Beyond Dangling Carrots: The Effect of Policy Maker Motives on Their Response to Corporate Political Activity
Bibliographic record
Abstract
Management scholars have predominantly theorized corporate political activity (CPA) as an exchange wherein a firm offers resources to a policy maker in return for favorable policy. While the CPA literature has extensively explored what drives firms to engage in CPA, it remains largely silent on the question of why a policy maker would accede to a firm’s attempt at influence. Drawing on social influence theory, we develop a framework that extends current CPA theory and centers a policy maker’s perspective when faced with CPA. We introduce a policy maker’s noninstrumental motives and identify novel types of CPA that appeal to these motives. We also consider the role of the bureaucrat—a heretofore largely ignored but important type of policy maker whose motives differ from those of politicians in key respects that shape whether and how a given policy maker is influenced. Lastly, we examine how third-party lobbyists affect the CPA process, identifying key pathways through which they are likely to enhance a firm’s CPA. By theorizing around the motives of the policy maker faced with CPA, we present a novel policy maker-centered framework that helps us better understand this socially and economically important strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".