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Article

Impact of Short Food Videos on the Tourist Destination Image—Take Chengdu as an Example

by
Yi Li
1,
Xiuxiu Xu
1,
Bo Song
2,* and
Hong He
1
1
School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
Shanghai Institute of Tourism, Shanghai Normal University, Shanghai 200234, China
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(17), 6739; https://doi.org/10.3390/su12176739
Submission received: 31 July 2020 / Revised: 12 August 2020 / Accepted: 16 August 2020 / Published: 20 August 2020
(This article belongs to the Special Issue City Branding and Sustainable Destination Management)
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Abstract

:
Taking Chengdu as an example, based on the destination image theory and employing the content analysis methodology, this paper conducts data mining on the online comment texts of TikTok short food videos, and analyzes the impact of short food videos on the destination image (cognitive image, affective image and conative image). The results show that: (1) in terms of cognitive image, short food videos have increased potential tourists’ attention to the destination image, especially their attention to the flavor characteristics of food in the destination and the local social environment; (2) in terms of affective image, the comments of short food videos are mainly neutral and positive, and the contents about the flavor characteristics of food and the local social environment are more likely to affect the affective image of the destination; and (3) in terms of conative image, the appearance description of food in short food videos brings about an obvious effect of intention, and it also creates the demand to travel together and obtain information. This paper is inspiring for city managers and tourism marketers to use TikTok short videos to establish and disseminate food-based city brands and destination images.

1. Introduction

Creating a unique city brand is one of the important means of city marketing and attracting tourists [1]. Local food is a unique attraction. Creating a city brand based on food is conducive to shaping a differentiated destination image [2,3]. As more and more cities attract tourists through food, food tourism is becoming a trend [4]. Food videos on social media can convey food information intuitively and vividly, thus helping to spread the destination image based on food [5,6]. In China, many cities are using social media to spread their destination images [7,8]. TikTok is the largest social platform for short videos in China. Food is one of the main themes of TikTok short videos, and the number of views of short food videos accounts for 14%, ranking third among all themes [9]. The short food videos of some cities are widely spread on TikTok. The contents of these short food videos involve food evaluation and food exploration stores, etc., forming a group of “influencers’ food places” and successfully shaping the destination image based on food. For example, Chengdu in China is the first city in Asia to win won the title of international “City of Gastronomy” and has formed a city brand about food. On TikTok, the number of views of short videos about Chengdu food is very large [10], conveying a unique destination image to potential tourists. Judging from the results, this kind of destination image does attract a large number of tourists [11]. However, it is still unclear how short food videos affect potential tourists’ impressions of destination cities, and even their tourism behavior. If city managers and tourism marketers want to use social media tools for city marketing, they need to understand the mechanism. This is an area to be studied.
In recent years, destination marketing, branding, and food have been widely discussed in academia. However, little attention has been paid to the impact of food content in social media on the image of destinations. The existing literature studies the impact of tourists’ food experience on the image of destinations [11,12], focusing on tourists who have been to these destinations. The audience of the short food video includes some users who have never been to the destination. How should one convey the destination image to these users (who may be potential tourists)? This is an issue that city managers and tourism marketers are concerned about and it is therefore necessary to study it.
There was also little previous literature on the role of TikTok, an emerging social media, in shaping and spreading the image of destinations. Previous studies have shown that social media based on text or pictures, such as Instagram, Travel Blogs and WeChat can shape and spread the image of destinations to some extent [13,14,15]. Then, as a social media based on short videos, does TikTok play a unique role in shaping and spreading the image of its destination? This is also worth studying.
This question studied in this paper is: What kind of destination image do short food videos convey to potential tourists? This paper takes Chengdu in China as an example to answer this question. This paper employs content analysis methodology to analyze user comments on short videos of Chengdu food on TikTok, and from three dimensions, including cognitive image, affective image and conative image, profiles the destination image conveyed to potential tourists through short videos of Chengdu food.

2. Literature Review

2.1. Destination Image

The concept of tourist destination image was put forward in the 1970s [16]. From the perspective of cognition, Crompton described the tourist destination image as the sum of all the ideas (such as belief, opinion and evaluation) generated by tourists in the tourist destination [17]. From the perspectives of cognition and emotion, Fakeye defines the tourism destination image as tourists’ spiritual construction and development by selecting a small number of impressions from the dominant overall impressions [18]. Stern and Krakover believe that the combination of cognitive image and affective image will form tourists’ overall cognition (i.e., overall image) of the destination [19]. On this basis, Gartner proposed the “three-dimensional structure” of the destination image—cognitive image, affective image and conative image—the correlations of which will determine the predisposition for visitation [20]. Among others, cognitive image refers to a tourist’s perception of the attributes or characteristics of the destination, including tourist attractions, infrastructure, tourism environment and other dimensions [21]. Affective image refers to a tourist’s cognition of the destination on the basis of personal values [20]. Conative image is a tourist’s intention to, action or possibility of visiting the destination at a specific time, equivalent to the behavior tendency of the tourist [22].
The studies on the factors that affect the destination image mainly focus on tourism information sources and tourism experience [23,24], tourists’ personal factors and stimulating factors [25], as well as tourism public service quality, [26] etc. In addition, they also involve the familiarity with and perceived distance of tourists from the destination, destination popular culture, film and television programs, and sports events [27,28,29,30,31]. The destination image is dynamic [32] and it may change with changes in influencing factors.

2.2. Food Image and Branding

Food, as one of the strong attractions of the destination, is an important part of the destination image [33]. More and more tourist destinations regard local food and related activities as the core products of tourism to attract more tourists [3]. Some scholars have studied the cognitive image of food. Through interviews, Peter and Hannele classified the cognitive image of food as organized and unorganized [34]. Richard et al. divided the cognitive image of food into seven dimensions: external appeal, flavor characteristics, familiarity, cooking methods and seasonings, local features, price, health and safety [35]. Seo et al. divided the cognitive image of food into five dimensions: quality and safety, external appeal, health promotion, family orientation, and cooking methods [36]. Food-related experience can not only change tourists’ perception of destinations, but also create positive emotions and values and affect tourists’ destination image [37,38]. In the tourism destination, an impressive food experience can bring higher satisfaction to tourists and further affect whether tourists revisit [39]. In the affective image, there is a positive, neutral or negative emotional connection between the destination food and the destination itself [40]. In the context of food tourism, the concept of conative image includes the willingness to revisit restaurants, recommend restaurants to others and use word of mouth to spread information [41,42].

2.3. User-Generated Content and Short Video

User-generated content (UGC) refers to content in the form of images, videos or texts posted by users through social media [43]. Governments, travel agencies, and related marketing organizations regard UGC as a powerful marketing tool [44]. Kim et al. took Sina Weibo as an example to study the impact of the quality of tourism information in social media on the image of destinations [45]. Nicolau et al. analyzed President Trump’s participation in Twitter and found that tourists’ association with public figures may affect the image of tourist destinations [46]. Afshardoost employed the meta-analysis methodology and found that destination image plays an important role in predicting the intentional behavior of tourists to varying degrees [47]. The impact of videos on the destination image has attracted much attention recently. Reality shows and movies and TV series can affect tourists’ cognitive image and affective image of the destination, and then affect tourists’ behavioral intention [30,48]. Short videos can effectively spread the attractions of the destination, such as scenic spots, facilities and delicacies, and have a significant impact on the destination image [49].
In sum, there are few studies on the connection between short food videos and the destination image, and there are also few studies on the impact of TikTok (a new social media) on the destination image. The work in this paper is helpful in making up for the gap in the previous literature.

3. Methodologies

3.1. Content Analysis Methodology

Content analysis methodology is a systematic research methodology for simplifying, condensing and classifying a large number of text contents [50]. This paper uses content mining software ROST Content Mining 6 to quantitatively analyze the users’ comments on TikTok short videos. ROST Content Mining 6 software has been recognized by scholars in the content analysis of Chinese texts [51,52,53,54]. First of all, this paper uses the word frequency analysis function of this software to extract the high-frequency words and frequency in the sample. By referring to previous studies [35,36] and combining them with the characteristics of TikTok short videos, this paper analyzes the cognitive image of the tourist destination from six dimensions: flavor characteristics, price, health and safety, local features, restaurant quality and local social environment. Secondly, this paper uses the emotion analysis function of this software to study the emotional tendency of TikTok users in TikTok short food videos, and profiles the affective image of the tourist destination based on semantic network diagram. In addition, the second author has also extracted key comments on the conative image, coded and refined the comments sentence by sentence (manually, without using software), explored the conative reasons and potential demands of users, and analyzed the conative image of the tourist destination.

3.2. Sample Selection

The texts analyzed in this paper come from users’ comments on short videos of Chengdu food on TikTok. For the purpose of this paper, we searched for “Chengdu Food” in the TikTok App, took the top 50 short food videos in the search results as the research objects, and extracted the user comments of these videos from 15 October to 15 November 2019. In order to facilitate the Chinese text analysis by the ROST Content Mining 6 software, we preprocessed the collected comments: First, we deleted the duplicate comments, corrected the wrong Chinese characters, and unified the names of scenic spots, which would not affect the original meaning of the comments. Secondly, we extracted the data with “@ user name” in the comments and processed them with other analysis methods. Otherwise, the user name texts in such comments would interfere with the analysis results of the software. After preprocessing the data, we obtained 6914 comments as samples, which were classified into two groups. The first group consisted of 5534 comments without “@username”, which were analyzed with ROST Content Mining 6 software. The second group consisted of 1380 comments with “@username”, which were analyzed manually by the authors.
The sample comments have several characteristics: First of all, most of the comments are short texts with a high real-time, strong interactivity and distinct emotional color, which are the inner thoughts of users when watching the videos. Secondly, the comments are rich in content, including not only comments on the delicious food in the scenic spots, but also comments on other delicacies on the streets and lanes of Chengdu, covering a wide geographical area and a large variety of delicacies. It is appropriate to use these comments to study the destination image conveyed to potential tourists through short food videos.

4. Results

4.1. Analysis of Cognitive Image

For the 5534 comments in the first group, the software ROST Content Mining 6 is used to filter out words and terms unrelated to Chengdu food, extract high-frequency words, combine similar synonyms and obtain the glossary of high-frequency words. The calculation formula of word frequency is Pk = n/N, where Pk represents the frequency of a word, n is the number of times a word appears, and N represents the total number of times all words appear. The word frequency distribution chart is obtained by ranking the word frequency from high to low (Figure 1).
The word frequency distribution conforms to the long tail theory, in which 95% of the words appear less than 20 times, while 5% of the words appear more frequently. These high-frequency words can reflect the important information in the comments. We select the top 50 high-frequency words as feature words to analyze the cognitive image of food, as shown in Figure 2.
In order to tap into the meaning of high-frequency words, we classify these words into six categories: local social environment, flavor characteristics, restaurant facilities and services, local features, price, health and safety, as shown in Table 1.
It can be seen from Table 1 that users paid the most attention to “local social environment” and “flavor characteristics”, reflected in the short food videos, followed by “restaurant facilities and services” and “local features”, and finally followed by “price” and “health and safety”.
From the perspective of the local social environment with the highest attention, many regional terms appeared frequently—“Chengdu” as many as 200 times and “Sichuan” as many as 175 times, indicating that short food videos brought users a strong regional cognition and enhanced the visibility of the city. Among others, the words describing Chengdu figures appeared 247 times, and the words ”soul” and “culture” also appeared frequently, reflecting the unique local social environment of Chengdu. From the perspective of flavor characteristics, the frequency of “delicious” was 239 times, the frequency of “yukky” was 44 times, and the frequency of “spicy” was 42 times. It can be seen that the food flavor presented in the short food videos is mainly “looking delicious”, which has a strong publicity effect on the city image of Chengdu as the “City of Gastronomy”. Besides, restaurant facilities and services such as “place” and “location” were described 305 times, showing that users were concerned not only with the authenticity of the food, but also with the attitude of the owner, the positioning of the restaurant and the surrounding environment.”Hotspicy”, “Hot Pot” and other food words with Chengdu features appeared more frequently, indicating that local features were attractive to users. In addition, comments about the description of the price occurred 163 times, and also involved food health; users will not only pay attention to the price of food, but are also concerned about food quality and safety.
In general, from the perspective of cognitive image, short food videos enhanced users’ attention to the destination image through audio-visual sensory experience, especially their attention to the flavor characteristics of the food in destination food and the local social environment.

4.2. Analysis of Affective Image

On the basis of extracting high-frequency words, we use ROST Content Mining 6 software to analyze the emotional attitude of users’ comments. As shown in Table 2, neutral comments accounted for the highest proportion, reaching 59.45%. Positive comments accounted for 27.07%, while negative comments accounted for 13.48%. Generally speaking, the affective images formed by users in TikTok short food videos were mainly neutral and positive emotions, indicating that the destination image spread well.
We use ROST Content Mining 6 software to construct the co-occurrence matrix of affective high-frequency words, and use Gephi software to visualize data, delete meaningless modal particles or prepositions, filter out the connection lines with low correlation, and form the semantic network diagram of high-frequency words. Among others, the nodes in Gephi analysis figure are high-frequency words, which are randomly positioned. The size of the nodes is proportional to the number of connecting lines of the nodes. The larger the nodes, the higher the correlation between the high-frequency words with other high-frequency keyword is. The links between nodes represent the relationship between keywords. The absence of links indicates that high-frequency words do not appear in the same comment. The thickness of line density between nodes shows the weight, the size of the line and more coarse lines shows two high-frequency words in the user at the same comment, and the number of lines indicate the number of occurrences of the same comments. The color itself has no practical significance, and the nodes with the same color are the same cluster subgroup, indicating that these kinds of high-frequency words have similar influence (see Figure 3 and Figure 4).
It can be seen from Figure 3 that the high-frequency words of positive emotions are mainly concentrated in three aspects including “flavor characteristics”, “local social environment” and “price”: first, the food tastes delicious; second, there are many kinds of delicacies in Chengdu, and tourists appreciate the places and figures; third, the price is cheap, and the owners of stores are honest and credible. It can be seen from Figure 4 that the high-frequency words of negative emotion are mainly concentrated in two aspects including “flavor characteristics” and “local social environment”: first, the food is yucky; second, the owners of stores are short-tempered. It can be seen that flavor characteristics and local social environment have a greater impact on the affective image. If the appearance of the food in TikTok short videos presents a delicious and attractive feeling, and the figures are positive, the users will have positive emotions. If the information presented in the video content is inconsistent with the information perceived by users, for example, the food in the spot is not as delicious and attractive as that displayed in the short video, users will have negative emotions.
In sum, from the perspective of affective image, the comments on short food videos are mainly neutral and positive, and the contents about the flavor characteristics of food and the local social environment are more likely to affect the affective image of the destination. This is conducive to improving neutral emotions and negative emotions by adding the content of local social environment while displaying the flavor characteristics. In addition, online promotion of food must be consistent with the actual taste of the food. Any inconsistency in information on any dimension of food image will arouse negative emotions in users.

4.3. Analysis of Conative Image

With respect to the 5534 comments in the first group: First of all, we used the key words “want to eat”, “go to eat”, “taste”, “recommend”, “delicious” and “worth” to extract the comments that express obvious intention, and obtained 285 comments. Secondly, we conceptualized these comments, encoded and refined them level by level. The first level coding was to extract the meaning of the text, and similar meaning is expressed in a unified way; the second level coding was to clarify the relations between concepts; the third level coding was to classify and compare the concepts, categories and relations obtained by coding. Finally, we summarized the reasons for the formation of TikTok users’ intention to consume delicious food, potential demand stimulus, and recommendation intention and reasons, as shown in Figure 5.
It can be seen from Figure 5 that, at the third level is consumption intention, travel intention and recommendation intention in the conative image. The key words in comments on consumption intention (first-level codes 101–108 and second-level codes 201–203) account for 67.01%, indicating that the audience’s consumption intention is obvious. From the perspective of reasons, individual perception (accounting for 44.91%) is the main reason (second-level code 201). In short food videos, “looking appetizing” has an obvious impact on consumption intention (first-level code 102), which is specifically reflected in the color, taste description, sound, restaurant business and other aspects shown in short food videos.
Users will not only have consumption demand because of the content of the short food video, but also may have other demands due to insufficient information acquisition, such as the information acquisition demand: they want to know the specific address (second-level code 108). Users who failed to taste the delicious food expressed regret after watching the video and said they might travel to Chengdu in the future because they wanted to eat delicious food (first-level code 107). Another type of users with experience in local food consumption or travel will post comments such as “really delicious” and “recommend everyone to eat” after watching the short food videos and recognizing the content (first-level code 111–113).
“@username” is TikTok’s way of passing information to a specified user. The second group includes 1380 comments with “@username”, which means that there is a large amount of information transmission for specific users, such as “@username, Let’s go to this restaurant next time”,”@username, Let’s eat sometime. Come on.” These comments show that many users shared information with friends after watching the food short video, and the effects of secondary communication and the intention to travel with friends were obvious.
To sum up, from the perspective of conative image, the appearance description of food in the food short video brings about an obvious effect of intention, and at the same time, it produces demand to travel together and obtain information.

5. Discussion and Conclusions

This study aimed to explore short food videos to potential tourists, and what destination image was passed on; to investigate the food elements that TikTok users focus on when watching short food videos; to apply big data and natural language analysis techniques as an innovative method. Based on users’ comments on TikTok short food videos, this paper analyzes the cognitive image, affective image and conative image of Chengdu food, and reaches the following conclusions: (1) in terms of cognitive image, short food videos have increased potential tourists’ attention to the destination image, especially their attention to the flavor characteristics of food in the destination and the local social environment; (2) in terms of affective image, the comments of short food videos are mainly neutral and positive, and the contents about the flavor characteristics of food and the local social environment are more likely to affect the affective image of the destination; (3) in terms of conative image, the appearance description of food in short food videos brings about obvious effect of intention, and it also creates the need to travel together and obtain information.

5.1. Theoretical Implications

The theoretical contributions of this paper mainly include the following two aspects:
(1)
This paper analyzes the image of destination conveyed to users by food content in social media. In the previous literature [11,12] on food experience and destination image, the research object only included the tourists who have been to the destination. Our research object of this paper also included a large number of potential tourists who have not been to the destination. The paper explores the impact of short food videos on the tourist destination image, including the cognitive image, affective image and conative image, based on food videos. In terms of cognitive image, we found that short food videos can not only arouse users’ attention to the flavor characteristics of food, but also cause the attention to the local social environment, which indicates that it is possible to convey the image of destination to potential tourists through food content in social media, which influences their travel decisions. In terms of affective image, this paper finds that flavor characteristics and local social environment are more likely to affect the affective image of destination compared with the other four dimensions of food cognitive image. This result supports the previous literature’s conclusion that there is an emotional connection between food and destination [40]. In addition, this paper also makes a more detailed discussion of these affective connections based on the different dimensions of the cognitive image of food. In terms of conative image, this paper finds that the conative image caused by short food videos includes the willingness to obtain information and the willingness to travel together, which have not been mentioned in the previous literature;
(2)
Most of the previous studies [45] used online travel notes and Sina Weibo as Chinese text materials to study the destination image. This paper takes TikTok users’ comments as text materials to explore the role of this new social media in the image management and communication of the tourist destination image. In fact, Sina Weibo only allows 140 characters, similar to Twitter, and such limited availability of information sharing or posting can barely influence destination image formation, particularly on the cognitive side [45]. As a social platform for short videos, TikTok is more targeted in its content dissemination than other social media thanks to its excellent recommendation algorithm. Moreover, TikTok combines audio and video forms, and is more life-oriented, situational and social in content, which seems to be more related to affective factors. As mentioned, the results show that TikTok also has an obvious effect on motion and stimulates the potential demand for tourism. Therefore, this study also contributes to the literature on destination image formation in the context of social media.

5.2. Practical Implications

The research results of this paper are helpful for city managers and tourism marketers to establish and disseminate food-based city brands and destination images through TikTok, thus attracting tourists. Relevant marketing suggestions include:
First, that the short food video should present the local social environment elements related to food, and strengthen the connection between the food and the destination image. The practice helps raise awareness of the city through short food videos. City managers can set up official accounts on TikTok and link the cultural connotation of food with the city image by releasing specially designed short videos, so as to promote the cultural connotation of food as a special tourist attraction of tourist destinations and establish and strengthen the city brand of gastronomy.
Secondly, poorly captured and displayed food videos could be as devastating in terms of negative word-of-mouth reputation. The food elements displayed in the short video should be objective and real, so as to avoid the negative emotion caused by the inconsistency between the short video content and the actual situation. Destination marketers can work with TikTok, strengthen content review to identify and remove short food videos that contain fake content.
Thirdly, tourism marketers make good use of TikTok new media communication tools in creating destination brands through food tourism creative marketing. By using the advantages of short videos with pictures and sounds, they can show attractive images of food and stimulate users’ potential needs and willingness to share. In addition, tourism marketers can also launch online and offline events or festivals on food themes, guide users to share the short video with their friends and invite them to travel together through the carefully designed activity process.

5.3. Limitations and Future Research

Limitations of this paper include that the comment texts used for content analysis in this paper only represent the attitude of the users who left comments, but ignores the users who watched the video but did not leave comments (the number of users may be larger), which may bias the results of the study. In addition, this paper only focuses on the users’ attitude as reflected in the comments of TikTok short video users. This kind attitude expressed in the virtual cyberspace may not be completely consistent with the real tourism behavior of users. However, it is the real tourism behavior that city managers and tourism marketers are more concerned about. Follow-up studies can collect users’ real tourism data from multiple data sources, and study the impact of short food videos on tourists’ behavior by combining with a questionnaire survey, experiments and other methods.

Author Contributions

Conceptualization, Y.L.; methodology, X.X. and Y.L.; data curation, X.X.; writing—original draft preparation, X.X.; writing—review and editing, H.H. and B.S.; supervision, B.S.; project administration, Y.L. and B.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Social Science Fund of Chongqing Federation of Social Science Circles [grant number 2019YBGL065]; the Undergraduate Scientific Research Training Program of Chongqing University of Posts and Telecommunications 2020.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Comment word frequency distribution chart.
Figure 1. Comment word frequency distribution chart.
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Figure 2. Comment word frequency distribution graph. Notes: The number is word frequency.
Figure 2. Comment word frequency distribution graph. Notes: The number is word frequency.
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Figure 3. Semantic Network Diagram of High-Frequency Words of Positive Emotions.
Figure 3. Semantic Network Diagram of High-Frequency Words of Positive Emotions.
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Figure 4. Semantic Network Diagram of High-Frequency Words of Negative Emotions.
Figure 4. Semantic Network Diagram of High-Frequency Words of Negative Emotions.
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Figure 5. Statistical Results of Conative image Analysis. Notes: From left to right are the first level, the second level and the third level. The number in the bracket of the first level coding represents the number of comments; the number in the brackets of the second and third level coding represents the proportion of such comments in the total comments.
Figure 5. Statistical Results of Conative image Analysis. Notes: From left to right are the first level, the second level and the third level. The number in the bracket of the first level coding represents the number of comments; the number in the brackets of the second and third level coding represents the proportion of such comments in the total comments.
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Table
Table 1. Cognitive Content Analysis Category Statistics.
Table 1. Cognitive Content Analysis Category Statistics.
CategoryHigh-Frequency WordsFrequency
Local Social EnvironmentChengdu/Sichuan/Hunan/Wife/Chongqing/
Guizhou/Brother/Story/Soul/Culture		654
Flavor CharacteristicsDelicious/Drooling/Taste/Yucky/
Spicy/Delicacies/Authentic		561
Restaurant Facilities and ServicesOwner/Place/Marvelous/Crowded/Location/
Attitude/Trick/Advertising/Workmanship/Queuing		305
Local FeaturesHotspicy/Chili/Hot Pot/Chicken/Self-help/Spicy Hot Pot/Pork256
PriceRich/Cheap/Free/Rise in Price/Cannot Afford/Consumption/Cost163
Health and SafetyWeight Loss/Appetite/Prod/Rummy/Junk/Problem/Flies/Health131
Table
Table 2. Affective Analysis Statistics Results.
Table 2. Affective Analysis Statistics Results.
Category of EmotionNumber of CommentsProportionIntensity Number of CommentsProportion
Positive Emotions (5, +∞)149827.07%High (25, +∞)1232.22%
Moderate (15, 25)2754.97%
General (5, 15]110019.88%
Neutral Emotions [−5, 5]329059.45%
Negative Emotions (−∞, −5)74613.48%High (−∞, −25)260.47%
Moderate [−25, −15)811.46%
General [−15, −5)63911.55%
Notes: The Numbers in brackets are the emotional scores of the comments “(” means not included, “[” means included.

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Li, Y.; Xu, X.; Song, B.; He, H. Impact of Short Food Videos on the Tourist Destination Image—Take Chengdu as an Example. Sustainability 2020, 12, 6739. https://doi.org/10.3390/su12176739

AMA Style

Li Y, Xu X, Song B, He H. Impact of Short Food Videos on the Tourist Destination Image—Take Chengdu as an Example. Sustainability. 2020; 12(17):6739. https://doi.org/10.3390/su12176739

Chicago/Turabian Style

Li, Yi, Xiuxiu Xu, Bo Song, and Hong He. 2020. "Impact of Short Food Videos on the Tourist Destination Image—Take Chengdu as an Example" Sustainability 12, no. 17: 6739. https://doi.org/10.3390/su12176739

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