A Study of Tourists’ Holiday Rush-Hour Avoidance Travel Behavior Considering Psychographic Segmentation
Abstract
:1. Introduction
2. Methodology
2.1. Model Framework
2.2. Structural Equation Model
2.3. Latent Class Model
3. Data
3.1. Sample
3.2. Data Analysis
4. Result Analysis
4.1. Analysis of SEM Estimation Results
4.2. Analysis of LCM Segmentation Results
5. Conclusions and Discussion
- (1)
- For the first segment (‘neutral’ type), although they are generally neutral about avoiding rush-hour travel during holidays, some tourists (about 40%) are still sensitive to preferential travel costs and travel experiences during holidays. Due to the small groups, the travel is free and flexible, and changing travel routes is convenient. They can potentially be used to stimulate groups to choose HRAT. We can encourage them to travel at different off-peak times by increasing travel discounts and recommending high-quality travel routes.
- (2)
- For the second segment (‘experiential’ type), they attach great importance to tourism travel experience and quality. The degree of caring about the travel experience exceeds the sensitivity to travel cost. Crowded scenic spots and congested roads will directly affect their travel choices. Therefore, this segment can be regarded as a group that is faithful to HRAT. Accurate information on avoiding rush hour can be used to help this group. Meanwhile, excellent service facilities at scenic spots and roads can help improve the HRAT experience of this group.
- (3)
- For the third segment (‘active’ type), they are the potential group for HRAT. They show strong sensitivity to external influences such as news media, preferential fees, and so on. The psychological manifestation of the intention to choose HRAT is more positive. However, the attitude evaluation of HRAT is weaker than that of the second segment and the sensitivity to cost is stronger. Most of the people in this group are middle income. It is more effective to use preferential fees (such as scenic spot tickets, travel costs) to attract them to travel at different times.
- (4)
- For the fourth segment (‘random’ type), because the number of tourists in these groups is usually large, they are very restrictive toward each other. It is relatively difficult to implement holiday rush-hour avoidance travel. However, because they are sensitive to fees and most have low incomes, we can increase the discounts to avoid rush hour for group tours, such as discount tickets for group attractions and preferential road tolls for multiple people.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variable | Description | Percentage (%) | |
---|---|---|---|
Gender | Gender 1 | Male | 52.82 |
Gender 2 | Female | 47.18 | |
Age (years) | Age 1 | 18–23 | 18.18 |
Age 2 | 24–34 | 49.38 | |
Age 3 | 35–44 | 19.01 | |
Age 4 | 45–54 | 8.68 | |
Age 5 | 55–65 | 4.75 | |
Occupation | Occupation 1 | Staff | 30.87 |
Occupation 2 | Worker | 23.56 | |
Occupation 3 | Teachers | 11.78 | |
Occupation 4 | Student | 21.18 | |
Occupation 5 | Retired/Unemployed | 5.58 | |
Occupation 6 | Freelance | 5.58 | |
Occupation 7 | Other | 1.45 | |
Education level | Education 1 | High school or below | 5.37 |
Education 2 | Junior college/Bachelor’s degree | 63.43 | |
Education 3 | Master’s degree | 22.73 | |
Education 4 | Doctorate degree | 8.47 | |
Monthly income (RMB) | Income 1 | 3000 | 35.95 |
Income 2 | 3000–5000 | 22.52 | |
Income 3 | 5001–8000 | 27.27 | |
Income 4 | 8000 | 14.26 | |
Disposable tourism time | Time 1 | Single day off and statutory holidays | 18.18 |
Time 2 | Two days off and statutory holidays | 25.83 | |
Time 3 | Two days off, statutory holidays, and paid annual leave | 16.12 | |
Time 4 | Two days off, statutory holidays, and summer/winter vacation | 23.34 | |
Time 5 | Lots of free time | 16.53 |
Latent Variables | Observed Variables | Mean | Cronbach’s α | |
---|---|---|---|---|
Attitude (ATT) | Y1 | Improve the tourism experience | 4.14 | 0.882 |
Y2 | Avoid travel congestion | 3.80 | ||
Y3 | Reduce the loss of time | 3.64 | ||
Subjective Norm (SN) | Y4 | Behavior of people around you | 3.31 | 0.841 |
Y5 | Advice and support from family members | 4.01 | ||
Y6 | Suggestions from friends/classmates/colleagues | 3.98 | ||
Y7 | News and social media promotion | 3.92 | ||
Perceived Behavior Control (PBC) | Y8 | Free travel time constraints | 3.89 | 0.906 |
Y9 | Accurate and perfect information guidance | 3.66 | ||
Y10 | Concessionary attraction of tickets to scenic spots | 3.58 | ||
Y11 | Preferential travel cost attraction | 3.76 | ||
Y12 | Convenient transportation facilities | 3.31 | ||
Y13 | Experience of holiday rush-hour avoidance travel | 3.25 | ||
Behavior Intention (BI) | Z1 | Willing to try | 4.00 | 0.814 |
Z2 | Willing to give priority | 3.33 | ||
Z3 | Recommend it to friends and relatives | 3.83 |
Fix Index | SEM Models | Criteria of Acceptable Fit |
---|---|---|
CMIN/DF (Likelihood-ratio Chi-square/degrees of freedom) | 1.723 | <3 |
RMSEA (root mean square error of approximation) | 0.039 | <0.08 |
RMR (root mean square residual) | 0.042 | <0.05 |
GFI (goodness-of-fit index) | 0.951 | >0.9 |
AGFI (adjusted goodness-of-fit index) | 0.934 | >0.9 |
NFI (normed fit index) | 0.955 | >0.9 |
TLI (Tacker-Lewis index) | 0.977 | >0.9 |
IFI (incremental fit index) | 0.981 | >0.9 |
CFI (comparative fit index) | 0.981 | >0.9 |
Number of Classes | χ2 | G2 | AIC | BIC | Entropy |
---|---|---|---|---|---|
2 | 3852.799 | 1512.216 | 7433.902 | 7630.460 | 0.892 |
3 | 3559.240 | 1367.385 | 7313.432 | 7610.360 | 0.841 |
4 | 3415.039 | 1316.357 | 7262.843 | 7660.141 | 0.872 |
5 | 3050.608 | 1216.701 | 7266.489 | 7714.157 | 0.859 |
Variables | Observed Variables | Level | Description | Conditional Probability of Latent Class | |||
---|---|---|---|---|---|---|---|
CL1 | CL2 | CL3 | CL4 | ||||
Personal attributes | Age (years) | 1 | 18–23 | 0.254 | 0.12 | 0.114 | 0.346 |
2 | 24–34 | 0.498 | 0.154 | 0.358 | 0.419 | ||
3 | 35–44 | 0.158 | 0.377 | 0.297 | 0.04 | ||
4 | 45–54 | 0.074 | 0.294 | 0.154 | 0.12 | ||
5 | 55–65 | 0.016 | 0.055 | 0.077 | 0.075 | ||
Monthly income (RMB) | 1 | 3000 | 0.426 | 0 | 0.313 | 0.789 | |
2 | 3000–5000 | 0.314 | 0.2 | 0.303 | 0.149 | ||
3 | 5001–8000 | 0.234 | 0.419 | 0.305 | 0.03 | ||
4 | 8000 | 0.026 | 0.381 | 0.079 | 0.032 | ||
Tourism characteristics | Tourist group (N) | 1 | 1 | 0.46 | 0.115 | 0.47 | 0.112 |
2 | 2–3 | 0.25 | 0.516 | 0.327 | 0.064 | ||
3 | 4–6 | 0.168 | 0.309 | 0.063 | 0.235 | ||
4 | ≥7 | 0.122 | 0.06 | 0.14 | 0.589 | ||
Psychological factors | Y1 | 1 | Strongly disagree | 0.016 | 0 | 0.047 | 0.256 |
2 | Disagree | 0.098 | 0.042 | 0.111 | 0.456 | ||
3 | Generally agree | 0.485 | 0.125 | 0.163 | 0.043 | ||
4 | Agree | 0.224 | 0.376 | 0.296 | 0.101 | ||
5 | Strongly agree | 0.177 | 0.457 | 0.383 | 0.144 | ||
Y7 | 1 | Strongly disagree | 0.012 | 0.018 | 0.043 | 0.131 | |
2 | Disagree | 0.05 | 0.075 | 0 | 0.323 | ||
3 | Generally agree | 0.893 | 0.146 | 0.039 | 0.144 | ||
4 | Agree | 0.045 | 0.502 | 0.26 | 0.372 | ||
5 | Strongly agree | 0 | 0.259 | 0.658 | 0.03 | ||
Y11 | 1 | Strongly disagree | 0.016 | 0.253 | 0.127 | 0.011 | |
2 | Disagree | 0.08 | 0.11 | 0.03 | 0.224 | ||
3 | Generally agree | 0.509 | 0.152 | 0.102 | 0.153 | ||
4 | Agree | 0.27 | 0.297 | 0.445 | 0.438 | ||
5 | Strongly agree | 0.125 | 0.188 | 0.296 | 0.174 | ||
Probability of latent class | 0.295 | 0.339 | 0.162 | 0.204 |
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Zhu, H.; Guan, H.; Han, Y.; Li, W. A Study of Tourists’ Holiday Rush-Hour Avoidance Travel Behavior Considering Psychographic Segmentation. Sustainability 2019, 11, 3755. https://doi.org/10.3390/su11133755
Zhu H, Guan H, Han Y, Li W. A Study of Tourists’ Holiday Rush-Hour Avoidance Travel Behavior Considering Psychographic Segmentation. Sustainability. 2019; 11(13):3755. https://doi.org/10.3390/su11133755
Chicago/Turabian StyleZhu, Haiyan, Hongzhi Guan, Yan Han, and Wanying Li. 2019. "A Study of Tourists’ Holiday Rush-Hour Avoidance Travel Behavior Considering Psychographic Segmentation" Sustainability 11, no. 13: 3755. https://doi.org/10.3390/su11133755