The Role of Traveler Type and Hotel Star Rating in the Effect of Hotel Service Attributes on Customer Satisfaction

Abstract

This study analyzes 136.413 reviews, sourced from booking.com, regarding 3, 4, and 5-star hotels in Athens, Greece. Using an R dedicated routine (LDA), to extract the most frequently used words from online reviews related to nine service attributes, and three-factor theory, a multi-attribute model is formulated to study the asymmetric effects of different hotel service attributes on customer satisfaction (CS) according to type of traveler (solo, group, couple, family) and hotel star rating. Results show that asymmetric effects are higher for the attributes of location, staff, and facilities. Location is a hybrid factor for 3*and 4* hotels but can delight couples and groups in 5* hotels and become a dissatisfier for families (3*& 5*) and groups (3*). Staff is a satisfier for 3* and a hybrid for 4* but a dissatisfier for 5* and for families and groups in 4*. Comfort is a dissatisfier for most customers but a hybrid for families and groups in 3* hotels and for solo travelers and families in 5 * hotels. Facilities is a satisfier for 3* and 5* and a hybrid for 4* but can delight solo and groups in 5*. Food is a hybrid for 3* but becomes a dissatisfier in 4* and 5*. Cleanliness, room, prices and processes are dissatisfiers/frustrators for all. Findings can help hotels customize their service mix for different customer segments and maximize satisfaction.

Full text

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
413
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The Role of Traveler Type and Hotel Star Rating in the Effect
of Hotel Service Attributes on Customer Satisfaction
Pinelopi Athanasopoulou 1*, Apostolos Giovanis 2, Dimitrios Papakyriakopoulos 3,
Krinanthi Gdonteli 4, Emmanouil Choustoulakis 5
1 Sport Organization & Management Department, University of Peloponnese, Sparti, Greece. Email: athanapi@go.uop.gr
2, 3 Department of Business Administration, University of West Attica, Athens, Greece.
4, 5 Sport Organization & Management Department, University of Peloponnese, Sparti, Greece
ARTICLE INFO
ABSTRACT
Received: 30 Dec 2024
Revised: 05 Feb 2025
Accepted: 25 Feb 2025
This study analyzes 136.413 reviews, sourced from booking.com, regarding 3, 4, and 5-star hotels
in Athens, Greece. Using an R dedicated routine (LDA), to extract the most frequently used words
from online reviews related to nine service attributes, and three-factor theory, a multi-attribute
model is formulated to study the asymmetric effects of different hotel service attributes on
customer satisfaction (CS) according to type of traveler (solo, group, couple, family) and hotel
star rating. Results show that asymmetric effects are higher for the attributes of location, staff,
and facilities. Location is a hybrid factor for 3*and 4* hotels but can delight couples and groups
in 5* hotels and become a dissatisfier for families (3*& 5*) and groups (3*). Staff is a satisfier for
3* and a hybrid for 4* but a dissatisfier for 5* and for families and groups in 4*. Comfort is a
dissatisfier for most customers but a hybrid for families and groups in 3* hotels and for solo
travelers and families in 5 * hotels. Facilities is a satisfier for 3* and 5* and a hybrid for 4* but
can delight solo and groups in 5*. Food is a hybrid for 3* but becomes a dissatisfier in 4* and 5*.
Cleanliness, room, prices and processes are dissatisfiers/frustrators for all. Findings can help
hotels customize their service mix for different customer segments and maximize satisfaction.
Keywords: Customer satisfaction, Customer type, Hotels, Online reviews, Service attributes,
Star rating.
INTRODUCTION
In today’s world where the internet and social media affect all parts of our life, the emergence and the increasing
importance of electronic word-of-mouth on consumer purchasing decisions is seen in all business sectors. The
proliferation of online platforms has enabled consumers to share their experiences and opinions about products and
services, creating a vast repository of information that potential buyers can access. One of the most important types
of electronic word-of-mouth is online reviews. Research shows that consumers rely increasingly on reviews to make
their purchasing decisions in many industries including hotels, restaurants and other service providers, and trust
these reviews more than any other promotional content shared by businesses [1]; [2]. Also, researchers agree that the
content of online reviews affects customer satisfaction from a service [3]; [4]; [5] and try to determine which service
attributes lead to satisfaction or dissatisfaction [6]; [7].
However, the effect of the different hotel service attributes on customer satisfaction can be nonlinear or asymmetric
[8]; [9]; [10]; [11]. In fact, researchers argue that the asymmetric effects differ for different types of customers (i.e.
business, leisure, couple, family, friends and solo) [3]; [12] and according to hotel star rating [13]; [14]. However,
there is still a need for further research on the impact of different service attributes on CS and on how this impact
differs for different customer segments [15]; [16]; [11]. The purpose of this study is to investigate the effect of hotel
service attributes on customer satisfaction for different types of travelers and hotel star ratings by analyzing online
hotel reviews.

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
414
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
LITERATURE REVIEW
Customers increasingly share their experiences and preferences online through online reviews. The power of
electronic word-of-mouth is increasing and affects many consumer purchase decisions. Especially in the hotel
industry, online reviews have become critical for consumer decision-making [17]; [18]. Research indicates that
potential guests base their evaluations of hotels and their ultimate decisions on reviews and tend to believe reviews
more than other traditional marketing messages or even personal suggestions from friends and family [1] [2]; [19].
Furthermore, researchers emphasize that online reviews affect customer satisfaction [3]; [4]; [5] and ultimately hotel
performance [20]; [15]; [21]; [22]. Customer satisfaction is a critical issue of research in the hospitality field in recent
years since it is considered as the key to success for hospitality businesses [11]; [23].
During the last ten years, several studies have used online reviews to assess customer satisfaction from services [24];
[25]; [26]; [27]). Researchers analyze online comments to determine which service attributes lead to customer
satisfaction [6]; [7] and dissatisfaction [28]. However, recent research also shows that the different hotel service
attributes can have asymmetric effects on customer satisfaction with a service [8]; [9]; [10]; [11]. Specifically, the
impact of different service attributes on CS differs substantially for various traveler compositions (i.e., leisure,
business, couple, family, friends, and solo), [29]; [30]; [31]; [3]; [12] and according to hotel star rating [32]; [30];
[13]; [14]. [29] find that although “value” and “rooms” are the most important attributes contributing to a high overall
rating for the hotel, couples find location more important whereas cleanliness is the least important for friends. [31]
show that customers report significantly lower levels of overall satisfaction with hotel services after business stays
than after leisure stays and this effect is moderated by the traveler's country of origin. [33] argue that travelers in
different group compositions perceive the quality of service differently because their needs and expectations are not
the same whereas [34] find that the expectations and satisfaction of travelers change when they travel in different
travel groups (solo, friends, couples, family). Therefore, it is important to study how different types of travelers are
affected by hotel service attributes. In fact, there is a continuous call for research to investigate how the various hotel
service characteristics affect CS and how this effect differs for various customer groups or for different types of hotels
[29] [32]; [15]; [35]; [11]; [16].
The purpose of this study is to investigate the effect of hotel service attributes on customer satisfaction for different
types of travelers and hotel star ratings by analyzing hotel online reviews. This will help hotel managers to design
more effective business strategies and adjust the services provided with an aim to increase marketing and economic
performance.
METHODOLOGY
This study analyzes 136.413 reviews, sourced from booking.com, regarding 120 3-, 4-, and 5-star hotels in Athens,
Greece posted between June 2022 and August 2024. To better evaluate the effects of service attributes on customer
satisfaction, this study considers only the reviews of customers that stayed in these hotels for more than three nights.
In order to investigate the asymmetric effect of hotel service attributes on customer satisfaction the text contained in
each review on Booking.com is analyzed by separating the positive and negative comments. The analysis includes two
stages. The first stage involves extracting information from the text regarding the evaluation of hotel service attributes
and overall hotel evaluation. Then, a content analysis is done to develop a multi-attribute model that shows the
relationship between positively and negatively assessed service attributes and customer satisfaction [36]. More
specifically, 150 words that are mentioned more frequently in the review texts are extracted using an R dedicated
routine (LDA) and then a group of three coders is used to classify the words into nine different service attributes:
cleanliness, location, staff, facilities, room, food, comfort, processes, and price. In case of conflict amongst coders, a
fourth experienced coder was used to resolve the issue. Each review text contains both positive and negative
evaluations of the hotel service. Therefore, each review text is considered to contain up to (18) attributes (9 positive
and 9 negative). These 18 attributes are independent variables and reflect multi-attributes of the hotel service mix.
Also, they are constructed based on the binary representation [36] in which positive (negative) service attribute
number variables refer to the number of words related to each attribute in pros (cons) of each review. Then, if the
number of words in pros (cons) is greater than 0, then the variable becomes 1, otherwise 0.

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
415
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In the second step, after the creation of the nine independent dummy variables, we use the penalty-reward contrast
analysis (PRCA) (e.g., [37]; [38]; [39]) and 3-factor theory ([40]) to investigate the asymmetric impact of different
hotel service attributes on customer satisfaction according to type of traveler (solo, group, couple, family) and hotel
star rating. The three-factor theory assumes that the effect of a service attribute on CS varies according to its
performance and proposes three types of attributes: basic/dissatisfiers (minimum requirements of service that do
not create high satisfaction, if they are offered but increase dissatisfaction if they are missing), performance/hybrids
that have a linear and symmetric effect on CS, and excitement/satisfiers that may produce high customer satisfaction
if they increase, but their absence does not create dissatisfaction.
Furthermore, the baseline model specification used is given by the following expression:
       




where CS = the overall customer satisfaction for each review, Xk = the kth negative dummy variable (negative
comment), Yk = the kth positive dummy variable (positive comment), pk = the penalty index for the kth negative
dummy variable, rk = the reward index for the kth positive dummy variable.
In order to find the impact asymmetry index (IA), which is used for categorizing service attributes according to the
3-factor theory, the following mathematical expressions need to be calculated:
 

    
The IA values for each service attribute vary between +1 and -1. These two extreme values represent attributes
characterized as perfect satisfiers and perfect dissatisfiers respectively. While, when for a service attribute IA = 0,
this represents a perfect hybrid. In between of the above values and using the taxonomy proposed by [8] and
presented in Table 1, the IA index is interpreted as follows:
Table 1: Attribute categorization rules
IA
Attribute Category
(0.4, 1]
Delighter
(0.1, 0.4]
Satisfier
(-0.1, 0.1]
Hybrid
(-0.1, -0.4]
Dissatisfier
(0.4, -1]
Frustrator
Moreover, the level of impact of each attribute is determined by splitting the RIOCS values into three equal intervals,
in which the lower interval values refer to the low impact attributes, the middle interval values refer to medium
impact attributes and the higher interval values refer to high impact attributes.
RESULTS
Sample Profile
Firstly, descriptive statistics are used to thoroughly understand the characteristics of our data set before the main
analysis. Table 2 presents the distribution of online reviews by type of hotel, measured by number of stars. The
majority of reviews concern 4-star hotels (55,1%), followed by those for 3-star hotels (29.7%) and 5-star hotels
(15.2%).

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
416
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Table 2: Distribution of online reviews by hotel star
Hotel stars
Frequency
Percent
3*
40,848
29.9
4*
73,359
53.8
5*
22,206
16.3
Total
136,413
100.0
Furthermore, Table 3 presents the distribution of online reviews by traveler type, in which couples and families are
the most frequent traveler group compositions. Couples make up almost half of reviews (43.6%) and families are
27.9%. Groups and solo travelers have lower percentages (12.7% and 15.8% respectively)
Table 3: Distribution of online reviews by traveler type
Type of customer
Frequency
Percent
Couple
59,466
43.6
Family
38,050
27.9
Group
17,322
12.7
Solo traveller
21,575
15.8
Total
136,413
100.0
Also, Table 4 divides review ratings into four categories according to the level of the overall rating. The percentage of
customer satisfaction ratings that were less than 6 out of 10 was 12.1%, 30.2% were between 6 and 8 and more than
half (57.7%) were more than 8.
Table 4: Online reviews distribution by review rating category
Frequency
Percentage
Mean
0-6
16,572
12.1
6-8
41,172
30.2
8-10
78,669
57.7
Total
136,413
1.0
Asymmetric Effects
The asymmetric effects of the different hotel service attributes on customer satisfaction are analyzed using a PRCA
analysis and the 3-factor theory of customer satisfaction. Table 5 describes the empirical results of the PRCA analysis
that includes the binary attribute variables as independent variables and the overall customer satisfaction rating as
the dependent variable. The proposed model explains 25.4 % of the variance in customer satisfaction.
The estimated coefficients show that all positive attributes cause positive effects, whereas all negative attributes lead
to negative effects, as expected. However, the impact of negative attributes seems to be greater than those of positive
attributes for eight out of nine attribute variables. The only exception is facilities that is classified as a satisfier.
Table 5: Impact of service attributes on overall customer satisfaction

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
417
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Service Attribute
RI
PI
RIOCS
SGP
DGP
IAI
Type
Impact
LOCATION
0.315
-0.367
0.682
0.462
-0.538
-0.076
Hybrid
Low Impact
STAFF
0.357
-0.375
0.732
0.488
-0.512
-0.025
Hybrid
Low Impact
FOOD
0.306
-0.452
0.759
0.404
-0.596
-0.192
Dissatisfier
Low Impact
CLEANLINESS
0.385
-0.892
1.277
0.301
-0.699
-0.397
Dissatisfier
High Impact
COMFORT
0.394
-0.557
0.951
0.414
-0.586
-0.172
Dissatisfier
Medium
Impact
FACILITIES
0.422
-0.262
0.684
0.617
-0.383
0.234
Satisfier
Low Impact
ROOM
0.168
-0.584
0.752
0.223
-0.777
-0.554
Frustrator
Low Impact
PRICES
0.106
-0.504
0.610
0.174
-0.826
-0.651
Frustrator
Low Impact
PROCESSES
0.473
-0.833
1.306
0.362
-0.638
-0.275
Dissatisfier
High Impact
Results indicate that staff, food, cleanliness, room and prices are categorized as basic factors
(dissatisfiers/frustrators) for all reviewers. Especially cleanliness and processes are dissatisfiers with a high impact
showing that customers are particularly demanding of these two attributes and that the high-quality service in these
two factors is critical for their satisfaction. Also, the absence or low quality of these two factors will increase
dissatisfaction. Furthermore, location and staff are considered to be hybrid factors and only facilities is a satisfier
factor. These results confirm the presence of asymmetric effects of hotel service attributes on customer satisfaction
and reveal that reviewers are very demanding with respect to the factors that are categorized as basic attributes, but
they do not seem to have high expectations from facilities, a factor categorized as a satisfier. Therefore, the quality of
facilities can be used by hotels to increase customer satisfaction together with a good location and high-quality staff.
Furthermore, results show that the asymmetric effects vary between different types of travelers and hotel star ratings
(see tables 6,7,8).
Table 6: Asymmetric effects for 3* hotels in different traveler compositions
3*
Service Attribute
All
Solo
Couple
Family
Group
LOCATION
Hybrid
Hybrid
Hybrid
Dissatisfier
Dissatisfier
STAFF
Satisfier
Satisfier
Satisfier
Satisfier
Hybrid
FOOD
Hybrid
Hybrid
Hybrid
Dissatisfier
Hybrid
CLEANLINESS
Dissatisfier
Frustrator
Dissatisfier
Dissatisfier
Frustrator
COMFORT
Dissatisfier
Dissatisfier
Dissatisfier
Hybrid
Hybrid
FACILITIES
Satisfier
Hybrid
Satisfier
Satisfier
Satisfier
ROOM
Frustrator
Frustrator
Frustrator
Frustrator
Frustrator
PRICES
Frustrator
Dissatisfier
Dissatisfier
Frustrator
Frustrator
PROCESSES
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Frustrator
Table 7: Asymmetric effects for 4* hotels in different traveler compositions
4*
Service Attribute
All
Solo
Couple
Family
Group
LOCATION
Hybrid
Satisfier
Hybrid
Hybrid
Hybrid
STAFF
Hybrid
Hybrid
Hybrid
Dissatisfier
Dissatisfier
FOOD
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Frustrator

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
418
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
CLEANLINESS
Dissatisfier
Dissatisfier
Frustrator
Dissatisfier
Dissatisfier
COMFORT
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
FACILITIES
Hybrid
Dissatisfier
Hybrid
Hybrid
Satisfier
ROOM
Frustrator
Frustrator
Frustrator
Dissatisfier
Frustrator
PRICES
Frustrator
Frustrator
Frustrator
Frustrator
Frustrator
PROCESSES
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Table 8: Asymmetric effects for 5* hotels in different traveler compositions
5*
Service Attribute
All
Solo
Couple
Family
Group
LOCATION
Satisfier
Hybrid
Delighter
Dissatisfier
Delighter
STAFF
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
FOOD
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
CLEANLINESS
Frustrator
Frustrator
Frustrator
Frustrator
Dissatisfier
COMFORT
Dissatisfier
Hybrid
Dissatisfier
Hybrid
Dissatisfier
FACILITIES
Satisfier
Delighter
Satisfier
Satisfier
Delighter
ROOM
Frustrator
Frustrator
Frustrator
Frustrator
Frustrator
PRICES
Frustrator
Frustrator
Frustrator
Frustrator
Frustrator
PROCESSES
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Dissatisfier
Evidently, different traveler types using different quality level accommodations evaluate differently the impact of
different service attributes on customer satisfaction. Specifically, for the whole sample results show that asymmetric
effects are higher for the attributes of location, staff, and facilities. However, location is a hybrid factor for 3*and 4*
hotels but can delight couples and groups in 5* hotels and become a dissatisfier for families (3*& 5*) and groups (3*).
Staff is a satisfier for 3* hotels and a hybrid for 4* hotels but a dissatisfier for 5* hotels and for families and groups in
4* hotels. Comfort is a dissatisfier for most customers but a hybrid for families and groups in 3* hotels and for solo
travelers and families in 5 * hotels. Facilities is a satisfier for 3* and 5* hotels and a hybrid for 4* hotels but can
delight solo travelers and groups in 5* hotels. Food is a hybrid for 3* hotels but becomes a dissatisfier in 4* and 5*
hotels. Finally, cleanliness, room, prices and processes are all dissatisfiers or frustrators. Specifically, cleanliness is a
dissatisfier for 3- and 4-star hotels, whereas it is a frustrator for 5-star hotels. This means that customers of 5 * hotels
are more demanding from this service attribute and its improvement will decrease their dissatisfaction faster than in
other types of hotels (3* or 4*). Furthermore, processes is a dissatisfier for almost all customers except groups in 3-
star hotels whereas prices is a frustrator for most customers except solo travelers and couples in 3-star hotels.
MANAGERIAL IMPLICATIONS, LIMITATIONS, SUGGESTIONS FOR FURTHER RESEARCH
This study presents results regarding the effects of hotel service attributes on the satisfaction of different customer
segments. The results confirm the moderating effects of traveler type and hotel star rating on the asymmetric
relationship between the evaluation of service attributes in online reviews and customer satisfaction. These results
can be used by hotels to customize their service mix according to different customer needs (e.g. different traveler
types or hotel star ratings) and as a result maximize customer satisfaction that ultimately affects hotel performance.
Hotels can provide a different service bundle to each customer according to which factors are more important for
each person.
This study is limited to reviews for the hotels of one city for 2 years. Future research can extend the sample in terms
of geographic area covered and number of reviews or investigate the role of other factors such as customer culture.
Also, primary research studies are needed to validate the results of review analysis. Finally, researchers can focus on

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
419
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
creating a system that analyzes reviews continuously and provides feedback to hotels and other businesses such as
airlines or cruise lines on how to manage effectively the satisfaction of each customer according to their specific needs.
REFERENCES
[1] D. Gavilan, M. Avello, and G. Martinez-Navarro, “The influence of online ratings and reviews on hotel booking
consideration,” Tour Manag, vol. 66, pp. 53–61, Jun. 2018, doi: 10.1016/j.tourman.2017.10.018.
[2] Q. Yan, S. Zhou, and S. Wu, “The influences of tourists’ emotions on the selection of electronic word of mouth
platforms,” Tour Manag, vol. 66, pp. 348–363, Jun. 2018, doi: 10.1016/j.tourman.2017.12.015.
[3] X. Xu, “Does traveler satisfaction differ in various travel group compositions?: Evidence from online reviews,”
International Journal of Contemporary Hospitality Management, vol. 30, no. 3, pp. 1663–1685, 2018, doi:
10.1108/IJCHM-03-2017-0171.
[4] A. Ahani et al., “Revealing customers’ satisfaction and preferences through online review analysis: The case of
Canary Islands hotels,” Journal of Retailing and Consumer Services, vol. 51, pp. 331–343, Nov. 2019, doi:
10.1016/j.jretconser.2019.06.014.
[5] Y. Zhao, X. Xu, and M. Wang, “Predicting overall customer satisfaction: Big data evidence from hotel online
textual reviews,” Int J Hosp Manag, vol. 76, pp. 111–121, Jan. 2019, doi: 10.1016/j.ijhm.2018.03.017.
[6] H. Li, Q. Ye, and R. Law, “Determinants of Customer Satisfaction in the Hotel Industry: An Application of
Online Review Analysis,” Oct. 2013. doi: 10.1080/10941665.2012.708351.
[7] K. Berezina, A. Bilgihan, C. Cobanoglu, and F. Okumus, “Understanding Satisfied and Dissatisfied Hotel
Customers: Text Mining of Online Hotel Reviews,” Journal of Hospitality Marketing and Management, vol. 25,
no. 1, pp. 1–24, Jan. 2016, doi: 10.1080/19368623.2015.983631.
[8] J. Mikulić and D. Prebežac, “Prioritizing improvement of service attributes using impact range-performance
analysis and impact-asymmetry analysis,” Managing Service Quality, vol. 18, no. 6, pp. 559–576, 2008, doi:
10.1108/09604520810920068.
[9] B. Kim, S. Kim, and C. Y. Heo, “Analysis of satisfiers and dissatisfiers in online hotel reviews on social media,”
International Journal of Contemporary Hospitality Management, vol. 28, no. 9, pp. 1915–1936, 2016, doi:
10.1108/IJCHM-04-2015-0177.
[10] X. Xu, “Examining an asymmetric effect between online customer reviews emphasis and overall satisfaction
determinants,” J Bus Res, vol. 106, pp. 196–210, Jan. 2020, doi: 10.1016/j.jbusres.2018.07.022.
[11] J. W. Bi, Y. Liu, Z. P. Fan, and J. Zhang, “Exploring asymmetric effects of attribute performance on customer
satisfaction in the hotel industry,” Tour Manag, vol. 77, Apr. 2020, doi: 10.1016/j.tourman.2019.104006.
[12] L. Wang, X. kang Wang, J. juan Peng, and J. qiang Wang, “The differences in hotel selection among various
types of travellers: A comparative analysis with a useful bounded rationality behavioural decision support
model,” Tour Manag, vol. 76, Feb. 2020, doi: 10.1016/j.tourman.2019.103961.
[13] R. Nunkoo, V. Teeroovengadum, C. M. Ringle, and V. Sunnassee, “Service quality and customer satisfaction:
The moderating effects of hotel star rating,” Int J Hosp Manag, vol. 91, Oct. 2020, doi: 10.1016/j.ijhm.
2019.102414.
[14] H. Li, Y. Liu, C. W. Tan, and F. Hu, “Comprehending customer satisfaction with hotels: Data analysis of
consumer-generated reviews,” International Journal of Contemporary Hospitality Management, vol. 32, no. 5,
pp. 1713–1735, May 2020, doi: 10.1108/IJCHM-06-2019-0581.
[15] G. Viglia, R. Minazzi, and D. Buhalis, “The influence of e-word-of-mouth on hotel occupancy rate,”
International Journal of Contemporary Hospitality Management, vol. 28, no. 9, pp. 2035–2051, 2016, doi:
10.1108/IJCHM-05-2015-0238.
[16] C. Zhang, Z. Xu, X. Gou, and S. Chen, “An online reviews-driven method for the prioritization of improvements
in hotel services,” Tour Manag, vol. 87, Dec. 2021, doi: 10.1016/j.tourman.2021.104382.
[17] C. T. P. Vincent, “Amateur versus professional online reviews: Impact on tourists’ intention to visit a
destination,” 2018.
[18] L. V. Casaló, C. Flavián, M. Guinalíu, and Y. Ekinci, “Avoiding the dark side of positive online consumer reviews:
Enhancing reviews’ usefulness for high risk-averse travelers,” J Bus Res, vol. 68, no. 9, pp. 1829–1835, Sep.
2015, doi: 10.1016/j.jbusres.2015.01.010.

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
420
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
[19] P. Phillips, S. Barnes, K. Zigan, and R. Schegg, “Understanding the Impact of Online Reviews on Hotel
Performance: An Empirical Analysis,” J Travel Res, vol. 56, no. 2, pp. 235–249, Feb. 2017, doi: 10.1177/
0047287516636481.
[20] K. L. Xie, Z. Zhang, and Z. Zhang, “The business value of online consumer reviews and management response
to hotel performance,” Int J Hosp Manag, vol. 43, pp. 1–12, 2014, doi: 10.1016/j.ijhm.2014.07.007.
[21] Z. Hou, F. Cui, Y. Meng, T. Lian, and C. Yu, “Opinion mining from online travel reviews: A comparative analysis
of Chinese major OTAs using semantic association analysis,” Tour Manag, vol. 74, pp. 276–289, Oct. 2019, doi:
10.1016/j.tourman.2019.03.009.
[22] S. C. Anagnostopoulou, D. Buhalis, I. L. Kountouri, E. G. Manousakis, and A. E. Tsekrekos, “The impact of
online reputation on hotel profitability,” International Journal of Contemporary Hospitality Management, vol.
32, no. 1, pp. 20–39, Jan. 2020, doi: 10.1108/IJCHM-03-2019-0247.
[23] G. Tontini, G. dos S. Bento, T. C. Milbratz, B. K. Volles, and D. Ferrari, “Exploring the nonlinear impact of
critical incidents on customers’ general evaluation of hospitality services,” Int J Hosp Manag, vol. 66, pp. 106–
116, Sep. 2017, doi: 10.1016/j.ijhm.2017.07.011.
[24] S. Chatterjee and P. Mandal, “Traveler preferences from online reviews: Role of travel goals, class and culture,”
Tour Manag, vol. 80, Oct. 2020, doi: 10.1016/j.tourman.2020.104108.
[25] M. M. Mariani, M. Borghi, and F. Okumus, “Unravelling the effects of cultural differences in the online appraisal
of hospitality and tourism services,” Int J Hosp Manag, vol. 90, Sep. 2020, doi: 10.1016/j.ijhm.2020.102606.
[26] P. Athanasopoulou, A. Giovanis N, and M. Ioakimidis, “How Are Guests Satisfied? Exploring the Asymmetric
Effects of Hotel Service Attributes on Customer Satisfaction by Analyzing Online Reviews,” Tourism, vol. 71,
no. 1, pp. 8–28, 2023, doi: 10.37741/T.71.1.1.
[27] Patil & Rane, “Customer experience and satisfaction: Importance of customer reviews and customer value on
buying preference,” International Research Journal of Modernization in Engineering Technology and Science,
vol. 5, no. 3, pp. 1–11, 2023, doi: 10.56726/irjmets36460.
[28] S. E. Levy, W. Duan, and S. Boo, “An Analysis of One-Star Online Reviews and Responses in the Washington,
D.C., Lodging Market,” 2013, SAGE Publications Inc. doi: 10.1177/1938965512464513.
[29] H. T. Rhee and S. B. Yang, “How does hotel attribute importance vary among different travelers? An exploratory
case study based on a conjoint analysis,” Electronic Markets, vol. 25, no. 3, pp. 211–226, Sep. 2015, doi:
10.1007/s12525-014-0161-y.
[30] S. Banerjee and A. Y. K. Chua, “In search of patterns among travellers’ hotel ratings in TripAdvisor,” Tour
Manag, vol. 53, pp. 125–131, Apr. 2016, doi: 10.1016/j.tourman.2015.09.020.
[31] T. Radojevic, N. Stanisic, N. Stanic, and R. Davidson, “The effects of traveling for business on customer
satisfaction with hotel services,” Tour Manag, vol. 67, pp. 326–341, Aug. 2018, doi: 10.1016/j.tourman.2018.
02.007.
[32] H. T. Rhee and S. B. Yang, “Does hotel attribute importance differ by hotel? Focusing on hotel star-
classifications and customers’ overall ratings,” Comput Human Behav, vol. 50, pp. 576–587, May 2015, doi:
10.1016/j.chb.2015.02.069.
[33] S. Ramanathan and A. L. Mcgill, “Consuming with Others: Social Influences on Moment-to-Moment and
Retrospective Evaluations of an Experience,” 2007. [Online]. Available: https://academic.oup.com/jcr/article/
34/4/506/1820258
[34] S. Liu, R. Law, J. Rong, G. Li, and J. Hall, “Analyzing changes in hotel customers’ expectations by trip mode,”
Int J Hosp Manag, vol. 34, no. 1, pp. 359–371, Sep. 2013, doi: 10.1016/j.ijhm.2012.11.011.
[35] X. (Roy) Zhao, L. Wang, X. Guo, and R. Law, “The influence of online reviews to online hotel booking
intentions,” International Journal of Contemporary Hospitality Management, vol. 27, no. 6, pp. 1343–1364,
Aug. 2015, doi: 10.1108/IJCHM-12-2013-0542.
[36] J. M. Kim, J. Liu, and K. K. cheon Park, “The dynamics in asymmetric effects of multi-attributes on customer
satisfaction: evidence from COVID-19,” International Journal of Contemporary Hospitality Management, vol.
35, no. 10, pp. 3497–3517, Aug. 2023, doi: 10.1108/IJCHM-02-2022-0170.
[37] V. Mittal, W. T. Ross, and P. M. Baldasare, “The asymmetric impact of negative and positive attribute-level
performance on overall satisfaction and repurchase intentions,” J Mark, vol. 62, no. 1, pp. 33–47, 1998, doi:
10.2307/1251801.

Journal of Information Systems Engineering and Management
2025, 10(34s)
e-ISSN: 2468-4376
https://www.jisem-journal.com/
Research Article
421
Copyright © 2024 by Author/s and Licensed by JISEM. This is an open access article distributed under the Creative Commons Attribution License which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
[38] E. W. Anderson and V. Mittal, “Strengthening the Satisfaction-Profit Chain,” J Serv Res, vol. 3, no. 2, pp. 107–
120, 2000, doi: 10.1177/109467050032001.
[39] J. Füller, K. Matzler, and R. Faullant, “Asymmetric effects in customer satisfaction,” Ann Tour Res, vol. 33, no.
4, pp. 1159–1163, Oct. 2006, doi: 10.1016/j.annals.2006.06.006.
[40] K. Matzler and E. Sauerwein, “The factor structure of customer satisfaction: An empirical test of the importance
grid and the penalty-reward-contrast analysis,” International Journal of Service Industry Management, vol. 13,
no. 4, pp. 314–332, Oct. 2002, doi: 10.1108/09564230210445078.