BIBLIOMETRIC ANALYSIS IN THE FIELD OF CROWDSOURCED LOGISTICS: GLOBAL RESEARCH TRENDS, COLLABORATION NETWORKS, AND THEMATIC STRUCTURE IN THE 2020–2026 PERIOD
DOI:
https://doi.org/10.5281/zenodo.22126710Keywords:
Crowdsourced Logistics, Bibliometric Analysis, Last-mile Delivery, Crowdshipping, Crowd LogisticsAbstract
This study aims to bibliometrically analyse the academic literature published in the field of kitle kaynak logistics between 2020 and 2026. Using Biblioshiny (bibliometrix R package), 155 articles compiled from the Web of Science database, covering 58 sources and 410 authors, were examined in terms of publication trends, journal and author performance, institutional and geographic distribution, keyword analysis, as well as co-citation and collaboration networks. Findings reveal that the field is growing rapidly at an annual rate of 21.03%. Transportation Research Part E, European Journal of Operational Research, and Transportation Science are the leading journals. Last-mile delivery, crowdshipping, and crowd logistics have been identified as the dominant research themes. China and the USA account for approximately 42% of total publications; Simoni et al. (2020) and Pourrahmani & Jaller (2021) are among the most cited pioneering studies. Results indicate that the field is entering a maturation phase characterised by optimisation and algorithm-based research, with increasingly deepening international collaboration. Thematic gaps and future research recommendations for researchers are also discussed.
References
Ahamed, T., Zou, B., Farazi, N. P., & Tulabandhula, T. (2021). Deep reinforcement learning for crowdsourced urban delivery. Transportation Research Part B: Methodological, 152, 227–257. https://doi.org/10.1016/j.trb.2021.08.015
Akbar, U., Jain, A. A., & Bråthen, S. (2024). Sustainability assessment of inter-urban crowdshipping - A case study approach. Research in Transportation Economics, 103, 101409. https://doi.org/10.1016/j.retrec.2024.101409
Archetti, C., Savelsbergh, M., & Speranza, M. G. (2016). The vehicle routing problem with occasional drivers. European Journal of Operational Research, 254(2), 472–480. https://doi.org/10.1016/j.ejor.2016.03.049
Arslan, A. M., Agatz, N., Kroon, L., & Zuidwijk, R. (2019). Crowdsourced delivery—A dynamic pickup and delivery problem with ad hoc drivers. Transportation Science, 53(1), 222–235. https://doi.org/10.1287/trsc.2017.0803
Behrendt, A., Savelsbergh, M., & Wang, H. (2023). A prescriptive machine learning method for courier scheduling on crowdsourced delivery platforms. Transportation Science, 57(4), 889–907. https://doi.org/10.1287/trsc.2022.1152
Boysen, N., Emde, S., & Schwerdfeger, S. (2022). Crowdshipping by employees of distribution centers: Optimization approaches for matching supply and demand. European Journal of Operational Research, 296(2), 539–556. https://doi.org/10.1016/j.ejor.2021.04.002
Buldeo Rai, H., Verlinde, S., & Macharis, C. (2021). Who is interested in a crowdsourced last mile? A segmentation of attitudinal profiles. Travel Behaviour and Society, 22, 22–31. https://doi.org/10.1016/j.tbs.2020.08.004
Castillo, V. E., Bell, J. E., Mollenkopf, D. A., & Stank, T. P. (2021). Hybrid last mile delivery fleets with crowdsourcing: A systems view of managing the cost–service trade–off. Journal of Business Logistics, 43(1), 36–61. https://doi.org/10.1111/jbl.12288
Castillo, V. E., Mollenkopf, D. A., Bell, J. E., & Esper, T. L. (2022). Designing technology for on–demand delivery: The effect of customer tipping on crowdsourced driver behavior and last mile performance. Journal of Operations Management, 68(5), 424–453. https://doi.org/10.1002/joom.1187
Cebeci, M. S., Tapia, R. J., Kroesen, M., de Bok, M., & Tavasszy, L. (2023). The effect of trust on the choice for crowdshipping services. Transportation Research Part A: Policy and Practice, 170, 103622. https://doi.org/10.1016/j.tra.2023.103622
Ciobotaru, G., & Chankov, S. (2021). Towards a taxonomy of crowdsourced delivery business models. International Journal of Physical Distribution & Logistics Management, 51(5), 460–485. https://doi.org/10.1108/IJPDLM-10-2019-0326
Devari, A., Nikolaev, A. G., & He, Q. (2017). Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers. Transportation Research Part E: Logistics and Transportation Review, 105, 105–122. https://doi.org/10.1016/j.tre.2017.06.011
Ermagun, A., Shamshiripour, A., & Stathopoulos, A. (2019). Performance analysis of crowd-shipping in urban and suburban areas. Transportation, 47(4), 1955–1985. https://doi.org/10.1007/s11116-019-10033-7
Fatehi, S., & Wagner, M. R. (2022). Crowdsourcing last-mile deliveries. Manufacturing & Service Operations Management, 24(2), 791–809. https://doi.org/10.1287/msom.2021.0973
Fessler, A., Thorhauge, M., Mabit, S., & Haustein, S. (2022). A public transport-based crowdshipping concept as a sustainable last-mile solution: Assessing user preferences with a stated choice experiment. Transportation Research Part A: Policy and Practice, 158, 210–223. https://doi.org/10.1016/j.tra.2022.02.005
Garcia-Herrera, A., Serrano-Hernandez, A., & Faulin, J. (2025). Understanding the dynamics of crowdshipping in last-mile distribution within urban mobility: A comprehensive framework. Socio-Economic Planning Sciences, 101, 102249. https://doi.org/10.1016/j.seps.2025.102249
He, B., Mirchandani, P., & Wang, Y. (2020). Removing barriers for grocery stores: O2O platform and self-scheduling delivery capacity. Transportation Research Part E: Logistics and Transportation Review, 141, 102036. https://doi.org/10.1016/j.tre.2020.102036
Horner, H., Pazour, J., & Mitchell, J. E. (2021). Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery. Transportation Research Part E: Logistics and Transportation Review, 153, 102419. https://doi.org/10.1016/j.tre.2021.102419
Macrina, G., Archetti, C., & Guerriero, F. (2024). Bundles generation and pricing in crowdshipping. EURO Journal on Transportation and Logistics, 13, 100142. https://doi.org/10.1016/j.ejtl.2024.100142
Moncef, B., & Monnet Dupuy, M. (2021). Last-mile logistics in the sharing economy: Sustainability paradoxes. International Journal of Physical Distribution & Logistics Management, 51(5), 508–527. https://doi.org/10.1108/IJPDLM-10-2019-0328
Nguyen, C. T., Yuen, K. F., Kim, T. Y., & Wang, X. (2024). An empirical research on the determinants of driver-partners' participative behaviour in crowd logistics: From a motivation theory perspective. The International Journal of Logistics Management, 35(2), 601-622. https://doi.org/10.1108/IJLM-12-2022-0496
Pahwa, A., & Jaller, M. (2022). A cost-based comparative analysis of different last-mile strategies for e-commerce delivery. Transportation Research Part E: Logistics and Transportation Review, 164, 102783. https://doi.org/10.1016/j.tre.2022.102783
Pourrahmani, E., & Jaller, M. (2021). Crowdshipping in last mile deliveries: Operational challenges and research opportunities. Socio-Economic Planning Sciences, 78, 101063. https://doi.org/10.1016/j.seps.2021.101063
Punel, A., & Stathopoulos, A. (2017). Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Research Part E: Logistics and Transportation Review, 105, 18–38. https://doi.org/10.1016/j.tre.2017.06.007
Punel, A., Ermagun, A., & Stathopoulos, A. (2018). Studying determinants of crowd-shipping use. Travel Behaviour and Society, 12, 30–40. https://doi.org/10.1016/j.tbs.2018.03.005
Rechavi, A., & Toch, E. (2020). Crowd logistics: Understanding auction-based pricing and couriers’ strategies in crowdsourcing package delivery. Journal of Intelligent Transportation Systems, 26(2), 129–144. https://doi.org/10.1080/15472450.2020.1797503
Sawik, B. (2024). Optimizing last-mile delivery: A multi-criteria approach with automated smart lockers, capillary distribution and crowdshipping. Logistics, 8(2), 52. https://doi.org/10.3390/logistics8020052
Seghezzi, A., & Mangiaracina, R. (2020). On-demand food delivery: Investigating the economic performances. International Journal of Retail & Distribution Management, 49(4), 531–549. https://doi.org/10.1108/IJRDM-02-2020-0043
Shen, H., & Lin, J. (2020). Investigation of crowdshipping delivery trip production with real-world data. Transportation Research Part E: Logistics and Transportation Review, 143, 102106. https://doi.org/10.1016/j.tre.2020.102106
Silva, M., Pedroso, J. P., & Viana, A. (2023). Deep reinforcement learning for stochastic last-mile delivery with crowdshipping. EURO Journal on Transportation and Logistics, 12, 100105. https://doi.org/10.1016/j.ejtl.2023.100105
Simoni, M. D., Marcucci, E., Gatta, V., & Claudel, C. G. (2019). Potential last-mile impacts of crowdshipping services: A simulation-based evaluation. Transportation, 47(4), 1933–1954. https://doi.org/10.1007/s11116-019-10028-4
Stoia, S., Laganà, D., & Ohlmann, J. W. (2025). Dynamic pickup-and-delivery for collaborative platforms with time-dependent travel and crowdshipping. European Journal of Operational Research, 322(1), 70-84. https://doi.org/10.1016/j.ejor.2024.09.048
Ta, H., Esper, T. L., Rossiter Hofer, A., & Sodero, A. (2023). Crowdsourced delivery and customer assessments of e-logistics service quality: An appraisal theory perspective. Journal of Business Logistics, 44(3), 345–368. https://doi.org/10.1111/jbl.12327
Tapia, R. J., Kourounioti, I., Thoen, S., de Bok, M., & Tavasszy, L. (2023). A disaggregate model of passenger-freight matching in crowdshipping services. Transportation Research Part A: Policy and Practice, 169, 103587. https://doi.org/10.1016/j.tra.2023.103587
Torres, F., Gendreau, M., & Rei, W. (2022a). Crowdshipping: An open VRP variant with stochastic destinations. Transportation Research Part C: Emerging Technologies, 140, 103677. https://doi.org/10.1016/j.trc.2022.103677
Torres, F., Gendreau, M., & Rei, W. (2022b). Vehicle routing with stochastic supply of crowd vehicles and time windows. Transportation Science, 56(3), 631–653. https://doi.org/10.1287/trsc.2021.1101
Ulmer, M. W., & Savelsbergh, M. (2020). Workforce scheduling in the era of crowdsourced delivery. Transportation Science, 54(4), 1113–1133. https://doi.org/10.1287/trsc.2020.0977
Upadhyay, C. K., Tiwari, V., & Tiwari, V. (2024). Generation “z” willingness to participate in crowdshipping services to achieve sustainable last-mile delivery in emerging market. International Journal of Emerging Markets, 19(9), 2446-2471. https://doi.org/10.1108/IJOEM-09-2021-1345
Wang, L., Xu, M., & Qin, H. (2023). Joint optimization of parcel allocation and crowd routing for crowdsourced last-mile delivery. Transportation Research Part B: Methodological, 171, 111–135. https://doi.org/10.1016/j.trb.2023.03.007
Wang, X., Wong, Y. D., Li, K. X., & Yuen, K. F. (2021). A critical assessment of co-creating self-collection services in last-mile logistics. The International Journal of Logistics Management, 32(3), 846–871. https://doi.org/10.1108/IJLM-09-2020-0359
Wicaksono, S., Lin, X., & Tavasszy, L. A. (2022). Market potential of bicycle crowdshipping: A two-sided acceptance analysis. Research in Transportation Business & Management, 45, 100660. https://doi.org/10.1016/j.rtbm.2021.100660
Zhang, Y., Xiang, C., Li, L., & Jiang, H. (2021). Evolutionary game analysis and simulation with system dynamics for behavioral strategies of participants in crowd logistics. Transportation Letters, 13(7), 540-554. https://doi.org/10.1080/19427867.2020.1783609
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 NEW ERA INTERNATIONAL JOURNAL OF INTERDISCIPLINARY SOCIAL RESEARCHES
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
