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Virginia Tech

1. Clark, Elizabeth Anne. Application of Automated Facial Expression Analysis and Facial Action Coding System to Assess Affective Response to Consumer Products.

Degree: PhD, Food Science and Technology, 2020, Virginia Tech

Sensory and consumer sciences seek to comprehend the influences of sensory perception on consumer behaviors such as product liking and purchase. The food industry assesses product liking through consumer testing but often does not capture consumer response as it pertains to emotions such as those experienced while directly interacting with a product (i.e., product-generated emotions, PG) or those attributed to the product based on external information such as branding, marketing, nutrition, social environment, physical environment, memories, etc.( product-associated emotions, PA). This research investigated the application of PA and PG emotion methodology to better understand consumer experiences. A systematic review of the existing scientific literature was performed that focused on the Facial Action Coding System (FACS), a process used determine facially expressed emotion from facial muscular positioning, and its use to investigate consumer behavior and characterize human emotional response to product-based stimuli; the review revealed inconsistencies in how FACS is carried out as well as how emotional response is determined from facial muscular activation. Automatic Facial Expression Analysis (AFEA), which automates FACS, was then used in a two-part study. In the first study (n=50 participants), AFEA, a Check-All-That-Apply (CATA) emotions questionnaire, and a Single-Target Implicit Association Test (ST-IAT) were used to characterize the relationship between PA as well as PG emotions and consumer behavior (acceptability, purchase intent) towards milk in various types of packaging (k=6). While the ST-IAT did not yield significant results (p>0.05), CATA data produced illustrated term selection based on motivation to approach and/or withdrawal from milk based on packaging color. Additionally, the lack of difference (p>0.05) between emotions that do not produce similar facial muscle activations, such as happy and disgust, indicates that AFEA software may not be determining emotions as outlined in the established FACS procedures. In the second study, AFEA data from the sensory evaluation (n=48 participants) of light-exposed milk stimuli (k=4) stored in packaging with various light blocking properties underwent time series statistical analysis to determine if the nature of the control stimulus itself could impact the analysis of AFEA data. When compared against the limited sensory engaging control (a blank screen), contempt, happy, and angry were expressed more intensely (p<0.025) and consistently for the light-exposed milk stimuli; neutral was expressed exclusively in the same manner for the blank screen. Comparatively, intense neutral expression (p<0.025) was brief, fragmented, and often accompanied by intense (although fleeting) expressions of happy, sad, or contempt for the sensory engaging control (water); emotions such as surprised, scared, and sad were expressed similarly for the light-exposed milk stimuli. As such, it was determined that care should be taken as facial activation of muscles/AUs related to sensory… Advisors/Committee Members: Duncan, Susan E. (committeechair), Gallagher, Daniel L. (committee member), Bell, Martha Ann (committee member), O'Keefe, Sean F. (committee member), Lahne, Jacob (committee member).

Subjects/Keywords: automated facial expression analysis; facial action coding system; milk; packaging; sensory science; emotion

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Clark, E. A. (2020). Application of Automated Facial Expression Analysis and Facial Action Coding System to Assess Affective Response to Consumer Products. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/97341

Chicago Manual of Style (16th Edition):

Clark, Elizabeth Anne. “Application of Automated Facial Expression Analysis and Facial Action Coding System to Assess Affective Response to Consumer Products.” 2020. Doctoral Dissertation, Virginia Tech. Accessed April 09, 2020. http://hdl.handle.net/10919/97341.

MLA Handbook (7th Edition):

Clark, Elizabeth Anne. “Application of Automated Facial Expression Analysis and Facial Action Coding System to Assess Affective Response to Consumer Products.” 2020. Web. 09 Apr 2020.

Vancouver:

Clark EA. Application of Automated Facial Expression Analysis and Facial Action Coding System to Assess Affective Response to Consumer Products. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2020 Apr 09]. Available from: http://hdl.handle.net/10919/97341.

Council of Science Editors:

Clark EA. Application of Automated Facial Expression Analysis and Facial Action Coding System to Assess Affective Response to Consumer Products. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/97341

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