Vol. 14 No. 3 (2026)
September 2026

Articles

Towards a sociotechnical framework and maturity model for artificial intelligence-driven supply chain due diligence

Published 2026-09-30

  • Marcel Welsen
    • ,
  • Łukasz Sułkowski

Marcel Welsen
WSB University

Łukasz Sułkowski
Jagiellonian University

Abstract

Objective: The objective of this article is to develop an integrated sociotechnical framework and a diagnostic maturity model for artificial intelligence-driven supply chain due diligence. It addresses the implementation gap under escalating mandatory regulations by demonstrating how technological capabilities, ethical governance, and cultural adaptation interact, shape each other, and must be developed together.

Research Design & Methods: We employed a theory-building approach and conducted a purposive literature synthesis. We applied thematic analysis, grounded in institutional theory, stakeholder theory, and the technology-organisation-environment framework to develop an integrated framework and maturity model, illustrated via analysis of European e-commerce sustainability disclosures.

Findings: We identified three critical sociotechnical interfaces (tech-ethics, ethics-culture, and tech-culture) and derived three formal propositions from the analysis. We propose, organisational readiness may function more as a coefficient than an additive factor, where the substantive effectiveness of technological tools depends on cultural commitment. A distinct ‘institutional governance gap’ emerges from the illustrative application.

Implications & Recommendations: Organisations, including small and medium-sized enterprises and entrepreneurial ventures operating within mandatory due diligence supply chains, should move beyond techno-centric approaches to assess artificial intelligence (AI) readiness simultaneously across operational, institutional, global, and strategic layers. The maturity model enables systematic capability gap identification.

Contribution & Value Added: We propose a diagnostic maturity model and formal propositions that bridge the gap between legal theory and organisational implementation. These contributions are conceptual and require empirical validation.

Keywords

artificial intelligence, supply chain due diligence, Corporate Sustainability Due Diligence Directive, EU AI Act, sociotechnical framework, TOE framework, organisational readiness

(PDF) Save

Author Biography

Marcel Welsen

PhD Student at the Department of Management and Quality Studies, WSB University (Poland) and lecturer at WSB campus Krakow. His research interests include International Business, AI-driven supply chain management, due diligence, and sociotechnical systems.

Łukasz Sułkowski

Professor at the Institute of Public Affairs, Jagiellonian University (Poland). His research interests include organisational culture, management of higher education institutions, and digital transformation.

References

  1. Abhulimen, A. O., & Ejike, O. G. (2024). Ethical considerations in AI use for SMEs and supply chains: Current challenges and future directions. International Journal of Applied Research in Social Sciences, 6(8), 1653-1679. https://doi.org/10.51594/ijarss.v6i8.1391
  2. Agrawal, N., Cohen, M. A., Deshpande, R., & Deshpande, V. (2024, March 1). How machine learning will transform supply chain management. Harvard Business Review. Retrieved from https://hbr.org/2024/03/how-machine-learning-will-transform-supply-chain-management on January 2, 2026.
  3. Akbarighatar, P., Pappas, I., & Vassilakopoulou, P. (2023). A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review. International Journal of Information Management Data Insights, 3(2), 100193. https://doi.org/10.1016/j.jjimei.2023.100193
  4. Akinrinola, O., Okoye, C. C., Ofodile, O. C., & Ugochukwu, C. E. (2024). Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability. GSC Advanced Research and Reviews, 18(3), 050-058. https://doi.org/10.30574/gscarr.2024.18.3.0088
  5. Alvesson, M., & Sandberg, J. (2020). The problematizing review: A counterpoint to elsbach and van Knippenberg’s argument for integrative reviews. The Journal of Management Studies, 57(6), 1290-1304. https://doi.org/10.1111/joms.12582
  6. Amazon. (2025). Amazon Sustainability Report 2024. Sustainability about Amazon.com. Retrieved from https://sustainability.aboutamazon.com/2024-amazon-sustainability-report.pdf on January 1, 2026.
  7. Amit, R., & Schoemaker, P. J. H. (1993). Strategic assets and organizational rent. Strategic Management Journal, 14(1), 33-46. https://doi.org/10.1002/smj.4250140105
  8. Arroyabe, M. F., Arranz, C. F. A., Fernandez De Arroyabe, I., & Fernandez de Arroyabe, J. C. (2024). Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment. Technology in Society, 79(102733), 102733. https://doi.org/10.1016/j.techsoc.2024.102733
  9. Bahangulu, J. K., & Owusu-Berko, L. (2025). Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications. World Journal of Advanced Research and Reviews, 25(2), 1746-1763. https://doi.org/10.30574/wjarr.2025.25.2.0571
  10. Barnes, A. J., Zhang, Y., & Valenzuela, A. (2024). AI and culture: Culturally dependent responses to AI systems. Current Opinion in Psychology, 58(101838), 101838. https://doi.org/10.1016/j.copsyc.2024.101838
  11. Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: a systematic literature review. AI and Ethics. https://doi.org/10.1007/s43681-024-00653-w
  12. Belenguer, L. (2022). AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry. AI and Ethics, 2(4), 771-787. https://doi.org/10.1007/s43681-022-00138-8
  13. Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: themes, knowledge gaps and future agendas. Internet Research, 33(7), 133-167. https://doi.org/10.1108/intr-01-2022-0042
  14. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
  15. Brauner, P., Glawe, F., Liehner, G. L., Vervier, L., & Ziefle, M. (2024). Cultural dimensions of AI perception: Charting expectations, risks, benefits, tradeoffs, and value in Germany and China. In arXiv [cs.CY]. https://doi.org/10.48550/ARXIV.2412.13841
  16. Brintrup, A., Kosasih, E., Schaffer, P., Zheng, G., Demirel, G., & MacCarthy, B. L. (2023). Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks. International Journal of Production Research, 62(13), 4674-4695. https://doi.org/10.1080/00207543.2023.2270719
  17. Buhmann, K., & Feld, L. (2024). Shifting boundaries: a transnational legal perspective on the EU Corporate Sustainability Due Diligence Directive. Transnational Legal Theory, 15(3), 370-396. https://doi.org/10.1080/20414005.2024.2426961
  18. Bundesministerium für Justiz. (2021, July 23). LkSG – Gesetz ueber die unternehmerischen Sorgfaltspflichten zur Vermeidung von Menschenrechtsverletzungen in Lieferketten. Retrieved December 23, 2024, from Budesamt für Justiz. https://www.gesetze-im-internet.de/lksg/BJNR295910021.htmlBundesregierung (2025, March 9). Kabinett beschließt Aussetzung der LkSG-Berichtspflicht. Die Bundesregierung informiert. Retrieved from https://www.bundesregierung.de/breg-de/aktuelles/lieferkettengesetz-2382748 on January 2, 2026.
  19. Butkeviciene, J., & Sekliuckiene, J. (2022). Exploring the institutional pressures that affect international new ventures. Entrepreneurial Business and Economics Review, 10(1), 97-112. https://doi.org/10.15678/EBER.2022.100107
  20. Campbell, J. L. (2007). Why would corporations behave in socially responsible ways? an institutional theory of corporate social responsibility. Academy of Management Review, 32(3), 946-967. https://doi.org/10.5465/amr.2007.25275684
  21. Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: a multiple case study research. International Journal of Production Research, 62(9), 3333-3360. https://doi.org/10.1080/00207543.2023.2232050
  22. Chang, T.-S., & Bau, D.-Y. (2025). eXplainable artificial intelligence (XAI) in business management research: a success/failure system perspective. Journal of Electronic Business & Digital Economics, 4(1), 36-53. https://doi.org/10.1108/jebde-07-2024-0019
  23. Chaudhary, G. (2024). Explainable artificial intelligence (xAI): Reflections on judicial system. Kutafin Law Review, 10(4), 872-889. https://doi.org/10.17803/2713-0533.2023.4.26.872-889
  24. Colangelo, E., Fries, C., Hinrichsen, T.-F., Szaller, Á., & Nick, G. (2022). Maturity model for AI in smart production planning and control system. Procedia CIRP, 107, 493-498. https://doi.org/10.1016/j.procir.2022.05.014
  25. Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162(104132), 104132. https://doi.org/10.1016/j.compind.2024.104132
  26. de Oliveira Serra, S. P. (2025). Corporate Governance and Due Diligence Analysis Under the Corporate Sustainability Due Diligence Directive (CSDDD): Legal and Extraterritorial Challenges (No. 105). Stanford Law School. Retrieved, from https://law.stanford.edu/publications/no-105-corporate-governance-and-due-diligence-analysis-under-the-corporate-sustainability-due-diligence-directive-csddd-legal-and-extraterritorial-challenges/ on January 2, 2026.
  27. Deming, E. (1982). Out of the Crisis. The MIT Press. https://mitpress.mit.edu/9780262541152/out-of-the-crisis/
  28. Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2023). Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small- and medium-sized enterprises. International Journal of Production Research, 5417-5456. https://doi.org/10.1080/00207543.2023.2179859
  29. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147. https://doi.org/10.2307/2095101
  30. Eisenhardt, K. M., & Graebner, M. E. (2007). Theory Building From Cases: Opportunities And Challenges. Academy of Management Journal, 50(1), 25-32. https://doi.org/10.5465/amj.2007.24160888
  31. El Mimouni, S., & Bouhdadi, M. (2025). Fairness and bias in AI: a sociotechnical perspective. Journal of Information Communication and Ethics in Society. https://doi.org/10.1108/jices-12-2024-0182
  32. Elhady, A. M., & Shohieb, S. (2025). AI-driven sustainable finance: computational tools, ESG metrics, and global implementation. Future Business Journal, 11(1). https://doi.org/10.1186/s43093-025-00610-x
  33. European Commission. (2024a, June 13). Directive (EU) 2024/1760 of the European Parliament and of the Council of 13 June 2024 on corporate sustainability due diligence and amending Directive (EU) 2019/1937 and Regulation (EU) 2023/2859. European Union. Retrieved from https://eur-lex.europa.eu/eli/dir/2024/1760/oj July 21, 2025.
  34. European Commission. (2024b, December 7). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations. EUR-Lex. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689 on March 6, 2025.
  35. European Commission. (2025a, January 4). Simplification. Omnibus Proposal. European Commission. Retrieved from https://commission.europa.eu/law/law-making-process/better-regulation/simplification-and-implementation/simplification_en on December 27, 2025.
  36. European Commission. (2025b, April 16). Directive (EU) 2025/794 of the European Parliament and of the Council of 14 April 2025 amending Directives (EU) 2022/2464 and (EU) 2024/1760 as regards the dates from which Member States are to apply certain corporate sustainability reporting and due diligence requirements. EUR-Lex. Retrieved from https://eur-lex.europa.eu/eli/dir/2025/794/oj/eng on January 5, 2026.
  37. Faheem, M. A. (2024). Ethical AI: Addressing bias, fairness, and accountability in autonomous decision-making systems. World Journal of Advanced Research and Reviews, 23(2), 1703-1711. https://doi.org/10.30574/wjarr.2024.23.2.2510
  38. Freeman, R. E. (2010). Strategic Management: A Stakeholder Approach. Cambridge University Press. https://doi.org/10.1017/cbo9781139192675
  39. Gustafsson, M.-T., Schilling-Vacaflor, A., & Lenschow, A. (2023a). Foreign corporate accountability: The contested institutionalization of mandatory due diligence in France and Germany. Regulation & Governance, 17(4), 891-908. https://doi.org/10.1111/rego.12498
  40. Gustafsson, M.-T., Schilling-Vacaflor, A., & Lenschow, A. (2023b). The politics of supply chain regulations: Towards foreign corporate accountability in the area of human rights and the environment?. Regulation & Governance, 17(4), 853-869. https://doi.org/10.1111/rego.12526
  41. Han, X., H. L. Kaas, M., & Wang, C. D. (2025). A cross-cultural examination of fairness beliefs in human-AI interaction. https://doi.org/10.2139/ssrn.5116823
  42. Hansen, H. F., Lillesund, E., Mikalef, P., & Altwaijry, Ν. (2024). Understanding Artificial Intelligence diffusion through an AI capability maturity model. Information Systems Frontiers: A Journal of Research and Innovation, 26(6), 2147-2163. https://doi.org/10.1007/s10796-024-10528-4
  43. Herold, D. M. (2018). Demystifying the link between institutional theory and stakeholder theory in sustainability reporting. Economics Management and Sustainability, 3(2), 6-19. https://doi.org/10.14254/jems.2018.3-2.1
  44. Hofmann, V., Kalluri, P. R., Jurafsky, D., & King, S. (2024). AI generates covertly racist decisions about people based on their dialect. Nature, 633(8028), 147-154. https://doi.org/10.1038/s41586-024-07856-5
  45. Hughes, L., Davies, F., Li, K., Gunaratnege, S. M., Malik, T., & Dwivedi, Y. K. (2026). Beyond the hype: Organisational adoption of Generative AI through the lens of the TOE framework-A mixed methods perspective. International Journal of Information Management, 86(102982), 102982. https://doi.org/10.1016/j.ijinfomgt.2025.102982
  46. Ioakeimidou, D., Chatzoudes, D., Symeonidis, S., & Chatzoglou, P. (2024). HRA adoption via organizational analytics maturity: examining the role of institutional theory, resource-based view and diffusion of innovation. International Journal of Manpower, 45(5), 958-983. https://doi.org/10.1108/ijm-10-2022-0496
  47. Kabir, S., Hossain, M. S., & Andersson, K. (2025). A review of Explainable Artificial Intelligence from the perspectives of challenges and opportunities. Algorithms, 18(9), 556. https://doi.org/10.3390/a18090556
  48. Leite, E., Koporcic, N., & Markovic, S. (2024). Corporate sustainability reporting: Shifting from optional due diligence to mandatory duty. Business Ethics, the Environment & Responsibility. https://doi.org/10.1111/beer.12753
  49. Li, N., Kim, M., Dai, J., & Vasarhelyi, M. A. (2024). Using artificial intelligence in ESG assurance. Journal of Emerging Technologies in Accounting, 1-17. https://doi.org/10.2308/jeta-2022-054
  50. Lin, B., & Zhu, Y. (2025). Does AI elevate corporate ESG performance? A supply chain perspective. Business Strategy and the Environment, 34(1), 586-597. https://doi.org/10.1002/bse.3999
  51. Marano, V., Wilhelm, M., Kostova, T., Doh, J., & Beugelsdijk, S. (2024). Multinational firms and sustainability in global supply chains: scope and boundaries of responsibility. Journal of International Business Studies, 55(4), 413-428. https://doi.org/10.1057/s41267-024-00706-6
  52. Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340-363. https://doi.org/10.1086/226550
  53. Milosevic, I., Bass, A. E., & Schulte, B. (2023). The interplay of conflicting and complementing institutional logics in sustainability practices. Management International Review, 63(3), 469-506. https://doi.org/10.1007/s11575-023-00503-7
  54. Mishra, N. K., Sahoo, S., Agarwal, S., Sharma, P. P., & Ilahi, F. (2024). Impact of institutional pressures and security on blockchain technology adoption and organization performance: an empirical study. The Journal of Technology Transfer. https://doi.org/10.1007/s10961-024-10098-2
  55. Mitchell, R. K., Agle, B. R., & Wood, D. J. (1997). Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts. Academy of Management Review, 22(4), 853-886. https://doi.org/10.2307/259247
  56. Neumann, O., Guirguis, K., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114-141. https://doi.org/10.1080/14719037.2022.2048685
  57. Nweje, U., & Taiwo, M. (2025). Leveraging Artificial Intelligence for predictive supply chain management, focus on how AI- driven tools are revolutionizing demand forecasting and inventory optimization. International Journal of Science and Research Archive, 14(1), 230-250. https://doi.org/10.30574/ijsra.2025.14.1.0027
  58. Otto Group. (2025). 2024/25 annual report – The Sustainability Strategy. Otto Group. Retrieved from https://www.ottogroup.com/medien/dynamic/docs/en/bpk/2025/otto-group_annual_report_2024_25_sustainabilty.pdf on January 1, 2026.
  59. Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885
  60. Paré, G., Trudel, M. C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183-199. https://doi.org/10.1016/j.im.2014.08.008
  61. Pinto, A. S., Abreu, A., Pérez Cota, M., & Paiva, J. (2025). A meta analysis of TOE factors driving organizational adoption of artificial intelligence across industries. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-025-00747-2
  62. Radanliev, P. (2024). Cyber diplomacy: defining the opportunities for cybersecurity and risks from Artificial Intelligence, IoT, Blockchains, and Quantum Computing. Journal of Cyber Security Technology, 1-51. https://doi.org/10.1080/23742917.2024.2312671
  63. Reis, J. F., & Pinheiro Junior, L. P. (2025). Institutional theory (IT) and diffusion of innovation (DOI): A theoretical approach on artificial intelligence (AI). Brazilian Administration Review, 22(4). https://doi.org/10.1590/1807-7692bar2025250060
  64. Romeo, E., & Lacko, J. (2025). Adoption and integration of AI in organizations: a systematic review of challenges and drivers towards future directions of research. Kybernetes. The International Journal of Cybernetics, Systems and Management Sciences. https://doi.org/10.1108/k-07-2024-2002
  65. Roy, A., & Mukherjee, P. (2022). Does national culture influence corporate ESG disclosures? Evidence from cross-country study. Vision The Journal of Business Perspective, 097226292210749. https://doi.org/10.1177/09722629221074914
  66. Rudko, I., Bashirpour Bonab, A., Fedele, M., & Formisano, A. V. (2025). New institutional theory and AI: toward rethinking of artificial intelligence in organizations. Journal of Management History, 31(2), 261-284. https://doi.org/10.1108/jmh-09-2023-0097
  67. Rühmkorf, A. (2023). Article: The German supply chain law: A first step towards more corporate sustainability. European Company Law, 20(1), 6-14. https://doi.org/10.54648/eucl2023003
  68. Sadiq, R. B., Safie, N., Abd Rahman, A. H., & Goudarzi, S. (2021). Artificial intelligence maturity model: a systematic literature review. PeerJ Computer Science, 7(e661), e661. https://doi.org/10.7717/peerj-cs.661
  69. Samuels, A. (2024). Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: a systematic literature review. Frontiers in Artificial Intelligence, 7, 1477044. https://doi.org/10.3389/frai.2024.1477044
  70. Sánchez, E., Calderón, R., & Herrera, F. (2025). Artificial intelligence adoption in SMEs: Survey based on TOE–DOI framework, primary methodology and challenges. Applied Sciences (Basel, Switzerland), 15(12), 6465. https://doi.org/10.3390/app15126465
  71. Shahzadi, G., Jia, F., Chen, L., & John, A. (2024). AI adoption in supply chain management: a systematic literature review. Journal of Manufacturing Technology Management, 35(6), 1125-1150. https://doi.org/10.1108/jmtm-09-2023-0431
  72. Shamsuddoha, M., Khan, E. A., Chowdhury, M. M. H., & Nasir, T. (2025). Revolutionizing supply chains: Unleashing the power of AI-driven intelligent automation and real-time information flow. Information (Basel), 16(1), 26. https://doi.org/10.3390/info16010026
  73. Singh, P. D. (2024). Generative AI through the lens of institutional theory. https://doi.org/10.2139/ssrn.4953180
  74. Sinnig, J., & Zetzsche, D. A. (2025). The EU’s Corporate Sustainability Due Diligence Directive: From disclosure to mandatory prevention of adverse sustainability impacts in supply chains. European Journal of Risk Regulation, 1-25. https://doi.org/10.1017/err.2024.100
  75. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039
  76. Stahl, B. C., & Eke, D. (2024). The ethics of ChatGPT – Exploring the ethical issues of an emerging technology. International Journal of Information Management, 74(102700), 102700. https://doi.org/10.1016/j.ijinfomgt.2023.102700
  77. Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2), 205395172211151. https://doi.org/10.1177/20539517221115189
  78. Steinmueller, W. E. (2001). ICTs and the possibilities for leapfrogging by developing countries. International Labour Review, 140(2), 193-210. https://doi.org/10.1111/j.1564-913x.2001.tb00220.x
  79. Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3(9), gae346. https://doi.org/10.1093/pnasnexus/pgae346
  80. Teixeira, A. R., Ferreira, J. V., & Ramos, A. L. (2025). Intelligent supply chain management: A systematic literature review on artificial intelligence contributions. Information, 16(5), 399. https://doi.org/10.3390/info16050399
  81. Tornatzky, L. G., & Fleischer, M. (1990). Processes of technological innovation. Free Press.
  82. Trindade, M. A. M., Bellieni, F., Stabilini, G., & De Silvio, M. (2025). Design principles for corporate sustainability due Diligence: An investigation with expert interviews. Journal of Purchasing and Supply Management, 31(4), 101032. https://doi.org/10.1016/j.pursup.2025.101032
  83. Vann Yaroson, E., Abadie, A., & Roux, M. (2025). Human-artificial intelligence collaboration in supply chain outcomes: the mediating role of responsible artificial intelligence. Annals of Operations Research, 354(1), 35-69. https://doi.org/10.1007/s10479-025-06534-7
  84. Welsen, M. (2025). A theoretical framework for analyzing multinational enterprise responses to the corporate sustainability due diligence directive. In Ł. Sułkowski & J. Dzieńdziora (Eds.), Contemporary concepts in social and technical sciences (pp. 433-456). WSB University Scientific Publishing. https://doi.org/10.2139/ssrn.5358592
  85. Wilhelm, M. (2024). Mandatory due diligence legislation: a paradigm shift for the governance of sustainability in global value chains?. Journal of International Business Policy. https://doi.org/10.1057/s42214-024-00193-4
  86. Wong, L.-W., Tan, G. W.-H., Ooi, K.-B., Lin, B., & Dwivedi, Y. K. (2022). Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis. International Journal of Production Research, 5535-5555. https://doi.org/10.1080/00207543.2022.2063089
  87. Yang, M., Lim, M. K., Qu, Y., Ni, D., & Xiao, Z. (2023). Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering, 175(108859), 108859. https://doi.org/10.1016/j.cie.2022.108859
  88. Yin, R. K. (2018). Case Study Research and Applications: Design and Methods. Sixth Edition. SAGE.
  89. Yuan, H., Che, Z., Zhang, Y., Li, S., Yuan, X., Huang, L., Hu, X., Peng, K., & Luo, S. (2025). The cultural stereotype and cultural bias of ChatGPT. Journal of Pacific Rim Psychology, 19. https://doi.org/10.1177/18344909251355673
  90. Zalando SE. (2025). Sustainability Reports (CDP corporate questionnaire, CSRD Digest 2024, Sustainability attitude behaviour gap report 2025, Sustainability Progress report 2023). Retrieved from Zalando Sustainability Reports. https://corporate.zalando.com/en/people-planet/sustainability/sustainability-reports on January 1, 2026.

Downloads

Download data is not yet available.

Most read articles by the same author(s)

1 2 > >>