Travel Vloggers on TikTok: Their Distribution and Impacts on
Regional Tourism Development
Xue PENG
Asian Growth Research InstituteWorking Paper Series Vol. 2024-14
May 2024

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Asian Growth Research InstituteTravel Vloggers on TikTok: Their Distribution and Impacts on Regional Tourism
Development
Xue PENG 1
Asian Growth Research Institute
Abstract: Anecdotal evidence from multiple cities suggests that short videos boost
destination awareness and subsequently, tourism visitation. TikTok has now
become one of the most popular short video platforms and accordingly, has
attracted intensive attention from the tourism industry and scholars querying theimplications. While previous tourism studies have primarily concentrated on theplatform’s viewers and user-generated content, studies focused on travel vloggers
are scarce, and their roles in tourism development remain understudied. This study
utilizes big data from TikTok in China and employs spatial analysis to investigate
the distribution characteristics and impacts of travel vloggers in regional tourism
development. The spatial analysis methods utilized include Moran’s I index andGeodetector. The Moran’s I analysis results indicate that cities with a similar
number of travel vloggers tend to be clustered; however, this tendency is higher
with regard to non-travel-themed vloggers. The Geodetector results reveal thattravel vloggers significantly influence provincial tourist arrivals, demonstratinglarger impacts than conventional variables such as scenic areas, travel agencies,
and transport infrastructure. The most crucial factor contributing to the impact oftravel vloggers is their number. This is followed by their productivity in terms ofthe number of videos they upload, which wields a larger impact than the shares or
likes they receive. Within the productivity metric, the cumulative productivity oftravel vloggers from previous years exerts a higher influence than their recent
productivity from the past year. Interestingly, the number of their followers does
not necessarily impact regional tourist arrivals. These insights can assist
policymakers and practitioners in leveraging vloggers for regional tourism
development.
Keywords: travel vloggers; TikTok; regional tourism development; big data;
spatial analysis methods, Geodetector; Moran’s I index.

1

Email: peng@agi.or.jp

11. Introduction
The advent of social media has brought radical transformations to the tourism industry
as well as research (Asongu & Odhiambo, 2019). Short videos are representative ofvideo-centric social media due to their high media richness, with TikTok emerging as themost prominent platform (Cheng, 2024). There have been cases of cities experiencing a
tourism boom triggered by short videos from time to time. A notable example is the cityof Zibo in the eastern region of China’s Shandong Province, whose tourism industry
recovered particularly rapidly from the COVID-19 pandemic downturn.
Although Zibo is now better known as an industrial city, it is where Ancient Linzi, thecapital of the powerful ancient State of Qi, was located. It is a tourist city famous for its
historical sites and museums. With a population of 4.7 million, the city experienced
serious setbacks in tourism due to measures such as lockdowns and social distancing
during the COVID-19 pandemic.
However, following the easing of COVID-19 restrictions, Zibo quickly emerged as oneof the hottest tourism destinations in early 2023. The upsurge in tourist arrivals began in
March and peaked during the May Day Golden Week, a five-day national vacation from
April 29th to May 3rd. During Golden Week, Zibo welcomed approximately 240,000
tourists, marking a 55% increase (85,000 persons) compared to 2019 (Cui, 2023). Thisrate of increase significantly exceeded the national average of 19% (MCTC, 2023).
Interestingly, tourists are not flocking to the city for its famous scenic spots or historicalsites but for its barbecue skewer meals. A series of short videos tagged “Zibo-Barbecue”
have spread exponentially nationwide since early March 2023, drawing people to the city.
This has made Zibo a notable example of a tourism boom triggered by short videos. The
successful contribution of short videos to tourism promotion has also been noted in other
cities. Despite this anecdotal evidence, research that empirically tests the relationshipbetween the short video boom and regional tourism development remains limited.
2

Existing research on short videos and tourism predominantly focuses on aspects suchas viewers’ tourism intention (Han et al., 2021; Jiang et al., 2022; Liu et al., 2023; Wu &Ding, 2023), tourism destination choices (Zhou et al., 2023), and tourism destinations’images (Li et al., 2019). Although TikTok has grown to be the largest and most prominent
online short video platform globally, previous tourism studies have primarily
concentrated on its viewers (Han et al., 2021; Zhou et al., 2023) and user-generated
content (Li et al., 2019; Tham et al., 2024). Video creators on TikTok have been largely
overlooked.
Individuals who create and share video content on online platforms are generally
referred to as video bloggers, i.e. vloggers. In this article, vloggers who specialize intravel content are termed “travel vloggers.” Vloggers of all topics are collectively referred
to as “vloggers in general.” Despite several existing studies investigating their online
sharing intentions (Zhao et al., 2022) and the impact of their motivations on media content
production (Tham et al., 2024), studies focused on travel vloggers are scarce, and theirroles in tourism development remain understudied.
To address this gap, this study aims to empirically examine travel vloggers on TikTok,by unveiling their distribution characteristics and their roles in regional tourism
development. China is selected as the case study area because TikTok, known as Douyin
in China, was established there and boasts the largest user base in the world. In addition,
since China implemented strict zero-coronavirus restrictions and did not fully lift them
until December 2022, China in 2023 serves as a well-suited case to observe the recoveryof the tourism industry since COVID-19. China’s May Day Golden Week in 2023 serves
as a significant milestone for evaluating this resurgence.
The research questions of interest are as follows:① What are the spatial distribution characteristics of travel vloggers in China?

3

② Does the distribution of travel vloggers have any impact on regional tourism
development? If so, does the impact come from the travel vloggers’ productivity(i.e., the number of videos a vlogger has uploaded) or viewer engagement (i.e., thenumber of likes, shares, and followers a vlogger has received)?
This study utilizes big data from TikTok to document the number of travel vloggers in
Chinese cities and employs the spatial analysis methods of Moran’s I index andGeodetector to conduct the analysis. Since there are relatively few data samples withinformation on tourist arrivals, the investigation of research question ② is an exploratory
study conducted with a limited sample size. Nevertheless, this study contributes totourism literature by expanding the academic understanding of the distribution
characteristics of travel vloggers in China and how they influence regional tourism
development.
The remainder of this paper is organized as follows. Section 2 reviews relevant
literature on factors influencing the regional tourism destination market in China and therole of TikTok in tourism. Section 3 details data and variables. Section 4 presents thespatial distribution characteristics of travel vloggers. Section 5 reports the results of theanalysis on the impact of travel vloggers on tourism. Section 6 concludes with academic
contributions, policy implications, and limitations.

2. Literature Review
2.1 Factors Influencing the Regional Tourism Destination Market
A rich body of literature has explored a wide range of factors influencing the tourism
market, including internal factors (tourists’ intention, motivation, destination choice
behavior, experience, culture, etc.), source market push factors (disposable income,population, vacation policy, etc.), destination pull factors (tourism resources, tourism

4

products, environment, infrastructures, service facilities, etc.), and resistance factors(distance, means of transport, budget, etc.) (Yang et al., 2011; Feng & Huang, 2015).
Several previous studies have explored the factors affecting provincial-level tourism
development in China. Wang and Chen (2011) employed a Pearson correlation analysis
in their empirical study and determined that both foreign exchange earnings and domestic
tourism revenues in Chinese provinces are positively affected by resource endowments,
transport infrastructure, and economic development levels. They argue that these factors
are essential to understanding regional differences in the tourism economy. Ao and Wei(2006) find that the main factors contributing to provincial differences in tourism revenue
are regional tourism resources, regional fundamental infrastructures represented bytransport infrastructure, service facilities represented by hotels, and economic
development levels. These results apply to foreign, domestic, and overall tourism revenue.
Ma and Yang (2003) argue that the scale of regional tourism industries—as measured by
the number of travel agencies, employees in the tourism industry, and tourism foreignexchange earnings—is positively associated with the number of AAAA-rated (4A) tourist
attractions in each province. A rating of 4A represents the second-highest level in the
tourist attraction rating system implemented by the China National Tourism
Administration to assess the quality of tourist attractions. According to this system, scenic
areas are classified into five categories, with A being the lowest level and AAAAA (5A)
the highest. Additional evidence reveals a correlation between provincial tourism
resources and foreign tourism in China, suggesting that tourism resources serve as theobjective foundation for developing tourism (Zhao, 2008). It is also argued that the impactof service facilities is complex. While service facilities are a significant factor affecting
foreign tourism in central and western China, they do not affect foreign tourism in eastern
China or at the national level (Zhao, 2008). However, existing studies on provincial-level5

tourism development mainly focus on traditional tourism-boosting factors, while studies
exploring the impact of social media on regional tourism development are scarce.

2.2 The Role of TikTok in Tourism
Social media is a group of Internet-based applications that builds on the ideologicaland technological foundations of Web 2.0 and allows for the creation and exchange ofuser-generated content (Kaplan & Haenlein, 2010). The popularity of social media has
facilitated the creation of an extensive repository of user-generated content, personalized
and readily accessible to its users. In the tourism landscape, people can use social media
to glimpse at other tourists’ experiences in a destination, view online content posted byfellow users, participate in conversations and reviews, share information with friends, andso on. People engage in these activities for various reasons—to inform others of theirpersonal preferences, for purely hedonic purposes to connect with others, or sometimes
to enhance their knowledge (Aleti et al., 2018).
The production of online short videos has become much easier and more popular due
to the widespread use of smartphones and advancements in applications. These videos
have started to exert a growing impact on the tourism landscape. For instance, an online
survey conducted in China revealed that, out of 2000 samples, 57% utilize short video
platforms as a channel for travel information. Remarkably, 90% of travelers are willingto share their travel experience online, with 59% opting for short video platforms(Oceanengine, 2022). Since its launch in 2016, TikTok has rapidly become one of thelargest social networks globally. As of January 2024, it boasts a monthly active user baseof 1,562 million in countries other than China (Statista, 2024). In China, TikTok has
amassed a monthly active user base of over 761 million as of December 2023, firmly
establishing its dominance in the Chinese online short video market (QuestMobile, 2024).

6

Short videos uploaded on TikTok typically can range from 15 seconds to 60 seconds.
Compared to longer videos, short videos are more flexible and better at reaching a large
audience. These short videos are particularly popular among young people, with the
majority of TikTok users in China falling in the 18–35 age range (Randeng Search
Academy, 2020). TikTok recommends videos to viewers based on algorithms. It
constantly analyzes a viewer’s watching behaviors and improves its model over time,showing more content tailored to the viewer’s preferences and interests (Herrman, 2019).
This personalized approach keeps viewers engaged and encourages them to return for
more.
Previous studies on TikTok have mainly focused on its viewers and explored its impacts
on the tourism intentions and destination choices of potential tourists (Han et al., 2021;
Zhou et al., 2023). Wengel et al. (2022) warn that a sudden surge of tourists triggered by
TikTok videos can burden the environment and facilities in a destination. Further issues
include users’ perceived trust (Zhou et al., 2023) and unverified tourism claims (Ying etal., 2021) resulting from TikTok videos. However, this strand of studies primarily relies
on surveys targeting TikTok end-users or the general public (Tham et al., 2024).
A few studies have used data on user-generated content from TikTok. For example,
Tham et al. (2024) manually decoded 30 videos related to a TikTok campaign for Penang,
Malaysia, and categorized their content based on a designated framework. Their findings
suggest that media content production is influenced by the roles and motives of different
stakeholders, including tourism destination management organizations (DMOs), TikTokinfluencers, and other TikTok end-users who participated in the campaign contest. The
results underscore the importance of information resources and raise concerns about how
influencers’ videos may result in the fragmentation and dilution of a destination’s image.
Li et al. (2019) also employed user-generated content and analyzed the online comments

7

under TikTok food short videos to investigate the impact of short videos on the tourism
destination image of Chengdu in China. TikTok allows its users to add geolocation
information by using geotags in their videos. Zhang et al. (2022) used this geo-tagged
information to analyze people’s virtual and physical visiting preferences in Beijing, China.
Despite these efforts, research incorporating user-generated content from online video
platforms, TikTok included, is still in its infancy.
In addition to the viewer and content, the content creator, i.e. vlogger, is crucial to
TikTok, serving as the lifeblood of the online short video platform. TikTok offers various
tools to simplify video creation, including filters, effects, auto-caption, and the ability tosearch for sounds to score one’s video, making the initial step of becoming a video creator
easy. The algorithm-driven content recommendation system enables a TikTok video topotentially experience a snowball effect in viewership, once it starts gaining traction.
These two factors attract a wide range of individuals and organizations to join the vlogger
group, boosting content creation across various niches, including tourism. Several studies
focusing on travel vloggers include Tham et al. (2024), who determined that vloggers’
different roles affect their content creation on TikTok. Although not specified to TikTok,
Zhao et al.’s (2022) study revealed that travel vloggers’ online sharing intention isaffected by factors such as the consistency of values, entertainment motivation, emotional
engagement, and the parasocial relationships between tourists and travel vloggers.
Nevertheless, travel vloggers have been largely overlooked and their roles in regional
tourism development remain understudied.

3. Data
The data on travel vloggers and vloggers in general were obtained from Chanmama, adata provider for TikTok in the mainland of China as of October 2023. For ethical8considerations, it needs to be declared that the data has undergone anonymization
processing. The number of travel vloggers is calculated based on the residence selfreported on TikTok. The data sample includes 185,655 travel vloggers distributed among
364 cities of 31 provinces. These cities include four direct-administered municipalities
under the control of the Central Government, 30 county-level administrative units, andone forest region directly administrated by the corresponding provinces, with the majority
being prefecture-level administrative units. Travel vloggers account for only 1.47% of all
12.61 million vloggers.
Variables for travel vloggers’ performance encompass productivity metrics
represented by the number of videos, as well as viewer engagement metrics represented
by the number of shares, likes, and followers. These metrics are considered for both theoverall term and specifically for the past year.
The data on tourist arrival numbers for each province during the 2023 May Day Golden
Week was released through news announcements by the Administration of Culture andTourism in each province. However, four provinces, namely Hebei, Shanxi, Jiangxi, andAnhui, have not released this data, leaving 27 provinces with data available for analysis
in Section 5.
This study employs several control variables. The number of scenic areas, travel
agencies, and hotels, are obtained from the China Statistical Yearbook of Culture,
Cultural Relics, and Tourism 2022. The index of transport infrastructure is constructedusing the method proposed by Ao and Wei (2006): Index of transport infrastructure =(Density of railway network in km/km²) * 30% + (Density of roads and highways in
km/km²) * 30% + (The percentage of secondary roads and above among all roads andhighways) * 20% + (Density of inland waterways in km/km²) * 20%. The relevant data9

are from the China Statistical Yearbook 2022. The descriptions and summary statistics ofvariables are listed in Table 1.
Table 1. Descriptions and summary statistics of the dependent variable and potential
determinants.
Variable

Description
Number of tourist arrivals
Touristsduring May Day Golden
Week
Tvloggers
Number of travel vloggers
Number of non-travel-themed
Ntvloggers
vloggers
Vloggers
Number of all vloggers
Number of travel vloggers’
Videosvideos in total (10 thousand)Number of travel vloggers’
Videos1year
videos within the previous
year (10 thousand)Number of followers in total
Followers(million)
Number of followers within
Followers1year
the previous year (million)
Number of likes in total
Likes(million)
Number of likes within the
Likes1year
previous year (million)
Number of shares in total
Shares(million)
Number of shares within the
Shares1year
previous year (million)
Number of A-level and above
Ascenicareas
scenic areas
Number of 5A-level scenic
A5scenicareas
areas
Tagencies
Number of travel agencies
HotelsNumber of starred hotels
Number of rooms in starred
Hotelrooms
hotelsNumber of beds in starred
Hotelbeds
hotelsIndex of transport
Transport
infrastructure

Obs

Mean

Std. Dev.

Min

Max

27

2196.831528.52

116.775518.0431

5988.874116.14

607

16653

31 401035.90 320189.80

15962 1355643

31 407024.80 324238.60

16790 1372296

31

92.57

62.89

7.33

250.47

31

33.77

23.39

2.75

95.90

31

21.70

17.01

1.33

63.13

31

118.04

89.60

7.26

358.0131

670.96485.2645.28

1973.5731

225.62173.0912.64

688.33

31

75.27

57.08

3.97

221.74

31

36.71

30.67

1.36

140.3831478.94288.19

84

1322

31

10.84

6.62

2

34

31
31

1000.03247.61617.02113.08

155
64

2455
483

31

36159.58

21858.43

2437

94818

31

61718.1340090.37

4130

183014

31

1.15

1.16

0.20

5.23

Note: The above variables are at the provincial level.

4. Spatial Distribution of Travel Vloggers
4.1 Visualization of Distribution
First, this study analyzes the spatial characteristics of travel vloggers by visualizing
their distribution using data from sample cities. The number of travel vloggers in each10

city is categorized into ten quantiles. The map is color-coded to reflect these levels, withdarker shades indicating a higher number. The results indicate that travel vloggers are
predominantly concentrated in areas east of the Hu Huanyong Line, the conceptual
geographic dividing line in China proposed by the renowned Chinese geographer Hu
Huanyong (Hu, 1935) (Figure 1a). These areas are also where most of the population andeconomic activities in China are located.
Out of 364 cities, 77 cities (21.2%) have less than 100 travel vloggers, while 246 cities(67.6%) have less than 500 travel vloggers. Only 37 cities (10.1%) have more than 1000
travel vloggers. The top ten cities with the highest number of travel vloggers are Beijing(5,446), Chengdu in Sichuan (4,340), Chongqing (4,245), Shanghai (4,109), Shenzhen in
Guangdong (3,536), Guangzhou in Guangdong (3,449), Xi’an in Shaanxi (3,247),
Hangzhou in Zhejiang (2,825), Zhengzhou in Henan (2,705), and Wuhan in Hubei (2,111).
These cities include three direct-administered municipalities and seven provincial capitals.
They are distributed across the eastern, central, and western regions, but not in thenortheastern region.
The maps in Figures 1a and 1b show that the distribution patterns of travel vloggers
and vloggers in general are broadly similar, although vloggers in general are slightly moreconcentrated in the southern regions east of the Hu Huanyong Line.

11

(a)

(b)
Figure 1. Decile map of the number of travel vloggers (a) and vloggers in general (b)by cities.

124.2 Spatial Autocorrelation Analysis
Tobler (1970, p. 236) proposed the first theory of geography, stating, “Everything isrelated to everything else but near things are more related than distant things.” This
principle may also be applicable in the distribution of travel vloggers. Global and local
indicators of spatial autocorrelation can be useful tools to identify such spatial
heterogeneity and agglomerations (Romão & Saito, 2017).

4.2.1 Global Spatial Autocorrelation Analysis
Moran’s I index is a commonly employed metric for assessing global spatial
autocorrelation. It was initially suggested by Moran (1948) and popularized through thework of Cliff and Ord (1973). For an observation at location i with attribute x, we havethe deviation calculated as 𝑧𝑧𝑖𝑖 = 𝑥𝑥𝑖𝑖 − 𝑥𝑥̅ , where 𝑥𝑥̅ is the mean of variable x. Moran’s I
index is then expressed as𝐼𝐼 =∑𝑖𝑖 ∑𝑗𝑗 𝑤𝑤𝑖𝑖𝑖𝑖 𝑧𝑧𝑖𝑖 · 𝑧𝑧𝑗𝑗 /𝑆𝑆0,
∑𝑖𝑖 𝑧𝑧𝑖𝑖2 /𝑛𝑛

where 𝑤𝑤𝑖𝑖𝑖𝑖 is the element of the spatial weight matrix, 𝑆𝑆0 = ∑𝑖𝑖 ∑𝑗𝑗 𝑤𝑤𝑖𝑖𝑖𝑖 is the sum of all theweights, and n is the number of observations.

Moran’s I index examines whether there are any associations between the locations
and the values of a variable. Its value is usually between −1 and 1. A positive value
indicates that the distribution of similar values tends to be spatially clustered. A negativevalue indicates that locations with similar attribute values tend to be spatially dispersed.
An approximate zero value indicates that the distribution of the values is truly randomly
distributed. The z-score and p-value are measures of statistical significance.
The spatial weights for cities are defined as a matrix based on contiguity order 1(Queen). In this approach, the neighboring cities of city i are assigned a weight of 1 if

13they share a common side or vertex with city i. If city j does not share any side or vertex
with city i, its weight, 𝑤𝑤𝑖𝑖𝑖𝑖 , is set as 0.

GeoDa software is utilized to calculate Moran’s I for travel vloggers (Tvloggers), non-travel-themed vloggers (nTvloggers), and vloggers in general (Vloggers). Table 2 reports
the results of these calculations and shows that the global Moran’s I for the three variables
are all significantly positive.
These results indicate that cities with similar numbers of travel vloggers tend to beclustered. There are several possible explanations for these results. First, travel vloggers
in nearby locations have access to comparable tourism resources. Geographic proximity
enables travel vloggers in nearby cities to easily access and showcase these resources inshort videos. Second, the spillover of travel vlogging know-how and its exemplary effects
are more easily diffused to nearby cities. Third, nearby cities share a similar cultural
background, making it similarly easy or difficult to encourage people into such new
endeavors.
Similar clustering tendencies also exist in regard to non-travel-themed vloggers.
However, the Moran’s I indices reveal that travel vloggers (0.156) show a lower tendency
toward clustering compared to non-travel-themed vloggers (0.257). It appears that travel
vloggers originate from a broader geographic range, resulting in a relatively more
scattered distribution. This indicates moderated regional differences compared to nontravel-themed vloggers.
Table 2. Global Moran’s I index.
Variable
Moran’s I index
z-scorep-value
Tvloggers
0.156**
4.45920.002
nTvloggers
0.257***
7.97570.001
Vloggers
0.256***
7.93880.001
Note: *** and ** denote significance levels of 0.001 and 0.01, respectively. A z-score above
1.96 indicates the existence of global spatial effects.

144.2.2 Local Spatial Autocorrelation Model
The local Moran’s I index, introduced by Anselin (1995), is an extension of the global
Moran’s I index. It assesses the degree of spatial autocorrelation at a local level by
evaluating the similarity or dissimilarity of a specific location with its neighboring
locations, considering both the attribute values and spatial proximity. For an observation
at city i, its spatially lagged variable representing the weighted average of the neighboringvalues is𝑛𝑛

𝐿𝐿𝑖𝑖 = � 𝑤𝑤𝑖𝑖𝑖𝑖
𝑗𝑗=1(𝑥𝑥𝑖𝑖 − 𝑥𝑥̅ )�∑𝑛𝑛𝑗𝑗=1(𝑥𝑥𝑗𝑗 − 𝑥𝑥̅ )2 /𝑛𝑛

·

1
,
𝑛𝑛

where 𝑥𝑥̅ is the mean of variable x, 𝑤𝑤𝑖𝑖𝑖𝑖 is the element of the spatial weight matrix, and n is

the number of observations. Clusters occur when the value of location i is more similar(in the case of positive autocorrelation) or dissimilar (in the case of negative
autocorrelation) to the value of the spatially lagged variable than it would be in the caseof spatial randomness. When positive and significant autocorrelation is identified, thelocation can be categorized as statistically significant clusters of high values (HH clusters)
or low values (LL clusters). When negative and significant autocorrelation is identified,the location can be categorized as an outlier in which a high value is surrounded primarily
by low values (HL outlier) or a low value is surrounded primarily by high values (LHoutlier).
The results of the local Moran’s I analysis of travel vloggers reveal 20 clusters withhigh values (HH clusters) (Figure 2a). They mainly include cities in the three most highly
developed regions (Beijing–Tianjin–Hebei Region, Yangtze River Delta, and Pearl River
Delta), the coastal province of Shandong, and several middle-region provinces (Guizhou,
Hunan, Hubei, and Shaanxi). It appears that travel vloggers tend to cluster in regions that15

provide attractive destinations, tourism opportunities, or a supportive environment for
travel vlogging.
There are 52 clusters with low travel vlogger numbers (LL clusters). They include mostcities in Xinjiang, Gansu, Ningxia, Qinghai, Xizang, and Inner Mongolia. A few cities inwestern Sichuan, northern Shaanxi, western Jilin, and western Heilongjiang are also
included. These are generally less economically developed regions.
Twenty-one outliers are markedly dissimilar from their surroundings, with 11 cities
being identified as low–high outliers (LH outliers). They are mainly located in Sichuan,
including Dazhou, Guang’an, Luzhou, Suining, and Ziyang. Other LH outliers include
Chengde and Zhangjiakou in Hebei, Shangluo in Shaanxi, Shaoguan in Guangdong, andShennongjia in Hubei. These LH outliers are typically smaller cities located near muchlarger cities and appear to be the “sole losers” of nurturing travel vloggers within theirregions while neighboring cities flourish.
There are ten significant high–low outliers (HL outliers). They include Haerbin in
Heilongjiang, Haikou and Sanya in Hainan, Lasa in Xizang, Lanzhou in Gansu, Nanningin Guangxi, Shenyang in Liaoning, Wulumuqi in Xinjiang, Xining and Yili in Xinjiang.
These are typically capital cities or central cities in less developed provinces, marked byeconomic disparities with their surrounding regions.
Last, no significant results were found for 275 cities. Interestingly, Zibo belongs to thiscategory. Its number of travel vloggers has no significant correlation with its neighboring
cities.
A comparison of Figures 2a and 2b reveals that the spatially clustering pattern ofvloggers in general is quite similar to that of travel vloggers. Both of their LL clusters
resemble the LL clusters of GDP (Figure 2c), and their HH clusters are similar to the HH
clusters of scenic areas (Figures 2d and 2e). It is speculated that regional economic size

16and tourism resources collectively shape the distribution of travel vloggers. This
speculation could initiate further investigations into the factors influencing travel vlogger
participation in these areas.

(a)

(b)

(c)

17

(d)

(e)

Figure 2. Spatial distribution map of local Moran’s I: (a) Travel vloggers; (b) Vloggers
in general; (c) GDP; (d) A-level and above scenic areas; (e) 5A-level scenic areas.

Section 4 has elucidated the distribution pattern of travel vloggers across Chinese cities.
Next, this study aims to determine whether this distribution influences regional tourism
development. If such an impact exists, regional disparities in the number of travel
vloggers may exacerbate discrepancies within the tourism industry, perpetuating aMatthew effect, where successful cities advance more rapidly while disadvantaged ones
progress at a much slower pace.
Anselin (1988) emphasized that omitting the spatial correlations in an econometricanalysis when variables are spatially correlated would lead to bias. The analysis in Section
4.2 has demonstrated the existence of spatial autocorrelation in the distribution of travel
vloggers. Contemporary tourists’ travel behavior also exhibits spatial correlation (Ma etal., 2015). Therefore, to reveal the impact of travel vloggers on tourism development,
spatial analysis methods are more appropriate than regular econometrical models. Hence,
the analysis in Section 5 employs the spatial analysis tool, Geodetector.

5. The Impact of Travel Vloggers on Tourism: A Geodetector Approach
Geodetector is a collection of statistical analysis tools that can explore the spatialdifferences and determinants of specific phenomena. It is designed to detect and quantify

18

the spatial heterogeneity and relationships between a dependent variable and a set ofindependent variables by utilizing a geographically weighted regression (GWR)
framework for estimation. The underlying assumption is spatial stratified heterogeneity,
which assumes that the within-strata variance is less than the between-strata variance(Wang et al., 2016; Wang & Xu, 2017). One advantage of Geodetector is that it does not
require a linear hypothesis and is sufficiently robust to handle multicollinearity among
multiple independent variables. It handles discrete data as independent variables,
assuming that the samples within each stratum are similar. As a result, Geodetector
provides more reliable estimation results for the relationship between dependent andindependent variables when the sample size is small (e.g., less than 30) (Wang & Xu,
2017). This is another notable advantage of Geodetector.
Since the original independent variables are continuous, this study uses the natural
breakpoint method to discretize them to meet the requirements of Geodetector. This
process involves dividing each variable into five strata.

5.1 Factor Detector
Geodetector’s Factor Detector helps uncover the underlying factors that shape theobserved spatial patterns of tourist arrivals. It reports a q-statistic for each independent
variable, representing the magnitude of a factor’s impact on spatial differences in tourist
arrivals. It is calculated as follows:
𝐿𝐿

1
𝑞𝑞 = 1 −� 𝑁𝑁ℎ 𝜎𝜎ℎ2 .
𝑁𝑁𝜎𝜎 2ℎ=1

N represents the number of the studied provinces (i.e., 27). 𝜎𝜎 2 is the discrete variance of

tourist arrivals in each province. L is the number of strata based on the stratification bydependent variable Y and independent variable X (in this case, L equals 5). 𝑁𝑁ℎ is thenumber of provinces in stratum h. 𝜎𝜎ℎ is the variance of tourist arrivals in provinces within
19stratum h. q∈[0,1], and a larger q-statistic value indicates a stronger impact of thecorresponding factor on the observed spatial patterns. The corresponding p-value
represents the significance level.
Table 3. Factor detection analysis results of the factors affecting tourist arrivals.
Variable
Tvloggers
NTvloggers
Vloggers
VideosVideos1year
Travelagencies
Shares1year
Likes1year
Sharesq-statistic
0.76240.74590.74590.70430.69130.56670.5534
0.5261
0.5112p-value
0.00000.00000.00000.00000.00000.01070.02670.01730.0209Variable
Likes
Transport
Ascenicareas
A5scenicareas
Hotelbeds
Followers
HotelsFollowers1year
Hotelrooms

q-statistic
0.50280.47870.47840.47470.46520.4304
0.41650.41280.3942p-value0.01500.04440.04680.05550.13170.06460.12160.06060.0757

Based on the results reported in Table 3 show that ten variables are statisticallysignificant factors affecting tourist arrivals. Their ranking in descending order based on
their magnitudes of impact is as follows: Travel vloggers (Tvloggers) > Non-travelthemed vloggers (NTvloggers) > All Vloggers (Vloggers) > Videos in total (Videos)>Videos within the previous year (Videos1year) > Travel agencies (Travelagencies) >Shares within the previous year (Shares1year) > Likes within the previous year
(Likes1year) > Total Shares (Shares) > Total Likes (Likes) > Transport infrastructure(Transport) > A-level and above scenic areas (Ascenicareas).
The results show that vlogger-related variables generally demonstrate greater impacts
than conventional tourism-boosting variables. Here, the conventional variables
encompass A-level and above scenic areas (representing tourism attractions), travel
agencies (representing tourism-receiving capacity), and transport infrastructure.
Travel vloggers exhibit a stronger influence than non-travel-themed vloggers, despite
their smaller number. This suggests that vloggers who specialize in travel-themed shortvideos are more likely to appeal to potential tourists. Regarding the performance of travel
vloggers, the number of videos they uploaded shows a higher influence than the shares20

or likes they received. Notably, the total number of videos uploaded has a more
pronounced influence than the number within the past year. This indicates that travel
vloggers primarily influence regional tourist arrivals through their productivity and theircontent’s enduring appeal. Not only are the newly uploaded videos attracting tourists, but
the older videos from a year prior are also proving to be influential, with the latter being
even more significant.
The viewer engagements received by travel vloggers, such as shares and likes, have asignificant but slightly weaker influence. Specifically, the shares and likes received in thepast year have a greater impact than the cumulative number from previous years.
Surprisingly, the number of a travel vlogger’s followers does not necessarily impact
regional tourist arrivals. This suggests that the influence of travel vloggers may extend
beyond their follower count, reaching a broader audience.
Of the conventional factors that contribute to regional tourism growth, A-level andabove scenic areas have a significant correlation with the number of tourist arrivals;
however, 5A-level scenic areas do not exhibit a significant association. This result
deviates from previous studies that suggested tourists typically select higher-level tourism
destinations during macro-spatial scale travel (Chen & Bao, 1988). While 5A-level scenic
areas have traditionally been believed to be particularly effective in attracting visitors,
our findings suggest a different narrative. Due to the current widespread use of social
media and years of tourism development, scenic areas of all levels are attracting inboundtourists to provinces.
This study’s findings show that transport infrastructure is significantly related to tourist
arrivals, which is in line with those of Ao and Wei (2006). However, the high-speed
railway and airports were not included in constructing the index due to data unavailability,

21

despite their growing influence. This constitutes one of the limitations of this study andshould be addressed in future research.
Last, the results reveal that the number of starred hotels, hotel rooms, and beds are notsignificantly related to regional tourist arrivals. This indicates that the accommodation
capacity is no longer a constraint for attracting tourists, as it was from 1990 to 2003 (Ao& Wei, 2006).

5.2 Ecological Detector
Ecological Detector compares the impact of two factors on the tourist arrivals’ spatial
distribution. This detector measures the impact differences between two variables with
the F statistic:
𝐹𝐹 =

𝐿𝐿

1
𝑁𝑁𝑥𝑥1 (𝑁𝑁𝑥𝑥2 − 1) ∑ℎ=1
𝑁𝑁ℎ 𝜎𝜎ℎ2

𝐿𝐿2
𝑁𝑁𝑥𝑥2 (𝑁𝑁𝑥𝑥1 − 1) ∑ℎ=1
𝑁𝑁ℎ 𝜎𝜎ℎ2.

𝑁𝑁𝑥𝑥1 and 𝑁𝑁𝑥𝑥2 are the sample sizes of 𝑥𝑥1 and 𝑥𝑥2 , respectively. 𝐿𝐿1 and 𝐿𝐿2 are the number ofstrata of variables 𝑥𝑥1 and 𝑥𝑥2 , respectively. The detection sets the null hypothesis as 𝐻𝐻0 :

𝐿𝐿1
𝐿𝐿2
∑ℎ=1
𝑁𝑁ℎ 𝜎𝜎ℎ2 = ∑ℎ=1
𝑁𝑁ℎ 𝜎𝜎ℎ2 . If 𝐻𝐻0 is rejected at the significance level of 5%, 𝑥𝑥1 and 𝑥𝑥2 playsignificantly different roles. In that case, the associated value in the table of results isreported as Y. If the roles of 𝑥𝑥1 and 𝑥𝑥2 are not significantly different, the associated value

is then reported as N. Here only the variables identified as significant in the Factor
Detector (Table 3) are included in this ecological detection analysis.

The results reveal significant differences between travel vloggers and three traditionalvariables including transport infrastructure, A-level and above scenic areas, and travel
agencies (Table 4). This suggests that travel vloggers exert a greater influence on tourists
compared to conventional factors.
Significant differences also exist in the pairs of non-travel-themed vloggers andtraditional variables. These results highlight vloggers’ growing importance as a new

22

influential force in the tourism industry. Nurturing vloggers can provide an additional
stimulus to tourism in conjunction with conventional tourism-promoting efforts.
The number of videos travel vloggers produce plays a different role than transport
infrastructure and A-level and above scenic areas but not travel agencies. This patternholds true for both the cumulative productivity of travel vloggers represented by the total
number of videos from previous years and their recent productivity represented by the
number of videos from the past year. The viewer engagement metrics of followers, likes,
and shares do not show significant differences with any traditional variable.
Table 4. Ecological detection analysis results of tourist arrivals at the provincial level.
Variable

Transport

Ascenicareas

Travelagencies

TvloggersY

YY

NTvloggers

Y

Y

Y

Vloggers

YYY

Followers

N

N

N

Videos

Y

Y

N

Likes

N

N

N

SharesN

N

N

Followers1year

N

N

N

Videos1year

Y

Y

N

Likes1year

N

N

N

N

N

Shares1year
N
Note: The significance level for the F-test is 0.05.

5.3 Interaction Detector
Interaction Detector tests the joint impact of two factors on the spatial disparities in
tourist arrivals. With factor 𝑥𝑥𝑚𝑚 , 𝑥𝑥𝑛𝑛 being interacted, the q-statistic of their interaction

𝑞𝑞(𝑥𝑥𝑚𝑚 ∩ 𝑥𝑥𝑛𝑛 ) will be compared to 𝑞𝑞(𝑥𝑥𝑚𝑚 ) and 𝑞𝑞(𝑥𝑥𝑛𝑛 ) . If 𝑞𝑞(𝑥𝑥𝑚𝑚 ∩ 𝑥𝑥𝑛𝑛 ) > max (𝑞𝑞(𝑥𝑥𝑚𝑚 ),𝑞𝑞(𝑥𝑥𝑛𝑛 )), the impact of the interaction is larger than each factor independently.

The results reveal a significant increase in the interactions between any two factors

compared to their individual components. For the interaction between conventionalvariables and vlogger-related factors, the effective combinations are as follows (Table 5):23•Scenic areas exhibit a greater influence when interacting with travel vloggers
compared to non-travel-themed vloggers. Specifically, scenic areas have thestrongest impact when interacting with travel vloggers’ shares in the past year.

•

Travel agencies similarly have a greater influence when interacting with travel
vloggers than with their non-travel-themed counterparts. They have the greatest
influence when interacting with travel vloggers’ likes in the past year.

•

Transport infrastructure exhibits a stronger influence when interacting with nontravel-themed vloggers.

These results provide valuable insights into effective collaborations. Governments andpractitioners within the tourism industry should acknowledge the importance of suchcollaborations and take the necessary actions to capitalize on them.
Table 5. Interaction detection analysis results of the affecting factors.
Variable

Ascenicareas

Travelagencies

Transport

Tvloggers

0.81220.85110.9313

NTvloggers

0.80140.8041

0.9445

Vloggers

0.80140.8041

0.9445

Followers

0.72670.74990.7934

Videos

0.87890.78560.8514Likes

0.83530.75940.7827

Shares

0.87430.75270.7692

Followers1year

0.7826

0.68660.7776

Videos1year

0.87920.78130.9070Likes1year

0.83290.85640.7643

Shares1year

0.88090.79130.7564

6. Concluding Remarks
Using big data from TikTok, the largest short video platform in China, this study
analyzes the spatial characteristics of travel vloggers and their impact on provincial tourist
arrivals. A primary conclusion of this work is the spatial heterogeneity and24

agglomerations of travel vloggers across Chinese cities. The 185,655 travel vloggers areprimarily concentrated in areas east of the Hu Huanyong Line, where most of China’s
population and economic activities are located. Cities with similar numbers of travel
vloggers tend to be clustered, but this clustering tendency is slightly lower than that ofcities with similar numbers of non-travel-themed vloggers. This indicates the potential toleverage the impacts of travel vloggers to promote regional development in less remoteand less developed areas.
This exploratory work reveals the pivotal role of content creators from short video
platforms in tourism development by confirming that travel vloggers have a significant
impact on regional tourist arrivals and their influence is greater than conventional
tourism-boosting factors. While studies on TikTok and tourism have primarily
concentrated on its viewers (Han et al., 2021; Zhou et al., 2023) and user-generated
content (Li et al., 2019; Tham et al., 2024), this study contributes to the social media
stream of tourism research by highlighting the valuable roles of content creators inregional tourism development.
This study is also among the first to examine the sources of impact of travel vloggers,
providing a fresh perspective on studying social media. The variables encompass various
aspects of travel vloggers, including their numbers, their productivity measured by the
number of videos they created and uploaded, and viewer engagement quantified by the
number of followers, likes, and shares they received. Furthermore, this study accounts forthe temporal effect by comparing travel vloggers’ overall performances to those from thepast year. The results suggest that, among various aspects, the number of travel vloggers
is the most crucial, with their productivity ranking second. Within the productivity metric,
the impact of the cumulative number of videos from previous years surpasses that of thenumber of videos from the previous year.

25The results of this study have several policy implications. Governments and DMOs cancapitalize on the impact of travel vloggers in tourism development and develop promotion
strategies that incorporate short videos. These include actions such as integrating shortvideo platforms into destination marketing plans, organizing tourism events to provide
topics for travel vloggers, and initiating collaborations between scenic areas and travel
vloggers. Among all Chinese cities, the 11 LH outliers (i.e., areas with low numbers oftravel vloggers while neighboring areas have high numbers of travel vloggers) areparticularly vulnerable to being left behind in the tourism industry. Thus, these areas
require special attention from policymakers and practitioners in destination planning toensure that they do not become the sole losers in the efforts to nurture travel vloggers.
Policymakers and practitioners must also be cautious about potential pressures anddamages to the environment and facilities at destinations arising from the TikTok tourism
boom (Wengel et al., 2022). Dispersing tourism over time and space is one of the mostfrequently adopted strategies to pursue sustainable tourism. Although people are awarethat certain short videos can become popular and boost local tourism, predicting which
videos will gain popularity is challenging. Consequently, constantly creating popular
videos related to a destination is very difficult. However, this exploratory study finds thatamong the various aspects of travel vloggers’ performances, the number of videos wields
a greater influence than the shares or likes they receive. Specifically, the total number ofvideos uploaded has an even larger impact than the number from the previous year. These
results suggest that setting aside the potential tourism boom brought about by popular
short videos, the productivity of travel vloggers, and their long-lasting and accumulative
efforts may be a more promising power to sustain tourism development. Nurturing therelationships with more local travel vloggers and encouraging them to consistently
showcase a city could be utile in counterbalancing and alleviating the adverse effects of26

the TikTok boom. Other effective strategies may include leveraging sponsored content
partnerships with travel vloggers to showcase lesser-known destinations and those in theoff-season and diversifying tourism products.
The discussion on sustainable tourism still predominantly revolves around a growthoriented perspective (Nieuwland, 2024). Scholars argue that instead of merely
minimizing the negative impacts of tourism, the focus should shift away from capitalist
thinking (Nieuwland, 2024). Society must prioritize social and ecological values overeconomic gains. New forms of tourism are being introduced, such as degrowing tourism
and regenerative tourism (Nieuwland, 2024), and virtual tourism can be embraced as a
substitute for physical travel (Frenzel et al., 2022). Since social media platforms are where
marginalized groups can be empowered and their voices can be heard (Cheng, 2024),
TikTok can be an extremely powerful instrument to advocate and showcase the ideas,
knowledge, and experiences of these new forms of tourism. Future research is necessary
to investigate the roles of travel vloggers in promoting these new types of tourism.
One limitation of this study is that the residence of a travel vlogger does not necessarilyalign with the locations featured in their video content. Nevertheless, despite theirinherent mobility, our findings confirm that travel vloggers are significantly associatedwith tourist arrivals in their respective provinces. Future research can delve deeper into
the locations featured in video content and conduct comparisons.

Acknowledgments
The author would like to express sincere gratitude to Dr. Fujii Daisuke from Osaka
University of Economics for providing valuable comments. The author also thanks C
Kato and Jen Stern from Cambridge Proofreading for carefully proofreading themanuscript.

27

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