シラバス Syllabus

授業名 Human Resources Analytics
Course Title Human Resources Analytics
担当教員 Instructor Name Kuok Kei Law
科目ナンバリングコード Course Numbering Code
授業形態 Class Type 講義 Regular course
授業形式 Class Format On Campus
単位 Credits 2
言語 Language EN
科目区分 Course Category 応用科目200系 / Applied
学位 Degree MSc in Business Analytics & AI
開講情報 Terms / Location 2026 GSM Nagoya Spring
コード Couse Code GLP157_G26N

授業の概要　Course Overview

Mission Statementとの関係性 / Connection to our Mission Statement

This course aligns with NUCB's mission to educate innovative and ethical leaders by challenging students to navigate the moral complexities of AI implementation. It also fosters a "Frontier Spirit" by immersing students in cutting-edge digital transformations in different case contexts.

授業の目的（意義） / Importance of this course

This course is designed for future managers and leaders who must make high-stakes people decisions driven by data, not just intuition. Students will move beyond the technical mechanics of how to calculate metrics and focus on the strategic question of why and when to use them.

学修到達目標 / Achievement Goal


1. Strategic alignment: Enable students to link human capital metrics (attrition, engagement, culture) to operational outcomes (profit, customer satisfaction, speed) to justify investment.
2. Diagnostic capability: Equip students to interpret descriptive data (pivot tables, correlations) to identify the root causes of turnover and performance gaps, thus moving from "I think" to "The data shows."
3. Implementation & ethics: Develop frameworks for implementing analytics in high-touch cultures and navigating the ethical risks of algorithmic decision-making.

本授業の該当ラーニングゴール　Learning Goals

*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。

LG1 Critical Thinking
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG7 Global Perspective (GLP)

受講後得られる具体的スキルや知識　Learning Outcomes


1. Diagnose root causes of organizational turnover and performance decline using descriptive analytics.
2. Formulate actionable implementation plans for HR technologies that account for cultural resistance and managerial agency.
3. Synthesize diverse data points to make real-time resource allocation decisions that balance cost, quality, and morale.

SDGsとの関連性　Relevance to Sustainable Development Goals

Goal 9 産業と技術革新の基盤をつくろう（Industry, Innovation and Infrastructure）

教育手法　Teaching Method

教育手法 Teaching Method % of Course Time
インプット型 Traditional 10 %
参加者中心型 Participant-Centered Learning ケースメソッド Case Method 90 %
フィールドメソッド Field Method 0 %
合計 Total 100 %

事前学修と事後学修の内容、レポート、課題に対するフィードバック方法　Pre- and Post-Course Learning, Report, Feedback methods

Participants are advised to allocate a minimum of 3 hours for preparation per case.

A laptop computer is essential for conducting information research and participating in simulation exercise during class.

Feedback for both pre-class and simulation tasks will be provided during open class discussions.

授業スケジュール　Course Schedule

第1日（Day1）

Day 1: The Business Case for Analytics
Theme: Moving from intuition to evidence-based management. Proving the value of "soft" data.


●使用するケース
HR Analytics at Barney (Product ID: IES843)
Google's Project Oxygen: Do Managers Matter? (Product ID: 9-313-110)

第2日（Day2）

Day 2: Retention & Internal Mobility
Theme: Fixing the "Leaky Bucket" and optimizing talent supply.

●使用するケース
Apturja Power Limited: Human Resources Analytics (Product ID: W21016)
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys

第3日（Day3）

Day 3: Implementation Challenges
Theme: When data meets reality — Culture, ethics, and resistance.


●使用するケース
Agoda: People Analytics and Business Culture (A) (Product ID: W17429)
Agoda: People Analytics and Business Culture (B) (Product ID: W17430)
Ethical Programming of Algorithms: How to Deal with Ethical Risks of AI Tools for Hiring Decisions? (A) (Product ID: UV8548)
Ethical Programming of Algorithms: How to Deal with Ethical Risks of AI Tools for Hiring Decisions? (B) (Product ID: UV8550)

第4日（Day4）

Day 4: Transformation & Application
Theme: Leading into the future and applying the toolkit.

●使用するケース
DBS Bank: A Tech Company Going All In on AI (Product ID: 056SMU)
HR Management Simulation: People Analytics (Product ID: FO0024-HTM-ENG)

第5日（Day5）



第6日（Day6）



第7日（Day7）



成績評価方法　Evaluation Criteria

*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 30 %
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 0 %
合計 Total 100 %

評価の留意事項　Notes on Evaluation Criteria

Preparation before class and active participation in both small group discussion and open class discussion are expected.

使用ケース一覧　List of Cases

    ケースは使用しません。

配布教材と教室における電子機器の利用マナーについて Guidelines for Classroom Technology and Proper Use of Course Materials

  1. ケースメソッド教育の中核は、積極的な参加と知識の共有です。この教育を支えるため参加者は授業中の電子機器（例：スマートフォン、ノートパソコン）の使用を制限するよう求められます。許可を得た場合でも、教室内では電子機器は、ケース討議に資する目的でのみ使用してください。授業中は、たとえケース討議に関連していても、検索エンジンや生成AIの使用は避けて下さい。
  2. 配布教材（ケースを含む）は指定された授業への参加以外の目的で利用しないで下さい。著者の権利、著作権、特定情報の機密性を保護するため、許可なく教材を個人や組織（生成AI を含む）に提供することはできません。このルールは、印刷物・電子教材のいずれにも適用されます。
  1. Active participation and shared learning is at the core of the case method learning.Participants are asked to limit their use of electronic devices (e.g., laptops, smartphones) during classroom sessions in support of this model. Even with permission granted, devices should only be used in the classroom in service to the case discussion. Online searches and generative AI tools, even if related to the case discussion, are discouraged while class is in session.
  2. Students are prohibited from using the course materials (including cases) distributed by the university for any purpose other than participation in the designated class.Students must not input, process or test course materials with any artificial intelligence (AI) tools, bots, software, or platforms without the author’s permission. These actions violate the terms of use for the course materials and may also constitute copyright infringement.

教科書　Textbook

  • 配布資料

参考文献・資料　Additional Readings and Resource

Additional readings will be recommended during classes.

授業調査に対するコメント　Comment on Course Evaluation

This is the first offering of the course.

担当教員のプロフィール　About the Instructor　


Kuok Kei (Eddie) Law is a Professor of Management at NUCB Business School. He earned his PhD from City University of Hong Kong. His primary research interests focus on knowledge management and human resource management. His work has been published in leading international journals, including European Management Journal, Human Resource Management Review, Journal of Knowledge Management, and Work, Employment & Society. Eddie’s research received Best Paper and Research Awards in 2013 and 2016, respectively. At NUCB, he has been honored with Teaching Awards for four consecutive academic years (2020/21–2023/24) and a Research Award for the 2020/21 and 2024/25 academic years.

（実務経験 Work experience）


Prior to joining NUCB, Eddie worked at the Open University of Hong Kong, where he began his academic career and progressed rapidly from Assistant Lecturer to Lecturer, and eventually to Assistant Professor over his nine-year tenure. He has extensive experience teaching business management courses at both undergraduate and postgraduate levels, as well as supervising research projects and dissertations.

Refereed Articles

  • (2025) The role of e-leadership and team dynamics in work-from-home performance: a replication study during the COVID-19 pandemic. Management Review Quarterly
  • (2025) Unlocking innovation potential: Harnessing intra-firm competition for organizational innovation performance. Journal of General Management
  • (2025) The Energy Ltd: A Threat from COVID-19 in a Third-party Logistics (3PL) Company. Asian Journal of Management Cases
  • (2024) ‘I am a Scaffolder’: Constructing Safety Knowledge and Machismo in ‘Dirty Work’. Work, Employment and Society
  • (2024) Integrating the adapted UTAUT model with moral obligation, trust and perceived risk to predict ChatGPT adoption for assessment support: A survey with students. Computers and Education: Artificial Intelligence 6 2666-920X

Refereed Proceedings

  • (2017). Knowledge appropriation, justice climate and performance management - A theoretical framework. Proceedings of the British Academy of Management 2017 Conference .British Academy of Management 2017 Conference. 1. 3. University of Warwick
  • (2016). Tacit knowledge management of firefighters: An exploratory study on the fire service department in Hong Kong. Proceedings of the 8th International Conference on Innovation and Knowledge Management in Asia Pacific .The 8th International Conference on Innovation and Knowledge Management in Asia Pacific. 1. 3. Ariston Hotel, Kobe
  • (2016). Exploring employees' communication behaviors in knowledge sharing: A hierarchal perspective. Proceedings of the 81st Annual Conference of the Association for Business Communication .The 81st Annual Conference of the Association for Business Communication. 1. 3. Albuquerque, New Mexico






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