广州市居民网络购物频率的影响因素及其空间差异

doi:10.13249/j.cnki.sgs.2020.06.008

摘要/Abstract

摘要：

基于广州市居民网络购物行为调查问卷和电子地图兴趣点（POI）数据，从全市和不同区位2个空间尺度，运用有序多分类Logistic回归模型探讨了个人社会经济属性、商品特征、空间环境及物流快递4类因素对居民网购频率的影响。研究发现：① 影响因素在不同空间尺度和不同区位产生作用的因子个数、作用强度和作用方向存在差异。影响因子数量在全市域范围最多，远郊区最少。各因子总体上在近郊区和全市域作用强度大，在远郊区最弱。退货服务重要性在近郊区和中心区作用方向相反；② 部分空间环境因子对网购频率有影响，城市化水平、商业中心可达性在全市域范围有影响，居住地城市化水平越高、离商业中心距离越近的居民网购频率越高，支持了创新扩散假说。快递点数量在中心区有影响，居住地快递点数量越多的居民网购频率越高。其它空间环境因子没有显著影响。③ 个人社会经济属性因素对网购频率影响较大，性别、年龄是最重要的影响因子，其次是学历、职业，月收入影响最小。商品特征、快递物流因素各因子在不同区域对网购频率产生较大影响。

关键词: 网购频率, 兴趣点, 有序多分类Logistic回归, 广州市

Abstract:

Online shopping has now become an important channel for residents to shop. To clarify the influencing factors of online shopping behavior is helpful to the formulation of e-commerce development strategy and the layout planning of physical business. Based on 1 156 online shopping behavior questionnaires of Guangzhou residents on November 2015 and March 2016 and the data of Point of Interest (POI) in 2016, this article explores the influence of 4 factors including 17 indicators on the online shopping frequency: personal socio- economic attributes, commodity characteristics, spatial environment and logistics express delivery, by using ordinal logistic regression model from whole city and different locations of Guangzhou. The results show that: 1) There are differences in the number, intensity and direction of influencing factors in different spatial scales and locations. The number of influencing factors is the largest in the whole city and the least in the outer suburbs. The effect of each factor is strong in the inner suburb and the whole city, but weakest in the outer suburb. The importance of return service plays an opposite role in the inner suburb and the central area. 2) Some spatial environmental indicators have an impact on the online shopping frequency. Urbanization level and accessibility of commercial centers have an impact on the whole city. Residents in higher urbanization level region and closer to the commercial center have higher online shopping frequency, which supports innovation diffusion hypothesis. The number of express delivery points has an impact on the central area. Residents with more express delivery points in residential areas have higher online shopping frequency. Other spatial environmental factors have no significant impact. 3) Personal socio-economic attributes have greater impact on the online shopping frequency than spatial environmental factors. Gender and age are the most important indicators, followed by education, occupation and monthly income. Women, young people, highly educated, upper-middle income residents have higher online shopping frequency. Commodity characteristics and express logistics indicators have a greater impact on the online shopping frequency in different regions. Residents who pay more attention to commodity characteristics have higher online shopping frequency. Residents with higher acceptance of the proportion of freight to transaction and express time have higher online shopping frequency.

Key words: online shopping frequency, Point of Interest (POI), ordinal logistic regression, Guangzhou

中图分类号:

K902

邓清华, 薛德升, 龚建周. 广州市居民网络购物频率的影响因素及其空间差异[J]. 地理科学, 2020, 40(6): 928-938.

Deng Qinghua, Xue Desheng, Gong Jianzhou. Influencing Factors of Online Shopping Frequency of Residents and Spatial Differences of These Factors in Guangzhou City[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(6): 928-938.

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样本属性		样本数（个）	比例（%）	样本属性		样本数（个）	比例（%）
性别	男	546	47.2	职业	机关/事业单位人员	111	9.5
	女	610	52.8		企业员工	305	26.4
年龄（岁）	15~24	463	40.0		私营业者	144	12.5
	25~34	358	31.0		在校学生	446	38.6
	35~44	208	18.0		自由职业者	82	7.1
	≥45	127	11.0		农民	5	0.4
学历	初中及以下	77	6.7		退休人员	15	1.3
	高中或高职	236	20.4		其他	48	4.2
	大专或本科	792	68.5	居住区位	中心区	408	35.3
	研究生	51	4.4	居住区位	近郊区	432	37.4
月收入（10⁴ 元）	<0.3	505	43.7		远郊区	316	27.3
	0.3~0.6	299	25.8
	0.6~1.0	224	19.4
	1.0~1.5	88	7.6
	>1.5	40	3.5

区位	网购频率
区位	≤5次/a	6~11次/a	1~3次/月	4~7次/月	≥8次/月
中心区	19.9	26.5	36.3	11.5	5.9
近郊区	17.1	37.7	31.9	8.8	4.4
远郊区	22.8	32.0	35.4	6.6	3.2

因素	自变量	符号	赋值说明	变量分类
个人社会经济属性	性别	X₁	1=男；0=女	二分类
	年龄	X₂	1=15~24岁；2=25~34岁；3=35~44岁；4=45岁以上	多分类
	学历	X₃	1=初中及以下；2=高中或高职；3=大专或本科；4=研究生	多分类
	职业	X₄	1=其他；2=企业员工；3=私营业者；4=在校学生；5=自由职业者；6=农民；7=退休人员；8=机关/事业单位人员	多分类
	月收入（元）	X₅	1=0.3×10⁴以下；2=0.3×10⁴~0.6×10⁴；3=0.6×10⁴~1.0×10⁴；4=1.0×10⁴~1.5×10⁴；5=1.5×10⁴以上	多分类
商品特征	商品价格重要性	X₆	1=不太重要；2=一般；3=比较重要	多分类
	商品种类重要性	X₇	1=不太重要；2=一般；3=比较重要	多分类
	实体店无售重要性	X₈	1=不太重要；2=一般；3=比较重要	多分类
	退货服务重要性	X₉	1=不太重要；2=一般；3=比较重要	多分类
	城市化水平	X₁₀	居住地所处行政区城市化水平，1=低；2=较低；3=中等；4=较高；5=高	多分类
空间环境	实体商店数量（个）	X₁₁	居住地1 km范围内实体商店数量	连续
	实体商店业态种类（种)	X₁₂	居住地1 km范围内实体商店的业态种类	连续
	交通站点数量（个）	X₁₃	居住地1 km范围内公交站、地铁站数量	连续
	商业中心可达性（km）	X₁₄	离最近的商业中心距离	连续
物流快递	快递点数量（个）	X₁₅	居住地1 km范围内快递点数量	连续
	运费占交易额比例	X₁₆	1=1/10以下；2=1/5以下；3=2/5以下；4=2/5以上	多分类
	快递时间接受度	X₁₇	1=1~2 d；2=3~4 d；3=5~6 d；4=7 d以上	多分类

自变量				全市		远郊区		近郊区		中心区
自变量				B	Exp(B)	B	Exp(B)	B	Exp(B)	B	Exp(B)
个人社会经济属性	性别	x₁	女性	0.679^***	1.972	0.422^**	1.525	0.397^**	1.487	0.921^***	2.512
	年龄（岁）	x₂	15~24	2.323^***	10.206			2.443^***	11.508	2.217^***	9.180
			25~34	1.629^***	5.099			2.018^***	7.523	1.185^***	3.271
			35~44	0.712^***	2.038			1.271^***	3.564
	学历	x₃	初中及以下	?0.938^**	0.391			?1.853^***	0.157	?0.99^*	0.372
			高中或高职	?0.570^*	0.566			?1.26^**	0.284
			大专或本科					?0.871^*	0.419
	职业	x₄	在校学生	?0.618^**	0.539			?1.376^***	0.253	?1.028^**	0.358
			其他	0.839^**	2.314	2.676^*	14.527
	月收入（10⁴元）	x₅	1.0~1.5	0.848^**	2.335
商品特征	商品价格重要性	x₆	不太重要			?1.536^**	0.215
	商品种类重要性	x₇	一般							?1.048^***	0.351
	实体店无售重要性	x₈	不太重要					?1.555^***	0.211
			一般	?0.394^***	0.674	?0.477^*	0.621
	退货服务重要性	x₉	不太重要					?0.615^**	0.541	0.566^**	1.761
空间环境	城市化水平	x₁₀	低	?0.507^**	0.602
	商业中心可达性（km）	x₁₄		?0.029^**	0.971
	快递点数量（个）	x₁₅								0.003^**	1.003
物流快递	运费占交易额比例	x₁₆	1/10以下	?1.456^***	0.233			?2.565^***	0.077
			1/5以下	?1.368^**	0.255			?2.417^**	0.089
			2/5以下	?1.360^**	0.257			?2.802^***	0.061
	快递时间接受度（d）	x₁₇	1~2	?0.877^**	0.416			?1.303^**	0.272
			3~4					?0.845^*	0.43
			5~6	?0.779^**	0.459			?0.998^*	0.369
样本数量				1156		316		432		408
似然比检验				P=0		P=0		P=0		P=0
平行线检验				P= 0.995		P= 0.995		P=1.000		P=0.948