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

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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.

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Notes

  1. https://www.statista.com/statistics/1270410/duty-free-shops-footfall-rate-us/.

  2. https://assets.kpmg/content/dam/kpmg/cn/pdf/en/2021/05/travel-retail-market-in-hainan-ftp.pdf.

  3. http://www.customs.gov.cn/haikou_customs/index/index.html.

  4. https://www.moodiedavittreport.com/china-duty-free-group-leads-global-travel-retailer-rankings-for-2020/.

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Acknowledgements

The authors are grateful for the constructive comments of the three reviewers on the earlier versions of this paper. This work was supported by National Natural Science Foundation of China (72201105, 62001314, 72131005), Guangdong Basic and Applied Basic Research Foundation (2023A1515012503), and Natural Science Foundation of Sichuan Province (24NSFSC3405).

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Authors and Affiliations

  1. School of Information Management, Sun Yat-Sen University, Guangzhou, Guangdong, China

    Dong Zhang

  2. Business School, Sichuan University, Chengdu, China

    Pengkun Wu

  3. School of Management, Harbin Institute of Technology, Harbin, China

    Chong Wu

  4. Department of Management & Marketing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Eric W. T. Ngai

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  1. Dong Zhang
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Correspondence to Pengkun Wu.

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Zhang, D., Wu, P., Wu, C. et al. Forecasting duty-free shopping demand with multisource data: a deep learning approach. Ann Oper Res 339, 861–887 (2024). https://doi.org/10.1007/s10479-024-05830-y

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