Erosion of Complement Portfolio Sustainability: Uncovering Adverse Repercussions in Steam’s Refund Policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maintaining a consistently trending portfolio of complements is vital to sustaining platform leadership. Prior research has highlighted the value of open innovation, but has largely disregarded the strategic identification and management of distinctive complements that drive extended platform value, particularly via platform policy modifications. The relevance of prior research around influential policies such as refund leniency becomes largely irrelevant once applied to platform conditions. Utilizing Steam as the medium of analysis, this paper distinguishes complements into three classifications of sustainability, representing its contribution to developing platform leadership. Steam's refund policy alteration is investigated for its effects on refund revenue reductions and additional demand on each classification, assessed using an indirectly related linear regression between playtime distribution and game age, and a binomially distributed t-test on the percentage of favorable games. The results reveal that, while all patterns experience significant volumes of refunds, corresponding revenue enhancements are perceived only among unsustainable games. This creates a disadvantageous foundation for high-value complements and consequently, an unforeseen disincentive for association, potentially inciting preferential linkage with competitors. This paper further proposes a precedent for future open innovation and platform management research, where complements of highest relevance are identified and granted heightened priority to protect their sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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