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.
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.
Personalized Learning and Adaptive Learning Tools
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.