Design and application of augmented reality query-answering system in mobile phone information navigation

• An augmented reality query-answering system (AR-QAS) is designed and implemented in this study.
• The average question classification accuracy in an artificial neural network was 98.76%.
• AR-QAS can provide a fast and convenient mobile information navigation service for users.
• Language variety, timely feedback, and personal focus improve behavioral intentions to us AR-QAS.
• This study confirms that the new model combining TAM and MRT can be applied to relevant AR research.

This study proposed an augmented reality query-answering system (AR-QAS) based on mobile cloud computing to provide natural language informational navigation services. Empirical research was performed to examine the effectiveness of the system in actual use. This study confirms that the new model developed by combining technology acceptance model (TAM), media richness theory, and the factors of self-efficacy can be applied to relevant AR research. The experiment results revealed that the average question classification accuracy of QAS when combined with artificial neural network and ontology was 98.76%. Moreover, the perceived media richness was found to be positively related to self-efficacy, perceived usefulness, perceived ease of use, user attitude, and use intention. Furthermore, this study reveals that combining the TAM and media richness theory provides a stronger explanation than does the TAM alone. Before new systems are created, designers are suggested to consider the four factors of media richness theory (i.e., multiple cues, language variety, timely feedback, and personal focus), to greatly improve user attitudes toward and behavioral intentions to use new technologies.