Exploring player cocreation dynamics on the gaming platform: Interplay of goal fulfillments, orchestration actions, and platform affordances
Bibliographic record
Abstract
Understanding player co-creation dynamics on gaming platforms is crucial for fostering engagement and driving innovation in digital marketing . This study investigates these dynamics on the Roblox platform, proposing an integrated framework that connects platform capabilities with player-driven orchestration actions and the pursuit of diverse goals − a model applicable to various digital marketing contexts. We identify three types of gaming platform affordances and three types of developers’ orchestration actions, ultimately shaping co-creation activities in terms of creative and social engagement . Using web crawling and text mining methodologies, we analyze a large, longitudinal dataset from Roblox developers engaged in co-creation projects. We employ three observable metrics to quantify co-creation activities, applying different perspectives including equality-based, effort-based weighted, and specialized measures of creative and social engagement. Our findings confirm the direct effects of platform affordances and orchestration actions on co-creation activities, with post-hoc analyses revealing goal fulfillment as an important antecedent mechanism. To validate our results, we conducted a two-stage survey with 206 experienced Roblox developers, providing additional robustness to our empirical findings. This research advances our understanding of digital co-creation and offers practical implications for designing more engaging and innovative gaming platforms. As gaming and digital marketing converge, particularly in the evolving metaverse landscape, this study underscores the importance of leveraging co-creation dynamics to enhance user engagement and drive platform growth.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".