An Interdisciplinary Study of the Impact of Playing A Marketing Simulation Game on Student Knowledge of Management Accounting/Finance Principles
Bibliographic record
Abstract
An interdisciplinary study of student knowledge of accounting/finance principles in concert with their application in a marketing simulation game was undertaken in three sections of an Introduction to Marketing. The subjects were 454 second year marketing students who took an accounting/finance knowledge test composed of 14 multiple choice questions focused on understanding definitions and making calculations for selected concepts including unit contribution margin, inventory carrying costs, working capital, gross margins, return on sales, straightforward breakeven calculation, current ratio and mark-ups. A total of 368 students agreed to participate in the study from which 308 usable responses were collected, a 67.8% response rate. The study employed a simple pretest-posttest design resulting in a pretest average score of 42.4% (32.7% corrected for guessing) and a posttest average score of 55.5% (43.7% corrected for guessing) for the accounting/finance test. Paired t-test comparisons of pretest versus posttest scores for both uncorrected and corrected for guessing results were significantly different at the .000 level. The overall conclusion was that the marketing simulation experience led to an improvement in knowledge and application of accounting/finance principles. This study provides further evidence for the external validity of business simulation games. Acknowledgements: The research undertaken in this paper was funded by a grant from the Certified Management Accountants of Ontario
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".