Measuring the Impact of a Marketing Simulation Game
Bibliographic record
Abstract
Background. The evidence from past research suggests that business simulation games (BSGs) do offer a meaningful educational experience. One characteristic lacking across past research studies is the trait of indecisiveness. Aim. This study sought to explore whether business students would self-report a change in their perceptions of their indecisiveness after participating in a business simulation games (BSG). In addition, whether higher performance simulation decision makers would self-report being less indecisive (i.e. able to make decisions in a timely manner) than lower performance simulation decision makers. Method. Using a pre-test and post-test design with a comparison to an untreated control group, the change in 386 business students’ perceptions of their indecisiveness was assessed using a self-reporting questionnaire. Results. The findings showed a statistically significant reduction in the level of perceived indecisiveness as a result of the simulation experience. The higher performance students reported being less indecisive than lower performance students while both higher performance and lower performance students reported a reduction in perceived indecisiveness. The level of self-reported perceived indecisiveness amongst a control group of 137 business students indicated no significant change. Conclusion. If the combination of practice and positive reinforcement increases the comfort level (reduce feelings of risk and threat) of decision makers then perceived indecisiveness should decrease as a result of simulation participation, which may generalize across situations demanding decisions.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".