The Implications of Offering Free Versions for the Performance of Paid Mobile Apps
Bibliographic record
Abstract
The mobile application (app) industry has grown tremendously over the past ten years, primarily fueled by small app development businesses. Lacking advertising budgets, these small and relatively unknown businesses often offer free versions of their paid apps to be noticed in the crowded app industry and to reduce customer uncertainty about app quality and fit. The authors build on the existing marketing and information systems literature on sampling and versioning to investigate the implications of offering free versions for the adoption speed of paid apps. Using a unique data set of 7.7 million observations from 12,315 paid apps, and accounting for endogeneity, the authors find that although the practice of offering free versions of paid apps is popular, it is negatively associated with paid app adoption speed. They also find that this negative association between free version presence and paid app adoption speed is stronger both for hedonic apps and in the later life stages of paid apps. The authors hope that the study's results will encourage app developers to reevaluate their current strategy of offering free versions of paid apps and prompt academics to produce more work focusing on this industry.
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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 | 0.011 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".