ArticleScientific reports2026
Simulation study of enterprise intelligent transformation behavior based on complex network evolutionary game.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accelerating global economic competition and the rapid development of intelligent technologies present both new opportunities and challenges for enterprises. Intelligent transformation has become an imperative trend for enhancing competitiveness, yet Chinese enterprises are still in the preliminary stages. Focusing on the supply-side (intelligent server providers) and the demand-side (adopting enterprises), this study develops a two-layer heterogeneous complex network model grounded in complex network and evolutionary game theories. We analyze the dynamic evolutionary mechanisms and key influencing factors of strategic choices for both types of firms under different scenarios. Python-based simulations reveal that increased government subsidies, reduced intelligent server costs, higher additional benefits from transformation, and appropriate pricing strategies all promote evolutionary cooperation between the two sides. Furthermore, the network structure significantly impacts strategic selection. The model's parameters are calibrated using 2023 financial data from Foxconn Industrial Internet Co., Ltd. to anchor the simulation in a representative large-enterprise scenario. This research extends the study of intelligent transformation from a static perspective to a dynamic, spatial-relationship-aware view, and addresses the limitation of participant homogeneity by employing a two-layer heterogeneous network model, thereby providing theoretical support and context-specific insights for enterprise intelligent transformation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.