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- Article
Birds of a Feather...Enforce Social Norms? Interactions Among Culture, Norms, and Strategy
By: Hongyi Li and Eric J. Van den Steen
Does culture eat strategy for breakfast? This paper investigates the interactions among corporate culture, norms, and strategy, in order to better understand this issue and related questions. It first shows, through microfoundations, how the forces that drive toward...
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- May 2021
- 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...
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Keywords:
Data;
Data Analytics;
Artificial Intelligence;
AI;
AI Algorithms;
AI Creativity;
Fashion;
Retail;
Retail Analytics;
Digital Marketing;
E-commerce;
E-Commerce Strategy;
Platform;
Platforms;
Big Data;
Preference Elicitation;
Predictive Analytics;
App Development;
＂Marketing Analytics＂;
Advertising;
Mobile App;
Mobile Marketing;
Apparel;
Referral Rewards;
Referrals;
Female Ceo;
Female Entrepreneur;
Female Protagonist;
Data and Data Sets;
Analysis;
Creativity;
Marketing Strategy;
Brands and Branding;
Consumer Behavior;
Demand and Consumers;
Forecasting and Prediction;
Marketing Channels;
Online Advertising;
Online Technology;
Mobile Technology;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products Industry;
United States
- 2021
- Working Paper
Time Dependence and Preference: Implications for Compensation Structure and Shift Scheduling
By: Doug J. Chung, Byungyeon Kim and Byoung G. Park
This study jointly examines agents’ time dependence—period effects within instantaneous utility—and time preference—behavior on discounting future utility. The study considers the start- and end-of-period effects for time dependence and exponential and hyperbolic...
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- 2021
- Working Paper
Extension Request Avoidance Increases Time Stress among Women
In eight studies using archival data, surveys, and experiments, we identify a novel factor that predicts gender differences in time stress and burnout. Across academic and professional settings, women are less likely to ask for more time when working under adjustable...
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- 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;
Digital Marketing;
E-commerce;
E-Commerce Strategy;
Platform;
Platforms;
Big Data;
Preference Elicitation;
Preference Prediction;
Predictive Analytics;
App Development;
＂Marketing Analytics＂;
Advertising;
Mobile App;
Mobile Marketing;
Apparel;
Referral Rewards;
Referrals;
Female Ceo;
Female Entrepreneur;
Female Protagonist;
Data and Data Sets;
Analysis;
Creativity;
Marketing Strategy;
Brands and Branding;
Consumer Behavior;
Demand and Consumers;
Forecasting and Prediction;
Marketing Channels;
Online Advertising;
Online Technology;
Mobile Technology;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products Industry;
United States
- 2020
- Working Paper
Heterogeneity of Gain-Loss Attitudes and Expectations-Based Reference Points
By: Lorenz Goette, Thomas Graeber, Alex Kellogg and Charles Sprenger
We examine the role of heterogeneity in gain-loss attitudes for identifying the leading behavioral model of expectations-based reference dependence (Kőszegi and Rabin, 2006, 2007) (KR). Failure to account for recently-documented heterogeneity in gain-loss attitudes is...
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- 2020
- Working Paper
Topic Preference Detection: A Novel Approach to Understand Perspective Taking in Conversation
By: Michael Yeomans and Alison Wood Brooks
Although most humans engage in conversations constantly throughout their lives, conversational mistakes are commonplace— interacting with others is difficult, and conversation re-quires quick, relentless perspective-taking and decision making. For example: during every...
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- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...
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- Article
Maimonides' Ladder: States of Mutual Knowledge and the Perception of Charitability
By: Julian De Freitas, Peter DiScioli, Kyle A. Thomas and Steven Pinker
Why do people esteem anonymous charitable giving? We connect normative theories of charitability
(captured in Maimonides’ Ladder of Charity) with evolutionary theories of partner choice to test predictions on how attributions of charitability are affected by states of...
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- 2018
- Working Paper
Channeled Attention and Stable Errors -- Previous Working Version
A common critique of models of mistaken beliefs is that people should recognize their error after observations they thought were unlikely. This paper develops a framework for assessing when a given error is likely to be discovered, in the sense that the error-maker...
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- November 2017
- Teaching Note
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...
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Keywords:
Brands;
Brand ＆ Product Management;
Big Data;
＂Marketing Analytics＂;
Consumer Behavior;
Predictive Analytics;
Forecasting;
Preferences;
Operation Management;
Distribution Channels;
Marketing;
Marketing Channels;
Marketing Strategy;
Brands and Branding;
Forecasting and Prediction;
Data and Data Sets;
Retail Industry;
Fashion Industry;
Apparel and Accessories Industry;
United States;
North America
- September 2017
- Article
The Belief in a Favorable Future
By: Todd Rogers, Don A. Moore and Michael I. Norton
People believe that future others’ preferences and beliefs will change to align with their own. People holding a particular view (e.g., support of President Trump) are more likely to believe that future others will share their view than to believe that future others...
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- 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;
E-commerce;
Fashion;
Marketing;
Marketing Strategy;
Marketing Channels;
Brands and Branding;
Consumer Behavior;
Demand and Consumers;
Data and Data Sets;
Forecasting and Prediction;
Apparel and Accessories Industry;
Consumer Products Industry;
Fashion Industry;
Retail Industry;
United States;
Canada;
North America
- August 2016
- Article
The Role of (Dis)similarity in (Mis)predicting Others' Preferences
By: Kate Barasz, Tami Kim and Leslie K. John
Consumers readily indicate liking options that appear dissimilar—for example, enjoying both rustic lake vacations and chic city vacations or liking both scholarly documentary films and action-packed thrillers. However, when predicting other consumers’ tastes for the...
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- Article
The Not-So-Common-Wealth of Australia: Evidence for a Cross-Cultural Desire for a More Equal Distribution of Wealth.
By: Michael I. Norton, David T. Neal, Cassandra L. Govan, Dan Ariely and Elise Holland
Recent evidence suggests that Americans underestimate wealth inequality in the United States and favor a more equal wealth distribution (Norton & Ariely, 2011). Does this pattern reflect ideological dynamics unique to the United States, or is the phenomenon evident in...
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- Article
The Allure of Unknown Outcomes: Exploring the Role of Uncertainty in the Preference for Potential
By: Daniella Kupor, Zakary L. Tormala and Michael I. Norton
Influence practitioners often highlight a target's achievements (e.g., "she is the city's top-rated chef"), but recent research reveals that highlighting a target's potential (e.g., "she could become the city's top-rated chef") can be more effective. We examine whether...
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- October 2014
- Teaching Note
Financial Policy at Apple, 2013 (A) and (B)
By: Mihir A. Desai and Elizabeth Meyer
This is the teaching note to HBS case 214-085: Financial Policy at Apple, 2013 (A).
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Keywords:
Apple;
Steve Jobs;
Forecast;
Forecasting;
Forecasting And Prediction;
Shareholder Activism;
Share Repurchase;
Dividends;
Financial Ratios;
Preferred Shares;
Cash Distribution;
Corporate Finance;
Borrowing and Debt;
Financial Management;
Financial Strategy;
Technology Industry;
Consumer Products Industry;
United States;
Republic of Ireland
- October 2014
- Supplement
Financial Policy at Apple, 2013 Excel Supplement
By: Mihir Desai and Elizabeth A. Meyer
This is the Excel Supplement to Teaching Note 215-022: Financial Policy at Apple, 2013 (A) - (B).
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Keywords:
Apple;
Steve Jobs;
Forecast;
Forecasting;
Forecasting And Prediction;
Shareholder Activism;
Share Repurchase;
Dividends;
Financial Ratios;
Preferred Shares;
Cash Distribution;
Corporate Finance;
Borrowing and Debt;
Financial Management;
Financial Strategy;
United States;
Republic of Ireland
- October 2014
- Supplement
Financial Policy at Apple, 2013 Powerpoint Supplement
By: Mihir Desai and Elizabeth A. Meyer
This is the PowerPoint supplement to the teaching note: Financial Policy at Apple, 2013 (A) - (B), number 215-022.
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Keywords:
Apple;
Steve Jobs;
Forecast;
Forecasting;
Forecasting And Prediction;
Shareholder Activism;
Share Repurchase;
Dividends;
Financial Ratios;
Preferred Shares;
Cash Distribution;
Corporate Finance;
Borrowing and Debt;
Financial Management;
Financial Strategy;
Technology Industry;
Consumer Products Industry;
United States;
Republic of Ireland
- June 2014
- Case
Financial Policy at Apple, 2013 (A)
By: Mihir A. Desai and Elizabeth A. Meyer
By the end of 2013, Apple had $137 billion dollars in cash and marketable securities. This case explores how companies can generate such large amounts of cash and how and if they should distribute it to shareholders, especially in the face of shareholder pressure. In...
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Keywords:
Apple;
Steve Jobs;
Forecast;
Forecasting;
Forecasting And Prediction;
Shareholder Activism;
Share Repurchase;
Dividends;
Financial Ratios;
Preferred Shares;
Cash Distribution;
Corporate Finance;
Borrowing and Debt;
Financial Management;
Financial Strategy;
Technology Industry;
Consumer Products Industry;
United States;
Republic of Ireland