Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory

Abstract

Understanding the mechanism underlying the mobile telecommunications technologies (MTTs) diffusion in a country is crucial for telecom planners to know how to accelerate their diffusion by designing appropriate scenarios. Considering the technology diffusion as a bottom-up process, this study is aimed at exploring this mechanism, drawing on insights from diffusion of innovation theory and social network theory. Accordingly, an agent-based model is proposed to investigate how MTTs are diffused in Iran over time. The results of this study show, (1) social network of Iranian society seems more similar to a Watts–Strogatz small-world network than a Barabási–Albert preferential attachment network, where the clustering coefficient is high and average path length is low, (2) compared to the compatibility parameter, the advertisement parameter not only is less influential on diffusion of a targeted MTT (i.e., 4G) but also is not necessary for it, and (3) scenarios having the least number of steps and turning points are more appropriate for continuous diffusion of 4G. The proposed study is empirically validated against real-world data ranging from 7/1/2017 to 12/31/2017. We believe it provides telecom planners insights regarding MTTs diffusion mechanism in a social complex structure and the how of scenario designing for increasing their diffusion.

An Agent-Based Model for 5G Technology Diffusion in Urban Societies: Simulating Two Development Scenarios

“…This finding indicates that to reach an unknown person worldwide, one can use a limited number of friend networks [19]. Results also found that handset compatibility is a significant factor compared to the advertisement and the most effective scenarios of incremental 4G adoption with lowest operational steps [16].…”

“…Then, this model was further expanded using other scenarios such as the threshold model of collective behavior and word-of-mouth analysis of a complex system [10,11]. Since then, this has become a powerful tool used for various purposes such as economy, management, climate change, healthcare and disease, land and housing, and energy [10][11][12][13][14][15][16][17].…”

“…Aligned with the Bass model, the two references' previous study [15,16] of 5G mobile adoption in South Korea and Iranian mobile network were both equipped with word of mouth, advertisement, and neighbor influence features. The 5G mobile adoption in the South Korea model has strength in combining System Dynamic and ABM to cover both macro aggregation and individual behavior.…”

Hybrid Approach for Modelling the Uptake of Residential Solar PV Systems, with Case Study Application in Melbourne, Australia

“…As there is no social network data available, households are connected in a social network according to a theoretically constructed Barabasi-Albert graph (a scale-free network). This is an algorithm that generates a random scale-free networks using a preferential attachment mechanism, which has been sometimes considered when developing ABMs (Sabzian et al 2020;Sznajd-Weron et al 2014;Wang & Collins 2015). Further description of the social network type is provided by Barabási & Albert (1999).…”

Optimal Agent Framework: A Novel, Cost-Effective Model Articulation to Fill the Integration Gap between Agent-Based Modeling and Decision-Making

“…e environment where the agents interact with each other must be determined. is environment can be a geographic information system (GIS) [20], a network [21], or a conceptual space.…”