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 develops the leadership capabilities required to operate effectively in organizations where decisions are increasingly shaped by analytics, algorithms, and artificial intelligence. It examines how data-driven approaches challenge established practices, reshape managerial decision-making, and influence organizational performance and talent development.
Students examine how leaders can initiate and sustain organizational change, build analytical capabilities, and manage the implications of AI for employees and organizational systems. Particular attention is given to balancing analytical evidence with human judgment, fairness, and accountability.

Students will be able to analyze leadership challenges arising when analytics and AI become embedded in organizational decision-making and management practices.
Students will develop the ability to evaluate how leaders can guide organizational change, build analytical capabilities, and address the implications of data-driven systems for performance management and talent development.
Students will also be able to articulate leadership trade-offs involving analytical evidence, managerial judgment, fairness, and accountability.

*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:
• Analyze organizational challenges arising from the adoption of analytics and AI.
• Apply organizational change and analytics capability concepts to evaluate how data-driven approaches can be adopted and sustained.
• Assess the benefits and limitations of performance metrics and algorithmic recommendations.
• Evaluate trade-offs involving analytical evidence, managerial judgment, fairness, and accountability.
• Propose practical leadership responses to organizational challenges associated with AI and analytics adoption.

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 and use the questions in the Assignment Sheet as guides for preparation and reading. Written Preparation Reports (10%) are required only for selected materials: Karen Bruck: Growing Managers at MercadoLibre, Competing on Analytics (Davenport), Employees in Foxconn’s Business Empire, and Part 1 (Case Study) of Trust the Algorithm or Your Gut? A VP Decides Which Candidate to Promote. Detailed submission instructions will be provided through Google Classroom.
Students should come prepared to identify the decision context, key organizational challenges, and relevant evidence in each assigned material. A laptop computer is required for in-class activities and electronic materials

"Class Discussion"
Classes combine case discussions, individual reflection, small-group discussion, and instructor-led synthesis. Students are encouraged to articulate their own judgments, consider alternative perspectives, and support their arguments with evidence from the assigned materials.

"In-class Activities"
Students participate in two structured activities: an idea-generation exercise comparing independent and AI-supported work, and an evaluation exercise using business ideas from previous student projects. These activities provide opportunities to reflect on human judgment, AI assistance, evaluation differences, and organizational implications.

"Reports / Written Assignments"
Preparation Reports account for 10% of the final grade and are submitted only for the selected materials listed above. Assignment questions for all materials serve as preparation and reading guides, whether or not a written report is required.

"Group Presentation"
Students work in groups to develop a practical leadership response to a specific organizational challenge arising from AI or analytics adoption. The final presentation takes place on Day 4 and draws on course cases, readings, and in-class activities. Peer assessment includes both within-team assessment of individual contributions and between-team assessment of other groups’ presentations.

"Feedback Methods"
• Real-time oral feedback during case discussions and group activities.
• Instructor comments and questions during group presentations.
• Collective feedback and synthesis of key insights at the end of class discussions.
• Reflection and debriefing following the two in-class activities.

*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	10 %
授業内での挙手発言 Class Contribution	60 %
クラス貢献度合計 Class Contribution Total	70 %
予習レポート Preparation Report	10 %
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	20 %
合計 Total	100 %

評価の留意事項　Notes on Evaluation Criteria

Attendance and Class Contribution (60%): Attendance, active participation in case discussions, engagement in individual and group activities, and constructive contributions to whole-class discussion are included. Students are expected to attend all sessions of this intensive course.
Preparation Report (10%): Written reports are required only for the selected cases and article specified under Pre-class Preparation. The Assignment Sheet questions for all materials are intended as preparation and reading guides. Detailed requirements and submission instructions will be announced through Google Classroom.
Cold Call (10%): Students are evaluated on preparedness and the ability to respond thoughtfully to questions about assigned materials and organizational issues.
Presentation (20%): The presentation is evaluated on the quality of the organizational analysis, use of course concepts and evidence, feasibility of recommendations, and clarity of communication. Peer assessment is included and covers both individual contributions within teams and evaluations across teams.

• 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.”
Iansiti, M., & Lakhani, K. R. (2020). Competing in the Age of AI. Harvard Business Review Press.

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