Examining the Impact of E-Commerce Growth on the Spatial Distribution of Fashion and Beauty Stores in Seoul
Abstract
:1. Introduction
2. Background
3. Methods
3.1. Outline of Analytical Procedures
3.2. Study Regions and Subjects
3.3. Analytical Techniques
3.3.1. Kernel Density Analysis
3.3.2. The Nearest Neighbor Ratio (NNR) Index
3.3.3. Spatial Regression Models
4. Results
4.1. Basic Analysis
4.2. Spatial Cluster Analysis
4.3. Spatial Regression Analysis
4.3.1. Collecting Spatial Data
4.3.2. Descriptive Statistics and Spatial Autocorrelation Tests
4.3.3. OLS and SEM Results
5. Conclusions
5.1. Summary of Findings
5.2. Spatial Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Previous (~2017) | Current (2018~) | |
---|---|---|
Integration | S/W(CD) | Computer, computer related appliances |
Music CDs and disks · videos · musical instruments | Others | |
Flowers | Household goods | |
Subdivision | Household goods motor vehicle parts and accessories | Household goods Motor vehicle parts and accessories |
Travel arrangement and reservation services | Travel arrangement and transportation services Culture and leisure services | |
Miscellaneous services | E-coupon services Food services Miscellaneous services |
2015 | 2019 | 2020 | 2015/2019 | 2019/2020 | |
---|---|---|---|---|---|
Wearing apparel—clothing | 27,370 | 23,531 | 21,745 | −14.03 | −7.59 |
Footwear, luggage, and accessories | 8107 | 6367 | 6117 | −21.46 | −3.93 |
Cosmetics | 8612 | 6405 | 5602 | −25.63 | −12.54 |
Hair, nails, and skin care—health | 24,947 | 31,747 | 32,055 | 27.26 | 0.97 |
Opening | Closure | Variation (2015/2019) | |
---|---|---|---|
Wearing apparel—clothing | 11,102 | 36,522 | −25,420 |
Footwear, luggage, and accessories | 2680 | 4408 | −1728 |
Cosmetics | 3144 | 5290 | −2146 |
Hair, nails, and skin care—health | 18,610 | 11,888 | 6722 |
Point (n) | Observed NND (m) | Expected NND (m) | NNR (Index) | Z-Score | p-Value | |
---|---|---|---|---|---|---|
Wearing apparel—clothing | 23,531 | 25.95 | 93.29 | 0.28 | −211.84 | 0.00 |
Footwear, luggage, and accessories | 6367 | 62.79 | 176.06 | 0.36 | −98.21 | 0.00 |
Cosmetics | 6405 | 63.23 | 173.79 | 0.36 | −97.40 | 0.00 |
Hair, nails, and skin care—health | 31,747 | 25.66 | 80.81 | 0.32 | −232.65 | 0.00 |
Dimension | Variables | Data Year | Data Source | |
---|---|---|---|---|
Dependent variables | The newly opened and closed fashion and beauty-related stores (n) (1) Wearing apparel—clothing; (2) footwear, luggage, and accessories; (3) cosmetics; and (4) hairs, nails, and skin care—health | 2019 | [69] | |
Independent variables | Population | Resident population (person) | 2015 | [8] |
The average of the daytime population (person) | ||||
The annual average of passengers in a subway station (person) | 2019 | [70] | ||
Industrial economic activities | Industrial establishments (n) | 2018 | [8] | |
Workers per establishment (person) | ||||
Food and beverage stores (n) | ||||
Business start-up rate (%) | ||||
Private tutoring institutes for adults (n) | 2019 | [71] | ||
Physical accessibility | Average nearest distance to a subway station (m) | 2019 | [70] | |
Average nearest distance to the main road (m) | 2019 | [72] | ||
Parking spaces (n) | 2018 | [69] | ||
Land value | Officially assessed individual land price (₩) | 2019 | [73] |
Variables | Min. | Max. | Mean | S.D. | VIF | |||
---|---|---|---|---|---|---|---|---|
POP | Resident population (person) | 268 | 57,761 | 2299 | 9448 | 3.9 | ||
Average of daytime population (person) | 35,633 | 919,903 | 254,147 | 124,232 | 6.4 | |||
Monthly average Passengers in subway station (n) | 0 | 367,882 | 27,319 | 44,733 | 1.9 | |||
IEA | Industrial establishments (n) | 19 | 15,787 | 1942 | 1917.8 | 4.2 | ||
workers per establishment (person) | 2.1 | 19.7 | 5.6 | 3.2 | 2.0 | |||
Food and beverage stores (n) | 0 | 20,718 | 308 | 290.5 | 6.8 | |||
start-up rate (%) | 3.1 | 25.9 | 11.0 | 2.8 | 1.4 | |||
Life-long vocational education and training institutes (n) | 0 | 181 | 4.7 | 13 | 2.6 | |||
ACC | Average nearest distance to main road (m) | opening | WAC | 0 | 1504.0 | 158.4 | 173.3 | 4.2 |
FLA | 0 | 1679.5 | 148.8 | 207.1 | 4.1 | |||
COS | 0 | 1566.2 | 134.5 | 177.2 | 4.2 | |||
HNSH | 0 | 1622.3 | 180.8 | 165.8 | 7.3 | |||
closure | WAC | 0 | 918.2 | 141.9 | 93.6 | 1.1 | ||
FLA | 0 | 803.1 | 129.0 | 93.7 | 1.5 | |||
COS | 0 | 1422.3 | 156.7 | 163.6 | 3.0 | |||
HNSH | 0 | 546.3 | 162.4 | 76.9 | 1.2 | |||
Average nearest distance to subway station (m) | opening | WAC | 0 | 3137.4 | 364.0 | 366.1 | 2.8 | |
FLA | 0 | 2833.4 | 360.4 | 353.9 | 3.3 | |||
COS | 0 | 2555.2 | 488.6 | 330.1 | 8.3 | |||
HNSH | 0 | 1023.5 | 398.8 | 150.6 | 1.2 | |||
closure | WAC | 0 | 1067.4 | 339.6 | 185.3 | 1.6 | ||
FLA | 0 | 2002.1 | 364.6 | 190.3 | 1.1 | |||
COS | 0 | 1638.4 | 451.1 | 164.6 | 1.3 | |||
HNSH | 0 | 2714.9 | 473.4 | 340.3 | 8.0 | |||
Parking spaces (n) | 388 | 53,414 | 10,025.2 | 6670.5 | 5.0 | |||
LV | Officially assessed individual land price (thousand won) | 742,238 | 22,528 | 3852 | 2438 | 1.7 |
Dependent | WAC | FLA | COS | HNSH | |
---|---|---|---|---|---|
Independent | |||||
CONSTANT | −42.738 *** (−2.749) | −1.602 (−0.596) | −2.448 (−1.081) | −29.008 *** (−4.527) | |
POP | RP (person) | −0.002 *** (−3.062) | −0.000 *** (−4.702) | −0.000 *** (−3.923) | 0.001 ***(2.833) |
ADP (person) | 0.0003 *** (4.810) | 4.858 *** (5.254) | 3.248 *** (−3.923) | 3.676 * (1.856) | |
MAPS S (n) | 5.264 (0.066) | −3.262 (−0.233) | 1.921 * (1.713) | −4.326 (−1.451) | |
IEA | IE (n) | 0.036 *** (13.006) | 0.003 *** (5.212) | −0.001 ** (−2.450) | −0.003 *** (−3.205) |
WPE (person) | −0.974 (−0.864) | −0.544 *** (−2.731) | −0.216 (−1.360) | −2.028 *** (−4.768) | |
FBS (n) | −0.128 *** (−5.534) | −0.002 (−0.518) | 0.008 ** (2.375) | 0.048 *** (5.424) | |
SR (%) | 3.613 *** (3.351) | 0.235 (1.232) | 0.165 (1.090) | 2.524 *** (6.158) | |
PTIA (n) | −0.449 (−1.419) | −0.031 (−0.549) | 0.260 *** (5.815) | 1.372 *** (11.419) | |
ACC | ANDM(m) | 0.009 (0.576) | 0.003 (1.110) | 0.003 (1.470) | 0.003 (0.473) |
ANSS(m) | 0.001 (0.179) | −0.000 (−0.097) | −0.001 (−0.514) | 0.009 (1.305) | |
PS (n) | −0.004 *** (−4.302) | −0.000 (−1.409) | 0.000 * (1.945) | 0.001 ** (2.368) | |
LV | OAILP (₩) | 3.789 *** (2.787) | 1.022 *** (4.258) | 7.738 *** (4.012) | 2.266 *** (4.396) |
Goodness of fit | R-squared | 0.451 | 0.429 | 0.598 | 0.763 |
Log-likelihood | −2285.4 | −155.74 | −1451.91 | −1873.18 | |
AIC | 4596.8 | 3127.48 | 2935.81 | 3772.37 | |
SC | 4649.44 | 3180.13 | 2988.46 | 3825.01 | |
Normality | Jarque–Bera test | 137,513.245 *** | 21,151.661 *** | 3888.313 *** | 2796.664 *** |
Heteroskedasticity | Breusch–Pagan test | 5969.289 *** | 951.028 *** | 1470.77 *** | 787.508 *** |
Koenker–Bassett test | 133.652 *** | 53.469 *** | 180.727 *** | 108.387 *** | |
White test | 414.866 *** | 289.771 *** | 350.66 *** | 348.789 *** | |
Spatial autocorrelation | Lagrange multiplier (lag) | 0.395 | 0.673 | 0.648 | 19.213 *** |
Robust LM (lag) | 0.605 | 0.015 | 4.791 | 3.320 * | |
Lagrange multiplier (error) | 0.034 | 0.839 | 1.053 | 27.716 *** | |
Robust LM (error) | 0.244 | 0.180 | 5.196 | 11.823 *** | |
Lagrange multiplier (SARMA) | 0.639 | 0.853 | 5.844 | 31.036 *** | |
Multi-collinearity (MCC-Number) | 23.549 | 23.285 | 23.924 | 24.836 |
Dependent | WAC | FLA | COS | HNSH | |
---|---|---|---|---|---|
Independent | |||||
CONSTANT | −66.582 *** (−3.647) | −0.117 (−0.029) | −9.763 *** (−3.321) | −12.329 ** (−2.484) | |
POP | RP (person) | −0.000 (−0.349) | −0.000 *** (−2.997) | 1.066 (0.118) | 0.001 *** (4.102) |
ADP (person) | 0.000 ** (2.464) | 4.102 *** (3.055) | 2.092 ** (2.360) | 2.303 (0.152) | |
MAPS S (n) | 3.055 (0.342) | −9.526 (−0.468) | 3.919 *** (2.926) | −1.153 (−0.496) | |
IEA | IE (n) | 0.027 *** (8.512) | 0.006 *** (8.455) | 0.000 (0.452) | −0.001 (−1.387) |
WPE (person) | −2.588 ** (−2.030) | −0.687 ** (−2.365) | −0.003 (−0.016) | −0.978 *** (−3.0) | |
FBS (n) | 0.043 (1.629) | 0.000 (0.084) | 0.015 *** (3.826) | 0.032 *** (4.781) | |
SR (%) | 1.678 (1.380) | −0.050 (−0.181) | 0.048 (0.257) | 0.750 ** (2.405) | |
PTIA (n) | −0.491 (−1.364) | −0.270 *** (−3.301) | 0.062 (1.148) | 0.586 *** (6.360) | |
ACC | ANDM(m) | 0.072 ** (2.241) | −0.003 (−0.399) | 0.004 (1.334) | 0.003 (0.305) |
ANSS(m) | 0.028 * (1.664) | −0.001 (−0.191) | −2.063 (−0.007) | −0.002 (−0.670) | |
PS (n) | −0.002 ** (−2.201) | −0.001 ** (−2.253) | 2.944 (0.2) | 0.000 (0.422) | |
LV | OAILP (₩) | 1.398 *** (9.113) | 2.384 *** (6.825) | 2.312 *** (10.043) | 2.984 *** (7.554) |
Goodness of fit | R-squared | 0.651 | 0.492 | 0.651 | 0.631 |
Log-likelihood | −2338.13 | −1711.09 | −1533.86 | −1760.89 | |
AIC | 4702.27 | 3448.18 | 3093.72 | 3547.77 | |
SC | 4754.91 | 3500.82 | 3146.37 | 3600.42 | |
Normality | Jarque–Bera test | 5244.515 *** | 104,576.226 *** | 2928.519 *** | 1043.274 *** |
Heteroskedasticity | Breusch–Pagan test | 1210.944 *** | 2286.395 *** | 172.578 *** | 435.167 *** |
Koenker–Bassett test | 129.997 *** | 58.677 *** | 236.148 *** | 92.491 *** | |
White test | 284.701 *** | 344.622 *** | 375.934 *** | 341.962 *** | |
Spatial autocorrelation | Lagrange multiplier (lag) | 4.840 ** | 1.354 | 5.813 ** | 30.389 *** |
Robust LM (lag) | 0.060 | 0.003 | 3.106 * | 2.927 * | |
Lagrange multiplier (error) | 9.116 *** | 2.057 | 30.149 *** | 40.642 *** | |
Robust LM (error) | 4.336 ** | 0.707 | 27.442 *** | 13.180 *** | |
Lagrange multiplier (SARMA) | 9.176 ** | 2.060 | 33.255 *** | 43.569 *** | |
Multi-collinearity (MCC-Number) | 24.697 | 24.144 | 25.328 | 25.049 |
Dependent | Opening | Closure | |||
---|---|---|---|---|---|
Independent | HNSH | WAC | COS | HNSH | |
CONSTANT | −23.780 *** (−3.756) | −64.073 *** (−3.517) | −10.063 *** (−3.475) | −9.493 ** (−1.995) | |
POP | RP (person) | 0.001 *** (2.987) | −0.000 (−0.430) | −2.438 (−0.283) | 0.001 *** (4.340) |
ADP (person) | 3.166 * (1.661) | 0.000 *** (2.743) | 2.539 *** (3.005) | 2.488 (0.175) | |
MAPS S (n) | −4.065 (−1.489) | 3.389 (0.396) | 3.439 *** (2.823) | −9.061 (−0.438) | |
IEA | IE (n) | −0.003 *** (−3.152) | 0.027 *** (8.456) | −1.267 (−0.003) | −0.001 (−1.287) |
WPE (person) | −1.601 *** (−3.874) | 0.027 *** (8.456) | 0.197 (1.062) | −0.444 (−1.432) | |
FBS (n) | 0.054 *** (6.287) | 0.042 (1.620) | 0.018 *** (4.644) | 0.037 *** (5.783) | |
SR (%) | 2.218 *** (5.674) | 1.478 (1.239) | 0.006 (0.316) | 0.457 (1.576) | |
PTIA (n) | 1.316 *** (11.291) | −0.446 (−1.255) | 0.050 (0.969) | 0.522 *** (5.950) | |
ACC | ANDM (m) | 0.003 (0.422) | 0.062 ** (1.981) | 0.005 (1.641) | −0.002 (−0.196) |
ANSS (m) | 0.004 (0.637) | 0.024 (1.482) | −0.001 (−0.221) | −0.002 (−0.855) | |
PS (n) | 0.000 (1.584) | −0.002 ** (−2.465) | −9.594 (−0.686) | −0.000 (−0.555) | |
LV | OAILP (₩) | 2.127 *** (3.760) | 1.389 *** (8.538) | 2.464 *** (9.623) | 2.805 *** (6.346) |
Goodness of fit | R-squared | 0.781 | 0.661 | 0.683 | 0.675 |
Log-likelihood | −1860.933 | −2334.068 | −1519.417 | −1741.723 | |
AIC | 3747.87 | 4694.14 | 3064.83 | 3509.45 | |
SC | 3800.51 | 4746.78 | 3117.48 | 3562.09 | |
Spatial effects | Spatial error coeff. () | 0.275 *** | 0.164 *** | 0.308 *** | 0.345 *** |
Spatial dependence | Likelihood ratio test | 24.50 *** | 8.130 *** | 28.890 *** | 38.326 *** |
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Park, S.; Lee, K. Examining the Impact of E-Commerce Growth on the Spatial Distribution of Fashion and Beauty Stores in Seoul. Sustainability 2021, 13, 5185. https://doi.org/10.3390/su13095185
Park S, Lee K. Examining the Impact of E-Commerce Growth on the Spatial Distribution of Fashion and Beauty Stores in Seoul. Sustainability. 2021; 13(9):5185. https://doi.org/10.3390/su13095185
Chicago/Turabian StylePark, Sohyun, and Keumsook Lee. 2021. "Examining the Impact of E-Commerce Growth on the Spatial Distribution of Fashion and Beauty Stores in Seoul" Sustainability 13, no. 9: 5185. https://doi.org/10.3390/su13095185