Advertisement

An Examination of Psychological Factors Affecting Drivers’ Perceptions and Attitudes Toward Car Navigation Systems

  • Eunil Park
  • Ki Joon Kim
  • Angel P. del Pobil
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 215)

Abstract

To examine drivers’ perceptions of and attitudes toward car navigation systems, the present study integrated perceived satisfaction and perceived locational accuracy into a modified technology acceptance model, and investigated hypothesized causal paths proposed by the research model with data collected from an online survey (N = 1,204). Results from the structural equation modeling indicated that perceived satisfaction and locational accuracy played crucial roles in determining perceived ease of use and perceived usefulness of the navigation systems. Implications and limitations are discussed.

Keywords

Car navigation systems Technology acceptance model Perceived satisfaction Perceived locational accuracy 

Notes

Acknowledgments

This research was partly supported by WCU program via the NRF of Korea funded by the KMEST (R31-2008-000-10062-0), by Ministerio de Ciencia e Innovacion (DPI2011-27846), by Generalitat Valenciana (PROMETEO/2009/052) and by Fundacio Caixa Castello-Bancaixa (P1-1B2011-54).

References

  1. 1.
    Adrados C, Girard I, Gendner JP, Janeau G (2002) Global positioning system (GPS) location accuracy improvement due to selective availability removal. CR Biol 325(2):165–170CrossRefGoogle Scholar
  2. 2.
    Kupper A (2005) Location-based services. Willey, ChichesterCrossRefGoogle Scholar
  3. 3.
    Kawasaki H, Murao M, Ikeuchi K, Sakauchi M (2001) Enhanced navigation system with real images and real-time information. In: Proceedings of the 8th world congress on intelligent transport systems, pp 1–11Google Scholar
  4. 4.
    Nobuyuki K, Tomohiro Y, Osamu S, Andrew L (2000) A driver behavior recognition meth-od based on a driver model framework. SAE Trans 109(6):469–476Google Scholar
  5. 5.
    Autos.ca, Product Review: five in-car navigation systems http://www.autos.ca/auto-articles/product-review-five-in-car-navigation-systems
  6. 6.
    Nowakowski C, Utsui Y, Green P (2000) Navigation system destination entry: the effects of driver workload and input devices, and implications for SAE recommended practice (Technical Report UMTRI-2000-20). University of Michigan, Transportation Research Institute, Ann ArborGoogle Scholar
  7. 7.
    Magellan, MiTAC International Corporation http://www.magellangps.com/
  8. 8.
  9. 9.
    Ariyoshi H, Iwaskaki A, Sugihara T, Ohe H, Sakamoto M (1988) Car navigation system. NEC Tech J 41:149–159Google Scholar
  10. 10.
    Narzt W, Pomberger G, Ferscha A, Kolb D, Müller R, Wieghardt J, Hörtner H, Lindinger C (2004) A new visualization concept for navigation systems. Lect Notes Comput Sci 3196:440–451CrossRefGoogle Scholar
  11. 11.
    Robertson DP, Cipolla R (2004) An image-based system for urban navigation. In: Proceedings of the 2004 BMVCGoogle Scholar
  12. 12.
    Gilliéron PY, Merminod B (2003) Personal navigation system for indoor application. In: Proceedings of the 11th IAIN world congress, pp 1–15Google Scholar
  13. 13.
    Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340CrossRefGoogle Scholar
  14. 14.
    Davis F (1993) User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. Int J Man Mach Stud 38:475–487CrossRefGoogle Scholar
  15. 15.
    Koufaris M (2003) Applying the technology acceptance model and flow theory to online consumer behavior. Inf Syst Res 13(2):205–223CrossRefGoogle Scholar
  16. 16.
    Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 46:186–204CrossRefGoogle Scholar
  17. 17.
    Park E, Kim KJ, Jin D, del Pobil AP (2012) Towards a successful mobile map service: an empirical examination of technology acceptance model. Commun Comput Inf Sci 293:420–428CrossRefGoogle Scholar
  18. 18.
    Park E, Kim S, del Pobil AP (2011) Can I go there? The effects of digital signage on psychology of wayfinding users. J Next Gener Inf Technol 2(4):47–58CrossRefGoogle Scholar
  19. 19.
    Chiu CM, Hsu M, Sun S, Lin T, Sun P (2005) Usability, quality, value and e-learning continuance decisions. Comput Educ 45:399–416CrossRefGoogle Scholar
  20. 20.
    LaBarbera P, Mazursky D (1983) A Longitudinal assessment of consumer satisfaction/dissatisfaction: the dynamic aspect of the cognitive process. J Mark Res 24:393–404CrossRefGoogle Scholar
  21. 21.
    Loomis JM, Silva JA, Philbeck JW, Fukusima SS (1996) Visual perception of location and distance. Curr Dir Psychol Sci 5:72–77CrossRefGoogle Scholar
  22. 22.
    Park E, del Pobil AP (2012) Modeling the user acceptance of long-term evolution (LTE) services. Ann Telecommun 1–9Google Scholar
  23. 23.
    Hair JF, Black WC, Babin BJ, Anderson RE (2006) Multivariate data analysis. Prentice Hall, Upper Saddle RiverGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Eunil Park
    • 1
  • Ki Joon Kim
    • 2
  • Angel P. del Pobil
    • 2
    • 3
  1. 1.Interaction Science Research CenterSungkyunkwan UniversitySeoulSouth Korea
  2. 2.Department of Interaction ScienceSungkyunkwan UniversitySeoulSouth Korea
  3. 3.Department of Computer Science and EngineeringUniversity Jaume-ICastellonSpain

Personalised recommendations