Digital Business Ecosystems: An Environment Of Collaboration, Innovation, And Value Creation In The Digital Age
Bibliographic record
Abstract
This article delves into the concept of Digital Business Ecosystems (DBEs), which have arisen due to the increasing interconnectedness of businesses and the growing reliance on digital technologies for value creation. DBEs are characterized by adaptability, scalability, and resilience, enabling businesses to collaborate, innovate, and adapt to changing market conditions. The article explores the components of DBEs, including actors, resources, and processes, and examines different DBE models, such as the hub-and-spoke, network, and layered models. Digital platforms play a critical role in DBEs, and effective platform design involves considering factors such as scalability, modularity, and openness. Various technologies, such as cloud computing, big data analytics, artificial intelligence, and the Internet of Things, underpin the development and operation of DBEs, and integrating these technologies presents both opportunities and challenges for businesses. The article addresses key DBE business issues, such as alliances, network analysis, value co-creation, governance, legal issues, trust, risk, security, knowledge development, dissemination, and management. It also highlights the importance of DBE strategies, processes, and management for businesses to thrive and achieve sustainability in the digital landscape. Finally, the article suggests future research themes, such as exploring new models and frameworks, investigating factors contributing to DBE success or failure, identifying best practices, and examining the implications of emerging technologies on DBEs.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".