Working with feeling AI in fashion retail: a stimulus-organism-response perspective on employee psychology and creativity

As fashion retail organizations increasingly integrate emotionally responsive artificial intelligence (AI) into employee-facing systems, understanding the psychological and behavioral implications of “feeling AI” has become essential, particularly its influence on employee creativity, defined as the generation of novel and useful ideas. This study investigates how two feeling AI capabilities, anthropomorphism and personalization, shape employees’ creative self-efficacy and job replacement anxiety, and how these psychological states influence two distinct forms of employee creativity: proactive and responsive creativity. Drawing on the stimulus-organism-response (SOR) framework, a structural model was developed and tested using survey data collected from fashion retail employees (N = 302). Findings reveal that feeling AI does not uniformly enhance workplace creativity; instead, its psychological impacts are context-dependent and behaviorally divergent. This study contributes to fashion and organizational literature by expanding the conceptualization of AI from a technical enabler to a psychological stimulus with dualistic effects. The findings also offer practical guidance for fashion retailers aiming to adopt feeling AI systems that support, rather than stifle, human creativity in a rapidly evolving industry.

Authors

Institutions

Publication Details

Journal
Fashion and Textiles
Published
2026-03-05
DOI
https://doi.org/10.1186/s40691-026-00465-y
Primary Topic
Creativity in Education and Neuroscience
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Working with feeling AI in fashion retail: a stimulus-organism-response perspective on employee psychology and creativity

Ishtehar Sharif Swazan
Fashion and Textiles
Creativity in Education and Neuroscience
article

Working with feeling AI in fashion retail: a stimulus-organism-response perspective on employee psychology and creativity

Ishtehar Sharif Swazan
article en

Abstract

As fashion retail organizations increasingly integrate emotionally responsive artificial intelligence (AI) into employee-facing systems, understanding the psychological and behavioral implications of “feeling AI” has become essential, particularly its influence on employee creativity, defined as the generation of novel and useful ideas. This study investigates how two feeling AI capabilities, anthropomorphism and personalization, shape employees’ creative self-efficacy and job replacement anxiety, and how these psychological states influence two distinct forms of employee creativity: proactive and responsive creativity. Drawing on the stimulus-organism-response (SOR) framework, a structural model was developed and tested using survey data collected from fashion retail employees (N = 302). Findings reveal that feeling AI does not uniformly enhance workplace creativity; instead, its psychological impacts are context-dependent and behaviorally divergent. This study contributes to fashion and organizational literature by expanding the conceptualization of AI from a technical enabler to a psychological stimulus with dualistic effects. The findings also offer practical guidance for fashion retailers aiming to adopt feeling AI systems that support, rather than stifle, human creativity in a rapidly evolving industry.

Fashion and TextilesVol. 13(1)
East Carolina University (US)
Decent work and economic growth
Openalex Percentile: Top 37%
Creativity in Education and Neuroscience
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.