Using the playful consumption experience model to uncover behavioral intention to play Multiplayer Online Battle Arena (MOBA) games
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Playing video gaming is one of the most popular forms of leisure activities. This study looks into a specific video game genre, under the category of Multiplayer Online Battle Arena (MOBA) games. The purpose of this research is to investigate the factors that lead to the consumption of MOBA games. Three factors, imaginal, emotional and sensory experiences, are investigated through an integration of the hedonic consumption model, Technology Acceptance Model (TAM) and Uses and Gratification Theory (UGT). Design/methodology/approach The study analyses a sample of 292 MOBA game players using the PLS-SEM model. The study comprises two stages; in the first stage, an estimation model was used to test the constructs' quality and legitimacy. In the second stage, we assessed the theoretical model to test the relationship between the principle constructs. Findings The study found that factors related to emotional experiences, namely enjoyment, emotional involvement, and arousal, led to greater intention to play MOBA games. Similarly, two factors related to imaginal experiences, escapism and role projection, also positively impacted while fantasy carried a negative impact. The study also found that sensory experiences had a significant positive impact on the intention to play MOBA games. Lastly, a positive correlation was also found between the intention to use and usage behavior in MOBA games. Originality/value This study contributes to the theoretical understanding of the playful-consumption experiences of pleasure-oriented information systems (I.S.) that is MOBA games that derive behavioral intention to play MOBA games, which in turn determines the usage behavior of MOBA players. The study also incorporates the uses and gratification theory to uncover the needs and experiences that trigger MOBA games' behavioral intention and its further impact on gamers' usage behavior. The study also presents useful insights for game developers and other relevant stakeholders in game development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 it