Exploring the Intersection of Contemporary Management Accounting Practices and Accounting Information Systems: The Impact on Hotel Performance
Bibliographic record
Abstract
Contemporary Management Accounting Practices (MAPs) were developed to address the weaknesses of traditional practices and to meet financial managers’ need for accurate and timely information. Consequently, they contribute to optimal decision-making that enhances firms’ efficiency and competitiveness, leading to improved organizational performance. Simultaneously, the success of Accounting Information Systems (AIS) is essential, as they improve the quality of information and reporting. In information- and competition-intensive environments such as the hotel industry, AIS user satisfaction, as an indicator of AIS success, can play a decisive role in the effective use of contemporary MAPs. The purpose of this paper is to explore the relationship between contemporary MAPs usage and hotel performance, and to investigate the moderating role of AIS user satisfaction. Using hierarchical multiple regression analysis, the findings indicate that the interaction of contemporary MAPs usage and AIS user satisfaction results in improved hotel performance. This study contributes to the current knowledge by developing a framework of the relationship of Management Accounting and Information Technology, through the lens of Contingency Theory and the Information Systems Success Model of DeLone and McLean. Additionally, the findings provide managerial implications for financial managers and IS developers.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".