Bridging the Distance: Spatial and Social Factors Influencing Audit Quality and Auditor Independence in Small and Medium-Sized Enterprises
Bibliographic record
Abstract
Audit quality is crucial, particularly for small and medium-sized enterprises (SMEs), due to their significant economic role. This study examined how spatial distance (physical separation) and social distance (perceived dissimilarity) between auditors and SME clients influence audit quality, focusing on technical quality (the tangible outputs of auditing) and process quality (the manner of service delivery). Using data from 449 SME executives across Thailand, the study investigated the mediating role of auditor independence within these relationships. The results from structural equation modeling revealed that spatial distance has no direct impact on audit quality but a negative effect on perceived auditor independence, which, in turn, diminishes audit quality indirectly. Conversely, social distance negatively impacts both technical and process quality directly and indirectly through auditor independence. The findings suggest that despite technological advancements facilitating remote auditing, maintaining some physical interaction remains vital for preserving client trust. Additionally, aligning auditor–client social similarities significantly enhances audit quality perceptions. This study provides practical implications for audit firms in managing client interactions effectively, particularly within SMEs.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".