Modeling the diffusion of complex innovations as a process of opinion formation through social networks
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{"title"=>"Modeling the diffusion of complex innovations as a process of opinion formation through social networks", "type"=>"journal", "authors"=>[{"first_name"=>"V. A.", "last_name"=>"Assenova"}], "year"=>2018, "source"=>"PLOS ONE", "identifiers"=>{"scopus"=>"2-s2.0-85046443895", "sgr"=>"85046443895", "doi"=>"10.1371/journal.pone.0196699", "pui"=>"621951401", "isbn"=>"1111111111", "issn"=>"19326203"}, "id"=>"c72e98ea-3982-34f1-bb0f-c532c2be001d", "abstract"=>"Complex innovations– ideas, practices, and technologies that hold uncertain benefits for potential adopters—often vary in their ability to diffuse in different communities over time. To explain why, I develop a model of innovation adoption in which agents engage in naïve (DeGroot) learning about the value of an innovation within their social networks. Using simulations on Bernoulli random graphs, I examine how adoption varies with network properties and with the distribution of initial opinions and adoption thresholds. The results show that: (i) low-density and high-asymmetry networks produce polarization in influence to adopt an innovation over time, (ii) increasing network density and asymmetry promote adoption under a variety of opinion and threshold distributions, and (iii) the optimal levels of density and asymmetry in networks depend on the distribution of thresholds: networks with high density (>0.25) and high asymmetry (>0.50) are optimal for maximizing diffusion when adoption thresholds are right-skewed (i.e., barriers to adoption are low), but networks with low density (<0.01) and low asymmetry (<0.25) are optimal when thresholds are left-skewed. I draw on data from a diffusion field experiment to predict adoption over time and compare the results to observed outcomes.", "link"=>"http://www.mendeley.com/research/modeling-diffusion-complex-innovations-process-opinion-formation-through-social-networks", "reader_count"=>4, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>2, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>2, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Social Sciences"=>1, "Business, Management and Accounting"=>1}, "reader_count_by_subdiscipline"=>{"Social Sciences"=>{"Social Sciences"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Unspecified"=>{"Unspecified"=>2}}, "group_count"=>0}

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