JOURNAL ARTICLE
Media Exposure and Information Seeking as Antecedents: Individual Behavioral Response to Corporate Political Advocacy in the Case of Lyft's Action Against the Texas Abortion Ban.
Published In: International Journal of Public Opinion Research, 2025, v. 37, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Cheng, Zicheng; You, Leping 3 of 3
Abstract
This study examines factors influencing U.S. individuals' social media engagement in response to Lyft's corporate political advocacy (CPA) opposing the Texas abortion ban (Senate Bill 8) and supporting drivers legally. Using two-wave panel survey data and the orientation–stimuli–orientation–response (O-S-O-R) model, the research finds that media exposure to abortion ban-related news does not directly predict social media engagement; instead, active information seeking mediates this relationship and significantly promotes engagement behaviors categorized as Consumer Digital Engagement in response to Corporate Political Advocacy (CDE-CPA). The study highlights the importance of information seeking as a cognitive process linking media exposure to online political activism and suggests that collective efficacy and issue importance also influence engagement. These findings offer theoretical contributions to media effects and CPA research and practical implications for corporations and advocates aiming to mobilize public support through strategic communication.
Additional Information
- Source:International Journal of Public Opinion Research. 2025/03, Vol. 37, Issue 1, p1
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:0954-2892
- DOI:10.1093/ijpor/edaf006
- Accession Number:184297082
- Copyright Statement:Copyright of International Journal of Public Opinion Research is the property of Oxford University Press / USA 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.