Understanding digital library adoption

Abstract

With the growth in operational digital libraries, the need for automatic methods capable of characterizing adoption and use has grown. We describe a computational methodology for producing two, inter-related, user typologies based on use diffusion. Use diffusion theory views technology adoption as a process that can lead to widely different patterns of use across a given population of potential users; these models use measures of frequency and variety to characterize and describe these usage patterns. The methodology uses computational techniques such as clickstream entropy and clustering to produce both coarse-grained and fine-grained user typologies. A case study demonstrates the utility and applicability of the method: it is used to understand how middle and high school science teachers participating in an academic year-long field trial adopted and integrated digital library resources into their instructional planning and teaching. The resulting fine-grained user typology identified five different types of teacher-users, including "interactive resource specialists" and "community seeker specialists" This typology was validated through comparison with qualitative and quantitative data collected using traditional educational field research methods.