How to Influence Users’ Willingness to Explore the Use of Sports and Fitness Apps in China
Bibliographic record
Abstract
The aim of this paper is to investigate how inertia affects users' willingness to explore the use of sports and fitness apps under the influence of status quo bias, and to explore the role of health goals in the process of exploring use based on goal setting theory. The population in this research is Chinese users who have already installed or used a sports and fitness app on their mobile device. Through an online survey technique, we collected 449 valid questionnaires by convenience sampling method. The results confirm that inertia negatively influences the users’ willingness to explore the use of sports and fitness apps and that inertia negatively influences perceived need, which, in turn, reduces the willingness to explore the use of sports and fitness apps; Furthermore, this study also verified health goal positive moderate the relationship between inertia and perceived need, as well as the relationship inertia and users’ willingness to explore the use of sports and fitness apps, revealing that health goals can effectively adjust for the effects of status quo bias in mobile fitness exercise. This study provides useful suggestions for the development and operation of sports and fitness app enterprises to help them make suitable marketing strategies according to users' needs, thus promoting the long-term development of sports and fitness app enterprises.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".