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- 2021
- Article
The Virtues and Limitations of Dynamic Capabilities
By: Bharat Anand and David J. Collis
Dynamic capabilities have been identified as a key determinant of competitive advantage. This paper explores the foundations of dynamic capabilities, and the limits to their effectiveness, first theoretically and then through the case of Danaher, the most successful...
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Keywords:
Dynamic Capabilities;
Danaher;
Resources;
Theory Of The Firm;
Value-based Strategy;
Organizations;
Performance Effectiveness;
Competitive Advantage;
Strategy
Anand, Bharat, and David J. Collis. "The Virtues and Limitations of Dynamic Capabilities." Strategic Management Review 2, no. 1 (2021): 47–78.
- 2021
- Working Paper
Information Avoidance and Image Concerns
By: Christine L. Exley and Judd B. Kessler
A rich literature finds that individuals avoid information, even information that is instrumental to their choices. A common hypothesis posits that individuals strategically avoid information to hold particular beliefs or to take certain actions--such as behaving...
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Exley, Christine L., and Judd B. Kessler. "Information Avoidance and Image Concerns." Harvard Business School Working Paper, No. 21-080, January 2021.
- January 2021
- Case
The FIRE Savings Calculator
By: Michael Parzen and Paul J. Hamilton
This case follows Carol Muñoz, a member of the Financial Independence, Retire Early (FIRE) lifestyle movement. At the age of 45, Carol is considering retiring and living off the $1 million she has accumulated. Using Monte Carlo simulation, Carol forecasts the...
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- January 2021
- Case
Rio Tinto Aluminum: Can Purpose Lead to Profit?
By: David Fubini and Agastya Muthanna
This case describes the tradeoffs Rio Tinto faces as it considers investments to ensure environmentally friendly, sustainability produced aluminum with the potential risks of competitive pricing and profit loses.
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Keywords:
Mining Industry
- January 2021
- Article
Using Models to Persuade
By: Joshua Schwartzstein and Adi Sunderam
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model...
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