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Sequential framework for analyzing mobile click-through decision in online travel agency with user digital footprints

Author

Listed:
  • Hongming Gao

    (Guangzhou University)

  • Di Deng

    (Sun Yat-Sen University)

  • Hongwei Liu

    (Guangdong University of Technology)

  • Zhouyang Liang

    (Guangdong University of Technology)

Abstract

In the hotel booking market, high click-through rates are essential for online travel agencies (OTAs) to earn commissions. Given the dominance of mobile devices in web traffic, analyzing the mobile click-through decision-making process plays a vital role in search engine optimization. This study proposes a sequential framework that leverages Bayesian inference to model individual users’ click-through behaviors using user digital footprints, which encompass sequences of search, browse, compare, and click-through actions. This framework extracts three categories of information based on the degrees of dynamism in the hotel search process, ranging from less dynamic to highly dynamic levels: static hotel attributes, information cues in the search results, and temporal characteristics of user behaviors. Extensive experiments on a global OTA mobile clickstream dataset with over 600,000 observations reveal the substantial superiority of the proposed framework over the baseline models like probit regression and Naive Bayes. Notably, temporal characteristics emerge as the most important category. Drawing on our model, we delve into the interpretability of these three information categories. Additionally, we compare their varying impacts across different devices. Beyond these findings, this study offers valuable managerial implications for mobile OTA search engine marketing and optimization.

Suggested Citation

  • Hongming Gao & Di Deng & Hongwei Liu & Zhouyang Liang, 2024. "Sequential framework for analyzing mobile click-through decision in online travel agency with user digital footprints," Information Technology & Tourism, Springer, vol. 26(4), pages 679-709, December.
  • Handle: RePEc:spr:infott:v:26:y:2024:i:4:d:10.1007_s40558-024-00294-z
    DOI: 10.1007/s40558-024-00294-z
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    References listed on IDEAS

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    1. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    2. Michael R. Baye & J. Rupert J. Gatti & Paul Kattuman & John Morgan, 2009. "Clicks, Discontinuities, and Firm Demand Online," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(4), pages 935-975, December.
    3. Anindya Ghose & Avi Goldfarb & Sang Pil Han, 2013. "How Is the Mobile Internet Different? Search Costs and Local Activities," Information Systems Research, INFORMS, vol. 24(3), pages 613-631, September.
    4. Savannah Wei Shi & Michael Trusov, 2021. "The Path to Click: Are You on It?," Marketing Science, INFORMS, vol. 40(2), pages 344-365, March.
    5. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao & Hui Li, 2022. "Ranking hotels through multi-dimensional hotel information: a method considering travelers’ preferences and expectations," Information Technology & Tourism, Springer, vol. 24(1), pages 127-155, March.
    6. Babur De los Santos & Sergei Koulayev, 2017. "Optimizing Click-Through in Online Rankings with Endogenous Search Refinement," Marketing Science, INFORMS, vol. 36(4), pages 542-564, July.
    7. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    8. Torsten Bornemann & Christian Homburg, 2011. "Psychological Distance and the Dual Role of Price," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 38(3), pages 490-504.
    9. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    10. Sulin Ba & Shu He & Shun‐Yang Lee, 2022. "Mobile App Adoption and Its Differential Impact on Consumer Shopping Behavior," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 764-780, February.
    11. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    12. Moorthy, Sridhar & Ratchford, Brian T & Talukdar, Debabrata, 1997. "Consumer Information Search Revisited: Theory and Empirical Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 23(4), pages 263-277, March.
    13. Singh, Sonika & Swait, Joffre, 2017. "Channels for search and purchase: Does mobile Internet matter?," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 123-134.
    14. Choudhary, Vidyanand & Currim, Imran & Dewan, Sanjeev & Jeliazkov, Ivan & Mintz, Ofer & Turner, John, 2017. "Evaluation Set Size and Purchase: Evidence from a Product Search Engine," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 16-31.
    15. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
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