社会网络视角的农户农产品电商参与行为分析——基于湖北、山东、安徽、甘肃349份农户抽样调查数据

doi:10.3969/j.issn.1004-1524.2021.10.21

浙江农业学报 ›› 2021, Vol. 33 ›› Issue (10): 1971-1981.DOI: 10.3969/j.issn.1004-1524.2021.10.21

社会网络视角的农户农产品电商参与行为分析——基于湖北、山东、安徽、甘肃349份农户抽样调查数据

胡月莉^a^,^b(), 夏春萍^a^,^b^,^*(), 贾铖^a^,^b, 王翠翠^a^,^b

华中农业大学 a. 经济管理学院;b. 电子商务研究所,湖北武汉 430070

收稿日期:2020-09-27 出版日期:2021-10-25 发布日期:2021-11-02
通讯作者: 夏春萍
作者简介:*夏春萍,E-mail: xcp@mail.hzau.edu.cn
胡月莉(1994—),女,河南周口人,硕士,主要从事农产品电子商务研究。E-mail: 1613158382@qq.com
基金资助:
中央高校基本科研业务费专项资金(2662019FW018);中国人民大学中国扶贫研究院“扶贫研究博士论文奖学金”

Analysis of farmers’ participation in e-commerce of agricultural products from perspective of social network: based on 349 household survey data from Hubei, Shandong, Anhui and Gansu in China

HU Yueli^a^,^b(), XIA Chunpinga^a^,^b^,^*(), JIA Cheng^a^,^b, WANG Cuicui^a^,^b

a. College of Economics and Management; b.Institute of E-Commerce, Huazhong Agricultural University, Wuhan 430070, China

Received:2020-09-27 Online:2021-10-25 Published:2021-11-02
Contact: XIA Chunpinga

摘要/Abstract

摘要：

以湖北、山东、安徽、甘肃4省的349份农户调研数据为基础,从社会网络视角对农户农产品电商参与行为进行理论分析,探究社会网络及其不同维度对农户农产品电商参与行为的影响,并采用Binary Logistic(二元逻辑回归)模型进行实证研究。在此基础上,根据受访农户的性别、学历和参加电商培训情况进行分组回归,以检验社会网络在不同群组间的影响差异。研究发现:社会网络及其2个维度——网络互动和网络学习在1%的置信水平上对农户农产品电商参与行为具有显著正向影响,且网络学习的影响程度要大于网络互动。与男性组农户和高学历组农户相比,社会网络、网络互动和网络学习对女性组农户和低学历组农户农产品电商参与行为具有更强的促进作用。此外,家庭经营耕地质量、政府或电商平台对电商的宣传力度等因素亦至少在10%的置信水平上对农户农产品电商参与行为具有显著影响。

关键词: 社会网络, 网络互动, 网络学习, 农产品电商

Abstract:

Based on 349 household survey data collected from Hubei, Shandong, Anhui and Gansu in China, a theoretical analysis of farmers’ participation in the e-commerce of agricultural products was conducted from the perspective of social network. The effect of social network and its four dimensions on farmers’ participation in the e-commerce of agricultural products was revealed by using the Binary Logistic model. On the basis, group regressions were conducted according to the gender, education, and e-commerce training status of the interviewed farmers, to examine the influences of the social network in different groups.The results showed that the social network and its two dimensions, namely, network interaction and network learning, had significant (P<0.01) impact on farmers’ participation in the e-commerce of agricultural products, and the impact of network learning was greater than that of network interaction. Compared with the male farmers and highly-educated farmers, the promotion effect of social network, network interaction and network learning was stronger for female farmers and lowly-educated farmers. In addition, other factors such as the farmland quality, and the e-commerce promotion level of government or e-commerce platform, also had significant (P<0.1 at least) impact on farmers’ participation in the e-commerce of agricultural products.

Key words: social network, network interaction, network learning, e-commerce of agricultural products

中图分类号:

S-9
F304.3

胡月莉, 夏春萍, 贾铖, 王翠翠. 社会网络视角的农户农产品电商参与行为分析——基于湖北、山东、安徽、甘肃349份农户抽样调查数据[J]. 浙江农业学报, 2021, 33(10): 1971-1981.

HU Yueli, XIA Chunpinga, JIA Cheng, WANG Cuicui. Analysis of farmers’ participation in e-commerce of agricultural products from perspective of social network: based on 349 household survey data from Hubei, Shandong, Anhui and Gansu in China[J]. Acta Agriculturae Zhejiangensis, 2021, 33(10): 1971-1981.

图/表 5

参考文献 32

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维度 Dimension	指标 Index	平均值 Mean	标准差 Standard deviation
F1	F1-1	3.788	0.878
	F1-2	3.782	0.921
F2	F2-1	4.284	0.756
	F2-2	4.132	0.743
F3	F3-1	3.358	0.929
	F3-2	3.668	0.776
F4	F4-1	2.052	0.853
	F4-2	2.086	0.958

维度 Dimension	指标 Index	平均值 Mean	标准差 Standard deviation
F1	F1-1	3.788	0.878
	F1-2	3.782	0.921
F2	F2-1	4.284	0.756
	F2-2	4.132	0.743
F3	F3-1	3.358	0.929
	F3-2	3.668	0.776
F4	F4-1	2.052	0.853
	F4-2	2.086	0.958

变量类别 Variable type	变量名称 Variable	平均值 Mean	标准差 Standard deviation
被解释变量 Dependent variable	农户农产品电商参与行为 Farmer's participation in e-commerce of agricultural products	0.490	0.501
核心解释变量	社会网络Social network	0	0.502
Independent variable	网络互动Network interaction	0	1.000
	网络信任Network trust	0	1.000
	网络学习Network learning	0	1.000
	网络互惠Network reciprocity	0	1.000
控制变量Control variable	性别Gender	0.527	0.500
	年龄Age	40.585	14.894
	健康状况Health	4.312	0.790
	学历Education	2.699	1.131
	家庭经营耕地面积Area of family-owned farmland	6.975	6.117
	耕地质量Farmland quality	2.418	0.779
	快递物流便利程度Express delivery convenience	4.057	0.872
	政府或电商平台对电商的宣传力度	3.814	1.068
	E-commerce promotion level of government or e-commerce platform

变量类别 Variable type	变量名称 Variable	平均值 Mean	标准差 Standard deviation
被解释变量 Dependent variable	农户农产品电商参与行为 Farmer's participation in e-commerce of agricultural products	0.490	0.501
核心解释变量	社会网络Social network	0	0.502
Independent variable	网络互动Network interaction	0	1.000
	网络信任Network trust	0	1.000
	网络学习Network learning	0	1.000
	网络互惠Network reciprocity	0	1.000
控制变量Control variable	性别Gender	0.527	0.500
	年龄Age	40.585	14.894
	健康状况Health	4.312	0.790
	学历Education	2.699	1.131
	家庭经营耕地面积Area of family-owned farmland	6.975	6.117
	耕地质量Farmland quality	2.418	0.779
	快递物流便利程度Express delivery convenience	4.057	0.872
	政府或电商平台对电商的宣传力度	3.814	1.068
	E-commerce promotion level of government or e-commerce platform

变量 Variable	模型1 Model 1		模型2 Model 2		模型3 Model 3		模型4 Model 4
变量 Variable	B	SE	B	SE	B	SE	B	SE
社会网络Social network	2.334^***	0.298	—	—	2.073^***	0.349	—	—
网络互动Network interaction	—	—	1.240^***	0.162	—	—	1.004^***	0.184
网络信任Network trust	—	—	0.035	0.130	—	—	0.088	0.147
网络学习Network learning	—	—	1.087^***	0.163	—	—	1.305^***	0.196
网络互惠Network reciprocity	—	—	0.270^*	0.139	—	—	0.081	0.16
年龄Age	—	—	—	—	-0.050^***	0.014	-0.058^***	0.015
性别Gender	—	—	—	—	0.403	0.274	0.412	0.298
健康状况Health	—	—	—	—	0.224	0.181	0.358^*	0.203
学历Education	—	—	—	—	0.459^***	0.160	0.457^***	0.174
家庭经营耕地面积	—	—	—	—	-0.003	0.024	-0.008	0.026
Area of family-owned farmland
耕地质量Farmland quality	—	—	—	—	0.424^**	0.187	0.462^**	0.201
快递物流便利程度	—	—	—	—	0.236	0.161	0.238	0.176
Express delivery convenience
政府或电商平台对电商的宣传力度	—	—	—	—	0.252^*	0.139	0.231^*	0.152
E-commerce promotion level of government or e-commerce platform
常数项Constant	0.177	0.180	0.479^***	0.208	-2.142	1.532	-1.443	1.621
χ²	85.942		103.59		148.88		192.594
最大似然估计值	397.735		350.086		334.796		290.082
Maximum likelihood estimator
Nagelkerke广义决定系数	0.291		0.424		0.463		0.566
Nagelkerke R²
正确预测率Correct prodiction rate/%	69.9		77.7		77.9		81.7

社会网络视角的农户农产品电商参与行为分析——基于湖北、山东、安徽、甘肃349份农户抽样调查数据

Analysis of farmers’ participation in e-commerce of agricultural products from perspective of social network: based on 349 household survey data from Hubei, Shandong, Anhui and Gansu in China

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变量 Variable	男性组 Male	女性组 Female	参加培训组 Trained	未参加培训组 Not trained	高学历组 Highly educated	低学历组 Lowly educated
社会网络 Social network	1.504^*** (0.464)	2.986^*** (0.600)	1.854^*** (0.642)	2.040^*** (0.496)	1.800^*** (0.480)	2.555^*** (0.579)
网络互动 Network interaction	0.846^** (0.258)	1.228^*** (0.305)	0.977^*** (0.345)	1.058^*** (0.275)	0.857^*** (0.260)	1.342^*** (0.340)
网络信任 Network trust	-0.026 (0.202)	0.204 (0.245)	0.164 (0.265)	0.042 (0.223)	0.162 (0.188)	-0.171 (0.302)
网络学习 Network learning	1.256^*** (0.275)	1.483^*** (0.304)	1.837^*** (0.456)	1.444^*** (0.285)	1.479^*** (0.310)	1.505^*** (0.318)
网络互惠 Network reciprocity	-0.081 (0.218)	0.405 (0.276)	-0.114 (0.289)	0.023 (0.223)	-0.415 (0.261)	0.566^** (0.238)

变量 Variable	模型5 Model 5			模型6 Model 6			模型7 Model 7
变量 Variable	B	SE	Exp(B)	B	SE	Exp(B)	B	SE	Exp(B)
社会网络Social network	1.159^***	0.301	3.186	—	—	—	—	—	—
网络互动Network interaction	—	—	—	0.432^***	0.146	1.541	0.832^***	0.215	2.299
网络信任Network trust	—	—	—	0.014	0.139	1.014	0.018	0.205	1.018
网络学习Network learning	—	—	—	0.692^***	0.164	1.997	1.302^***	0.219	3.677
网络互惠Network reciprocity	—	—	—	0.142	0.144	1.153	0.189	0.200	1.208
年龄Age	-0.034^**	0.015	0.967	-0.046^***	0.016	0.955	-0.041^**	0.017	0.960
性别Gender	0.492^*	0.271	1.635	0.514^*	0.278	1.672	0.400	0.301	1.492
健康状况Health	0.239	0.186	1.271	0.283	0.192	1.328	0.079	0.206	1.082
学历Education	0.410^**	0.164	1.506	0.407^**	0.166	1.503	0.489^***	0.184	1.630
家庭经营耕地面积	0.002	0.024	1.002	-0.001	0.025	0.999	-0.006	0.029	0.994
Area of family-owned farmland
耕地质量Farmland quality	0.308	0.188	1.360	0.340^*	0.193	1.405	0.450^**	0.209	1.569
快递物流便利程度	0.289^*	0.158	1.336	0.261	0.164	1.298	0.196	0.181	1.216
Express delivery convenience
政府或电商平台对电商的宣传力度	0.367^***	0.137	1.444	0.398^***	0.142	1.489	0.256^***	0.157	1.291
E-commerce promotion level of government or e-commerce platform
常数项Constant	-4.291	1.534	0.014	-4.206^***	1.561	0.015	-10.855^***	2.141	0
χ²	73.058			85.022			126.287
最大似然估计值	327.903			315.939			274.674
Maximum likelihood estimator
Nagelkerke广义决定系数	0.296			0.337			0.469
Nagelkerke R²
正确预测率 Correct prodiction rate/%	71.4			74.8			79.3

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