Skip to main content

What Drives the Usage of Intelligent Traveler Information Systems?

  • Chapter
  • First Online:
Disrupting Mobility

Part of the book series: Lecture Notes in Mobility ((LNMOB))

Abstract

Rising mobility demand and increasing complexity of transportation options put a higher pressure on transportation systems and are a challenge in urban areas. A solution requires changes on coordination and behavioral levels. Today’s technology, e.g., omnipresent smartphones, comprises the capabilities to induce such change via supply and demand coordination through intelligent traveler information systems. To identify the driving forces behind the decision to use such systems on an individual level the UTAUT 2 is transferred to the context of mobility by enriching it with explanatory insights from transportation research. The results indicate that the driving forces are user-specific and depend on diverse influencing factors that exceed pure economic and socio-demographic dimensions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    www.uber.com.

  2. 2.

    www.citibikenyc.com.

  3. 3.

    www.moovel.com.

References

  1. Gehl, J.: Cities for People. Island Press, Washington (2010)

    Google Scholar 

  2. Knoflacher, H.: Zurück zur Mobilität!: Anstöße zum Umdenken. Ueberreuter, Wien (2013)

    Google Scholar 

  3. Batty, M.: The New Science of Cities. The MIT Press, Cambridge (2013)

    Google Scholar 

  4. Larson, K. et al.: Updates from our future city. www.youtube.com/watch?v=pUum3OQuI24c. Accessed 20 June 2016

  5. Kenyon, S., Lyons, G.: The value of integrated multimodal traveller information and its potential contribution to modal change. Transp. Res. Part F Traffic Psychol. Behav. 6(1), 1–21 (2003)

    Article  Google Scholar 

  6. Sussman, J.S.: Perspectives on Intelligent Transportation Systems (ITS). Springer, New York (2005)

    Google Scholar 

  7. Hilty, L.M., et al.: The relevance of information and communication technologies for environmental sustainability—a prospective simulation study. Environ. Model Softw. 21(11), 1618–1629 (2006)

    Article  Google Scholar 

  8. Eriksson, L., et al.: Interrupting habitual car use: the importance of car habit strength and moral motivation for personal car use reduction. Transp. Res. Part F Traffic Psychol. Behav. 11(1), 10–23 (2008)

    Article  Google Scholar 

  9. Gardner, B.: Modelling motivation and habit in stable travel mode contexts. Transp. Res. Part F Traffic Psychol. Behav 12(1), 68–76 (2009)

    Article  Google Scholar 

  10. Verplanken, B., et al.: Attitude versus general habit: antecedents of travel mode choice. J. Appl. Soc. Psychol. 24(4), 285–300 (1994)

    Article  Google Scholar 

  11. Schneider, R.J.: Theory of routine mode choice decisions: An operational framework to increase sustainable transportation. Transp. Policy 25, 128–137 (2013)

    Article  Google Scholar 

  12. Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transp. Res. Part C Emerg. Technol. 6(3), 157–172 (1998)

    Article  Google Scholar 

  13. Kramers, A.: Designing next generation multimodal traveler information systems to support sustainability-oriented decisions. Environ. Model Softw. 56, 83–93 (2014)

    Article  Google Scholar 

  14. Blau, B. et al.: How to coordinate value generation in service networks, Bus. Inf. Syst. Eng. 5(1), 343 (2009)

    Google Scholar 

  15. Lisson, C. et al.: Decisions in Mobility Service Networks—Coordinating Demand and Supply Using a Mechanism Design Approach. In: Proceedings of the Forty-Ninth Annual Hawaii International Conference on System Sciences (HICSS) (2016)

    Google Scholar 

  16. Bamberg, S., et al.: Choice of travel mode in the theory of planned behavior: the roles of past behavior, habit, and reasoned action. Basic Appl. Soc. Psych. 25(3), 175–187 (2003)

    Article  MathSciNet  Google Scholar 

  17. Steg, L., et al.: Instrumental-reasoned and symbolic-affective motives for using a motor car. Transp. Res. Part F Traffic Psychol. Behav. 4(3), 151–169 (2001)

    Article  Google Scholar 

  18. Bamberg, S., et al.: Social context, personal norms and the use of public transportation: two field studies. J. Environ. Psychol. 27(3), 190–203 (2007)

    Article  Google Scholar 

  19. Schiefelbusch, M.: Rational planning for emotional mobility? The case of public transport development. Plan. Theory 9(3), 200–222 (2010)

    Google Scholar 

  20. Ben-Akiva, M., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand. The MIT Press, Cambridge (1985)

    Google Scholar 

  21. Grotenhuis, J.-W., et al.: The desired quality of integrated multimodal travel information in public transport: customer needs for time and effort savings. Transp. Policy 14(1), 27–38 (2007)

    Article  Google Scholar 

  22. Chorus, C.G., et al.: Use and effects of advanced traveller information services (ATIS): a review of the literature. Transp. Rev. 26(2), 127–149 (2006)

    Article  Google Scholar 

  23. Abou-Zeid, M., Ben-Akiva, M.: Well-being and activity-based models. Transportation (Amst) 39(6), 1189–1207 (2012)

    Article  Google Scholar 

  24. Maslow, A.H.: Motivation and Personality. Harper Row, New York (1970)

    Google Scholar 

  25. Anable, J.: “Complacent Car Addicts” or “Aspiring Environmentalists”? Identifying travel behaviour segments using attitude theory. Transp. Policy 12(1), 65–78 (2005)

    Article  Google Scholar 

  26. Vallerand, R. J.: Toward a hierarchical model of intrinsic and extrinsic motivation. Adv. Exp. Soc. Psychol. 29 (1977)

    Google Scholar 

  27. Vallerand, R.J., Lalande, D.R.: The MPIC model: the perspective of the hierarchical model of intrinsic and extrinsic motivation. Psychol. Inq. 22(1), 45–51 (2011)

    Article  Google Scholar 

  28. Axhausen, K.W.: Social networks, mobility biographies, and travel: survey challenges. Environ. Plann. B. Plann. Des. 35(6), 981–997 (2008)

    Article  Google Scholar 

  29. Gärling, T., Axhausen, K.: Introduction: habitual travel choice. Transportation (Amst) 30(1), 1–11 (2003)

    Article  Google Scholar 

  30. Götz, K.: Mobilitätsstile. In: Schöller, O. et al. (eds.) Handbuch Verkehrspolitik, VS Verlag für Sozialwissenschaften, 964 (2008)

    Google Scholar 

  31. Seebauer, S., et al.: Technophilia as a driver for using advanced traveler information systems. Transp. Res. Part C Emerg. Technol. 60, 498–510 (2015)

    Article  Google Scholar 

  32. Parasuraman, A., Colby, C.L.: An updated and streamlined technology readiness index: TRI 2.0. J. Serv. Res. 18(1), 59–74 (2014)

    Article  Google Scholar 

  33. Rammstedt, B., John, O.P.: Measuring personality in one minute or less: a 10-item short version of the big five inventory in English and German. J. Res. Pers. 41(1), 203–212 (2007)

    Article  Google Scholar 

  34. Chlond, B. et al.: Data quality and completeness issues in multiday or panel surveys. In: Zmud, J. et al. (eds.) Transport Survey Methods: Best Practice for Decision Making, Emerald Group Publishing Limited, pp. 373–392 (2013)

    Google Scholar 

  35. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)

    Article  Google Scholar 

  36. Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(4), 144–176 (1995)

    Article  Google Scholar 

  37. Limayem, M., et al.: How habit limits the predictive power of intention: the case of information systems continuance. MIS Q. 31(4), 705–737 (2007)

    Google Scholar 

  38. Venkatesh, V. et al.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. (2012)

    Google Scholar 

  39. Grönroos, C.: A service quality model and its marketing implications. Eur. J. Mark. 18(4), 36–44 (1984)

    Article  Google Scholar 

  40. Cronin, J.J., Taylor, S.A.: Measuring quality: a reexamination and extension. J. Mark 56(3), 55–68 (1992)

    Article  Google Scholar 

  41. Diana, M.: Measuring the satisfaction of multimodal travelers for local transit services in different urban contexts. Transp. Res. Part A Policy Pract. 46(1), 1–11 (2012)

    Article  MathSciNet  Google Scholar 

  42. Ding, D.X., et al.: e-SELFQUAL: a scale for measuring online self-service quality. J. Bus. Res. 64(5), 508–515 (2011)

    Article  Google Scholar 

  43. MacKenzie, S.B., et al.: Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques. MIS Q. 35(2), 293–334 (2011)

    Google Scholar 

  44. Gefen, D. et al.: An update and extension to SEM guidelines for administrative and social science research. MIS Q. 35(2), iii–ixiv (2011)

    Google Scholar 

  45. Anable, J., Gatersleben, B.: All work and no play? The role of instrumental and affective factors in work and leisure journeys by different travel modes. Transp. Res. Part A Policy Pract. 39(2–3), 163–181 (2005)

    Article  Google Scholar 

  46. Klein, H.K., Myers, M.D.: A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Q. 23(1), 67–93 (1999)

    Article  Google Scholar 

  47. Flick, U.: An introduction to qualitative research. Sage Publications, Thousand Oaks (2014)

    Google Scholar 

  48. Glaser, B.G., Strauss, A. L.: The Discovery of Grounded Theory: Strategies for Qualitative Research. Transaction Publishers (2009)

    Google Scholar 

  49. Krippendorf, K.: Content Analysis: An Introduction to its Methodology. Sage Publications (2012)

    Google Scholar 

  50. Venkatesh, V., et al.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)

    Google Scholar 

  51. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3), 319–339 (1989)

    Article  Google Scholar 

  52. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four field studies. Manage. Sci. 45(2), 186–204 (2000)

    Article  Google Scholar 

  53. Davis, F.D., et al.: Extrinsiacnd intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psychol. 22(14), 1111–1132 (1992)

    Article  Google Scholar 

  54. Rogers, E.: Diffusion of Innovation. Free Press, New York (1995)

    Google Scholar 

  55. Bandura, A.: Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Cliffs (1986)

    Google Scholar 

  56. Stradling, S.G., et al.: Performance, importance and user disgruntlement: a six-step method for measuring satisfaction with travel modes. Transp. Res. Part A Policy Pract. 41(1), 98–106 (2007)

    Article  Google Scholar 

  57. Davis, F.D.: JSTOR. MIS Q. 13(3), 319–340 (1989)

    Article  Google Scholar 

  58. Thompson, R.L.: Personal computing: toward a conceptual model of utilization. MIS Q. 15(1), 124–143 (1991)

    Article  Google Scholar 

  59. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 192–222 (1991)

    Article  Google Scholar 

  60. Xin Ding, D. et al.: The impact of service system design and flow experience on customer satisfaction in online financial services. J. Serv. Res, Vol. 13(1), 96–110 (2009)

    Google Scholar 

  61. Taylor, S., Todd, P.A.: Assessing IT usage: the role of prior experience. MIS Q. 19(2), 561–570 (1995)

    Article  Google Scholar 

  62. Brown, S.A., Venkatesh, V.: Model of adoption and technology in households: a baseline model test and extension incorporating household life cycle. MIS Q. 29(3), 399–436 (2005)

    Google Scholar 

  63. van der Heijden, H.: User acceptance of hedonic information systems. MIS Q. 28(4), 695–704 (2004)

    Google Scholar 

  64. Ben-Akiva, M., Boccara, B.: Discrete choice models with latent choice sets. Int. J. Res. Mark. 12(1), 9–24 (1995)

    Article  Google Scholar 

  65. Dodds, W.B. et al.: Effects of price, brand, and store information on buyers’ product evaluations

    Google Scholar 

  66. Zeithaml, V.A., et al.: Problems and strategies in service marketing. J. Mark. 49(2), 33–46 (1985)

    Article  Google Scholar 

  67. Kim, S.S. et al.: Research note—two competing perspectives on automatic use: a theoretical and empirical comparison. Inf. Syst. Res (2005)

    Google Scholar 

  68. Ajzen, I., Fishbein, M.: The influence of attitudes on behavior. Handb. Attitudes 173, 173–221 (2005)

    Google Scholar 

  69. Groves, R.M., et al.: Survey Methodolog. Wiley, LondonWile (2009)

    Google Scholar 

  70. Malhotra, M.: An assessment of survey research in POM: from constructs to theory. J. Oper. Manage. 16(4), 407–425 (1998)

    Article  Google Scholar 

  71. Churchill, G.A.: A paradigm for developing better measures of marketing constructs. J. Mark. Res. 16(1), 64–73 (1979)

    Article  MathSciNet  Google Scholar 

  72. Harman, H.: Modern Factor Analysis. University of Chicago Press, Chicago (1976)

    MATH  Google Scholar 

  73. El Hedhli, K., Chebat, J.-C.: Developing and validating a psychometric shopper-based mall equity measure. J. Bus. Res. 62(6), 581–587 (2009)

    Article  Google Scholar 

  74. Bagozzi, R.P., Baumgartner, H.: The evaluation of structural equation models and hypothesis testing. In: Bagozzi, R. (ed.) Principles of marketing research, pp. 386–422. Blackwell Publishers (1994)

    Google Scholar 

  75. Fronell, C., Larcker, D.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)

    Article  Google Scholar 

  76. Hair, J., et al.: Multivariate Data Analysise. Prentice Hall, Englewood Cliffs (1998)

    Google Scholar 

  77. Cronbach, L.: Coefficient alpha and the internal structure of tests. Psychometrika 16(3), 297–334 (1951)

    Article  Google Scholar 

  78. Caliński, T., Harabasz, J.: A dendrite method for cluster analysis, Commun. Stat. Theory Methods (2007)

    Google Scholar 

  79. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (2005)

    MATH  Google Scholar 

  80. Gefen, D. et al.: An update and extension to SEM guidelines for administrative and social science research. Editorial Comment. 35(2), III–XII (2011)

    Google Scholar 

  81. Ringle, C. et al.: Editor’s comments: a critical look at the use of PLS-SEM in mis quarterly. http://aisel.aisnet.org/misq/vol36/iss1/2 (2012). Accessed 20 June 2016

  82. Hu, L., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. A Multidiscip. J. 6(1), 1–55 (1999)

    Article  Google Scholar 

  83. Chin, W.W., et al.: A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf. Syst. Res. 14(2), 189–217 (2003)

    Article  MathSciNet  Google Scholar 

  84. Henseler, J., et al.: Common beliefs and reality about PLS: comments on Ronkko and Evermann (2013). Organ. Res. Methods 17(2), 182–209 (2014)

    Article  Google Scholar 

  85. Stibe, A.: Towards a framework for socially influencing systems: meta-analysis of four PLS-SEM Based Studies. In: MacTavish, T., Basapur, S. (eds.) Persuasive Technology, pp. 172–183. Springer International Publishing, New York (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christopher Lisson .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Lisson, C., Hall, M., Michalk, W., Weinhardt, C. (2017). What Drives the Usage of Intelligent Traveler Information Systems?. In: Meyer, G., Shaheen, S. (eds) Disrupting Mobility. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-319-51602-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-51602-8_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51601-1

  • Online ISBN: 978-3-319-51602-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics