Publications
Publications
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- Faculty Publications (393)
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- June 1982 (Revised May 1995)
- Case
Ellis Manufacturing Co.
By: Roy D. Shapiro
Ellis finds itself in a weakening competitive position largely due to the lack of rationalization in its plants. Driven by a strong traditionally decentralized sales organization, Ellis finds that all plants want control over all product lines. As a result, overall... View Details
- March 1980 (Revised February 1987)
- Case
Sweco, Inc. (A)
By: Michael E. Porter and George S. Yip
Describes Sweco's decision about whether to enter the mud-processing equipment industry (used in oil well drilling). This is an internal entry decision, and the case describes Sweco's existing businesses as well as the mud-processing industry and competitors. The case... View Details
- Teaching Interest
Data Science and Artificial Intelligence for Leaders
By: Chiara Farronato
With artificial intelligence (AI)... View Details
- Forthcoming
- Article
Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data
By: AJ Chen, Omri Even-Tov, Jung Koo Kang and Regina Wittenberg-Moerman
To mitigate information asymmetry about borrowers in developing economies, digital lenders use machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of... View Details
- Teaching Interest
Harvard Business Analytics Program
The Harvard Business Analytics Program is offered through a collaboration between Harvard Business School (HBS), the John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Faculty of Arts and Sciences (FAS).
Designed for... View Details
- Teaching Interest
Harvard Business Analytics Program: Operations and Supply Chain Management
By: Dennis Campbell
Digital technologies and data analytics are radically changing the operating model of an organization and how it connects to its broader supply chain and ecosystem. This course emphasizes managing product availability, especially in a context of rapid product... View Details
- Forthcoming
- Article
Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
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
- Teaching Interest
Overview
By: John A. Deighton
I teach about the ecosystem of big data, the role of data in advertising and creative industries, and customer management and personal privacy in an era of individual addressability. View Details
- Research Summary
Overview
I have spent my career studying novel talent management practices and their effect on collaboration and performance. My core research focuses on two interrelated organizational trends that have become salient in the 21st century: workplace transparency (who gets to... View Details
Keywords: Privacy; Transparency; Productivity; Field Experiments; Communication; Design; Human Resources; Leadership; Management; Organizational Design; Organizational Structure; Performance; Groups and Teams; Networks; Behavior; Social and Collaborative Networks; Satisfaction; North America; Europe; Asia; China; Japan; Latin America
- Research Summary
Overview
Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the... View Details
- Research Summary
Overview
By: Ayelet Israeli
Professor Israeli utilizes econometric methods and field experiments to study data driven decision making in marketing context. Her research focuses on data-driven marketing, with an emphasis on how businesses can leverage their own data, customer data, and market data... View Details
- Article
Paradise Lost (and Restored?): A Study of Psychological Safety over Time
By: Derrick P. Bransby, Michaela Kerrissey and Amy C. Edmondson
Although prior research indicates that psychological safety can fluctuate, questions about when and why remain. To gain insights into the emergence and temporal dynamics of psychological safety, we explored longitudinal data representing more than 10,000 health care... View Details
- Forthcoming
- Article
Slowly Varying Regression Under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
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