{ "$schema": "https://json-schema.org/draft/2020-12/schema", "manifest_version": "1.0", "collection": { "id": "tourism-ai-bible", "title": "Tourism AI 1.0 Bible", "title_th": "คัมภีร์ Tourism AI 1.0", "description": "A structured knowledge collection for Thailand Together / Pattaya Together / Thailand Tourism LLM, covering strategic doctrine, research corpus, and platform intelligence design.", "version": "v3", "language": "th", "type": "knowledge_base", "category": "Tourism AI / Strategic Doctrine", "owner": "Thailand Together", "created": "2026-04-22", "total_documents": 40, "total_cards": 3000, "portal_url": "/tourism-ai-bible-portal.html", "pdf_base_path": "/tourism-ai-bible/pdfs", "tags": ["tourism", "ai", "strategy", "research", "national-platform", "thai", "bible", "doctrine"] }, "documents": [ { "doc_id": "tab-a-01", "set": "A", "part": 1, "type": "strategic", "title": "Tracking Doctrine & First Mindset", "title_full": "Set A v3 - Part 01 - Strategic TH", "tagline": "วางรากฐาน mindset ของทีม Tourism AI ทั้งระบบ", "description": "TRACK-01 Doctrine, 4 Team Traps, 12 Platform Layers, Big Data Pipeline 4 ขั้น, 7 Core Terms, Object/Display/Workflow Standards, 9 Persona Blind Spots, 6 Together Modes — เอกสารฐานที่ทุกส่วนต่อยอด", "pdf_url": "/tourism-ai-bible/pdfs/set-a/01.pdf", "gamma_url": "https://gamma.app/docs/lx8ukvlcu8emlsf", "gamma_id": "lx8ukvlcu8emlsf", "tags": ["Foundation", "TRACK-01", "Persona", "6 Modes", "Object Standard", "Workflow", "Tourism AI"], "chapters": ["TRACK-01 Mindset Shift", "4 Team Traps", "12 Platform Layers", "Big Data Pipeline", "7 Core Terms", "TRACK-01 5-Layer Framework", "Object Standard Fields", "Display Standard", "Workflow Standard", "Persona + Experience Measurement", "9 Persona Blind Spots", "6 Together Modes", "Strategic Roadmap", "References"], "search_keywords": "tracking doctrine mindset persona demographic journey object workflow outcome 6 modes trip mice rewards shopping services stay platform layers big data pipeline blind spots energy decision planning privacy social novelty explanation budget group" }, { "doc_id": "tab-a-02", "set": "A", "part": 2, "type": "strategic", "title": "Platform Merge Blueprint", "title_full": "Set A v3 - 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Part 13 - Strategic TH", "tagline": "เชื่อม persona, journey state และ Together Mode เข้าด้วยกันอย่างเป็นระบบ", "description": "กำหนดวิธี map คนแต่ละแบบกับ mode ที่ควรเด่น card ที่ควรเปิด/ปิด ข้อมูลที่ควรเน้น และ workflow ที่ควรเริ่มก่อนในแต่ละสถานการณ์", "pdf_url": "/tourism-ai-bible/pdfs/set-a/13.pdf", "gamma_url": "https://gamma.app/docs/0j3m8o0qgic4qo1", "gamma_id": "0j3m8o0qgic4qo1", "tags": ["Persona", "Mode Mapping", "Journey State", "Dynamic UI", "Card Logic"], "chapters": ["Persona-to-Mode Premise", "Journey States", "Mode Priority Rules", "Card Activation", "Data Emphasis", "Workflow Start Rules", "Fallback Logic", "Cross-Mode Shifts", "Presentation Implication", "References"], "search_keywords": "persona mode mapping journey state dynamic ui card activation workflow mode priority" }, { "doc_id": "tab-a-14", "set": "A", "part": 14, "type": "strategic", "title": "Merchant Value Translation", "title_full": "Set A v3 - Part 14 - Strategic TH", "tagline": "แปล insight และ AI recommendation ให้เป็นภาษาของผู้ประกอบการ", "description": "merchant-facing value map: discovery uplift, mood-fit recommendations, time-window demand, audience-fit menus/places, UGC propensity, referral spending, group travel dynamics", "pdf_url": "/tourism-ai-bible/pdfs/set-a/14.pdf", "gamma_url": "https://gamma.app/docs/d4rtsn4j2z2cau7", "gamma_id": "d4rtsn4j2z2cau7", "tags": ["Merchant Value", "Recommendation", "Audience Fit", "UGC", "Demand Windows"], "chapters": ["Merchant Language", "Discovery Uplift", "Mood Fit", "Time-window Demand", "Audience Fit Logic", "UGC Propensity", "Referral Spending", "Group Travel Impact", "Merchant Dashboard Implication", "References"], "search_keywords": "merchant value translation audience fit ugc propensity demand windows referral spending recommendation" }, { "doc_id": "tab-a-15", "set": "A", "part": 15, "type": "strategic", "title": "City Intelligence Translation", "title_full": "Set A v3 - Part 15 - Strategic TH", "tagline": "แปลสัญญาณและ insight ให้เมืองใช้ตัดสินใจได้จริง", "description": "แปลง platform intelligence ให้เป็น city-facing outcomes เช่น crowd balancing, time-based activation, economic distribution, hotspot early warning, underserved area opportunity", "pdf_url": "/tourism-ai-bible/pdfs/set-a/15.pdf", "gamma_url": "https://gamma.app/docs/4zx5d0lfjzhh9h6", "gamma_id": "4zx5d0lfjzhh9h6", "tags": ["City Intelligence", "Crowd Balancing", "Economic Distribution", "Hotspots", "Policy Insight"], "chapters": ["City Translation Layer", "Crowd Balancing", "Time-based Activation", "Economic Distribution", "Hotspot Alerts", "Underserved Zones", "Preparedness Signals", "Policy Dashboards", "City Recommendation Logic", "References"], "search_keywords": "city intelligence translation crowd balancing economic distribution hotspot policy insight preparedness" }, { "doc_id": "tab-a-16", "set": "A", "part": 16, "type": "strategic", "title": "Trust, Privacy & Honest AI Responses", "title_full": "Set A v3 - 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Part 18 - Strategic TH", "tagline": "วัดผล AI และประสบการณ์ แล้วป้อนกลับเข้าระบบอย่างเป็นวงจร", "description": "ออกแบบ feedback loop ระหว่าง user behavior, merchant response, city signals, recommendation performance, trust outcomes, and non-use recovery", "pdf_url": "/tourism-ai-bible/pdfs/set-a/18.pdf", "gamma_url": "https://gamma.app/docs/3ex7h4qqznkn9m2", "gamma_id": "3ex7h4qqznkn9m2", "tags": ["Measurement", "Feedback Loop", "Recommendation Performance", "Trust", "Recovery"], "chapters": ["Feedback Loop Overview", "Behavior Signals", "Merchant Response", "City Signals", "Recommendation Performance", "Trust Outcomes", "Non-use Recovery", "Loop Speed", "Learning Layer", "References"], "search_keywords": "measurement feedback loop recommendation performance trust outcomes non-use recovery learning layer" }, { "doc_id": "tab-a-19", "set": "A", "part": 19, "type": "strategic", "title": "Operating Model for Tourism AI Teams", "title_full": "Set A v3 - Part 19 - Strategic TH", "tagline": "ทีมจะทำงานร่วมกันอย่างไร เมื่อ doctrine ทั้งระบบเริ่มชัด", "description": "นิยามการทำงานร่วมกันของ Strategy, Product, UX, AI, Data, Merchant, City, Ops, and Dev เพื่อเปลี่ยน doctrine เป็นระบบจริงโดยไม่หลุด coherence", "pdf_url": "/tourism-ai-bible/pdfs/set-a/19.pdf", "gamma_url": "https://gamma.app/docs/f1i6ohsr8s7hi7x", "gamma_id": "f1i6ohsr8s7hi7x", "tags": ["Operating Model", "Teams", "Coherence", "Cross-functional", "Execution"], "chapters": ["Why Team Operating Model Matters", "Role Stack", "Doctrine Ownership", "Cross-functional Loops", "Decision Rhythm", "Conflict Resolution", "Coherence Controls", "Execution Bridges", "From Doctrine to Build", "References"], "search_keywords": "operating model tourism ai teams coherence cross functional doctrine ownership execution" }, { "doc_id": "tab-a-20", "set": "A", "part": 20, "type": "strategic", "title": "Integrated Master Blueprint", "title_full": "Set A v3 - Part 20 - Strategic TH", "tagline": "สรุปทุกแกนหลักให้เป็น master blueprint กลางของทั้งอาณาจักร", "description": "รวม 10 master principles, 12 system layers, intelligence flow, 6 engines, 8 outcome families, risk/value taxonomy, user-merchant-city integrated view, and doctrine questions", "pdf_url": "/tourism-ai-bible/pdfs/set-a/20.pdf", "gamma_url": "https://gamma.app/docs/5z3axlmle6n2ft2", "gamma_id": "5z3axlmle6n2ft2", "tags": ["Master Blueprint", "Principles", "System Layers", "Engines", "Taxonomy", "Integrated View"], "chapters": ["10 Master Principles", "12 System Layers", "Master Intelligence Flow", "Input Types", "Output Types", "6 Intelligence Engines", "8 Outcome Families", "Value Taxonomy", "Risk Taxonomy", "Improvement Workflow", "User-Merchant-City View", "Doctrine Questions", "Asset Map", "Synthesis Diagram"], "search_keywords": "integrated master blueprint principles system layers intelligence flow engines taxonomy user merchant city" }, { "doc_id": "tab-b-01", "set": "B", "part": 1, "type": "research", "title": "Tourist Identity & Latent Demand Research", "title_full": "Set B v3 - Part 01 - Research TH", "tagline": "วิจัยเชิงลึกว่าตัวตนของนักท่องเที่ยวถูกนิยามอย่างไรในโลกจริงและโลก AI", "description": "สำรวจ latent demand, identity clusters, observable vs hidden motives, aspiration states, and why tourists often cannot fully articulate what they want at the beginning of a trip", "pdf_url": "/tourism-ai-bible/pdfs/set-b/01.pdf", "gamma_url": "https://gamma.app/docs/36l6bzx9b5m4r62", "gamma_id": "36l6bzx9b5m4r62", "tags": ["Identity", "Latent Demand", "Research", "Aspiration", "Tourist Psychology"], "chapters": ["Latent Demand", "Identity Clusters", "Observable vs Hidden Motives", "Aspiration States", "Trip Start Ambiguity", "Research Implication", "Signal Capture", "Design Consequences", "References"], "search_keywords": "tourist identity latent demand aspiration hidden motives tourist psychology research" }, { "doc_id": "tab-b-02", "set": "B", "part": 2, "type": "research", "title": "Travel Decision Research Under Uncertainty", "title_full": "Set B v3 - Part 02 - Research TH", "tagline": "การตัดสินใจเดินทางจริงเกิดขึ้นภายใต้ความไม่แน่นอนอย่างไร", "description": "วิเคราะห์ bounded rationality, group negotiation, heuristic shortcuts, situational bias, time pressure, weather uncertainty, and how AI can become decision prosthetics", "pdf_url": "/tourism-ai-bible/pdfs/set-b/02.pdf", "gamma_url": "https://gamma.app/docs/gbjgqjlwmjujcsk", "gamma_id": "gbjgqjlwmjujcsk", "tags": ["Decision Making", "Uncertainty", "Group Negotiation", "Heuristics", "AI Prosthetics"], "chapters": ["Decision Under Uncertainty", "Bounded Rationality", "Group Negotiation", "Heuristics", "Situational Bias", "Time Pressure", "Weather Uncertainty", "AI as Decision Prosthetics", "References"], "search_keywords": "decision under uncertainty group negotiation heuristics time pressure ai prosthetics travel decision" }, { "doc_id": "tab-b-03", "set": "B", "part": 3, "type": "research", "title": "Memory, Nostalgia & Return Intent", "title_full": "Set B v3 - Part 03 - Research TH", "tagline": "ความทรงจำ การกลับมาอีกครั้ง และ nostalgia economy", "description": "ศึกษาความสัมพันธ์ระหว่าง prior visits, family memory, social storytelling, and repeat intent เพื่อให้ AI อ่าน experience memory เป็นสัญญาณเชิงกลยุทธ์", "pdf_url": "/tourism-ai-bible/pdfs/set-b/03.pdf", "gamma_url": "https://gamma.app/docs/f6nco1rfxsh19mh", "gamma_id": "f6nco1rfxsh19mh", "tags": ["Memory", "Nostalgia", "Return Intent", "Family Story", "Repeat Visit"], "chapters": ["Experience Memory", "Nostalgia Economy", "Return Intent", "Family Story", "Social Storytelling", "Repeat Visit Logic", "Memory Signals", "AI Implication", "References"], "search_keywords": "memory nostalgia return intent family story repeat visit experience memory" }, { "doc_id": "tab-b-04", "set": "B", "part": 4, "type": "research", "title": "Mood, Energy & Situational State Research", "title_full": "Set B v3 - Part 04 - Research TH", "tagline": "อารมณ์ พลังงาน และ state เฉพาะช่วงเวลา มีผลต่อการเที่ยวมากกว่าที่ทีมส่วนใหญ่คิด", "description": "วิจัย mood-state recommendation, energy-aware suggestion, day-phase transitions, decision fatigue, heat/crowd/weather effects, and how AI should adapt surfaces accordingly", "pdf_url": "/tourism-ai-bible/pdfs/set-b/04.pdf", "gamma_url": "https://gamma.app/docs/1gjj8mrhaq3oa8l", "gamma_id": "1gjj8mrhaq3oa8l", "tags": ["Mood", "Energy", "Situational State", "Decision Fatigue", "Adaptive UX"], "chapters": ["Mood-State Research", "Energy Curves", "Day-phase Transitions", "Decision Fatigue", "Heat/Crowd/Weather Effects", "Adaptive UX", "State Signals", "Recommendation Implication", "References"], "search_keywords": "mood energy situational state decision fatigue adaptive ux heat crowd weather" }, { "doc_id": "tab-b-05", "set": "B", "part": 5, "type": "research", "title": "Social Proof, Review & Popularity Mechanics", "title_full": "Set B v3 - Part 05 - Research TH", "tagline": "เพราะเหตุใด review, popularity และ UGC จึงเป็นตัวเร่งการตัดสินใจที่ทรงพลัง", "description": "วิเคราะห์ review psychology, social proof cascades, influencer trace, virality, UGC propensity, popularity windows, and why 'everybody goes there' is sometimes good and sometimes harmful", "pdf_url": "/tourism-ai-bible/pdfs/set-b/05.pdf", "gamma_url": "https://gamma.app/docs/k9j4xg2u4r7iy9p", "gamma_id": "k9j4xg2u4r7iy9p", "tags": ["Social Proof", "Review", "Popularity", "UGC", "Virality"], "chapters": ["Review Psychology", "Social Proof Cascades", "Influencer Trace", "Virality", "UGC Propensity", "Popularity Windows", "Healthy vs Harmful Popularity", "AI Use Cases", "References"], "search_keywords": "social proof review popularity ugc virality influencer trace popularity windows" }, { "doc_id": "tab-b-06", "set": "B", "part": 6, "type": "research", "title": "Group Travel Dynamics Research", "title_full": "Set B v3 - Part 06 - Research TH", "tagline": "เวลาเดินทางกันหลายคน การตัดสินใจแทบไม่ใช่เรื่องของบุคคลเดียว", "description": "วิจัยบทบาทของ dominance, compromise, hidden preference, group splitting, family authority, friend influence, and trip role asymmetry ในการเลือกอาหาร สถานที่ และกิจกรรม", "pdf_url": "/tourism-ai-bible/pdfs/set-b/06.pdf", "gamma_url": "https://gamma.app/docs/86nd03qp3glbo0g", "gamma_id": "86nd03qp3glbo0g", "tags": ["Group Travel", "Dominance", "Compromise", "Family", "Friend Influence"], "chapters": ["Group Dynamics", "Dominance", "Compromise", "Hidden Preference", "Group Splitting", "Family Authority", "Friend Influence", "Trip Role Asymmetry", "AI Mediation", "References"], "search_keywords": "group travel dominance compromise family authority friend influence hidden preference group splitting" }, { "doc_id": "tab-b-07", "set": "B", "part": 7, "type": "research", "title": "Budget Psychology & Spend Curves", "title_full": "Set B v3 - Part 07 - Research TH", "tagline": "งบประมาณไม่ใช่แค่จำนวนเงิน แต่คือสภาวะทางจิตวิทยาระหว่างทริป", "description": "ศึกษาความมั่นใจในการใช้จ่าย, spend curve ต้นทริป-ท้ายทริป, premium moments, budget anxiety, splurge logic, and how perceived value alters purchase behavior", "pdf_url": "/tourism-ai-bible/pdfs/set-b/07.pdf", "gamma_url": "https://gamma.app/docs/ek1n1x1ulwlmgc2", "gamma_id": "ek1n1x1ulwlmgc2", "tags": ["Budget", "Spend Curve", "Anxiety", "Splurge", "Value Perception"], "chapters": ["Budget Psychology", "Spend Curves", "Start vs End of Trip", "Budget Anxiety", "Premium Moments", "Splurge Logic", "Perceived Value", "AI Pricing Implication", "References"], "search_keywords": "budget psychology spend curve splurge value perception anxiety premium moments" }, { "doc_id": "tab-b-08", "set": "B", "part": 8, "type": "research", "title": "Time Use, Trip Pace & Activity Density", "title_full": "Set B v3 - Part 08 - Research TH", "tagline": "การใช้เวลาระหว่างทริปมี pattern ที่บอกอะไรได้มากกว่าที่เห็น", "description": "วิเคราะห์ trip pace, dwell time, slow vs dense itineraries, transition friction, queue aversion, activity stacking, and the economics of time allocation", "pdf_url": "/tourism-ai-bible/pdfs/set-b/08.pdf", "gamma_url": "https://gamma.app/docs/d6d0xum3t6g1d1h", "gamma_id": "d6d0xum3t6g1d1h", "tags": ["Time Use", "Trip Pace", "Dwell Time", "Queue Aversion", "Activity Density"], "chapters": ["Trip Pace", "Dwell Time", "Slow vs Dense Itineraries", "Transition Friction", "Queue Aversion", "Activity Stacking", "Time Economics", "Optimization Logic", "References"], "search_keywords": "time use trip pace dwell time queue aversion activity density transition friction" }, { "doc_id": "tab-b-09", "set": "B", "part": 9, "type": "research", "title": "Search Friction, Logging Friction & Silent Behavior", "title_full": "Set B v3 - Part 09 - Research TH", "tagline": "ทำไมผู้ใช้ไม่ค้น ไม่กด ไม่บันทึก และไม่บอกเรา ทั้งที่ยังสนใจอยู่", "description": "วิจัยความฝืดของ interaction, non-input behavior, camera-first users, voice-first users, passive scanning, silent comparison, and low-effort signal capture design", "pdf_url": "/tourism-ai-bible/pdfs/set-b/09.pdf", "gamma_url": "https://gamma.app/docs/0h2rb93jujlwm9k", "gamma_id": "0h2rb93jujlwm9k", "tags": ["Friction", "Silent Behavior", "Voice First", "Camera First", "Signal Capture"], "chapters": ["Interaction Friction", "Non-input Behavior", "Camera-first Users", "Voice-first Users", "Passive Scanning", "Silent Comparison", "Low-effort Signals", "Design Research Implication", "References"], "search_keywords": "search friction logging friction silent behavior camera first voice first passive scanning low effort signals" }, { "doc_id": "tab-b-10", "set": "B", "part": 10, "type": "research", "title": "Spatial Behavior, Route Choice & Place Sequencing", "title_full": "Set B v3 - Part 10 - Research TH", "tagline": "มนุษย์เลือก route และลำดับสถานที่อย่างไรเมื่ออยู่ในเมืองจริง", "description": "ศึกษา route choice, anchor places, detour willingness, place sequencing logic, adjacency effect, and why 'nearby' does not always mean 'next'", "pdf_url": "/tourism-ai-bible/pdfs/set-b/10.pdf", "gamma_url": "https://gamma.app/docs/gd7wtxf2m95r4h5", "gamma_id": "gd7wtxf2m95r4h5", "tags": ["Spatial Behavior", "Route Choice", "Anchor Place", "Detour", "Adjacency"], "chapters": ["Route Choice", "Anchor Places", "Detour Willingness", "Place Sequencing", "Adjacency Effect", "Nearby vs Next", "Spatial Recommendation", "Research Implication", "References"], "search_keywords": "spatial behavior route choice anchor place detour adjacency nearby next place sequencing" }, { "doc_id": "tab-b-11", "set": "B", "part": 11, "type": "research", "title": "Food, Dining Mood & Social Meal Logic", "title_full": "Set B v3 - Part 11 - Research TH", "tagline": "การกินเป็น social decision engine ที่เชื่อม mood, group, budget, identity เข้าด้วยกัน", "description": "วิจัย dining mood, cuisine curiosity, meal sociality, comfort vs novelty, family fit, friend fit, meal timing, queue tolerance, and menu storytelling", "pdf_url": "/tourism-ai-bible/pdfs/set-b/11.pdf", "gamma_url": "https://gamma.app/docs/hk4lrh2wz0y44qz", "gamma_id": "hk4lrh2wz0y44qz", "tags": ["Dining", "Food", "Mood", "Social Meal", "Menu Storytelling"], "chapters": ["Dining Mood", "Cuisine Curiosity", "Meal Sociality", "Comfort vs Novelty", "Family Fit", "Friend Fit", "Meal Timing", "Queue Tolerance", "Menu Storytelling", "References"], "search_keywords": "dining mood social meal menu storytelling comfort novelty family fit queue tolerance" }, { "doc_id": "tab-b-12", "set": "B", "part": 12, "type": "research", "title": "Retail, Souvenir & Purchase Identity", "title_full": "Set B v3 - Part 12 - Research TH", "tagline": "การซื้อของฝาก ของใช้ หรือสินค้าท้องถิ่น บอกตัวตนและความตั้งใจของทริป", "description": "ศึกษาซื้อเพื่อตัวเอง ซื้อเพื่อคนอื่น souvenir signaling, identity purchase, trip memory tokens, gift pressure, and merchant storytelling opportunities", "pdf_url": "/tourism-ai-bible/pdfs/set-b/12.pdf", "gamma_url": "https://gamma.app/docs/e00109yhbla5g5o", "gamma_id": "e00109yhbla5g5o", "tags": ["Retail", "Souvenir", "Gift", "Identity Purchase", "Memory Token"], "chapters": ["Souvenir Logic", "Identity Purchase", "Gift Pressure", "Memory Tokens", "Social Signaling", "Merchant Storytelling", "Retail Recommendation", "Research Implication", "References"], "search_keywords": "retail souvenir gift identity purchase memory token merchant storytelling" }, { "doc_id": "tab-b-13", "set": "B", "part": 13, "type": "research", "title": "MICE Participant Behavior Beyond the Venue", "title_full": "Set B v3 - Part 13 - Research TH", "tagline": "พฤติกรรมของผู้ร่วมงาน MICE นอก venue มีมูลค่าทางเศรษฐกิจที่มักถูกมองข้าม", "description": "สำรวจ pre/post-event movement, side meetings, local exploration, partner/family extension, city spillover, and the business logic of event-to-city journey extension", "pdf_url": "/tourism-ai-bible/pdfs/set-b/13.pdf", "gamma_url": "https://gamma.app/docs/p71ecpw7ol39zuy", "gamma_id": "p71ecpw7ol39zuy", "tags": ["MICE", "Venue Spillover", "City Extension", "Business Traveler", "Event-to-City"], "chapters": ["Beyond the Venue", "Pre/Post-event Movement", "Side Meetings", "Local Exploration", "Partner/Family Extension", "City Spillover", "Economic Logic", "MICE AI Implication", "References"], "search_keywords": "mice participant beyond venue city extension business traveler spillover side meetings" }, { "doc_id": "tab-b-14", "set": "B", "part": 14, "type": "research", "title": "Support, Safety & Help-Seeking Research", "title_full": "Set B v3 - Part 14 - Research TH", "tagline": "เวลาคนต้องการความช่วยเหลือ เขาตัดสินใจอย่างไร และ AI ควรเข้ามาตรงไหน", "description": "วิจัย help-seeking thresholds, embarrassment avoidance, emergency vs non-emergency support, trust in official vs local help, and escalation design", "pdf_url": "/tourism-ai-bible/pdfs/set-b/14.pdf", "gamma_url": "https://gamma.app/docs/64eq67wq0nvpxu5", "gamma_id": "64eq67wq0nvpxu5", "tags": ["Support", "Safety", "Help-Seeking", "Escalation", "Trust"], "chapters": ["Help-Seeking Thresholds", "Embarrassment Avoidance", "Emergency vs Non-emergency", "Official vs Local Help", "Escalation Design", "Support Surfaces", "Trust in Help", "AI Support Logic", "References"], "search_keywords": "support safety help-seeking escalation official vs local help embarrassment avoidance" }, { "doc_id": "tab-b-15", "set": "B", "part": 15, "type": "research", "title": "Trust in Recommendations & Authority Sources", "title_full": "Set B v3 - Part 15 - Research TH", "tagline": "คนเชื่อ recommendation จากใคร และเพราะอะไร", "description": "วิเคราะห์ authority layers เช่น friend, influencer, review crowd, local expert, official city, AI assistant, merchant, and hybrid trust models", "pdf_url": "/tourism-ai-bible/pdfs/set-b/15.pdf", "gamma_url": "https://gamma.app/docs/o32m6g1l0n7n3qv", "gamma_id": "o32m6g1l0n7n3qv", "tags": ["Trust", "Authority", "Recommendation", "Influencer", "AI Assistant"], "chapters": ["Authority Layers", "Friend Trust", "Influencer Trust", "Review Crowd", "Local Expert", "Official City", "AI Assistant", "Merchant Voice", "Hybrid Models", "References"], "search_keywords": "trust authority recommendation influencer ai assistant review crowd local expert" }, { "doc_id": "tab-b-16", "set": "B", "part": 16, "type": "research", "title": "Signal Quality, Noise & Inference Reliability", "title_full": "Set B v3 - Part 16 - Research TH", "tagline": "ไม่ใช่ทุก signal จะดีพอสำหรับการอนุมาน ต้องแยก noise ให้เป็น", "description": "ศึกษาความน่าเชื่อถือของ digital traces, inferred preference confidence, contradictory signals, missingness, signal decay, and calibration strategies", "pdf_url": "/tourism-ai-bible/pdfs/set-b/16.pdf", "gamma_url": "https://gamma.app/docs/bx8p33f9mz8b0wh", "gamma_id": "bx8p33f9mz8b0wh", "tags": ["Signal Quality", "Noise", "Inference", "Confidence", "Calibration"], "chapters": ["Signal Quality", "Inference Confidence", "Contradictory Signals", "Missingness", "Signal Decay", "Calibration", "Reliability Scoring", "Research Implication", "References"], "search_keywords": "signal quality noise inference reliability confidence calibration contradictory signals" }, { "doc_id": "tab-b-17", "set": "B", "part": 17, "type": "research", "title": "Micro-Moments & Trigger Design", "title_full": "Set B v3 - Part 17 - Research TH", "tagline": "ช่วงเวลาสั้น ๆ ที่เหมาะกับการสะกิดมีมูลค่ามหาศาล", "description": "วิจัย micro-moments, trigger timing, interruption tolerance, glanceability, frictionless prompts, and how timing changes conversion more than content alone", "pdf_url": "/tourism-ai-bible/pdfs/set-b/17.pdf", "gamma_url": "https://gamma.app/docs/3u3r89tvzg1n79s", "gamma_id": "3u3r89tvzg1n79s", "tags": ["Micro-Moments", "Trigger", "Timing", "Glanceability", "Prompt Design"], "chapters": ["Micro-Moments", "Trigger Timing", "Interruption Tolerance", "Glanceability", "Frictionless Prompting", "Timing vs Content", "Conversion Windows", "Research Implication", "References"], "search_keywords": "micro moments trigger timing interruption tolerance glanceability frictionless prompts conversion windows" }, { "doc_id": "tab-b-18", "set": "B", "part": 18, "type": "research", "title": "Emotional Outcomes & Experience Meaning", "title_full": "Set B v3 - Part 18 - Research TH", "tagline": "ผลลัพธ์ที่แท้จริงของการเที่ยวไม่ใช่แค่ action แต่คือความหมายที่คนพกกลับไป", "description": "ศึกษาความประทับใจ ความภูมิใจ ความใกล้ชิด ความผ่อนคลาย ความรู้สึกว่าคุ้มค่า และผลทางอารมณ์อื่น ๆ ที่ AI ควรเรียนรู้ที่จะ optimize", "pdf_url": "/tourism-ai-bible/pdfs/set-b/18.pdf", "gamma_url": "https://gamma.app/docs/o0kw36r9olb1w7j", "gamma_id": "o0kw36r9olb1w7j", "tags": ["Emotion", "Meaning", "Experience Outcome", "Pride", "Worthwhile"], "chapters": ["Emotional Outcomes", "Meaning of Experience", "Pride", "Closeness", "Relaxation", "Worthwhileness", "Memory Value", "Optimization Logic", "References"], "search_keywords": "emotional outcomes meaning pride closeness relaxation worthwhile memory value" }, { "doc_id": "tab-b-19", "set": "B", "part": 19, "type": "research", "title": "Merchant-side Readiness & Participation Research", "title_full": "Set B v3 - Part 19 - Research TH", "tagline": "ฝั่งผู้ประกอบการเองมี readiness และ friction แบบไหนที่ต้องเข้าใจ", "description": "วิจัยข้อจำกัดของ merchant onboarding, content readiness, willingness to respond, deal participation, data sharing comfort, AI adoption hesitation, and incentive alignment", "pdf_url": "/tourism-ai-bible/pdfs/set-b/19.pdf", "gamma_url": "https://gamma.app/docs/j2fyntg4h6y0xi2", "gamma_id": "j2fyntg4h6y0xi2", "tags": ["Merchant Readiness", "Onboarding", "Deal Participation", "AI Adoption", "Incentives"], "chapters": ["Merchant Readiness", "Onboarding Friction", "Content Readiness", "Response Willingness", "Deal Participation", "Data Sharing Comfort", "AI Adoption Hesitation", "Incentive Alignment", "References"], "search_keywords": "merchant readiness onboarding deal participation ai adoption hesitation incentive alignment" }, { "doc_id": "tab-b-20", "set": "B", "part": 20, "type": "research", "title": "National Tourism LLM Research Agenda", "title_full": "Set B v3 - Part 20 - Research TH", "tagline": "สรุปว่าถ้าจะสร้าง Thailand Tourism LLM จริง ต้องวิจัยอะไรต่ออีกบ้าง", "description": "วาง research agenda สำหรับ Thailand Tourism LLM ตั้งแต่ corpus strategy, ontology, signal integrity, recommendation evaluation, multimodal capture, merchant-city-user alignment, and governance", "pdf_url": "/tourism-ai-bible/pdfs/set-b/20.pdf", "gamma_url": "https://gamma.app/docs/cx4ghpoa0vjlwmn", "gamma_id": "cx4ghpoa0vjlwmn", "tags": ["Thailand Tourism LLM", "Research Agenda", "Corpus", "Ontology", "Governance"], "chapters": ["Why a National Tourism LLM", "Corpus Strategy", "Ontology", "Signal Integrity", "Recommendation Evaluation", "Multimodal Capture", "Merchant-City-User Alignment", "Governance", "Future Research Questions", "References"], "search_keywords": "thailand tourism llm research agenda corpus ontology signal integrity multimodal governance" } ] }