The Impact of Entrepreneurial Ecosystems on Value Co-Creation in SME: The Moderating Role of Marketing Innovations
Bibliographic record
Abstract
Value co-creation is essential for the success and sustainability of Small and Medium Enterprises (SMEs), enabling them to integrate resources and knowledge from multiple stakeholders, such as customers, suppliers, and universities, to develop innovative offerings. However, research drawing on Service-Dominant Logic (SDL) and Resource-Based View (RBV) has devoted limited attention to how entrepreneurial ecosystem cooperation and marketing innovation jointly shape SME value co-creation, particularly in smaller and peripheral economies. This study examines the impact of entrepreneurial ecosystems (EEs) on value co-creation in SMEs, focusing on the moderating role of marketing innovation. EEs provide SMEs with access to new knowledge, technologies, and financial resources, which support innovation and enhance their competitiveness. Using microdata from the Portuguese Community Innovation Survey (CIS) 2020 and logistic regression models, we investigate how cooperation with key stakeholders (universities, customers, suppliers, consultants, competitors and government agencies) affects the likelihood that SMEs engage in value co-creation with users. The results show that ecosystem cooperation significantly contributes to value co-creation, with cooperation with universities, customers and suppliers exerting the strongest effects. Marketing innovation further strengthens the association between ecosystem cooperation and value co-creation, especially for knowledge-intensive and market-oriented partners. Theoretically, the study extends SDL by applying its multi-actor value co-creation perspective to entrepreneurial ecosystem configurations and specifying how cooperation with distinct actors activates co-creation mechanisms in SMEs. It extends RBV by conceptualising ecosystem cooperation as an externally orchestrated bundle of strategic resources and by positioning marketing innovation as a dynamic capability that shapes the returns to such cooperation. The findings also provide practical guidance for SMEs and policymakers seeking to design ecosystems and marketing strategies that support collaborative innovation in the knowledge economy.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".