An empirical analysis of usage dynamics in a mobile music app: evidence from large-scale data
Bibliographic record
Abstract
Purpose – The purpose of this paper is to quantify how mobile app usage relates to the unique characteristics of behavioral orientations and content types, focussing on the interrelationship among content usage in the context of in-app purchase. Design/methodology/approach – Using a large-scale data set of individual content usage in a particular music mobile app, the author builds a simultaneous equation panel data model to examine dynamic interdependent usage of mobile app. Findings – The paper finds a positive temporal effect of self-oriented content usage (download) on other-oriented content usage (gift), based on behavioral orientation, and also a temporal interdependence between external (ringtone) and internal usage (mp3) based on types of content. The paper also finds that the fourth generation communications standard increases content usage in this mobile app. Practical implications – These findings provide useful insights for mobile app developers, mobile network operators, content providers, and mobile device manufacturers. Originality/value – This paper is one of the first to consider and empirically test the interrelationship between various kinds of content usage in music apps.
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.
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".