Data elements and innovation in manufacturing corporates

Owing to their reproducibility, non-rival nature, and high value-addition attributes, data elements have become the most important factors of production in the era of digital economy. Hence, this study mainly investigates the effect and mechanisms of data elements on corporate innovation. Focusing on A-share listed firms from 2014 to 2022, this study adopts various methods to compute the provincial-level data element index in China and carries out empirical analysis of the effect of data elements on corporate innovation. The study finds that data elements boost the innovation level of the enterprises. Notably, data elements exert a promotional effect on corporate innovation through two channels: elevating the innovation capability of enterprises and easing financing constraints. However, the mechanism analysis finds that the opening of regional public data does not significantly promote the innovation-driven effect of data as a factor. Heterogeneity studies show that non-manufacturing firms and large-scale companies can better use data elements to promote corporate innovation level. Furthermore, this study reveals that the digital transformation of manufacturing enterprises at the current stage hinders the innovative effects of data elements. This study offers valuable evidence for accelerating the release of the power of data elements and promoting the innovation in Chinese listed companies.

Authors

Institutions

Publication Details

Journal
Journal of Innovation & Knowledge
Published
2026-03-27
DOI
https://doi.org/10.1016/j.jik.2026.101031
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data elements and innovation in manufacturing corporates

Guanglin Sun, Lehe Yang, Chuanxin Xia, Zian Yan
Journal of Innovation & Knowledge
Big Data and Business Intelligence
article

Data elements and innovation in manufacturing corporates

Guanglin Sun, Lehe Yang, Chuanxin Xia, Zian Yan
article en

Abstract

Owing to their reproducibility, non-rival nature, and high value-addition attributes, data elements have become the most important factors of production in the era of digital economy. Hence, this study mainly investigates the effect and mechanisms of data elements on corporate innovation. Focusing on A-share listed firms from 2014 to 2022, this study adopts various methods to compute the provincial-level data element index in China and carries out empirical analysis of the effect of data elements on corporate innovation. The study finds that data elements boost the innovation level of the enterprises. Notably, data elements exert a promotional effect on corporate innovation through two channels: elevating the innovation capability of enterprises and easing financing constraints. However, the mechanism analysis finds that the opening of regional public data does not significantly promote the innovation-driven effect of data as a factor. Heterogeneity studies show that non-manufacturing firms and large-scale companies can better use data elements to promote corporate innovation level. Furthermore, this study reveals that the digital transformation of manufacturing enterprises at the current stage hinders the innovative effects of data elements. This study offers valuable evidence for accelerating the release of the power of data elements and promoting the innovation in Chinese listed companies.

Journal of Innovation & KnowledgeVol. 16
Nanjing University of Finance and Economics (CN), Peking University (CN), Chinese Academy of International Trade and Economic Cooperation (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Big Data and Business Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Data elements and innovation in manufacturing corporates — Guanglin Sun, Lehe Yang, et al. · Journal of Innovation & Knowledge (2026) | TGRS Research Map | TGRS