Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group


First Advisor

Witold Henisz


The main goal of this dissertation is to better understand how external corporate stakeholder perceptions of relatedness affect important outcomes for companies. In pursuit of this goal, I apply the lens of category studies. Categories not only help audiences to distinguish between members of different categories, they also convey patterns of relatedness. In turn, this may have implications for understanding how audiences search, what they attend to, and how the members are ultimately valued.

In the first chapter, I apply incites from social psychology to show how the nationality of audience members affects the way that they cognitively group objects into similar categories. I find that the geographic location of stock market analysts affect the degree to which they will revise their earnings estimates for a given company in the wake of an earnings miss by another firm in the same industry. Foreign analysts revise their earnings estimates downward more so than do local analysts, suggesting that foreign analysts ascribe the earnings miss more broadly and tend to lump companies located in the same country into larger groups than do local analysts.

In the second chapter, I demonstrate that the structure of inter-category relationships can have consequential effects for the members of a focal category. Leveraging an experimental-like design, I study the outcomes of nanotechnology patents and the pattern of forward citations across multiple patent jurisdictions. I find that members of technology categories with many close category 'neighbors' are more broadly cited than members of categories with few category 'neighbors.’ My findings highlight how category embeddedness and category system structure affect the outcomes of category members as well as the role that classification plays in the valuation of innovation.

In the third chapter, I propose a novel and dynamic measure of corporate similarity that is constructed from the two-mode analyst and company coverage network. The approach creates a fine-grained continuous measure of company similarity that can be used as an alternative or supplement to existing static industry classification systems. I demonstrate the value of this new measure in the context of predicting financial market responses to merger and acquisition deals.