## 7 Discussions and Managerial Implications  
This paper studied the determinants of ICT fluency and the evolution of technology inequalities among students from the 1 st year to 4 th year, using the two developed globally applicable models. The evaluation of the models is carried out on the undergraduate students' data from three East African (EA) universities (KU, UDSM, and UB), collected during the summer of 2019. The classification of the frequencies of students by ICT fluency indicated that the number of technology fluent students grow faster from the 1 st year to the 4 th year as shown by Figure 4. In every class year, the ICT fluent students can be categorized into two main groups that are the fluent group and the no fluent--which implies a digital divide, thus, the justification of the necessity of the investigations into the determinants of technology fluency in the region. Regression computational findings show that the most significant proxy determinants of ICT fluency were the student urban/rural origin, computer ownership, class year, major, and prior computer experience. Surprisingly, gender was not a very significant predictor of ICT fluency among students, given that usually in EA, males and females possess different gender roles [39]. It might be that since the number of technology fluent students among the enrolling students (in the 1 st year) was very \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **1 st Year** & **2 nd Year** & **3 rd Year** & **4 th Year** \\ \hline
**Internet access** & & & & \\ Full sample & & & & \\ Concentration Index & 46\% & 28\% & 19\% & **16\%** \\ Horizontal Inequality & 32\% & 19\% & 13\% & 11\% \\ No PC owners & & & & \\ Concentration Index & 62\% & 50\% & 38\% & 32\% \\ Horizontal Inequality & 43\% & 35\% & 26\% & 21\% \\
**Computer property** & & & & \\ Full sample & & & & \\ Concentration Index & 28\% & 17\% & 9\% & **7\%** \\ Horizontal Inequality & 18\% & 10\% & 5\% & 3\% \\ No PC owners & & & & \\ Concentration Index & 38\% & 26\% & 15\% & 12\% \\ Horizontal Inequality & 19\% & 14\% & 6\% & 4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evolution of the Concentration Index (CI) and Horizontal Inequality (HI). reduced, both male and female students started the digital learning at university studies, which led to the insignificant gender differences along the four years of studies. This study assumed that the inequality related to individual and external factors was the basis of the digital divide in utilizing available online resources. With that assumption, the digital divide evolution measured with inequality indexes indicates that: (i) computer experience or the lack of computer skills is the main barriers to online resources utilization and (ii) the capacity to purchase a personal computer (influenced by family income), as well as a big number of students in a class with an unmatched number of lab computers constitute the major barriers to accessing online resources. The influence of external barriers to technology fluency was reducing from the 1 st year to the 4 th year. However, still, the effect of prior computer experience remained expressive in every part. Computer ownership rationalizes most of the inequalities in individual technology skills, this implies that the model shows evidence that the improvements in the capacity to buy a computer, accommodating more computer labs, and providing more computer usage training may be valuable strategies to decrease: (i) the digital divide among the students and (ii) the barriers associated with ICT fluency, an argument that was supported by [PERSON] et al. [26] as well.