Predicting Video Game Addiction Through the Dimensions of Consumer Video Game Engagement: Quantitative and Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Video games are expanding exponentially with their increased popularity among users. However, this popularity has also led to an increase in reported video game addiction. There may be consumer engagement-related factors that may influence video game addiction. OBJECTIVE: This study aims to empirically examine the impact of the dimensions of consumer video game engagement on video game addiction. The dimensions are dedication, absorption, conscious attention, social connection, enthusiasm, and interaction. We utilize the uses and gratifications theory to study the video game engagement dimensions as potential factors through which gamers feel gratified and engaged in video game playing. Additionally, this study incorporates the cultivation theory to investigate how video game engagement factors trigger video game addiction. METHODS: A two-step process was applied for data analysis on valid cases of 176 gamers aged 15-25 years: video game addiction was specified and validated as a reflective-formative construct, and hypothesis testing was later performed using the WarpPLS on valid respondents. RESULTS: The analysis uncovered 2 dimensions of video game engagement: social connection with P=.08 and interaction with P=.49, which did not significantly contribute to video game addiction. CONCLUSIONS: This study offers unique insights to a myriad of stakeholders, mostly psychologists and psychiatrists, who routinely prescribe behavior modification techniques to treat video game addiction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".