Episodes

  • AI Marketing & Cashless Payments: Consumer Trust Research

    AI Marketing & Cashless Payments: Consumer Trust Research
    May 28 2026
    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.
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    9 mins
  • AI Marketing Ethics, Data Privacy & Industry 5.0: Research Brief

    AI Marketing Ethics, Data Privacy & Industry 5.0: Research Brief
    May 27 2026
    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.
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    16 mins
  • AI Marketing Research: Data Gaps, Trust Risks & Personalization

    AI Marketing Research: Data Gaps, Trust Risks & Personalization
    May 26 2026
    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.
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    18 mins
  • AI Marketing Research: Virtual Influencers, Personalization & SME Tools

    AI Marketing Research: Virtual Influencers, Personalization & SME Tools
    May 26 2026
    # 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.
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    20 mins
  • AI Chat Logs, Privacy & Dependency: 2 Research Signals

    AI Chat Logs, Privacy & Dependency: 2 Research Signals
    May 25 2026
    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.
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    14 mins
  • AI Marketing Performance Research: What Actually Creates the Edge?

    AI Marketing Performance Research: What Actually Creates the Edge?
    May 23 2026
    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.
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    13 mins
  • AI Marketing in Emerging Markets: What the Research Shows

    AI Marketing in Emerging Markets: What the Research Shows
    May 22 2026
    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.
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    13 mins
  • AI in Arts Marketing: One Framework Paper, Three Big Claims

    AI in Arts Marketing: One Framework Paper, Three Big Claims
    May 21 2026
    # 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.
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    10 mins