B2C VS.O2O：线上渠道拓展类型对线下渠道的影响及门店密度的调节作用

doi:10.16381/j.cnki.issn1003-207x.2023.1242

Abstract

Abstract:

In the evolving landscape of digital commerce， online channel expansion strategies are increasingly changing. While some companies have added conventional B2C e-commerce platforms such as JD.com， others have introduced O2O platforms like Doordash that offer instant delivery services. Despite the growth in such strategies， there is a scarcity of research comparing the effects of different types of online channel expansions on offline sales. Drawing upon the omni-channel continuum framework， B2C e-commerce platforms are delineated as “vertical strategies” and O2O instant delivery platforms are delineated as “complete strategies.” This paper investigates their relative impacts on physical store sales and examines the moderating role of store density on the effectiveness of omni-channel strategies. Associative Network Theory is used to formulate our hypotheses. Unique daily sales data from 1，696 offline stores and 9 online platforms of a high-end wine brand in China are analyzed， spanning from April 2019 to June 2022. Through linear and panel regression models， it is found that both complete and vertical strategies significantly enhance offline sales. However， complete （vs. vertical） strategies have a stronger synergistic effect on offline sales. Moreover， the results indicate that higher store density amplifies the positive effects of vertical strategies on offline sales. Several robustness checks validate our results. This research shed light on the strategic importance of selecting different types of channels in enhancing offline sales.

Key words: online channel expansion, omni-channel continuum, associative network theory, panel regression

CLC Number:

F274

Wei Li,Zecheng Fan,Sha Zhang. The Effects of Adding B2C Versus O2O Channel on Offline Sales: Store Density as a Moderator[J]. Chinese Journal of Management Science, 2026, 34(2): 323-335.

Figures/Tables 5

References 55

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变量类别	变量符号	定义
线下门店销售额	offline_sales	线下门店日销售额
完全策略渠道销售额（cs_sales）	elm_sales	饿了么平台日销售额
	mt_sales	美团平台日销售额
	xcx_sales	外卖小程序日销售额
垂直策略渠道销售额（vs_sales）	jd1_sales	京东1店日销售额
	jd2_sales	京东2店日销售额
	jdzy_sales	京东自营日销售额
	tm_sales	天猫日销售额
	tmcs_sales	天猫超市日销售额
	app_sales	自营App日销售额
控制变量-日期特征	weekday	周几
	after_covid19	是否在疫情之后
	holiday	是否节假日
	pre_holidays	是否在节假日前三天
	trend	据统计开始之日的天数
控制变量-省份特征	density	门店密度，反映消费者的可触及性，各省门店数量除以地区面积
	age	平均门店开业时间，当前日期减去门店开业日，单位天
	square	平均门店面积，各省所有门店的平均面积，单位平方米
	worktime	平均营业时间，省份所有门店平均每天营业时长，单位小时

变量		各子渠道销售额
变量		均值	标准差	观测天数
offline_sales		15.043	0.897	1182
elm_sales		8.282	1.741	1182
mt_sales		9.147	1.247	1182
xcx_sales		6.547	2.639	1182
jd1_sales		8.976	5.083	1182
jd2_sales		6.95	5.664	1182
jdzy_sales		3.317	5.226	1182
tm_sales		5.061	4.675	1182
tmcs_sales		2.188	4.356	1182
app_sales		12.948	1.305	1182
门店特征
变量	均值	标准差	最小值	最大值
density	10.508	14.102	0.075	68.254
age	637.742	349.745	0.000	4128.000
square	117.119	48.337	38.000	560.000
worktime	13.182	1.371	6.000	23.980

变量	模型1 控制变量	模型2 完全策略	模型3 垂直策略	模型4 完全+垂直策略
cs_impact		0.303^***(0.039)		0.222^*** (0.035)
vs_impact			0.200^*** (0.028)	0.177^*** (0.028)
elm_sales		0.057^*(0.018)		0.022(0.013)
mt_sales		0.326^***(0.036)		0.270^*** (0.036)
xcx_sales		0.113^***(0.010)		0.047^*(0.009)
jd1_sales			0.318^**(0.025)	0.295^*(0.024)
jd2_sales			0.309^*** (0.012)	0.311^*** (0.012)
jdzy_sales			-0.115^*** (0.004)	-0.102^*** (0.004)
tm_sales			-0.052(0.006)	-0.056^*(0.006)
tmcs_sales			0.022(0.006)	0.087^*** (0.005)
app_sales			0.175^*** (0.016)	0.125^*** (0.016)
Tuesday	-0.018(0.108)	-0.012(0.106)	-0.026(0.109)	-0.021(0.108)
Wednesday	0.017(0.063)	0.019(0.059)	0.014(0.055)	0.018(0.053)
Thursday	0.015(0.064)	0.000(0.060)	0.009(0.055)	0.001(0.054)
Friday	0.043*(0.061)	0.007(0.059)	0.041^**(0.053)	0.016(0.055)
Saturday	-0.009(0.062)	-0.047^**(0.058)	0.023(0.053)	-0.013(0.052)
Sunday	-0.033(0.062)	-0.039^*(0.059)	0.014(0.055)	-0.003(0.054)
after_covid19	-0.155^*** (0.107)	-0.118^**(0.111)	-0.481^*** (0.271)	-0.391^*** (0.263)
holiday	-0.136^*** (0.075)	-0.158^*** (0.070)	-0.076^*** (0.067)	-0.104^*** (0.065)
pre_holidays	0.123^*** (0.072)	0.096^*** (0.070)	0.132^*** (0.071)	0.114^*** (0.069)
trend	0.678^*** (0.000)	0.300^*** (0.000)	0.506^*** (0.000)	0.149^**(0.000)
observations	1182	1182	1182	1182
R-squared	0.364	0.421	0.461	0.485

变量	垂直策略		完全策略
变量	模型5	模型6	模型7	模型8
vs_sales	0.088^***(0.031)	-0.057^*** (0.017)
cs_sales			0.066^**(0.028)	0.049^*(0.028)
vs_sales×density		0.007^*** (0.001)
cs_sales×density				0.001(0.002)
density	0.024^**(0.010)	-0.079^*** (0.01)	0.022^**(0.009)	-0.008(0.018)
age	0.001^**(0.001)	0.001(0.001)	0.001^***(0.001)	0.001(0.001)
square	0.004^***(0.001)	0.003^***(0.001)	0.005^***(0.001)	0.004^***(0.001)
worktime	0.011(0.053)	0.029(0.036)	-0.009(0.058)	0.014(0.040)
Tuesday	0.032(0.070)	-0.577(0.486)	0.042(0.068)	-.466(0.555)
Wednesday	0.059(0.063)	-1.189^*(0.601)	0.082(0.064)	-1.115(0.663)
Thursday	0.117^**(0.059)	0.082(0.503)	0.162^***(0.056)	0.159(0.531)
Friday	0.229^***(0.065)	-0.051(0.629)	0.246^***(0.064)	0.052(0.645)
Saturday	0.120(0.088)	-1.382^**(0.560)	0.102(0.089)	-1.187^**(0.568)
Sunday	0.039(0.057)	-1.907^***(0.676)	0.003(0.070)	-1.724^***(0.708)
after_covid19	0.664^***(0.220)	3.768^***(0.643)	0.78^***(0.226)	4.455^***(0.653)
holiday	0.117(0.099)	0.793(0.521)	0.044(0.107)	0.764(0.545)
pre_holidays	0.768^***(0.086)	-0.285(0.654)	0.774^***(0.087)	-0.249(0.666)
fixed province effect	No	Yes	No	Yes
fixed time effect	No	Yes	No	Yes
const	5.783^***(0.698)	6.294^***(0.876)	6.23^***(0.701)	4.87^***(1.000)
observations	9105	9105	8448	8448

The Effects of Adding B2C Versus O2O Channel on Offline Sales: Store Density as a Moderator

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