Artificial Intelligence and Social Well-Being in the Yellow River Basin: A Cultural Lag Theory Perspective

Amid comprehensive reforms, artificial intelligence (AI) has emerged as a vital force in solving people’s problems and enhancing quality of life. Yet, theoretical inquiries into the mechanisms by which AI influences social well-being remain limited. Drawing upon cultural lag theory, this study constructs a social well-being index system based on the Gini coefficient objective weighting method. By integrating a moderated mediation model with a spatial econometric model, it examines the mechanisms and impacts of artificial intelligence on social well-being. The findings reveal that AI induces multiple cultural lags and exerts a U-shaped impact on social well-being. AI enhances well-being through the channels of employment opportunities, human capital, and green innovation, while digital inclusion and foreign direct investment (FDI) further reinforce this relationship. Additionally, AI generates spatial spillover effects on social well-being, and the region’s well-being landscape exhibits convergence. However, both digital inclusion and FDI negatively moderate the convergence process, slowing its overall pace. These insights provide substantial practical guidance for crafting informed policies aimed at elevating public well-being.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2025-02-26
DOI
https://doi.org/10.3390/su17052006
Citations
2
Primary Topic
Culture, Economy, and Development Studies
Type
article
Field-Weighted Citation Impact
11.18

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence and Social Well-Being in the Yellow River Basin: A Cultural Lag Theory Perspective

Z. X. Song, Guanying Wang, Y. P. Duan, Shuoxun Cheng
2 citations
Sustainability
Culture, Economy, and Development Studies
11.18
article

Artificial Intelligence and Social Well-Being in the Yellow River Basin: A Cultural Lag Theory Perspective

Z. X. Song, Guanying Wang, Y. P. Duan, Shuoxun Cheng
article en
2 citations

Abstract

Amid comprehensive reforms, artificial intelligence (AI) has emerged as a vital force in solving people’s problems and enhancing quality of life. Yet, theoretical inquiries into the mechanisms by which AI influences social well-being remain limited. Drawing upon cultural lag theory, this study constructs a social well-being index system based on the Gini coefficient objective weighting method. By integrating a moderated mediation model with a spatial econometric model, it examines the mechanisms and impacts of artificial intelligence on social well-being. The findings reveal that AI induces multiple cultural lags and exerts a U-shaped impact on social well-being. AI enhances well-being through the channels of employment opportunities, human capital, and green innovation, while digital inclusion and foreign direct investment (FDI) further reinforce this relationship. Additionally, AI generates spatial spillover effects on social well-being, and the region’s well-being landscape exhibits convergence. However, both digital inclusion and FDI negatively moderate the convergence process, slowing its overall pace. These insights provide substantial practical guidance for crafting informed policies aimed at elevating public well-being.

SustainabilityVol. 17(5)
Nanjing University of Finance and Economics (CN), Tianjin University (CN), Inner Mongolia University of Science and Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 3%
Culture, Economy, and Development Studies
11.18
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.