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The Driving Impact of Digital Innovation Ecosystems on Enterprise Digital Transformation: Based on an Interpretable Machine Learning Model

This paper is based on data from Chinese digital creative enterprises from 2015 to 2023. A regression model is constructed to test the driving mechanism of the digital innovation ecosystem on the digital transformation of enterprises. The Shapley Additive exPlanations (SHAP) machine learning method is also employed to reveal the key factors driving enterprise digital transformation in the digital innovation ecosystem. The results show that (1) the digital innovation ecosystem can significantly drive the digital transformation of enterprises. And this driving effect is influenced by the moderating effect of the dynamic capabilities of enterprises. The moderating effect of innovation capability and absorptive capacity on enterprise digital transformation is the most significant. (2) The heterogeneity test finds that among state-owned enterprises and enterprises with a high degree of industry competition, the digital innovation ecosystem significantly drives enterprises’ digital transformation more strongly. (3) The results of SHAP value identification indicate that the digital intelligence foundation is the most important factor in driving enterprise digital transformation. The degree of participant diversity, the enabling capacity of the system environment, and the comprehensive benefits play different roles in driving enterprise digital transformation under different heterogeneous conditions.

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How Does Digital Innovation Empower the Development of New Quality Productive Forces? An Empirical Study Based on Double Machine Learning

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New quality productive forces (NQPFs) are a key driver for sustainable and high-quality development, where digital innovation (DI) plays a crucial role in promoting the evolution of NQPFs. Based on this, this paper takes 2740 A-share listed companies from 2011 to 2022 as research…

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crossrefSustainability2023-07-26Cited by 6

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

Xing Liu, Xiaojing Zhang, Aliasghar Baziar

This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems, which face challenges due to t…

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crossrefSustainability2023-08-25Cited by 23

Digital Mapping of Soil pH Based on Machine Learning Combined with Feature Selection Methods in East China

Zhi-Dong Zhao, Ming-Song Zhao, Hong-Liang Lu, Shi-Hang Wang, Yuan-Yuan Lu

This study aimed to evaluate and compare the performances of the random forest (RF) and support vector regression (SVR) models combined with different feature selection methods, including recursive feature elimination (RFE), simulated annealing feature selection (SAFS), and selec…

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crossrefSustainability2025-03-09Cited by 34

Predicting Fuel Consumption and Emissions Using GPS-Based Machine Learning Models for Gasoline and Diesel Vehicles

Fahd Alazemi, Asmaa Alazmi, Mubarak Alrumaidhi, Nick Molden

The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective tra…

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crossrefSustainability2024-10-13Cited by 4

A Machine Learning and Deep Learning-Based Account Code Classification Model for Sustainable Accounting Practices

Durmuş Koç, Feden Koç

Accounting account codes are created within a specific logic framework to systematically and accurately record a company’s financial transactions. Currently, accounting reports are processed manually, which increases the likelihood of errors and slows down the process. This study…

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crossrefSustainability2026-07-14

Can Digital Infrastructure Predict Regional Innovation Capacity? Evidence Based on Machine Learning and the SHAP Explanatory Framework

Shasha Xie, Shusheng Xu, Wei Xu, Guo Yu, Yunli Li, Jianqiu Wu, et al.

Digital infrastructure is a critical foundation for promoting regional innovation and achieving sustainable development. However, existing studies have primarily focused on its impact effects, while paying limited attention to whether digital infrastructure can effectively identi…

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The Driving Impact of Digital Innovation Ecosystems on Enterprise Digital Transformation: Based on an Interpretable Machine Learning Model

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