Valentina Assenova, Management, The Wharton School
Abstract: Why do the diffusion rates of the same innovations differ across markets? Network topologies serve as important pathways through which individuals obtain information about innovations and are influenced to adopt them. Often, the same innovations can be met with very different success, depending on the topologies of the networks in which they are introduced. I propose to investigate how multiplexity in these topologies — the overlap between different kinds of relations within dyads — affects rates of innovation diffusion on networks through endogenous communication and social influence mechanisms.
I conduct simulations of innovation diffusion on multiplex networks to understand how changes in model parameters affect rates of diffusion. I then apply the model to simulate and compare the predicted to the actual rates of diffusion across 43 villages in India (344 network layers) that adopted microfinance, a financial service innovation. The proposed research contributes to our understanding of how multiplex network topologies shape social learning, information exchange, and influence processes on multiplex networks. I provide evidence of how network topologies differ in their benefits for innovation diffusion (e.g. advice networks versus economic exchange networks). I posit that multiplexity in network topologies affects how social influence evolves on a network over time, and thereby affects the rates at which people uptake innovations over time.