This course is firstly about managing and leading, the constant grind of analyzing problems well, making better insights, gathering relevant data to support insights, and ultimately, making sound decisions. This course is secondly about managers leveraging Data science—AI, machine learning, and statistics-data visualization (aka “analytics”)—to help this cycle of problem analysis to decision-making? Last and most importantly, the course is for non-techie MBAs. No programming, no Git, no data cleaning. You will learn just enough to communicate management needs to techies. More, you will learn how: how to use GenAI to smoothen processes, how to visualize data, how to judge AI quality, how to use AI ethically, and how to manage human–AI interaction.

Before COVID most MBA courses emphasized broad functional knowledge like marketing, operations, finance, etc. across business domains. In just five years AI has mooted knowledge: AI can now gather and assemble knowledge faster than humans. Knowledge alone is therefore no longer an MBA advantage. While modern business leaders must continue to develop strong foundational business skills, good judgment, and empathy--they must also work comfortably with AI and data analysis.

By the end of the course, students will be able to use generative AI as a data analyst, a workflow assistant, a creative collaborator, and a helper for critical thinking. Students must also learn when AI can and cannot substitute for thinking. Students will develop a conceptual understanding of the core purposes and mechanisms of data science. Without having deep technical expertise, students should have skills to articulate business problems, translate managerial needs into analytical requirements, and collaborate productively with specialists like data analysts and data scientists to achieve complex business objectives.

*本学の教育ミッションを具現化する形で設定されています。

By the end of the course, participants will be able to:

1. Develop a personal toolkit of effective generative AI prompts and workflow notebooks to support managerial workflows.
2. Identify when and how to apply fundamental stats, including hypothesis testing (e.g., t tests), regression, classification, and clustering. Be able to interpret specific goodness and quality of results.
3. Explain how stats underpin prediction and prescriptive decision making in AI enabled systems.
4. Demonstrate a conceptual understanding of the core mechanisms of data science sufficient to communicate business requirements to technical experts.
5. Assess how AI can enhance personal productivity, redesign business processes, and inform business strategy.
6. Develop skills to judge AI quality and know when humans develop overdependence, and when humans should intervene.

教育手法 Teaching Method		% of Course Time
インプット型 Traditional		0 %
参加者中心型 Participant-Centered Learning	ケースメソッド Case Method	100 %
	フィールドメソッド Field Method	0 %
合計 Total		100 %

GenAI / LLM use policy:

1. AI generates vast, unlimited, and often hallucinated knowledge. But knowledge is only one component of the NUCB case experience. The others are skills (Critical thinking), wisdom (judgment, ethical reasoning) and social engagement (empathy, active listening). To build these we generally limit the use of GenAI, to prevent overdependence on the tool.

2. Therefore, though the course is about AI, unlimited use of GenAI is not allowed.

3. Case materials must not be submitted to LLMs for analysis. Legally we are prohibited from uploading any case material to the web. Critical thinking-wise we want students to process the material in a personal way, rather than have LLMs do the work for them.

4. GenAI is allowed for specific class exercises, e.g. brainstorming, statistical testing, editing, presentation formulation, etc.

5. Otherwise, turn GenAI off during general case discussions, i.e. when we do many-to-many interactions, debates, elaborations. From time to time, I will allow GenAI in exercise or to source external information—but unless the professors say so, GenAI stays off during class.

General learning approach:

1. Though we have 14 sessions, not all sessions are case discussions. Some will be hands-on exercises and spontaneous projects in class.

2. Read the 7-8 cases in your PDF packet ahead of time. Attempt to solve the problems ahead of time. For each case you might want to allocate at least three hours to read and understand and propose solutions to problems, if any.

3. There are no exams in this course. You will instead be assigned a group project at course end with one week to complete. You will also submit a semi-peer evaluation where you vote for the two best team members in your team (excluding yourself). The top two members will get a 5% grade bonus.

Before Day 1: NotebookLM setup

CREATE a set of your OWN COURSE NOTEBOOKs. I suggest NotebookLM in Google platform, though you can also use AnythingLLM, NotionAI, etc. Google assures that notes and data uploaded to NotebookLM are private and will not be used for training.

1. Use your NUCB (Google) account to set up NotebookLM app. See https://notebooklm.google.com.

2. DO NOT UPLOAD any Harvard or NUCB CCJ case material to Notebook.

3. You are free to upload public docs and links, your notes, photos, data, etc to NotebookLM.

4. Suggest you use separate notebooks for each session. Over-stuffed notebooks may slow down your processing time.

5. To save processing time, tell Notebook NOT to automatically roduce slide decks or infographics in the studio panel. Create instructions like “When I say ‘KISS’, do not generate a slide deck and infographic.” For succeeding prompts, type KISS and Notebook will obey.

6. Notebook sometimes gets into an endless loops. You may have to delete the notebooks and start over, in order to stop the looping.

*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。

講師用内規準拠 Method of Assessment	Weights
コールドコール Cold Call	0 %
授業内での挙手発言 Class Contribution	70 %
クラス貢献度合計 Class Contribution Total	70 %
予習レポート Preparation Report	0 %
小テスト Quizzes / Tests	0 %
シミュレーション成績 Simulation	0 %
ケース試験 Case Exam	0 %
最終レポート Final Report	30 %
期末試験 Final Exam	0 %
参加者による相互評価 Peer Assessment	0 %
合計 Total	100 %

評価の留意事項　Notes on Evaluation Criteria

• 配布資料

To be announced.

For past course evaluations, please see various course evaluations.

https://docs.google.com/spreadsheets/d/1bmWo77TeHNgayBaU3TlOprS-9mK58-mL/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true

https://docs.google.com/spreadsheets/d/1bqH3sXynAEyf32ItAq97muKRVmTwdX3J/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true

https://docs.google.com/spreadsheets/d/1uVN4AMzY7wE7E95Ucpe4J6aTF-YZ_j_-/edit?usp=sharing&ouid=103967654037341470318&rtpof=true&sd=true

Refereed Articles

• (2023) Determinants of Conspicuous Consumption in Smartphones. Asia Pacific Journal of Information Systems 33(3): 2288-5404
• (2023) A Study of Satisfaction and Loyalty for Continuance Intention of Mobile Wallet in India. International Journal of E-Adoption (IJEA) 15(1): 1937-9633
• (2021) Developing and Testing a Smartphone Dependency Scale Assessing Addiction Risk. International Journal of Risk and Contingency Management 10(4): 2160-9624
• (2021) Business Model Innovation: A Study of Empowering Leadership. Creativity and Innovation Management 1467-8691
• (2021) The Effect of Reciprocity on Mobile Wallet Intention: A Study of Filipino Consumers. International Journal of Asian Business and Information Management 12(2): 1947-9638