Customers place great value on the timely delivery of goods purchased online. This study explores how time preferences influence consumer satisfaction regarding e-commerce delivery, examines consumers’ willingness to pay for time-sensitive services and analyzes the role of demographic factors in shaping last-mile delivery expectations. We have collected 700 questionnaires and performed statistical and econometric analysis. Results reveal that while demographic variables affect consumers’ time preferences, certain behaviours, such as willingness to pay a premium for faster delivery, are less strongly associated with them. Education is the most influential factor, as highly educated individuals are less satisfied with delivery times, more likely to abandon shopping carts, prefer flexible delivery options and show greater willingness to pay extra for enhanced services. This article highlights the strategic role of time in last-mile delivery services and offers actionable insights for e-commerce stakeholders to better align with evolving consumer expectations.
AljohaniK. (2024). The role of last-mile delivery quality and satisfaction in online retail experience: An empirical analysis. Sustainability, 16(11), 4743. https://doi.org/10.3390/su16114743
2.
AlSokkarA. A. M., LawE. L.-C., AlMajaliD. A., Al-GasawnehJ. A., & AlshinwanM. (2024). An indexed approach for expectation-confirmation theory: A trust-based model. Electronic Markets, 34(1). https://doi.org/10.1007/s12525-024-00694-3
3.
AlverhedE., HellgrenS., IsakssonH., OlssonL., PalmqvistH., & FlodénJ. (2024). Autonomous last-mile delivery robots: A literature review. European Transport Research Review, 16(1). https://doi.org/10.1186/s12544-023-00629-7
4.
AmorimP., DeHoratiusN., Eng-LarssonF., & MartinsS. (2024). Customer preferences for delivery service attributes in attended home delivery. Management Science, 70(11), 7559–7578. https://doi.org/10.1287/mnsc.2020.01274
5.
BerryL. L. (1979). The time buying consumer. Journal of Retailing, 55(Winter), 58–69.
6.
BiancolinM., RotarisL., & PernotD. (2025). Willingness to pay for sustainable delivery: Evidence from young consumers in Italy and Norway. Transportation Research Part A: Policy and Practice, 199, 104580. https://doi.org/10.1016/j.tra.2025.104580
7.
BoysenN., FedtkeS., & SchwerdfegerS. (2021). Last-mile delivery concepts: A survey from an operational research perspective. OR Spectrum, 43(1), 1–58. https://doi.org/10.1007/s00291-020-00607-8
8.
BryllaD., & WalshG. (2022). When faster online delivery backfires: Examining the negative consequences of split deliveries. International Journal of Electronic Commerce, 26(4), 497–525. https://doi.org/10.1080/10864415.2022.2123647
9.
ChebatJ., & FiliatraultP. (1993). The impact of waiting in line on consumers. International Journal of Bank Marketing, 11(2), 35–40. https://doi.org/10.1108/02652329310025938
10.
CorreiaD., TeixeiraL., & MarquesJ. L. (2021). Last-mile-as-a-service (LMaaS): An innovative concept for the disruption of the supply chain. Sustainable Cities and Society, 75, 103310. https://doi.org/10.1016/j.scs.2021.103310
11.
CoşarC., PanyiK., & VargaA. (2017). Try not to be late!—The importance of delivery service in online shopping. Organizations and Markets in Emerging Economies, 8(2), 177–192. https://doi.org/10.15388/omee.2017.8.2.14186
12.
CouperM. P. (2000). Web surveys: A review of issues and approaches. The Public Opinion Quarterly, 64(4), 464–494.
13.
CuiR., LuZ., SunT., & GoldenJ. M. (2024). Sooner or later? Promising delivery speed in online retail. Manufacturing & Service Operations Management, 26(1), 233–251. https://doi.org/10.1287/msom.2021.0174
14.
DabićM., FerreiraJ. J., LopesJ. M., & GomesS. (2025). Consumer preferences and barriers in the adoption of drone delivery services: A comprehensive analysis. IEEE Transactions on Engineering Management, 72, 47–61. https://doi.org/10.1109/TEM.2024.3494051
15.
DaviesM. A. P. (2018). Using time in branding: Reflections and orientations in an increasingly competitive world. Journal of Brand Management, 25(2), 93–100. https://doi.org/10.1057/s41262-017-0066-0
16.
De ToniA., & MeneghettiA. (2000). Traditional and innovative paths towards time-based competition. International Journal of Production Economics, 66(3), 255–268. https://doi.org/10.1016/S0925-5273(99)00140-1
17.
DemirE., SyntetosA., & Van WoenselT. (2022). Last mile logistics: Research trends and needs. IMA Journal of Management Mathematics, 33(4), 549–561. https://doi.org/10.1093/imaman/dpac006
18.
D’HaenR., BraekersK., & RamaekersK. (2023). Integrated scheduling of order picking operations under dynamic order arrivals. International Journal of Production Research, 61(10), 3205–3226. https://doi.org/10.1080/00207543.2022.2078747
19.
DillmanD. A., SmythJ. D., & ChristianL. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method. Wiley.
20.
DuarteA. L. D. C. M., TeixeiraR., AraujoF. V. M., & Picanco RodriguesV. (2025). Punctuality and customer satisfaction in attended home delivery: The moderation effects of price-promotional, emotional and pandemic events. Benchmarking: An International Journal. https://doi.org/10.1108/BIJ-02-2023-0119
21.
EdwardsD., SubramanianN., ChaudhuriA., MorlacchiP., & ZengW. (2024). Use of delivery drones for humanitarian operations: Analysis of adoption barriers among logistics service providers from the technology acceptance model perspective. Annals of Operations Research, 335(3), 1645–1667. https://doi.org/10.1007/s10479-023-05307-4
22.
EsperT. L., CastilloV. E., RenK., SoderoA., WanX., CroxtonK. L., KnemeyerA. M., DeNunzioS., ZinnW., & GoldsbyT. J. (2020). Everything old is new again: The age of consumer-centric supply chain management. Journal of Business Logistics, 41(4), 286–293. https://doi.org/10.1111/jbl.12267
23.
EtikanI. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1. https://doi.org/10.11648/j.ajtas.20160501.11
24.
EvansJ. R., & MathurA. (2005). The value of online surveys. Internet Research, 15(2), 195–219. https://doi.org/10.1108/10662240510590360
25.
FisherM., GallinoS., & XuJ. J. (2015). The value of rapid delivery in online retailing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2573069
26.
GuQ., FanT., & ChenJ. (2024). Integrated schedule of order picking and delivery for instant delivery. Journal of Management Science and Engineering, 9(1), 88–100. https://doi.org/10.1016/j.jmse.2023.11.004
27.
HagenT., & Scheel-KopeinigS. (2021). Would customers be willing to use an alternative (chargeable) delivery concept for the last mile?Research in Transportation Business & Management, 39, 100626. https://doi.org/10.1016/j.rtbm.2021.100626
28.
HarterA., StichL., & SpannM. (2024). The effect of delivery time on repurchase behavior in quick commerce. Journal of Service Research, 28(2), 10946705241236961. https://doi.org/10.1177/10946705241236961
29.
HoT. H., & ZhengY.-S. (2004). Setting customer expectation in service delivery: An integrated marketing-operations perspective. Management Science, 50(4), 479–488. https://doi.org/10.1287/mnsc.1040.0170
30.
HuB., GaertigC., & DietvorstB. J. (2024). How should time estimates be structured to increase customer satisfaction?Management Science, 71(9), 7497–7515. https://doi.org/10.1287/mnsc.2023.00137
31.
HumS., & SimH. (1996). Time-based competition: Literature review and implications for modelling. International Journal of Operations & Production Management, 16(1), 75–90. https://doi.org/10.1108/01443579610106373
32.
KandulaS., KrishnamoorthyS., & RoyD. (2021). A prescriptive analytics framework for efficient E-commerce order delivery. Decision Support Systems, 147, 113584. https://doi.org/10.1016/j.dss.2021.113584
33.
KapserS., AbdelrahmanM., & BerneckerT. (2021). Autonomous delivery vehicles to fight the spread of COVID-19—How do men and women differ in their acceptance?Transportation Research Part A: Policy and Practice, 148, 183–198. https://doi.org/10.1016/j.tra.2021.02.020
34.
KitchinR. (2023). Digital timescapes: Technology, temporality and society. Polity Press.
35.
KohL. Y., XiaZ., & YuenK. F. (2024). Consumer acceptance of the autonomous robot in last-mile delivery: A combined perspective of resource-matching, perceived risk and value theories. Transportation Research Part A: Policy and Practice, 182, 104008. https://doi.org/10.1016/j.tra.2024.104008
36.
KokkinouA., & CranageD. A. (2015). Why wait? Impact of waiting lines on self-service technology use. International Journal of Contemporary Hospitality Management, 27(6), 1181–1197. https://doi.org/10.1108/IJCHM-12-2013-0578
37.
KrejcieR. V., & MorganD. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308
38.
LaiP.-L., JangH., FangM., & PengK. (2022). Determinants of customer satisfaction with parcel locker services in last-mile logistics. The Asian Journal of Shipping and Logistics, 38(1), 25–30. https://doi.org/10.1016/j.ajsl.2021.11.002
39.
LeonS., ChenC., & RatcliffeA. (2023). Consumers’ perceptions of last mile drone delivery. International Journal of Logistics Research and Applications, 26(3), 345–364. https://doi.org/10.1080/13675567.2021.1957803
40.
LiX., LeeG. J. X., & YuenK. F. (2024). Consumer acceptance of urban drone delivery: The role of perceived anthropomorphic characteristics. Cities, 148, 104867. https://doi.org/10.1016/j.cities.2024.104867
41.
LiangY., ChenY., & LiY. (2024). Research on demand assessment and forecasting of express service based on TOPSIS and LSTM models. 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA) (pp. 1415–1419). IEEE. https://doi.org/10.1109/ICIPCA61593.2024.10709202
42.
LimS. F. W. T., & WinkenbachM. (2019). Configuring the last-mile in business-to-consumer e-retailing. California Management Review, 61(2), 132–154. https://doi.org/10.1177/0008125618805094
43.
Ling SimK., & CuratolaA. P. (1999). Time-based competition. International Journal of Quality & Reliability Management, 16(7), 659–674. https://doi.org/10.1108/02656719910268215
44.
LiuS., HeL., & Max ShenZ.-J. (2021). On-time last-mile delivery: Order assignment with travel-time predictors. Management Science, 67(7), 4095–4119. https://doi.org/10.1287/mnsc.2020.3741
45.
MaB., WongY. D., & TeoC.-C. (2022). Parcel self-collection for urban last-mile deliveries: A review and research agenda with a dual operations-consumer perspective. Transportation Research Interdisciplinary Perspectives, 16, 100719. https://doi.org/10.1016/j.trip.2022.100719
46.
MohammadW. A., Nazih DiabY., ElomriA., & TrikiC. (2023). Innovative solutions in last mile delivery: Concepts, practices, challenges, and future directions. Supply Chain Forum: An International Journal, 24(2), 151–169. https://doi.org/10.1080/16258312.2023.2173488
47.
MondalA., MajumderA., & ChaojiV. (2022). ASPIRE: Air shipping recommendation for e-commerce products via causal inference framework. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3584–3592). https://doi.org/10.1145/3534678.3539197
48.
MusaH. G., FatmawatiI., NuryakinN., & SuyantoM. (2024). Marketing research trends using technology acceptance model (TAM): A comprehensive review of researches (2002–2022). Cogent Business & Management, 11(1), 2329375. https://doi.org/10.1080/23311975.2024.2329375
49.
NagadeepaC., DyczekB., MishraAr. K., ValeriiB., OleksandrO., & SokoliukK. (2024). Last-mile delivery innovations: The future of e-commerce logistics. InEl KhouryR.(Ed.), Technology-driven business innovation (Vol. 223, pp. 283–296). Springer Nature. https://doi.org/10.1007/978-3-031-51997-0_24
50.
Navarro-VillatoroK., Ixcot-HernándezP., Jadán-GuerreroJ., Pusey-AlvaradoL., & Díaz-AlvarezN. (2024). Improve the efficiency of online grocery deliveries—Advanced GPS-. 2024 IEEE 4th International Conference on Advanced Learning Technologies on Education & Research (ICALTER) (pp. 1–4). https://doi.org/10.1109/ICALTER65499.2024.10819250
51.
NguyenD. H., De LeeuwS., DullaertW., & FoubertB. P. J. (2019). What is the right delivery option for you? Consumer preferences for delivery attributes in online retailing. Journal of Business Logistics, 40(4), 299–321. https://doi.org/10.1111/jbl.12210
52.
NogueiraG. P. M., De Assis RangelJ. J., & ShimodaE. (2021). Sustainable last-mile distribution in B2C e-commerce: Do consumers really care?Cleaner and Responsible Consumption, 3, 100021. https://doi.org/10.1016/j.clrc.2021.100021
53.
OksmanV., KuljuM., & KramarV. (2024). Exploring the factors influencing public acceptance and intention to use automated drone delivery systems: A trust theory approach. 2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC) (pp. 1–9). https://doi.org/10.1109/DASC62030.2024.10748821
54.
OlssonJ., HellströmD., & VakulenkoY. (2023). Customer experience dimensions in last-mile delivery: An empirical study on unattended home delivery. International Journal of Physical Distribution & Logistics Management, 53(2), 184–205. https://doi.org/10.1108/ijpdlm-12-2021-0517
55.
OyamaY., FukudaD., ImuraN., & NishinariK. (2024). Do people really want fast and precisely scheduled delivery? E-commerce customers’ valuations of home delivery timing. Journal of Retailing and Consumer Services, 78, 103711. https://doi.org/10.1016/j.jretconser.2024.103711
56.
PaluzziH., ChenH., HoweM., DaughertyP. J., & TokarT. (2025). Consumer impatience in the e-commerce home delivery context: A consumer-centric supply chain perspective for time-based competition. International Journal of Physical Distribution & Logistics Management, 55(1), 1–21. https://doi.org/10.1108/IJPDLM-03-2024-0102
57.
ParasuramanA. P., ZeithamlV., & BerryL. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12–40.
58.
PramanikP., & JanaR. K. (2025). A consumer acceptance model in the artificial intelligence era. Management Decision. https://doi.org/10.1108/MD-03-2024-0574
59.
RajG., RoyD., De KosterR., & BansalV. (2024). Stochastic modeling of integrated order fulfillment processes with delivery time promise: Order picking, batching, and last-mile delivery. European Journal of Operational Research, 316(3), 1114–1128. https://doi.org/10.1016/j.ejor.2024.03.003
60.
RajammaR. K., PaswanA. K., & HossainM. M. (2009). Why do shoppers abandon shopping cart? Perceived waiting time, risk, and transaction inconvenience. Journal of Product & Brand Management, 18(3), 188–197. https://doi.org/10.1108/10610420910957816
61.
RajendranS. D., & WahabS. N. (2022). Investigating last-mile delivery options on online shoppers experience and repurchase intention. International Journal of Electronic Marketing and Retailing, 13(2), 224. https://doi.org/10.1504/IJEMR.2022.121826
62.
ReitanoM., PappalardoG., SelvaggiR., ZarbàC., & ChinniciG. (2024). Factors influencing consumer perceptions of food tracked with blockchain technology. A systematic literature review. Applied Food Research, 4(2), 100455. https://doi.org/10.1016/j.afres.2024.100455
SaidM., AeschlimanS., & StathopoulosA. (2023). Robots at your doorstep: Acceptance of near-future technologies for automated parcel delivery. Scientific Reports, 13(1), 18556. https://doi.org/10.1038/s41598-023-45371-1
65.
SamaniA. R., TalebianA., MishraS., & GoliasM. (2025). Evaluating consumer shopping, delivery demands, and last-mile preferences: An integrated MDCEV-HCM approach. Transportation Research Part E: Logistics and Transportation Review, 197, 104067. https://doi.org/10.1016/j.tre.2025.104067
66.
SchmidtS., & SaraceniA. (2024). Consumer acceptance of drone-based technology for last mile delivery. Research in Transportation Economics, 103, 101404. https://doi.org/10.1016/j.retrec.2023.101404
67.
SeghezziA., SiragusaC., & MangiaracinaR. (2022). Parcel lockers vs. home delivery: A model to compare last-mile delivery cost in urban and rural areas. International Journal of Physical Distribution & Logistics Management, 52(3), 213–237. https://doi.org/10.1108/IJPDLM-03-2020-0072
68.
ShangW., & LiuL. (2011). Promised delivery time and capacity games in time-based competition. Management Science, 57(3), 599–610. https://doi.org/10.1287/mnsc.1100.1292
69.
ShuaibuA. S., MahmoudA. S., & SheltamiT. R. (2025). A review of last-mile delivery optimization: Strategies, technologies, drone integration, and future trends. Drones, 9(3), 158. https://doi.org/10.3390/drones9030158
70.
SinghR. (2024). The impact of quick commerce on consumer behavior and economic trends in India: A systematic review. International Journal of Social Science and Economic Research, 09(08), 2859–2874. https://doi.org/10.46609/IJSSER.2024.v09i08.020
71.
SmithA., DickinsonJ. E., MarsdenG., CherrettT., OakeyA., & GroteM. (2022). Public acceptance of the use of drones for logistics: The state of play and moving towards more informed debate. Technology in Society, 68, 101883. https://doi.org/10.1016/j.techsoc.2022.101883
72.
SorooshianS., Khademi SharifabadS., ParsaeeM., & AfshariA. R. (2022). Toward a modern last-mile delivery: Consequences and obstacles of intelligent technology. Applied System Innovation, 5(4), 82. https://doi.org/10.3390/asi5040082
73.
SouzaN. L. S. D., Stradioto NetoL. A., FettermannD. D. C., & FrazzonE. M. (2022). Evaluation of the customer requirements for last mile delivery in Brazil. Brazilian Journal of Operations & Production Management, 19(3), 1–12. https://doi.org/10.14488/BJOPM.2022.1360
SugunaM., ShahB., RajS. K., & SureshM. (2022). A study on the influential factors of the last mile delivery projects during COVID-19 era. Operations Management Research, 15(1–2), 399–412. https://doi.org/10.1007/s12063-021-00214-y
76.
TapiaJ., FariñaP., UrbinaI., & DujovneD. (2024). Examining the retail delivery choice behavior in a technology-aware market. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 1392–1410. https://doi.org/10.3390/jtaer19020070
77.
TomG., & LuceyS. (1995). Waiting time delays and customer satisfaction in supermarkets. Journal of Services Marketing, 9(5), 20–29. https://doi.org/10.1108/08876049510100281
78.
VakulenkoY., HellströmD., & HjortK. (2018). What’s in the parcel locker? Exploring customer value in e-commerce last mile delivery. Journal of Business Research, 88, 421–427. https://doi.org/10.1016/j.jbusres.2017.11.033
79.
VinkJ., Koskela-HuotariK., TronvollB., EdvardssonB., & Wetter-EdmanK. (2021). Service ecosystem design: Propositions, process model, and future research agenda. Journal of Service Research, 24(2), 168–186. https://doi.org/10.1177/1094670520952537
80.
VrhovacV., DakićD., MilisavljevićS., ĆelićĐ., StefanovićD., & JankovićM. (2024). The factors influencing user satisfaction in last-mile delivery: The structural equation modeling approach. Mathematics, 12(12), 1857. https://doi.org/10.3390/math12121857
81.
VrhovacV., VasićS., MilisavljevićS., DudićB., ŠtarchoňP., & ŽižakovM. (2023). Measuring e-commerce user experience in the last-mile delivery. Mathematics, 11(6), 1482. https://doi.org/10.3390/math11061482
82.
WangG., ListyaM., AliantoH., & NugrohoD. A. (2023). Real time tracking system integrated with IoT for package delivery in Indonesia’s logistic company with TOGAF implementation. 2023 10th International Conference on ICT for Smart Society (ICISS) (pp. 1–4). https://doi.org/10.1109/ICISS59129.2023.10291211
83.
WangT., LiangS., & SunY. (2023). So curious that I want to buy it: The positive effect of queue wait on consumers’ purchase intentions. Journal of Consumer Behaviour, 22(4), 848–866. https://doi.org/10.1002/cb.2169
84.
WangX., WongY. D., TeoC.-C., YuenK. F., & LiK. X. (2019). Decomposing service conveniences in self-collection: An integrated application of the SERVCON and Kano models. International Journal of Physical Distribution & Logistics Management, 49(4), 356–375. https://doi.org/10.1108/ijpdlm-10-2018-0336
85.
WrightK. B. (2005). Researching internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer-Mediated Communication, 10(3). https://doi.org/10.1111/j.1083-6101.2005.tb00259.x
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
