FACTOR ANALYSIS OF HEALTHY FOOD PHOTOGRAPH

Abstract

Lockdown is one way to reduce the transmission rate of COVID-19. Nevertheless, on the other hand, lockdowns also increase human psychological problems to cause the emergence of emotional eating. In addition, social media exposure that presents food photos can trigger the desire to eat. However, this only applies to high-fat and high-calorie foods, while healthy foods do not have the same stimuli. Therefore, more research is needed on the properties of healthy food photos desired by consumers in order to be able to create or design healthy food photos with an effect that resembles photos of high-fat and high-calorie foods. This study employed the Kansei Engineering approach in designing healthy food photos. Through Kansei Engineering, we can determine the nature of healthy food photos consumers want. The type of Kansei engineering used in this study was Kansei Engineering Type I and was limited to the Semantic Space stage. The process of factor reduction from the results of the semantic differential was carried out by using factor analysis to obtain the most critical factors related to healthy food photos. The semantic space spanning resulted in 23 pairs of Kansei words that related and represented healthy food photos. Based on the factor analysis results, these Kansei words were then into 6-factor groups. Each of the factor groups was represented by the Kansei word pair with the highest loadings value. The selected pair of Kansei words showed that healthy food photos could be represented by Kansei words attractive, contrast, proper lighting, neat, high-quality image, and straightforward.