Heterogeneity in innovation outcomes is the primary focus of this thesis. Heterogeneity among innovative products in online markets, cross-country comparison of innovation impacted by institutional quality, and the varied nature of climate impacting innovation across countries are the three key subjects of the three chapters. This thesis employs fixed effects regression and quantile regressions to understand 1) online demand dynamics and 2) determinants of innovation outcomes contributing to the fields of microeconomics and macroeconomics. By using quantile regression, this work offers a unified methodological framework that captures distributional heterogeneity, yielding insights with both theoretical and practical significance. While conventional regression techniques aim to reduce the gaps between the observed values and the values predicted by the regression line, this thesis highlights quantile regression methodology to present a more detailed knowledge of how variables relate at different quantiles, and thus helping reveal hidden patterns.