Using Qualitative Comparative Analysis to Identify Complex Solutions and Optimal Combinations of Conditions Influencing COVID Vaccine Acceptance: A Primer for QCA

Date
2023-08-09
Authors
Brush, GJ
Guo, X
Hunting, A
Frethey-Bentham, C
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
SAGE Publications
Abstract

Quantitative studies in marketing are dominated by variance-based approaches. These have limitations for understanding macromarketing outcomes that often derive from different combinations of causal conditions, and where factors productive of the same outcome may be different from those impeding it. In this paper we draw on set-theoretic theory and propose qualitative comparative analysis (QCA) as an analytical method able to complement and extend macromarketing research programs. Fuzzy-set QCA is used to explore combinations of conditions influencing COVID vaccine adoption, with readers provided with detailed guidance through the process and current best practices. We consider a number of important but often neglected issues in fuzzy-set QCA; outlining how to conduct robustness checks, appropriateness of a two-step approach, identifying individual cases with specific conditions for further analysis, and examining the problems and opportunities provided by irrelevant cases and contradictions. A summary of macromarketing issues that may benefit from QCA, and recommended practices for conducting a QCA, are provided.

Description
Keywords
3503 Business Systems In Context , 35 Commerce, Management, Tourism and Services , Immunization , 1399 Other Education , 1505 Marketing , 2103 Historical Studies , Marketing , 3506 Marketing
Source
Journal of Macromarketing, ISSN: 0276-1467 (Print); 1552-6534 (Online), SAGE Publications, 44(2), 276-306. doi: 10.1177/02761467231182300
Rights statement
© The Author(s) 2023. Creative Commons License (CC BY 4.0). This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).