The Impact of Big Data on Purchasing and Procurement in Egypt
DOI:
https://doi.org/10.47604/gjppm.1956Keywords:
Impact, Big Data, Purchasing, Procurement, EgyptAbstract
Purpose: The study sought to analyze the impact of big data on purchasing and procurement in Egypt.
Methodology: The research was conducted entirely on computers. Secondary data, or data that doesn't require actual observation in the field, are the focus of desk research. Because it requires little more than an executive's time, telephone rates, and directories, desk research is generally seen as a low-cost strategy in comparison to field research. As a result, the research used data that had already been collected and reported. This secondary data was readily available via the internet's digital library and scholarly articles.
Findings: The results show that in conclusion, big data has had a significant impact on the way businesses operate, particularly when it comes to purchasing and procurement. It provides businesses with valuable insights into their customers' purchasing patterns and preferences. Big data is having a significant impact on purchasing and procurement in Egypt. It is enabling businesses, organizations and individuals to make more informed decisions, enhance efficiency and reduce costs
Unique Contribution to Theory, Practice and Policy: Future research in the field of purchasing and procurement may be grounded in the behavioral theory and the resource dependency theory. Policymakers, researchers and academics from all across the world will all stand to gain from this study's findings. Executives in charge of national purchasing and procurement initiatives will also use the study's findings to boost the big data performance across the board. The research suggests that the purchasing and procurement sector should implement big data policies to boost the effectiveness of their primary operations and activities.
Downloads
References
AbdelLatif, L., & Zaky, M. (2013, December). The Macro-Micro Nexus and Public Procurement Support Policy for SMEs: The Case of Pharmaceuticals in Egypt. In Economic Research Forum Working Papers (No. 818).
AlNuaimi, B. K., Khan, M., & Ajmal, M. M. (2021). The role of big data analytics capabilities in greening e-procurement: A higher order PLS-SEM analysis. Technological Forecasting and Social Change, 169, 120808.
Bag, S. (2016). Fuzzy VIKOR approach for selection of big data analyst in procurement management. Journal of Transport and Supply Chain Management, 10(1), 1-6.
Bienhaus, F., & Haddud, A. (2018). Procurement 4.0: factors influencing the digitisation of procurement and supply chains. Business Process Management Journal.
Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170-180.
Brewer, B., Wallin, C., & Ashenbaum, B. (2014). Outsourcing the procurement function: Do actions and results align with theory? Journal of purchasing and Supply Management, 20(3), 186-194.
Dey, N., Ella Hassanien, A., Bhatt, C., S Ashour, A., & Chandra Satapathy, S. (2018). Internet of things and big data analytics toward next-generation intelligence. by Springer Nature.
Elgendy, A. (2021). The mediating effect of big data analysis on the process orientation and information system software to improve supply chain process in Saudi Arabian industrial organizations. International Journal of Data and Network Science, 5(2), 135-142.
ElZahed, M., & Marzouk, M. (2022). Smart archiving of energy and petroleum projects utilizing big data analytics. Automation in Construction, 133, 104005.
Faccia, A., Mosteanu, N. R., Fahed, M., & Capitanio, F. (2019, August). Accounting information systems and ERP in the UAE: an assessment of the current and future challenges to handle big data. In Proceedings of the 2019 3rd International Conference on Cloud and Big Data Computing (pp. 90-94).
Gholizadeh, H., Fazlollahtabar, H., & Khalilzadeh, M. (2020). A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data. Journal of Cleaner Production, 258, 120640.
Guangting, Z., & Junxuan, Z. (2014). The Study of Impact of" Big Data" to Purchasing Intention. International Journal of Business and Social Science, 5(10).
Ivanov, D. (2017). Simulation-based single vs. dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management, 11(1), 24-43.
Jaber-Chehayeb, R. I. (2022). How big data will be an added value to SCM? BAU Journal-Creative Sustainable Development, 4(1), How-Big.
Kholaif, M. M. N. H. K., & Xiao, M. (2023). Is it an opportunity? COVID-19's effect on the green supply chains, and perceived service's quality (SERVQUAL): the moderate effect of big data analytics in the healthcare sector. Environmental Science and Pollution Research, 30(6), 14365-14384.
Lamba, K., & Singh, S. P. (2017). Big data in operations and supply chain management: current trends and future perspectives. Production Planning & Control, 28(11-12), 877-890.
Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of broadcasting & electronic media, 57(1), 34-52.
Mageto, J. (2021). Big data analytics in sustainable supply chain management: A focus on manufacturing supply chains. Sustainability, 13(13), 7101.
Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2023). Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management & Data Systems, 123(1), 278-301.
Maroufkhani, P., Tseng, M. L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International journal of information management, 54, 102190.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
Megeid, A., & Sobhy, N. (2022). The Role of Big Data Analytics in Supply Chain "3Fs": Financial Reporting, Financial Decision Making and Financial Performance "An Applied Study". الÙکر المØاسبى, 26(2), 207-268."Ž
Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses (Vol. 578). John Wiley & Sons.
Moretto, A., Ronchi, S., & Patrucco, A. S. (2017). Increasing the effectiveness of procurement decisions: The value of big data in the procurement process. International Journal of RF Technologies, 8(3), 79-103.
Mwai, N. W., Kiplang'at, J., & Gichoya, D. (2014). Application of resource dependency theory and transaction cost theory in analyzing outsourcing information communication services decisions: A case of selected public university libraries in Kenya. The Electronic Library.
Radhakrishnan, A., David, D. J., Sridharan, S. V., & Davis, J. S. (2018). Re-examining supply chain integration: a resource dependency theory perspective. International Journal of Logistics Systems and Management, 30(1), 1-30.
Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., & Mehta, A. (2018). Impact of big data on supply chain management. International Journal of Logistics Research and Applications, 21(6), 579-596.
Razaghi, S., & Shokouhyar, S. (2021). Impacts of big data analytics management capabilities and supply chain integration on global sourcing: a survey on firm performance. The Bottom Line, 34(2), 198-223.
Richey Jr, 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.
Saleha, W. A., Abdelkaderb, S. M., Rashada, H., & Abdelgawad, A. (2023). Statistical techniques for big data analytics in IoT-enabled green supply chain management: a survey. المجلة العربية للقياس والتقويم, 4(7). "Ž
Shim, J. P., & Taylor, R. (2019). Purchase-based analytics and big data for actionable insights. IT Professional, 21(5), 48-56.
Sundarakani, B., Ajaykumar, A., & Gunasekaran, A. (2021). Big data driven supply chain design and applications for blockchain: An action research using case study approach. Omega, 102, 102452.
Tamym, L., Benyoucef, L., Moh, A. N. S., & El Ouadghiri, M. D. (2021). A big data based architecture for collaborative networks: supply chains mixed-network. Computer Communications, 175, 102-111.
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330.
Wibowo, S., Hidayat, R., Suryana, Y., Sari, D., & Kaltum, U. (2020, October). Measuring the Effect of Advertising Value and Brand Awareness on Purchase Intention through the Flow Experience Method on Facebook's Social Media Marketing Big Data. In 2020 8th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-5). IEEE.
Yang, S., Su, Y., Wang, W., & Hua, K. (2019). Research on developers' green procurement behavior based on the theory of planned behavior. Sustainability, 11(10), 2949.
Yildiz Çankaya, S. (2020). The effects of strategic sourcing on supply chain strategies. Journal of Global Operations and Strategic Sourcing, 13(2), 129-148.
Zhang, J., Chan, F. T., & Xu, X. (2022). Data-driven analysis on optimal purchasing decisions in combined procurement. International Journal of Production Research, 1-14.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Layla Sayed
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
