How Passion for Playing World of Warcraft Predicts In-Game Social Capital, Loneliness, and Wellbeing
Bibliographic record
Abstract
Playing digital games can nurture wellbeing by helping players recover from daily stressors, cope with life's challenges, practice emotion regulation, and engage in meaningful social interaction; however, this same leisure activity can also result in problematic gaming (i.e., harmful play at the expense of healthy behaviors), and social isolation that damages wellbeing. Research consistently demonstrates that the value or harm of gaming on wellbeing cannot be determined solely from whether and how much people play, but rather depends on contingent factors related to the player, the game, and the gaming context. In this paper, we aim to model contingent factors that differentiate between beneficial and harmful outcomes within players of the same massively multiplayer online role playing game (MMORPG). We model how passion for gaming-defined as a strong desire to engage in a beloved activity that is enjoyed and valued, in which time and energy is invested, and that ultimately integrates into a person's identity-affects loneliness and wellbeing. We employ the dualistic model that divides passion into harmonious passion (HP)-characterized by a balanced and authentic relationship with the beloved activity, and obsessive passion (OP)-characterized by preoccupation and inflexible persistence toward the loved activity. We sampled 300 frequent World of Warcraft (WoW) players, recruited from online forums, and used structural equation modeling (SEM) to investigate the effects of their passion for playing WoW on in-game social capital, loneliness, and wellbeing. We demonstrate that HP for playing WoW facilitates in-game social capital (both bridging and bonding), combats loneliness, and increases wellbeing, whereas OP also builds social capital, but these social ties do not combat loneliness, and OP is directly associated with increased loneliness. Further, the positive effect of HP on wellbeing is mediated through an increase in bonding social capital and a resulting decrease in loneliness. Our findings highlight that passion orientation is important for characterizing the relationship between gaming and wellbeing. We contribute to the conversation on combating problematic gaming, while also promoting digital gaming as an appealing leisure activity that provides enjoyment, recovery, and meaningful social interaction for the millions of gamers who benefit from its captivation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".