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Forecasting duty-free shopping demand with multisource data: a deep learning approach

Author

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  • Dong Zhang

    (Sun Yat-Sen University)

  • Pengkun Wu

    (Sichuan University)

  • Chong Wu

    (Harbin Institute of Technology)

  • Eric W. T. Ngai

    (The Hong Kong Polytechnic University)

Abstract

Accurate forecasting of duty-free shopping demand plays a pivotal role in strategic and operational decision-making processes. Despite the extensive literature on sustainability, operations management, and consumer behavior in the context of duty-free shopping, there is a noticeable absence of an integrated end-to-end solution for precise demand forecasting. Furthermore, existing forecasting models often encounter limitations in effectively leveraging multi-source data as reliable indicators for duty-free shopping demand. To address these gaps, our study introduces a pioneering deep-learning architecture known as the Attention-Aided Interaction-Driven Long Short-Term Memory-Convolutional Neural Network Model (AI-LCM). Designed to capture intricate cross-correlations within multi-source data, encompassing search queries, COVID-19 impact, economic factors, and historical data; this model represents a significant methodological advancement. Rigorous evaluation against state-of-the-art benchmarks conducted on robust real-world datasets confirms the superior forecasting performance exhibited by our AI-LCM model. We elucidate the manifold implications for various stakeholders while illustrating the extensive applicability of our model and its potential to inform data-driven decision-making strategies.

Suggested Citation

  • Dong Zhang & Pengkun Wu & Chong Wu & Eric W. T. Ngai, 2024. "Forecasting duty-free shopping demand with multisource data: a deep learning approach," Annals of Operations Research, Springer, vol. 339(1), pages 861-887, August.
  • Handle: RePEc:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-024-05830-y
    DOI: 10.1007/s10479-024-05830-y
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    1. Ying Liu & Yibing Chen & Sheng Wu & Geng Peng & Benfu Lv, 2015. "Composite leading search index: a preprocessing method of internet search data for stock trends prediction," Annals of Operations Research, Springer, vol. 234(1), pages 77-94, November.
    2. Faruk Balli & Syed Jawad Hussain Shahzad & Gazi Salah Uddin, 2018. "A tale of two shocks: What do we learn from the impacts of economic policy uncertainties on tourism?," Post-Print hal-02044848, HAL.
    3. Li, Cheng & Zheng, Weimin & Ge, Peng, 2022. "Tourism demand forecasting with spatiotemporal features," Annals of Tourism Research, Elsevier, vol. 94(C).
    4. Jiao, Xiaoying & Li, Gang & Chen, Jason Li, 2020. "Forecasting international tourism demand: a local spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 83(C).
    5. Peng Chen & Andrew Vivian & Cheng Ye, 2022. "Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine," Annals of Operations Research, Springer, vol. 313(1), pages 559-601, June.
    6. Pham, Tien Duc & Dwyer, Larry & Su, Jen-Je & Ngo, Tramy, 2021. "COVID-19 impacts of inbound tourism on Australian economy," Annals of Tourism Research, Elsevier, vol. 88(C).
    7. Tai Vovan & Luan Nguyenhuynh & Thuy Lethithu, 2022. "A forecasting model for time series based on improvements from fuzzy clustering problem," Annals of Operations Research, Springer, vol. 312(1), pages 473-493, May.
    8. Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2021. "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Post-Print hal-03331805, HAL.
    9. Yi-Chung Hu, 2021. "Forecasting tourism demand using fractional grey prediction models with Fourier series," Annals of Operations Research, Springer, vol. 300(2), pages 467-491, May.
    10. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    11. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    12. Li, Cheng & Ge, Peng & Liu, Zhusheng & Zheng, Weimin, 2020. "Forecasting tourist arrivals using denoising and potential factors," Annals of Tourism Research, Elsevier, vol. 83(C).
    13. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    14. Vidar Christiansen & Stephen Smith, 2008. "Optimal commodity taxation with duty-free shopping," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 15(3), pages 274-296, June.
    15. Jinsoo Hwang & Kwang-Woo Lee & Seongseop (Sam) Kim, 2021. "The Antecedents and Consequences of Rapport between Customers and Salespersons in the Tourism Industry," Sustainability, MDPI, vol. 13(5), pages 1-22, March.
    16. Fasone, Vincenzo & Kofler, Lukas & Scuderi, Raffaele, 2016. "Business performance of airports: Non-aviation revenues and their determinants," Journal of Air Transport Management, Elsevier, vol. 53(C), pages 35-45.
    17. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
    18. Yu-Jin Choi & Jin-Woo Park, 2020. "Investigating Factors Influencing the Behavioral Intention of Online Duty-Free Shop Users," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
    19. Martín, Juan Carlos & Martín-Domingo, Luis & Lohmann, Gui & Spasojevic, Bojana, 2019. "The role of travel patterns in airport duty-free shopping satisfaction: A case study from an Australian regional airport," Journal of Air Transport Management, Elsevier, vol. 80(C), pages 1-1.
    20. Zhang, Hanyuan & Song, Haiyan & Wen, Long & Liu, Chang, 2021. "Forecasting tourism recovery amid COVID-19," Annals of Tourism Research, Elsevier, vol. 87(C).
    21. Kwon, Ryeok-Hwan & Kim, Kwang-Jae & Kim, Ki-Hun & Hong, Yoo-Suk & Kim, Bohyun, 2015. "Evaluating servicescape designs using a VR-based laboratory experiment: A case of a Duty-free Shop," Journal of Retailing and Consumer Services, Elsevier, vol. 26(C), pages 32-40.
    22. Park, Eunhye & Park, Jinah & Hu, Mingming, 2021. "Tourism demand forecasting with online news data mining," Annals of Tourism Research, Elsevier, vol. 90(C).
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    1. Ru-Xin Nie & Chuan Wu & He-Ming Liang, 2024. "Exploring Appropriate Search Engine Data for Interval Tourism Demand Forecasting Responding a Public Crisis in Macao: A Combined Bayesian Model," Sustainability, MDPI, vol. 16(16), pages 1-21, August.

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