Exploring knowledge workers’ perceptions of the influence of external generative AI on knowledge creation processes in organizations

This study explores how knowledge workers perceive the influence of external generative AI tools on knowledge creation processes. While most existing research has focused on integrated AI tools within organizational systems, this study addresses the gap in understanding how external generative AI tools like ChatGPT are individually used and perceived by employees. Through a qualitative multiple-case study design, data were collected from nine knowledge workers across two Swedish organizations through semi-structured interviews. Thematic analysis was conducted using the SECI model of knowledge creation as a theoretical lens. Findings reveal that external generative AI tools can positively influence knowledge creation by supporting brainstorming, enhancing communication, and increasing efficiency. However, the study also identifies risks related to data security, trust in AI-generated content, and its impact on collaboration and communication. Differences between the two organizations suggest that organizational culture, formal training, and support play a key role in shaping perceptions and usage of these tools. The study contributes to research on generative AI in knowledge management by examining how unregulated use of external generative AI interacts with established knowledge creation models and highlights the need for clearer policies and guidance.

Publication Details

Journal
Dalarna University College Electronic Archive
Published
2025-01-01
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring knowledge workers’ perceptions of the influence of external generative AI on knowledge creation processes in organizations

Dalarna University College Electronic Archive
Artificial Intelligence in Healthcare and Education
article

Exploring knowledge workers’ perceptions of the influence of external generative AI on knowledge creation processes in organizations

article en

Abstract

This study explores how knowledge workers perceive the influence of external generative AI tools on knowledge creation processes. While most existing research has focused on integrated AI tools within organizational systems, this study addresses the gap in understanding how external generative AI tools like ChatGPT are individually used and perceived by employees. Through a qualitative multiple-case study design, data were collected from nine knowledge workers across two Swedish organizations through semi-structured interviews. Thematic analysis was conducted using the SECI model of knowledge creation as a theoretical lens. Findings reveal that external generative AI tools can positively influence knowledge creation by supporting brainstorming, enhancing communication, and increasing efficiency. However, the study also identifies risks related to data security, trust in AI-generated content, and its impact on collaboration and communication. Differences between the two organizations suggest that organizational culture, formal training, and support play a key role in shaping perceptions and usage of these tools. The study contributes to research on generative AI in knowledge management by examining how unregulated use of external generative AI interacts with established knowledge creation models and highlights the need for clearer policies and guidance.

Dalarna University College Electronic Archive
Openalex Percentile: Top 53%
Artificial Intelligence in Healthcare and Education
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.