Value Cocreation for Service Innovation: Examining the Relationships between Service Innovativeness, Customer Participation, and Mobile App Performance
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 innovation is critical to firms’ competitive advantage and, thus, firms desire to make their services increasingly innovative. However, the relationship between the innovativeness and performance of a new service is unclear. Conflicting findings and the related literature suggest that service innovativeness is multidimensional and its impact on performance could be nonlinear. However, limited research has studied these aspects, both theoretically and empirically. Furthermore, prior research has mainly considered customers as inputs to value creation, which may not capture their precise role. Drawing on service-dominant logic, we propose two dimensions of service innovativeness, namely novelty and intensity, which differentially influence the performance of a new service. We further posit that customers are part of the value cocreation process, thereby directly and indirectly affecting new service performance. The model was tested using a panel dataset of 234 mobile apps over 14 months. Results indicate important asymmetries in the impacts of novelty and intensity on mobile app performance: novelty shows a curvilinear relationship with mobile app performance whereas intensity shows a positive linear relationship. Furthermore, customer participation positively impacts mobile app performance and positively moderates the effects of intensity and novelty on mobile app performance.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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