Utilizing the theory of planned behavior to predict COVID-19 vaccination intention: A structural equational modeling approach

A study on commuters' public transportation mode choice behavior in river valley-type cities considering terrain spatial perception: evidence from Lanzhou, China

Existing research rarely examines the subjective and objective built environment of river valley-type cities in relation to travel mode choice, particularly overlooking the heterogeneity among travelers in these cities. In this paper, based on questionnaire survey data and built environment data, terrain spatial perception (TSP) is introduced to expand the theory of planned behavior (TPB), and a Structural Equation Model (SEM) is established. Factor analysis and path analysis are conducted using SPSS and AMOS to estimate latent variables. An integrated model of SEM and random parameter Logit model (RPLM), which can not only analyze the psychological perception factors of commuters in river valley-type cities but also consider the heterogeneity of psychological perception, was constructed to analyze the impact of personal attributes, objective built environment factors, and psychological latent variables on the commuting mode choice behavior of public transport users in river valley-type cities. The results indicate that the five observation indicators corresponding to the proposed terrain spatial perception latent variables can better explain the terrain spatial perception of commuters in river valley-type cities. Different from plain cities, the subjective and objective built environment of river valley-type cities notably influence the travel behavior of commuters. Moreover, the parameters of terrain spatial perception follow a normal distribution, indicating that the sensitivity of different commuters to the terrain spatial perception of river valley-type cities is heterogeneous. The results of our study can provide a reference for alleviating traffic issues in valley cities.

Understanding Parental Intentions for COVID-19 Child Vaccination: A Cross-Sectional Study From Jordan Using Theory of Planned Behavior

Introduction

Using the Theory of Planned Behavior, this study addresses the factors that influence parental intentions to vaccinate their 12- to 17-year-old children against COVID-19. The study looked at how attitudes, subjective norms, perceived behavioral control, and fear of the COVID-19 vaccine impact these intentions.

Methods

Between November and December 2021, 396 Jordanian parents completed an anonymous online survey. A multivariate logistic regression analysis was used for analyzing the relationships.

Results

While 94.7% of children had received routine vaccinations, only 23.5% intended to vaccinate their children against COVID-19, indicating a vaccine acceptance gap. The analysis revealed that attitudes are the most significant positive predictor of vaccination intent, accounting for 75% of the variance. Subjective norms had a positive influence on parents' decisions, whereas fear of the COVID-19 vaccine was a significant barrier. Perceived behavioral control had a small but negative effect, indicating significant challenges to vaccination.

Conclusion

The Theory of Planned Behavior (TPB) clarifies numerous factors that influence parents' decisions to immunize their children against COVID-19. Understanding these factors is critical for narrowing the gap between high rates of routine vaccinations and low rates of COVID-19 vaccinations, as well as developing effective strategies to increase vaccine acceptance among parents.

Validating part of the social media infodemic listening conceptual framework using structural equation modelling

Background

The literature has identified various factors that promote or hinder people's intentions towards COVID-19 vaccination, and structural equation modelling (SEM) is a common approach to validate these associations. We propose a conceptual framework called social media infodemic listening (SoMeIL) for public health behaviours. Hypothesizing parameters retrieved from social media platforms can be used to infer people's intentions towards vaccination behaviours. This study preliminarily validates several components of the SoMeIL conceptual framework using SEM and Twitter data and examines the feasibility of using Twitter data in SEM research.

Methods

A total of 2420 English tweets in Toronto or Ottawa, Ontario, Canada, were collected from March 8 to June 30, 2021. Confirmatory factor analysis and SEM were applied to validate the SoMeIL conceptual framework in this cross-sectional study.

Findings

The results showed that sentiment scores, the log-numbers of favourites and retweets of a tweet, and the log-numbers of a user's favourites, followers, and public lists had significant direct associations with COVID-19 vaccination intention. The sentiment score of a tweet had the strongest relationship, whereas a user's number of followers had the weakest relationship with the intention of COVID-19 vaccine uptake.

Interpretation

The findings preliminarily validate several components of the SoMeIL conceptual framework by testing associations between self-reported COVID-19 vaccination intention and sentiment scores and the log-numbers of a tweet's favourites and retweets as well as users' favourites, followers, and public lists. This study also demonstrates the feasibility of using Twitter data in SEM research. Importantly, this study preliminarily validates the use of these six components as online reaction behaviours in the SoMeIL framework to infer the self-reported COVID-19 vaccination intentions of Canadian Twitter users in two cities.

Funding

This study was supported by the 2023-24 Ontario Graduate Scholarship.

Extending the theory of planned behavior to envisage social distancing behavior in containing the COVID-19 outbreak

The study investigates the antecedents that influence social distancing behavior during the COVID-19 outbreak and develops a hypothetical model integrating additional pertinent variables into the classic theory of planned behavior (TPB) to grasp the social distancing behavior accurately. The extended model was investigated employing structural equation modeling using the data collected through Snowball Technology from 331 respondents. The results unearth significant influences of all the predictor variables on their outcome variables. Surprisingly, the supportive government strategy has no crucial impact on perceived behavioral control toward social distancing. Furthermore, the study reports no trivial difference between young and old and high-low income groups on the intention-actual social distancing. The study outlines policy interventions to prevent mass transmission of the COVID-19 catastrophe. Lastly, limitations are specified to open up novel insights for future researchers.

Testing an extended theory of planned behaviour in predicting Covid-19 vaccination intention over the course of the pandemic: A three-wave repeated cross-sectional study

Background

Mass vaccination against Covid-19 has been recognised as the most effective strategy for overcoming the pandemic emergency and remains crucial in the ongoing efforts to mitigate the impact of the virus. The present study aimed to test the efficacy of an extended Theory of Planned Behaviour (TPB) model in predicting vaccination intention in three different phases of the pandemic. Understanding how psychological drivers of vaccine acceptance may have changed throughout the pandemic is essential for informing public health strategies and addressing vaccine hesitancy, even in the current post-pandemic context.

Methods

Using a repeated cross-sectional survey, we tested the hypothesised extended TPB model (intention, attitude, subjective norms, perceived behavioural control, anticipated affective reactions, risk perception, trust in science, trust in institutions and religiosity) across three independent samples: before (T1: November-December 2020; N = 657), during (T2: March-May 2021; N = 818), and after (T3: February-March 2022; N = 605) the start of the vaccination campaign in Italy.

Results

Results indicated significant differences between the time points in all investigated variables, pointing to a general trend of improvement in vaccine acceptability levels at T2 compared to T1, and a worsening at T3 compared to the other two time points. Interestingly, net of these differences, a multi-group Structural Equation Modeling analysis supported the invariance, across time, of the structural relationships examined within the extended TPB.

Conclusion

Findings demonstrated the efficacy of the TPB in predicting Covid-19 vaccination intention at different stages of the pandemic, suggesting that the model, in its extended version, represents a valuable framework for designing interventions aimed at promoting vaccine acceptance.