Does client participation in an external audit affect their satisfaction with the audit service?
Bibliographic record
Abstract
Purpose – The purpose of this paper is to determine if self-serving bias affects audit client satisfaction level with their audit firm. Design/methodology/approach – A 2×2 between-subjects design is used, where the authors experimentally manipulate the level of client involvement in the audit and the extent of value-added services the client received. Findings – Using a sample of 115 financial managers (audit clients), the authors find no evidence that self-serving bias exists among clients in the experimental setting. Rather, they find that clients appear to be more satisfied with their auditor when they (clients) participate more in the service exchange. Research limitations/implications – The research is limited to a specific context within the privately held company audit setting. Practical implications – Audit firms may consider encouraging their privately held clients to participate more in the audit process by clearly communicating expectations and providing clients with audit preparedness materials, including templates and training where necessary. Originality/value – Although the self-serving bias has been shown to exist in the marketing literature, the authors present a setting where the relationship between service provider (auditor) and customer (client) is such that the self-serving bias may not hold.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".