What drives subscribing to premium in freemium services? A consumer value‐based view of differences between upgrading to and staying with premium
Bibliographic record
Abstract
Abstract Fostering the conversion of free users to premium subscribers and retaining those premium users are critical objectives for freemium service providers. Building on consumer value theory, we empirically examine the differences between basic and premium users in terms of the emotional, functional, social, epistemic, and economic values driving basic users' decisions to upgrade to premium subscriptions and premium users' decisions to retain their paid subscriptions. We employ enjoyment, intrusiveness of advertising in the free subscription, ubiquity, social connectivity, the discovery of new content, and the price value of the premium subscription as drivers of intentions and test our model using data from a leading digital content service that employs the freemium model. Our results show that enjoyment and price value of the premium subscription predict the intention to upgrade to premium, whereas the intention to retain the premium subscription is driven by ubiquity and the discovery of new content. Interestingly, social connectivity has no effect on the intention to upgrade but does have a small negative effect on the intention to retain the premium subscription. Contrary to our expectations, intrusiveness of advertising in the free subscription had a negative effect on the price value of the premium subscription. Collectively, our results imply that the intention to retain the premium subscription is influenced by attribute‐level value perceptions such as ubiquity, the discovery of new content, and social connectivity whereas the intention to upgrade is driven by benefits, ie, enjoyment and price value of the premium subscription.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".