Integrating SOR and TAM Models to Explore Consumer Emotions and Preferences in Fur Fashion Design
Abstract
This research examines how design perception, emotional experience, and technology acceptance jointly influence consumer preferences for fur and faux-fur clothing. By proposing an integrated SOR-TAM framework combining the Stimulus-Organism-Response (SOR) model and the Technology Acceptance Model (TAM), the study explains consumer attitudes toward fur clothing. Cultural-Aesthetic Value (CAV), Emotional-Experience Value (EEV), Functional-Applicability Value (FAV), and Innovative-Sensory Value (ISV) are defined as stimulus factors influencing Consumer Satisfaction (CS), which subsequently affects Purchase Intention (PI). Perceived Usefulness (PU) and Perceived Ease of Understanding (PEOU) are supplementary factors that affects Consumer Satisfaction and Purchase Intention. 11 hypotheses were made for further verification. A questionnaire was used to collect data, which were developed based on the SOR-TAM framework. 469 valid responses were analyzed using AMOS 25.0. The results showed that FAV has the strongest effect on consumer satisfaction, followed by CAV and EEV, while ISV has a weaker influence. Consumer satisfaction mediates the relationship between product perception and purchase intention. In addition, PEOU positively affects PU, which further strengthens this relationship. This study extends the SOR-TAM framework to fur clothing research and proposes design strategies integrating function, culture, and experience, offering insights for enhancing consumer satisfaction and supporting the cultural continuity of fur clothing design.
Keywords: SOR–TAM Model, Fur Apparel Design, Consumer Satisfaction, Purchase Intention
DOI: 10.54941/ahfe1007335
Cite this paper
More from this volume
- An Embodied Interaction System for Five-Tone Music Therapy: A Guqin-Inspired Multimodal Design
- Beyond Function: An Analysis of Affective Design Factors in Japanese Mechanical Watches with High Auction Prices
- Environment Providing Necessary Information to Users Using Multiple IoT Avatars
- i-EyFuze: An Eye-Shaped eHMI in Autonomous Vehicles that Provides Intentions for Pedestrians
- Voice-Based Human Relaxation Assessment Using Autoencoder-Driven Anomaly Detection of Calm Speech
- Feasibility study of estimating visuospatial cognition and mental states using eye movement and brain activity during domain-specific tasks
- Deep Learning of Latent Gaze Representations for Cognitive Ability and Mental State Estimation
- Lightweight Driver Drowsiness Detection Model Using MediaPipe Blendshapes and a Dual-Attention Hierarchical BiLSTM
- Estimating 3D Ground Reaction Forces During Gait Using a Deep Learning Model with IMU and Plantar Pressure Data
- Effect of Changing Task Sequence on Physical Workload in Agricultural Operations
- Influence of Social Appearance Attributes of Cyber Driving Support Agents on the Passenger Effect
- Design of a Community-Based Digital Platform for Standardized Stray Cat Rescue Based on Service System Design


AHFE Open Access