Exploring the Relationships among ICTs: A Scalable Computational Approach Using KL Divergence and Hierarchical Clustering

Different information and communication technologies (ICTs) are related in complex ways and, accordingly, their diffusion trajectories are related, too. How can the relationships among multiple ICTs be described and analyzed in a scalable way? In this study, we offer a scalable methodology, based on computational analysis of discourse, to examine the relationships among ICTs. Specifically, we employed Kullback-Leibler (KL) divergence to compare the semantic similarity of forty-seven ICTs discussed in the trade magazine InformationWeek over a decade. Using hierarchical clustering, we have found that the similarity of the technologies can be mapped in a hierarchy and similar technologies demonstrated similar discourses. The results establish the validity of our approach and demonstrate its scalability and richness. This analytical approach not only enables diffusion researchers to undertake multi-innovation, multi-source, and multi-period studies, but also helps practitioners effectively adopt and efficiently use new ICTs in their organizations.