Students’ Acceptance of Simulation Games in Management Courses: Evidence from Saudi Arabia
Bibliographic record
Abstract
As a new trend in learning, simulation games play an active and essential role in the area of educational technology. Gaming makes a positive impact on the learning process. It has the capability to enhance creativity, problem-solving, communication, decision-making, and collaboration (Faizan et al., 2015). This paper is aimed at exploring the factors that affect students’ acceptance and use of simulation games in management courses. In this study, the unified theory of acceptance and use of technology (UTAUT) is utilized to investigate students’ intentions regarding using simulation games for learning. The proposed model and its hypotheses are tested by surveying 115 students at Yanbu University College in Saudi Arabia. Data are gathered and analyzed using smart partial least square. After analysis, the results prove that performance expectancy, effort expectancy, and social influence have positive effects on behavioral intentions (BI) and that facilitating conditions have a positive impact on use behavior (UB). In addition, a positive effect is found between BI and UB. The authors utilize the study findings to highlight some recommendations that could improve the implementation of simulation games. Finally, future studies are recommended to increase the sample size for more reliable results and conclusions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".