Eliciting tacit knowledge about requirement analysis with a Grammar-targeted Interview Method (GIM)

The assumption that tacit knowledge cannot be articulated remains dominant in knowledge elicitation. This paper, however, claims that linguistic theory does not support such a position and that language should not be factored out of accounts of tacit knowledge. We argue that Polanyi's (1966, p. 4) widely cited notion that ‘we know more than we can tell’ uses a folk model of language. This model does not acknowledge the linguistic patterns that competent language speakers deploy without direct awareness. This paper draws upon Systemic Functional Linguistics (SFL) to propose a Grammar-targeted Interview Method (GIM). The GIM uses SFL to unpack linguistic patterning, which we refer to as ‘under-representation’, to reveal tacit assumptions. It is a strategy that can be applied within a traditional interview method when the interviewer feels that there is confusion resulting from assumptions, such as those often embedded in terminology, that have not been directly expressed. This paper reports findings from an empirical study of tacit knowledge about requirements analysis in a Content Management System redevelopment. We compared the GIM with a Content-motivated Interview Method (CMIM) and show that, when the GIM is used, interviewees respond with less nominalised talk, that is the less nominalised content has more meaning unpacked as verbs and agents rather than hidden tacitly in nouns.