杏吧论坛

Dr Chunyun Li NAB 3.18

This course is available on the MSc in Human Resources and Organisations (Human Resource Management/CIPD), MSc in Human Resources and Organisations (International Employment Relations and Human Resource Management) and MSc in Human Resources and Organisations (Organisational Behaviour). This course is not available as an outside option.

The course will be capped at 30 students. Interested students will be expected to submit a 200 words maximum outline for why they are motivated to study the course.

This course explores the role of metrics and analytics in human resource management (HRM). The current world of work contains all kinds of information and metrics, which guide employee behaviors and could be analyzed to make the work more engaging to employees and organizations more efficient. Instead of making human resource decisions based on traditions or gut instinct, we can bring more science to the way people are managed. This course combines substantive people management issues such as performance management and employee turnover with data-driven decision-making skills. In addition, the course will discuss emerging technologies such as big data and AI in HRM and the ethics involved. It prepares students to analyze data and evaluate evidence to form ethical people decisions and become better managers. 

This course will help students in three important ways. First, weekly lectures will present up-to-date literature and analytics insights pertinent to particular aspects of managing people. This will provide baseline knowledge for students to think about what may work in their future management. Second, students will work on multiple sample datasets from real-world case studies to identify the HRM problems, evaluate evidence, and analyze data to make people decisions, building their analytics capability and insights in HRM. This practical experience will prepare them to gather and analyze data on their own for their dissertation or future projects. Third, the students will develop the understanding necessary to be thoughtful and critical evaluator of the impact and deployment of emerging technologies in HRM.

This course will explore the following topics:

1. Introduction to people analytics: why, tools, and case studies
2. HR metrics and performance incentives
3. Performance feedback and a case on workplace experimental design
4. Analyzing employee turnover
5. HR investment and business performance analysis
6. Workforce forecasts
7. Workforce forecasts and staffing
8. Big data and algorithmic HRM
9. AI in HRM and relevant law & ethics

15 hours of lectures and 15 hours of seminars in the LT.

This course will be comprise interactive and practical discussions in lectures and hands-on data analysis experiences during seminars. The teaching is also built heavily on case studies, allowing students to analyze data from real life case studies of companies. Students will be also be encouraged to attend the relevant Digital Skills Lab workshops.

There are two elements in formative assessment. The first element - quizzes - will allow the students to check whether they understand the analytics techniques correctly. The second element - a short analytics project outline (500 words)- allows the students to present a plan to collect and analyze data before they actually undertake an analytics project to analyze data.

• Text book: Edwards, Martin and Kirsten Edwards. 2019. Predictive HR Analytics: Mastering the HR Metric. Publisher: Kogan Page. ISBN: 9780749484446.
• Davenport, Thomas H., Jeanne Harris, and Jeremy Shapiro. 2010. Competing on talent analytics. Harvard Business Review, 88(10): 52-58.
• Tambe, Prasanna, Peter Cappelli, and Valery Yakubovich. 2019. Artificial intelligence in human resources management: challenges and a path forward." California Management Review 61(4): 15-42.
• Hamilton, R. H., and William A. Sodeman. 2020. "The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources." Business Horizons, 63(1): 85-95.
• Allen, David G., Phillip C. Bryant, and James M. Vardaman. 2010. "Retaining talent: Replacing misconceptions with evidence-based strategies." Academy of management Perspectives 24(2): 48-64.
• Case study: Garvin, David. 2013. How Google sold its engineers on management. Harvard Business Review, 91 (12): 74-82.
• Case study: Frei, Frances X., and Dennis Campbell. 2017. Store24(A): Managing employee retention. Harvard Business School Case 602-096.
• Case study: Feeney, Justin, Frost, Ann, and Chris Street. 2020. Banishing performance ratings at iQmetrix, Ivey Publishing, W20064-PDF-ENG.

Project (50%, 2000 words) and essay (40%, 2000 words) in the ST.
Class participation (10%).

The course will be assesed via the following methods:

Class participation assessed on identifying HRM issues and the of role data and analytics in people decisions during seminar discussion (10%).

An individual analytics report (2000 words) to identify the problem(s), analyze data, present findings, and write a report (50%).  

An essay (2000 words) to critically evaluate current areas of people analytics and/or emerging technologies’ application in HRM (40%).

The summative assessment aims to gauge students’ capability to identify main HRM issues and decide on relevant evidence and analytics techniques needed to resolve the issues as based on case studies. Critical evaluation of analytics decisions and potential impacts are encouraged in the essay.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.