Contract negotiation, incomplete contracting, and asymmetric information : (essays in managerial accounting research)
Bibliographic record
Abstract
This thesis contributes to the managerial accounting research literature. The methodology used is basically analytical modelling. Part I focuses on voluntary financial accounting disclosure. Following a detailed survey of the existing literature, an analytical model of an entry game with continua of types is provided to advance the results of prior research. By explicitly considering both a potential entrant and potential investors, this model incorporates two opposing forces that may influence an incumbent's decision to disclose or withhold private information. Various equilibria are characterized and discussed. Part II of the thesis focuses on firms' contractual relationships. The analyses extend traditional agency theory analysis to situations in which complete contracting is costly. Two models related to incomplete contracting are offered. One model analyzes the influence of contracting costs on a firm's contracting strategy in the context of the firm's internal transfer of goods and services. The results of this analysis provide insights and a new basis for the research of the transfer pricing issue. The second model deals with the incentive issues within organizations. The analysis focuses on the situations in which verifiable performance measures are unavailable. In the model, two kinds of incentives, namely, high-powered and low-powered incentives, are analyzed. We find that contract renewal based on observable (but non-verifiable information) can provide useful low-powered incentives in an hierarchical organization in which employees build up human capital. This may provide useful insights into managerial accounting system design.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".