How can innovation affect ways of life full#

How can innovation affect ways of life full#

In this paper, we analyze the adoption dynamics of iWiW, a social media platform that used to be popular in Hungary, over its full life cycle (2002вЂ“2012). Facilitated by the observation of a large scale Online Social Network (OSN) over a decade, we capture for the first time these dynamics and provide insights into social network diffusion in its geographical space. Social contagion-similar to geolocated routing 21-occurs initially between two large settlements located at long distances and then becomes more locally concentrated reaching smaller towns and short distance paths. It is argued that spatial diffusion resembles geolocated routing through social networks 20. Nevertheless, understanding how physical geography affects social contagion dynamics is still lacking 1.Д®arly work on spatial diffusion has highlighted that adoption rate grows fast in large towns and in physical proximity to initial locations of adoption 19, 20. The structure of social networks in diffusion, such as community or neighborhood structure of egos, are still topics of interest 17, 18. This approach is similar to other network diffusion approaches regarding the increasing pressure on the individual to adopt as network neighbors adopt however, spontaneous adoption is also possible in the Bass ABM 16. In order to incorporate the role of social networks in technology adoption, the Bass model has been implemented through an agent-based model (ABM) version 15. Complex contagion models, in which adoption depends on the ratio of the adopting neighbors, often referred to as adoption threshold 1, 11, have been efficiently applied to characterize the diffusion of online behavior 12 and online innovations 13, 14. In spreading phenomena, individuals perform a certain action only when a sufficiently large fraction of their network contacts have performed it before 6, 7, 8, 9, 10. Only in the past two decades, the importance of the social network structure has become increasingly clear in the mechanism of peersвЂ™ influence 5. The differential equations of the Bass model have been extensively used to describe the diffusion process and forecast market size of new products and the time of their adoption peaks 4. The model distinguishes between exogenous and peersвЂ™ influence and reproduces the observation that few early adopters are followed by a much larger number of early and late majority adopters, and finally, by few laggards 3. This feature was first modelled in the 1960s with the Bass model of innovation diffusion 2. Individuals are influenced both by media and by their social ties in their decision-making. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.Д¬ollective behavior, such as massive adoption of new technologies is a complex social contagion phenomenon 1. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption.

During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. The urbanвЂ“rural divide is increasing in modern societies calling for geographical extensions of social influence modelling.