Ryan T. Allen

Ryan T Allen HBS


I’m a PhD candidate at Harvard Business School. I will be joining the University of Washington as an Assistant Professor in the Management and Organization department starting Fall 2023.

I study how organizational context (like tools, culture, and structure) shapes strategy and innovation. In my dissertation, I develop and test new theories to explain when data-driven decision-making helps or hinders new product innovation. Click for more details:

Click to display a brief dissertation summary See my research page for full abstracts
  • In chapter 1, I ask whether organizations with data-driven cultures are more innovative. This question has evoked polarized viewpoints: some argue that reliance on quantitative data increases innovative foresight by reducing the biases inherent in softer methods, whereas others argue that such reliance leads to merely incremental innovation. I show that it depends on what I call the methodological pluralism of organizational culture—the extent to which different kinds of analyses are valued in the organization. Using data on 3,500 product launches and text from employee résumés at 61 large consumer product firms, I show that increasing quantitative analysis decreases innovation performance when qualitative analysis is low, and, conversely, increases when qualitative analysis is high. A culture dominated by quantitative analysis is especially harmful when launching products that are novel, or when targeting markets characterized by higher uncertainty.
  • In chapter 2, I use an agent-based model to explain the underlying reason why demand for new innovations can be difficult to anticipate using data-driven methods. In my model, customers are more uncertain of their evaluations of novel products, so their evaluations are more interdependent with neighbors’ evaluations. This leads to counterintuitive diffusion dynamics that can systematically downward bias ex ante estimates of new products’ quantitative market size estimates. I empirically validate the model with data on over 1,600 consumer product launches.
  • In chapter 3, I shift my focus to product innovation in a different organizational form—user communities. Using longitudinal data on experimental PC games under development, I show that firms’ innovation trajectories can get derailed when innovators are too responsive to experimentation in homogenous user communities that are not representative of the broader market.

My dissertation work follows my prior publications on algorithms+judgment, machine learning methods, innovation in global R&D, and industry dynamics.

Before my PhD, I studied Economics at BYU, served a 2-year mission in Taiwan for my church, conducted economic research on environmental health issues, managed at a nonprofit, and did a stint as data analyst at Amazon. I currently live in Cambridge, MA with my wife and 3 children.

Click the links under my picture to contact me, or to see my research and teaching experience.