IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v43y2024i5p953-970.html
   My bibliography  Save this article

Using Text Analysis in Parallel Mediation Analysis

Author

Listed:
  • 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)

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 .

Suggested Citation

  • Judy (Zijing) Zhang & H. Alice Li & Greg M. Allenby, 2024. "Using Text Analysis in Parallel Mediation Analysis," Marketing Science, INFORMS, vol. 43(5), pages 953-970, September.
    • Handle: RePEc:inm:ormksc:v:43:y:2024:i:5:p:953-970
    DOI: 10.1287/mksc.2023.0045
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2023.0045
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2023.0045?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mummolo, Jonathan & Peterson, Erik, 2019. "Demand Effects in Survey Experiments: An Empirical Assessment," American Political Science Review, Cambridge University Press, vol. 113(2), pages 517-529, May.
    2. Daniel Zizzo, 2010. "Experimenter demand effects in economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 13(1), pages 75-98, March.
    3. Kuangjie Zhang & Shaobo (Kevin) Li & Sharon Ng, 2022. "Sizes Are Gendered: The Effect of Size Cues in Brand Names on Brand Stereotyping [Consumer Evaluations of Brand Extensions]," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 49(2), pages 252-267.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea F.M. Martinangeli & Lisa Windsteiger, 2019. "Immigration vs. Poverty: Causal Impact on Demand for Redistribution in a Survey Experiment," Working Papers tax-mpg-rps-2019-13, Max Planck Institute for Tax Law and Public Finance.
    2. Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2023. "Designing Information Provision Experiments," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 3-40, March.
    3. Lergetporer, Philipp & Piopiunik, Marc & Simon, Lisa, 2021. "Does the education level of refugees affect natives’ attitudes?," European Economic Review, Elsevier, vol. 134(C).
    4. Grewenig, Elisabeth & Lergetporer, Philipp & Werner, Katharina & Woessmann, Ludger, 2022. "Incentives, search engines, and the elicitation of subjective beliefs: Evidence from representative online survey experiments," Journal of Econometrics, Elsevier, vol. 231(1), pages 304-326.
    5. Bjorn Van Campenhout & David J. Spielman & Els Lecoutere, 2021. "Information and Communication Technologies to Provide Agricultural Advice to Smallholder Farmers: Experimental Evidence from Uganda," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 317-337, January.
    6. Manzoni, Elena & Murard, Elie & Quercia, Simone & Tonini, Sara, 2024. "News, Emotions, and Policy Views on Immigration," IZA Discussion Papers 17017, Institute of Labor Economics (IZA).
    7. Lergetporer, Philipp & Woessmann, Ludger, 2023. "Earnings information and public preferences for university tuition: Evidence from representative experiments," Journal of Public Economics, Elsevier, vol. 226(C).
    8. Lergetporer, Philipp & Woessmann, Ludger, 2019. "The Political Economy of Higher Education Finance: How Information and Design Affect Public Preferences for Tuition," Rationality and Competition Discussion Paper Series 145, CRC TRR 190 Rationality and Competition.
    9. Stefanie Stantcheva, 2021. "Understanding Tax Policy: How do People Reason?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(4), pages 2309-2369.
    10. Grewenig, Elisabeth & Lergetporer, Philipp & Werner, Katharina & Woessmann, Ludger, 2020. "Do party positions affect the public's policy preferences? Experimental evidence on support for family policies," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 523-543.
    11. Martinangeli, Andrea F.M. & Windsteiger, Lisa, 2023. "Immigration vs. poverty: Causal impact on demand for redistribution in a survey experiment," European Journal of Political Economy, Elsevier, vol. 78(C).
    12. Eßer, Jana & Flörchinger, Daniela & Frondel, Manuel & Sommer, Stephan, 2024. "Avoiding cognitive dissonance: Experimental evidence on sustainable online shopping," Ruhr Economic Papers 1063, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    13. Mukherjee, Ashesh & Burnham, Thomas & King, Dan, 2021. "Anticipated firm interaction can bias expressed customer satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    14. Banerjee, Sanchayan & Picard, Julien, 2023. "Thinking through norms can make them more effective. Experimental evidence on reflective climate policies in the UK," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 106(C).
    15. Campedelli, Gian Maria & Daniele, Gianmarco & Martinangeli, Andrea F.M. & Pinotti, Paolo, 2023. "Organized crime, violence and support for the state," Journal of Public Economics, Elsevier, vol. 228(C).
    16. Thomas, Ranjeeta & Galizzi, Matteo M. & Moorhouse, Louisa & Nyamukapa, Constance & Hallett, Timothy B., 2024. "Do risk, time and prosocial preferences predict risky sexual behaviour of youths in a low-income, high-risk setting?," Journal of Health Economics, Elsevier, vol. 93(C).
    17. Banerjee, Sanchayan & Picard, Julien, 2023. "Thinking through norms can make them more effective. Experimental evidence on reflective climate policies in the UK," LSE Research Online Documents on Economics 120057, London School of Economics and Political Science, LSE Library.
    18. Choi, Syngjoo & Choi, Chung-Yoon & Kim, Seonghoon, 2023. "Tackling misperceptions about immigrants with fact-checking interventions: A randomized survey experiment," Labour Economics, Elsevier, vol. 84(C).
    19. Flörchinger, Daniela & Frondel, Manuel & Sommer, Stephan & Andor, Mark Andreas, 2024. "Pro-environmental behavior and environmentalist movements: Evidence from the identification with Fridays for Future," Ruhr Economic Papers 1077, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    20. Bao, Te & Wei, Lijia & Yu, Yang, 2022. "The impact of information interventions on public opinion on social media regulation: Evidence from a survey on Twitter’s Trump Ban," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 101(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:43:y:2024:i:5:p:953-970. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.