The Effect of Information Systems on Honesty in Managerial Reporting: A Behavioral Perspective*
Bibliographic record
Abstract
Abstract This study examines the behavioral impact of an information system, and how that impact varies with the information system's precision, in an internal reporting environment. We propose that a manager's reporting decisions are affected by his or her trade‐off of the benefits of appearing honest against the benefits of misrepresentation. The information system affects the manager's trade‐off by improving the owner's ability to make an inference regarding the manager's level of honesty. Thus, to the extent that the manager perceives benefits to appearing honest, the presence of an information system can increase managerial honesty. As the information system becomes more precise, however, the manager must forgo greater benefits of misrepresentation in order to achieve the same appearance of honesty. For managers under a precise system, this will shift the trade‐off decision toward the benefits of misrepresentation and away from the benefits of appearing honest. Notably, in our experiment, the only benefit of appearing honest is an intrinsically motivated desire for social approval. We find that, although the existence of an information system increases managerial honesty, honesty is lower under a precise than under a coarse information system. We also compare profit earned by the owners in our experiment, which relies on a behavioral role of an information system, with the maximum profit theoretically possible given a contractual use of the information system. This comparison suggests that, unless the available information system is sufficiently precise, the owner will obtain greater profits by not contracting on its output, even if that output is fully contractible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".