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Article
The Impact of Hotel Customer Experience on Customer
Satisfaction through Online Reviews
Yae-Ji Kim 1 and Hak-Seon Kim 2,3, *
1 School of Global Studies, Kyungsung University, 309 Suyoungro, Nam-gu, Busan 48434, Korea;
yaeji76@ks.ac.kr
2 Wellness & Tourism Big Data Research Institute, Kyungsung University, 309 Suyoungro, Nam-gu,
Busan 48434, Korea
3 School of Hospitality & Tourism Management, Kyungsung University, 309 Suyoungro, Nam-gu,
Busan 48434, Korea
* Correspondence: kims@ks.ac.kr; Tel.: +82-51-663-4473
Abstract: With the growing popularity of the internet, customers can easily share their experiences
and information in online reviews. Consumers recognize online reviews as a useful source of infor-
mation prior to consumption, and many online reviews influence consumer purchasing decisions.
Understanding the customer experience in online reviews is thus necessary to maintain customer
satisfaction and repurchase intention for the sustainable development of the hotel business. This
study assessed the fundamental selection attributes of customers from online reviews reflecting the
hotel customer experience, and investigated their association with customer satisfaction. A total of
8229 reviews were collected from Google travel websites from December 2019 to July 2021. Text min-
ing and semantic network analysis were adopted for big data analysis. Factor and regression analyses
were then used for quantitative analysis. Based on linear regression analysis, the Service and Dining
factors significantly affected customer satisfaction. Service is a critical selection attribute for cus-
tomers, and the provision of more particular services is necessary, especially after COVID-19. These
results indicate that understanding online reviews can provide theoretical and practical implications
Citation: Kim, Y.-J.; Kim, H.-S. The for developing sustainable strategies for the hotel industry.
Impact of Hotel Customer Experience
on Customer Satisfaction through Keywords: online review; customer experience; customer satisfaction; selection attribute; text mining;
Online Reviews. Sustainability 2022, semantic network analysis
14, 848. https://doi.org/10.3390/
su14020848
2. Literature Review
2.1. Electronic Word of Mouth
E-commerce has a strategic emphasis for business and consumers, and WOM has been
reconceptualized as eWOM since traditional, face-to-face WOM is changing into eWOM, as
consumers can obtain information regarding products or services online before making
a purchase decision [20,21]. When purchasing intangible products or services, consumers
tend to rely more on eWOM for products or services they have not used or experienced
before [22], as this helps customers obtain specific information influenced by customers’
selection attributes and shared personal experiences, opinions, photos, hotel reviews, and
vacation suggestions [23].
Online review websites such as Tripadvisor serve as an essential platform for con-
sumers to share purchasing experiences and express their opinions about products and
services [24]. Reviews exchanged among consumers contain information about the user’s
experience and how that experience is perceived [25], such that customers are more likely to
trust eWOM than information provided by sellers [22]. By reading other people’s reviews,
potential consumers better construct their interpretation of the product and become more
aware of the risks to their transactions. Vermeulen and Seegers [26] found that consumers’
exposure to online reviews improved the probability of booking a hotel. Stringam, Gerdes,
and Vanleeuwen [27] analyzed reviews on Expedia.com, an online travel agency platforms,
and found that the overall satisfaction evaluation had a high correlation between recom-
mendation intentions. Ban and Kim [19] analyzed the online reviews on Skytrax.com
and quantitatively identified the relationship among six evaluation factors (seat comfort,
staff, F&B, entertainment, ground service, and value for money), customer satisfaction,
and recommendations.
This study thus investigates customer experience through text-mining analysis of
reviews of hotel products, which are representative experience goods greatly affected by
online reviews.
to customer satisfaction, and customers who positively evaluate the experience can be said
to feel satisfied [29].
Customer satisfaction is a complex experience in the hospitality industry, and assessing
what the customer has experienced is complicated [30]. Focusing on customer experience
and satisfaction has inevitably increased as the market has changed from a producer-driven
market to a buyer-driven one [20]. Customer-satisfaction management is the only strategy
that can respond to such market changes. Corporate marketing activities have made it
a fundamental goal to focus on customer satisfaction, which, through customer experience,
can increase customer loyalty, repurchase intention, positive WOM, and consequently,
contribute to higher profitability [31].
Customers prefer and think about hotel selection attributes, which play a critical role in
the information search and alternative selection process and is thus a target for evaluating
hotel satisfaction and dissatisfaction [32,33]. Identifying the customer’s hotel selection
attributes is essential for improving service quality and increasing customer satisfaction to
gain a competitive advantage [34].
When collecting data, researchers identify the type of information they want, clarify
concepts, limit the scope of what they want to collect, and familiarize themselves with
the characteristics of the keywords. Data refining and preprocessing involves converting
unstructured textual data into structured forms. For accurate results, sophisticated pre-
treatment is essential. As a step to analyze text-based technologies such as information
extraction, document summary, and clustering are involved in the next step for analyzing
text-based data, and analysis methods suitable for research purposes are then applied to
manage information systems at worksites and to accumulate knowledge [19,38].
Text mining for the hospitality industry has recently been the subject of active research.
Boo and Busser [8] qualitatively and quantitatively investigated meeting planners’ online
reviews of destination hotels. The results yielded insights to respond to the online reviews
and formed the basis for hotel evaluation criteria. Stepchenkova and Morrison [39] analyzed
Russia-related texts on 212 websites, measured Russia’s tourist destination image, and
Sustainability 2022, 14, 848 4 of 13
identified the differences between US and Russian websites. He, Zha, and Li [40] described
an in-depth case study that applied text mining to analyze text on the Facebook and Twitter
sites of three pizza chains: Pizza Hut, Domino’s Pizza, and Papa John’s Pizza, which are
representative franchises in the industry. The results confirmed the value of social media
competitive analysis and the power of text mining as an effective technique for extracting
business value from the vast amount of available social media data. The formation of
social issues through SNS (Social Networking Service) is accelerating in various fields.
With the development of SNS, network analysis is becoming essential to extract different
meanings and concepts inherent in text-based messages and to understand their relational
characteristics [41]. Semantic network analysis assesses the structure of a semantic network
retrieved according to the given text, and it also explores meaning through the structural
relationship of words as message components, rather than lexical units [42].
Semantic network analysis uses individual words to clarify network structure and
meaning within a text. Selecting a specific term and repeatedly using it when emphasizing
a particular meaning is one method for content analysis of the relationship between words
that appear simultaneously in a sentence or paragraph. The core of semantic network
analysis is indicating the influence of words, and analysis based on structural identity
consists of an index for classifying subgroups based on word similarity [19,30,43].
3. Methodology
3.1. Data Collection
The data collection procedure for this study is as follows. Hotel reviews were collected
from Google Travel (www.google.com/travel), the largest search engine globally. Google
hotel reviews include detailed information about the hotel brand used by the customer, the
reviewer’s ID, review date, comment, rating, and type of trip. Figure 2 shows a specific
example from Google Online Reviews. SCTM (Smart Crawling & Text Mining, developed
by the Wellness and Tourism Big Data Research Institute at Kyungsung University) and
TEXTOM (a big data analysis solution to collect and refine data and generate matrix data)
were used to collect and refine online data.
Words were ranked according to the frequency of their occurrence, to analyze the
unstructured data. Table 1 shows the 25 recommended hotels and the number of reviews for
each hotel. A total of 8448 reviews were collected, and 8229 reviews and 314,813 words were
extracted, excluding data that were not readable or had only ratings with no review content.
The data collection period was from December 2019, when COVID-19 first appeared, to
Sustainability 2022, 14, 848 5 of 13
July 2021, to determine if there were any different implications from a prior study of Ban,
Choi, Choi, Lee, and Kim [44], who conducted research using online hotel reviews before
the COVID-19 pandemic, making this a longitudinal study.
Table 1. Number of reviews according to 2021 Hotel ranking source from Tripadvisor website.
Factor analysis was performed to retrieve the main factors affecting hotel customer sat-
isfaction, using 55 out of the 90 top-frequency words. In addition, linear regression analysis,
which consisted of four independent variables derived from the factor analysis and overall
ratings as a dependent variable, was performed to verify the following hypothesis: The
hotel experience shown in the online review can be used to explain customer satisfaction.
4. Results
4.1. Frequency Analysis
The data were transformed from unstructured information such as sentences to struc-
tured data such as single words and their corresponding frequency. A total of 314,813 words
were collected. After deleting repeated, unnecessary, and low-frequency words, a total of
90 high-frequency words were extracted. Table 2 shows the 90 highest frequency words
with Term Frequency-Inverse Document Frequency (TF-IDF). Figure 3 illustrates the net-
work visibility of these 90 top frequency words.
Table 2. Top 90 frequent words with TF-IDF from the online review.
Rank Word Freq. TF-IDF Rank Word Freq. TF-IDF Rank Word Freq. TF-IDF
1 service 2523 4027 31 waldorf 253 908/33 61 option 120 553
2 ciudad 2104 2892/3 32 quality 252 967 62 coffee 120 550
3 méxico 2085 2882/4 33 astoria 243 873/36 63 window 119 564
4 hotel 1244 2899/2 34 family 238 927 64 variety 119 543
5 restaurant 893 2412 35 people 231 907 65 night 118 545
6 resort 798 2303/6 36 guest 223 893 66 moment 115 555
7 place 701 1865 37 hospitality 216 836 67 villa 114 508
8 experience 671 1892 38 luxury 214 863 68 brunch 112 555
9 breakfast 651 1902 39 belgrave 212 791/43 69 thank 102 471
10 locate 620 1792 40 belize 209 794/42 70 holiday 100 471
11 ritz carlton 572 1510/14 41 custom 209 822 71 park 98 470
12 chandys 551 1509/15 42 island 202 834 72 class 97 449
13 staff 544 1595 43 property 198 817 73 entrance 96 473
14 everything 507 1618 44 ambience 194 742 74 city 95 453
15 trip 478 1364 45 dinner 177 733 75 decorate 94 470
16 attention 458 1571 46 reception 175 745 76 Zermatt 94 504/69
17 mandapa 440 1350/18 47 architecture 172 751 77 Tokoriki 93 429/81
18 lisbon 412 1317/19 48 everyone 166 694 78 amenity 92 430
19 serve 402 1228 49 security 155 667 79 conference 90 428
20 france 377 1202/23 50 history 153 703 80 employee 90 446
21 hamanasi 377 1424/16 51 price 151 667 81 bathroom 89 423
22 seminyak 352 1143/25 52 atmosphere 151 657 82 garden 89 431
23 terrace 341 1284 53 manage 150 657 83 elevator 88 438
24 adventure 314 1179 54 drink 146 636 84 world 86 409
25 room 313 1019 55 build 142 643 85 environment 85 408
26 buffet 311 1243 56 food 142 596 86 stay 84 389
27 facility 278 1037 57 accommodate 131 586 87 excellent 84 384
28 residence 276 941 58 view 129 570 88 wait 83 415
29 amsterdam 275 987/28 59 friend 127 573 89 afternoon 82 422
30 hacienda 264 910/32 60 station 121 577 90 balcony 82 404
The TF-IDF weight model evaluates how important words inside a document are in
text mining. TF-IDF is a value obtained by multiplying TF and IDF, and the higher the
score, the less frequently the word appears in other documents and the more frequently it
appears in the document considered. A word with a larger TF-IDF value is more likely to
determine the topic or meaning of the document to which it belongs, and this can be used
as a measure to extract critical keywords. For example, the words indicating the location or
included in hotel brand, such as “ciudad” or “mexico”, which are second and third in the
frequency ranking, have a TF-IDF of 2892 and 2882, respectively, and the TF-IDF rankings
are also highly ranked, at third and fourth. Words representing the hotel brand or location
were meaningful words in the review data.
It is undeniable but surprising that “service” had the highest frequency, and this
implies that, for hotel consumers, a hotel’s service is the aspect they mentioned most. There
were also words related to service, such as “restaurant”, “experience”, and “staff”, that
Sustainability 2022, 14, 848 7 of 13
appeared with high frequency. Referring to the number of online hotel reviews, although
the “Gran Hotel Ciudad de México” ranked 20th in the hotel brand among 25 hotels,
it recorded the highest number of reviews, at 2064, which suggests that the two words
“ciudad” and “mexico” have high web visibility. Words related to location or name of
the hotel were also common, such as “ciudad”, “ritz carlton”, “chandy”, “mandapa”,
“lisbon”, and “france”, as were words such as “experience”, “trip”, “adventure”, “family”,
“security”, and “price”, which could reflect the purpose of the trip.
sociality, such as “family” and “friend”. After conducting CONCOR analysis, 55 words
were used to determine the main factors affecting hotel customer satisfaction.
Unstandardized Standardized
Model Coef. Coef. p t
B Std. Error Beta
(Constant) 4.733 0.008 0.000 610.319 ***
Service (S) 0.019 0.008 0.027 0.014 2.449 **
Physical Environments (P) 0.004 0.008 0.005 0.620 0.496
Dining (D) −0.015 0.008 −0.021 0.055 −1.916 *
Location (L) −0.005 0.008 −0.007 0.521 −0.642
Notes: Dependent variable: Customer Satisfaction (CS); R2 = 0.100; adjusted R2 = 0.100; F = 2.573, p < 0.05.
*** p < 0.001, ** p < 0.05, * p < 0.1.
The Service (S) factor holds the highest standardized coefficients, which means staff
service is the essential factor associated with customer satisfaction. The Dining (D) factor
was found to negatively influence customer satisfaction, indicating that customers have
a negative view of the dining factor, which included words such as restaurant and brunch.
with food and beverage prices, quality, safety, improved service, overall feeling, image,
comfortable condition, and location [11]. This work adds to the understanding of customer
hotel selection attributes that are a prerequisite for improving service quality and enhancing
customer satisfaction to gain a competitive advantage. Additionally, the keywords were
visualized by drawing networks and nodes using NetDraw in UCINET 6.0. Factor and
linear regression analyses were performed to determine the relationships between extracted
factors and customer satisfaction.
This study provides five academic and practical implications based on the research
results. For the theoretical implications, this study demonstrates the significance of extend-
ing the application area of semantic network analysis. This work extends our knowledge
and serves as a benchmark for researchers and stakeholders concerning the factors that
provide pleasing outcomes within the hotel context. Given the importance of the hotel
sector in the tourism industry, this study empirically explored the hotel experience and
satisfaction through big data analysis. Understanding online reviews as an expression of
customer experience can help the hotel industry identify key attributes needed to achieve
positive repurchase intention and increase revenue. Online reviews provide an efficient
way for the hotel industry to collect feedback from hotel customers and discover how to
generate positive revisit intention after the experience.
Second, the use of a semantic network analysis provides a valuable tool for exploring
customers’ comments about their hotel experiences. This method reveals features that
explain why customers evaluate their hotel experience positively or negatively. By ana-
lyzing customer comments, we can assess the power and meaning of words commonly
used by customers when sharing their experiences and how those word choices can inform
recommendations through online sources. The power expressed in words contains the
customers’ expressions and evaluations of their experiences, and serves as a basis for
understanding the reality of their experience.
Third, in terms of practical implications, “service” is an important factor influencing
customer satisfaction (as in previous studies [46,47]), and a more remarkable result is that
customers expect to receive more attentive service during COVID-19. Thus, managers or
operators in the hotel industry should pay more attention to maintaining the standard
service quality and providing more proactive or extra service to customers during this
pandemic. Offering service with warm and sincere hospitality can create positive eWOM
and satisfaction, as shown in the following quotations from online reviews: “José Luis and
Víctor were always greeting us with a big smile probably one of the best treatment I have
had in a hotel” and “The staff is simply amazing! They make every effort to make you feel
comfortable and welcome.” Appropriate staff service at the service point leads to positive
reviews, which then form a positive image of the hotel. There is a need for continuous and
systematic education and training to motivate employees with customer-centered thinking.
Fourth, it is noteworthy for hotel operators and managers that dining negatively
influences customer satisfaction. One customer mentioned the restaurant and brunch
within their review and stated they were not pleased with the dining experience. This
indicates that customers using the hotel are paying the same cost as before the pandemic,
but they are restricted from using some facilities (such as restaurants) due to COVID-19 or
cannot receive regular services. Due to COVID-19, restrictions on restaurants that confine
many people into a narrow space are unavoidable, but consideration from the hotel is
necessary to ensure that customers do not feel uncomfortable. For example, the hotel
could provide a service where customers can eat all the menu offerings from the restaurant
in the room. Using a mobile app, the hotel could deliver the food ordered to the room
without contacting others. If this exceptional dining service is implemented, it could affect
customer satisfaction, revisit intention, and positive WOM. There is need for a service that
does not cause inconvenience to customers despite the limited availability of F&B services
due to COVID-19. If the hotel minimizes contact with employees and provides a service
that allows the customer to dine in a private space through a delivery service or to order
through a mobile app and watch the cooking process on the screen, no problems with
Sustainability 2022, 14, 848 12 of 13
hygiene or cleanliness that customers are particularly concerned about with COVID-19
would arise.
Finally, online reviews are a source of information for potential customers to make
decisions. Considering that tourism decision making through the internet is rapidly
increasing, this study can guide hotel marketing strategy, facility operation, and complaint
management through big data analysis of online reviews.
This study has some limitations, and results should be interpreted with caution; these
limitations also provide suggestions for future research. First, online reviews were collected
from Google, the world’s largest search engine. However, there is a possibility that using
a specific online channel may not capture all customer preferences, so for representative-
ness, future samples should be based on analysis of various websites and should use data
from many years. Second, it is not easy to understand the additional meaning of words
when analyzing their frequency. Future research should adopt the further analysis of
positives and negatives, and sentimental analysis could better clarify customer experience
and satisfaction. Finally, in this study, reviews of 25 hotels recommended by TripAdvisor
were collected from Google Travel and analyzed. However, the quantity of review data
collected for each hotel differed widely due to the different sizes, types, and average costs
of the hotels considered. Future studies should collect and analyze review data from hotels
that share the same characteristics to derive more meaningful research results.
Author Contributions: Y.-J.K. and H.-S.K. designed the research model, analyzed online review data
and wrote the paper. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by the Ministry of Education of the Republic of Korea and the
National Research Foundation of Korea (NRF-2019S1A5A2A03049170).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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