  Prior studies of technology non-use demonstrate the need for approaches that
go beyond a simple binary distinction between users and non-users. This paper
proposes a set of two different methods by which researchers can identify types
of non/use$^{1}$ relevant to the particular sociotechnical settings they are
studying. These methods are demonstrated by applying them to survey data about
Facebook non/use. The results demonstrate that the different methods proposed
here identify fairly comparable types of non/use. They also illustrate how the
two methods make different trade offs between the granularity of the resulting
typology and the total sample size. The paper also demonstrates how the
different typologies resulting from these methods can be used in predictive
modeling, allowing for the two methods to corroborate or disconfirm results
from one another. The discussion considers implications and applications of
these methods, both for research on technology non/use and for studying social
computing more broadly.
