AI & Marketing Research Radar

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

``` === AI MARKETING RESEARCH RADAR === === TODAY'S RADAR QUESTION === 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. === PAPERS COVERED === 1. "With great power comes great responsibility": A meta-narrative review of ethical considerations and implications in the cro -- 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.

``` === AI MARKETING RESEARCH RADAR === === TODAY'S RADAR QUESTION === 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. === PAPERS COVERED === 1. Ad Genie: A Multimodal Generative AI Framework for Automated Marketing Campaign Creation Using Product Images, Textual Prompts, and Web Intellig -- 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.

``` === AI MARKETING RESEARCH RADAR === === TODAY'S RADAR QUESTION === 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. === PAPERS COVERED === 1. Fine-Tuned LLM as a Co -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

# 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. --- ## Papers Covered ### 1. Unveiling Trends in AI-Powered Marketing - **Source type:** Academic book cha -- 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.

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. -- 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.

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. -- 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.

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. -- 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.

# 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 -- 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.

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. -- 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.

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. -- 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.

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.

# 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

# 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

# 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

# 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

# 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

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.