Artificial intelligence in logistics and supply chain management: A primer and roadmap for research
Open accessAbstract
The dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling with an interplay between the promising capabilities and potential drawbacks. This conversation frequently includes dystopian forecasts of mass unemployment and detrimental repercussions concerning academic research integrity. Despite the current hype, existing research exploring the intersection between AI and the logistics and supply chain management (L&SCM) sector remains limited. Therefore, this editorial seeks to fill this void, synthesizing the potential applications of AI within the L&SCM domain alongside an analysis of the implementation challenges. In doing so, we propose a robust research framework as a primer and roadmap for future research. This will give researchers and organizations comprehensive insights and strategies to navigate the complex yet promising landscape of AI integration within the L&SCM domain.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A major hurdle lies in continuously upskilling and reskilling employees to align with the new technological paradigm and to ensure they have the capabilities to contribute to the management of GAI risks and sophistication.
“A major hurdle lies in continuously upskilling and reskilling employees to align with the new technological paradigm, and to ensure they have the capabilities to contribute to the management of GAI risks and sophistication (Richey et al., 2023).”
From Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework (2024) · cites this paper as Richey et al., 2023
The Workforce Dynamics section (page unavailable) calls for training, reskilling, and continuous learning so employees can adapt to AI-related changes in logistics and supply chain management. The paper also discusses generative AI risks, but the cited passages do not specifically link employee upskilling to managing those risks or the technology’s growing sophistication. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 3× · see citing works
“This might involve collaborative efforts, bringing together experts from the fields of technology, ethics, law, sociology, and psychology to build a cohesive and comprehensive blueprint for ethical or responsible AI use (Ashok et al., 2022; Dwivedi et al., 2021).”
Self-citationHaenlein, M., & Kaplan, A. (2019) · California Management Review · doi:10.1177/0008125619864925 · Cited by 2,655 · see citing works
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.01.021 · Cited by 2,628 · cited 2× · see citing works
“In the rapidly evolving landscape of AI, the exploration of factors both propelling and hindering the adoption of emerging technologies forms a critical frontier for future research (Duan et al., 2019; Hasija & Esper, 2022).”
Self-citationBudhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A., et al. (2023) · Human Resource Management Journal · doi:10.1111/1748-8583.12524 · Cited by 908 · cited 2× · see citing works
“…of AI within existing frameworks propel a complex array of hurdles spanning ethical considerations, data privacy, transparency, and visibility (Morgan et al., 2023), copyright issues, the lack of regulatory templates, and workforce adaptability (Budhwar et al., 2023; Dwivedi et al., 2023a).”
Self-citationMakarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.07.045 · Cited by 780 · see citing works
Chowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.01.069 · Cited by 531 · cited 1× · see citing works
“Organizations might increasingly lean towards a workforce that can collaborate seamlessly with AI systems, fostering a symbiotic relationship that leverages the strengths of both humans and machines, i.e., collaborative intelligence (Chowdhury et al., 2022; Peng et al., 2022).”
Self-citationEthical framework for Artificial Intelligence and Digital technologies
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102433 · Cited by 469 · see citing works
Flagged first, then by citations
Research works printed but not linked: 28
- Brau, R. I., Sanders, N. R., Aloysius, J., & Williams, D. (2023). Utilizing people, analytics, and AI for digitalized retail supply chain decision-making. Journal of Business Logistics. https://doi.org/10.1111/jbl.12355. · DOI
- Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M. & Albanna, H., (2023a). "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 · DOI · Paper page
- Dwivedi, Y.K., Pandey, N., Currie, W. & Micu, A., (2023b). Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: practices, challenges, and research agenda. International Journal of Contemporary Hospitality Management, doi: https://doi.org/10.1108/IJCHM-05-2023-0686 · DOI
- Friedrich, A., Lange, A., & Elbert, R. (2022). Make-or-buy decisions for industrial additive manufacturing. Journal of Business Logistics, 43(4), 623-653.
- Gligor, D. M., Davis-Sramek, B., Tan, A. Vitale, A., Russo, I., Golgeci, I., & Wan, X. (2022). Utilizing blockchain technology for supply chain transparency: A resource orchestration perspective. Journal of Business Logistics, 43(1), 140-159. https://doi.org/10.1111/jbl.12287 · DOI
- Hahn, T., & Tampe, M. (2021). Strategies for regenerative business. Strategic Organization, 19(3), 456- 477. https://doi.org/10.1177/147612702097922 · DOI
- Hasija, A., & Esper, T. L. (2022). In artificial intelligence (AI) we trust: A qualitative investigation of AI technology acceptance. Journal of Business Logistics, 43(3), 388-412. https://doi.org/10.1111/jbl.12301 · DOI
- Hendriksen, C. (2023). AI for Supply Chain Management: Disruptive Innovation or Innovative Disruption? Journal of Supply Chain Management, 59(3), 65-76. https://doi.org/10.1111/jscm.12304 · DOI
- Johnson, M., Albizri, A., Harfouche, A., & Fosso-Wamba, S. (2022). Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence. International Journal of Information Management, 64, 102479. https://doi.org/10.1016/j.ijinfomgt.2022.102479 · DOI
- Kelan, E. K. (2023). Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring. Human Resource Management Journal. https://doi.org/10.1111/1748-8583.12511 · DOI · Paper page
- Klumpp, M., & Ruiner, C. (2022). Artificial intelligence, robotics, and logistics employment: The human factor in digital logistics. Journal of Business Logistics, 43(3), 297-301. https://doi.org/10.1111/jbl.12314 · DOI
- Konietzko, J., Das, A., & Bocken, N. (2023). Towards regenerative business models: A necessary shift? Sustainable Production and Consumption, 38, 372-388. https://doi.org/10.1016/j.spc.2023.04.014 · DOI
- Kumar, A., Gupta, N., & Bapat, G. (2023). Who is making the decisions? How retail managers can use the power of ChatGPT. Journal of Business Strategy. https://doi.org/10.1108/JBS-04-2023-0067 · DOI
- Loske, D. (2022). Empirical evidence on human learning and work characteristics in the transition to automated order picking. Journal of Business Logistics, 43(3), 302-342. https://doi.org/10.1111/jbl.12300 · DOI
- Mentzer, J. T., Flint, D. J., & Hult, G. T. M. (2021). Logistics service quality as a segment-customized process. Journal of Marketing, 65(4), 82-104. https://doi.org/10.1509/jmkg.65.4.82.183 · DOI
- Morgan, T. R., Gabler, C. B., & Manhart, P. S. (2023). Supply chain transparency: Theoretical perspectives for future research. The International Journal of Logistics Management, 34(5), 1422- 1445. https://doi.org/10.1108/IJLM-02-2021-0115 OECD Employment Outlook. (2023). Artificial and jobs. An urgent need to act. OECD survey on AI use in the manufacturing and finance sectors. https://www.oecd.org/employment- outlook/2023/#ai-jobs. · DOI
- Pan, S. L., & Nishant, R. (2023). Artificial intelligence for digital sustainability: An insight into domain- specific research and future directions. International Journal of Information Management, 72, 102668. https://doi.org/10.1016/j.ijinfomgt.2023.102668 · DOI
- Peng, C., van Doorn, J., Eggers, F., & Wieringa, J. E. (2022). The effect of required warmth on consumer acceptance of artificial intelligence in service: The moderating role of AI-human collaboration. International Journal of Information Management, 66, 102533. https://doi.org/10.1016/j.ijinfomgt.2022.102533 · DOI
- Pessot, E., Zangiacomi, A., Marchiori, I., & Fornasiero, R. (2023). Empowering supply chains with Industry 4.0 technologies to face megatrends. Journal of Business Logistics, https://doi.org/10.1111/jbl.12360 · DOI
- Rese, A., & Tränkner, P. (2024). Perceived conversational ability of task-based chatbots-Which conversational elements influence the success of text-based dialogues? International Journal of Information Management, 74, 102699. https://doi.org/10.1016/j.ijinfomgt.2023.102699 · DOI
- Richey, R. G., & Davis-Sramek, B. (2022a). A research agenda to reflect reality: On being responsive. Journal of Business Logistics, 43(1), 4-8. https://doi.org/10.1111/jbl.12297 · DOI
- Richey, R. G., & Davis-Sramek, B. (2022b). Scholarship that matters. Journal of Business Logistics, 43(2), 164-168. https://doi.org/10.1111/jbl.12308 · DOI
- Richey Jr, R. G., & Davis-Sramek, B. (2022c). What about policy research? Journal of Business Logistics, 43(4), 416-420. https://doi.org/10.1111/jbl.12324 · DOI
- Richey, R. G., Roath, A. S., Adams, F. G., & Wieland, A. (2022). A responsiveness view of logistics and supply chain management. Journal of Business Logistics, 43(1), 62-91. https://doi.org/10.1111/jbl.12290 · DOI
- Treiblmaier, H., & Garaus, M. (2023). Using blockchain to signal quality in the food supply chain: The impact on consumer purchase intentions and the moderating effect of brand familiarity. International Journal of Information Management, 68, 102514. https://doi.org/10.1016/j.ijinfomgt.2022.102514 · DOI
- Treiblmaier, H., & Rejeb, A. (2023). Exploring blockchain for disaster prevention and relief: A comprehensive framework based on industry case studies. Journal of Business Logistics, https://doi.org/10.1111/jbl.12345 · DOI
- von Krogh, G., Roberson, Q., & Gruber, M. (2023). Recognizing and utilizing novel research opportunities with artificial intelligence. Academy of Management Journal, 66(2), 367-373. https://doi.org/10.5465/amj.2023.4002 · DOI · Paper page
- Wamba, S. F., Queiroz, M. M., Jabbour, C. J. C., & Shi, C. V. (2023). Are both generative AI and ChatGPT game changers for 21st-century operations and supply chain excellence? International Journal of Production Economics, 265, 109015. https://doi.org/10.1016/j.ijpe.2023.109015 · DOI
Cited by
Sorted by citations
Amankwah-Amoah, J., Abdalla, S., Mogaji, E., Elbanna, A., & Dwivedi, Y. K. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102759 · Cited by 305 · see citing works
Self-citationChowdhury, S., Budhwar, P., & Wood, G. (2024) · British Journal of Management · doi:10.1111/1467-8551.12824 · Cited by 221 · see citing works
“It seeks to strike a harmonious balance between productivity, value creation and sustainable/responsible innovation (Richey et al. , 2023).”
BackgroundSelf-citationنويجى, ع. د. ك. (2025) · مجلة البحوث الإدارية · doi:10.21608/jso.2024.305553.1330
- Authors
- Richey, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K.
- Affiliations
- Auburn University Auburn AL USA
- TBS Education (FR)
- Auburn University (US)
- Audencia Business School (FR)
- Swansea University (GB)
- Venue
- Journal of Business Logistics
- Publisher
- Wiley
- Published
- 2023-09-29
- Volume
- 44
- Issue
- 4
- Pages
- 532-549
- ISSN
- 0735-3766 (ISSN-L)
- 2158-1592
- OpenAlex
- W4387163553
- Cited by
- 471
- References
- 7
- Citations by year
- 2026: 155 · 2025: 182 · 2024: 112 · 2023: 22
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- Journal of Business Logistics (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- generative AI, logistics, supply chain management, artificial intelligence applications, implementation barriers, research framework, research roadmap, research integrity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
