A Social Network Analysis of Chinese Tourists’ Image of South Korea in Conflict Lijuan Sua, Xiangyi Daib, Svetlana Stepchenkovaa a. Dept. of Tourism, Hospitality & Event Management University of Florida b. College of Resource Environment and Tourism Capital Normal University Destination Image

Quantitative-qualitative approach (Echtner & Ritchie, 1993): Neural networks (Govers et al., 2007) Visualization along bipolar continua: • Attribute based-holistic • Functional-psychological • Common-unique

Long-tail distribution: A few phrases are well-known to most Statistically-based way to connect respondents; hundreds of niche images in a network (Stepchenkova & phrases are rarely mentioned, but Zhan, 2013) account for a large volume (Pan & Li, 2011). Context: South Korea - China • 2002: World Cup – mostly positive image of stability, strong

product brand, and tourism resources (Kim & Morrison, 2005). • 2008: South Korea impressed Chinese people by its snow,

seafood, and cultural ties with China (Hsu & Song, 2012). • 2014: Largely positive image in aspects of culture, safety,

cleanliness, and friendly people (Zeng et al., 2015). • 2017: Installation of THAAD damaged Chinese tourists’ images of South Korea and led to cancelled trips, especially

by very patriotic and risk averse respondents. (Juan et al., 2017).

Mar – Oct, 2017: 2.38 m (- 60.1%), loss USD $4.6 billion (Yonhap, 2017). 2018 Olympics (15-day visa-free): • Expected: 200,000 • Actual: 20,000 (Hinsdale 2018) South Korea-China conflict: THAAD project

Mar/06/2017: THAAD launcher at Osan Air Base, South Korea. Mar/03/2017: China’s National Tourism Administration Alert. Mar/2017: Ctrip and Tuniu canceled tours to South Korea, airline companies canceled flights to South Korea. Mar- Oct/2017: 2.38 million Chinese tourists (decrease of 60.1%) Feb/2018: Olympics Expected Chinese tourists (200,000) v.s. Actual (20,000) Research Purpose

1. To investigate the structure of South Korea’s image in the context of bilateral conflicts between South Korea and China using network analysis. 2. Determine which images and image clusters affect the overall attitude of Chinese people toward South Korea and their intention to visit the country. Data collection

Online survey on WeChat in March-May 2018. Four travel companies sent links to the past, current, and potential clients. Targeted active Chinese outbound tourists, who had taken a leisure trip (four night or more) outside Mainland China in the past three years or planned to take one in the coming two years. 531 respondents completed the survey and provided valid data.

1. What three images come to your mind when you think about South Korea? Indicate how positive each image is. 2. Evaluate overall national image of South Korea. 3. What is your intention to visit South Korea as a tourist in the next 3 years? 4. What is your intention to visit South Korea for a special event, e.g., Olympic Games? Preliminary Manual Classification: 1524 short phrases were classified into 62 image codes and 9 categories.

Code Subcategory Freq. Category Code Subcategory Freq. Category 2001 Plastic surgery 140 2041 Park Geun-hye and political instability 26 2002 Cosmetic & skincare 97 2042 Nationality 24 2003 Beauty 44 2043 historic relationship to China 24 2004 Handsome guy 29 2044 THAAD 21 Politics Beauty Industry 2005 Shaping & facelift 25 2045 Relationship to China 18 (144) (373) 2006 Manage image 13 2046 Relationship to USA 11 2007 Makeup style 12 2047 relationship to other county 12 3.35 2008 Appearance 8 4.47 2048 Civilization 8 2009 Artificial beauty 6 2051 Cleaniness 41 2011 138 2052 small country 23 2012 Food & cuisine 34 2053 Culture 22 Environment &Culture 2013 Grilled meat 25 2054 Good & beautiful environment 15 (121) 2014 Noodle & rice stample 20 2055 Hanbok 11 Food 5.23 2015 Fried chicken & snacks 11 2056 Cold 9 (261) 2016 Soup, drink, & coffee 8 2061 Tourist attraction 25 2017 Lack food variety 10 2062 attractions 19 4.98 Tourism & Sport 2018 delicious food 7 2063 Sports 19 (95) 2019 Eat & diet 8 2064 Jeju island & sea sites 18 2021 Soap Opera 114 2065 Travel barriar (crowed, language problem) 14 4.76 2022 Entertainment celebrity 40 2071 Arrogant 18 Entertainment 2023 Idol group 33 2072 Shameless 20 (220) 2024 Hallyu 17 2073 Selfish 12 Negative Affect 2025 TV show 16 4.97 2074 Unhonest 9 (88) 2031 Shopping 46 2075 Masculinity & unfriendly 8 2032 Fashion & clothing 32 2076 Poor quality 8 2.51 2033 Samsung & high-tech 23 2077 Negtive feeling 13 Economy 2034 Developed country 20 2081 Polite 25 (146) 2035 Price 9 2082 Friendly & enthusiastic 11 2036 Manufacturing & Hyundai 8 2083 Diligent & hardworking 10 Positive Affect 2037 Undeveloped 8 5.14 2084 Confident& independent 10 (75) 2085 Good quality 11 2086 Have no idea 8 4.97 Network Analysis

Directed network: 62 nodes, 458 edges. Average degree 12.89. Average geodesic distance 1.90

Edges or links

Nodes or vertices Network analysis

CORE • Kimchi (0.72) • Plastic surgery (0.43) • Soap opera (0.35) • Cosmetics & skin care (0.24)

PERIPHERY 58 image codes: • image associations that they represent appear in the respondents’ answers together with some of the core associations but rarely appear with other periphery codes. Network Analysis: 8-cluster solution

Faction algorithm computationally arrange nodes into mutually exclusive groups, maximizing density of internal ties and minimizing density of external ties (Glover, 1989; 1990)

Main images Industries Idol, fashion, & shopping Good environment & people Small country, culture, & diplomatic Beauty industry & civilization Historic China & negative affect Culture, USA, & lack food variety Core - Periphery Clusters

# Coreness Clusters Key members nodes Freq Fav Ave Max Kimchi, Plastic surgery, Soap 1 Main images 14 742 4.53 0.16 0.72 opera, Cosmetic & skincare 2 Industries Grilled meat; Samsung; Sports 6 106 4.86 0.03 0.07 Idol groups; Fashion & clothing; 3 Idol, fashion, & shopping 8 169 4.88 0.04 0.12 Shopping 4 Good environment & Cleaniness; Polite; Good 8 134 5.38 0.02 0.06 people environment Small country; TV show; 5 Small country 7 109 4.87 0.02 0.05 Relationship to China 6 Beauty industry Beauty; Makeup style; Unhonest 6 88 4.90 0.04 0.13 Historic relationship with China; 7 History & negative affect 7 108 2.75 0.02 0.10 Arrogant; Poor quality 8 Culture, relationship with Culture; Relationship with U.S.; 6 68 4.43 0.01 0.02 U.S. Lack food variety Total 62 1524 4.33 0.06 0.72 Software: UCINET Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. Ucinet 6 for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies. CORE

Cluster 1: Main images Overall national image & Intention to visit: Regression

Overall National Image β SE of β Std. β t p Value CL6 Beauty industry 0.42 0.18 0.12 2.32 0.021 CL4 Good environment & people 0.50 0.16 0.22 3.22 0.001 CL7 Historic China & negative affect -0.81 0.16 -0.30 -4.95 0.000 R2/ adj. R2 = 0.168/ 0.155; F(8,522) = 13.19, p -value < 0.001; D.-W. = 1.356

Intention to Visit β SE of β Std. β t p Value CL6 Beauty industry 0.72 0.34 0.11 2.12 0.035 CL4 Good environment & people 0.92 0.29 0.22 3.16 0.002 CL7 Historic China & negative affect -1.05 0.31 -0.21 -3.42 0.001 R2/ adj. R2 = 0.121/ 0.107; F(8,522) = 8.96, p -value < 0.001; D.-W. = 2.040

Visit for an Event β SE of β Std. β t p Value CL3 Entertainment, fashion, shopping -0.49 0.27 -0.12 -1.82 0.069 CL5 Small country -0.53 0.30 -0.11 -1.80 0.073 CL7 Historic China & Negative affect -1.01 0.29 -0.23 -3.50 0.001 CL8 Culture, relation with the U.S. -0.86 0.33 -0.14 -2.63 0.009 R2/ adj. R2 = 0.058/ 0.044; F(8,522) = 4.05, p -value < 0.001; D.-W. = 2.030 PERIPHERY

Cluster 4: Good environment and people Cluster 7: Historic relationship with China & Negative affect

Cluster 8: Culture, relationship with U.S.

Cluster 6: Beauty industry Conclusions ❑ Established destination brand can withhold negative publicity caused by political, diplomatic, and military confrontations. ▪ Core images are positive. Core cluster is positive. Predominantly negative clusters (Historic relationship to China & negative affect; Culture, relationship to USA, & lack food variety) are on the periphery of the image network. ❑ People tend to describe South Korea from different angles, thus, network clusters contain images from various categories. ❑ Core cluster does not differentiate between tourists with respect to overall national image and desire to visit. ❑ Clusters on periphery of the image network do differentiate between tourists. Thank you for listening! Questions?

Lijuan Su ([email protected]) is a doctoral student at the Dept. of Tourism, Hospitality and Event Management at the University of Florida. She is also pursuing master degree in Statistics.

Interests: tourism and hospitality marketing, with the focus on issues of corporate image recovery and tourists' spatial behavior after the highly publicized online incidents of service failure. Big data analytics.

M.S. in Urban and City Planning at Peking University B.S. in Tourism Management at Zhejiang University. Supplement: Sample Profile

Variable Freq (N=531) % Variable Freq (N=531) % Gender Travel abroad in past 3 years Male 224 42.2 Yes 336 63.3 Female 307 57.8 No, but plan to 194 36.5

Age Been to South Korea 18-24 114 21.5 Yes 161 30.3 25-34 226 42.6 No 370 69.7 35-44 119 22.4 Have friends or relatives in South Korea 45-59 65 12.2 Yes 82 15.4 60 and above 7 1.3 No 449 84.6

Education Monthly income Below High School 6 1.1 Below 3000 102 19.2 High School/Tech School 32 6.0 3001-5000 72 13.6 Some College 85 16.0 5001-8000 112 21.1 College Degree 266 50.1 8001-10000 86 16.4 Postgraduate Degree 142 26.7 10001-20000 111 20.9 Above 20000 48 9.0 Data Preparation

1524 short phrases were classified into 62 image codes and 9 categories.

Original Response (CN) EN Freq. Code Subcatgory Category 综艺 Variety show 8 2025 综艺文化发展蓬勃综艺文化发展蓬勃The development of variety culture is flourishing 1 2025 RUNNING MAN RUNNING MAN 1 2025 跑男 Running man 1 2025 TV show 无限挑战与韩国综艺 Infinite Challenge and Korean Variety 1 2025 李光洙 Li Guangyu 1 2025 Entertainment 刘在石 Liu Zaishi 1 2025 喜欢模仿 Like imitation 1 2025 … Korean drama, Love drama, Full House… 114 2021 Soap Opera … Celebrity, Star factory, Entertainment… 40 2022 Entertainment … Idol, Idol group, Male group, Trainee… 33 2023 Idol group … Hallyu, K-pop, Swag, G-Dragon… 90 2024 Hallyu