How AI policies influence urban innovation in China: analysis based on feature extraction and fsQCA DOI
Kaili Wang, Tao Cang,

Jiang Wu

et al.

Aslib Journal of Information Management, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Purpose This study aims to understand the patterns that characterize impact of artificial intelligence (AI) policies on urban innovation performance, and reveal how these vary across different regions, thereby helping AI policy-making promoting innovation. Design/methodology/approach research focuses influence using city as unit analysis. policy patent data were collected from 156 Chinese cities over a decade. Coding machine learning methods applied extract features, including three types instruments, continuity, intensity, count. The fuzzy set Qualitative Comparative Analysis (fsQCA) method is used identify explain performance further explore regional differences. Findings Comparing four models for extracting ERNIE 3.0 has been proven be most accurate effective model. Three are found fsQCA: environment-safeguard, demand-pull, supply-environment-demand triple-drive patterns. Moreover, reflect development distinction eastern, middle, western cities, respectively. Hence, governments should focus intricate interplay synergistic application multiple levers, enhance creativity in formulation based their specific developmental characteristics. Originality/value analyzed national perspective. Automated introduced feature extraction, particularly identifying significantly cutting down labor enhancing efficiency Besides, this highlights among various factors, utilizing fsQCA collaborative dynamics at work, which compensates deficiency independent assumptions regression analysis, analyze effects factors systematic

Language: Английский

How AI policies influence urban innovation in China: analysis based on feature extraction and fsQCA DOI
Kaili Wang, Tao Cang,

Jiang Wu

et al.

Aslib Journal of Information Management, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Purpose This study aims to understand the patterns that characterize impact of artificial intelligence (AI) policies on urban innovation performance, and reveal how these vary across different regions, thereby helping AI policy-making promoting innovation. Design/methodology/approach research focuses influence using city as unit analysis. policy patent data were collected from 156 Chinese cities over a decade. Coding machine learning methods applied extract features, including three types instruments, continuity, intensity, count. The fuzzy set Qualitative Comparative Analysis (fsQCA) method is used identify explain performance further explore regional differences. Findings Comparing four models for extracting ERNIE 3.0 has been proven be most accurate effective model. Three are found fsQCA: environment-safeguard, demand-pull, supply-environment-demand triple-drive patterns. Moreover, reflect development distinction eastern, middle, western cities, respectively. Hence, governments should focus intricate interplay synergistic application multiple levers, enhance creativity in formulation based their specific developmental characteristics. Originality/value analyzed national perspective. Automated introduced feature extraction, particularly identifying significantly cutting down labor enhancing efficiency Besides, this highlights among various factors, utilizing fsQCA collaborative dynamics at work, which compensates deficiency independent assumptions regression analysis, analyze effects factors systematic

Language: Английский

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