Examining situational continuous mobile game play behavior from the perspectives of diversion and flow experience
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate users’ continuous adoption behaviors on mobile game playing from the perspective of situational habit formation. Design/methodology/approach Based on the literature research, a continuous adoption model for situational mobile game is proposed. And the research model is assessed based on data gathered from a sample of 226 mobile game players by employing the structural equation model methodology. Findings The results show that situational cues represented by availability, perceived ease of use and diversion lead to repeated performance that can be represented by flow experience and satisfaction in the situational mobile game playing context. But only flow experience and diversion influence continuous usage directly. Additionally diversion, as a critical situational variable, not only indirectly affects continuous usage intention through flow experience, but also directly affects continuous usage intention for situational mobile game playing. Originality/value Mobile game adoption has been studied from different perspectives, but most research is based on the technology acceptance model. They could not explain the common fact that young people tend to be highly motivated by mobile games and can be regarded as pro-active mobile game players, but many people play mobile games only when they are bored and need a diversion. So this study attempts to illustrate the phenomena to fill the gaps.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".