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#LayerTitleAuthorsJournalYearVolume/Issue/PagesDOI URLMethodTeaching NoteAssigned WeekPresenter
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1L1How Generative AI Is Shaping the Future of MarketingGrewal, D.; Satornino, C. B.; Davenport, T.; Guha, A.Journal of the Academy of Marketing Science202553(3), 702-722https://doi.org/10.1007/s11747-024-01064-3Conceptual / Framework四象限框架（輸入性質 × 人類增強程度）。清單 anchor paper。提醒學生：框架式論文的貢獻在於組織既有現象，不等於理論貢獻。
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2L1The GenAI Future of Consumer ResearchHuang, M.-H.; Rust, R. T.Journal of Consumer Research202552(1), 4-17https://doi.org/10.1093/jcr/ucaf013Conceptual / Framework提出 democratization → average trap → model collapse 三階段軌跡。本層唯一具可證偽命題結構者，教學價值最高。
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3L2AI Companions Reduce LonelinessDe Freitas, J.; Oguz-Uguralp, Z.; Uguralp, A. K.; Puntoni, S.Journal of Consumer Research202652(6), 1126-1148https://doi.org/10.1093/jcr/ucaf040Multi-study ExperimentAI 伴侶對孤獨感的緩解效果。注意其 Study 1 使用 app 評論的觀察性資料與 Study 2 實驗的互補設計。
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4L2Automated Versus Human-Operated: Impact of AI-Driven Autonomous Stores on Prosocial BehaviorLiu, X. (J.); Hoang, C.; Ng, S.Journal of Marketing2026OnlineFirst (Apr 13, 2026)https://doi.org/10.1177/00222429261445436Behavioral Experiment把 AI 研究從採用/滿意度推向道德行為外溢。L2 中理論野心最大者。
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5L2Enhancing AI-Assisted Purchase Decisions: The Role of the Sense of AutonomyHou, J.; Yang, S.; Xiong, G.; Pavlou, P. A.MIS Quarterly202650(2), 589-614https://doi.org/10.25300/MISQ/2025/17607Behavioral Experiment提出 AI 的 uniqueness neglect。核心命題：競爭優勢不在賦能 AI，而在賦能消費者。IS 與 Marketing 交界最漂亮的一篇。
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6L2Lower Artificial Intelligence Literacy Predicts Greater AI ReceptivityTully, S. M.; Longoni, C.; Appel, G.Journal of Marketing2025OnlineFirst (Jan 13, 2025)https://doi.org/10.1177/00222429251314491Behavioral Experiment常見引用錯誤：本文常被誤植為 JMR。DOI 前綴 00222429 為 Journal of Marketing。反直覺發現：AI 素養越低接受度越高，挑戰 TAM 預設。本層最強 hook。
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7L3AdGazer: Improving Contextual Advertising with Theory-Informed Machine LearningYe, J.; Wedel, M.; Pieters, R.Journal of Marketing202590(4), 122-144https://doi.org/10.1177/00222429251396114Machine Learning / Theory-driven標題中的 Theory-Informed 就是答案。回應「data-rich but theory-poor」批評的教科書級示範。
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8L3Leveraging Generative Artificial Intelligence to Create Visual Content in Digital Advertising（官方目次未列，待補）Marketing Science2026Articles in Advance (May 5, 2026)https://doi.org/10.1287/mksc.2024.1130Computational / Empirical與 #12 配對閱讀。同題不同刊、不同方法，本學期最佳 methodological contrast 案例。
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9L3Picture Perfect: Engaging Customers with Visual Generative AIHeitmann, M.; Jansen, T. P. J.; Reisenbichler, M.; Schweidel, D. A.Journal of Marketing202590(4), 74-96https://doi.org/10.1177/00222429251356993Computational / Model Fine-tuning必讀。開源模型在 marketing mindset metrics 上做 fine-tuning。示範行銷學者如何不只使用模型而是改造模型——脫離 application paper 的關鍵動作。
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10L4A User Purchase Motivation-Aware Product Recommender System（官方目次未列，待補）Information Systems Research2026Articles in Advance (Feb 6, 2026)https://doi.org/10.1287/isre.2024.1028Design Science / ML區分 stable preference vs. exploratory intent，並提出資料效率高的量測（STB）。
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11L4Algorithmic Targeting and the Precision-Recall Tradeoff（官方目次未列，待補）Marketing Science2026Articles in Advance (Mar 23, 2026)https://doi.org/10.1287/mksc.2024.0930Analytical / Empirical把 ML 評估指標（precision / recall）直接翻譯為行銷決策取捨。全清單中技術—管理橋接做得最乾淨的一篇，是回答「黑箱的 So What」的標準答案。與 #18 形成 L4 雙篇組。
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12L5How Costs Influence Preferences for Control in Generative Artificial Intelligence (GenAI): Human-Guided vs. GenAI-Based Delegated Search（官方目次未列，待補）Information Systems Research2026Articles in Advance (Apr 30, 2026)https://doi.org/10.1287/isre.2025.1836Experiment委任 vs. 掌控的成本權衡。與 #24 形成 delegation 主題的雙篇組。
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13L5When Influencers Delegate Replies: How Social AI Agents Shape User Engagement（官方目次未列，待補）Information Systems Research2026Articles in Advance (May 8, 2026)https://doi.org/10.1287/isre.2025.2270Field Experiment最新最前沿。網紅將互動委任給 AI 代理，直接觸及 authenticity 與 parasocial relationship 的理論張力。強烈建議作為期末報告主題來源。
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14L6A Whole New World, a New Fantastic Point of View: Charting Unexplored Territories in Consumer Research with Generative Artificial IntelligenceYoo, K.; Haenlein, M.; Hewett, K.Journal of the Academy of Marketing Science202553(3), 723-759https://doi.org/10.1007/s11747-025-01097-2Methodological / Replication方法論警世之作。以 ChatGPT-4o 複製五本行銷期刊 35 篇論文的研究流程，同時是對合成受試者效度的嚴肅檢驗。與 #28、#29 形成方法論三部曲。
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15L6Frontiers: ChatGPT Referrals to E-Commerce Websites: How Do LLMs Compare Against Traditional Channels?Kaiser, M.; Schulze, C.Marketing Science2026Articles in Advance (Apr 21, 2026)https://doi.org/10.1287/mksc.2025.0489Descriptive / EmpiricalLLM 作為新流量通路的實證描述。對「AI 是否正在取代搜尋引擎」這個產業級問題的第一手學術證據。
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16L6Large Language Models for Market Research: A Data-Augmentation Approach（官方目次未列，待補）Marketing Science2026Articles in Advance (Mar 17, 2026)https://doi.org/10.1287/mksc.2025.0009MethodologicalLLM 作為資料擴增工具。與 #30 對照閱讀：兩者對合成資料的效度立場不同。
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