Research on Customer Satisfaction of Budget Hotels Based on Revised IPA and Online Reviews

Full text

Science Journal of Business and Management
2020; 8(2): 50-56
http://www.sciencepublishinggroup.com/j/sjbm
doi: 10.11648/j.sjbm.20200802.11
ISSN: 2331-0626 (Print); ISSN: 2331-0634 (Online)
Research on Customer Satisfaction of Budget Hotels Based
on Revised IPA and Online Reviews
Xue Liu, Ning Zhang
*
Department of Management Science and Engineering, Business School of Qingdao University, Qingdao, China
Email address:
*
Corresponding author
To cite this article:
Xue Liu, Ning Zhang. Research on Customer Satisfaction of Budget Hotels Based on Revised IPA and Online Reviews. Science Journal of
Business and Management. Special Issue: Tourism and Sustainability. Vol. 8, No. 2, 2020, pp. 50-56. doi: 10.11648/j.sjbm.20200802.11
Received: February 14, 2020; Accepted: March 6, 2020; Published: March 17, 2020
Abstract:
Online reviews are the emotional expressions of customers after product or service experience. Compared with
survey questionnaires, they can more truly reflect customers' perception of product or service. Therefore, combined with online
reviews and importance-performance analysis (IPA), managers can make corresponding corporate strategic according to the
priority of features. This research uses the Meituan.com hotel online reviews of budget hotels as an example. First, we uses
natural language processing technology to preprocess online reviews, and uses K-means to build a feature lexicon. Second, based
on the sentiment dictionary, we perform fine-grained sentiment analysis on “feature-view pairs” to obtain feature satisfaction
scores. Third, combined with the revised IPA, we obtain implicitly derived importance, and then the priority of each feature
improvement is determined. The conclusions show that (1) service, location, and price are the advantages of budget hotels.
Managers should maintain a competitive advantage and ensure the supply of resources. (2) Catering and room facilities are the
main disadvantages of budget hotels. Managers should improve these two features to improve customer satisfaction as soon as
possible. This study implements the method of managing IPA through online reviews, which replaces the previous questionnaire
method. At the same time, revised IPA provides more realistic and concrete reference for hotel managers when making decisions.
Keywords:
Budget Hotel, Customer Satisfaction, Online Review, Revised IPA
1. Introduction
With the rapid development and popularity of the Web 2.0
era, people are more inclined to post their ideas and interact on
social platforms. Previous studies have shown that online
reviews are an important form of social interaction [1], so
online customer reviews (OCRs) become a major source of
information for consumers and industry managers. Compared
with the objective descriptions published by merchants, OCRs
are derived from the emotional expression of consumers after
the experience, so they are more trustworthy and persuasive,
especially for experiential products. Hotels are one of the most
typical experiential products, OCRs will have a significant
impact on the consumer behavior of potential customers.
Marketing and economic theory [2] believe that products
and services have multi-dimensional features, and consumers'
preferences for each feature are different, so the degree of the
emotional expression of each feature displayed in OCRs will
be different. Recognizing the importance of
multi-dimensional features can be more accurately improved
according to consumer needs. At present, most consumer
satisfaction surveys are in the form of questionnaires. Some
researches set a large number of items in the questionnaire to
investigate the comprehensiveness and accuracy of the
information, such as the classic service quality model
(SERVQUAL) [3], and its theory is relatively strong, there are
certain limitations in both the size of the data and the richness
of the content. Summarizing the existing research, it is found
that the researches on hotel satisfaction have been more
in-depth, but most of them use questionnaires or directly use
website numerical scores, and rarely analyze the text of OCRs.
At present, the most widely used hotel satisfaction
measurement model is importance-performance analysis
(IPA), which was proposed by Martilla and James in 1977 [4],
and has been widely used in many fields since then. IPA
mainly determines the improvement strategy to achieve the
optimal use of resources by comparing the satisfaction and
importance values of each feature. Although IPA is widely

51 Xue Liu and Ning Zhang: Research on Customer Satisfaction of Budget Hotels Based on
Revised IPA and Online Reviews
used, it also has certain defects [5]. When mapping each
feature to a matrix figure, it is required that the two
coordinates, that is, importance and satisfaction are
independent of each other. However, due to the similarity
between importance and satisfaction, the interviewees could
not distinguish the two well, so that the final quadrant
distribution results were also biased. Based on the above
problems, many scholars have revised the IPA analysis
method. Deng has summarized the research results of other
scholars and optimized the IPA analysis method statistically
[6]. The revised model has achieved good results.
This research uses the reviews of Meituan.com to conduct
research to explore consumer preferences and attention to
hotel features. First, combined with natural language
processing, machine learning and other methods, we process
review texts. Second, we transform the crawled hotel reviews
into word vectors through Word2Vec, and use the K-means
method to cluster the word vectors. Third, the sentiment
dictionary method is used to assign features to obtain features
satisfaction. Finally, according to revised IPA to acquire
importance, the hotel features are judged through the IPA
matrix figure, so as to provide more accurate and reasonable
suggestions to the hotel industry.
2. Related Research
2.1. Features of Budget Hotels and Consumer Satisfaction
The features of the hotel are divided into many categories,
such as intangible features and tangible features; practical
features and experience features. The perception of hotel is the
evaluation of the importance and satisfaction of all the features
by consumers during the process of staying in the hotel. Callan
and Bowman [7] conducted a survey of 38 features of the hotel.
They found that the features that consumers consider important
include employee service attitude, service efficiency, health
environment, etc. At the same time, many respondents said that
the experiential features are relative important. Rhee and Yang
[8] researched the literature on hotel features and summarized
the six categories that consumers are more concerned about. At
the same time, many tourism websites also used these six
categories of hotel features to gauge consumer evaluation.
Consumers' perception of the importance and satisfaction of
hotel features is uncertain. Some features may have high
satisfaction but may not be very important to consumers. Some
features are important but their existence and optimization do
not improve satisfaction. The KANO model proposed by Kano
[9] explains the above features. KANO model is classified
according to the provision of product attribute and consumer
satisfaction, it mainly includes: basic attributes, performance
attributes, and excitement attributes, where the basic attributes
and the excitement attributes are non-linearly related.
Combining and summarizing the existing literature, we found
that there are more domestic researches on the tourism market,
but less research on the features of budget hotels. Although
there are literature survey on domestic consumers' needs and
satisfaction in hotels, they only studies specific features. Gao et
al. [10] found that hotel location is an important feature that
consumers will pay special attention to when making decisions.
Shan et al. [11] researched the four features of hotel type and
room size, etc. when portraying user portraits of online reviews
for hotels. In general, the analysis of all features of budget
hotels lacks systematic and in-depth research. Therefore, in the
domestic hotel industry research, it is necessary to further
explore consumer perceptions of budget hotel features.
2.2. Traditional IPA and Revised IPA
The IPA method uses the satisfaction and importance of
features as a combination evaluation of various factors, and
analyzes each factor based on the quadrant distribution in the
matrix, thereby to find out the factors that need urgent
improvement. The IPA method is relatively widely used at
home and abroad. Wu [12] studied typical domestic budget
hotels and found that the most important thing for consumers
is still the hotel location. Kuo et al. [13] explored the factors
affecting Hong Kong consumers' choice of hotel
accommodation, and used IPA analysis to study 26 hotel
service features, and found that the hotel's location,
environment, catering and other aspects urgently need to be
improved and optimized.
There are two necessary prerequisites for the use of IPA
analysis. First, the two coordinate axes in the matrix diagram
are required to be independent of each other; second, the
relationship between the satisfaction of each feature and
overall satisfaction must be linearly related and symmetrical.
In reality, because of the similarity between importance and
satisfaction, consumers cannot distinguish them well. Many
scholars have revised and expanded the IPA analysis method.
Deng summarized previous research and revised the problems
existing in traditional IPA. He proposed to replace the
self-reported importance with the implicitly derived
importance, that is, to analyze the correlation logarithm of the
satisfaction evaluation of each factor, and then conduct a
partial correlation analysis of overall satisfaction and the
satisfaction of each feature. The obtained partial correlation
coefficient is used as the implicitly derived importance [6].
The partial correlation coefficient excludes the impact of other
satisfaction variables on the correlation between the specified
variable and the overall satisfaction, and can reflect the true
situation of each feature [14].
2.3. Sentiment Analysis of Online Reviews
Sentiment analysis, also is known as opinion mining or
comment extraction. It is an objective and subjective analysis
of unstructured text data, and classifies an emotional polarity
of the extracted subjective sentences. The purpose of
sentiment analysis is to obtain praise or disapproval opinions
on a certain commodity, and to provide a basis for
decision-making [15]. Sentiment analysis is divided into
coarse-grained sentiment analysis and fine-grained sentiment
analysis. Coarse-grained sentiment analysis includes
chapter-level and sentence-level sentiment analysis.
Fine-grained sentiment analysis is based on the analysis of

Science Journal of Business and Management 2020; 8(2): 50-56 52
feature level. Medhat et al. [16] believe that the main steps in
fine-grained analysis of online reviews are: emotion
recognition, feature extraction, emotion classification, and
emotion polarity recognition. Zhao [17] used the
DBSCAN-based text clustering process to mine the hotel's
reputation dimension. The results show that consumers are
more concerned about the features include hardware, service,
environment, diet and value. This research draws on the
previous method based on sentiment dictionary, and calculates
sentiment scores based on sentiment polarity and intensity to
obtain satisfaction which are used to analyze the revised IPA.
2.4. K-means Features Clustering
K-means clustering is an unsupervised machine learning
algorithm. Because of its simple principle, easy
implementation, and good results, it is a more commonly used
clustering method. It calculates the distance between objects
and judges the similarity between them, and then classifies
them. The main purpose of clustering is to divide the data set
into K classes, so that the data within the class is smallest, and
the inter-class clustering is the largest.
The main process of K-means clustering includes four steps.
First, determining the number of clusters K, and then it can
randomly select K initial points as the cluster centers. Second,
calculating the distance between each point in the data set and
K initial points, they can assign the points to the cluster center
with the smallest distance. Third, after classifying all points in
the data set, it should recalculate the cluster center. Finally,
repeating second and third steps until the cluster center does
not change, then it is considered that the optimal clustering has
been reached.
3. Research Design
The overall research framework of this paper mainly
includes text preprocessing, Word2Vec, K-means clustering,
fine-grained sentiment analysis, and partial correlation
analysis. The concrete process is shown in Figure 1. The
stage1 main includes data processing and features extraction.
The stage2 main obtain satisfaction and implicitly derived
importance of features. In stage3, we construct the IPA figure
to analysis the features of hotel and provide suggestions.
Figure 1. Research framework.

53 Xue Liu and Ning Zhang: Research on Customer Satisfaction of Budget Hotels Based on
Revised IPA and Online Reviews
3.1. Data Sources
The representative brands of Chinese budget hotels
including Home Inn, Hanting Inn and 7Days Inn, etc.
Therefore, this study chooses 21 budget hotels from Qingdao
including Home Inn, Hanting Inn and 7Days Inn, etc. By
writing a Python program for the web crawler, a total of
35,398 online reviews were obtained, including text reviews,
star scores, and review usefulness scores. Through clearing all
data and deleting invalid reviews, this research finally gets
30,877 reviews.
3.2. Hotel Features Clustering Based on K-means
1. Online reviews are segmented form a Word2Vector
corpus, and then we use the Word2Vec technology trains
the segmented corpus (window = 10, vector = 300),
finally, the words map to the K-dimensional vector space
to form the corresponding word vector.
2. K-means algorithm is used clusters nouns in the
dictionary. First we need to determine the number of
clusters of K-means. There are two best clustering
methods: the elbow method and the contour method. The
core idea of the elbow method is that as the number of
clusters increases, the sample segmentation will be more
accurate, and the sum of the squared errors will gradually
become smaller. When the number of true clusters is
reached, the degree of aggregation obtained by increasing
the number of clusters will rapidly decrease, so the
decrease in the sum of squared errors will decrease
sharply. Then it becomes flat as the number of clusters
increases, that is, the elbow corresponds to the true
number of clusters in the data. The core index of the
contour method is the Silhouette Coefficient. The larger
the average contour coefficient, the better the clustering
effect. Therefore, the maximum average contour
coefficient is the optimal number of clusters.
Because the number determined by the contour method is
not necessarily the optimal, it is sometimes necessary to judge
by SSE, so the elbow method is selected to determine the
optimal number of clusters. The number of clusters is set to 1
to 15 for repeated prediction. The final results show that when
the number of clusters is 13, the number of clusters is optimal.
Therefore, this paper divides features into 13 categories for
research.
The process of K-means clustering is as follows: (1)
Segmenting the reviews, the nouns in the reviews are selected
as candidate words for feature clustering. And keep the nouns
that appear at least 10 times to form a noun dictionary. (2)
After the words in the dictionary are trained by Word2Vec,
they are transformed into word vectors as a clustered corpus.
(3) The K-means algorithm is used to cluster the word vectors.
According to the optimal number of clusters, the number of
clusters is 13 and judged based on the distance formula. At the
same time, referring to previous related research and manual
classification, the final hotel feature is classified. The concrete
classification is shown in Table 1.
Table 1. Hotel Features and representative feature words.
Code Feature Representative feature words
1 Catering Breakfast, yogurt...
2 Bedding Sheets, quilts...
3 Service Attitude, politeness...
4 Insulation Voice, movement...
5 Environment Construction, environment...
6 Price Money, price...
7 Infrastructure Elevators, corridors...
8 Room Facility Heating, TV...
9 Cleanliness Toilet, toilet paper...
10 Brand Reputation, brand...
11 Network Network, wireless...
12 Bathroom Bathroom, sprinkler...
13 Location Squares, scenic spots...
3.3. Sentiment Analysis Based on Sentiment Dictionary
Consumers usually express their opinions on specific
features when they post reviews, so "feature-view pairs" can
be extracted as evaluation units. The central idea of sentiment
analysis is to judge sentiment through adjectives and adverbs
closest to feature nouns. After extracting the "feature-view
pairs" of each sentence, we will classify the features by
sentiment dictionary. In order to make the classification
results more accurate, the words related to hotels are added in
this research to form a special dictionary for hotels. The
concrete construction principles are as follows.
HowNet2007 [18] dictionary is used as the basic dictionary.
The positive basic sentiment dictionary is a combination of
positive evaluation words and positive emotional words. The
negative basic sentiment dictionary is a combination of
negative evaluation words and negative emotional words. In
the end, 7020 positive emotion words and 5949 negative
emotion words are obtained. In order to improve the
classification accuracy of the hotel-specific sentiment
dictionary, manual processing is required: all valid reviews
are processed by word segmentation, etc., and then word
frequency statistics are performed on the adjectives, and the
sentiment polarity of the selected adjectives is judged to form
a special sentiment dictionary.
Except for nouns and adjectives, consumers have different
emotional strengths when they post reviews. Therefore, this
research determines the quantitative standard of emotional
polarity based on the emotional level of degree adverbs in
HowNet2007, as shown in Table 2. The sentiment polarity score
of the feature is equal to the degree adverb multiplied by the
sentiment polarity value, if there is no degree adverb, the score is
1 or -1. The extracted “feature-view pairs” are calculated, and
then calculate the average of all features in a category to get the
sentiment score of every category of the hotel.
Table 2. Sentiment polarity quantification criteria.
Degree adverb Degree value
Very, absolutely... 2
Especially, really... 1.5
More, even more... 1
Slightly, a little... 0.5

Science Journal of Business and Management 2020; 8(2): 50-56 54
Degree adverb Degree value
Positive emotion words without degree adverbs 1
Negative emotion words without degree adverbs -1
3.4. Implicitly Derived Importance and Satisfaction of
Features
Through the above experiments, 13 categories of hotel
features are obtained. The sentiment score represents the
satisfaction score of each feature. The correlation test is
performed on the extracted 13 categories of features. There are
a total of 78 correlation coefficients, of which there are only 8
features that are not related to each other, including:
catering-infrastructure, catering-room facilities,
catering-network, insulation-network, environment-network,
brand-bathroom, brand-location and network-location.
In order to resolve the mutual influence between the
features, the existing data was revised according to Deng's
conversion method, and the implicitly derived importance is
used to replace the self-stated importance. The conversion
method mainly has two steps: First, the natural
logarithmln() is taken for each feature. Since the sentiment
score in this paper is calculated, the minimum positive score is
-2, therefore, ln( + 3) is used as the independent variable
of each attribute to make it linearly distributed. Secondly,
using ln( + 3) and overall satisfaction  , we perform
partial correlation analysis. The overall satisfaction score is
obtained by the crawler for the hotel star rating, with an
integer ranging from 1 to 5, where 1 is very dissatisfied and 5
is very satisfied. Partial correlation coefficients obtained from
partial correlation analysis are made to be implicitly derived
importance. The implicitly derived importance and
satisfaction scores of hotel features are shown in Table 3.
Table 3. Implicitly derived importance and satisfaction score.
Code
Feature Implicitly derived importance
Satisfaction
1 Catering 0.127 -0.022
2 Bedding 0.036 0.084
3 Service 0.162 0.464
4 Insulation 0.003 0.046
5 Environment 0.069 0.269
6 Price 0.083 0.175
7 Infrastructure 0.060 0.102
8 Room Facility 0.073 0.105
9 Cleanliness 0.056 -0.026
10 Brand 0.069 0.123
11 Network 0.011 0.022
12 Bathroom 0.054 -0.010
13 Location 0.111 0.386
3.5. Revised IPA
The satisfaction is taken as the horizontal axis and
implicitly derived importance is taken as the vertical axis. The
mean value of all feature satisfaction and implicitly derived
importance are used as the center points to divide the matrix
into four quadrants. The 13 features are mapped in the matrix
quadrant according to the scores of each feature in Table 3.
The results are shown in Figure 2.
Figure 2. IPA analysis of implicitly derived importance and satisfaction.
According to the IPA analysis figure, the satisfaction and
importance of the quadrant I are high, including three features:
service (3), location (13), and price (6). Among them, the
satisfaction and importance of service are the highest,

55 Xue Liu and Ning Zhang: Research on Customer Satisfaction of Budget Hotels Based on
Revised IPA and Online Reviews
reflecting the customer's attention to service personnel and
service attitude. With the development of society and the
improvement of civilized quality, customers pay more
attention to the requirements of intangible features and more
emphasis on the consumption of things. As a typical
experience product, hotels should pay more attention to the
improvement of service. The second is the hotel location. The
hotel location has always been used as an important reference
feature. In the past research on hotel features, the "hotel
location" was also researched separately. Therefore, location
is very important. It is more inclined to choose hotels with
convenient transportation, around the scenic area or
surrounding facilities. Finally, for the price, the rapid
development of budget hotels in recent years has benefited
from its price positioning. Compared with other types of
hotels, such as four-star and high-end hotels, budget hotels
have obvious price advantages. Since most consumers'
economic conditions are relatively ordinary, and they are still
sensitive to price issues. Therefore, budget hotels should
continue to give play to their price advantages, and strive to
optimize based on the market share they have already
occupied. Because the features of the quadrant I represent the
competitive advantage of the company, the strategy adopted is
"keep up the good work."
The quadrant II is low satisfaction and high importance, and
includes two features: catering (1) and room facilities (8).
Catering is essential for people to travel. The initial service
mode of budget hotels is accommodation and breakfast. With
the modern people's pursuit of healthy living, breakfast has
become the standard of daily life. Therefore, customers are
paying more attention to the free food and beverage provided
by hotels. Because the features of quadrant II represent the
aspects that enterprises urgently need to improve, if they are
ignored, it will pose a serious threat to the development of the
enterprise. People’s demand for hotel catering is not harsh,
and does not require the variety and richness of catering types,
therefore, it is necessary for hotel managers to provide a
relatively simple diet and improve it. For room facilities, such
as air conditioners, kettles, and other basic room types, they
are the basis for the consumer experience and the most basic
service of the hotel. They directly affect consumers’ overall
perception and satisfaction of the hotel. The strategy adopted
by this quadrant is "concentrate here."
The quadrant III, that is, satisfaction and importance are
relatively low, including seven features: brand (10),
infrastructure (7), cleanliness (9), bathroom (12), bedding (2),
network (11), and insulation (4). Infrastructure such as
parking lots, bathrooms such as bathing facilities, beddings
such as quilts, and insulation of rooms, are the basic
equipment of the hotel, so they do not attract much attention.
Customers are accustomed to these essential features, so they
are of low importance and have a low impact on overall
satisfaction. Regarding brand feature, this research finds that a
certain amount of online reviews will mention the hotel name,
such as "Hanting Inn", "Home Inn", etc. Therefore, customers
will be affected by the brand to a certain extent when making
consumer choices, but most customers prioritize other factors
before considering the brand. The cleanliness feature is also an
important basic condition. Under the condition that other basic
features are provided, the cleanliness of the hotel should reach
a certain standard. Network as an indispensable factor for
modern cities, especially tourism cities, it can already reach a
high coverage rate, and it is no longer an additional feature
that customers pay attention to. Therefore, the strategy
adopted for the features of the quadrant III is "low priority",
and it can be improved under the condition of sufficient hotel
resources.
The quadrant IV is high satisfaction and low importance,
and includes one feature: environment (5). The environment
may be noisy in places with convenient transportation or
around the scenic area, and the environment may be relatively
quiet in relatively remote places, however, there is also less
convenient transportation. Because location is a feature that
customers pay great attention to when consuming, and the
convenience of location contradicts the quietness of the
environment to a certain extent. Therefore, the general
customer will give priority to the hotel location and ignore the
quiet environment. It is considered to be "possible overkill".
When the resources of the hotel are limited, the features of the
other quadrants are prioritized, and the excessive resources are
transferred to other aspects to increase the overall satisfaction
of the hotel.
4. Conclusion
The IPA analysis method is widely used because of its
simplicity and ease of operation. However, due to the
limitations of the IPA method and the complexity of
traditional questionnaires, the use of IPA has also been
restricted. Therefore, from the perspective of management, the
focus of this article is the empirical application of Bi et al.'s
[19] method of introducing online reviews into IPA.
Compared with the questionnaire, online reviews are more
authentic and easier to obtain. Based on a large amount of data,
more objective and accurate results can be obtained. Hotel
managers determine more accurate and reasonable
improvement strategies based on the objective and precise
results, while effectively reducing the company's human,
material, and time costs. Secondly, we use the existing
principles and methods to revise IPA to meet the assumptions
of the use of IPA, so as to make the conclusions more accurate.
From the perspective of practice, it is possible to understand
the current development status of budget hotels in Qingdao
more realistically and effectively. Based on a more
trustworthy conclusion, it is proposed the improvement
strategy. The strategy can obtain higher satisfaction through
meeting customer needs as much as possible and provide
reference for the improvement of hotels for Qingdao to build a
national central city.
The research in this paper also has certain limitations.
Firstly, the selected hotel sample is only targeted at the
economy of Qingdao, and cannot represent the overall
development of the hotel industry in Qingdao. Follow-up
research and analysis will be conducted for different types of

Science Journal of Business and Management 2020; 8(2): 50-56 56
hotels. Secondly, although the revised IPA referenced in this
article includes the three-factor theory of asymmetry, it cannot
accurately reflect the asymmetry between the satisfaction and
importance of each feature. Therefore, we will carry out more
in-depth research on the revision method of IPA.
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