Enable and orchestrate—How keystone actors shape institutions for smart service innovation in ecosystems
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
Abstract This study explores the role of keystone actors in shaping institutions to drive collaborative innovation within service ecosystems, focusing on smart services in industrial B2B settings. Smart services leverage data analytics for enhanced customer insights, marking a strategic shift for product-oriented companies. Transitioning to smart services involves adapting business models and fostering effective collaborations. Keystone actors facilitate this by promoting collaboration and aligning participants toward shared goals without exerting direct control. While previous research emphasizes understanding keystone actors in service ecosystems, how they shape institutions for collaboration is rarely investigated. This study aims to provide insights into driving smart service innovation, enhancing companies’ competitive advantage in the digital era. Using a multiple case study design, the research identifies two keystone actor types: the Orchestrator and the Enabler. The findings offer valuable insights into institution shaping and keystone actors’ influence, guiding practitioners in managing smart service innovation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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