Title: A study on the impact of digital transformation of business clusters on the economic performance of innovation in the context of the digital economy

Authors: Haikun Zhang

Addresses: School of Economics and Management, XinYu University, Xinyu, 338004, China; Faculty of Business Management, Universiti Teknologi MARA Kampus Kota Kinabalu Malaysia, UiTM Sabah, 88997 Sabah, Malaysia

Abstract: 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.

Keywords: business clusters; digital economy; financial performance; XGBoost; growth.

DOI: 10.1504/IJTIP.2024.145507

International Journal of Technology Intelligence and Planning, 2024 Vol.13 No.4, pp.309 - 326

Received: 09 Oct 2023
Accepted: 27 Mar 2024
Published online: 02 Apr 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article