高管个人特征与企业创新——基于SGBT算法的经验证据

doi:10.13306/j.1672-3813.2026.03.015

复杂系统与复杂性科学

2026, Vol. 23

Issue (3): 121-128 DOI: 10.13306/j.1672-3813.2026.03.015

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高管个人特征与企业创新——基于SGBT算法的经验证据

刘诗绮¹, 谢甜梦²

1.天津大学管理与经济学部,天津 300072;
2.青岛大学经济学院,山东青岛 266061

Managerial Characteristics and Corporate Innovation-evidence Based on SGBT Algorithm

LIU Shiqi¹, XIE Tianmeng²

1. School of Management and Economics, Tianjin University, Tianjin 300072, China;
2. School of Economics, Qingdao University, Qingdao 266061, China

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摘要为研究高管个人特征对企业创新水平的预测能力以及预测机制和预测模式,利用随机梯度提升树(SGBT)算法全面分析高管的不同特征对企业创新水平的预测能力,同时深入研究不同特征在预测企业创新水平方面的重要性及其预测模式。结果发现:高管个人特征有助于预测企业的创新水平,在众多的高管个人特征中,高管的薪酬激励、任期和过度自信对企业创新水平的预测能力较强,且上述3种特征与企业创新水平之间的关系均呈现出区间效应,即具有非线性特点。

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关键词 ：高管个人特征, 企业创新, SGBT算法

Abstract：The personal characteristics of top executives and corporate innovation have become a key research focus in corporate finance. While existing studies primarily examine causal relationships, few explore the predictive ability, mechanisms, and patterns of executive characteristics on innovation levels. This study employs the Stochastic Gradient Boosting Tree (SGBT) algorithm to analyze the predictive power of executive characteristics. Results show that executive characteristics, particularly compensation incentives, tenure, and overconfidence, strongly predict corporate innovation levels, with nonlinear (interval) effects. These findings highlight the importance of executive characteristics in forecasting innovation.

Key words： managerial characteristics corporate innovation SGBT algorithm

收稿日期: 2024-11-19 出版日期: 2026-07-14

ZTFLH:	F832.5
	F224-3

通讯作者: 谢甜梦(1998-),女,山东菏泽人,硕士研究生,主要研究方向为公司金融。

作者简介: 刘诗绮(1994-),女,山东胶州人,博士,主要研究方向为绿色金融与绿色制造。

引用本文:

刘诗绮, 谢甜梦. 高管个人特征与企业创新——基于SGBT算法的经验证据[J]. 复杂系统与复杂性科学, 2026, 23(3): 121-128.
LIU Shiqi, XIE Tianmeng. Managerial Characteristics and Corporate Innovation-evidence Based on SGBT Algorithm[J]. Complex Systems and Complexity Science, 2026, 23(3): 121-128.

链接本文:

https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2026.03.015 或 https://fzkx.qdu.edu.cn/CN/Y2026/V23/I3/121

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