The Double-Edged Sword of Backward Compatibility: The Adoption of Multigenerational Platforms in the Presence of Intergenerational Services
Bibliographic record
Abstract
We investigate the impact of the intergenerational nature of services, via backward compatibility, on the adoption of multigenerational platforms. We consider a mobile Internet platform that has evolved over several generations and for which users download complementary services from third-party providers. These services are often intergenerational: newer platform generations are backward compatible with respect to services released under earlier generation platforms. In this paper, we propose a model to identify the main drivers of consumers’ choice of platform generation, accounting for (i) the migration from older to newer platform generations, (ii) the indirect network effect on platform adoption due to same-generation services, and (iii) the effect on platform adoption due to the consumption of intergenerational services via backward compatibility. Using data on mobile Internet platform adoption and services consumption for the time period of 2001–2007 from a major wireless carrier in an Asian country, we estimate the three effects noted above. We show that both the migration from older to newer platform generations and the indirect network effects are significant. The surprising finding is that intergenerational services that connect subsequent generations of platforms essentially engender backward compatibility with two opposing effects. Whereas an intergenerational service may accelerate the migration to the subsequent platform generations, it may also, perhaps unintentionally, provide a fresh lease on life for earlier generation platforms due to the continued use of earlier generation services on newer platform generations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".