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← Page 19 of 1,483 Results →
  • August 1997 (Revised June 2023)
  • Case

Fabritek, 1992

By: Janice H. Hammond
Describes a large-volume automotive parts contract in a high-quality machine work company. Quality and delivery problems arise when one of the four men on the job is replaced with a high producer who cannot earn a substantial bonus because of machine interference. View Details
Keywords: Machinery and Machining; Compensation and Benefits; Selection and Staffing; Production; Quality; Manufacturing Industry
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Hammond, Janice H. "Fabritek, 1992." Harvard Business School Case 698-014, August 1997. (Revised June 2023.)
  • 2024
  • Working Paper

Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions

By: Caleb Kwon, Ananth Raman and Jorge Tamayo
We investigate whether corporate officers should grant managers discretion to override AI-driven demand forecasts and labor scheduling tools. Analyzing five years of administrative data from a large grocery retailer using such an AI tool, encompassing over 500 stores,... View Details
Keywords: AI and Machine Learning; Forecasting and Prediction; Working Conditions; Performance Productivity
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Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, April 2024.
  • 01 Mar 2019
  • News

Alumni and Faculty Books for March 2019

As the Ottoman Empire crumbled, the Middle East and Balkans became the site of contestation and cooperation between the traditional forces of religion and the emergent View Details
  • 2024
  • Working Paper

Displacement or Complementarity? The Labor Market Impact of Generative AI

By: Wilbur Xinyuan Chen, Suraj Srinivasan and Saleh Zakerinia
Generative AI is poised to reshape the labor market, affecting cognitive and white-collar occupations in ways distinct from past technological revolutions. This study examines whether generative AI displaces workers or augments their jobs by analyzing labor demand and... View Details
Keywords: Generative Ai; Labor Market; Automation And Augmentation; Labor; AI and Machine Learning; Competency and Skills
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Chen, Wilbur Xinyuan, Suraj Srinivasan, and Saleh Zakerinia. "Displacement or Complementarity? The Labor Market Impact of Generative AI." Harvard Business School Working Paper, No. 25-039, December 2024.
  • 12 Apr 2022
  • Research & Ideas

Swiping Right: How Data Helped This Online Dating Site Make More Matches

some estimates, with players such as Bumble, Tinder, and OKCupid vying to help people find love. While McFowland is not a dating expert, his work in machine learning and social... View Details
Keywords: by Kara Baskin
  • February 2025
  • Supplement

Intenseye: Powering Workplace Health and Safety with AI (B)

By: Michael W. Toffel, Shane Greenstein and Sadika El Hariri
Intenseye used its $25 million series A funds to refine and expand its digital safety platform while refining its target markets and ideal customer profile. As the company implemented new approaches to create value for its clients, such as developing an AI-powered... View Details
Keywords: Safety Performance; Occupational Safety; Innovation; Safety; Operations; Health; AI and Machine Learning; Analytics and Data Science; Digital Transformation; Supply Chain Management; Performance Improvement; Entrepreneurship; Product Development; Customer Relationship Management; Value Creation; Venture Capital; Growth and Development Strategy; Information Technology Industry; United States; Europe; Middle East; Turkey
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Toffel, Michael W., Shane Greenstein, and Sadika El Hariri. "Intenseye: Powering Workplace Health and Safety with AI (B)." Harvard Business School Supplement 625-025, February 2025.
  • Web

Buy Now, Pay Later: Credit and Information Technology

thought differently, and, in 1874, placed an order with E. Remington and Sons for one hundred machines at $55 each. The ability to paste typewritten entries into the credit ledgers considerably speeded up... View Details

    Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity

     

    Fintech, Small Business & the American Dream describes the needs of small businesses for capital and demonstrates how technology—novel data sources, artificial intelligence, machine learning—will transform the small business lending market. This market... View Details
    • 08 Feb 2016
    • Research & Ideas

    The Civic Benefits of Google Street View and Yelp

    to use it, and when not to use it.” Taming the data flow To get a handle on all this data and to better predict outcomes of policies, Luca believes cities need to develop algorithms to coordinate their own... View Details
    Keywords: by Michael Blanding; Consumer Products
    • 26 Jul 2022
    • News

    Burgers with Bugs? What Happens When Restaurants Ignore Online Reviews

    • 01 Nov 1999
    • Research & Ideas

    John H. Patterson and the Sales Strategy of the National Cash Register Company, 1884 to 1922

    described the register for the first time and explained how it would prevent theft and give an accurate account of the day's receipts. The goal of this stage was to schedule a demonstration of the View Details
    Keywords: by Walter A. Friedman
    • 11 Feb 2020
    • Sharpening Your Skills

    10 Rules Entrepreneurs Need to Know Before Adopting AI

    Although adoption of artificial intelligence (AI) and machine learning (ML) for the enterprise is still in the early days, the technology has matured enough for entrepreneurs to start gathering inspiration... View Details
    Keywords: by Rocio Wu
    • February 2025
    • Article

    Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots

    By: Julian De Freitas and I. Glenn Cohen
    In the wake of recent advancements in generative AI, regulatory bodies are trying to keep pace. One key decision is whether to require app makers to disclose the use of generative AI-powered chatbots in their products. We suggest that some generative AI-based chatbots... View Details
    Keywords: AI and Machine Learning; Governing Rules, Regulations, and Reforms; Applications and Software; Well-being
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    De Freitas, Julian, and I. Glenn Cohen. "Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots." New England Journal of Medicine AI 2, no. 2 (February 2025).
    • 10 Mar 2015
    • Research & Ideas

    The Surprising Winners and Losers in the Retail Revolution

    and you say, holy cow. That's the conundrum that Walmart is in. Think about this. There are more than 30,000 dollar stores in the US, you can find one everywhere: in rural regions you find Dollar General and... View Details
    Keywords: by Sean Silverthorne; Retail
    • March 2025
    • Case

    Xfund and Sam Altman: Finding Harvard’s Best Generative AI Founders

    By: Suraj Srinivasan
    On May 1, 2024, Xfund Managing Partners Patrick Chung and Brandon Farwell, hosted a high-stakes venture pitch session designed to select one startup for a minimum $100,000 investment. This “Xperiment Stake” competition, dedicated to startups in the Generative AI... View Details
    Keywords: AI and Machine Learning; Venture Capital; Innovation Leadership; Technological Innovation; Technology Industry; United States
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    Srinivasan, Suraj. "Xfund and Sam Altman: Finding Harvard’s Best Generative AI Founders." Harvard Business School Case 125-090, March 2025.
    • 19 Jan 2023
    • Research & Ideas

    What Makes Employees Trust (vs. Second-Guess) AI?

    products were grouped in 241 “style-colors'' and sizes. When the allocators received a recommendation from an interpretable algorithm, they often overruled it based on their own intuition. But when the same allocators had a recommendation... View Details
    Keywords: by Rachel Layne
    • 2021
    • Chapter

    Towards a Unified Framework for Fair and Stable Graph Representation Learning

    By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
    As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual... View Details
    Keywords: Graph Neural Networks; AI and Machine Learning; Prejudice and Bias
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    Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.
    • 03 Oct 2017
    • First Look

    First Look at Research and Ideas, October 3, 2017

    using people analytics in hiring. The case also provides an accessible yet thorough explanation of the key aspects of artificial intelligence (supervised, unsupervised, and reinforcement machine learning).... View Details
    Keywords: by Sean Silverthorne
    • 22 Feb 2021
    • Blog Post

    I Found My Future at HBS and You Can Too

    lenses, our place in the world appeared immutable. Fortunately for me, that was about to change. Two military aircraft suddenly flew low and fast over the field where I was riding. As they tore through the sky, I imagined the sensation of... View Details
    • 2018
    • Working Paper

    Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 7 The Value Structure of Technologies, Part 2: Technical and Strategic Bottlenecks as Guides for Action

    By: Carliss Y. Baldwin
    The purpose of this chapter is to present analytic tools based on functional maps that can be used to identify investment opportunities and to formulate strategy in large, evolving technical systems. I argue that the points of value creation and value capture in a... View Details
    Keywords: Bottlenecks; Information Technology; System; Investment; Opportunities; Strategy
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    Baldwin, Carliss Y. "Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 7 The Value Structure of Technologies, Part 2: Technical and Strategic Bottlenecks as Guides for Action." Harvard Business School Working Paper, No. 19-042, October 2018.
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