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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".