The effects of consumer esports videogame engagement on consumption behaviors
Bibliographic record
Abstract
Purpose This study aims to propose a model for predicting consumers’ esports videogame engagement on their ensuing consumption behaviors, which remains nebulous to date. Design/methodology/approach After approaching esports consumers in different gaming zones in Pakistan, this paper collected data from 364 videogame-based esports consumers. This paper deployed SmartPLS 3.2.8 software to perform the partial least squares-structural equation modeling-based analyzes. Findings The structural model results show that consumers’ affective and behavioral esports videogame engagement positively affects their consumption behavior, including heightened community engagement, purchase intent, coproduction, word-of-mouth and new player recruitment. However, while consumers’ cognitive esports engagement was found to positively impact community engagement, new player recruitment and coproduction, it failed to predict consumers’ esports-related purchase intent or word-of-mouth behaviors. Practical implications The findings reveal that a strategic focus on consumers’ esports game engagement will enable practitioners to nurture desirable consumer behaviors, including enhanced purchase intent, coproduction, word-of-mouth and new player recruitment behaviors, thus warranting consumer engagement’s strategic value as a key esports gaming metric. Originality/value Empirical research into the role of consumers’ esports videogame engagement on their ensuing consumption behaviors remains scant to date. Based on this gap, this study offers a timely contribution by exploring and validating a model that gauges the effect of consumers’ cognitive, emotional and behavioral esports videogame engagement on their community engagement, purchase intention, coproduction, word-of-mouth and new player recruitment. It, thus, offers important insight into the rapidly advancing field of digital esports games.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| 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".