Using Text Analysis in Parallel Mediation Analysis

Judy (Zijing) Zhang (), H. Alice Li () and Greg M. Allenby ()
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Judy (Zijing) Zhang: The Ohio State University, Columbus, Ohio 43210
H. Alice Li: The Ohio State University, Columbus, Ohio 43210
Greg M. Allenby: The Ohio State University, Columbus, Ohio 43210

Marketing Science, 2024, vol. 43, issue 5, 953-970

Abstract: Text data are widely used in marketing research. In this paper, we propose a model that uses text data to identify multiple mediators in a parallel mediation analysis. Our model is based on the Latent Dirichlet Allocation (LDA) model that incorporates treatment and outcome variables. Treatment variables can affect topic composition in the text data, with topic probabilities used to predict outcomes via a logistic regression model. Lexical priors are introduced to seed topics that researchers consider relevant to an analysis, whereas non-seeded topics allow researchers to find other potential mediation paths. The resulting analysis of mediation replaces the use of rating scales with text that more flexibly reflects the reasons for respondent choices. The assessment of stimuli’s effect on topic probabilities provides information on which aspects of stimuli contribute to the change in respondents’ choices of words and their latent meanings behind these words. History: Olivier Toubia served as the senior editor for this article. Conflict of Interest Statement: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or nonfinancial interest in the subject matter or materials discussed in this article. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0045 .

Keywords: topic modeling; lexical priors; semi-supervised LDA; machine learning; heterogeneous effects (search for similar items in EconPapers)
Date: 2024
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