My research focuses on developing and testing theories of network and ecosystem design using field experiments that capture both rich micro-level data and important macro-level outcomes. Policy makers and scholars increasingly recognize that ecosystems are key drivers of innovation and economic growth. Underlying this insight is the idea that the networks that connect firms and people within an ecosystem magnify knowledge, talent, and competition. Building on this insight, the question I try to tackle with my research is the following: how can we improve ecosystems by designing better social networks between people and firms?
Towards this end, for my dissertation, I co-founded Innovate Delhi Entrepreneurship Academy, a three-week-long, full-time, startup boot camp and field experiment. Innovate Delhi was designed to examine the network processes that underlie startup boot camps, accelerators, and incubators, organizations that are becoming central to entrepreneurial ecosystems around the world. Building on the network mechanisms identified in my dissertation, I am working with a fantastic team on a new project that attempts to enrich the founders' networks in the Indian Software StartUp ecosystem. This project should yield insights into how network ties change firm performance and strategy, which interventions are zero-sum and which raise all boats, and when network interventions are the most efficacious.
Academically, my work lies at the intersection of research on Innovation, Network Theory, and Strategy. My focus on the design of ecosystems draws on recent work in the innovation literature which tests the optimal design of online competitions and crowdsourcing platforms. My focus on the underlying network processes that generate ecosystem growth connects me to important debates on structure and agency in Network Theory and Sociology. That said, my research goes beyond the network mechanisms themselves and is instead primarily focused on how we can design networks that improve firm and ecosystem performance, central concerns within strategy research.
Beyond my dissertation work, I have two other completed papers. The first uses a field experiment to look at social information and inequality in crowdfunding outcomes. The second paper is an observational analysis of how the technical linkages between jobs buffer positions from elimination. Despite the very different contexts in these two studies, they are linked by leveraging machine learning to capture theoretically significant, but hard to observe, aspects of each context. Moving forward, I hope to further leverage machine learning to model the heterogeneity that is necessarily present in ecosystems in order to design more targeted treatments and to develop more robust theories of ecosystem design.
My work is fundamentally interdisciplinary and collaborative. If you are interested in analyzing the data we have collected, teaming up on a new project, or trying to build a better ecosystem with science, feel free to drop a line.
Rembrand Koning (Job Market Paper) Download a draft here
Do networks plentiful in ideas provide startups with performance advantages? On the one hand, network positions that provide access to a multitude of ideas are thought to increase team performance. On the other hand, research on network formation argues that such positional advantages should be fleeting as entrepreneurs both strategically compete for the most valuable network positions and form relationships with others who have similar characteristics and abilities. I embed a field experiment in a three-week-long startup bootcamp and pre-accelerator to test if networks that are plentiful in ideas lead to performance advantages. Using detailed data from the bootcamp's custom-designed learning management platform, I find support for the first hypothesis. Teams with networks more plentiful in ideas receive better peer evaluations and more crowdfunding page views. I find little evidence that entrepreneurs actively build networks to others who could have provided a greater quantity of information and ideas. Instead, entrepreneurs seek feedback from those they have collaborated with in the past or who share similar ascriptive characteristics. These findings provide first-order evidence for the importance of knowledge spillovers within bootcamps, incubators, and accelerators. Furthermore, the findings provide a potential explanation for the durability of idea and information-based network advantages.
Sharique Hasan and Rembrand Koning Download a draft here
Social interaction is thought to affect individual and organizational innovation. We argue that individuals and teams are better able to generate high quality ideas if they converse with peers who provide them with new insight, perspective and information. Further, we argue that not all individuals can equally capitalize on this new information. Specifically, extroverted peers, because they are more willing to share and transmit their experiences facilitate idea generation. Moreover, innovators who are open to experience absorb this new information better and can more effectively convert it to better ideas. We test our claims using novel data from a randomized field experiment at an entrepreneurship bootcamp where individuals were randomized to both teams and conversational peers. We find that conversations with extroverted peers help individuals generate higher quality ideas, more open individuals benefit more from such peers, and teams with more cohesion can convert these raw ideas into better performance.
Rembrand Koning and Jacob Model Download a draft here
The diffusion of online marketplaces has increased our access to ``social information'' -- records of past behavior and opinions of consumers. One concern with this development is that social information may create cumulative advantage dynamics that distort marketplaces by increasing inequality in the distribution of success. Critically, this argument assumes that products that are likely to succeed disproportionately benefit from social information. We challenge this assumption and argue that cumulative advantage processes can aggregate to have nearly any effect on distribution of success, even decreasing the level of inequality in some cases. We assess these claims using archival data and a field experiment in a crowdfunding marketplace. Consistent with prior work, randomized changes to social information generate cumulative advantage. However, our treatments did not change the distribution of success. Products benefited equally from our treatments regardless of their predicted likelihood of success. Our treatments still affected marketplace dynamics by weakening the relationship between predicted and achieved success.