Internal Control, Organizational Culture, and Quality of Information Accounting to Prevent Fraud: Case Study From Indonesia's Agriculture Industry
Bibliographic record
Abstract
This research wants to find out how far internal control, organization culture and the quality of accounting information system will help the small-medium enterprises (SMEs) to prevent fraud. by applying the case study approach in achieving its aims and objectives. This study is done by a used case study from SMEs in the agriculture industry in Lampung province, Indonesia. The data were collected through observations and semi-structured interviews with employed and managerial staff. This research applied a mixed method in collecting and analyzing data, which were document analyses and interviews. Applying more than a single method in collecting data enables the researcher to compare and to verify the information accuracy (Brewer and Hunter 2006). This method can increase the credibility and validity of the findings because the final bias will depend on one method which later can be avoided (Yin 2012). This type of research is quantitative descriptive research. The purpose of this descriptive research is to provide a descriptive, systematic, factual and accurate description of the facts, properties, and relationships between the phenomena investigated. All data that will be used in this study is sourced from the results of respondents' answers to the questionnaire given to employees at PT. XYZ as many as 70 respondents with the unit of analysis are part of Business Control, Human Capital, Finance, Marketing, and Operations. The sampling technique that uses saturated sampling, which is a sampling technique where all members of the population will be used as samples. The results of the study show that some weaknesses of the internal controls have been identified as one of the factors of fraud. The results show that Internal Control Organizational Culture and Quality of Information Accounting have a positive significant effect to prevent fraud.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".