Shedding Light on the Dark Side of Firm Lobbying: A Customer Perspective
Bibliographic record
Abstract
Firms spend a substantial amount on lobbying—devoting financial resources on teams of lobbyists to further their interests among regulatory stakeholders. Previous research acknowledges that lobbying positively influences firm value, but no studies have examined the parallel effects for customers. Building on the attention-based view (ABV) of the firm, the authors examine these customer effects. Findings reveal that lobbying negatively affects customer satisfaction such that the positive relationship between lobbying and firm value is mediated by losses to customer satisfaction. These findings suggest a dark side of lobbying and challenge current thinking. However, several customer-focused moderators attenuate the negative effect of lobbying on customer satisfaction, predicted by ABV theory, including the chief executive officer’s background (marketing vs. other functional area) and the firm’s strategic use of resources (advertising spending, research-and-development spending, or lobbying for product market issues). These moderators ensure consistency between lobbying and customer priorities or direct firm attention toward customers even while firms continue to lobby. Finally, the authors verify that lobbying reduces the firm’s customer focus by measuring this focus directly using text analysis of firm communications with shareholders. Collectively, the research provides managerial implications for navigating both lobbying activities and customer priorities, and public policy implications for lobbying disclosure requirements.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".