In spite of high expectations for augmented reality (AR) technology, many smartphone AR applications have failed and terminated their services in Korea because of low usage rates. As failures of smartphone AR applications have increased, investigating factors that influence users’ continuance intention to use the AR applications become an important topic. This study develops a theoretical model that explains continuance intention of users based on AR and motivation theories, and empirically examines the influencing factors with collected data from smartphone AR application users. The results showed that information quality was the most influencing factor for AR continuance intention among AR factors, while perceived enjoyment was not a significant antecedent. The implications of this study provide additional insights for smartphone AR researchers and developers.
AnimeshAPinsonneaultAYangS-B. (2011) An odyssey into virtual worlds: exploring the impacts of technological and spatial environments on intention to purchase virtual products. MIS Quarterly35(3): 789–813.
2.
ArmitageCJConnerM (1999) The theory of planned behaviour: Assessment of predictive validity and’ perceived control. British Journal of Social Psychology38(1): 35–54.
3.
AtkinsonMKyddC (1997) Individual characteristics associated with World Wide Web use: an empirical study of playfulness and motivation. ACM SIGMIS Database28(2): 53–62.
4.
AzumaRBaillotYBehringerR. (2001) Recent advances in augmented reality. Computer Graphics and Applications, IEEE21(6): 34–47.
5.
AzumaRT (1997) A survey of augmented reality. Presence6(4): 355–385.
6.
BagozziRPYiY (1988) On the evaluation of structural equation models. Journal of the Academy of Marketing Science16(1): 74–94.
7.
BarnesSJ (2011) Understanding use continuance in virtual worlds: Empirical test of a research model. Information & Management48(8): 313–319.
8.
BhattacherjeeA (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly25(3): 351–370.
9.
BimberORaskarRInamiM (2005) Spatial augmented reality: AK Peters Wellesley.
10.
BrownSPChinWW (2004) Satisfying and retaining customers through independent service representatives. Decision Sciences35(3): 527–550.
11.
CarmignianiJFurhtBAnisettiM. (2011) Augmented reality technologies, systems and applications. Multimedia Tools and Applications51(1): 341–377.
12.
ChangK-EChangC-THouH-T. (2014) Development and behavioral pattern analysis of a mobile guide system with augmented reality for painting appreciation instruction in an art museum. Computers & Education71(1): 185–197.
13.
ChenY-CChiH-LHungW-H. (2011) Use of tangible and augmented reality models in engineering graphics courses. Journal of Professional Issues in Engineering Education & Practice137(4): 267–276.
14.
ChinWW (2010) Bootstrap cross-validation indices for PLS path model assessment. Handbook of Partial Least Squares. Springer, 83–97.
15.
ChiouJ-S (2004) The antecedents of consumers’ loyalty toward Internet service providers. Information & Management41(6): 685–695.
16.
CoyleJRThorsonE (2001) The effects of progressive levels of interactivity and vividness in web marketing sites. Journal of Advertising30(3): 65–77.
17.
CyrDHeadMIvanovA (2009) Perceived interactivity leading to e-loyalty: Development of a model for cognitive–affective user responses. International Journal of Human-Computer Studies67(10): 850–869.
18.
DavisFD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly13(3): 319–340.
19.
DavisFDBagozziRPWarshawPR (1989) User acceptance of computer technology: a comparison of two theoretical models. Management Science35(8): 982–1003.
20.
DavisFDBagozziRPWarshawPR (1992) Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology22(14): 1111–1132.
21.
DunleavyMDedeCMitchellR (2009) Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology18(1): 7–22.
22.
EfronBTibshiraniRJ (1994) An introduction to the bootstrap. CRC press.
23.
FornellCLarckerDF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research18(1): 39–50.
24.
Gartner (2010) Gartner’s 2010 Hype Cycle Special Report evaluates maturity of 1,800 technologies. Available at:http://www.gartner.com/newsroom/id/1447613 (accessed 10 May 2013)
25.
HürstWvan WezelC (2013) Gesture-based interaction via finger tracking for mobile augmented reality. Multimedia Tools and Applications62(1): 233–258.
26.
HsiehS-THsuP-Y (2013) Mentoring effects in the successful adaptation of information systems. Information Development. Epub ahead of print 16 December 2013. DOI: 0266666913511263
27.
JiangZBenbasatI (2007) Research note—investigating the influence of the functional mechanisms of online product presentations. Information Systems Research18(4): 454–470.
28.
KatoHBillinghurstM (1999) Marker tracking and hmd calibration for a video-based augmented reality conferencing system Augmented Reality, 1999.(IWAR’99) Proceedings. 2nd IEEE and ACM International Workshop onSan Francisco, CA, 20 Oct 1999, pp. 85–94. IEEE
29.
KimB (2010) An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Systems with Applications37(10): 7033–7039.
30.
KimMJ (2013) A framework for context immersion in mobile augmented reality. Automation in Construction33(1): 79–85.
31.
KlopferE (2008) Augmented learning: Research and design of mobile educational games: The MIT Press.
32.
KlopferESquireK (2008) Environmental Detectives—the development of an augmented reality platform for environmental simulations. Educational Technology Research and Development56(2): 203–228.
33.
KnightS-ABurnJM (2005) Developing a framework for assessing information quality on the World Wide Web. Informing Science: International Journal of an Emerging Transdiscipline8(5): 159–172.
34.
KwonOWenY (2010) An empirical study of the factors affecting social network service use. Computers in Human Behavior26(2): 254–263.
35.
LarckerDFLessigVP (1980) Perceived usefulness of information: a psychometric examination. Decision Sciences11(1): 121–134.
36.
LeeDYLehtoMR (2013) User acceptance of YouTube for procedural learning: an extension of the technology acceptance model. Computers & Education61(1): 193–208.
37.
LeeMKCheungCMChenZ (2005) Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management42(8): 1095–1104.
38.
LiawS-SHuangH-M (2003) An investigation of user attitudes toward search engines as an information retrieval tool. Computers in Human Behavior19(6): 751–765.
39.
LiawSS (2002) Understanding user perceptions of world wide web environments. Journal of Computer Assisted Learning18(2): 137–148.
40.
LinCSWuSTsaiRJ (2005) Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management42(5): 683–693.
41.
McDougallGHLevesqueT (2000) Customer satisfaction with services: putting perceived value into the equation. Journal of Services Marketing14(5): 392–410.
42.
MilgramPKishinoF (1994) A taxonomy of mixed reality visual displays. IEICE TRANSACTIONS on Information and Systems77(12): 1321–1329.
43.
NarztWPombergerGFerschaA. (2006) Augmented reality navigation systems. Universal Access in the Information Society4(3): 177–187.
44.
NeeAOngSChryssolourisG. (2012) Augmented reality applications in design and manufacturing. CIRP Annals-Manufacturing Technology61(2): 657–679.
45.
NunnallyJ (1978) Psychometric methods. McGraw-Hill, New York, NY.
46.
OlatokunWNwaforCI (2012) The effect of extrinsic and intrinsic motivation on knowledge sharing intentions of civil servants in Ebonyi State, Nigeria. Information Development28(3): 216–234.
47.
OlssonTIhamäkiPLagerstamE. (2009) User expectations for mobile mixed reality services: an initial user study European Conference on Cognitive Ergonomics: Designing beyond the Product–Understanding Activity and User Experience in Ubiquitous Environments pp. 19. VTT Technical Research Centre of Finland
48.
OlssonTLagerstamEKärkkäinenT. (2013) Expected user experience of mobile augmented reality services: a user study in the context of shopping centres. Personal and Ubiquitous Computing17(2): 287–304.
49.
OumSHanD (2011) An empirical study of the determinants of the intention to participate in user-created contents (UCC) services. Expert Systems with Applications38(12): 15110–15121.
50.
PapagiannakisGSinghGMagnenat-ThalmannN (2008) A survey of mobile and wireless technologies for augmented reality systems. Computer Animation and Virtual Worlds19(1): 3–22.
51.
PattersonPGSprengRA (1997) Modelling the relationship between perceived value, satisfaction and repurchase intentions in a business-to-business, services context: an empirical examination. International Journal of Service Industry Management8(5): 414–434.
52.
PenceHE (2010) Smartphones, smart objects, and augmented reality. The Reference Librarian52(1-2): 136–145.
53.
ReevesB (1991) Being there: Television as symbolic versus natural experience. Unpublished manuscript, Institute for Communication Research, Stanford University, Stanford, CA.
RyanRMDeciEL (2000) Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology25(1): 54–67.
57.
SaeedKAAbdinnour-HelmS (2008) Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management45(6): 376–386.
58.
SeddonPKiewM-Y (2007) A partial test and development of DeLone and McLean’s model of IS success. Australasian Journal of Information Systems4(1): 90–109.
59.
ShinD-H (2007) User acceptance of mobile Internet: Implication for convergence technologies. Interacting with Computers19(4): 472–483.
TeoTNoyesJ (2011) An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education57(2): 1645–1653.
62.
ThengY-LMei-LingCLLiuW. (2007) Mixed reality systems for learning: a pilot study understanding user perceptions and acceptance. Virtual Reality. Springer, pp. 728–737.
63.
ThongJYHongS-JTamKY (2006) The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies64(9): 799–810.
64.
UcelliGContiGDe AmicisR. (2005) Learning using augmented reality technology: multiple means of interaction for teaching children the theory of colours. Intelligent Technologies for Interactive Entertainment. Springer, 193–202.
65.
Van der HeijdenH (2004) User acceptance of hedonic information systems. MIS Quarterly28(4): 695–704.
66.
VenkateshVDavisFD (1996) A model of the antecedents of perceived ease of use: Development and test. Decision Sciences27(3): 451–481.
67.
WagnerDSchmalstiegD (2003) First steps towards handheld augmented reality. Seventh IEEE International Symposium on Wearable ComputersWhite Plains, New York, USA, Oct 21, pp. 127. IEEE.
68.
WalkerGSheppardP (1997) Telepresence-The future of telephony. British Telecommunications Technology Journal, Special Issue on Telepresence.
69.
WojciechowskiRCellaryW (2013) Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Computers & Education68(1): 570–585.
70.
WuH-KWen-Yu LeeSChangH-Y. (2012) Current status, opportunities and challenges of augmented reality in education. Computers & Education62(1): 41–49.
71.
YangZCaiSZhouZ. (2005) Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Information & Management42(4): 575–589.
72.
YooW-SLeeYParkJ (2010) The role of interactivity in e-tailing: Creating value and increasing satisfaction. Journal of Retailing and Consumer Services17(2): 89–96.
73.
ZhouT (2011) The effect of initial trust on user adoption of mobile payment. Information Development27(4): 290–300.
