Understanding artificial intelligence usage and knowledge sharing: The social ties perspective

Although research has increasingly explored the consequences of artificial intelligence (AI) for workplace knowledge exchange, it remains unclear how AI reconfigures the relational foundations that sustain knowledge sharing among coworkers. Drawing on social exchange theory and conservation of resources theory, we propose that AI usage restructures coworker relationships along two distinct pathways: weakening instrumental ties while strengthening expressive ties. Using a two-wave time-lagged survey of 561 employees in technology firms, we find that AI usage is negatively related to instrumental ties but positively related to expressive ties. Both types of ties, in turn, are positively associated with explicit and tacit knowledge sharing. We further find that shared leadership weakens both the negative indirect association via instrumental ties and the positive indirect association via expressive ties. These findings advance a relational restructuring perspective on AI and knowledge sharing by showing that AI's effects are contingent on how it reconfigures task-based and socioemotional interaction channels among coworkers.

Authors

Institutions

Publication Details

Journal
Acta Psychologica
Published
2026-04-30
DOI
https://doi.org/10.1016/j.actpsy.2026.106967
Primary Topic
Knowledge Management and Sharing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding artificial intelligence usage and knowledge sharing: The social ties perspective

Lu Zhang, Rui Yan
Acta Psychologica
Knowledge Management and Sharing
article

Understanding artificial intelligence usage and knowledge sharing: The social ties perspective

Lu Zhang, Rui Yan
article en

Abstract

Although research has increasingly explored the consequences of artificial intelligence (AI) for workplace knowledge exchange, it remains unclear how AI reconfigures the relational foundations that sustain knowledge sharing among coworkers. Drawing on social exchange theory and conservation of resources theory, we propose that AI usage restructures coworker relationships along two distinct pathways: weakening instrumental ties while strengthening expressive ties. Using a two-wave time-lagged survey of 561 employees in technology firms, we find that AI usage is negatively related to instrumental ties but positively related to expressive ties. Both types of ties, in turn, are positively associated with explicit and tacit knowledge sharing. We further find that shared leadership weakens both the negative indirect association via instrumental ties and the positive indirect association via expressive ties. These findings advance a relational restructuring perspective on AI and knowledge sharing by showing that AI's effects are contingent on how it reconfigures task-based and socioemotional interaction channels among coworkers.

Acta PsychologicaVol. 266
Hohai University (CN), Yancheng Institute of Technology (CN)
Life in Land
Openalex Percentile: Top 10%
Knowledge Management and Sharing
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.

Understanding artificial intelligence usage and knowledge sharing: The social ties perspective — Lu Zhang, Rui Yan · Acta Psychologica (2026) | TGRS Research Map | TGRS