這門課以《Become Judy》長個案為核心。你將從一位跨國創作者如何看見市場機會開始，逐步走過決策、策略、組織、人才、營運、行銷、資金、控制與變革，最後提出一份可以讓公司真正運作的建立藍圖。
This course is built around the long teaching case Become Judy. You begin with how a cross-border creator noticed a market opportunity, then move through decisions, strategy, organization, talent, operations, marketing, cash, control, and change before proposing a company-building blueprint that could actually operate.
個案聲明：本課程使用的《Become Judy》為教學案例。素材由元智大學吳相勲副教授與 Codex 協作整理，主要依據公開影片、公開報導與公開可查資料製作；未經 Judy 本人同意，亦未訪問 Judy 或其身邊相關人士。課程目的在於學習管理決策與公司建立過程，不在評價當事人或周邊人員的好壞。 Case note: Become Judy is used here as a teaching case. The material was prepared by Associate Professor Hsiang-Hsun Wu of Yuan Ze University in collaboration with Codex, mainly from public videos, public reports, and publicly verifiable materials. Judy has not approved this case, and neither Judy nor people around her were interviewed. The learning purpose is to study management decisions and company building, not to judge the people involved.
這不是一門「看 YouTuber」的課，而是一門把真實創業線索拆解成管理制度的課。每一週都把個案推進一點，讓你把抽象的管理學概念轉成可執行的公司設計。
This is not a course about watching a YouTuber. It is a course about turning real entrepreneurial clues into management systems. Each week moves the case forward and asks you to convert management concepts into executable company design.
你要能說明市場機會不是一句「我喜歡」或「觀眾喜歡」，而是由需求、供給、競爭、通路、收入來源與限制條件共同構成。
You should be able to explain that market opportunity is not simply "I like it" or "the audience likes it." It is built from demand, supply, competition, channels, revenue sources, and constraints.
你要能把工作分工、人才管理、內容品質、客戶交付、財務壓力與風險控制轉成流程、角色與指標。
You should be able to turn division of work, talent management, content quality, customer delivery, financial pressure, and risk control into processes, roles, and metrics.
期末建議不只是「應該擴張」或「應該保守」，而必須說清楚公司要選擇哪一種商業模式，以及下一步應該建立哪些管理能力。
The final recommendation should not stop at "expand" or "be conservative." It must specify the business model choice and the management capabilities that must be built next.
長個案提供完整事實、產業背景與公司形成脈絡；短個案把其中一個高張力決策改寫成對話場景。課程會以長個案為主，短個案作為決策模擬與課堂討論的切入點。
The long case provides the factual base, industry background, and company-formation context. The short case turns one high-tension decision into a dialogue-driven scene. The course uses the long case as the core and the short case as a decision simulation entry point.
長個案從旅行影片、頻道成長、公司化、韓國啦啦隊供給、台灣運動娛樂需求、YouTuber 變現困境、人才簽約與現金壓力一路鋪陳。它要求你判斷：當一個人的內容能量變成公司機會時，管理者應該先擴張、先制度化，還是先收斂商業模式？
The long case moves from travel videos, channel growth, company formation, Korean cheerleading supply, Taiwanese sports-entertainment demand, YouTuber monetization pressure, talent contracts, and cash pressure. It asks: when one person's content energy becomes a company opportunity, should the manager expand, institutionalize, or narrow the business model first?
短個案把真實脈絡簡化成一個場景：創辦人面對可以快速提高曝光的球隊合約，但合約會帶來人力、排練、內容、現金與心理壓力。角色之間的對話讓你看見同一個決策在創辦人、營運、隊長、品牌與財務角度上的不同代價。
The short case simplifies the context into one scene: a founder faces a team contract that could sharply increase visibility, while also adding pressure on people, rehearsals, content, cash, and morale. The dialogue reveals how the same decision looks different to the founder, operations lead, captain, brand side, and financial advisor.
第 1、9、17 週分別是進場、期中進度瞭解與期末個案建議，不安排完整正式授課。其餘 15 週依序走過管理學核心主題。每週都會產出一個可累積到期末報告的管理工具。
Weeks 1, 9, and 17 are orientation, midterm progress review, and final case recommendation rather than full formal lectures. The other 15 weeks move through core management topics. Each week produces one management tool that accumulates into the final report.
你們的團隊專案不是另外一份作業，而是每週把個案公司多理解一點。到了期末，所有工具會合併成你們版本的 Judy 公司：它的顧客、價值主張、收入組合、組織設計、營運流程、資金需求與風險控制。
Your team project is not a separate assignment. Each week helps you understand one more piece of the case company. By the final, the tools combine into your version of the Judy company: customers, value proposition, revenue mix, organization, operations, cash needs, and risk controls.
建立團隊資料庫。你們開始蒐集個案證據、外部資料、影片線索、產業資訊與待查問題。
Build the team evidence base. Start collecting case evidence, external sources, video clues, industry information, and unresolved questions.
起點Start把資料整理成公司目標與商業模式草案，說明你們認為公司要解決的核心問題。
Turn the material into a draft of company goals and business model, explaining the core problem the company should solve.
初版假設First hypothesis提交期中進度包。重點不是完美答案，而是證據是否夠清楚、假設是否可檢驗、下一步資料缺口是否明確。
Submit the midterm progress package. The goal is not a perfect answer; it is clear evidence, testable assumptions, and explicit data gaps.
期中檢核Midterm check完成收入與資金壓力分析。每一個成長選項都要說清楚現金流、固定成本、人才成本與風險。
Complete the revenue and cash-pressure analysis. Every growth option must clarify cash flow, fixed costs, talent costs, and risk.
財務壓力Cash pressure提出期末個案分析與建議。你們需要選擇一條成長路線，並說明相對應的管理制度。
Present the final case analysis and recommendation. Choose a growth path and explain the management system that supports it.
期末建議Final recommendation把期末建議修成公司建立藍圖。這份藍圖應該讓讀者看懂：如果你們真的接手這家公司，下一季要先做什麼。
Revise the final recommendation into a company-building blueprint. The reader should understand what your team would do first next quarter if you actually took over the company.
藍圖Blueprint
本課程允許學生使用 AI 協助理解教材、整理概念、檢查作業結構與修正文句；但 AI 不能取代閱讀、判斷、查證與個人責任。評量重點不是交出看起來完整的文字，而是能否把管理學概念、個案證據與情境條件連在一起，提出可辯護的判斷。
Students may use AI to understand readings, organize concepts, review assignment structure, and revise wording. AI cannot replace reading, judgment, verification, or personal responsibility. Assessment focuses on whether students can connect management concepts, case evidence, and contextual conditions into defensible judgments.
本課程重視三件事：你是否理解本週管理學概念；你是否能把概念用在個案中；你是否能清楚說明自己如何使用 AI 完成學習與作業。小考、期中考與期末考不可使用 AI；正式作業若使用 AI，必須附上對話紀錄、使用聲明與個人修正說明。
The course emphasizes three responsibilities: understanding the weekly management concepts, applying them to the case, and explaining how AI was used in learning and assignments. AI is not allowed in quizzes, the midterm, or the final exam. Formal assignments that use AI must include dialogue records, an AI-use statement, and a note on the student's own revisions.
| 項目Item | 比例Weight | 說明Purpose |
|---|---|---|
| 每週概念診斷小考Weekly concept quizzes | 15% | 重點在概念理解、判斷條件與常見混淆，不只看答對幾題。Checks concept understanding, judgment conditions, and common confusions. |
| 課前 NotebookLM 學習紀錄Pre-class NotebookLM learning records | 10% | 檢查是否完成課前閱讀，並能用 AI 協助自己理解材料。Checks preparation and responsible AI-supported reading. |
| 每週個案工具作業與 AI 對話紀錄Weekly case-tool assignments and AI dialogue records | 20% | 每週產出管理分析工具，並提交完整 AI 使用紀錄。Builds management-analysis tools and documents AI use. |
| 期中考Midterm exam | 15% | 個人作答，不可使用 AI。Individual exam; AI is not allowed. |
| 期末考Final exam | 15% | 個人作答，不可使用 AI。Individual exam; AI is not allowed. |
| 期末個案整體評估方案Final case evaluation plan | 20% | 小組完成，需附個人貢獻與 AI 對話紀錄。Team deliverable with individual contribution notes and AI records. |
| 個人反思與同儕互評Individual reflection and peer evaluation | 5% | 檢查學習責任與小組貢獻。Checks learning responsibility and team contribution. |
每週上課前，學生需完成指定閱讀。閱讀材料可能包含管理學教材、課程個案、補充材料，以及 NotebookLM 中預先放入的學習材料。可以使用 NotebookLM 或其他 AI 工具協助理解，但不能只複製 AI 摘要。
Before each class, students complete the assigned readings. Materials may include the management textbook, course cases, supplemental materials, and NotebookLM sources. Students may use NotebookLM or other AI tools for understanding, but may not simply copy AI summaries.
| 學習紀錄欄位Record Field | 學生需要填寫的內容What to Submit |
|---|---|
| 本週主題Weekly topic | 例如：決策、組織設計、人才管理、風險控制。For example: decision making, organization design, talent management, risk control. |
| 三個核心概念Three core concepts | 寫出本週最重要的三個管理學概念。Identify the three most important management concepts of the week. |
| 我問 AI 的問題Questions I asked AI | 列出 2 到 3 個實際提問。List two to three actual questions asked. |
| AI 幫我理解了什麼What AI helped me understand | 用自己的話說明，不直接貼上 AI 回答。Explain in your own words; do not paste AI output directly. |
| 我仍然不懂的地方What I still do not understand | 至少寫出一個具體問題。Write at least one specific question. |
| 本週個案中最關鍵的證據Most important case evidence | 引用或整理個案中的具體內容。Quote or summarize specific case evidence. |
| 最容易混淆的概念Most confusing concept | 說明為什麼容易混淆。Explain why the concept is easy to confuse. |
每週上課一開始會有 8 到 12 分鐘的小考。小考主要用來診斷理解狀況，幫助老師和學生看出哪些概念容易混淆、哪些情境條件被忽略、哪些判斷需要重新思考。平時小考不可使用 AI。
Each class begins with an 8- to 12-minute quiz. The quiz diagnoses understanding: which concepts are confused, which contextual conditions were missed, and which judgments need revision. AI is not allowed during quizzes.
| 題型Question Type | 檢查重點What It Checks |
|---|---|
| 概念題Concept questions | 是否理解基本定義。Understanding of basic definitions. |
| 概念比較題Concept comparison | 是否能分辨相近概念。Ability to distinguish similar concepts. |
| 個案判斷題Case judgment | 是否能把概念用在個案情境。Application of concepts to the case context. |
| 邊界條件題Boundary-condition questions | 是否知道哪些條件會改變管理判斷。Awareness of conditions that change managerial judgment. |
| 錯誤答案修正題Answer revision | 是否能看出空泛答案的問題，並重新改寫。Ability to identify weak answers and rewrite them. |
正式作業若使用 AI，需繳交最終作業檔、AI 對話紀錄、AI 使用聲明表、參考資料清單與個人修正說明。AI 對話紀錄可使用 PDF、Markdown、HTML 或 TXT；單純截圖原則上不建議，因為不易搜尋與比對。
Formal assignments that use AI must include the final assignment file, AI dialogue records, an AI-use statement, a reference list, and a personal revision note. Dialogue records may be submitted as PDF, Markdown, HTML, or TXT. Screenshots alone are not recommended because they are hard to search and compare.
| 聲明欄位Statement Field | 填寫內容Required Content |
|---|---|
| 使用工具與日期Tools and dates | 例如 ChatGPT、NotebookLM、Claude，以及主要使用日期。For example ChatGPT, NotebookLM, Claude, and the main dates used. |
| 使用目的Purpose | 例如理解概念、整理個案、改寫段落、檢查推理。For example concept learning, case organization, paragraph revision, or reasoning review. |
| 是否使用 AI 產生初稿AI-generated draft | 明確填寫是或否。State yes or no clearly. |
| AI 協助範圍Scope of AI assistance | 具體說明最終作業中哪些段落、表格或分析項目受到 AI 協助。Identify the paragraphs, tables, or analysis items influenced by AI assistance. |
| 個人修改與查證Personal revision and verification | 說明自己如何修正、刪除、補充或重寫 AI 內容，並列出查證來源。Explain how AI content was revised, removed, supplemented, or rewritten, and list verification sources. |
| 評量面向Criterion | 高品質表現Strong Performance | 低品質風險Low-Quality Risk |
|---|---|---|
| 重疊度說明Overlap explanation | 清楚說明哪些內容來自 AI，哪些由自己修改。Clearly separates AI-assisted content from personal revisions. | 作業與 AI 回答高度相似，卻沒有修正說明。Final work closely matches AI output without explanation. |
| Prompt 結構Prompt structure | 包含任務目標、使用材料、分析步驟、輸出格式、限制條件與檢查標準。Includes goals, materials, steps, format, constraints, and quality checks. | 只要求 AI 代寫、整理或摘要。Only asks AI to write, organize, or summarize. |
| 知識掌握Knowledge mastery | 能主動使用管理概念，並檢查適用條件。Uses management concepts and checks when they apply. | 只有名詞，缺少真正理解與查證。Uses terminology without understanding or verification. |
| 對話推進Dialogue progression | 從釐清事實、整理概念、比較選項，到檢查條件與修正答案。Moves from facts to concepts, options, conditions, and revision. | 問題零散，彼此沒有關係，看不出學習過程。Questions are scattered and do not show a learning process. |
期中考與期末考皆為個人作答，考試期間不可使用 AI。期中考確認前半學期的管理學概念與個案應用能力；期末考確認學生能否整合整學期概念，針對個案做出有根據的管理判斷。
The midterm and final exam are individual assessments, and AI is not allowed. The midterm checks the first half of the semester; the final exam checks whether students can integrate semester-long concepts into evidence-based managerial judgment.
本課程不把 AI 視為禁止使用的工具；相反地，學生需要學會正確使用 AI。但是，使用 AI 不代表可以放棄閱讀、思考、查證與判斷。每一份作業都必須能說明產出過程。
This course does not treat AI as a banned tool. Students are expected to learn how to use it properly. Using AI does not remove the responsibility to read, think, verify, and judge. Every assignment must be explainable through its production process.
課程由元智大學管理學院國際企業學群吳相勲副教授設計，將管理學、個案教學、產業分析與生成式 AI 輔助學習結合，讓學生從真實資料出發，練習建立可被討論、檢驗與執行的管理判斷。
The course is designed by Associate Professor Hsiang-Hsun Wu, International Business Group, College of Management, Yuan Ze University. It combines management, case teaching, industry analysis, and AI-supported learning so students can build management judgment from real materials.
元智大學管理學院國際企業學群副教授，並擔任元智大學終身教育部主任。主要關注商業模式創新、新創投資評估、策略管理、產業分析、數位轉型，以及 AI 進入知識工作與管理教育之後所帶來的能力變化。
Associate Professor in the International Business Group, College of Management, Yuan Ze University, and Director of Continuing Education at Yuan Ze University. His interests include business model innovation, startup investment evaluation, strategic management, industry analysis, digital transformation, and how AI changes knowledge work and management education.
本課程重視證據、推論與行動之間的連結。學生不只需要說出看法，還要說明看法從哪些資料而來、哪裡仍有不確定性，以及如果自己是管理者，會先建立哪些制度來降低風險。
The course emphasizes the connection among evidence, inference, and action. Students must do more than state opinions; they must explain what evidence supports those opinions, what remains uncertain, and what systems they would build first as managers to reduce risk.