What Drives Cost System Sophistication? Empirical Evidence from the Greek Hotel Industry
Bibliographic record
Abstract
The increasing complexity of the hotel industry necessitates the implementation of sophisticated cost systems capable of delivering accurate and relevant cost information to support managerial decision-making. Investigating the determinants of cost system design is crucial, given that no single accounting system is universally applicable across all business contexts. This study addresses a critical gap by examining the key drivers of cost system sophistication through the theoretical frameworks of contingency and upper echelons theories, focusing specifically on the Greek hotel sector. Employing multiple regression analysis, the findings reveal that firm size, cost structure, the importance of cost information in decision-making, and the integration of information technology significantly influence the complexity of cost systems. Conversely, factors such as competition, service diversity, business strategy, organizational life cycle, and executive characteristics showed no statistically significant impact. These findings contribute to management accounting and hospitality literature by integrating theoretical perspectives and identifying key determinants of cost system sophistication. Moreover, the study offers practical insights for designing cost systems that meet the specific needs of the hotel industry.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".