Bibliographic record
Abstract
Purpose – This paper uses the multidimensional definition of value – ecosystemic value – and employs lifecycle theory to identify the different stages of evolution of value-creation and -capture processes in an ecosystem. Specifically, the aim of this paper is to show the uneasy transition from supply chains to ecosystems. Design/methodology/approach – Based on a field study of a Canadian ICT ecosystem, this paper adopts a multilevel perspective on value-creation and value-capture processes and illustrates how these processes need to move from a dyadic economic focus to a network socioeconomic one. Findings – The findings pinpoint the uneasy transition from supply-chains management to ecosystems management and provide a framework for understanding how value creation and value capture should be coupled throughout the ecosystem lifecycle. Finally, five theoretical and managerial propositions are suggested to better leverage ecosystemic capabilities and better manage value creation and value capture in ecosystems. Practical implications – Five theoretical and managerial propositions are suggested to better leverage ecosystemic capabilities and better manage value creation and value capture in ecosystems. Originality/value – Many marketing and management scholars discuss the limitations of unbalanced perspectives (customer- or seller-centric) in building a comprehensive view of how value is created and captured. This multi-actors case study highlights how ecosystemic value creation may be obstructed by a firm's focus on value capture.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".