## 6 Conclusions  
Developing the appropriate scenarios for increasing the 4G technology diffusion in Iran has been the motivation behind the development of this model. In this regard, the mechanism underlying the diffusion of MTT in Iranian society was studied. To better identify this mechanism, prior studies were first investigated, and because of reasons such as (1) simplistic neoclassical assumptions like unbounded rationality and perfect information, (2) non-inclusion of network effects and non-linear interactions of micro-level individual adopters, (3) learnability and adaptability of individual adopters, (4) bottom-up nature of technology adoption, (5) heterogeneity and multiplicity of influential factors, agent-based modeling (ABM) was chosen as the modeling approach in tandem with social network theory (SNT). This model is mainly theory-based, where real-world empirical data, theories, and extension of the works of [64; 65; 66; 67] have been taken into account, and it is to a lesser extent evidence-based because some new variables (e.g., handset compatibility) are added authors. The model's input parameters can be grouped as (1) number of adopters (i.e., 2G, 3G, and 4G), (2) adopters' memory parameters (i.e., innovators, early-adopters, early-majority, late-majority, and laggards), (3) network topology parameters (i.e., WSN and PAN) and (4) advertisement and handset compatibility parameters. They were directly calibrated according to empirical studies. The model was implemented by Netlogo 6.0.1, and the data were analyzed by R 3.5.3. The performance of the model was simulated in two different social network topologies by 9000 experiment runs for the second half of 2017 actually, 1500 simulation runs for each MTT in each social network structure. The Shapiro-Wilk test was used to check the normality of data, showing that none of them were normal. Data normalization methods such as standard (z-score), log transformation, and box-cox were used  
Figure 17: Scenario A.  
Figure 18: Scenario B.  
to normalize the data, but none of them could normalize the data. Therefore, as a non-parametric test of correlation, spearman test was used to measure the model's outputs with real data for the second half of 2017, and the results indicated the high predictive accuracy of model's performance, and it was figured out that Watts-Strogatz small-world network (WSN) has the highest similarity with the social network of Iranian people where the clustering coefficient (CC) is high (i.e., 0.34 for this study because of the size of sample, but it tends to \(\frac{3}{4}\) (0.75) in case of large N) and average path length (L) is low (i.e., 2.90 for this study, but it will approximately converge to 7.90 for real number of Iranian population). High CC indicates that the friends of a person tend to be friends of each other, and low L shows that when two persons want to reach each other, they can do it by a limited number of steps. Several number of studies affirmed this finding that social networks mainly follow a WSN structure [96; 97; 98; 99; 67; 80]. In order to formulate scenarios for accelerating 4G diffusion, the decision parameters were selected from input parameters, and the necessity and influence of decision parameters were studied in 100 simulation runs for the first quarter of 2018. In terms of necessity, the results showed that if the value of advertisement is zero, 4G technology could get diffused over the society, which is mainly because of the technology push nature of MTTs [102; 103; 104] and WSN topology of Iran's social network, where world-of-mouth plays a critical role [100; 101], while 4G could never be diffused if the handset compatibility is zero. In addition, in terms of influence, the compatibility parameter was found to have a higher performance in increasing 4G technology than advertisement parameter. Thereafter, a number of operational steps were extracted through the states of each parameter (i.e., six states for advertisement and five states for compatibility).