2021/05/23 by Jie Xu, Xu, Jie, Min Ding +1
Business, Management and Accounting · Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Consumer Behavior in Brand Consumption and Identification #Consumer Retail Behavior Studies #Evolutionary Psychology and Human Behavior #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2106.07558
6 figures and 6 tables
arxiv created 2021/05/23 · openalex publication_date 2021/05/23 · arxiv updated 2021/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent advancements in computational power and algorithms have enabled unabridged data (e.g., raw images or audio) to be used as input in some models (e.g., deep learning). However, the black box nature of such models reduces their likelihood of adoption by marketing scholars. Our paradigm of analysis, the Transparent Model of Unabridged Data (TMUD), enables researchers to investigate the inner workings of such black box models by incorporating an ex ante filtration module and an ex post experimentation module. We empirically demonstrate the TMUD by investigating the role of facial components and sexual dimorphism in face perceptions, which have implications for four marketing contexts: advertisement (perceptions of approachability, trustworthiness, and competence), brand (perceptions of whether a face represents a brand's typical customer), category (perceptions of whether a face represents a category's typical customer), and customer persona (perceptions of whether a face represents the persona of a brand's customer segment). Our results reveal new and useful findings that enrich the existing literature on face perception, most of which is based on abridged attributes (e.g., width of mouth). The TMUD has great potential to be a useful paradigm for generating theoretical insights and may encourage more marketing researchers and practitioners to use unabridged data.