INVESTIGATING VALUE CREATION AND COMPETITIVE ADVANTAGE OF DIGITAL ECOSYSTEMS: NEXT-GENERATION COLLABORATION AND BIG DATA ENVIRONMENTS
Bibliographic record
Abstract
Purpose- The purpose of this article is to examine the potential of digital ecosystems in creating value and providing a competitive advantage for businesses and industries. Additionally, it aims to provide an understanding of how digital ecosystems function within a big data environment. Methodology- This study presents a general understanding of digital ecosystems and big data by reviewing previous research and literature. Focusing on two critical advantages of digital ecosystems in creating value and providing a competitive advantage, the analysis is conducted using example companies such as Amazon, Apple, and UBER. Findings- Digital ecosystems emerge as complex and dynamic structures that enable value creation processes and collaboration among technology, businesses, and users. These structures significantly differ from traditional collaborative ecosystems by relying on digital technologies and platforms for value creation processes. A successful digital ecosystem is based on three main elements: platform, network effects, and market expectations. Big data is considered one of the fundamental components of digital ecosystems and has the potential to increase their effectiveness and value. Conclusion- Digital ecosystems allow businesses and industries to increase their productivity, gain a competitive advantage, and achieve sustainable growth. In particular, big data analytics can be used to optimize the performance and decision-making processes of digital ecosystems. Examples such as Amazon, Apple, and UBER demonstrate the potential of digital ecosystems in creating value and providing a competitive advantage. Therefore, it is crucial for businesses to adopt digital transformation and innovation to benefit from the advantages offered by digital ecosystems. Keywords: Digital ecosystems, big data, value creation, competition JEL Codes: L86, D46, D41
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".