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Artificial-intelligence-powered customer service management in the logistics industry

Abstract

Objective: The article aims to show how people perceive future implications for logistics customer service resulting from the implementation of new technologies in the form of game-changer artificial intelligence (AI) solutions in the spirit of economy 4.0 and society 5.0.

Research Design & Methods: The research process used a nomothetic approach based on the methodology of mixed research. The qualitative approach included a research study of monographs, publications, reports, and netographic sources. We used the technique of critical content analysis based on the co-occurrence of terms. In turn, we based the quantitative approach on the diagnostic survey method with the computer-assisted web interviewing (CAWI) technique. The sample size was 233. For further analysis, we used the statistical package for the social sciences (SPSS).

Findings: The research shows that customer service in logistics already uses different forms of AI-based solutions (like Chabtbots, Voicebots, and voice assistants). Even customers positively evaluate those solutions, among others, for efficiency, competence, and service quality. Moreover, customers are aware of AI-based solutions and know that their usage will deepen in the future, as it is a game changer for the competitiveness of customer service in logistics.

Implications & Recommendations: The conducted research indicates the need to constantly improve the digital competences of the users of last-mile logistics services in the context of technologization of transaction processes. Different areas of business will widely use AI-based solutions, because there is a need to develop systems which will help with the human-machine communication. This technology should be constructed as safe for people and easy to use; both with regard to users and customers. As a result of these processes, there is a greater need to educate people about AI-based solutions to develop awareness and improve future outcomes.

Contribution & Value Added: The article’s main advantage is determining new possibilities in the area of logistics customer service as a result of the dissemination of solutions in the AI field, which may be a helpful instrument for enterprises in managing the last-mile scenario in the future.

Keywords

artificial intelligence; , automation; , logistics; , logistics customer service; , Industry 4.0

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Author Biography

Marta Brzozowska

PhD in Management (2018, University of Social Sciences, Lodz, Poland); Associate Professor at the Jan Kochan-owski University of Kielce (Poland). Her research interests include Logistics services, supply chain management and innovative technologies.
Correspondence to: dr inż Marta Brzozowska, Uniwersytet Jana Kochanowskiego w Kielcach, Wydział Prawa i
Nauk Społecznych, Instytut Zarządzania, ul. Żeromskiego 5, 25-369 Kielce, Poland, e-mail: marta.
brzozowska@ujk.edu.pl

Katarzyna Kolasińska-Morawska

Adjunct professor at the Cracow University of Economics (CUE). She holds PhD in management - specialization
logistics management. Her area of research interests includes but not limited to distribution processes, logistics
and consumer behaviour.
Correspondence to: dr Katarzyna Kolasińska-Morawska, Uniwersytet Ekonomiczny w Krakowie, Instytut Zarządzania, Katedra Zarządzania, ul. Rakowicka 27, 31-510 Kraków, Poland, e-mail: kolasink@uek.krakow.pl

Łukasz Sułkowski

Full professor of economic sciences, specializing in management sciences. Currently employed in the Institute
of Public Affairs of the Jagiellonian University. Vice-Rector of the WSB Academy in Dąbrowa Górnicza. He is also
the President of PCG Polska.

Paweł Morawski

Adjunct professor at the Cracow University of Economics (CUE). He holds PhD in logistics management and
master degree in computer sciences.
Correspondence to: dr inż. Paweł Morawski, Uniwersytet Ekonomiczny w Krakowie, Instytut Zarządzania, Katedra
Zarządzania, ul. Rakowicka 27, 31-510 Kraków, Poland, e-mail: morawskp@uek.krakow.pl
ORCID 


References

  1. Agnihotri, R., Trainor, K.J., Itani, O.S., & Rodriguez, M. (2017). Examining the role of sales-based CRM tech-nology and social media use on post-sale service behaviours in India. Journal of Business Research, 81, 144-154. https://doi.org/10.1016/j.jbusres.2017.08.021
  2. Agnusdei, G.P., Gnoni, M.G., Sgarbossa, F., & Govindann, K. (2022). Challenges and perspectives of the In-dustry 4.0 technologies within the last-mile and first-mile reverse logistics: A systematic literature re-view. Research in Transportation Business & Management, 45, Part C, 100896. https://doi.org/10.1016/j.rtbm.2022.100896
  3. Akter, S., Hossain, M.A., Sajib, S., Sultana, S., Rahman, M., Vrontis, D., & McCarthy, G. (2023). A framework for AI-powered service innovation capability: Review and agenda for future research. Technovation, 125, 102768. https://doi.org/10.1016/j.technovation.2023.102768
  4. Balinado, J.R., Prasetyo, Y.T., Young, M.N., Persada, S.F., Miraja, B.A., & Redi, A.A.N.P. (2021). The Effect of Service Quality on Customer Satisfaction in an Automotive After-Sales Service. Journal of Open Innova-tion: Technology, Market, and Complexity, 7(2), 116. https://doi.org/10.3390/joitmc7020116
  5. Barik, K., Misra, S., Ray, A.K., & Shukla, A. (2023). A blockchain-based evaluation approach to analyse cus-tomer satisfaction using AI techniques. Heliyon, 9(6), https://doi.org/10.1016/j.heliyon.2023.e16766
  6. Behie, S.W., Pasman, H.J., Khan, F.I., Shell, K., Alarfaj, A., El-Kady, A.H., & Hernandez, M. (2023). Leadership 4.0: The changing landscape of industry management in the smart digital era. Process Safety and Envi-ronmental Protection, 172, 317-328. https://doi.org/10.1016/j.psep.2023.02.014
  7. Chaabi, M. (2022, September). Roadmap to Implement Industry 5.0 and the Impact of This Approach on TQM. In International Conference on Smart Applications and Data Analysis (pp. 287-293). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-20490-6_23
  8. Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023). Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty. Internet Research (Vol. and No ahead-of-print). https://doi.org/10.1108/INTR-09-2021-0686
  9. Cortés-Leal, A., Cárdenas, C., & Del-Valle-Soto, C. (2022). Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing. Applied Sciences, 12(22), 11330. https://doi.org/10.3390/app122211330
  10. Du, P., He X., Cao, H., Garg, S., Kaddoum, G., & Hassan, M.M. (2023). AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems. Computer Communications, 207, 46-55. https://doi.org/10.1016/j.comcom.2023.04.032
  11. Giza, W., & Wilk, B. (2021). Revolution 4.0 and its implications for consumer behaviour. Entrepreneurial Business and Economics Review, 9(4), 195-206. https://doi.org/10.15678/EBER.2021.090412
  12. Gobble, M.A.M. (2018). Digitalization, Digitization, and Innovation. Research-Technology Management. https://doi.org/10.1080/08956308.2018.1471280
  13. Hassoun, A., Kamiloglu, S., Garcia-Garcia, G., Parra-López, C., Trollman, H., Jagtap, S., Aadil, R.M., & Esatbe-yoglu, T. (2023). Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0. Food Chemistry, 409, 135503. https://doi.org/10.1016/j.foodchem.2022.135303
  14. Hsu, Ch.-L., & Lin, J.Ch.-Ch. (2023). Understanding the user satisfaction and loyalty of customer service chat-bots. Journal of Retailing and Consumer Services, 71, 103211. https://doi.org/10.1016/j.jretconser.2022.103211
  15. Huang, S., Wang, B., Li, X., Zheng, P., Mourtzis, D., & Wang, L. (2022). Industry 5.0 and Society 5.0 – Compari-son, complementation and co-evolution. Journal of Manufacturing Systems, 64, 424-428. https://doi.org/10.1016/j.jmsy.2022.07.010
  16. Iqbal, M., Lee, C.K.M., & Ren, J.Z. (2022, December). Industry 5.0: From Manufacturing Industry to Sustaina-ble Society. In 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1416-1421). IEEE. https://doi.org/10.1109/IEEM55944.2022.9989705
  17. Jan, I.U., Ji, S., & Kim, Ch. (2023). What (de) motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective. Journal of Retailing and Consumer Ser-vices, 75, 103440. https://doi.org/10.1016/j.jretconser.2023.103440
  18. Janjevic, M., Merchán, D., & Winkenbach, M. (2021). Designing multi-tier, multi-service-level, and multi-modal last-mile distribution networks for omni-channel operations. European Journal of Operational Re-search, 294(3), 1059-1077. https://doi.org/10.1016/j.ejor.2020.08.043
  19. Javed, M.K., & Wu, M. (2020). Effects of online retailer after delivery services on repurchase intention: An empirical analysis of customers’ past experience and future confidence with the retailer. Journal of Re-tailing and Consumer Services, 54, 101942. https://doi.org/10.1016/j.jretconser.2019.101942
  20. Jucha, P. (2021). Use of artificial intelligence in last mile delivery. In SHS Web of Conference (92, p. 04011). EDP Sciences. https://doi.org/10.1051/shsconf/20219204011
  21. Korzynski, P., Kozminski, A.K., & Baczynska, A. (2023a). Navigating leadership challenges with technology: Uncovering the potential of ChatGPT, virtual reality, human capital management systems, robotic pro-cess automation, and social media. International Entrepreneurship Review, 9(2), 7-18. https://doi.org/10.15678/IER.2023.0902.01
  22. Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023b). Generative artificial intelligence as a new context for management theories: analysis of ChatGPT. Central European Management Journal, 31(1), 3-13. https://doi.org/10.1108/CEMJ-02-2023-0091
  23. Kunrath, T.L., Dresch, A., & Veit, D.R. (2023). Supply chain management and industry 4.0: A theoretical ap-proach. Brazilian Journal of Operations and Production Management, 20(1), 1263-1263. https://doi.org/10.14488/BJOPM.1263.2023
  24. Lajante, M., Tojib, D., & Ho, T. (2023). When interacting with a service robot is (not) satisfying: The role of customers’ need for social sharing of emotion. Computers in Human Behavior, 146, 107792. https://doi.org/10.1016/j.chb.2023.107792
  25. Li, B., Liu, L., Mao, W., Qu, Y., & Chen, Y. (2023). Voice artificial intelligence service failure and customer complaint behavior: The mediation effect of customer emotion. Electronic Commerce Research and Ap-plications, 146, 107792. https://doi.org/10.1016/j.elerap.2023.101261
  26. Li, F., & Xu, G. (2022). AI-driven customer relationship management for sustainable enterprise performance. Sustainable Energy Technologies and Assessments, 52, 102103. https://doi.org/10.1016/j.seta.2022.102103
  27. Li, S., Peng, G., Xing, F., Zhang, J., & Zhang, B. (2021). Value co-creation in industrial AI: The interactive role of B2B supplier, customer and technology provider. Industrial Marketing Management, 98, 105-114. https://doi.org/10.1016/j.indmarman.2021.07.015
  28. Liu, S., & Wang, J. (Eds.) (2019). The Internet Society in China: A 2016 Raport. Springer.
  29. Menti, F., Romero, D., & Jacobsen, P. (2023). A technology assessment and implementation model for evalu-ating socio-cultural and technical factors for the successful deployment of Logistics 4.0 technologies. Technological Forecasting and Social Change, 190, 122469. https://doi.org/10.1016/j.techfore.2023.122469
  30. Modgil, S., Singh, R.K., & Hannibal, C. (2021). Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management, 33(4), 1246-1268. https://doi.org/10.1108/IJLM-02-2021-0094
  31. Ngai, E.W.T., Lee, M.C.M., Luo, M., Chan, P.S.L., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50, 101098. https://doi.org/10.1016/j.elerap.2021.101098
  32. Noble, S.M., Mende, M., Grewal, D., & Parasuraman, A. (2022). The Fifth Industrial Revolution: How Harmo-nious Human–Machine Collaboration is Triggering a Retail and Service [R]evolution. Journal of Retailing, 98(2), 199-208. https://doi.org/10.1016/j.jretai.2022.04.003
  33. Pengfei, D., He, X., Cao, H., Garg, S., Kaddoum, G., & Hassan, M.M. (2023). AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems. Computer Communication, 207, 46-55. https://doi.org/10.1016/j.comcom.2023.04.032
  34. Prentice, C., & Nguyen, M. (2020). Engaging and retaining customers with AI and employee service. Journal of Retailing and Consumer Services, 56, 102186. https://doi.org/10.1016/j.jretconser.2020.102186
  35. Prentice, C., Weaven, S., & Wong, I.A. (2020). Linking AI quality performance and customer engagement: The moderating effect of AI preference. International Journal of Hospitality Management, 90, 102629. https://doi.org/10.1016/j.ijhm.2020.102629
  36. Qin, J., Liu, Y., & Grosvenor, R. (2016). A Categorial Framework of Manufacturing for Industry 4.0 and Beyond. Procedia CIRP, 52, 173-178. https://doi.org/10.1016/j.procir.2016.08.005
  37. Rosendorf, A., Hodes, A., & Fabian, B. (2021). Artificial Intelligence for last-mile logistics - Procedures and architecture. Online Journal of Applied Knowledge Management (OJAKM), 9(1), 46-61. https://doi.org/10.36965/OJAKM.2021.9(1)46-61
  38. Ruan, Y., & Mezei, J. (2022). When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type. Journal of Retailing and Consumer Services, 68, 103059. https://doi.org/10.1016/j.jretconser.2022.103059
  39. Sarfraz, Z., Sarfraz, A., Iftikar, H.M., & Akhund, R. (2021). Is covid-19 pushing us to the fifth industrial revolu-tion (Society 5.0)?. Pakistan Journal of Medical Sciences, 37(2), 591. https://doi.org/10.12669/pjms.37.2.3387
  40. Shahidan, N.H., Latiff, A.S.A., & Wahab, S.A. (2021). Moving Towards Society 5.0: A Bibliometric and Visuali-zation Analysis. In Society 5.0: First International Conference, Society 5.0 2021, Virtual Event, June 22-24, 2021, Revised Selected Papers 1 (pp. 93-104). Springer International Publishing. https://doi.org/10.1007/978-3-030-86761-4_8
  41. Taj, I., & Jhanjhi, N.Z. (2022). Towards Industrial Revolution 5.0 and Explainable Artificial Intelligence: Chal-lenges and Opportunities. International Journal of Computing and Digital Systems, 12(1), 295-320. https://doi.org/10.12785/ijcds/120124
  42. Turban, E., Outland, J., King, D., Lee, J.K., Liang, T., & Turban, D.C. (2018). Electronic Commerce 2018: A Man-agerial and Social Networks Perspective (Vol. 97). Cham: Springer.
  43. Vasyltsiv, T., Mulska, O., Panchenko, V., Kohut, M., Zaychenko, V., & Levytska, O. (2021).Technologization Process and Social and Economic Growth: Modelling Impact and Priorities for Strengthening the Techno-logical Competitiveness of the Economy. Regional Science Inquiry, 13(1), 117-134.
  44. Wach, K., Duong, C., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7-30. https://doi.org/10.15678/EBER.2023.110201
  45. Wang, X., Lin, X., & Shao, B. (2022). How does artificial intelligence create business agility? Evidence from chatbots. International Journal of Information Management, 66, 102535. https://doi.org/10.1016/j.ijinfomgt.2022.102535
  46. Wijaya, A.P. (2017). Role of Experience in Customer Self Congruity to Maintaining Loyalty: A Study on Fash-ion Store. Entrepreneurial Business and Economics Review, 5(3), 189-198. http://doi.org/10.15678/EBER.2017.050310
  47. Xu, Y., Shieh, Ch.-H., van Esch, P., & Ling, I-L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian Marketing Journal, 28(4), 189-199. https://doi.org/10.1016/j.ausmj.2020.03.005
  48. Yang, B., Sun, Y., & Shen, X.-L. (2023). Understanding AI-based customer service resistance: A perspective of defective AI features and tri-dimensional distrusting beliefs. Information Processing & Management, 60(3), 103257. https://doi.org/10.1016/j.ipm.2022.103257

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