IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i3p1166-d1329569.html
   My bibliography  Save this article

Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review

Author

Listed:
  • Humaid Al Naqbi

    (Department of Industrial Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Zied Bahroun

    (Department of Industrial Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Vian Ahmed

    (Department of Industrial Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

Abstract

In this review, utilizing the PRISMA methodology, a comprehensive analysis of the use of Generative Artificial Intelligence (GAI) across diverse professional sectors is presented, drawing from 159 selected research publications. This study provides an insightful overview of the impact of GAI on enhancing institutional performance and work productivity, with a specific focus on sectors including academia, research, technology, communications, agriculture, government, and business. It highlights the critical role of GAI in navigating AI challenges, ethical considerations, and the importance of analytical thinking in these domains. The research conducts a detailed content analysis, uncovering significant trends and gaps in current GAI applications and projecting future prospects. A key aspect of this study is the bibliometric analysis, which identifies dominant tools like Chatbots and Conversational Agents, notably ChatGPT, as central to GAI’s evolution. The findings indicate a robust and accelerating trend in GAI research, expected to continue through 2024 and beyond. Additionally, this study points to potential future research directions, emphasizing the need for improved GAI design and strategic long-term planning, particularly in assessing its impact on user experience across various professional fields.

Suggested Citation

  • Humaid Al Naqbi & Zied Bahroun & Vian Ahmed, 2024. "Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review," Sustainability, MDPI, vol. 16(3), pages 1-37, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1166-:d:1329569
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/3/1166/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/3/1166/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Walkowiak, Emmanuelle, 2023. "Task-interdependencies between Generative AI and Workers," Economics Letters, Elsevier, vol. 231(C).
    2. Peng Zhang & Maged N. Kamel Boulos, 2023. "Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges," Future Internet, MDPI, vol. 15(9), pages 1-15, August.
    3. Theo Araujo & Ward van Zoonen & Claartje ter Hoeven, 2022. "“A Large Playground†: Examining the Current State and Implications of Conversational Agent Adoption in Organizations," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 19(07), pages 1-23, November.
    4. James Lappeman & Siddeeqah Marlie & Tamryn Johnson & Sloane Poggenpoel, 2023. "Trust and digital privacy: willingness to disclose personal information to banking chatbot services," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(2), pages 337-357, June.
    5. Dhir Gala & Amgad N. Makaryus, 2023. "The Utility of Language Models in Cardiology: A Narrative Review of the Benefits and Concerns of ChatGPT-4," IJERPH, MDPI, vol. 20(15), pages 1-14, July.
    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. Li, Chia-Ying & Fang, Yu-Hui & Chiang, Yu-Hung, 2023. "Can AI chatbots help retain customers? An integrative perspective using affordance theory and service-domain logic," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    2. Swaraj S. Bharti & Kanika Prasad & Shwati Sudha & Vineeta Kumari, 2023. "Customer acceptability towards AI-enabled digital banking: a PLS-SEM approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 779-793, December.

    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:gam:jsusta:v:16:y:2024:i:3:p:1166-:d:1329569. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.