.png){kind=icon}
.png){kind=icon}
Return
Avoiding algorithm errors in textual analysis: A guide to selecting software, and a research agenda toward generative artificial intelligence
.png){kind=icon}
OA
AI
DOI：10.1016/j.jbusres.2025.115571.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• We develop a systematic process to select textual analysis software for complex constructs. • Our study evaluates four software packages using value-based management (VBM) as a test case. • We show that software from the same methodological family yields near-identical results. • We quantify how unsuitable tools distort results despite being established in other fields. • Our framework links AI prompts to theory-driven constructs for valid analysis.
Keywords:
Generative AI
Large language models
Textual analysis
Software selection
Algorithm error
Validity
Reliability
Value-based management
C80
C88
M10
M15
L86
Journal
IF:
9.8
Papers:
1.0W
Citations:
8.7W
Organization
No organization information available