The Role of Artificial Intelligence Applications in Building Sustainable Supply Chains: The Moderating Role of Achieving Sustainable Development Goals (SDGs)
DOI:
https://doi.org/10.14419/t9wak682Published
06-11-2025Keywords:
Artificial Intelligence (AI), Sustainable Supply Chains, Sustainable Development Goals (SDGs), Pharmaceutical Sector, JordanAbstract
This Research aims to investigate the significance of the application of Artificial Intelligence in establishing and implementing sustainable supply chains, and to scrutinize the mediating role of the attainment of the Sustainable Development Goals in the pharmaceutical sector in the state of Jordan. This Research was designed based on the Dynamic Capabilities Theory to realize that the application of AI in organizations can improve organizational intelligence, efficiency, and sustainability performance, while the achievement of SDGs can enhance the application of AI in sustainability performance, integrating organizational, natural, and social aspects. The Research used a quantitative methodology with questionnaires, distributed to 412 managers of administrative, technical, and healthcare sections in the industry, out of 500 questionnaires sent to them in total. The Research used a five-point Likert scale in measuring the constructs of AI application, SDG achievement, and sustainability performance of supply chain management in organizations. The Research used SmartPLS version 4 to examine the measurement and structural models, scrutinizing assumptions, robustness, and model fit indexes via bootstrapping and Latent Variable analyses. The reliability test applied indicated high internal consistency across constructs, with values higher than 0.80 for Cronbach's alpha, ensuring the reliability and consistency of the data retrieved, validating the measurement models with theoretical assumptions in the Research. The models were scrutinized via multiple analytical assumptions to avoid any contradictions in data analysis results. The models' assumptions were valid due to consistent analytical results to validate AI, SDG, and sustainability performance in the Research across models and theories applied in this Research on AI application in sustainability performance in the pharmaceutical sector in the state of Jordan with insight support to AI evolving as approving sustainability factor in organizational performance for pharmaceutical organizations in Jordan cornered on the Dynamic Capabilities Theory Assuming AI Application.
References
Al-Awamleh, H. K., Omoush, M. M., Ahmed, R. T., Assaf, N., Alqudah, M. Z., & Samara, H. (2025). Less innovation in energy management: Mapping knowledge development and technological change. International Journal of Energy Sector Management. https://doi.org/10.1108/IJESM-03-2025-0016
Al-Khatib, A. W. (2022). Big data analytics capabilities and green supply chain performance: Investigating the moderated mediation model for green innovation and technological intensity. Business Process Management Journal, 28(5), 1446–1471. https://doi.org/10.1108/BPMJ-07-2022-0332
Al-Khatib, A. W., & Khattab, M. (2024). How can generative artificial intelligence improve digital supply chain performance in manufacturing firms? Analysing the mediating role of innovation ambidexterity using hybrid analysis through CB-SEM and PLS-SEM. Technology in Society, 78, 102676. https://doi.org/10.1016/j.techsoc.2024.102676
Al-Olfi, S. A. A., Song, Y., & Al-Hajj, Y. M. (2025). The impact of sustainable supply chain strategies on e-commerce consumer purchasing behav-iour: Considering the moderating effect of big data analytics. Journal of the Knowledge Economy.
Alshawabkeh, R., Al-Awamleh, H., Alkhawaldeh, M., Kanaan, R., & Al-Hawary, S. (2022). The mediating role of supply chain management on the relationship between big data and supply chain performance using SCOR model. Uncertain Supply Chain Management, 10(3), 729–736.*
Atieh Ali, A. A., Sharabati, A. A. A., Allahham, M., & Nasereddin, A. Y. (2024). The relationship between supply chain resilience and digital sup-ply chain and the impact on sustainability: Supply chain dynamism as a moderator. Sustainability, 16(6), 3082. https://doi.org/10.3390/su16073082
Bag, S., & Rahman, M. S. (2024). Navigating circular economy: Unleashing the potential of political and supply chain analytics skills among top supply chain executives for environmental orientation, regenerative supply chain practices, and supply chain viability. Business Strategy and the Environment, 33(1), 504–528. https://doi.org/10.1002/bse.3623
Bag, S., Gupta, S., Kumar, S., & Sivarajah, U. (2021). Role of technological dimensions of green supply chain management practices on firm per-formance. Journal of Enterprise Information Management, 34(1), 1–27. https://doi.org/10.1108/JEIM-03-2019-0089
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Baron, R. M., & Kenny, D. A. (1986). The moderator variable distinction in social psychological research: Conceptual, strategic, and statistical con-siderations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Behl, A., Sampat, B., Pereira, V., & Chiappetta Jabbour, C. J. (2023). The role played by responsible artificial intelligence (RAI) in improving sup-ply chain performance in the MSME sector: An empirical inquiry. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05624-8
Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resil-ience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, 333(1), 627–652. https://doi.org/10.1007/s10479-021-03956
Chen, L., Li, Y., & Wang, H. (2024). AI-driven predictive analytics and sustainable operations: Evidence from emerging markets. Journal of Clean-er Production, 378, 139412. https://doi.org/10.1016/j.jclepro.2024.139412
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Del Giudice, M., Scuotto, V., Caputo, F., & Carayannis, E. G. (2021). Intuition, intelligence and innovation in the digital entrepreneurship era: A new conceptual framework. Journal of Innovation & Knowledge, 6(3), 145–153. https://doi.org/10.1016/j.jik.2021.02.001
Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Capstone.
Guo, X., Zhang, L., & Li, J. (2025). Research on supply chain resilience mechanism of AI: Evidence from the Chinese manufacturing industry. Sci-entific Reports. https://doi.org/10.1038/s41598-025-17138-3
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modelling (PLS-SEM) (3rd ed.). SAGE Publications.
Iqbal, M., Ahmad, S., & Khan, M. (2025). AI-enabled optimisation for green logistics in pharmaceutical supply chains. Sustainable Operations and Computers, 6, 100234. https://doi.org/10.1016/j.susoc.2025.100234
Javed, A., Raza, S. A., & Akbar, M. (2024). AI and SDG alignment: Examining responsible consumption practices in manufacturing firms. Journal of Sustainable Production Systems, 12(4), 205–219.
Khan, M. D. A., Ahmed, S., & Kamal, F. (2025). AI-driven business models for sustainability transitions: A systematic review. Journal of Cleaner Production, 367, 136021. https://doi.org/10.1016/j.jclepro.2025.136021
Lin, L., Wang, H., & Chen, Z. (2025). Research on the impact of enterprise artificial intelligence adoption on supply chain resilience. Sustainability, 17(19), 8576. https://doi.org/10.3390/su17198576
Meena, P. L., Kumar, S., & Agrawal, R. (2025). Green and circular supply chains for sustainability: The role of digitalisation and AI adoption. Re-sources, Conservation & Recycling, 210, 107185. https://doi.org/10.1016/j.resconrec.2025.107185
Omoush, M. (2021). The impact of green productivity strategy on environmental sustainability through measurement of management support: A field Research in the industry sector in Jordan. Management Science Letters, 11(3), 737–746. https://doi.org/10.5267/j.msl.2020.10.033
Omoush, M. (2025). The impact of supply chain integration via mediator—Supply chain resilience on improvement in the performance of manufac-turing sectors. International Review of Management and Marketing, 15(2), 157–170.
Omoush, M. M. (2022). The impact of the practices of logistic management on operational performance: A field Research of road transport compa-nies [Special issue]. Journal of Governance & Regulation, 11(4), 237–245.
Omoush, M. M. (2025). Harnessing logistics in the era of generative artificial intelligence. In A. Al-Marzouqi, S. Salloum, K. Shaalan, T. Gaber, & R. Masa’deh (Eds.), Generative AI in Creative Industries (Vol. 1208). Springer. https://doi.org/10.1007/978-3-031-89175-5_39
Omoush, M. M. (2025). Human–AI collaboration in HRM and employee-centric outcomes: Evidence from E-supply chain management. Human Systems Management, Advanced online publication. https://doi.org/10.1177/01672533251365119
Omoush, M., Al-frejat, A. S., & Masa’deh, R. (2024). A systematic analysis of digital supply chain, big data and manufacturing lean time in indus-trial companies. Business Process Management Journal, 30(5), 1696–1715.
Patil, R., & Singh, S. (2025). AI integration with Sustainable Development Goals for competitive and resilient supply chains. Circular Economy and Sustainability. https://doi.org/10.1007/s43615-025-00416-8
Raman, R., Gupta, V., & Sharma, P. (2023). Leveraging artificial intelligence for achieving Sustainable Development Goals: An integrative frame-work. Technological Forecasting and Social Change, 191, 122512. https://doi.org/10.1016/j.techfore.2023.122512
Regona, M. (2024). Artificial intelligence and sustainable development goals (SDGs). Environmental Science and Policy, 158, 112–123. https://doi.org/10.1016/j.envsci.2024.07.012
Srivastava, R., Dey, P., & Bag, S. (2025). AI adoption and circular supply chains: Empirical insights from sustainable manufacturing sectors. Jour-nal of Business Research, 176, 114229. https://doi.org/10.1016/j.jbusres.2025.114229
Štreimikienė, D., Mikalauskienė, A., & Khan, M. (2025). AI, sustainability, and renewable energy: A pathway toward circular industrial ecosystems. Renewable & Sustainable Energy Reviews, 188, 113692. https://doi.org/10.1016/j.rser.2025.113692
Teixeira, C., Lopes, F., & Ferreira, J. (2025). Artificial intelligence in sustainable operations and supply chain management: A bibliometric and em-pirical review. Journal of Cleaner Production, 370, 136148. https://doi.org/10.1016/j.jclepro.2025.136148
Vishwakarma, S., Verma, S., & Singh, M. (2024). Dynamic capabilities in AI-enabled organisations: Linking digital transformation and sustainabil-ity outcomes. Technovation, 132, 102790. https://doi.org/10.1016/j.technovation.2024.102790
Wang, H., & Zhang, L. (2025a). Machine learning-based sustainability indicators for pharmaceutical logistics. Expert Systems with Applications, 240, 122551. https://doi.org/10.1016/j.eswa.2025.122551
Wang, H., & Zhang, L. (2025b). Artificial intelligence and sustainable manufacturing: Integrating SDGs into Industry 4.0 strategies. Sustainability, 17(7), 5121. https://doi.org/10.3390/su17075121