This course supports the NUCB Business School mission of cultivating innovative, ethical, and globally minded leaders. By emphasizing responsible decision-making, organizational design, and stakeholder accountability in analytics-driven environments, the course encourages students to integrate advanced analytical capabilities with sound judgment and integrity in addressing complex business and societal challenges.

This course is important for students because it develops the leadership capabilities required to operate effectively in organizations where decisions are increasingly shaped by analytics and data-driven systems.

Through this course, students will be able to analyze how analytics changes what is considered a reasonable managerial decision, rather than simply improving technical accuracy. Students will develop an understanding of how decision authority, accountability, and legitimacy are redistributed when data and analytics are embedded in everyday organizational processes.

In addition, students will develop the ability to design and evaluate leadership systems that integrate analytics while preserving human judgment, ethical responsibility, and stakeholder considerations. These capabilities are particularly important for students pursuing careers in business analytics, where leadership roles require not only analytical proficiency but also sound judgment under structural and institutional constraints.

Students will be able to analyze leadership challenges in organizations where decisions are shaped by analytics and data-driven systems.

Students will develop the ability to examine how organizational structures and decision rights influence managerial judgment, rather than attributing outcomes solely to individual leadership traits.

Students will also be able to articulate leadership trade-offs involving data use, responsibility, and stakeholder considerations in complex organizational settings.

*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。

Upon completion of this course, students will be able to:
- Diagnose organizational decision systems influenced by analytics
- Identify misalignments between data, incentives, and accountability
- Explain leadership challenges related to power, coordination, and legitimacy
- Propose organizational design solutions that responsibly integrate analytics

教育手法 Teaching Method		% of Course Time
インプット型 Traditional		20 %
参加者中心型 Participant-Centered Learning	ケースメソッド Case Method	80 %
	フィールドメソッド Field Method	0 %
合計 Total		100 %

"Pre-class Preparation"
Students are expected to read or view the assigned materials for each class day and familiarize themselves with the key issues of each case.
Rather than preparing full written answers, students should focus on understanding the decision context, main actors, and core organizational challenges.
Preparation time is expected to be approximately 2–3 hours per day, even when multiple cases are assigned.
A laptop computer is required for in-class activities and electronic distribution of materials.

"Class Discussion"
Classes are conducted primarily through open discussions and group-based activities. Students are encouraged to exchange ideas, reconsider their initial views, and respond to perspectives raised by others.
During discussions, no individual written comments are provided for each opinion. However, students can assess the quality and relevance of their contributions through:
- peer responses during discussion, and
- instructor facilitation and follow-up questions.

"Learning Checks (In-class Pop-up Quizzes)"
Short in-class pop-up quizzes may be conducted to confirm engagement with assigned materials.
These quizzes focus on key concepts and decision issues, rather than detailed factual recall.
The quizzes are ungraded individually and are included as part of the overall participation assessment.

"Reports / Written Assignments"
There are no take-home preparation reports or final reports in this course.
Learning outcomes are demonstrated primarily through in-class participation, group discussions, and the group presentation conducted during the course.

"Group Presentation"
Students work in groups to develop a preliminary design of an analytics-augmented leadership system, based on cumulative in-class discussions and activities from Days 1 to 3.
All preparation for the presentation is conducted during class hours, and no additional meetings outside class are required.

"Feedback Methods"
Feedback is provided primarily through the following methods:
- Real-time oral feedback during class discussions and group activities
- Instructor comments and questions during group presentations
- Collective feedback sessions at the end of each class day, summarizing key insights and common issues
- Debriefing discussion following the Everest V3 simulation
- Written individual feedback is not provided; instead, feedback is embedded in the learning process through discussion and interaction.

*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	20 %
授業内での挙手発言 Class Contribution	50 %
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	0 %
期末試験 Final Exam	0 %
参加者による相互評価 Peer Assessment	30 %
合計 Total	100 %

評価の留意事項　Notes on Evaluation Criteria

Evaluation emphasizes continuous engagement and active participation throughout the intensive course.
Multiple evaluation components are used to ensure fair assessment of individual contribution in a discussion- and team-based learning environment.

Attendance is a prerequisite for evaluation and is reflected in Class Contribution. Students are expected to attend all class sessions of this intensive course and actively participate in discussions and group activities. Absence, late arrival, or lack of engagement will negatively affect the class contribution score.

Class Contribution includes attendance, active participation in class discussions, responsiveness to cold calls, engagement in group activities, and preparation for class as demonstrated through in-class interaction. Short in-class pop-up quizzes may be used to confirm engagement with assigned materials. These quizzes focus on key concepts and decision contexts and are included as part of the overall class contribution assessment.

There are no preparation reports or take-home written assignments in this course. Preparation is evaluated continuously through students’ participation, discussion, and in-class responses rather than through written submissions.

Group presentations are evaluated primarily through peer assessment, which is used to assess individual contributions to team-based work. Peer assessment covers both the group presentation and the simulation activity. Instructor observation during class discussion is used to complement peer-based evaluation, and the instructor may adjust peer assessment results in cases of clear imbalance or inconsistency.

The simulation is evaluated based on participation, decision-making rationale, and students’ ability to reflect on structural constraints revealed during the exercise. Simulation outcomes or performance results are not used as evaluation criteria.

• 配布資料

Galbraith, J. R. (2002). Designing Organizations: An Executive Guide to Strategy, Structure, and Process.
Chapter 2: “Choosing an Effective Organizational Design.”
This chapter is provided as a conceptual reference for understanding organizational design choices discussed in class.

Iansiti, M., & Lakhani, K. R. “Competing in the Age of AI.” (HBP, R2001C-PDF-ENG)
This article is provided as a supplementary reference to support understanding of how analytics and AI reshape organizational processes and managerial roles.

Selected film (optional reference): No Other Choice (어쩔 수가 없다).
Directed by Park Chan-wook. Produced by Moho Film; distributed by CJ ENM.
This film is provided as an optional reference to illustrate how individual judgment and responsibility are shaped by structural and institutional constraints.

This course has been newly designed for the MSc in Business Analytics & AI (2026) to focus on leadership and organizational judgment in analytics-driven environments. Rather than teaching technical tools, the course examines how managers design decision systems, exercise responsibility, and lead effectively when data and AI shape organizational processes.

The course emphasizes active discussion, structured reasoning, and collaborative problem-solving in class rather than additional take-home assignments. Evaluation reflects this design and focuses on meaningful participation, analytical clarity, and constructive contribution in a team-based learning environment.

Students are encouraged to approach the course as an opportunity to strengthen their strategic perspective and leadership judgment in complex, data-informed organizations.

Refereed Articles

• (2025) Social Entrepreneurial Intention Change by Gender during the COVID-19 Pandemic. Journal of Social Entrepreneurship 16(3): 19420676
• (2025) Gendered Morality and Positivity in Social Entrepreneurship: A Trend Analysis across Global Crises. Journal of Social Entrepreneurship 19420676, 19420684
• (2024) The Effects of Morality and Positivity on Social Entrepreneurial Intention. Journal of Social Enterpreneurship 15(1): 9420676/19420684

Refereed Proceedings

• (2022). The Mediating Effect of PsyCap on Social Entrepreneurial Intention by Gender and Pandemic. Academy of Management Proceedings .Academy of Management Annual Meeting. 1. 4. Seattle, USA