Cognitive Factors Influencing COVID-19 Vaccination Intentions: An Application of the Protection Motivation Theory Using a Probability Community Sample

Abstract

COVID-19 vaccines, as one of the effective ways of dealing with the COVID-19 pandemic, can lower COVID-19 risk and protect against severe disease and death; however, substantial individual differences in vaccination intentions have hindered the realization of a high vaccination rate among the public. To look for efficient strategies to promote vaccination, this study tested whether the protection motivation theory (PMT), a cognitive model based upon threat and coping appraisals, would account for the differences in vaccination intentions under three scenarios (i.e., vaccines in general and with high- or low- efficacy of lowering the risk of developing COVID-19). A phone survey was conducted in early 2021 and obtained a probability community sample (n = 472; 49.2% men) in Macao, China. We found that 54.0% of respondents indicated their relatively strong intention to receive COVID-19 vaccination for high-efficacy vaccines, compared to 29.5% for low-efficacy vaccines and 31.0% for vaccines in general. After controlling for demographics, self-efficacy (i.e., the perceived capability of receiving COVID-19 vaccines) and maladaptive response reward (i.e., the perceived benefits of not receiving COVID-19 vaccines) were consistently associated with COVID-19 vaccination intentions under all three scenarios. The perceived severity of COVID-19 infection and response cost of COVID-19 vaccination were significantly associated with vaccination intention for high-efficacy vaccines, while the response efficacy of lowering the COVID-19 impact with COVID-19 vaccination was positively associated with vaccination intention for general and low-efficacy vaccines. Given that the relative strength of PMT constructs depends on perceived vaccine efficacy, we recommend taking PMT constructs and vaccine efficacy into account for encouraging vaccination.

Predicting COVID‐19 booster vaccine intentions

“…Again, other unmeasured constructs may mediate this association. For example, threat and coping appraisals from protection motivation theory, such as perceived severity and vulnerability, and perceived efficacy of the booster vaccine, respectively, may have accounted for effect of vaccine hesitancy on intentions (see Tong et al, 2021 for an example). Similarly, the affective beliefs individuals hold with respect to their booster vaccination intentions such as fears or concerns about vaccine procedures themselves (e.g.…”

Applying an extended protection motivation theory to predict Covid-19 vaccination intentions and uptake in 50–64 year olds in the UK

“…PMT has been applied to explain a wide range of health behaviours (for reviews, see Floyd et al, 2000 ; Milne et al, 2000 ; Norman et al, 2015 ) including intentions to receive a seasonal influenza vaccine ( Ling et al, 2019 ) and adherence to Covid-19 protection behaviours ( Scholz and Freund, 2021 ). PMT encompasses many of the beliefs associated with intended uptake of a Covid-19 vaccine identified in the Lin et al (2021) review and, since this review, a number of cross-sectional studies have used PMT to explain Covid-19 vaccination intentions ( Ansari-Moghaddam et al, 2021 ; Eberhardt and Ling, 2021 ; Huang et al, 2021 ; Tong et al, 2021 ) although, to date, no PMT studies have predicted subsequent uptake. For example, Eberhardt and Ling (2021) reported that PMT explained 68% of the variance in Covid-19 vaccination intentions.…”

Cross-sectional assessment of predictors for COVID-19 vaccine uptake: an online survey in Greece

“…Several studies have investigated the predictors for COVID-19 vaccine uptake in the general population ( Al-Mansour et al, 2021 ; Al-Qerem and Jarab, 2021 ; Glampson et al, 2021 ; Kreps and Kriner, 2021 ; Lau et al, 2021 ; McCabe et al, 2021 ; Nguyen et al, 2021 ; Seboka et al, 2021 ; Tong et al, 2021 ; Wong et al, 2022 ; Yu et al, 2021 ). Furthermore, these studies investigated several demographic, health condition and lifestyle characteristics of the general population, such as gender, age, race/ethnicity educational level, living status, profession, income, perceived health status, chronic medical conditions, smoking status, perceived COVID-19 exposure, knowledge of COVID-19, contact with known/suspected case of COVID-19, sources of knowledge about COVID-19 vaccines, etc.…”