This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.

We propose a new model of flrm reputation that interprets reputation directly as the market belief about product quality. Quality is persistent and is determined endogenously by the flrm’s past investments. We analyse how investment incentives depend on the flrm’s reputation and derive implications for reputational dynamics. We consider three types of consumer learning. When consumers learn about quality through good news, investment incentives are decreasing in reputation, leading to a unique work-shirk equilibrium and convergent dynamics. When consumers learn through bad news, investment incentives are increasing in reputation, leading to a continuum of shirk-work equilibria and divergent dynamics. Finally, when consumers learn through Brownian news and the cost of investment is low, incentives are hump-shaped but a work-shirk equilibrium exists and is essentially unique.