IDEAS home Printed from https://ideas.repec.org/a/rnd/arjebs/v16y2024i1p82-93.html
   My bibliography  Save this article

The Role of Artificial Intelligence on Market Performance: Evidence from Scientific Review

Author

Listed:
  • Endalkachew Desta
  • Chalchissa Amantie

Abstract

The study's primary purpose was to review studies on the role of artificial intelligence in market performance. Artificial intelligence significantly impacts market performance by providing data analysis, personalization, demand forecasting, pricing optimization, customer support automation, risk assessment, and enhanced decision-making capabilities. By leveraging artificial intelligence (AI) effectively, Businesses can improve their competitiveness, improve customer satisfaction, increase revenue, and achieve sustainable growth in the market. A thorough assessment of the literature was done, and screening standards were applied, all to improve the study. Based on the inclusion and exclusion criteria for the articles, data extraction was done by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 45 published articles were analyzed, and significant data was extracted. The review’s findings collectively emphasize the crucial role of AI in enhancing market performance by improving sales, customer satisfaction, demand forecasting, pricing optimization, risk mitigation, and decision-making processes. As AI continues to advance, further research and practical implementations will likely uncover additional benefits and insights into its impact on market performance. To help more scholars understand and advance the numerous theories and models related to the topic, this concept overview provides guidance.

Suggested Citation

  • Endalkachew Desta & Chalchissa Amantie, 2024. "The Role of Artificial Intelligence on Market Performance: Evidence from Scientific Review," Journal of Economics and Behavioral Studies, AMH International, vol. 16(1), pages 82-93.
    • Handle: RePEc:rnd:arjebs:v:16:y:2024:i:1:p:82-93
    DOI: 10.22610/jebs.v16i1(J).3511
    as

    Download full text from publisher

    File URL: https://ojs.amhinternational.com/index.php/jebs/article/view/3511/2419
    Download Restriction: no
    File URL: https://ojs.amhinternational.com/index.php/jebs/article/view/3511
    Download Restriction: no
    File URL: https://libkey.io/10.22610/jebs.v16i1(J).3511?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. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    2. Janssen, Marijn & van der Voort, Haiko & Wahyudi, Agung, 2017. "Factors influencing big data decision-making quality," Journal of Business Research, Elsevier, vol. 70(C), pages 338-345.
    3. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    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. Rahman, Muhammad Sabbir & Bag, Surajit & Hossain, Md Afnan & Abdel Fattah, Fadi Abdel Muniem & Gani, Mohammad Osman & Rana, Nripendra P., 2023. "The new wave of AI-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    2. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Dr. Muhammad Waqas Rana & Mohammad Shahnawaz Ashfaq & Dr. Faizah Yasir Jalbani, 2024. "The Role of Artificial Intelligence in Influencer Marketing," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 1-13.
    4. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
    5. Robertson, Jeandri & Ferreira, Caitlin & Botha, Elsamari & Oosthuizen, Kim, 2024. "Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction," Business Horizons, Elsevier, vol. 67(5), pages 499-510.
    6. Mohammad Ali Yamin, 2021. "Investigating the Drivers of Supply Chain Resilience in the Wake of the COVID-19 Pandemic: Empirical Evidence from an Emerging Economy," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    7. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    8. Klein, Daniel & Ludwig, Christopher A. & Nicolay, Katharina, 2020. "Internal digitalization and tax-efficient decision making," ZEW Discussion Papers 20-051, ZEW - Leibniz Centre for European Economic Research.
    9. Jingmei Gao & Zahid Sarwar, 2024. "How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?," Information Technology and Management, Springer, vol. 25(3), pages 283-304, September.
    10. Drummond, Conor & McGrath, Helen & O'Toole, Thomas, 2023. "Beyond the platform: Social media as a multi-faceted resource in value creation for entrepreneurial firms in a collaborative network," Journal of Business Research, Elsevier, vol. 158(C).
    11. Mustak, Mekhail & Hallikainen, Heli & Laukkanen, Tommi & Plé, Loïc & Hollebeek, Linda D. & Aleem, Majid, 2024. "Using machine learning to develop customer insights from user-generated content," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    12. Ling Pan & Zeshui Xu & Marinko Skare, 2023. "Sustainable business model innovation literature: a bibliometrics analysis," Review of Managerial Science, Springer, vol. 17(3), pages 757-785, April.
    13. Hoyer, Wayne D. & Kroschke, Mirja & Schmitt, Bernd & Kraume, Karsten & Shankar, Venkatesh, 2020. "Transforming the Customer Experience Through New Technologies," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 57-71.
    14. Mari, Alex & Mandelli, Andreina & Algesheimer, René, 2024. "Empathic voice assistants: Enhancing consumer responses in voice commerce," Journal of Business Research, Elsevier, vol. 175(C).
    15. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    16. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    17. Zhang, Yaqiong & Wang, Shifu, 2023. "The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    18. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    19. Hosany, A. R. Shaheen & Hosany, Sameer & He, Hongwei, 2022. "Children sustainable behaviour: A review and research agenda," Journal of Business Research, Elsevier, vol. 147(C), pages 236-257.
    20. Di Caprio, Debora & Santos-Arteaga, Francisco J. & Tavana, Madjid, 2019. "The role of anticipated emotions and the value of information in determining sequential search incentives," Operations Research Perspectives, Elsevier, vol. 6(C).

    More about this item

    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:rnd:arjebs:v:16:y:2024:i:1:p:82-93. 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: Muhammad Tayyab (email available below). General contact details of provider: https://ojs.amhinternational.com/index.php/jebs .

    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.