When to be vaccinated? What to consider? Modelling decision-making and time preference for COVID-19 vaccine through a conjoint experiment approach

  1. Samson W.H. Yuen
  2. Ricci P.H. Yue
  3. Bobo H.B. Lau
  4. Cecilia L. W. Chan
  5. Siu-Man Ng

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

How do citizens choose COVID-19 vaccines, and when do they wish to be vaccinated? A choice-based conjoint experiment was fielded in Hong Kong to examine factors that shape citizens’ preference toward COVID-19 vaccines and their time preference to be vaccinated, which is overlooked in extant literature. Results suggest people are most concerned about vaccines’ efficacy and severe side-effects, and that cash incentives are not useful in enhancing vaccine appeal. The majority of respondents show low intention for immediate vaccination, and many of them want to delay their vaccination. Further analysis shows that their time preference is shaped more by respondent characteristics than vaccine attributes. In particular, confidence in the vaccine, trust in government, and working in high-risk professions are associated with earlier timing for vaccine uptake. Meanwhile, forced COVID testing would delay vaccination. The findings offer a novel view in understanding how people decide whether and when to receive new vaccines, which have pivotal implications for a head start of any mass vaccination programs.

Highlights

  • People are most concerned about vaccines’ efficacy and severe side-effects when choosing COVID-19 vaccines

  • Cash incentives are not useful in enhancing vaccines’ appeal

  • Time preference of vaccination is shaped more by respondent characteristics than vaccine attributes

  • Forced COVID testing might delay vaccination decision

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.06.05.21258416: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.06.05.21258416v1 on medRxiv