Developing Innovative Practice in Service Industries
Bibliographic record
Abstract
This chapter presents the argument that service innovation is promoted by supporting divergent interpretations, enlarging the scope of employee and organizational skills and competencies, making interactions and knowledge sharing between people easy, and by encouraging close ties with customers. The chapter further argues that service organizations that utilize sociotechnical mechanisms for knowledge sharing through the use of a successful community of innovation (which we term a CoInv), and that build into their innovative capacities a strong relationship with their customers and suppliers, are very likely to innovate successfully. The argument is demonstrated through a qualitative case study where data analysis was deductive from multiple data sources. The chapter also demonstrates the power and efficacy of channeling activities through community innovation lenses. We argue that identifying innovation champions and comprehensively supporting them will potentially trigger more successful innovations thus improving service competitiveness in the market place.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".