneurs and Axis Bank branch officials, and secondary data from RBI publications and bank annual reports (FY 2021–2024), the study analyses loan products, disbursement trends, interest rate structures, appraisal processes, and sectorwise distribution of MSME credit. Findings reveal that collateral inadequacy, low CIBIL scores, and insufficient income documentation are the primary barriers to SSI credit access. The paper recommends simplification of documentation norms, alternate collateral frameworks, and digital appraisal tools to enhance financial inclusion of small industries.
Kamuni Snehith¹, Gajarla Manisha2 , Neela Sai Krishna3 , Chamineti Rohit Raj4 , Gaddam Vara Prasad5 , Dr. Y. Azith6
A STUDY ON DATA SECURITY PRACTICES IN IT ORGANISATION AT AMAZON
Data security has become a paramount concern for modern IT organisations as cyber threats grow in sophistication and frequency. Amazon, one of the world’s largest technology and ecommerce conglomerates, manages vast repositories of sensitive customer, transactional, and operational data across its global cloud and enterprise infrastructure. This study examines the data security practices adopted by Amazon, with emphasis on AWS (Amazon Web Services) security architecture, encryption standards, access control frameworks, compliance mechanisms, and incident response strategies. Primary data was collected through structured surveys and secondary data was sourced from Amazon’s published security whitepapers, AWS documentation, and academic literature. The study finds that Amazon employs a multi-layered defensein-depth strategy underpinned by Zero Trust architecture, automated threat detection, and continuous compliance monitoring. Findings indicate that while Amazon’s security posture is robust, challenges persist in thirdparty vendor risk management and insider threat mitigation. Recommendations include enhanced vendor risk scoring, AI-driven anomaly detection, and strengthened employee awareness training.
G S Damini¹, Murthy Sai Kumar2 , Mohammed Asif3 , Gogula Manichandan4 , Mr. G. Mahesh5
A Study on Role of Insurance in Minimizing Business Risk at ICICI Lombard General Insurance
Business risk is an inherent dimension of commercial activity, encompassing operational, financial, market, legal, and natural hazards that can disrupt enterprise continuity and erode stakeholder value. Insurance functions as the primary institutional mechanism for transferring and mitigating these risks, enabling businesses to sustain operations, maintain credit, and plan for growth with greater certainty. This paper examines the role of insurance in minimizing business risk at ICICI Lombard General Insurance, India's largest private general insurer. The study investigates the insurance products offered for business risk coverage, the risk assessment methodologies employed, the effectiveness of claims settlement in business continuity, and the challenges faced by enterprises in accessing adequate insurance protection. Primary data were collected through structured questionnaires administered to 60 business clients and ICICI Lombard relationship managers. Secondary data were sourced from ICICI Lombard Annual Reports (2021–2024), IRDAI publications, academic journals, and industry research. Findings demonstrate that property insurance and liability coverage are the most widely adopted risk mitigation instruments, while awareness gaps and premium affordability constrain uptake among SMEs. The study recommends customized SME insurance packages, digital underwriting acceleration, and enhanced financial literacy programs to broaden business risk coverage.
Kavali Sravan Kumar1 , Puri Veeresh2 , Polagoni Manasa3 , Kandi Shiva Krishna4 , Gandham Sheshikiran5 , P. Amulya6
A Study on Risk Adjusted Returns Analysis at Kotak Mahindra Bank
Risk-adjusted return analysis constitutes a fundamental pillar of modern portfolio theory, enabling investors and financial institutions to evaluate investment performance relative to the inherent risk assumed. Kotak Mahindra Bank, one of India's pre-eminent private sector banks with a diversified financial services portfolio spanning banking, asset management, insurance, and securities, presents an enriched subject for such analysis given its multi-segment business model and sustained outperformance relative to sector benchmarks. This study examines the riskadjusted return profile of Kotak Mahindra Bank over a five-year period (FY 2019–20 to FY 2023–24), employing classical and contemporary risk-measurement tools including the Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Standard Deviation, Beta Coefficient, and the Coefficient of Variation. Secondary financial data were sourced from Kotak Mahindra Bank annual reports, BSE/NSE historical price data, SEBI disclosures, and published research. Results reveal that the bank consistently generated positive risk-adjusted returns across the study period, with Sharpe Ratios exceeding the banking sector benchmark in four of five years. Jensen's Alpha indicates persistent positive abnormal returns, suggesting skilled portfolio and treasury management. Beta values below unity confirm lower systematic risk relative to the NIFTY 50, making the stock attractive to risk-averse institutional investors. The study provides actionable insights for investors, fund managers, and bank strategists.
Risk-adjusted returns, Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Beta coefficient,
Goka Aarthi¹, Mohammed Rashath Adeeb², Ekula Bhaskar³, Md Saif⁴, P. Amulya5
CURRENT ISSUES AND CHALLENGES OF WATER MANAGEMENT IN INDIA
India has witnessed a substantial rise in water demand over recent decades due to rapid population growth, urbanization, industrialization, and socio-economic development. To satisfy this increasing demand, extensive exploitation of surface and groundwater resources has taken place, leading to declining groundwater levels, deterioration in water quality, overuse of river basins, reservoir siltation, and increasing freshwater pollution from industrial and municipal wastes. Per capita freshwater availability has decreased significantly from 6008 m³ per year in 1947 to nearly 2200 m³ annually after five decades, highlighting the growing severity of water scarcity. This scarcity poses major challenges to food security, ecosystem conservation, public health, and social stability, while also intensifying conflicts among different water-use sectors. The study discusses the emerging water-related issues and management challenges in India and emphasizes that future water demand is expected to increase further due to expanding urban populations, rising incomes, and rapid industrial growth. The paper also identifies major barriers to sustainable water resource management, including inadequate scientific data and limited technological advancements for developing environmentally sustainable, economically feasible, and socially acceptable water management solutions.
Mergers and Acquisitions as Catalysts of Globalisation: A Review of the Literature
In this paper, past research studies on Mergers and Acquisitions’ (M&As) are reviewed around the world. This paper contains two parts viz., Financial performance of firms and mergers effect on shareholders wealth has been summed up by identifying the gap in the literature to be fulfilled it by this study.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
From Human Authorship to Machine Creativity: Rethinking Copyright Law in the AI Age
Artificial Intelligence (AI) has brought about a rapid transformation within the creative sphere, thereby unsettling the traditional foundations of intellectual property law, particularly copyright. Works generated through AI systems become increasingly prevalent. Legal frameworks across jurisdictions are confronted with persistent difficulties in defining authorship, ownership, and copyrightability. This article tries to critically evaluatee the evolving legal platform governing AI-generated content and particular reference to comparative developments in the European Union, the United States, the United Kingdom, and India. The analysis focus upon the fundamental issue of whether an AI system can be regarded as an author? Who should be recognised as the rightful holder of rights in such works? While traditional copyright frameworks are firmly rooted in the principle of human authorship, the growing confusion created by AI systems in content generation necessitates a reconsideration of established traditional assumptions. The notion of “work made for hire” has been advanced as a possible mechanism for allocating authorship. Hhowever, disputes regarding ownership continue to arise among developers, users, and corporate entities. In addition, the scope and applicability of economic and moral rights in relation to AI-generated works remain deeply contested underscoring the urgent need for greater legal clarity.With particular emphasis on the Indian copyright framework, this study highlights the urgent need for legislative reform capable of addressing the realities of AI-driven creativity. It advances policy-oriented suggestions, including the incorporation of AI-specific provisions.The mentioning of clear standards concerning human intervention, and the establishment of structured mechanisms for attribution and rights protection also suggested. Ultimately, the article argues that a balanced legal approach is essential with the one that simultaneously promotes technological innovation and safeguards the interests of human creators. By aligning copyright principles with contemporary technological developments, legal systems can more effectively navigate the complexities posed by AI-generated creativity. It thereby ensuring both progress and equitable protection in the digital age.
ASSET PRICING IN EMERGING MARKETS AN EMPIRICAL INVESTIGATION
Emerging financial markets present an important yet challenging context for testing the validity of traditional asset pricing theories. While models such as the Capital Asset Pricing Model (CAPM), the Fama– French Three-Factor Model, and the Arbitrage Pricing Theory (APT) have been widely examined in developed and mature markets, their empirical performance in emerging markets remains comparatively underexplored. These markets differ fundamentally in structure and behavior, exhibiting higher volatility, liquidity constraints, segmented capital flows, and heightened exposure to macroeconomic and political risk. Such characteristics raise questions about whether standard asset pricing frameworks can adequately capture return dynamics in these environments. This study investigates the applicability and performance of mainstream asset pricing models across selected emerging market economies using panel data and crosssectional regression approaches. By incorporating both market-based risk factors and macroeconomic variables, the analysis examines how country-specific characteristics influence asset returns and whether multifactor frameworks outperform single-factor benchmarks. Particular attention is given to inflation, exchange rate movements, interest rate fluctuations, and institutional factors that uniquely shape the risk–return landscape in emerging markets. Empirical findings reveal that while traditional models retain some explanatory power, they often fail to fully account for return variation in these markets, especially during periods of heightened uncertainty or structural change. Multi-factor and macro factor augmentations yield improved performance, suggesting the need for context-specific adaptations. The study contributes to ongoing debates on asset pricing theory by highlighting the limitations of universal model application and advocating for tailored approaches that reflect the heterogeneous and evolving nature of emerging economies. These results carry implications for investors, policymakers, and academics seeking to understand asset behavior in high-growth but complex financial environments.
Asset Pricing, Emerging Markets, Capital Asset Pricing Model (CAPM), Fama– French Model, Arbitrage Pricing Theory (APT), Risk–Return Relationship, Panel Data Analysis, Cross-Sectional Regression, Macroeconomic Factors, Market Volatility, Exchange Rates, Interest Rates, Inflation, Liquidity Constraints, Financial Econometrics
DIVIDEND POLICY DECISIONS: TRENDS, DETERMINANTS, AND FIRM VALUE
This paper examines the trends, determinants, and impact of dividend policy decisions on firm value. Dividend policies, which reflect how companies distribute profits to shareholders, have been a critical area of research due to their potential influence on shareholder value, market perceptions, and corporate performance. This study explores the various factors affecting dividend decisions, such as profitability, liquidity, market conditions, and agency costs. Furthermore, it discusses the effects of these policies on firm value, considering the signaling theory and agency theory. Through a comprehensive literature review and data analysis, this paper presents an understanding of how dividend policies evolve in different economic environments and their implications for firm performance.
Dividend Policy, Firm Value, Shareholder Wealth, Profitability, Liquidity, Agency Theory, Signaling Theory, Corporate Finance, Dividend Payout Ratio, Market Conditions, Corporate Performance, Earnings Distribution
भारत के चीन और अमेरिका के साथ ऐतिहासिक संबंध: एक विश्लेषण (1945-2003)
यह अध्ययन भारत के चीन और अमेरिका के साथ संबंधों के ऐतिहासिक विश्लेषण (1945 से 2003 तक) पर केंद्रित है । इसमें भारत की विदेश नीति और दोनों देशों के साथ रिश्तों की मुख्य पहलुओं का मूल्यांकन किया है। इस अध्ययन से हमें 1945 से 2003 तक भारत के बाह्य नीति में चीन और अमेरिका के साथ संबंधों के परिणाम समझने में मदद मिलेगी। साथ ही, हमें अमेरिका और चीन के साथ भारत के राजनयिक संबंधों की वर्तमान स्थिति पर ऐतिहासिक नीतियों और घटनाओं के प्रभाव को समझने में भी मदद मिलेगी।
भू राजनीति, गुटनिरपेक्षता, विश्व व्यवस्था, रचनात्मक सहयोग, राजनयिक सम्बन्ध, अलगाव।
A COMPARATIVE STUDY OF ANTI-DISCRIMINATION LAWS FOR TRANSGENDER PERSONS
The recognition of transgender rights has emerged as a central concern in contemporary constitutional and human rights law. Antidiscrimination frameworks across jurisdictions increasingly acknowledge gender identity as a protected characteristic; however, their effectiveness remains uneven. This paper provides a comprehensive comparative analysis of anti-discrimination laws for transgender persons in India, the United States, the United Kingdom, South Africa, and Argentina. It examines constitutional guarantees, statutory provisions, judicial interpretation, and enforcement practices. Drawing on substantive equality theory and intersectional analysis, the paper argues that legal recognition alone is insufficient without robust enforcement, socioeconomic inclusion, and institutional reform. Using recent scholarly literature and comparative legal methods, the study identifies best practices and persistent structural gaps. It concludes by proposing a model framework for effective transgender-inclusive antidiscrimination law.
transgender rights, antidiscrimination law, comparative law, equality, constitutional law, human rights
The Integrity of the Undying Spirit amidst Ethical Erosion: A Socio-Ethical Critique of Sudha Murty’s Wise and Otherwise
Sudha Murty’s Wise and Otherwise serves as a profound socio-cultural document that captures the dichotomy of human nature in contemporary India. This research paper explores the "undying spirit" of individuals who uphold values of honesty, humility, and compassion amidst a society increasingly driven by materialism and ethical erosion— metaphorically termed a "money laundering society." Through a thematic analysis of selected vignettes, this study highlights how Murty’s protagonists navigate the conflict between traditional integrity and modern greed, ultimately saluting the resilience of the human soul. It argues that Murty uses the short narrative form to reconstruct a moral compass for a post-liberalization India struggling with the weight of consumerist culture.
Sudha Murty, Wise and Otherwise, Social Realism, Materialism, Ethical Erosion, Indian Literature in English, Humanism.
A STUDY ON INTEREST RATES IN UNION BANK OF INDIA
Interest rates are one of the most powerful instruments of monetary policy and a primary determinant of a commercial bank's financial performance, lending behaviour, and deposit mobilization capacity. Union Bank of India, a major public sector bank formed through the amalgamation of Andhra Bank and Corporation Bank in 2020, offers a compelling case study for examining how interest rate dynamics shape banking outcomes in post-merger restructuring. This paper studies the interest rate structure—covering lending rates, deposit rates, MCLR, and repo-linked lending rates—at Union Bank of India over FY 2019–2024, and evaluates their impact on the bank's Net Interest Margin (NIM), credit growth, deposit mobilization, asset quality, and profitability. Primary data was collected through structured questionnaires administered to 30 respondents comprising bank employees, customers, and finance professionals. Secondary data was sourced from Union Bank's annual reports, RBI publications, and BSE financial disclosures. Analysis employs ratio analysis, trend analysis, and correlation methods. Findings reveal that Union Bank's shift from MCLR-based to repo-linked lending rates improved credit transmission efficiency and supported NIM recovery from 2.21% (FY20) to 3.14% (FY24). The study recommends further optimization of deposit rate strategy, acceleration of digital lending to reduce cost of funds, and proactive ALM practices to manage interest rate risk in a volatile monetary environment.
Interest rates, Union Bank of India, MCLR, repo rate, Net Interest Margin, deposit rates, lending rates, NIM, credit growth, monetary policy, public sector bank, assetliability management.
Bkurri Uma Shanker1 , Yerrolla Satishkumar2 , Vemula Ajay3 , Karnam Shivaram4 , Sardar Rahul Singh5 , K.V. Chiranjeevi6
A STUDY OF DIGITAL PAYMENTS IMPACT ON CUSTOMER BEHAVIOUR AND ADOPTION OF UPI MOBILE WALLETS AMONG VARIOUS DEMOGRAPHICS AT HDFC BANK
The rapid proliferation of smartphone penetration and digital infrastructure has fundamentally transformed the payments ecosystem in India. The Unified Payments Interface (UPI), launched by the National Payments Corporation of India (NPCI) in 2016, has emerged as the dominant digital payments rail, processing over 13.4 billion transactions worth ₹21.55 lakh crore in March 2024 alone. HDFC Bank, India’s largest private sector bank, has positioned itself as a key UPI enabler through its PayZapp wallet, UPIintegrated mobile banking app, and merchant payment solutions. This paper investigates the impact of digital payments on customer behaviour and examines the adoption patterns of UPI and mobile wallets across varied demographic segments—age, gender, income, education, and occupation—at HDFC Bank. Primary data was collected through structured questionnaires from 120 respondents, and secondary data sourced from NPCI reports, RBI circulars, and HDFC Bank annual reports. Findings reveal that adoption is highest among the 18–35 age cohort, salaried urban professionals, and post-graduate educated customers, while cash dependency persists among rural, elderly, and low-income demographics. The study identifies awareness gaps, infrastructure challenges, and trust barriers as primary adoption inhibitors and recommends targeted financial literacy campaigns, simplified UX design, and enhanced security communication to achieve inclusive digital payment adoption.
UPI, digital payments, mobile wallets, HDFC Bank, customer behaviour, fintech adoption, demographics, PayZapp, financial inclusion, NPCI.
Boddu Kathyayini1 , J Balakrishna2 , Karthik Pochamgalla3 , Golla Swathi4 , K.V. Chiranjeevi5
A STUDY ON COST OF CAPITAL AND PROFITABILITY ANALYSIS AT ITC LIMITED
Cost of capital is a fundamental concept in corporate finance, representing the minimum return a company must earn on its investments to satisfy the expectations of its capital providers. For conglomerates like ITC Limited, which operates across cigarettes, FMCG, hotels, paperboards, and agribusiness, optimizing the cost of capital while sustaining strong profitability is a complex strategic challenge. This paper examines the cost of capital structure and profitability performance of ITC Limited over the period FY 2019–2024, analyzing the Weighted Average Cost of Capital (WACC), cost of equity, cost of debt, debtequity mix, and their relationship with key profitability indicators. Secondary data sourced from ITC's annual reports, BSE filings, and CMIE Prowess database is analyzed using ratio analysis, trend analysis, and correlation methods. Primary data was collected through structured questionnaires administered to 25 finance professionals and MBA students. Findings reveal that ITC's conservative capital structure—predominantly equity-financed with minimal long-term debt—results in a relatively higher WACC, but its strong brand portfolio and operating leverage deliver profitability ratios significantly above FMCG sector averages. The study recommends judicious use of debt financing to lower WACC, further diversification of FMCG revenues, and continued capital efficiency improvements to enhance Return on Capital Employed (ROCE) and shareholder value.
Mudimala Mrudula Goud1 , Shaik Hazi2 , Thummanapelly Sriram3 , Thota Chaitanya4 , K.V. Chiranjeevi5
A STUDY ON IMPACT OF BRANDING STRATEGIES ON PURCHASE INTENT OF HERITAGE FOOD PRODUCTIONS
Branding is a pivotal strategic tool in the highly competitive fast-moving consumer goods (FMCG) and dairy sector. Heritage Foods Limited, one of southern India’s most trusted dairy and food brands, has sustained market leadership through consistent branding, product diversification, and consumer engagement. This study investigates the impact of branding strategies—comprising brand awareness, brand image, brand loyalty, perceived quality, and brand association—on consumer purchase intent toward Heritage food products. Primary data was gathered through a structured questionnaire administered to 120 respondents across Hyderabad, Telangana. Secondary data was drawn from Heritage Foods annual reports, academic journals, and FMCG sector publications. Analysis employs percentage analysis, weighted average method, correlation analysis, and chi-square testing. Findings reveal that brand trust, product quality perception, and packaging design are the most significant drivers of purchase intent. Price sensitivity moderates the brand loyalty–purchase intent relationship, particularly among middle-income consumers. The study concludes with strategic recommendations for strengthening Heritage’s brand equity through digital engagement, regional branding, and premium product positioning.
Katakam Sushmitha¹, Gundala Varsha², G Sathish³, Dr. Sanjay Kumar Padhy⁴
A STUDY ON IMPACT OF EMPLOYEE ATTITUDE ON PRODUCTIVITY AT AMAZON
Employee attitude—encompassing job satisfaction, organisational commitment, and workplace engagement—is a fundamental determinant of individual and collective productivity in modern organisations. This study investigates the impact of employee attitude on productivity at Amazon, one of the world’s largest e-commerce and cloud computing corporations, employing a global workforce exceeding 1.5 million associates, corporate employees, and technologists. Using a descriptive research design, structured questionnaires were administered to 100 Amazon employees across fulfilment, corporate, and technology functions in India. Both primary and secondary data were employed. Analytical tools including percentage analysis, Likert scale mean score computation, weighted average ranking, and chi-square testing were applied to examine attitude dimensions and their productivity correlates. Findings reveal that positive attitude clusters—particularly job satisfaction, supervisor support, recognition, and a sense of purpose—exert significant positive influence on individual output, quality, and collaborative behaviour. Conversely, negative attitudes linked to workload stress, limited career visibility, and perceived inequity suppress productivity. The study recommends targeted interventions including structured feedback systems, mental wellness programmes, career development pathways, and equitable workload distribution to sustain positive employee attitudes and maximise productivity across Amazon’s diverse workforce segments
Priyanka Dewangan¹, Bandi Sweety², Mohammed Gulamuddin Khan³, Kodimela Gayathri Sanjana⁴, Punyamurthi Divya⁵, Ms. A. Swathi 6
A STUDY ON EFFECTIVENESS OF REWARD SYSTEMS ON MOTIVATIONAL LEVEL OF EMPLOYEES AT WIPRO
Reward systems play a pivotal role in shaping employee motivation, engagement, and organisational performance in the highly competitive Information Technology sector. This study investigates the effectiveness of reward systems on the motivational levels of employees at Wipro Limited, one of India’s foremost IT services and consulting corporations. Using a descriptive research design and structured questionnaire survey administered to 100 employees across various departments and hierarchical levels, the study examines both monetary and non-monetary reward mechanisms including performance bonuses, recognition programmes, career development opportunities, and work-life balance initiatives. Data were analysed using percentage analysis, weighted average method, and chi-square test. Findings reveal that intrinsic rewards—particularly career growth, recognition, and skill development—exert the strongest motivational impact, while monetary rewards retain significant importance for junior and mid-level employees. The study identifies gaps between employee reward expectations and actual delivery and recommends personalised reward frameworks, transparent appraisal mechanisms, and enhanced non-monetary recognition programmes to maximise workforce motivation and retention at Wipro.
reward systems, employee motivation, Wipro, IT sector, intrinsic rewards, extrinsic rewards, performance appraisal, recognition, employee engagement, retention.
Vadluri Srisahithi¹, Maripelly Nithya², K. Manisha³, Nangunuri Harika⁴, Panjagari Likitha⁵, Dr. A. Jyothi6
A STUDY ON MARKET RESEARCH ON HEALTH CARE PRODUCTS AT KAMINENI
The healthcare industry in India is one of the fastest-growing sectors, driven by increased health awareness, rising disposable incomes, and expanding access to medical products. Kamineni, a well-established healthcare conglomerate in Telangana, offers a wide portfolio of health care products including pharmaceuticals, nutraceuticals, and wellness products. This study examines consumer perception, buying behavior, and market trends related to health care products at Kamineni through structured primary research and secondary data review. A structured questionnaire was administered to a sample of 100 respondents comprising patients, caregivers, and general consumers. Findings indicate that product quality, doctor recommendation, brand trust, and availability are the dominant purchase drivers. The study identifies pricing sensitivity and lack of product awareness as key barriers to market penetration in semi-urban segments. Data analysis using percentage analysis, weighted average method, and chi-square testing reveals significant associations between income level and purchase frequency of premium health products. Practical recommendations include targeted digital marketing, enhanced hospital-outlet integration, and loyalty program development to strengthen Kamineni's market position in the competitive healthcare product landscape.
Vinay Mathangi¹, Mothe Vijay², Jamboju Rajakishore³, M Dinesh⁴, Borra Nithin Kumar⁵, Dr. Sanjay Kumar Padhy⁶
COST EFFICIENCY AND ITS IMPACT ON PROFIT MARGINS AT HINDUSTAN UNILEVER LIMITED
Cost efficiency is a critical determinant of profitability and long-term competitiveness in the Fast-Moving Consumer Goods (FMCG) sector. Hindustan Unilever Limited (HUL), India's largest FMCG company, has consistently leveraged cost optimization strategies to sustain and enhance its profit margins despite volatile input prices and evolving market dynamics. This paper examines the relationship between cost efficiency measures and profit margins at HUL over the period 2019–2024, analyzing key cost drivers including raw material costs, operational expenses, supply chain efficiency, and technology investments. Primary data was gathered through structured questionnaires from industry professionals, while secondary data was sourced from HUL's annual reports, Bombay Stock Exchange (BSE) filings, and industry publications. Data analysis employs ratio analysis, trend analysis, and correlation methods to evaluate cost-profit relationships. Findings reveal that HUL's savings-led cost reduction programs—particularly in procurement, manufacturing, and distribution—directly improve gross margins and operating profit margins. The study recommends sustained investment in digital supply chain management, green manufacturing, and zero-based budgeting to further strengthen HUL's cost-efficiency framework.
Nandyala Dharani1 , Lyada Ruthika2 , Palle Lava Kumar3 , Dharmaram Shiva Krishna4 , Anthadupula Ankitha5 , A. Jyothsna6
FINANCIAL PLANNING AND FORECASTING AT AMBUJA CEMENT
Financial planning and forecasting are vital instruments for sustainable corporate growth and strategic resource allocation. This study examines the financial planning and forecasting practices at Ambuja Cements Limited, one of India's premier cement manufacturers. The research analyses key financial parameters including revenue trends, capital expenditure planning, working capital management, profitability forecasting, and budget variance analysis over five financial years (FY 2019–20 to FY 2023–24). Secondary data sourced from annual reports, financial statements, and industry publications form the empirical base. Tools including ratio analysis, trend analysis, and common-size statements are employed to interpret data. Findings reveal a consistent upward revenue trajectory with strong EBITDA margins, disciplined capital allocation, and effective short-term financial planning. However, challenges in raw material cost volatility and energy price fluctuations affect forecast accuracy. The study concludes that advanced analytical tools and integrated financial modelling can significantly enhance Ambuja Cement's forecasting precision and strategic planning effectiveness.
Soppari Umesh¹, Rachapally Kavya², G. Balakrishna³, Lokanaboina Bhanu Prasad⁴, A. Jyothsna⁵
A REPORT ON AUTOMATION IN FINANCIAL OPERATIONS AT KOTAK MAHINDRA BANK
The financial services industry is undergoing a transformative shift driven by automation and digital technologies. Kotak Mahindra Bank, one of India’s leading private-sector banks, has strategically deployed Robotic Process Automation (RPA), Artificial Intelligence (AI), and machine learning across its core financial operations including account management, credit appraisal, fraud detection, payment processing, and regulatory compliance. This paper investigates the scope, implementation, and impact of automation initiatives at Kotak Mahindra Bank, examining how technology integration has enhanced operational efficiency, reduced turnaround times, minimized error rates, and improved customer satisfaction. Primary data was gathered through structured questionnaires and secondary data sourced from annual reports, RBI publications, and industry research. Findings reveal that automation has reduced routine processing costs by up to 40%, accelerated loan disbursement by 35%, and improved fraud detection accuracy to over 94%. The study identifies implementation challenges including change management resistance, legacy system integration complexity, and cybersecurity concerns, and recommends a phased automation roadmap for sustainable digital transformation in banking financial operations.
Munge Basi Reddy1 , Sheri Akhila2 , Chetti Ravinder3 , Cholleti Akhila4 , Md Maqsud Ali5 , Dr. B. Narmada6
A COMPARATIVE STUDY OF TRADITIONAL AND DIGITAL MARKETING STRATEGIES
The ever changing environment in technology and wide application of internet has tremendously changed the marketing environment and marketing has taken on a new dimension of being a viable alternative to the traditional way of marketing with the advent of digital marketing. The present research article alludes to a comparative study of the conventional and the digital marketing methods in regard to their efficacy, reach and cost effectiveness and their contribution to the behavioral change of the consumers. Digital marketing incorporates the services of online markets such as social networks, search engines, email and websites to engage target continuous and interactive audiences as compared to traditional digital marketing, which typically involves print, television, radio and outdoor advertising and is mainly aimed at the creation of brand awareness and reaching mass audiences. Primary and secondary data is used in conducting the study. Structured questionnaires are used as a way of collecting primary data to get to know consumer preference, perceptions and responsiveness to the two marketing strategies. Tertiary data will be obtained through the use of the academic journals, industry report and verified internet sources to provide a theoretical background. The effectiveness of the conventional and digital advertising techniques are discussed through the assistance of statistical analysis such as percentage analysis, mean, and comparative analysis. The findings reveal that digital marketing is more advantageous because it is cost effective, real time, they are personalized and the outcomes are measurable. It enables firms to access specific and targeted groups of consumers and target them based on the data analytics. Conversely, conventional promotions proved to be better when it comes to creating brand trust and reaching out audiences who have minimum or no access to the internet and especially rural population. The other observation made in the study is that, despite the rapid emergence of digital marketing being the dominant aspect, in integrated marketing strategies, traditional marketing up to date, is still pertinent. The study finds out that a blend of both early and modern techniques of marketing is the best strategy a company can adopt when it seeks to have maximum exposure and attention. Every business is supposed to take the advantages of the two approaches to formulate an inclusive marketing campaign to meet the emerging dynamics of consumers and technology. The study has a relevance to the marketers, business and policy makers on the dynamic ways of marketing strategies in the digital age.
Traditional Marketing, Digital Marketing, Consumer Behavior, Marketing Strategies, Online Advertising, Brand Awareness, Integrated Marketing
URBAN ALIENATION AND THE SEARCH FOR MEANING IN PATRICIA LOCKWOOD’S NO ONE IS TALKING ABOUT THIS
Patricia Lockwood’s No One Is Talking About This (2021) dramatizes the psychic condition of being “extremely online” and models with formal daring and tonal oscillation how contemporary digital life can produce both a diffuse communal intimacy and a profound kind of alienation. The novel divides into two contrasting halves: the first renders the protagonist’s immersion in the “portal” (a social-media ecosystem) through fragmentary, aphoristic prose; the second pivots into intimate, embodied grief when a family medical crisis demands attention beyond the screen. This paper argues that Lockwood stages urban alienation as an emergent property of networked culture a condition in which continuous mediated sociality displaces rooted attachment and attenuates language and that the protagonist’s search for meaning ultimately depends on an uneasy reinvestment in physical relations, narrative continuity, and the limits of representability. Close reading of the novel’s form, voice, and recurring motifs (the portal, the “we,” the phone, and the hospital room) together with critical reception and Lockwood’s own commentary illuminates how the book is both a diagnosis of contemporary alienation and a tentative ethics of presence.
Ethnic Federalism, Governance Fragmentation, and Educational Resource Allocation in Post-Conflict Tigray, Ethiopia: A Qualitative Case Study
Education recovery in post-conflict settings is shaped not only by the scale of destruction but also by the institutional design through which reconstruction resources are allocated. This article examines how Ethiopia’s ethnic federal system has structured the allocation of educational resources in post-conflict Tigray. Drawing on a qualitative case study, the analysis combines 30 semistructured interviews conducted between March and April 2025 with federal, regional, and district officials, educational administrators, teachers, lecturers, students, parents, community leaders, NGO actors, and international partners, alongside a review of policy and program documents. The article asks how ethnic federalism has affected the distribution of educational finance, infrastructure, teachers, and learning materials during the early recovery period. Four findings stand out. First, population-based and project-based financing mechanisms did not adequately reflect war damage, displacement, or teacher attrition, producing chronic underfunding at school level. Second, authority was formally decentralized but operationally fragmented: blurred responsibilities across federal, regional, and woreda institutions delayed approvals, disbursements, and implementation. Third, recovery was spatially uneven, with rural and internally displaced persons-hosting communities receiving fewer resources than urban centers. Fourth, shortages of teachers, salary interruptions, and inadequate psychosocial support limited the substantive reopening of schools even where facilities had partially resumed operation. The article contributes to scholarship on ethnic federalism and education in emergencies by showing that post-conflict inequality is reproduced through governance fragmentation rather than scarcity alone. It argues for a conflict-sensitive, needs-based allocation formula, ring-fenced recovery grants, integrated education data systems, and teacher reintegration packages that align reconstruction with equity and durable peace.
ethnic federalism; governance fragmentation; education in emergencies; post-conflict recovery; Tigray; Ethiopia
AN INTELLIGENT FRAMEWORK FOR MONITORING CITIZEN REACTIONS TO GOVERNMENT POLICIES USING SOCIAL MEDIA DATA
The rapid growth of social media platforms has transformed the way citizens express their opinions and reactions to government policies and decisions. Platforms such as Twitter, Facebook, and Instagram provide large volumes of real-time user-generated content that reflects public sentiment and societal concerns. Analyzing this data can provide valuable insights for policymakers, government agencies, and researchers to understand public perception and evaluate the impact of government actions. This study proposes an intelligent framework for monitoring citizen reactions to government policies using social media data. The framework utilizes advanced data analytics, natural language processing (NLP), and machine learning techniques to collect, process, and analyze social media posts related to government initiatives. The system extracts relevant features from textual data and performs sentiment analysis, topic modeling, and trend detection to identify public attitudes toward specific policies. The proposed framework includes modules for data collection, preprocessing, sentiment analysis, feature extraction, machine learning-based classification, and visualization. Social media data is first collected through APIs or web scraping tools and then cleaned and processed to remove noise, irrelevant content, and duplicate posts. Natural language processing techniques are applied to analyze textual information and determine whether public opinions are positive, negative, or neutral. Machine learning algorithms are employed to classify citizen reactions and detect emerging public concerns related to government actions. The system also provides visualization dashboards that display sentiment trends, topic distributions, and geographic patterns of public opinion. These insights can help government authorities monitor public feedback, identify policy challenges, and improve communication with citizens. The results demonstrate that integrating social media analytics with intelligent data processing techniques can significantly enhance the understanding of citizen reactions to government policies. The proposed framework offers a scalable and efficient solution for real-time public opinion monitoring, enabling governments to make informed decisions and strengthen citizen engagement.
M. SURESH BABU, JAVVAJI NARESH, MUSKU BHARGAVI, MARAM SHIVA KUMAR, MANNE VINAY
AI-BASED PREDICTION OF HEART RATE CHANGES FROM BODY TEMPERATURE IN PEDIATRIC INTENSIVE CARE PATIENTS
Monitoring vital signs such as heart rate and body temperature is critical for assessing the health condition of children admitted to the Pediatric Intensive Care Unit (PICU). Variations in body temperature often influence heart rate, and abnormal patterns may indicate infections, inflammation, or other medical complications. Early identification of such physiological changes can assist healthcare professionals in making timely clinical decisions and improving patient outcomes. This study proposes an Artificial Intelligence (AI)-based approach to predict heart rate changes based on body temperature data collected from pediatric patients in intensive care settings. The system utilizes machine learning algorithms to analyze large volumes of patient monitoring data and identify patterns between temperature fluctuations and heart rate variations. Historical patient data, including vital sign measurements and clinical observations, are used to train predictive models capable of estimating heart rate responses to temperature changes. The proposed framework involves data preprocessing, feature extraction, model training, and prediction, enabling the system to detect abnormal physiological patterns and support real-time patient monitoring. Machine learning models such as Random Forest, Support Vector Machine, and Neural Networks can be employed to improve prediction accuracy and reliability. The developed system aims to assist clinicians by providing early warning indicators of potential health deterioration, thereby enabling proactive medical intervention. By integrating AI with pediatric healthcare monitoring systems, the proposed approach enhances the efficiency of patient monitoring and contributes to improved decision-making in critical care environments.
SHRAVANI JASTHI, HARSHA GANESH KARLE, KURME RAGHU, MORISHETTI SAI PRASANNA KUMAR, MUSKU SATHWIK REDDY
INTELLIGENT CREDIT CARD FRAUD PREVENTION USING GRAPH NEURAL NETWORKS AND AUTOENCODER MODELS
Credit card fraud has become a major concern in modern digital banking due to the rapid growth of online transactions and electronic payment systems. Traditional fraud detection methods often rely on rule-based systems or conventional machine learning algorithms, which may struggle to detect complex and evolving fraud patterns in real time. To address these challenges, this study proposes an intelligent credit card fraud prevention framework using Graph Neural Networks (GNN) and Autoencoder models. The proposed approach leverages the ability of Graph Neural Networks to capture relationships and interactions between entities such as users, transactions, and merchants. By representing transaction data as a graph structure, the model can identify suspicious patterns and hidden connections that may indicate fraudulent activities. Additionally, an autoencoder-based anomaly detection mechanism is employed to learn normal transaction behavior and detect unusual deviations that may signal potential fraud. The integration of graph-based learning and deep anomaly detection enhances the system’s capability to detect both known and unknown fraud patterns. The proposed framework processes transaction data in near real time, enabling faster and more accurate fraud identification while reducing false positives. Experimental evaluation demonstrates that the combined use of GNN and autoencoder models significantly improves fraud detection performance compared to traditional methods. This intelligent fraud detection system can assist financial institutions in strengthening security, minimizing financial losses, and improving trust in digital banking services by providing an effective solution for real-time credit card fraud prevention.
SRIKANTH JINDAM, LIKKI LEELA VARDHAN, LINGAM RAM AKSHITH, MATURI KALYANI, M A SHASHANK YADAV, MALYALA GOUTHAM GOUMITH