Methods for Generating Typologies of Non/use
This provides incremental methods for researchers in social computing to better analyze technology non-use, improving typology development and predictive modeling.
The paper tackles the problem of categorizing technology non-use beyond a simple binary, proposing two methods to identify types of non/use in sociotechnical settings, demonstrated on Facebook survey data where the methods produced comparable typologies with trade-offs in granularity and sample size.
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.