TASK-TECHNOLOGY FIT - Human-Computer Interaction and Management Information Systems: Foundations - page 174

The impact of TTF on performance comes about through two very different pathways. First, as
shown by the arrow from TTF to “Precursors to Utilization” in Figure 9.3, higher TTF will
increase (in particular) beliefs about the positive consequences of utilization. These more positive
beliefs should lead to stronger intentions to utilize the system, and ultimately to a greater likeli-
hood of utilization.
Secondly, as shown by the arrow from TTF to Performance Impacts in Figure 9.3, at any given
level of utilization, a system with higher TTF will lead to better performance, since it more
closely meets the task needs of the individual. This second important link is completely missing
in the utilization focus research illustrated in Figure 9.1A and Figure 9.2. This link explains why
the IRS agents who used the database and spreadsheet capabilities of their new system more
extensively had poorer performance (in the earlier example from Pentland, 1989).
Feedback is an important aspect of the model. Once a technology has been utilized and perfor-
mance effects have been experienced, there will inevitably be a number of kinds of feedback. First,
the actual experience of utilizing the technology may lead users to conclude that it has a better (or
worse) impact on performance than anticipated, changing their expected consequences of utilization
and therefore affecting future utilization. The individual may also learn from experience better ways
of utilizing the technology, improving individual-technology fit, and hence overall TTF.
Relative Task-Technology Fit
Unless utilization is absolutely mandatory, there is always an alternative to the use of a new infor-
mation technology. For example, one might continue to use the old technology, whatever it is.
This alternative option is not typically described in the literature, but has a number of important
consequences. Suppose that a new technology is introduced to support a particular type of mana-
gerial decision making. The likelihood of utilizing that new technology is not dependent upon the
absolute task-technology fit of the new technology, but upon the relative TTF compared to the old
technology (or to some third possible technology). Suppose the new technology has very good
TTF, but it is only a little better than the old technology. In this situation, the impetus to use the
new technology will be slight, and any performance gains will also be slight. Thus, it is important
to consider the alternative technologies as one tries to predict or explain utilization and perfor-
mance impacts of a new technology.
MEASURING TASK-TECHNOLOGY FIT
The above arguments for the impact of TTF on performance are made in general terms. As is
often the case, as we begin to think about measuring a concept and testing its relationships to
other constructs, we realize the need to define it more carefully. “Fit” can be conceptualized in a
number of different ways, and this has important implications for measurement and testing
(Schoonhoven, 1981; Van de Ven and Drazin, 1985; Venkatraman, 1989). The argument devel-
oped here is that the strength of the link between a technology characteristic and its task-technol-
ogy fit is dependent upon how important that technology characteristic is, given the task demands
and the capabilities of the user. For example, the more a task requires information from many dif-
ferent parts of the organization (a task characteristic), the stronger the link from the degree of
organizational-wide data integration (a technology characteristic) is to TTF. On the other hand, if
all information needs are local, then organizational-wide data integration may not be important to
TTF. This corresponds exactly to one of Venkatraman's (1989) categories of fit (fit as moderation)
and generally to Van de Ven and Drazin's (1985) interaction approach.