Journal of Robotics, Networking and Artificial Life
Vol. 8(3); December (2021), pp. 165–169
DOI: https://doi.org/10.2991/jrnal.k.210922.003; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Identifying the Determinants of Michinoeki Performance
Takao Ito1, Tsutomu Ito2,*, Seigo Matsuno2, Rajiv Mehta3, Makoto Sakamoto4
Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi Hiroshima 739-7527, JapanDepartment of Business Administration, National Institute of Technology, Ube College, 2-14-1, Tokiwadai, Ube City, Yamaguchi Pref. 755-8555, Japan
3
Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102-1982, USA
4
Faculty of Engineering, University of Miyazaki, Miyazaki-City, Miyazaki 889-2192, Japan
12ARTICLE INFOABSTRACT

Article History

Widely recognized as providing vital services in the transportation sector, it is important to identify the determinants as wellas estimate their impact on Michinoeki today. Seemingly, no research has studied their influence using data on all the 1107
Michinoeki established as of April 2017. To attain a better understanding, Japan was divided into nine areas to detect regional
differences in the determinants of Michinoeki. The findings of the study indicate that, as expected, there are wide regionalvariations in the key characteristics of Michinoeki. This paper makes a contribution to the knowledge by: (1) Understanding thestatus quo of Michinoeki and (2) Identifying important salient factors for their further development. Based on the results, theadministrative implications are identified, the research limitations are discussed and avenues for further research are proffered.

Received 01 October 2018
Accepted 22 June 2021Keywords
Michinoekirevitalization
sales revenue
number of purchasers© 2021 The Authors. Published by Atlantis Press International B.V.
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
Michinoeki have widely been acknowledged as an importantmodel for revitalization of Japan’s regional economies. Maryvonneobserved that “Michinoeki can be an effective tool for attenuating
poverty because they address social issues and benefit local residents directly” [1]. Although Michinoeki originated and became
pervasive in Japan, they have also spread to Kenya and China.
Seemingly, no research has been undertaken to identify their key
determinants nor estimate the impact of Michinoeki using thecomplete data set of all 1107 such transportations hubs established
as of April 2017. Using data drawn from the complete populationof 1107 Michinoeki, this investigation attempts to shed light on
identifying the salient characteristics these transportation hubs, sothat important insights can be attained that provide guidance for
improving the efficiency, effectiveness as well as augmenting thequality of management.
This paper is structured as follows: Section 2 introduces the background of this research. In Section 3, the paper explicates datacollection and multivariate regression model using specific 18 variables selected from the data set of whole Japan. Section 4 shows
the results and discussions on our findings. The conclusions anddirections for further research and managerial implications areproffered in the final section.

2. BACKGROUND
Michinoeki are located along major national highways withfour functions of (1) providing free parking space, restrooms,
Corresponding author. Email: t_ito@ube-k.ac.jp*(2) spreading information, (3) allying with regional society, and(4) preventing disaster. Accordingly, the four-function model ofMichinoeki could be illustrated in Figure 1.
Numerous theories and analyses of Michinoeki rooted in the fourviewpoints have been published. For instance, research by Sakamotoand Toda examined the relationship between communication of
tourist information from Michinoeki and regional revitalization
based on the viewpoint of local tourist policy [2]. Kumano et al. [3]identified successful determinants of Michinoeki in Chugoku area
using regression modelling. Hiraoka et al. [4] investigated the linkage
between the number of customers and information associated withagriculture, forestry, and fisheries in the vicinity of Michinoeki. Inaddition, Kumano et al. [5] identified the antecedents of Michinoeki
in the Kyushu area. Recently Ito et al. [6] proposed a new approachto calculate the efficiency of the Michinoeki in Yamaguchi area
using Data Envelopment Analysis (DEA) model after determining
the significant factors using a regression model. Seemingly, no investigations have estimated the characteristics of all 1107 Michinoeki
established as of April 2017 in aggregate form. To identify the salient
factors of Michinoeki, this study divided Japan into nine areas that
include: Chubu, Chugoku, Kanto, Hokkaido, Hokuriku, Kinki,
Kyushu/Okinawa, Shikoku, and Tohoku. Eighteen factors (subsequently discussed) were selected from the database to ascertain thekey antecedents of Michinoeki in each of the nine regions.

3. DATA COLLECTIONS AND
MODEL BUILDING
Data from 2015 were drawn from the internal databases ofMichinoeki headquarters. The 18 variables selected as the determinants express the four critical functions in Michinoeki, which0.48‡
0.38†−0.15†−0.22‡−0.50§

1.18‡−1.02‡−0.20†
0.55§

0.35−0.19‡−0.19†−0.28†

0.55‡

1.21†

−0.78‡

0.54‡
0.58‡
0.57‡
†

−0.17‡
0.30†−0.64‡
1.34†−1.20†

Note: †Less than 1%, ‡less than 5%, §less than 10%.

0.36
0.64†
0.17‡
−0.91†−0.16§−0.25−1.10†

†

0.20
0.66§

‡

−0.61‡
0.14‡
2.07†

0.30†
−0.28††−0.45‡
0.17§

1.30†

−0.22§−1.30†

1.02†
0.21†

0.19‡
0.40†
−0.19†
1.25†

0.42†

−0.49†
0.40‡

0.12§−0.43†
0.10‡−0.49§−0.44§
0.12†
0.49‡
0.78†
0.83†
0.33†
22.33‡−21.82‡−0.66†

0.26‡

OCPCL
HTNS
WTNS
SMFRP TSFRP
TNR
SMPA
NLV

Hokkaido
Tohoku
Kanto
Hokuriku
Chubu
Kinki
Chugoku
Shikoku
Kyushu &Okinawa

First, the correlation coefficients of (1) SMLS are all negative. The
possible reason is that the large size of Michinoeki is to provide
free space for visitors. Second, the correlation coefficients of (17)SMFMP, and (18) SMFS are all positive. Thus, the size of marine

0.22§

0.21‡
0.92†
0.44†
0.15§
0.32†
0.18‡
0.23†

SMFMP
NRF
NOP
NSI
NLP

The standardized correlation coefficients between the 18 variables
and sales revenue and number of purchasers are calculated respectively. The results are shown as in Tables 1 and 2.

NSSCPL

4. CALCULATION AND DISCUSSIONSLMS

y = a1SMLS + a2NSSC + a3NLV + a 4SMPA +a5TNR + a6SMFRP + a 7TSFRP + a8WTNS +a9HTNS + a10PCL + a11OC + a12NAP +(1)a13NLP + a14 NSI + a15NOP + a16NRF +a17SMFAP + a18SMFS + e

Table 1 | Empirical Findings of standardized coefficients of the 18 determinants of sales revenue

include: (1) Square Meters of Land Space (SMLS), (2) Number ofStandard-Sized Car Parking Lot (NSSCPL), (3) Number of Large
Vehicles (NLV), (4) Square Meters of Parking Area (SMPA), (5)Total Number of the Restroom (TNR), (6) Square Meters of Free
Rest Place (SMFRP), (7) Total Seats of the Free Rest Place (TSFRP),(8) Weekdays’ Traffic Near the Station (WTNS), (9) Holidays’ Traffic
Near the Station (HTNS), (10) Population of the City Located (PCL),(11) Operating Cost (OC), (12) Number of the Agriculture andMarine Products (NAP), (13) Number of the Local Products (NLP),(14) Number of the Selling Items (NSI), (15) Number of Original
Products (NOP), (16) Number of the Registered Farmers (NRF),(17) Square Meters of the Facilities for Marine Products (SMFMP),and (18) Square Meters of Facilities Space (SMFS). SMLS, SMPA,SMFAP, SMFS, and OC collectively serve the function of spreading
information and disaster prevention. NSSC, NLV, TNR, SMFPR,TSFRP, WTNS, and HTNS are viewed as indicants of providing freeparking space and restrooms. PCL, NAP, NLP, NSI, NOP, and NRF
in concert play an important role in building alliances and fosteringclose relationships with regional society. Sales revenue and numberof purchasers are selected as performance indicators of Michinoeki.
Thus, this leads to the formation of the following multivariate
regression equation (1):NAP

Figure 1 | Four-function conceptual model of Michinoeki.

0.52†

T. Ito et al. / Journal of Robotics, Networking and Artificial Life 8(3) 165–169

SMFS

166

0.44†
0.82†

Hokkaido
Tohoku0.09‡−0.46†
0.18†

Kanto
Hokuriku
Chubu

0.21†

0.83†
0.41†

0.33

Area

†

Chugoku

0.86−1.06†

NLP

0.56†

Kinki

NAP

Shikoku

†

−0.25

0.27‡

0.42§
−0.23
−0.42§†

3.03†−1.05†
0.35†
1.26†−3.21†

0.21‡

0.39
0.47†
−0.64‡
0.73†
0.17‡

Weekdays’ traffic near
the station (WTNS)
Total seats of the freerest place (TSFRP)

Square meters offacilities space (SMFS)
Number of the local
products (NLP)
Holidays’ traffic near
the station (HTNS)

−1.02

†

−0.31†
1.00†−1.08

Note: †Less than 1%, ‡less than 5%, §less than 10%.

0.54††−0.49†0.53†−1.45†–0.37§
0.36†
0.43§

Hokkaido
Tohoku
Kanto
Hokuriku
Chubu
Kinki
Chugoku
Shikoku
Kyushu &Okinawa

3.52†

Square meters of the
facilities for marine
products (SMFMP)
Square meters of thefacilities for marine
products (SMFMP)
Number of standardsized car parking lot(NSSCPL)
Number of the local
products (NLP)
Number of the sellingitems (NSI)
Holidays’ traffic near
the station (HTNS)
Number of the agriculture
and marine products (NAP)
Square meters of parking
area (SMPA)
Number of the agriculture
and marine products (NAP)

5. CONCLUSION

1.09†
0.16‡
1.44†
0.30†

0.87‡

0.42§
−0.36
−0.24‡−1.89†
0.30
0.24

−0.21
−0.27†−1.19‡Key determinants

product retailers and the larger scale of the facilities contributes
both to increase sales revenues as well as the number of purchasers.
Third, (17) SMFMP has strong impact on the performance for theHokkaido, Tohoku, and Kyushu/Okinawa. Interestingly, the results
reveal that Kyushu/Okinawa have a pronounced effect by (17)SMFMP, and (9) HTNS. Thus, this indicates that performance iseffected by both SMFMP and HTNS, which differs from Hokkaidoand Tohoku. Fourth, the highest value for the Kanto region is (2)NSSCPL. This indicates that traffic is considered a key determinant in the Kanto region. Fifth, the key determinants of Hokuriku,Chubu, Chugoku, and Kinki are (13) NLP, (14) NSI, (12) NAP,and (9) HTNS. Kanto and Shikoku are effected by (4) SMPA, (2)NSSCPL, and NLP. These findings reveal that the key determinantsfor different regions are divergent in their characteristics. Taken
together, the key antecedents of each regional area are summarizedin Table 3.

−0.15‡
0.49‡
0.38†−0.32†

OCPCL
WTNS

HTNS

Kyushu andOkinawa†TSFRP
SMFRP
TNR

†
†

SMPA
NLV

‡§

NSSCPLSLMS

Table 2 | Empirical Findings of standardized coefficients of the 18 determinants of number of purchasers

167

Table 3 | Key determinants of Michinoeki by region

1.36†
0.33†
−0.16†

0.46†0.29‡
0.32†
0.56†
0.42†
0.20
0.35†
‡NRF
NSINOP†

SMFMP SMFS

T. Ito et al. / Journal of Robotics, Networking and Artificial Life 8(3) 165–169

The statistical findings of the linkages among the 18 factors as
predictors of sales revenues and number of purchasers were estimated using multiple regression modelling. Evidently, the key
determinants are divergent for each regional area. For example,sales revenues of Hokkaido, Tohoku, and Kyushu/Okinawa regions
are primarily dependent on the square meter size of the facilities
for marine products, but the influence of Tohoku is weaker than
Hokkaido, and Kyushu/Okinawa, which are seemingly affected by
the square meter size of the facilities for agriculture products aswell as HTNS.
Firm conclusions should not be made given the exploratory natureof the study; thus, some caution is suggested. Consequently,additional replication of this investigation is warranted to findconclusive support for the preliminary results. As findings basedon one fiscal year data set is not sufficient, additional avenues
for future research will be fruitful with longitudinal studies thatT. Ito et al. / Journal of Robotics, Networking and Artificial Life 8(3) 165–169

168

might shed light if the divergent determinants of Michinoeki aretemporal in nature.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

REFERENCES[1][2]

The World Bank, Guidelines for Roadside Stations “Michinoeki”,
p. 7, retrieved June 2, 2021.
S. Yasumasa, T. Kaori, Communication of Tourist Informationfrom Michinoeki and Activation of Local Area, retrieved June 2,2021.

[3]

[4]

[5]
[6]

M. Kumano, T. Ito, T. Ito, T. Hiraoka, H. Nonaka, M. Hirota,
Discovering Successful Determinants of Efficiency of Michinoekiin Chugoku Area, Proceedings of The 2018 International
Conference on Artificial Life and Robotics, B-Con Plaza, Beppu,Oita, Japan, 2018, pp. 352–354.
T. Hiraoka, S. Nishimura, H. Nonaka, T. Ito, M. Kumano,
A correlation analysis between information related to thenumber of customers and the agriculture, forestry, and fisheries of Michi-no-eki in Japan, J. Inst. Ind. Appl. Eng. 6 (2018),
173–178.
M. Kumano, T. Ito, T. Ito, A determinants analysis of theMichinoeki in Japan, Int. J. Econ. Manage. Syst. 2 (2017),287–291.
T. Ito, M. Kumano, T. Ito, Measuring efficiencies of Michinoeki
using DEA model in Yamaguchi area, J. Robot. Netw. Artif. Life 7(2020), 204–207.

AUTHORS INTRODUCTION
Dr. Takao Ito

Dr. Seigo Matsuno

He is Professor of Management of Technology(MoT) in Graduate School of Engineering
at Hiroshima University. He is serving concurrently as Professor of Harbin Institute ofTechnology (Weihai) China. He has publishednumerous papers in refereed journals andproceedings, particularly in the area of management science,and computer science. He has published more than eight academic books including a book on Network Organizations andInformation (Japanese Edition). His current research interests
include automata theory, artificial intelligence, systems control,
quantitative analysis of inter-firm relationships using graphtheory, and engineering approach of organizational structuresusing complex systems theory.

He is Professor of the Department of Business
Administration at National Institute ofTechnology, Ube College, Japan. He received
his PhD degree in Economics from Kyushu
University, Japan in 2004. His current researchinterests are in the areas of IT management andstrategy, information systems outsourcing, andinterfirm relationship management. He has published several
research papers and books in that area. He is a member of theBoard of the Directors of Japan Society for Information andManagement.

Dr. Tsutomu ItoDr. Rajiv Mehta

He is Assistant Professor of the Department ofBusiness Administration at National Instituteof Technology, Ube College, Japan. He has
published many papers in refereed journalsand proceedings, particularly in the area ofindustrial management, and computer science.
His current research interests include internet of things (IoT),
mechanical engineering, artificial intelligence (AI), automata
theory, quantitative analysis of Japanese Keiretsu. He was oneof the winners of the Best Paper Award in the International
Conference on Artificial Life and Robotics (ICAROB) in 2015
and 2016. He earned his Doctor degree of Engineering fromHiroshima University, Japan in 2018.

He (PhD, Drexel University) is Professorof Marketing at Martin Tuchman Schoolof Management, New Jersey Institute ofTechnology, USA. Previously, he worked for
an international manufacturer of steel cables.
He has co-authored four university textbooks,including Sales Force Management, 2nd ed.
(2020). Focusing on sales management, global marketing channels, Keiretsus, and international strategic alliances, his researchis published in Sloan Management Review, Journal of BusinessResearch, Industrial Marketing Management, European Journalof Marketing, International Marketing Review, and Artificial
Life and Robotics, among others.

T. Ito et al. / Journal of Robotics, Networking and Artificial Life 8(3) 165–169

Dr. Makoto Sakamoto
He received the PhD degree in computer science and systems engineering from Yamaguchi
University. He is presently Professor in theFaculty of Engineering, University of Miyazaki.
He is a theoretical computer scientist, and hiscurrent main research interests are automata
theory, languages and computation. He is alsointerested in digital geometry, digital image processing, computer vision, computer graphics, virtual reality, augmented reality, entertainment computing, complex systems and so on.

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