Information Technology Reference
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whole organization. The relevant question is whether the data are accurate enough for the tasks
the user needs to accomplish. For example, to the statement “The data that I use or would like to
use is accurate enough for my purposes,” respondents are asked to agree or disagree on a seven-
point scale (Goodhue, 1995, p. 1842). TTF questions need to clearly ask users to rate the systems
and services they use, based on the fit with their personal task needs, something they are arguably
capable of doing. Researchers must be careful in borrowing word for word from existing
questionnaires.
The point is not that the measures developed by Goodhue are the way to assess task-technology
fit, but rather that the approach used by Goodhue is one general way to develop a measure of task-
technology fit in any task domain.
EMPIRICAL EVIDENCE
Here we will briefly summarize some of the findings from five empirical studies involving TTF at
various levels of analysis: Goodhue (1995); Goodhue and Thompson (1995); Goodhue et al. (2000);
Dennis et al. (2001); and Karimi et al. (2004).
The Moderating Effect of Task on the Impact of Technology
Goodhue (1995) investigated whether task characteristics moderated the impact of technology on
user evaluations of TTF. For measures of the technology he looked at a number of aspects of
information systems and services provided to users, including the extent of integrated common
data, number of PCs per user, ratio of assistance personnel to users, and the decentralization of
assistance personnel. Task characteristics included interdependence of user tasks with other parts
of the organization, and the non-routineness of tasks. He also measured one individual character-
istic, computer literacy. Finally he measured twelve dimensions of task-technology fit, based on
the task model described earlier. Overall he found that for nine of the twelve dimensions of TTF,
interactions between task characteristics and technology, or between individual characteristics
and technology, explained a statistically significant amount of additional variance in TTF. In other
word, people's assessments of whether the technology was meeting their needs was not just
a function of the technology, or of the task, but was also a function of the interaction of the two.
For example, assessments of the accessibility of the data were not significantly explained by
the main effect of the extent to which users had integrated databases, or by the main effect of
whether they engaged in tasks that were interdependent with other areas of the organization.
However, the
interaction
of integrated databases with interdependent tasks was statistically sig-
nificant. For users with very little interdependence with other parts of the organization, increased
data integration did not increase their perceptions of data accessibility. For users with high inter-
dependence, increased data integration did increase their perceptions of data accessibility.
The Relative Importance of Utilization Versus TTF in Predicting Performance Impacts
Goodhue and Thompson (1995) investigated a number of aspects of the technology-to-performance
chain, including the relative importance of TTF and utilization in predicting performance impacts.
They measured eight dimensions of TTF, as well as utilization (the extent to which users had become
dependent upon the major systems in their organization) and perceived performance impacts.
Utilization alone produced an adjusted r-square of only .04 in explaining performance impacts. The
eight TTF dimensions alone produced an adjusted r-square of .14. Together utilization and TTF
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