Personality Classification of Consumers: A Comparison of Variables, Standardization and Clustering Methods

The use of personality trait measurement is increasing in sensory evaluation for linking certain variables (i.e., consumption behavior and product preferences) to particular attributes. For this study, 976 consumers rated agreement on 44 statements from the Big Five Inventory using a 5‐point Likert‐type scale. Data handling methods for personality segmentation were compared: (1) the original 44 variables versus the five computed personality variables; (2) standardization versus nonstandardization of data; and (3) k‐means versus Ward's hierarchical clustering method used with principal component analysis. Results indicate using the five computed variables in mapping gave higher percentages of explained variability because of the small number of input variables. However, maps created from the 44 individual variables illustrated that participants were distributed throughout and separated visually into groups. Standardization of the data set did not affect mapping or classification. k‐means and Ward's clustering methods provided different classification results within the same data set. Results suggest that when using the Big Five personality traits measurement, the original 44 unstandardized variables and k‐means clustering should be used for obtaining consumer segmentation because this captures the greater variability inherent in the 44 variable tests and easily separates consumers into personality groups. Practical Applications Previous research has demonstrated that demographic and economic information provides insufficient explanation for consumer preference and other subjective responses. An application of personality research would aid researchers in understanding psychological factors that influence subjective responses. The study suggests that when using the Big Five personality traits tool, taking time to compute five personality traits is not needed and, in fact, detracts from grouping consumers into clusters. In addition, there is no need to standardize data during data preparation. However, selecting and using an appropriate clustering method for placing consumers into personality groups does impact the outcome. Based on this research k‐means clustering is recommended. The personality classification could be applied in consumer segmentation for a better understanding of consumers.