SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial Life
Vol. 9(4); March (2023), pp. 358–362
ISSN (Online):2352-6386; ISSN (Print):2405-9021
https://alife-robotics.org/jrnal.html

Tsutomu Ito1, Seigo Matsuno1, Takao Ito2, Makoto Sakamoto3, Satoshi Ikeda3, Rajiv Mehta4
1

Department of Business and Administration, Ube National College of Technology, 2-14-1 Tokiwadai, Ube, Yamaguchi, 755-0096, Japan
Graduate School of advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, 739-8527, Japan3
Faculty of Engineering, University of Miyazaki, 1-1 Gakuen Kibanadai-Nishi, Miyazaki, 889-2192, Japan
4Martin Tuchman School of Management, New Jersey Institute of Technology, University Heights, Newark, New Jersey, 07102-1982, U.S.A
2

Received 24 November 2021
Accepted 07 February 2023

Regional revitalization
Touristic destination
Tourism resource
Travel time
Four-cell model

Currently, Japan is facing serious headwinds from declining birthrates as well as an aging society,and their economic consequences are widely known. Among many possible strategies to address
the aging and shrinking population, the revitalizing the local economy in each prefecture become
more prominent with an emphasis on spurring tourism. To shed light on this issue, this manuscript
focuses on determining empirically the interrelationship between tourists and tourism destination
characteristics using regression modelling. More specifically, the inter-linkages among tourists
and tourism destinations are conducted by developing a 4-cell model. The results find support for
the importance of the development of public traffic among different tourism destinations as apromising economic revitalization strategy.
© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

1. Introduction

regional revitalization. This research makes acontribution to the literature by developing andempirically testing an innovative 4-cell model, whichinvestigates the impact of strategies for increasing thenumber of tourists to tourism destinations.
This paper is organized as follows: In Section 2 therelevant literature is reviewed. Section 3, develops andexplicates the four-cell model and its associated research
hypotheses. Section 4 discusses the antecedents ofsuccessful tourism, while Section 5 analyzes andexplicates the relationship between the four-cell modeland the distance between touristic destinations. In the
final section, managerial implications and conclusions
are discussed.

Japan is in the midst of confronting the economic
consequences resulting from the dual problems ofdeclining birthrates and an aging population. The
declining population from low birthrates, and an aging
labor force, many commercial problems may cause
headwinds for its national economy and society-at-large.
Thus, it is pivotal that strategies be implemented torevitalize commercial activities in local communities,
which will contribute to the development of the overall
Japanese economy. Recently, tourism, in particular, hasbeen identified as an important sector for revitalizing
regional cities in all prefectures that could contribute toeconomic growth approaches. Consequently, if therelationship between tourism and the number of tourists
can be determined, it will be possible to allocate
resources to spur tourism as a driving force towards

2. Background

Corresponding author’s E-mail itotakao@hiroshima-u.ac.jp358

Table 1. The planning and control characteristics in different prefectures.
Hokkaido

Aomori

Iwate

Miyagi

Akita

Wakayama

EhimeMiyazaki

Nature

12

113

124

12

14

1

History and culture

12422

1112

1

14

1

Hot spring and healthcare

13

1232233

144

4Sports

1

13233332

1

112

Urban tourism

1

14

1

1

13342

14

1Event1212

114

1

1

143

3Others

1

112

1

111111

1

2

Many countries are primarily dependent on tourism. Assuch, they develop special schemes to foster tourism—assuggested by the plethora of literature published onJapanese tourism. For instance, using SNS data, Kudo etal. proposed a new method of event collection for tourist
information distribution to foreign tourists [1]. Aihara
analyzed tourist behavior and sentiments using big data[2] whereas Okamura and Fukushige’s study emphasized
the importance of promoting repeater tourists in Kansai
area [3]. Butler presented a concept of a recognizable
cycle on the evolution of tourist areas and explained thecontinuing decline in the environment quality and theattractiveness of many tourist areas using a basic S curve[4]. Accordingly, to counteract the deleterious effect theaging and declining population will have on the Japanese
economy, the role of promoting tourism has taken on
greater importance in becoming the most effective
economic development strategy with a special focus onrevitalization of regional tourism destinations in eachprefecture in Japan. However, studies that identify thedeterminants of tourism and the effects of tourists’promotion strategies are scant. To provide more practicalguidance for the development of tourism in Japan, thispaper proposes a 4-cell model that investigates theinterrelationships among tourists and tourism
destinations using a regression analysis. Moreover, itseeks to find support for placing importance on thedevelopment of public traffic among different tourism
destinations.

Tottori Shimane Okayama Hiroshima Yamaguchi

number of tourist facilities—as depicted in Figure 1. The
horizontal and vertical axis indicate the change of thenumber of tourism destinations, and the increase ordecrease in the number of tourists, respectively.
Based on the 4-cell model, positioning wasperformed for each quadrant by assigning weights todifferent characteristics, which was used as an index
indicating the tourism strategies for each prefecture.
Based on the positioning, research hypotheses for eachquadrant were proposed as follows:
H1. If it belongs to the first quadrant, the tourism industry
can be considered successful.
H2. If it belongs to the second quadrant, tourism is also
successful, albeit less.
H3. If it belongs to the third quadrant, tourism isdeclining.
H4. If it belongs to the fourth quadrant, tourism has failed.
With respect to Hypothesis 1, both the number oftourists and the number of tourism destinations areincreasing, and is considered a desirable tourist
attraction; thus, a weight of 4 is given to the first quadrant.
For Hypothesis 2, the number of tourism attractions isdecreasing despite of the increasing in the number oftourists suggesting that the number of visitors revisit the3. Four-cell model and 4 Hypotheses
It is crucial to observe and evaluate changes in thenumber of tourists and touristic destinations inchronological order. Accordingly, the linkage between
the change in the number of tourists and different tourism
characteristics is ascertained using the proposed 4-cellmodel on 13 Japanese prefectures. The longitudinal, dataon 7 types of tourism features reported in Table 1 wasdrawn over a period of 7 years [5], [6]. By employing thisprocedure, it is possible to collectively express andcompare fluctuations in the number of tourists and theFigure 1. Results of 4-cell model359

tourist spots as they do not venture to discover new
tourism areas. Therefore, a weight of 3 is given to thesecond quadrant. With regard to hypothesis 3, both
number of tourist spots and the number of tourists isdecreasing, and it is possible that they are entering aperiod of decline. Accordingly, a weight of 2 is given tothe third quadrant. Relative to hypothesis 4, the numberof tourists is decreasing despite of the increase in thenumber of tourism attractions, thus indicating that there
is a mismatch in supply and demand do not match.
Therefore, a weight of 1 is assigned to the fourth quadrant
as the desired tourism attraction has not been achieved.
The findings for each prefecture and different tourism
characteristics are depicted in Table1.
Figure 2 exhibits the evaluation index for calculating
the tourism attraction status for each prefecture bysummarizing the tourism elements belonging to eachquadrant by considering the weight of each quadrant.

Next, famous tourism destinations were selectedfrom Jaran Net [9] to ascertain whether it is possible toaccess different tourism destinations from nearby tourism
attractions and the approximate travel time among these
destinations. If public transportation was used, travel
time was ascertained by rail. If not, the travel time byautomobile was measured. The travel time between
tourist spots is summarized in a matrix format in Tables
2 and 3 for railways and highways in the four prefectures,
respectively.
The evaluation travel time is calculated by thefollowing formula.
Evaluation value of travel time =Travel time required
Area of each prefecture(1)

Equation (1) shows that evaluation value of travel
time is calculated as a ratio between travel time required
and the area of each prefecture. The reason whyevaluation value of travel time should be divided by areaof different prefecture is the area of each prefecture isdifferent. For comparative study among different
prefecture, a relative index is required.
The evaluation value of travel time of all prefectures
has been calculated. Hokkaido, Hiroshima, Shimane, andEhime as the typical examples of the best and the worst
prefectures have been selected. Tables 2 and 3 show a listof evaluation value of travel times for railways andhighways in the four prefectures, respectively.
Table 2. Evaluation value of average travel time for railroad(min/km2).

Figure 2. Evaluation index of the selected prefecture.

Hokkaido

Hiroshima

ShimaneEhime

0.00270.01040.0146

0.0268

4. Determinants of successful tourism
Table 3. Evaluation value of average travel time for highway(min/ km2).

The successful factors representing the best twoprefectures and the least attractive two prefectures
depicted in the 4-cell model. To identify the salient
factors involved in attracting tourists, the study focusedon the time required to move between tourism
destinations. It is highly unlikely that tourists will visit
tourism destinations considering only one characteristic.
It is more likely that they will visit several tourism
destinations in the prefecture. Therefore, based upon theaccessibility index proposed by the Ministry of Land,
Infrastructure, Transport and Tourism [7], [8], the travel
time between different tourism destinations wascompared with tourism trends of typical prefectures.

Hokkaido

Hiroshima

ShimaneEhime

0.00210.00740.01070.0104

5. Analysis of the relationship between the fourcell model and the distance between tourist
spots
In comparing these results with the tourist attraction
evaluation index specified in the 4-cell model, the travel
time per unit area of kilometers tends to be short in
Hokkaido and Hiroshima prefectures, while in theShimane and Ehime prefectures are considerably360lengthier and more time consuming. On the other hand,
the travel time per unit area of Hokkaido and Hiroshima
prefectures was shorter even when using an automobile,
but in Shimane and Ehime, the result was inverse by rail.
As revealed by the findings illustrated in Figure 3, blue
represents the value obtained by dividing the movingaverage time when using railways, while gray represents
the value obtained by dividing the moving average timeby area. The value of evaluation in the 4-cell model, and
it is desirable, thus assigning a high value is represented
by orange. As is evident from Figure 3, an inversely
proportional relationship between the evaluation value
and the distance between tourist spots is found with acorrelation coefficient of -0.959, which indicates it ishighly correlated with the number of tourists attracted totourism destinations. However, relative to travel time, apositive result was obtained.

Furthermore, the development status of the transportation
network of the 4-cell model and the local indication itself
confirmed that there is a high correlation among thedevelopment status of public transportation and the index
obtained by the proposed method. This result indicatesthat the development of public transportation thatconnects tourist spots is one of the factors for the successof developing tourism in different prefectures.
Accordingly, these findings should be considered infuture research efforts for planning tourism policy.
References[1] Ruriko Kudo, Miki Enoki, Akihiro Nakao, Shu Yamamoto,
Saneatsu Yamaguchi, and Masato Oguchi, Event namecollection method for information distribution corresponding toplace and time using SNS data (Japanese edition), Proceedingsof the 81st National Convention, 411-412, 2019, retrieved
2021/4/20.
[2] Kenro Aihara , Grasping dynamism of tourist behavior andthoughts through big data analysis -Case studies on analyzing
trajectories and microblogs of foreign visitors (Japaneseedition), Information Management Vol.59, 743-754, February
2017.
[3] Kaoru Okamura, Mototsugu Fukushige, How to Promote
Repeaters? Empirical Analysis of Tourist Survey Data in
Kansai Region (Japanese edition), Discussion Papers in
Economics And Business，Vol. 07-42，November 2007.
[4] Richard W. Butler, The concept of a tourist area cycle ofevolution: implications for management of resources, Canadian
Geographer 24, 5-12, June 2008.
[5] Japan tourism agency, Statistics of the number visitors,
Statistics/White paper (Japanese edition).
[6] Regional economy society analyzing system, A regionaleconomic analysis system (Japanese edition).
[7] Ministry of Land, Infrastructure, Transportation, andTourism, An evaluation method of “accessibility of service” forlocal public traffic (Japanese edition).
[8] Urban Research Department, National Institute for Land andInfrastructure Management, A guideline for usage ofaccessibility indexes (Draft) (Japanese edition).
[9] Recruit Co., Ltd., Tourism Guidance, Jalan Net.

Figure 3. Relationship between evaluation value and distance
between tourist spots.

6. Conclusion
This empirical research investigation attempted toidentify the salient factors for promoting tourism in 13
Japanese prefectures such that appropriate provision oftourism resources will lead to regional revitalization in
the age of declining population and aging population.
This paper proposed a method to clarify the linkage
between the number of tourists and tourism destinations.
In study confirmed that it is difficult to ascertain thefactors that affect the number of tourists by rudimentary
contrasting the number of tourists and the number ofrestaurants in each attraction vicinity. Therefore, thisstudy proposed a novel procedure for evaluating thetendency of attracting tourists by identifying the factorsof attracting tourists in Japan with the 4-cell model.
361

Dr. Satoshi Ikeda
He received PhD degree fromHiroshima University. He is anassociate professor in the Faculty ofEngineering,
UniversityofMiyazaki. His research interest
includes graph theory, probabilistic
algorithm, fractal geometry andmeasure theory.

Authors Introduction
Dr. Tsutomu ItoDr. Tsutomu Ito is AssistantProfessor of the Department ofBusiness Administration at National
Institute of Technology, Ube
College, Japan. He has publishedmany papers in refereed journals andproceedings, particularly in the areaof industrial management, andcomputer science. His current
research interests include internet of things (IoT),
mechanical engineering, artificial intelligence (AI),
automata theory, quantitative analysis of JapaneseKeiretsu. Dr. Ito earned his doctor degree of Engineeringfrom Hiroshima University, Japan in 2018.

Dr. Rajiv Mehta
Dr. Rajiv Mehta (Ph.D., DrexelUniversity) is Professor of Marketingat Martin Tuchman School ofManagement, New Jersey Institute ofTechnology, USA. Focusing on sales
management,
globalmarketing
channels, Keiretsus, and international
strategic alliances, his research is
published in Sloan Management Review, Journal ofBusiness Research, Industrial Marketing Management,
European Journal of Marketing, International Marketing
Review, and Artificial Life and Robotics, among others.

Dr. Seigo Matsuno
Dr. Seigo Matsuno is Professor of theDepartment of Business Administration
at National Institute of Technology,
Ube College, Japan. He received hisPh.D. degree in Economics fromKyushu University, Japan in 2004. His
current research interests are in theareas of IT management and strategy,
information systems outsourcing, and interfirm
relationship management.
Dr. Makoto Sakamoto
Makoto Sakamoto received the Ph.D.
degree in computer science andsystems engineering from Yamaguchi
University. He is presently Professor
in the Faculty of Engineering,
University of Miyazaki. He is atheoretical computer scientist, and his
current main research interests areautomata theory, languages, andcomputation. He is also interested in digital geometry,
digital image processing, computer vision, computergraphics, virtual reality, augmented reality, entertainment
computing, complex systems.
Dr. Takao Ito
Dr. Takao Ito is Professor ofManagement of Technology (MOT)in Graduate School of Engineering at
Hiroshima University. He is serving
concurrently as Professor of Harbin
Institute of Technology (Weihai)
China. He has published numerous
papers in refereed journals andproceedings, particularly in the area of management
science, and computer science362