## 2 Literature Review
However, the results show that two groups had chosen the criterion of \"customer services\" as the most critical factor influencing the effectiveness of electronic banking [20]. [PERSON] and colleagues (2014) used FAHP to rank critical factors of mobile services adoption by Taiwan citizens. They grouped all factors into three groups of \"factors related to mobile,\" \"factors related to mobile hardware,\" and \"psychological factors of users\" [18]. Nevertheless, these studies had two major shortcomings. The first of which was that the cause and effect relationships among factors affecting technology adoption were not considered, and the second one was that the intensity of these relationships was not also taken into consideration. Generally, as it can be observed in all EBM studies of technology adoption and diffusion, in modeling how a technology is adopted and diffused in a society over time, the whole society is reduced into some socio-economic factors with the qualities of unbounded rationality and (most often) perfect information, and the model built from the relationships among such factors is analyzed to explain the technology adoption and diffusion in the society, while very important factors such as adaptability and the evolutionary nature of all engaged actors along with network effects go unaddressed. For overcoming deficiencies and limitations of reductionism approach, in the past two decades, the framework of complex adaptive system (CAS) has shown lots of promise. In contrast to reductionism approach, under this framework, the socio-economic phenomena such as technology adoption and diffusion are studied in an organic manner where the economic agents are supposed to be both boundedly rational and adaptive. Based on CAS framework, the socio-economic aggregates such as technology diffusion emerge out of the ways agents of a socio-economic system interact and decide. As the most powerful methodology for CAS modeling, agent-based modeling (ABM) has gained an increasing application among academia and industry. ABMs show how simple behavioral rules of agents and local interactions among them at a micro-scale can create surprisingly complex patterns at a macro-scale. This methodology has gained an increasing popularity, both in academia and industry such as the study of critical infrastructures dependence [22], consumption of services of sustainable eco-systems [23], economic sciences [24; 25], and management [26; 27]. For adoption and diffusion studies, a number of works have used ABM for studying the adoption and diffusion of technologies such as environmental technologies [28], energy technologies [29], health and medical technologies [30; 31; 32], agricultural technologies [33], cinema technologies [34], transportation technologies [35], electricity production [36], alternative fuels [37; 38], organic farming practices [39], automobile markets [40], fuel cell vehicles [41; 42], mobile phone [43], and smart metering [44]. However, to the best of authors' knowledge, no systematic study has been conducted on the adoption and diffusion of mobile telecommunications technologies (MTTs) so far. Using agent ABM and social network theory (SNT), the prime purpose of this study is to discover the mechanism underlying the diffusion of three competing MTTs in Iran, through which all telecoms planners would get practical insights in order to speed up the diffusion of a targeted MTT.