Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
  • Research
    • Research
    • Publications
    • Global Research Centers
    • Case Development
    • Initiatives & Projects
    • Research Services
    • Seminars & Conferences
    →
  • Publications→

Publications

Publications

Filter Results : (172) Arrow Down
Filter Results : (172) Arrow Down Arrow Up

Show Results For

  • All HBS Web  (601)
    • Faculty Publications  (172)

    Show Results For

    • All HBS Web  (601)
      • Faculty Publications  (172)

      Algorithms Remove Algorithms →

      Page 1 of 172 Results →

      Are you looking for?

      → Search All HBS Web
      • 2023
      • Article

      Association Between Regulatory Submission Characteristics and Recalls of Medical Devices Receiving 510(k) Clearance

      By: Alexander O. Everhart, Soumya Sen, Ariel D. Stern, Yi Zhu and Pinar Karaca-Mandic
      Importance: Most regulated medical devices enter the U.S. market via the 510(k) regulatory submission pathway, wherein manufacturers demonstrate that applicant devices are “substantially equivalent” to 1 or more “predicate” devices (legally marketed medical devices...  View Details
      Keywords: Recalls; Governing Rules, Regulations, and Reforms; Medical Devices and Supplies Industry
      Citation
      Find at Harvard
      Purchase
      Related
      Everhart, Alexander O., Soumya Sen, Ariel D. Stern, Yi Zhu, and Pinar Karaca-Mandic. "Association Between Regulatory Submission Characteristics and Recalls of Medical Devices Receiving 510(k) Clearance." JAMA, the Journal of the American Medical Association 329, no. 2 (2023): 144–156.
      • January 2023
      • Case

      Proday: Calling the Right Play

      By: Lindsay N. Hyde, Thomas R. Eisenmann and Tom Quinn
      Sarah Kunst knew the elements of a successful startup from her tenure at venture capital firms. In April 2018, however, her own app – Proday, a home fitness platform featuring exercises filmed by professional sports stars – was floundering. Kunst theorized that...  View Details
      Keywords: Social Media; Entrepreneurship; Advertising; Digital Marketing; Product Launch; Social Marketing; Failure; Sports; Applications and Software; Technology Industry; United States
      Citation
      Educators
      Related
      Hyde, Lindsay N., Thomas R. Eisenmann, and Tom Quinn. "Proday: Calling the Right Play." Harvard Business School Case 823-005, January 2023.
      • 2023
      • Working Paper

      When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions

      By: Himabindu Lakkaraju and Chiara Farronato
      Citation
      Related
      Lakkaraju, Himabindu, and Chiara Farronato. "When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions." Working Paper, 2023.
      • 2022
      • Working Paper

      Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions

      By: Caleb Kwon, Ananth Raman and Jorge Tamayo
      We empirically analyze how managerial overrides to a commercial algorithm that forecasts demand and schedules labor affect store performance. We analyze administrative data from a large grocery retailer that utilizes a commercial algorithm to forecast demand and...  View Details
      Keywords: Employees; Human Capital; Performance; Applications and Software; Management Skills; Management Practices and Processes; Retail Industry
      Citation
      SSRN
      Related
      Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, December 2022.
      • 2022
      • Working Paper

      Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence

      By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
      Even if algorithms make better predictions than humans on average, humans may sometimes have “private” information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by...  View Details
      Keywords: Cognitive Biases; Algorithm Transparency; Forecasting and Prediction; Behavior; AI and Machine Learning; Analytics and Data Science; Cognition and Thinking
      Citation
      Read Now
      Related
      Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, December 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 1

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Data-driven Decision-making; Decisions; Negotiation; Bids and Bidding; Valuation; Consumer Behavior; Real Estate Industry
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 1." Harvard Business School Exercise 923-016, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 2

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 2." Harvard Business School Exercise 923-017, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 3

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 3." Harvard Business School Exercise 923-018, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 1

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 1." Harvard Business School Exercise 923-019, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 2

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Market Timing; Measurement and Metrics
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 2." Harvard Business School Exercise 923-020, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 3

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 3." Harvard Business School Exercise 923-021, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Updated Confidential Information for Homebuyer

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Market Timing; Measurement and Metrics
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for Homebuyer." Harvard Business School Exercise 923-022, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Updated Confidential Information for iBuyer

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Measurement and Metrics; Market Timing; Decision Making
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for iBuyer." Harvard Business School Exercise 923-023, October 2022.
      • 2022
      • Working Paper

      Price Monitoring and Response: A Supply Side Characterization

      By: Ayelet Israeli and Eric Anderson
      With the growth of e-commerce and increasing price transparency, there has been tremendous interest in the adoption and competitive consequences of algorithmic pricing. A premise of algorithmic pricing is that firms monitor competitors' prices and then their pricing...  View Details
      Keywords: Algorithmic Pricing; Ecommerce; Price Monitoring; Price; Competition; Retail Industry
      Citation
      Related
      Israeli, Ayelet, and Eric Anderson. "Price Monitoring and Response: A Supply Side Characterization." Working Paper, October 2022.
      • 2022
      • Article

      Dynamic Pricing Algorithms, Consumer Harm, and Regulatory Response

      By: Alexander MacKay and Samuel N. Weinstein
      Pricing algorithms are rapidly transforming markets, from ride-sharing apps, to air travel, to online retail. Regulators and scholars have watched this development with a wary eye. Their focus so far has been on the potential for pricing algorithms to facilitate...  View Details
      Keywords: Competition Policy; Regulation; Algorithmic Pricing; Dynamic Pricing; Economics; Law And Economics; Law And Regulation; Consumer Protection; Antitrust Law; Industrial Organization; Antitrust Issues And Policies; Technological Change: Choices And Consequences; Competition; Policy; Price; Governing Rules, Regulations, and Reforms; Microeconomics; Duopoly and Oligopoly; Law
      Citation
      SSRN
      Read Now
      Related
      MacKay, Alexander, and Samuel N. Weinstein. "Dynamic Pricing Algorithms, Consumer Harm, and Regulatory Response." Washington University Law Review 100, no. 1 (2022): 111–174. (Direct download.)
      • September 2022 (Revised November 2022)
      • Teaching Note

      PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

      By: Ayelet Israeli
      Teaching Note for HBS Case No. 522-046.  View Details
      Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
      Citation
      Purchase
      Related
      Israeli, Ayelet. "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion." Harvard Business School Teaching Note 523-020, September 2022. (Revised November 2022.)
      • August 2022
      • Supplement

      Zalora: Data-Driven Pricing Recommendations

      By: Ayelet Israeli
      This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the...  View Details
      Keywords: Pricing; Pricing Algorithms; Dynamic Pricing; Ecommerce; Pricing Strategy; Pricing And Revenue Management; Apparel; Singapore; Startup; Demand Estimation; Data Analysis; Data Analytics; Exercise; Price; Internet and the Web; Apparel and Accessories Industry; Retail Industry; Fashion Industry; Singapore
      Citation
      Purchase
      Related
      Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.
      • 2022
      • Working Paper

      Dynamic Pricing and Demand Volatility: Evidence from Restaurant Food Delivery

      By: Alexander J. MacKay, Dennis Svartbäck and Anders G. Ekholm
      Pricing technology that allows firms to rapidly adjust prices has two potential benefits. Prices can respond more rapidly to demand shocks, leading to higher revenues. On the other hand, time-varying prices can be used to smooth out demand across periods, reducing...  View Details
      Keywords: Pricing Algorithms; Dynamic Pricing; Demand Volatility; Delivery Services
      Citation
      SSRN
      Read Now
      Related
      MacKay, Alexander J., Dennis Svartbäck, and Anders G. Ekholm. "Dynamic Pricing and Demand Volatility: Evidence from Restaurant Food Delivery." Harvard Business School Working Paper, No. 23-007, July 2022.
      • July 7, 2022
      • Other Article

      Are Online Prices Higher Because of Pricing Algorithms?

      By: Zach Y. Brown and Alexander J. MacKay
      This article reviews recent work examining pricing strategies of major online retailers and the potential effects of pricing algorithms. We describe how pricing algorithms can lead to higher prices in a number of ways, even if some characteristics of these algorithms...  View Details
      Keywords: Pricing Algorithms; Online Marketplace; Digital Strategy; Internet and the Web; Retail Industry
      Citation
      Read Now
      Related
      Brown, Zach Y., and Alexander J. MacKay. "Are Online Prices Higher Because of Pricing Algorithms?" Brookings Series: The Economics and Regulation of Artificial Intelligence and Emerging Technologies (July 7, 2022).
      • 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
      Citation
      SSRN
      Read Now
      Related
      Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
      • 1
      • 2
      • …
      • 8
      • 9
      • →

      Are you looking for?

      → Search All HBS Web
      ǁ
      Campus Map
      Harvard Business School
      Soldiers Field
      Boston, MA 02163
      →Map & Directions
      →More Contact Information
      • Make a Gift
      • Site Map
      • Jobs
      • Harvard University
      • Trademarks
      • Policies
      • Accessibility
      • Digital Accessibility
      Copyright © President & Fellows of Harvard College