JOURNAL ARTICLE
Maintaining or Altering the Status Quo in the Nonmarket Arena: Theory and Evidence from Government Contract Disputes.
Published In: Organization Science (INFORMS), 2023, v. 34, n. 3. P. 1004 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Abdurakhmonov, Mirzokhidjon; Hasija, Dinesh; Ridge, Jason W.; Hill, Aaron D. 3 of 3
Abstract
This article develops a theory examining how corporate political activity (CPA) differs when firms seek to maintain versus alter the status quo in political arenas. It argues that relational embeddedness—long-term relationship-building with government officials—more strongly benefits firms aiming to maintain the status quo than those attempting to change it, due to government officials’ inherent status quo bias and risk aversion. The study further finds that this effect is moderated by firms’ prior targeting of specific political domains (legislative or administrative) and their social reputation, with these factors amplifying relational embeddedness benefits primarily for firms maintaining the status quo. Empirical analysis of U.S. government contract disputes supports these claims, highlighting coordination challenges among firms seeking to alter the status quo and underscoring the nuanced dynamics of interfirm rivalry in political markets. The findings contribute to a more refined understanding of how CPA strategies and firm characteristics interact to influence political outcomes.
Additional Information
- Source:Organization Science (INFORMS). 2023/05, Vol. 34, Issue 3, p1004
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2023
- ISSN:1047-7039
- DOI:10.1287/orsc.2022.1606
- Accession Number:163655240
- Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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