Table 6

Factors influencing farmers' willingness to adopt digital application tools

Marginal effects
CoefficientStandard errort-valuedy/dxStandard errort-value
Female−0.466*0.242−1.92−0.081*0.041−1.950
Age−0.0050.01−0.45−0.0010.002−0.450
Primary education0.591*0.3291.790.1040.0591.770
Secondary education0.762**0.3552.150.133**0.0632.100
Tertiary education0.2740.4770.570.0490.0860.570
Years of experience in farming0.021*0.0121.800.004*0.0021.820
Training0.51**0.2352.180.088**0.0402.210
Access to internet0.743***0.2413.080.129***0.0403.200
Access to extension services0.150.4030.370.0260.0700.370
Awareness of the IITA herbicide calculator0.654**0.3242.020.113**0.0552.040
Awareness of Akilimo−0.779**0.303−2.57−0.135**0.051−2.630
Ownership of smartphone1.64***0.3614.540.284***0.0594.860
Use a paid phone application1.183**0.4972.380.205**0.0852.400
Calibration spraying0.1620.2420.670.0280.0420.670
Cost of application0.78***0.2862.730.135***0.0482.800
Ease of use of the application0.2740.2920.940.0470.0500.940
Innovativeness of application−0.6520.646−1.01−0.1130.112−1.010
The application used previously by other farmers0.5990.8160.730.1040.1410.740
Constant−1.788***0.635−2.81   
Pseudo r-squared = 0.211Number of observations = 564
Chi-square = 155.869Prob > χ2 = 0.000

Note(s): ***p < 0.01, **p < 0.05, *p < 0.1

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