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• Case | HBS Case Collection | November 2020

  Creating a Virtual Internship at Goldman Sachs

  Prithwiraj Choudhury, Iavor Bojnov and Emma Salomon

  Goldman Sachs runs an annual internship for over 3,000 participants, spread across dozens of the firm's global offices. In 2020, the team brought all its resources to bear to transform the internship program into a fully virtual format in just a few short weeks. The new all-virtual internship faced challenges, but also benefited from unexpected opportunities. As the program ended, the team reflected on what worked, what they would change, and what the future of the internship program at Goldman Sachs could look like in 2021 and beyond.

  Keywords: remote work; remote operations; remote internship; internship; virtual socialization; human capital management; Human Resources; Management; Health Pandemics; Adaptation;

  Citation:

  Choudhury, Prithwiraj, Iavor Bojnov, and Emma Salomon. "Creating a Virtual Internship at Goldman Sachs." Harvard Business School Case 621-035, November 2020.  View Details

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• Case | HBS Case Collection | September 2020 (Revised October 2020)

  Moderna (A)

  Marco Iansiti, Karim R. Lakhani, Hannah Mayer and Kerry Herman

  In summer 2020, Stephane Bancel, CEO of biotech firm Moderna, faces several challenges as his company races to develop a vaccine for COVID-19. The case explores how a company builds a digital organization, and leverages artificial intelligence and other digital resources to speed its operations, manage its processes and ensure quality across research, testing and manufacturing.
  Built from the ground up as such a digital organization, Moderna was able to respond to the challenge of developing a vaccine as soon as the gene sequence for the virus was posted to the Web on January 11, 2020. As the vaccine enters Phase III clinical trials, Bancel considers several issues: How should Bancel and his team balance the demands of developing a vaccine for a virus creating a global pandemic alongside the other important vaccines and therapies in Moderna’s pipeline? How should Moderna communicate its goals and vision to investors in this unprecedented time? Should Moderna be concerned it will be pegged as “a COVID-19 company?”

  Keywords: COVID-19; vaccine; digital organizations; Organizational Structure; Operations; Management; Health Pandemics; Research and Development; Goals and Objectives;

  Citation:

  Iansiti, Marco, Karim R. Lakhani, Hannah Mayer, and Kerry Herman. "Moderna (A)." Harvard Business School Case 621-032, September 2020. (Revised October 2020.)  View Details

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• Working Paper | HBS Working Paper Series | 2020

  Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models

  Fiammetta Menchetti and Iavor Bojinov

  Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless impacted by the intervention because of cross-unit interactions. This paper extends the synthetic control methods to accommodate partial interference, allowing interactions within predefined groups, but not between them. Focusing on a class of causal estimands that capture the effect both on the treated and control units, we develop a multivariate Bayesian structural time series model for generating synthetic controls that would have occurred in the absence of an intervention enabling us to estimate our novel effects. In a simulation study, we explore our Bayesian procedure’s empirical properties and show that it achieves good frequentists coverage even when the model is misspecified. Our work is motivated by an analysis of a marketing campaign’s effectiveness by an Italian supermarket chain that permanently reduced the price of hundreds of store-brand products. We use our new methodology to make causal statements about the impact on sales of the affected store-brands and their direct competitors. Our proposed approach is implemented in the CausalMBSTS R package.

  Keywords: causal inference; partial interference; synthetic controls; Bayesian structural time series; Mathematical Methods;

  Citation:

  Menchetti, Fiammetta, and Iavor Bojinov. "Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models." Harvard Business School Working Paper, No. 21-048, October 2020.  View Details

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