Solutions for the commercialization challenges of Horizon Europe and earth observation consortia: co-creation, innovation, decision-making, tech-transfer, and sustainability actions
Bibliographic record
Abstract
Abstract European Community (EC) Horizon-funded projects and Earth Observation-based Consortia aim to create sustainable value for Space, Land, and Oceans. They typically focus on addressing Sustainable Development Goals (SDGs). Many of these projects (e.g. Commercialization and Innovation Actions) have an ambitious challenge to ensure that partners share core competencies to simultaneously achieve technological and commercial success and sustainability after the end of the EC funds. To achieve this ambitious challenge, Horizon projects must have a proper governance model and a systematized process that can manage the existing paradoxical tensions involving numerous European partners and their respective agendas and stakeholders. This article presents the VCW-Value Creation Wheel (Lages in J Bus Res 69: 4849–4855, 2016), as a framework that has its roots back in 1995 and has been used since 2015 in the context of numerous Space Business, Earth Observation, and European Community (EC) projects, to address complex problems and paradoxical tensions. In this article, we discuss six of these paradoxical tensions that large Horizon Consortia face in commercialization, namely when managing innovation ecosystems, co-creating, taking digitalization, decision-making, tech-transfer, and sustainability actions. We discuss and evaluate how alliance partners could find the optimal balance between (1) cooperation, competition, and coopetition perspectives; (2) financial, environmental, and social value creation; (3) tech-push and market-pull orientations; (4) global and local market solutions; (5) functionality driven and human-centered design (UX/UI); (6) centralized and decentralized online store approaches. We discuss these challenges within the case of the EC H2020 NextLand project answering the call for greening the economy in line with the Sustainable Development Goals (SDGs). We analyze NextLand Online Store, and its Business and Innovation Ecosystem while considering the input of its different stakeholders, such as NextLand’s commercial team, service providers, users, advisors, EC referees, and internal and external stakeholders. Preliminary insights from a twin project in the field of Blue Economy (EC H2020 NextOcean), are also used to support our arguments. Partners, referees, and EC officers should address the tensions mentioned in this article during the referee and approval processes in the pre-grant and post-grant agreement stages. Moreover, we propose using the Value Creation Wheel (VCW) method and the VCW meta-framework as a systematized process that allows us to co-create and manage the innovation ecosystem while engaging all the stakeholders and presenting solutions to address these tensions. The article concludes with theoretical implications and limitations, managerial and public policy implications, and lessons for Horizon Europe, earth observation, remote sensing, and space business projects.
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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 | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".