A model of the relationship between tourism
information retrieval and tourists' satisfaction: A case of
the Japanese and Korean tourists
Jongmin Bae
Gradaute School, College of Commerce, Nihon University
Domirukoike 103, 2-32, Tomihisa-cho, Shinjuku-ku, Tokyo 162-0067, JAPAN
+81-80-4634-5979
[email protected]
A model of the relationship between tourism information retrieval and
tourists' satisfaction: A case study of Japanese and Korean tourists
Abstract
The priority of foreign tourism has been increasing globally in recent decades. To increase the
satisfaction of the visitors, it is very important to develop new tourism products. Recently, the trends
of information searching have changed with the development of ICT. It is particularly noticeable in the
middle step of travel, where prospective travellers search for information on destinations. It has been
determined that the activity of information searching is one of the factors affecting satisfaction. In this
study, we elucidate the structure of information searching in the middle step of travel, and verify the
relationship between satisfaction and information searching.
Keyword : Consumer Behavior, Travel and Tourism, Knowledge Management Systems, Big-data,
East-North Asia
■ INTRODUCTION
relationship between that and satisfaction.
Through this research, I hope to examine the
basic structure of Big-Data for the application to
tourism information.
Globally, the overseas tourism market is
growing rapidly in the proportion of the whole
tourist market, with the number of overseas
visitors increasing faster than ever. In
accordance with this trend, it has become crucial
to the industry to consider the overseas tourism
market. To visit another country has advantages
of making special experiences, but there is some
risk at the same time. Tourists gather
information about travel region destinations to
protect themselves from risk, and the
information retrieval methods are various.
Mostly, the tourism information retrieval
activities of tourists are done on the pre-travel
step. However, because of the many limitations
on information retrieval and language difficulty,
there is a barrier to access tourism information
about foreign countries. For that reason, tourism
information retrieval is mainly studied about the
pre-travel step.
Many studies have been done on the relationship
between information retrieval and satisfaction.
However, to the author’s knowledge, there has
been no research about the in-travel step. In
order to confirm the relationship between the
information retrieval, information use, and
satisfaction, the present study proceeds as
follows:
First, theoretical research on tourism
information, information retrieval, and the use
and satisfaction of information technology are
conducted. In addition, literature on information
retrieval infrastructure is examined. In the next
step, a hypothetical model is established on the
basis of the theoretical studies. In the final step,
an empirical survey of actual Korean and
Japanese tourists is done to verify the model.
However, this tendency is been changing by
constant exertions of tourist sites and
development of ICT technology. According to
these environmental changes, information
retrieval activities of tourists are now found to
appear consistently in all steps of tourism –
before, during, and after the trip. Because the intravel step is now a period of information
retrieval, the collection and verification of the
data corresponding to the tourist activity can
take place at the same time. These changes have
induced a need to extend the research scope
regarding tourism information retrieval. In
particular, extending the range of the research
will help to determine the emerging data
structure and ways of applying big data.
■ META-ANALYSIS
- Tourism Information and Big-data
Tourism Information refers to all information
that tourists needed for tourist activities. And the
range of data types and sources ranges according
to the needs of tourists. Generally, tourism
information can be divided into six categories as
follows.
The first category is general information about
tourist areas. This refers to climate information,
society information, economic information, and
cultural assets of the area. The second category
is accommodation information. This means the
information on the type, location, rating, number
of rooms, availability, price, and dining and
other facilities of accommodation. The third
category is the transportation information. This
means information about the type of
transportation, cost, available times, and
duration. This can be subdivided into two parts.
That is, travel to the target area and travel within
the target area. The fourth category is the
information about restaurants. This means
information on famous and/or unique
For expanding the scope of the research to intravel step with the tourist, some considerations
are needed. First, the structure of the information
retrieval in the in-travel steps should be
elucidated. This is because the information
retrieval structure in the in-travel step differs
significantly from that in other travel steps.
Subsequently, the method of processing and
applying tourism data according to the identified
structures should be developed.
The goal of this study is to find out the purpose
of tourism information retrieval, and the
1
restaurants, and about the style, menu, services,
and how to access. Fifth category is information
on enjoyable facilities or events. This means
information about location, type, cost, and event
information. The final category is information
about safety and security. Activities of tourism
in unfamiliar places are a great pleasure.
However, it is very important to ensure a sense
of psychological relief so as to enjoy it
completely. The information in this category
refers to information that confirms the safety of
tourism. For example, tourist insurance
information, medical facilities information,
emergency contact information, location and
contact details of embassies, and the
geographical positioning information.
volume refers to the amount of data. The variety
refers to different types of data and data sources.
The velocity refers to data in motion, which
implies data uncertainty. The Samsung
Economic Research Institute of Korea has
defined big-data as "The collection of data that
is difficult to handle [including data from] the
organization and technology personnel to
management and analysis." 3 According to this
definition, it is included in its meaning that the
human resources and organization are essential
for handling information besides a scraps of
information.
Deep-learning is defined as a set of machinelearning algorithms to attempt a high level of
abstraction using a combination of different nonlinear transformation techniques. 4 Based on
these theories, the meaning of information can
include pieces of information, technical
personnel, and the management of information.
This can be applied equally well to tourism
information.
There are differences between online
information and offline information. Online
information has the following six features. ① It
is easy to update to the latest information
(Freshness). ② Transfer of information is simple
(Accessibility). ③ It allows two-way
communication of information (Interactivity). ④
It is possible to provide various types of
information by using multimedia (Abundance).
⑤ Access to relevant information is simple
(Ease of use). ⑥ The information structure is of
a massive and deep structure (Immensity).1
- Tourism information retrieval
Tourism information retrieval according to the
features of the activity can be classified in
various ways, as follows:
As mentioned above, the conceptual scope of
information started from the simple pieces of
information, and it has been expanding widely.
Nowadays, information does not refers to just a
scraps of information. Information is understood
in terms of structure and the function. This trend
can be found in the concept of big-data and
Deep-learning.
First, information retrieval can be divided into
searching and surfing. Searching refers to
finding specific features and purchasing with the
purpose of information retrieval. Surfing is an
ongoing search seeking simple or general
information.
Pan (2003)
The concept of big-data is proposed in 2012.
IBM defines big-data as "The data of 4V
(Volume, Variety, Velocity, Veracity)". 2 The
5
divided tourism information
enterprises extract value from uncertain data.
IBM Institute for Business Value. Retrieved from
http://www-03.ibm.com/systems/hu/resources/the
real word use of big data.pdf.
3
Yugun, H., & Sungbyung, C. 2012. Big-data,
Chandethe management. The Samsung Economic
Research Institute of Korea
4
Y. Bengio, A. Courville, & Vincent, P. 2013.
Representation learning: A review and new
perspectives. IEEE Trans. PAMI, Special Issue
Learning Deep Architectures.
5
Pan B. 2003. Travel Information Search on the
1
Hyojin, J. 2006. Studies in Online Travel Information
Credibility and Relation Continuance Trust. Ph.D.
Thesis, Department of Tourism Management, The
Graduate School of Kyonggi University, Kyonggi ,
Republic of Korea: 14–15.
2
Schroeck, M., Shockley, R., Smart, J., RomeroMorales, D., & Tufano, P. 2012. Analytics: The
real-world use of big data. How innovative
2
retrieval into five steps. It is based on the flow of
tourism activities. The five steps of this model
are Ongoing Search, Prepurchase Search,
Planning Search, En-route Search, and After-trip
Search, as presented in Figure 1.
search. Internal search is a process of retrieving
the necessary information from the past of own
experience or knowledge. External search is a
process of retrieving the necessary information
from outside one’s own experience and
knowledge. Sources of information in the
external search process include books, radio, TV,
Internet, and external human resources.
Finally, there is a criterion of classification about
knowledge regarding time flow, which can be
divided into knowledge gained before and after
the search. Earlier knowledge often has features
that affect the later knowledge. Based on these
theories, it is determined that previously gained
knowledge may be affected by the information
search activity of an En-route Search step.
In a study by the Pan, we can confirm that the
information search influence satisfaction.
Considering this together, it has been determined
that the satisfaction is influenced by information
search activity in this process.
Fig.1. A process Model of Travel
Information Search6
The conventional concept of the tourist
information search refers only to the step of
Planning Search. However, Pan divided the
planning step into two steps: Ongoing Search
and Prepurchase Search. He thought of each step
as having different features of information
search. He also asserted that there is the
information search in the After-trip step.
Especially, he argued that the information search
in the En-route Search step and After-trip Search
step are very important factors determining
intention to revisit.
Infrastructure,
Satisfaction
Information,
and
Recently information technology is changing
rapidly. These changes are lowering the barriers
between countries. In particular, they have
lowered the barrier of communication method
and language problem, the most. These two
barrier is the highest barrier to international
tourism.
However,
accessing
tourism
information from overseas is becoming very
convenient, by changes such as supporting
multi-language tourism websites, diversification
of internet platforms, supporting for mobile
device,
and
unified
standards
for
communications technology. Furthermore, the
environment is making possible to collect
marketing information such as location
information, health information, and billing
information. Therefore, these changes in
technology can promote search activity in the
En-route step.
Another classification criterion of the
information search activity is the range of
searches. By this criterion, search activities can
be divided into internal search and external
Internet: An Exploratory Study. Submitted in partial
fulfillment of the requirements for the degree of
Doctor of Philosophy in Leisure Syudies in the
Graduate College of the University of Illinois at
Urbana-Champaign: 10–11.
6
Pan, B. 2003. Travel Information Search on the
Internet: An Exploratory Study. Submitted in partial
fulfillment of the requirements for the degree of
Doctor of Philosophy in Leisure Syudies in the
Graduate College of the University of Illinois at
Urbana-Champaign: 11.
There are many theoretical studies on the use of
technology and satisfaction. The most relevant
theory is Expectation Confirmation Theory
(ECT), illustrated in Figure 2.
3
ECT is primarily used in the research of general
customer satisfaction, pre-purchase behavior,
and marketing services. When it the perceived
performance and the expectation match, the
perceived performance7 and the disconfirmation
become satisfaction factors. If the information is
as useful as expected, the satisfaction will be
increased. In addition, the continuous use
intention of the used information retrieval
method is affected at the same time. Consumer's
buying activities and ongoing search activities
increase the repurchase and reuse of the
purchase methods.
factors. Personal factors are attitudes toward
behavior, and social factors are subjective social
norms. These two factors affect the intention of
activities and the actual action. Davis asserted
that perceived usefulness and perceived ease of
use are the major factor in information
technology. He explained the process of
influence. The attitude caused by these factors
affects the intention of action, and the intention
of action affects the actual behavior.
And,
according
to the
research
of
Bandura(1982) 9 , The User of information
technology has higher confidence, when the ease
of use is high level. And, that confidence makes
the attitude of the information technology a
positive.
Lederer et al.(1998)10 have found that this TAM
can be applied to homepage information search.
In their study, the perceived ease of use had a
positive impact on attitude, for use of the
homepage, and behavior of the information
search.
Fig.2. The structure of Expectation
Confirmation Theory
We can explain the relationship about the
information search activities by using this
model. When searching information, if
confirmed the usefulness and ease of use, the
information search behavior change the attitudes
of tourists. This changed attitude affects the
actual tour activities by intention of action.
The similarity is very high between information
retrieval of the purchase tourism product to
information retrieval of the general consumption
process. Thus, it is possible to apply this theory
to the information retrieval intention of tourism
and tourism products.
- Technology Acceptance Model
As a model to explain the behavior using
information technology, Davis (1989) 8 has
proposed the Technology Acceptance Model
(TAM). This theory is a model that extends the
Theory of Reasoned Action (TRA) proposed by
the Ajen & Fishbein (1967). According to the
TRA, there are personal factors and social
Fig.3. Technology Acceptance Model.11
Bandura, A. 1982. Self-efficacy mechanism in
human agency. American Psychologist, 37(2):
122–147.
10
Lederer, A.L., Maupin, D.J., Sena, M.P., &
Zhuang, Y. 2000. The technology acceptance
model and the World Wide Web. Decision
Support Systems, 29: 277–280.
11
Davis, F.D. 1989. Perceived Usefulness, Perceived
Ease of Use, and User Acceptance of Information
9
Oliver, R.L. 1980. A Cognitive Model for the
Antecedents and Consequences of Satisfaction,
Journal of Marketing Research, Vol. 17: 460-469.
8
Davis, F. D. 1989. Perceived usefulness,
perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3) :
319–339.
7
4
 Hypothesis 6: The search behavior and
confirmation of information are positively
correlated.
■ RESEARCH MODEL
Here, we postulate the factors affecting the
information search behavior of tourists. We then
build the model for understanding the behavior
of the information search in En-route step. There
are three factors with corresponding questions as
follow:
 Hypothesis 7: The search behavior and
satisfaction are positively correlated.
 Hypothesis 8: The confirmation of
information and satisfaction are positively
correlated.
1. What is the condition of information search?
 Hypothesis 9: The satisfaction of the tourist
and the earlier knowledge are positively
correlated
2. What is the purpose of tourists' information
search?
3. Do the tourists have earlier knowledge about
the target of the search?
Search conditions can be classed into three
factors: the time, the place, and the method of
search. The purpose of the information search
can be classed into three factors, practical
usefulness, fun, and threat avoidance. Earlier
knowledge refers to the information identifiable
without a search activities, because of the
tourists already having it. We decided on the
three factors described above as factors affecting
the search attitude and retrieval activities. In
addition, the result can also reflect factors that
influence the first earlier knowledge factor
according to the Bayesian inference.
Fig.4. Research Model
■ SURVEY AND RESULTS
The survey was conducted in the international
airport terminals in Japan and South Korea. The
survey period was from the 9th of August to
18th of September. It was conducted on 68
tourists. Among them, 46 were Japanese and 22
Korean. The questionnaire of this survey was
designed to address the above hypotheses by
Likert scale. The correlation analysis was used
to examine how the each factor is related to each
of the others. The analysis software is SPSS 18.
Given the above, the following hypotheses are
set here, as indicated by the research model
shown in Figure 4:
 Hypothesis 1: The situation of the tourist and
the search attitude are positively correlated.
 Hypothesis 2: The earlier knowledge of the
tourist and the search attitude are positively
correlated.
5
 Hypothesis 3: The searching purpose and
search attitude are positively correlated.
3
 Hypothesis 4: The search attitude and search
behavior are positively correlated.
-1
Technology. MIS Quarterly, 13(3): 321-323.
5
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q13
Q14
Q15
Q16
1
 Hypothesis 5: The condition of the tourist
and search behavior are positively correlated.
Fig.5. The average value of each item
appeared to have the highest relevance to the
search attitude.
Table 1 shows the correlation analysis results
relating to hypothesis 1. In the first hypothesis, I
defined the time, location, and retrieval method
as determining state of the tourists, and verified
that there a connection between these three
factors and search attitude. As shown in Table 1,
the relation coefficients are -.025(the time) and .046 (the place). This means there is no
connection between these factors (time and
place) to search attitude. However, the
coefficient of retrieval method is .819. This
means there is a very strong connection between
the retrieval method and search attitude.
Table3. Hypothesis3
Q4
Q7 Usefulness
Q5
Q6
Q4 Time
1
Q5 Place
-.058
1
Q6 Method
-.157
-.068
1
Q11 Search Attitude
-.025
-.046
.819
1
Q8 Fun
.618
1
Q9 Avoid Risk
.567
.582
1
Q11 Search Attitude
.731
.748
.740
1
Table4. Hypothesis4
Q11
1
Q10
Q11
1
.613
Q11 Search Attitude
1
Q12 Search Activity
.470
Q12
1
Table 5 shows the correlation analysis results
relating to hypothesis 5. Hypothesis 5 is for
verifying the relation between the situation of
the tourists to search behavior. The same factors
are used as in the first hypothesis: time, the
place, and the search method. The coefficient of
time was .070, and that for place was .071.
These coefficients mean there is no relevance
between the time and place to the search
behavior. However, the coefficient of the search
method was .758, which indicates that the
situation of the tourists has relevance to the
search behavior.
Table2. Hypothesis2
Q11 Search Attitude
Q11
Q11
Table 2 shows the correlation analysis results
relating to hypothesis 2. The coefficient of the
result is .613. This means the earlier knowledge
has relevance to the search attitude.
Q10 Earlier Knowledge
Q6
Table 4 shows the correlation analysis results
relating to hypothesis 4. As shown in Table 4,
the relation coefficient is .470 between the
search attitude and search behavior, which
indicates a significant relationship.
Table1. Hypothesis1
Q4
Q5
1
Table 3 shows the correlation analysis results
relating to hypothesis 3. In hypothesis 3, the
searching purpose is defined as containing three
factors (usefulness, fun, avoid risk). As shown in
Table 3, the relation coefficients are .618
(usefulness), .567 (fun) and .731 (avoid risk).
This means these three factors have relevance to
the search attitude. Especially, avoid risk
Table5. Hypothesis 5
6
Q1
Q2
Q1 Time
1
.
Q2 Place
-.180
1
Q3
Q12
Q3 Method
-.012
.015
1
Q12 Search Behavior
.071
.070
.758
Table9. Hypothesis9
1
Table 6 shows the correlation analysis results
relating to hypothesis 6. The coefficient
was .409. This means that search behavior has
relevance to confirmation.
Q12
Q15 Confirmation
Q16 Earlier Knowledge
Q12 Search Activity
Q13 Confirmation
Q13
1
.409
1
.490
1
As the results of analysis indicate, some factors
(the time and place) of the hypothesis 1 and
hypothesis 5 were found to be non-relevant.
Hypothesis 7 was also denied. The other
hypotheses are all proven.
Table6. Hypothesis6
Q12
Q13
1
The results indicate the following conclusions.
First, retrieval method of information affects
attitude and search activity. Second, purpose of
search and earlier knowledge affect attitude
toward search. Third, the attitude to the search
affects search behavior. Finally, if it is perceived
that the search activity is actually useful, the
satisfaction rises.
Table 7 shows the correlation analysis results
relating to hypothesis 7. The coefficient was .001. This means the search behavior has no
relevance to satisfaction.
Table7. Hypothesis7
Q12
Q12 Search Activity
Q14 Satisfaction
■ DISCUSSION
Q13
Direct meanings of this research are as follows:
1
-.001
The activity of searching in the En-route step is
more common than ever before. According to
this possibility in the En-route step, factors
affecting satisfaction are increased. The results
of the present study indicate the following:
1
Table 8 shows the correlation analysis results
relating to hypothesis 8. The coefficient
was .556. This means the confirmation has
relevance to satisfaction.
First, provision of tourism information on the
En-route step is a way to increase the
satisfaction of tourists. If it can achieve the
highest satisfaction of tourists, increased repurchase of tourism products, and boost the
economies of the tourist areas. Thus, the
marketers and managers of the tourist area have
to focus on providing information in the Enroute step.
Table8. Hypothesis8
Q12
Q13 Confirmation
Q15 Satisfaction
Q13
1
.556
1
Second, it is confirmed that a review is required
for the configuration of the online tourism
information. This research has established a
model for the use of tourism information in the
En-route step. Accordingly, to find a way of
providing tourism information, we have to study
the structure of the tourism information. It is
Table 9 shows the correlation analysis results
relating to hypothesis 9. The coefficient of
analysis was .490. This means the satisfaction
has relevance to early knowledge.
7
■ REFERENCE
determined that this research provides guidance
for the construction and configuration of the
tourism information.
Bandura, A. 1982. Self-efficacy mechanism in
human agency. American Psychologist, 37(2):
122–147.
Third, it was confirmed that we must build data
about the tourists in the tourism industry. If we
can check the activities of tourists in the Enroute step, we will raise the satisfaction of
tourism and intention to repurchase tourism
products. Data collection about the tourists in
the En-route step is necessary for looking for
better marketing methods and making new
tourism products. This study can provide a
theoretical model for the structure of
information searching for tourists.
Davis, F.D. 1989. Perceived Usefulness, Perceived
Ease of Use, and User Acceptance of Information
Technology. MIS Quarterly, 13(3): 319-339.
Davis, F.D. 1989. Perceived Usefulness, Perceived
Ease of Use, and User Acceptance of Information
Technology. MIS Quarterly, 13(3): 321-323.
Hyojin, J. 2006. Studies in Online Travel Information
Credibility and Relation Continuance Trust. Ph.D.
Thesis, Department of Tourism Management, The
Graduate School of Kyonggi University, Kyonggi ,
Republic of Korea: 14–15.
Fourth, the big data is positively necessary in the
tourism industry. Building big data has two
meanings. First, it is possible to integrate the
management of information related to tourism. If
this can be done, the cost of managing
information is reduced. In addition, the
reliability can be acquired by the reliable
managing. Second, we can create opportunities
for new business by analyzing the pattern of
tourists. Through the gathering of relevant
information, new or emerging trends of tourists
can be identified. Besides, it is also possible to
find out new needs of tourists.
Lederer, A.L., Maupin, D.J., Sena, M.P., &
Zhuang, Y. 2000. The technology acceptance
model and the World Wide Web. Decision
Support Systems, 29: 277–280.
Oliver, R.L. 1980. A Cognitive Model for the
Antecedents and Consequences of Satisfaction,
Journal of Marketing Research, Vol. 17: 460-469.
Pan B. 2003. Travel Information Search on the
Internet: An Exploratory Study. Submitted in partial
fulfillment of the requirements for the degree of
Doctor of Philosophy in Leisure Syudies in the
Graduate College of the University of Illinois at
Urbana-Champaign: 10–11.
This study has determined the relationships
between information search and satisfaction on
the En-route step. The structure of the
relationship provides guidelines for the
information providing method in the En-route
step. Furthermore, I believe this result will help
elucidate the building structure of big data
tourism information system.
Schroeck, M., Shockley, R., Smart, J., RomeroMorales, D., & Tufano, P. 2012. Analytics: The realworld use of big data. How innovative enterprises
extract value from uncertain data. IBM Institute for
Business Value. Retrieved from http://www03.ibm.com/systems/hu/resources/the real word use of
big data.pdf.
Y. Bengio, A. Courville, & Vincent, P. 2013.
Representation learning: A review and new
perspectives. IEEE Trans. PAMI, Special Issue
Learning Deep Architectures.
Yugun, H., & Sungbyung, C. 2012. Big-data,
Chandethe management. The Samsung Economic
Research Institute of Korea
8