However, few studies have focused on the use of AR and
QAS in mobile information navigation. Therefore, this study proposes
and implements an AR-QAS that includes mobile devices
and cloud servers. Mobile devices provide AR functions to recognize
camera images and to present 3D objects on device screens.
A cloud server provides a QAS based on data mining and expert
system techniques to analyze the mobile device messages and to
reply to questions. This paper presents a case study of a mobile
phone informational navigation service based on an AR-QAS.
For user behavior analysis, the current study combines the technology
acceptance model (TAM) (Davis, Bagozzi, & Warshaw,
1989) and media richness theory (Daft & Lengel, 1984) to explore
whether using AR positively benefits informational navigation.
Specifically, a natural language query AR navigation system was
designed in this study, and user attitudes and behavioral intentions
to use the system were examined. The study comprised the design
of the natural language query AR navigation system and the use of
empirical research to determine the acceptance of and behavioral
intention toward the system, which was used as an informational
navigation guide at a museum.
The remainder of the paper is organized as follows. Related
technologies and the background to the study are discussed in
Section 2. In Section 3, an AR-QAS is presented and evaluated. In
Section 4, the TAM and media richness theory are employed to
explore user attitudes and behavioral intentions toward AR-QAS.
Finally, conclusions and suggestions are provided in Section 5.