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Benefits and Risks of Introducing Artificial Intelligence Into Trade and Commerce: The Case of Manufacturing Companies in West Africa

Author

Listed:
  • Zelin Zhuo

    (Institute of International and Comparative Education, Research Center for Hong Kong &Macau Youth Education, South China Normal University, Guangzhou, China)

  • Frank Okai Larbi

    (Institute of International and Comparative Education, Research Center for Hong Kong &Macau Youth Education, South China Normal University, Guangzhou, China)

  • Eric Osei Addo

    (School of International Trade and Economics, University of International Business and Economics, Beijing, China)

Abstract

With innovations in technology, the application of artificial intelligence (A.I) in the area of commerce is rising to the top with an expected growing number of business transactions not just for entrepreneurs but for consumers as well. It advances the understanding of how A.I. can be used to enhance businesses around the world by establishing their presence online to reach customers beyond borders. This study highlights the benefits and risks of introducing A.I. into trade in terms of how the commerce industry operates and revolutionize products shopping. Significantly, the primary aim of this paper is to explore ways A.I. is integrated into commerce to help understand its impact on existing/potential customers and its efficiency in sales processes. With a sample size of 2,903 manufacturing companies in four West-African countries, the empirical results show that value-based adoption of A.I. approach outperforms the traditional/human search of customers’ products delivery in both convenience, accuracy and profitability. Furthermore, A.I. approach within commerce achieved competitive advantage with several modernized customer service machine learning approach such as automated content creation, voice assistance, image search among others. Clearly, this shows that the application of A.I system into commerce introduces significant competitive advantages in terms of trust, quality, openness and security.

Suggested Citation

  • Zelin Zhuo & Frank Okai Larbi & Eric Osei Addo, 2021. "Benefits and Risks of Introducing Artificial Intelligence Into Trade and Commerce: The Case of Manufacturing Companies in West Africa," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 174-174, February.
    • Handle: RePEc:aes:amfeco:v:23:y:2021:i:56:p:174
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    References listed on IDEAS

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    1. Richard H. Thaler, 2008. "Mental Accounting and Consumer Choice," Marketing Science, INFORMS, vol. 27(1), pages 15-25, 01-02.
    2. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    3. John Sutton & Daniel Trefler, 2016. "Capabilities, Wealth, and Trade," Journal of Political Economy, University of Chicago Press, vol. 124(3), pages 826-878.
    4. Sarv Devaraj & Rajiv Kohli, 2003. "Performance Impacts of Information Technology: Is Actual Usage the Missing Link?," Management Science, INFORMS, vol. 49(3), pages 273-289, March.
    5. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    6. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial Intelligence; Human Interaction; Commerce; Value-based Adoption model (VAM); Probit Model; West Africa;
    All these keywords.

    JEL classification:

    • O2 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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