SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial
Life Vol. 10(4); March (2024), pp. 368–373
ISSN (Online): 2352-6386; ISSN (Print): 2405-9021
https://alife-robotics.org/jrnal.html

Research Article

The Antecedents of Michinoeki’s Efficiency in Japan: A
Longitudinal Study
Satoshi Ikeda1, Tsutomu Ito2, Seigo Matsuno2, Makoto Sakamoto1, Rajiv Mehta3, Takao Ito4
Faculty of Engineering, University of Miyazaki, Japan
Department of Business and Administration, Ube National College of Technology, Japan
3
Martin Tuchman School of Management, New Jersey Institute of Technology, U.S.A
4
Graduate School of advanced Science and Engineering, Hiroshima University, Japan
1
2

ARTICLE INFO
Article History
Received 24 November 2023
Accepted 23 July 2024
Keywords
Michinoeki
Efficiency
DEA
Revitalization
PRC

ABSTRACTThe expansion of Michinoeki along Japanese highway systems is widely considered to be a
successful socio-economic experiment, which has enhanced interstate commerce as well as
spurred tourism. Although descriptive studies on Michinoeki have been conducted, a review ofthe literature indicates that quantitative investigations are sparse. It is pivotal to identify thedeterminants that contribute to the success and efficiency of Michinoeki’s, which can provide
insights to further augment their development, thus revitalizing the surrounding local economy.
To address this gap in the literature, the present investigation drew data from the entire populationof Michinoeki towards ascertaining their relative efficiency by employing the DEA model, whichenables data-driven inferences as well as comparative analyses. Based on the findings of the study,
managerial implications are identified and avenues for future research are forwarded.
© 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/).

spaces and rest areas offer significant benefits for longdistance travelers and truck drivers, ensuring safety andcomfort during their journeys. Moreover, as disasterprevention hubs, Michinoeki provide crucial resources
and support during emergencies, offering shelter andinformation to affected individuals.
In addition to these roles, the collaboration with local
communities has been a cornerstone of Michinoeki's
success. By promoting local products and services, thesestations boost local economies and create opportunities
for small businesses. The information dissemination
function of Michinoeki further enhances their utility,
offering travelers valuable insights into local attractions,
events, and services. Despite the evident importance andmultifaceted benefits of Michinoeki, empirical research
specifically focusing on them remains limited. This paper
aims to fill this gap by evaluating the efficiency ofMichinoeki using the DEA (Data Envelopment Analysis)model, facilitating comparative performance assessments

1. Introduction
The continuous expansion of Michinoeki along Japanese
highways has been widely recognized as a successful
socio-economic initiative. Since its establishment in 1993
with 103 stations, the network has grown significantly toencompass over 1,193 stations today. Michinoeki serve
multiple crucial functions: (1) providing parking spaces
and rest areas, (2) coordinating disaster prevention efforts,(3) collaborating with local communities to foster
commercial and economic development, and (4)disseminating information [1]. Japan's economy has
grappled with deflation over the past three decades
amidst a declining birthrate and aging population.
Michinoeki have been regarded as effective measures tostimulate local commerce and economic revitalization
amid these challenges. The multifunctional role ofMichinoeki has proven to be essential in addressing someof Japan's pressing socio-economic issues. The parking
Corresponding author E-mail: saka2000@cc.miyazaki-u.ac.jp368

residents' reliance on nearby Michinoeki [2]. Other
research has evaluated their economic impact andsustainability. Ozuka et al. explored methods to boost the
local economy around Michinoeki [3], while Ito et al.
assessed their efficiency in supporting local commerce
and tourism in Yamaguchi prefecture [4]. This paper aims
to further contribute by evaluating the efficiency ofMichinoeki across Japan and comparing performance
between different prefectures.

across different regions. By doing so, it seeks to provide
a comprehensive understanding of how effectively thesestations are operating and identify areas for potential
improvement.
The structure of this paper is as follows: Section 2
reviews previous research, Section 3 outlines the datacollection process and operationalizations, Section 4
presents the research findings, and finally, administrative
implications and conclusions are discussed. This
comprehensive approach aims to shed light on theoperational efficiency of Michinoeki and provide insights
for enhancing their contributions to local and nationalsocio-economic landscapes.

3. Methods and Construct Operationalizations
3.1. Procedure
Information on free parking space, systems to spread
information, disaster prevention facilities, local
population size, number of retail establishments selling
local products from 2014 to 2015 and management style
were drawn from the internal databases of Michinoeki
headquarters (see Table 1).

2. BackgroundPrevious studies on Michinoeki have primarily focusedon their four main functions. For instance, Yoshida et al.
examined their role during natural disasters, noting local

Table 1. 20 Selected Variables as Explanatory Variables in the Multiple Regression Model.
No.

VariableNo.

Variablex1

Square Meters of Land Space

SMLS

x11

Operating CostOC

x2

Number of Standard-Sized Car Parking Lot

NSSCPLx12

Total Customers

TC

x3

Number of Large Vehicles Parking Lot

NLVPL

x13

Number of the Agriculture Products and Sales

NAPS

x4

Square Meters of Parking Area

SMPA

x14

Number of the Agriculture and Marine Products

NAP

x5

Total Number of the Restroom

TNR

x15

Local Products

NLP

x6

Square Meters of Free Rest Place

SMFRP

x16

Number of the Selling Items

NSI

x7

Total Seats of the Free Rest PlaceTSFRP)

x17

Number of Original Products

NOP

x8

Weekdays’ Traffic Near the Station

WTNS

x18

Number of the Registered Farmers

NRF

x9

Holidays’ Traffic Near the Station

HTNS)

x19

Square Meters of the Facilities for Marine Products

SMFMP

x10

Population of the City Located

PCML

x20

Square Meters of Facilities Space

SMFS

Table 2. Standard Coefficients between Selected Variables and the PRC and SRRC.
Standard Coefficients (Probability)
PRC（2014）

SRRC（2015）

PRC（2014）

SRRC（2015）-0.0227(0.7142)

x1
x10

0.0776(0.0374)-0.0004(0.9948)

0.0771(0.0253)

0.0981(0.0031)

x11

0.2783(0)0.034(0.5951)

0.2086(0)0.1747(0)x120.3982(0)-0.0127(0.837)

0.4458(0)0.5054(0)x15-1.2764(0.0014)-0.3527(0.6106)-1.596(0)x16

1.256(0.0016)

0.3478(0.6158)

1.6157(0)x18

0.0791(0.0506)-0.0064(0.9137)

0.1067(0.0043)

0.0849(0.0142)

x19

0.1871(0)0.0191(0.7569)

0.2279(0)0.3218(0)Intercept

0(0.729)0(0.5323)

0(0.2775)0(0.0038)

R2

0.65020.04466

0.699

0.7956369

Increased efficiency and lower coefficient ofvariation scores exhibit higher performance. In Okinawa,
the efficiency in 2015 is higher than in other areas as is
its coefficient of variation. These findings suggest that
this area could be considered as one of the best
performers when compared to the other ten areas.
Fig. 4 depicts high efficiency and low coefficient ofvariation, indicating that the Chubu Michinoeki is themost optimal while the Michinoeki in Shikoku, Hokkaido,
Hokuriku, and Okinawa also exhibit positive
performance. Michinoeki in Okinawa and Hokuriku
exhibit inconsistent efficiency performance levels as2015 is higher than in 2014. The results are stable forMichinoeki located in Kyushu, Kanto, Chugoku, Tohoku,
and Kinki as they exhibit lower levels of efficiency,
indicating the potential for enhancing performance inthese regions.

Passengers of Register Count (PRC) and Sales
Revenue of Register Count (SRRC) were considerred as
Efficiency—the dependent variables in the regression
equation. Additionally, the 20 selected variables as
explanatory variables in the multiple regression modelare detailed in Table 1, which were used to develop themultiple regression model below [1].
𝑦𝑦 = 𝑎𝑎1 𝑥𝑥1 + 𝑎𝑎2 𝑥𝑥2 + 𝑎𝑎3 𝑥𝑥3 + 𝑎𝑎4 𝑥𝑥4 + 𝑎𝑎5 𝑥𝑥5 + 𝑎𝑎6 𝑥𝑥6 + 𝑎𝑎7 𝑥𝑥7 +𝑎𝑎8 𝑥𝑥8 + 𝑎𝑎9 𝑥𝑥9 + 𝑎𝑎10 𝑥𝑥10 + 𝑎𝑎11 𝑥𝑥11 + 𝑎𝑎12 𝑥𝑥12 + 𝑎𝑎13 𝑥𝑥13 +𝑎𝑎14 𝑥𝑥14 + 𝑎𝑎15 𝑥𝑥15 + 𝑎𝑎16 𝑥𝑥16 + 𝑎𝑎17 𝑥𝑥17 + 𝑎𝑎18 𝑥𝑥18 + 𝑎𝑎19 𝑥𝑥19 +(1)
𝑎𝑎20 𝑥𝑥20 + 𝑐𝑐.

(See Table 1 for independent variable descriptions)

Eight variables (SMLS, PCML, OC, TC, NLP, NSI,
NRF, SMFMP) were selected as key determinants thatwere expected to have strong impact on PRC and SRRC.
Standard coefficients between selected variables and
PRC and SRRC are reported in Table 2.
3.1. Operationalizations

Hokkaido (32 Stations)

0.6
0.5

Tohoku (101 Stations)

1

0.8

0.4

0.6

0.3

0.4

0.2

0.2

0.1

0
390
401
404
426
304
278
366
361
396
296
370
424
351
425
331
346
309
402
418
408

0

455
451
503
494
445
463
471
512
443
454
442
483
507
461
504
486

DEA (Data Envelopment Analysis) is a prominent
method for evaluating the relative efficiency of decisionmaking units such as businesses. One of the basic models
of DEA is the Charnes, Cooper, and Rhodes (CCR)
model, in which the problem of determining theefficiency of a specific Michinoeki, denoted as j, as adecision-making unit is formulated as the following
mathematical programming problem:∑𝑠𝑠𝑟𝑟=1 𝑢𝑢𝑟𝑟 𝑦𝑦𝑟𝑟𝑟𝑟(2)
maximize 𝐷𝐷𝑗𝑗 = 𝑚𝑚∑𝑖𝑖=1 𝑣𝑣𝑖𝑖 𝑥𝑥𝑖𝑖𝑖𝑖∑𝑠𝑠𝑟𝑟=1 𝑢𝑢𝑟𝑟 𝑦𝑦𝑟𝑟𝑟𝑟≤ 1 (𝑗𝑗 = 1,2, … , 𝑛𝑛)
subject to𝑣𝑣𝑖𝑖 𝑥𝑥𝑖𝑖𝑖𝑖
𝑢𝑢𝑟𝑟 ≥ 0 (𝑟𝑟 = 1,2, … , 𝑠𝑠)𝑣𝑣𝑖𝑖 ≥ 0 (𝑖𝑖 = 1, ,2, … , 𝑚𝑚)
Here xi1,…,xim and v1,…,vm represent the inputs to theMichinoeki i and their respective weights, while yr1,…,yrs
and u1,…,us signify the outputs from Michinoeki r andtheir respective weights.
Using three variables (SMLS, NRF, SMFMP) as
input, and PRA as output of equation (1), the efficienciesof 554 stations in 2014, and 561 stations in 2015, out ofa total of 1,107 stations, are calculated due to a lack ofdata. The results of the efficiencies of 561 Michinoeki
across ten areas in 2015 are illustrated in Fig. 1.

Kanto (96 Stations)
1.2

Hokuriku (32 Stations)
0.7

1

0.6
0.5

0.8

0.4

0.6

0.3

0.4

0.2

0.2

0.1
0
562
544
603
601
568
621
554
616
527
539
550
569
549
558

725
653
645
749
731
670
666
712
648
730
774
674
690
702

0

1.2

Chubu (55 Stations)

Kinki (42 Stations)

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0
853
829
794
818
840
845
866
867
862
877
839
855
854
859
819
857
883
833
836
849

944
928
943
932
948
920
929
941
937
890
956
909
899
955
912

0

Chugoku (60 Stations)

Shikoku (30 Stations)
1
0.8
0.6

4.

Empirical Results and Explanation

0.4
0.2
0

Fig. 1 shows that the efficiency of Chugoku, Chubu,
Hokkaido, Tohoku, and Okinawa is higher than that of
Hokuriku, Kinki, Shikoku, and Kyushu. The mean andcoefficient of variation of Michinoeki efficiency are
depicted in Fig. 2 and Fig. 3, respectively.

Kyushu (109 Stations)

1100

1102

1106

1107

Okinawa (4 Stations)Fig. 1 Michinoeki’ Efficiency of 10 Areas (2015, 561
Stations)

370

0.6

management systems are established in these
Michinoekis. In contrast, the hypothesis of a higher
number of passengers or sales revenue of register count,is associated with lower efficiency in Abucho (No. 210),
Chugoku, Shinshuhiraya (No.241), Chubu, Kawamata(No.445), Tohoku—as shown in Fig. 5. Moreover,Mizubenosatoooyama
(No.
442)
in
Kyushu,Toyohiradonngurimura (No. 455) in Chugoku—asrevealed in Fig. 6, and Mikamo (No. 218) and Nishikata(No. 249), and Tohoku, Kamisekikaikyo (No. 444) in
Chugoku—as depicted in Fig. 7. These findings suggestthat additional effort is required to improve theirefficiency.

0.5
0.4
0.3
0.2
0.1
0

Hokkaido Tohoku

Kanto Hokuriku Chubu

Kinki

Efficency Mean (H26)

Efficency Mean (H27)

Chugoku Shikoku Kyushu Okinawa

Fig. 2. Mean Value of Michinoeki’ Efficiency in 10 Areas.

0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0

Hokkaido Tohoku

Kanto

Hokuriku Chubu

Coefficient of Variation (H26)

Kinki

Chugoku Shikoku Kyushu Okinawa

Coefficient of Variation (H27)

Fig. 3. Coefficient of Variation of Michinoeki’ in 10 Areas.

Fig. 5. Relationship between Efficiency and Passengers ofRegister Count (PRC) in 2014.

Fig. 4. Transition of Michinoeki’ Rank of 10 Areas in 2014 and2015.

Lastly, the linkage between efficiency and registeredpassenger count (PRC) and sales register count (SRRC)
in 2014 and 2015 are plotted as follows: The horizontal
axis represents efficiency, and the vertical axis depicts
passengers and sales revenues in Fig. 5, Fig. 7 and Fig. 6,
Fig. 8, respectively.
Higher efficiency was associated with smaller
numbers of passengers or sales revenue of register countin Siranuikoitoi (No.1) and Kamiyuubetuonsen (No.2) in
Hokkaido, Murayama (No.3) and Tuchiyu (No.4) inTouhoku,Fujikawarakuza (No. 5) in Chubu—asrevealed in Fig. 5 and Fig. 6, Oribenosato (No. 4) in
Chubu, and Suparakan and Tekhara (No. 7) in Chugoku
in Fig. 7 and 8. Thus, it is possible to infer that effective

Fig. 6. Relationship between Efficiency and Sales Revenueof Register Count (SRRC) in 2014.

371

-resulted from questionaries survey of the Michinoeki in
Kinki area- (Japanese edition), 2018,
4. Ta. Ito, Ts. Ito, S. Matsuno, R. Mehta, M. Sakamoto,
Identifying the Determinants of Michinoeki Performance,
Journal of Robotics, Networking and Artificial Life, Vol.
8(3); 165–169, December 2021.

Authors Introduction
Dr. Satoshi Ikeda

He received Ph.D. at Hiroshima
University. He is Associate Professor,
Faculty of Engineering, University ofMiyazaki.

Fig. 7. Relationship between Efficiency and Passengers ofRegister Count (PRC) in 2015.
Dr. Tsutomu Ito

He received Ph.D. at Hiroshima
University. He is Associate Professor,of Business Administration at
National Institute of Technology, Ube
College, Japan

Dr. Seigo Matsuno

Fig. 8. Relationship between Efficiency and Sales Revenue ofRegister Count (SRRC) in 2015.

5. Conclusion

He received Ph.D. at Kyushu
University. He is Professor of Business
Administration at National Institute ofTechnology, Ube College, Japan.

Dr. Makoto Sakamoto
He received Ph.D. at Yamaguchi
University. He is Professor, Faculty ofEngineering, University of Miyazaki

Several determinants were used to predict their impact on
the efficiency of all Michinoeki in 2014 and 2015. Thus,
this study partially bridged the gap in the current
literature. However, a drawback of this paper is thatanalyzing the transition of Michinoeki only using two
year’s data is insufficient. Thus, in future research studies,
additional factors, such as NLP and NSI, should be tested
as antecedents of efficiency using longitudinal data.

Dr. Rajiv Mehta

References
1. T. Ito, M. Kumano, T. Ito, Measuring Efficiencies ofMichinoeki using DEA Model in Yamaguchi Area,
Journal of Robotics, Networking and Artificial Life,
Volume 7, Issue 3, 204–207, December 2020,
2. S. Yoshida, Y. Matsuda, N. Takada, A consideration on
functions and roles of Michinoeki’ for preventing disaster(Japanese edition), 2014,
3. M. Ozuka, A study on finding the relationship between
Managerial issues and effects as expected of Michinoeki

372

He received Ph.D. at Drexel University.
He is Professor of Marketing, Martin
Tuchman School of Management, New
Jersey Institute of Technology, USA.

Dr. Takao Ito

He received at Ph.D. at Kyoto
University. He is Professor ofManagement of Technology (MOT),
Graduate School of Engineering at
Hiroshima University373