Relationship Between Passion and Motivation for Gaming in Players of Massively Multiplayer Online Role-Playing Games
Bibliographic record
Abstract
Passion represents one of the factors involved in online video gaming. However, it remains unclear how passion affects the way gamers are involved in massively multiplayer online role-playing games (MMORPGs). The objective of the present study was to analyze the relationships between passions and motivations for online game playing. A total of 410 MMORPG players completed an online questionnaire including motives for gaming and the Passion Scale. Results indicated that passionate gamers were interested in relating with others through the game and exhibited a high degree of interest in discovery of the game, gaining leadership and prestige but little interest in escape from reality. However, some differences were observed with respect to the role of the two types of passion in the different types of motivation. Specifically, harmonious passion (HP) predicted higher levels of exploration, socialization, and achievement, in that order, while obsessive passion (OP) predicted higher levels of dissociation, achievement, and socialization. The present findings suggest that HP and OP predict different ways of engaging in MMORPGs and confirm that passion is a useful construct to help understand different motivational patterns demonstrated by MMORPG players.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".