ISRO: Explore Space or Exploit CubeSats?

Prithwiraj  Choudhury

Teaching Note | HBS Case Collection | March 2018

ISRO: Explore Space or Exploit CubeSats?

Abstract

ISRO, India’s space agency, established to address domestic challenges with space technology, is one of the six largest space agencies in the world. Through its 59 missions, ISRO has launched numerous satellites for communication, disaster management, navigation, scientific exploration, and other diverse purposes. Its commercial arm, Antrix Corporation, which launches satellites for both domestic and international customers, has 0.6% of the global space launch market. However, there is increasing demand for satellite launches from both the Indian government and commercial organizations. Unfortunately, ISRO can neither manufacture the required number of launch vehicles to support the huge demand for its services nor can it launch them. In light of these constraints, what should ISRO prioritize? How should we think of this question in light of the theory of exploration-exploitation? What kind of human capital resources should it develop? What processes need to be strengthened? What external collaborations need to be fostered? Teaching Note for HBS No. 617-062.

Keywords: Strategic Planning; Decision Choices and Conditions; Operations; Management; Aerospace Industry; India;

Language: English

Format: Print

13 pages

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Citation:

Choudhury, Prithwiraj. "ISRO: Explore Space or Exploit CubeSats?" Harvard Business School Teaching Note 618-049, March 2018.

Ethnic migrant inventors may differ from locals in terms of the knowledge they bring to host firms. We study the role of first-generation ethnic migrant inventors in cross-border transfer of knowledge previously locked within the cultural context of their home regions. Using a unique dataset of Chinese and Indian herbal patents filed in the United States, we find that an increase in the supply of first-generation ethnic migrant inventors increases the rate of codification of herbal knowledge at U.S. assignees by 4.5%. Our identification comes from an exogenous shock to the quota of H1B visas and from a list of entities exempted from the shock. We also find that ethnic migrant inventors are more likely to engage in reuse of their prior knowledge, whereas knowledge recombination is more likely to be pursued by teams comprising inventors from other ethnic backgrounds.

Working Paper | HBS Working Paper Series | 2018

Work from Anywhere or Co-locate? Autonomy versus Learning Effects at the United States Patent Office

Prithwiraj Choudhury, Cirrus Foroughi and Barbara Larson

While employees might prefer work arrangements that offer greater autonomy, such as work from anywhere (WFA) policies, there are possible negative productivity effects of WFA, due to a lack of learning from co-located peers and increased coordination costs. We study the effects of WFA on productivity at the United States Patent and Trademark Office (USPTO) and exploit two features of the setting that lend themselves to econometric analysis: First, our setting allows us to tease out how the lack of opportunities to learn from co-located peers affects productivity. Second, we exploit a natural experiment in which the implementation of WFA was driven by negotiations between managers and the union of patent examiners, leading to exogeneity in the timing of individual examiners’ transition to WFA. We observe mixed results of WFA across experienced and new hires: for experienced hires, WFA results in a 3.9% increase in output and a 24% reduction in turnover, without affecting the proportion of rework; however, for new hires, an increase in output due to WFA is followed by an additional increase in rework, indicating a negative learning effect from the lack of co-location with experienced peers. We employ micro-data on cost of living, degree of autonomy, and proxies for examiner effort to shed light on mechanisms. Back-of-the-envelope welfare estimates indicate that the 3.9% increase in patent grants at the USPTO could potentially create $1.16 billion in value for the U.S. economy.

Keywords: Working Conditions; Performance Productivity; Learning; Cost vs Benefits;

Citation:

Choudhury, Prithwiraj, Cirrus Foroughi, and Barbara Larson. "Work from Anywhere or Co-locate? Autonomy versus Learning Effects at the United States Patent Office." Harvard Business School Working Paper, No. 19-054, November 2018. View Details

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We describe how to employ machine learning (ML) methods in theory development. Compared to traditional causal inference methods, ML methods make far fewer a priori assumptions about the functional form of the underlying model that best represents the data. Thus researchers could use such methods to explore novel and robust patterns in data, which could in turn lead to inductive theory building. ML’s strengths include replicable identification of novel patterns in data. ML methods also address several issues raised by scholars (such as “p-hacking” and confounding local effects with global effects) pertinent to the norms of empirical research in the fields of strategy and management. We provide a step-by-step roadmap that illustrates how to use four ML methods (decision trees, random forests, K-nearest neighbors, and neural networks) to reveal patterns in data that could be used for theory building. We also illustrate how ML methods can illuminate interactions and non-linear effects better than traditional methods. In summary, ML methods could serve as a complement to both existing inductive theory-creating methods, such as multiple-case inductive studies and traditional methods of causal inference.

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ISRO: Explore Space or Exploit CubeSats?

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