Shanlax International Journal of Management

Responsible and Ethical AI in Management: Governance Frameworks, Bias Mitigation, Transparency, and Strategic Policy Implications

Archana Ambekar — 2026-03-25

The growing adoption of Artificial Intelligence (AI) in organizational decision-making has significantly reshaped managerial practices across functional domains such as human resources, marketing, finance, and strategic management. While AI-driven systems enhance operational efficiency, predictive analytics, and competitive advantage, they simultaneously raise critical ethical, governance, and regulatory concerns. Issues such as algorithmic bias, lack of transparency, limited accountability, and regulatory non-compliance present substantial risks to organizational credibility and stakeholder trust. This research paper explores the concept of Responsible and Ethical AI in management, focusing on governance frameworks, bias detection and mitigation strategies, transparency and interpretability mechanisms, and the strategic policy implications of regulatory compliance.
The study first examines frameworks for responsible AI governance within organizations. Effective AI governance requires structured oversight mechanisms that integrate ethical principles into AI design, deployment, and monitoring processes. The research highlights the importance of establishing AI ethics committees, cross-functional governance boards, risk assessment models, and internal audit systems to ensure responsible decision-making. Embedding AI governance within broader corporate governance structures enhances executive accountability and aligns AI initiatives with organizational values and long-term sustainability goals.
A key dimension of the study is the detection and mitigation of ethical bias in AI systems. AI models often rely on historical data that may reflect social and institutional biases, leading to discriminatory outcomes in recruitment processes, credit approvals, targeted marketing, and financial risk assessments. The paper evaluates various bias detection tools, including fairness metrics, algorithmic audits, and data validation techniques. It also discusses mitigation strategies such as diverse data sourcing, model retraining, human oversight mechanisms, and continuous performance monitoring. The research emphasizes that bias management must be treated as an ongoing organizational responsibility rather than a purely technical adjustment.
Transparency, accountability, and interpretability are identified as essential pillars of responsible AI management. Complex “black-box” algorithms can obscure decision logic, making it difficult for managers and stakeholders to understand or challenge AI-driven outcomes. The study explores explainable AI (XAI) techniques that enhance interpretability without compromising performance. Transparent documentation, impact assessments, and disclosure policies are proposed as mechanisms to strengthen stakeholder confidence and managerial control. Ensuring accountability through clear role definitions and decision-review processes further supports ethical AI deployment. The research also investigates the policy implications of regulatory compliance and AI ethics in corporate strategy. Emerging global regulations on data protection, algorithmic fairness, and digital accountability require organizations to align AI initiatives with legal standards. Proactive compliance not only reduces legal and reputational risks but also fosters sustainable innovation and competitive advantage. Ethical AI practices can strengthen brand reputation, improve customer trust, and enhance employee engagement.

Inclusive HR Policies and Workplace Equality: A Framework for Organizational Success

M Raga Keerthika — 2026-03-25

This paper highlights the relationship between human resource policies and workplace equality in the organization. By studying organizational based Case studies and reviewing experimental research. This paper establishes the relationship between the HR policies and how they are engaged in the organization. The policies are related to organizational performance and retention in the workplace. This paper studies and identifies the key dimensions of workplace inclusivity in the Organization which are represented by equitable pay structures, gender and anti-discrimination HR policies. The findings of the paper indicates that Organizations need to essentially implement Diversity, Equity and Inclusion initiatives in their workplaces. By implementing DEI initiatives, it demonstrates a better financial performance, stronger employee branding and increased innovation. This paper offers an evidence-based recommendations for the HR leaders, enabling them to institutionalize equality as a workplace strategic organizational priority.

AI-Driven Adaptive Trust Management Framework for Secure Vehicular Networks in Smart Cities Using NS-3 and SUMO Co-Simulation

R Nirmala — 2026-03-25

To address the security challenges of Vehicular Ad Hoc Networks (VANETs) in smart cities, which arise from their high mobility and decentralization nature, robust and adaptive security mechanisms are essential. This paper introduces an AI-Driven adaptive trust management framework. Moving beyond the traditional perimeter-based security, the proposed solution integrates Zero-Trust principles with entropy-weighted trust scoring and edge assisted decision-making. This was developed using integrated SUMO mobility modeling and NS-3 simulations. This framework specifically targets the Sybil, replay and data falsification attacks. Extensive experiments involving 500 to 5000 vehicles and 20 roadside units helps to demonstrate the greater performance over the centralized and the static models. While reducing the authentication latency by 71% the system achieves 94.1% detection accuracy. Furthermore, the framework scales effectively to 10,000 nodes by maintaining high efficiency in dense environments. A one-way ANOVA confirms the statistical significance of these results by validating the system as a robust and a scalable security solution. This approach significantly enhances both the network reliability and the real time responsiveness.

A Study to Understand Consumer Perception on the Role of AI-Driven ‘Trending Now’ Recommendations in Apparel Retail

V Michael Belwin Raj — 2026-03-25

The use of the Artificial Intelligence (AI) technologies in the apparel retail business has dramatically changed the industry, especially in online and omnichannel retailing platforms. Among other AI uses, Trending Now recommendation features have become popular as mechanisms that control consumer browsing and product discovery, as well as purchase choices. These machine recommendations emphasize the most popular and frequently bought products as they happen, inform consumers about the relevance of the product, social evidence, and fashionability. The perception of consumers with such AI-driven recommendations can be seen as the key to successful retail strategy and brand positioning in an extremely competitive apparel market.
The paper is intended to explore the consumer perception of the role of AI-based Trending Now recommendation in retailing apparel using secondary research methodology. The research is premised on a systemic review and thematic analysis of the available literature generated by the peer review journals, books, and the industry reports published in the years between 2018 and 2024. The review allows combining the knowledge concerning AI-driven recommendation systems, consumer perception, and decision-making, trust in the retail situations. According to the literature findings, AI-powered recommendations of Trending Now have a beneficial impact on perceived usefulness, convenience, and purchase confidence, and also, bring up the issue of transparency and bias in the algorithm. The research is relevant to the literature because it presents the combined conceptual knowledge of the influence of AI-based recommendation cues on consumer perception in apparel retail. The managerial implications point to the necessity of ethical implementation of AI, balance of personalization, and transparency that will help to boost consumer trust and retail efficiency.

AI-Driven Social Media Content Strategies and Brand Image of Indian E-Commerce Brands

Lida Merolin — 2026-03-25

The high rate of digital technology development has greatly changed the marketing communication especially in the e-commerce market. The social media platforms have become very important platforms through which the brands can communicate and relate with the consumers, and create perceptions of the brands. Artificial Intelligence (AI) has become more actively incorporated into social media marketing over the last few years to improve the personalization of the content, interaction with the consumer, and decision-making processes. The use of AI-based social media content strategies in influencing brand image is very important in the Indian e-commerce market because the competition is stiff and the consumer is digitally empowered. This paper will be an attempt to analyze how AI-based social media content strategies, such as informational, promotional, and interactive, impact the brand image of Indian e-commerce brands with the use of secondary research method. It is founded on the systematic review and thematic analysis of the available literature that was published by the time of 2018 and 2024 in peer-reviewed journals, academic books, and industry reports. The results of the literature suggest that AI-based personalization and interactive content are highly valuable to consumer engagement, trust, and favorable brand associations, and too much promotional material can have a negative influence on perceived authenticity. The research is significant to the existing knowledge in that it offers a comprehensive conceptual knowledge of AI-based content strategy and brand image of the Indian e-commerce industry. Managerial implications demonstrate that it is crucial to use AI ethically, balance academic content and focus on consumers to make a brand sustainable.

A Study of Consumer Behavioral Responses to AI-Driven Sales Promotion in Physical Retail Stores

P Maheswarar — 2026-03-25

The introduction of Artificial Intelligence (AI) in retailing marketing has greatly changed the way retailers make and execute sales promotion strategies. AI technologies can help retailers analyze high amounts of consumer data, forecast buying behaviors, and send personalized promotional messages in their real-time. AI-based sales promotion, including personalized discounts, smart suggestions, digital displays, and location-based offers, has gained significant dimensions in the physical retail stores and has become more crucial in driving consumer behavior. This paper provides secondary research where the researcher analyzes the consumer behavioral reaction to AI-based sales promotion in the physical retail setting. The study relies on the systematic review and thematic analysis of the literature on peer-reviewed journals, books, and industry reports that are published between 2016 and 2024. The results indicate that AI-inspired promotion tactics increase consumer interest, perceived value, intention to purchase and impulse buying. Individual promotions with the help of AI enhance customer experience, providing customers with pertinent offers, depending on personal preferences and buying history. Nevertheless, the issues concerning privacy, transparency of the data, and the use of algorithms to make decisions can contribute to consumer distrust in marketing via AI. The research is relevant to the existing literature as it incorporates knowledge on artificial intelligence, sales promotion, and consumer behavior in relation to the physical retail stores. The results are also related to the managerial implications of retailers who are willing to implement AI-based promotional approaches to promote consumer satisfaction and competitive strength in the dynamic retailing environment.

From Resistance to Readiness: Healthcare Workforce Adaptation in AI- Driven Digital Transformation

Retheka Saravanan — 2026-03-25

Artificial Intelligence (AI) has been identified as one of the key change drivers in the evolution of health care facilities across the globe. However, the implementation of AI-based digital technology is not only the advancement of technology but also the workforce’s alignment with it. Though AI- based digital technology is increasingly becoming popular in health care facilities across the globe, workforce resistance to its implementation remains a major issue in its sustainability. This study aims to identify the perceptions of employees in Bengaluru on the prospects and limitations of AI- based digital technology in health care facilities and the major factors contributing to workforce resistance. The study employed a qualitative methodology. Fifteen employees from various departments of a health care facility were interviewed through in-depth interviews, including doctors, nurses, technicians, administrative staff, and IT employees. The findings revealed seven major factors contributing to workforce resistance: dependency on digital technology, reliability of digital technology, data security apprehensions, training perceptions, emotional attachment to traditional methods, alignment of technology with tasks, and uncertainty regarding performance outcomes.
Employees are aware of the operational efficiency of AI technologies, particularly in improving workflow efficiency. However, resistance escalates when technology reliability is low, organizational support is limited, and change is not communicated in a structured manner. Perceived usefulness alone was not adequate to prepare the workforce. The research contributes to the human capital discourse in the broader AI adoption movement by offering a framework on management strategies involved in the transition from resistance to readiness.

AI Governance and Leadership Emerging Roles, Organizational Structures, Enterprise Frameworks, and Ethical Deployment

A Rekha — 2026-03-25

The accelerating integration of Artificial Intelligence (AI) into core business functions has elevated governance and leadership to strategic imperatives for contemporary organizations. As AI systems increasingly influence decision-making in operations, finance, marketing, and human resources, firms must establish robust leadership roles and governance mechanisms to manage innovation, mitigate risks, and ensure ethical deployment. This research paper examines the evolving landscape of AI governance and leadership, focusing on emerging executive roles such as the Chief AI Officer (CAIO), organizational structures that support AI innovation and risk management, enterprise-wide governance frameworks, and the importance of ethical leadership in AI implementation.
The study begins by analyzing the emergence of specialized leadership roles, particularly the Chief AI Officer, as organizations seek to institutionalize AI strategy and oversight. The CAIO is positioned as a strategic leader responsible for aligning AI initiatives with corporate objectives, managing AI portfolios, ensuring regulatory compliance, and fostering cross-functional collaboration. Unlike traditional IT leadership roles, the CAIO integrates technological expertise with strategic vision, ethical accountability, and organizational transformation capabilities. The research evaluates the effectiveness of this role in driving innovation, enhancing competitive advantage, and reducing governance gaps. It further explores how leadership commitment at the executive and board levels influences the success of AI adoption initiatives.
The paper also investigates organizational structures that facilitate AI innovation while managing associated risks. Effective AI governance requires cross-functional collaboration among data scientists, legal experts, risk managers, HR professionals, and business leaders. Matrix structures, AI centers of excellence, and innovation hubs are identified as structural mechanisms that promote experimentation while maintaining oversight. The study highlights the importance of clearly defined accountability frameworks, risk assessment protocols, and reporting mechanisms to ensure transparency in AI-driven decision-making. Organizational culture plays a critical role in supporting responsible AI adoption, requiring openness to innovation alongside strong ethical standards.
A central component of the research focuses on governance frameworks for enterprise-wide AI adoption. As AI systems scale across departments, organizations must develop standardized policies addressing data governance, algorithmic fairness, cybersecurity, model validation, and lifecycle management. The study proposes an integrated AI governance framework that includes strategic alignment, risk management, compliance monitoring, stakeholder engagement, and performance evaluation. Continuous auditing and monitoring systems are emphasized as essential tools for identifying bias, ensuring reliability, and maintaining accountability. Enterprise governance frameworks also require alignment with global regulatory standards and evolving digital policies to mitigate legal and reputational risks.
Ethical leadership emerges as a foundational pillar in AI deployment. Leaders play a critical role in embedding ethical values into AI strategy, ensuring fairness, transparency, and accountability in algorithmic systems. Ethical leadership involves proactive bias mitigation, inclusive decision-making processes, and transparent communication with stakeholders regarding AI usage. The research underscores that ethical AI is not solely a technical responsibility but a leadership-driven commitment that influences organizational trust, employee morale, and customer confidence. Leaders must balance innovation with social responsibility to sustain long-term organizational legitimacy.

A Study on AI in E - Commerce Personalization

TA Poornima — 2026-03-25

The quick development of artificial intelligence (AI) has drastically changed the e-commerce industry, altering how companies engage with clients and provide tailored experiences. This study looks at how AI-driven personalisation can improve consumer satisfaction, engagement, and conversion rates on digital commerce platforms. Personalisation has become a strategic necessity for competitive differentiation due to the exponential rise of online marketplaces like Amazon, Alibaba Group, and eBay. AI technologies, such as machine learning algorithms, natural language processing, and predictive analytics, allow platforms to evaluate enormous amounts of consumer data in order to provide intelligent search results, dynamic pricing, personalised marketing messages, and product suggestions.
This study uses a mixed-methods approach, integrating consumer surveys and industry experts’ qualitative views with quantitative analysis of user interaction data. The study looks into how AI models employ contextual, transactional and behavioural data to forecast consumer preferences and enhance user experiences. Collaborative filtering, content-based filtering and deep learning models for recommendation systems are important analytical methods. The results show that important performance metrics like click-through rates, average order value and customer retention are greatly enhanced by AI-powered personalisation.
However, the report also highlights important obstacles to the application of AI in e-commerce, such as issues with algorithmic bias, data protection, transparency and the moral use of customer data. In order to strike a balance between innovation and consumer trust, regulatory frameworks and data protection regulations are essential. In order to guarantee ethical personalisation techniques, the research emphasises the significance of explainable AI models and strong data governance strategies.

A Study on Impact of Artificial Intelligence on Demand Forecasting on Supply Chain Management

B Likitha — 2026-03-25

By greatly increasing prediction accuracy, operational efficiency, and strategic responsiveness, artificial intelligence (AI) is revolutionizing demand forecasting in supply chain management. Conventional forecasting methods frequently fall short of capturing intricate and nonlinear demand patterns impacted by quickly shifting market conditions since they mainly rely on statistical models and past sales data. Organizations can evaluate enormous amounts of organized and unstructured data, including customer purchase patterns, economic indicators, seasonal trends, and external market signals, thanks to AI-driven techniques like machine learning, deep learning, and predictive analytics.
AI improves prediction accuracy, lowers uncertainty, and lessens the risks related to excess inventory and stock outs by utilizing sophisticated algorithms and real-time data processing. Better distribution networks, more effective production scheduling, better procurement planning, and optimum inventory control are all directly impacted by improved demand forecasts. Additionally, AI increases supply chain resilience and adaptation in unstable global situations by facilitating dynamic decision-making through ongoing learning and scenario analysis..
Despite its advantages, the adoption of AI in demand forecasting presents challenges including data quality issues, integration complexity, high implementation costs, and the need for skilled personnel. Nevertheless, AI-powered forecasting systems represent a critical innovation for achieving competitive advantage and sustainable performance in modern supply chain management.

A Study on the Impact of Influencer Marketing on the Generation

GS Gagana — 2026-03-25

Influencer marketing has emerged as a powerful digital strategy that significantly shapes consumer behavior, particularly among younger generations With the rapid growth of social media platforms such as Instagram, YouTube, and TikTok, influencers have become key opinion leaders who affect purchasing decisions, brand perceptions, and lifestyle choices This study examines the impact of influencer marketing on Generation Z and Millennials, focusing on factors such as credibility, authenticity, relatability, and trust It explores how influencer-generated content drives engagement, brand awareness, and purchase intention compared to traditional advertising methods The findings suggest that influencer marketing strongly affects brand loyalty and consumer decision-making due to perceived personal connections and peer-like communication styles However, issues such as sponsored content transparency and influencer credibility also influence consumer skepticism
Overall, the study highlights the growing importance of influencer marketing in shaping generational consumption patterns and emphasizes the need for ethical and strategic implementation by brands

A Study on Artificial Intelligence (AI) in Job Transformation

L Charan — 2026-03-25

The fast development and application of Artificial Intelligence (AI) technology is radically changing the global labour market by rethinking organisational structures, employment positions, and skill needs. The dual processes of automation and augmentation are the main subject of this study, which explores the revolutionary effects of AI on employment across industries. AI-driven systems improve human capacities by facilitating sophisticated decision-making, data analysis, and innovative problem-solving, even as they increasingly automate repetitive, predictable, and routine jobs. The study examines how this technological change is reorganising employment rather than just destroying them, resulting in new occupational categories, task reconfiguration, and hybrid human-AI collaboration models.
The report finds trends in workforce transformation using cross-sectoral data from manufacturing, healthcare, finance, retail, and information technology. While jobs requiring emotional intelligence, flexibility, and sophisticated cognitive abilities show resilience and growth, routine physical and clerical tasks are more vulnerable to automation. Furthermore, the emergence of AI has created a need for new positions like data scientists, machine learning engineers, AI ethicists, and AI governance experts, indicating a dramatic change in the nature of the labour market.
The impact of AI adoption on skill gaps, wage polarisation, and worker inequality is further examined in the research. It contends that AI-driven change could worsen socioeconomic inequality in the absence of focused legislative changes and inclusive upskilling programs. Simultaneously, workers can move into higher-value professions through smart investments in education, digital literacy, and lifelong learning. In order to ensure that technology enhances rather than replaces human expertise, the study highlights the significance of organisational policies that prioritise human-centred AI integration.
The study addresses ethical and legal issues, such as algorithmic bias, accountability, transparency, and data privacy in workplace systems, in addition to economic effects. In AI-enabled workplaces, these factors are essential for fostering sustainability and trust.
In the end, the results point to an evolutionary rather than solely destructive impact of AI on employment. Collaboration between humans and intelligent systems will probably define the nature of employment in the future, necessitating flexibility, ongoing education, and legislative frameworks that strike a compromise between social protection and innovation. This report adds to the continuing conversation on technological change by offering a thorough analysis of AI-driven job transformation and makes suggestions for legislators, educators, and business executives navigating the changing nature of the workplace.

A Study on AI in Media, Communication, and Culture

A Keerthana Shree — 2026-03-25

By changing how content is produced, disseminated, and understood in digital contexts, artificial intelligence (AI) is radically changing media, communication, and culture. Artificial intelligence (AI) tools like recommendation engines, automated journalism, and predictive analytics improve user experiences and expedite production processes in the media sectors. By using machine learning algorithms to manage content, evaluate audience behaviour, and maximize engagement, platforms like Netflix and Google are rethinking traditional editorial positions and gatekeeping procedures.
Chabot’s, virtual assistants, speech recognition, and real-time translation are examples of AI-powered communication tools that enable quicker, more accessible international connection. Although these systems increase productivity and diversity, they also raise issues with data privacy, algorithmic bias, surveillance, and false information. Algorithmic filtering and AI-driven content moderation are affecting social movements, political narratives, and cultural visibility in public discourse.
AI challenges conventional ideas of authorship, originality, and intellectual property by expanding creative possibilities through generative art, music composition, and automated video production. AI undermines creative labour markets and cultural authenticity while democratizing content creation and enabling new kinds of storytelling. In order to promote responsible innovation and inclusive cultural evolution in an increasingly automated world, critical thought on ethics, governance, and digital equality is crucial as AI becomes ingrained in everyday communication ecosystems.

A Study on The Impact of Artificial Intelligence on Quick Commerce

Shawar Saleem — 2026-03-25

Quick- Commerce is redesigning the entire retail sector by promising deliveries of the products and other essentials in less than 10 to 30 minutes. Its rapid growth in the urban markets shows how the consumers give value to speed and convenience. Operating such quick- delivery system requires a real-time decision making, effective logistics and personalised client requirements. The role played by AI (Artificial Intelligence) is very significant as it changes the entire consumer experiences and provides the operational backbone to the industry.
In this paper the researcher has explored how Artificial Intelligence is changing the marketing effectiveness and the various operational efficiencies in the quick-commerce sector. The paper also brings to light some of the important technologies like various personalization engines, predictive analysis, the dynamic pricing strategies and the route optimization. Personalization driven AI significantly increases consumer satisfaction and their loyalty (Haneefa et.al, Gupta et.al). From the operational side AI increases the speed of delivery and the management of inventory (Dasarraju,2024, Taghipour et.al,2025). This is not free from challenges there exist issues related to transparency, fairness and data privacy being the main challenging concern (Munshi et.al,2025, Naik et.al,2025).

The Impact of Virtual Influencers: Emerging Trends and Opportunities

K Shobitha Kumari — 2026-03-25

With the introduction of technology, Marketing has drastically changed over time. From traditional marketing to digital marketing now we are in era of virtual influencers. Emerged This report has combines evidences from 25 scholarly papers to identify the effectiveness of virtual influencers across various industries. The analysis reveals that virtual influencers also able to capture the consumer trust if they have good content quality, and authenticity in the post with disclosure details. As human influencers have already captured the market with these dimensions, when it comes to virtual also same dimensions are inculcated to capture the market. Key findings of the study indicate that virtual influencers can match or even can exceed human performance when properly presented in front of consumers. This research contributes to the growing body of knowledge which navigates through direction which is rapidly Gowing domain.

Urban Indian Millennials Have Low Consumer Trust in AI-Generated Rapid Commerce Advertisements on YouTube Shorts

JN Darshan — 2026-03-25

The rapid integration of Artificial Intelligence (AI) in online marketing has altered the interactions between the brand and the consumer. The automatic creation of advertisements has become an absolute need in the high-speed quick commerce industry in India instead of a technological choice. Generative AI tools are growing in popularity on platforms such as Blinkit, Zepto, and Swiggy Instamart and are primarily used to generate high-frequency and personalized short video ads, primarily shown in YouTube Shorts. Although the application of this technology enhances scalability, cost-saving and hyper-personalization, it also leads to issues of authenticity, transparency, and ethical disclosure among conscious consumers.
The primary viewers of quick commerce services are urban Indian millennials, 18-35 years of age, who are particularly wary of AI-induced communication. However, they do value convenience and speed, but this population group is getting more suspicious of AI-generated advertisements that are not labeled or synthetic. This conflict is efficiency or trust and that is the central topic of this research. The study examines the influences on consumer trust in AI-generated quick commerce creatives on YouTube Shorts and the impact of the perceived usefulness, ease of use, and transparency labeling on trust, brand attitude, and purchase intention.
The paper integrates the Technology Acceptance Model (TAM) with such attributes of trust as benevolence, integrity, and competence to develop a larger framework that can be applicable in the context of AI-driven advertising. The proposed research design will be the quantitative cross-sectional research, where data will be gathered among 300 urban millennials in Bengaluru, Mumbai, and Delhi. The relationships between the variables will be analyzed with the help of Partial Least Squares Structural Equation Modeling (PLS-SEM).
The reason is that the expected results suggest that the application of transparency labelling, in other words, AI-Made disclosures, have a strong positive impact on perceived integrity and general trust, which is a solid predictor of purchase intention. The study contributes to the theory, as it uses TAM in the context of synthetic media environments and offers realistic conclusions that can be applied to fast commerce brands desiring to enhance AI-based marketing without losing consumer confidence. Making AI trust a part of the fast-moving quick commerce scenario in India, the study will solve significant knowledge gaps in the academic literature and in the industry application.

Role of Artificial Intelligence in Customer Acquisition and Customer Retention in the Insurance Industry: A Comprehensive Empirical and Theoretical Study in the Indian Context

S Nandagopal — 2026-03-25

The concept of Artificial Intelligence (AI) has become one of the most powerful technological innovations that transform the modern business landscape, especially in the industry of financial services where data-based decision-making has become a key priority. The insurance industry, which is marked by its unique characteristics of manual labor, interaction through intermediaries, and complex administrative procedures, is witnessing a major transformation with the advent of intelligent technologies. In the insurance domain, AI-based technologies are becoming increasingly popular for assisting insurance firms in optimizing their operations and customer relationship management and improving competitive advantage. Although there is a significant focus of scholars on exploring the facets of efficiency and risk management capabilities of AI application, there is a scarcity of research works on exploring the impact of AI on customer lifecycle outcomes, customer purchase and customer retention, in a developing economy, India.
This paper aims at exploring the relationship of AI application with customer lifecycle outcomes in the Indian insurance domain. For developing the research framework, there is a significant integration of Relationship Marketing Theory, Technology Acceptance Model (TAM), and Customer Lifetime Value (CLV) theory.
The research design was also quantitative, with primary data being gathered with 300 insurance policyholders who had already experienced the AI-powered insurance service, which can be chatbots, automated onboarding systems, digital policy recommendation engines, and AI-assisted claims processing platforms. To test the hypothesized relationships among variables, the statistical analyses, such as reliability testing, correlation analysis, regression modeling, and mediation analysis, were performed.
The results demonstrate that the adoption of AI boosts the effectiveness of customer acquisition by many folds, through providing personalized communication, decreasing the complexity of the procedure, and enhancing the availability of insurance services. Moreover, the use of AI positively affects customer retention intentions, which is mostly achieved via the enhancement in the responsiveness of the services and quality of customer experience. The level of customer satisfaction was found to be a partial mediating variable when technology innovation is applied to customer retention and adoption outcomes, implying that technology innovation can produce loyalty mainly via the enhancement of perceived service value. The theoretic relevance of the study lies in the fact that it combines two different views, technological acceptance and relationship marketing, in one empirical framework. In practice, the results are strategic guidance to the insurance companies that want to apply AI responsibly without losing customers and sustaining relationships. In general, the study demonstrates that AI is an operation-enabling and relationship-enhancing tool that can transform the approaches to customer management in the changing insurance ecosystem in India.

Analyzing the Effectiveness of Multimedia Advertising Campaigns on Audience Reach and Recall a Study of Red Bull

A Bhargav — 2026-03-25

Within the modern media setting, brands are turning to multimedia-based advertisement campaigns to attract the interest of the audience and improve message recall. In this paper we used the Hierarchy of Effects Model and the Integrated Marketing communications (IMC) model in order to research the significant impact of exposure to multimedia advertisements on the perception of the consumers towards the reach of the audience and brand recognition. The model indicates that exposure of the multimedia campaigns created by Red Bull, which includes television, digital video, social media, and experience events, as well as branded content has a direct relationship on the audience reach, which in turn has an indirect relationship with the recall of the message created by the brand. This research will adopt a quantitative research design approach that will involve the use of structured questionnaires to sample out young adults between the ages of 18 and 35 years in India and their exposure to the advertisement of Red Bull. The major results will be that multimedia advertising will perform much better than the single-media campaigns with different levels of performance being differentiated by the platform. Using established concepts of communication, the hypotheses proposed prove the concepts of cross-platform synergy as effective in creating increased awareness, as well as being a type of catalyst to cognitive message retention.

The Role of AI-Driven Personalization in Brand Storytelling and Its Impact on Consumer Engagement in D2C Brands

Meenamrutha Chereddy — 2026-03-25

Artificial Intelligence (AI) is now a disruptive technology in digital marketing, especially in the support of providing customers with personalized experiences. Direct-to-Consumer (D2C) brands are heavy users of online services in engaging consumers and developing long-term relationships with the consumers. Brand storytelling is one of the best methods employed by these brands as one of them because it lets a company share its values, identity, and mission in the form of captivating stories. Storytelling is more impactful when integrated with AI-based personalization since the marketing messages will be customized based on the preferences and behaviors of different consumers.
The research will discuss how AI-based personalization can be used to tell brand stories and determine how it affects consumer interest in D2C brands. As a research method, the study is based on a secondary data research, which means that the authors examine the existing academic materials, industry reports, and scholarly articles devoted to the topics of artificial intelligence, digital marketing, personalization and consumer engagement. The results suggest that AI technologies, including machine learning algorithms, recommendations system, predictive analytics, and generative AI, are important in facilitating the improvement of personalized storytelling experience. These technologies help the brands to analyze the information on the consumers, know their behavioral patterns and provide personalized information that is appealing to the consumer.
Another point that is raised in the study is that AI-based storytelling enhances consumer engagement through relevancy, emotional attachment, and interactions between a brand and a consumer. Nonetheless, privacy issues, data safety and ethical factors are some of the challenges that organizations should grapple with. The research reaches the conclusion that AI-based personalization can be an effective solution that D2C brands can use to enhance consumer interaction and build brand relationships in online spaces. Further studies can also apply empirical validation with primary data and can examine how AI-driven storytelling can affect consumer loyalty and brand, in the long run.

The Economics of AI Chatbots in Retail: Cost Savings Vs. Brand Loyalty

Maitri Manjunath — 2026-03-25

The rapid integration of Artificial Intelligence (AI) in retail has significantly transformed customer service operations. This study examines the economic impact and strategic role of AI chatbots in the retail sector, with a particular focus on cost efficiency and customer loyalty. The research explores how AI-powered chatbots help retailers reduce operational costs, improve response time, and enhance customer engagement through automated communication systems.
Primary data was collected through a structured questionnaire from 200 online shoppers to evaluate awareness, satisfaction levels, and preferences regarding chatbot services. The findings reveal that a majority of respondents are aware of AI chatbots and generally express satisfaction with their performance, particularly for simple queries and quick responses. However, the results also indicate that customers still prefer human assistance for complex issues, highlighting the importance of maintaining a balance between automation and human interaction.
The study further analyzes the relationship between chatbot efficiency and brand loyalty. While many respondents believe that efficient chatbot services positively influence loyalty, some customers feel that excessive automation reduces emotional connection with brands. These findings suggest that although AI chatbots provide measurable economic benefits through cost reduction and operational efficiency, their long-term effectiveness depends on service quality, personalization, and trust.
The research concludes that a hybrid service model combining AI chatbot technology with human support offers the most effective strategy for retailers. Such an approach enables businesses to maximize cost savings while maintaining strong customer relationships and long-term brand loyalty.

Emerging Technologies: Transforming Society

Sushanth S Shetty — 2026-03-25

Emerging technologies are rapidly transforming modern society by changing how people communicate, work, learn, and access services. Innovations such as artificial intelligence, robotics, blockchain, and biotechnology are creating new opportunities for economic growth, enhanced healthcare, and increased efficiency across various industries. These technologies support automation, data analysis, and digital communication, enabling organizations to enhance productivity and improve their decision-making processes. However, the rapid growth of technology also raises important challenges. Issues such as job displacement due to automation, privacy risks, cybersecurity threats, and the digital divide are becoming significant concerns for society. This paper examines the major emerging technologies and analyses their positive and negative social implications. The study uses qualitative research based on secondary data collected from books, research articles, and online sources. The findings show that while emerging technologies provide significant benefits such as innovation and economic development, they also require careful management and regulation. Governments, educational institutions, and organizations must adopt responsible policies and promote digital literacy to ensure that technological progress benefits all members of society. Understanding the social implications of emerging technologies is therefore essential for achieving sustainable and inclusive development.

Societal Impacts of Artificial Intelligence: Opportunities, Risks and Ethical Challenges

C Rishik — 2026-03-25

Artificial Intelligence (AI) has become one of the most transformative technologies of the 21st century, significantly influencing the social, economic, and cultural aspects of modern life. This paper examines the societal impacts of AI by exploring its opportunities, risks, and ethical challenges. AI is increasingly used in sectors such as healthcare, education, finance, transportation, and governance, helping improve efficiency, accuracy, and innovation. Applications like advanced medical diagnostics, personalised learning systems, automated decision-making, and smart infrastructure enhance human capabilities and support sustainable development.
However, the rapid expansion of AI also raises several societal concerns. Automation may lead to job displacement, while issues such as data privacy violations, cybersecurity threats, and algorithmic bias highlight the risks associated with AI systems. Additionally, limited transparency and the concentration of technological power among a few organisations create challenges related to accountability, fairness, and equal access.
AI systems operate by analysing large datasets to identify patterns and make predictions or decisions. While AI promotes economic growth and technological advancement, responsible development, ethical guidelines, and effective regulations are necessary to ensure that its benefits are distributed fairly across society.

Digital Note-Taking in Higher Education: A Thematic Analysis of Student Adoption, Benefits, and Learning Outcomes

M Subbu Arravind — 2026-03-25

Digital technologies have changed the way of teaching and learning in higher education institutions. In modern learning environments, learners increasingly use digital devices, including laptops, tablets, and smartphones, for their learning activities. In traditional learning environments, learners have used handwritten notes for writing down what they have learned in class and for summarizing key concepts. However, the growth of computer technology and internet -based learning platforms has encouraged students to adopt digital note-taking tools for organizing and managing academic information which help students store, organize, and retrieve academic information more efficiently in a productive manner. Multimedia, storage, and search, along with collaborative editing, are some other features of digital note-taking applications that could also add value to the learning experience.
Despite the increased rate of using digital tools of taking notes, the issue of whether these tools could add value to the learning experiences of students is still a matter of debate among educators and researchers. Some researchers argue that digital note-taking tools improve accessibility, productivity, and collaboration among students.
Other problems that were identified in other studies include digital distraction, reduced processes of cognitive information, and addiction to technology. As a result, the overall impact of the digital note taking practices of the higher education settings needs to be examined.
This study will discuss the use, benefits, and problems associated with the use of digital note taking tools in tertiary institutions. The research adheres to a qualitative research design and uses thematic analysis to put together findings of the existent academic literature on the subject of digital learning tools and note taking patterns. None of them were older than 2019 or later than 2025, and academic articles that were published during this period were analyzed to identify widespread patterns and themes that were relevant to the use of technology, learning outcomes, and implications of education.
As the result of the conducted analysis, the five major themes were defined such as the switching between the traditional handwritten notes and the digital note-taking technology, the problems that affect the use of digital technologies by students, the educational worth of utilizing digital note- taking technology, the barriers and limitations of the use of digital technologies in the learning process, and the future implications of digital note-taking to the higher education institutions.
The findings have revealed that digital note taking technologies possess a number of advantages including improved organization of academic knowledge, more convenient access to learning resources as well as enhanced collaboration among students. However, distractions on the Internet, cognitive overload, and disparities in access to the technology-related resources may be the factors that diminish the usefulness of these tools. The survey finds that a moderate approach that is achieved through adopting both the digital and traditional approach to taking notes may be the most beneficial way to provide the maximum learning to students in modern educational establishment.

Assessing the Influence of AI-Enabled HR Analytics on Employee Satisfaction in IT Organizations

B Vinutha — 2026-03-25

Another significant technological change which has had an impact on contemporary human resource management practices is the development of Artificial Intelligence. HR analytics based on AI are increasingly being used by organizations to analyze high amounts of information on employees and aid in their strategic decision-making regarding hiring, performance appraisal, employee engagement, and workforce planning. These analytics technologies are important in enhancing workforce management and employee experience in the IT sector where the competition on skilled professionals is fierce. Nevertheless, the success of AI-based HR systems is highly conditional upon the perception and acceptance of employee of these technologies in the work place. This paper considers how AI-powered HR analytics affects employee satisfaction in IT companies. The study also examines the role of perceived usefulness and ease of use on the attitude of employees towards such systems by use of the Technology Acceptance Model (TAM). The information was gathered by means of a structured questionnaire among IT professionals and was examined using statistical methods. The results show that positive attitudes toward AI-based HR analytics can help enhance employee satisfaction and technological acceptance of HR practices. The paper also emphasizes the role of transparency, fairness and good communication in the implementation of AI-based HR systems.

AI in Cybersecurity and Data Privacy: Emerging Trends and Techniques

Ramya P — 2026-03-25

AI has been adopting both the benefits and the challenges, even though it considers itself as an element of current cybersecurity and data privacy strategies. With this research aiming to analyse how Artificial Intelligence advances the responses automation, threat detection, strengthens the data protection methods and the ethical issues and also raises new privacy concerns. The research approach is based on surveys focussing on recent innovations, examining the practical applications of Artificial Intelligence applications based on cybersecurity scenarios. The studies establish how the various Artificial Intelligence driven methods like machine learning, deep learning and also anomaly detection extensively detect the undiscovered threats and also better the conventional signature-based strategies. In order to protect sensitive data using artificial intelligence while considering model training and data analysis, examples like federated learning, differential privacy and behavioural pattern analysis focuses on AI. This research mainly analyses on Artificial Intelligence systems that are vulnerable to model extraction, algorithmic bias, data leakage; wherein the opaque model decision-making process and the human mistakes continue to be the important risk concerns. The implementation of transparent, accountable governance structures is required to prevent the erosion of public trust and also to minimize ethical issues. Therefore, with the collective effects and the multidisciplinary procedures cooperating Artificial Intelligence enabled ethical design principles, regulatory compliance, strict privacy safeguards and security measures are required to create robust and reliable digital ecosystems.

A Study on AI and the Digital Divide and its Impact on Education

D Nisha — 2026-03-25

The rapid integration of Artificial Intelligence (AI) into educational systems is reshaping teaching methodologies, learning experiences, and institutional management worldwide. However, the expansion of AI-driven technologies has also intensified concerns regarding the digital divide—the gap between individuals and communities with adequate access to digital tools, infrastructure, and skills and those without.
This study examines how AI influences educational equity, focusing on disparities in access to devices, high-speed internet, digital literacy, and AI-powered learning platforms. While AI technologies such as adaptive learning systems, intelligent tutoring systems, and predictive analytics offer personalized instruction and improved academic outcomes, their benefits are often concentrated in well-resourced schools and regions. Marginalized populations, including low-income, rural, and underrepresented communities, risk further exclusion due to limited technological infrastructure and insufficient policy support.
The study analyzes existing literature and policy frameworks to evaluate both the transformative potential of AI and the structural inequalities it may reinforce. It concludes by proposing strategic interventions—such as equitable infrastructure investment, inclusive AI design, digital literacy initiatives, and ethical governance frameworks—to ensure that AI serves as a tool for educational inclusion rather than a driver of inequality.

AI Learning Intention, Engagement Dynamics, and Behavioral Consequences: A Critical Re-examination

HK Prasad — 2026-03-25

Purpose: While much scholarship evaluates learning effectiveness within conventional training environments, limited attention has been given to how AI-centered learning ecosystems reshape learner agency and outcomes. This study reconceptualizes the connection between AI-driven learning systems and employee behavioral consequences. Rather than assuming direct positive outcomes, this research critically examines whether AI-learning intention genuinely translates into meaningful behavioral transformation. Drawing from the Theory of Planned Behavior (TPB) and Self-Determination Theory (SDT), the study reassesses the relationships among learning intention, engagement processes, and behavioral outcomes.
Design/Methodology/Approach: Using survey data collected from Indian industry professionals, the study applies structural equation modeling (SPSS-AMOS 27) and Hayes PROCESS macro to test direct and indirect relationships between AI learning intention, engagement patterns, and behavioral outcomes.
Findings: Results indicate that although AI learning intention factors (attitude, subjective norm, perceived behavioral control) show statistical associations with behavioral outcomes, the magnitude of actual behavioral transformation depends heavily on the depth and quality of engagement. Engagement does not merely mediate but conditions the extent to which AI intention results in observable workplace change.
Originality/Value: This study challenges overly optimistic assumptions regarding AI-based learning systems and emphasizes the conditional nature of behavioral outcomes in technology- driven learning environments.

The Role of AI-Driven Content Quality in Improving Digital Engagement and Conversions in Digital Marketing

Sanika Jadhav — 2026-03-25

The fast development of digital technologies has drastically changed marketing landscape with many organizations adopting superior tools to enhance their interaction with consumers. Content is an important aspect in the digital world in terms of attracting customer attention, conveying brand messages, and affecting their purchasing decisions. Good content with the right information, relevance and captivity assists the business to create better relationships with the consumers and improve their online presence. As the concept of Artificial Intelligence (AI) has appeared, digital marketing plans are becoming more informational, allowing businesses to design tailored and very specific content to their target audience. Machine learning, natural language processing, predictive analytics, and generative AI types of AI technologies help organizations to process a significant amount of data on consumers and to identify behavioral patterns and to create content matching the preferences and interests of each individual user. This paper will focus on the contribution of AI-based content quality to digital engagements and conversion in digital marketing.
Digital engagement is the intensity or degree of interaction between the consumer and the online material and this could be in form of likes, comments, sharing, clicking, and time spent on the digital material. On the other hand, the conversion is the activities done by the consumers after viewing the digital content such as purchasing products, subscribing to services and registering among other intended results to the businesses. Artificial intelligence technologies can be used to improve quality, relevance, and personalization of digital content to make organizations offer better consumer experiences and promote the level of engagement. The study follows a qualitative approach of research whose research follows a complete secondary research. The study data has been gathered in the form of academic journals, scholarly articles, industry reports, books, and credible Internet databases devoted to the topics of artificial intelligence, digital marketing, content marketing, and consumer behavior. The systematic literature review and thematic analysis of the gathered materials help the study to reveal major tendencies and findings on how AI technologies affect the content creation, marketing communication, and consumer engagement strategies.
The discussion shows how AI-based tools can help marketers generate more personalized and relevant content that can make consumers more interested and engaged in digital platforms. The research outcomes obtain that AI-powered content strategies are effective in improving digital interaction with the consumer through offering personalized content experiences that reflect the unique needs and interests of each consumer. Individualized suggestions, robot content creation, and predictive marketing will help increase the customer satisfaction and enhance brand affiliation. More engagement will also result in a higher rate of conversation since the consumer tends to trust and connect more with brands that add value and relevance to their lives. Nevertheless, the paper also states a number of drawbacks related to AI use in digital marketing, such as the problem of data privacy, the legitimate use of consumer data, and the possibility of algorithm bias in general.
Altogether, the study concludes that artificial intelligence is instrumental at enhancing the quality of content and personalization to make the digital marketing strategies more effective. Those organizations that have been able to implement AI technologies into their content marketing efforts can achieve a greater degree of consumer interactions, enhanced brand retention, and higher conversion rates. With the further development of AI technologies, companies should also not neglect to be responsible and transparent in the use of consumer data to be trusted and have long-term relations with the audience.

Strategic Recruitment in the Digital Era: Exploring AI, Gen Z Employer Branding, and Online Recruitment Platforms

Veeksha Ravi — 2026-03-25

The digital revolution in the domain of Human Resource Management has impacted the recruitment practices in different industries. The recruitment practices in the contemporary recruitment scenario comprise Artificial Intelligence, digital employer branding, and digital recruitment platforms, which have become essential tools for different firms to attract and retain highly skilled employees, particularly the new generation of employees, known as Generation Z (Gen Z). The major objectives of the present study are to investigate the impact of Artificial Intelligence recruitment tools, digital employer branding for Gen Z, and digital recruitment platforms on recruitment effectiveness.
The present research has employed the Technology Acceptance Model, the Employer Branding Theory, and the Person Organisation Fit Theory to develop a conceptual model for understanding the influence of recruitment tools on recruitment effectiveness. The research methodology employed for the present study is quantitative research, and the data for the present study have been collected from 250 Gen Z employees.
The findings of the study show that the application of AI-based tools in the recruitment process improves the efficiency of the screening process and the candidate experience. Employer branding using social media is useful in the recruitment of Gen Z. The application of recruitment platforms is useful in enhancing the accessibility of the organisation. The study concluded that the application of AI- based tools, employer branding using digital tools, and recruitment platforms improves the performance of the organisation. This study is useful to the field of digital HR.

Impact of AI-Enabled Personalization on Impulse Buying Behaviour in Online Shopping Platforms

Jagadhiesh . — 2026-03-25

The high rate of adoption of Artificial Intelligence (AI) in online retailers has revolutionized the way consumers shop as a result of dynamic personalization approaches. The recommendation of products, advertisements, prices, and content are done using AI-based personalization, which relies on data analytics, machine learning algorithms, and predictive modeling to adjust personal preferences to specific consumers. Though personalization increases convenience and relevance, it as well has major implications on impulse buying behaviour. Impulse buying is spontaneous, unplanned purchases that are driven by the emotions, situational stimuli and psychological stimuli.
This paper will set out to review the effects of AI-facilitated personalization on impulse buying behaviour in online shopping sites using the secondary research methodology. The article summarizes the results of peer-reviewed publications, academic books, and industrial reports concerning between 2018 and 2024. The research will capture thematic dimensions that determine the most dominant dimensions of impact of personalized recommendations, real-time targeting, dynamic pricing, urgency cues, and algorithm-based social evidence to make impulse purchases using thematic analysis.
The results indicate that personalization with the use of AI has a powerful effect on increasing the tendencies towards impulse buying due to less cognitive effort, better emotional involvement, and the perception of urgency. Nevertheless, ethical issues and worries about the safety of data, the manipulation of algorithms, etc. Could cool down consumer reactions. The research is relevant to current literature because it combines the concept of AI personalization with the existing theories of impulse buying. Managerial implications highlight the need to consider ethical approaches to personalization and open AI practices to retain consumer confidence and ensure sales performance.

AI and Emotional Intelligence: Exploring the Convergence of Technology and Human-Centered Leadership

Archana Ambekar — 2026-03-25

The rapid advancement of Artificial Intelligence (AI) is transforming organizational processes, decision-making systems, and workplace dynamics. At the same time, Emotional Intelligence (EI)—the ability to perceive, understand, regulate, and manage emotions—remains a critical determinant of effective leadership and organizational success. This research paper explores the intersection of AI and Emotional Intelligence, examining how AI technologies influence emotional competencies in managerial contexts and how emotionally intelligent leadership shapes responsible AI adoption.
The study investigates two primary dimensions: first, the integration of AI systems capable of recognizing and responding to human emotions through affective computing, sentiment analysis, and natural language processing; and second, the role of human emotional intelligence in guiding ethical AI deployment and human–AI collaboration. AI-driven tools are increasingly used in recruitment, customer service, performance analytics, and employee engagement, where emotional data interpretation enhances personalization and decision accuracy. However, reliance on algorithmic emotion recognition raises concerns regarding privacy, bias, and authenticity of emotional responses.
The research further analyzes how emotionally intelligent leaders facilitate smoother AI adoption by fostering trust, empathy, adaptability, and effective communication within organizations. Leaders with high EI are better equipped to manage resistance to technological change, address employee anxieties about automation, and ensure ethical considerations are embedded in AI strategies. The paper argues that AI should augment rather than replace human emotional capabilities, promoting a collaborative model where technology supports data-driven insights while humans provide contextual judgment and empathy.
Using a conceptual framework approach supported by literature review and emerging organizational case insights, the study concludes that the synergy between AI capabilities and human emotional intelligence can enhance decision quality, organizational resilience, and sustainable innovation. Ultimately, the integration of AI and EI represents a balanced pathway toward technologically advanced yet human-centered management practices.

A Study of the Impact of Artificial Intelligence on Marketing Advertisement

S Shriya — 2026-03-25

The field of marketing and advertising has seen a dramatic change due to the quick development of artificial intelligence (AI). This study investigates how artificial intelligence (AI) technologies, including machine learning, generative models, natural language processing, and predictive analytics, affect the production, customization, and distribution of ads. The study demonstrates how AI facilitates hyper-targeted marketing, improves client engagement, and maximizes advertising efficiency through real-time data analysis by examining case studies and existing industry practices. It also benefits society. In the end, this study emphasizes how AI will influence marketing communication in the future by acting as both a disruptive force and a strategic enabler.
Results indicate that although AI-driven advertising presents previously unheard- of chances for creativity and efficacy, cautious governance is necessary to strike a balance between business advantages and social responsibility. In the end, this study emphasizes how AI will influence marketing communication in the future by acting as both a disruptive force and a strategic enabler.
Keywords: Marketing strategy, advertising campaigns, brand awareness, consumer behaviour, market segmentation, target audience, digital marketing, social media advertising, content marketing, influencer marketing, search engine optimization (SEO), pay-per-click (PPC), customer engagement, promotional techniques, brand positioning, marketing communication, product promotion, advertising effectiveness, media planning, integrated marketing.

Sustainable Consumption Patterns of Gen Z Consumers in the Age of Quick Commerce

Mohammed Farooq — 2026-03-25

The rapid growth of quick commerce (Q-commerce) has redefined the retail industry by providing ultra-fast delivery of groceries and daily needs within minutes. Despite an extremely high convenience promise to the customers, sustainability issues such as packaging waste and carbon emission as a result of frequent delivery services arise. Generation Z (Gen Z), being a digital and eco-conscious group, is one of the key consumer segments on quick commerce platforms. However, a sustainability awareness and consumption behaviour gap often exists in the convenience-dominant environment. The present study looks at sustainable consumption behaviour of Gen Z consumers on quick commerce platforms. The present study is conducted on primary data collected through structured questionnaire from 100 respondents by employing simple random sampling technique. Mean score analysis was applied to analyze the data. Findings of the present study imply that Gen Z consumers are quite conscious about environmental sustainability, however various factors such as convenience and price have a strong influence on their buying decision. The present study implies that quick commerce platforms should work on sustainable packaging and eco-friendly delivery to promote responsible consumption behaviour.

Edge–Cloud Continuum: A Conceptual Perspective on Distributed Cloud Computing

OR Aruna — 2026-03-25

The rapid growth of connected devices, artificial intelligence applications, and data-driven services has led to an extraordinary increase in distributed data generation. Traditionally, machine learning models are trained using centralized datasets collected from multiple devices and stored in cloud servers. Although this approach provides strong computational capabilities, it raises major concerns related to privacy, data security, and communication overhead. In many real-world environments such as healthcare systems, smart cities, and industrial IoT networks, transmitting raw data to centralized locations may be impractical or even prohibited due to privacy regulations.
Federated learning has arisen as a promising solution to these challenges by enabling collaborative model training while keeping data on local devices. Instead of sharing raw datasets, participating devices train models locally and send only model updates to a central coordinator for aggregation. This approach significantly reduces the exposure of sensitive information and allows organizations to utilize distributed data resources more efficiently.
The combination of federated learning with edge computing and cloud infrastructures has created new opportunities for distributed intelligence across the edge–cloud continuum. In such environments, computation is performed across multiple layers including devices, edge nodes, and centralized cloud systems. This paper presents a conceptual examination of federated learning within distributed edge–cloud environments. The study reviews existing research, analyzes current challenges, and proposes a conceptual framework for implementing federated learning in distributed computing systems. The findings suggest that federated learning can play a key role in enabling scalable, privacy-aware, and efficient artificial intelligence applications in future computing ecosystems.

AI and Organizational Innovation: Business Model Transformation, Product Development, Learning, and Digital Transformation

Archana Ambekar — 2026-03-25

The rapid advancement of Artificial Intelligence (AI) is reshaping the foundations of organizational innovation, enabling firms to reconfigure business models, accelerate product and service development, enhance organizational learning, and drive large-scale digital transformation. As AI technologies such as machine learning, predictive analytics, natural language processing, and generative AI become increasingly integrated into business operations, organizations are shifting from traditional innovation processes toward data-driven, intelligent ecosystems. This research paper examines the multifaceted role of AI in organizational innovation, focusing on AI-enabled business model innovation and value creation, its impact on product and service development cycles, its contribution to organizational learning and knowledge management, and case-based insights into AI-led digital transformation.
The study first explores how AI enables business model innovation and sustainable value creation. AI allows organizations to move beyond conventional revenue models by leveraging data as a strategic asset. Intelligent automation, predictive customer insights, and personalized service platforms facilitate the creation of new value propositions, dynamic pricing strategies, and platform-based ecosystems. AI-driven analytics help firms identify emerging market opportunities, optimize resource allocation, and design adaptive strategies. As a result, organizations are transitioning from product-centric models to service-oriented and outcome-based business models, enhancing competitiveness and long-term value creation.
Secondly, the research analyzes AI’s impact on product and service development cycles. Traditional development processes are often linear, time-consuming, and resource-intensive. AI accelerates innovation by enabling rapid prototyping, predictive modeling, simulation testing, and real-time customer feedback analysis. Machine learning algorithms can identify patterns in consumer preferences, forecast demand, and guide design improvements. In service industries, AI-powered chatbots, recommendation systems, and intelligent automation enhance customer experiences while reducing operational costs. By shortening development cycles and improving accuracy in decision-making, AI strengthens an organization’s capacity for agile innovation.
The paper further examines AI’s contribution to organizational learning, creativity enhancement, and knowledge management. AI systems facilitate knowledge discovery by processing large volumes of structured and unstructured data, identifying hidden patterns, and generating actionable insights. Intelligent knowledge management platforms enhance information accessibility, collaboration, and decision support across departments. Additionally, generative AI tools stimulate creativity by assisting in idea generation, content development, and problem-solving. AI-supported learning systems enable personalized training programs and continuous skill development, fostering a culture of adaptive learning within organizations. However, the research also highlights the importance of balancing human judgment with algorithmic insights to maintain creativity and ethical standards.
To provide practical insights, the study includes case analyses of organizations that have undergone digital transformation through AI integration. These cases illustrate how AI adoption requires strategic alignment, leadership commitment, data infrastructure readiness, and change management practices. Successful digital transformation is not solely technology-driven but involves cultural shifts, employee reskilling, and governance mechanisms that ensure responsible AI deployment. The cases demonstrate that organizations leveraging AI strategically achieve improved operational efficiency, enhanced innovation capacity, and stronger competitive positioning.

The Study on the Perception of Gen Z and Gen Y on Rented Versus Purchase Clothing

Preeti Rani — 2026-03-25

This study explores an extreme paradigm shift in fashion consumption around the world whereby consumers are moving towards non-ownership of products towards the new models of access-based liquid consumption. The main idea is to discuss the so-called attitude-behavior gap, when the environmental concerns do not always lead to the actual rental transactions, in terms of AI-based intervention and the generational gap.
The study considers the moderating effect of AI-personalization, including algorithmic style recommendations and virtual try-ons, on the intentions of Generation Z and Generation Y to adopt the services of the rental business by relying on the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and the Psychological Ownership Theory. The data collected in the form of 300 respondents will be analyzed with the help of Structural Equation Modeling (SEM) to identify the effect of digital stimuli and utility of AI-powered platforms on rental intentions.
This will be supplemented by semi-structured qualitative interviews to investigate how AI-enhanced transparency in sanitization tracking can help reduce deep-seated “hygiene stigmas” the anticipated results are that although both cohorts are motivated by environmental consciousness, both are driven by AI-curated hedonic value and social media aesthetics respectively, with Gen Y running on utilitarian efficiency with the assistance of predictive logistics. In addition, the paper examines how virtual ownership based on AI can be used as a source of psychological connection with people who are restrained by the desire to own. On the managerial side it gives a framework by which brands can use generative AI and data analytics to shift their one-size-fits-all marketing strategies to segmented and hyper-personalized rental experiences.

The Role of AI Adoption in Shaping Consumer Behaviour and Purchase Intention in Digital Commerce

AM Ramya Shree — 2026-03-25

E-commerce marketing now uses artificial intelligence (AI) technologies to provide personalized recommendations together with intelligent search functions and automated customer support systems which create marketing systems that drive consumer decision- making during online shopping. The research investigates how AI implementation AIP affects customer PI through the extended TAM framework. The model evaluates how AIP affects three fundamental beliefs which include PU and PEOU and PR while it studies how these beliefs impact CX which leads to PI. The researchers collected information through a structured questionnaire which they distributed to 384 online consumers and they used SEM to examine their research propositions. The researchers conducted reliability and validity tests on the measurement model before testing the structural path. The study results demonstrate that AI adoption leads to positive impacts on PU and PEOU while it creates negative impacts on PR in customer experience. The researchers found that customer experience serves as the main process which transforms AI technology acceptance beliefs into actual purchasing behaviour. The study shows that customers trust AI systems when these systems provide beneficial features and their data handling methods are completely visible to users. The research establishes new evidence through its investigation of AI usage in marketing and online retailing which shows how AI technology affects customer behaviour through experiential channels, while also offering practical guidance to e-commerce businesses that want to enhance their conversion rates.

AI-Powered Tools for Screening Green Bonds in Emerging Markets: Opportunities for Indian SMEs

HM Ashwini — 2026-03-25

Green bonds play an important role in facilitating financial support in achieving sustainable development in emerging economies, particularly in enabling Small and Medium Enterprises in India, who contribute 30% to GDP, to raise funds for low-carbon projects. This paper seeks to explore the potential impact that AI technologies, including machine learning and NLP, can have on improving the screening process. This study was conducted with reference to financial innovation theory and the triple bottom line approach. This study has utilized secondary data from RBI, SIDBI, S&P Green Bond Indices (2021-2026), and 20 case studies on anonymized SME data. From these findings, it was concluded that AI technologies have the potential to revolutionize the screening process, overcoming issues faced in manual screening, including high costs and time required. Contrary to these issues, AI technologies have shown superior results in comparison, achieving 92-95% accuracy, reducing costs by 70%, and saving 80% in time, from 4-6 weeks to 2-3 days. This study has shown that AI technologies have the potential to ensure that green capital reaches SMEs, thereby supporting India in achieving its target of becoming net-zero by 2070 and achieving SDGs 7, 9, and 13. This study has provided important implications on how RBI can ensure AI technologies are integrated into the green bond market, how Skill India can play an important role in enabling SMEs, and how refinements can be made in taxonomy in AI-verified issuances.

Influence of AI-Based Personalized Marketing on Consumer Purchase Intentions

N Ashwini — 2026-03-25

Artificial Intelligence (AI) has significantly transformed modern marketing by allowing companies to provide consumers with highly tailored experiences. AI-based personalized marketing offers customized communication, targeted ads, and product suggestions based on user data, browsing patterns, and prediction algorithms. Digital platforms and e-commerce businesses are using these tailored tactics further frequently to sway consumer choices and boost customer engagement. The purpose of this study is to investigate how consumer purchase intentions are affected by AI-based tailored marketing. The study focuses on comprehending how tailored suggestions, focused ads, and AI-driven Customer interactions have an impact on consumers’ opinions, level of trust, and propensity to buy. The study also explores the role of factors such as perceived relevance, consumer trust, and privacy concerns in shaping purchase intentions. The research will adopt a qualitative data approach, reviewing the literature based on the key terms of AI-based marketing tools and digital marketing. platforms. The findings are expected to provide insights into how AI-powered personalization strategies impact consumer Behaviour and help Marketers design more effective marketing campaigns while maintaining consumer trust and satisfaction.

Impact of AI Tools on Learning Statistical Analysis Among Students

Aparna Patil — 2026-03-25

One of the most revolutionary technologies affecting contemporary education is artificial intelligence (AI). AI-driven technologies, such as generative AI assistants, machine learning platforms, automated statistical software, and intelligent tutoring systems, have been increasingly used in recent years to improve learning in quantitative domains. For academic research, business decision-making, and data-driven problem solutions, statistical analysis is crucial. However, because statistical notions involve complicated mathematical reasoning and computing procedures, many students struggle to grasp them.
This study aims to investigate how AI technologies affect statistical analysis learning through secondary data sources. In order to comprehend how AI technologies affect statistical learning results, student engagement, analytical skills, and learning efficiency, the study synthesizes findings from scholarly reports, research articles, and academic journals. Potential hazards including an over reliance on AI technologies and a potential decline in autonomous analytical thought are also discussed in the article.
The results of the study show that artificial intelligence (AI) technologies significantly enhance learning by simplifying complex statistical procedures, providing instant feedback, enabling data visualization, and enabling customized learning experiences. AI-based learning tools enable students to do tasks like data purification, hypothesis testing, regression analysis, and result interpretation more successfully. Nonetheless, the findings suggest that an excessive dependence on AI technologies without conceptual understanding could negatively affect critical thinking and problem-solving abilities.
The study comes to the conclusion that, when utilized in conjunction with conventional teaching techniques, AI tools can significantly enhance statistics education. To ensure that students acquire both computational abilities and a conceptual knowledge of statistical analysis, educational institutions and teachers must concentrate on integrating AI technology in a balanced way.

Impact of Flight Delays and Cancellations on Passenger Satisfaction: A Case Study of IndiGo Crisis

Sarthak Choudhary — 2026-03-25

Flight delays and cancellations are common problems in the airline business that have a big effect on how happy passengers are. Changes to flight schedules can be annoying, cost money, and make people think less of the quality of airline service. This research investigates the effect of flight delays and cancellations on passenger satisfaction, utilizing a case study of IndiGo, India’s largest airline. The study concentrates on the operational disruptions resulting from regulatory modifications and operational limitations. The study looks at the reasons for delays and cancellations and how they affect customer satisfaction by looking at secondary data sources like aviation reports, research journals, and passenger feedback. The results show that good communication, good customer service, and good planning of operations are all important for keeping passengers’ trust during flight delays.

Effect of Discounts and Offers on Student Purchasing Behaviour

Nakul Dev Maroli — 2026-03-25

Discounts and promotional offers are widely used marketing tools to influence consumer purchasing behaviour. Among college students, who are generally price-sensitive and highly responsive to promotions, discounts significantly shape buying decisions. This study examines the effect of discounts and offers on student purchasing behaviour using primary data collected from 45 respondents through a structured questionnaire. Statistical tools such as percentage analysis, mean score analysis, correlation analysis, and reliability analysis were applied in SPSS. The findings reveal that discounts strongly influence students’ purchase decisions, increase impulse buying behaviour, and motivate them to prefer discounted products over regular-priced alternatives. The study concludes that discounts and offers positively impact student purchasing behaviour, but excessive discounting may influence brand perception over time.

Artificial Intelligent in Digital Commerce: Application, Benefits and Challenges

V Hemalatha — 2026-03-25

Digital business is being redefined by Artificial Intelligence (AI), which has become a game-changing technology. Artificial Intelligence (AI) technologies including machine learning, natural language processing, predictive analytics and intelligent automation which have completely changed how businesses handle operations, interact with clients and offer customized services. The applications of artificial intelligence, benefits and challenges in Digital Commerce are examined in this research paper. The article also covers how AI improves e-commerce platform’s operational effectiveness, consumer experience and strategic decision-making. Despite the various benefits, businesses still face challenges like data security, expensive implementation, moral dilemmas and complicated technology. The study concludes that even though AI has a lot of potential to enhance digital commerce, businesses must overcome these challenges through responsible adoption and sound governance in order to fully realize it’s potential.

Personal Interest: Its Role in Learning, Career Development, and Performance

Syed Mohammed Tajeel — 2026-03-25

Personal interest is a fundamental psychological factor that influences learning behaviour, career selection, motivation, and performance. Individuals tend to invest more time and effort inactivities they find personally interesting and meaningful. This research paper explores the concept of personal interest and its importance in academic achievement, career development, and workplace productivity.
The study discusses theoretical perspectives on interest development, the relationship between interest and motivation, and the influence of personal interest on decision making. It also examines the benefits of encouraging interest-based learning in educational institutions and organisations. The research concludes that understanding and nurturing personal interest can significantly improve engagement, creativity, and long-term success among individuals

Consumer Perception on Personalized Advertisement and Brand Integrity

Rashida Aiman — 2026-03-25

The use of artificial intelligence and machine learning is being deeply used in digital marketing. There is a thin line between personalized service and surveillance. This research talks about the creepiness, psychological discomfort faced by customers when the things they search or talk about is hyper accurate in their algorithm. the privacy paradox suggests that being too accurate can trigger a defensive reaction leading to a drop in brand image and trust. The purpose of this research is to see how tracking too much of private information of a customer can decrease their trust. There is a very thin line between using the data for personalization and misusing the data to exploit the customers so they hesitate to give their credentials most of the time.
This research uses a quantitative approach; the data was collected through a questionnaire creating primary data. People were provided with scenario of predictive ads appearing after private conversations. The data was analyzed using correlation analysis and use of SPSS. The study suggests people find the hyper personalized ads to be annoying and add a creepy factor. The use of artificial intelligence in promoting brand ads and using bots for customer service makes the customer loose the human touch Therefore, the brands should focus on privacy first to retain the integrity and increase the brand loyalty for a long term in era of algorithm governance. Let the brand speak for itself and retain its originality than being diluted in the world of artificial intelligence.

Ethical Implications of AI in Decision-Making in the Corporate Sector in Bangalore

A Trisha — 2026-03-25

Artificial Intelligence (AI) is transforming the corporate decision-making model. Bangalore is the technology hub of India. AI is being increasingly adopted in the corporate sector of Bangalore. AI is capable of increasing efficiency, precision, and data-driven decision-making. However, AI is creating ethical issues such as algorithmic bias, transparency, data privacy, and accountability. This research paper aims to address the ethical issues associated with AI-based decision-making in the corporate sector of Bangalore. This research paper is based on the existing literature on the subject. This research paper has evaluated the case studies of the corporations. This research paper has evaluated the ethical issues associated with AI-based decision-making. This research paper has found that AI-based decision-making is the need of the hour for the corporate sector of Bangalore. This research paper concludes with the recommendations on AI-based decision-making.

AI Driven Consumer Behaviour: Trends, Effects and Implications

M Raga Keerthika — 2026-03-25

It is significantly noted that Artificial Intelligence (AI) is playing a pivotal role in changing the global economy. This paper also proposes a framework that tends to connect artificial intelligence capabilities to consumers trust, satisfaction and loyalty but also raises important concerns about data privacy bias algorithms and the need for transparency. Artificial intelligence has deeply transformed to consumers behaviour, Influencing the individuals in recognizing the needs, search for information, evaluating alternative products, make purchase decisions on engaging in post-purchase activities. The findings of this paper suggest that AI enhances market efficiency, contributes to sustainable competitive advantage and also improves customer experience in online shopping. However, it also highlights that responsible management and the use of an appropriate strategy are needed in order to handle risks and maintain customer’s trust.