Don Bosco Institute of Technology Delhi Journal of Research
Don Bosco Institute of Technology Delhi Journal of Research Don Bosco Institute of Technology Delhi Journal of Research is a multidisciplinary journal that welcomes research articles from various fields including Social Sciences, Humanities, Education, Science and Technology, Management, Health Sciences, and Arts and Culture. The journal aims to foster intellectual growth and facilitate cross-disciplinary collaboration by providing a platform for researchers to share innovative work and findings. About Don Bosco Institute of Technology The Don Bosco Society, also known as the Salesians of Don Bosco, was founded in 1859 by St. John Bosco, who sought to educate and help homeless and orphaned child laborers in Turin, Italy. Today, the society has over 32,000 members working in 134 countries, offering educational and social services through schools, colleges, technical institutes, and youth centers. In India Don Bosco Institute of Technology (DBIT) (Unit of Don Bosco Group of Educators), Okhla Road is situated on the bank of Sukhdev Vihar Metro Station, South Delhi, 110025. The college offers opportunities to grow professionally and personally. It is affiliated with GGSIP University, Dwarka, New Delhi, and is one of the best institutes for BCA, BBA & B. Com (Hons.) programs in Delhi NCR.
An Intelligent Approach to Network Intrusion Detection using Machine Learning
Keeping networks secure is a big challenge for today’s organizations, especially with cyber threats growing more sophisticated all the time. Traditional signature-based intrusion detection systems aren’t enough anymore—they rely on known patterns, so they often miss new or complex attacks. This study introduces a machine learning-based intrusion detection system that spots threats using data-driven analysis instead of just matching signatures. The system sorts network traffic into safe or malicious categories by using multiple machine learning models, like Random Forest, Support Vector Machine, Decision Tree, and K-Nearest Neighbor. These models were trained on well-known benchmarking datasets (CICIDS 2017 and CICIDS 2019), which gives them a solid foundation. To improve accuracy and cut down on false alarms, the system goes through thorough data preparation, careful feature selection, and fine-tuning during training. Plus, the Streamlit platform adds interactive visualizations and motion, making it easier to explore and understand what’s happening across the network.
Copyright (c) 2026 Don Bosco Institute of Technology Delhi Journal of Research
A Hybrid Machine Learning Approach for Heart Disease Prediction with Ecg & Echo Analysis and Explainable Ai
Cardiovascular diseases remain one of the leading causes of mortality worldwide, underscoring the urgent need for early detection and accessible diagnostic solutions. This paper presents a hybrid machine learning and deep learning framework that integrates electrocardiogram (ECG) and echocardiography (ECHO) analysis with explainable AI techniques to enhance heart disease prediction. The developed application incorporates five core features: individualized patient prediction, batch prediction via CSV input, model transparency through algorithmic explanations, ECG-based arrhythmia and myocardial irregularity detection, and an extended framework for ECHO evaluation, personalized recommendations, hospital locator integration, and automated report summarization. By combining multiple machine learning and deep learning algorithms with explainable AI methodologies, the system not only improves predictive accuracy but also ensures interpretability and user trust. Furthermore, the model highlights critical risk factors such as cholesterol and blood pressure, bridging the gap between predictive analytics and practical healthcare applications.
Crm Strategies and Their Effect on Customer Satisfaction: An Empirical Study
Customer relationship management has become an essential tool for strategy for organizations to enhance customer satisfaction and attain a sustainable competitive edge. Effective CRM techniques allow organizations to get a better grasp of customer needs, customize services, build stronger customer relationships and improve service quality. In this study the effect of different CRM strategies on customer satisfaction has been studied. This study considers eight CRM characteristics, namely customer service quality, customization, customer communication, technology-based CRM, customer retention programs, employee competency and trust and relationship development. Around 300 participants answered a structured questionnaire with a 5 point Likert scale as part of the quantitative research approach used to gather the data. The data gathered using SPSS software was subjected to reliability analysis, descriptive statistics, multiple regression analysis, and correlation analysis. The study investigates that in order to improve customer experiences and long-term customer relationship, businesses should concentrate on implementing CRM comprehensively.
Green Marketing as a Strategic Tool for Sustainable Growth in the Automobile Sector
The automobile industry is undergoing a fundamental strategic inflection: the convergence of accelerating climate change awareness, tightening regulatory standards, and a structural shift in consumer values has transformed green marketing from a peripheral promotional exercise to a core strategic imperative. This research examines the influence of green marketing strategies on consumer behavior, brand perception, competitive dynamics, and sustainable innovation within the automobile sector, with particular focus on the Indian market context and cross-national comparative evidence from global automotive leaders. Employing a mixed-methods design grounded in an inductive research philosophy, the study integrates primary survey data collected from 100 urban, environmentally aware consumers — including students, working professionals, and active vehicle users — with secondary data triangulated across corporate sustainability reports (Tesla, Toyota, BMW, Maruti Suzuki), industry analyses (SIAM, JATO Dynamics), peer-reviewed academic literature, and regulatory policy documents. The theoretical framework synthesizes the Theory of Planned Behavior (Ajzen, 1991), the Green Marketing Mix framework, Legitimacy Theory (Suchman, 1995), and Brand Equity theory (Keller, 1993) to analyze the mechanisms through which green marketing creates consumer value and competitive advantage. Key findings indicate that 68% of respondents incorporate environmental considerations into vehicle purchase decisions; that brands with credible green positioning — Tesla, Toyota, and BMW — demonstrate significantly higher consumer trust and recall; and that the majority of respondents express willingness to pay a moderate price premium for verified eco-friendly vehicles. EV and hybrid adoption in India reached 19.23% of total passenger vehicle sales in 2023–24 (JATO Dynamics), reflecting the commercial impact of sustained green marketing investment. The study also examines greenwashing as a structural market failure with severe reputational and regulatory consequences, and proposes a multi-level countermeasure framework for manufacturers. The research contributes to the emerging literature on green marketing in the automotive sector by providing India-contextualized empirical evidence, developing a practical Green Marketing Strategic Framework, and generating a differentiated research agenda. It concludes with actionable recommendations for automobile manufacturers, marketing managers, and policy makers seeking to align commercial strategy with environmental imperatives.
Optimizing Direct Tax Reconciliation in a Corporate Setting: An Excel-based Financial Workflow with Special Reference at Exl Services
Direct tax reconciliation is a critical element of financial governance in large, multi-entity organizations, particularly where parallel accounting frameworks and high transaction volumes increase the risk of mismatches and non-compliance. This study, titled “Optimizing Direct Tax Reconciliation in a Corporate Setting: An Excel-Based Financial Workflow with special reference at EXL,” investigates how Excel-driven processes support direct tax reconciliation at EXL Services, a global analytics and digital solutions company maintaining dual books of accounts following US GAAP and India GAAP across multiple entities. Using a descriptive–exploratory case study design, the research combines primary data from 150 tax, finance domain people. The primary analysis shows that Excel is the dominant reconciliation tool, with most respondents using it daily and demonstrating high confidence in its use. However, manual errors, incorrect classifications, posting issues, and data availability constraints remain major sources of mismatches. Respondents strongly favour Excel-based automation, standardized templates, dashboards, and LDC trackers to strengthen TDS compliance and reduce reconciliation risk. The study concludes that structured automation, standardized Excel workflows, better data integration, and targeted training can significantly enhance the accuracy, efficiency, and reliability of direct tax reconciliation in complex, multi-entity corporate settings, while reinforcing overall financial reporting quality.
Copyright (c) 2026 ACS Publisher Journal
Ensuring Human-in-the-loop Integrity: A Strategic HR Framework for AI-driven Compliance in Federal Agencies
The rapid adoption of AI in public administration is altering decision-making processes and service delivery across government institutions. Federal agencies increasingly use AI systems for tasks such as fraud detection, benefit administration and public service management. Although these technologies are offering innovative efficiency, they also raise concerns regarding accountability, transparency and regulatory compliance. Several public organizations still face challenges in ensuring effective human oversight and governance of AI systems. So, how organizational governance mechanisms can strengthen AI-driven compliance effectiveness in government institutions have been examined in this work. The main purpose of the research is to analyze the effect of three organizational factors on AI compliance effectiveness: Human-in-the-Loop (HITL) oversight mechanisms, governance of AI infrastructure and strategic human resource capability for AI oversight. A quantitative research design using a structured survey instrument has been applied in the research. Data are analyzed using PLS-SEM to examine the relationships among the variables and to evaluate the validity and reliability of the measurement model. The findings confirm all three independent variables significantly influence AI compliance effectiveness. Human-in-the-Loop oversight has a positive strong effect, which indicates that structured human supervision improves accountability in algorithmic decision-making. AI governance infrastructure also demonstrates a significant connection with compliance effectiveness. Additionally, Strategic human resource capability strengthens compliance outcomes. The findings suggest that successful AI governance requires strong human oversight, clear institutional policies and skilled personnel who can manage algorithmic risks. Managers and policymakers should therefore develop formal AI governance frameworks, strengthen human supervision systems and invest in workforce training on AI ethics and compliance. Despite its contributions, the study has some limitations since cross-sectional design and reliance on survey responses were used.
Dark Patterns: Understanding Consumer Awareness & Regulatory Mechanism
Persuasive interface designs are gaining a lot of importance in directing user behavior on digital platforms. While many of th tactics which forms part of the interface are important & also within the legal boundaries, other design practices—known as “dark patterns”—cross ethical boundaries and are used purposively for deceiving the consumers through various social media channels. Three main aspects are examined in this conceptual paper: (1) the meaning and types of dark patterns; (2) the extent to which consumers are aware of these trends when using online platforms; and (3) the level of awareness among users regarding the legal protections and regulatory framework available against manipulative dark patterns used digitally . The paper focused on how consumers are actually aware of the various types of dark patterns used on the digital platforms.The research paper is beneficial for the consumers as it creates awareness among them, for policymakers on how to create a user friendly digital user interface for digital literacy.
Assessing Interface Dependency Complexity in Components based Software Systems (CBSS)
Systems with high complexity typically exhibit a balanced combination of order and disorder, characterized by the coexistence of randomness and regularity. In component-based software engineering, each component contributes to overall system complexity through its inherent constraints, interaction mechanisms with other components, and the degree of customizability it supports. As software systems grow in scale and interconnectivity, understanding and managing such complexity becomes critical to ensuring system reliability, maintainability, and quality. Measuring system dependency and interaction complexity provides valuable insights into potential design weaknesses and helps assess software quality at both component and system levels. Complexity metrics play a crucial role in enabling objective comparison across different software systems by applying standardized measurement frameworks. Existing complexity measures, however, often focus primarily on internal code structure, overlooking the significance of component interfaces that govern communication, data exchange, and functional coordination. The author has pointed out a novel interface complexity metric for software components in the study. Based on the quantity, kind, and structural features of interface methods and attributes that a component exposes, the suggested metric measures complexity. Factors such as method signatures, parameter types, data dependencies, access constraints, and interaction frequency are considered to capture the true interaction burden imposed by component interfaces. By focusing on interface-level complexity, the metric provides a more accurate representation of component interdependencies and their impact on overall system behaviour. The proposed approach supports improved software design evaluation, facilitates early detection of architectural vulnerabilities, and aids developers in enhancing modularity, reusability, and long-term system quality.
Order Processing Delay in Logistics: A Review of Quality and Maintenance Approach
Delays in order processing are a serious concern in logistics and affect customer service, operational efficiency, and supply chain performance. This paper systematically investigates order processing delays and examines how quality management and maintenance approaches can be employed to reduce them. The paper is based on a Structured Literature Review (SLR) of peer-reviewed academic articles published between 2020 and 2025. We have organized the causes of delays into operational (e.g. equipment failures, manual inefficiencies, data errors) and regulatory aspects (e.g. transport documentation, and legal requirements) and explored quality management approaches to reduce errors, wastage and ensure consistent quality during order processing (Total Quality Management {TQM}, Six Sigma, Kaizen, ISO 9001, Lean Six Sigma). We also reviewed maintenance approaches, starting from a traditional preventive maintenance approach, to a diagnostic maintenance approach that integrates predictive and prescriptive methods using artificial intelligence (AI) to reduce the chance of unplanned downtimes and improve system reliability. This proposal has developed an integrated conceptual model that uses quality and maintenance interventions to target root causes for delays in the logistics value chain. The dual-intervention model presents a whole-scale solution to improve order-filling performance. The framework is essential to provide original value to logistics research and literature and connecting process and equipment perspectives while also providing practical implications for actionable steps for freight forwarders, third-party logistics (3PL) operations, and logistics managers that intend to build their operations to be a resilient and delay-resistance organization. Future research could include empirically testing it and looking at actual data and a systems thinking perspective.
Influence of YouTube Finance Influencers and Traders’ Tips on Investment Decisions of Working Professionals: The Mediating Role of Trust and Perceived Advice Quality in Stock Market Participation
This study investigates the burgeoning influence of YouTube-based financial influencers (“finfluencers”) on the investment behaviors of working professionals. As digital platforms democratize financial advice, understanding the cognitive and psychological drivers of investment becomes paramount. Grounded in Source Credibility Theory (SCT), this research examines how influencer content drives stock market participation through two critical mediators: Trust and Perceived Advice Quality. A quantitative study was conducted using a hypothetical sample of 400 working professionals across urban centers. The findings indicate that while direct “trading tips” stimulate immediate investment intent, long-term and sustained stock market participation are heavily dependent on the established trust between the viewer and the influencer. Furthermore, perceived advice quality acts as a secondary mediator, ensuring that professionals feel competent in their decision-making. The results underscore a significant shift from traditional institutional advisory to social-media-driven peer advisory. This paper contributes to behavioral finance literature by identifying the “Trust-Quality” bridge in digital ecosystems and suggests that regulatory bodies should focus on the quality metrics of digital financial content to protect retail investors.
Unveiling Trends and Insights: A Bibliometric analysis on Social Media Marketing
Objective: Significant transformations have taken place in the marketing industry over the past century. The extent of the transformation in marketing strategies, tools, and tactics is truly difficult to grasp. In a traditional economy, the market was restricted to a particular physical space, facing constraints concerning time, location, and utility. Globalization has led to a significant contraction of the market, bringing it within the reach of our hands, as all markets are now easily accessible. Initially created as a means for social interaction, social media has evolved into a powerful instrument for brand positioning, advertising, and various other aspects of marketing management. Staying informed about the most recent developments in the marketing and tooling sectors is essential for any organization. The main objectives of the paper are to enhance readers’ knowledge and to improve their understanding of social media marketing. This study provides a bibliometric analysis of social media marketing research spanning from 2009 to 2024 to assist scholars in the field.Approach/Methodology/Design: The Scopus database was employed to examine how research in social media marketing has developed over the years, to find out the leading authors, countries, documents and journals in this research domain, to study the keyword occurrence pattern in the field of social media marketing and to investigate the association between the citations and co-citations of social media marketing research articles.Results: It has been noted that the researches in social media marketing were minimal till 2014, gaining momentum after 2015 and peaking in 2024. Ko, Eunju is found to be most impactful author whereas Dwivedi, Yogesh K. is most productive author. The country that has significantly influenced the realm of social media marketing is United States. Most prominent article is Kozinets titled “Networked narratives: Understanding word-of-mouth marketing in online communities” and the most impactful journal in this field is “Journal of Business Research”.
The Intelligence Behind the Click: A Bibliometric Study of E-Commerce and Emerging Cognitive Technologies
In the digital era, online transactions are integral to everyday life—from grocery shopping and clothing purchases to streaming services like Netflix and Amazon Prime. E-Commerce has progressed beyond simple transactions, now leveraging intelligent systems for personalized user experiences via recommendation engines analyzing user behavior, reviews, and past transactions. This transformation is largely powered by the integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Over the last decade, this convergence has garnered substantial academic attention. This study presents a bibliometric analysis of 2,464 peer-reviewed publications from 2018 to 2025, sourced from the Dimensions database and filtered through the PRISMA framework and UGC CARE List-II criteria. The analysis explores publication trends, influential authors and institutions, significant countries, high-impact papers, and emerging thematic clusters. The findings reveal an uptrend in research activity, heightened technological integration, and shifting research priorities. The study offers a roadmap for future inquiry at the intersection of E-Commerce and AI technologies.
An Analysis of Indian Firm Levels’ Digital Finance, ESG Performance, and Financial Performance: A Road Map to Long-Term Sustainability
This research aims to investigate the processes for using digital finance to address the sustainability triple-bottom-line challenge, as digital finance is essential for attaining sustainability. Within the context of digital finance, this article investigates the inner workings of the relationship between financial performance and ESG (environmental, social, and governance) factors. Indirectly improving both short- and long-term financial success for organizations, digital finance promotes ESG performance, according to the research. Furthermore, the key enabler for ESG and short-term financial success is the depth of digital finance adoption. According to the results of the mechanism study, improving green innovation and bolstering digital transformation would strengthen the favorable correlation between digital finance and ESG. Furthermore, according to heterogeneity study, big businesses and SOEs (state-owned enterprises) have a stronger positive effect on ESG and financial performance when it comes to digital finance use depth, but small firms and non-SOEs have a stronger positive effect on ESG when it comes to coverage breadth. By outlining potential avenues for sustainable behavior, this article deepens our understanding of digital money.
A Study on Examining the Impact of AI Tools on Students’ Study Habits and Academic Success
This study examines the role of AI tools in shaping students’ study habits and academic outcomes, focusing on their integration into educational routines. A mixed-methods approach was employed, combining survey data and qualitative feedback from 141 students across diverse demographics and disciplines. Key findings reveal a moderate level of AI usage, with 57% of students utilizing AI tools for 26-50% of their study time. AI tools were reported to enhance efficiency, motivation, and GPA while reducing study hours. Despite these benefits, challenges such as over-reliance and integration with traditional methods were identified. The study underscores the importance of a balanced approach to AI adoption, advocating for its role as a complementary tool alongside conventional study practices. Actionable recommendations include promoting responsible usage, addressing privacy concerns, and fostering inclusivity to maximize AI’s educational potential.
Cloud Computing: Redefining Data Management and IT Services in the Digital Age
This paper delves into cloud computing, a transformative technology reshaping data management, storage, and IT services. The study explores the fundamentals, growth, and impact of cloud computing, addressing its advantages and challenges, with a focus on security, scalability, and cost-efficiency. Through a comprehensive literature review, this research investigates existing studies, methods, and findings on cloud adoption across industries. The paper analyses data from industry reports and surveys, providing insights into cloud adoption trends and user experiences. The conclusion underscores the potential and challenges of cloud computing, suggesting directions for future research.
A Comparative Study of Deep Learning Models in Medical Image Analysis for Pulmonary Disease Detection: A Review
This survey provides the view of current scenario of Deep Learning models used to detect pulmonary diseases, Stroke Disease and some other diseases related to blood clots. The diagnosis and categorization of lung disorders using medical images, especially chest X-rays and CT scans, has been greatly improved by recent developments in deep learning. This survey highlights the use of deep learning techniques in the detection of lung diseases by compiling and analyzing 20 research publications published between 2016 and 2020. By offering a thorough taxonomy based on seven essential characteristics—image kinds, features, data augmentation, deep learning models, transfer learning, ensemble methods, and lung disease types—this study fills that gap. Convolutional neural networks (CNNs) dominate, transfer learning models like VGG, ResNet, and Inception-V3 are widely used, and diseases including COVID-19, TB, and pneumonia are the main emphasis, according to key findings. The paper also indicates promising avenues for future research, such as combining clinical and demographic data, creating lightweight models for contexts with limited resources, and implementing ensemble learning techniques.
The fast-paced growth coupled with the global acceptance of cloud computing has significantly changed how information technology is viewed. The advantages presented in scale, cost and operations have never been experienced before. Nevertheless this change has also created another layer of issues that organizations need to deal with full scale in order to protect their cloud resources against unauthorized access, use and destruction. This long and detailed essay includes review of numerous threats, solutions and best practices regarding cloud computing security. This study is based on an understanding of cloud security at present by employing qualitative and quantitative methods. For example, the first 1000 security incidents were evaluated; surveys of 500 IT security experts and 50 industry leaders were conducted. The analysis showed that the cloud security landscape is becoming complex, and while the security technologies and practices have progressed tremendously, there are challenges still persistent. According to the research, physical security controls are in place for 76% of organizations practicing multi-factor authentication, yet only 68% have supplied and implemented an all-inclusive cloud security plan, a disturbing discrepancy in the organizations’ approaches to security [1]. The research not only identifies and examines these issues but also offers practical suggestions and looks at the technologies that will influence cloud computing security in the years to come, therefore answering the questions posed by both academics and practitioners
Evolving Deepfake Technologies: Advancements, Detection Techniques, and Societal Impact
The rapid advancement of Deepfake technology, powered by deep learning algorithms and Generative Adversarial Networks (GANs), presents a paradigm shift in digital content creation and manipulation. This technology, capable of generating highly realistic but entirely synthetic audiovisual content, has implications that stretch across various domains, from entertainment to politics, posing both opportunities for innovation and risks for misinformation and privacy violations. This paper provides a comprehensive overview of the evolution of Deepfake technology, highlighting key developments in AI- driven synthetic media creation. It delves into the state-of-the-art detection techniques that leverage both appearance-based and geometric features to combat the proliferation of Deepfakes, emphasizing the importance of precise geometric analysis and temporal modelling in enhancing detection robustness, especially in the face of sophisticated manipulation techniques that evade traditional detection methods. Through an analysis of contemporary datasets and detection frameworks, the paper assesses the effectiveness and limitations of current approaches, underscoring the challenges posed by video compression and digital noise in real-world scenarios. Furthermore, it discusses the profound societal impact of Deepfakes, from the erosion of trust in digital media to legal and ethical dilemmas, and proposes future directions for both technological advancements in Deepfake generation and detection, and policy measures to mitigate their adverse effects. By bridging the gap between technological capabilities and ethical considerations, the research aims to foster a deeper understanding of Deepfakes’ dual potential to both enrich and deceive, calling for a balanced approach to harnessing and regulating this powerful technology.
Study on Data Mining Techniques in Healthcare Sector: AReview
Today, the Healthcare sector is generating bulks of data be it from the medical history of the patients to their personal details, their clinical data, or the genetic data. Electronic Health Records (EHR), the medical data, is very complex and varied and hence cannot be processed using the traditional manual tools. Hence, Data Mining Analysis is used extensively in the Healthcare Industry to uncover the hidden patterns and relationships to study the similarity between patients, identify their symptoms and diagnose the disease at an early stage so that proper treatment could be given to the patients well in time. Today, Heart Diseases are very common and can lead to the risk of life. Due to the lack of extensive medical facilities and resources in the healthcare sector, it is very important to diagnose the risk factors leading to cardiovascular disease. This paper reviews and compares the work done by the different researchers and highlights the various risk factors that can cause heart problems and apply the different data mining algorithms that can be used to diagnose the early symptoms so that the disease can be cured.
A Comparative Analysis of LSTM, ARIMA, and MCMC Models for Forecasting Financial Market
Machine learning, with its sophisticated predictive capabilities, has fundamentally transformed numerous fields, including stock trading. This research presents a comparative study of Bayesian inference using the Markov Chain Monte Carlo (MCMC) method in stock price prediction, the new paradigm of long short-term memory (LSTM) networks, and conventional auto-regressive integrated moving average (ARIMA) models. The study uses historical stock price data from a diverse array of companies to evaluate these models concerning their accuracy, robustness, and computational efficiency. The findings contribute to the ongoing discourse on effective forecasting methodologies in financial markets, offering valuable insights to stakeholders, practitioners, and researchers. These insights are particularly pertinent for navigating financial environments’ inherently unpredictable and dynamic nature.
Psychographic Behavioural Background of Watching Hindi Web Series on Over-The-Top Platforms
This study inspects the psychographic and personal conduct standards of watchers of Hindi web series contents on beyond ridiculous (Over-The-Top) platforms. The investigation centers around grasping the motivations, segment profiles, seeing propensities and the more extensive psychosocial effect of this pattern. This study utilized Biblioshiny, an online bibliometric analysis tool, to examine a number of papers sourced from different databases including the Scopus database. The keywords “psychographics”, “digital consumption” and “web series” will let to identify over-the-top content preferences. This review gives a thorough outline of the developing scene of online content consumption in India, focusing on the language of Hindi. Key discoveries show a transcendent youthful, metropolitan and educated crowd, with unmistakable inclinations and standards of conduct that have huge implications for content makers and advertisers.
A Study of Key Drivers of Fintech Disruption and its Impact on Traditional Banking – A Comprehensive Literature Review
The advent of financial technology (Fintech) has significantly reshaped the landscape of the banking industry, introducing innovations that challenge traditional banking models. This study aims to identify and analyse the key drivers of fintech disruption and assess their impact on conventional banking institutions. Through a comprehensive literature review, this research identifies critical factors such as technological advancements, regulatory changes, customer expectations, and competitive dynamics as primary catalysts of fintech disruption. The study further investigates how these drivers influence the operational and strategic responses of traditional banks, leading to changes in service delivery, customer engagement, and business models. By examining the literature, the research highlights the adaptive strategies employed by traditional banks to remain competitive in a rapidly evolving financial ecosystem. The findings provide valuable insights for policymakers, financial institutions, and fintech companies, offering a nuanced understanding of the interplay between innovation and regulation, and paving the way for more resilient and customer-centric banking solutions.
Exploring the Interplay of Scale and Innovation: A Multidisciplinary Perspective
This paper explores the dynamic interplay between the concepts of scale and in-novation, emphasizing the multifaceted and interdisciplinary nature of their re-lationship. Drawing insights from human geography, political ecology, and eco-nomics, the study delves into how scale—understood as hierarchical, relational, and fluid—shapes and is shaped by innovative processes. It argues that innovation is not only about the introduction of new products or processes but also about their diffusion and adaptation across various scales. By examining theories like “scale jumping” and “scale bending,” the paper illustrates how organizations and innovators navigate different levels of operation to optimize resources, align with socio-political contexts, and address market demands. Through case studies the paper highlights the relational and strategic dimensions of scale in innovation. It also addresses challenges such as resource standardization, control dynamics, and localized policy needs. Ultimately, the paper underscores the critical role of scale as a tool for fostering sustainable and inclusive innovation, advocating for a nuanced understanding of its implications in a globalized world.
CSR in Business for Sustainable Enterprise
Over time, businesses have added additional components, such corporate governance, service-dominant thinking, and sustainable business. Examples of these include corporate social responsibility. It used to be said that an organization’s main responsibility was to maximize profits for its stakeholders at the expense of its ability to improve society as a whole. Ecology, another important component that was previously disregarded, is now receiving the attention it deserves. A “business case” for corporate sustainability is a concept that more and more businesses, environmental groups, and others are adopting to bolster their sustainable business practises. No company can stay in business for very long if it withdraws from these operations. Therefore, it is now essential to engage in responsible corporate citizenship in the contemporary economic environment by Understanding these phrases and integrating them into company social culture is increasingly essential. The theoretical examination of corporate social responsibility (CSR) in this article will primarily center on the triple bottom line (TBL) in terms of the economic, social, and environmental components. The study of corporate social responsibility (CSR) for sustainable business will be built around the triple bottom line, with a focus on the importance of this concept for running sustainable businesses. The value that companies may provide to their clientele will determine how corporate businesses turn out.
Sentiment Analysis with ChatGPT Across Domains
This paper explores ChatGPT’s applications in sentiment analysis across various domains, including business, Reddit data, and scientific citations. It demonstrates ChatGPT’s utility in understanding customer needs and public opinion, as well as its ability to identify nuanced sentiment and biases in scholarly research evaluation. Analysis of app reviews on the Google Play Store shows predominantly positive sentiments, with models like Random Forest and SVM achieving high effectiveness. The research evaluates ChatGPT and other large language models such as Gemini and LLaMA for multilingual sentiment analysis, revealing their proficiency alongside biases and inconsistencies across languages. The study emphasizes the importance of standardized evaluation methodologies and the need for data and algorithm improvements to enhance ChatGPT’s performance and applicability in sentiment analysis.
With more and more businesses adopting cloud computing, cloud-based database adoption is becoming a common practice. A well-liked NoSQL database, MongoDB is ideal for cloud deployment because of its scalable and adaptable data storage features. The purpose of this study is to examine the advantages and factors to be taken into account while implementing MongoDB in a cloud environment. The cloud environment presents several advantages for MongoDB deployment, such as high availability, scalability, and integration capabilities. To guarantee a successful and effective implementation, enterprises must carefully take into account elements like data security, network connectivity, and cost management. Organizations may make well-informed decisions and fully utilize MongoDB in the cloud by being aware of the advantages and factors related to MongoDB hosted on the cloud.
Enhancing Customer Experience in the Hospitality Industry through Artificial Intelligence
The hospitality industry is continuously evolving, driven by the need to enhance customer experience and satisfaction. Artificial intelligence (AI) has emerged as a transformative technology that can significantly elevate service standards in this sector. This research paper explores the multifaceted applications of AI in the hospitality industry, focusing on its potential to revolutionize customer service, personalize guest experiences, and optimize operational efficiency. Key AI technologies discussed include machine learning algorithms, chatbots, voice assistants, predictive analytics, and IoT-enabled smart rooms. Through personalized recommendations, automated customer service, and data-driven insights, AI enables hotels to anticipate and meet guest needs more effectively. The implementation challenges—such as data protection issues, integrating with current systems, and requiring staff training—are also covered in the article. The hospitality sector may increase operational efficiency and profitability while also improving visitor pleasure and loyalty by utilizing AI. This research attempts to give a thorough grasp of AI’s effects on hospitality and provide tactical advice for their effective use.
Indian Banks Mergers and Acquisitions- Conceptual Review
Purpose- The paper aims to provide a conceptual review of mergers and acquisitions (M&A) in the Indian banking sector, highlighting the evolution, strategic motivations, legal aspects, and ethical considerations of M&A activities. Design/methodology/approach- The research methodology involves a qualitative literature review of scholarly articles, books, and relevant academic sources. The review focuses on identifying key themes related to the motivations, processes, and outcomes of M&A in the banking sector. Findings- The study finds that M&A activities in the Indian banking sector are primarily driven by the need for synergy, improved managerial efficiency, growth, and technological advancements. Synergies achieved through M&A can enhance revenue, performance, and cost efficiency. The paper also highlights the importance of ethical considerations and cultural integration during M&A processes to ensure smooth transitions and maintain organizational stability. Research limitations/implications- This study is based on existing literature, and empirical validation through case studies or primary data could further substantiate the findings. Future research could explore the long-term impact of M&A on financial performance and stakeholder value in the Indian banking sector. Practical implications- The findings provide valuable insights for banking sector stakeholders, by elucidating the strategic motivations and critical factors influencing successful M&A transactions. Originality/value- The paper offers a detailed conceptual framework for understanding the dynamics of M&A in the Indian banking sector, contributing to the existing body of knowledge and guiding future research and practice in this field.