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Tracking the Impact of Media on Voter Choice in Real Time: A Bayesian Dynamic Joint Model

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journal contribution
posted on 14.02.2019, 10:03 by B Pareek, P Ghosh, HN Wilson, EK Macdonald, P Baines
Commonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents’ exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications, such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor consumers’ perceptual responses over time. This study based on a new data collection technique using cell-phone text messaging (called real-time experience tracking or RET) offers the potential to address these weaknesses. We propose an RET-based model of the impact of communications and apply it to a unique choice situation: voting behavior during the 2010 UK general election, which was dominated by three political parties. We develop a Bayesian zero-inflated dynamic multinomial choice model that enables the joint modeling of: the interplay and dynamics associated with the individual voter's choice intentions over time, actual vote, and the heterogeneity in the exposure to marketing communications over time. Results reveal the differential impact over time of paid and earned media, demonstrate a synergy between the two, and show the particular importance of exposure valence and not just frequency, contrary to the predominant practitioner emphasis on share-of-voice metrics. Results also suggest that while earned media have a reducing impact on voting intentions as the final choice approaches, their valence continues to influence the final vote: a difference between drivers of intentions and behavior that implies that exposure valence remains critically important close to the final brand choice. Supplementary materials for this article are available online.

History

Citation

Journal of the American Statistical Association, 2018, pp. 1-19

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/School of Business

Version

AM (Accepted Manuscript)

Published in

Journal of the American Statistical Association

Publisher

Taylor & Francis: STM, Behavioural Science and Public Health Titles

issn

0162-1459

eissn

1537-274X

Copyright date

2018

Available date

14/02/2019

Publisher version

https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1419134

Language

en

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