ISA 701 and Materiality Disclosure as Methods to Minimize the Audit Expectation Gap
Bibliographic record
Abstract
Purpose: The main purpose of this paper is to determine how particular audit firms deal with ISA 701 requirements and the society expectations towards reporting the materiality levels. Additionally, the aim of this paper is to range the assertions in terms of the frequency of their occurrence. Design/methodology/approach: The tested sample consisted of 317 companies listed on Warsaw (158 companies) or London (159 companies) stock exchange. The analysis was divided into companies from the following ten market indexes (WIGs): construction, IT, real estate, food, media, oil and gas, mining, energy, automotive and chemicals. The research was executed based on the analysis of annual consolidated financial statements (annual reports) and independent auditor reports that were published by in-scope entities for the latest twelve-months period available as at the date of the research (mostly periods ended on 31 December 2017 and 31 March 2018). All values were denominated to euro (EUR) with use of average exchange rates published by the National Bank of Poland. All performed analyses and developed charts were supported by Microsoft Power BI data analysis tool. Findings: The general conclusion, which may be drawn from this research, is that implementation of ISA 701 and materiality disclosure limited the audit expectation gap. Detailed observations are described throughout the paper and summarized in the conclusions section. Originality/value: This study extends the prior research by providing various dimensions of the analysed matters. It contributes to understanding of the audit expectation gap and investigates on methods of minimizing it.
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 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 | 0.051 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".