Predicting consumer preferences for vegetable juices based on emotional reactions and the intensity of sensory attributes
A new study has found that the optimal model for predicting consumer acceptance and preference of commercially-available vegetable juice products involves a combination of sensory attribute intensities and emotional responses. The research, which involved one hundred participants, fifty of whom were females, aimed to develop a more accurate predictive model than previous notions.
The study extended the previous notion that a combination of sensory intensities and emotional responses can better predict consumer acceptance. It found that a combination of self-reported emotions, facial expression analysis, and perceived sensory intensities performed best in predicting overall liking. The results showed that emotional responses, measured using a self-reported emotion questionnaire and facial expression analysis, along with perceived sensory intensities, performed best in predicting overall liking.
The study participants were asked to evaluate five different vegetable juice samples. Over a period of two weeks, a majority of independent predictors showed neither differences between test and retest sessions nor interactions between session and test product. This indicates that the emotional responses and sensory intensities remained consistent over the testing period.
The use of autonomic nervous system (ANS) measures made limited contributions to predicting overall liking. The amount of overall variation attributed to these independent predictors was low in terms of preference rank. This suggests that while ANS measures can provide valuable insights, they are not the primary drivers of consumer acceptance in vegetable juice products.
The findings of this study highlight the importance of integrating sensory evaluation data with consumer emotional response measurements to enhance prediction accuracy. This approach, which leverages the multidimensional nature of consumer experience, can be refined to the vegetable juice product category for improved predictive power.
In conclusion, the optimal predictive model is a category-specific, machine learning-based framework integrating sensory attribute intensities and emotional response metrics to predict consumer acceptance and preference of vegetable juice products. Such models outperform simple nutrient-based or sensory-only models by capturing the complex interplay between product characteristics and consumer feelings.
- Future consumer research in the health-and-wellness and lifestyle sectors, particularly food-and-drink, could benefit from adopting an approach like the one in this study, incorporating both eye tracking for emotional responses and sensory attribute intensities during product evaluation.
- The findings of this study emphasize the significance of honing prediction models for the health-and-wellness and lifestyle sectors, such as in the sphere of food-and-drink, using science to integrate sensory evaluation data and consumer emotional responses, refined to specific product categories to improve predictive power.
- As a novel and innovative approach to consumer research, this study showcases how merging eye tracking technology with traditional sensory evaluation methods for vegetable juice products can contribute to more accurate predictions and insights into consumer preferences and acceptance within the health-and-wellness, lifestyle, and food-and-drink sectors.
