Essential Factors When Designing a Cost Accounting System in Greek Manufacturing Entities
Bibliographic record
Abstract
We examine the extent to which basic factors, such as the structure, complexity, and usefulness of a cost system, affect the design of cost systems and the resulting satisfaction and help companies make the right decisions. Moreover, we examine the relationship between the structure and complexity of cost systems with (a) a company’s demographic data, such as the volume of its activities, the number of years it has been operating, its sector, its size, and the gender, age, level of training, and position of its employees; and (b) information concerning production and competition, such as the number of products that a company produces, the number of a company’s production lines, the level of competition, and the extent to which competition affects a company’s pricing policy. Empirical research was conducted via a questionnaire in which a sample of 114 industrial companies in Greece took part. The findings revealed that the structure and the usefulness of a cost system, but not its complexity, significantly affect the satisfaction users get from the system when they are called to make fast and correct decisions. The results point out a positive correlation between the satisfaction a user gets from a cost system and the range of information (R), the calculation of deviations (CS), the provision of accurate information (CS), the quality of information (CS), the number of cost pools (C), the number of allocation bases (C), and the cost information (U). Companies that produce more goods and have a complex production process have cost systems that not only have a more detailed structure and provide more detailed information with the calculation of deviations as well as accurate information but also have more cost pools and cost allocation bases. The more competition affects a company’s pricing policy, the more a company seeks systems that categorize costs based on behavior (structure) and more cost allocation bases (complexity). The larger a company is, with a long (>20 years) and international presence, the higher the probability a company will have a system with a more detailed cost information structure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".