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Impact of the COVID-19 pandemic on travel behavior: A case study of domestic inbound travelers in Turkey

Mehmet Sariişik

Mehmet Sariişik

Sakarya University of Applied Sciences, Faculty of Tourism, Dept. of Gastronomy and Culinary ArtsTurkey
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Sariişik, Mehmet
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Veli Ceylan

Veli Ceylan

Sakarya University of Applied Sciences, Faculty of Tourism, Dept. of Gastronomy and Culinary ArtsTurkey
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Ceylan, Veli
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Mustafa İnanç Kapucuoğlu

Mustafa İnanç Kapucuoğlu

Sakarya University of Applied Sciences, Faculty of Tourism, Dept. of Gastronomy and Culinary ArtsTurkey
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Kapucuoğlu, Mustafa İnanç
  
31. Jan. 2024
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Artikel-Kategorie: Research Article
Online veröffentlicht: 31. Jan. 2024
Seitenbereich: 211 - 224
Eingereicht: 04. Juli 2022
Akzeptiert: 23. Okt. 2023
DOI: https://doi.org/10.2478/ejthr-2023-0017
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© 2023 Mehmet Sariişik et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Research Model
Research Model

Figure 2:

Path Analysis Results
Path Analysis Results

Demographics of Respondents

Variable n %
Gender
Female 342 56.0
Male 269 44.0
Age
30 and younger 256 41.9
31–40 166 27.2
41 and older 189 30.9
Education
High school 104 17.0
Bachelor’s 301 49.3
Postgraduate 206 33.7
Number of Children
None 327 53.5
1 120 19.6
2 164 26.8
Marital Status
Married 305 49.9
Single 306 50.1
Income
400 dollars or less 352 57.6
401–600 dollars 80 13.1
601 dollars or more 179 29.3
Who do you go on vacation with?
Alone 41 6.7
With immediate family 376 61.2
With a friend 93 15.5
With multiple friends 83 13.6
With relatives 18 2.9

Analysis of the Convergent Validity of the Theory of Planned Behaviour

ITEMS Outer loading Cronbach’s alpha CR AVE
HOS During the travel ban. the city I intended on visiting remained welcoming to visitors from parts of the country hardest hit by the pandemic 0.953 0.845 0.948 0.901
The city I intended on visiting showed a great deal of resilience in ensuring the health and safety of visitors 0.945
IMP My impression of the city will be affected by the number of coronavirus cases reported 0.869 0.843 0.860 0.754
My impression of the city will be affected by its reported coronavirus recovery rate 0.868
ATT Once this epidemic is over, I believe it is still a good idea to go on holiday to the city I intended on visiting 0.875 0.776 0.813 0.705
Once this epidemic is over, I would be excited about going on holiday to the city I intended on visiting 0.778
SUN Once this epidemic is over, we intend on going on holiday to the destination we had chosen to visit originally 0.875 0.772 0.808 0.709
Once this epidemic is over, my friends and colleagues intend on going on holiday to the destination they had chosen to visit originally 0.769
PBC Once this epidemic is over, I will remain financially able to go on holiday in the city I intended on visiting 0.862 0.779 0.814 0.695
Once this epidemic is over, I will continue to have availability in my schedule to go on holiday in the city I intended on visiting originally 0.794
PPT After this epidemic, I will go on holiday to the city I intended on visiting originally 1

Analysis of Goodness of Fit of the Model

Goodness-of-Fit Index Acceptable Range Before Correction After Correction Goodness of Fit of the Model
χ2 (chi-square) Smaller the Better 49.481 4.123 pass
χ2 and degrees of freedom < 3 3.54 2.72 pass
GFI > 0.80 .930 .977 pass
AGFI > 0.80 .930 .945 pass
RMSEA < 0.08 .079 .064 pass
CFI > 0.90 .974 .966 pass
PCFI > 0.50 .326 .371 pass

Path Analysis and Effect Size

Path Coefficients T-statistics f2
IPP-ATT .145 3.825 0.213
IPP-PPT .105 2.662 0.138
ATT-PPT .346 6.258 0.375
PBC-PPT .686 15.510 0.480
SUN-PPT .262 5.387 0.303
R2 Q2
ATT 0.226 0.184
PPT 0.620 0.590

PLS path analysis results

Model Path β t Label
H1 IPP → ATT .305 3.118 Accepted
H2 ATT → PTT .458 12.291 Accepted
H3 IPP → PPT .280 2.348 Accepted
H4 PBC → PPT .872 47.964 Accepted
H5 SUN → PPT .624 35.542 Accepted
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