Photo of Adam Castor

Adam Castor

Doctoral Student

Links: CV

Contact Information

Address: 3114 SH-DH, 3620 Locust Walk, Philadelphia, PA 19104
Office: (215) 573-8823
Office Fax: (215) 898-0401



The Wharton School, University of Pennsylvania
Ph.D. Managerial Science & Applied Economics, (expected) May 2016
Fields: Innovation, Strategy, International Business

Johns Hopkins University
M.A. Economics, May 2006
Fields: Econometrics, Industrial Organization

University of Wyoming
B.S. Mathematics &Economics, May 2002


Working Papers

  • Castor, A. "Cognitive Neighborhoods and the Valuation of Innovations Across National Patent Systems: Comparing Patents in the US, Germany, and Japan" (job market paper)
  • Castor, A. "Painted with the Same National Brush?  How Nationality Affects Categorization and Information Spillovers."
  • Castor, A. "Relatedness is in the Eye of the Beholder:  The Differences in Cognitive Models of Corporate Relatedness Across Corporate Stakeholders."

Book Chapters

  • Castor, A. & T. Wry. (2016). Category learning: Search outcomes as an antecedent to category system change in technological classification. Research in the Sociology of Organizations. (forthcoming, 2016)


Conference Presentations

  • Castor, A. "Cognitive Neighborhoods and the Valuation of Innovations Across National Patent Systems: Comparing Patents in the US, Germany, and Japan"
    Wharton-INSEAD PhD Consortium, Philadelphia, PA, 2015
  • Castor, A. & T. Wry. "Category Structures as Attention Primes: Category Similarity and the Variable Value of Patent Families Across Patent Systems"
        Academy of Management, Vancouver, Canada, 2015
  • Castor, A. "Relatedness is in the Eye of the Beholder:  The Differences in Cognitive Models of Corporate Relatedness Across Corporate Stakeholders"
         NYU / Columbia Doctoral Conference, New York, New York, 2014
         Wharton-INSEAD PhD Consortium, Fontainebleau, France 2014
         Strategic Management Society Annual International Conference, Madrid, Spain, 2014
  • Castor, A. "Painted with the Same National Brush?  How Nationality Affects Categorization and Information Spillovers."
         Wharton-INSEAD PhD Consortium, Fontainebleau, France, 2012
         Strategic Management Society Annual International Conference, Atlanta, GA, 2013
         Academy of Management, Orlando, FL, 2013
         Consortium for Competitiveness and Cooperation, Kansas City, KS, 2013

         Trans-Atlantic Doctoral Conference, London, England, 2014

Teaching Experience


The Wharton School, University of Pennsylvania, Philadelphia, PA
     Introduction to Management (Undergrad): Summer 2015 (instructor rating 3.5/4.0)


Teaching Assistantships:

The Wharton School, University of Pennsylvania, Philadelphia, PA
      Network Theory & Applications (PhD): Spring 2013, 2015
      Foundations of Teamwork & Leadership (MBA): Fall 2010-2015
      Managing the Enterprise - Strategy (EMBA): Summer 2013
      Managing the Enterprise - International Business (EMBA): Summer 2014
      Introduction to Management (Undergrad): Fall 2011, Spring 2012

Booth School of Business, University of Chicago, Chicago, IL
      Managerial Decision Making (MBA): Winter 2009

Johns Hopkins University, Baltimore, MD
      Microeconomics (Undergrad): Spring 2005
      Development Economics (Undergrad): Fall 2004


Awards, Grants, & Fellowships

Research Grant, Mack Institute for Innovation Management (with T. Wry), 2012, $8,000
T. Rowe Price Fellowship, Johns Hopkins University, 2003-2005


Professional Activities

2010-present    Member, Academy of Management
2010-present    Member, Strategic Management Society




In the broadest sense, my research program centers around understanding relatedness and its consequences for firms.  In pursuing this program, while I apply the logic of social categorization, I deviate from much of the existing literature which assumes categories to be reified, homogenous across audiences, and stable over time.  Instead, I operate on the presumption that categories and classification are more malleable and may vary across individual audience members.  Empirically, I tackle the question of relatedness and categorization via two theoretically related research streams.  In the first stream, I examine the factors that affect the construction of industry categories and thus the perceived relatedness among individual firms.  Relatedness, from the perspective of external stakeholders, plays a crucial role in determining patterns of informational spillovers as well as the evaluation of corporate strategic actions including mergers, acquisitions and alliance deals.  In the second stream, I examine perceptions of relatedness among technologies and technology classes.  Relatedness, from the perspective of patent examiners, shapes citation patterns among patents and across technology classes and ultimately affects the realized value of intellectual property.  By taking this alternative approach, I embark on a research program that leverages data at the audience-member level to understand macro-level phenomenon, such as financial market reactions to deal announcements and the valuation of technological innovations.

Dissertation Research

My dissertation represents initial progress towards answering the question of relatedness posed by my larger research program in both streams as well creating a new methodological tool to allow me to do so.  The focal question of my dissertation is to elucidate how relatedness affects audience member cognition and the sense-making of firms and their innovations.  There are two main ideas that I empirically support: 1) audience member characteristics affect their perceptions of firm relatedness, which in turn affects the degree of informational spillovers and 2) classification system characteristics affect audience member perceptions of technology relatedness.  To help demonstrate these ideas, I also create a novel measure that captures firm relatedness from an external stakeholder perspective.

In the first chapter, I indirectly show, via information spillovers, how the characteristics of individuals within a single stakeholder group, i.e., stock market analysts, affect the way that they each individually categorize firms.  Information spillovers are determined by category boundaries.  When new information emerges related to one firm, it will be more readily ascribed to, and have a stronger effect on, other firms in the same category compared to firms in different categories.  For instance, take the case of two firms:  an event firm which reports a large earnings miss and another focal firm that is being covered by the analyst.  If the analyst sees the two firms as members of the same category, the earnings miss from the event firm will also be ascribed to the focal firm.  Conversely, if the analyst sees the two firms as members of different categories, the analyst will not ascribe the event firm's earnings miss to the focal firm.  When the event firm's earnings miss is ascribed to the focal firm, the analyst will lower the earning forecast for the focal firm.  Hence, the pattern of informational spillovers for an analyst reflects the way they categorize companies.  Empirically, I look at event companies with large earnings misses and focal firms that are co-located in the same country.  I find that foreign analysts (i.e., analysts not located in the same country as the company they cover) revise their earnings estimates downward more so for focal companies than do local analysts.  This suggests that foreign analysts ascribe the earnings miss more broadly and tend to categorize companies located in the same country into larger groups than do local analysts.

In the second chapter, I investigate how technology classification affects the valuation of intellectual property.  Different national patent systems use different technology classification schemes to asses and organize intellectual property.  Technology classification schemes are designed to help patent examiners locate similar prior intellectual property when examining and evaluating a focal patent.  They do so by helping the examiner focus their attention on a subset of prior patents that are most likely to be relevant to the focal patent when conducting prior art searches. These searches then determine what patents are cited as relevant prior art to the focal patent.  If categorization schemes differ across patenting systems, examiners in different systems may focus on different sets of prior art when conducting their prior art search.  This may lead to differences across national patent systems in what prior art is cited as related for a given patent.  By looking at patent families, collections of patents across different national systems that are based on the same underlying innovation, I show that a patent is cited more or less often based on the characteristics of the national category scheme.  This is important for prior art as citations proxy for the value of the patent.  Thus, I show how the characteristics of the technology classification scheme affect the realized value of intellectual property.

In the third chapter of my dissertation, I propose a dyadic measure of company-to-company relatedness that is based on the stock market analyst coverage network.  Analysts specialize by industry, so similar firms will be covered by the same analysts.  As the perceived relatedness or similarity of two firms increases, so will the number of analysts they have in common.  This fact is used in combination with network methods to construct an analyst-based measure of relatedness which can be used as an alternative or supplement to existing industry category systems.  Moreover, I argue that the analyst-based measure directly captures the way by which stock market analysts, influential financial market actors, categorize firms.

Related Work

In another paper, I start with the premise that while an analyst-based measure best captures relatedness among firms according to shareholders, production-oriented measures such as technological similarity best capture relatedness according to internal stakeholders.  In the context of corporate mergers and acquisitions, relatedness between the involved companies is an important factor in determining the potential value of each deal.  Empirically, I show that: a) technological similarity more strongly predicts the target choices for merger and acquisition deals than does analyst-based similarity, and b) analyst-based similarity more strongly predicts the financial market reactions to M&A deal announcements than does technological similarity.  This suggests that internal stakeholders may have different perceptions of firm relatedness and may categorize firms in different ways than external stakeholders.  This yields one explanation as to why shareholders may respond negatively to merger or acquisition deals that seem beneficial to internal stakeholders.



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