{
 "lead": "IDAEO 的每一個設計都有學理出處。上半是圖§8-2 列出的 17 項學理的完整書目（19 筆出處）；下半是 78 筆延伸文獻，分成行銷學基礎、生成式引擎與 AI 搜尋研究、AI 顧客與 AI Agent、資訊檢索與可信度、成效量測、啟動與累積、AI 平台官方文件與代理協定、七大定律與旅程的學理八類。",
 "note": "全部書目已於 2026/10/11 逐筆對照出版資料與官方頁面查證作者、年份、題名與出處；標「預印本」者尚未經同儕審查。古德哈特定律的論文於 1975 年發表、1976 年收入論文集出版。官方文件會持續更新，以查閱日的內容為準。表中「→」之前是文獻原本的說法，「→」之後是 IDAEO 的用法；IDAEO 以實測得到的結論，依據是各章的實測數據，文獻是它的學理參照。",
 "cats": [
  {
   "name": "本書圖§8-2 列出的 17 項學理（19 筆出處）",
   "items": [
    {
     "theory": "檢索增強生成",
     "citation": "Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., 等（2020）。Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks。Advances in Neural Information Processing Systems 33（NeurIPS 2020）。arXiv:2005.11401。",
     "use": "AI 回答前先檢索 → 當場來讀"
    },
    {
     "theory": "委託代理",
     "citation": "Jensen, M. C., & Meckling, W. H.（1976）。Theory of the firm: Managerial behavior, agency costs and ownership structure。Journal of Financial Economics, 3(4), 305–360。",
     "use": "AI Agent 是客人的代理人"
    },
    {
     "theory": "AI 可讀標準（robots 協定）",
     "citation": "Koster, M., Illyes, G., Zeller, H., & Sassman, L.（2022）。Robots Exclusion Protocol。RFC 9309，IETF（Proposed Standard），2022 年 9 月。",
     "use": "AI 讀網站有規矩 → AI 放行"
    },
    {
     "theory": "AI 可讀標準（結構化資料詞彙）",
     "citation": "Schema.org。About Schema.org。Schema.org 社群（創始公司：Google、Microsoft、Yahoo、Yandex）。",
     "use": "AI 讀網站有規矩 → AI 放行"
    },
    {
     "theory": "顧客旅程（消費者決策旅程）",
     "citation": "Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J.（2009）。The consumer decision journey。McKinsey Quarterly，2009 年 6 月。",
     "use": "消費決策是有回流的循環 → 十步驟"
    },
    {
     "theory": "顧客旅程（零關鍵時刻 ZMOT）",
     "citation": "Lecinski, J.（2011）。Winning the Zero Moment of Truth。Google（免費電子書）。",
     "use": "客人在購買前先上網查，路徑可前進也可回頭 → 十步驟"
    },
    {
     "theory": "注意力稀缺",
     "citation": "Simon, H. A.（1971）。Designing Organizations for an Information-Rich World。收於 M. Greenberger（編），Computers, Communications, and the Public Interest（頁 37–72）。Baltimore: The Johns Hopkins Press。",
     "use": "資訊愈多，注意力愈稀缺 → S6 AI 一次只點名幾家"
    },
    {
     "theory": "資訊不對稱",
     "citation": "Akerlof, G. A.（1970）。The Market for \"Lemons\": Quality Uncertainty and the Market Mechanism。The Quarterly Journal of Economics, 84(3), 488–500。",
     "use": "品質資訊不對稱影響交易判斷 → 以驗證補足可信依據"
    },
    {
     "theory": "來源可信度",
     "citation": "Hovland, C. I., & Weiss, W.（1951）。The Influence of Source Credibility on Communication Effectiveness。Public Opinion Quarterly, 15(4), 635–650。",
     "use": "誰說的，會影響人信不信 → 主張溯源定律：每項主張都附來源"
    },
    {
     "theory": "信任三要素",
     "citation": "Mayer, R. C., Davis, J. H., & Schoorman, F. D.（1995）。An Integrative Model of Organizational Trust。The Academy of Management Review, 20(3), 709–734。",
     "use": "信任看能力、善意、正直，並隨行為更新 → 信任隨時效更新"
    },
    {
     "theory": "實體解析",
     "citation": "Fellegi, I. P., & Sunter, A. B.（1969）。A Theory for Record Linkage。Journal of the American Statistical Association, 64(328), 1183–1210。",
     "use": "同一家要多個欄位對上 → 身分連續定律"
    },
    {
     "theory": "資料溯源",
     "citation": "Moreau, L., & Missier, P.（編）（2013）。PROV-DM: The PROV Data Model。W3C Recommendation，2013 年 4 月 30 日。",
     "use": "每筆資料從哪來、何時 → 主張溯源定律"
    },
    {
     "theory": "第三方認證中介",
     "citation": "Lizzeri, A.（1999）。Information Revelation and Certification Intermediaries。The RAND Journal of Economics, 30(2), 214–231。",
     "use": "認證中介決定揭露多少資訊 → 驗證獨立定律"
    },
    {
     "theory": "信號理論",
     "citation": "Spence, M.（1973）。Job Market Signaling。The Quarterly Journal of Economics, 87(3), 355–374。",
     "use": "信號的成本因類型而異，高品質一方較能承擔 → 服務分級的可信信號"
    },
    {
     "theory": "能力成熟度模型",
     "citation": "Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V.（1993）。Capability maturity model, version 1.1。IEEE Software, 10(4), 18–27。",
     "use": "能力分級，逐級往上 → 服務分級"
    },
    {
     "theory": "版本化定價",
     "citation": "Shapiro, C., & Varian, H. R.（1998）。Versioning: The Smart Way to Sell Information。Harvard Business Review, 76(6), 106–114。",
     "use": "同一產品分版本提供 → 服務分級內容不同"
    },
    {
     "theory": "效果層級",
     "citation": "Lavidge, R. J., & Steiner, G. A.（1961）。A Model for Predictive Measurements of Advertising Effectiveness。Journal of Marketing, 25(6), 59–62。",
     "use": "知道、有好感、行動有先後 → 曝光、口碑、品牌公關"
    },
    {
     "theory": "聲量佔比",
     "citation": "Jones, J. P.（1990）。Ad Spending: Maintaining Market Share。Harvard Business Review, 68(1), 38–42。",
     "use": "廣告聲量佔比要和市佔一起看 → 借這個概念量 AI 點名率"
    },
    {
     "theory": "古德哈特定律",
     "citation": "Goodhart, C. A. E.（1976）。Problems of Monetary Management: The U.K. Experience。收於 Papers in Monetary Economics, Vol. I。Sydney: Reserve Bank of Australia。（1975 年 7 月於雪梨的研討會發表，通行引作 Goodhart 1975）",
     "use": "指標成為目標會削弱其衡量效力 → 同時看點擊與 AI 每一步的取用"
    }
   ]
  },
  {
   "name": "延伸文獻：行銷學基礎",
   "items": [
    {
     "theory": "行銷的官方定義",
     "citation": "American Marketing Association。Definition of Marketing。AMA 官方網站（2026/10/11 查閱）。",
     "use": "行銷是為顧客與社會創造並交換價值的活動 → 對象擴及 AI"
    },
    {
     "theory": "AIDA（注意、興趣、慾望、行動）",
     "citation": "Strong, E. K., Jr.（1925）。Theories of selling。Journal of Applied Psychology, 9(1), 75–86。",
     "use": "Strong 整理注意、興趣、慾望、行動的銷售階段說法 → 顧客旅程分階段的早期參照"
    },
    {
     "theory": "顧客旅程與顧客體驗",
     "citation": "Lemon, K. N., & Verhoef, P. C.（2016）。Understanding Customer Experience Throughout the Customer Journey。Journal of Marketing, 80(6), 69–96。",
     "use": "旅程分消費決策前、中、後與多接觸點 → 十步驟加入 AI 接觸點"
    },
    {
     "theory": "以顧客為基礎的品牌權益",
     "citation": "Keller, K. L.（1993）。Conceptualizing, Measuring, and Managing Customer-Based Brand Equity。Journal of Marketing, 57(1), 1–22。",
     "use": "品牌資產來自顧客腦中的品牌知識 → 延伸到 AI 腦中的知識"
    },
    {
     "theory": "服務主導邏輯",
     "citation": "Vargo, S. L., & Lusch, R. F.（2004）。Evolving to a New Dominant Logic for Marketing。Journal of Marketing, 68(1), 1–17。",
     "use": "價值在使用中與顧客共創 → AI 代辦也是共創的一環"
    },
    {
     "theory": "口碑對新產品擴散的影響",
     "citation": "Arndt, J.（1967）。Role of Product-Related Conversations in the Diffusion of a New Product。Journal of Marketing Research, 4(3), 291–295。",
     "use": "正面口碑能促進消費者採用新產品 → 口碑是「AEO+ 口碑」的核心"
    },
    {
     "theory": "兩級傳播與意見領袖",
     "citation": "Katz, E., & Lazarsfeld, P. F.（1955）。Personal Influence: The Part Played by People in the Flow of Mass Communications。Glencoe, IL: Free Press。",
     "use": "意見領袖替大眾解讀、轉述訊息 → AI 扮演同樣的中介角色"
    },
    {
     "theory": "傳統與社群贏得媒體的銷售效果",
     "citation": "Stephen, A. T., & Galak, J.（2012）。The Effects of Traditional and Social Earned Media on Sales: A Study of a Microlending Marketplace。Journal of Marketing Research, 49(5), 624–639。",
     "use": "贏得媒體能帶動銷售 → 第三方證據比自說有力"
    },
    {
     "theory": "服務藍圖",
     "citation": "Shostack, G. L.（1984）。Designing Services That Deliver。Harvard Business Review, 62(1), 133–139。",
     "use": "服務藍圖標示流程的潛在失敗點 → 十步驟逐步確認改善重點"
    },
    {
     "theory": "承諾與信任（關係行銷）",
     "citation": "Morgan, R. M., & Hunt, S. D.（1994）。The Commitment-Trust Theory of Relationship Marketing。Journal of Marketing, 58(3), 20–38。",
     "use": "信任與承諾是關係行銷成功的關鍵 → 信任驗證接到 S10 回來再問"
    },
    {
     "theory": "顧客價值",
     "citation": "Gupta, S., Lehmann, D. R., & Stuart, J. A.（2004）。Valuing Customers。Journal of Marketing Research, 41(1), 7–18。",
     "use": "顧客的長期價值可以用來估算企業價值 → 成效一路看到回訪"
    }
   ]
  },
  {
   "name": "延伸文獻：生成式引擎與 AI 搜尋研究",
   "items": [
    {
     "theory": "生成式引擎優化（GEO）",
     "citation": "Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A.（2024）。GEO: Generative Engine Optimization。Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining（KDD '24），頁 5–16。",
     "use": "首次提出 GEO 優化框架並量測可見度 → 本書沿用其核心方向：提升內容在生成式回答中的可見度。"
    },
    {
     "theory": "生成式搜尋引擎的可驗證性",
     "citation": "Liu, N. F., Zhang, T., & Liang, P.（2023）。Evaluating Verifiability in Generative Search Engines。Findings of the Association for Computational Linguistics: EMNLP 2023，頁 7001–7025。",
     "use": "生成式搜尋的引用支撐程度有差異 → 正確率要另行量測"
    },
    {
     "theory": "對話式搜尋引擎的排名操縱",
     "citation": "Pfrommer, S., Bai, Y., Gautam, T., & Sojoudi, S.（2024）。Ranking Manipulation for Conversational Search Engines。Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing（EMNLP 2024），頁 9523–9552。arXiv:2406.03589。",
     "use": "網頁文字可操縱 AI 推薦排序 → 所以要有三方驗證"
    },
    {
     "theory": "操縱語言模型提高產品能見度",
     "citation": "Kumar, A., & Lakkaraju, H.（2024）。Manipulating Large Language Models to Increase Product Visibility。arXiv 預印本 arXiv:2404.07981。",
     "use": "在商品頁加一段字就能擠進推薦首位 → 點名率可被操縱"
    },
    {
     "theory": "針對語言模型的對抗式 SEO",
     "citation": "Nestaas, F., Debenedetti, E., & Tramèr, F.（2025）。Adversarial Search Engine Optimization for Large Language Models。ICLR 2025。arXiv:2406.18382。",
     "use": "競相操縱 AI，各方表現一起變差 → IDAEO 主張以可驗證事實長期經營"
    },
    {
     "theory": "AI 搜尋偏好贏得媒體",
     "citation": "Chen, M., Wang, X., Chen, K., & Koudas, N.（2025）。Generative Engine Optimization: How to Dominate AI Search。arXiv 預印本 arXiv:2509.08919。",
     "use": "AI 搜尋引第三方來源多於品牌自有 → 第三方證據最關鍵"
    },
    {
     "theory": "對話式 SEO 是否有效的基準測試",
     "citation": "Puerto, H., Gubri, M., Green, T., Oh, S. J., & Yun, S.（2025）。C-SEO Bench: Does Conversational SEO Work?。Advances in Neural Information Processing Systems 38（NeurIPS 2025 Datasets & Benchmarks），頁 31145–31169。arXiv:2506.11097。",
     "use": "多數改寫技巧效果有限 → 以實測成效判斷優化成果"
    },
    {
     "theory": "AI 搜尋「有引用就可信」的落差",
     "citation": "Narayanan Venkit, P., Laban, P., Zhou, Y., Mao, Y., & Wu, C.-S.（2025）。Search Engines in the AI Era: A Qualitative Understanding to the False Promise of Factual and Verifiable Source-Cited Responses in LLM-based Search。Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency（FAccT '25），頁 1325–1340。arXiv:2410.22349。",
     "use": "附來源的 AI 回答仍常出錯 → 要逐條事實驗證"
    },
    {
     "theory": "AI 摘要降低點擊",
     "citation": "Chapekis, A., & Lieb, A.（2025）。Google users are less likely to click on links when an AI summary appears in the results。Pew Research Center，2025 年 7 月 22 日。",
     "use": "出現 AI 摘要時使用者較少點連結 → 同時衡量點擊與 AI 取用"
    }
   ]
  },
  {
   "name": "延伸文獻：AI 顧客與 AI Agent",
   "items": [
    {
     "theory": "演算法消費者",
     "citation": "Gal, M. S., & Elkin-Koren, N.（2017）。Algorithmic Consumers。Harvard Journal of Law & Technology, 30(2), 309–353。",
     "use": "演算法替人挑選並下單 → 行銷對象延伸到 AI 顧客"
    },
    {
     "theory": "自動購物系統的採用障礙",
     "citation": "de Bellis, E., & Johar, G. V.（2020）。Autonomous Shopping Systems: Identifying and Overcoming Barriers to Consumer Adoption。Journal of Retailing, 96(1), 74–87。",
     "use": "消費者交由系統代作消費決策時面臨心理障礙 → S8 需要信任基礎"
    },
    {
     "theory": "AI 購物代理如何選品",
     "citation": "Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., & Kumar, A.（2025）。What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce。arXiv:2508.02630；另收於 Proceedings of the ACM Web Conference 2026，頁 8697–8700。",
     "use": "AI 代理選品有位置與模型偏好 → 商家要為代理優化"
    },
    {
     "theory": "代理對代理的市場實驗環境",
     "citation": "Bansal, G., Hua, W., Huang, Z., Fourney, A., Swearngin, A., Epperson, W., Payne, T., Hofman, J. M., 等（2025）。Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets。arXiv 預印本 arXiv:2510.25779。",
     "use": "用模擬市場研究交易雙方代理互動 → S8 的實驗依據"
    }
   ]
  },
  {
   "name": "延伸文獻：資訊檢索與可信度",
   "items": [
    {
     "theory": "PageRank 與大型搜尋引擎架構",
     "citation": "Brin, S., & Page, L.（1998）。The anatomy of a large-scale hypertextual Web search engine。Computer Networks and ISDN Systems, 30(1–7), 107–117。",
     "use": "爬取、索引、以連結算權威 → S2 與 S4 的技術原型"
    },
    {
     "theory": "權威來源與樞紐（HITS）",
     "citation": "Kleinberg, J. M.（1999）。Authoritative sources in a hyperlinked environment。Journal of the ACM, 46(5), 604–632。",
     "use": "權威由他人指向來認定 → 第三方引用是權威的來源"
    },
    {
     "theory": "以事實正確性評估來源可信度（KBT）",
     "citation": "Dong, X. L., Gabrilovich, E., Murphy, K., Dang, V., Horn, W., Lugaresi, C., Sun, S., & Zhang, W.（2015）。Knowledge-based trust: Estimating the trustworthiness of web sources。Proceedings of the VLDB Endowment, 8(9), 938–949。",
     "use": "用網頁事實對不對來評可信度 → 逐條事實驗證的依據"
    },
    {
     "theory": "知識圖譜（實體而非字串）",
     "citation": "Singhal, A.（2012）。Introducing the Knowledge Graph: things, not strings。Google 官方部落格，2012 年 5 月 16 日。",
     "use": "搜尋從比對字串改成認識實體 → 店家要成為可辨識實體"
    },
    {
     "theory": "知識圖譜綜述",
     "citation": "Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Labra Gayo, J. E., 等（2021）。Knowledge Graphs。ACM Computing Surveys, 54(4), 1–37。",
     "use": "知識圖譜如何建立、驗證與更新 → S4 的學術總覽"
    },
    {
     "theory": "E-E-A-T（經驗、專業、權威、可信）",
     "citation": "Google Search Central（2022）。Our latest update to the quality rater guidelines: E-A-T gets an extra E for Experience。Google Search Central Blog，2022 年 12 月 15 日。另見 Google Search Central。Creating helpful, reliable, people-first content（2026/10/11 查閱）。",
     "use": "Google 的品質評分指南看四個面向，信任最重要 → 驗證的對照"
    },
    {
     "theory": "事實查核資料集 FEVER",
     "citation": "Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A.（2018）。FEVER: a Large-scale Dataset for Fact Extraction and VERification。NAACL-HLT 2018（Long Papers），頁 809–819。",
     "use": "每條聲明對證據判支持、反駁或不足 → 驗證三態的原型"
    },
    {
     "theory": "原子事實正確率 FActScore",
     "citation": "Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W., Koh, P. W., Iyyer, M., Zettlemoyer, L., 等（2023）。FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation。EMNLP 2023，頁 12076–12100。",
     "use": "把回答拆成單一事實逐條算正確比例 → 正確率的算法"
    },
    {
     "theory": "語言生成的幻覺綜述",
     "citation": "Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., 等（2023）。Survey of Hallucination in Natural Language Generation。ACM Computing Surveys, 55(12), 1–38。",
     "use": "語言模型會生成無依據內容 → 為何要量正確率"
    }
   ]
  },
  {
   "name": "延伸文獻：成效量測",
   "items": [
    {
     "theory": "淨推薦值（NPS）",
     "citation": "Reichheld, F. F.（2003）。The One Number You Need to Grow。Harvard Business Review, 81(12), 46–54。",
     "use": "推薦意願和企業成長相關 → 口碑量測的參考，另外追蹤回訪量"
    },
    {
     "theory": "多通路轉換歸因",
     "citation": "Li, H., & Kannan, P. K.（2014）。Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment。Journal of Marketing Research, 51(1), 40–56。",
     "use": "轉換要分攤給各接觸通路 → AI 通路也要算進歸因"
    },
    {
     "theory": "資料驅動的多點觸及歸因",
     "citation": "Shao, X., & Li, L.（2011）。Data-driven multi-touch attribution models。Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining（KDD '11），頁 258–264。",
     "use": "用資料估每個接觸點的貢獻 → 連接讀取量到導客量"
    },
    {
     "theory": "坎貝爾定律（指標被用於決策就會被扭曲）",
     "citation": "Campbell, D. T.（1979）。Assessing the impact of planned social change。Evaluation and Program Planning, 2(1), 67–90。",
     "use": "量化指標越用於決策越被扭曲 → 九個成效指標要互相制衡"
    },
    {
     "theory": "廣告成效的實驗與觀察比較",
     "citation": "Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D.（2019）。A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook。Marketing Science, 38(2), 193–225。",
     "use": "觀察資料算出的成效，和實驗結果常有落差 → 成效比較一律用同一口徑"
    }
   ]
  },
  {
   "name": "延伸文獻：啟動與累積",
   "items": [
    {
     "theory": "先行者優勢",
     "citation": "Lieberman, M. B., & Montgomery, D. B.（1988）。First-mover advantages。Strategic Management Journal, 9(S1), 41–58。",
     "use": "先進場有優勢，也要付出代價 → 早期紅利靠持續投入守住"
    },
    {
     "theory": "報酬遞增與路徑依賴",
     "citation": "Arthur, W. B.（1989）。Competing Technologies, Increasing Returns, and Lock-In by Historical Events。The Economic Journal, 99(394), 116–131。",
     "use": "報酬遞增時，早期領先可能鎖定市場 → 複利領先的學理參照"
    },
    {
     "theory": "冷啟動問題",
     "citation": "Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M.（2002）。Methods and metrics for cold-start recommendations。Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval（SIGIR '02），頁 253–260。",
     "use": "新項目還沒有累積資料時，推薦系統難以推薦 → 冷啟動的學理參照"
    }
   ]
  },
  {
   "name": "延伸文獻：AI 平台官方文件與代理協定",
   "items": [
    {
     "theory": "OpenAI 爬蟲說明",
     "citation": "OpenAI。Overview of OpenAI Crawlers。OpenAI 開發者文件（2026/10/11 查閱）。",
     "use": "GPTBot、OAI-SearchBot、ChatGPT-User 各有用途 → 訓練、建目錄、當場來讀三種讀取"
    },
    {
     "theory": "Anthropic 爬蟲說明",
     "citation": "Anthropic。Does Anthropic crawl data from the web, and how can site owners block the crawler?。Claude Help Center（2026/10/11 查閱）。",
     "use": "ClaudeBot、Claude-SearchBot、Claude-User 各有用途 → 同一家平台也分三種讀取"
    },
    {
     "theory": "Perplexity 爬蟲說明",
     "citation": "Perplexity。Perplexity Crawlers。Perplexity 官方文件（2026/10/11 查閱）。",
     "use": "PerplexityBot 建搜尋結果，Perplexity-User 隨使用者提問來讀 → 建目錄與當場來讀"
    },
    {
     "theory": "Amazon 爬蟲說明",
     "citation": "Amazon。About Amazonbot。Amazon Developer（2026/10/11 查閱）。",
     "use": "Amazonbot 改善產品與服務、可能用於訓練，Amzn-User 隨使用者需求來讀 → 訓練與當場來讀"
    },
    {
     "theory": "Google 爬蟲總覽",
     "citation": "Google。Google's common crawlers。Google Crawling Infrastructure 文件（2026/10/11 查閱）。",
     "use": "Googlebot 建搜尋目錄，Google-Extended 管理 AI 用途 → 搜尋引擎爬蟲與 AI 用途分開看"
    },
    {
     "theory": "AI 爬取與導客的外部量測",
     "citation": "Tomé, J.（2025）。The crawl-to-click gap: Cloudflare data on AI bots, training, and referrals。Cloudflare Blog，2025 年 8 月 29 日。",
     "use": "外部量測把 AI 爬取和導客分開統計 → AI 曝光數與 AI 帶客分開量"
    },
    {
     "theory": "模型情境協定（MCP）",
     "citation": "Model Context Protocol。Specification（2025-06-18 版）。modelcontextprotocol.io。",
     "use": "AI Agent 透過標準接口取用資料與工具 → S8 替客人辦事"
    },
    {
     "theory": "代理對代理協定（A2A）",
     "citation": "Google（2025）。Announcing the Agent2Agent Protocol (A2A)。Google Developers Blog，2025 年 4 月 9 日。",
     "use": "不同的 AI Agent 用共同標準交接任務 → AI Agent 是新的行銷受眾"
    },
    {
     "theory": "代理支付協定（AP2）",
     "citation": "Google Cloud（2025）。Announcing Agent Payments Protocol (AP2)。Google Cloud Blog，2025 年 9 月。",
     "use": "代理交易要有使用者授權與可追溯的憑證 → S8 到 S9 的信任驗證"
    },
    {
     "theory": "llms.txt 提案",
     "citation": "Howard, J.（2024）。The /llms.txt file（現行為 v2）。llmstxt.org，2024 年 9 月 3 日首次發布。",
     "use": "替模型準備一份內容導覽 → S1 店家準備好的 AI 可讀層"
    }
   ]
  },
  {
   "name": "延伸文獻：七大定律與旅程的學理",
   "items": [
    {
     "theory": "受眾適配定律｜語意網",
     "citation": "Berners-Lee, T., Hendler, J., & Lassila, O.（2001）。The Semantic Web。Scientific American, 284(5), 34–43。",
     "use": "網頁加上機器讀得懂的結構，軟體代理才能替人辦事 → 對人、對 AI 各用對方讀得懂的表達"
    },
    {
     "theory": "受眾適配定律｜媒介豐富度",
     "citation": "Daft, R. L., & Lengel, R. H.（1986）。Organizational Information Requirements, Media Richness and Structural Design。Management Science, 32(5), 554–571。",
     "use": "不同的資訊需求適合不同的表達方式 → 依受眾安排內容形式"
    },
    {
     "theory": "身分連續定律｜品牌即信號",
     "citation": "Erdem, T., & Swait, J.（1998）。Brand Equity as a Signaling Phenomenon。Journal of Consumer Psychology, 7(2), 131–157。",
     "use": "一致、可信的品牌信號降低客人的不確定 → 身分長期一致，信任才累積得起來"
    },
    {
     "theory": "身分連續定律｜實體連結",
     "citation": "Shen, W., Wang, J., & Han, J.（2015）。Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions。IEEE Transactions on Knowledge and Data Engineering, 27(2), 443–460。",
     "use": "系統靠名稱與上下文把文字連回同一個實體 → 名稱一致，AI 才連得回同一家店"
    },
    {
     "theory": "驗證獨立定律｜審計獨立性",
     "citation": "DeAngelo, L. E.（1981）。Auditor size and audit quality。Journal of Accounting and Economics, 3(3), 183–199。",
     "use": "驗證品質看能不能發現問題、敢不敢如實報告 → 驗證結論不隨委託關係改變"
    },
    {
     "theory": "驗證獨立定律｜品質揭露與認證",
     "citation": "Dranove, D., & Jin, G. Z.（2010）。Quality Disclosure and Certification: Theory and Practice。Journal of Economic Literature, 48(4), 935–963。",
     "use": "第三方認證的效果取決於認證者的誘因與做法 → IDA 驗證的整體學理參照"
    },
    {
     "theory": "事實先行定律｜資料品質",
     "citation": "Wang, R. Y., & Strong, D. M.（1996）。Beyond Accuracy: What Data Quality Means to Data Consumers。Journal of Management Information Systems, 12(4), 5–33。",
     "use": "資料品質除了正確，還包括完整、及時、可理解 → 店家事實逐項核實並標示時效"
    },
    {
     "theory": "主張溯源定律｜生成內容的歸因",
     "citation": "Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., 等（2023）。Measuring Attribution in Natural Language Generation Models。Computational Linguistics, 49(4), 777–840。",
     "use": "AI 生成內容裡講到事實的句子，要能指回已識別的來源 → 每項主張附可追溯的依據"
    },
    {
     "theory": "主張溯源定律｜資料來源譜系",
     "citation": "Buneman, P., Khanna, S., & Tan, W.-C.（2001）。Why and Where: A Characterization of Data Provenance。Database Theory — ICDT 2001，Lecture Notes in Computer Science 1973，頁 316–330。",
     "use": "資料要能回答「為何產生、來自哪裡」 → 每條主張連回具體證據"
    },
    {
     "theory": "外部印證定律｜傳播者偏差",
     "citation": "Eagly, A. H., Wood, W., & Chaiken, S.（1978）。Causal inferences about communicators and their effect on opinion change。Journal of Personality and Social Psychology, 36(4), 424–435。",
     "use": "聽眾會推論說話者為什麼這樣說 → 本方說法配上獨立印證更有力"
    },
    {
     "theory": "外部印證定律｜三角驗證",
     "citation": "Jick, T. D.（1979）。Mixing Qualitative and Quantitative Methods: Triangulation in Action。Administrative Science Quarterly, 24(4), 602–611。",
     "use": "不同來源的證據可以互相檢查 → 官方、本方、第三方合看"
    },
    {
     "theory": "全程成效定律｜不點擊也滿足需求",
     "citation": "Li, J., Huffman, S., & Tokuda, A.（2009）。Good abandonment in mobile and PC internet search。Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval（SIGIR '09），頁 43–50。",
     "use": "使用者沒有點擊就離開，有一部分是需求已在結果頁滿足 → 回答階段的取用本身就是成效"
    },
    {
     "theory": "全程成效定律｜顧客權益與行銷報酬",
     "citation": "Rust, R. T., Lemon, K. N., & Zeithaml, V. A.（2004）。Return on Marketing: Using Customer Equity to Focus Marketing Strategy。Journal of Marketing, 68(1), 109–127。",
     "use": "行銷投入要連到顧客的長期價值 → 九個指標從前段一路看到回訪"
    },
    {
     "theory": "S6 AI 點名幾家｜考慮名單",
     "citation": "Hauser, J. R., & Wernerfelt, B.（1990）。An Evaluation Cost Model of Consideration Sets。Journal of Consumer Research, 16(4), 393–408。",
     "use": "評估有成本，客人只會把部分品牌放進考慮名單 → AI 點名率就是進入候選名單的機會"
    },
    {
     "theory": "S6 AI 點名幾家｜被想起就會被選",
     "citation": "Nedungadi, P.（1990）。Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations。Journal of Consumer Research, 17(3), 263–276。",
     "use": "品牌被想起、進入考慮名單，就能改變選擇 → 被 AI 點名本身就是成效"
    },
    {
     "theory": "S8 替客人辦事｜自動化的層級",
     "citation": "Parasuraman, R., Sheridan, T. B., & Wickens, C. D.（2000）。A model for types and levels of human interaction with automation。IEEE Transactions on Systems, Man, and Cybernetics – Part A, 30(3), 286–297。",
     "use": "自動化分成取得資訊、分析、決策、執行四種 → 分清 AI Agent 是查資料還是替客人辦成事"
    },
    {
     "theory": "S10 回來再問｜期望確認",
     "citation": "Bhattacherjee, A.（2001）。Understanding Information Systems Continuance: An Expectation-Confirmation Model。MIS Quarterly, 25(3), 351–370。",
     "use": "用過以後符合期望，較願意繼續用 → 回訪量看客人願不願意再問、再來"
    },
    {
     "theory": "AI 曝光數｜被讀到的次數與模型記得多少",
     "citation": "Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C.（2023）。Large Language Models Struggle to Learn Long-Tail Knowledge。Proceedings of the 40th International Conference on Machine Learning（ICML 2023），PMLR 202，頁 15696–15707。arXiv:2211.08411。",
     "use": "模型答對一件事的機會，和訓練資料裡相關文件的數量有關 → 被抄回去訓練的次數值得量"
    },
    {
     "theory": "AI 曝光數｜重複出現與模型記憶",
     "citation": "Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramèr, F., & Zhang, C.（2023）。Quantifying Memorization Across Neural Language Models。ICLR 2023。arXiv:2202.07646。",
     "use": "內容在訓練資料裡重複愈多次，模型愈容易原樣記住 → S3 抄回去訓練的價值"
    },
    {
     "theory": "AI 能見度｜品牌顯著性",
     "citation": "Romaniuk, J., & Sharp, B.（2004）。Conceptualizing and measuring brand salience。Marketing Theory, 4(4), 327–342。",
     "use": "品牌顯著性是購買情境中容易被注意、被想起的程度 → AI 點名率量的是品牌在 AI 回答裡被想起的機會"
    },
    {
     "theory": "成效指標｜構念效度",
     "citation": "Cronbach, L. J., & Meehl, P. E.（1955）。Construct validity in psychological tests。Psychological Bulletin, 52(4), 281–302。",
     "use": "指標要證明自己量到所命名的概念 → 每個成效指標都寫清楚量的是什麼"
    },
    {
     "theory": "IDA 第三方驗證｜信任財",
     "citation": "Darby, M. R., & Karni, E.（1973）。Free Competition and the Optimal Amount of Fraud。The Journal of Law and Economics, 16(1), 67–88。",
     "use": "有些品質客人用過也難判斷 → 執照、專業這類事實交給第三方驗證"
    },
    {
     "theory": "熱啟動｜爬蟲重訪排程",
     "citation": "Cho, J., & Garcia-Molina, H.（2003）。Effective page refresh policies for Web crawlers。ACM Transactions on Database Systems, 28(4), 390–426。",
     "use": "爬蟲資源有限，重訪頻率要權衡頁面變動來分配 → 熱啟動的機制參照"
    },
    {
     "theory": "複利領先｜偏好連結",
     "citation": "Barabási, A.-L., & Albert, R.（1999）。Emergence of Scaling in Random Networks。Science, 286(5439), 509–512。",
     "use": "網路成長時，新連結偏向已有較多連結的節點 → 複利領先的網路科學參照"
    },
    {
     "theory": "複利領先｜累積優勢",
     "citation": "Merton, R. K.（1968）。The Matthew Effect in Science。Science, 159(3810), 56–63。",
     "use": "已有名望的人會得到更多認可與關注 → 早被取用的內容更有機會再被取用"
    },
    {
     "theory": "活的學說｜研究綱領",
     "citation": "Lakatos, I.（1970）。Falsification and the Methodology of Scientific Research Programmes。收於 I. Lakatos & A. Musgrave（編），Criticism and the Growth of Knowledge（頁 91–196）。Cambridge: Cambridge University Press。",
     "use": "研究綱領有核心主張，並靠持續預測新事實而進步 → 七大定律是核心，每月新數據持續檢驗"
    },
    {
     "theory": "活的學說｜設計科學",
     "citation": "Hevner, A. R., March, S. T., Park, J., & Ram, S.（2004）。Design Science in Information Systems Research。MIS Quarterly, 28(1), 75–106。",
     "use": "設計出的方法要在實際情境反覆建造、評估、修正 → 每月實測用來修訂診斷與指標"
    }
   ]
  }
 ]
}