Culture vs. distance: comparing the effects of geographic segmentation variables on tourists’ destination images based on social media data

Mengtong Wu , Chao Jiang , Yi Zhang , Jingjing Cao , Ying Cheng , Yu Liu

Computational Urban Science ›› 2021, Vol. 1 ›› Issue (1) : 14

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Computational Urban Science ›› 2021, Vol. 1 ›› Issue (1) : 14 DOI: 10.1007/s43762-021-00012-z
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Culture vs. distance: comparing the effects of geographic segmentation variables on tourists’ destination images based on social media data

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Abstract

Culture and distance are two major factors for geographically segmenting tourists in tourism marketing and advertising. Previous empirical studies on the destination image, however, have examined extensively the effect of the culture while inadequately the effect of the distance, let alone comparing the effects of the two variables. Using social media data, this study compares the effect of distance-based segments of tourists with that of culture-based segments in producing diverse perceived images of a destination. From Sina Weibo data, 282,532 Chinese mainland tourists who visited Suzhou, China during 2012–2016 and their perceived destination images are extracted and analyzed. The main results include: 1) for distance-based segments, the image differences increased with distance and the short-haul tourists perceived a more comprehensive image than the long-haul tourists; 2) for culture-based segments, the image differences were clear and relatively complex, while tourists from Wuyue cultural region had similar image perceptions with the local visitors; 3) the q-statistic of the Geodetector method shows that the culture-based segmentation can explain 65.8% of image variations while the distance-based segmentation can explain 46.6% of image variations, suggesting that culture is a more appropriate variable to segment the tourism market.

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Mengtong Wu, Chao Jiang, Yi Zhang, Jingjing Cao, Ying Cheng, Yu Liu. Culture vs. distance: comparing the effects of geographic segmentation variables on tourists’ destination images based on social media data. Computational Urban Science, 2021, 1(1): 14 DOI:10.1007/s43762-021-00012-z

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Ministry of Science and Technology of the People's Republic of China,(2018YFB0505004)

National Natural Science Foundation of China,(41830645)

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