2023 年 11 巻 1 号 p. 24-43
To examine the bus travel behaviour of the elderly in the context of the COVID-19 pandemic, this study analysed the mechanisms influencing the elderly’s risk perceptions regarding behavioural intention towards bus travel whilst focusing on the normalisation stage of pandemic prevention and control. Based on the theory of planned behaviour, a structural equation model of the elderly’s bus travel intention was constructed. The interactions among six factors—including attitudes, subjective norms, perceived behavioural control, cognitive risk perception, affective risk perception and the behavioural intention of the elderly’s bus travel—were quantitatively analysed. Valid sample data were used for empirical research. The results of this study show that perceived behavioural control, attitudes and subjective norms have a significant positive impact on the behavioural intentions of the elderly’s bus travel during the normalisation stage of pandemic prevention and control, with perceived behavioural control being the most influential factor. Moreover, perceived behavioural control also has a significant positive impact on attitudes, which indirectly influences behavioural intention. Cognitive risk perception has a direct and significant negative impact on attitudes, perceived behavioural control and subjective norms; however, affective risk perception only has a significant negative impact on subjective norms. Additionally, there is a positive correlation between the two, with both indirectly and negatively influencing the behavioural intentions of the elderly’s bus travel. This study can provide a basis for the formulation and improvement of pandemic prevention measures for bus travel during the normalisation stage of pandemic prevention and control to safeguard the elderly’s bus travel rights.
The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on people’s daily travel behaviour and the urban transportation system. Based on the characteristics of the pandemic, the operation of the urban transportation system can be divided into five stages: the normal stage, virus incubation stage, outbreak stage, post-pandemic stage and recovery stage (Li, Y., Liu et al., 2020). There is essentially minimal pandemic interference during the normal stage and virus incubation stage. Although there may be a limited number of infected people during the virus incubation stage, there is no risk of the pandemic spreading. These two stages can be collectively referred to as the pre-pandemic stage. Even though the pandemic has been efficiently controlled during the post-epidemic and recovery stages by essentially blocking its spread, there remains the possibility of a rebound. The two stages can be collectively referred to as the normalisation of pandemic prevention and control (hereinafter referred to as the normalisation stage). For example, Beijing entered the outbreak stage in early 2020, travel risks increased sharply and people’s activities and travel decreased significantly. From early April of that year, it entered the normalisation stage, resulting in travel gradually returning to normal.
Regular public transportation is one of the most important travel modes used to meet the daily needs of residents. Affected by the pandemic since February 2020, overall monthly bus passenger volumes in Beijing have been lower than those of the same periods in 2019. In the normalisation stage, bus passenger volume has shown a trend of gradual recovery but has not fully recovered to the pre-pandemic level (Figure 1). Previous studies on bus travel under the influence of COVID-19 have mainly focused on identifying the main factors influencing bus travel (Hu, Wang et al., 2021; Hu, Weng et al., 2020) and analysing of the impact of the pandemic on bus travel (De Vos, 2020; Kwok, Li et al., 2020; Qi, Liu et al., 2021).
(Year on year compared to 2019)
Additionally, the fear of contracting COVID-19 can also affect people’s travel psychology, thereby affecting travel behaviour. A few studies have found that COVID-19 risk perception affected travel behaviour and intention during the outbreak stage (Abdullah, Ali et al., 2021; Bae and Chang, 2021; Hui, Liao et al., 2020; Li, J., Nguyen et al., 2020; Luo, Dong et al., 2020; Tan and Ma, 2021; Wu, J., Wang et al., 2020). Since the domestic pandemic has entered the normalisation stage, some scholars have also studied this stage. For example, recent literature has examined the impact mechanisms of COVID-19 on leisure travel preferences and found that perceived risk has indirect negative effects on leisure travel intention (Shi and Long, 2022).
The elderly are a vulnerable group in urban transportation and are more susceptible to COVID-19. The travel of the elderly has been greatly affected since the outbreak. During the outbreak stage, the number of trips decreased sharply. In the normalisation stage, the recovery of bus travel among the elderly is better than that of younger people; however, the number of trips has not recovered to pre-pandemic levels (Figure 2). Compared to ordinary younger people, the elderly in China are more reliant on bus travel (Liu, W., Lu et al., 2017; Zhang, Yao et al., 2019). This also shows that the pandemic still has a certain inhibitory effect on bus travel among the elderly. This may be due to the fear of infection and health risks in the travel psychology of the elderly, which affects their travel behaviour.
In particular, studies have found that the impact of COVID-19 on the propensity to stop travelling, bus travel satisfaction and the bus travel behaviour of the elderly in the normalisation stage also relates to the bus travel of the elderly under the influence of COVID-19. Almlöf, Rubensson et al. (2021) analysed the propensity to stop travelling via public transport during the pandemic and found that the elderly avoided public transport during the outbreak stage. Sun, Jin et al. (2022) explored the heterogeneity of elderly bus travel satisfaction before and after COVID-19 based on the survey data of the elderly in Taiyuan, China. Liu, J., Hao et al. (2020) analysed short-distance (1 km) travel behaviour, bus travel choice behaviour, bus usage frequency and outgoing frequency using Logit modelling to study the impact of COVID-19 on the elderly’s bus travel behaviour during the post-pandemic stage.
Data source: Beijing’s bus smart card data for typical weekdays and weekends during three stages. Two smart card types are used: old-age cards for the elderly and regular cards for younger people.
Overall, these studies have provided important insights to date. However, additional research is still required for a more comprehensive understanding. Most of the existing studies are oriented towards the outbreak stage of COVID-19 and rarely involve the normalisation stage. Most of the subjects include the general population, with less attention paid to the elderly. Notably, no previous study has investigated how the risk perception of COVID-19 affects bus travel behavioural intention from the psychological level of the elderly. Consequently, this study aims to answer the following questions:
- Are the elderly still willing to give priority to bus travel during the normalisation stage?
- How does the risk perceptions of COVID-19 affect bus travel behavioural intention among the elderly during the normalisation stage?
Therefore, this study takes the bus travel behavioural intentions of the elderly during the normalisation stage as the research subject. We obtained travel data through a travel survey and quantitatively analysed the influence mechanisms of bus travel behavioural intention among the elderly during the normalisation stage.
Based on the theory of planned behaviour (TPB), this study adopted the analysis method of the structural equation model, focused on the psychological motivation of the elderly and quantitatively analysed the potential impact of different psychological factors on the behavioural intentions of the elderly’s bus travel. Also, the latent variables of risk perceptions (cognition/affective) were incorporated into the extended model of planned behaviour theory, which enhanced the analytical power of the model and provided a more reliable basis for the analysis of the impact mechanism whilst helping to more accurately explore the bus travel behaviour and psychological characteristics of the elderly under the influence of COVID-19.
The remainder of this paper is organised as follows. Section 2 describes the basic theory, research model, hypotheses, research methods and data collection process. Section 3 presents the results of our study and Section 4 discussion the policy insights of the key fundings. Section 5 contains the conclusions and limitations of this study.
According to the TPB, attitudes, subjective norms, and perceived behavioural control act as determinants of behavioural intention, which influences behaviour (Ajzen, 1991). Attitudes refer to an individual’s positive or negative assessments of action. Subjective norms refer to social pressures that encourage or discourage individuals from taking certain actions. Perceived behavioural control is a concept related to an individual’s perception of their capability to act. The TPB has been widely used in various disciplines, and many studies have attempted to include additional variables in the TPB to improve its explanatory power. In the study of bus travel behaviour and intention, variables such as residential environment, environmental concern and travel habits were added to enhance the understanding of bus travel behaviour and intention (Chen, J. , Zhang et al., 2020; Zhong, Shao et al., 2020)
Risk perception refers to the psychological cognition of emergencies and represents an individual’s attitude toward and subjective evaluation of risk (Glik, 2007). Previous studies have found that risk perception determines attitudes, which in turn affects behavioural intention (Ajzen, 1985a, 1985b; Quintal, Lee et al., 2010). Risk perception has also been identified as an important antecedent of subjective norms and perceived behavioural control (Lee, M., 2009). Generally, a lower level of risk perception results in a positive attitude, with individuals being more confident that family or friends will show a positive attitude towards their behavioural intention, which will enhance their perception of behavioural ability (Jarvenpaa, Tractinsky et al., 1999). Some studies have verified that travel intention will change with the risk perception caused by emergencies such as Severe Acute Respiratory Syndrome (SARS), H1N1 influenza strain, etc. (Huang, Li et al., 2015; Lee, C., Song et al., 2012). The risk perception of COVID-19 has a significant effect on the choice of leisure places (Bayrsaikhan, Lee et al., 2021).
Furthermore, cognition and affectivity are the two key variables that promote an individual’s consumption willingness and behavioural motivation during emergencies (Gutnik, Hakimzada et al., 2006). Risk perception at the cognitive and affective levels can affect travel intentions during COVID-19 (Bae and Chang, 2021). Therefore, this study included two variables to emphasise the degree of an individual’s perception of pandemic risk whilst considering the crisis impact of COVID-19 (i.e., cognitive risk perception and affective risk perception). Cognitive risk perception focuses on the judgement of the possibility of infection, whereas affective risk perception focuses on the emotional response to infection.
Research model and hypothesesWe initiated this study to further clarify the relationship between risk perception and the bus travel behaviour intention of the elderly. Thus, with an additional variable of risk perception (cognitive/affective), this study applied an extended TPB to construct a theoretical model of psychological factors and the behavioural intention of bus travel for the elderly during the normalisation stage (Figure 3).
This study proposed the following hypotheses:
(1) In the normalisation stage, attitudes, subjective norms and perceived behavioural control of the elderly’s bus travel positively influence behavioural intention. Subjective norms and perceived behavioural control have a direct positive impact on attitudes and indirectly affect behavioural intention.
H1: Subjective norms exert a significant positive influence on behavioural intention.
H2: Attitudes exert a significant positive influence on behavioural intention.
H3: Perceived behavioural control exerts a significant positive influence on behavioural intention.
H4: Perceived behavioural control exerts a significant positive influence on attitudes.
H5: Subjective norms exert a significant positive influence on attitudes.
(2) In the normalisation stage, risk perception will influence the TPB variables positively as follows.
H6: Cognitive risk perception exerts a significant positive influence on subjective norms.
H7: Cognitive risk perception exerts a significant positive influence on attitudes.
H8: Cognitive risk perception exerts a significant positive influence on perceived behavioural control.
H9: Affective risk perception exerts a significant positive influence on subjective norms.
H10: Affective risk perception exerts a significant positive influence on attitudes.
H11: Affective risk perception exerts a significant positive influence on perceived behavioural control.
H12: Cognitive risk perception and affective risk perception influence each other.
Research methodsOur research design consists of four major steps: (1) based on the basic theory, construct a conceptual framework and propose research hypotheses; (2) conduct a questionnaire survey to collect data; (3) perform descriptive statistical analysis, reliability analysis and validity analysis; (4) construct a structural equation model to achieve a more in-depth study (Figure 4).
The literature emphasises the complex relationships between risk perception, attitudes, subjective norms, perceived behavioural control and behavioural intention. Additionally, several variables not only influence behavioural intention but also affect each other. Moreover, behavioural intention is a complex concept that is difficult to observe and requires several variables to represent it. Therefore, the structural equation model (SEM) can be used as an appropriate method to evaluate the complex relationship between behavioural intention and its influencing factors.
SEM is one of the important statistical analysis methods in quantitative research. Nowadays, SEM has been widely applied in the domain of transport (De Oña, De Oña et al., 2013; Mokarami, Alizadeh et al., 2019; Wang, Y., Cao et al., 2022; Ye and Titheridge, 2017). It integrates the two statistical analysis methods of factor analysis and path analysis, which can realise the establishment, estimation and testing of causality. Additionally, the SEM can include latent unobservable psychological variables, thereby reflecting the specific process and influence mechanism (Wu, M., 2009). Compared to the disaggregated model, SEM has several advantages. Firstly, SEM can deal with multiple dependent variables simultaneously. Secondly, the measurement errors of independent variables and dependent variables can be ignored. Thirdly, it is similar to factor analysis, which allows a latent variable to consist of one or more observed variables. Generally, it consists of a measurement model and a structural model. The measurement model indicates the corresponding relationship between latent variables and observed variables, whereas the structural model represents the causal structural relationship between each latent variable.
This study used the SEM to quantitatively describe the psychological decision-making process of individual psychological latent variables on behavioural intention and analysed the influence mechanism of the attitudes, subjective norms, perceived behavioural control, cognitive risk perception and affective risk perception of the elderly related to the behavioural intentions of bus travel during the normalisation stage.
The research subject of this study was the urban elderly. The questionnaire was designed to obtain empirical data. It included observation variables and unobservable psychological latent variables. Among them, the questionnaire of observation variables was mainly divided into two categories: socio-demographic characteristics and travel characteristics. The questionnaire involving psychological latent variables referred to the scale measurement items of other scholars for variables of planned behaviour theory (Chen, Jian , Fu et al., 2017; Chen, J. , Zhang et al., 2020; Zhong, Shao et al., 2020) and variables of risk perception (Bae and Chang, 2021; Brug, Aro et al., 2004). All observation variables were measured on a 5-point Likert scale (Table 1).
| Variables | Items | |
|---|---|---|
| CRP1 | There is a high likelihood of acquiring COVID-19 in general. | |
| CRP2 | There is a high likelihood that I will acquire COVID-19 compared to other people. | |
| CRP3 | There is a high likelihood of acquiring COVID-19 compared to other diseases. | |
| ARP1 | I am worried that I will contract COVID-19. | |
| ARP2 | I am worried about my family members contracting COVID-19. | |
| ARP3 | I am worried about COVID-19 occurring in my region. | |
| ATT1 | Compared to the pre-pandemic stage, I still think that bus travel is accessible in the normalisation stage. | |
| ATT2 | Compared to the pre-pandemic stage, I still think that bus travel is safe during the normalisation stage. | |
| ATT3 | Compared to the pre-pandemic stage, I still think that bus travel is comfortable during the normalisation stage. | |
| SN1 | Compared to the pre-pandemic stage, the opinions of family and friends still have a great influence on my choice of bus travel during the normalisation stage. | |
| SN2 | Compared to the pre-pandemic stage, media such as television, the internet, newspapers etc. still greatly influence my choice regarding bus travel during the normalisation stage. | |
| PBC1 | Compared to the pre-pandemic stage, I still prefer bus travel to other travel modes in general during the normalisation stage. | |
| PBC2 | Compared to the pre-pandemic stage, the bus remains an integral part of my daily trip during the normalisation stage. | |
| PBC3 | Compared to the pre-pandemic stage, bus travel remains the best choice for safety during the normalisation stage. | |
| BI1 | Compared to the pre-pandemic stage, I still often travel by bus during the normalisation stage. | |
| BI2 | Compared to the pre-pandemic stage, I still prefer to travel by bus rather than other transportation modes (e.g., private cars, taxis, etc.) during the normalisation stage. | |
| BI3 | Compared to the pre-pandemic stage, I am still willing to recommend others to travel by bus during the normalisation stage. | |
Elderly people in Beijing (aged 60 and above) were selected for analysis. Considering the risk of virus transmission and the strong awareness of travel prevention during COVID-19, a combination of online questionnaires and on-site questionnaires was adopted. The link for the questionnaire was sent to the panel of the Questionnaire Star ( http://www.wjx.cn/), a professional survey platform. Part of the online questionnaires was completed by the elderly themselves, whilst the other parts were completed by their family members. A total of 314 participants completed the survey, which was conducted from July 22 to August 8, 2021. Overall, 289 valid responses were used for the final data analysis, with an effective recovery rate of 92.04%.
Table 2 present the socio-demographic characteristics of the participants. Of the 289 respondents, there were more males (50.17%) than females (49.89%). More than half of the elderly were on the younger side (60–69 years old). Most participants were educated at the high school level or below (79.24%). The largest proportion of respondents reported a monthly average income of 2,000–5,000 CNY (47.06%). Overall, 82.70% of the participants had old-age disability cards and were enjoying preferential bus policies for the elderly in Beijing. The majority of the elderly (82.70%) travelled independently. Also, 84.78% of the elderly lived with other family members and 62.98% of them lived in the central area of Beijing. Moreover, 73.36% of the elderly participants’ families did not have cars.
| Categories | n | % | Categories | n | % | ||
|---|---|---|---|---|---|---|---|
| Gender | Health | ||||||
| Male | 145 | 50.17 | Travel independently | 239 | 82.70 | ||
| Female | 144 | 49.83 | Require assistance from other people | 25 | 8.65 | ||
| Age | Require a wheelchair | 8 | 2.77 | ||||
| 60–64 | 91 | 31.49 | Require a crutch | 17 | 5.88 | ||
| 65–69 | 87 | 30.10 | Others | 0 | 0.00 | ||
| 70–74 | 55 | 19.03 | Residence Status | ||||
| 75–79 | 38 | 13.15 | Solitude | 44 | 15.22 | ||
| ≥80 | 18 | 6.23 | Couples | 99 | 34.26 | ||
| Education | Parents and children | 61 | 21.11 | ||||
| Primary school | 84 | 29.07 | Grandparents and children | 19 | 6.57 | ||
| Junior high | 72 | 24.91 | Three or four generations | 58 | 20.07 | ||
| High school | 73 | 25.26 | Others | 8 | 2.77 | ||
| Junior college | 33 | 11.42 | Residence location | ||||
| College and above | 27 | 9.34 | Central area | 182 | 62.98 | ||
| Monthly average income (CNY) | Inner suburb | 92 | 31.83 | ||||
| <2 thousand | 66 | 22.84 | Outer suburb | 15 | 5.19 | ||
| 2–5 thousand | 136 | 47.06 | Number of cars owned by families | ||||
| 5–10 thousand | 74 | 25.61 | 0 | 77 | 26.64 | ||
| >10 thousand | 13 | 4.50 | 1 | 144 | 49.83 | ||
| Has old-age disability card | 2 | 49 | 16.96 | ||||
| Yes | 239 | 82.70 | ≥3 | 19 | 6.57 | ||
| No | 50 | 17.30 | |||||
Descriptive statistics were used to determine the characteristics of bus travel among the elderly during the normalisation stage (Figure 5), as well as the characteristics of bus travel recovery under the influence of COVID-19 (Figure 6). Specifically, the characteristics of bus travel among the elderly during the normalisation stage mainly included travel purpose, travel frequency and travel duration. The characteristics of bus travel recovery under the influence of COVID-19 included the general status of bus travel recovery in the normalisation stage and a change in bus travel frequency and travel duration when compared to the normal stage.
Characteristics of bus travel among the elderly during the normalisation stageIn terms of bus travel purposes, the main activities of the elderly were related to shopping (29.31%) and recreational activity (15.46%), followed by medical treatment (14.81%) and visiting someone (10.79%). In terms of bus travel frequency, 35.99% of the elderly seldom travelled by bus, whereas 29.76% travelled by bus once or twice a week. The travel duration for most of the elderly was from 11 to 20 minutes, accounting for 35.99%.
With the gradual remission of the pandemic, bus passenger volume has also shown a progressive recovery trend. In terms of overall recovery, only 10.73% of the elderly thought that bus travel had not recovered to pre-pandemic levels, which was in line with the data analysis presented in Figure 1. However, nearly half of the elderly claimed that their bus travel frequency was lower in the pre-pandemic stage but higher in the outbreak stage. Moreover, 30.10% of the elderly stated that their only bus trip was between the pre-pandemic and outbreak stages.
Before constructing the SEM, the quality of scale item design should be fully considered. The tests of scale in this study mainly included reliability tests and validity tests (Table 3 and Table 4).
| Latent variable | Observation variable | Cronbach's alpha | Factor loading | CR | AVE |
|---|---|---|---|---|---|
|
Cognitive risk perception |
CRP1 | 0.873 | 0.799*** | 0.874 | 0.699 |
| CRP2 | 0.868*** | ||||
| CRP3 | 0.840*** | ||||
|
Affective risk perception |
ARP1 | 0.896 | 0.861*** | 0.900 | 0.751 |
| ARP2 | 0.953*** | ||||
| ARP3 | 0.777*** | ||||
| Attitudes | ATT1 | 0.754 | 0.613*** | 0.763 | 0.520 |
| ATT2 | 0.752*** | ||||
| ATT3 | 0.787*** | ||||
| Subjective norms | SN1 | 0.796 | 0.751*** | 0.801 | 0.669 |
| SN2 | 0.881*** | ||||
| Perceived behavioural control | PBC1 | 0.821 | 0.812*** | 0.829 | 0.619 |
| PBC2 | 0.847*** | ||||
| PBC3 | 0.692*** | ||||
| Behavioural intention | BI1 | 0.826 | 0.783*** | 0.827 | 0.614 |
| BI2 | 0.791*** | ||||
| BI3 | 0.775*** |
Note: *: p <0.05, **: p<0.01, ***: p<0.001
CR: Composite reliability; AVE: Average variance extracted
Cronbach’s alpha is frequently used to measure reliability. A coefficient greater than 0.9 suggests exceptional reliability, whilst a coefficient greater than 0.8 shows good reliability, a coefficient greater than 0.7 indicates acceptable reliability and a coefficient greater than 0.6 indicates basic acceptance (Wu, M., 2010). The reliability analysis revealed that the Cronbach’s alpha coefficient was 0.724 and the coefficient of each latent variable was likewise above the standard of more than 0.7, suggesting that the scale had high reliability and internal consistency (Table 3).
Validity analysisTo understand the relationships between each construct, we first conducted a validity analysis, which was mainly achieved by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The results of the validity analysis are presented in Table 3.
The EFA was designed for the unknown or uncertain relationship between the observed variables and the potential factors to determine if and to what extent the observed variables are associated with the latent variables. The results showed good validity and met the conditions of factor analysis:
The CFA was used to test the matching degree between the data and measurement model based on theoretical presupposition or EFA. The analysis showed that the estimated coefficients of the standardised factor loading of the corresponding items of each latent variable were greater than 0.5. The statistical result was significantly indigenous (p < 0.001), indicating that the latent variable items were representative and the selected observation variables were reasonable.
Additionally, the goodness-of-fit reached the ideal standard:
Furthermore, the combination reliability (CR) was above 0.700, whilst the average variance extraction (AVE) was above 0.500, which is within the standard range and shows good internal consistency. Further analysis of the discriminant validity (Table 4) found that the correlation between latent variables was significant (p<0.01), and
| 1 | 2 | 3 | 4 | 5 | 6 | |
|---|---|---|---|---|---|---|
| 1. Cognitive risk perception | 0.836 | |||||
| 2. Affective risk perception | 0.597*** | 0.867 | ||||
| 3. Attitudes | -0.393*** | -0.292*** | 0.721 | |||
| 4. Subjective norms | 0.453*** | 0.453*** | -0.311** | 0.818 | ||
| 5. Perceived behavioural control | -0.247*** | -0.137* | 0.696*** | -0.155* | 0.787 | |
| 6. Behavioural intention | -0.343*** | -0.202** | 0.749*** | -0.299*** | 0.771*** | 0.783 |
Note: Diagonal values indicate the squared root of AVE and the lower triangle is the Pearson correlation coefficient between the latent variables.
*: p <0.05, **: p<0.01, ***: p<0.001
Figure 7.Structural equation model analysis result
The proposed research model was analysed by the SEM using AMOS software (version 24). Figure 7 presents the optimal model structure and parameter estimation results after repeated revision. The SEM analysis revealed strong goodness-of-fit indicators:
The effects between variables were divided into direct and indirect effects. The sum of the direct effect and the indirect effect was the total effect value (Table 5). The standardised path coefficient of each variable reflected the direct effect between the variables (Figure 7). According to the parameter estimation results of the SEM and the influence effect between latent variables, the interactions between latent variables and the relationship between latent variables and observation variables were analysed.
| Impact path | Direct effect | Indirect effect | Total effect |
|---|---|---|---|
| CRP → PBC | -0.25 | 0.00 | -0.25 |
| CRP → SN | 0.30 | 0.00 | 0.30 |
| CRP → ATT | -0.25 | -0.16 | -0.41 |
| ARP → SN | 0.28 | 0.00 | -0.28 |
| PBC → ATT | 0.64 | 0.00 | 0.64 |
| PBC → BI | 0.49 | 0.24 | 0.73 |
| SN → BI | 0.12 | 0.00 | 0.12 |
| ATT → BI | 0.38 | 0.00 | 0.38 |
| CRP → BI | 0.00 | -0.31 | -0.31 |
| ARP → BI | 0.00 | -0.03 | -0.03 |
Perceived behavioural control, attitudes and subjective norms exhibited a positive influence on behavioural intention (direct effect = 0.49, 0.38 and 0.12, respectively). Perceived behavioural control was an important influencing factor of the behavioural intention of the elderly’s bus travel, and the effect was greater than that for attitudes and subjective norms. The behavioural intention was directly related to subjective norms and attitude. Also, attitudes exhibited a mediating effect on the relationship between perceived behavioural control and behavioural intention.
Cognitive risk perception exhibited a significant negative influence on perceived behavioural control, attitudes and subjective norms (direct effect = -0.25, -0.25 and -0.29, respectively), indicating that the degree of cognitive risk perception among the elderly was higher, the possibility of pandemic perception was higher, whilst attitude, perceived behavioural control and subjective norms were lower. Affective risk perception also exhibited a significantly negative influence on subjective norms, indicating that the affective risk perception of the elderly was higher, worry about the pandemic increased and the subjective norms of bus travel were lower.
Although risk perception (cognition/affective) exhibited no direct effect on behavioural intention, it exhibited an indirect negative effect on behavioural intention through perceived behavioural control, attitudes and subjective norms. The indirect effect of cognitive risk perception (-0.31) was greater than that for affective risk perception (-0.03), indicating that the behavioural intention of the elderly’s bus travel was more vulnerable to the influence of cognitive risk perception. Additionally, there was a positive interaction between cognitive risk perception and affective risk perception, with a correlation coefficient of 0.60.
Measurement modelThe measurement model results indicate that these observation variables could well reflect their corresponding latent variables since the standardised path coefficients were greater than 0.6.
Attitudes. ATT3 (comfort) and ATT2 (safety) significantly contributed to explaining participation through the main effect.
Subjective norms. The bus travel of the elderly was more susceptible to SN2 (publicity of media such as television, internet, newspapers, etc.).
Perceived behavioural control. The elderly paid more attention to PBC2 (the importance of buses in daily travel) and PBC1 (higher choice intention than other traffic modes), with standardised path coefficients of 0.85 and 0.81, respectively.
Cognitive risk perception. CRP2 (possibility of infection compared to others) had the greatest impact, with a standardised path coefficient of 0.87.
Affective risk perception. ARP2 (worry about family infection) had the greatest impact, with a standardised path coefficient of 0.95.
Behavioural intention. The standardised path coefficients of BI1, BI2 and BI3 were 0.61, 0.63 and 0.60, respectively. The influences of the three observation variables were relatively close, indicating that the elderly were still willing to give priority to bus travel and use it frequently in the normalisation stage, whilst also being willing to recommend others to travel by bus.
Based on the above analysis, the hypothesis verification results of the SEM model are presented in Table 6.
| Hypothesis | Impact path | Influence | Verified result |
|---|---|---|---|
| H1 | SN → BI | Positive | True |
| H2 | ATT→ BI | Positive | True |
| H3 | PBC → BI | Positive | True |
| H4 | PBC → ATT | Positive | True |
| H5 | SN → ATT | Positive | False |
| H6 | CRP → SN | Negative | True |
| H7 | CRP → ATT | Negative | True |
| H8 | CRP → PBC | Negative | True |
| H9 | ARP →SN | Negative | True |
| H10 | ARP → ATT | Negative | False |
| H11 | ARP → PBC | Negative | False |
| H12 | CRP ←→ARP | Interaction | True |
The key findings of this study are as presented as follows.
First, from the above analysis, we found that the recovery of bus travel among the elderly was better than that of younger people in the normalisation stage, with the elderly still being willing to recommend bus travel to others (corresponding to the BI variable). This also showed that the elderly were more dependent on public transport. Therefore, it is necessary to consider the psychological needs and travel intentions of the elderly to adjust and improve the bus priority policy and epidemic prevention measures.
Second, attitudes, subjective norms and perceived behavioural control exhibited a positive influence on behavioural intention (corresponding H1–H3), which is consistent with the basic hypothesis of TPB and the findings of previous TPB research (Bae and Chang, 2021; Chen, Jian , Fu et al., 2017; Hu, Weng et al., 2020). Moreover, the elderly valued the comfort and safety of bus travel (corresponding to the ATT variable). Media publicity also had a guiding effect on their use of buses (corresponding to the SN variable).
Third, affective risk perception exhibited a negative effect on attitudes and perceived behavioural control (corresponding H10–H11); however, cognitive risk perception was not a significant antecedent of both. That is, positive attitudes and perceptions of ability towards public transport were not based on the elderly’s safety concerns for themselves and their families; instead, they were based on judgements regarding the possibility of infection. Additionally, both cognitive risk perception and affective risk perception exhibited negative effects on the subjective norm (corresponding to H6 and H9). That is, when the elderly cognitively and emotionally perceived the risk of the pandemic, the influence of the outside world regarding whether they choose to travel by public transport would be relatively weakened and the effect of media publicity would be less obvious.
Fourth, risk perception (cognition/affective) exhibited no direct effect on behavioural intention; however, it exhibited an indirect negative effect on behavioural intention through perceived behavioural control, attitudes and subjective norms. The results of smart card data analysis also showed that the sensitivity to risk perception affected the elderly’s behavioural intention to travel by bus. For example, elderly participants who were more optimistic about the risk perception showed a more positive attitudes towards public transport and were more susceptible to external influences. Therefore, policies and advocacy guidance to encourage public transport were not effective for all elderly individuals.
With the continuously changing situation and requirements for the prevention and control of COVID-19, determining how to ensure the travel needs of the elderly and meet their living demands under the new normal requires the continuous adjustment and improvement of various policies and measures. Both groups of the elderly (i.e., the elderly who are more optimistic about risk perception and still prefer to use public transport; the elderly who are more sensitive about risk perception and likely to abandon public transport) should be considered.
For the elderly who are more optimistic about risk perception and still prefer to use public transport, their bus travel must remain needs guaranteed. Many urban public transport systems were proposing, adjusting and improving various prevention and control measures to improve the accuracy and effectiveness of pandemic prevention (Xu, 2020). Combined with the analysis of this study, in terms of ensuring the bus travel of the elderly, effective coordination should be achieved among all stakeholders (i.e., policymakers, service providers and bus users). Taking appropriate measures to minimise the risk perception of the elderly will help to enhance the trust and confidence of the elderly in public transportation during the normalisation stage.
Service providers must understand the needs of the elderly in the normalisation stage, properly ventilate and disinfect public transport vehicles, provide epidemic prevention materials (e.g., masks, hand sanitisers, etc.), use audio-visual technology to provide epidemic prevention-related guidance, and limit bus loading rates. Additionally, it is necessary to further improve ‘ageing-friendly’ existing transportation facilities to ensure the safety and comfort of the elderly and provide more convenient travel services to a certain extent (e.g., providing community buses for the elderly, setting priority queuing channels, etc.).
Policymakers can encourage service providers to prepare sufficient anti-epidemic materials, organise anti-epidemic training for bus attendants and pay attention to media publicity. Notably, they can focus on more than only popularising pandemic prevention knowledge through media channels (e.g., television, mobile phones, radios, etc.) for the elderly. Instead, they can also strengthen publicity regarding the comfort and safety of buses so that the elderly can realise the advantages of bus travel. The interventions of risk management aim to mitigate infectious diseases and benefit public health (Hee-Jin and Gim, 2021).
It is advocated that bus users should consciously abide by preventive measures, wear masks and maintain social distance during the entire process.
For the elderly people who are more sensitive about risk perception and likely to abandon public transport during the normalisation stage, their basic living and health needs also need to be guaranteed.
Policymakers gradually transform and construct elderly-friendly communities to create a safe and comfortable walking environment. Specifically, highly walkable communities can be improved, whilst commercial, medical, entertainment and other supporting facilities can be reasonably planned and constructed near residential areas to create a life circle for the elderly to travel short distances (Wang, X. and Zeng, 2019; Yan, Gao et al., 2018; Zhang, Yao et al., 2019). Additionally, policymakers can plan services such as door-to-door medical care and living materials in community life circles based on the needs and characteristics of the elderly and the purpose of travel.
Service providers can also provide flexible transportation services according to the special needs of the elderly to ensure their travel (e.g., customised buses, demand-responsive transit, community transportation services, scheduled car services, minibuses, etc.) (He, Cheung et al., 2018; Mulley, Ho et al., 2020).
Notably, policymakers must also consider that the elderly group is slow to accept new things (Herrenkind, Nastjuk et al., 2019). Thus, digital accessibility should be regarded as an important public policy arrangement in the ageing era to help elderly bus users overcome the ‘digital divide’ in the context of the COVID-19 pandemic. For example, Beijing has already automatically upgraded the elderly card so that the elderly can swipe their cards to synchronously verify their ‘health code’ without any additional operation.
This study has provided a meaningful discussion on the influence mechanisms of the elderly’s behavioural intentions regarding bus travel during the normalisation stage. The highlights of our findings are presented as follows.
First, perceived behavioural control exhibited the greatest effect on behavioural intention during the normalisation stage, with the importance of the bus in daily travel being the most important observation variable. Attitudes and subjective norms also exhibited a positive effect on behavioural intention.
Second, cognitive risk perception exhibited a significant negative impact on attitudes, perceived behavioural control and subjective norms. Compared to the other elements, the possibility of infection had the greatest impact. Affective risk perception also exhibited a significant negative impact on subjective norms, among which family infections had the greatest impact. Although risk perception (cognition/affective) exhibited no direct effect on behavioural intention, it exhibited an indirect negative effect. Moreover, cognitive risk perception had a greater effect than affective risk perception.
Third, the elderly were still willing to prioritize bus travel and recommend it to others during the normalisation stage. Therefore, to guarantee the elderly’s bus travel during the normalisation stage, it is necessary to consider their psychological factors to evaluate different bus response measures under the requirements of pandemic prevention. Effective coordination between all stakeholders (i.e., government, service providers and bus users) will help increase the trust and confidence of the elderly in bus travel during the normalisation stage.
The limitations of this study and future research recommendations are as follows. First, this study only focused on the influence mechanism of behavioural intention during the normalisation stage. As such, future studies should consider discussing behavioural intention in other stages of the pandemic or the relationship between behavioural intention and human behaviour. Secondly, the impact of the pandemic on the elderly’s bus travel may have long-term effects. Therefore, future research should also consider collecting data for longitudinal studies. Also, the research subject of this study is limited to the elderly living in Beijing. In different cities and regions, the travel behaviour of the elderly may be different. In future research, we will compare and validate the model in different cities and regions.
Conceptualization, Hai Yan and Ruixin Jin; methodology, Hai Yan and Ruixin Jin; software, Ruixin Jin; investigation, Ruixin Jin; data curation, Ruixin Jin; writing—original draft preparation, Hai Yan and Ruixin Jin; writing—review and editing, Hai Yan and Ruixin Jin; supervision, Hai Yan. All authors have read and agreed to the published version of the manuscript.
The authors declare that they have no conflicts of interest regarding the publication of the paper.
This research was supported by National Natural Science Foundation of China (No. 71971005), Beijing Municipal Natural Science Foundation (No. 8202003).
