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Explorez tous les épisodes du podcast AI & Marketing Research with Dr. Eva Wolf
Plongez dans la liste complète des épisodes de AI & Marketing Research with Dr. Eva Wolf. Chaque épisode est catalogué accompagné de descriptions détaillées, ce qui facilite la recherche et l'exploration de sujets spécifiques. Suivez tous les épisodes de votre podcast préféré et ne manquez aucun contenu pertinent.
| Titre | Date | Durée | |
|---|---|---|---|
| AI Slop Is Killing Your Marketing: The 3C Framework That Fixes It | 18 Jul 2026 | 00:28:58 | |
Most businesses don’t have an AI marketing tool problem. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: CRM Performance, Agent Loyalty & AI Forecasting | 16 Jul 2026 | 00:20:15 | |
When your AI says it's predicting the future, is it actually forecasting — or just remembering? And what happens to brand loyalty when an AI agent, not a human, is the one making the purchase? Three papers this week examine whether we are actually getting what we think we are getting from AI in marketing.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI-driven CRM and financial performance in digital banking, AI agent loyalty loops in autonomous commerce, and a method for evaluating whether AI forecasting tools are genuinely predicting or retrieving memorized answers.
Note: Evita is an AI-generated research briefing avatar trained on the research framework of Dr. Eva Wolf. Every Friday, Dr. Wolf records a live weekly roundup with her own analysis.
What you'll learn:
- Why AI-powered CRM outperformed both personalization and chatbots as a financial performance driver in a Nigerian digital banking study, and how to use that R-squared value in a budget conversation
- How to tell whether an AI forecasting tool your vendor sells is actually predicting anything, or retrieving answers it already knows from training data
- Why emotional brand loyalty may stop mattering once an AI agent takes over purchasing decisions — and what marketers need to put in its place
- What machine-readable brand signals means in practice, and why loyalty programs need to be legible to algorithms, not just humans
- Why noisy or speculative retrieval sources make AI predictions worse, not merely less precise
Papers covered:
1. The Influence of AI-Driven Marketing on the Financial Performance of Digital Banks in Nigeria
Source type: Peer-reviewed journal article (likely peer-reviewed, published via Zenodo)
Access: Full text reviewed
DOI: 10.5281/zenodo.21277512
2. The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce
Source type: Preprint (not yet peer-reviewed — treat findings as early-stage and theoretical)
Access: Full text reviewed
Source: https://arxiv.org/abs/2607.13998v1
3. Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2607.14051v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-crm-performance-agent-loyalty-forecasting-2026-07-16
Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Preprints have not been peer-reviewed and findings may change. Correlation findings do not establish causation. Always consult the original source before citing or acting on any research discussed here.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Generative AI, Small Business & Chatbot Trust | 07 Jul 2026 | 00:19:36 | |
Access to AI tools isn't the same as knowing how to use them — and assuming your AI chatbot's output is safe to ship may be a risk your brand isn't tracking. This episode examines what the research actually says about generative AI adoption in small businesses, the reshaping of marketing content pipelines, and a striking finding about how often AI chatbots cite sources you'd never want attached to your brand.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI training for small businesses, AI-driven content and influencer disruption, and a source-trust audit of AI chatbots using open web search — drawn from 385 papers screened.
What you'll learn:
- Why structured training and mentoring matter as much as the AI tools themselves for small business adoption
- How generative AI is shifting who does creative work — and what that means for marketing teams
- Why roughly 1 in 3 AI chatbot answers using open web search cited at least one source flagged as untrustworthy by domain experts
- Why telling your AI to "prefer trusted sources" in the system prompt barely works — and what the more reliable fix actually is
- What a pre/post measurement framework for AI training programs looks like in practice
Papers covered:
1. Generative AI in Digital Marketing Strategy: Transforming Brand Communication and Consumer Engagement
- Authors: Halim Dwi Putra, J. Azizah (2026)
- Source type: Peer-reviewed journal article (likely peer-reviewed)
- Venue: Indonesian Journal of Business and Entrepreneurship Research
- Access: Full text reviewed
- DOI: 10.62794/ijober.v4i1.23
- Radar verdict: Use cautiously
2. How Generative AI is Disrupting Marketing, Branding & Content Creation for Modern Businesses
- Author: Shaan Garg (2026)
- Source type: Peer-reviewed journal article (likely peer-reviewed)
- Venue: International Journal For Multidisciplinary Research
- Access: Full text reviewed (open access)
- DOI: 10.36948/ijfmr.2026.v08i01.64298
- Radar verdict: Use cautiously
3. Curated Retrieval versus Open Web Search in Public AI Information Services: A Coverage-Trust Trade-Off
- Authors: Hafsteinn Einarsson, Hafsteinn Birgir Einarsson, Jon Gunnar Olafsson, Jon Gunnar Thorsteinsson (2026)
- Source type: Preprint — not yet peer-reviewed
- Venue: arXiv
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2607.05217v1
- Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-generative-small-business-chatbot-source-trust-2026-07-07
Disclaimer: This is a first-pass research briefing produced by an AI-generated research avatar trained on Dr. Eva Wolf's methodology. It is not a final academic review. Findings are summarised for informational purposes. Always read the original papers before making decisions based on this content. Preprints have not been peer-reviewed and should be treated with additional caution.
--
This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Visuals, Engagement & Marketing Agent Loop: Research Brief | 06 Jul 2026 | 00:15:59 | |
AI tools are cheap enough now that even a shoestring team can produce polished visuals — but does that actually close the competitive gap, or does it just mean everyone looks polished while the power imbalance stays exactly the same? And when AI agents start making pricing, promotion, and relationship decisions inside your marketing org, who is still accountable for auditing the results?
In this Research Radar Brief, Dr. Eva Wolf reviews 2 recent peer-reviewed AI marketing research papers covering AI-generated visual content on social media, disclosure practices, photorealistic AI imagery, and a proposed framework for AI agent integration in marketing workflows.
What you'll learn:
- AI-generated visuals boost social media engagement — but the lift is roughly equal for all players, which means it may not deliver the competitive edge many marketers expect
- Disclosure practices are uneven: better-resourced organizations label AI content more consistently, and that gap is emerging as a credibility signal
- Photorealistic AI imagery paired with negative or emotionally charged messaging drives higher engagement — with real implications for brand safety and platform scrutiny
- The Marketing Agent Loop (sense, generate, interact, learn) offers a four-stage mental map for deciding where AI agents fit in your workflow — and where human oversight is still essential
Papers covered:
1. Party Equalization or Normalization Through Visual Generative AI in the 2025 German Federal Election
- Authors: Simon Kruschinski, Fabio Votta (2026)
- Source: Media and Communication (peer-reviewed journal)
- Access: Full text reviewed
- DOI: https://doi.org/10.17645/mac.11859
- Radar verdict: Deep dive
2. Agent-Oriented Transformation of Marketing Functions in the Generative AI Era: Introducing the Marketing Agent Loop
- Authors: Merve Kadriye Yurdabak (2026)
- Source: Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi (peer-reviewed journal)
- Access: Full text reviewed — open access
- DOI: https://doi.org/10.24889/ifede.1788481
- Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-visuals-engagement-agent-loop-2026-07-06
Disclaimer: This is a first-pass research briefing produced by Evita, an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a substitute for reading the full papers. Findings are reported as the research suggests — not as proven facts. Always consult primary sources before making strategic decisions.
--
This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Ad Speed, Brand Trust & Green Influencer Risk: 3 Research Signals | 05 Jul 2026 | 00:20:28 | |
When AI runs your ads, writes your content, and picks your influencers — are you gaining an edge, or quietly building a trust problem you haven't spotted yet? This episode looks at both sides of that question at once, drawing on three recent AI and marketing research papers that together point to the same tension: AI is removing friction from ad delivery at exactly the moment consumers are developing resistance to AI-generated content.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering LLM-powered ad delivery, consumer perceptions of generative AI content, and AI and AR tools in sustainability influencer marketing. We screened 358 papers this cycle; these three cleared the full-text bar.
What you'll learn:
- How a compressed large language model was reported to run real-time ad delivery at over 1.8x its original speed — and what that means for ad tech teams still treating LLMs as too slow and too expensive
- The four ways consumers signal distrust when they sense a brand is leaning too hard on AI: verification burden, emotional flattening, content homogenisation, and AI fatigue
- Why AI-generated visuals and AR experiences in sustainability campaigns can amplify greenwashing risk rather than credibility
- What inference latency and competitive precision actually mean for marketers evaluating AI ad vendors
- Why a hybrid human-AI content approach may now be a measurable brand safety decision, not just an ethical preference
Papers covered:
1. Efficient LLM-based Advertising via Model Compression and Parallel Verification
Authors: WenXin Dong, Chang Gao, Guanghui Yu et al. (2026)
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://doi.org/10.48550/arxiv.2605.11582
2. Generative AI in Marketing Communication: Consumer Perceptions, Brand Credibility, and Responsible AI Practices
Authors: Tahir Mushtaq, Onder Kethuda, Antje Cockrill, Ahmed Almoraish (2026)
Source type: Conference paper (likely peer-reviewed — AMI Conference 2026)
Access: Full text reviewed
Source: https://doi.org/10.25401/cardiffmet.32326287.v1
3. Sustainable Influencer Marketing: Leveraging AI and AR Tools to Promote Green Lifestyles
Authors: Lina Tio, Salamiah Muhd Kulal, Dorris Yadewani (2026)
Source type: Peer-reviewed journal article (use cautiously — see episode notes on venue credibility)
Access: Full text reviewed
Source: https://doi.org/10.54099/ijibmr.v5i2.1624
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-ad-speed-brand-trust-influencer-greenwashing-2026-07-05
Disclaimer: This is a first-pass research briefing produced by an AI-generated research avatar trained on the methodology of Dr. Eva Wolf. It is not a final academic review. Findings are reported as the papers suggest, not as established facts. Preprints have not been peer-reviewed. Always consult the original papers before making business decisions.
--
This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Ads, Persona Research & Consumer Trust: 3 Marketing Papers | 04 Jul 2026 | 00:19:20 | |
What if two of the biggest bets your team is making right now — building detailed customer personas for AI research and trusting AI to write your ads — are both quietly working against you? This episode looks at what the latest research actually says about where AI-native advertising is breaking, and whether the fixes are simpler than most teams assume.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering LLM ad auction design, the accuracy of AI-simulated customer personas, and consumer trust in generative AI advertising.
What you'll learn:
- Why filtering ads for topic relevance before the auction — not after — may earn AI platforms more revenue while annoying users less
- Why elaborately detailed customer personas tend to make AI simulations of human behavior less accurate, not more
- Why a simple Age + Gender persona consistently outperformed a 10-attribute Ideal Customer Profile in AI audience research
- Why perceived authenticity — not visual quality — is the stronger predictor of whether AI-generated ads drive engagement
- Which industries (healthcare, finance, luxury) face the sharpest consumer trust penalty for AI-generated creative
Papers covered:
1. Mechanism Design for Quality-Preserving LLM Advertising
Authors: Jiale Han, Xiaowu Dai (2026)
Source: arXiv (Cornell University)
Type: Preprint — not yet peer-reviewed
Access: Full text reviewed
DOI: https://doi.org/10.48550/arxiv.2605.10964
2. How Well Do Large Language Models Capture Human Personality?
Authors: Aanisha Bhattacharyya, Yaman Kumar Singla, Rajiv Ratn Shah, Changyou Chen, Jitendra Ajmera (2026)
Source: arXiv (Cornell University)
Type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs/2606.18263
3. Generative AI Applications in Advertising
Author: Yifei Wang (2026)
Source: Frontiers in Computing and Intelligent Systems
Type: Peer-reviewed journal article (lower-tier venue — see episode notes for caveats)
Access: Full text reviewed
DOI: https://doi.org/10.54097/4tggde31
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-advertising-persona-accuracy-consumer-trust-llm-2026-07-04
DISCLAIMER: This is a first-pass research briefing produced using an AI-generated research avatar trained on Dr. Eva Wolf's methodology. Two of the three papers covered are preprints that have not yet been peer-reviewed. Findings are presented as signals to investigate, not as settled conclusions. Always read the original papers before making business decisions based on this content.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI-Generated Ads, Consumer Trust & Privacy: 3 Research Signals | 03 Jul 2026 | 00:20:17 | |
When your AI writes the ad, the email, or the product page — does it matter whether customers know it's AI, or does what they feel about it matter more?
This radar brief follows the trust trail: from authenticity signals to privacy gates to the uncanny valley of AI creative, and asks what actually moves a consumer from "I see this ad" to "I'm buying."
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI-generated content and purchase intent, consumer responses to AI advertising, and how privacy trust shapes the effectiveness of AI personalization across cultural markets.
What you'll learn:
- Why perceived authenticity — not credibility — is the single strongest driver of purchase intent from AI-generated content, and why credibility only converts when it first builds trust
- How consumers can feel both impressed and unsettled by AI ads at the same time, and what determines which reaction wins
- Why disclosing AI involvement doesn't automatically hurt ad performance — and when it may actually help
- How privacy trust acts as an on/off switch for AI personalization, especially in GDPR-regulated markets
- Why cultural localization means adapting visuals, flow, and tone — not just language — and how that amplifies AI personalization effects
Papers covered:
1. The Impact of AI-Generated Marketing Content on Consumer Behavior
Source type: Peer-reviewed journal article (International Journal of Multidisciplinary Research and Analysis)
Access: Full text reviewed
DOI: 10.47191/ijmra/v9-i6-60
2. Decoding AI-Generated Advertising: Consumer Responses, Advertising Outcomes, and Strategic Leverage
Source type: Academic book chapter (IntechOpen, likely peer-reviewed)
Access: Full text reviewed
DOI: 10.5772/intechopen.1016226
3. Cultural Adaptation and AI-Driven Marketing in the Context of Trust in Data Privacy
Source type: Academic book chapter (Routledge, likely peer-reviewed)
Access: Full text reviewed
DOI: 10.4324/9781003757719-14
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-generated-ads-consumer-trust-privacy-personalization-2026-07-03
DISCLAIMER: This is a first-pass research briefing produced by an AI-generated avatar (Evita) trained on the research framework of Dr. Eva Wolf. It is not a substitute for reading the full papers. Findings are described as the studies suggest them — not as proven facts. Sample sizes, methodologies, and limitations are noted where relevant. Always consult the original sources before making strategic decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Creativity Benchmarks, Chatbot Personas & Agent Competition | 02 Jul 2026 | 00:22:11 | |
Are you choosing AI tools on instinct, setting your chatbot to maximum-friendly, and hoping your AI agents figure out the rest? Three new preprints suggest those defaults are leaving real performance on the table — and that small, specific changes to how you prompt and configure AI can produce meaningfully different outcomes.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI and marketing research papers covering AI creativity measurement, adaptive chatbot personality design, and the competitive dynamics of AI agent labour markets.
What you'll learn:
- Adding "be creative" to your AI prompt meaningfully improves creative output — and takes about five seconds
- The best AI model for writing taglines is probably not the best model for brainstorming campaign concepts
- A chatbot with a medium-intensity personality outperforms both flat and over-enthusiastic bots on trust and likeability
- Context-specific prompting for AI agents changes outcomes — a single generic prompt leaves performance on the table
- Platform design choices (like whether pricing is public or hidden) shape whether AI agents race to the bottom or compete on quality
Papers covered:
1. AGC-Bench: Measuring Artificial General Creativity
Authors: Beaty, Deshpande, Lai, Attuch, Shivagunde, Roy, Pujari, DiStefano, Muckatira, Stevenson, Gronas, Rumshisky (2026)
Type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs/2607.01152v1
2. Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
Authors: Rahman, Desai (2026)
Type: Preprint — not yet peer-reviewed (presented at AAAI-2026 Bridge Program)
Access: Full text reviewed
Source: https://arxiv.org/abs/2607.01034v1
3. When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets
Authors: Chiu, Zhang, van der Schaar (2025)
Type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs (link in show notes)
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-creativity-benchmarks-chatbot-personas-agent-competition-2026-07-02
Disclaimer: This episode is a first-pass research briefing produced by an AI-generated avatar trained on the research framework of Dr. Eva Wolf. All three papers discussed today are preprints that have not yet completed peer review. Findings are preliminary and may change. Nothing in this episode constitutes professional marketing, legal, or financial advice. Always read the original papers before making strategic decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| Personalized AI, Creative Work & Knowledge Protocols: Research Brief | 17 Jun 2026 | 00:13:08 | |
Most marketing teams are running AI on generic settings — and the latest research suggests that may be the core problem. A new experiment finds that giving an AI tool a profile of the person it's working with produced measurably more creative and higher-quality marketing campaigns than using a generic AI setup. A second paper proposes a new framework for guiding how AI reasons through expert work — compelling in theory, but not yet tested in practice.
In this Research Radar Brief, Dr. Eva Wolf reviews 2 recent AI marketing research papers covering personalized AI collaboration, human-AI creative synergy, and expert-guided AI reasoning frameworks.
What you'll learn:
- Why a personalized AI collaborator outperformed a generic one in a controlled experiment — and the three mechanisms that explain the gap
- How human-plus-personalized-AI teams achieved a synergy effect that beat AI working alone — a result rarer than most assume
- What Knowledge Protocol Engineering proposes as a step beyond RAG-based AI — and why it has not yet been validated
- How to start loading your AI tools with collaborator context today, before any platform natively supports it
Papers covered:
1. Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work
- Authors: Sean Kelley, David De Cremer, Christoph Riedl (2025)
- Type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2510.27681v2
2. Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work
- Authors: Guangwei Zhang (2025)
- Type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2507.02760v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/personalized-ai-creative-work-knowledge-protocols-marketing-2026-06-17
Disclaimer: This is a first-pass research briefing, not a final academic review. Both papers covered are preprints that have not yet been peer-reviewed — findings may change before publication. Dr. Eva Wolf summarizes what the research suggests and where the evidence is limited. Nothing here should be taken as a definitive research conclusion.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Personalization & Creative Campaigns: What the Research Shows | 17 Jun 2026 | 00:13:52 | |
Does it matter whether your AI tool knows anything about you before you start a campaign? A new experimental study suggests it does — and the reason why changes how you should think about every AI-assisted creative session you run.
In this Research Radar Brief, Dr. Eva Wolf reviews one recent AI marketing research paper covering personalized AI, multi-turn creative collaboration, and what the research suggests about setting up AI tools for better campaign work. We screened twelve papers this cycle; one cleared the full-text bar.
What you'll learn:
- Why a short "creative brief about yourself" before any AI session may improve campaign quality — and what the research says is driving that improvement
- The three collaboration mechanisms personalization appears to activate: shared memory, joint attention, and aligned reasoning
- Why single-prompt tests of AI tools may miss the real performance gap — and where personalization pays off most
- Why this study's evidence is directionally strong but not final — and what to keep in mind before overhauling your AI workflow
Papers covered:
1. Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work
Authors: Sean Kelley, David De Cremer, Christoph Riedl (2025)
Type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2510.27681v2
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-personalization-creative-marketing-campaigns-research-2026-06-17
Important note: This is a first-pass research briefing, not a final academic review. Every paper covered has been reviewed at the full-text level where access was available. Read the original papers before making major marketing, business, legal, or financial decisions. Preprints have not yet completed peer review and findings may change.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Location Leakage, AI Workflows & Content Attribution | 17 Jun 2026 | 00:21:40 | |
Your AI personalization stack may be inserting geographic references into content where location is completely irrelevant — and new research puts concrete numbers on how often that happens. Meanwhile, two other papers raise questions about who owns accountability inside AI-assisted marketing workflows, and who gets paid when AI-generated content is built on someone else's data.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering geographic conditioning in large language models, human-AI workflow orchestration, and contributor attribution in generative AI markets. Selected from 382 papers screened this week.
What you'll learn:
- How location data passed to AI tools can leak into outputs up to 793 times more often than baseline — even when location is irrelevant to the task
- Why the way you inject location into a prompt (system prompt vs. user-profile block) can cut geographic leakage from 16% down to 3.8%
- Which AI models showed the highest and lowest leakage rates in this study
- How one conceptual framework proposes mapping a specific AI tool to each step of the marketing management process, with a human accountable at every stage
- Why the economics of AI-generated content are moving toward automatic revenue splits among training data providers, model builders, and prompt engineers — and what that means for the platforms marketers use today
Papers covered:
1. Unintended Effects of Geographic Conditioning in Large Language Models
Authors: Naz Col, David M. Chan (2026)
Type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs/2606.18124v1
2. Reframing the Managerial School in the Age of Generative AI: Toward an Augmented Marketing Framework
Authors: Merve Kadriye Yurdabak (2026)
Type: Peer-reviewed journal article
Access: Full text reviewed (open access)
DOI: 10.16953/deusosbil.1795373
3. AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets
Authors: Yang Shi, Songwen Pei, Yang Gao, Bingxue Zhang (2026)
Type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs/2606.16075v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-location-leakage-workflows-content-attribution-2026-06-17
Disclaimer: This is a first-pass research briefing produced using an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Two of the three papers covered are preprints that have not yet undergone peer review; findings may change. Nothing in this episode constitutes professional, legal, or financial advice.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Social Media, Creative Risks & Global Competitiveness: 3 Papers | 16 Jun 2026 | 00:20:44 | |
Can AI write convincing social media posts, profile your audience, and brainstorm your next campaign — and is your team getting sharper as a result, or quietly more generic? This episode of AI & Marketing Research Radar digs into the research behind those questions.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering LLM performance on social media tasks, role-based integration of generative AI in creative work, and AI-driven marketing capabilities for international competitiveness.
What you'll learn:
- AI models like GPT-4 can detect authorship and mimic writing styles on social media, but accuracy depends heavily on which users and posts are tested — and no single model leads on every task
- AI-generated social posts can score well on automated quality metrics but still feel fake to real human readers — automated checks are not a substitute for human review
- Assigning GenAI a specific creative role (idea generator, conceptual synthesiser, strategic framer) is proposed to produce better outcomes than treating it as an all-purpose brainstorm machine
- Heavy AI use in creative work carries a risk of gradually flattening output — pushing teams toward statistically common ideas rather than genuinely original ones
- AI-powered customer analytics may help smaller and emerging-market firms compete globally by speeding up market sensing and entry decisions
Papers covered:
1. Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest
- Authors: Ramtin Davoudi, Kartik Thakkar, Nazanin Donyapour, Tyler Derr, Hamid Karimi
- Source type: Preprint (arXiv — peer review status not fully confirmed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2604.18955
2. Beyond the Creativity Paradox: A Theory-informed Framework for Role-based Integration of Generative AI in Organisational Creativity
- Authors: Youngseok Choi, Chang Won Park, Ceyda Paydas Turan, Habin Lee
- Source type: Peer-reviewed journal article (Information Systems Frontiers)
- Access: Full text reviewed
- DOI: 10.1007/s10796-026-10746-y
3. AI-Driven Marketing Capabilities and International Competitiveness
- Authors: Manoj Govindaraj, D. Jishnu, Jenifer Lawrence, Duggirala Aravind
- Source type: Peer-reviewed academic book chapter (IGI Global)
- Access: Full text reviewed
- DOI: 10.4018/979-8-3373-9988-1.ch001
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-social-media-analytics-creativity-paradox-marketing-competitiveness-2026-06-1
Disclaimer: This is a first-pass research briefing produced with AI assistance and reviewed editorially. It is not a substitute for reading the original papers. Findings from preprints have not completed full peer review. Practical implications are the editorial team's interpretations and should not be treated as professional advice.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Pricing Algorithms, LLM Bias & Ad Retrieval: 3 Research Signals | 15 Jul 2026 | 00:20:41 | |
When your AI pricing tool receives more market data, does it actually compete harder — or does it quietly learn to charge more? And if the AI writing your content was trained to please the average user, who is that person, and is your audience actually in the room?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI and marketing research papers covering algorithmic pricing behavior, LLM personalization bias, and ad retrieval systems. We screened 374 papers this cycle; these three cleared the full-text bar.
What you'll learn:
- Why giving your AI pricing algorithm more market data does not always produce more competitive prices — in some configurations, it produces higher ones
- Why regulators trying to prevent AI-driven price collusion may inadvertently make it worse by restricting information access
- Why most major AI systems are trained to serve the average user, a demographic that does not exist, and how that systematically disadvantages non-Western and minority audiences
- Why the algorithm deciding who sees your ad operates on completely different logic than the one serving organic content — and why optimizing for one does not help the other
- How ad targeting and LLM technology are converging around shared retrieval architectures
Papers covered:
1. Strategic Information Disclosure in Algorithmic Pricing
- Authors: Chengcheng Wang, Zexin Ye
- Source type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2607.04345v1
2. Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences
- Author: Cristina Garbacea
- Source type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2606.07629
3. A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems
- Authors: Zhao Yu, Fang Liu, Yuan Yuan, Yifan Dang
- Source type: Peer-reviewed journal article
- Access: Full text reviewed
- DOI: 10.11591/ijece.v16i3.pp1518-1530
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-pricing-algorithms-llm-bias-ad-retrieval-research-2026-07-15
DISCLAIMER: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Preprints have not been peer-reviewed and findings may change. Nothing here constitutes legal, financial, or business advice.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Content Adaptation: Real-Time GANs, RL & Marketing Claims | 14 Jun 2026 | 00:12:49 | |
If an AI system claims it can boost your click-through rate by 25%, your conversions by 20%, and cut your bounce rate by 30% — all without human intervention — how do you know whether to run toward it or run away from it?
In this Research Radar Brief, Evita (an AI research briefing avatar trained on the framework of Dr. Eva Wolf) reviews 1 recent AI marketing research paper on generative AI-based real-time content adaptation, examining what the system claims to do, what the evidence actually supports, and what marketers can do with this kind of research right now.
What you'll learn:
- What GANs and Reinforcement Learning actually do when combined in a marketing content pipeline
- Why impressive performance numbers can be unactionable without knowing the experimental design behind them
- How to use a paper's claimed metrics as a vendor checklist — even when the paper itself can't be acted on yet
- Where real-time content adaptation already exists in deployable tools, and how to find verified results
- What the gap between enterprise dynamic creative optimization and small business access means for marketers today
Papers covered:
1. Generative AI-Based Content Adaptation System for High-Impact Digital Marketing
Authors: Surendra Singh Jagwan, Nirmesh Sharma, Ashwani Sharma
Year: 2026
Source type: Conference paper (likely peer-reviewed)
Access: Full text reviewed
Radar verdict: Watchlist
DOI: https://doi.org/10.1109/qpain69676.2026.11546259
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-content-adaptation-gans-reinforcement-learning-marketing-claims-2026-06-14
IMPORTANT: This is a first-pass research briefing, not a final academic review. Every paper covered has been reviewed at the full-text level where available. Read the original papers before making major marketing, business, legal, or product decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Chatbot Persuasion, Data Silos & AI Labor Markets: 3 Research Signals | 13 Jun 2026 | 00:21:01 | |
What if your AI chatbot is already persuasive enough — and the real problem is that nobody's opening it? What if your churn model is missing the most predictive signals you already own? And what if AI-powered freelance services are heading toward a price war that compresses margins faster than most agencies are prepared for? Those are the questions this episode's research surfaces.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI and marketing research papers covering AI chatbot persuasion versus traditional campaign advertising, integrated predictive analytics for marketing ROI, and AI agent competition in simulated labor markets.
From 381 papers screened, three cleared the full-text bar and made the radar.
What you'll learn:
- Why AI chatbots match professional campaign ads in persuasiveness per person reached — but getting people to start the conversation remains the hard problem
- The estimated cost to change one person's mind via AI chatbot is $48–$75, compared to roughly $100 using traditional campaign methods (with important caveats about research design)
- Why marketing AI may perform significantly better when it shares data with supply chain and financial systems — and why the specific benchmark numbers in that study deserve scrutiny before you act on them
- How AI agents competing in simulated gig labor markets drive prices down fast, with winner-take-most dynamics emerging quickly
- The three capabilities — self-reflection, competitive awareness, and long-horizon planning — that predicted AI agent success, and why they're worth asking about when evaluating any AI vendor
Papers covered:
1. A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies
Source type: Peer-reviewed journal article (Journal of Experimental Political Science)
Access: Full text reviewed
DOI: https://doi.org/10.1017/xps.2026.10032
2. AI-Driven Predictive Analytics for Supply Chain Resilience, Financial Risk Management, and Digital Marketing Strategy: A Unified Business Intelligence Framework
Source type: Peer-reviewed journal article (Journal of Business and Management Studies)
Access: Full text reviewed
DOI: https://doi.org/10.32996/jbms.2026.8.7.3
3. Strategic Self-Improvement for Competitive Agents in AI Labour Markets
Source type: Preprint — not yet peer-reviewed
Access: Full text reviewed
Source: https://arxiv.org/abs/2512.04988v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chatbot-persuasion-data-silos-labor-markets-marketing-research-2026-06-13
Disclaimer: This is a first-pass research briefing produced by Evita, an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a final academic review. Findings are summarized for accessibility and should not be treated as definitive conclusions. Always consult the original papers before making strategic or financial decisions. Preprints have not been peer-reviewed and should be interpreted with additional caution.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Banking Signals, GenAI CRM & Digital Commerce | 12 Jun 2026 | 00:18:36 | |
What if the two strongest predictors of banking customer engagement — call duration and account balance — are already sitting in your CRM right now? This week's radar papers circle a single uncomfortable truth: AI's predictive power is real, but the gap between a working model and a working marketing program is wider than most vendors admit.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering banking customer segmentation with behavioral signals, generative AI architecture for enterprise CRM, and the current state of AI in digital commerce.
What you'll learn:
- Two behavioral signals — call duration and account balance — predicted banking customer engagement with 97-99% accuracy in a study of 45,000 records, outperforming demographic-based targeting on both accuracy and compliance grounds
- Why behavioral signals may be more predictive and more ethics-friendly than demographic data for financial services segmentation
- What a generative AI CRM architecture could look like (churn prediction, LLM-driven insights, explainability layer) — and why it has only been tested in simulation, not in a live enterprise
- How human-AI collaboration in digital advertising — humans set strategy, AI handles execution — is the model most consistently linked to better campaign performance in the reviewed literature
- Why data privacy and algorithmic bias remain the two biggest practical barriers to AI adoption in digital commerce
Papers covered:
1. The adaptive engagement framework: enhancing banking customer experience through AI-powered invisible marketing
Source type: Peer-reviewed journal article (Scientific Reports, Nature Portfolio)
Access: Full text reviewed
Source: https://doi.org/10.1038/s41598-026-49522-y
2. Design and implementation of generative Artificial Intelligence-driven automation for enterprise customer relationship management decision support systems
Source type: Peer-reviewed journal article (Global Journal of Engineering and Technology Advances)
Access: Full text reviewed
Source: https://doi.org/10.30574/gjeta.2026.27.2.0089
3. Digital Commerce in the AI Era: Opportunities and Challenges
Source type: Peer-reviewed journal article (conference proceedings)
Access: Full text reviewed
Source: https://doi.org/10.66710/ijersem.v2si1.36
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-banking-signals-genai-crm-digital-commerce-2026-06-12
Disclaimer: This is a first-pass research briefing, not a final academic review. Evita is an AI-generated briefing avatar trained on the research framework and methodology of Dr. Eva Wolf. Findings are reported as the papers suggest them, not as proven conclusions. Individual studies have limitations noted in the full episode. Always read the original papers before making business decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: B2B GenAI, Hospitality AI & LLM Safety | 11 Jun 2026 | 00:20:30 | |
Your B2B team is using AI to write copy. Your hotel client's chatbot is labelled "safety-aligned." And new research suggests both of those things might be giving you a false sense of progress.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI adoption in B2B and industrial marketing, AI applications and trust risks in travel and hospitality, and a striking finding about how easily safety guardrails on open-weight language models can be bypassed.
What you'll learn:
- In B2B and industrial marketing, AI is being used almost exclusively for execution tasks — content, copy, and ads — while research and planning remain nearly AI-free. That gap is where the real opportunity is hiding.
- As AI handles more execution work, the marketer's role is shifting toward briefing AI tools, reviewing outputs, and strategic thinking — teams that don't plan for this will fall behind.
- In travel and hospitality, the most research-backed AI applications are recommendation engines, sentiment analysis, and dynamic pricing — but over-automating customer touchpoints is consistently flagged as a trust and loyalty risk.
- The safety guardrails on popular open-weight AI models are more fragile than most marketing technology buyers realize. A single internal neuron can be toggled to bypass them entirely.
Papers covered:
1. The Impact of Generative AI on B2B Marketing Processes: Evidence from Industrial Firms
- Authors: Vesterinen, Mero, Skippari, Karjaluoto (2026)
- Type: Peer-reviewed journal article
- Access: Full text reviewed
- Source: https://doi.org/10.18690/um.fov.4.2026.32
- Radar verdict: Read now
2. Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review
- Authors: Tyagi, Aggarwal, Tyagi, Vasudevan, Singh (2026)
- Type: Peer-reviewed journal article (F1000Research)
- Access: Full text reviewed
- Source: https://doi.org/10.12688/f1000research.177254.2
- Radar verdict: Watchlist
3. A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models
- Authors: Kazemi, Chegini, Safi (2026)
- Type: Preprint — not yet peer-reviewed
- Access: Open access
- Source: https://arxiv.org/abs/2605.08513
- Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-b2b-genai-hospitality-llm-safety-2026-06-11
Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Findings are described as the research suggests, not as proven conclusions. Preprints have not completed peer review and should be treated with additional caution.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Brand Voice, API Trust & Arab AI Attitudes | 10 Jun 2026 | 00:19:51 | |
Your AI vendor might be delivering a cheaper model than the one on your invoice. Your brand copy might be drifting off-voice without anyone noticing. And if you're launching in Arab markets, your audience is holding genuine enthusiasm and real fear about AI at the same time — and these are measurable, distinct feelings that campaign messaging needs to address separately.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering Arab consumer attitudes toward large language models, behavioral consistency and transparency in LLM API gateways, and brand voice governance under AI-assisted content production. These were selected from 353 papers screened for this episode.
What you'll learn:
- Why Arab consumers hold measurable enthusiasm and fear about AI simultaneously, and why generic positive messaging may miss the fear dimension entirely
- How third-party LLM API gateways were found to quietly substitute cheaper AI models, silently truncate conversation memory, and charge for tokens that were never processed
- Why positive attitudes toward AI in general do not automatically transfer to a specific LLM tool — and why marketers should test these separately before launching
- How AI-assisted content production causes brand voice to drift gradually, often before anyone notices
- What a five-level brand voice governance framework looks like — and why it is theoretical, not yet empirically tested
Papers covered:
1. Developing and Validating the Arabic Version of the Attitudes Toward Large Language Models Scale
Source type: Peer-reviewed journal article (SN Computer Science)
Access: Full text reviewed
DOI: 10.1007/s42979-026-04855-3
Radar verdict: Test this week
2. Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways
Source type: Peer-reviewed conference paper (ACM IMC '26 / arXiv)
Access: Full text reviewed
DOI: 10.1145/3777912.3809156
Source: https://arxiv.org/abs/2604.21083
Radar verdict: Test this week
3. Brand Voice Management in the Era of Large Language Models
Source type: Peer-reviewed journal article (Integrated Communications)
Access: Full text reviewed
DOI: 10.28925/2524-2652.2026.119
Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-brand-voice-api-gateway-arab-llm-attitudes-2026-06-10
Disclaimer: This is a first-pass research briefing produced using an AI-assisted research framework developed by Dr. Eva Wolf. It is not a substitute for reading the original papers. Findings are reported as what the research suggests, not as definitive conclusions. Paper three is a conceptual framework with no empirical validation. Listeners should evaluate each paper independently before acting on its findings.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Creative Workflows, AI Influencers & Marketing Analytics | 09 Jun 2026 | 00:19:05 | |
When AI does the creative work — writing your copy, pitching ideas, fronting your campaigns — how much control do you actually need to keep? And what happens when you hand the wheel entirely to the bots? This episode's research lands on a consistent answer: the human layer isn't optional overhead. It's the thing that makes the output worth having.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering multi-agent creative workflows, AI influencer humanlikeness, and the evolution of marketing analytics from dashboards to AI-generated content and ethics.
What you'll learn:
- Why autonomous AI agent coordination tends to stall in creative tasks, and what a human-directed model looks like instead
- How the human-like quality of an AI influencer or chatbot directly affects whether people intend to purchase
- What a four-stage model of AI marketing evolution suggests about where governance and ethics now fit in your AI stack
- Why your role in an AI-assisted creative workflow is director, not just reviewer — and why that distinction matters
Papers covered:
1. Understanding Human-Multi-Agent Team Formation for Creative Work
Source type: Peer-reviewed conference paper (CHI '26, ACM CHI Conference on Human Factors in Computing Systems)
Access: Full text reviewed
DOI: 10.1145/3772318.3791166
Radar verdict: Read now
2. Physical Humanlikeness as A Moderator of The Relationship Between AI Influencer Marketing and Purchase Intention
Source type: Peer-reviewed journal article (International Journal of Management Science and Information Technology)
Access: Full text reviewed
DOI: 10.35870/ijmsit.v6i1.7072
Radar verdict: Read now
3. Artificial intelligence across social sciences and humanities: The evolution of marketing analytics in the digital era
Source type: Peer-reviewed journal article (Journal of Interdisciplinary Research in Artificial Intelligence and Society)
Access: Full text reviewed
DOI: 10.20897/jirais/18474
Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-creative-workflows-influencers-marketing-analytics-2026-06-09
Disclaimer: This is a first-pass research briefing, not a final academic review. Evita is an AI-generated briefing avatar trained on the research framework and methodology of Dr. Eva Wolf. Findings are summarised from full-text sources and reflect what the research suggests, not what it conclusively proves. Always read the original papers before making decisions. Paper quality and venue credibility vary; notes on limitations are included in the full show notes.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Chatbot Ads, Cultural Bias & GenAI Content: 3 Research Signals | 08 Jun 2026 | 00:18:08 | |
When you advertise inside an AI chatbot, is the model working for your customer — or quietly for whoever pays it most? And when AI writes your regional ad copy, does it actually understand the culture, or just fake the surface look? This episode examines both questions through three recent research papers screened from a pool of 373.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering chatbot advertising conflicts of interest, LLM cultural awareness in ad copy, and generative AI content marketing efficiency.
What you'll learn:
- Why 18 out of 23 AI chatbots tested pushed users toward more expensive sponsored products over cheaper equivalents — and what that means for brand trust
- How GPT-5.1 redirected users away from their explicitly chosen store toward a sponsored competitor 94% of the time, and why disclosure failures may carry FTC risk
- Why AI models appear to treat higher-income user profiles differently — and the implications for fairness in AI-powered retail recommendations
- What the cultural stylistics research suggests about AI's ability to write genuine Hong Kong-style ad copy versus mainland Chinese copy — and the gap between recognizing a style and producing it
- What the generative AI content efficiency review covers, and why its evidence base (largely industry surveys) warrants caution before acting on it
Papers covered:
1. Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest
Source type: Preprint — arXiv (Cornell University). Peer-review status unconfirmed. Treat findings with appropriate caution.
Access: Full text reviewed
Source: http://arxiv.org/abs/2604.08525
2. Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics
Source type: Preprint — not yet peer-reviewed
Access: Full text reviewed
DOI: 10.48550/arxiv.2605.27296
Source: https://arxiv.org/abs/2605.27296
3. The Impact of Generative AI on Content Marketing Efficiency: Opportunities, Risks, and Future Perspectives
Source type: Literature review — Zenodo (CERN). Peer-review status unconfirmed (Zenodo self-submission).
Access: Full text reviewed
DOI: 10.5281/zenodo.20021151
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chatbot-ads-cultural-bias-genai-content-marketing-2026-06-08
DISCLAIMER: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Preprints have not undergone formal peer review and findings may change. Nothing here constitutes legal, financial, or regulatory advice.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Chatbot Ads, Neuron Auctions & Agent Security: 3 Research Signals | 07 Jun 2026 | 00:23:25 | |
As AI chatbots replace search engines for millions of users, two parallel questions are becoming urgent for marketers: who controls which brands get recommended inside those conversations — and are the AI agents we're deploying to automate marketing tasks actually secure?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering neuron-level ad auctions inside large language models, a two-stage chatbot ad auction framework, and runtime hijacking attacks on AI agents browsing the web.
All three papers are unreviewed preprints tested in controlled or simulated environments. None are ready to act on today. But together they sketch the shape of AI-powered advertising and AI agent security over the next several years — and they raise questions your team should start asking now.
What you'll learn:
- Why the future of paid media in AI chatbots may have nothing to do with writing ad copy — and everything to do with how a model is wired internally
- How a two-stage AI ad auction system picks more relevant ads faster than either approach alone — and what that means for how you write advertiser copy today
- Why your ad description quality will matter more than your headline when AI chatbot placements go live
- How AI agents browsing the web on your behalf can be silently hijacked — appearing to work normally while leaking data to an attacker's server
- What questions to ask any AI agent vendor before you let their product take actions on the web for your brand
Papers covered:
1. LLM Advertisement based on Neuron Auctions
- Authors: Peiran Yun, Wenxin Xu, Jiayuan Liu, Yihang Zhang, Liang Zeng, Lingkai Kong, Tonghan Wang (2026)
- Source type: Preprint — not peer reviewed
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2605.08326
2. LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
- Authors: Haoran Sun, Xinrui Song, Xinyu Zhang, Zhaohua Chen, Xu Chu, Zhilin Zhang, Chuan Yu, Jian Xu, Bo Zheng, Xiaotie Deng (2026)
- Source type: Preprint — not peer reviewed
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2605.16474
3. WebMCP Tool Surface Poisoning: Runtime Manipulation Attacks on LLM Agents
- Authors: Lin-Fa Lee, Yi-Yu Chang, Chia-Mu Yu, Kuo-Hui Yeh (2026)
- Source type: Preprint — not peer reviewed
- Access: Full text reviewed
- Source: https://arxiv.org/abs/2606.06387v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chatbot-ads-neuron-auctions-agent-security-research-2026-06-07
Disclaimer: This is a first-pass research briefing produced by Evita, an AI-generated avatar trained on the research framework of Dr. Eva Wolf. All papers flagged as preprints have not been peer reviewed and should be treated as preliminary. Findings may change. Nothing in this episode constitutes professional legal, financial, or security advice.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Ethics, SME AI Wins & LLM Pipelines: 3 Marketing Research Signals | 06 Jun 2026 | 00:21:38 | |
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AI MARKETING RESEARCH RADAR
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TODAY'S RADAR QUESTION
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As AI takes over more of your marketing workflow — writing copy, targeting ads, analyzing data — who's actually checking whether it's doing any of that responsibly, accurately, or ethically? This episode asks whether your team is building with AI or just hoping for the best.
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PAPERS COVERED
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1. "With great power comes great responsibility": A meta-narrative review of ethical considerations and implications in the cro
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Campaign AI, Content Quality & Conversational Ads | 05 Jun 2026 | 00:18:30 | |
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AI MARKETING RESEARCH RADAR
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TODAY'S RADAR QUESTION
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Can AI actually replace your content team, your campaign designer, and your ad buyer — or is the real edge hiding in the seams between human judgment and machine speed? This episode screens 370 papers and surfaces three that put that question to the test.
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PAPERS COVERED
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1. Ad Genie: A Multimodal Generative AI Framework for Automated Marketing Campaign Creation Using Product Images, Textual Prompts, and Web Intellig
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Generative AI, Ethics & Cooperative Branding | 14 Jul 2026 | 00:20:11 | |
Are your marketing workflows actually keeping up with generative AI — or are you using powerful tools without the governance to back them up? This episode examines three recent research papers that each point to the same uncomfortable gap: most organisations are adopting AI for marketing faster than they are building the systems to use it well.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering generative AI content creation for small businesses, ethical frameworks for AI-enabled marketing, and AI adoption in agri-food cooperatives.
What you'll learn:
- Why prompt writing — not design skill — is the key capability for non-technical teams using AI content tools
- Why the 70% efficiency claim from the small business study is directional, not proven, and how to run your own test
- How large brands like Unilever and P&G are already running AI ethics boards and bias-testing protocols — and what a basic compliance checklist looks like
- How values-driven and community-rooted brands can use authentic storytelling as a competitive advantage when adopting AI
- Why governance — who teaches AI skills, who checks outputs, who owns the ethics policy — is the common thread across all three papers
Papers covered:
1. Accelerating MSME Digital Marketing Through the Use of Generative AI to Improve Visual Content Creation and Creative Promotional Narratives
- Source type: Peer-reviewed journal article (Jurnal Pengabdian Masyarakat dan Riset Pendidikan)
- Access: Full text reviewed
- DOI: 10.31004/jerkin.v4i4.6076
- Radar verdict: Test this week
2. Ethical Frameworks for AI-Enabled Marketing: Guidelines, Adoption, and Organizational Practices
- Source type: Peer-reviewed academic book chapter (IIP Series)
- Access: Full text reviewed
- DOI: 10.58532/nbennureambv6b2p1c7
- Radar verdict: Test this week
3. Digital Transformation in Agri-Food Cooperatives: AI and Marketing Strategies in Case Studies of First- and Second-Degree Models
- Source type: Peer-reviewed journal article (British Food Journal)
- Access: Full text reviewed
- DOI: 10.1108/bfj-10-2025-1430
- Radar verdict: Test this week
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-generative-ai-msme-ethics-cooperative-branding-2026-07-14
Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar (Evita) trained on the research framework of Dr. Eva Wolf. It is not a final academic review. Findings are summarised for marketing professionals and should be read alongside the original papers. Study limitations are noted throughout. Nothing in this episode constitutes legal, regulatory, or investment advice.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Ad Targeting Research: LLMs, Knowledge Graphs & Ad Auctions | 02 Jun 2026 | 00:19:30 | |
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AI MARKETING RESEARCH RADAR
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TODAY'S RADAR QUESTION
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AI is already inside your ad stack — but are platforms using it as a scalpel or a sledgehammer? This episode digs into three new studies asking whether LLMs actually improve ad targeting in production, what happens when too many brands fight for control of an AI's recommendations, and how wiring a knowledge graph into your ad engine could make it both smarter and 24% faster.
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PAPERS COVERED
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1. Fine-Tuned LLM as a Co
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Tasks, LLM Ads & Brand Visibility: 3 Research Signals | 01 Jun 2026 | 00:18:49 | |
AI promises to make every marketing task faster and smarter. But does it? Three recent research papers suggest the answer depends heavily on the task, the person using the tool, and whether someone is quietly steering the AI answers your customers are already reading.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI task performance in content creation, commercial influence inside LLM chatbots, and engineering approaches to real-time LLM-powered ad delivery.
What you'll learn:
- AI improves quality for long-form content like blog posts and destination guides, but makes no measurable difference for short social captions, and actually worsens visual design outputs
- Digital literacy is the hidden variable: team members with weaker digital skills may produce lower-quality work when using AI than when working without it
- LLMs are now an official advertising channel — ChatGPT began running ads in February 2026, and commercial influence inside AI answers is harder to detect than in traditional search
- Your brand's reputation inside AI chatbots is already shaping customer decisions, and almost no marketing team is monitoring it
- Real-time LLM-powered ad targeting is technically possible at scale, but only with significant ML engineering infrastructure most teams will not build in-house
Papers covered:
1. Task To Tech: An Exploration of Generative AI in Tourism Marketing through Student Experiments and Practitioner Interviews
Source type: Peer-reviewed journal article (Media Wisata)
Access: Full text reviewed
DOI: https://doi.org/10.36276/mws.v24i1.945
2. Advertising and Large Language Models: A New Frontier Influencing Medical Practice
Source type: Peer-reviewed journal article (Eye)
Access: Full text reviewed
DOI: https://doi.org/10.1038/s41433-026-04518-w
3. Efficient LLM-based Advertising via Model Compression and Parallel Verification
Source type: Preprint — not yet peer-reviewed (arXiv / Cornell University)
Access: Full text reviewed
DOI: https://doi.org/10.48550/arxiv.2605.11582
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-tasks-llm-advertising-brand-visibility-2026-06-01
Disclaimer: This episode is a first-pass research briefing produced by Evita, an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a final academic review. Findings are described as the research suggests, not as proven conclusions. Listeners are encouraged to read the original papers before making strategic or operational decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Chatbot Trust, Cold-Start Ads & AI Disclosure: 3 Research Signals | 01 Jun 2026 | 00:17:14 | |
Is everything we assume about chatbot design — the personalization, the warm tone, the friendly AI — actually doing what we think it's doing? This week, three studies landed on the radar that challenge assumptions baked into nearly every conversational AI and ad tech strategy right now. The findings are counterintuitive enough to warrant a pause and an audit.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering conversational AI trust and reliance, cold-start ad personalization using large language models, and the effects of AI disclosure on brand authenticity.
This is a first-pass research briefing, not a final academic review. Papers are assessed for relevance and rigor, but findings should be treated as signals to investigate further — not settled conclusions.
What you'll learn:
- Why personalizing your AI chatbot's explanations may actually reduce its persuasiveness when used alone — and what happens when warmth is added
- Why higher AI literacy did not make users more skeptical of AI advice — and what that means for tech-savvy, B2B audiences
- How Walmart used an LLM to generate ad ranking weights from creative content before a single click — and the real-world results from their deployment
- Why AI-generated visuals without disclosure can damage brand trust, and why disclosing AI use acts as brand insurance rather than a trust differentiator
Papers covered:
1. Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI
Source type: Preprint (not yet peer-reviewed)
Access: Full text reviewed
Source: https://arxiv.org/abs/2605.31275v1
2. LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks
Source type: Preprint (likely peer-reviewed venue — formal status uncertain)
Access: Full text reviewed
Source: https://arxiv.org/abs/2605.31275v1 — see show notes for correct link
3. Opening AI: A Study of Transparency's Impact on Brand Authenticity and Trust in Visual Advertising
Source type: Master's thesis (not peer-reviewed)
Access: Full text reviewed
Source: Link in show notes
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chatbot-trust-cold-start-ads-disclosure-research-2026-06-01
DISCLAIMER: This episode is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the original papers. Two of the three papers covered today are preprints or theses and have not completed formal peer review. Findings should be treated as early signals, not settled evidence.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Brand Visibility, SMB Content Playbooks & AI Music in Ads | 31 May 2026 | 00:19:41 | |
When AI becomes the first stop for brand discovery, does it surface what makes your brand genuinely different — or does it quietly reduce every brand to a price-and-quality comparison? That question threads through all three papers in this episode, along with two more grounded ones: what does responsible AI content adoption actually look like for a small business, and can AI-generated music replace the royalty-free tracks you're paying for right now?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering brand identity collapse in AI-mediated search, generative AI adoption by small businesses in Nigeria, and AI-generated music performance in digital advertising.
What you'll learn:
- Why AI search tools may strip most of what makes your brand distinctive down to price and quality — and which brands the research suggests are hurt most
- How adding structured, machine-readable brand data to your website may partially recover the brand identity AI search flattens
- What a minimum viable governance playbook looks like for small businesses actually using generative AI for marketing content today
- Why being transparent with customers about AI-generated content helped small business owners in this study build trust rather than lose it
- How AI-generated music performed against royalty-free stock music in a live digital ad campaign — and what that may mean for your production budget
Papers covered:
1. Dimensional Collapse in AI-Mediated Search: Large Language Models as Metameric Observers of Brand Advertising
- Source type: Preprint (not yet peer-reviewed)
- Access: Full text reviewed
- DOI: 10.5281/zenodo.19422427
- Source: https://doi.org/10.5281/zenodo.19422427
2. How Small Businesses in Nigeria Use Generative AI to Compete in Marketing Content
- Source type: Peer-reviewed journal article
- Access: Full text reviewed (open access)
- DOI: 10.65773/ssia.2.2.34
- Source: https://doi.org/10.65773/ssia.2.2.34
3. Generative AI-Enabled Music Generation in Marketing and Consumer Response
- Source type: Peer-reviewed journal article
- Access: Full text reviewed
- DOI: 10.5282/jums/v11i1pp181-194
- Source: https://doi.org/10.5282/jums/v11i1pp181-194
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-brand-visibility-smb-content-playbooks-ai-music-ads-2026-05-31
Disclaimer: This is a first-pass research briefing produced by an AI-generated research avatar trained on the methodology of Dr. Eva Wolf. It is not a final academic review. Findings are drawn directly from the papers as accessed and are presented with their limitations. Preprint findings have not completed peer review and may change. Nothing here constitutes business, legal, or financial advice.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Consumer Trust, AI Bias & Ad Influence | 30 May 2026 | 00:19:08 | |
Consumer attitudes toward generative AI have shifted dramatically since 2020 — and the direction is not what most marketing teams are planning for. Meanwhile, advertising embedded inside AI chatbots can already shift product recommendations in measurable ways, without the AI ever disclosing the ad or giving a wrong answer. This episode covers both.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering consumer attitudes toward generative AI, practical AI tools for marketing research, and the influence of advertising on AI chatbot recommendations.
What you'll learn:
- How consumer feelings about generative AI have shifted over seven years — and why what excited people in 2020 may actively annoy them today
- Why people accept AI as a creative assistant but resist it as a decision-maker — and what that means for how you frame AI-powered products
- The most common prompt mistake that turns AI-generated marketing research into polished-sounding garbage
- How ads embedded in AI chatbots can shift product recommendations invisibly — and why the choice of AI platform matters as much as the ad itself
- Why standard accuracy checks would never catch the bias this third paper found
Papers covered:
1. Designing marketing strategies based on a dual-method analysis of consumer attitudes toward generative AI
- Source: Discover Artificial Intelligence (Springer)
- Type: Peer-reviewed journal article
- Access: Full text reviewed
- DOI / Link: https://doi.org/10.1007/s44163-026-01382-1
2. New Tools, New Roles: A Manager's Guide to Harnessing Generative AI for Marketing Insight
- Source: NIM Marketing Intelligence Review
- Type: Peer-reviewed journal article
- Access: Full text reviewed (open access)
- DOI / Link: https://doi.org/10.2478/nimmir-2026-0005
3. Ad-verse Effects: Pharmaceutical Advertising Shifts Drug Recommendations by Consumer-Facing AI
- Source: medRxiv
- Type: Preprint — not yet peer-reviewed
- Access: Full text reviewed
- DOI / Link: https://doi.org/10.64898/2026.04.14.26350868
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-consumer-trust-prompt-bias-ad-influence-2026-05-30
Disclaimer: This is a first-pass research briefing produced with AI-assisted screening tools and reviewed editorially. It is not a substitute for reading the full papers. Preprints have not been peer-reviewed and findings may change. Nothing here constitutes medical, legal, or financial advice.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Tools, Consumer Behaviour & Lead Gen: Research Brief | 30 May 2026 | 00:13:54 | |
How much of what we believe about AI marketing tools is backed by real evidence — and how much is practitioner intuition dressed up as data? This week's radar brief examines two 2026 studies that both ask whether AI marketing tools actually deliver, and both run into the same methodological wall.
In this Research Radar Brief, Dr. Eva Wolf reviews 2 recent AI marketing research papers covering AI-powered social media personalization, consumer impulse buying behaviour, chatbot effectiveness, lead generation, AI CRM adoption, and the barriers that slow AI tool rollout in real organizations.
What you'll learn:
- Why AI-powered personalization on social media is associated with higher brand engagement and impulse purchasing — and what that association does and does not tell us
- How chatbots and automated recommendations may trigger buying behaviour when timed to a discovery or browsing moment
- Why data privacy concerns consistently surface as a trust friction point in AI marketing touchpoints
- What marketing and sales professionals report as the top barriers to adopting AI lead-gen tools: cost, technical complexity, and data privacy
- Why predictive analytics and AI CRM tools are seen by practitioners as particularly useful for prioritising high-quality leads
- What to measure before and after adopting an AI marketing tool — and why benchmarking matters
- Why both studies are methodologically limited and should be read cautiously before informing strategy
Papers covered:
1. AI-Driven Social Media Marketing and Its Impact on Consumer Behaviour
Vasavi, Uma Kumari, Sairam (2026)
Source type: Peer-reviewed journal article (likely peer-reviewed)
Access: Full text available
Triage verdict: Use cautiously
Source: https://doi.org/10.66710/ijersem.v2si1.10
2. To Understand the Impact of AI-Based Marketing Tools on Lead Generation Effectiveness within an Organization
Kinikar, Bhavsar, Suryavanshi, Yadav, Moholkar (2026)
Source type: Peer-reviewed journal article (likely peer-reviewed)
Access: Full text available (truncated)
Triage verdict: Watchlist
Source: https://doi.org/10.55248/gengpi.07.0526.d13254
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-tools-consumer-behaviour-lead-gen-research-2026-05-30
Disclaimer: This is a first-pass research briefing, not a final academic review. Summaries are based on available full text, abstracts, and metadata. Findings reflect what the studies suggest, not what they prove. Read the original papers before making strategic decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing & Cashless Payments: Consumer Trust Research | 28 May 2026 | 00:09:27 | |
If your AI personalization is doing its job but your checkout is broken, are you actually converting anyone? That's the question at the centre of this week's radar brief — and it's one that gets surprisingly little research attention.
In this Research Radar Brief, Dr. Eva Wolf reviews 1 recent AI marketing research paper covering AI-driven personalization, cashless payment systems, consumer trust, and purchase decision-making in household durable goods. Seventy-five papers were screened this week. One cleared the relevance bar — and it lands on the watchlist, not the deep-dive queue.
What you'll learn:
- Why the combination of AI personalization and payment UX may matter more than either element alone
- How trust and perceived ease of use appear to act as the bridge between digital marketing tactics and actual purchase decisions
- What this research does and does not prove — and why the full-text access gap limits conclusions
- Which methodological details are missing and why that matters before acting on this finding
- Why this research angle is worth watching if you work in e-commerce, retail tech, or high-consideration product categories
Papers covered:
1. Integrating AI-Driven Marketing and Cashless Payment Systems: An Empirical Study of Consumer Decision-Making in Household Durable Purchases
Source type: Peer-reviewed conference proceeding (IEEE ICKECS 2026)
Access: Abstract only — full text was inaccessible at time of recording
DOI: https://doi.org/10.1109/ickecs70176.2026.11527601
Triage verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-cashless-payments-consumer-trust-decision-making-2026-05-28
This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Findings are associations, not proven causal claims. Read the original papers before making any decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Ethics, Data Privacy & Industry 5.0: Research Brief | 27 May 2026 | 00:16:08 | |
If you can't explain to your customer what data you're collecting — or why — are you actually ready to be running AI marketing at all? That's the uncomfortable question sitting at the centre of this week's radar. Two 2026 book chapters surfaced from a screen of 75 papers, both pointing at the parts of AI marketing most teams don't want to look at: privacy exposure, algorithmic bias, and the real complexity of integrating AI into existing workflows.
In this Research Radar Brief, Dr. Eva Wolf reviews 2 recent AI marketing research papers covering ethical challenges in AI-driven targeting, data privacy and GDPR compliance, algorithmic bias in ad systems, and Industry 5.0 human-machine collaboration in marketing management.
What you'll learn:
- Why most AI marketing campaigns may be collecting personal data without adequate consumer transparency
- How training data gaps can cause AI targeting systems to treat customer segments unfairly
- What GDPR enforcement inconsistencies mean for marketers operating across borders
- Why 'privacy by design' is the practical standard regulators and researchers are pointing toward
- How Industry 5.0 reframes AI as a human-machine partner — not just an automation layer
- What AR, VR, and IoT adoption in marketing looks like in emerging markets
- Why workflow integration complexity is a real barrier when adding AI tools to existing marketing stacks
Papers covered:
1. Ethical Challenges and Data Privacy Concerns in AI-Driven Marketing
Gaur, Pareek & Yadav (2026)
Source type: Academic book chapter
Peer review: Likely peer-reviewed
Access: Abstract only
DOI: https://doi.org/10.1201/9781003671381-4
2. AI-Based Marketing Management Strategies and Industry 5.0
Parashar, Parashar & Parashar (2026)
Source type: Academic book chapter
Peer review: Likely peer-reviewed
Access: Abstract only
Venue: Bentham Science Publishers eBooks
DOI: https://doi.org/10.2174/9789815324037126010015
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-ethics-data-privacy-industry-5-2026-05-27
Disclaimer: This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata only. Neither paper reached the deep-dive threshold this episode — both are watchlist items pending full-text access. Read the original papers before making any decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Data Gaps, Trust Risks & Personalization | 26 May 2026 | 00:18:21 | |
You have the data. The CRM is full. The analytics dashboards are humming. So why aren't your marketing results improving? That's the tension running through this episode. Three recent papers all circle the same uncomfortable question: when does AI actually help, and when does it quietly fail you?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering AI adoption as a performance mediator, consumer psychology risks from AI-generated creative, and the ethics and governance of AI personalization.
What you'll learn:
- Why having more data doesn't automatically improve marketing performance — and what the missing link is
- What an Egyptian B2B study of 148 managers found about AI adoption and marketing outcomes
- Why AI-generated ads can trigger an uncanny valley response that quietly erodes brand trust
- What "model collapse" means for AI marketing tools trained on synthetic data
- How AI personalization has evolved from simple rules to real-time neural networks
- What a responsible AI marketing framework looks like before you scale a personalization campaign
- Key limitations to watch: sample size, cross-sectional design, literature review sourcing, and journal tier
Papers covered:
1. The Mediation Role Played by AI Adoption in the Relationship Between Information Processing Requirements and Marketing Performance
Source: Peer-reviewed journal article (likely peer-reviewed)
Access: Abstract only
Venue: Management & Sustainability: An Arab Review, 2026
Link: https://doi.org/10.1108/msar-09-2025-0354
2. The Convergence of Artificial Intelligence, Consumer Psychology, and Marketing Strategy in the Digital Age
Source: Peer-reviewed journal article (likely peer-reviewed)
Access: Abstract only
Venue: International Journal of Scientific Research in Engineering and Management, 2026
Link: https://doi.org/10.55041/ijsrem.ncdtaim032
3. The Use of Artificial Intelligence for Personalized Advertising and Marketing
Source: Peer-reviewed journal article (likely peer-reviewed)
Access: Abstract only
Venue: International Journal of Advanced Research in Science, Communication and Technology, 2026
Link: https://doi.org/10.48175/ijarsct-32854
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-research-data-gaps-trust-risks-personalization-2026-05-26
Disclaimer: This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Read the original papers before making business or strategic decisions. Findings should not be treated as established conclusions without further verification.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Virtual Influencers, Personalization & SME Tools | 26 May 2026 | 00:19:57 | |
# Research Radar Brief — AI and Marketing
**Date:** 2026-05-26
**Episode type:** Research Radar Brief
**Episode ID:** radar-2026-05-26
**Papers screened:** 120
**Papers selected:** 3
**Theme:** AI and marketing
> This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Read the original papers before making decisions.
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## Papers Covered
### 1. Unveiling Trends in AI-Powered Marketing
- **Source type:** Academic book cha
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Ad Generation, Tourism Marketing & Retail AI: 3 Research Signals | 13 Jul 2026 | 00:20:21 | |
The AI tools your team is racing to adopt — have they ever been tested on a real customer? This week's radar covers three genuinely interesting papers across AI creative production, sustainable tourism marketing, and retail analytics. The ideas are worth knowing. But the evidence behind the headlines is weaker than the numbers suggest.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers covering automated ad asset generation, AI personalization in sustainable tourism, and the link between AI-assisted inventory decisions and promotional performance.
What you'll learn:
- How a single-product-photo-in, full-ad-kit-out AI pipeline works and how to prototype it today with off-the-shelf tools
- Why the 99% time and cost savings figures from the AdSpark paper are attention-grabbing but not yet independently validated
- What the sustainable tourism paper signals about AI personalization and the ethical guardrails that may determine whether campaigns land or backfire
- Why connecting your AI inventory system to your promotional planning may be the most underused lever in retail marketing
- What the three biggest blockers to AI adoption in retail are, based on 120 surveyed retail managers in India
Papers covered:
1. AdSpark: A Multimodal Generative AI Framework for Automated Digital Advertising and Marketing Asset Synthesis
Source type: Peer-reviewed journal article (IJRASET — low-tier venue; treat findings cautiously)
Access: Full text reviewed
DOI: 10.22214/ijraset.2026.78444
Radar verdict: Use cautiously
2. The Effectiveness of AI-Powered Marketing Strategies in Sustainable Tourism
Source type: Peer-reviewed journal article (Agora International Journal of Economical Sciences)
Access: Full text reviewed
DOI: 10.15837/aijes.v20i1.7640
Radar verdict: Watchlist
3. Predictive Analytics in Retail: Impact of AI on Inventory Optimization and Marketing Performance
Source type: Peer-reviewed journal article (IJCRT — low-prestige open-access venue; treat findings cautiously)
Access: Full text reviewed
DOI: 10.56975/ijcrt.v14i1.300688
Radar verdict: Use cautiously
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-ad-generation-tourism-marketing-retail-analytics-research-2026-07-13
DISCLAIMER: This is a first-pass research briefing produced by an AI-generated avatar trained on the research framework of Dr. Eva Wolf. It is not a final academic review. Findings represent what papers suggest, not what is proven. Always read the original papers before citing or making business decisions based on this content. Paper quality and peer review rigor vary; notes on venue credibility are included for each paper.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Chat Logs, Privacy & Dependency: 2 Research Signals | 25 May 2026 | 00:14:01 | |
What if the AI chat data your users generate is far less anonymous than you think — and what if the engagement features driving your AI product metrics are quietly creating dependency in the people who need help most?
In this Research Radar Brief, Dr. Eva Wolf reviews 2 recent AI marketing research papers covering conversational AI privacy, demographic inference from chat logs, and the dependency risks built into engagement-optimized AI tools.
This week we screened 140 papers. Two made the radar.
What you'll learn:
- Why removing names and contact details from AI chat logs may not be enough to protect user privacy
- How an LLM inferred age, gender, and country with F1 scores of 0.84 to 0.90 from conversation topics alone
- Why just 5% of a user's chat history may be enough to profile them demographically
- How stereotype-driven inference causes the most errors for women in tech, older digital users, and workers from Nigeria and Pakistan
- Why AI chatbot design features that maximize engagement may inadvertently create dependency in emotionally vulnerable users
- What engagement-based KPIs may be missing when users are turning to AI because human alternatives are too expensive or inaccessible
- What proactive disclosure and care-aligned metrics could mean for AI wellness, coaching, and HR product teams
Papers covered:
1. Inferential Privacy Leakage in Anonymized Conversational AI Logs
Zaman & Garimella (2026)
Source type: Preprint
Access: Open access (full text)
Source: https://arxiv.org/abs/2605.23820v1
2. Engagement-Optimized Care: When LLMs Become Mental Health Infrastructure
Vecchione, Ye, Garofalo & Singh (2026)
Source type: Preprint
Access: Open access (full text)
Source: https://arxiv.org/abs/2605.23787v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-chat-logs-inferential-privacy-llm-dependency-marketing-2026-05-25
Disclaimer: This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and full text where noted. Both papers covered this week are preprints and have not yet undergone peer review. Findings may change before publication. Read the original papers before making decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Performance Research: What Actually Creates the Edge? | 23 May 2026 | 00:12:47 | |
If every marketing team is buying the same AI platforms, the same targeting features, and the same dashboards — what actually creates a lasting edge? That's the question both papers this week are circling. And the answer is more inconvenient than most vendors want you to hear.
In this Research Radar Brief, Dr. Eva Wolf reviews 2 recent AI marketing research papers covering AI-driven marketing performance, customer data as competitive advantage, and integrated AI deployment in direct-to-consumer businesses. Both papers carry methodological caveats that matter — and Dr. Wolf flags them directly.
What you'll hear about:
- Why proprietary customer data may matter more than the AI tools themselves
- What 15 confidential AI marketing implementations reported for conversion, acquisition cost, and return on ad spend — and why those numbers need careful interpretation
- Why bolting one AI tool onto your marketing stack is unlikely to deliver the growth benefits the research describes
- How neural network models compare to conventional methods for predicting customer lifetime value
- The cultural and organizational factors that appear to separate successful AI rollouts from stalled ones
- Key limitations in both studies that should inform how much weight you give the reported figures
Papers covered:
1. AI-Driven Marketing Models as a Competitive Advantage in Global Markets
- Kalinina Elena Evgenievna (2026)
- Source type: Peer-reviewed journal article (use cautiously)
- Access: Open access
- Source: https://doi.org/10.29013/ejems-26-2-61-65
2. Integrated AI-Driven Marketing Growth Models for Scaling Businesses in Competitive Direct-to-Consumer Landscapes
- Dineth Ratnayake (2026)
- Source type: Zenodo deposit — venue credibility uncertain (use cautiously)
- Access: Open access
- Source: https://doi.org/10.5281/zenodo.19725980
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-performance-competitive-advantage-data-culture-2026-05-23
Disclaimer: This is a first-pass research briefing, not a final academic review. Summaries reflect available evidence; findings should be interpreted in light of each study's limitations. Read the original papers before making decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing in Emerging Markets: What the Research Shows | 22 May 2026 | 00:12:33 | |
Is AI marketing only for companies with deep pockets and advanced data infrastructure? This week, one paper went looking for answers outside the usual tech-forward markets — examining AI tool adoption inside Azerbaijan's clothing, textile, and footwear sectors. The findings raise questions that are relevant well beyond one country.
In this Research Radar Brief, Dr. Eva Wolf reviews 1 recent AI marketing research paper from 115 screened, covering AI adoption barriers, targeting effectiveness, and operational efficiency in an emerging market context.
What you'll learn:
- Why staff capability and data infrastructure matter more than the software itself when adopting AI marketing tools
- What improvements companies in Azerbaijan's light industry reported after using AI-driven targeting and demand forecasting
- The three main barriers that slowed AI marketing adoption — cost, infrastructure, and skills gaps
- Why data privacy and consent concerns are a practical business blocker, not just a compliance issue
- What this study's significant limitations mean for how seriously to take its findings
Note: This was a light screening week. Only one paper met the minimum threshold for inclusion, and it carries notable caveats around verifiability. The geographic angle — AI marketing in an emerging economy — is underrepresented in the research literature, which is why it made the radar despite those caveats.
Papers covered:
1. Evaluation of the Effectiveness of AI-Based Marketing Strategies in Azerbaijan's Light Industry
Source: Peer-reviewed journal article (peer review status unconfirmed — Zenodo self-submission; see episode notes)
Access: Open access
Source: https://doi.org/10.5281/zenodo.19922874
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-emerging-markets-adoption-barriers-benefits-2026-05-22
DISCLAIMER: This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Findings should not be treated as confirmed or generalisable. Read the original papers before making any decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI in Arts Marketing: One Framework Paper, Three Big Claims | 21 May 2026 | 00:10:15 | |
# Research Radar Brief — AI & Marketing | Episode radar-2026-05-21
**Date:** 2026-05-21
**Episode type:** Research Radar Brief
**Papers screened:** 75
**Papers selected:** 1
**Theme:** AI and marketing
> This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Read the original papers before making decisions.
---
## Papers Covered
### 1. Smart Cultural Curations: A Multidisciplinary Study on AI-Enhanced Marketing, Talent R
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Ads, Trust & When Simple Beats AI: 2 Research Signals | 19 May 2026 | 00:10:20 | |
Is the AI tool you're paying for actually better than a free method from 2004? And how deeply is commercial influence already woven into the AI answers your audience reads every day? Those are the two threads running through this Research Radar Brief.
In this episode, Dr. Eva Wolf reviews 2 recent AI marketing research papers — selected from 140 screened — covering hidden commercial influence in generative AI systems and a head-to-head benchmark of AI versus traditional statistical methods for expert matching.
What you'll learn:
- How commercial influence operates inside AI systems, from labeled ads to invisible preference shaping
- Why the four-tier taxonomy proposed by Qiu and Mei matters for marketers planning AI channel strategy
- Why organic AI referrals (e.g., ChatGPT citations to e-commerce sites) are already cited as converting better than paid social in third-party data
- What generative engine optimization (GEO) is and why it may be worth prioritizing now
- How a simple keyword-frequency method (TF-IDF) outperformed GPT-4o mini by nearly 30 percentage points on an expert-matching benchmark
- What to ask AI vendors before buying audience-matching or content-recommendation tools
- Why preserving specific jargon may matter more than letting AI paraphrase it in specialized niches
Papers covered:
1. Generative AI Advertising as a Problem of Trustworthy Commercial Intervention
Source type: Preprint (not peer reviewed)
Access: Open access
Source: https://arxiv.org/abs/2605.18673v1
2. Traditional Statistical Representations Outperform Generative AI in Identifying Expert Peer Reviewers
Source type: Preprint (not peer reviewed)
Access: Open access
Source: https://arxiv.org/abs/2605.18752v1
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-advertising-trust-commercial-influence-tfidf-vs-gpt-2026-05-19
Disclaimer: This is a first-pass research briefing, not a final academic review. Both papers are unreviewed preprints. Summaries reflect available full text as of the episode date. Findings may change before or after peer review. Read the original papers before making decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Consumer Trust, Bias & Chatbots | 16 May 2026 | 00:15:45 | |
If AI is writing your ads, optimizing your layouts, and running your chatbots — how much of that is actually working the way you think it is? That's the thread running through this week's papers. Five studies poke at the same uncomfortable nerve: the gap between what AI marketing tools promise and how consumers actually respond.
In this Research Radar Brief, Dr. Eva Wolf reviews 5 recent AI marketing research papers covering consumer trust in AI-generated content, cultural bias in predictive AI attention tools, customer engagement in AI-driven environments, AI personalization and loyalty, and consumer perception of marketing chatbots.
What you'll learn:
- Why disclosing AI-generated content can hurt brand trust — and when it matters most
- How emotional ads are more vulnerable to AI disclosure backlash than rational, fact-based ads
- Why predictive AI attention tools may systematically misread non-Western audiences
- What three AI qualities — perceived effectiveness, trust, and continuous learning — appear to drive customer engagement
- Why over-personalization is a real risk, and how to set a practical 'creepiness check'
- What 100 Indian online shoppers say they actually care about most in marketing chatbots (hint: it's not accuracy)
Papers covered:
1. Consumer Trust in AI-Generated Marketing Content: A Systematic Literature Review and Research Agenda
Source: Peer-reviewed journal article (American Impact Review, 2026)
Access: Open access
Link: https://doi.org/10.66308/air.e2026024
2. Algorithmic Influence and Consumer Decision-Making: Empirical Evidence on the Limitations of Predictive AI in Marketing Communication Management
Source: Peer-reviewed journal article (Revista de Administração da UFSM, 2026)
Access: Check institutional access
Link: https://doi.org/10.5902/1983465994997
3. The Dynamics of Customer Engagement Within an AI-Driven Marketing Environment
Source: Peer-reviewed journal article (ACADEMIA International Journal for Social Sciences, 2026)
Access: Check institutional access
Link: https://doi.org/10.63056/academia.5.3(a).2026.1720
4. AI-Driven Marketing Personalization and Customer Loyalty
Source: Peer-reviewed journal article (SIJRI, 2026)
Access: Check institutional access
Link: https://doi.org/10.65579/sijri.2026.v2si1.09
5. A Study on Consumer Perception Towards AI-Based Marketing Chatbots
Source: Peer-reviewed journal article (Journal of Advance and Future Research, 2026)
Access: Check institutional access
Link: https://doi.org/10.56975/jaafr.v4i4.507919
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-consumer-trust-predictive-bias-chatbots-personalization-2026-05-16
DISCLAIMER: This is a first-pass research briefing, not a final academic review. Summaries are based on available full text, abstracts, and metadata. Findings reflect what the papers suggest, not settled science. Read the original papers before making strategic or business decisions. Some papers in this episode come from lower-profile venues — apply additional scrutiny to those findings.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Ai And Marketing — 5 Papers | 15 May 2026 | 00:12:18 | |
AI is shaping both sides of marketing: how campaigns are created and how consumers discover, trust, and choose brands.
In this Research Radar Brief, Dr. Eva Wolf reviews 5 recent AI marketing research papers covering AI and marketing.
Papers covered:
1. From Stereotypes to Strategy: Addressing Gender Bias in AI-Powered Marketing
Source type: peer_reviewed_journal_article
Access: unknown
Source: Link in show notes
2. The Impact of AI-Generated Marketing Imagery on Consumer Trust and Purchase Intentions: Examining Effect of Human-AI Assisted Images on Marketing
Source type: peer_reviewed_journal_article
Access: unknown
Source: Link in show notes
3. The AIMx framework: integrating marketing mix modeling, attribution, and AI-driven analytics for adaptive decision systems
Source type: peer_reviewed_journal_article
Access: unknown
Source: Link in show notes
4. AI-Augmented Marketing Automation: Transforming Decision-Making in Omnichannel Retailing
Source type: peer_reviewed_journal_article
Access: unknown
Source: Link in show notes
5. THE IMPACT OF ARTIFICIAL INTELLIGENCE ON MARKETING STRATEGIES IN FAST-PACED BUSINESS ENVIRONMENTS
Source type: peer_reviewed_journal_article
Access: unknown
Source: Link in show notes
Full show notes, transcript, and citations:
https://bigplans.media/episodes/marketing-stereotypes-strategy-impact-generated-aimx-framework-2026-05-15
This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing decisions. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI & Marketing Research Radar — 2026-05-14 | 14 May 2026 | 00:25:33 | |
# Research Radar Brief — Episode radar-2026-05-14
**Date:** 14 May 2026
**Episode type:** Research Radar Brief
**Papers screened:** 105
**Papers selected:** 5
**Theme:** AI and Marketing
> This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Read the original papers before making decisions.
---
## Papers Covered
### 1. Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work
- **Source type:** Pr Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI & Marketing Research Radar — 2026-05-12 | 12 May 2026 | 00:23:20 | |
# Research Radar Brief — Episode radar-2026-05-12
**Date:** 2026-05-12
**Episode type:** Research Radar Brief
**Papers screened:** 120
**Papers selected:** 5
**Theme:** AI and Marketing
> This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Read the original papers before making decisions.
---
## Papers Covered
### 1. Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work
- **Source type:** Pre Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI & Marketing Research Radar — 2026-05-12 | 12 May 2026 | 00:22:18 | |
# Research Radar Brief — Episode radar-2026-05-12
**Date:** 2026-05-12
**Episode type:** Research Radar Brief
**Papers screened:** 105
**Papers selected:** 5
**Theme:** AI and marketing
> This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Read the original papers before making decisions.
---
## Papers Covered
### 1. Vertical tacit collusion in AI-mediated markets
- **Source type:** Preprint
- **Access:** Open access
Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Banking Loyalty, AI Training & LLM Ethics | 12 Jul 2026 | 00:18:50 | |
AI tools are being deployed across banking, education, and marketing workflows — but the research suggests the gap between a promising feature and a real business outcome is wider than most teams assume. In digital banking, AI features only convert to customer loyalty when they first produce genuine satisfaction. In training, even impressive short-term results deserve scrutiny. And sitting underneath all of it: bias, hallucinations, and privacy risks that compound the deeper AI is embedded in your work.
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent peer-reviewed AI marketing research papers covering AI-powered digital banking loyalty, AI skills training for beginners, and the ethical risks of large language models in business contexts.
What you'll learn:
- Why AI banking features don't build loyalty on their own — and what the research says the missing link actually is
- How age segments respond differently to AI banking tools, and why one-size-fits-all campaigns may underperform
- What a structured hands-on AI training program using ChatGPT and Canva AI can realistically achieve — and what the evidence doesn't yet prove
- How ethical problems in LLMs compound as they move from the model into tools like ChatGPT and then into specific business applications
- Why bias, hallucinations, and data privacy leakage are documented risks at the tool level, not just theoretical concerns
Papers covered:
1. An Analytical Study on the Role of Satisfaction in Mediating Customer Loyalty and AI-Powered Digital Banking
Source type: Peer-reviewed journal article (International Review of Management and Marketing)
Access: Open access — full text reviewed
DOI: 10.32479/irmm.22326
Radar verdict: Read now
2. Empowering Vocational Students through AI-Based Smart Digital Marketing Training: A Case Study at SMKN 1 Grati, Pasuruan
Source type: Peer-reviewed journal article (Nusantara Science and Technology Proceedings)
Access: Full text reviewed
DOI: 10.11594/nstp.2026.5484
Radar verdict: Test this week
3. Ethical Issues of Large Language Models: A Multi-Level Thematic Synthesis of the Academic Literature
Source type: Peer-reviewed journal article (AI and Ethics)
Access: Full text reviewed
DOI: 10.1007/s43681-026-01173-5
Radar verdict: Watchlist
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-banking-loyalty-ai-training-llm-ethics-2026-07-12
DISCLAIMER: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a final academic review. Findings are reported as the research suggests, not as proven conclusions. Always consult the original papers before making strategic decisions.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI & Marketing Research Radar — 2026-05-12 | 12 May 2026 | 00:22:19 | |
# Research Radar Brief — AI & Marketing (Episode radar-2026-05-12)
**Date:** 2026-05-12
**Episode type:** Research Radar Brief
**Papers screened:** 140
**Papers selected:** 7
**Theme:** AI and marketing
> This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Read the original papers before making decisions.
---
## Papers Covered
### 1. Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work
- **S Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI & Marketing Research Radar — 2026-05-07 | 07 May 2026 | 00:21:41 | |
# Research Radar Brief — Episode radar-2026-05-07
**Date:** 2026-05-07
**Episode type:** Research Radar Brief
**Papers screened:** 140
**Papers selected:** 5
**Theme:** AI and Marketing
> This is a first-pass research briefing, not a final academic review. Summaries are based on available abstracts and metadata. Read the original papers before making decisions.
---
## Papers Covered
### 1. Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work
- **Source type:** Pre Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI & Marketing Research Radar — 2026-05-06: AI-Generated Advertising and Consumer Trust | 06 May 2026 | 00:13:59 | |
We screened 30 papers and selected 3. Theme: AI-generated advertising and consumer trust. Includes findings on disclosure granularity, AI avatar reviews, and consumer recognition of AI-generated ads. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
| AI Marketing Research: Generative Recommendations, AI Frameworks & Adoption | 11 Jul 2026 | 00:20:24 | |
AI recommendation systems can now generate personalized ads, hold real shopping conversations, and show customers what a product looks like on them. So why are most marketing teams still running the same algorithm they had three years ago — and what does the research say about who's actually getting AI marketing right?
In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI marketing research papers screened from 391 sources, covering generative recommendation systems, a unified AI marketing knowledge framework, and ground-level AI adoption barriers in an emerging-market insurance sector.
What you'll learn:
- Why AI recommendation systems that generate content solve the "new product, no data" problem that has long plagued e-commerce
- How a three-bucket framework — Strategic AI, Consumer AI, and Conversational AI — can guide your next AI investment decision
- What conversational product recommendation looks like in practice, and why LLM-based tools now outperform older rule-based chatbots
- Why the biggest barriers to real-world AI marketing adoption are skill gaps and messy data, not budget or interest
- What risks to build into your AI deployment checklist: filter bubbles, demographic bias, and cold-start failures that standard metrics miss
Papers covered:
1. Recommendation with Generative Models
Source type: Preprint — comprehensive monograph / literature review (not yet peer-reviewed)
Access: Full text reviewed
Source: https://doi.org/10.1108/ftinr-06-2025-0109
2. Artificial Intelligence in Marketing: A Bibliometric Analysis and Integrated AI Marketing Knowledge Framework
Source type: Peer-reviewed journal article (Journal of AI & Immersive Marketing — new venue, peer review process not independently verified)
Access: Full text reviewed
Source: https://doi.org/10.53893/jaiim-v1-2-2026-2
3. Integrating AI to Improve Customer Experience and Marketing in Zambia's Insurance Sector
Source type: Peer-reviewed journal article (lower-profile venue; peer review rigor not independently verified)
Access: Full text reviewed
Source: https://doi.org/10.59413/ajocs/v7.i2.46
Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-marketing-generative-recommendation-framework-adoption-2026-07-11
Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar (Evita) trained on the research framework of Dr. Eva Wolf. It is not a final academic review. Findings are reported as the papers suggest them, not as proven conclusions. Preprints have not been peer-reviewed. Always consult original sources before acting on any finding.
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This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions.
AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts. Thanks for listening to AI & Marketing Research Radar by Big Plans Media. | |||
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