1 - 10 of 12 results for: HRMGT

How can we use big data, machine learning and artificial intelligence to inform design, hiring, promotion and human resource management processes in organizations? We will discuss the theoretical and practical challenges that these issues present, and the ways by which data can help resolve them. In doing so, we will explore various data analytic methods and different data types, as well as the pitfalls and ethical issues their use introduces.

This elective will bring you to the cutting edge of how organizations are (re)-designed using analytics. You will learn about a variety of tools which enable Perception (i.e. understanding what is happening in the organization right now), Prediction (i.e. forecasting what is likely to happen in the future, based on sophisticated extrapolation of past data) and Prototyping (i.e. determining which decisions are likely to be successful based on pilot tests) in organizations. This is a hands-on class: we will rely extensively on group exercises, in which you will be able to learn enough about analytical tools and programming to be able to collaborate with analysts and evaluate their work. You will also have the chance to interact with industry speakers who have been applying these techniques in practice. The suite of new ideas that characterize ¿Org2.0¿ represents a major departure from the mainstream approach to organization design, which relies extensively on copying ¿best practices¿ from other companies, represents organizations as ¿boxes and arrows¿ organization charts, and is typically obsessed with incentive compensation and reporting as the key organizational/HR decisions. In Org2.0, you will learn how to use analytics to find out what works specifically for your company (rather than what worked in others), develop detailed models of interactions among employees (rather than among boxes) to help them collaborate successfully, and think of organization design as a combination of factors which can all be better understood through analytics. This approach has been made possible both by recent theoretical developments in organization design and by access to vast computational power and data arising from digitalization. Some of the skills you will learn include: -Thinking about complex organizations by breaking them down into a few basic building blocks or ¿micro-structures¿ -Using graph theory to map networks of interaction within organizations -Using machine learning to answer the fundamental ¿people¿ questions in organizations: whom to hire, develop and retain? - Prototyping organizational changes in silico using agent-based computational models -Using A/B testing (a.k.a. Randomized Controlled Trials) to know rather than guess which organizational designs will be effective Who should take this course? If you are likely to be involved in strategy execution, post-merger integration, re-organizations, or HR in either a direct or advisory role (regardless of how big or small your company is), this course will put you at the cutting edge of thinking and methodology in these areas. Some of the exercises will involve programming (in the Python language, specifically). However, you DO NOT need to know statistics (beyond that covered in core courses) or computer programming to take and benefit from this course. We will provide technical support and will design exercises in a way that you can focus your energies on mastering the concepts and thinking of solving business problems. The course won't turn you into data analysts, but it will help you manage them.

This course focuses attention on human resource strategies for startups. It discusses recruitment, incentives, design of jobs, development of talent, leadership and empowerment challenges in startups. We will deal with questions ranging from equity splits to founding team dynamics, hiring talent to the sequencing of hires. We will use a mix of "live cases" from the field, and lectures.

The average CEO, C-suite manager, or funder, will say that a firm¿s success hinges on hiring and motivating the right people. Therefore, this class aims to examine the people management strategies you might choose from as you build a young firm, or as you grow a larger firm. There are three facets to this course. The first is to acknowledge that the People Management Strategy that you choose must be selected with the overall external strategy of the firm in mind. Technology-based firms will have different strategies than brick-n-mortar retail firms. The second facet is to discuss the evolution of people management strategies as the firm grows over time: a very small firm will have few formal processes; a large firm typically can¿t live without them and may need to change them over time. The third facet covered is an analysis of the detailed issues you should consider as you set up your HR practices surrounding hiring, compensation, and promotion. Classes will have guest speakers, and cases will draw from LinkedIn, Box, Royal Bank of Canada, Tesla, the Gap, and various startups.

The world of work has changed fundamentally - firms are now integrally linked to the global economy and many of you will manage teams of people located in different countries. What are the typical "people" challenges that arise when working across borders? We will answer this question by looking at topics such as hiring, job design, teamwork, training, pay and promotions. We will use exercises, cases from both developed and developing economies and guest lectures.

This course is designed to teach the student how to use economics to solve practical personnel problems that affect worker productivity. Topics include: selecting the best workers to hire, training workers, turnover, setting compensation strategically, structuring salespersons' commissions, downsizing, using promotions as an incentive mechanism, and other topics. Examples and cases will be presented to demonstrate the importance of using economic techniques to structure human resources programs. The course will appeal most to the student who expects to be a general manager or who hopes to run his or her own business. Although the human resources specialist may benefit from this course, the emphasis will be on decisions that affect personnel, but are made primarily by general managers. The class format is somewhat unusual. Most classes consist of lecture with questions, but two are class workshops. The lecture will present a theoretical development of a topic. The questions discussed during the last part of the lecture period will involve practical business application of the theory presented in lecture. This course is more technical than other human resources courses, but should be accessible to anyone who has successfully completed the economics and statistics courses in the MBA core. Every student is expected to know calculus and basic probability and statistics. Although I will not emphasize the technical aspects on the final exam, the problem sets will require some knowledge of mathematics. To ease your fears, many "poets" have taken variants of this course in the past and have done well.There will be graded team problem sets and a final exam.

This course focuses attention on human resource strategies for startups. It discusses recruitment, incentives, design of jobs, development of talent, leadership and empowerment challenges in startups. We will deal with questions ranging from equity splits to founding team dynamics, hiring talent to the sequencing of hires. We will use a mix of "live cases" from the field, and lectures.

The purpose of the course is to review the key trends transforming the world of work in a post-covid environment, and then get students to define and sharpen their definition of entrepreneurial opportunities to improve the world of work. It will be offered as a 'lab' - in that in each session will focus on the transition from a transformative trend to an entrepreneurial opportunity.

How can we use big data, machine learning and artificial intelligence to inform design, hiring, promotion and human resource management processes in organizations? We will discuss the theoretical and practical challenges that these issues present, and the ways by which data can help resolve them. In doing so, we will explore various data analytic methods and different data types, as well as the pitfalls and ethical issues their use introduces.