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Paper A

Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work

Sean Kelley, David De Cremer, Christoph Riedl — 2025 — arXiv

preprint  ·   ·  deep dive

Key findings

• Participants working with fully personalized AI produced marketing campaigns of significantly higher quality and creativity than those using generic AI, and also outperformed AI alone — indicating genuine human-AI synergy rather than mere AI substitution.
• Personalized AI led to higher self-reported levels of assistance, feedback quality, trust, and confidence compared to generic AI.
• Causal mediation analysis found that personalization improves performance indirectly by enhancing three mechanisms: collective memory (what the pair retains and reuses), attention (what the pair focuses on), and reasoning (how choices are justified and prioritized) within the human-AI interaction.
• The findings support a theoretical framework in which AI personalization functions as external conversational scaffolding that builds 'common ground' and shared partner models between the user and AI, reducing uncertainty and enhancing joint cognition.

Marketing implications

• Marketing teams deploying AI writing or ideation tools should invest in personalization mechanisms — onboarding users with psychometric or work-style profiles may significantly improve the quality and creativity of AI-assisted campaign outputs.
• The finding that personalized AI improves trust and perceived usefulness (not just output quality) suggests that personalization could increase AI tool adoption and sustained engagement among marketing professionals.
• Agencies and brands building internal AI assistants should consider multi-turn conversation design with user context embedded from the start, rather than treating each AI session as a blank-slate interaction — this 'scaffolding' approach appears to unlock genuine human-AI creative synergy.

Paper B

From Avatars to Algorithms: Virtual Streamers and AI-Enabled Consumer Behavior in Live Streaming Commerce—A Systematic Review

Ling Wang, Jasmine A. L. Yeap, Jiaqi Liu, Zongwei Li — 2026 — Journal of Theoretical and Applied Electronic Commerce Research

systematic review  ·   ·  deep dive

Key findings

• Three primary mechanisms explain how virtual streamers influence consumer behavior: trait-based trust, perceived social presence, and message framing.
• These three mechanisms collectively form a 'triadic integration model' (CIMCO-derived) that explains how AI-driven virtual streamers shape consumer engagement across platforms and product types.
• Current research is geographically concentrated and relies heavily on self-reported data, limiting broader generalizability.
• Transparency about whether a streamer is AI- or human-operated is identified as important for maintaining user trust.

Marketing implications

• Retailers and platform operators should align virtual avatar traits and communication styles with the product categories being sold and the expectations of their target consumer segments.
• Brands deploying AI virtual streamers should proactively disclose AI/human status to consumers to preserve trust, particularly as regulatory scrutiny of AI disclosures grows.
• Investing in the social presence signals and framing strategies used by virtual streamers—not just visual realism—may be a more reliable lever for driving consumer engagement in live commerce.

Paper C

Vertical tacit collusion in AI-mediated markets

Felipe M. Affonso — 2026 — arXiv

preprint  ·   ·  deep dive

Key findings

• Joint exploitation of AI shopping agent biases by platforms (via ranking/architecture) and sellers (via product descriptions) produces consumer harm more than double what either party could achieve independently — a super-additive effect.
• Platform ranking determines which products occupy bias-triggering positions (e.g., primacy bias), while seller manipulation of descriptions determines conversion rates; these two mechanisms act as complements rather than substitutes.
• Vertical tacit collusion requires no communication or coordination between platforms and sellers — harm emerges from independently aligned profit-maximizing incentives, making it structurally invisible to antitrust frameworks that require evidence of coordination or agreement.
• AI shopping agents exhibit uniform, predictable biases across millions of interactions (unlike heterogeneous human consumers), making them a concentrated and systematically exploitable target; LLMs show cognitive biases in ~40% of tested scenarios and, e.g., select top-half listings 77% of the time in benchmark tests.

Marketing implications

• Sellers and platforms that optimize AI-facing content (product descriptions, rankings, endorsements) may inadvertently — or deliberately — exploit LLM biases; marketers should audit their AI-optimization strategies for potential consumer harm and regulatory exposure as oversight frameworks evolve.
• Brands and agencies developing AI-mediated retail or recommendation experiences should be aware that position effects, anchoring language, and endorsement signals interact multiplicatively, not additively, in influencing AI agent recommendations — with potentially outsized effects on consumer outcomes.
• The paper signals an emerging regulatory risk area: as AI shopping agents scale (ChatGPT, Amazon Rufus, Perplexity), marketing practices that exploit AI biases may attract antitrust or consumer protection scrutiny, even absent any explicit coordination or intent to harm.

Paper D

The Impact of AI-Generated Marketing Imagery on Consumer Trust and Purchase Intentions: Examining Effect of Human-AI Assisted Images on Marketing

Rushikesh Lahane — 2026 — International Journal for Research in Applied Science and Engineering Technology

peer reviewed journal article  ·   ·  deep dive

Key findings

• Human-generated ads produced significantly higher consumer trust (M = 5.63) than AI-generated ads (M = 4.24, p < 0.001) and higher purchase intentions (M = 5.62 vs. 4.65, p < 0.001).
• Hybrid (human-AI assisted) ads partially closed the trust gap relative to fully AI-generated ads (M = 5.08 vs. 4.24, p < 0.001), but did not significantly improve purchase intentions compared to AI-only ads (p > 0.05).
• Human-generated ads were perceived as more authentic and effortful; AI-generated ads were seen as less authentic and requiring less effort; hybrid ads fell in between on both dimensions.
• Increased consumer trust from human involvement does not automatically translate into higher purchase intentions, suggesting trust and purchase intent are not perfectly coupled outcomes.

Marketing implications

• Brands using AI-generated imagery should consider clearly disclosing AI involvement only when it aligns with brand values around transparency — disclosed AI origin depresses both trust and purchase intent compared to human-made content.
• Hybrid workflows (human creative direction + AI execution) may represent a middle-ground strategy that partially preserves consumer trust without sacrificing AI efficiency gains, though they may not fully recover purchase intent.
• Marketers should not assume that regaining consumer trust through human involvement automatically converts to sales lift — separate strategies may be needed to bridge the trust-to-purchase gap.

Paper E

Persuading the proxy: a framework for AI-mediated marketing decisions

Anil Bilgihan, Melanie P. Lorenz, Ye Zhang, Massimiliano Ostinelli — 2026 — International Journal of Contemporary Hospitality Management

peer reviewed journal article  ·   ·  deep dive

Key findings

• Persuasion in AI-mediated hospitality environments is bifurcated into two distinct pathways: one targeting human psychology via AI-assisted decision support, and another targeting algorithmic evaluation via machine-readable signals and structured data.
• The framework proposes four key mediators — trust, cognitive load, preference alignment, and perceived agency — and three moderators — service type, technological fluency, and emotional salience — that shape the effectiveness of influence across both pathways.
• Customer loyalty, satisfaction, and decision quality are increasingly shaped by a hybrid interplay between human sentiment and agentic (AI-driven) logic, not by human cognition alone.
• Effective marketing strategies must now combine emotionally resonant narratives for human customers with structurally optimized, verifiable, machine-readable content for AI agents.

Marketing implications

• Hospitality marketers and content strategists must now design for two distinct persuasion targets simultaneously: emotionally compelling narratives and brand stories for human consumers, and structured, machine-readable, verifiable data (e.g., schema markup, standardized attributes, verified ratings) for AI booking agents.
• As AI agents increasingly make or heavily influence booking decisions, data quality, transparency, and preference alignment within hotel or hospitality listings become as strategically important as traditional advertising creative.
• Brands should audit how their content and product information appear to AI systems (not just human users), ensuring that key decision signals — price, availability, amenities, reviews — are consistently structured and accessible to algorithmic evaluation.

Paper F

The AIMx framework: integrating marketing mix modeling, attribution, and AI-driven analytics for adaptive decision systems

Thi Phuong Lan Nguyen — 2026 — Future Business Journal

peer reviewed journal article  ·   ·  deep dive

Key findings

• Integrating MMM, MTA, and incrementality testing within a single AI-driven feedback architecture (AIMx) is proposed to reduce decision fragmentation and improve marketing responsiveness to market fluctuations.
• Simulation results suggest that firms using the integrated AIMx framework demonstrate more stable budget adjustments and faster adaptation compared to fragmented measurement approaches, though this is based on simulated rather than empirical data.
• The framework conceptualizes AI and human judgment as complementary: algorithmic systems enhance analytical precision and scalability, while strategic interpretation, ethical oversight, and accountability remain human responsibilities.
• At a broader level, widespread adoption of AI-integrated marketing analytics may generate stabilizing feedback effects within digital ecosystems, though the authors explicitly note these system-level implications remain conceptual.

Marketing implications

• The AIMx framework offers marketing leaders a structured conceptual architecture for unifying their measurement stack — integrating MMM for long-run budget optimization, MTA for touchpoint attribution, and incrementality testing for causal validation — rather than running these models in isolation.
• The paper reinforces the case for investing in integrated AI-driven marketing analytics platforms that enable adaptive budget reallocation under volatile market conditions (e.g., inflationary or rapidly shifting consumer behavior environments).
• The framework explicitly calls for human governance alongside algorithmic systems, providing a useful framing for marketing organizations designing accountability structures around AI-assisted budget and channel decisions.

Paper G

The prospect of AI-enhanced agile marketing: boosting marketing ROI through customer engagement and sales performance

Luoxi Pu, Robert Radics, Muhammad Umar, Faith Jeremiah et al. — 2026 — Marketing Intelligence & Planning

peer reviewed journal article  ·   ·  deep dive

Key findings

• AI-enhanced marketing agility operates through a 'parallel dual-mediation' mechanism, simultaneously improving both customer engagement and sales performance — rather than requiring trade-offs between the two.
• AI enables marketing agility to bypass traditional resource constraints that typically force prioritization between engagement-focused and sales-focused activities.
• AI-enhanced agility demonstrates both autonomous operation (independent effects on ROI) and synergistic integration (amplifying existing marketing capabilities), challenging linear capability–performance assumptions.
• The study validates a theoretical framework suggesting AI transforms marketing agility into a more flexible, multi-pathway driver of marketing ROI in e-commerce SME contexts.

Marketing implications

• For SMEs in e-commerce, investing in AI-enabled marketing tools may allow teams to pursue customer engagement and sales conversion goals simultaneously, rather than treating them as competing priorities under budget constraints.
• Marketing leaders should consider AI not just as an efficiency tool but as a structural enabler that can expand the operational reach of agile marketing practices without proportional resource increases.
• Brands operating in high-velocity e-commerce environments may benefit from framing AI adoption in terms of agility enhancement — specifically, the ability to sense and respond to market shifts faster and across multiple performance dimensions at once.

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