JOURNAL ARTICLE
Measuring Public Preferences for Strategic Choices in an Era of Great Power Competition: Taiwan as a Case Study.
Published In: Issues & Studies, 2024, v. 60, n. 2. P. 1 1 of 3
Database: America: History and Life with Full Text 2 of 3
Authored By: LEE, KUAN-CHEN 3 of 3
Abstract
As the U.S.-China relationship tilts toward competition and confrontation, scholars and policymakers have focused on the strategic choices faced by the citizens of small and medium-sized countries caught between the two great powers. However, methodological deficiencies remain in how public preferences for these strategic choices are measured. This paper attempts to fill this gap by developing a new means of measuring public opinion regarding strategic choices. Using survey data collected in Taiwan in March 2023, we demonstrate how this measure is constructed and why it aligns more closely with the theoretical concept of strategic choices. Our findings indicate that preferences for different strategies in Taiwan follow a U-shaped distribution that is centered either on hedging with the United States or with China. Multivariate analyses show that factors such as the threat of China, the U.S. security commitment to Taiwan, skepticism toward the United States, confidence in Taiwan's military, and partisanship are all correlated with public preferences for different strategies. This paper helps us understand these preferences among citizens in small and medium-sized countries and contributes to the literature on public opinion, international relations theory, and policymaking. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Issues & Studies. 2024/06, Vol. 60, Issue 2, p1
- Document Type:Article
- Subject Area:Economics
- Publication Date:2024
- ISSN:1013-2511
- DOI:10.1142/S1013251124500061
- Accession Number:178117060
- Copyright Statement:Copyright of Issues & Studies is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.