When to respond to technological platform changes: empirical evidence from the video game industry
Bibliographic record
Abstract
Generational platform innovations prompt complementors to adapt their product development strategies. This paper examines how the speed of response to such platform changes affects new product quality and how this relationship is shaped by the complementor’s prior experience. Adopting a contingency perspective on the strategy-by-doing literature, we differentiate between depth of experience (repeated NPD experience with the same platform generation) and breadth of experience (accumulation of NPD experience across different generations), and analyse their interplay with the radicalness of the platform innovation. Using panel data from 1,014 PC video games released by 378 developers between 1995 and 2014 in response to DirectX platform generations and updates, we find that faster responses lead to higher product quality for complementors with greater depth of experience in incremental platform innovation contexts. In contrast, for radical platform changes, faster responses are harmful for complementors with a greater breadth of experience, suggesting that translating and integrating diverse platform knowledge into effective responses requires more time. Our findings contribute to research on platform evolution and strategy-by-doing by clarifying when faster technology responses help (or hinder) product performance in generational platform contexts.
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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 | 0.005 | 0.053 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".