A study on the impact of digital transformation of business clusters on the economic performance of innovation in the context of the digital economy
Haikun Zhang
What the paper says
This study focuses on evaluating the growth prospects of enterprises by analysing panel data of digitally listed companies. Principal component extraction is used to construct growth evaluation indicators, and the extreme gradient boosting (XGBoost) model is employed to predict enterprise growth. The study confirms the indicator system's relevance through the Kaiser-Meyer-Olkin (KMO) test. In simulation experiments, the prediction performance of different classification algorithms was compared, and the prediction accuracy of XGBoost in the training dataset was 0.8366, which is higher than other algorithms under the same conditions. The proposed XGBoost model provides a more reliable and accurate method for financial status classification and growth prediction compared to traditional methods. This research aims to guide the ongoing development of enterprise innovation economy by offering an effective growth prediction method for digital cluster enterprises.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.38 × 0.4 = 0.15 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.