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- August 28, 2018
- Article
How Intermittent Breaks in Interaction Improve Collective Intelligence
By: Ethan Bernstein, Jesse Shore and David Lazer
People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a... View Details
Keywords: Transparency; Social Influence; Collective Intelligence; Interaction; Problem Solving; Collaboration; Intermittant; Breaks; Always On; Communication Technologies; Communication; Design; Information; Management; Leadership; Organizational Design; Organizational Structure; Performance; Social and Collaborative Networks; Information Technology
Bernstein, Ethan, Jesse Shore, and David Lazer. "How Intermittent Breaks in Interaction Improve Collective Intelligence." Proceedings of the National Academy of Sciences 115, no. 35 (August 28, 2018).
- November–December 2023
- Article
Network Centralization and Collective Adaptability to a Shifting Environment
By: Ethan S. Bernstein, Jesse C. Shore and Alice J. Jang
We study the connection between communication network structure and an organization’s collective adaptability to a shifting environment. Research has shown that network centralization—the degree to which communication flows disproportionately through one or more... View Details
Keywords: Network Centralization; Collective Intelligence; Organizational Change and Adaptation; Organizational Structure; Communication; Decision Making; Networks; Adaptation
Bernstein, Ethan S., Jesse C. Shore, and Alice J. Jang. "Network Centralization and Collective Adaptability to a Shifting Environment." Organization Science 34, no. 6 (November–December 2023): 2064–2096.
- August 2017 (Revised July 2019)
- Case
GROW: Using Artificial Intelligence to Screen Human Intelligence
By: Ethan Bernstein, Paul McKinnon and Paul Yarabe
Over 10% of all 2017 university graduates in Japan used GROW, an artificial intelligence platform and mobile app developed by Tokyo-based people analytics startup IGS, to recruit for a job. This case puts participants in the shoes of IGS founder and CEO Masahiro... View Details
Keywords: Big Data; Artificial Intelligence; Talent and Talent Management; Recruitment; Selection and Staffing; Human Resources; Information Technology; AI and Machine Learning; Analytics and Data Science; Financial Services Industry; Air Transportation Industry; Advertising Industry; Manufacturing Industry; Technology Industry; Japan
Bernstein, Ethan, Paul McKinnon, and Paul Yarabe. "GROW: Using Artificial Intelligence to Screen Human Intelligence." Harvard Business School Case 418-020, August 2017. (Revised July 2019.)
Do Experts or Collective Intelligence Write with More Bias?
Co-authored by Feng Zhu
Which source of information contains greater bias and slant-text written by an expert or that constructed via collective intelligence? Do the costs of acquiring, storing, displaying, and revising information shape those... View Details
Which source of information contains greater bias and slant-text written by an expert or that constructed via collective intelligence? Do the costs of acquiring, storing, displaying, and revising information shape those... View Details
- 2014
- Other Unpublished Work
Neutral Point of View and Collective Intelligence Bias: The Case of Wikipedia
By: Shane Greenstein
- 07 Nov 2014
- Working Paper Summaries
Do Experts or Collective Intelligence Write with More Bias? Evidence from Encyclopædia Britannica and Wikipedia
- 2024
- Chapter
Regulating Collective Emotions
By: Amit Goldenberg
When we think of emotion and emotion regulation, we typically think of them as processes occurring at the individual level. Even when emotions are experienced by multiple people who interact with each other, analysis is typically centered around individual-level... View Details
Goldenberg, Amit. "Regulating Collective Emotions." Chap. 22 in Handbook of Emotion Regulation. Third Edition edited by James J. Gross and Brett Q. Ford, 183–189. Guilford Press, 2024.
- Article
A Collective Biological Processing Algorithm for EKG Signals
By: Mike Horia Teodorescu
We establish and explore an analogy between hunting by packs of agents and signal processing. We present a version of adaptive ‘Hunting Swarm’ algorithm (HSA), apply it to EKG signals, and investigate the influence of the model parameters on the filtering of stationary... View Details
Teodorescu, Mike Horia. "A Collective Biological Processing Algorithm for EKG Signals." Proceedings of the International Conference on Bio-inspired Systems and Signal Processing 4th (2011): 413–420. (IEEE BIOSIGNALS 2011.)
Do Experts or Collective Intelligence Write with More Bias? Evidence from Encyclopædia Britannica and Wikipedia
Organizations today can use both crowds and experts to produce knowledge. While prior work compares the accuracy of crowd-produced and expert-produced knowledge, we compare bias in these two models in the context of contested knowledge, which involves subjective,... View Details
- 02 Jan 2019
- What Do You Think?
SUMMING UP: Do We Need an Artificial Intelligence Czar?
with it. At the same time, we have made giant strides in methods of addressing nearly any problem one can imagine. Many are associated with the development of artificial intelligence (AI). In a nutshell, cloud-enabled data View Details
- May 2021
- Article
Ideology and Composition Among an Online Crowd: Evidence From Wikipedians
By: Shane Greenstein, Grace Gu and Feng Zhu
Online communities bring together participants from diverse backgrounds and often face challenges in aggregating their opinions. We infer lessons from the experience of individual contributors to Wikipedia articles about U.S. politics. We identify two factors that... View Details
Keywords: User Segregation; Online Community; Contested Knowledge; Collective Intelligence; Ideology; Bias; Wikipedia; Knowledge Sharing; Perspective; Government and Politics
Greenstein, Shane, Grace Gu, and Feng Zhu. "Ideology and Composition Among an Online Crowd: Evidence From Wikipedians." Management Science 67, no. 5 (May 2021): 3067–3086.
- 2019
- Working Paper
Intelligent Design of Inclusive Growth Strategies
By: Robert S. Kaplan, George Serafeim and Eduardo Tugendhat
Improving corporate engagement with society, as advocated in the Business Roundtable’s 2019 statement, should not be viewed as a zero-sum proposition where attention to new stakeholders detracts from delivering shareholder value. Corporate programs for sustainable and... View Details
Keywords: Inclusion; Sustainability; Performance Measures; Environmental Sustainability; Social Issues; Strategy; Governance; Corporate Social Responsibility and Impact; Business and Stakeholder Relations
Kaplan, Robert S., George Serafeim, and Eduardo Tugendhat. "Intelligent Design of Inclusive Growth Strategies." Harvard Business School Working Paper, No. 20-050, October 2019.
- January 2021 (Revised March 2021)
- Case
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Jill Avery, Ayelet Israeli and Emma von Maur
THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on... View Details
Keywords: Data; Data Analytics; Artificial Intelligence; AI; AI Algorithms; AI Creativity; Fashion; Retail; Retail Analytics; E-Commerce Strategy; Platform; Platforms; Big Data; Preference Elicitation; Preference Prediction; Predictive Analytics; App Development; ＂Marketing Analytics＂; Advertising; Mobile App; Mobile Marketing; Apparel; Online Advertising; Referral Rewards; Referrals; Female Ceo; Female Entrepreneur; Female Protagonist; Analytics and Data Science; Analysis; Creativity; Marketing Strategy; Brands and Branding; Consumer Behavior; Demand and Consumers; Forecasting and Prediction; Marketing Channels; Digital Marketing; Internet and the Web; Mobile and Wireless Technology; AI and Machine Learning; E-commerce; Digital Platforms; Fashion Industry; Retail Industry; Apparel and Accessories Industry; Consumer Products Industry; United States
Avery, Jill, Ayelet Israeli, and Emma von Maur. "THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)." Harvard Business School Case 521-070, January 2021. (Revised March 2021.)
- May 2021 (Revised February 2024)
- Teaching Note
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Ayelet Israeli and Jill Avery
THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on... View Details
Keywords: Data; Data Analytics; Artificial Intelligence; AI; AI Algorithms; AI Creativity; Fashion; Retail; Retail Analytics; E-Commerce Strategy; Platform; Platforms; Big Data; Preference Elicitation; Predictive Analytics; App Development; ＂Marketing Analytics＂; Advertising; Mobile App; Mobile Marketing; Apparel; Online Advertising; Referral Rewards; Referrals; Female Ceo; Female Entrepreneur; Female Protagonist; Analytics and Data Science; Analysis; Creativity; Marketing Strategy; Brands and Branding; Consumer Behavior; Demand and Consumers; Forecasting and Prediction; Marketing Channels; Digital Marketing; Internet and the Web; Mobile and Wireless Technology; AI and Machine Learning; E-commerce; Digital Platforms; Fashion Industry; Retail Industry; Apparel and Accessories Industry; Consumer Products Industry; United States
- 22 Oct 2019
- Research & Ideas
Use Artificial Intelligence to Set Sales Targets That Motivate
advanced analytics that incorporate artificial intelligence (AI). “Chung has seen companies dramatically improve productivity after adopting advanced analytics to guide compensation.” In an ideal world, a company would use trial and error... View Details
Keywords: by Michael Blanding
- September 2018
- Article
Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia
By: Shane Greenstein and Feng Zhu
Organizations today can use both crowds and experts to produce knowledge. While prior work compares the accuracy of crowd-produced and expert-produced knowledge, we compare bias in these two models in the context of contested knowledge, which involves subjective,... View Details
Keywords: Online Community; Collective Intelligence; Wisdom Of Crowds; Bias; Wikipedia; Britannica; Knowledge Production; Knowledge Sharing; Knowledge Dissemination; Prejudice and Bias
Greenstein, Shane, and Feng Zhu. "Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia." MIS Quarterly 42, no. 3 (September 2018): 945–959.
- 03 Jan 2023
- Book
Confront Workplace Inequity in 2023: Dig Deep, Build Bridges, Take Collective Action
back up and running, many women are asking: What’s it going to take to effect real change? According to Tina Opie, visiting scholar at Harvard Business School and author of Shared Sisterhood: How to Take Collective Action for Racial and... View Details
Keywords: by Pamela Reynolds
- Web
Introduction - The Human Factor – Baker Library | Bloomberg Center, Historical Collections
HBS Home HBS Index Contact Us Harvard Business School Baker Library Historical Collections The Human Factor Introducing the Industrial Life Photograph Collection at the Baker Library Introduction The... View Details
- May 2017 (Revised March 2018)
- Case
Predicting Consumer Tastes with Big Data at Gap
By: Ayelet Israeli and Jill Avery
CEO Art Peck was eliminating his creative directors for The Gap, Old Navy, and Banana Republic brands and promoting a collective creative ecosystem fueled by the input of big data. Rather than relying on artistic vision, Peck wanted the company to use the mining of big... View Details
Keywords: Retailing; Preference Elicitation; Big Data; Predictive Analytics; Artificial Intelligence; Fashion; Marketing; Marketing Strategy; Marketing Channels; Brands and Branding; Consumer Behavior; Demand and Consumers; Analytics and Data Science; Forecasting and Prediction; E-commerce; Apparel and Accessories Industry; Consumer Products Industry; Fashion Industry; Retail Industry; United States; Canada; North America
Israeli, Ayelet, and Jill Avery. "Predicting Consumer Tastes with Big Data at Gap." Harvard Business School Case 517-115, May 2017. (Revised March 2018.)