代写MG-GY 6343-INET (6343) Human Capital Engineering & Analytics Fall 2023代做Python编程
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MG-GY 6343-INET (6343) Human Capital Engineering & Analytics, Online
Fall 2023
Course Pre-requisites: Graduate Standing
Course Description:
This course examines and applies the valuation and management of intangible assets in designing and managing post-industrial organizations. As organizations increasingly rely on technology to produce value, these technological solutions require interactions with other forms of value creation like Human Capital Management, Intellectual Property development and Organization Culture. The first part of the course focuses on human capital engineering using an interdisciplinary approach, drawing on diverse fields including industrial-organizational psychology, industrial engineering, economics, and artificial intelligence to create a holistic view of how work in its various forms creates value. The second part of the course addresses people analytics, providing the student with a knowledge and understanding of current best practices, issues, and decision points in building an effective human capital analytic program. This part of the course will also focus on data structure and design to enable automation and predictive modeling and will place an emphasis on technology-enabled reporting.
Course Objective:
By the end of the course, students should:
· Understanding of how to apply Human Capital Theory to HR Management, Industrial Engineering, and related fields to building a Human Capital Strategy
· Understand the types of HC/HR data that exist and analytic options that are commonly used.
· Understand how the interplay between human competencies and the capabilities of technology combine to accomplish work and create value.
· Demonstrate how analytics are applied to human capital and used to make decisions.
· Identify standard Key Performance Indicators often used in HR scorecards and measurement.
· Identify and create data displays and dashboard designs that enable organization decision making and change.
· Develop a working knowledge in the area through focused projects, presentations, and individual assignments.
Course Structure:
Since the first part of this course investigates new and cutting-edge theories and their application, it will be structured more as a research tutorial to facilitate individual student’s exploration. Lectures and on-line experiences will be used to build an understanding of the topic and a basic toolkit for strategic analysis and human capital management. These tools (e.g., Strategy Map, Competency Model, HR Scorecard) will be applied to a case of the student’s own choosing and form. the basis of a Human Capital Strategy which will become the basis for the Analytics part of the course.
The second part of the course will develop students’ hands-on skills in human capital analytics. Building on the first half of the course, students will receive instructions and templates for designing a Human Capital scorecard (KPI’s), a data structure and a HC dashboard. Each student will be expected to deliver an oral and visual presentation of a data dashboard in addition to a detailed final paper tying together all the deliverables in the course.
Most of the course content will be delivered through Brightspace but student teams will be given an opportunity to interact with the instructor through regularly scheduled synchronous Zoom video-conference sessions.
Readings:
· Alan Burton-Jones, J.C. Spender (2011) The Oxford Handbook of Human Capital, Oxford University Press, Inc., New York. (Available on-line through the NYU Library)
· Robert S. Kaplan and David P. Norton (2004). Strategy Maps: Converting Intangible Assets into Tangible Outcomes. Harvard Business Press.
· Lyle M. Spencer, Jr. and Signe M. Spencer (1993) “Competence at Work: Models for Superior Performance.” Wiley & Sons, Inc. ISBN 0-471-54809-X (optional and on reserve in the Birn Dibner Library)
· Andrew McAfee and Erik Brynjolfsson (2017) “Machine, Platform, Crowd: Harnessing our digital future.” WW Norton and Company, New York.
· Pease, G., Byerly, B. & Fitz-enz, J. (2013). “Human Capital Analytics” Hoboken, NJ: Wiley
Additional Suggested Readings (Optional):
Deiz, Fermin, Bussin, Mark, and Lee, Venessa (2020) “Fundamentals of HR Analytics,” Emerald Publishing.
Edwards, Martin R. and Edwards Kirsten, (2019) “Predictive HR Analytics: Mastering the HR metric,” 2nd ed. Kogan Page.
Waters, S.D., Streets, V.N., McFarlane, L., and Johnson-Murray, R. (2018) “The Practical Guide to HR Analytics,” SHRM.
Other readings will be distributed in class or through Brightspace
Course Assignments and Grading:
|
Participation and Graded Assignments/Activities |
Percent |
|
Team Project Deliverables and Participation (Group shared grade). |
40% |
|
Exams/Quizzes (Mid-term and Final) |
30% |
|
Individual Class Assignments (Individual Grade) |
20% |
|
Class Participation (based on attendance at all group and class assignments) |
10% |
|
Total |
100% |
Major Assignment
Team Project Paper and Deliverables (40%)
Each team will prepare a HC Strategy and Measurement for an organization of their choice. There will be one organization studied by each team. The final document will include a summary of the organization’s strategy, a Human Capital Strategy, A set of Competency Models for 2 Strategic Job Families, and a Human Capital Scorecard.
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Session |
Topics |
|
Module 1 Friday Sept 8th
|
Overview and Introduction to HC · Historical Perspective on Human Capital Theory · Application to Human Resources Strategy · Emerging issues of Human/Technology Integration · Intellectual Property rights and Developing Economies Readings: Blair, M.M. An Economic Perspective on the Notion of “Human Capital” in the Oxford Handbook of Human Capital. (Chapter 1) |
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Module 2 Friday Sept 15th
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Using the strategy Map to Inventory and/or Design Intangible Assets · Which intangible assets are required by the Organization’s Strategy? · How can these assets be defined in terms of human labor? · How can these assets be defined in terms of technology? · How do the interactions of human labor and technology enhance the organization’s outputs and market valuation? Readings: Kaplan and Norton Text Part II: Intangible Assets, pp. 199-275 |
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Module 3 Friday Sept 22nd
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Defining, Designing and Enhancing Human Capabilities · New Competency Models for the Post-Industrial Era · Changes in Performance and Reward Practices in the new work paradigm. · Exploring the outer reaches of Human Potential in high tech and data rich environments. · How engineers build Human Capability into machines and the state of the art in emulating human performance through machines. Readings: · Spencer, L.M. Jr., and Spencer, S.M. “Competence at Work,” Part I, II and III. (Optional) |
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Module 4 Friday Sept 29th
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Introduction to Analytics & Alignment • Creating measurement from organization objectives. • Case examples of HR Measurement and Benchmarking • Understanding Cause and Effect Relationships through Lead and Lag Measures Pease: Chapter 1 & Chapter 2 |
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Module 5 Friday Oct 6th
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Designing Work in the New Era of AI and Robotics · What to automate and what should people do? · Impact of the People/AI/Robotics interface in the future of work. · Issues of motivation and personal development in an automated work environment · Creating a blueprint for work design that makes the best of Human Capital · TBD |
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Module 6 Friday Oct.13th
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Culture, Employee Turnover, Understanding What They Want and Why They Leave · Changes in the Employee/Employer Contract? · Using Surveys, Regression & Path Analyses to define cause-Effect Relationships · How do Organization and National cultures Contribute to or detract from Human Capital? · Do Robots become part of the culture? |
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Oct 19th – Oct 23rd |
Assessment - Mid-Term Exam (On-line on Brightspace – there will be no regular Zoom session) Assignment – Upload one page outline of Class Project on Brightspace (see instructions |
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Module 7 Friday Oct 27th
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Measurement Plan and Supporting Data Foundation • Hypotheses or business questions • Defining the metrics • Demographics • Data sources and requirements • Types of data and linking these together • Importance of data structures for automation • Ethics of measurement and evaluation Pease: Chapters 3 and 4 Assignment – Upload First Draft of Term Project Strategy Overview |
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Module 8 Friday Nov 3rd |
Demographic analysis and selection validation · Analyzing demographic characteristics of a candidate pool. Validating selection techniques to predict performance of applicants and internal hires. Assignment – Upload first draft of Team Project HC Strategy |
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Module 9 Friday Nov 10th
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Dashboards and KPIs (Key Performance Indicators) • Types of analysis/measurements and their impacts. • Visual representations of data • Time Series • Enhanced metrics and descriptive stats • Correlation & Regression Mapping Cause and Effect (Path Analysis) • Statistical significance • Reward Systems and their Impact on Behavior. • HR Scorecards & Dashboard Design Pease: Chapter 5 and 6 Assignment – Upload first draft of Team Project Competency Model |
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Module 10 Friday Nov 17th
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ROI to Optimization • Optimization • Presenting the financials • Telling the story and adding up the numbers • Preparing for Sponsor meetings • Economic Value Added (EVA) and how it derives from organization effectiveness. Pease: Chapters 7, 8 and 9 Assignment – Upload First Draft of HC Scorecard |
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Friday Nov 24th |
Thanksgiving Holiday & Native American Heritage Day No Class |
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Friday Dec 1st |
Social Media, Data Scraping and other hot Topics in Big Data |
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Friday Dec 8th |
Team Presentations via Zoom |
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Friday Dec 15th |
Team Presentations via Zoom |
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Week of Dec 16th through 22nd |
Assessment - Final Exam (On-line on Brightspace)
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