Encouraging Innovations of Quality from User Innovators: An Empirical Study of Mobile Data Services
Bibliographic record
Abstract
Can mobile data service platforms support users to make quality mobile apps? If so, what should the platforms do to encourage quality mobile data services from users? Cognitive evaluation theory is useful in explaining human behaviors based on individuals’ innate psychological needs. In this article, the authors use this theory to explain how platforms can design their features (i.e., software development tools and design rules and regulations) to fulfil user needs for competence and autonomy. As a result, users can make mobile data services of better quality. The authors propose that toolkits can support the need for competence in terms of ease of effort and idea exploration, whereas regulations in design autonomy can support the need for autonomy in terms of decision-making autonomy, scheduling autonomy, and work method autonomy; and they find that indeed toolkits supported idea exploration and ease of effort, decision-making autonomy, and work method autonomy, enhancing the quality of users’ service innovations. The insights for managers are that platforms can mindfully design their regulations and tools to support users to develop quality innovations, and that platform regulations and tools should be developed complementarily rather than separately.
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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 | 0.024 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".