Journal of Robotics, Networking and Artificial Life
Vol. 7(3); December (2020), pp. 204–207
DOI: https://doi.org/10.2991/jrnal.k.200909.013; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Measuring Efficiencies of Michinoeki using DEA Modelin Yamaguchi Area
Tsutomu Ito1, Minoru Kumano2, Takao Ito3,*
Department of Business Administration, National Institute of Technology, Ube College, 2-14-1 Tokiwadai, Ube City, Yamaguchi Pref. 755-8555, Japan
Faculty of Regional Innovation, University of Miyazaki, 1-1 Gakuendai-Nishi, Miyazaki 889-2192, Japan
3
Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi Hiroshima 739-7527, Japan
1
2ARTICLE INFOABSTRACTArticle History

Identifying the key determinants of Michinoeki and assessing its impact on efficiency is considered as an urgent issue. Accordingly,
this paper proposed a new approach to calculate the efficiency using DEA model after determining the significant factors usinga regression model. The data were collected from Yamaguchi area, which is considered the birthplace of Michinoeki in Japan.
This paper contributes to the literature by: (1) Identifying the key factors of Michinoeki in Yamaguchi area, and (2) calculating
the efficiencies of the Michinoeki, and (3) developing a four-cell matrix to ascertain the relative position of Michinoeki for future
development. Based on the findings, the managerial implications are discussed, the study limitations are identified and directions
for further research are suggested.

Received 30 September 2018
Accepted 04 June 2020
Keywords
Efficiency
DEA model
four-cell matrix modelMichinoeki© 2020 The Authors. Published by Atlantis Press 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
A plethora of studies on Michinoeki from the viewpoint of breakfunction, information dispatch function, regional cooperation
function, and disaster prevention function have been published in
the past decades. However, studies focusing on the calculation ofefficiency to make managerial improvements are still sparse. First,
this paper proposes a new two-step approach to calculate efficiency
using a DEA model. Second, the relevant factors are determinedusing a multivariate regression model. The data were drawn fromYamaguchi area, which is considered the birthplace of Michinoeki inJapan. This paper makes a contribution to the management literatureby: (1) Identifying the key factors of Michinoeki in Yamaguchi
area, and (2) calculating the efficiencies of the Michinoeki, and(3) proposing a four-cell matrix model to ascertain the relative
position of Michinoeki for future development.
This manuscript is structured as follows: Section 2 introduces thebackground of this research. In Section 3, the paper explicatesdata collection and the models of multivariate regression modeland DEA model to identify the key factors and efficiencies ofMichinoeki using selected variables. Section 4 shows the results
and discusses our findings. Based on the results, the managerial
implications are discussed, the study limitations are identified anddirections for future research are proffered in the final section.

2. BACKGROUND
Michinoeki are located along major national highways that serve
four primary functions: (1) Providing free parking space andCorresponding author. Email: itotakao@hiroshima-u.ac.jp*restroom facilities, (2) spreading information, (3) allying with
regional businesses and society, and (4) a platform for disaster prevention. Accordingly, the four-function model of Michinoeki isillustrated in Figure 1.
Especially Michinoeki are well-known institutions that have beenrecognized as playing an important role in regional economic revitalization in Japan. Recently, many studies focusing on Michinoeki
have been published. They have been the subject of research attention as regional economic revitalization is considered a focal partof Abenomics. For instance, Yokota [1] introduced many successful stories of Michinoeki not only in Japan, but also in Kenya andChina and indicated the future direction of the Michinoeki’s furtherdevelopment. Mitsuhashi [2] analyzed the economic backgroundof Michinoeki. Ogata et al. [3] explained the relationship between
Michinoeki and regional revitalization based on the viewpointof business model. Hiraoka et al. [4] discussed the relationshipbetween the number of customers and information associated withagriculture, forestry, and fisheries of Michinoeki. Recently Kumanoet al. [5] discovered successful determinants of Michinoeki in
Chugoku area using a regression model. Furthermore, the keycharacteristics of each area in Japan have been analyzed in order
to finding effective strategy for further development [6]. All ofthese studies suggest that it is important to calculate efficiency toimprove the quality of Michinoeki. Thus, identifying the key determinants and measuring its efficiency are considered as the mosturgent tasks for Michinoeki. This paper will focus on the discovering the determinants, and calculating the efficiency of Michinoeki
in Yamaguchi area. The number of Michinoeki—as identified inTable 1—23 Michinoeki as of July 21, 2017.
The geographic distribution map of Michinoeki in Yamaguchi area
is shown as in Figure 2.

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205

Traffic near the Station (HTNS), Operating Cost (OC), Total
Parking Number (TPN), Total Number of the Restroom (TNR),
Square Meters of Free Rest Place (SMFRP), Number of the Selling
Items (NSI), Number of Local Products Items (NLPI), Number ofthe Registered Farmers (NRF), Square Meters of Facilities Space(SMFS) and Population of the City Located (PCL). SMLS and OC
could be considered as the determinants of variables of information dispatch function, the WTNS, HTNS, NSI, NLPI, PCL, andNRF are designed as the indexes of regional cooperation function, the TPN, TNR, SMFRP and SMFS will play important role inbreak function and disaster prevention function. Sales revenue andnumber of purchasers are selected as the performance indicantsof Michinoeki. Thus, this leads to the formation of the following
regression equation:  Figure 1 | Four-function model of Michinoeki.
Table 1 | Michinoeki list in Yamaguchi area123456789101112Abu Town
Hagi Okan
Asahi
AioYutori Park Tamagawa
Sazanseto Towa
Kikugawa
Happiness Fukue
Chomonkyo
Pure Line Nishiki
Ofuku
Mitou

131415161718192021222324

Niho no Sato
Hagi Seamart
Ganjoju Onsen
Uribo no Sato Katamata
Hotaru Kaido Nishi no Ichi
Kirara Ajisu
Hagi Sansan Sammi
Kitaura Kaido Hohoku
Solene Shunan
Kaminoseki Kaikyo
Shiosai Ichiba Houfu
Senzakitchen*y = a1SMLS + a2 WTNS + a3HTNS + a4OC + a5TPN+ a6TNR + a7SMFRP + a8 NSI + a9 NLP
PI + a10 NRF (1)+ a11SMFS + a12PCL + e

After determining the key factors of Michinoeki, the efficiency wascalculated using the following DEA model [7,8].
s∑ u y ( j = 1, 2, …, n)		 maxq =(2)
∑ vx
r rm

rj

i =1 i ij

Subject tos∑ u y ≤ 1 ( j = 1, 2, …, n) (3)		
∑ vx
r rm

rj

i =1 i ij		 ur ≥ 0 (r = 1, 2, …, s) (4)		 vi ³ 0 (i = 1, 2, ¼, m) (5)Semzakitchen has been opened from 2018 fiscal year.

*

4. CALCULATION AND DISCUSSIONS
The results of the relationship between the 12 variables and sales
revenue and the number of purchasers are shown in Table 2.
The key factors are finally determined using backward elimination
method.
The determinants of those two models are different. The adjusted R2of sales revenue and number of purchases are 0.9069 and 0.96604,
respectively. The four variables in the model of sales revenue are:
TPN, SMSRP, SMFS, and PCL, and the six variables of number ofpurchasers are: OC, NSI, NPLI, SMFS, and PCL—each of which arestatistically significant. Accordingly, regional cooperation function,break function, and disaster function are becoming much important
rather than information dispatch function of Michinoeki in theYamaguchi prefecture as it is the birthplace with a long history.

3. DATA COLLECTION AND MODELSAs illustrated as Figure 3, the efficiency of the 23 Michinoeki inYamaguchi area are calculated using DEA model. The efficiency ofYutori Park Tamagawa, Chomonkyo, Kitaura Kaido Hohoku, andShiosai Ichiba Houfu cannot be calculated because of lack of data.

The data from 2015 were drawn from the database of theMichinoeki headquarters. Based on the advice from senior managers of Michinoeki, 12 variables were selected as the determinants of Michinoeki. They are: Square Meters of Land Space(SMLS), Weekdays’ Traffic near the Station (WTNS), Holidays’

The correlation coefficient of sales revenue and number of purchasers is 0.318, which is not statistically significant. In order to ascertain the future direction of these Michinoeki, a four-cell matrix isproposed based on the efficiency of sales revenue and number ofpurchasers, which is reported in Figure 4.

Figure 2 | Geographic distribution map of Michinoeki.

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206

Table 2 | Results of the regression models of sales and number of thepurchasers
ModelsSales revenue
Standardized
Probability
coefficient
SMLS
WTNS
OC
TPN
TNR
SMFRP
NSI
NLPI
NRF
SMFS
PCL
Intercept
R2
Multiple R2
Adjusted R2AIC
DW ratio––––––
0.64860––0.24120.0128––––––
0.41650.01280.548
0.000200.0011
0.946100.97268
0.90969445.972.2853

Number of the purchasers
Standardized
coefficient

Probability––––−0.34020.04360.72710.0004––––−1.28530.01071.43180.0059––0.799700.13270.050600.0006
0.97644
0.0.98815
0.96604
207.332.6311The Michinoeki located in the 4th quadrant could be excellent
because they have high efficiency of sales revenue and the numberof purchases both. The Michinoeki located in the 2nd quadrant
could be considered as sales oriented one because they have highefficiency of sales revenue even with low efficiency of numberof purchases. The Michinoeki located in the 3rd quadrant could
be poor because there is still room to improve. The Michinoeki
located in the 4th quadrant could be called visitor-oriented because
the efficiency of the number of purchasers are lower. Accordingly,Abu Town, Hagi Seamart, Ganjoju Onsen, Hotaru Kaido Nishi noIchi, Kirara Ajisu, Solene Shunan, and Sazanseto, Pure Line Nishiki,and Kikugawa are considered as excellent Michinoeki while
Niho no Sato, Uribo no Sato Katamata, Hagi Sansan Sammi, andHappiness could be the poor Michinoeki. Ofuku could be sales
revenue oriented Michinoeki while Hagi Okan, Asahi, Mitou, andKaminoseki Kaikyo could be visitors oriented Michinoeki.

5. CONCLUSION
The relationship between 12 variables and sales revenue andnumber of purchasers are calculated and discussed. Only fourand six variables were found to be significant statistically for thesales revenue and number of purchaser’s models, respectively. The
18 Michinoeki could be divided into four groups, as follows: Abu
Town and other eight Michinoeki are excellent, and Niho no Satoand other three could be poor. Ofuku needs to improve its efficiency of number of purchaser while Hai Okan and other three
Michinoeki need to improve their sales revenue efficiency. One ofthe drawbacks of this study is that only a 1-year data sample wasused. To draw firmer conclusions, future studies should examine
Michinoeki using multi-year data. Furthermore, additional qualitative support for these conclusions is required.

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

Figure 3 | Efficiency of the 23 Michinoeki.

Figure 4 | The positions of the Michinoeki.

REFERENCES[1] T. Yokota, Guidelines for Roadside Stations “Michinoeki”, p. 7,
available from: http://documents.worldbank.org/curated/en/753051468137999706/pdf/356830Guidelin1de0stations01PUBLIC1.pdf#search=%27woeld+bank+Michinoeki%27.
[2] M. Mitsuhashi, Why is regional revitalization difficult?
[3] J. Ogata, D. Nozokito, H. Murata, K. Yamamoto, Developing
Michinoeki using Michinoeki: an analysis based business model.
[4] T. Hiraoka, S. Nishimura, H. Nonaka, T. Ito, M. Kumano, A correlation analysis between information related to the number ofcustomers and the agriculture, forestry, and fisheries of Michino-­eki in Japan, J. Inst. Ind. Appl. Eng. 6 (2018), 173–178.
[5] M. Kumano, T. Ito, T. Ito, T. Hiraoka, H. Nonaka, M. Hirota,
Discovering successful determinants of efficiency of michinoekiin chugoku area, Proceedings of International Conference on
Artificial Life and Robotics, B-Con Plaza, Beppu, Oita, Japan,
2018, pp. 352–354.

T. Ito et al. / Journal of Robotics, Networking and Artificial Life 7(3) 204–207[6] M. Kumano, T. Ito, T. Ito, T. Hiraoka, H. Nonaka, Discovering
the characteristics of Michinoeki in Japan, Proceedings ofInternational Conference on Artificial Life and Robotica, B-ConPlaza, Beppu, Oita, Japan, 2019, pp. 140–143.

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Soc. Ser. A 120 (1957), 253–290.
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AUTHORS INTRODUCTION
Dr. Tsutomu Ito

Dr. Takao Ito

He is Engineer at Hino Motors, Ltd. Hehas published many papers in refereed
journals and proceedings, particularly inthe area of industrial management, andcomputer science. His current researchinterests include Internet of Things(IoT), Mechanical Engineering, Artificial
Intelligence (AI), automata theory, quantitative analysis of Japanese Keiretsu. He wasone of 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 from
Hiroshima University, Japan in 2018.

He is Professor of Management ofTechnology (MOT) in Graduate Schoolof 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 and proceedings,
particularly in the Area of Management
Science, and Computer Science. He earned his Doctor degree ofEconomics from Kyoto University and PhD of Engineeringfrom Miyazaki University, Japan. He was a visiting Research
Professor of School of Management, New Jersey Institute ofTechnology (NJIT) from March 29, 2008 to March 28, 2009.

Dr. Minoru Kumano
He is Professor and Deputy Dean ofFaculty of Regional Innovation, National
University of Miyazaki. He has publishedmany papers in the area of regional Urbanplanning including Michinoeki. He is
the member of editorial review board ofArchitectural Institute of Japan’s Planning
papers and the Japanese Rural Planning
Association. He earned his Doctor degree of Engineering from
Nagaoka University of Technology, Japan in 2002.