An Investigation of the Key Attributes of Korean Wellness Tourism Customers Based on Online Reviews

Abstract

With its fast-growing trend, wellness tourism is transforming the client base and service and product offerings, and it is attracting new suppliers. The purpose of understanding the customer experience as portrayed in online reviews is to sustainably maintain customer loyalty and satisfaction. The objective of this research is to identify the critical attributes and their structural relationships to Korean wellness tourism. The study analyzed 24,060 Google-based customer reviews on 11 wellness tourism destinations in South Korea. Following the calculation of word frequencies in a matrix, UCINET 6.0 was utilized to analyze the centrality of the network and perform a CONCOR analysis. Based on the findings of the CONCOR analysis, the review data were sorted into four distinct categories. Following the quantitative analysis led to the identification of six variables that were grouped together through exploratory factor analysis.: wellness, tangible, value, F&B, purpose, and service. Whereas value, F&B, and service negatively affected the satisfaction of guests, the study also revealed that wellness, tangible, and purpose all had positive impacts and contributed to increased trust among wellness tourism customers. In terms of managerial implication, the results will enable wellness tourism destination managers to focus more on improving the factors of value, food, and service.

1. Introduction

The search for a meaningful lifestyle in today’s complex and challenging social landscape is led by modern versions of ancient practices, including meditation, yoga, massage, acupuncture, ayurveda, and naturopathy [1]. The concept of wellness is becoming an integral part of a sustainable future and has proven indispensable to individuals wishing to remain physically and mentally healthy [2]. Wellness concepts are gaining in popularity as a result of a growing number of individuals taking control of their health. A growing wellness trend has brought about significant changes in the tourism and hospitality fields [3]. In these field, there has been an increased need for methods for achieving wellness due to the need for substantial reward improvements in a state of spiritual balance [4].

The wellness tourism sector has experienced substantial expansion over the last few years, reaching $720.4 billion in 2019; despite previous growth, the industry suffered a decline to $435.7 billion in 2020 owing to the pandemic [1]. The industry experienced an 8.1% growth rate recorded in this sector from 2017 to 2019, which means a 50% higher growth rate than the 5.2% experienced by the tourism industry as a whole [1]. As a result of this growth, wellness retreat centers, hotel and resort brands, and specialty travel packages now offer a range of both active and passive wellness services [5]. Moreover, global tourism has seen an increase in wellness demand as people travel abroad to boost their health and wellbeing while enjoying tourism experiences [6,7]. Wellness-oriented businesses have expanded and now offer services such as fitness classes, massages, spas, healthy gourmet meals, and clinical treatments as part of their product offerings [8]. Globally, Asia–Pacific ranks third in terms of expenditures with $6.4 billion in national and international expenditures annually [9].

The historical context of Korean wellness tourism reveals that its origins can be traced back to the Joseon Dynasty (1392–1910), during which hot springs and mineral springs were utilized for medicinal purposes [10]. The popularity of Korean wellness tourism has been on the rise in recent times, with a significant number of visitors coming from neighboring Asian countries, including China and Japan [11]. According to a report by the Korea Tourism Organization, the total count of tourists from China visiting Korea for medical tourism has increased from approximately 18,000 in 2010 to over 350,000 in 2018. The report also notes that the number of Japanese tourists visiting Korea for medical tourism has also been steadily increasing [11]. In terms of stay duration, Korean wellness tourism destinations offer a variety of options for visitors, from short-term weekend retreats to longer stays of a week or more, and many wellness centers and resorts offer comprehensive programs that include accommodations, meals, and a range of wellness activities such as yoga, meditation, and spa treatments [11]. For example, Seoul, the capital city of South Korea, serves as a hub for both modern wellness and traditional Korean medicine practices, with many high-end wellness centers and hotels offering an array of treatments such as acupuncture and cosmetic surgery [11]. South Korea’s success in wellness tourism is evident from its ranking as the 12th largest market in the world and its ability to attract over 3 million international wellness tourists in 2018, which accounted for 5% of the total number of international tourists to the country [1].

Prior studies in tourism and hospitality have primarily concentrated on expanding the utilization of review sites and their effects on measurements of enterprise performance as a result, such as the booking of hotel rooms [12], the intention to book the hotel and trust in the hotel [13], as well as consumer outcomes such as hotel considerations [14]. Until now, this information resource has only been applied to the wellness tourism industry in a limited manner. The study of consumer satisfaction and experience is necessary in order to determine the effectiveness of wellness tourism, especially given its increasing popularity.

The purpose of this study is to evaluate and identify several key influencers within customer networks at wellness tourism destinations by analyzing online guest reviews and categorizing their respective significance. This study investigates the correlation between guest feedback and guest satisfaction using its rating system and provides a description of the evaluation focus and its influencing factors. Furthermore, by analyzing the rating system, this research investigates how guest feedback correlates with guest satisfaction at wellness tourism destinations in South Korea and provides general suggestions about wellness tourism marketing. A number of these recommendations may be beneficial to the industry in the future as well.

2. Literature Review

2.1. Wellness Tourism

Prior to discussing wellness tourism, it is essential to understand health in its broader context in order to grasp the context of wellness. As stated by the World Health Organization [15] “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”. Alternatively, the National Institute Wellness [16] defines “high-level wellness” in the following way: “functioning optimally within the current environment, it is achieved when there are sustainable shifts to behaviors, mindsets, and practices”. Even though wellness and health are two different perspectives, they are interconnected in the pursuit of better health outcomes. Health is seen as the ultimate objective, with wellness serving as the means to achieve it. However, it is important to note that wellness and well-being are separate concepts with subtle variations. Ultimately, wellness plays a critical role in overall health and should not be overlooked. As previously discussed, wellness can be viewed holistically as having dimensions in the domains of physical, mental, emotional, and environmental aspects of an individual’s well-being [17,18]. Scholars and organizations have long recognized the importance of wellness in preventing disease and maintaining health. The concept of wellness tourism has been defined in a number of different ways, with some emphasizing the demand side and others the supply side [19]. The supply and demand for wellness tourism may be defined as follows “the sum of all the relationships resulting from a journey by people whose motive in whole or in part is to maintain or promote their health and well-being, and who stay at least one night at a facility that is specifically designed to enable and enhance people’s physical, psychological, spiritual, and/or social well-being” [20].

Tourism related to wellness and healthcare provides a range of services to travelers, including medical and non-medical options, and can be classified into three different categories [21]. Firstly, wellness and health tourism is a form of tourism designed to ensure health, as well as well-being, to attain spiritual as well as physical relaxation in order to make people more conscious and enthusiastic about participating in society’s various activities [21,22]. Secondly, wellness and health can be defined as a means of attaining mental and physical wellness without requiring medical intervention during vacation [23]. The scholars holding this view argue that tourists travel for health reasons, as well as to find non-tangible values, such as maintaining a healthy balance between mind, body, and spirit [24]. As for the third definition, wellness and health tourism are integrated into the definition, and the focus is more on the end result [21,23]. Studies suggest that a tourist’s decision to choose a wellness and health tourism destination is significantly influenced by the quality of their experience, which can be used to forecast the number of visitors during the development of such tourism [25].

The meaning of wellness varies across different contexts and countries. In Europe, spa relaxation is a conventional practice that contrasts significantly with the spiritual traditions of Asia and the contemporary wellness practices [8]. Wellness tourism during the Middle Ages was mainly focused on achieving a sense of physical and spiritual equilibrium [26]. The nineteenth century saw a rise in the popularity of spa visits in an effort to improve health conditions. Because of this, the healing properties of spas were highly esteemed, leading to the development of numerous spas as tourist attractions [27]. In order to achieve well-being, tourists may choose activities based on their interests, life stages, and motivations: relaxation at luxury spas, life coaching, water activities or amusement parks, sports and fitness, medical treatments, or spiritual or meditation advancement [8]. As the spa industry continues to expand globally, the importance of service quality cannot be overstated [28]. Spas have historically been associated with the pursuit of health, but during the Renaissance, the practice of hot spring therapy evolved from medical treatment to a recreational pursuit [9]. Hot spring resorts and recreation centers were built to cater to the tourism trend of actively seeking to enhance health conditions [26].

The rising demand for wellness services has led numerous scholars to investigate the concept of wellness tourism in the tourism industry. It has become apparent that wellness tourism is an emerging pattern in the global travel and tourism sector, as well as a popular tourist offering for several reasons, due to various factors, such as the growing awareness of health concerns among the affluent population and the focus on physical, mental, or social wellness promotion [29]. Researchers believe customers who prioritize wellness are attracted to services that enhance their well-being and physical appearance, such as addressing discomfort and stress, improving healing, managing weight, and delaying the aging process [30]. This assumption has led to the development of wellness tourism, which focuses on enhancing the psychological, spiritual, and emotional well-being of customers [31].

2.2. Online Reviews and Tourism

The tourism and hospitality industry requires a high-speed and effective technology infrastructure as well as software applications to facilitate tourism development as it enables customer and supplier system integration into a single source to facilitate a wide range of operations, including selection of the products, placing orders, fulfilling orders, order management, payment and reporting, and using a single, intuitive interface [32]. In tourism and hospitality, reviews of online customers—with the growth of technology and Internet usage—and digital data are becoming increasingly available online for a wide range of products and services [33]. Travelers consider reviews and ratings from other tourists when choosing a destination when visiting the website of the destination or using social networking sites [34]. Furthermore, diverse Internet data have been exploited for the purpose of generating new insights for industries and business intelligence. For example, an aspect-oriented comparative analysis was conducted by Gao et al. to analyze the competitive advantages of competing restaurants using online customer reviews [35]. Additionally, Xiang et al. employed online hotel visitor reviews to analyze facets relating to the experience of customers and their relationships with their satisfaction [36]. Several recent studies have demonstrated the significant importance of analyzing customer reviews online for the industry’s development, offering new perspectives and suggestions for research [37]. Studies have shown that customer reviews provide valuable textual data that can be used to identify hidden topics and information important for various business intelligence purposes [38].

Tourism has long been recognized as a domain that relies heavily upon information and communication technology to manage operations on a day-to-day basis [39,40,41]. Nevertheless, Web 2.0 is a relatively recent term that refers to the second phase of development of the World Wide Web [18]. UGC, or user-generated content, is considered a central component of Web 2.0, as it is created by consumers and puts them at the center of information creation [18]. Managing UGC is best accomplished by actively promoting it through specialized user-generated marketing channels, such as the word-of-mouth protocol. Tourism is greatly impacted by word-of-mouth, fundamentally due to the intangible nature of tourism services [42]. As a result, online word-of-mouth can reduce the amount of chance, ambiguity, and uncertainty associated with a product or service [43]. Although the foregoing studies examine these statistics sources as components that impact the perceived image of a tourism destination, there is an absence of explanation regarding how this affects destination trust, particularly when it comes to wellness, where uncertainty and adverse effects are prevalent.

The service offered is unique in that it involves both the purchase and consumption of intangible goods simultaneously, setting it apart from typical services [44,45]. Due to the potential risks involved, customers are increasingly looking for precise and useful information that can help them minimize the chances of failure. These days, in addition to the limited information that they receive from those around them, people will be able to hear more varied and specific opinions via the Internet, allowing them to compare and contrast different points of view from a wider group of experienced individuals [46]. Customers tend to place greater reliance on reviews from fellow customers when buying experience products compared to general merchandise due to the nature of the products and the importance of first-hand experiences in informing purchase decisions [47,48]. Compared to information provided by companies, online reviews tend to be more detailed, provide a more accurate representation of the product or service, and are generally perceived as more trustworthy [49]. Thus, customers are more sympathetic to their own perspectives than to the advertising content offered by the company.

2.3. Big Data Analytics

The phrase “big data analysis” pertains to the examination of vast quantities of both organized and unorganized data available on the Internet [50]. The popularity of sentiment analysis for analyzing business performance is growing rapidly, particularly in analyzing large online datasets [51]. The purpose of this technique is to determine how users feel about a product or service [52], and the results of these surveys can be useful to managers in determining what the true feelings of customers are. Through sentiment analysis, researchers have been able to draw meaningful conclusions from the massive, unstructured online data, partly owning to the rapid technological advancements [53]. Recently, sentiment analysis has been applied to hotel customer satisfaction data, which is a relatively new method of analyzing data.

Questionnaires have been the primary method for conducting customer satisfaction research [54,55]. Several techniques were employed during the study to perform sentiment analysis, such as utilizing both R language and RStudio software. Despite their similarity, they are not identical, and therefore, a clear distinction must be made before using them together [56]. As stated on the GitHub RStudio site, R is a programming language that is optimized for statistical computing, whereas RStudio is an IDE that allows users to work with R seamlessly. Aside from this, while the R language can function on its own, it is recommended to use RStudio in conjunction with R for optimal results [57]. Borgatti, Freeman, and Everett have stated that UCINET is a software program that is designed to analyze network relationships, especially in social science research [58]. Hanneman and Riddle’s book also introduced the UCINET dataset as an innovative method for storing datasets. In the example, two different files were included: file.##h, a dataset containing information about the header; and file.##d, which contained lines of data [59]. Researchers in the hospitality field employ the UCINET software for conducting sentiment analysis [60]. The general concept of semantic network analysis involves studying the semantic structure of information by analyzing the correlation between the frequency of words and the order in which they appear in a sentence without assigning specific meanings to them; this type of analysis often involves examining the frequency of keywords on the web and evaluating the relationship between these keywords and the network structure, as well as their link status [61].

Based on previous research [62,63,64], the network analysis literature has discussed two basic centralities to determine node power in the network: the Eigenvector centrality and the Freeman-degree centrality. Eigenvector centrality is based on the concept that degree differences propagate throughout a network, which enhances the concept of connective centrality by taking into account not only the number of words linked, but also the importance of those connections [65]. Freeman’s centrality is defined as how many direct relationships a word has, the greater the degree of centrality, and the more connections that a word has with other nodes in the network [66]. In terms of Eigenvector centrality, it is based on the fact that differences in degree may propagate throughout a network and thus enhances the concept of connective centrality by taking into account not only the number of words connected, but also the significance of a connected relationship [65].

A CONCOR analysis was conducted to cluster the identified centralized words and to extract different attributes describing the customer experience [67,68]. By using the CONCOR analysis, one can analyze text in terms of meaning structure, using a “keyword” co-occurrence matrix to uncover hidden subgroups and relationships among the groups. Subjects are grouped by combining the inverse and matrix of the subjects [69]. In order to determine the vital words based on semantic network analysis, quantitative analysis was performed using exploratory factor analysis and linear regression analysis. As a result, a quantitative analysis of the variables reflecting customer experience and their relationship with customer satisfaction was conducted. This process included the use of a dummy variable to convert qualitative data to quantitative data [36]. Based on the previous literature, and the conducted linear regression analysis in this study, the following hypotheses were formulated:

Hypothesis 1 (H1).

Wellness has a positive impact on customer satisfaction at South Korean Wellness Destinations.

Hypothesis 2 (H2).

Tangible has a positive impact on customer satisfaction at South Korean Wellness Destinations.

Hypothesis 3 (H3).

Value has a positive impact on customer satisfaction at South Korean Wellness Destinations.

Hypothesis 4 (H4).

F&B has a positive impact on customer satisfaction at South Korean Wellness Destinations.

Hypothesis 5 (H5).

Purpose has a positive impact on customer satisfaction at South Korean Wellness Destinations.

Hypothesis 6 (H6).

Service has a positive impact on customer satisfaction at South Korean Wellness Destinations.

3. Methodology

The utilization of big data in this study was driven by limitations in the survey method’s ability to explore a more diverse set of outcomes. In this study, both qualitative and quantitative methods were used. This study employed three primary procedures for data analysis. As can be seen in Figure 1, three processes were used in this study. To begin with, the data-containing sentences were subjected to pre-processing using text mining techniques. During this stage, the data were divided into individual words based on their frequency. The data collection process was conducted using a sophisticated platform for data crawling and text mining known as SCTM 3.0, which is commonly referred to as smart crawling and text mining. Next, to analyze the data, RStudio was used to identify the most frequently used words and create a matrix. Then, we conducted two types of analyses—Freeman’s degree and Eigenvector—to understand which words were most important in the dataset. We also used UCINET to analyze the importance of these words and CONCOR analyses to see how they were related to each other. This helped us to understand people’s perceptions and understandings of wellness tourism destinations in South Korea by showing us which aspects were most important and how they were related to each other.

The top-frequency words selected by the author underwent semantic network analysis, which was carried out with the specific criterion of considering only those words relevant to the research topic. Generally, semantic network analysis is performed using a co-occurrence matrix (keyword by keyword) of words with the highest frequency [70]. Additionally, for analyzing the data and visualizing the results, the study employed Ucinet 6.0 along with Netdraw as a package. As a final step, a quantitative analysis was conducted based on factor analysis as well as linear regression analysis to determine the factors that reflect customer experience and their relationship with customer satisfaction. A customer satisfaction rating of 1 star representing the most dissatisfied customer, and a customer satisfaction rating of 5 stars representing the most satisfied customer was used in this study.

To identify the factors that influence customer satisfaction levels, review data from wellness destinations in South Korea were collected from Google Travel. There are 21 destinations listed on the Korea Tourism Organization Official website (www.visitkorea.com; accessed on 1 February 2023). To refine the dataset, online reviews that only presented a score without any written feedback were deleted; the clean review number stood at 24,650. The study exclusively focused on destinations that had received more than 310 reviews (310 reviews was the mean number of 24,650—the total number of all the reviews), thus only 11 wellness destinations were selected in this study, with a total number of 24,060 reviews, as shown in

Table 1. Number of reviews.

No	Destination	Number of Reviews	Average Satisfaction
1	Hwadam Botanical Garden	6447	4.3
2	Damyang Juknokwon	4743	4.4
3	Suncheon Bay Wetlands	4494	4.4
4	Hanwha Resort Geoje Belvedere	3127	4.4
5	Jeju Bijarim Forest	2056	4.6
6	Seolhaewon	816	4.4
7	Park Roche Resort & Wellness	653	4.5
8	Tongyeong Cable Car	595	4.5
9	The We Hotel Wellness Center	490	4.4
10	Healience Seonmaeul	329	4.4
11	Forest Resom Have 9 Healing Spa	310	4.3
	Total	24,060
	Average Rating	4.41

.

4. Result

4.1. Data Pre-Processing

From the text mining process, 24,060 reviews with 640,820 word frequencies were collected and calculated. To assess the level of customer satisfaction,

Table 2. Summary of overall satisfaction rating.

Rating	Frequency	Percent	Cumulative Percent (%)
1	377	2%	2%
2	1222	5%	7%
3	2265	9%	16%
4	6002	25%	41%
5	14,194	59%	100%
total	24,060	100%
Average Rating		4.41

presents a breakdown of the frequency of numerical ratings spanning from 1 star to 5 stars. The average satisfaction rating was 4.41 out of 5 stars, and 84% of reviewers indicated that they were very satisfied with their experience in South Korea’s wellness tourism destinations, posting a rating of 4 stars or 5 stars. According to the data, 9% of customers rated their stays as unsatisfactory, with a rating of 3 stars. Moreover, 7% of customers gave a rating of either 1 star or 2 stars, which suggests they were not content with their experiences.

The frequencies of the words’ appearances in the valid comments were used as the basis for determining their rankings.

Table 3. Frequency of the top 60 keywords of customers reviews.

Rank	Words	Freq	Rank	Words	Freq
1	good	13,957	31	around	904
2	relax	6592	32	family	885
3	hotel	6562	33	fee	816
4	room	5027	34	feel	796
5	place	4780	35	pretty	766
6	healing	3375	36	children	730
7	calm	3361	37	expensive	725
8	nature	3266	38	restaurant	715
9	forest	2898	39	enjoy	672
10	rest	2871	40	pool	626
11	walk	2805	41	garden	610
12	like	2327	42	hot	556
13	quiet	2276	43	summer	550
14	nice	2261	44	admission	513
15	mental	2164	45	worth	457
16	scenery	1871	46	park	419
17	food	1481	47	stay	419
18	beautiful	1476	48	bay	416
19	time	1392	49	staff	396
20	great	1347	50	mountain	392
21	autumn	1344	51	fall	391
22	people	1245	52	season	390
23	resort	1214	53	sunset	384
24	recommend	1174	54	delicious	381
25	best	1138	55	spring	379
26	clean	1130	56	eat	371
27	facility	1127	57	price	366
28	view	1083	58	spa	290
29	comfortable	981	59	breakfast	270
30	cool	911	60	atmosphere	244

displays the extraction and sorting of the 60 most frequently occurring words that were indicative of the customer experience. The top frequency words were selected based on their close relationship to the subject of the research [67]. The ranking of words was determined by their proportionate frequency in the comments, which was calculated using the overall frequency of the words.

Table 3 displays the high visibility of words such as “good”, “relax”, “hotel”, “room”, and “place”, which appeared frequently in the comments. For example, “good” was mentioned 13,957 times, “relax” 6592 times, “hotel” 6562 times, and so on. Figure 2 illustrates the network of the most frequent words, with intertwined and complex connections between them.

Topics could be identified manually from texts, but overlapping words between topics were challenging to comprehend [71]. The word segmentation list was inadequate in reflecting certain aspects of customers’ experiences, necessitating the use of a data-driven method to discover the internal meaning and relationship among the words [67]. As a result, a semantic network analysis of these high-frequency words was executed to acquire a more comprehensive understanding of the latent meaning conveyed in the textual reviews [67].

4.2. Semantic Network Analysis

Semantic network analysis is a type of network analysis that focuses on paired associations based on shared meaning rather than behavior or perceptions of communication [72]. This method involves examining the interconnections among concepts that are situated in close proximity to one another [73]. By linking these concepts together, semantic network analysis offers valuable insights into texts, allowing researchers to gain a deeper understanding of the underlying meaning and connections between different ideas [74]. Freeman’s degree centrality and Eigenvector centrality were employed to gauge the proximity of each word to the center of the network in order to analyze these top frequency words [75]. Semantic network analysis is a powerful tool for uncovering the hidden meanings and relationships among concepts within a given context. Furthermore, this technique enables an examination of word frequency and clustering to comprehend how a specific word impacts relationships within a group, as well as the inter-group relationships [61,76]. As a result, semantic network analysis provides a powerful theoretical and methodological basis for describing the semantics of online reviews [70].

In a general sense, Freeman’s degree centrality calculates the level of interconnectivity among nodes within a network, while Eigenvector centrality is a valuable tool for identifying the most significant nodes within a network [62]; at the end of the analysis, clustering analysis was conducted in conjunction with the CONCOR analysis. CONCOR (CONvergence of iterated CORrelation) is a technique used to identify the degree of similarity between keywords by performing correlation analysis repeatedly. Furthermore, this technique is capable of identifying blocks of nodes by utilizing the correlation coefficient between metrics of concurrent keywords. As a result, clusters are formed that include keywords that are similar [77]. Hence,

Table 4. Comparison of word frequency and centrality.

Factor	Frequency		Degree		Eigenvector			Frequency		Degree		Eigenvector
	Freq	Rank	Coef.	Rank	Coef	Rank		Freq	Rank	Coef	Rank	Coef	Rank
good	13,957	1	21.9556942	1	0.404197127	1	around	904	31	3.980383158	24	0.080615662	23
relax	6592	2	17.27293396	2	0.335057944	2	family	885	32	3.053624868	33	0.06428121	33
hotel	6562	3	15.91495419	3	0.30321309	3	fee	816	33	3.058741093	32	0.05710239	38
room	5027	4	15.53708839	4	0.301844865	4	feel	796	34	3.228305578	30	0.066443041	32
place	4780	5	13.01481533	5	0.25941357	5	pretty	766	35	2.270119429	39	0.045719009	42
healing	3375	6	10.32955456	6	0.201851308	6	children	730	36	3.042661667	35	0.061191104	36
calm	3361	7	8.99861908	9	0.180179372	9	expensive	725	37	2.634829521	37	0.062477779	35
nature	3266	8	8.36786747	11	0.165996537	12	restaurant	715	38	3.045585155	34	0.059211947	37
forest	2898	9	8.088670731	12	0.167633936	10	enjoy	672	39	3.07847476	31	0.063117079	34
rest	2871	10	9.318014145	7	0.181923479	8	pool	626	40	2.60632515	38	0.049515173	40
walk	2805	11	9.005927086	8	0.195045128	7	garden	610	41	2.1897223	41	0.043981124	43
like	2327	12	6.457342148	16	0.141071886	14	hot	556	42	1.849862218	45	0.036298826	47
quiet	2276	13	7.485692978	13	0.155085385	13	summer	550	43	2.088129759	44	0.039665788	45
nice	2261	14	6.501194954	15	0.137619883	16	admission	513	44	2.091053247	43	0.039167475	46
mental	2164	15	8.515505791	10	0.166269615	11	worth	457	45	1.50415504	54	0.028161401	55
scenery	1871	16	6.845440388	14	0.139437005	15	park	419	46	1.730728507	47	0.03305877	50
food	1481	17	5.990308285	17	0.11733795	17	stay	419	47	2.107132673	42	0.041279174	44
beautiful	1476	18	4.111941814	23	0.083156928	22	bay	416	48	1.254193425	57	0.025184641	58
time	1392	19	5.227267742	18	0.104405515	18	staff	396	49	1.595515251	52	0.077659726	25
great	1347	20	4.224497795	21	0.092316926	20	mountain	392	50	1.704416752	48	0.069867291	31
autumn	1344	21	4.203301907	22	0.079113893	24	fall	391	51	1.40621686	55	0.026256492	57
people	1245	22	4.633791447	19	0.094311848	19	season	390	52	1.670796156	49	0.034129128	49
resort	1214	23	3.411026001	29	0.077552229	26	sunset	384	53	1.055393577	59	0.021625007	59
recommend	1174	24	4.444493294	20	0.086741425	21	delicious	381	54	1.575781465	53	0.030103663	54
best	1138	25	2.860671997	24	0.053548742	39	spring	379	55	1.663487315	50	0.031149046	53
clean	1130	26	3.515542269	27	0.072417051	29	eat	371	56	1.763618112	46	0.035691302	48
facility	1127	27	3.854671478	26	0.076274633	28	price	366	57	1.615980029	51	0.032140009	51
view	1083	28	3.425643682	28	0.070797436	30	spa	290	58	1.186952353	58	0.031503849	52
comfortable	981	29	3.87952137	25	0.07693769	27	breakfast	270	59	1.271734595	56	0.02775248	56
cool	911	30	2.219688416	30	0.047260575	41	atmosphere	244	60	0.966225922	60	0.019759074	60

presents a comparison of the centralities (Freeman’s degree and Eigenvector centrality) with the frequencies of the top 60 most frequently used words. In terms of their centrality ranking, Freeman’s degree centrality and Eigenvector centrality exhibit similar distribution patterns, with their centrality values being nearly equal.

According to the results, “good”, “relax”, and “hotel” recorded the highest degree and eigenvector centrality. Regarding the measures of degree centrality and eigenvector centrality, the words “calm”, “nature”, and “forest” were ranked lower than in terms of frequency. Moreover, the words “rest” and “walk” were rated higher in terms of the degree of centrality and the eigenvector centrality. The data revealed that despite reviewers using some words less frequently, these words still held significant weight and contributed to the network’s overall structure and impact. Overall, centrality analysis identifies significant words within a semantic network in order to facilitate a more precise understanding of the hidden meaning and relationships among these nodes.

CONCOR-enabled semantic network analysis revealed identifiable clusters that were associated with reviewers’ experiences of wellness tourism destinations in South Korea. The resulting clusters generated from the semantic networking were named based on the CONCOR clustering method, which took into account the keywords and their respective meanings as expressed in the original reviews. A visual representation of the CONCOR analysis is shown in Figure 3. To facilitate the identification of words belonging to each cluster, the clustered words and those of note are compiled into

Table 5. Summarization of words from CONCOR Analysis.

	Extracted Words	Significant Words
Hospitality-oriented Cluster	admission/breakfast/eat/best/clean/ delicious/expensive/facility/fee/food/pool/recommend/ restaurant/room/staff/stay/worth	breakfast/eat/best/clean/ facility/fee/food/pool/ restaurant/room/staff/stay
Destination-oriented Cluster	around/bay/children/family/forest/ garden/hotel/mountain/park/ people/place/resort/spa/sunset	bay/forest/garden/hotel/ mountain/park/place/ resort/spa
Nature-oriented Cluster	atmosphere/autumn/beautiful/cool/fall/hot/nature/scenery /season/spring/summer/view	autumn/fall/nature/scenery/ season/spring/summer
Wellness-oriented Cluster	calm/comfortable/enjoy/feel/good/ great/healing/like/mental/nice/ pretty/quiet/relax/rest/time/walk	calm/comfortable/healing/ mental/quiet/relax/rest

.

As a result, the analysis of online customer reviews generated four distinct clusters. First was the “Hospitality-oriented Cluster”, which included terms such as “Room”, “Staff”, “Stay”, “Facility”, etc. These words were expressed in reviews in the following manner: “The room was clean, overall the facilities and the staff were amazing”, “I recommend staying here. The food in the restaurant was so delicious!”, “The admission was quite expensive but I think it was worth it”, etc. These expressions are hospitality related.

The second cluster, “Destination-oriented Cluster” presented words such as “Hotel”, “Place”, “Spa”, and “Resort”. These words were associated with destinations. There were several examples of them in the reviews, including the following: “The deck path is calm and made for walking through a peaceful forest. Baby strollers are fine. It looks like a perfect family vacation destination.”, “In the morning, there will be fewer people at the bay, whereas in the evening, there will be a lot of people watching the sunset.”, etc. The third cluster was “Nature-oriented cluster”, covering words such as “Nature”, “Scenery”, “Season”, and more, which are related to nature. For example, “The autumn leaves are especially beautiful, but there are many things to do in any season”, and “The scenery is equally stunning in every four seasons”.

The last cluster was named “Wellness-oriented Cluster”, since it contained many words related to wellness, such as “Calm”, “Healing”, “Mental”, “Relax”, “Rest”, etc. These words were also of relatively high frequency (e.g., “Relax” was used 6592 times and ranked at 2, “Healing” and “Calm” were used 3375 and 3361 time and ranked at 6 and 7, respectively. The following words were expressed in customer reviews as follows: “A great place for healing, very peaceful and calm, good for mental health”, “If you live in the metropolitan area, you must visit here. This is really calm-maintained, great for walking or resting. It’s quiet and nice.”, etc.

4.3. Factor Analysis

Factor analysis is a statistical technique that can reveal the underlying patterns and relationships among keywords based on the variances of keywords within an online review. Using oblique rotation, factor analysis reduces numerous variables into smaller factors. For the extraction of factors, common factorial criteria were used, and the minimum factor loading in the final model was set at 0.400. Additionally, the factors must have Eigenvalues greater than 1.0 and must explain a substantial portion of the variance. According to the results, 22 key words within six factors contributed 91.020% of the variance. These factors were then used as independent variables to derive the customer satisfaction factors that were important. According to

Table 6. Result of the factor analysis.

Factor	Words	Factor Loading	Eigen Value	Cum. Variance
Wellness	healing	0.932	4.397	19.988
	calm	0.917
	relax	0.902
	mental	0.899
	spa	0.888
Tangible	pool	0.918	4.133	38.776
	hotel	0.893
	room	0.882
	resort	0.876
	park	0.859
Value	worth	0.995	3.768	55.904
	fee	0.928
	feel	0.925
	price	0.913
F&B	restaurant	0.977	2.954	69.332
	breakfast	0.972
	food	0.966
Purpose	walk	0.935	2.834	82.214
	enjoy	0.929
	stay	0.923
Service	staff	0.955	1.937	91.020
	facility	0.940
KMO (Kaiser Meyer Olkin) = 0.666
Bartlett chi-square (p) = 1473.065 (p < 0.001)

, the KMO index was 0.666, close to 0.700, meaning that it was fundamentally based on the recommended value. In Bartlett’s sphericity test, χ² was 1473.065, with the overall significance of the correlation matrix p < 0.001, which in other words indicated that the use of factor analysis was appropriate for this study.

4.4. Linear Regression Analysis

After conducting the factor analysis, a linear regression analysis was performed to examine the relationship between guest experiences and satisfaction, which can be observed in

Table 7. Result linear regression analysis.

Model	Unstandardized Coef		Standardized Coef	p	t
	B	Std. Error	Beta
(Constant)	2.480	0.121		0.000	20.551 ***
Wellness	0.460	0.122	0.259	0.000	3.774 ***
Tangible	0.251	0.122	0.142	0.045	2.062 *
Value	−1.432	0.122	−0.807	0.000	−11.747 ***
F&B	−0.341	0.122	−0.192	0.008	−2.798 **
Purpose	0.250	0.122	0.141	0.046	2.055 *
Service	−0.096	0.122	−0.054	0.436	−0.786

Dependent variable: Customer Satisfaction (CS); R² = 0.444; adjusted R² = 0.443; F = 633.654; * p < 0.05, ** p < 0.01, *** p < 0.001.

. The linear regression analysis had six independent variables: Wellness (W), Tangible (T), Value (V), F&B (FB), Purpose (P), and Service (S). All the variance explained by six variables were 44.44% (R² = 0.444). All variables were significant at 0.01. However, three factors, namely, “Value” (ß = −0.807, p < 0.001), “F&B” (ß = −0.192, p < 0.001), and “Service” (ß = −0.054, p < 0.001) were negative impacts to the customer average satisfaction rating based upon its standardized coefficient values.

There was a number of reviews written by guests that indicated a lack of satisfaction with the factor “Value”, which ranked first for the beta value with a negative impact on customer satisfaction, such as “It doesn’t seem worth it to pay more to use the other facility when it’s already so pricey to stay here”. Price is perceived by the customer in accordance with quality, resulting in satisfaction or dissatisfaction [77]. Customers are satisfied when they perceive the price as fair, but perceived price fairness may be negatively affected by the customer’s vulnerability [78,79].

There was also a number of reviews written by guests that indicated a lack of satisfaction with the “Food and Beverage”, and “Service” factors. Some examples of reviews are as follows: “We were unable to get information due to the fact that the staff could not speak English well”. Additionally, a lack of variety of food was mentioned quite often when visiting destinations with restaurants: “there were not a lot of food options.”

There were also positive impacts on customer satisfaction from the three other variables, namely, “Wellness” (ß = 0.259, p < 0.001), “Tangible” (ß = 0.142, p < 0.05), and “Purpose”, as indicated by reviews such as the following: “I enjoyed the spa very much, it was very relaxing and calming, exactly what I needed for my mental and physical health”; “The room was very comfortable, I loved the pool, I’d definitely recommend this resort”; “Walking around the park was very healing and enjoyable”. By focusing on positive aspects of Wellness, Tangible, and Purpose, more guests are inclined to trust wellness tourism destinations.

5. Discussion

In this research, big data were analyzed through semantic network analysis of 60 words. The analysis emphasized the use of centrality measures (Freeman’s degree centrality and Eigenvector centrality), proximity analysis, and CONCOR analysis. For visualization purposes, UCINET 6.0 packaged with Netdraw was employed. “Good” was ranked as the most frequent word. CONCOR analysis was conducted in order to categorize Internet users’ understandings and awareness of the Internet. As a result of these data, the implications for empirical applications were clarified. Eleven wellness tourism destinations in South Korea were selected for this study. In the CONCOR analysis, there were four clusters, which were “Hospitality”, “Destination”, “Nature”, and “Wellness”.

The cluster related to destinations included the word “spa”, but the quantitative analysis revealed distinct factors that impacted guest satisfaction/dissatisfaction. The outcomes of the linear regression analysis revealed that the wellness factor had the highest beta value with a positive impact, indicating its significance in determining guest satisfaction for Korean wellness tourism. This factor comprised keywords such as “healing”, “calm”, “relax”, “mental”, and “spa.” The words identified through frequency analysis were closely related to the research topic of wellness tourism and were positioned accordingly in the analysis results. Thus, even though the word “spa” is a destination-oriented word, it had a significant impact on the wellness factor. This study could lead to new insights into the research of wellness tourism by using online guest reviews. The purpose of this study was to gather qualitative and quantitative data to support research and development in the field of wellness tourism. The present study’s findings emphasize the crucial attributes of Korean wellness tourism as perceived by its customers through online reviews. Based on this, sharing recommendations and examples with managers of wellness tourism destinations could be advantageous in attracting and retaining clients in the future.

The Global Wellness Institute (GWI) conducted an assessment of the wellness economy in its 2018 Global Wellness Economy Monitor. The world has undergone significant changes and disturbances in recent years, experiencing a period of robust growth (2017–2019) followed by the impact of the COVID-19 pandemic in 2020. It is particularly important and compelling to provide an update on the global wellness economy now, as 2020 has become a watershed year that will forever divide history into “before” and “after” COVID-19. The pandemic certainly had a negative impact on wellness tourism. Likewise, with the wellness tourism destination managers, the Korean government is making significant efforts to revitalize its wellness tourism industry. Based on the factor analysis, the examined factors such as Value, Food and Beverage, and Service, which dissatisfied customers, would be the basis for developing a marketing strategy relative to these factors. Therefore, managers should devote more attention to improving these aspects, since the majority of their customers were more concerned with these dimensions, and in this case were dissatisfied.

The three other variables (Wellness, Tangible, and Purpose) are significant contributors to improving the quality of wellness tourism. The analysis of reviews conducted in this study can serve as a valuable foundation for formulating effective marketing strategies for wellness destinations in South Korea. The findings of this study can guide wellness destinations in South Korea on areas that need improvement and maintenance. In addition to analyzing the customer reviews of their competitors, managers can use this method to benchmark their customer satisfaction levels against those of their competitors. The reviews analyzed in this study can be utilized to inform strategic marketing decisions aimed at achieving a sustainable competitive advantage by identifying areas that require improvement and maintenance. Although the wellness industry is expanding at a fast pace globally, there has been minimal research conducted on this particular sector. In a crowded market, broad conceptualizations of well-being may provide significant opportunities for destinations to gain competitiveness and thus prolong their lifespan [80]. Most of the research on wellness tourism has centered on tourists who partake in activities such as visiting spas, using health products, or receiving medical services [81], and although there has been considerable research on traditional health care and recreational health care, only a few studies have been conducted regarding wellness hotels, which are considered to be one of the most prominent segments of wellness tourism in the engagement of tourists with spas, health products, or medical service [82]. Despite the limited research on wellness tourism, it is evident that this sector plays a significant role in promoting the well-being of many individuals [83].

There has been considerable research conducted in the field of wellness tourism and guest satisfaction. However, there is still no precedent for studies on wellness tourism in South Korea that utilize big data. The aim of this study was to investigate the various aspects that contribute to the customer experience and establish a correlation between customer experience and satisfaction. The research data sources selected for this study were wellness tourism destinations, and online customer reviews of these places were collected as part of the research process. Additionally, the integration of text mining, semantic network analysis, and quantitative analysis techniques was utilized to reveal implicit meanings and connections among the gathered reviews.

Previous studies that analyzed online customer reviews have used smaller datasets compared to the one used in this study. For instance, one study applied the same method but used less than 5000 reviews. For example, one illustration of the study’s specificity is its focus on online reviews of a single theme hotel, namely, the Hong Kong Disneyland Hotel [33] with 655 reviews. Research that examined customer reviews and satisfaction with an upscale hotel in Dubai [51] included 2051 reviews, and another study that analyzed cruiser reviews to understand their experience and satisfaction [70] collected 665 reviews for cruise tourism in Asia. However, small sample sizes have a greater likelihood of exhibiting selection biases and estimation biases. This results in incorrect analysis results that may differ from the real situations and generalized conclusions. Previous studies that used smaller datasets revealed that some of the categories that were identified may be typical only of a certain destination and may not apply to the entire market or industry. As a result, a larger sample size is recommended for future research [33]. In order to avoid these problems, we used a larger dataset in this paper (24,060 reviews were analyzed). In addition, this study did not focus on one destination but on 11 destinations recommended by the Korea Tourism Organization, thereby making it more generalized to the entire wellness industry market in South Korea.

In concluding this study, it is important to consider and acknowledge some limitations that should be approached with caution. The intricacy and free-form nature of human language necessitated the use of more exact and scientific techniques to handle the data. Analyzing data in human language is challenging due to its intricate and unstructured nature, requiring more rigorous and scientific methods. Semantic network analysis proved to be among the most effective approaches in this study [75]. However, to gain a deeper understanding of our customers, advanced technology should be utilized to uncover any hidden information present in the text. In future research, it would be beneficial to use more advanced techniques based on data to analyze these reviews. However, it should be acknowledged that there may be a lack of objectivity, as data coding and interpretation are subject to the researcher’s knowledge, skill, and subjective judgment [84].

6. Conclusions

There were several steps involved in this study. A frequency analysis was conducted to determine the most influential words from all keywords. Subsequently, 60 words with the highest frequency were analyzed for degree and eigenvector centrality to identify their importance. The subsequent step in this study was to conduct a CONCOR analysis, which was employed to cluster the keywords. The findings of this analysis revealed that the top 60 keywords could be classified into four distinct groups. These keywords were grouped into four clusters, namely, “Hospitality”, “Destination”, “Nature”, and “Wellness”. In addition to keyword groups, UCINET’s NetDraw function was used to visualize nodes and networks.

After conducting a factor analysis, 22 words were divided into six categories, namely, “Wellness”, “Tangible”, “Value”, “F&B”, “Purpose”, and “Service”. There were six independent variables in the linear regression: Wellness (W), Tangible (T), Value (V), Food & Beverage (FB), Purpose (P), and Service (S). The total variance explained by the six variables was 44.4%, (R² = 0.444). All variables were significant at 0.01. In terms of average guest satisfaction ratings, three factors, namely, “Value”, “Food and Beverage”, and “Service”, were negatively impacted by their standardized coefficient values. The findings of this research paper indicated that hypotheses 1, 2, and 5 were supported by the data, whereas hypotheses 3, 4, and 6 were not supported.

A wellness destination’s management team should pay more attention to dining, facilities, and prices in order to make visitors feel that the services provided are worthwhile, for example, focusing on improving the food quality, the cleanliness of the environment, and the friendliness of the staff. In contrast, all three variables of “Wellness”, “Tangible”, and “Purpose” positively influenced guest satisfaction. Wellness tourism destination managers in South Korea may utilize this finding to improve aspects in which guests remain dissatisfied. These details can be useful to guests as they provide information about quality and previous experiences.