Specialization and career dynamics: Evidence from the Indian Administrative Service

Administrative Science Quarterly58(2), 233-256.

(with John-Paul Ferguson)

In this article, we attempt to resolve the tension between two conflicting views on the role of specialization in workers’ careers. Some scholars argue that specialization is a net benefit that allows workers to get ahead, while others argue that broad experience across several domains is the only way to be truly exceptional. We use rich longitudinal data from 1974 to 2008 on the careers of Indian Administrative Service officers, members of the Republic of India’s elite bureaucratic service, to test both these hypotheses. We find that specialization benefits officers throughout their career. We distinguish between skill-based and signal-based mechanisms that relate specialization to promotion, by exploring the match (or lack thereof) between the skills officers acquire and the jobs to which they are promoted, and we find that both mechanisms operate, but at different points in the career. Specialization is rewarded later in …

Group based trajectories of network formation and dynamics

Social Networks34(4), 506-514.

In this paper, we propose the application of a semi-parametric statistical methodology called Group-Based Developmental Trajectory Analysis to studying the dynamics of social networks. We begin with a discussion of theoretical problems in network analysis that may benefit from this approach. Next, we describe the methodology and how it can be applied to dyadic network data as well as aggregated node level data. We then demonstrate the methodology by analyzing the Newcomb Fraternity and the van de Bunt student data sets. Finally, we conclude with a discussion of potential directions for further research.

Automatic detection of omissions in medication lists

Journal of the American Medical Informatics Association 18.4 (2011): 449-458.

(with George T. Duncan, Daniel B. Neill and Rema Padman)

Objective

Evidence suggests that the medication lists of patients are often incomplete and could negatively affect patient outcomes. In this article, the authors propose the application of collaborative filtering methods to the medication reconciliation task. Given a current medication list for a patient, the authors employ collaborative filtering approaches to predict drugs the patient could be taking but are missing from their observed list.

Design

The collaborative filtering approach presented in this paper emerges from the insight that an omission in a medication list is analogous to an item a consumer might purchase from a product list. Online retailers use collaborative filtering to recommend relevant products using retrospective purchase data. In this article, the authors argue that patient information in electronic medical records, combined with artificial intelligence methods, can enhance medication reconciliation. The authors formulate the detection of omissions in medication lists as a collaborative filtering problem. Detection of omissions is accomplished using several machine-learning approaches. The effectiveness of these approaches is evaluated using medication data from three long-term care centers. The authors also propose several decision-theoretic extensions to the methodology for incorporating medical knowledge into recommendations.

Results

Results show that collaborative filtering identifies the missing drug in the top-10 list about 40–50% of the time and the therapeutic class of the missing drug 50%–65% of the time at the three clinics in this study.

Conclusion

Results suggest that collaborative filtering can be a valuable tool for reconciling medication lists, complementing currently recommended process-driven approaches. However, a one-size-fits-all approach is not optimal, and consideration should be given to context (eg, types of patients and drug regimens) and consequence (eg, the impact of omission on outcomes).

 

Keywords: Collaborative filtering, machine learning, medication reconciliation

iSPIRT: M&A Connect (Part A)

Stanford GSB Cases

(with Sarah Rosenthal)

Part A of the case describes the founding of the Indian Software Product Industry Roundtable (iSPIRT, pronounced “ispirit”), a nonprofit organization formed in 2012 by a small group of Indian entrepreneurs and technology professionals who believed that India’s tremendous engineering talent could be harnessed to transition the country from its role as the “back office of the world” into a “product nation” in its own right.  Led almost entirely by volunteers, the group identified three major obstacles blocking the path of entrepreneurship and innovation in India: 1) obstructive government regulations and policies; 2) entrepreneur readiness (or lack thereof); and 3) the process by which potential acquirers/partners could “discover” Indian startups.  Though iSPIRT engaged in numerous initiatives to address these challenges, it is the challenge of “discovery” that provides the focus of Part B of the case.

Learning Objective

 

The learning objective of the case is to provide students with an opportunity to apply social network theory to a real life business challenge. As Rao, students are asked to navigate the challenges and opportunities and determine a pathway forward for launching M&A Connect based on limited financial resources and numerous constraints. What personal and professional networks should he tap into, what resources can he use, what value can he bring to the respective audiences with whom he is speaking? Once students learn the details around how Rao actually launched the program, they are then asked to evaluate the next steps in Part B. How can he overcome the dual challenge/opportunity that iSPIRT’s status as a nonprofit brings? What new challenges does he face as he attempts to identify the “top” entrepreneurs throughout India while at the same time trying to establish credibility with the top tier technology firms in the U.S.? As Rao refines his model, how can he go about scaling it such that iSPIRT can have

iSPIRT: M&A Connect (Part B)

Stanford GSB Case Study

(with Sarah Rosenthal)

Part B explores the launch of M&A Connect, a one-man initiative led by Sanat Rao, to serve as a matchmaker between viable, high-potential Indian startups and U.S.-based acquirers such as Google, AutoDesk, and Intel.  Students learn how Rao approached the challenge of finding inroads into the corporate development departments of these American companies in order to connect them with virtually unknown Indian startups.  While he has achieved success, the process is ongoing and the future of M&A Connect continues to unfold. Also see Part A.

 

Learning Objective

 

The learning objective of the case is to provide students with an opportunity to apply social network theory to a real life business challenge. As Rao, students are asked to navigate the challenges and opportunities and determine a pathway forward for launching M&A Connect based on limited financial resources and numerous constraints. What personal and professional networks should he tap into, what resources can he use, what value can he bring to the respective audiences with whom he is speaking? Once students learn the details around how Rao actually launched the program, they are then asked to evaluate the next steps in Part B. How can he overcome the dual challenge/opportunity that iSPIRT’s status as a nonprofit brings? What new challenges does he face as he attempts to identify the “top” entrepreneurs throughout India while at the same time trying to establish credibility with the top tier technology firms in the U.S.? As Rao refines his model, how can he go about scaling it such that iSPIRT can have