The Impact of Digital Technology Adoption on Supply Chain Performance: A Case Study of Sonatrach
Keywords:
Internal Marketing, Organizational Creativity, Service sector, Banking sector, Algeria, Digital technology adoption, Supply chain performance, IoT and remote sensing, Big Data analytics, ERP systemsAbstract
This study rigorously examines the transformative impact of digital technology adoption on supply chain performance within the specific context of Sonatrach, Algeria. It directly addresses the pivotal question of how the strategic integration of advanced digital technologies reshapes performance outcomes. A quantitative, correlational research design is employed, analyzing survey data from 150 supply chain management professionals at Sonatrach. Pearson's correlation and regression techniques rigorously assess the relationships between adoption levels of IoT, remote sensing, Big Data analytics, and ERP systems and key performance indicators. The findings emphatically demonstrate significant, positive correlations between Big Data analytics and ERP adoption and enhanced supply chain performance. These results underscore the crucial role of data-driven decision-making and integrated systems, while also revealing vital areas for optimized IoT deployment strategies to maximize their potential.References
Aćimović, S., Mijušković, V., Marković, D., & Todorović-Spasenić, A. (2022). The relationship between logistics and organizational performance in a supply chain context. Serbian Journal of Management, 17(2), 333–349. https://doi.org/10.5937/sjm17-37401
Blichfeldt, H., & Faullant, R. (2021). Performance effects of digital technology adoption and product & service innovation – A process-industry perspective. Technovation, 105, 102275. https://doi.org/10.1016/j.technovation.2021.102275
Brintrup, A., Kosasih, E. E., MacCarthy, B. L., & Demirel, G. (2022). Digital supply chain surveillance. In The Digital Supply Chain (pp. 379–396). Elsevier. https://doi.org/10.1016/B978-0-323-91614-1.00022-8
Brous, P., Janssen, M., & Herder, P. (2020). The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations. International Journal of Information Management, 51, 101952. https://doi.org/10.1016/j.ijinfomgt.2019.05.008
Chen, Y. (2016a). Industrial information integration—A literature review 2006–2015. Journal of Industrial Information Integration, 2, 30–64. https://doi.org/10.1016/j.jii.2016.04.004
Chen, Y., Li, B., & Huo, B. (2025). Building operational resilience through digitalization: The roles of supply chain network position. Technological Forecasting and Social Change, 211, 123918. https://doi.org/10.1016/j.techfore.2024.123918
Dutta, P., Choi, T.-M., Somani, S., & Butala, R. (2020). Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transportation Research Part E: Logistics and Transportation Review, 142, 102067. https://doi.org/10.1016/j.tre.2020.102067
Govindan, K., Kannan, D., Jørgensen, T. B., & Nielsen, T. S. (2022). Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence. Transportation Research Part E: Logistics and Transportation Review, 164, 102725. https://doi.org/10.1016/j.tre.2022.102725
Gupta, S., Meissonier, R., Drave, V. A., & Roubaud, D. (2020). Examining the impact of Cloud ERP on sustainable performance: A dynamic capability view. International Journal of Information Management, 51, 102028. https://doi.org/10.1016/j.ijinfomgt.2019.10.013
Jabbar, A., Akhtar, P., & Dani, S. (2020). Real-time big data processing for instantaneous marketing decisions: A problematization approach. Industrial Marketing Management, 90,
569. https://doi.org/10.1016/j.indmarman.2019.09.001
Karnik, N., Bora, U., Bhadri, K., Kadambi, P., & Dhatrak, P. (2022). A comprehensive study on current and future trends towards the characteristics and enablers of industry 4.0. Journal of Industrial Information Integration, 27, 100294. https://doi.org/10.1016/j.jii.2021.100294
Konopasky, A., Artino, A. R., Battista, A., Ohmer, M., Hemmer, P. A., Torre, D., Ramani, D., Van Merrienboer, J., Teunissen, P. W., McBee, E., Ratcliffe, T., & Durning, S. J. (2020). Understanding context specificity: The effect of contextual factors on clinical reasoning. Diagnosis, 7(3), 257–264. https://doi.org/10.1515/dx-2020-0016
Lakhtaria, K. I., & Iyer, S. S. (2020). Data Analytics Using Satellite Remote Sensing in Healthcare Applications. In A. E. Hassanien, A. Darwish, & H. El-Askary (Eds.), Machine Learning and Data Mining in Aerospace Technology (Vol. 836, pp. 125–146). Springer International Publishing. https://doi.org/10.1007/978-3-030-20212-5_7
Laulita, N. B., Yulfiswandi, Ana, Agustino, M., Rusiana, N., & Lim, V. E. (2022). THE EFFECT OF ENTERPRISE RESOURCE PLANNING IMPLEMENTATION ON INCREASING COMPANY PERFORMANCE. MARGINAL : JOURNAL OF MANAGEMENT, ACCOUNTING, GENERAL FINANCE AND INTERNATIONAL ECONOMIC ISSUES, 1(3), 43–52. https://doi.org/10.55047/marginal.v1i3.188
Leminen, S., Rajahonka, M., Wendelin, R., & Westerlund, M. (2020). Industrial internet of things business models in the machine-to-machine context. Industrial Marketing Management, 84, 298–311. https://doi.org/10.1016/j.indmarman.2019.08.008
Moysiadis, T., Adamides, G., Stylianou, A., Zotos, N., Giannakopoulou, M., & Alexiou, G. (2021). Use of IoT technologies for irrigation and plant protection. In Bio-Economy and Agri- production (pp. 175–194). Elsevier. https://doi.org/10.1016/B978-0-12-819774-5.00010-2
Osrof, H. Y., Tan, C. L., Angappa, G., Yeo, S. F., & Tan, K. H. (2023). Adoption of smart farming technologies in field operations: A systematic review and future research agenda. Technology in Society, 75, 102400. https://doi.org/10.1016/j.techsoc.2023.102400 - 91 -
Reid, J., & Castka, P. (2023). The impact of remote sensing on monitoring and reporting— The case of conformance systems. Journal of Cleaner Production, 393, 136331. https://doi.org/10.1016/j.jclepro.2023.136331
Richey, R. G., Morgan, T. R., Lindsey-Hall, K., & Adams, F. G. (2016). A global exploration of Big Data in the supply chain. International Journal of Physical Distribution & Logistics Management, 46(8), 710–739. https://doi.org/10.1108/IJPDLM-05-2016-0134
Shahadat, M. M. H., Chowdhury, A. H. M. Y., Nathan, R. J., & Fekete-Farkas, M. (2023). Digital Technologies for Firms’ Competitive Advantage and Improved Supply Chain Performance. Journal of Risk and Financial Management, 16(2), 94. https://doi.org/10.3390/jrfm16020094
Swetha, M., E., N., & Parakh, S. (2024). An Impact of Empirical Data Analysis in the World of Business Environment. International Research Journal of Business Studies, 15(1), 97–109. https://doi.org/10.21632/irjbs.15.1.97-109
Townsend, M., Le Quoc, T., Kapoor, G., Hu, H., Zhou, W., & Piramuthu, S. (2018). Real- Time business data acquisition: How frequent is frequent enough? Information & Management, 55(4), 422–429. https://doi.org/10.1016/j.im.2017.10.002
Tripathi, S., & Gupta, M. (2019). A Current Review of Supply Chain Performance Measurement Systems. In K. Shanker, R. Shankar, & R. Sindhwani (Eds.), Advances in Industrial and Production Engineering (pp. 27–39). Springer Singapore. https://doi.org/10.1007/978-981-13-6412-9_4
Wang, X., Kumar, V., Kumari, A., & Kuzmin, E. (2022). Impact of Digital Technology on Supply Chain Efficiency in Manufacturing Industry. In V. Kumar, J. Leng, V. Akberdina, & E. Kuzmin (Eds.), Digital Transformation in Industry (Vol. 54, pp. 347–371). Springer International Publishing. https://doi.org/10.1007/978-3-030-94617-3_25
Zhao, Y., Isozaki, A., Herbig, M., Hayashi, M., Hiramatsu, K., Yamazaki, S., Kondo, N., Ohnuki, S., Ohya, Y., Nitta, N., & Goda, K. (2023). Intelligent sort‐timing prediction for image‐activated cell sorting. Cytometry Part A, 103(1), 88–97. https://doi.org/10.1002/cyto.a.24664
