Transparent Model of Unabridged Data (TMUD)

TL;DR

The paper introduces TMUD, a transparent modeling paradigm that allows investigation of black box models using unabridged data, demonstrated through face perception analysis with implications for marketing.

Contribution

TMUD provides a novel framework for understanding black box models with unabridged data, enhancing interpretability in marketing research.

Findings

Revealed new insights into face perception related to marketing contexts.

Demonstrated the utility of unabridged data over abridged attributes.

Showed TMUD's potential to generate theoretical insights in marketing.

Abstract

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.