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- November–December 2024
- Article
Why Employees Quit
By: Ethan Bernstein, Michael Horn and Bob Moesta
The so-called war for talent is still raging. But in that fight, employers continue to rely on the same hiring and retention strategies they’ve been using for decades. Why? Because they’ve been so focused on challenges such as poaching by industry rivals, competing in... View Details
Keywords: Retention; Recruitment; Talent and Talent Management; Employee Relationship Management; Motivation and Incentives
Bernstein, Ethan, Michael Horn, and Bob Moesta. "Why Employees Quit." Harvard Business Review 102, no. 6 (November–December 2024): 44–54.
- October 2024
- Case
Grain Management
By: Archie Jones, Michael Norris, Alliyah Gary and Chelsea Grain-Jefferson
- October 2024
- Article
Founder-CEO Compensation and Selection into Venture Capital-Backed Entrepreneurship
By: Michael Ewens, Ramana Nanda and Christopher Stanton
We show theoretically that a critical determinant of the attractiveness of VC-backed entrepreneurship for high-earning potential founders is the expected time to develop a startup’s initial product. This is because founder-CEOs’ cash compensation increases... View Details
Ewens, Michael, Ramana Nanda, and Christopher Stanton. "Founder-CEO Compensation and Selection into Venture Capital-Backed Entrepreneurship." Journal of Finance 79, no. 5 (October 2024): 3361–3405.
- October 2024
- Article
Strategic Decision Making at Platform Transitions: The Case of Nokia (2010-2011).
By: Timo O. Vuori and Michael Tushman
We studied Nokia’s decision to adopt the Windows platform in 2011 to induce new theory on the emotional dynamics of incumbent firms’ strategic decision making at platform transitions. We find that platform companies’ entry into an established industry activates a... View Details
Vuori, Timo O., and Michael Tushman. "Strategic Decision Making at Platform Transitions: The Case of Nokia (2010-2011)." Strategic Management Journal 45, no. 10 (October 2024): 2018–2062.
- September 2024
- Supplement
Allbirds: Decarbonizing Fashion (B)
By: Michael W. Toffel, Kenneth P. Pucker and Stacy Straaberg
The Allbirds: Decarbonizing Fashion (B) case encourages students to assess Allbirds’ product development and go-to-market strategies now that Allbirds is a publicly listed company. The (B) case provides a 2024 update on Allbirds including its initial public offering,... View Details
Keywords: Financial Markets; Initial Public Offering; Global Strategy; Collaborative Innovation and Invention; Innovation Strategy; Knowledge Sharing; Distribution; Corporate Social Responsibility and Impact; Mission and Purpose; Going Public; Performance; Diversification; Stocks; Transformation; Fashion Industry; United States; San Francisco; California; Europe; Asia; New Zealand
Toffel, Michael W., Kenneth P. Pucker, and Stacy Straaberg. "Allbirds: Decarbonizing Fashion (B)." Harvard Business School Supplement 625-004, September 2024.
- September 20, 2024
- Article
It’s Time to Unbundle ESG
By: Aaron K. Chatterji and Michael W. Toffel
ESG is at an inflection point. It has come to represent a broad and inchoate aspiration for what business should be doing beyond maximizing shareholder value. With ESG advocates on the defensive, business leaders need a new roadmap to determine which factors to... View Details
Keywords: ESG; ESG (Environmental, Social, Governance) Performance; ESG Ratings; ESG Reporting; ESG Disclosure; Sustainability; Climate; Climate Finance; Climate Risk; Social Accounting; Investment; Governance; Safety; Climate Change; Environmental Sustainability; Corporate Social Responsibility and Impact; Financial Services Industry
Chatterji, Aaron K., and Michael W. Toffel. "It’s Time to Unbundle ESG." Harvard Business Review (website) (September 20, 2024).
- September 2024
- Case
Google Quantum AI
By: David B. Yoffie, Michael A. Cusumano and Matt Higgins
Quantum computing may be the most important nascent computing technology of the 21st century. It has the potential to impact industries ranging from drug discovery to cybersecurity. Google's Quantum AI is one of the leaders in quantum research. This case explores... View Details
Keywords: Information Technology Industry
- 2024
- Working Paper
Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python
By: Melissa Ouellet and Michael W. Toffel
This paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in... View Details
Keywords: Empirical Methods; Empirical Operations; Statistical Methods And Machine Learning; Statistical Interferences; Research Analysts; Analytics and Data Science; Mathematical Methods
Ouellet, Melissa, and Michael W. Toffel. "Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python." Harvard Business School Working Paper, No. 25-010, August 2024.
- 2024
- White Paper
Modernizing the U.S. Exchange Visitor Skills List
By: William R. Kerr and Michael C. Clemens
Kerr, William R., and Michael C. Clemens. "Modernizing the U.S. Exchange Visitor Skills List." Peterson Institute for International Economics Policy Brief, 24-8, Peterson Institute for International Economics, September 2024.
- September–October 2024
- Article
Where Data-Driven Decision-Making Can Go Wrong
By: Michael Luca and Amy C. Edmondson
When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any... View Details
Luca, Michael, and Amy C. Edmondson. "Where Data-Driven Decision-Making Can Go Wrong." Harvard Business Review 102, no. 5 (September–October 2024): 80–89.
- August 2024
- Background Note
Mitigating Climate Change with Machine Learning
By: Michael W. Toffel, Kelsey Carter, Amy Chambers, Avery Park and Susan Pinckney
This note highlights how machine learning is being used to decarbonize (reduce GHG emissions) several key sectors including electricity, transportation, building, industrial processes, and agriculture -- and how machine learning is being used to accelerate efforts to... View Details
Keywords: Climate; Artificial Intelligence; Adaptation; Climate Change; AI and Machine Learning; Innovation and Invention
Toffel, Michael W., Kelsey Carter, Amy Chambers, Avery Park, and Susan Pinckney. "Mitigating Climate Change with Machine Learning." Harvard Business School Background Note 625-014, August 2024.
- 2024
- Article
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
By: Michael Lingzhi Li and Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
- August 2024
- Article
Not a One-Trick Pony: Price Impact of Rating Agency Information
By: Michael Machokoto and Anywhere Sikochi
Prior literature on the informational role of credit rating agencies has largely focused on announcements by the rating agencies regarding rating actions. We take a tangent in this paper and examine the relevance of rating agencies' other information disclosures beyond... View Details
Machokoto, Michael, and Anywhere Sikochi. "Not a One-Trick Pony: Price Impact of Rating Agency Information." Art. 111837. Economics Letters 241 (August 2024).