97,953 research outputs found
When Shall I Tell? Relational Promotion and Timing of Information Technology Diffusion
This study adds to existing knowledge about information technology diffusion within organizations by examining the effects of social embeddedness on behavior of individual diffusers. Building on a social capital perspective of relationships, the authors theorize that individuals make intentional decisions to promote or suppress an innovation contingent on the nature of their relationship(s) with the potential adopters. Hypotheses regarding the likelihood of diffusion of an IT innovation through friendship, advice and multiplex friend and advisor relationships at early versus later stages in the diffusion process were tested using social network and panel survey data in two organizations. Results support predictions that individual diffusion behavior is contingent on the relation type and the progress of the innovation in the organization
DySuse: Susceptibility Estimation in Dynamic Social Networks
Influence estimation aims to predict the total influence spread in social
networks and has received surged attention in recent years. Most current
studies focus on estimating the total number of influenced users in a social
network, and neglect susceptibility estimation that aims to predict the
probability of each user being influenced from the individual perspective. As a
more fine-grained estimation task, susceptibility estimation is full of
attractiveness and practical value. Based on the significance of susceptibility
estimation and dynamic properties of social networks, we propose a task, called
susceptibility estimation in dynamic social networks, which is even more
realistic and valuable in real-world applications. Susceptibility estimation in
dynamic networks has yet to be explored so far and is computationally
intractable to naively adopt Monte Carlo simulation to obtain the results. To
this end, we propose a novel end-to-end framework DySuse based on dynamic graph
embedding technology. Specifically, we leverage a structural feature module to
independently capture the structural information of influence diffusion on each
single graph snapshot. Besides, {we propose the progressive mechanism according
to the property of influence diffusion,} to couple the structural and temporal
information during diffusion tightly. Moreover, a self-attention block {is
designed to} further capture temporal dependency by flexibly weighting
historical timestamps. Experimental results show that our framework is superior
to the existing dynamic graph embedding models and has satisfactory prediction
performance in multiple influence diffusion models.Comment: This paper has been published in Expert Systems With Application
The Local Emergence and Global Diffusion of Research Technologies: An Exploration of Patterns of Network Formation
Grasping the fruits of "emerging technologies" is an objective of many
government priority programs in a knowledge-based and globalizing economy. We
use the publication records (in the Science Citation Index) of two emerging
technologies to study the mechanisms of diffusion in the case of two innovation
trajectories: small interference RNA (siRNA) and nano-crystalline solar cells
(NCSC). Methods for analyzing and visualizing geographical and cognitive
diffusion are specified as indicators of different dynamics. Geographical
diffusion is illustrated with overlays to Google Maps; cognitive diffusion is
mapped using an overlay to a map based on the ISI Subject Categories. The
evolving geographical networks show both preferential attachment and
small-world characteristics. The strength of preferential attachment decreases
over time, while the network evolves into an oligopolistic control structure
with small-world characteristics. The transition from disciplinary-oriented
("mode-1") to transfer-oriented ("mode-2") research is suggested as the crucial
difference in explaining the different rates of diffusion between siRNA and
NCSC
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