  In my dissertation, I will analyze how the product market position of a
mobile app affects its pricing strategies, which in turn impacts an app's
monetization process. Using natural language processing and k-mean clustering
on apps' text descriptions, I created a new variable that measures the
distinctiveness of an app as compared to its peers. I created four pricing
variables, price, cumulative installs, and indicators of whether an app
contains in-app ads and purchases. I found that the effect differs for
successful apps and less successful apps. I measure the success here using
cumulative installs and the firms that developed the apps. Based on third-party
rankings and the shape of the distribution of installs, I set two thresholds
and divided apps into market-leading and market-follower apps. The
market-leading sub-sample consists of apps with high cumulative installs or
developed by prestigious firms, and the market-follower sub-sample consists of
the rest of the apps. I found that the impact of being niche is smaller in the
market-leading apps because of their relatively higher heterogeneity. In
addition, being niche also impact utility apps differently from hedonic apps or
apps with two-sided market characteristics. For the special gaming category,
being niche has some effect but is smaller than in the market follower
sub-sample. My research provides novel empirical evidence of digital products
to various strands of theoretical research, including the optimal
distinctiveness theory, product differentiation, price discrimination in two or
multi-sided markets, and consumer psychology.
