Accounting for Negative Attention: Status and Costs in the Market for Audit Services
Bibliographic record
Abstract
Prior work has emphasized the role of positive attention spillovers in driving cost advantages for high-status firms, with exchange partners offering preferential terms to high-status organizations because they anticipate benefits. Yet, spillovers from a client to a supplier may also be negative. These negative spillovers can be exacerbated when high-status actors are involved, because of the high level of publicity they attract. In this paper, we propose that suppliers’ concerns about negative attention are an important contingent factor determining whether high-status firms enjoy cost advantages or, instead, pay a premium. We expect that when suppliers anticipate that negative spillovers are more likely than positive ones and when they enjoy some bargaining power over their clients, a positive relationship between status and costs will result. To test this argument, we analyze fees paid by clients of varying status levels in the U.S. market for audit services. Consistent with our theory, we find that (1) high-status clients are charged more than their lower status peers and (2) the media attention clients receive does mediate this relationship. Indicative of the role of the supplier’s expectation of negative spillovers and their bargaining power, we also demonstrate that the positive relationship becomes stronger when auditors view clients as presenting a greater risk of future negative events and when clients have more bargaining power. Our efforts at theoretical integration result in a fuller picture of the role of status in shaping a firm’s costs, suggesting that status involves advantages in some settings but disadvantages in others. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.15814 .
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 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".