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The Journal of International Management Studies, Volume 11 Number 2, August, 201614

The Selection of the Store Location with Fuzzy Analytic Hierarchy Process

Chen Iong- Rong, PhD candidate of Business School, Fu Jen Catholic University,

Graduate Institute of Business Administration, Taiwan

Shih Yi-Nuo, Associate Professor, Fu Jen Catholic University,

Department of Occupational Therapy, Taiwan

Gong Shang-Chi, Professor, Fu Jen Catholic University,

Graduate Institute of Business Administration, Taiwan

ABSTRACT

Min-Yu Yang (2012) divided the factors of selecting the locations for chain restaurant as four

measurements, which are geographic factor, business area, traffic factor, financial factor and sixteen

minor criterion. This research was based on the 6 measures which Berman (2001) used to evaluate the

location for setting the store with traffic, parking facility, transport, store composition, location and entry

requirement. The purpose of this research was to prioritize the measures while selecting the location of

the store. What is the evaluative criteria and the order of the priority of the variable in each measure?

The research method is Fuzzy AHP with the questionnaire answered by the students and teachers

(scholars) at the Institute of Business and Technology. The conclusion gave the order of the priority as

parking facility> traffic> transportation> store composition> location> entry requirement. The first

three priorities of the variable criteria are the number of the vehicle> the distance between the parking

lot and the store> the size of the parking lot while the last three conditions are condition for buying or

renting> other policies> the condition and year of the building and ground.

Keywords: selection and estimation of the location, fuzzy analytic hierarchy process, hierarchy analysis

INTRODUCTION

Motivation

The economic miracle of Taiwan during 1980 and 1990 was witnessed by the world. The spirit of

being practical and persistent is a symbol of Taiwanese. Lately, the environment in Taiwan has changed,

the lack of devotion and effort to the work makes the young people think about having their own careers

after working for just a few years. To start a business is a self-affirmation and the accomplishment of

the dreams. However, the rick of starting a business with the limited capital is very high in this

competitive situation. Therefore, many young people choose to open a store as a start and it is important

to face the first issue about the location. This research aimed to provide an evaluation to select the

location for setting the store through the relevant literatures and the actual studies.

Purpose The purposes of this study is as following,

1. The order of the priority to evaluate the measures while choosing the location of the store.

2. The evaluative criteria and order of the priority of the variables in each measures.

The Journal of International Management Studies, Volume 11 Number 2, August, 2016 15

Review of the literatures

Berman, Barry and Joel R. Evans (2001) included the items to evaluate the store section (or

location), such as passenger traffic, vehicle traffic, parking facility, transportation, store composition,

location and entry requirement in the below table.

Table 1: The check list to evaluate the store section (or location) Evaluation items Evaluation content

Passenger traffic The number and type of the passengers

Vehicle traffic The number and type of the vehicles, traffic jam

Parking facility Size and quality of the parking lot, the distance between the parking lot and store, the parking space of the staff

Transportation The public transportation, convenience of the highway, the workability of the delivery

Store composition The number and size of the store, synergy and the retail balance.

Specific location Visibility, location, shape and size of the ground, shape and size of the building, the condition and year of the building and ground.

Entry requirement The condition for buying or renting, cost of operation and maintenance, the restriction of the urban area and other policies.

Source of information: Berman, Barry and Joel R. Evans(2001),Retail Management-A Strategic Approach, Eighth Ed., Englewood Cliffs, NJ: Prentice Hall, chapter 10.

G.-H.Tzeng (2002) chose the location to set the hotel based on criteria of the economics,

transportation, competition, business area and environment as the criteria. The table is as below

Table 2: The level of the criteria for the location of the hotel Purpose Goal Criteria

Economic Cost of the rental, cost of the transportation

TransportationThe convenient public transportation system、volume of the parking lot、pedestrian volume

Competition The number of the competitor, the strength of the competition Business area The measure of area, the extension of public facility Environment The convenience for the storage of the garbage, and volume of the swage

Source of the information: G.-H. Tzeng et al. 1 Hospitality Management21(2002)171-187

Min-Yu Yang (2012) divided the factors of selecting the locations for chain restaurant as four

measurements, which are geographic factor, business area, traffic factor, financial factor and sixteen

minor criterion. The result indicates the first factor the chain restaurant considered while setting the

location is the business area, and comes to the traffic, then the geographic factor and finally the financial

factor.

Design of the research 1. Selection of the research method

Fuzzy AHP The statement of the Fuzzy AHP from Buckley (1995) is as following, The concept to use Fuzzy

AHP for the evaluation of the criteria or proposal is because such judgement or measurement is a

subjective and mental appraisal which contains uncertainty. To solve this deficiency, the application of

Fuzzy AHP is the best method.


The exposition about the amendment of Buckley’s fuzzy AHP is as following.

This research was based on the fuzzy AHP of Buckley (1955) which included (1) have each expert use

the trapezoidal fuzzy number to present the opinion about the relative importance of the paired factors and (2)

the deficiency of over complex in the operation by using the triangular fuzzy number as the fuzzy

membership function. While giving the questionnaire, ask the decision maker to fill the interval scale

based on the own feeling. To calculate the fuzzy weight of each interviewer and use Saaty’s method to take

geometric mean as the integrated function to integrate the overall decisions. Then obtain the weight as the

fuzzy value with Buckley’s method. This study developed the method of ranking fuzzy numbers based on

Zhau & Govind, 1991; Teng & Tzeng, 1996; Tang & Tzeng, 1999, which was to use center of gravity

method to defuzzy the triangular fuzzy number and find out the best non-fuzzy value or the best crisp value.

This research amended the operation for the weight by Buckley’s Fuzzy AHP as stated below,

Step 1: Establish the fuzzy matrix of paired comparison Through the questionnaire, get the viewpoint from each expert about the importance of the paired

factors with trapezoidal fuzzy number, then establish the fuzzy matrix of paired comparison Ãi.

1~~1

~1~

~~1

~~~

~~~

~~~

~~

21

221

112

21

22221

11211

nn

n

n

nnnn

n

n

iji

aa

aa

aa

aaa

aaa

aaa

aA ， ni ,2,1

ijijijij umla ,,~ 1~~ jiij aa．， nij ,,2,1

ijl : The minimum value of the interval scale from the interviewer.

ijm : The average of the interval scale.

iju : The maximum value of the interval scale from the interviewer.

Step 2: The consistency verification of the fuzzy matrix

According to the Buckley’s research, the following result was obtained Set ijaA as a positive

reciprocal matrix, and ijaA ~~ is a fuzzy positive reciprocal matrix.

If ijaA is consistent, then ijaA ~~ is also consistent.

Determine if this questionnaire is effective accordingly.

Step 3: Calculation of the fuzzy weight iZ~

Buckley introduced the evaluation of the weight with the concept of the geometric mean

Step 4: The series connection of all levels

Set the decision framework to 3 levels, if k 1k nkf f , , f is the fuzzy weight of each proposal kA,

and keee ~,,~~

1 is the fuzzy weight of the previous level, then the formula of the overall hierarchical

weight of each proposal is as following, kjkjj efefw ~~~~~11

Step 5: Defuzzy This study developed the method of ranking fuzzy numbers based on Zhau & Govind, 1991; Teng

&Tzeng, 1996; Tang & Tzeng, 1999, which was to use center of gravity (Center of Area, COA or Center

Index, CI) method to defuzzy the triangular fuzzy number and find out the best non-fuzzy value or the

best crisp value, which was to obtain the specific weight of each proposal. The operation is as below


iiiii llmluDFw 3/1

Step 6: Normalization Normalize the weight obtained from each proposal to get the below ration ii DFwDFwW /1

Framework The framework of this research is shown as the below figure


Traffic: (the number of the vehicle, type of the vehicle and traffic jam). Pedestrian including the

age (child are exclusive) and gender, the record of different time (rush hour, off-peak and lunch break)

and the number and feature of the vehicle on the road as well as the degree and time interval of the traffic

jam.

Parking facility: (the size and quality of the parking lot, the distance between the parking lot and the

store, the staff’s parking lot). The size of the parking lot including the number of the vehicle it can

accommodate; the quality of the parking lot including the space for the vehicle, shelter from sun and rain,

route planning for the vehicle, the availability of the security and the safety facility, the distance between

the parking lot and the store, and the exclusive parking lot for the staff out of charge.

Transportation: (public transportation means, convenience of the highway and the workability of

the delivery). The crowd and business opportunity which MRT and highway will bring, the distance

between the delivery driveway and parking lot are the factors to be considered.

Store composition and competition: (the number and size of the store, synergy and the retail balance,

number of the competitor and the strength of the competition). If a store can be blending and

complementary with the other regional stores, the synergy will create a high revenue for all store

composition. The number and strength of the competitor will affect the revenue and profit of the store as

well as the alternative choice for the customers.

Specific location: (visibility, location, the shape and size of the ground and building, the condition

and year of the ground and building). Ex. Quarter window and the route for school and work…etc. such

specific locations are the factors to be considered.

Entry requirement: (the condition for buying or renting, the cost of operation and maintenance, tax,

restriction of the urban area and other policies). In view of the operational cost and policies, if the entry

requirement is worthy needs to be evaluated.

Sample selection on the object

This study chose the Fuzzy AHP with the questionnaire done by the students and teachers (scholars)

at the business school. The sources of the sample were the students and teachers (scholars) at the

Institute of Business and Technology, with 20 questionnaires for both groups filled in complete, then

analyzed 10 effective ones for the consistency to further organize.

Verification of the consistency

The effectiveness of the questionnaire can be achieved by having the paired comparison be coherent

and proceed with the verification of the consistency to make the Consistency Index (CI).

C.I.=(λmax-n)/(n-1) n means the criterion numeral; λ is the greatest characteristic root. Normally, when C.I.≦0.1, the consistency is acceptable. Use the Expert choice software to verify the C.I and determine

the consistency of the questionnaire.

The analysis of the variance Guo Li-Fen (2001) applied fuzzy multiple criteria decision making to evaluate the IC designing

company in her thesis, and analyzed the variance with the result of the questionnaire to verify the

difference of the cognitive weight. If there is a remarkable difference, use T test of the LSD method to

proceed all the paired comparison among the grouped means but not to make any adjustment on the error

from the multiple comparison which is like to make the multiple T test between each two groups.


Furthermore, Douglas A.Lind,Robert D.Mason&William G. Marchal (2000) verified the sample

selection on the object with T test on the paired sample for the small sampling ad assumed the verification

of the relevant samples, paired the T test t= d /(Sd/ n )， Sd =2

(d d / (n 1))

Df=n-1， d : the difference of means among the paired or relative observations; Sd: the standard

deviation of the variant assignment among the paired or relative observation; n= the paired observations; H0：u0=0，H1：u1≠0. This research proceeded the T test towards the students and teachers to understand

the difference.

Data Analysis

The research used linguistic variable to express the evaluation of the relative important between two

proposals while having each students and teacher answer the questionnaire. These linguistic variables

can be presented with positive triangular fuzzy number and positive reciprocal fuzzy number as shown in

table 3.

Table 3: The evaluation scale of the relative importance Linguistic variable Positive triangular fuzzy number Positive reciprocal fuzzy number Extremely strong （9,9,9）（1/9,1/9,1/9）

In between （7,8,9）（1/9,1/8,1/7）

Extremely （6,7,8）（1/8,1/7,1/6）

In between （5,6,7）（1/7,1/6,1/7）

Quite strong （4,5,6）（1/6,1/5,1/6）

In between （3,4,5）（1/5,1/4,1/3）

A little strong （2,3,4）（1/4,1/3,1/2）

In between （1,2,3）（1/3,1/2,1）

Equally strong （1,1,1）（1,1,1） Source of information: Wang Chung-Hsiang- A Study on the Application of Fuzzy Analytic Hierarchy Process to IC Industrial Policy

Adoption (2003)

In the questionnaire answered by the students and teachers from the Institute of Technology, the

fuzzy weight (Wi) can be obtained from the four measures of level one, and fuzzy weight (Xi) can be

obtained from the eighteen evaluations of level 2, and then to obtain the combined fuzzy weight (Wi×Xi) with the triangular fuzzy number ijijijij umla ,,~ , and defuzz to obtain the fuzzy number. The center of

area (COA) proposed by Zhau & Govind(1991) can be used to obtain the fuzzy number: DFw1=[(ui－li)

＋(mi－li)]/3＋li. Take the measure of the student as example, the operation is shown as Table 4; the

fuzzy weights (Wi) of the traffic measure are (0.16452, 0.22, 0.292) which develops the fuzzy number

ranking method, then defuzz the triangular fuzzy number to get the non-fuzzy value or the best crisp

value that will obtain the specific weight of each proposal. The operation is as following, DFw1=[(ui－li)＋(mi－li)]/3＋li

[(0.292-0.16452)+(0.22-0.16452)]/3 +0.16452=0.226。


Table 4: The analysis table of fuzzy weight and fuzzy number of each

measure for the store section (location)-- Student of the Institute of Technology Measure Fuzzy weight(Wi) Fuzzy number Importance ranking Traffic (0.16452, 0.22, 0.292) 0.226 2

Parking lot (0.188, 0.243, 0.315) 0.249 1 Transportation (0.078, 0.11, 0.155) 0.114 6

Store composition and competition (0.123, 0.167, 0.223) 0.171 3 Specific location (0.107, 0.149, 0.21) 0.155 4

Entry requirement (0.08, 0.111, 0.156) 0.116 5

Table 5: The analysis table of fuzzy weight and fuzzy number of each evaluation

for the store section (location)-- Student of the Institute of Technology Level 1 The fuzzy

weight of each measure (Wi)

Level 2 The evaluation Level 2 The fuzzy

weight of the evaluation (Xi)

Combination of the fuzzy weights (Wi×Xi)

Fuzzy number

Importance Ranking

Traffic (0.16452, 0.22,

0.292)

Number of the vehicle (0.607, 0.673, 0.748) (0.100,0.148,0.218) 0.15545 1

Type of the vehicle (0.189,0.227, 0.27) (0.031,0.050,0.079) 0.05329 10

Traffic jam (0.09,0.1, 0.112) (0.015,0.022,0.033) 0.02317 17

Parking lot (0.188, 0.243,

0.315)

Size of the vehicle (0.28,0.348,0.43) (0.053,0.085,0.135) 0.09088 3 Quality of the parking lot (0.185,0.234,0.294) (0.035,0.057,0.093) 0.06142 6

Distance between the parking lot and the store

(0.309,0.363,0.427) (0.058,0.088,0.135) 0.09360 2

Staff’s parking lot (0.048,0.055,0.065) (0.009,0.013,0.020) 0.01429 21

Transportation (0.078, 0.11, 0.155)

Public transportation (0.331, 0.437, 0.57) (0.026, 0.048, 0.088) 0.05408 9 Convenience of the highway (0.274, 0.366, 0.483) (0.021, 0.040, 0.075) 0.04550 11 Workability of the delivery (0.157, 0.198, 0.258) (0.012, 0.022, 0.040) 0.02467 12

Store composition and competition (0.123, 0.167,

0.223)

Number and size of the store (0.266, 0.362, 0.49) (0.033, 0.060, 0.109) 0.06748 4 Synergy and the retail balance (0.139, 0.213, 0.313) (0.017, 0.036, 0.070) 0.04082 13

Number of the competitor (0.146, 0.202, 0.287) (0.018, 0.034, 0.064) 0.03856 14 Strength of the competition (0.16, 0.224, 0.318) (0.020, 0.037, 0.071) 0.04267 12

Specific location (0.107, 0.149, 0.21)

Visibility (0.254, 0.344, 0.46) (0.027, 0.051, 0.097) 0.05834 8 Location (0.308, 0.393, 0.501) (0.033, 0.059, 0.105) 0.06557 5

Shape and size of the building and ground

(0.158, 0.2, 0.255) (0.017, 0.030, 0.054) 0.03342 15

Condition and year of the building and ground

(0.053, 0.063, 0.077) (0.006, 0.009, 0.016) 0.01041 23

Entry requirement (0.08, 0.111, 0.156)

Condition for buying or renting

(0.085, 0.117, 0.161) (0.007, 0.013, 0.025) 0.01497 20

Cost of operation and maintenance

(0.361, 0.473, 0.609) (0.029, 0.053, 0.095) 0.05880 7

Rental (0.121, 0.151, 0.196) (0.010, 0.017, 0.031) 0.01901 19 Restriction of the urban area (0.13, 0.171, 0.226) (0.010, 0.019, 0.035) 0.02155 18

Other policies (0.066, 0.088, 0.119) (0.005, 0.010, 0.019) 0.01120 22 Source of information: Organized by this study


Table 6: The analysis table of fuzzy weight and fuzzy number of each measure

for the store section (location)-- Teacher of the Institute of Technology Measure Fuzzy weight (Wi) Fuzzy number Important ranking Traffic (0.13799, 0.2, 0.286) 0.208 3

Parking lot (0.192, 0.271, 0.382) 0.282 1 Transportation (0.152, 0.215, 0.304) 0.224 2

Store composition and competition (0.105, 0.156, 0.231) 0.164 4 Specific location (0.075, 0.105, 0.149) 0.110 5

Entry requirement (0.039, 0.052, 0.074) 0.055 6

Table 7: The analysis table of fuzzy weight and fuzzy number of each evaluation for the store

section (location)-- Teacher of the Institute of Technology

Level 1 The fuzzy weight of each measure (Wi)

Level 2 The evaluation Level 2 The fuzzy

weight of the evaluation (Xi)

Combination of the fuzzy weights (Wi×Xi)

Fuzzy number Importance

Ranking

Traffic (0.13799, 0.2, 0.286)

Number of the vehicle (0.402, 0.561, 0.765) (0.055, 0.112, 0.219) 0.12882 1

Type of the vehicle (0.137, 0.196, 0.273) (0.019, 0.039, 0.078) 0.04539 11

Traffic jam (0.177, 0.243, 0.357) (0.024, 0.049, 0.102) 0.05838 8

Parking lot (0.192, 0.271, 0.382)

Size of the parking lot (0.244, 0.361, 0.52) (0.047, 0.098, 0.199) 0.11444 4 Quality of the parking lot (0.145, 0.203, 0.285) (0.028, 0.055, 0.109) 0.06391 6

Distance between the parking lot and the store

(0.26, 0.369, 0.531) (0.050, 0.100, 0.203) 0.11759 3

Staff’s parking lot (0.053, 0.068, 0.089) (0.010, 0.018, 0.034) 0.02087 17

Transportation (0.152, 0.215, 0.304)

Public transportation (0.352, 0.486,0.666) (0.054, 0.104, 0.202) 0.12015 2 Convenience of the highway (0.249, 0.362, 0.52) (0.038, 0.078, 0.158) 0.09125 5 Workability of the delivery (0.118, 0.152, 0.206) (0.018, 0.033, 0.063) 0.03775 14

Store composition and competition (0.105, 0.156,

0.231)

Number and size of the store (0.211, 0.321, 0.479) (0.022, 0.050, 0.111) 0.06096 7 Synergy and the retail balance (0.146, 0.222, 0.33) (0.015, 0.035, 0.076) 0.04206 12

Number of the competitor (0.121, 0.173, 0.25) (0.013, 0.027, 0.058) 0.03248 15 Strength of the competition (0.185, 0.284, 0.448) (0.019, 0.044, 0.103) 0.05574 9

Specific location (0.075, 0.105, 0.149)

Visibility (0.309, 0.434, 0.608) (0.023, 0.046, 0.091) 0.05311 10 Location (0.236, 0.341, 0.484) (0.018, 0.036, 0.072) 0.04187 13

Shape and size of the building and ground

(0.108, 0.147, 0.203) (0.008, 0.015, 0.030) 0.01793 18

Condition and year of the building and ground

(0.061, 0.078, 0.105) (0.005, 0.008, 0.016) 0.00947 20

Entry requirement (0.039, 0.052, 0.074)

Condition for buying or renting (0.087, 0.137, 0.219) (()0.003, 0.007, 0.016) 0.00891 21 Cost of operation and maintenance (0.244, 0.39, 0.594) (0.010, 0.020, 0.044) 0.02458 16

Rental (0.079, 0.127, 0.217) (0.003, 0.007, 0.016) 0.00858 23 Restriction of the urban area (0.128, 0.212, 0.357) (0.005, 0.011, 0.026) 0.01414 19

Other policies (0.084, 0.133, 0.223) (0.003, 0.007, 0.017) 0.00890 22 Source of information: Organized by this study


Table 8: The comprehensive analysis table of fuzzy weight and fuzzy

number of each measure for the store section (location)—Teacher & student

Measure Student Teacher Student & teacher

Fuzzy number Ranking Fuzzy number Ranking Fuzzy mean Importance ranking

Traffic 0.226 2 0.208 3 0.217 2 Parking lot 0.249 1 0.282 1 0.2655 1

Transportation 0.114 6 0.224 2 0.169 3 Store composition and competition 0.171 3 0.164 4 0.1675 4

Specific location 0.155 4 0.110 5 0.1325 5 Entry requirement 0.116 5 0.055 6 0.0855 6

In the assumed verification of the relative sample, the paired T test t= d /(Sd/ n )， Sd = 2

( d d / ( n 1 ))

H0：u0=0，H1：u1≠0，two-tailed test n=6，df=n-1=5, when α=0.05, threshold is 2.571. Then the

acceptance region is -2.571≦α≧2.57. From the measures to obtain the paired T test of the student and teacher Sd = 2

( d d / ( n 1 )) =0.062103

t= d /(Sd/ n )=-0.07888 is within the acceptance region, to accept u0 means there is no difference

between the sample of the student and teacher. Thus, proceed with the average of two groups and rank

again.

In the measure of level 1, the average of the teacher and student from table 7, obtained the

importance ranking of the fuzzy weight that except for the remarkable difference in transportation, the

fuzzy number and the ranking of the teacher and student is almost the same. Which means the parking

lot > traffic? Transportation> store composition and competition> specific location> entry requirement

Table 9 The comprehensive analysis table of fuzzy weight and fuzzy number of each evaluation for

the store section (location)-- Teacher of the Institute of Technology


Source of information: Organized by this study

In the assumed verification of the relative sample, the paired T test t t= d /(Sd/ n )， Sd

= 2(d d / (n 1))

H0：u0=0，H1：u1≠0, two-tailed test n=6，df=n-1=22，whenα=0.05 threshold is 2.074. Then the

acceptance region is -2.074≦α≧2.074. From the variable (evaluation) to obtain the paired T test of the

student and teacher Sd = 2(d d / (n 1)) =0.02337

t= d /(Sd/ n )=-0.68182, is within the acceptance region, to accept u0 means there is no difference

between the sample of the student and teacher. Thus, proceed with the average of two groups and rank

again.

In the measure of level 2, the overall average of the student and teacher as table 9 showed, obtained

the ranking of the fuzzy weight as the fuzzy weight of evaluation

The order of the priority is: Number of the vehicle> the distance of the parking lot and the store>

the size of the parking lot> the public transportation means> the convenience of the highway? The

number and size of the store> the quality of the parking lot> visibility> location> type of the vehicle>

strength of the competition> cost of operation and maintenance> synergy and the retail balance> traffic

jam> number of the competitor> workability of the delivery> shape and size of the building and ground>

restriction of the urban area> staff’s parking lot> rental> condition for buying or renting> other policies>

condition and year of the building and ground. The orders of the priority for the first three items and last

items are as below

1. The order of the priority for the first three items: Number of the vehicle (1) >distance between the

parking lot and the store (2) > the size of the parking lot (3).

2. The order of the priority for the last three items: Condition for buying or renting (1) >other policies (2)

> condition and year of the building and ground (3).


CONCLUSION AND SUGGESTION

Conclusion From the overall average of the teacher and student in all measures of level 1, except for the

transportation, the fuzzy weight and the importance ranking are almost the same. That is the parking lot

> traffic> transportation> store composition and competition> specific location> entry requirement.

However, from the evaluation of level 2, the fuzzy weight and the importance ranking are different with

the first three and last three prioritized as below.

1. The order of the priority for the first three items: Number of the vehicle(1) >distance between the

parking lot and the store(2) > the size of the parking lot(3)

2. The order of the priority for the last three items: Condition for buying or renting(1) >other policies(2) >

condition and year of the building and ground(3)

Discussion and suggestion The study indicated the parking lot and traffic were the priority factors to be considered in all

measures of level 1 because in the commercial area, it could drop the consumer’s desire to buy simply

because the lack of the parking space or force to purchase somewhere else which was very pity. The

traffic and people can stimulate sales and that’s what we say “money follows people”. An impulse

purchase happens is likely to happen in the crowd. Therefore, to select a store location with busy people

and traffic is one of the important considerations.

In the evaluation of level 2, the number of vehicle, distance between the parking lot and storage

were the priority factors to be considered. The study showed the number of vehicle was the factor of the

crowd and the distance between the parking lot and store was to think of how long it would take from the

parking lot to the store for purchasing the product. Some inconvenient parking lot or a distant parking

space would also drop consumer’s will. The size of the parking lot will affect if people can find the

parking space if it is too large or if any parking space will be available if it is small.

As to the condition for buying and renting, other policies, condition of the building and ground

were the least consideration. The condition for buying or renting covers the mutual contracts and the

cost of the operator. However, with busy crowd, nice sales and profit, even if the condition for buying

or renting is not good, people are still willing to buy. The other policies and condition of the building

and ground don’t directly affect the operation. It’s ok to solve when any problem or difficulty occurs.

At last, a suggestion was given to those who would like to open stores as the owners, while

choosing the store location, an overall consideration including finical, labors, product assortment,

customers and competitors…etc. Only the comprehensive consideration can reduce the risk.

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