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Explaining Corporate Litigation Activity in an Integrated Framework of Interest Mobilization

Published online by Cambridge University Press:  20 January 2017

Isaac Unah*
Affiliation:
University of North Carolina
*
1.Department of Political Science, University of North Carolina, Chapel Hill, NC 27599–3265. E-mail: [email protected].

Abstract

Although the pluralist theory of politics predicts that the focus of organizational activity should shift to the judicial arena whenever the expectations of government as regulator and the demands of regulated interests fail to converge, there has been little systematic research focusing on the question of business litigation as a specific form of interest mobilization. This article develops an integrated organizational choice model of interest mobilization to explain corporate litigation against the United States government. I argue that a company's decision to proceed with litigation is predicated upon the company's (1) resource capacity, (2) constraints of the regulatory environment, and (3) perception of procedural unfairness of the government in the administrative process. The argument is tested with data from a survey of top U.S. business executives whose companies unsuccessfully petitioned the government for administered protection between 1990 and 1995. The argument receives strong empirical support, and suggests that U.S. corporations facing import competition consider litigation an important component of their overall political strategy for obtaining nonmarket benefits.

Type
Research Article
Copyright
Copyright © V.K. Aggarwal 2003 and published under exclusive license to Cambridge University Press 

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Footnotes

Author's note: An earlier version of this study was presented at the annual meeting of the American Political Science Association, Washington, D.C., 1–3 September 2000. For their constructive comments, I thank Wendy Hansen, Herbert Kritzer, Thomas Oatley, Albert Yoon, and participants at the Jürg Steiner Discussion Group at UNC-Chapel Hill. I am grateful to George Rabinowitz for his assistance on imputation algorithm and to Todd McNoldy for his assistance on data gathering. The University of North Carolina Research Council provided funding. All remaining errors are my own.

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