Person:

Choudhury, Prithwiraj

We develop and test predictions on how early-career challenges arising from the workplace context affect short- and long-term career advancement of individuals. Typically an organization’s decision to deploy a manager to one of several possible contexts is endogenous to unobservable factors, and selection makes it challenging to disentangle the effect of workplace context on individual career advancement. We work around this problem by studying an organization, the Indian Administrative Services, which deploys entry-level managers quasi-randomly across India. We find that managers deployed to more challenging contexts early in their careers experience faster career advancement in the short term. We present suggestive evidence that this is because challenging contexts provide managers more opportunities to develop skills (“crucible experiences”) and a greater motivation to relocate out of the challenging context. We also find that managers deployed to a challenging context early in their careers continue to experience faster advancement in the long term, suggesting that initial deployment to a challenging context is associated with human capital development. Managers initially deployed to more challenging contexts were not, however, more likely to break into the upper echelons of the organization.

Prior research has established a relation between intra-firm mobility and innovation outcomes at distributed organizations. The literature has also uniformly agreed on the mechanism underlying this relationship: the sharing of tacit knowledge and recombination of ideas that occurs because of intra-firm mobility. But a second mechanism may also be at work: intra-firm mobility might help distant employees secure access to resources for their innovative projects. Using unique data on travel, employment, and patenting for 1,315 inventors at the Indian R&D center of a Fortune 50 multinational, I find that intra-firm mobility in the form of short-duration business trips from a distant R&D location to headquarters is positively related to higher subsequent patenting at the individual level. I also find mobility immediately prior to meetings at which R&D funds are most likely to be disbursed to be related to higher subsequent patenting. This study sheds new light on how intra-firm mobility and possible face-to-face interactions with those who allocate resources might affect innovation outcomes and the matching of resources to individuals within a distributed organization.

In this article, we provide a synthesizing framework that we call the "dynamic trajectories" framework to study the evolution of multinational enterprises (MNEs) in host countries over time. We argue that a change in the policy environment in a host country presents an MNE with two sets of interrelated decisions. First, the MNE has to decide whether to enter, exit, or stay in the host country at the onset of each policy epoch; second, conditional on the first choice, it has to decide on its local responsiveness strategy at the onset of each policy epoch. India, which experienced two policy shocks—shutting down to MNEs in 1970 and then opening up again in 1991—offers an interesting laboratory to explore the "dynamic trajectories" perspective. We collect and analyze a unique dataset of all entry and exit events for Fortune 50 and FTSE 50 firms (as of 1991) in India in the period from 1858 to 2013 and, additionally, we document detailed case studies of four MNEs (that arguably represent outliers in our sample).

We build on the emerging literature of influence-based models to study how multinational firms can navigate host governments. Our "core-periphery" framework posits that the actions that an MNC takes with actors in what we call the "periphery"—comprised of state, quasi-state, and civil society actors—can lead to positive or negative influence with interconnected state actors in a "core." There are two mechanisms by which this can happen: engaging the periphery may either change the information set of the core or help align incentives of multiple core actors. Engaging the periphery might be particularly relevant in settings where the institutional framework is still emerging. We build a case study of a multinational firm in the biotechnology sector to illustrate how the core-periphery framework works in multiple emerging markets across institutional differences. The analysis is based on 32 interviews conducted with the CEO and other executives of Genzyme at the corporate headquarters in Cambridge, Massachusetts, and in subsidiaries in Brazil, China, Costa Rica, France, India, and the United States.

I study whether return migrants facilitate knowledge production by local employees working for them at geographically distant R&D locations. Using unique personnel and patenting data for 1,315 employees at the Indian R&D center of a Fortune 500 technology firm, I exploit a natural experiment where the assignment of managers for newly hired college graduates is mandated by rigid HR rules and is uncorrelated to observable characteristics of the graduates. Given this assignment protocol, I find that local employees with returnee managers file disproportionately more US patents. I also find some evidence that return migrants act as a 'bridge' to transfer knowledge from the MNE headquarters to the local employees working for them.

The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic behavior by agents. We theorize that domain expertise of users can complement ML by mitigating this bias. Our observational and experimental analyses in the patent examination context support this conjecture. In the face of “input incompleteness,” we find ML is biased towards finding prior art textually similar to focal claims, and domain expertise is needed to find the most relevant prior art. We also document the importance of vintage-specific skills and discuss the implications for artificial intelligence and strategic management of human capital.

We study migration in the right tail of the talent distribution using a novel dataset of Indian high school students taking the Joint Entrance Exam (JEE), a college entrance exam used for admission to the prestigious Indian Institutes of Technology (IIT). We find a high incidence of migration after students complete college: among the top 1,000 scorers on the exam, 36% have migrated abroad, rising to 62% for the top 100 scorers. We next document that students who attended the original “Top 5” Indian Institutes of Technology (IIT) were 5 percentage points more likely to migrate for graduate school compared to equally talented students who studied in other institutions. We explore two mechanisms for these patterns: signaling, for which we study migration after one university suddenly gained the IIT designation; and alumni networks, using information on the location of IIT alumni in U.S. computer science departments.

We exploit plausibly exogenous variation in the staggered entry of new managers into India’s 42 public R&D labs between 1994 and 2006 to study how alignment between the CEO and middle-level managers affect research productivity. We show that the introduction of new lab managers aligned with the national R&D reforms raised patenting and multinational licensing revenues by 58% and 75%, respectively, and scientist research productivity, including a 16%, 10%, 11%, and 22% increase in h-indices, number of coauthors, publications, and citations per scientist, respectively. Using natural language processing (NLP) techniques on the set of research abstracts produced among these scientists, we also find that overall mood and sentiment increased by 8.5% following the first managerial change.

Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented worker performance. Reconciling these perspectives, we theorize that intermediate levels of domain experience are optimal for algorithm-augmented performance, due to the interplay between two countervailing forces—ability and aversion. Although domain experience can increase performance via increased ability to complement algorithmic advice (e.g., identifying inaccurate predictions), it can also decrease performance via increased aversion to accurate algorithmic advice. Because ability developed through learning by doing increases at a decreasing rate, and algorithmic aversion is more prevalent among experts, we theorize that algorithm-augmented performance will first rise with increasing domain experience, then fall. We test this by exploiting a within-subjects experiment in which corporate information technology support workers were assigned to resolve problems both manually and using an algorithmic tool. We confirm that the difference between performance with the algorithmic tool versus without the tool was characterized by an inverted U-shape over the range of domain experience. Only workers with moderate domain experience did significantly better using the algorithm than resolving tickets manually. These findings highlight that, even if greater domain experience increases workers’ ability to complement algorithms, domain experience can also trigger other mechanisms that overcome the positive ability effect and inhibit performance. Additional analyses and participant interviews suggest that, even though the highest experience workers had the greatest ability to complement the algorithmic tool, they rejected its advice because they felt greater accountability for possible unintended consequences of accepting algorithmic advice.

We study whether, when, and how better connectivity through nonstop flights leads to positive innovation outcomes for firms in the global context. Using unique data of all flights emanating from 5,015 airports around the globe from 2005 to 2015 and exploiting a regression discontinuity framework, we report that a 10% increase in nonstop flights between two locations leads to a 3.4% increase in citations and a 1.4% increase in the production of collaborative patents between those locations. This effect is driven primarily by firms as opposed to academic institutions. We further study the characteristics of firms and firm locations that are salient to the relation between nonstop flights and innovation outcomes across countries. Using a gravity model, we posit and find that the positive effect of nonstop flights on innovation is stronger for firms and subsidiaries with greater innovation mass (e.g., stocks of inventors and R&D spending), located in innovation hubs or countries that are deemed technology leaders, and that are separated by large cultural or temporal distance. This paper was accepted by Alfonso Gambardella, business strategy. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4682 .