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Article

What Does Northern European Travel Means to South Koreans? Focusing on Travel Decision Process

1
Department of Arts and Cultural Management, Cyber University of Korea, 106 Bukchon-ro, Seoul 03051, Korea
2
School of Business Administration, Hongik University, 94 wausan-ro, Seoul 04066, Korea
3
Department of Film and Entertainment Business, Chugye University for the Arts, 7 Bugahyeon-ro 11ga-gil, Seoul 03762, Korea
*
Author to whom correspondence should be addressed.
Adm. Sci. 2022, 12(3), 109; https://doi.org/10.3390/admsci12030109
Submission received: 8 July 2022 / Revised: 25 August 2022 / Accepted: 25 August 2022 / Published: 30 August 2022

Abstract

:
The main purpose of this study is to analyze different types of information that may have diverse effects on the travel decision-making process. We collected the most frequently used words related to “Northern European travel” from Korean news sources and blogs, to determine whether there were any systematic differences through network text analysis and CONCOR analysis. We found that Koreans are exposed to words such as “nature” and “cruise activities”, which may lead them to perceive traveling in Northern Europe as a special experience. While political and diplomatic issues are frequently discussed in the news, practical information is discussed in blogs.

1. Introduction

The role of travel information has been of interest in many prior studies (Bieger and Laesser 2004; Choi et al. 2012; Fodness and Murray 1997; Jacobsen and Munar 2012). When people plan for a trip, they seek to collect information from diverse sources to make the travel experience better. In this process, destinations, transportation options, and accommodation businesses compete fiercely to provide attractive features for potential visitors.
Among the various sources, private ones such as social networking services or blogs, have attracted people’s attention more than public ones such as news and other types of public communication methods as Internet technology advances (Schmallegger and Carson 2008). Online word-of-mouth plays an important role in consumer decision-making (Arenas-Márquez et al. 2021; Arasli et al. 2021), and this may make tremendous differences from just using traditional news services. However, prior studies have not made enough contributions by identifying how private sources of information would have different characteristics from private ones. For example, Huang et al. (2010) found that people tend to collect specific information from private sources even though they have already collected information from public sources. No and Kim (2015) found that the roles of public and private sources of information on travel decisions are different, while Choi et al. (2012) found that tourists utilize different sources of information at different stages of travel decision-making.
However, what we could not hear from these studies was how the private and public sources are different, and without testing real and specific models we might not be able to get the answer. This study aims to find the answer by examining how the words frequently searched for in the news (public sources) and in blogs (private sources) are different. Online text analysis enables us to do this work (e.g., Brochado et al. 2019). Especially, this study used the term of “Northern European travel” to see the difference between Korean news (public sources) and blogs (private sources) by comparing key concepts the two sources produce for Korean potential tourists.
On the largest Korean portal site, Naver, we found 10,481 words on news pages and 9190 words on blog pages. The top 92 words were selected in order to measure the degree of centrality and betweenness centrality among selected keywords. Additionally, CONCOR analysis was conducted to determine whether clusters with different meanings were formed between the two groups. Then, we discussed the results and suggested the implications of the study.

2. Literature Review

2.1. Information Sources in Travel Decision Processes

Tourism is an intangible service that receivers consume upon production (Munar 2012). Because of this characteristic individual lifestyles greatly influence tourism, and tourism is closely associated with information search and storytelling (Jacobsen 1994, as cited by Munar 2012). In the past, tourists obtained travel information from recommendations by people they knew and kept records of their travels in the form of a journal or photo album.
When purchasing a product, consumers use multiple channels to gather information, such as sellers, shopping sites, reviews, online communities, and blogs (Hyde 2008). Likewise, in the tourism industry, potential tourists use multiple sources of information on travel destinations. The main motivation for potential tourists to search for information is to improve the quality of travel and reduce the related risks (Jacobsen and Munar 2012). For example, travelers tend to invest a great deal of money, time, and mental energy in planning their summer vacation. They make a serious effort to search for information that can help them lower various risks and maximize their satisfaction.
Potential tourists initially decide on their travel destinations based on their own experience and knowledge (Fodness and Murray 1997). However, if they believe they do not have enough experience and knowledge, they seek out external sources, such as news or advertisements (Fodness and Murray 1997). Other external sources of information potential tourists can use include recommendations from family and friends as well as travel books, travel agencies, and expert advice (Baloglu 2000). Vogt and Fesenmaier (1998) classified various sources of travel information into social, personal, corporate and editorial-oriented sources, whereas Fodness and Murray (1997) classified travel information based on the type and source of information, as shown in Table 1.
In recent years, with the growth of the Internet, it has become the key source for travel information (Jacobsen and Munar 2012). According to a previous study, people who book travel services online highly trust them and use vast amounts of online tourist information (Morrisonn et al. 2001). Furthermore, as a growing number of travelers purchase travel products directly on the Internet, the power of travel service providers such as airlines has risen (Buhalis and Licata 2002). With the advent of social media, the influence of electronic word of mouth has also grown in the tourism decision-making process (Huang et al. 2010). The development of social networking services has led to the creation of new communities, enabling more diverse social interactions (Munar 2010). It has also allowed people to record and share their travel experiences in “real time”, and such advances in social media have brought changes to the development and management of online tourism content. Tourist-generated content and corporate-generated content are now in direct competition with each other (Schmallegger and Carson 2008). No and Kim (2015) classified online travel information sources into personal blogs, public sources, company sources, and social media websites and compared their characteristics. They found that potential tourists want more up-to-date and useful travel information, while tourism information sources are becoming increasingly diverse to meet these demands.
Travel decision-making is the process by which travelers select and purchase travel products to satisfy their needs. It is a multifaceted decision-making process that occurs while traveling to and staying at a destination. Earlier studies on travel decision-making were traditional consumer behavior studies (Choi et al. 2012). Van Raaij and Francken (1984) proposed a travel decision-making process based on prior studies of consumption decisions, which are generic decisions on expenditure, information acquisition, joint decision-making, vacation activities, satisfaction, and complaints. Woodside and Lysonski (1989) described travel destination selection as a process of destination awareness, preferences, intentions, and the final choice.
To travel, it is necessary to decide on various items in addition to the destination. Dynamic and multistage contingent decision processes include scheduling, budgeting, finding, accommodation, planning routes, and finding companions (Choi et al. 2012). Fesenmaier and Jeng (2000) proposed three levels of a travel decision model in which all sub-decisions, including destination selection, have different levels of importance: the core decisions that are predetermined before the travel (e.g., destination, travel date, companions), the predetermined decisions that are flexible to change (e.g., secondary destination, activity, tourist attractions), and the route decisions that are made at the destination and actively seek alternatives (e.g., restaurants, rest areas, shopping places).
The exploration and selection of tourism information is a key element of travel decision-making and a continuous activity in the travel process. Bieger and Laesser (2004) classified the travel decision-making process based on “decisions that are difficult to change once made”. Additionally, they analyzed the information sources used in pre- and post-decision-making. They found that the pattern of using information sources in both stages differed depending on the individual and travel characteristics. Choi et al. (2012) divided the travel decision-making stage into four levels based on purchases for the vacation trip and analyzed differences in the information used in each level. As a result, prior to purchase, recommendations from relatives and public media such as TV/radio commercials had a significant impact. After a purchase, experience-based information and private information such as travel guidebooks and tour guides became important sources of information.
Based on Choi et al.’s (2012) findings, the current study identifies differences in information that have diverse effects on the travel decision-making process. More specifically, text analysis is used to differentiate public travel information and private travel information and suggest implications.

2.2. News vs. Blogs

Today, we live in the Web 2.0 era, which is characterized by a hybrid media system. The Web 1.0 era was dominated by mass media, but the Web 2.0 era has witnessed the emergence of blogs and social networking sites (Chadwick 2014). These days, news spreads quickly through various online media platforms without being bound by time and space constraints. However, the process of generating news on online platforms is not much different from the traditional process. Through editorial meetings, the media generates information that is unilaterally given to readers (Lewis 2012). The vast majority of major online news providers still belong to offline news media groups (Ceron 2015). Moreover, the editorial desks of online news sources rely on information that is selected according to the reliability and authority of the source (Hermida et al. 2014). These similarities between traditional and new media have led consumers to assume that online news is as reliable as offline news (Ceron 2015).
Tourism-related information provided by the news media can have a strong influence on the notoriety and perception of travel destinations (Yoo and Kim 2013). For example, research on the impact of the Tiananmen Square conflict on tourism showed that the media has a significant influence on people’s perception of the region (Gartner and Shen 1992). Peel and Steen (2007) pointed out that print media takes great interest in issues where tourism can have a positive impact on the local economy, and negative reports on local crimes significantly affect the perception of a region. Stepchenkova and Eales (2011) classified reports related to Russia that appeared in British newspapers from 1993 to 2007 into seven categories, including culture, economy, society, and tourism. Their results confirmed the impact of media on tourism. Yoo and Kim (2013) studied the characteristics and effects of press releases and tourism information distributed by U.S. state tourism offices via news media. They emphasized the importance of official materials and the need for more user-friendly content.
With the emergence of social media, traditional media has gradually lost its domination over the production and distribution of information. Social media provides a convenient environment for users to create content and share information. It allows users to avoid going through the information-generation system that traditional media relies on (Hermida 2010). Information created through social media is considered organic and personal (Jacobsen and Munar 2012). Murray (1991) argued that regarding services, consumers prefer personal information to impersonal information and this is also applicable to the process of selecting travel destinations (Prebensen et al. 2010).
A blog is an online communication tool and a social networking service through which users can directly produce information (Hur and Byun 2012). Blogs enable real-time communication and can reach thousands of readers simultaneously (Munar and Jacobsen 2013). Blogs convey emotion and allow users to talk about their feelings using emojis and informal language (Munar and Jacobsen 2013). Like blogs, personal media is an important tool for interpersonal communication in modern society (Hennig-Thurau et al. 2010); users utilize personal media to obtain the information they want without encountering any advertising messages or irrelevant information (Bacile et al. 2014). The content found on travel blogs, in particular, is composed of information on areas that potential travelers are interested in. By examining such blogs, the preferences of tourists can be better understood (Sasaki and Nishii 2012).
With the growing influence of blogs in the tourism industry, research is increasingly being conducted on travel blogs. Huang et al. (2010), for instance, argued that travel blogs play a positive role in advertising. They offer advantages for tourism marketing because they serve as a communication tool. Blog users exchange information besides typical information such as the weather conditions at the travel destination and the recommended itinerary (Huang et al. 2010). Schmallegger and Carson (2008) discussed how to create and utilize tourism content through a case analysis of travel blogs used as a strategic marketing tool. Meanwhile, Volo (2010) analyzed the impact of blogs on the travel experience of tourists and on their decision-making. Mack et al. (2008) examined the reliability of travel blogs using a scenario-based approach and found that users did not trust blogs as much as traditional word-of-mouth sources.
Online news is an impersonal channel and a blog is a personal channel, but both are important sources of travel information. Nevertheless, few studies have conducted a text comparison of the differences between travel information provided by news articles and blogs. Therefore, the current research is an empirical study that involves text analysis of the type of information tourists obtain from both channels. Information provided in South Korean news articles and blogs about traveling in Northern Europe is analyzed to examine Koreans’ perspectives. Furthermore, we suggest practical implications that can be used in tourism marketing in Northern Europe.

3. Methods

3.1. Research Questions

Recently, long-distance travel has been attracting travelers’ interest, with the rise in popularity of keywords such as “work-life balance”, “leisure”, and “break”. Thus, it appears that the number of potential tourists interested in traveling to Northern Europe has been growing in South Korea. There has not been much research on the destination image or major issues that Koreans have with traveling in Northern Europe. Additionally, to analyze the differences between news articles and blogs as information channels, this study asks the following research questions:
Q1. How can we describe the difference between private and public sources for travel?
Q2. What are the main issues in Korean news articles and blogs, respectively, associated with traveling in Northern Europe?
Q3. What are the implications of the types of information that may have different effects on the travel decision-making process?

3.2. Data Collection and Analysis

The scope of this study included news articles and blogs registered on Naver, the largest portal site in Korea. We analyzed news reports and blogs published over the course of one year, from 1 January 2019 to 31 December 2019. To select the analysis target, we searched “Northern European1 travel” on Naver, and then we collected the top 1000 articles and posts that were sorted by relevance. Text mining and network text analysis (NTA) were conducted to find words related to traveling in Northern Europe, and a comparative analysis was conducted of the different characteristics of news articles and blogs.
Text mining is an analysis method used to discover new knowledge or patterns by extracting key concepts from a large amount of text data using a computer (Hearst 1999). This is distinct from general data mining in that text mining processes unstructured data such as emails and newspaper articles (Fan et al. 2006). Text mining involves a pre-processing step in which documents are collected using search terms, and the collected documents are analyzed using multiple tokens to remove unnecessary words. In this study, text mining was performed using the TEXTOM program, an online unstructured data collection program that supports English, Chinese, and Korean and is capable of processing data. Following this, a network analysis was conducted.
The implications of this study were derived through NTA and CONCOR (CONvergence of iterated CORrelations) analysis. NTA is one of the text analysis methods derived from social network analysis (SNA), which is used in social science research (Kim and Lee 2019). In network analysis, the network between keywords implies symbolism and characteristics of the connection, and thus it is possible to draw meaningful results (Presenza and Cipollina 2010). Additionally, centrality analysis is a method that analyzes the degree to which nodes are centrally located in the network as a means of grasping the connection between words (Bhat and Milne 2008). Through this, the structure between a specific central word and the surrounding words can be examined. In the case of CONCOR analysis, it is possible to form a cluster of similar words within a network and visually derive a connection pattern to examine the relationships between groups (Kim and Lee 2019).
In this study, a matrix of the frequency of co-occurrence of words was derived using TEXTOM for network analysis. Furthermore, centrality analysis and CONCOR analysis were carried out using the UCINET 6 program, and visualization was performed using Netdraw. Data collection methods are summarized in Table 2.

4. Results

4.1. Text Frequency Analysis

In this study, text frequency analysis was conducted using data collected by searching for “Northern European travel”. A total of 10,481 words were found in news articles about traveling in Northern Europe, and 9190 words were found on blogs about traveling in Northern Europe. The top 96 keywords among them—excluding the terms used in the search, such as “Northern Europe”, “travel”, and “Northern European travel”—are summarized in Table 3.
The words that appeared with the highest frequency, except for country names and geographical names, were “nation”, “nature”, “traveler”, “Scandinavian mood”, and “region”. In the blogs, “cruise”, “design”, “Scandinavian mood”, “backpack”, “accommodation”, and so on appeared most frequently. The results of the frequency analysis showed that both news articles and blogs mention the unique aspects of Northern Europe, such as its grand natural environments, the characteristics of North Europeans, and cruises.
Regarding the differences among the keywords found in the news articles and blogs, political and diplomatic issues indicated by words such as “nation”, “President Moon”, and “presidential visit to Northern Europe” were frequently discussed in the news. “Bon Voyage” was also founded from the news articles. Bon Voyage is a travel entertainment program featuring a famous K-pop idol group called BTS. Its first season was filmed in Northern Europe in 2016, and “Bon Voyage” continued to be mentioned even in 2019 when the fourth season aired. Additionally, “welfare”, “environment”, and “education” were frequently mentioned in the news articles. This indicates an interest in Northern Europe’s welfare states.
In the case of blogs, words representing practical information necessary for planning and preparing for a trip, such as “weather”, “schedule”, “preparation”, “recommendation”, and “information”, were extracted. This shows the function of blogs that share personal travel experiences and recommendations for potential travelers. “Price” and “toilet” were mentioned in relation to unfamiliar situations to Korean travelers, such as having to pay to use a toilet. Words that were not mentioned in the news, such as “minimalism” and “practical”, were derived from blogs, revealing the perspectives of Koreans toward Northern European designs.

4.2. Degree of Centrality of Keywords

Degree centrality is the measure of the degree to which a specific word is directly connected to another word in a network (Yan and Ding 2009). The higher the degree centrality, the closer the word is to the center of the network, and the stronger its influence is as an important discussion point (Freeman 1978).
In the case of degree centrality of the news, the mean was 0.021, the median was 0.015, and the standard deviation was 0.019. As you can see in Table 4, the country that showed the highest level of degree centrality of the news was Finland, followed by Sweden, Norway, and Denmark, suggesting that these countries were identified as top travel destinations. Furthermore, the degree centrality of words such as “nature”, “purity”, and “fine dust” was high. This shows that Koreans admire and think highly of Northern Europe’s well-preserved natural environment, which contrasts with Korea’s own environment, with its serious air quality problem.
As for the degree centrality of the words extracted from blogs, the mean was 0.010, the median was 0.006, and the standard deviation was 0.012. The country with the highest degree centrality was Norway, followed by Denmark, Sweden, and Iceland. Words related to traveling in Iceland, such as Iceland, Ring Road, and Reykjavik, had high degree centrality. This was because Youth Over Flowers—Iceland Edition, a travel entertainment program that aired in 2016 in Korea, caught the attention of Koreans as a travel destination in Northern Europe. According to prior research on tourism marketing, publicity through mass media has a positive effect on reliability and purchase intention (Loda et al. 2005). In fact, shortly after the broadcast of Youth Over Flowers—Iceland Edition, inquiries regarding traveling in Northern Europe skyrocketed, increasing by more than 10 times the previous number (Ki 2016).
In the case of both news articles and blogs, “museum”, “art gallery”, and “culture” displayed low degree centrality, whereas “aurora”, “forest”, and “lake” showed relatively high levels of degree centrality. Based on this, it appears that Koreans are more interested in natural scenery than cultural experiences when it comes to visiting Northern Europe. Furthermore, “Russia” was a non-Northern Europe country mentioned frequently both in the news articles and blogs in connection with Northern European travel. This was likely because there are not many direct flights between Korea and Northern Europe, which necessitates transit through Russia.

4.3. Betweenness Centrality of Keywords

Betweenness centrality is a measure of the degree to which a word acts as an intermediary between other words in the network (Freeman 1978). Words with a high degree of betweenness centrality play an important role in the flow of discussion on the network (Yan and Ding 2009). This is an important analysis concept because, without going through a specific node, it may not be possible to connect certain nodes irrespective of the degree of connection within the network (Lim et al. 2020).
The results of analyzing betweenness centrality of words used in the news articles showed the mean as 0.747, median as 0.318, and standard deviation as 1.103. As you can see in Table 5, words that objectively refer to the object, such as “travelers” and “destinations”, topped the list. This suggests that news media is leading discussions on travel in Northern Europe regarding “travelers” and “travel destinations”. Furthermore, “travel product”, “tour package”, and other words related to group travel through travel agencies showed a high degree of betweenness centrality. Keywords such as “happiness”, “life”, “culture”, and “book” had lower values than the median in degree centrality but had higher values than the median in betweenness centrality. Based on this, it can be inferred that these words influence the flow of discussions regarding traveling in Northern Europe.
Meanwhile, the mean betweenness centrality in the case of blogs was 0.811, the median was 0.392, and the standard deviation was 1.206. Capitals of Northern European countries such as Copenhagen, Helsinki, Oslo, and Stockholm recorded high betweenness centrality. City-level information seemed to have an impact on the overall flow of discussion on blogs. Additionally, the fact that “summer” and “July” showed a high degree of betweenness centrality suggests that Koreans believe the best time to travel in Northern Europe is during summer.
Figure 1 and Figure 2 are Venn diagrams that show the intersections of words ranking high in degree centrality and betweenness centrality2. Among the words with high values of both degree centrality and betweenness centrality, the ones that were not directly related to the keyword “travel” were “Scandinavian mood” and “design”. “Scandinavian mood”, in particular, was ranked high in both news articles and blogs. This is in line with the fact that Korean consumers are highly interested in Scandinavian interior design and Nordic style (Lee 2015). In fact, the concept of “Scandinavian mood” has led trends in several areas such as fashion and interior design in Korea, and it is now often mentioned in the field of travel as an influential keyword. Thus, “Scandinavian mood” is a notable concept from the Northern Europe tourism marketing perspective. “Scandinavian mood”, which is said to be nature-friendly, comfortable, simple, and pragmatic (Kang and Kwon 2016) is applied to the field of travel. This travel concept is drawing Koreans’ interest because it is geared toward enjoying nature to the fullest and exploring the everyday lives of local people.

4.4. CONCOR Analysis Results

With CONCOR analysis, it is possible to see how words, each with a different meaning in the centrality analysis, are clustered within the network to form a topic. As a result of CONCOR analysis, eight clusters were derived from the news articles and blogs. Each word is shown in Table 6, and the visualization results are shown in Table 7.
Regarding the results of the news articles’ analysis, Cluster 1 consisted of issues related to politics and society in Northern Europe, with words such as “welfare”, “freedom”, “nation”, and “society”. The quality of education and social safety of Northern European countries is well-known, as indicated by welfare states being a part of the discussion. Cluster 2 was composed of words that provided a glimpse of Northern European culture and Koreans’ perception of Northern Europe. Words such as “Scandinavian mood”, “design”, “Moomin”, “book”, and “author” showed Koreans’ interest in the Northern European style, which is gaining popularity in Korea. As for Cluster 3, it consisted of words related to flight, such as “Finnair”, “Helsinki”, and “Route”, along with a discussion of the first direct flight connecting Busan, the second-largest city in Korea, to Northern Europe. Cluster 4 contained words related to President Moon Jae-in’s visit to countries in Northern Europe in June 2019, whereas Cluster 5 contained words such as “Hawaii”, “Malta”, and “New Zealand”, related to BTS’ travel entertainment program. Cluster 6 had words representing different types of travel such as “independent tour” and “tour package” along with words pertaining to the natural environment, such as “fine dust” and “purity”. It showed that Koreans often plan a trip to Northern Europe to witness the beauty of nature. Cluster 7 contained words related to cruise trips, suggesting an interest in cruise travel, although cruises are rare in Korea. Lastly, Cluster 8’s words were related to neighboring countries in Europe, implying Koreans’ tendency to travel to multiple countries when taking a trip to Europe.
According to the results of the blog analysis, Cluster 1 consisted of words representing travel information related to Scandinavia. In addition to geographical names such as “Sweden” and “Norway”, words related to attractions, such as “pleasure craft”, “museum”, and “art gallery”, as well as informative words, such as “price” and “bus”, were found. This showed that practical information necessary for traveling is found on blogs. In Cluster 2, there were words related to “Russia”, a major transit point between Korea and Northern Europe. There was also information on planning an independent tour, such as “schedule”, “hotel”, and “food”. In the case of Cluster 3, words such as “Scandinavian mood”, “design”, and “minimalism” were derived. This was a discussion topic related to the image of Northern Europe that was similar to Cluster 2 in the news articles. In Cluster 4 “summer”, considered as the best time to travel to Northern Europe by Koreans, and Northern European “myths” formed a cluster. Cluster 5 was formed under the theme of traveling to Iceland. Cluster 6 consisted of information about the kind of travel that would allow one to experience nature, such as “trekking”, “waterfall”, and “national park”. In Cluster 7, words such as “blue”, “sky”, and “glacier” were derived together and reaffirmed the perception of Northern Europe as a clean destination among Koreans. Lastly, Cluster 8 consisted of “Western Europe” and “Jokulsarlon”.

5. Conclusions and Implications

In this study, we collected information from Korean news and blog sites to determine whether different sites provide diverse types of information that affect the travel decision-making process. Specifically, we asked three research questions and used several methodologies. Our results are summarized below.
First, we confirmed that different sources of travel information focus on different aspects of travel. For example, the CONCOR analysis shows that BTS’ travel entertainment program cluster was formed in the news, and the Iceland cluster, which was the destination of Youth Over Flowers, was formed in the blog (Table 8 and Table 9). In the case of the news articles, the density was the highest for Clusters 5–6 (3.111). The connection between Cluster 5, consisting of words related to the travel entertainment program featuring BTS, and Cluster 6, pertaining to traveling to enjoy a clean natural environment, was the strongest. From this, it can be inferred that the program captured the exquisite natural environment of Northern Europe, and as a result the number of potential travelers interested in traveling to Northern Europe grew. In the case of the blogs, the density value was the highest for Clusters 5–6 (1.685); these two clusters displayed the highest degree of connection, consisted of information related to traveling in Iceland and traveling to experience nature. Based on this, it can be speculated that a significant number of viewers of Youth Over Flowers are interested in nature travel. Exposure through mass media will help to recognize Northern Europe as a tourist destination for potential travelers. The main contribution of this study to prior research lies in showing this empirical and clear distinction between difference travel information sources.
Second, whereas the main issues associated with traveling in Northern Europe in Korean news articles include political, educational, and environmental issues, those in Korean blogs include the weather, schedules, preparation, and recommendation (see Table 4 and Table 5). The former source of information, news articles, is what Koreans are inadvertently exposed to, and the latter source, blogs, is what Koreans intentionally use for information search.
Third, in our CONCOR analyses, public information (e.g., news) had three clusters (cluster 1, about political subjects; cluster 4, about President Moon’s visit; and cluster 5, about BTS’s tour) out of eight that were not relevant to travel. On the other hand, private information (e.g., blogs) had only one cluster (cluster 3, about general Scandinavian mood) out of eight clusters that was not relevant to travel (see Table 7). This means that Koreans are exposed to traveling in Northern Europe when they read news about political and cultural topics. When they are motivated to search for more information about traveling in Northern Europe in blogs, they find more direct information.
This study has the following theoretical implications: While many prior studies (Bieger and Laesser 2004; Choi et al. 2012; Fodness and Murray 1997; Jacobsen and Munar 2012) suggest that potential tourists collect information from diverse channels, this study empirically showed how the difference exists and may affect travelers’ decision making. By analyzing the difference between travel-related words that can be obtained from public and private channels through text analysis, this study found empirical evidence supporting the claim that travelers are exposed to different types of information from different sources
Several important managerial implications can be found in this study. First, public channels collectively exposing diverse information can be useful to increase the awareness of tourist destinations; on the other hand, it is possible to establish a differentiated marketing strategy that utilizes private channels for specific information to sell tourism products.
Additionally, Northern Europe, although an attractive tourist destination, hides still many unexplored areas for Koreans who are reluctant to navigate them due to long-distance flights and lack of information. Northern European tourism developers, therefore, need to further emphasize the image of clean nature and promote tourism programs centered on the natural landscape.
This study has some limitations. First, in the study, news articles represented public information and blogs represented private information. However, there are other information sources that can be defined as public or private information sources. Therefore, future studies can collect and analyze data from other information sources such as Facebook and Twitter. Recently, video clips such as on YouTube have become an important source of travel information, so follow-up research on these new information sources should be conducted.
Second, in this study, the travel information search stage was classified into “prior to purchase” and “after purchase”. However, exploration of tourism information is a continuous action that occurs even at the travel destination. In the future, we want to analyze further levels of the decision-making process for travel.
Special remarks: This study has a limitation that it was conducted based on data collected before the COVID-19 pandemic, which had a great impact on the global tourism industry. However, interest in travel is growing again as the pandemic turns into an endemic. A survey on leisure activities after the coronavirus shows that Koreans are more interested in ‘tourism and travel’ than in ‘society, culture, and art’ by 20 percent (Consumer Insight 2022). In this respect, even though the data used were outdated, this study may offer better implications for, hopefully, ‘normal’ travel situations after the COVID-19 pandemic.

Author Contributions

Conceptualization, J.H.P., H.-D.S. and Y.-D.S.; methodology, J.H.P., H.-D.S. and Y.-D.S.; software, J.H.P.; validation H.-D.S. and Y.-D.S.; formal analysis, J.H.P.; investigation, J.H.P. and H.-D.S.; resources, J.H.P.; data curation, J.H.P.; writing—original draft preparation, J.H.P.; writing—review and editing, H.-D.S.; visualization, J.H.P.; supervision, H.-D.S. and Y.-D.S.; project administration, J.H.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Notes

1
The Korean expression for “Northern European” refers to Northern European, Scandinavian, and Nordic countries without any distinction.
2
Although these Venn diagrams were based on the top 20 words, 19 words were extracted from the news articles and 21 were extracted from blogs to prevent omission of words with the same value of degree centrality.

References

  1. Arasli, Huseyin, Mehmet Bahri Saydam, Tugrul Gunay, and Kaveh Jafari. 2021. Key attributes of Muslim-friendly hotels’ service quality: Voices from booking.com. Journal of Islamic Marketing 12: 22. [Google Scholar] [CrossRef]
  2. Arenas-Márquez, Francisco José, María del Rocío Martínez-Torres, and Sergio Toral. 2021. How can trustworthy influencers be identified in electronic word-of-mouth communities? Technological Forecasting and Social Change 166: 120596. [Google Scholar] [CrossRef]
  3. Bacile, Todd J., Christine Ye, and Esther Swilley. 2014. From firm-controlled to consumer-contributed: Consumer co-production of personal media marketing communication. Journal of Interactive Marketing 28: 117–33. [Google Scholar] [CrossRef]
  4. Baloglu, Seyhmus. 2000. A path analytic model of visitation intention involving information sources, socio-psychological motivations, and destination image. Journal of Travel & Tourism Marketing 8: 81–90. [Google Scholar] [CrossRef]
  5. Bhat, Sushma Seth, and Simon Milne. 2008. Network effects on cooperation in destination website development. Tourism Management 29: 1131–40. [Google Scholar] [CrossRef]
  6. Bieger, Thomas, and Christian Laesser. 2004. Information sources for travel decisions: Toward a source process model. Journal of Travel Research 42: 357–71. [Google Scholar] [CrossRef]
  7. Brochado, Ana, Paulo Rita, Christina Oliveira, and Fernando Oliveira. 2019. Airline passengers’ perceptions of service quality: Themes in online reviews. International Journal of Contemporary Hospitality Management 31: 855–73. [Google Scholar] [CrossRef]
  8. Buhalis, Dimitrios, and Maria Cristina Licata. 2002. The future eTourism intermediaries. Tourism Management 23: 207–20. [Google Scholar] [CrossRef]
  9. Ceron, Andrea. 2015. Internet, news, and political trust: The difference between social media and online media outlets. Journal of Computer-Mediated Communication 20: 487–503. [Google Scholar] [CrossRef]
  10. Chadwick, Andrew. 2014. The hybrid media system: Politics and power. Public Administration 92: 1106–14. [Google Scholar]
  11. Choi, Soojin, Xinran Y. Lehto, Alastair M. Morrison, and Soocheong Jang. 2012. Structure of travel planning processes and information use patterns. Journal of Travel Research 51: 26–40. [Google Scholar] [CrossRef]
  12. Consumer Insight. 2022. Post-COVID-19 Leisure Activities—‘Tourism and Travel’ Took the Overwhelming First Place. Available online: https://www.consumerinsight.co.kr/voc_view.aspx?no=3252&id=pr10_list&PageNo=1&schFlag=0 (accessed on 8 August 2022).
  13. Fan, Weiguo, Linda Wallace, Stephanie Rich, and Zhongju Zhang. 2006. Tapping the power of text mining. Communications of the ACM 49: 76–82. [Google Scholar] [CrossRef]
  14. Fesenmaier, Daniel R., and J. M. Jeng. 2000. Assessing structure in the pleasure trip planning process. Tourism Analysis 5: 13–27. [Google Scholar]
  15. Fodness, Dale, and Brian Murray. 1997. Tourist information search. Annals of Tourism Research 24: 503–23. [Google Scholar] [CrossRef]
  16. Freeman, Linton Clarke. 1978. Centrality in social networks conceptual clarification. Social Networks 1: 215–39. [Google Scholar] [CrossRef]
  17. Gartner, William C., and Jingqing Shen. 1992. The impact of Tiananmen Square on China’s tourism image. Journal of Travel Research 30: 47–52. [Google Scholar] [CrossRef]
  18. Hearst, Marti A. 1999. Untangling text data mining. Paper presented at the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, ML, USA, June 20–26; Stroudsburg: Association for Computational Linguistics, pp. 3–10. [Google Scholar] [CrossRef]
  19. Hennig-Thurau, Thorsten, Edward C. Malthouse, Christian Friege, Sonja Gensler, Lara Lobschat, Arvind Rangaswamy, and Bernd Skiera. 2010. The impact of new media on customer relationships. Journal of Service Research 13: 311–30. [Google Scholar] [CrossRef]
  20. Hermida, Alfred. 2010. Twittering the news: The emergence of ambient journalism. Journalism Practice 4: 297–308. [Google Scholar] [CrossRef]
  21. Hermida, Alfred, Seth C. Lewis, and Rodrigo Zamith. 2014. Sourcing the Arab Spring: A case study of Andy Carvin’s sources on Twitter during the Tunisian and Egyptian revolutions. Journal of Computer-Mediated Communication 19: 479–99. [Google Scholar] [CrossRef]
  22. Huang, Ching-Yuan, Chia-Jung Chou, and Pei-Ching Lin. 2010. Involvement theory in constructing bloggers’ intention to purchase travel products. Tourism Management 31: 513–26. [Google Scholar] [CrossRef]
  23. Hur, Kyung-Suk, and Jeoung-Woo Byun. 2012. Effects of storytelling factors at travel power blogs on motivation and behavioral intention. Journal of Korean Institute of Information Technology 10: 93–106. [Google Scholar]
  24. Hyde, Kenneth F. 2008. Information processing and touring planning theory. Annals of Tourism Research 35: 712–31. [Google Scholar] [CrossRef]
  25. Jacobsen, Jens Kr. Steen, and Ana María Munar. 2012. Tourist information search and destination choice in a digital age. Tourism Management Perspectives 1: 39–47. [Google Scholar] [CrossRef]
  26. Kang, Jun Ho, and Gi Young Kwon. 2016. A study on the formative characteristics and meanings of the Scandinavian fashion. Journal of Basic Design & Art 17: 17–30. [Google Scholar]
  27. Ki, Soojung. 2016. The Effect of ‘Youth Over Flowers’… ‘a Huge Surge’ in the Inquiry of Travel to Iceland. Aju Business Daily. Available online: https://www.ajunews.com/view/20160105172431975 (accessed on 6 January 2016).
  28. Kim, Sohyeon, and Won SeokLee. 2019. Network text analysis of medical tourism in newspapers using text mining: The South Korea case. Tourism Management Perspectives 31: 332–39. [Google Scholar] [CrossRef]
  29. Lee, Woo-young. 2015. For New Look, Think Outside the Box. The Korea Herald. Available online: http://www.koreaherald.com/view.php?ud=20150320001001&ACE_SEARCH=1 (accessed on 20 March 2015).
  30. Lewis, Seth C. 2012. The tension between professional control and open participation: Journalism and its boundaries. Information, Communication & Society 15: 836–66. [Google Scholar] [CrossRef]
  31. Lim, Eunjung, Shindo Mari, and Arita Shin. 2020. A Korean-Japanese comparison research on social discussion about accommodation sharing service: Using network text analysis on Korean-Japanese articles. Journal of Consumer Policy Studies 51: 1–34. [Google Scholar]
  32. Loda, Marsha D., William Norman, and Kenneth Backman. 2005. How potential tourists react to mass media marketing: Advertising versus publicity. Journal of Travel & Tourism Marketing 18: 63–70. [Google Scholar] [CrossRef]
  33. Mack, Rhonda W., Julia E. Blose, and Bing Pan. 2008. Believe it or not: Credibility of blogs in tourism. Journal of Vacation Marketing 14: 133–44. [Google Scholar] [CrossRef]
  34. Morrisonn, Alastair M., Su Jing, Joseph T. O’Leary, and Liping A. Cai. 2001. Predicting usage of the Internet for travel bookings: An exploratory study. Information Technology & Tourism 4: 15–30. [Google Scholar] [CrossRef]
  35. Munar, Ana María. 2010. Digital exhibitionism: The age of exposure. Culture Unbound: Journal of Current Cultural Research 2: 401–22. [Google Scholar] [CrossRef]
  36. Munar, Ana María. 2012. Social media strategies and destination management. Scandinavian Journal of Hospitality and Tourism 12: 101–20. [Google Scholar] [CrossRef]
  37. Munar, Ana María, and Jens Kr. Steen Jacobsen. 2013. Trust and involvement in tourism social media and web-based travel information sources. Scandinavian Journal of Hospitality and Tourism 13: 1–19. [Google Scholar] [CrossRef]
  38. Murray, Keith B. 1991. A test of services marketing theory: Consumer information acquisition activities. Journal of Marketing 55: 10–25. [Google Scholar] [CrossRef]
  39. No, Eunjung, and Jin Ki Kim. 2015. Comparing the attributes of online tourism information sources. Computers in Human Behavior 50: 564–75. [Google Scholar] [CrossRef]
  40. Peel, Victoria, and Adam Steen. 2007. Victims, hooligans and cash-cows: Media representations of the international backpacker in Australia. Tourism Management 28: 1057–67. [Google Scholar] [CrossRef]
  41. Prebensen, Nina, Kåre Skallerud, and Joshep S. Chen. 2010. Tourist motivation with sun and sand destinations: Satisfaction and the wom-effect. Journal of Travel & Tourism Marketing 27: 858–73. [Google Scholar] [CrossRef]
  42. Presenza, Angelo, and Maria Cipollina. 2010. Analysing tourism stakeholders networks. Tourism Review 65: 17–30. [Google Scholar] [CrossRef]
  43. Sasaki, Kuniaki, and Kazuo Nishii. 2012. Study of blog mining for examination of tourist travel behavior in Japan. Transportation Research Record 2285: 119–25. [Google Scholar] [CrossRef]
  44. Schmallegger, Doris, and Dean Carson. 2008. Blogs in tourism: Changing approaches to information exchange. Journal of Vacation Marketing 14: 99–110. [Google Scholar] [CrossRef]
  45. Stepchenkova, Svetlana, and James S. Eales. 2011. Destination image as quantified media messages: The effect of news on tourism demand. Journal of Travel Research 50: 198–212. [Google Scholar] [CrossRef]
  46. Van Raaij, W. Fred, and Dick A. Francken. 1984. Vacation decisions, activities, and satisfactions. Annals of Tourism Research 11: 101–12. [Google Scholar] [CrossRef]
  47. Vogt, Christine A., and Daniel R. Fesenmaier. 1998. Expanding the functional information search model. Annals of Tourism Research 25: 551–78. [Google Scholar] [CrossRef]
  48. Volo, Serena. 2010. Bloggers’ reported tourist experiences: Their utility as a tourism data source and their effect on prospective tourists. Journal of Vacation Marketing 16: 297–311. [Google Scholar] [CrossRef]
  49. Woodside, Arch G., and Steven Lysonski. 1989. A general model of traveler destination choice. Journal of Travel Research 27: 8–14. [Google Scholar] [CrossRef]
  50. Yan, Erjia, and Ying Ding. 2009. Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology 60: 2107–18. [Google Scholar] [CrossRef]
  51. Yoo, Kyung-Hyan, and Jangyul Robert Kim. 2013. How US state tourism offices use online newsrooms and social media in media relations. Public Relations Review 39: 534–41. [Google Scholar] [CrossRef]
Figure 1. Centrality Venn diagram of news articles.
Figure 1. Centrality Venn diagram of news articles.
Admsci 12 00109 g001
Figure 2. Centrality Venn diagram of blogs.
Figure 2. Centrality Venn diagram of blogs.
Admsci 12 00109 g002
Table 1. Classification of tourism information sources.
Table 1. Classification of tourism information sources.
Source of InformationType of Information
ImpersonalPersonal
CommercialBrochures
Guide books
Local tourist offices
State travel guides
Auto clubs
Travel agents
NoncommercialMagazines
Newspapers
Friends or relatives
Highway welcome centers
Personal experience
Table 2. Data collection.
Table 2. Data collection.
ItemsDetails
Collection RangeNaver news articles, Blog
Period1 January 2019–31 December 2019
Data-mining toolTEXTOM
KeywordNorthern European travel
Analysis toolUCINET 6
Table 3. Frequency of top 96 keywords.
Table 3. Frequency of top 96 keywords.
NewsBlog
RankKeywordsFrequencyRankKeywordsFrequencyRankKeywordsFrequencyRankKeywordsFrequency
1Finland18449relax251Norway20149South Korea16
2Nation15450culture242Iceland15150capital city16
3Sweden14251Iceland243Sweden12851photo16
4Norway12652building244Finland11952trekking15
5Nature10453Christmas235Copenhagen11253schedule15
6Europe10254lake236Denmark11154preparation15
7Denmark10055Balkan Peninsula237cruise10555people15
8traveler6656Western Europe238design9256story15
9Scandinavian mood5957Stockholm239Russia8457sightseeing15
10New Zealand5758the United States2310Scandinavian mood6758minimalism15
11region5459the Mediterranean Sea2311Oslo6559scenery15
12city5360book2212Helsinki6360tour15
13cruise5361author2213Stockholm6261friend14
14Russia5262scenery2114Ring road5362white night14
15aurora5163travel2015back pack5363travelogue14
16summer4964art gallery2016accommodation5064information14
17winter4665fine dust2017hotel4965price14
18destination4666happiness2018nation4766charm13
19travel product4567popularity2019Bergen4567glacier13
20overseas travel4268landscape1920nature4268National Park13
21BTS4269life1921city3769Flam13
22President Moon3870Bergen1922move3770recommendation13
23Hawaii3871hotel1923Europe3271practical13
24people3872Germany1824Estonia3272traveler13
25presidential visit to Northern Europe3773Asia1825summer2973destination13
26Copenhagen3674gift of nature1826weather2874Viking13
27South Korea3675direct flight1827brand2875world tour12
28design3476furniture1828fjord2776plane12
29welfare3477safety1829independent tour2677Germany12
30route3378environment-friendly1830book2678interior12
31environment3379history1731Scandinavian style2679road12
32education3280society1732tour package2580museum12
33forest3281comfort1733aurora2481toilet12
34Alaska3282Moomin1734Saint Peterburg2482Western Europe12
35representative3283Incheon1735art gallery2383the Little Mermaid12
36purity3284boarding1736Moscow2384sky12
37Helsinki3185independent tour1637plan2285god of Northern Europe12
38Scandinavian style3186program1638winter2286myth12
39Eastern Europe3087Spain1639Reykjavik2287church12
40Finnair2888passenger1640introduce2288Eastern Europe12
41tour package2889Busan1641booking2189Jokulsarlon11
42Malta2890brand1642July2190port11
43Switzerland2791travel to Europe1643bus2191culture11
44booking2792flight1644Tallin1892blue11
45Bon Voyage2793Australia1545waterfall1893food11
46Oslo2794freedom1546airport1794ferry11
47Caucasus2695Scandinavia1547pleasure craft1795Scandinavia11
48Canada2696fjord1548Greenland1796healing11
Table 4. Degree centrality of top 96 keywords.
Table 4. Degree centrality of top 96 keywords.
NewsBlog
RankKeywordsDegree CentralityRankKeywordsDegree CentralityRankKeywordsDegree CentralityRankKeywordsDegree Centrality
1Finland0.11349direct flight0.0151Norway0.0749book0.005
2Sweden0.08350South Korea0.0142Denmark0.05150plan0.005
3Norway0.08351relax0.0143Sweden0.04851winter0.005
4nature0.07752Western Europe0.0144Iceland0.04252airport0.005
5nation0.07553gift of nature0.0145Copenhagen0.04253preparation0.005
6Denmark0.06654boarding0.0146Finland0.0454minimalism0.005
7Europe0.06455presidential visit to Northern Europe0.0137cruise0.03255tour0.005
8New Zealand0.04256design0.0138Russia0.0356white night0.005
9region0.04257Oslo0.0139Oslo0.02857information0.005
10traveler0.0458Caucasus0.01310design0.02758recommendation0.005
11Russia0.03259Stockholm0.01311Stockholm0.02259world tour0.005
12city0.03160Incheon0.01312Helsinki0.01960Germany0.005
13purity0.03161Iceland0.01213Scandinavian mood0.01861museum0.005
14cruise0.0362the United States0.01214Ring road0.01862blue0.005
15Hawaii0.0363travel0.01215Bergen0.01763pleasure craft0.004
16Scandinavian mood0.02864happiness0.01216accommodation0.01564trekking0.004
17route0.02665comfort0.01217hotel0.01565sightseeing0.004
18travel product0.02566passenger0.01218nation0.01566friend0.004
19overseas travel0.02567building0.01119back pack0.01467travelogue0.004
20destination0.02468scenery0.01120nature0.01468charm0.004
21Switzerland0.02469life0.01121move0.01469National Park0.004
22BTS0.02370Asia0.01122city0.01270Flam0.004
23summer0.02271safety0.01123Estonia0.01171traveler0.004
24tour package0.02272Busan0.01124fjord0.0172road0.004
25winter0.02173flight0.01125Moscow0.0173sky0.004
26welfare0.02174culture0.0126summer0.00974port0.004
27Finnair0.02175the Mediterranean Sea0.0127weather0.00975culture0.004
28Copenhagen0.0276Spain0.0128brand0.00976Scandinavia0.004
29aurora0.01977freedom0.0129aurora0.00877Greenland0.003
30environment0.01978fjord0.0130Saint Peterburg0.00878South Korea0.003
31Helsinki0.01979hotel0.00931introduce0.00879photo0.003
32education0.01880Germany0.00932Europe0.00780schedule0.003
33Alaska0.01881environment-friendly0.00933independent tour0.00781people0.003
34Eastern Europe0.01882Scandinavia0.00934Scandinavian style0.00782story0.003
35fine dust0.01883Scandinavian style0.00835tour package0.00783price0.003
36representative0.01784booking0.00836Reykjavik0.00784destination0.003
37Malta0.01785Christmas0.00837booking0.00785plane0.003
38Bon Voyage0.01786book0.00838July0.00686Western Europe0.003
39independent tour0.01787popularity0.00839bus0.00687church0.003
40Canada0.01688Moomin0.00840Tallin0.00688healing0.003
41Balkan Peninsula0.01689program0.00841waterfall0.00689art gallery0.002
42landscape0.01690author0.00742capital city0.00690toilet0.002
43Australia0.01691furniture0.00743scenery0.00691god of Northern Europe0.002
44President Moon0.01592history0.00744glacier0.00692Eastern Europe0.002
45people0.01593art gallery0.00645practical0.00693food0.002
46forest0.01594society0.00646Viking0.00694interior0.001
47lake0.01595brand0.00447the Little Mermaid0.00695myth0.001
48Bergen0.01596travel to Europe0.00448ferry0.00696Jokulsarlon0
Table 5. Betweenness centrality of top 96 keywords.
Table 5. Betweenness centrality of top 96 keywords.
NewsBlog
RankKeywordsBetweenness CentralityRankKeywordsBetweenness CentralityRankKeywordsBetweenness CentralityRankKeywordsBetweenness Centrality
1Finland5.4949landscape0.3071Norway8.48549plan0.39
2Norway4.79550hotel0.2812Sweden4.76650people0.386
3region3.95251President Moon0.2713Iceland3.58551church0.382
4Sweden3.8452flight0.2714Denmark3.52652Aurora0.332
5nature3.74153Balkan Peninsula0.2665Copenhagen3.20753sky0.316
6traveler3.32954Eastern Europe0.2656Finland2.94754brand0.314
7Europe3.31855purity0.2557nature2.3855god of Northern Europe0.284
8destination3.12156author0.2548nation2.15156information0.275
9nation2.68157comfort0.2529Helsinki2.09257healing0.25
10Scandinavian mood2.37258history0.24310Oslo2.08658Moscow0.247
11Denmark1.99559Australia0.23711design2.02159white night0.237
12city1.52960presidential visit to Northern Europe0.23712cruise1.86460capital city0.227
13travel product1.45661building0.21813summer1.75761airport0.226
14South Korea1.42562Christmas0.21814Scandinavian mood1.74862blue0.222
15New Zealand1.33463Hawaii0.21515Stockholm1.71263Saint Peterburg0.216
16Russia1.12264Finnair0.21416Russia1.65564winter0.216
17tour package1.09765Booking0.21217July1.64165waterfall0.211
18overseas travel0.99666independent tour0.20518accommodation1.59966museum0.209
19summer0.9267program0.20419hotel1.42267ferry0.208
20Western Europe0.91668Scandinavia0.220Europe1.34968back pack0.203
21Cruise0.89669passenger0.19821city1.21369Viking0.193
22Helsinki0.88370Stockholm0.19322move1.15470schedule0.191
23Route0.76771Caucasus0.18823charm1.1271glacier0.186
24life0.72372lake0.17324tour package1.0672Western Europe0.175
25the United States0.71373Aurora0.17225Bergen0.89573world tour0.174
26Oslo0.68674Moomin0.16926story0.86774Tallin0.173
27culture0.67375the Mediterranean Sea0.16527road0.77875trekking0.16
28design0.60476Alaska0.16428independent tour0.67676plane0.153
29winter0.60277Incheon0.14729scenery0.65477pleasure craft0.152
30representative0.58278fine dust0.1430introduce0.61178friend0.144
31environment0.53679art gallery0.12931travelogue0.59579food0.125
32book0.52480brand0.1232bus0.5980price0.102
33Asia0.47781Canada0.1233photo0.56581art gallery0.086
34travel0.45882scenery0.11234fjord0.55482myth0.083
35happiness0.45583gift of nature0.10435tour0.54483Scandinavia0.08
36welfare0.45484boarding0.10436destination0.54184port0.078
37people0.44885fjord0.10237Ring road0.52485toilet0.069
38Iceland0.41686freedom0.08438weather0.5186the Little Mermaid0.06
39Switzerland0.37287BTS0.0839Estonia0.48587Germany0.045
40Scandinavian style0.37188Germany0.07240South Korea0.4888National Park0.045
41relax0.36189furniture0.06641preparation0.46789Reykjavik0.034
42Busan0.35590safety0.06442traveler0.46490Greenland0.033
43Bergen0.3591travel to Europe0.06243sightseeing0.45391Eastern Europe0.031
44forest0.34292education0.0544booking0.44692interior0.029
45Copenhagen0.3493Bon Voyage0.0545recommendation0.42993Flam0.024
46direct flight0.33894Spain0.02146Scandinavian style0.40594practical0.018
47society0.33695environment-friendly0.01947book0.495minimalism0.01
48popularity0.32996Malta0.00348culture0.39496Jokulsarlon0
Table 6. CONCOR analysis results.
Table 6. CONCOR analysis results.
ClusterNewsBlog
1freedom, welfare, nation, education, Christmas, South Korea, popularity, society, safety, Germany, the United States, people, forest, happiness, traveler, Finland, fjordScandinavia, Sweden, Norway, Finland, recommendation, book, tour package, pleasure craft, god of Northern Europe, Oslo, preparation, white night, introduce, Stockholm, museum, art gallery, culture, bus, nation, price, nature, fjord, city, booking, move, Europe, Viking, port, winter, healing, Bergen, Flam
2Scandinavian mood, design, Scandinavian style, furniture, culture, brand, history, Moomin, author, city, Europe, Scandinavia, Denmark, Sweden, Copenhagen, Iceland, Norway, relax, winter, hotel, summer, Oslo, Stockholm, Bergen, city, life, book, environment, travel, travel product, Saint Peterburg, Russia, Cruise, plan, schedule, hotel, independent tour, food, photo, world tour, Estonia, Moscow, Eastern Europe, destination, Denmark, Copenhagen, Helsinki, the Little Mermaid, Tallin, South Korea, Germany
3Finnair, Helsinki, representative, environment-friendly, route, flight, passenger, Russia, Busan, Incheon, lake, AsiaScandinavian mood, Scandinavian style, design, interior, back pack, brand, charm, practical, story, minimalism, people
4President Moon, presidential visit to Northern Europe, travel to Europe, art gallery, buildingsummer, traveler, myth
5BTS, Bon Voyage, program, Hawaii, Malta, New Zealand, auroraIceland, Reykjavik, Ring road, aurora, friend, road, accommodation, scenery, sightseeing
6purity, fine dust, Switzerland, Australia, overseas travel, tour package, independent tour, booking, regiontravelogue, information, capital city, plane, ferry, weather, tour, July, Greenland, trekking, waterfall, National Park
7cruise, the Mediterranean Sea, Canada, Alaska, comfort, boarding, gift of nature, direct flight, destinationairport, blue, sky, glacier, church, toilet
8Western Europe, Spain, Balkan Peninsula, Eastern Europe, Caucasus, scenery, landscapeWestern Europe, Jokulsarlon
Table 7. CONCOR analysis visualization.
Table 7. CONCOR analysis visualization.
NewsBlog
Admsci 12 00109 i001 Admsci 12 00109 i002
Table 8. News density matrix.
Table 8. News density matrix.
12345678
14.184
22.1333.007
31.0340.9614.455
40.7180.7330.63.6
50.2020.5430.012016.19
60.8891.070.380.5113.1119.333
70.5690.930.9260.2441.270.4326.361
80.3781.2050.750.7710.0610.6670.9054.81
Table 9. Blog density matrix.
Table 9. Blog density matrix.
12345678
12.071
21.3502.976
30.5650.4635.018
40.7400.3170.7270.333
50.6220.3700.2020.2227.500
60.4270.4130.0680.1941.6850.667
70.4060.4370.0000.1670.3520.2222.000
80.1560.1670.0910.1670.1110.0830.0000.000
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Park, J.H.; Shin, H.-D.; Seo, Y.-D. What Does Northern European Travel Means to South Koreans? Focusing on Travel Decision Process. Adm. Sci. 2022, 12, 109. https://doi.org/10.3390/admsci12030109

AMA Style

Park JH, Shin H-D, Seo Y-D. What Does Northern European Travel Means to South Koreans? Focusing on Travel Decision Process. Administrative Sciences. 2022; 12(3):109. https://doi.org/10.3390/admsci12030109

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Park, Ji Hyon, Hyung-Deok Shin, and Young-Doc Seo. 2022. "What Does Northern European Travel Means to South Koreans? Focusing on Travel Decision Process" Administrative Sciences 12, no. 3: 109. https://doi.org/10.3390/admsci12030109

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