Value Cocreation for Service Innovation: Examining the Relationships between Service Innovativeness, Customer Participation, and Mobile App Performance
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.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".