MODELING FACTORS THAT INFLUENCE ONLINE TRAVEL BOOKING

Michael Conyette

Abstract

Data was collected from an online questionnaire completed by 1,198 respondents in 2008. Analysis of the dataset involved, correlation analysis, exploratory factor analysis, and logistic regression. In the final model building stage, a logistic regression model is generated containing key factors that lead to online travel booking intention. These factors are a unique set of socio and psychographic variables that can be used to more accurately predict website booking of travel products. The contribution to literature that this research makes is that it appears to be one of only a few models available for predicting travel product booking. For instance, this model predicts that consumers who previously booked specific travel products such as hotels or airline tickets will have a greater intention to book other travel products online. This research study also shows the relevance of the Theory of Reasoned Action to online travel but it goes further by enabling the quantification of the strength of variables such as key beliefs, attitudes and subjective norms.

References

  1. Bei, Chen & Widdows (2004). Consumers' Online Information Search Behavior and the Phenomenon of Search vs. Experience Products. Journal of Family and Economic Issues, 25(4), (Winter), 449-467.
  2. Beldona, S. (2003, July). Examining the structure of online pleasure travel planning. Paper presented at ICHRIE 2005 in Las Vegas, NV.
  3. Conyette, M. (2010). “Determinants of Online Leisure Travel Planning Decision Processes: A Segmented Approach (Unpublished doctoral dissertation, University of Newcastle, Newcastle)”.
  4. Conyette, M. (2011). Demographics for Segmentation in Online Travel, International Journal of Trade, Economics and Finance, 2 (1), 93-98.
  5. Dabholkar, P. A (1996). Consumer evaluations of new technology-based self-service options: an investigation of alternative models of service quality. International Journal of Research in Marketing, 13 (1), 29-51.
  6. Dabholkar, P. A, Bobbitt, L. M. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service: The Internet as an illustration. International Journal of Service Industry Management, 12 (5), 423-450.
  7. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003.
  8. Fishbein, M. & Raven B. H. (1962). The AB scales: an operational definition of belief and attitude. Human Relations, 15, 35-44.
  9. Fishbein, M. (1967). Readings in attitude theory and measurement. New York, John Wiley & Sons, Inc.
  10. Georgia Institute of Technology: Graphics, Visualization, and Usability Center (GVU). (1998). GVU's tenth WWW survey. Atlanta: Author.
  11. Hilbe, J. (2009). Logistic regression models, Norwell, MA, Chapman & Hall/CRC Press.
  12. Hyde, K. F. (2008). Tourist information processing and touring planning theory. Unpublished manuscript.
  13. Jarvelainen, J., Puhakainen, J., (2004). Distrust of one's own web skills: A reason for offline booking after online information search. Electronic Markets London 14, (4), 333-343.
  14. Law, R., Leung, K. & Wong, J. (2004). The impact of the internet on travel agencies, International Journal of Contemporary Hospitality Management 16, (2), 100- 107.
  15. Li, Y.M., Chen, C. W. (2009). A synthetical approach for blog recommendation: Combining trust, social relation, and semantic analysis. Expert Systems with Applications, 36, 6536-6547.
  16. Lord, C. G. (2004). The role of exemplar stability in attitude consistency and attitude change. In Haddock, G., & Maio, G. R. (Eds.), Contemporary Perspectives on the Psychology of Attitudes (pp. 299-323). Hove, UK: Psychology Press.
  17. Morrison, A. M., Jing, S., O'Leary, J. T., Cai, L. A. (2001). Predicting usage of the Internet for travel bookings: An exploratory study. Information Technology & Tourism, 4, 15-30.
  18. Trafimow, D. & Sheeran, P. (2004). A theory about the translation of cognition into affect and behavior. In Haddock, G., & Maio, G. R. (Eds.), Contemporary Perspectives on the Psychology of Attitudes (pp. 57- 75). Hove, UK: Psychology Press.
  19. Zimbardo, P. & Ebbesen, E. B. (1970). Influencing attitudes and changing behavior. Reading, AddisonWesley Publishing Company.
Download


Paper Citation


in Harvard Style

Conyette M. (2011). MODELING FACTORS THAT INFLUENCE ONLINE TRAVEL BOOKING . In Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2011) ISBN 978-989-8425-70-6, pages 205-210. DOI: 10.5220/0003455902050210


in Bibtex Style

@conference{ice-b11,
author={Michael Conyette},
title={MODELING FACTORS THAT INFLUENCE ONLINE TRAVEL BOOKING},
booktitle={Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2011)},
year={2011},
pages={205-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003455902050210},
isbn={978-989-8425-70-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2011)
TI - MODELING FACTORS THAT INFLUENCE ONLINE TRAVEL BOOKING
SN - 978-989-8425-70-6
AU - Conyette M.
PY - 2011
SP - 205
EP - 210
DO - 10.5220/0003455902050210