Author ORCID Identifier

https://orcid.org/0000-0001-9392-5106

Date of Award

7-5-2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Marketing

First Advisor

DENISH SHAH

Second Advisor

NAVEEN DONTHU

Third Advisor

YI ZHAO

Fourth Advisor

SARANG SUNDER

Fifth Advisor

AJAY KOHLI

Sixth Advisor

ALIREZA AGHASI

Abstract

The two essays in this dissertation examine the role of Marketing in advancing social good. One of the essays focuses on behavioral outcomes in the context of a global health crisis while the other essay investigates the impact of residents’ sentiments on the competitiveness of cities.

The first essay examines whether TV advertising can affect societal outcomes beyond traditional marketing outcomes (e.g., sales, demand). We investigate this in the context of social distancing behavior during the COVID-19 pandemic by analyzing daily advertising and mobility data across 204 Designated Market Areas in the US. By employing a border identification strategy that exploits discontinuities across television markets, we find a significant positive causal relationship between COVID-19 related brand advertising and social distancing while controlling for government policy interventions (e.g., shelter-in-place, mask mandates). The estimated effects are almost 11 times larger in counties without government policy interventions (compared to counties with policy interventions). We find the effects to be heterogeneous across several brand and demographic variables. However, the government ad effect is negative (positive) in counties with (without) policy interventions and in predominantly rural counties. The study’s findings underscore the critical role that brand-sponsored TV ads can potentially play during major health crises, including mitigating the lack of policy interventions from local governments and people’s reactance to government-sponsored communications.

The second essay investigates the relationship between city-related citizen sentiments and key drivers of a city’s competitiveness, namely, the economic performance of a city and in-migration and visits to a city. Using a combination of in-depth interviews, topic modeling, text classification, and a Panel Vector Autoregression model we demonstrate the relationships between the sentiment of city-related Twitter conversations and the drivers of a city’s competitiveness. Further, we use BERT to classify relevant Twitter conversations (88% accuracy) and the sentiment of these relevant conversations (best-in-class accuracy of 85%) for six major cities in the US. Finally, using Impulse Response Functions we show that citizen sentiments have a positive and significant effect on all three drivers of a city’s competitiveness. The findings highlight the need for city managers to focus on the well-being of citizens.

DOI

https://doi.org/10.31922/98w3-sk32

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