Identifying the Degree of Compatibility between Computer-based Accounting Information Systems, Requirements and Barriers to E-Commerce in Iranian Service Companies According to a Joint Project of the United States and Canada
Bibliographic record
Abstract
The purpose of this study is to identify the degree of compatibility between computer-based accounting information systems, requirements, and barriers to e-commerce in Iranian service companies according to a joint project of the United States and Canada. The present study is applied in terms of purpose and descriptive-analytical in terms of method. The statistical population is service companies listed on the Iranian Stock Exchange (fifty-five companies). The statistical sample included 48 employees of accounting departments. So, the questionnaire was distributed among 48 employees in the accounting departments. All participants in the sample answered the questionnaire. We used the resources, references, books, papers, and scientific journals available in libraries (extracting studies related to the research topic) and the Internet (discovering new topics in the field of research) to collect data and information. Also, the main tool for collecting primary data was a questionnaire whose questions were prepared and designed based on a five-point Likert scale and a joint project between the United States and Canada (Al-Qishi, 2003 and Mahern, 2009). Before distributing the questionnaire, some experienced professors and experts in the field of accounting sciences evaluated the apparent validity of the questions. The reliability of the questionnaire was also assessed through Cronbach's test. To analyze the data, SPSS software and arithmetic mean test and standard deviations were used to determine the degree of compatibility between the studied variables. The results showed that there is sufficient compatibility between computer-based accounting information systems and e-commerce requirements in Iranian service companies according to a joint US-Canadian project.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".