iORB’s core mission is to nurture and grow outlier research. Consistent with this mission, iORB provides funding to support ambitious research projects that require additional resources but have significant potential impact. iORB aims to annually fund several outstanding proposals that will positively impact business and society.
A call for proposals is sent out each Fall as part of a competitive submission process and respected business scholars review the proposals. Based on the reviews, the iORB executive board makes final funding decisions.
The proposals for funding are evaluated according to the following main criteria:
- Is there potential for creative and rigorous research that has a likelihood of significant impact on business and society, and will result in publications in premier academic journals?
- Is the work of broad impact; i.e., is there a path forward for disseminating the results of the work beyond the proposer’s own academic community?
Examples of Recently Funded Outlier Research Proposals
1. A Paradigm of FDR Control in High-Dimensional Nonlinear Models
Jinchi Lv and Yingying Fan (Data Sciences and Operations)
The wide availability of massive data in such diverse areas as marketing, economics, finance, operations management, genomics, etc. poses unprecedented challenges to statistical methods, theory, and algorithms. A common issue is that we have a deluge of explanatory variables, often many more than the number of observations, knowing that the outcome only actually depends on a small fraction of them. An important question in any such study is to select the variables or causal factors that are important in explaining outcomes. Note that traditionally we determine the variables that are statistically significant by considering the p-value that is output by the “regression” software. However, these p-values do not make any sense in these high-dimensional settings, and would lead to wrong conclusions. The authors propose to develop a novel method for controlling the False Discovery Rate (FDR) in high-dimensional non-linear models. The proposed method will provide an important step in the pursuit of key causal factors in a wide range of important applications in various disciplines with scalability and statistical guarantees.
2. The Nomenklatura State Institutions in the Knowledge Economy
Nan Jia (Management and Organization)
A pivotal factor in the rapid surge in China’s indigenous innovations in recent years is the direct and powerful role of the Chinese government, which creates a very different institutional environment for innovation as compared to countries such as the U.S. This paper aims to understand how key features of the political governance in China’s political systems shape the incentives in developing innovations. Contrary to the ideal type of Weberian bureaucracy, at the heart of China’s state institutions is a nomenklatura model which generates incentives for state officials to promote certain ideas, for example to promote knowledge production in the 21st century. We plan to use longitudinal data from 1990 to 2015 covering all patents produced in each of the 333 Chinese municipal-level cities and 32 provinces (including province-level cities) every year, to study the relationship between an outstanding incentive feature of the nomenklatura governance system and the patenting landscape in China. We predict that the same incentive structure that resulted in excessive grain extraction and famine in the 1950s also produced greater activism in patenting following the national campaign promoting indigenous innovations—but with certain distortions: a larger number of patents at the expense of the quality or novelty of these patents. This project has the potential to bridge the gap between theories in political science and the economics of innovation literature on the topic of how state institutions influence economic outcomes (innovation outcomes in particular).
3. Optimization in the Small-Data Regime
Vishal Gupta and Paat Rusmevichientong (Data Sciences and Operations)
Modern decision making under uncertainty often requires making thousands of decisions simultaneously at a highly granular level in a time-varying environment. Because of these three features - large-scale, high-granularity, changing environments - the relative amount of relevant data per decision is often quite small. We term this emerging application setting the small-data optimization regime. This proposal aims to: (1) formulate customized methods for decision making in the small-data regime that exploit large-scale optimization structure, (2) promote the adoption of these methods by creating open-source software, (3) partner with the Operations Innovation team in the City of LA's Mayor's office to implement these methods on real-world problems, and (4) host a Hackathon/conference showcasing this software and the above real-world case-studies for academics and practitioners.
4. Institutional knowledge and local information advantage of US versus Chinese information intermediaries
T.J. Wong (Leventhal School of Accounting)
Using textual analysis of a comprehensive set of analysts’ reports and corporate news articles of Chinese firms, we propose to address three research questions in two related projects. First, when covering the Chinese listed firms, do the Chinese intermediaries (financial analysts or journalists) have local information advantage over their US counterparts as measured by forecast quality for the analysts and the level of bias and market response to the information generated by the analysts or journalists? Second, do the Chinese intermediaries focus more on sociopolitical rather than market and economic factors in the reports, and is this focus a key contributing factor to their local information advantage? Third, can the institutional knowledge about the sociopolitical factors be acquired through education or work experience? These projects would shed light on the information advantage of local information intermediaries in the literature. They would potentially impact practice as US intermediaries and investors are increasing their investment in emerging markets such as China but experiencing severe information asymmetry.
5. Digital Entrepreneurship and Innovation: Outlier Behavior in the Mobile App Ecosystem
Pai-Ling Yin (Greif Center for Entrepreneurship) & Milan Miric (Data Sciences and Operations)
This project aims to study: 1) how mobile app entrepreneurs finance their launch in ways that differ from traditional tech entrepreneurs, and 2) how mobile app developers employ novel business models. The mobile app context presents a unique opportunity to identify many failed developers, allowing us to correctly measure the prevalence of different strategies and correctly infer the success of these strategies. Using a mix of large-scale empirical analysis and qualitative case studies, we examine the drivers of bootstrapping among entrepreneurs and the drivers of non-monetary and freemium business models in mobile apps. We accomplish this by merging two unique datasets to explore these questions. The resulting findings will more generally speak to digital and platform environments, where low-cost entry, low-cost production, and the presence of network effects lead to intense competition. We hope to help industry participants and investors in the mobile app industry as well as similar digital and platform environments better understand successful resource acquisition and business model options.