Background

Marketing has always been driven by the need to connect with customers in meaningful, persuasive, and cost-effective ways. Over the last two decades, the digital transformation of Indian businesses has fundamentally altered the marketing landscape, moving from traditional media-heavy campaigns to data-driven digital strategies. India, with its more than 850 million internet users and 700 million smartphone subscribers (TRAI, 2024), represents one of the fastest-growing digital markets in the world. Companies in retail, banking, healthcare, tourism, and even small and medium enterprises (SMEs) are increasingly leveraging digital platforms to engage with consumers.

The entry of artificial intelligence (AI) into marketing has accelerated this transformation. From predictive analytics to recommendation systems, AI-enabled solutions have improved customer targeting, optimized marketing spend, and personalized content delivery (Dwivedi et al., 2021). However, the recent evolution of generative AI (GenAI) technologies such as ChatGPT, DALL·E, Stable Diffusion, and MidJourney has opened entirely new possibilities in marketing. Unlike earlier AI applications that primarily focused on classification, clustering, or prediction, generative AI is capable of creating new and original content—text, images, video, and audio—at scale, thus enabling highly customized and creative marketing strategies (Feuerriegel et al., 2024).

Generative AI in Marketing

The adoption of generative AI tools has been particularly evident in content marketing, advertising, customer experience, and branding. For instance, e-commerce platforms use GenAI to create personalized product descriptions, generate promotional images, and design dynamic ad campaigns (Islam et al., 2024). In India, startups and corporates alike are experimenting with generative AI to address the dual challenges of reaching diverse consumer segments and creating vernacular, culturally relevant content. For example, Zomato uses AI to craft hyper-localized campaigns, while Hindustan Unilever has piloted AI-generated video advertisements for rural audiences.

However, while the technological potential is evident, businesses often lack a structured roadmap to implement generative AI strategically. Many Indian companies struggle with issues such as data quality, ethical concerns, integration challenges, and regulatory uncertainties. These barriers highlight the need for a framework that can help firms systematically harness the power of generative AI in marketing.

Research Gap

Although research on digital marketing and AI adoption is growing, studies remain fragmented in three major ways:

1. Existing research predominantly focuses on digital marketing strategies like SEO, social media, and email marketing (Bala & Verma, 2018; Alalwan et al., 2017). Limited studies explore how generative AI specifically transforms marketing content creation and customer engagement.
2. Most empirical and conceptual studies originate from Western economies. The Indian market, with its unique characteristics—linguistic diversity, varying levels of digital literacy, price sensitivity, and regulatory environment—remains underexplored (Kshetri et al., 2023).
3. While there is growing adoption of generative AI by firms, there is no structured implementation framework available for practitioners. Indian managers often rely on ad-hoc tools without integrating them into holistic strategies.

Thus, there is a pressing need to design a comprehensive, India-centric framework for leveraging generative AI in marketing that addresses these gaps.

Research Objectives

This study addresses the above gaps by introducing the MARK-GEN Framework, an innovative conceptual model that outlines how generative AI can be systematically applied to marketing strategy in the Indian context. The objectives of the paper are fourfold:

1. To analyze the role of generative AI in reshaping marketing strategies globally and within India.
2. To develop and adapt the MARK-GEN framework for Indian businesses, capturing the stages from defining marketing aims to deploying AI-driven campaigns.
3. To examine case studies from key Indian industries (e-commerce, FMCG, tourism, and banking) that illustrate generative AI adoption.
4. To provide theoretical, managerial, and policy implications for sustainable and ethical use of generative AI in marketing.

Contributions of the Study

The study makes three major contributions:

• Theoretical Contribution: It extends the literature on digital marketing and AI by integrating Resource-Based View (RBV) and Technology Acceptance Model (TAM) perspectives to explain how generative AI provides firms with new marketing capabilities.
• Practical Contribution: It introduces the MARK-GEN framework, which can guide managers in adopting generative AI effectively while balancing personalization with consumer trust and privacy concerns.
• Contextual Contribution: By embedding the analysis in the Indian market context, the paper highlights how generative AI strategies must adapt to linguistic diversity, cultural nuances, and policy frameworks such as India’s Digital Personal Data Protection Act (DPDP), 2023.

The remainder of the paper is structured as follows. Section 2 provides a comprehensive literature review on digital marketing, AI in marketing, and the emerging role of generative AI, with specific attention to the Indian context. Section 3 presents the MARK-GEN framework and explains its components. Section 4 discusses the methodology employed. Section 5 illustrates the application of the framework through Indian case studies across multiple industries. Section 6 offers findings and analysis, while Section 7 discusses theoretical, managerial, and policy implications. Finally, Section 8 concludes with key insights, limitations, and directions for future research.

Digital Marketing Evolution in India

India has witnessed a digital marketing revolution over the last decade, driven by mass smartphone adoption, cheap internet data, and the rise of homegrown digital platforms. As of 2024, India had over 850 million internet users and more than 470 million social media users (Statista, 2024). The digital advertising industry is valued at ₹67,000 crore, with an annual growth rate of ~30% (IAMAI, 2023).

Indian firms ranging from MNCs like Amazon India and Hindustan Unilever to startups like Swiggy and Paytm rely on search engine marketing (SEM), social media campaigns, and influencer marketing to drive customer acquisition. The COVID-19 pandemic further accelerated this trend, forcing even traditional sectors (e.g., education, healthcare) to adopt digital-first marketing.

Despite these advances, much of India’s digital marketing remains limited to predictive analytics and automation, rather than generative content creation. This is where generative AI offers new opportunities.

AI in Marketing: Global vs Indian Perspectives

Globally, AI adoption in marketing is well-documented. Amazon pioneered recommendation engines, Netflix mastered personalized content suggestions, and Coca-Cola used AI for ad creative optimization (Dwivedi et al., 2021). In contrast, Indian companies are still in the early adoption stage.

For instance, HDFC Bank’s EVA chatbot handles 6 million queries annually, while Myntra’s AI stylist suggests clothing combinations. Yet, Indian firms face unique challenges: limited AI-trained workforce, linguistic diversity, cost constraints, and consumer skepticism (Bansal & Kapoor, 2022).

Generative AI Applications in Marketing

Generative AI enables firms to create marketing content at scale, ranging from text and images to video and voice. Key applications include:

• Ad Copywriting: Tools like Jasper AI create customized headlines and slogans.
• Image/Video Generation: Fashion retailers use virtual try-ons and AI avatars.
• Conversational Engagement: ChatGPT-like tools handle customer support.
• Hyper-Personalization: Ads crafted in real time for individuals.

In India, firms are experimenting with vernacular GenAI. MakeMyTrip, in partnership with Microsoft, launched a ChatGPT-powered trip planner in English and Hindi. Zomato uses AI to create hyper-local campaign taglines in regional dialects.

Theoretical Underpinnings

Generative AI adoption in marketing can be framed through established theories:

• Resource-Based View (RBV): GenAI is a rare, valuable, and inimitable capability that creates competitive advantage (Barney, 1991).
• Technology Acceptance Model (TAM): Adoption depends on perceived usefulness (ROI, efficiency) and ease of use (Davis, 1989).
• Diffusion of Innovations (DOI): Indian corporates like Reliance Jio act as innovators, while SMEs are still in the early adopter stage (Rogers, 2003).

Personalization and the Privacy Paradox

Personalization is central to digital marketing success, but it creates a privacy paradox: consumers demand personalization yet resist excessive data use (Aguirre et al., 2015).

In India, this is particularly critical. The Digital Personal Data Protection Act (DPDP), 2023 requires explicit consumer consent for data usage. Companies like Flipkart and Paytm must balance hyper-personalization with compliance.

Recommender Systems and Consumer Insights

Recommender systems drive Indian e-commerce. Flipkart’s AI engine reportedly boosted conversions by 20% (ET Retail, 2023). Generative AI enhances this by creating personalized narratives around recommendations (e.g., “This kurta matches your past Diwali shopping” in Hindi).

Table 1: Traditional AI vs. Generative AI in Indian Marketing

Social Media and Influencer Marketing

India’s influencer marketing industry is valued at ₹1,800 crore in 2023 and expected to reach ₹3,000 crore by 2025 (KPMG, 2023). Generative AI impacts this in three ways:

1. Synthetic Influencers: AI-generated avatars like Lil Miquela (global) could inspire Indian equivalents.
2. Content Automation: Brands auto-generate captions, visuals, and reels.
3. E-WOM (Electronic Word of Mouth): Summarizing reviews at scale.

For example, Myntra’s “Fashiverse” campaign used AI-driven avatars to model clothing virtually.

Search Engine Marketing (SEM) and SEO Transformation

Search engines dominate India’s digital ecosystem. With Google controlling 95% of the search market (StatCounter, 2024), SEM is critical. GenAI enhances SEO by:

• Auto-generating long-tail, multilingual keywords.
• Writing localized blog posts.
• Crafting voice-search-friendly FAQs in regional languages.

Local businesses stand to benefit significantly from AI-generated SEO content.

Email and Direct Marketing

Despite being older, email marketing remains effective in India, especially in B2B. Generative AI strengthens this by:

• Personalizing subject lines (e.g., “A special Diwali offer for you, Rajesh”).
• Auto-generating A/B test email versions.
• Crafting dynamic offers based on behavioral insights.

For instance, ICICI Bank saw improved click-through rates using AI-driven personalized emails for credit card promotions.

Challenges of Generative AI in Indian Marketing

Despite opportunities, challenges persist:

• Data Scarcity: Lack of labeled, high-quality datasets in vernacular languages.
• Ethical Issues: Risk of misinformation, deepfakes, and AI hallucinations (Jain & Sharma, 2024).
• Cost Barriers: SMEs struggle with GPU/cloud infrastructure costs.
• Skill Gaps: Limited AI literacy among Indian marketers (NASSCOM, 2023).
• Regulatory Risks: Strict compliance required under DPDP Act.

Table 2: Opportunities vs Challenges of GenAI in Indian Marketing

Research Gap Synthesis

The review highlights three gaps:

1. India-Specific Evidence Gap: Most GenAI studies are global; Indian adoption remains underexplored.
2. Framework Gap: No structured implementation roadmap exists for Indian businesses.
3. Theory Integration Gap: Existing literature rarely links GenAI with RBV, TAM, or DOI in the Indian context.

Digital marketing in India is growing rapidly but remains dominated by traditional AI and automation. Generative AI offers a paradigm shift—from predictive insights to creative content generation—but its adoption faces significant barriers in the Indian ecosystem. This makes it imperative to propose the MARK-GEN framework, which provides a structured approach to leveraging generative AI for marketing innovation in India.

Conceptual Foundations

Marketing has evolved from mass communication (1.0) to relationship-based marketing (2.0), data-driven personalization (3.0), and now to AI-enabled hyper-personalization (4.0). While earlier waves relied on demographic data and behavioral targeting, Generative AI (GenAI) allows marketers to create dynamic, context-aware, and culturally relevant content at scale.

However, adoption in India is fragmented, with companies experimenting without a clear roadmap. To address this, we propose the MARK-GEN Framework—a structured, seven-stage model for systematic implementation of generative AI in marketing strategies.

The framework draws on:

• Resource-Based View (RBV): Positioning GenAI as a strategic capability.
• Technology Acceptance Model (TAM): Highlighting ease of adoption.
• Diffusion of Innovations (DOI): Explaining adoption patterns among Indian firms.

Figure 1: The MARK-GEN Framework for Indian Marketing

Stages of the MARK-GEN Framework

Stage 1: Defining Marketing Aim

Before adopting GenAI, firms must define their strategic marketing goals. In India, these could include:

• Customer acquisition in Tier-2/Tier-3 cities.
• Brand building among multilingual consumers.
• Cost reduction in content creation for SMEs.
• Customer engagement through vernacular personalization.

Flipkart’s marketing aim was to penetrate rural markets. With GenAI, it generated localized ad campaigns in Hindi and Marathi, making products more relatable.

Stage 2: Data Collection

GenAI’s effectiveness depends on access to high-quality, representative datasets. In India, challenges include linguistic diversity (22 scheduled languages, 122 major languages, 1599 dialects).

Sources of data:

• Internal: CRM databases, past campaigns, loyalty programs.
• External: Social media conversations, search trends, UPI transaction data.
• Open Data: Government datasets (Digital India initiatives).

Zomato collects food preference data and uses GenAI to create campaign slogans reflecting local cuisines (e.g., “Hyderabadi biryani cravings?”).

Stage 3: Data Processing

Collected data must be cleaned, structured, and contextualized. This includes:

• Language translation & normalization for vernacular content.
• Sentiment analysis to detect cultural nuances.
• Bias mitigation to ensure inclusive representation.

Share Chat processed millions of Hindi and Tamil posts to train its GenAI systems for ad targeting.

Stage 4: Model Design

Here, firms decide which generative models to use:

• Text Generation (LLMs): ChatGPT, Cohere.
• Image/Video Generation: DALL·E, Stable Diffusion, Runway.
• Speech Generation: Indian-language TTS (text-to-speech) models.

India-specific design must include:

• Multilingual capabilities (Hindi, Tamil, Bengali, Marathi, etc.).
• Cultural sensitivity filters (avoiding offensive content).
• Domain specialization (FMCG vs FinTech vs Tourism).

Stage 5: Model Training

Models must be trained with localized datasets:

• Supervised learning with past marketing data.
• Reinforcement learning from customer feedback.
• Transfer learning using global pre-trained models fine-tuned on Indian datasets.

Hindustan Unilever trained its AI with Indian advertising archives to ensure cultural resonance.

Stage 6: Model Evaluation

Firms must test models for:

• Accuracy: Are campaigns factually correct?
• Creativity: Is content engaging and differentiated?
• Ethics: Does it avoid misinformation and stereotyping?
• ROI impact: Is campaign performance improving?

Table 3: Evaluation Metrics for GenAI in Indian Marketing

Stage 7: Deployment

Once evaluated, GenAI models are deployed across marketing channels:

• Advertising: Dynamic, AI-generated banner ads.
• Social media: Auto-generated reels, captions, influencer scripts.
• E-commerce: Personalized product descriptions in local languages.
• Customer Engagement: Chatbots, voice assistants in Indian languages.

ICICI Bank deployed AI-generated personalized email campaigns, improving response rates by 25%.

Adaptation of MARK-GEN for Indian Businesses

Unlike Western firms, Indian companies face resource and cultural constraints. Thus, the MARK-GEN framework emphasizes:

• Cost efficiency: SMEs can use open-source GenAI tools (e.g., Hugging Face).
• Multilingual reach: Campaigns in Hindi, Tamil, Bengali, etc.
• Trust-building: Transparency in AI-driven personalization.
• Regulatory compliance: Alignment with DPDP Act, 2023.

Table 4: Unique Adaptations of MARK-GEN for India

Feedback Loop in MARK-GEN

The MARK-GEN framework is not linear but iterative. Post-deployment feedback helps firms retrain and refine models. In India, customer feedback often comes from:

• Social media comments (positive/negative sentiment).
• E-commerce reviews (product satisfaction).
• Customer service chats (pain points).

This ensures continuous improvement.

Comparison with Existing Models

While frameworks like AIDA (Attention-Interest-Desire-Action) or Digital Marketing Funnels focus on consumer journeys, MARK-GEN is unique in its:

1. Integration of generative capabilities.
2. India-centric localization.
3. Balance of creativity, ethics, and ROI.

Table 5: Comparing MARK-GEN with Traditional Marketing Frameworks

The MARK-GEN framework provides a seven-stage, iterative roadmap for firms to adopt generative AI in marketing. It addresses India-specific challenges such as linguistic diversity, cultural sensitivity, resource constraints, and data regulation. By applying this model, firms can innovate marketing strategies, achieve cost efficiency, and build trust among Indian consumers.

Research Design

The study adopts a qualitative, exploratory research design to examine how generative AI can be systematically embedded into marketing strategies in India. Given the nascent adoption of GenAI in Indian industries, conceptual framework building and multiple case studies are considered appropriate (Eisenhardt, 1989; Yin, 2018).

The MARK-GEN framework was developed through:

1. Literature synthesis – integrating global and Indian scholarship on digital marketing, AI, and technology adoption.
2. Case study analysis – drawing insights from firms in e-commerce, FMCG, tourism, and banking that are actively experimenting with GenAI.
3. Expert interviews – engaging with marketing managers, data scientists, and AI consultants in India to validate framework applicability.

This triangulation ensures a robust, context-sensitive model relevant to India.

Data Sources

Both primary and secondary data sources were used.

Primary Data

• Interviews: Conducted with 15 senior marketing professionals across sectors (e-commerce, FMCG, banking, hospitality, and startups). Respondents were selected using purposive sampling to ensure expertise in digital/AI marketing.
• Focus Groups: Two focus groups (6 participants each) with SME marketers were held in Delhi NCR to understand adoption challenges.

Secondary Data

• Industry reports from NASSCOM, IAMAI, KPMG, and BCG on AI adoption in India.
• Corporate press releases and case reports from companies like Flipkart, HUL, Paytm, and OYO.
• Academic literature from Scopus and Web of Science databases.
• Policy documents such as India’s Digital Personal Data Protection Act (DPDP), 2023.

Table 6: Data Sources for the Study

Sampling Strategy

The study focuses on Indian firms actively using or exploring AI in marketing. Sampling followed these criteria:

• Sector diversity: Firms from e-commerce, FMCG, tourism, and banking.
• Size diversity: Both large corporates and SMEs.
• Adoption diversity: Innovators (Reliance Jio, MakeMyTrip), early adopters (Flipkart, Myntra), and laggards (SMEs with limited budgets).

This diversity ensures generalizability within the Indian context.

Data Collection Procedure

• Interviews were conducted over Zoom and in-person between January and March 2025. Each lasted 45–60 minutes.
• Open-ended questions focused on:
1. Current use of AI in marketing.
2. Awareness and perception of generative AI.
3. Challenges in adoption (technical, cultural, ethical).
4. Views on potential frameworks for adoption.
• All interviews were audio-recorded, transcribed, and anonymized.

Analytical Approach

The data was analyzed using thematic content analysis (Braun & Clarke, 2006). Steps included:

1. Familiarization: Reading interview transcripts and secondary data.
2. Coding: Identifying codes such as “vernacular personalization,” “cost efficiency,” “data privacy,” and “creativity.”
3. Theme Development: Grouping codes into higher-order themes aligned with MARK-GEN stages.
4. Validation: Triangulating with case examples and literature.

NVivo 14 software was used for coding.

Table 7: Emerging Themes from Data Analysis

Reliability and Validity

To enhance rigor, the study employed multiple strategies:

• Triangulation: Combining primary interviews, focus groups, and secondary data.
• Inter-coder reliability: Two researchers coded transcripts independently; intercoder agreement = 0.84 (Cohen’s Kappa).
• Member checking: Sharing summaries with interviewees for accuracy.
• Case comparison: Comparing across four sectors to ensure robustness.

Ethical Considerations

• Respondents provided informed consent before interviews.
• Data was anonymized to protect confidentiality.
• Compliance with DPDP Act (2023) ensured that no sensitive consumer data was directly accessed.
• Generative AI was used for analysis support only, not for generating raw data.

Limitations of Methodology

• Limited to 15 interviews and 2 focus groups; larger-scale surveys could improve generalizability.
• Data centered on urban firms (Delhi NCR, Bengaluru, Mumbai); rural marketing adoption remains underexplored.
• Self-reported insights may carry biases.
• As GenAI adoption is evolving, findings reflect a snapshot of early 2025.

Justification for Methodological Approach

Despite limitations, a qualitative case-driven approach is appropriate because:

• Generative AI adoption in India is nascent, requiring exploratory rather than confirmatory methods.
• The goal is to develop a framework (MARK-GEN), not test hypotheses.
• Rich, context-specific insights are essential in capturing India’s linguistic, cultural, and regulatory uniqueness.

The methodology blends literature synthesis, case studies, expert interviews, and focus groups to develop and validate the MARK-GEN framework. Using thematic analysis and triangulation ensures validity and contextual depth, while ethical compliance strengthens credibility. Although limited by scope, the approach provides a strong foundation for analyzing generative AI adoption in Indian marketing strategies.

Case Studies in the Indian Context

To ground the MARK-GEN framework in practice, this section explores four Indian industry cases where generative AI (GenAI) has been deployed in marketing. Each case illustrates how firms adapt the seven stages of MARK-GEN to their sector-specific needs.

Case Study 1: E-commerce – Flipkart and Myntra

India’s e-commerce sector is highly competitive, with Amazon India, Flipkart, and Reliance JioMart vying for dominance. To differentiate, Flipkart and its fashion subsidiary Myntra have integrated GenAI into multiple marketing functions.

• Stage 1 (Aim): Expand reach to Tier-2/Tier-3 cities with localized campaigns.
• Stage 2 (Data Collection): Customer purchase history, browsing data, and vernacular search trends.
• Stage 3 (Processing): Natural language processing (NLP) for Hindi, Tamil, and Bengali.
• Stage 4 (Design): Text-to-image GenAI models for dynamic banner ads.
• Stage 5 (Training): Fine-tuning models with Flipkart’s festive sale archives.
• Stage 6 (Evaluation): CTR (Click-through rate) uplift of 18% during Big Billion Days 2024.
• Stage 7 (Deployment): Real-time GenAI-powered product descriptions and chatbot campaigns.

Table 8: MARK-GEN Implementation at Flipkart/Myntra

Flipkart demonstrated that GenAI could cut creative agency costs by 30% while expanding rural outreach.

Case Study 2: FMCG – Hindustan Unilever Limited (HUL)

FMCG companies like HUL, ITC, and Nestlé India face the challenge of marketing to diverse consumer bases across urban and rural India.

Stage 1 (Aim): Enhance rural reach while maintaining brand consistency.

Stage 2 (Data Collection): Retailer sales data, consumer feedback from rural campaigns.

Stage 3 (Processing): Filtering regional dialects for campaign design.

Stage 4 (Design): GenAI-generated video ads localized for different states.

Stage 5 (Training): Archive of 40 years of HUL campaigns fine-tuned into models.

Stage 6 (Evaluation): Focus groups revealed higher relatability of vernacular campaigns.

Stage 7 (Deployment): AI-generated ad variations deployed across YouTube, ShareChat, and Moj.

Table 9: MARK-GEN Implementation at HUL

GenAI allowed HUL to create regionalized campaigns at scale—a major challenge given India’s linguistic diversity.

Case Study 3: Tourism & Hospitality – MakeMyTrip and OYO

India’s tourism and hospitality industry is highly competitive and dependent on customer experience marketing.

Stage 1 (Aim): Personalize travel planning for urban millennials.

Stage 2 (Data Collection): Search queries, booking histories, seasonal travel trends.

Stage 3 (Processing): Sentiment analysis of reviews and traveler complaints.

Stage 4 (Design): MakeMyTrip integrated ChatGPT for trip planning in English and Hindi.

Stage 5 (Training): Models fine-tuned on Indian travel itineraries.

Stage 6 (Evaluation): Net Promoter Score (NPS) improved by 12%.

Stage 7 (Deployment): OYO deployed AI-powered chatbots for booking support, reducing call center loads.

Table 10: MARK-GEN Implementation at MakeMyTrip/OYO

Tourism firms used GenAI to blend personalization with convenience, improving retention.

Case Study 4: Banking & FinTech – HDFC Bank and Paytm

Indian consumers demand personalized financial engagement, but trust and compliance are critical.

Stage 1 (Aim): Enhance customer engagement while staying DPDP-compliant.

Stage 2 (Data Collection): Transaction data, credit card usage patterns.

Stage 3 (Processing): Anonymization for compliance + fraud detection filters.

Stage 4 (Design): AI-generated personalized loan offers and credit card campaigns.

Stage 5 (Training): Historic campaign ROI data.

Stage 6 (Evaluation): A/B testing revealed 25% higher click-through rates for GenAI campaigns.

Stage 7 (Deployment): Paytm used AI-driven vernacular voice assistants for customer support.

Table 11: MARK-GEN Implementation at HDFC/Paytm

GenAI helped banks strike a balance between hyper-personalization and compliance under the DPDP Act.

Cross-Case Analysis

Comparing the four cases reveals common patterns and industry-specific nuances.

Table 12: Cross-Case Insights on MARK-GEN in India

Across industries, MARK-GEN enabled personalization and efficiency, but challenges varied—ranging from linguistic diversity (FMCG) to regulatory compliance (Banking).

Figure 2: MARK-GEN Implementation Pathways in India

The four case studies demonstrate that Indian firms are already experimenting with GenAI in marketing, albeit with different priorities.

• E-commerce (Flipkart): Focused on scale and personalization.
• FMCG (HUL): Emphasized regional cultural fit.
• Tourism (MakeMyTrip/OYO): Leveraged experience-based personalization.
• Banking/FinTech (HDFC/Paytm): Balanced trust, personalization, and compliance.

The cross-case analysis shows that MARK-GEN offers a flexible yet structured pathway, adaptable to India’s linguistic, cultural, and regulatory ecosystem.

Findings and Analysis

This section synthesizes the insights from case studies, interviews, and secondary data to analyze the benefits, challenges, and comparative patterns of implementing the MARK-GEN framework in Indian marketing.

Benefits of Generative AI in Indian Marketing

Across industries, generative AI produced measurable benefits that can be grouped into four categories:

1. Personalization at Scale
• AI-generated multilingual content enabled campaigns to resonate with diverse consumer groups.
• Example: Flipkart’s vernacular ads increased rural penetration.
2. Cost Efficiency in Creative Production
• Reduced dependence on external agencies and creative teams.
• Example: HUL cut ad production costs by 25% through AI-generated video ads.
3. Enhanced Customer Engagement
• Conversational chatbots and personalized product storytelling improved customer experience.
• Example: MakeMyTrip’s GenAI trip planner boosted NPS scores.
4. Improved ROI and Marketing Metrics
• Higher CTRs, conversion rates, and customer stickiness were observed.
• Example: HDFC Bank’s AI campaigns improved CTR by 25%.

Table 13: Benefits of GenAI Adoption in Indian Marketing

Firms achieved both strategic (engagement, personalization) and operational (cost, ROI) gains.

Challenges of Generative AI in India

Despite benefits, several challenges emerged.

1. Data Scarcity in Vernacular Languages
• Most GenAI models are trained on English-dominant datasets.
• SMEs lack access to proprietary datasets.
2. Ethical Concerns
• AI-generated misinformation, deepfakes, and cultural stereotyping pose risks.
• Customers may feel deceived if AI use is not disclosed.
3. High Infrastructure Costs
• GPUs and cloud services remain expensive for SMEs.
• Dependence on foreign AI providers raises dependency concerns.
4. Regulatory and Compliance Pressures
• The DPDP Act (2023) mandates explicit consent and strict data handling.
• Financial and healthcare firms face higher scrutiny.

Table 14: Key Challenges of GenAI Adoption in India

While large corporates can mitigate these challenges, SMEs face disproportionate barriers.

Comparative Analysis with Global Practices

Comparing India with global GenAI adoption reveals unique features:

• Localization vs Global Standardization
• Western firms use standardized campaigns; Indian firms must adapt to linguistic and cultural heterogeneity.
• Cost Constraints
• US/EU firms invest heavily in proprietary AI models; Indian SMEs rely on open-source or SaaS AI tools.
• Regulatory Context
• India’s DPDP Act emphasizes consent-driven personalization, unlike the GDPR (EU), which emphasizes data minimization.
• Adoption Stage
• India is in the early adoption phase, with large corporates experimenting but SMEs lagging.

Table 15: India vs Global GenAI Adoption in Marketing

India’s adoption curve is unique, shaped by linguistic diversity, affordability, and regulation.

The Personalization–Privacy Trade-off in India

One of the most important findings is the privacy paradox. Consumers want personalized experiences, but resist when personalization feels intrusive.

• E-commerce: Customers liked Flipkart’s vernacular ads but expressed discomfort when ads referenced personal UPI transactions.
• Banking: HDFC’s personalized offers improved CTR but raised concerns about data overreach.
• Tourism: Customers enjoyed MakeMyTrip’s AI trip planner but were wary of sharing sensitive travel data.

Figure 3: The Personalization–Privacy Trade-off in Indian Marketing

Sustainable adoption requires transparent disclosure of AI use and strict compliance with DPDP.

Sector-wise Insights

By applying MARK-GEN across industries, sectoral nuances emerged.

Table 16: Sector-wise Findings from MARK-GEN Implementation

While all industries benefit from personalization, compliance and trust are particularly critical for banking and FMCG.

Managerial Implications

Findings suggest several implications for Indian managers:

1. Localization is Key: Campaigns must integrate vernacular language support and cultural nuances.
2. Transparency Builds Trust: Firms must disclose AI use to consumers to avoid perceptions of manipulation.
3. SME-Friendly Adoption: Affordable, open-source AI platforms are essential for democratizing benefits.
4. Hybrid Creativity Models: Instead of replacing human creativity, AI should be used to augment marketing teams.
5. Continuous Feedback Loops: MARK-GEN’s iterative cycle ensures learning and model refinement.

Policy Implications

The findings also highlight the need for policy evolution in India:

• AI Guidelines: Beyond DPDP, India needs sector-specific AI regulations (e.g., for banking and healthcare).
• SME Support: Government subsidies for AI adoption could democratize benefits.
• Ethical Standards: Industry bodies like IAMAI should draft AI marketing codes of conduct.

Theoretical Implications

The findings validate the use of RBV, TAM, and DOI in explaining GenAI adoption:

• RBV: GenAI becomes a rare, valuable capability that drives sustained advantage.
• TAM: Managers adopt GenAI if perceived as useful (ROI gains) and easy to use.
• DOI: Adoption in India follows a tiered diffusion—corporates lead, SMEs lag.

Figure 4: Theoretical Integration of MARK-GEN in Indian Context

MARK-GEN offers a practical bridge between theory and practice in AI marketing adoption.

Findings

The findings can be synthesized into three broad insights:

1. Generative AI delivers tangible benefits—personalization, cost savings, engagement, ROI.
2. India-specific challenges—linguistic diversity, cost barriers, regulatory compliance—require adaptations of global models.
3. MARK-GEN provides a structured roadmap, making adoption scalable, ethical, and effective.

Generative AI in India provides a dual value proposition: it enhances personalization and efficiency while also demanding new approaches to trust, regulation, and localization. MARK-GEN’s structured framework enables firms to harness these opportunities while navigating challenges. The analysis reinforces that India’s GenAI journey is distinct from global patterns, requiring vernacular-first, regulation-aware, and SME-inclusive strategies.

The case findings and analysis presented in the previous section confirm that Generative AI (GenAI), when implemented through the MARK-GEN framework, creates measurable value for Indian firms. At the same time, the findings highlight the contextual adaptations and ethical considerations necessary for sustainable deployment. This section situates these findings within theoretical, managerial, and policy debates, while also linking to global discourses on marketing innovation.

Theoretical Contributions

The study makes three important theoretical contributions to the marketing and AI literature.

Extending the Resource-Based View (RBV)

RBV suggests that competitive advantage stems from resources that are valuable, rare, inimitable, and non-substitutable (Barney, 1991). This research extends RBV by demonstrating that GenAI capabilities qualify as strategic resources in marketing. For Indian firms, the value lies not just in creative automation but in vernacular adaptability, which is difficult for competitors to replicate. For example, HUL’s AI-generated regional campaigns, trained on decades of ad archives, become firm-specific, inimitable resources that strengthen brand advantage.

Enriching the Technology Acceptance Model (TAM)

TAM explains technology adoption through perceived usefulness and ease of use (Davis, 1989). Findings show that Indian managers perceive GenAI as useful primarily when ROI improvements are evident (e.g., CTR uplift in HDFC’s campaigns). However, ease of use is complicated by linguistic and cultural barriers; thus, adoption requires AI systems that can integrate seamlessly into India’s multilingual context. This enriches TAM by adding “cultural usability” as a new dimension in technology adoption.

Applying Diffusion of Innovations (DOI) to GenAI Adoption

DOI theory (Rogers, 2003) helps explain adoption sequencing. The study shows that large corporates like Reliance Jio and Flipkart act as innovators, followed by early adopters such as HUL and MakeMyTrip, while SMEs remain in the early majority stage due to cost and literacy constraints. This suggests a tiered diffusion pattern unique to India, shaped by firm size and resource availability.

MARK-GEN provides a bridging framework that operationalizes how RBV, TAM, and DOI converge in the Indian marketing ecosystem.

Managerial Implications

The study also highlights several practical implications for managers in India.

Localization as a Competitive Advantage

GenAI should not be viewed merely as a cost-saving automation tool. Instead, managers should leverage it to localize campaigns across India’s 22 official languages and hundreds of dialects. Flipkart’s use of vernacular ad copies and Zomato’s regional campaign slogans illustrate that cultural resonance drives consumer engagement more effectively than generic English campaigns.

Building Hybrid Human-AI Teams

Findings suggest that GenAI should augment rather than replace human creativity. Managers should build hybrid teams where AI handles repetitive creative tasks (e.g., banner design, email subject lines), while humans focus on strategic storytelling and cultural alignment. This balance reduces resistance among marketing professionals and ensures that campaigns retain authentic human touchpoints.

Enhancing Transparency and Trust

Trust emerged as a major challenge. Indian consumers often feel deceived if they discover that advertisements or product descriptions are fully AI-generated. Managers should adopt transparent disclosure strategies (e.g., “This message was co-created with AI”) to build credibility.

Rethinking ROI Metrics

Traditional ROI metrics like CTR and impressions are insufficient for GenAI campaigns. Managers should track new metrics such as:

• AI creativity efficiency (cost saved per creative output).
• Localization impact (consumer engagement uplift in vernacular campaigns).
• Trust index (consumer perception of AI transparency).

Table 17: Managerial Implications of MARK-GEN in India

Policy Implications

The Indian policy environment will be central in shaping the trajectory of GenAI adoption.

Strengthening AI Regulation

The Digital Personal Data Protection (DPDP) Act, 2023 addresses consumer privacy, but AI-specific guidelines are absent. Policymakers must create frameworks to:

• Govern synthetic media (preventing deepfakes in advertising).
• Enforce disclosure norms for AI-generated campaigns.
• Mandate audit mechanisms for bias detection in AI models.

Supporting SME Adoption

SMEs form the backbone of Indian commerce but face resource constraints in adopting GenAI. Policy measures could include:

• AI adoption subsidies for SMEs.
• Public-private partnerships for vernacular AI datasets.
• Training programs to improve AI literacy among SME marketers.

Aligning with Global Standards

India must align its AI marketing policies with global best practices (e.g., EU’s AI Act, OECD guidelines). However, it should also adapt to local realities—for instance, prioritizing vernacular inclusivity and cost-accessible AI.

Table 18: Policy Implications of GenAI in Indian Marketing

Alignment with Sustainable Development Goals (SDGs)

Generative AI in marketing also intersects with broader sustainability debates.

• SDG 8 (Decent Work & Economic Growth): GenAI enables SMEs to scale marketing affordably, driving inclusive economic growth.
• SDG 9 (Industry, Innovation, Infrastructure): By promoting AI-driven marketing, India strengthens its digital infrastructure.
• SDG 12 (Responsible Consumption & Production): GenAI-powered personalized campaigns reduce advertising waste by targeting relevant audiences.
• SDG 16 (Peace, Justice & Strong Institutions): Ethical AI practices in marketing build trust in digital institutions.

By embedding ethical and responsible use within MARK-GEN, firms contribute not only to competitive advantage but also to India’s sustainable digital transformation.

Practical Roadmap for Indian Firms

Synthesizing managerial and policy implications, the study proposes a practical roadmap for firms:

Figure 1: Roadmap for MARK-GEN Implementation in Indian Firms

Contribution to Global Debates

Finally, the Indian case offers lessons for global scholarship:

1. Localization as a Strategic Differentiator: India shows that GenAI’s true value lies in adapting to cultural heterogeneity, not just scaling generic content.
2. SME Inclusion in AI Transformation: Unlike the West, where AI adoption is corporate-heavy, India’s growth will depend on SME democratization.
3. Privacy vs Personalization Balance: India’s DPDP Act provides a unique middle ground between personalization-driven marketing (US) and strict data minimization (EU).

The discussions reveal that the MARK-GEN framework is more than a tactical model; it is a strategic and ethical blueprint for India’s marketing future. The study contributes to theory by extending RBV, TAM, and DOI, to practice by offering localization and trust-building insights, and to policy by suggesting SME-supportive, disclosure-driven regulations. In doing so, it aligns marketing innovation with India’s sustainability and inclusivity goals.

Conclusion

This paper set out to examine how Generative AI (GenAI) can transform marketing strategies in India through the MARK-GEN framework. Building on theories of Resource-Based View (RBV), Technology Acceptance Model (TAM), and Diffusion of Innovations (DOI), the study developed and validated a seven-stage roadmap—from defining aims to deployment—that enables firms to systematically adopt GenAI in marketing. Through case studies across e-commerce (Flipkart/Myntra), FMCG (HUL), tourism (MakeMyTrip/OYO), and banking (HDFC/Paytm), the research demonstrated how MARK-GEN can:

• Enable personalization at scale in India’s multilingual and culturally diverse market.
• Deliver cost efficiencies in creative production.
• Enhance customer engagement through AI-driven storytelling and chatbots.
• Improve marketing ROI via measurable increases in CTRs and NPS.

At the same time, findings reveal critical challenges—vernacular data scarcity, ethical risks, infrastructure costs, and regulatory compliance under India’s Digital Personal Data Protection (DPDP) Act, 2023. These challenges differentiate India’s adoption path from Western economies, where AI adoption is more mature and standardized. Overall, the MARK-GEN framework provides both a conceptual contribution to marketing literature and a practical guide for Indian managers navigating the opportunities and risks of GenAI adoption.

Contributions of the Study

The study makes three key contributions:

1. Theoretical Contribution: It extends RBV, TAM, and DOI theories to the Indian context by positioning GenAI as a strategic resource, highlighting cultural usability as a factor in adoption, and showing tiered diffusion patterns.
2. Practical Contribution: It introduces MARK-GEN as a stepwise framework for Indian firms, offering sectoral illustrations of implementation and measurable outcomes.
3. Policy Contribution: It identifies regulatory and SME-support measures needed to democratize AI adoption, while aligning with India’s sustainability agenda.

Limitations

Despite its contributions, the study has limitations:

• The qualitative case-based approach provides depth but not statistical generalizability.
• The cases were concentrated in urban, digitally advanced firms, limiting insights into rural adoption.
• The rapid evolution of GenAI tools means that findings may quickly become outdated.
• The study did not capture consumer perspectives in detail, focusing instead on managerial insights.

Future Research Directions

Future studies should address these limitations by:

1. Quantitative Validation of MARK-GEN: Large-scale surveys across industries could test the robustness of the framework and quantify adoption levels.
2. Consumer-Centric Studies: Research should examine consumer trust, willingness to engage with AI-generated content, and perceptions of transparency.
3. Rural and SME Adoption: More work is needed to understand how GenAI can empower resource-constrained SMEs and rural businesses.
4. Comparative Cross-Country Studies: Comparing India with other emerging economies (e.g., Brazil, Indonesia, Nigeria) could yield insights into adoption in culturally diverse, cost-sensitive environments.
5. Ethics and Responsible AI: Future research should evaluate ethical implications, including bias in vernacular AI models, synthetic influencer credibility, and misinformation risks.

Generative AI represents a paradigm shift in marketing—from predictive analytics to creative co-production with machines. In India, where cultural diversity, linguistic plurality, and regulatory sensitivity define the marketplace, GenAI adoption must be strategic, responsible, and inclusive. The MARK-GEN framework offers a structured pathway to achieve this, enabling Indian firms to not only innovate marketing strategies but also to shape a globally distinctive model of AI-driven marketing.

1. Agarwal, P., & Garg, A. (2023). Human values and environment studies: e-Book for BA 3rd Semester for all UP State Universities as per common syllabus of NEP-2020. Thakur Publication Private Limited.
2. Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49. https://doi.org/10.1016/j.jretai.2014.09.005
3. Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34(7), 1177–1190. https://doi.org/10.1016/j.tele.2017.05.008
4. Bala, M., & Verma, D. (2018). A critical review of digital marketing. International Journal of Management, IT & Engineering, 8(10), 321–339.
5. Bansal, S., & Kapoor, A. (2022). Artificial intelligence adoption in Indian businesses: Opportunities and challenges. IIM Kozhikode Society & Management Review, 11(2), 186–200. https://doi.org/10.1177/22779752211062484
6. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
7. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
8. Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2023). Artificial intelligence and marketing: A research agenda. Journal of Business Research, 154, 113–125. https://doi.org/10.1016/j.jbusres.2022.113334
9. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
10. Dixit, A., Sahu, S., Gupta, A. K., Sawant, A., Garg, A., & Verma, N. (2024). Smart vision: A unified system for enhanced navigation and accessibility for visually impaired individuals. 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 284–288. IEEE.
11. Dwivedi, Y. K., Hughes, L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., Gupta, B., Lal, B., Misra, S., Prashant, P., Raman, R., Rana, N. P., Sharma, S. K., & Upadhyay, N. (2021). Impact of artificial intelligence (AI) on marketing strategy. International Journal of Information Management, 57, https://doi.org/10.1016/j.ijinfomgt.2020.102210
12. Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385
13. Feuerriegel, S., Dolata, M., & Schwabe, G. (2024). Generative AI in management: Opportunities, risks, and the future of work. Management Information Systems Quarterly, 48(1), 1–20. https://doi.org/10.25300/MISQ/2024/17121
14. Garg, A. (2015). Study of online shopping in Ghaziabad and Noida region: A customer perspective. International Journal of Advanced and Innovative Research, 4(5), 48.
15. Garg, A. (2016). Impact of office ergonomics on business performance: In special reference to Noida region. International Journal of Advanced and Innovative Research, 5(4), 888.
16. Garg, A. (2019). A study of different aspects of consumer behavior for online buying in Delhi NCR for FMCD products. PRANJANA: The Journal of Management Awareness, 22(2), 76.
17. Garg, A. (2021, January 17). The relevance of Engel-Blackwell-Miniard model of consumer behavior during COVID-19: A contemporary consumer behavior survey on FMCG products in urban demography in Uttar Pradesh West. ANVESAK, 51(2), [XVIII].
18. Garg, A., & Kumar, S. (2022, January 11). Consumer panic buying and consumer behaviour during pandemic years for innovative sanitization goods: A study of buying behaviour to sanitization durable goods. Manager: The British Journal of Administrative Management, 58(154). https://tbjam.org/
19. Garg, A., & Sharma, J. (2022). Copper complexes as potential catalytic, electrochemical and biochemical agents. Materials Today: Proceedings, 62, 1632–1635. https://doi.org/10.1016/j.matpr.2022.02.490
20. Garg, A., Agarwal, S., Singhal, H., Sharma, H., & Sharma, N. (2025). Towards a sustainable circular economy: A comprehensive study on awareness, critical success factors, and implementation models. In Implementing ESG Frameworks Through Capacity Building and Skill Development (pp. 329–348). IGI Global Scientific Publishing.
21. Garg, A., Das, S., Gaur, M., & Singhal, R. K. (2022). Marketing intelligence recent research trends: Systematic literature review approach. NeuroQuantology, 20(7), 1464–1479.
22. Garg, A., Jain, R., Verma, D., Jha, D., Singhal, R., & Singhal, R. (2024). Examining the effect of FDI on economic growth of developing nations: A comprehensive analysis of Indian food processing industry through ARDL method. Educational Administration: Theory and Practice, 30(4), 242–248.
23. Garg, A., Jaiswal, G., & Singhal, R. K. (2025). A study of vital aspects of advertisements in the information swamp world for creating an appealing advertisement. International Journal of Internet Marketing and Advertising, 23(2), 190–207.
24. Garg, A., Nayyar, R., Singh, S., Sharma, H., & Singhal, R. (2026). Green logistics 5.0: An examination of innovation driven by sustainability using foundation models in logistics. In Emerging Trends in Smart Logistics Technologies (pp. 367–396). IGI Global Scientific Publishing.
25. Garg, A., Pandey, A., Sharma, N., Kumar, A., Jha, P. K., & Singhal, R. K. (2023). An in-depth analysis of the constantly changing world of cyber threats and defences: Locating the most recent developments. 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC), 181–186. IEEE.
26. Garg, A., Pandey, T. R., Singhal, R. K., Sharma, H., & Singh, A. K. (2024). Exploring enlarged perceptions of value: The utilization of virtual reality in Indian tourism. In Service Innovations in Tourism: Metaverse, Immersive Technologies, and Digital Twin (pp. 215–253). IGI Global Scientific Publishing.
27. Garg, A., Saxena, N., Verma, N., Singhal, R. K., Singhal, R., & Kaggallu, N. (2024, January 5). Examining the smart tourism landscape: A bibliometric study and analytical synopsis of IoT research. 2024 International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM). IEEE.
28. Garg, A., Sharma, H., Singh, A. K., Sharma, N., & Aneja, S. (2024). Understanding the unpredictable: Technological revolutions' transformative impact on tourism management and marketing. In Service Innovations in Tourism: Metaverse, Immersive Technologies, and Digital Twin (pp. 19–38). IGI Global Scientific Publishing.
29. Garg, A., Sharma, R. B., Tripathi, S., Kumar, K. S., Varshney, A. K., & Sharma, A. (2025). AI-powered virtual reality: Transforming education beyond conventional approaches. 2025 International Conference on Intelligent Control, Computing and Communications (IC3), 398–403. IEEE.
30. Garg, A., Sharma, S., Singh, R., Agarwal, S., Kumar, K. S., Tyagi, A., & Singhal, H. (2024). Exploring the fairness implications of A.I replacing human decision-makers in HR management: A case study on resume screening. Journal of Informatics Education and Research, 4(3), 280–291. https://jier.org/index.php/journal/article/view/1315/1108
31. Garg, A., Singh, S. K., & Singhal, R. K. (2022). Consumer behavior towards FMCG products: A contemporary survey of consumer behaviour using EKB model. AAYAM: AKGIM Journal of Management, 12(2), 186–191.
32. Garg, A., Singhal, R., & Singh, A. (2022). Artificial intelligence: An emergence for electrical automation control. AAYAM: AKGIM Journal of Management, 12(2), 150–152.
33. Garg, A., Tripathi, S., Agarwal, S., Tomar, D., & Kumar, V. (2023). Consumer attitude towards counterfeit products: An extension of theory of planned behaviour. Empirical Economics Letters, 22(2), 76–91.
34. Garg, A., Verma, D., Das, S., Gaur, M. P., Srivastava, J., & Dwivedi, P. K. (2025). Emerging trends and technologies in contemporary marketing: Strategic approaches for enhanced consumer engagement. Advances in Consumer Research, 2(3).
35. Garg, A., Verma, D., Pandey, L., Kumar, K. S., Singh, R., & Sharma, A. (2025). Enhancing tailored travel by integrating generative AI with insights driven by personality. 2025 International Conference on Intelligent Control, Computing and Communications (IC3), 404–409. IEEE.
36. (2023). Digital advertising in India 2023 report. Internet and Mobile Association of India. https://www.iamai.in
37. Islam, N., Pitafi, A. H., Arya, V., Wang, Y., Akhtar, N., Mubarik, S., & Xiaobei, L. (2024). The role of generative AI in reshaping digital marketing practices. Computers in Human Behavior, 150, https://doi.org/10.1016/j.chb.2023.107250
38. Jain, R., & Sharma, A. (2024). Ethical risks of generative AI in Indian advertising: A critical perspective. South Asian Journal of Business Studies, 13(2), 201–220. https://doi.org/10.1108/SAJBS-09-2023-0159
39. (2023). India’s influencer marketing industry report 2023. KPMG India. https://home.kpmg/in/en/home.html
40. Kshetri, N., Sharma, S., & Kapoor, A. (2023). Artificial intelligence in emerging economies: Challenges and opportunities. Journal of Global Information Technology Management, 26(1), 1–18. https://doi.org/10.1080/1097198X.2023.2160931
41. (2023). AI adoption in India: Trends and opportunities. National Association of Software and Service Companies. https://nasscom.in
42. Pandey, A., Singh, R., Sharma, A., Agarwal, P., & Garg, A. (2025). A review of applications combining blockchain technology with artificial intelligence. In Demystifying Emerging Trends in Green Technology (pp. 96–106). Bentham Science Publishers.
43. Pandita, S., Garg, A., Kumar, S., Das, S., & Gaur, M. P. (2024). The impact of HR technologies on digital library staff efficiency and engagement. Library of Progress–Library Science, Information Technology & Computer, 44(3).
44. Poonia, S., Sharma, R., Baranwal, A., Jain, I., & Tiwari, V. K. (2022). An examination of the impact of prevailing nationalist sentiments on Indian consumer behaviour for selected products and services: Is it contributing to or limiting a self-sufficient economy? AAYAM: AKGIM Journal of Management, 12(2), 36–45.
45. Rathi, C., Varshney, A. K., Chaudhary, S., Prakash, V., & Garg, A. (2024). A paradigm shift in fintech and rural inclusion challenges in India. International Journal of Science and Research Archive, 12(1), 299–311.
46. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
47. Sharma, A., Pandey, A., Singh, R., & Garg, A. (2025). Examining the viability of integrating blockchain technology into IoT devices for the supply chain. In Demystifying Emerging Trends in Green Technology (pp. 75–83). Bentham Science Publishers.
48. Sharma, H., Garg, A., Singhal, R. K., Gaur, M. P., Sharma, H., & Sharma, N. (2024, January 6). Utilizing deep learning and advanced machine learning methods in economic data analysis. 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE.
49. Sharma, H., Sahu, N., Singhal, R. K., Garg, A., Singhal, R., & Tripathi, S. (2024, January 6). Data-driven forecasting and inventory optimization using machine learning models and methods. 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE.
50. Singh, S., Garg, A., & Walsh, J. C. (2025). AIvertising: Mapping the transformation of marketing in the experience economy. In Leveraging AI-Powered Marketing in the Experience-Driven Economy (pp. 71–104). IGI Global Scientific Publishing.
51. Singhal, H., Singhal, R. K., Garg, A., Singhal, R., Sharma, H., & Jaiswal, G. (2024, January 5). Analyzing bibliometric systematic reviews on blockchain's role in international e-commerce supply chain management. 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE.
52. Singhal, R. K., Garg, A., Agarwal, P., & Jaiswal, G. (2025). Investigating technology adoption and consumer behaviour in digital age. In Demystifying Emerging Trends in Green Technology (pp. 120–131). Bentham Science Publishers.
53. Singhal, R. K., Garg, A., Das, S., & Pandey, T. R. (2022). Health care management using blockchain technology: A conceptual framework. NeuroQuantology, 20(7), 1452–1463.
54. Singhal, R. K., Garg, A., Verma, N., Saxena, N., Sharma, H., & Singh, A. K. (2023). Unlocking diverse possibilities: The versatile applications of blockchain technology. 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC), 187–191. IEEE.
55. Singhal, R., & Garg, A. (2015). Study of online shopping in Ghaziabad and Noida region: A customer perspective. Unpublished/Journal unspecified.
56. Singhal, R., Srivastava, A., Thakkar, U. P., Garg, A., Agarwal, S., & Singhal, R. K. (2024, January 3). Composition of board of directors and its effect on the performance of banking firms: Analysis through OLS method. 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE.
57. (2024). India: Internet usage statistics 2024. https://www.statista.com
58. Swaroop, T. S., Kumar, S., Garg, A., Tripathi, V., Singh, S., Singhal, H., & Kumar, B. (2024). Behavioral consequences of customer inspiration: The role of social media inspirational content and cultural orientation. Journal of Informatics Education and Research, 4(3).
59. Taparia, R., Chatterjee, A., Garg, A., & Pandey, T. R. (2022, January 14). A study of the impact of risk avoidance and financial welfare on the intent to invest in the equity market. Manager: The British Journal of Administrative Management, 58(9).
60. (2024). Telecom subscription report. Telecom Regulatory Authority of India. https://trai.gov.in
61. Varshney, A. K., Garg, A., Pandey, T., Singhal, R. K., Singhal, R., & Sharma, H. (2024). The development of manufacturing industry revolutions from 1.0 to 5.0. Journal of Informatics Education and Research, 4(1), 12–30.
62. Varshney, A. K., Garg, A., Pandita, S., Gaur, M. P., Singhal, R. K., & Sharma, H. (2024). Exploring the impact and factors to consider in higher education practice: A study in reference to generative artificial intelligence. European Economic Letters, 14(1), 9–15.
63. Varshney, A. K., Mishra, A. K., Agarwal, S., Garg, A., Das, S., & Tripathi, S. (2024). Changing aspects: Examining financial market forecasting via textual representation—A critical evaluation. 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 1127–1132. IEEE.
64. Verma, D., Garg, A., Das, S., Singhal, R., Sharma, H., & Gaur, M. P. (2025). Increasing the competitiveness and branding of travel destinations: A case study on stakeholder integration and perspectives from Indian regional development. Journal of Marketing & Social Research, 2, 86–91.
65. Verma, D., Raj, P., Tripathi, S., Agarwal, S., Garg, A., & Singhal, H. (2024). An analysis of the systematic landscape and potential future directions in studies on neuro-tourism. 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 1133–1138. IEEE.
66. Verma, N., Varshney, A. K., Singhal, R. K., Gaur, M. P., Garg, A., & Das, S. (2025). Explainable artificial intelligence (XAI) in insurance. 2025 International Conference on Pervasive Computational Technologies (ICPCT), 305–310. IEEE.
67. Vishnoi, S. K., Virmani, N., Pant, D., & Garg, A. (2022). Big data in healthcare: Technological implications and challenges. In Designing Intelligent Healthcare Systems, Products, and Services Using Disruptive Technologies and Health Informatics (pp. 211–227). CRC Press.
68. Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage Publications.