Capturing high and low levels of participant engagement in sensory and consumer evaluations via a known groups design and an implicit correlate. (November 2022)

• Record Type: Journal Article
• Authors: Hannum, Mackenzie E.; Simons, Christopher T.
• Is Part Of: Food research international. Volume 161(2022)
• Publication Date: 2022-11
• Subjects: Subject engagement -- Emotion tracking -- Known groups analysis -- Implicit method -- Scale validation -- Disengagement -- Facial expressions; Food -- Analysis -- Periodicals; Food industry and trade -- Periodicals; Food industry and trade -- Canada -- Periodicals; Food Technology -- Periodicals; Food -- Periodicals; Food-Processing Industry -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Industrie et commerce -- Canada -- Périodiques; Aliments -- Recherche -- Périodiques; Food industry and trade; Canada; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.foodres.2022.111786
• Languages: English
• ISSNs: 0963-9969

Abstract

Graphical abstract: Highlights: The experimental manipulations resulted in two different experiential conditions. The Engagement Questionnaire (EQ) was successfully validated via known groups. Positive and negative conditions respectively engaged and disengaged subjects. Subjects were more emotive in the engaged condition. Abstract: Recently developed to characterize consumer engagement during sensory and consumer testing, the Engagement Questionnaire (EQ) can quantify differences in participant engagement; however, the limits of the scale to detect disengagement has not been established. We conducted a known-group experimental design to verify the EQ's ability to differentiate between an engaged and disengaged participant. Participants were randomly assigned to a more engaging (positive condition) and a less engaging sensory task (negative condition). In the positive condition, participants (N = 31) completed 20 tetrad tests; however, stimuli varied in type and difficulty. For the negative condition, participants (N = 28) completed 20 tetrads of the same sucrose concentration to promote a state of helplessness and encourage disengagement with the task. Facial expressions were collected through video recordings, using automatic facial emotion recognition software (FaceReader, Noldus Technology). Accounting for sex differences across the conditions, the EQ successfully discriminated between the positive and negative conditions. When in the positive condition, participants were more actively involved in the task (F1, 59 = 23.99, p < 0.001), and experienced higher levels of purposeful intent (F1, 59 = 11.10, p = 0.002) and affective value (F1, 59 = 26.03, p < 0.001). Participants were more emotive in general, and experienced increased happiness (F1, 42 = 22.24, p < 0.001), anger (F1, 42 = 12.10, p = 0.001) and disgust (F1, 42 = 4.33, p = 0.04) as well as lower levels of a neutral expression (F1, 42 = 24.88, p < 0.001) in the positive condition. Now validated via known-groups analysis, the EQ can confidently differentiate a greater range of engagement. Such an instrument allows for a deeper understanding of participant engagement and its potential effect on data quality.