JOURNAL OF NATURAL RESOURCES

LI Jia-hui, CHENG Wen-xin, LU Qian

JOURNAL OF NATURAL RESOURCES, 2024, 39(12): 2980-3004. DOI: 10.31497/zrzyxb.20241215

作用类型	作用路径	调节变量	效应值	标准误	95%置信区间
中介效应	数字金融→借贷可得性→精准农业技术采用	—	0.282	0.079	[0.153, 0.466]
	数字金融→信息可得性→精准农业技术采用	—	0.247	0.066	[0.141, 0.398]
	数字金融→风险承担水平→精准农业技术采用	—	0.363	0.099	[0.207, 0.598]
有中介的调节效应	数字金融→借贷可得性→精准农业技术采用	社会网络	0.912	0.340	[0.381, 1.698]
	数字金融→信息可得性→精准农业技术采用	社会网络	1.118	0.331	[0.609, 1.915]
	数字金融→风险承担水平→精准农业技术采用	社会网络	0.634	0.264	[0.218, 1.246]

Table 8 Influence mechanism test results

Other figure/table from this article

Fig. 1 The influence mechanism of digital finance and social network on farmers' adoption of precision agricultural technology
Table 1 Index system of social network
Table 2 Variable definition and descriptive statistics
Table 3 Basic regression results of digital finance and social network on farmers' adoption of precision agricultural technology
Table 4 The average treatment effect of digital finance on farmers' adoption of precision agricultural technology
Table 5 CMP model estimation results
Table 6 Mediation effect test results
Table 7 Mediated moderation effect test results
Table 9 Differences in the impact of different functions of digital finance and social network on farmers' adoption of precision agricultural technology
Table 10 Estimated results of the mediated moderation effect based on sequential test (the moderating variable is strong social network)
Table 11 Estimated results of the mediated moderation effect based on the sequential test method (the moderating variable is weak social network)
Table 12 Estimation results of mediated moderation effect based on Bootstrap method
Table 13 The differences in the impact of digital finance and social network on the adoption of precision agricultural technology by different types of farmers
Table 14 Robustness test results