JOURNAL ARTICLE
When consumers lose power: An examination of the stakeholder dynamics in the pharmaceutical industry.
Published In: Business Ethics, the Environment & Responsibility, 2023, v. 32, n. 3. P. 986 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Tang, Zhi; Leo, Ezekiel; Hull, Clyde; Fu, Xudong; Stromeyer, William 3 of 3
Abstract
Primary stakeholder pressure has long been considered the main reason that firms engage in responsible behaviors. However, prior studies are generally silent on how industry characteristics reshape the relationships among stakeholders. By integrating information asymmetry in credence goods industries with the stakeholder power framework, we posit that the extent to which consumers can evaluate the qualities of goods alters the dynamics between a firm and its two primary stakeholders, regulators and consumers. Longitudinal data collected on 72 pharmaceutical companies indicate that consumers of pharmaceutical products can only influence firms indirectly through the regulator. Further, contrary to the conventional wisdom that innovative firms are more capable of resisting stakeholder pressure, we find that R&D investment increases the dependency of pharmaceutical firms on the regulator. Lastly, we do not find that lobbying reduces dependency on the regulator except for high R&D firms. These findings provide important theoretical, ethical, and policy implications. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Business Ethics, the Environment & Responsibility. 2023/07, Vol. 32, Issue 3, p986
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2023
- ISSN:2694-6416
- DOI:10.1111/beer.12544
- Accession Number:164396799
- Copyright Statement:Copyright of Business Ethics, the Environment & Responsibility is the property of Wiley-Blackwell 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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