This study aims to highlight the generalization problem as a core challenge in organizational learning and strategic decision-making and explore how decision-makers may address this problem via different learning and choice strategies.…Read More
Using a structural model, we show that workers are highly heterogenous in their preferences for both multihoming and repositioning. We provide counterfactual estimates on the effects of proposed firm and regulatory policies aimed at multihoming and repositioning.…Read More
This research explores how a new relation of production—specifically, the shift from human to algorithms as managers on digital platforms—reconfigures and repurposes workplace consent.…Read More
We design new algorithms that leverage tools from survival analysis to provably perform well in these settings. We demonstrate the value of our approach on a large-scale clinical trial database on metastatic breast cancer.…Read More
This project aims to estimate the welfare effects of this strategy in the biotechnology industry, using a structural model of the investment decisions of VCs during the drug development process and the product market for the successful drugs.…Read More
The main questions we analyze in this project are, what are the key engagement drivers in online discussion platforms, and how can a platform leverage these drivers to adjust the user feed to maximize user engagement throughout the path of the discussion.…Read More
We examine the assumptions that underlie our understanding of profit maximizing firms and test whether product/service scope has different implications for social impact creation. This has both theoretical and practical implications for business model innovation that aims to deliver both financial and social value.…Read More
In this study we plan to fill this research gap by examining whether social media can help startups to access broader knowledge and subsequently facilitate innovation. Our goal is twofold in this research. First, we explore the effect of social media adoption on startup’s knowledge diversity. Second, we analyze whether startups can successfully transfer the diversified knowledge into innovations.…Read More
Providing access to cleaner and cheaper lighting solutions is necessary to lift people out of poverty. The magnitude of the economic, health, and educational impacts created by these lighting solutions as we move up the energy ladder, however, is not clear.…Read More
We examine how industry-university collaboration (IUC) enhances Chinese firms’ commercialization of their technologies using a comprehensive dataset of medium- sized and large industrial firms and research universities in China.…Read More
Little is known currently about the effect of scale and scope on productivity and innovation in healthcare. We propose using unique and hitherto untapped Census economic files on the healthcare sector to answer these questions.…Read More
What is a feasible and efficient policy to regulate air pollution from vehicles? A Pigouvian tax is technologically infeasible. Most countries instead rely on exhaust standards that limit air pollution emissions per mile for new vehicles. We assess the effectiveness and efficiency of these standards, which are the centerpiece of US Clean Air Act regulation of transportation, and counterfactual policies.…Read More
The goal of the project is to identify cheap talk and faithful signaling in environmental reports issued by the U.S. largest firms and understand the determinants and consequences of cheap talk about firms’ climate responsibility.…Read More
We show that bank credit affects entrepreneurship, but only in low-income regions. We use a novel methodology to identify credit supply shock from regional demand shock using comprehensive data on small business loans between pairs of banks and counties in the US.…Read More
Entrepreneurial strategies to cope with dynamic environments are becoming increasingly important globally with the heightened technological and institutional disruptions in the recent and coming years. These dynamic environments are usually characterized by high velocity, complexity, ambiguity and unpredictability, creating additional challenges for entrepreneurs to find and realize opportunities…Read More
Supervised machine learning algorithms fail to perform well in the presence of endogeneity in the explanatory variables. In this paper, we borrow from literature on partial identification to propose deep causal inequalities that overcomes this issue.…Read More
People are frequently asked to engage in collective action—voting, protesting, signing petitions, donating—to uplift members of traditionally marginalized groups and encourage social change. Prior research suggests that minority group members who advocate for collective action are penalized for doing so, while majority group members are not. In this work, I shift focus from perceptions of people who take collective action to how effectively people are able to persuade others to engage in collective action.…Read More
This project examines destigmatization, or “the process by which low-status groups gain recognition and worth.” Destigmatization of identity is a central goal of egalitarian social movements. Yet destigmatization represents a paradox because while destigmatization benefits identity groups as a whole and creates opportunities for many individual members, organizations perceived as belonging that identity group may suffer.…Read More
We propose to develop and validate a model that uses automatically extracted, high-dimensional sentence embeddings to predict the likeability and memorability of new and existing slogans.…Read More
The first study aims to investigate the online data network, in which personal information flows between websites, in order to analyze which privacy policy interventions would be most effective. The second study focuses on individual household’s internet browsing behavior and analyzes vulnerability of different socioeconomic groups to privacy violating data collection activities.…Read More
