Information Technology Reference
In-Depth Information
C HAPTER 18
ON THE RELATIONSHIP BETWEEN HCI AND
TECHNOLOGY ACCEPTANCE RESEARCH
F RED D. D AVIS
Abstract: This paper examines the relationship between human-computer interaction (HCI)
research and information systems research on technology acceptance. The technology acceptance
model (TAM) is a motivational model of the user that traces the effects of system design character-
istics (functionality and interface features), through perceived ease of use and perceived usefulness,
on people's intentions to use the system. TAM was introduced in the 1980s to predict and explain
user acceptance of new information systems based on usage intentions of potential users measured
after a brief trial experience using a prototype of the new system. Influenced by early HCI research,
TAM extends traditional user interface testing methodologies, which emphasize objective usability
criteria (e.g., task performance times and error rates). Whereas HCI emphasizes how best to design
the user interface to improve task performance when using a system, TAM focuses on how to
increase people's willingness to use a system. Since both user acceptance and task performance are
vital to a system's success, HCI and TAM offer complementary perspectives. The paper concludes
by discussing the evolution and current status of TAM research.
Keywords: Technology Acceptance Model, Information-System Usefulness, Ease of Use, Usability,
User Acceptance
The purpose of this brief commentary is to introduce researchers to the stream of research on user
acceptance of information technology and explain its relationship to HCI research. Specifically, I
provide a personal reflection on the inspiration for and the development and evolution of the tech-
nology acceptance model (TAM).
INSPIRATION FOR TAM
The general problem originally motivating TAM was the high failure rate of information technol-
ogy applications in workplace settings. In the late 1970s, I worked as a consultant developing and
implementing hospital budget and planning systems. The growing power of computers for driv-
ing model-based and data-oriented decision support systems was evident. As an industrial engi-
neering student, I was inspired by Little's (1970) influential Management Science paper, “Models
and Managers: The Concept of a Decision Calculus,” which laid out key concepts of decision sup-
port and specifically raised user acceptance as a key issue: “The big problem with management
science models is that managers practically never use them” (p. B466).
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