Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Service innovativeness, or the propensity to introduce service innovations to satisfy customers and improve firm value at acceptable risk, has become a critical organizational capability. Service innovations are enabled primarily by the Internet or people, corresponding to two types of innovativeness: e- and p-innovativeness. The authors examine the determinants of service innovativeness and its interrelationships with firm-level customer satisfaction, firm value, and firm risk and investigate the differences between e- and p-innovativeness in these relationships. They develop a conceptual model and estimate a system of equations on a unique panel data set of 1049 innovations over five years, using zero-inflated negative binomial regression and seemingly unrelated regression approaches. The results reveal important asymmetries between e- and p-innovativeness. Whereas e-innovativeness has a positive and significant direct effect on firm value, p-innovativeness has an overall significantly positive effect on firm value through its positive effect on customer satisfaction but only in human-dominated industries. Both e- and p-innovativeness are positively associated with idiosyncratic risk, but customer satisfaction partially mediates this relationship for p-innovativeness to lower this risk in human-dominated industries. The findings suggest that firms should nurture e-innovativeness in most industries and p-innovativeness in human-dominated industries.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it