IDEAS home Printed from https://ideas.repec.org/a/cwk/ajocsk/2026-19.html

Artificial Intelligence Demand Forecasting and Supply Chain Performance of Large Supermarkets in Nairobi City County, Kenya

Author

Listed:
  • Kithandi, Charles Katua

    (Department of Economics, Daystar University)

  • Mwove, Reuben Musyoka

    (Daystar University)

Abstract

Artificial Intelligence (AI) has become a transformative force in contemporary supply chain management, offering capabilities that optimize operational efficiency, reduce costs, and facilitate data-driven decision-making. This study examined the effect of artificial intelligence demand forecasting on supply chain performance among large supermarkets in Nairobi City County, Kenya. The study was anchored on the Hybrid Intelligence Model and the Technology Acceptance Theory. A descriptive research design was adopted. The population comprised employees working in the supply chain departments of ten large supermarkets operating in Nairobi City County, Kenya. A sample size of 70 employees was selected, and questionnaires were pretested using seven respondents drawn from two Naivas supermarkets in Kiambu County, Kenya. Primary data were collected through structured questionnaires and analyzed using descriptive statistics including percentages, means, and standard deviations, as well as inferential statistics such as correlation and regression analysis using SPSS version 30. The regression results revealed that AI-demand forecasting had a statistically significant positive effect on supply chain performance (β1 = 0.714, p-value = 0.000

Suggested Citation

  • Kithandi, Charles Katua & Mwove, Reuben Musyoka, 2026. "Artificial Intelligence Demand Forecasting and Supply Chain Performance of Large Supermarkets in Nairobi City County, Kenya," African Journal of Commercial Studies, African Journal of Commercial Studies, vol. 7(1).
  • Handle: RePEc:cwk:ajocsk:2026-19
    DOI: 10.59413/ajocs/v7.i1.19
    as

    Download full text from publisher

    File URL: https://ijcsacademia.com/index.php/journal/article/view/424
    Download Restriction: no

    File URL: https://libkey.io/10.59413/ajocs/v7.i1.19?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Abderahman Rejeb & John G. Keogh & Horst Treiblmaier, 2019. "Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management," Future Internet, MDPI, vol. 11(7), pages 1-22, July.
    2. Tan, Weng Chun & Sidhu, Manjit Singh, 2022. "Review of RFID and IoT integration in supply chain management," Operations Research Perspectives, Elsevier, vol. 9(C).
    3. Min, Hokey, 2019. "Blockchain technology for enhancing supply chain resilience," Business Horizons, Elsevier, vol. 62(1), pages 35-45.
    4. Charles Guandaru Kamau & Nancy Nkatha Kinyua, 2025. "Application of Artificial Intelligence in Detecting Creative Accounting Tendencies Among Corporations in Kenya," African Journal of Commercial Studies, African Journal of Commercial Studies, vol. 6(6).
    5. Kuang-Sheng Liu & Ming-Hung Lin, 2021. "Performance Assessment on the Application of Artificial Intelligence to Sustainable Supply Chain Management in the Construction Material Industry," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    6. Kinkel, Steffen & Baumgartner, Marco & Cherubini, Enrica, 2022. "Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies," Technovation, Elsevier, vol. 110(C).
    7. Rohit Sharma & Anjali Shishodia & Angappa Gunasekaran & Hokey Min & Ziaul Haque Munim, 2022. "The role of artificial intelligence in supply chain management: mapping the territory," International Journal of Production Research, Taylor & Francis Journals, vol. 60(24), pages 7527-7550, December.
    8. Cubric, Marija, 2020. "Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study," Technology in Society, Elsevier, vol. 62(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Šimon Hána & Bart Lameijer, 2026. "AI-based systems adoption in business operations: barriers and performance effects," Operations Management Research, Springer, vol. 19(1), pages 1-18, March.
    2. Hu, Hui & Xu, Jiajun & Liu, Mengqi & Lim, Ming K., 2023. "Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning," Journal of Business Research, Elsevier, vol. 156(C).
    3. Yang, Yimin & Yi, Chaoqun & Li, Hailing & Dong, Xuesong & Yang, Lulu & Wang, Zilong, 2025. "An analysis on the role of artificial intelligence in green supply chains," Technological Forecasting and Social Change, Elsevier, vol. 217(C).
    4. Weili Yin & Wenxue Ran, 2021. "Theoretical Exploration of Supply Chain Viability Utilizing Blockchain Technology," Sustainability, MDPI, vol. 13(15), pages 1-25, July.
    5. Hangl, Johannes & Krause, Simon & Behrens, Viktoria Joy, 2023. "Drivers, barriers and social considerations for AI adoption in SCM," Technology in Society, Elsevier, vol. 74(C).
    6. Ulpan Tokkozhina & Ana Lucia Martins & Joao C. Ferreira, 2023. "Uncovering dimensions of the impact of blockchain technology in supply chain management," Operations Management Research, Springer, vol. 16(1), pages 99-125, March.
    7. Ghobakhloo, Morteza & Asadi, Shahla & Iranmanesh, Mohammad & Foroughi, Behzad & Mubarak, Muhammad Faraz & Yadegaridehkordi, Elaheh, 2023. "Intelligent automation implementation and corporate sustainability performance: The enabling role of corporate social responsibility strategy," Technology in Society, Elsevier, vol. 74(C).
    8. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2021. "Sustainable Supply Chains with Blockchain, IoT and RFID: A Simulation on Order Management," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    9. Yuk Ming Tang & Ka Yin Chau, 2025. "Blockchain evolution and management theoretical (BEMT) model based on integrated semantic similarity and co-citation analysis for next generation service industry," Operations Management Research, Springer, vol. 18(2), pages 475-494, June.
    10. Luisanna Cocco & Roberto Tonelli & Michele Marchesi, 2021. "Blockchain and Self Sovereign Identity to Support Quality in the Food Supply Chain," Future Internet, MDPI, vol. 13(12), pages 1-19, November.
    11. Sharma, Mahak & Antony, Rose & Sehrawat, Rajat & Cruz, Angel Contreras & Daim, Tugrul U., 2022. "Exploring post-adoption behaviors of e-service users: Evidence from the hospitality sector /online travel services," Technology in Society, Elsevier, vol. 68(C).
    12. Syed Imran Zaman & Sharfuddin Ahmed Khan & Sahar Qabool & Himanshu Gupta, 2023. "How digitalization in banking improve service supply chain resilience of e-commerce sector? a technological adoption model approach," Operations Management Research, Springer, vol. 16(2), pages 904-930, June.
    13. Vaggelis Papachristos & Constantinos Antonopoulos & Nikolaos P. Rachaniotis & Dimitris Spontas & Thomas K. Dasaklis, 2023. "The Potential of ICT Adoption in Promoting Sustainable and Resilient Supply Chains: Evidence from Greek Logistics Firms," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    14. Skare, Marinko & Soriano, Domingo Riberio, 2021. "Technological and knowledge diffusion link: An international perspective 1870–2019," Technology in Society, Elsevier, vol. 66(C).
    15. Mahmoona Khalil & Kausar Fiaz Khawaja & Muddassar Sarfraz, 2022. "The adoption of blockchain technology in the financial sector during the era of fourth industrial revolution: a moderated mediated model," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2435-2452, August.
    16. Bo Yuan & Faguo Wu & Zhiming Zheng, 2023. "Post quantum blockchain architecture for internet of things over NTRU lattice," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-21, February.
    17. Vimal Kumar Dixit & Rakesh Kumar Malviya & Sachin K. Patil & Himanshu Prajapati & Abhishek Agarwal, 2026. "Analysing the empirical research in digital supply chain: a state of art literature review and future research directions," Operational Research, Springer, vol. 26(1), pages 1-40, January.
    18. Zhang, Weidong & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, 2021. "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C).
    19. Samadhiya, Ashutosh & Yadav, Sanjeev & Kumar, Anil & Majumdar, Abhijit & Luthra, Sunil & Garza-Reyes, Jose Arturo & Upadhyay, Arvind, 2023. "The influence of artificial intelligence techniques on disruption management: Does supply chain dynamism matter?," Technology in Society, Elsevier, vol. 75(C).
    20. Sachin Kumar Mangla & Yiğit Kazançoğlu & Abdullah Yıldızbaşı & Cihat Öztürk & Ahmet Çalık, 2022. "A conceptual framework for blockchain‐based sustainable supply chain and evaluating implementation barriers: A case of the tea supply chain," Business Strategy and the Environment, Wiley Blackwell, vol. 31(8), pages 3693-3716, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cwk:ajocsk:2026-19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Charles G. Kamau (email available below). General contact details of provider: https://ijcsacademia.com/index.php/journal .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.