# Mobile Internet Technology Adoption for Sustainable Agriculture: Evidence from Wheat Farmers
## 5 Results and Discussion
### Study Hypothesis 1a-d  
Figure 5 summarizes the hypothesis test outcomes. H1a indicates the age impact on the adoption of MIT, and the coefficient has a negative sign in statistics, indicating that, ceteris paribus, the older age is negatively correlated with the MIT adoption. Therefore, H1a could not be rejected. This outcome is consistent with the aforementioned research on MIT and the internet in sustainable agriculture development. Young farmers may be more interested in the use of innovative technologies, as [83, 84] also pointed out. In addition, skills used in conjunction with information technology and MDs are generally well among young farmers [85]. It is consistent with the previous findings. For example, [PERSON] et al. [86] found that older farmers have less computer and smartphones experience. However, young farmers have less agricultural experience [87]. Young farmers may use MIT as a source of additional knowledge for decision making. In short, young farmers are more likely to adopt MIT. H1b assumes gender variances in the MIT adoption among wheat farmers. The measurement has anticipated indication but is not statistically positive at the 10% significance level. Therefore, the analysis cannot support H1b that gender is associated with MIT adoption. This is consistent with the regression outcomes of [PERSON] et al. [88]. Although the past study shows that men are generally enthusiastic about modem technologies [53, 89], especially mobile phones [90], their research shows that by comparing the statistics on the adoption of MIT over time, the gender difference is narrowing quickly [50]. By comparing the regression outcomes in 2017 and 2019, it was found that the statistical indication of gender is no longer significant for MIT adoption. This may describe the fact that there is also no association between the farmer's gender and the MIT adoption in the study. Therefore, men and women farmers have equal opportunities in the MIT adoption. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Hypothesis**} & **MD Adoption (SS)** & **MIT Adoption (OS)** \\ \cline{3-4}  & & **Coefficient (S.E)** & **Coefficient (S.E)** \\ \hline IT use (Regular) & - & 0.7802 *** (0.0986) & - \\ Age & Hypothesis 1a & \(-\)0.0218 *** (0.0044) & \(-\)0.0201 *** (0.0054) \\ Gender & Hypothesis 1b & 0.0202 (0.1578) & 0.0617 (0.1677) \\ Education & Hypothesis 1c & 0.3177 ** (0.1335) & 0.0299 (0.1347) \\ Innovativeness & Hypothesis 1d & - & 0.192 8 *** (0.0485) \\ Awareness of IT risks & Hypothesis 2 & - & 0.0955 ** (0.0463) \\ Farm size & Hypothesis 3a & 0.033 (0.015) & 0.010 * (0.005) \\ Region & & Hypothesis3b & \\ North & & - & 0.3344 *** (0.1277) \\ West & & - & 0.4798 *** (0.1263) \\ South & & - & 0.3135 (0.1134) \\ East & & - & 0.0740 (0.2090) \\ Farm diversification & Hypothesis 3c & \(-\)0.1066 (0.2106) & \(-\)0.1102 (0.1347) \\ Constant & & 0.8708 *** (0.2890) & 0.4165 (0.3383) \\ Atanh (\(p\)) & & - & \(-\)1.3407 *** (0.2809) \\ \(p\) & & - & \(-\)0.8718 (0.0674) \\ Likelihood ratio test & & 21.42 *** & - \\ for \(p=0\) & & 42.55 *** & - \\ \(\text{Wald }\chi^{2}\) & & \(-\)777.88 & - \\ Log-likelihood & & \(-\)777.88 & - \\ \hline \hline \end{tabular}  
* Note: SE, standard error, \({}^{*}\)\(p<0.10\), \({}^{**}\)\(p<0.05\), and \({}^{***}\)\(p<0.01\). (SS) denotes the selection stage, and (OS) outcome stage.  
\end{table} 
Table 2: Outcomes of the bivariate probit model, including sample selection of MDs and MIT adoption for wheat farmers. According to H1c, the impact of farmer's education on MIT adoption was verified. The coefficient has an anticipated indication, but it is significant at the five percent level of significance. Therefore, the research cannot confirm H1c, which is that farmer's schooling is associated with the adoption of MIT (ceteris paribus). The outcomes are consistent with [PERSON] et al. [91] and [PERSON] et al. [92], but contrary to the previous research results, the latter shows an optimistic correlation between schooling and MIT adoption.