of the results from their work, and Yin (1989)
notes that this issue is often raised with respect to case
studies:
'How can you generalize from a single case study?' is a
frequently heard question .... The short answer is that
case studies . . . are generalizable to theoretical propositions
. . . (p. 21).
We will extend Yin's answer in this section to four
types of generalization from interpretive case studies:
the development of concepts, the generation of theory,
the drawing of specific implications, and the contribution
of rich insight. However, before discussing each of
these generalizations in more detail, a short introduction
is necessary on the nature of theorising in the social
sciences, viewed from an interpretive stance.
Bhaskar (1979) describes the scientific process in the
natural sciences as involving three phases in which
phenomena are identified, explanations for the phenomena
are constructed and empirically tested, and the
generative mechanisms at work are described. Bhaskar
argues that the human or social sciences can be tackled
using a similar methodology, but there are differences
in that social structures do not exist independently of
the actions and conceptions of the human agents in
them, and the generative mechanisms of such structures
are not space-time invariant. Thus, generative
mechanisms identified for phenomena in the social
sciences should be viewed as 'tendencies', which are
valuable in explanations of past data but are not wholly
predictive for future situations. The generalizations
which we discuss below should, therefore, be seen as
explanations of particular phenomena derived from
empirical interpretive research in specific IS settings,
which may be valuable in the future in other organizations
and contexts.
We will now illustrate each of the four types of
generalizations using specific examples, although it
should be noted that the four types are not mutually
exclusive categories. A summary of these examples is
given in Table 3.
The first type of generalization concerns concepts.
Zuboff (1988) used her interpretive case studies of IT
use in US organizations to develop the 'informate'
concept, which has been widely quoted in the IS
literature and beyond. She introduced this concept as
follows:
Thus, information technology, even when it is applied to
automatically reproduce a finite activity, is not mute. It
not only imposes information (in the form of programmed
instructions) but also produces information . . . information
technology supersedes the traditional logic of automation.
The word that I have coined to describe this unique
capacity is informate. Activities, events, and objects are
translated into and made visible by information when a
technology informates as well as automates, (pp. 9-10)
A single concept such as 'informate' can be part of
a broader network or an integrated clustering of
concepts, propositions and world-views which form
theories in social science (Layder, 1993). As an
illustration in the IS field, it was noted earlier that
Orlikowski and Robey (1991) drew on their empirical