Open Innovation in Services? A Conceptual Model of Barriers to Service Innovation Adoption
Bibliographic record
Abstract
Recently, there has been an increased focus on the service sector as a source for economic growth and development. This is particularly true in the knowledge-based services where the need for innovative service offerings in the global market continues to grow. The open innovation model is one which has been gaining in popularity as the technology continues to improve the ability for global collaborations and partnerships. Currently, little is understood of innovation in the services, and in particular open service innovation. This paper presents an extension of existing models of open innovation focusing on innovation sources and diffusion of open service innovation. Particular attention is paid to the potential barriers to open service innovation in order to demonstrate the additional complexities in managing open service innovations in comparison to their physical good counterparts. The conceptual model provides insight into areas for future research at the individual, meso-, and macro-levels to better understand the factors that influence open services innovation, situations in which open innovation is most practical, and intricacies necessary to support open innovation in services.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".