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January 2022 (Revised November 2023)
Case

Expanding the Culture of Learning at Kraft Heinz

By: Ashley V. Whillans and Carolyn Watson

The Kraft Heinz Company (KHC) was an American food company formed in 2015 by the merger of Kraft Foods Group, Inc and the H.J. Heinz Company. The company sold food products like Heinz Ketchup, Kraft Mac & Cheese, Kool-Aid, and Philadelphia cream cheese to supermarkets,... View Details

Keywords: Learning; Culture; Work Culture; Workplace Practices; Mergers; Mergers and Acquisitions; Competitive Advantage; Human Capital; Training; Performance Evaluation; Growth and Development; Personal Development and Career; Employee Relationship Management; Organizational Change and Adaptation; Organizational Culture; Food and Beverage Industry

August 2020
Article

Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation

By: Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal

The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic... View Details

Keywords: Machine Learning; Bias; Human Capital; Management; Strategy

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April 2020
Article

Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning

By: Ariel Dora Stern and W. Nicholson Price, II

In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has been engaging... View Details

Keywords: Machine Learning; Causal Inference; Health Care and Treatment; Safety; Governing Rules, Regulations, and Reforms

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Research Summary

Learning Motives

In another research stream, Professor Myers probes the underlying reasons that motivate people to learn. He has confirmed a conceptual framework that identifies four distinct learning motives that vary to the extent that they are intrinsic, extrinsic, and self- or... View Details

Keywords: Learning

2025
Working Paper

Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli

Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships between customer states, company actions, and long-term value. However, its practical implementation often faces significant challenges.... View Details

Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science

December 2023
Background Note

Organizational Learning

This is a background note that surveys part of the extensive literature on organizational learning. The focus is on learning from experiences, how those learnings get translated into organizational routines and processes, and how that can also lead to getting stuck in... View Details

Keywords: Learning; Knowledge; Business Processes; Manufacturing Industry; Service Industry

August 2020 (Revised September 2020)
Technical Note

Assessing Prediction Accuracy of Machine Learning Models

By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne

The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details

Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods

Research Summary

Learning Organizations

David A. Garvin is studying how companies pursue improvement and change through efforts to stimulate organizational learning. He has found the following activities to be common in learning organizations: intelligence gathering; experimentation; learning from... View Details

January 2015 (Revised April 2015)
Case

Zeal: Launching Personalized and Social Learning

By: John J-H Kim and Christine S. An

Set in 2014, this case follows John Danner and his team at Zeal as they consider their product development strategy. In February 2013, serial entrepreneurs John Danner and Sanjay Noronha co-found Zeal, an education technology start up providing a web-based, mobile... View Details

Keywords: Entrepreneurship; Education Technology; MVP; Product Development; Product Market Fit; Monetization Strategy; SaaS Business Models; Education; Personalized Learning

Research Summary

Social Learning

One major area of my research is social learning: the ways and extent to which people discover what they want and need from the behavior and opinions of others. Social learning takes many forms. Probably most obvious is word of mouth—the advice and... View Details

Research Summary

Learning Agility

This research in both field and experimental settings is targeted at examining whether and how employees in the 'midst of learning' are aware of the optimal learning behavior as well as determining if this awareness does indeed increase learning agility. Further,... View Details

18 Apr 2000
Research & Ideas

Learning in Action

"The most effective learning strategy depends on the situation," writes David A. Garvin. "There is no stock answer, nor is there a single best approach." In Learning in Action, he illustrated the diversity... View Details

Keywords: by David A. Garvin

July–September 2020
Article

Innovation Contest: Effect of Perceived Support for Learning on Participation

By: Olivia Jung, Andrea Blasco and Karim R. Lakhani

Background: Frontline staff are well positioned to conceive improvement opportunities based on first-hand knowledge of what works and does not work. The innovation contest may be a relevant and useful vehicle to elicit staff ideas. However, the success of the... View Details

Keywords: Contest; Innovation; Employee Engagement; Organizational Learning; Health Care; Health Care Delivery; Innovation and Invention; Organizations; Learning; Employees; Perception; Health Care and Treatment

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2012
Working Paper

Learning by Supplying

By: Juan Alcacer and Joanne Oxley

Learning processes lie at the heart of our understanding of how firms build capabilities to generate and sustain competitive advantage: learning by doing, learning by exporting, learning from competitors, users, and alliance partners. In this paper we focus attention... View Details

Keywords: Learning; Supply Chain; Competitive Advantage; Mobile and Wireless Technology; Competency and Skills; Relationships; Telecommunications Industry

January 2021
Article

Machine Learning for Pattern Discovery in Management Research

By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres

Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect... View Details

Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; ROC Curve; Confusion Matrix; Partial Dependence Plots; AI and Machine Learning

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February 2014
Article

Learning by Supplying

By: Juan Alcacer and Joanne Oxley

Learning processes lie at the heart of our understanding of how firms build capabilities to generate and sustain competitive advantage: learning by doing, learning by exporting, learning from competitors, users, and alliance partners. In this paper we focus attention... View Details

Keywords: Competitive Advantage; Organizations; Learning

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2022
Working Paper

Machine Learning Models for Prediction of Scope 3 Carbon Emissions

By: George Serafeim and Gladys Vélez Caicedo

For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details

Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting

2020
Working Paper

Team Learning and Superior Firm Performance: A Meso-Level Perspective on Dynamic Capabilities

By: Jean-François Harvey, Henrik Bresman, Amy C. Edmondson and Gary P. Pisano

This paper proposes a team-based, meso-level perspective on dynamic capabilities. We argue that team-learning routines constitute a critical link between managerial cognition and organization-level processes of sensing, seizing, and reconfiguring. We draw from the... View Details

Keywords: Dynamic Capabilities; Innovation; Strategic Change; Teams; Team Learning; Groups and Teams; Learning; Innovation and Invention; Change; Performance

May 2012 (Revised February 2014)
Teaching Note

Learning About Reducing Hospital Mortality at Kaiser Permanente

By: Anita Carson Tucker

Keywords: Patient Safety; Process Improvement; Standardization; Organizational Learning; Knowledge Development; Safety; Knowledge Management; Health Care and Treatment; Business Processes; Health Industry

February 1989
Background Note

Learning with Cases

Gives some tips to maximize all learning; offers the pros and cons of experiential learning (cases) as a method; and gives some guidelines for effective case preparation, discussion, and learning. View Details

Keywords: Cases; Learning

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