Unveiling multi-agent dynamics in digital transformation: A niche-based evolutionary game analysis of high-end manufacturing
Bibliographic record
Abstract
The digital transformation of the high-end equipment manufacturing industry is crucial for optimizing industrial structure and establishing a digital eco-economy. However, digital transformation is a complex and systemic process that requires the participation of multiple stakeholders. This study, which is grounded in the niche theory perspective, constructs a four-party evolutionary game model involving leading enterprises (LEs), small and medium-sized enterprises (SMEs), the government, and consumers and analyses the driving mechanisms of corporate digital transformation. The findings reveal that (1) the cost of digital transformation significantly influences the pace of transformation for both LEs and SMEs; (2) the digital transformation of LEs is strongly impacted by government policies, whereas that of SMEs is more influenced by consumer purchasing intentions; (3) government incentive policies promote corporate digital transformation, and consumer subsidies for digital products positively affect their sales; and (4) consumer preferences for digital products can accelerate the rate of digital transformation for enterprises. On the basis of the stability analysis and simulation results of the evolutionary game participants, five policy recommendations are proposed to guide the government in formulating and implementing digital transformation policies.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".