Behavioral Law and Economics

Most developing economies continue to be overdependent on agriculture, yet farmers fail to access professional
knowledge and information in a timely fashion that may help them generate higher yields, reduce risks, and make
decisions. In the past, farmer-advising systems have relied on face-to-face meetings, call centers, or fixed platforms
in relaying information. Another revolutionary solution is the appearance of artificial intelligence that can be used in
the form of multilingual chatbots with the help of Natural Language Processing (NLP). These chatbots provide farmers
with highly personalized, context-sensitive, and readily available consulting services in their own languages. This
study explores the concept, design, and effectiveness of multilingual chatbots in terms of advising farmers using
natural language processing. It reviews the existing studies, discusses methods of chatbot development, analyzes
challenges, including a variety of dialects and low-resource languages, and presents potential conclusions in the
context of usability, functionality, and socioeconomic consequences. To ensure trust, scalability, and inclusivity, the
discussion ends with a glimpse of the future and the need to focus on explainable AI, advanced NLP models, and
interaction with Internet of Things (IoT) devices.

Personalization and recommendation systems have become a cornerstone of modern digital experiences, providing tailored content to users and enhancing engagement across various industries. The integration of artificial intelligence (AI) and machine learning (ML) in recommendation systems has revolutionized how businesses interact with consumers by analyzing vast amounts of user data to generate highly relevant content suggestions. This paper explores the critical components of recommendation systems, focusing on their architecture, algorithmic implementation, management strategies, and the challenges they encounter. By addressing fundamental issues such as data sparsity, scalability, privacy, and algorithmic bias, organizations can develop more efficient and ethical AI-driven recommendation frameworks that improve user experiences while maintaining trust and transparency.

The rapid evolution of artificial intelligence (AI) has significantly transformed the global banking sector, driving consumers toward AI-powered platforms for daily financial management (Chukwudi et al., 2023). In response, fintech companies are focused on enhancing user experience through a seamless blend of intelligent functionalities and intuitive design. The intersection of user interface (UI) design and AI-driven personalization has become a critical factor for user trust, satisfaction, and engagement (Md Ashrafuzzaman et al, 2025). This study investigates how UI design elements-including color palee, information density, and gamification and AI-driven features, such as savings recommendations, fraud alerts, and spending categorization, collectively impact consumer engagement (the dependent variable) in AI-powered financial platforms. Using a mixed-methods approach, the research combines a quantitative survey to measure engagement metrics (session frequency and feature adoption) with qualitative insights to understand user perceptions of UI aesthetics and AI functionality. Statistical analyses, including correlation and regression, will identify key predictors of engagement, while open-ended responses will provide a richer understanding of user trust and perceived value. The findings indicate that UI design establishes the initial conditions for trust and ease of use, while AI features provide the sustained, functional value that drives consistent engagement. These results underscore the importance of a dual-focus development strategy in fintech, one that equally prioritizes high-quality interface design and impactful, value-driven AI capabilities to cultivate strong and enduring consumer relationships.

The incorporation of Artificial Intelligence (AI) is rapidly advancing and significantly shaping consumer purchasing behavior and marketing approaches. This research examines AI's role in increasing the understanding of consumer preferences and digital marketing performance within Pakistan. Despite the potential of technologies such as analytics, machine learning, and recommender systems to enhance personalization, process automation, and user engagement, their application in moderating social media platforms remains underexplored. This study examines the effect and role of Artificial Intelligence (AI) on consumer-buyer behavior and digital marketing, with a key moderating variable being social media. The study, based on the Technology Acceptance Model (TAM), aims to investigate the impact of AI-based technologies on consumer decision-making, which promotes personalization and effectiveness in marketing. Data was collected from 658 Foodpanda users in Pakistan to complete a structured online survey with some standard Likert-scale measurement items. The findings indicate that significant and positive relationships exist among AI, consumer purchasing behavior, digital marketing, and social media engagement. The consumer's purchasing behavior to purchase a product mediates the connection between AI and digital marketing, and social media amplifies the effects of consumer behavior on marketing performance. This study is beneficial to both theory and practice since it validates the theory of TAM in an AI-driven marketing setting and provides practical steps targeted towards those marketers who want to maximize campaigns via AI and social media. There are also limitations and areas of future research discussed.

AI decision-making process, especially within CMC systems, it is paramount to consider ethical and explicable
aspects for regulated applications. This study examines the significance of ethics and explainability in AI-driven
CMC decisions by assessing governance frameworks that promote model validation, bias control, and auditability.
We look at how XAI methods increase user confidence, enhance decision transparency, and ensure regulatory
compliance. The research leverages recent advancements in ethical AI models, bias mitigation techniques, and
governance frameworks to ensure the safe implementation of AI systems within cybersecurity, healthcare, and
financial risk analysis domains. Additionally, it touches on the challenges of introspection, interpretability, and
bias elimination in opaque models as well as related regulatory issues and best practices to combat biases.
Ultimately, there is a need to pay attention to the ethics and explainability of AI as design features to create
trustworthy CMC.

Supply chain resilience has become a central theme in the face of global disruptions, from pandemics, natural disasters, climate change, and geopolitical instability. This review explores data-driven approaches that enhance resilience by integrating predictive analytics, optimization, and risk modeling. Predictive analytics enables organizations to anticipate demand fluctuations, supplier risks, and external shocks through statistical, machine learning, and hybrid methods. Optimization techniques strengthen supply chain continuity by balancing cost efficiency, flexibility, sustainability, and redundancy. Risk modeling frameworks, including stochastic simulations, network-based approaches, and AI-driven systems, provide structured tools for quantifying vulnerabilities and stress-testing supply chain networks. Applications across sectors such as healthcare, retail, energy, and transportation demonstrate how these methods support adaptability and continuity in uncertain environments. Building on this synthesis, the paper proposes an integrated framework that connects predictive analytics, optimization, and risk modeling, offering a pathway toward robust, adaptive, and sustainable supply chains. The review concludes with a discussion of limitations, emerging trends, and future research opportunities to guide both academics and practitioners in advancing supply chain resilience.

The healthcare industry has seen a major transformation in patient engagement and service delivery as a result of the integration of digital marketing and artificial intelligence (AI). Healthcare accessibility and efficiency are increased through individualized experiences provided by AI-driven marketing techniques that improve communication between patients and healthcare providers. Digital marketing optimizes patient encounters, boosts engagement, and offers real-time assistance by utilizing automation, machine learning, and data analytics. Moreover, AI facilitates precision marketing, allowing healthcare organizations to analyze vast amounts of patient data to tailor communication strategies that enhance preventive care and treatment adherence. AI algorithms also support healthcare decision-making by predicting patient behaviors and medical needs, leading to proactive interventions. Healthcare organizations must navigate regulatory frameworks to ensure patient data security and compliance with medical ethics. Additionally, AI models require continuous updates to maintain accuracy and relevance in medical marketing campaigns. Despite these challenges, the potential of AI in healthcare marketing remains unparalleled. The convergence of AI with telemedicine, wearable technology, and electronic health records (EHRs) fosters a holistic patient-centered approach, revolutionizing service delivery. As healthcare providers continue to adopt AI-driven marketing strategies, the industry is poised for a paradigm shift, ensuring enhanced patient engagement, personalized treatment plans, and improved health outcomes. Future research should focus on refining AI models for healthcare marketing, addressing ethical concerns, and maximizing patient trust. The synergy between digital marketing and AI is not just a trend but a transformative force shaping the future of healthcare service delivery.

In this article, opinion of people of some parts of Durg district of Chhattisgarh, India about investing in LIC policies and the returns received from the same was collected. Four hypothesis were considered in order to understand the gender wise and marital status wise opinion of people about investing in LIC policies and the returns obtained through them. Test of significance (t-test) was applied to reach to the conclusion. Gender and marital status don't influence the opinion about investing in LIC Policies and about the return obtained. A text review was also asked in the questionnaire. Sentiment Analysis using Natural Language Processing (NLP) Library available in Python language. Negative, positive, neutral and compound sentiment index were calculated using NLP to analyse the sentiments of the target people about the topic concerned.

This study aims to analyze the impact of customer experience on online shopping behavior in Ho Chi Minh City. Data were collected from 400 respondents, primarily students and young workers, who use e-commerce platforms. The customer experience factors examined include website interface, product information quality, customer service and personalized experience. A mixed-method approach combining qualitative and quantitative methods was employed, using a 5-point Likert scale and regression analysis. The findings reveal that personalized experience and product information quality are the most influential factors affecting online shopping behavior. The study suggests that businesses should optimize user experience, enhance product information and leverage technology to personalize the shopping journey in order to promote online purchasing behavior.

This thesis investigates a Deep Reinforcement Learning (DRL)-based control strategy for the fixed-length (cutto-length) problem in integrated continuous casting and rolling (CCRI). The task is formulated as a Markov Decision Process (MDP), and a Deep Q-Network (DQN) controller is developed to learn both optimal shear points and fine-tuned length offsets. The objective is to maximize material utilization and yield while minimizing shear frequency, energy consumption, and secondary cutting losses. The approach leverages rich process-aware state features-including billet temperature profiles, geometric dimensions, expansion and oxidation losses, and equipment constraints-encoded into a structured state representation. A high-fidelity, disturbance-rich digital twin simulation environment is constructed to mimic real production scenarios such as billet length deviations, thermal drifts, and shear delays. Within this platform, the DQN agent is trained and evaluated under both nominal and perturbed conditions. Experimental results demonstrate that the learned policies achieve competitive or superior performance compared to rule-based and optimization-based baselines, while maintaining robustness and adaptability. Additional analyses include sensitivity to reward weighting, evaluation of architectural variants (Double/Dueling DQN, prioritized replay), and deploymentoriented metrics such as inference latency and online responsiveness. The findings indicate that value-based DRL, when combined with careful process modeling and reward shaping, provides a viable route toward robust and adaptive control in steel manufacturing. Beyond immediate contributions, this thesis also aligns with the supervisor's 2025-2027 research roadmap, which emphasizes progressive algorithmic refinement, expansion toward multi-agent and hybrid control, and eventual pilot-scale industrial deployment.

In the United States, the growth of digital banking has redefined how people utilize financial services, make
investments, and create economic security for the future. With the growth, digital financial literacy is becoming an
important factor that can aid users in maximizing the benefits of digital services. This paper investigates the impact
of digital banking and investment access on financial literacy, economic inequality, and financial inclusion. In
particular, this paper looks at the digital banking opportunities for disadvantaged groups and the new means of
digital banking training in order to address the financial literacy gaps. Special consideration is given to the impact of
mobile banking, robo-advisors, and micro-investment platforms, as these digital banking tools provide investment
opportunities that were previously unavailable to disadvantaged and minority groups. The paper also looks at other
disadvantages, such as technological disadvantages, exclusion risks, and the need for compliance to changes in
regulation that affect certain groups. In the end, the paper concludes that digital banking can drastically improve
economic inequality in the U.S., if combined with appropriate access policies and financial literacy programs.

Enterprise business intelligence systems benefit from predictive analytics functions that help organizations use
identified data streams to build data-driven decisions through their BI systems. The capability delivers fundamental
organizational benefits that help organizations enhance their operational abilities and market position. SQL-based
analytics, a primary predictive analytics enabler, operates as one of the main querying and structured data management
systems. This role of SQL in predictive analytics is crucial, as it allows organizations to process raw data into
applicable insights and execute predictive model applications after connecting their ML models with SQL software.
Businesses integrating SQL platforms with machine learning technology establish automated decision systems that
decrease operational risks and optimize their financial health, care, retail, and manufacturing activities.
For adopting machine learning in predictive analytics, an organization needs statistical methods comprising regression
analysis cla, ossification models, and clustering algorithms. Through these methods, organizations obtain new
capabilities that enable them to forecast patterns and identify threats while dividing their customer base into segments.
Organizations track market trend modification through historical and current data to develop unique client interactions
and discover anomalous events for improved business strategic choices. The fundamental aspect of this process
depends on data cleaning procedures followed by feature design operations to produce quality datasets that boost
predictive modeling precision levels. SQL-based analytics enhances data extraction and querying processes by
offering advanced capabilities to operate data query systems more effectively. Business inquiries can achieve realtime responses through fast structured data manipulation, allowing organizations to make quicker decisions.
Systems achieve better prediction results when TensorFlow and Scikit-learn frameworks integrate with SQL analytics
protocols. Organizations use AutoML to gain predictive analytics features requiring minimal human involvement
when developing models. Process automation is simplified by combining elements, improving model efficiency, and
boosting decision-making process effectiveness. Organizations achieve enhanced predictive modeling capacity
through this technology implementation, which allows them to monitor consumer activities better, operate an efficient
supply chain, and protect their financial resources with accurate predictive systems.
International companies must overcome the main challenges during the deployment of predictive analytics solutions
for business intelligence applications. The efficacy problems of ML analytics stem from poor data quality, difficulty
interpreting models, and high computational operation requirements. Predictive model reliability depends on
organizations' ability to perform biased algorithm revisions with appropriate data protection protocols and ethical
implementation of AI procedures. AI systems must undergo proper and transparent management so that every
automated decision process functions properly. Enterprise data processing of whole data sets has become possible
through AutoML technologies that make deploying models at speed and scale operations easy using cloud-based
analytics services. Normative methods help businesses remedy their data processing problems, creating new business
prospects from data-centric business models.
The article explores the impact of predictive analytics on BI operations by explaining how SQL analytics enables
enhanced machine learning performance. When organizations unite their predictive ML functions with SQL databases,
they gain enhanced operational capabilities and data-driven support systems, which result in enduring digital
marketplace success. By implementing these technological tools through strategic frameworks, organizations gain
adaptable business operations in future times

The digital transformation era has reshaped consumer engagement, with artificial intelligence (AI) emerging as a critical enabler of hyper personalized marketing strategies. Businesses now leverage AI-driven behavioral analytics to gain deeper insights into customer preferences, enhancing both user experience and revenue growth. This study examines the integration of advanced machine learning techniques, natural language processing (NLP) and predictive modelling into marketing frameworks to refine consumer targeting. Analyzing vast amounts of structured and unstructured data allows AI to uncover nuanced patterns in consumer interactions, sentiment and purchasing behavior. The ability to anticipate customer needs with high precision has led to widespread adoption of AI-powered recommendation systems, generative AI models and real-time data processing. These technologies facilitate dynamic personalization across various touchpoints including e-commerce platforms, social media and digital advertising. Ethical considerations surrounding AI-driven personalization, particularly in relation to data privacy, bias mitigation and consumer trust, remain key areas of focus. Case studies from leading industries illustrate successful AI implementation in hyper personalized marketing, demonstrating significant improvements in conversion rates, customer retention and brand loyalty. Future research directions include the role of multimodal AI, reinforcement learning and federated learning in refining marketing strategies. This research provides a comprehensive framework for businesses to effectively harness AI-driven consumer insights, ensuring that marketing strategies remain adaptive, ethical and impactful in an increasingly data driven world.

Artificial intelligence (AI) can transform the services that are placed on customers and lead to smart routing and resolution of cases via cloud-based applications. Customer requests in clouds vary, with queries as simple as inquiring about a cloud to the technical requests such complex issues as triggering a cloud failure. The manual systems which are traditional may not be efficient and this may lead to delays and customer dissatisfaction. Case routing automation, which uses machine learning and natural language processing, will automatically classify, prioritize and assign cases to the most appropriate agents and will be faster and more accurate. The main benefit of artificial intelligence is that it can analyze customer history and contextual data in real-time to make sure that the cases are channeled to those agents that can be best equipped to solve an issue in the shortest amount of time. Predictive analytics can also enable the system to predict the level of complexity of issues, resource allocation and higher first-contact resolution levels. Consequently, there is increased responsiveness and customer satisfaction that is enabled by AI-powered chatbots and sentiments analysis tools. AI capabilities in service cloud provide a pressurized picture of general efficiency, initiating customer experience and competiveness

The use of AI-powered personalization in real-money gaming has raised significant ethical and regulatory concerns in recent years. These systems are usually designed to maximize user engagement with the platform, which leads to higher monetization, but this sometimes comes at the cost of opaque and potentially discriminatory targeting systems, which risk exploiting user behavior, particularly among the vulnerable population. This study aims to address these issues by developing a regulatory-aligned framework that integrates ethical constraints such as fairness, transparency, and harm mitigation directly into the design of personalization algorithms. Drawing on secondary qualitative sources such as regulatory frameworks, peer-reviewed literature, and documented platform practices, this study analyzes current standards and gaps in industry practice. These insights inform the creation of the Responsible Personalization Framework (RPF), which operationalizes regulatory principles through concrete design patterns. The findings reveal that widely adopted personalization strategies often contradict responsible AI principles. However, the proposed framework, featuring mechanisms like Harm-Aware Personalization and the Offer Parity Rule, offers actionable, regulation-compliant solutions for ethical personalization. Overall, the study demonstrates that embedding ethical and regulatory safeguards at the design stage, instead of post hoc reactive enforcement, enables the development of safer, fairer, and more responsible AI systems in the context of real-money gaming.

The early education monitoring of child behavior in early education is important to assist the development of the child, conflict handling and to improve learning in the child. The established observational approaches are usually subjective, constrained by time, and scope, which spawns the necessity of new technological applications. The present paper discusses multimodal artificial intelligence (AI) and predictive analytics to be implemented to observe child behavior in real-time during early education. Multimodal AI enables contactless monitoring of children, including their behavior in the classroom based on a combination of data on their behavior across modalities, including facial expressions, speech patterns, body movements and classroom interactions. Predictive analytics further empowers this system through the aspect of predicting the possible trends in behavior and the early warning signs of learning challenge or emotional discomfort. The suggested model illustrates how the approach based on AIdriven monitoring would provide teachers and caregivers with practical information to enhance subjectivity in behavioral assessments, making interventions timely. Along with it, the paper speaks about ethical and privacy issues of implementing AI in sensitive educational contexts and methods of deploying it securely, bias-consciously, and transparently. The evidence indicates that incorporating predictive analytics along with multimodal AI would revolutionize early education because of its potential to encourage personalization learning in the classroom, help manage students more effectively, and enhance the long-term child development.

A machine learning-based system that employs several algorithms to analyse personal health data, detect vitamin deficiencies, and forecast related illnesses is suggested as a solution to these problems. To guarantee accurate disease prediction, the most accurate model is used. Additionally, the system makes use of extensive nutritional databases to suggest meals high in vitamins based on the user's dietary requirements and interests. This technique attempts to improve general well-being and encourage the adoption of balanced, nutrient-dense eating habits by providing individualized dietary and health advice.

How else can you say it than that AI is rapidly metamorphosing diagnostic offerings in healthcare, creating frontier solutions in disease detection, clinical decision support, and predictive analytics? From radiology, pathology, and ophthalmology to rare disease identification, these computer-assisted diagnostic solutions are heralded for timeliness, accuracy, and cost-effectiveness. But implementation raises serious ethical issues beyond technical performance: Can they assure the privacy of the patients? Are they able to obtain true informed consent for data use? Are they fair in counteracting the biases toward some populations in their algorithms? How transparent and explainable are they? Who remains accountable when something goes wrong? How secure are they with regard to cyber breaches? Are they based on business principles that are still aligned with the regulatory framework guiding their applications-because regulations by their nature constantly evolve? Such questions are discussed within this article with the scope of the entire AI lifecycle-from data acquisition, model development to clinical integration, and post-market surveillancewith an emphasis on the necessity for ethical governance throughout-auditability-beyond the design time. When implemented alongside ethical principles, operationalized toward strategy, these solutions can help healthcare organizations build trust among their stakeholders, protect patient welfare, and ensure equitable and clinically meaningful benefits for AI-based diagnostics.

At its core, generative AI is changing creative industries by enveloping, readdressing, and promoting how ideas are
formulated, shaped, and channeled toward art, music, design, film, gaming, advertising, and publishing. Generative
systems fabricate assets for the commercial world using foundation model breakthroughs and advances in diffusion
architectures from ideation to prototyping; asset generation, post-production, audience-testing, and adaptive
personalization at scale. The article attempts to lay together evidence bearing on productivity, costs, and enabling
conditions for new creativity, with the risks surrounding authorship, labor displacement, data governance, privacy,
and cultural homogenization. The authors differentiate between impacts at the model, tool, workflow, and ecosystem
level, outline nascent patterns for human-AI co-creation, and teach response governance waves as to provenance
standards, licensing regimes, and transparency mechanisms. In that sense, we empirically map capability frontiers in
text, image, audio, video, coding, and multimodal generation while aligning these new capabilities to role
reconfiguration along the creative supply chains. Finally, we propose research and policy priorities for responsible
deployment focusing on human agency, fair compensation, safety, and inclusivity for generative AI to amplify human
creativity instead of replacing it.

The airline industry is poised to undergo a digital revolution powered by Artificial Intelligence (AI). However, due to the complexity of adopting AI, considerable and significant obstacles exist in enabling its adoption in Airlines. This research considers the enablers of AI adoption in the airline industry to determine and construct the significant enablers of AI adoption and to utilize Interpretive Structural Modeling (ISM) to identify interdependencies and levels of structural complexity of the dimensions of enabling AI adoption into the industry. Based on an extensive literature review combined with expert opinion, it was identified there are twelve significant enablers including: Top Management Support, Technological Infrastructure, Data Availability and quality, and Trust in AI systems and Explainability. Utilizing ISM methodology, these twelve enablers were structured into eight levels, resulting in a structural model, and identifying the base level drivers and levels of dependence on assessment outcomes. From the ISM model, Top Management Support and Technological Infrastructure Readiness were identified as the base level drivers. The top two dependent structural elements of them overall model were Government and Regulatory Support, and Customer-Centric Innovation Culture. The research indicated there are systematic discretionary actions exist which are dependent on Leadership commitment, data systems readiness, available of data, and inter departmental team collaboration to enable the successful adoption AI into their operations. As a result of the research, it was recommended research participants consider some of the practical operational AI applications based on enablers within their own context. Some of the recommended application include; predictive maintenance, dynamic pricing and selling, AI customer service support, and customer sentiment analysis. Further, because of the investigation is practiced based, it is anticipated applications will lead to improved operational efficiency and customer experience. Furthermore, the findings will offer strategic insight into decision making for airline managers concerned with the adoption of AI applications in their operations, and offers a structured framework for the adoption of AI applications within an Airline context that is characterized as complex process with high regulation. Finally, future research is recommended in order to validate the proposed model of enablers for AI adoption across multiple airline contexts and determine the actual outcomes of implementation in the real world.

The fact that systems in cybersecurity, healthcare, finance and critical infrastructure have grown so complex and are so related has made predicting incidents in advance a main part of risk management. Artificial Intelligence (AI) has emerged as a transformative technology in this context, offering sophisticated tools and methodologies to predict, detect, and mitigate potential incidents before they escalate into critical failures. It investigates Artificial Intelligence Incident Forecasting by looking at its theoretical basis, important technologies and practical usages. The field reviews current machine learning models, techniques for finding anomalies and predictive methods meant to notice and predict things like system outages, security breaches, crises in public health and natural disasters. It also points to a number of events where the use of AI in forecasting strengthened resilience and stopped crisis from occurring. Data quality issues, how models are explained and ethical matters are also identified and addressed by fairness in predictive technologies. A detailed examination demonstrates how AI could greatly change incident management and suggests points to keep in mind for more work and use of AI in this upcoming field.

Web development is an ever-growing process in the fast-pacing, AI-driven world. This survey paper has been conducted to portray the extensive impact that AI has on the development of webs and management, even in recent times. Basically, it is how AI technologies are being used toward the fulfilment of myriads of tasks in a manner including generated code, design alteration, or content management of a web page, hence easing development workflows overall. More so, it then goes further to show how AI-driven algorithms can tune a user's experience towards what is best for them: dynamic content recommendations, individually designed interfaces, and design features that lock in much higher levels of engagement and friendliness. It tells us about how AI supports the optimization of website performance by analysing user data, predicting traffic patterns, and oriented optimization strategies toward search engine optimization. It further elaborates on expected problems and limitations in using AI that will be implemented in web development, in terms of data privacy and requisites for experts who can use their AI systems. With this, there would then be a created dependence on the tool by automatizing too much. All this will come out clearly in the survey that is about to make reference to the well-reviewed recent advances in current AI tools applied to website development. This section is hence supposed to instruct the reader on how AI is all about to change the role in shaping the future of the developers, designers, and end users of websites. This paper investigates how Artificial Intelligence (AI) has transformed web development by comparing the traditional and the new methods of web development by AI. Some of the areas studied include code writing, detects bugs, personalizes content, and enhances performance. There is an evident time savings of about 50%, and further efficiencies in performance of about 30-40% are observed in various tasks that were performed. It was noted that all the activities stored by the use of tools employing AI were performed faster and more accurately, which indicates that such tools will be needed in the further development of the web. As a result, some remarkable improvements have been seen in the case of integration of AI into web development. For instance, the process of generating code by using AI reduces the development time by 30%, while in generation of user specific content saw its efficiency rise by 50%. Tools focuses on design of web improved the accuracy rate by 25-40%, whereas bug detection efficiency increased by 35%. Other than that, the methodology of optimization improved and because of that general performance of websites increased by 45%, creating the websites much more user friendly and interactive.

As artificial intelligence becomes deeply embedded in business ecosystems, the challenge for brands is no longer just adopting new technology-it is ensuring that innovation remains human-centered, ethical, and culturally inclusive. This article examines Olga Denysiuk's (2025) advanced framework for AI-driven brand development, which prioritizes fairness, accessibility, and emotional authenticity. Her approach integrates ethical AI principles with adaptive brand storytelling to build trust, deepen loyalty, and create scalable market value without sacrificing cultural nuance.

The proposed work describes the design and implementation of ShopAssist, an offline-capable customer support
chatbot system designed for e-commerce applications. ShopAssist offers a chatbot that supports customer queries
by combining semantic and contextual query understanding with user authentication and personalized customer
support. By combining TF-IDF vectorization, fuzzy string matching, and transformer-based embeddings, the
model can provide the most accurate and relevant answers in direct response to queries. With a graphical user
interface, the chatbot supports login-session-based control as well as contextual support. By combining user
authentication, contextual query understanding, and methods such as the Sentence-BERT method, Random Forest
classifiers, and Tkinter GUI elements for user interaction, ShopAssist provides offline usability in resolving
queries. In summary, the proposed human-centred solution has immediate implications for deriving significant
value by automating customer interactions, such as FAQs or customized responses, thereby enhancing customer
engagement in a seamless digital marketplace.

In the digital era, the rapid expansion of online content has made it increasingly difficult for readers to identify books that align with their individual tastes and preferences. Traditional search methods such as keyword-based filtering or user reviews are often insufficient, leading to an overwhelming user experience. To address this challenge, this paper proposes a robust and scalable hybrid book recommendation system that integrates content-based filtering and collaborative filtering techniques to enhance recommendation accuracy and relevance. The content-based component utilizes metadata such as book titles, authors, and genres, along with natural language processing (NLP) techniques to analyze textual descriptions. TF-IDF (Term Frequency-Inverse Document Frequency) is used to vectorize book descriptions, and cosine similarity is employed to measure the closeness between books. In parallel, the collaborative filtering module leverages user ratings and interaction history to detect user behavior patterns. By applying Singular Value Decomposition (SVD), the system can infer preferences even for unseen books, helping to mitigate cold-start problems. The hybrid approach combines the strengths of both methods, resulting in a recommendation engine that is both personalized and diverse. Implemented using Python and machine learning libraries such as Scikit-learn and Surprise, the system is deployed through a web interface built with Flask or Django, making it easily accessible and interactive for users. Evaluation was conducted using benchmark datasets like Goodbooks-10K and Book-Crossing, with performance measured using metrics such as Root Mean Square Error (RMSE), Precision, Recall, and F1-Score. The system demonstrates a significant improvement in recommendation quality over standalone methods. It effectively addresses challenges such as sparsity, over-specialization, and scalability. The proposed solution is not only technically sound but also practical for deployment in educational platforms, e-commerce sites, and digital libraries. Overall, this work contributes to the growing field of intelligent recommender systems by delivering a flexible, accurate, and user-centric model for book discovery

Financial fraud has become a growing concern in the modern digital economy, with vast amounts of money lost annually due to unauthorized and deceptive transactions. As financial systems become increasingly complex and data-rich, traditional rule-based fraud detection systems struggle to keep up with sophisticated fraudulent techniques. This project proposes the use of neural networks-a subset of deep learning-to build an intelligent and adaptive system for financial fraud detection. The core objective of this project is to design, train, and evaluate a neural network model capable of classifying financial transactions as either legitimate or fraudulent. The model is trained using a publicly available financial dataset containing anonymized records of credit card transactions, where each transaction is labelled as fraudulent or not. The dataset is highly imbalanced, with fraudulent transactions making up a small fraction of the total, posing a challenge that is addressed using resampling techniques such as SMOTE (Synthetic Minority Oversampling Technique) or under sampling of the majority class. The neural network architecture employed in this study consists of multiple fully connected layers (also called dense layers), with activation functions like ReLU (Rectified Linear Unit) for the hidden layers and sigmoid activation for the output layer to produce a binary classification. To prevent overfitting and improve generalization, regularization techniques such as dropout and early stopping are used during training. The model is optimized using an appropriate optimizer (e.g., Adam), and the loss function used is binary cross-entropy. Performance is evaluated using key classification metrics such as accuracy, precision, recall, and F1-score, which provide a comprehensive view of how well the model identifies fraudulent activity while minimizing false positives. The recall score is especially important in fraud detection, as it measures the ability to correctly identify actual fraud cases. The experimental results show that the neural network can effectively learn complex patterns in transaction data and outperform traditional machine learning models in terms of detection accuracy and robustness. This demonstrates the suitability of neural networks for real-world fraud detection systems, where adaptability and timely detection are critical. In conclusion, this project illustrates how neural networks can be successfully applied to financial fraud detection, offering a scalable and intelligent solution that adapts to evolving fraudulent behaviours. Future enhancements could include incorporating recurrent neural networks (RNNs) for sequence-based transaction analysis or deploying the model in real-time financial systems for proactive fraud prevention.

The recent age of digital transformation, characterized by a data-driven approach, renders real-time methods of
managing and synchronizing enterprise data as a strategic necessity. Conventional Master Data Management (MDM)
systems have become lesser and less effective in dealing with the complexity of the contemporary, multi-cloud, and
hybrid world. This paper will discuss how Artificial Intelligence (AI) can be used in MDM systems to promote realtime synchronization of data to ensure that businesses obtain more value. Intelligent data governance. Artificial
intelligence-based MDM systems allow intelligent data governance, anomaly dynamic detection, automation of data
cleansing, and dynamic metadata management to achieve consistent and superior quality data silos throughout the
organization. Enterprises can enhance operational efficiency, decision-making, and scalability through machine
learning, natural language processing as well and stream analytics. The article discusses modern architectures,
examples of enterprise utilization, implementation issues, and challenges, and provides a conceptual blueprint of
organizations that show interest in utilizing AI-empowered MDM systems. Finally, the study highlights the radical
possibility of AI in restructuring the enterprise data strategies and availing long-term competitive advantage.

This paper describes the design of a comprehensive, holistic tinnitus management system that incorporates artificial intelligence, machine learning, evidence-based therapeutic interventions, and further standardized mitigative methods. The comprehensive system and holistic tinnitus management includes a conversational AIbased chatbot, clinically validated self-assessment tools, self-symptom diary, and predictive modeling to support individuals experiencing tinnitus. Using natural language processing, transformer-based models, and TF-IDF similarity, the chatbot is able to retrieve information from the knowledge base with high accuracy to assist the user. The self-monitoring diary system employs Random Forest classification and K-means cluster analysis to identify smart triggers and patterns more accurately and predict severity of symptoms. The self-assessment tools (e.g., Tinnitus Handicap Inventory (THI), Tinnitus Functional Index (TFI), and other clinically validated questionnaires) are digitized for usability, such that they may be used in digital self-monitoring. The complete system also had therapeutic modules for breathing exercises and mindfulness meditation. Results from the preliminary evaluation are positive based on amount of usage/user engagement, identification of patterns in user symptom reporting, and assessment of the type of support provided by the system to engage with the therapeutic elements.

This research presents the design and implementation of an intelligent e-commerce web application that leverages machine learning for enhanced customer profiling and personalized product recommendations. The platform is built using the Flask web framework and SQL Alchemy ORM, integrating key functionalities such as secure user authentication, behave oral tracking, and dynamic product display. A core component of the system is the application of the K-Means clustering algorithm, which segments users into groups based on demographic factors including age, gender, and location. This segmentation allows the system to deliver tailored recommendations that align with the preferences of similar users. In addition to demographic clustering, the recommendation engine incorporates behavioural metrics such as search frequency, interest trends, and recency of interactions to further refine product suggestions. The system also includes a comprehensive administrative module that enables efficient product and category management, including image uploads and real-time content updates. By combining data-driven user profiling with scalable web technologies, the proposed solution offers a robust, user-centric approach to improving the ecommerce shopping experience.

The increasing complexity of modern surgical procedures has led to a pressing need for accurate and timely assessments of post-operative risks and recovery timelines. This project presents a data-driven approach to predicting post-surgical risk levels and expected recovery durations using machine learning techniques. By leveraging patientspecific data such as age, blood pressure, heart rate, diabetes status, BMI, and other clinical indicators, the system is capable of providing personalized predictions that support clinical decision-making. A comprehensive dataset comprising both pre-operative and post-operative parameters was used to train multiple machine learning models, with Random Forest and Logistic Regression showing the highest accuracy. The final predictive model categorizes post-surgical risk into five levels-Very Low, Low, Moderate, High, and Very Highand estimates recovery time in days. To ensure practical usability, the system is implemented as a web-based application with an intuitive user interface, enabling healthcare professionals to input patient data and receive predictions instantly. The proposed system addresses common issues in surgical recovery forecasting by reducing reliance on subjective clinical judgments. It enhances patient care by enabling proactive interventions for high-risk individuals, optimizing resource allocation, and improving post-operative planning. Furthermore, the application supports easy updates and retraining, allowing integration with hospital information systems for real-time analysis. This research highlights the potential of machine learning in transforming healthcare delivery by improving the accuracy and efficiency of post-surgical assessments. The system not only empowers surgeons and caregivers with actionable insights but also contributes to safer, more personalized treatment pathways. As healthcare continues to embrace digital transformation, such predictive tools are poised to play a vital role in supporting evidence-based clinical decisions and improving overall surgical outcomes. I.

In the digital age, social media influencers play a pivotal role in shaping consumer behavior and brand perception. However, the rise of disingenuous influencer content-characterized by clickbait tactics, misleading endorsements, and staged promotions-has begun to undermine consumer trust and weaken brand equity. This article explores the mechanisms behind digital deception in influencer marketing, examines its impact on consumer attitudes and purchasing decisions, and analyzes the consequences for brand reputation. Through real-world examples, such as the Payless "Palessi" experiment and instances of influencers promoting fake products, the study highlights the urgent need for transparency and authenticity in influencer-brand collaborations. Ultimately, the article proposes strategies for restoring credibility in influencer marketing to protect consumer trust and sustain long-term brand value.

This project presents the design and implementation of an intelligent Telugu Language Chatbot that interact with
user and answer queries related to Telugu poets and literacy information. The system utilizes Natural Language
Processing (NLP) techniques and a deep learning-based intent classification model to understand user input,
determine the intent, and generate contextually relevant responses in Telugu. Built using Python, TensorFlow,
and Flask, the chatbot is trained on a curated dataset of intents and responds to input using a lightweight neural
model consisting of embedding, pooling, and dense layers. The system also incorporates fuzzy logic for fallback
responses and is deployed via a web interface for -friendly interaction. The chatbot aim to promote digital access
to Telugu literature through intelligent conversation in the regional language

This project introduces a smart and user-friendly wellness application that brings together the power of machine learning and the benefits of yoga to support better sleep and overall health. In today's fast-paced world, sleeprelated issues have become alarmingly common, affecting both physical and mental well-being. Traditional methods used to evaluate sleep quality such as clinical sleep studies or personal logs are often expensive, timeconsuming, and not easily accessible to everyone. To overcome these challenges, our system uses a machine learning model Random Forest Regression to predict a user's sleep quality score on a scale from 1 to 10. This prediction is made using basic and easy-to provide personal and lifestyle inputs such as age, stress level, sleep duration, and physical activity. The model is implemented using data from a publicly available Sleep Health and Lifestyle dataset. What sets our application apart is the integration of a rule-based yoga recommendation system. Based on the user's profile and current stress or fitness levels, the system suggests suitable yoga poses aimed at improving their sleep and reducing stress. The entire solution is deployed through a Streamlit web interface, ensuring accessibility and ease of use for all users. By merging AI-driven insights with traditional wellness techniques like yoga, this project highlights how technology can offer simple yet powerful tools for preventive health care and self-care in everyday life.

The current project describes an end-to-end sentiment analysis paradigm with a focus on hotel review systems for the Hilton London Paddington dataset. The goal: develop an automated system for classifying customer feedback as positive or negative, based on textual review content, making use of a hybrid combination of classical machine learning and NLP techniques. The journey with the review data begins with preprocessing, including entity anonymization, lemmatization, and the removal of noise (punctuation, stop words). The next step is to address class imbalance with a balanced dataset. After that, TF-IDF vectorization with n-gram features is employed to preserve contextual information from the text data. Classifiers such as LinearSVC, Logistic Regression, and Random Forests are evaluated using Stratified K-Fold Cross-Validation while hyperparameters were tuned using GridSearchCV and RandomizedSearchCV. The best-performing model was then fine-tuned and saved for future inference. In addition, Named Entity Recognition (NER) is applied for anonymization, apart from topic modelling using Latent Dirichlet Allocation (LDA) to identify thematic structures in the reviews, and word cloud visualization to provide insights into highly recurrent and distinctive words under positive and negative sentiments. Performance measures include the construction of confusion matrices and generation of classification reports and ROC-AUC curves, which illustrate the high accuracy and robustness of the classification pipeline. The entire model is saved using Joblib, enabling real-time sentiment prediction on new review data.

The healthcare industry is rapidly evolving due to technological advancements, and one of the most transformative developments is the integration of Artificial Intelligence (AI) and Machine Learning (ML) into digital healthcare platforms. The Digital Health Companion is a smart healthcare solution developed using machine learning techniques to offer real-time assistance, disease prediction, health monitoring, and personalized medical guidance to users. This system is designed to empower individuals in managing their health proactively, reducing dependency on clinical visits, and facilitating early detection of illnesses. The primary objective of this project is to develop a machine-learning-based application that predicts diseases by analysing user-provided symptoms and health parameters. It uses a trained classifier model to suggest possible illnesses and corresponding recommendations. This not only helps in early diagnosis but also provides preventive healthcare tips and alerts, contributing to improved quality of life. Machine learning serves as the brain of this system, enabling it to learn from historical health data, recognize patterns, and make predictions based on user inputs. The ML model is trained using a healthcare dataset that includes various symptoms and associated diseases. Algorithms such as Random Forest, Decision Trees, and Naïve Bayes are employed for accurate classification and prediction. These models are evaluated for accuracy, precision, recall, and F1-score to ensure dependable performance. The system is built using Python, with libraries like Scikit-learn, Pandas, NumPy, and Seaborn for data handling, model training, and visualization.

Managing big data in cloud environments requires a combination of strong security measures and efficient
clustering techniques. With the increasing volume of sensitive data being processed, ensuring secure
storage, retrieval, and management is essential. Advanced cryptographic methods can protect data from
unauthorized access, while machine learning algorithms improve clustering accuracy and efficiency. By
integrating these technologies, cloud infrastructures can enhance both security and performance, reducing
vulnerabilities and optimizing resource allocation. Businesses and organizations relying on cloud platforms
need robust mechanisms to safeguard their data while maintaining operational efficiency. Efficient
clustering techniques help in organizing and processing large datasets, making data management more
effective. Secure cloud based operations ensure compliance with regulatory standards and protect against
cyber threats. Implementing scalable security frameworks strengthens data confidentiality, integrity, and
availability. Leveraging AI-driven models for anomaly detection further enhances security within cloud
environments. The proposed system will integrate security and clustering by using encrypted data storage
combined with machine learning-based data clustering within a cloud environment.

In the modern digital economy, the surge in online financial transactions has led to a parallel increase in fraudulent
activities, posing a significant threat to individuals, businesses, and financial institutions. Traditional fraud
detection systems often struggle with adaptability and accuracy, especially when faced with evolving fraudulent
patterns and highly imbalanced datasets. This paper presents a hybrid machine learning approach to detect
fraudulent transactions by combining the strengths of the Random Forest Classifier and Graph Neural Networks
(GNNs). The proposed system begins with a detailed preprocessing pipeline transforming raw transaction data
into structured form, handling missing values, encoding categorical data, and applying standardization. To address
the class imbalance problem commonly present in fraud datasets, balancing techniques are applied to enhance
model robustness. The Random Forest algorithm is used to identify key transaction features and perform initial
fraud predictions. In parallel, a GNN-based model captures complex relationships between users and transaction
elements, effectively uncovering hidden fraud networks that are typically missed by conventional methods. The
model’s performance is evaluated using standard metrics, and visualizations are generated to illustrate feature
importance and fraud distribution. The results indicate a significant improvement in detection accuracy and
interpretability, making this approach highly effective for real-time fraud prevention in financial systems

The Multi-Language Interview Practice Portal is a dynamic full-stack web application developed using React.js, Node.js, Express.js, and MongoDB to assist candidates in preparing for software and IT job interviews. Designed with inclusivity in mind, the platform features a language-switching option that enables users to translate questions and explanations into regional languages, helping bridge communication gaps while strengthening English proficiency. The portal offers a wide range of modules including a structured DSA learning path with progress tracking, subject-wise quizzes on core topics, an ATS-compatible Resume Analyzer, and realistic mock interview simulations covering both technical and HR rounds. Registered users benefit from personalized dashboards, detailed feedback, and a seamless learning experience, while new visitors can explore its offerings through an open landing page. By combining technical preparation, soft skill development, and language flexibility, the platform delivers a complete and accessible solution for modern interview readiness.

The rapid growth of online financial transactions has increased the risk of credit card fraud, making early detection crucial for securing user data and minimizing financial loss. This project presents a credit card fraud detection system using machine learning algorithms to effectively identify and classify fraudulent transactions. The system leverages algorithms such as Random Forest, Logistic Regression, and Support Vector Machine (SVM) to analyze patterns in transactional data and distinguish legitimate from fraudulent behavior. The dataset used consists of anonymized credit card transactions with features reduced via Principal Component Analysis (PCA) to protect sensitive user information while maintaining predictive accuracy. Preprocessing steps include feature scaling using StandardScaler and balancing the class distribution to improve model generalization. Among the tested models, the Random Forest classifier demonstrated the highest accuracy and robustness in handling class imbalance. The final model is deployed in a Flask-based web application, allowing users to input transaction parameters and receive real-time fraud predictions. The interface is styled for clarity and ease of use. By integrating machine learning with a web interface, the project showcases a practical solution for financial institutions to detect fraudulent activity efficiently and proactively, contributing to enhanced cybersecurity in the digital payment ecosystem.

The goal of this study was to showcase how leveraging machine learning can facilitate timely treatment and
improve healthcare outcomes for patients with chronic diseases like kidney failure. To achieve this, the study
employed Logistic Regression to predict the presence of the disease based on medical features such as blood
pressure, glucose levels, and creatinine levels. Predicting an outcome based on multiple features is known as
binary classification, for which Logistic Regression is widely used due to its simplicity, efficiency, and
interpretability. At the initial stage, patient data was pre-processed by handling missing values and anomalies
while normalizing the dataset to enhance model performance and robustness. The dataset was meticulously
cleaned to guarantee that the model was fed only high-quality inputs. After the Logistic Regression model was
trained, its reliability in diagnosing chronic kidney disease (CKD) was evaluated using a variety of measures, such
as recall, accuracy, precision, and F1-score. The model also contributed to illness severity estimates, which were
useful for planning early medical interventions. This study demonstrated that Logistic Regression can be an
effective tool in the healthcare sector, particularly for early-stage CKD detection. Its interpretability, simplicity
of implementation, and low cost make it a promising candidate for practical medical applications that might help
healthcare providers enhance patient care via informed decision-making

New opportunities are opening up for women as a result of the digital revolution. New media environments
require women to navigate their digital lives and online privacy. Through this research, we hope to understand
some of the factors that contribute to women's vulnerability to online exploitation. Women who have been
victims of cybercrime are studied using a qualitative research method. A reliable news source is used to gather
case-specific information for this study in order to assure that the content is accurate and reliable. There are a
number of obstacles women face in the current media age, and they should be protected and acknowledged.
Cybercrime victims, especially women, face a variety of challenges, coping strategies, and wider social
repercussions, as we analyze the challenges and coping strategies in this study

Artificial Intelligence (AI) is rapidly transforming the UK’s information technology (IT) industry by driving
innovation, enhancing operational efficiency, and enabling new forms of automation. This paper offers a
comprehensive examination of AI adoption across four core domains of the UK IT sector: cybersecurity, software
development, IT operations (AIOps), and data analytics. Drawing on government reports, industry case studies,
and recent surveys, we trace how UK firms are deploying AI for threat detection, code generation, autonomous
infrastructure, and intelligent forecasting. Notably, by late 2024, around 15% of UK businesses—30% among
large enterprises—had integrated some form of AI into their operations. At the same time, the UK government’s
regulatory response emphasizes a “pro-innovation” stance, while generative AI and ethical challenges continue to
reshape the policy landscape. We conclude by discussing the future trajectory of AI in UK IT, highlighting the
importance of public trust, workforce readiness, and international competitiveness.

This study looks at the importance of openness in government communication, emphasizing how it affects accountability, public trust, and efficient governance. This paper examines the mechanisms, difficulties, and results of transparency initiatives in the government communication strategies of the northern Indian state of Himachal Pradesh through a thorough case study. The study employs a qualitative research methodology, utilizing anecdotal evidence, policy statements, news reports, and publicly accessible documents. It seeks to determine the degree to which the state's communication practices are transparent, the effectiveness of the Right to Information (RTI) Act's implementation, the function of digital platforms, and the public's perceptions of government openness. According to the findings, despite Himachal Pradesh's admirable efforts to increase transparency, there are still large gaps in proactive disclosure, accessibility for all societal segments, and the responsiveness of information distribution. The study ends with suggestions for improving Himachal Pradesh's government communication transparency that might be used as a template by other states.

Nowadays, Artificial Intelligence (AI) has played an important role in decision making in different fields such as healthcare, finance and public administration. However, AI brings a lot of efficiency and scale at the same time and that implies a ton of ethical challenges as well. The goal of this systematic review is to pinpoint and assess the ethical issues with AI placed decisions. Since our PRISMA guidelines we have chosen only peer reviewed articles and trusted sources from 2015-2025. Algorithmic bias, lack of transparency, accountability, data privacy and human autonomy are among the key ethical challenges. These findings highlight the importance of developing strong ethical frameworks, interdisciplinary cooperation and constant oversight when deploying AI.

The evaluation of creditworthiness and loan eligibility remains a fundamental yet complex task within financial institutions. Traditional loan assessment processes often involve manual analysis of applicant profiles, which can be time-consuming, prone to inconsistencies, and influenced by subjective judgment. To address these limitations, this study presents the development of an intelligent, automated loan approval prediction system that leverages machine learning techniques to enhance decision-making efficiency and reliability. The proposed model employs a K-Nearest Neighbors (KNN) classification algorithm, trained on synthetically generated datasets designed to simulate realistic applicant scenarios. This system is integrated into a web-based platform built using Flask, HTML, CSS, and JavaScript, enabling users to interactively submit personal and financial information. Upon submission, the model evaluates the application and determines the likelihood of loan approval. For approved applications, the system further computes an estimated loan amount, tenure, and monthly instalment using dynamic interest rate inputs. A distinguishing feature of the system is its ability to provide interpretable feedback. Users receive either a detailed breakdown of loan terms in the case of approval or a clear explanation supported by visual analytics when the application is declined. This transparency fosters user understanding and builds trust in the automated process. The proposed solution demonstrates the potential to improve the accuracy, consistency, and speed of loan processing in financial institutions. Moreover, its explainable nature aligns with the principles of responsible AI in financial technology applications.

Resumo: A defesa de que o legislador preveja normas legais próprias e distintas para os contratos empresariais ressurgiu nas últimas décadas por força do projeto de Código Comercial, da Lei de Liberdade Econômica e de partes do Projeto de reforma do Código Civil. O artigo observa que esse ressurgimento é movido por novos interesses, particularmente a busca por menos intervenção judicial nas relações empresariais. Analisa-se, então, se a reforma legislativa, do modo geral, é meio idôneo para atender a esses interesses e, em particular, o conteúdo das normas propostas na iniciativa mais recente de reforma. Ao final, são abordadas outras soluções possíveis, envolvendo a esfera doutrinária e jurisprudencial, para o problema da arbitrariedade das decisões judiciais.

Abstract: The argument that the legislator should provide distinct legal rules for business contracts has resurfaced in recent decades due to the draft of a Commercial Code, the Economic Freedom Law and parts of the Civil Code reform project. The article notes that this resurgence is driven by new interests, particularly to reduce judicial intervention in business relationships. Then it analyzes whether the legislative reform, in general, is an appropriate means to meet these interests and, in particular, the content of the norms proposed in the most recent reform initiative. Finally, other possible solutions are addressed, involving the doctrinal and jurisprudential sphere, for the problem of arbitrariness of judicial decisions.

The rapid evolution of digital technologies has fundamentally reshaped consumer behavior, presenting new challenges and opportunities for marketing and management decision-making. This study examines how the digital age has transformed consumers' purchasing patterns, information-seeking behavior, and brand engagement. Employing a mixed-methods approach that combined surveys of 500 consumers with in-depth interviews of marketing professionals, the research explored key behavioral trends influenced by digital platforms, social media, and mobile technologies. The findings reveal that consumers in the digital era exhibit heightened expectations for personalized experiences, increased reliance on peer reviews and digital communities, and a preference for seamless online-offline brand interactions. These behavioral shifts demand that marketers adopt data-driven strategies and that managers rethink customer engagement models to remain competitive. The study concludes that organizations must integrate consumer insights into strategic planning, leverage technology for personalized marketing, and prioritize agility in decisionmaking processes. These adaptations are critical for sustaining relevance in an increasingly digital and consumer-driven marketplace.

Emphasizing hyper-personalization enabled by Artificial Intelligence (AI) algorithms that drive meaningful engagement with the clients in the production of state-of-the-art investment strategies and advice to leverage the sensitivity of client portfolios to systematic risksprovides asset management firms with potentially unprecedented differentiation opportunities. However, practical implementation challenges require execution through a multi-faceted approachenhancing back-end data, systems infrastructure, and data science capabilities; re-purposing client engagement models and front-end tools; refining talent requirements, workflows, and processes; as well as addressing important ethical considerations regarding data usage, advice quality, and client privacy. Such differentiation initiatives can be fueled by embedding lower-cost AI-driven data collection and processing techniques that channel and sort the digital exhaust created by clients in their high-frequency interactions with the broader worldand tie them to actionable dynamic financial strategies that aim to translate the AIenhanced understanding of their motivations and triggers into timely portfolio repositionings to protect the upside and downside for both aggressive and defensive investors during "normal" and "bad" market regimes respectively . As such, this chapter offers a present-day reflection on how sector players, at the leading edge of such industry transitions, are addressing these new avenues for differentiation, illustrating lessons from best practices, and discussing the challenges encountered, through the lens of client engagement models and front-end tools, as well as the backend challenges associated with creating the required predictive data science infrastructure. However, traditional providers of asset management services that are considering enhancing existing offerings face a conundrum. On the one hand, amidst declining revenues, greater regulatory scrutiny, and rising competitive pressuresfrom Deep Science Publishing

This systematic review investigates the strategic application of engagement marketing in digital platforms, focusing on its impact on return on investment (ROI) and consumer psychology. Drawing from a comprehensive analysis of 87 peer-reviewed articles published between 2005 and 2024, this study synthesizes existing literature to uncover the mechanisms, measurement models, and ethical dimensions of digital engagement. The review reveals that engagement is a multidimensional construct encompassing emotional, cognitive, and behavioral dimensions, with emotional resonance and psychological alignment playing a central role in fostering meaningful consumer-brand interactions. Advanced ROI measurement frameworks such as customer lifetime value (CLV), multi-touch attribution models, and sentiment analytics have emerged as superior alternatives to traditional metrics like impressions or click-through rates. Moreover, platform-specific strategies tailored to the structural and algorithmic features of social media channels like TikTok, Instagram, Facebook, and LinkedIn are shown to be more effective in generating sustained user engagement. Cultural variability, symbolic representation, and ethical considerations especially regarding data privacy, manipulation, and algorithmic bias further shape engagement practices in global contexts. The study also highlights the rise of prosumerism, wherein consumers act as cocreators of content and community narratives, contributing significantly to brand visibility and authenticity. Regulatory frameworks such as GDPR and CCPA have begun to reshape engagement strategies, emphasizing transparency, user consent, and ethical responsibility. Overall, this review positions engagement marketing not merely as a tactical function but as a strategic, culturally adaptive, and ethically grounded approach that is central to digital brand success. The findings contribute to a more holistic understanding of engagement as both a measurable business outcome and a human-centered experience, offering insights for marketers, researchers, and platform developers seeking to optimize digital interaction in an increasingly complex media landscape.