The Effect of Ethics in Business on Happiness, Aggressiveness and Inconsistency of Efforts and Rewards
Bibliographic record
Abstract
The present study investigates the effect of business ethics on happiness, aggression and inconsistency of effort and reward of auditors in Iran and Iraq. The statistical population of the present study includes all partners, managers and auditors working in audit institutions in Iran and partners of the audit institutions, assistant auditors, auditors, individual second rank and individual first rank, with a total of 365 questionnaires completed by Iranian respondents out of 450 questionnaires and 250 questionnaires completed by Iraqi respondents out of 350 questionnaires, a total of 615 questionnaires from the two countries in 2022. Also, the methods of variance analysis and ordinary least squares regression and Smart PLS 3 and Stata 15 software were used to analyze the data and test the hypotheses. The results from testing this research’s hypotheses indicate a negative and significant relationship between business ethics and aggression, effort-reward mismatch and a positive and significant relationship between business ethics and happiness. Since the current research was conducted in the emerging financial markets of Iran and Iraq, which are highly competitive, along with having special economic conditions, and since the occupation of the ISIS terrorist group, the civil wars in Iraq, severe world economic sanctions against Iran and the global crisis of Covid-19 in Iran and Iraq have led to special conditions, the current research can bring helpful information to readers and help the development of science and knowledge in this field.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".