IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2301.00666.html
   My bibliography  Save this paper

E-commerce users' preferences for delivery options

Author

Listed:
  • Yuki Oyama
  • Daisuke Fukuda
  • Naoto Imura
  • Katsuhiro Nishinari

Abstract

Many e-commerce marketplaces offer their users fast delivery options for free to meet the increasing needs of users, imposing an excessive burden on city logistics. Therefore, understanding e-commerce users' preference for delivery options is a key to designing logistics policies. To this end, this study designs a stated choice survey in which respondents are faced with choice tasks among different delivery options and time slots, which was completed by 4,062 users from the three major metropolitan areas in Japan. To analyze the data, mixed logit models capturing taste heterogeneity as well as flexible substitution patterns have been estimated. The model estimation results indicate that delivery attributes including fee, time, and time slot size are significant determinants of the delivery option choices. Associations between users' preferences and socio-demographic characteristics, such as age, gender, teleworking frequency and the presence of a delivery box, were also suggested. Moreover, we analyzed two willingness-to-pay measures for delivery, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS), and applied a non-semiparametric approach to estimate their distributions in a data-oriented manner. Although VODT has a large heterogeneity among respondents, the estimated median VODT is 25.6 JPY/day, implying that more than half of the respondents would wait an additional day if the delivery fee were increased by only 26 JPY, that is, they do not necessarily need a fast delivery option but often request it when cheap or almost free. Moreover, VOTS was found to be low, distributed with the median of 5.0 JPY/hour; that is, users do not highly value the reduction in time slot size in monetary terms. These findings on e-commerce users' preferences can help in designing levels of service for last-mile delivery to significantly improve its efficiency.

Suggested Citation

  • Yuki Oyama & Daisuke Fukuda & Naoto Imura & Katsuhiro Nishinari, 2022. "E-commerce users' preferences for delivery options," Papers 2301.00666, arXiv.org, revised Aug 2023.
  • Handle: RePEc:arx:papers:2301.00666
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2301.00666
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hsiao, Ming-Hsiung, 2009. "Shopping mode choice: Physical store shopping versus e-shopping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(1), pages 86-95, January.
    2. Ramanathan, Ramakrishnan, 2010. "The moderating roles of risk and efficiency on the relationship between logistics performance and customer loyalty in e-commerce," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(6), pages 950-962, November.
    3. Fosgerau, Mogens & Mabit, Stefan L., 2013. "Easy and flexible mixture distributions," Economics Letters, Elsevier, vol. 120(2), pages 206-210.
    4. Xinan Yang & Arne K. Strauss & Christine S. M. Currie & Richard Eglese, 2016. "Choice-Based Demand Management and Vehicle Routing in E-Fulfillment," Transportation Science, INFORMS, vol. 50(2), pages 473-488, May.
    5. Niels Agatz & Yingjie Fan & Daan Stam, 2021. "The Impact of Green Labels on Time Slot Choice and Operational Sustainability," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2285-2303, July.
    6. Hess, Stephane & Daly, Andrew & Dekker, Thijs & Cabral, Manuel Ojeda & Batley, Richard, 2017. "A framework for capturing heterogeneity, heteroskedasticity, non-linearity, reference dependence and design artefacts in value of time research," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 126-149.
    7. Holguín-Veras, José & Amaya Leal, Johanna & Seruya, Barbara B., 2017. "Urban freight policymaking: The role of qualitative and quantitative research," Transport Policy, Elsevier, vol. 56(C), pages 75-85.
    8. Robert Klein & Michael Neugebauer & Dimitri Ratkovitch & Claudius Steinhardt, 2019. "Differentiated Time Slot Pricing Under Routing Considerations in Attended Home Delivery," Service Science, INFORMS, vol. 53(1), pages 236-255, February.
    9. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    10. Joan L. Walker & Moshe Ben-Akiva & Denis Bolduc, 2007. "Identification of parameters in normal error component logit-mixture (NECLM) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1095-1125.
    11. Fosgerau, Mogens, 2006. "Investigating the distribution of the value of travel time savings," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 688-707, September.
    12. Chowdhury, Priyabrata & Paul, Sanjoy Kumar & Kaisar, Shahriar & Moktadir, Md. Abdul, 2021. "COVID-19 pandemic related supply chain studies: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    13. Niels Agatz & Ann Campbell & Moritz Fleischmann & Martin Savelsbergh, 2011. "Time Slot Management in Attended Home Delivery," Transportation Science, INFORMS, vol. 45(3), pages 435-449, August.
    14. Joan L. Walker & Yanqiao Wang & Mikkel Thorhauge & Moshe Ben-Akiva, 2018. "D-efficient or deficient? A robustness analysis of stated choice experimental designs," Theory and Decision, Springer, vol. 84(2), pages 215-238, March.
    15. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    16. Riccardo Scarpa & Mara Thiene & Kenneth Train, 2008. "Utility in Willingness to Pay Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 994-1010.
    17. Farag, Sendy & Schwanen, Tim & Dijst, Martin & Faber, Jan, 2007. "Shopping online and/or in-store? A structural equation model of the relationships between e-shopping and in-store shopping," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(2), pages 125-141, February.
    18. Kollmann, Tobias & Kuckertz, Andreas & Kayser, Ina, 2012. "Cannibalization or synergy? Consumers' channel selection in online–offline multichannel systems," Journal of Retailing and Consumer Services, Elsevier, vol. 19(2), pages 186-194.
    19. Scarpa, R. & Thiene, M. & Train, K., 2008. "Appendix to Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps," American Journal of Agricultural Economics APPENDICES, Agricultural and Applied Economics Association, vol. 90(4), pages 1-9, January.
    20. Martin Savelsbergh & Tom Van Woensel, 2016. "50th Anniversary Invited Article—City Logistics: Challenges and Opportunities," Transportation Science, INFORMS, vol. 50(2), pages 579-590, May.
    21. Emin Dinlersoz & Han Li, 2006. "The shipping strategies of internet retailers: Evidence from internet book retailing," Quantitative Marketing and Economics (QME), Springer, vol. 4(4), pages 407-438, December.
    22. Axhausen, Kay W. & Hess, Stephane & König, Arnd & Abay, Georg & Bates, John J. & Bierlaire, Michel, 2008. "Income and distance elasticities of values of travel time savings: New Swiss results," Transport Policy, Elsevier, vol. 15(3), pages 173-185, May.
    23. Hua, Guowei & Wang, Shouyang & Cheng, T.C.E., 2010. "Price and lead time decisions in dual-channel supply chains," European Journal of Operational Research, Elsevier, vol. 205(1), pages 113-126, August.
    24. Xu, Xun & Munson, Charles L. & Zeng, Shuo, 2017. "The impact of e-service offerings on the demand of online customers," International Journal of Production Economics, Elsevier, vol. 184(C), pages 231-244.
    25. Hess, Stephane & Bierlaire, Michel & Polak, John W., 2005. "Estimation of value of travel-time savings using mixed logit models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(2-3), pages 221-236.
    26. Takanori Sakai & Yusuke Hara & Ravi Seshadri & André Romano Alho & Md Sami Hasnine & Peiyu Jing & ZhiYuan Chua & Moshe Ben-Akiva, 2022. "Household-based E-commerce demand modeling for an agent-based urban transportation simulation platform," Transportation Planning and Technology, Taylor & Francis Journals, vol. 45(2), pages 179-201, February.
    27. Michael Lewis & Vishal Singh & Scott Fay, 2006. "An Empirical Study of the Impact of Nonlinear Shipping and Handling Fees on Purchase Incidence and Expenditure Decisions," Marketing Science, INFORMS, vol. 25(1), pages 51-64, 01-02.
    28. Yang, Xinan & Strauss, Arne K., 2017. "An approximate dynamic programming approach to attended home delivery management," European Journal of Operational Research, Elsevier, vol. 263(3), pages 935-945.
    29. Klapp, Mathias A. & Erera, Alan L. & Toriello, Alejandro, 2020. "Request acceptance in same-day delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    2. Koch, Sebastian & Klein, Robert, 2020. "Route-based approximate dynamic programming for dynamic pricing in attended home delivery," European Journal of Operational Research, Elsevier, vol. 287(2), pages 633-652.
    3. Waßmuth, Katrin & Köhler, Charlotte & Agatz, Niels & Fleischmann, Moritz, 2023. "Demand management for attended home delivery—A literature review," European Journal of Operational Research, Elsevier, vol. 311(3), pages 801-815.
    4. Strauss, Arne & Gülpınar, Nalan & Zheng, Yijun, 2021. "Dynamic pricing of flexible time slots for attended home delivery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1022-1041.
    5. Schmid, Basil & Jokubauskaite, Simona & Aschauer, Florian & Peer, Stefanie & Hössinger, Reinhard & Gerike, Regine & Jara-Diaz, Sergio R. & Axhausen, Kay W., 2019. "A pooled RP/SP mode, route and destination choice model to investigate mode and user-type effects in the value of travel time savings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 262-294.
    6. Zemo, Kahsay Haile & Termansen, Mette, 2018. "Farmers’ willingness to participate in collective biogas investment: A discrete choice experiment study," Resource and Energy Economics, Elsevier, vol. 52(C), pages 87-101.
    7. Lu, Hui & Hess, Stephane & Daly, Andrew & Rohr, Charlene & Patruni, Bhanu & Vuk, Goran, 2021. "Using state-of-the-art models in applied work: Travellers willingness to pay for a toll tunnel in Copenhagen," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 37-52.
    8. Broberg, Thomas & Daniel, Aemiro Melkamu & Persson, Lars, 2021. "Household preferences for load restrictions: Is there an effect of pro-environmental framing?," Energy Economics, Elsevier, vol. 97(C).
    9. Kingsley Adjenughwure & Basil Papadopoulos, 2019. "Towards a Fair and More Transparent Rule-Based Valuation of Travel Time Savings," Sustainability, MDPI, vol. 11(4), pages 1-19, February.
    10. Haider, Zulqarnain & Hu, Yujie & Charkhgard, Hadi & Himmelgreen, David & Kwon, Changhyun, 2022. "Creating grocery delivery hubs for food deserts at local convenience stores via spatial and temporal consolidation," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    11. Punel, Aymeric & Stathopoulos, Amanda, 2017. "Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 18-38.
    12. van der Hagen, L. & Agatz, N.A.H. & Spliet, R. & Visser, T.R. & Kok, A.L., 2022. "Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management," ERIM Report Series Research in Management ERS-2022-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. Riccardo Scarpa & Cristiano Franceschinis & Mara Thiene, 2017. "A Monte Carlo Evaluation of the Logit-Mixed Logit under Asymmetry and Multimodality," Working Papers in Economics 17/23, University of Waikato.
    14. Stephane Hess & John Rose, 2012. "Can scale and coefficient heterogeneity be separated in random coefficients models?," Transportation, Springer, vol. 39(6), pages 1225-1239, November.
    15. Bliemer, Michiel C.J. & Rose, John M., 2013. "Confidence intervals of willingness-to-pay for random coefficient logit models," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 199-214.
    16. Keskin, Merve & Branke, Juergen & Deineko, Vladimir & Strauss, Arne K., 2023. "Dynamic multi-period vehicle routing with touting," European Journal of Operational Research, Elsevier, vol. 310(1), pages 168-184.
    17. Krueger, Rico & Rashidi, Taha H. & Vij, Akshay, 2020. "A Dirichlet process mixture model of discrete choice: Comparisons and a case study on preferences for shared automated vehicles," Journal of choice modelling, Elsevier, vol. 36(C).
    18. Hancock, Thomas O. & Hess, Stephane & Daly, Andrew & Fox, James, 2020. "Using a sequential latent class approach for model averaging: Benefits in forecasting and behavioural insights," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 429-454.
    19. Hancock, Thomas O. & Broekaert, Jan & Hess, Stephane & Choudhury, Charisma F., 2020. "Quantum probability: A new method for modelling travel behaviour," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 165-198.
    20. William Greene & David Hensher, 2010. "Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models," Transportation, Springer, vol. 37(3), pages 413-428, May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2301.00666. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.