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Labor vs. Big Business: Interest Groups, Cue-Taking, and Voting Behavior.

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Title: Labor vs. Big Business: Interest Groups, Cue-Taking, and Voting Behavior.
Authors: Daneri, Daniel R. (AUTHOR)
Source: Political Behavior. Dec2025, Vol. 47 Issue 4, p1735-1780. 46p.
Subjects: Labor movement, Big business, Decision making, Policy analysis, Partisanship, Voting research, Public interest groups, Gig economy, Uber Technologies Inc.
Abstract: How do individuals interpret interest group cues to make informed voting decisions that are aligned with their partisan identities and ideologies? In the 2020 election cycle, Californians voted on a ballot proposition that concerned the employment status of gig economy workers such as Uber and Lyft drivers. In a manner uncharacteristic of most policy issues, votes for and against the measure did not neatly align with partisan identities. I conduct a content analysis of newspaper coverage and paid social media advertising and find that voters received potentially imbalanced exposure that favored arguments by app-based companies and their allies. I theorize that voters were persuadable due to low attitude crystallization and a new information environment with respect to independent contractor status as a policy issue. To test this, I conduct an experiment among self-identifying Democrats in which I expose them to a series of cue-taking treatments from businesses and labor unions regarding legislation on an independent contractor status policy (low attitude crystallization) or a paid family/medical leave program (high attitude crystallization). The results support my theory and suggest that, despite a half-century of decline, labor unions' endorsements continue to serve as a compass to guide individual voter decision-making towards progressive positions. [ABSTRACT FROM AUTHOR]
Copyright of Political Behavior is the property of Springer Nature 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: How do individuals interpret interest group cues to make informed voting decisions that are aligned with their partisan identities and ideologies? In the 2020 election cycle, Californians voted on a ballot proposition that concerned the employment status of gig economy workers such as Uber and Lyft drivers. In a manner uncharacteristic of most policy issues, votes for and against the measure did not neatly align with partisan identities. I conduct a content analysis of newspaper coverage and paid social media advertising and find that voters received potentially imbalanced exposure that favored arguments by app-based companies and their allies. I theorize that voters were persuadable due to low attitude crystallization and a new information environment with respect to independent contractor status as a policy issue. To test this, I conduct an experiment among self-identifying Democrats in which I expose them to a series of cue-taking treatments from businesses and labor unions regarding legislation on an independent contractor status policy (low attitude crystallization) or a paid family/medical leave program (high attitude crystallization). The results support my theory and suggest that, despite a half-century of decline, labor unions' endorsements continue to serve as a compass to guide individual voter decision-making towards progressive positions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Political Behavior is the property of Springer Nature 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.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2025
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