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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Walkowiak, Emmanuelle, 2023. "Task-interdependencies between Generative AI and Workers," Economics Letters, Elsevier, vol. 231(C).
    5. 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.
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    Cited by:

    1. Hyun Yong Ahn, 2024. "AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
    2. Bejinaru Ruxandra & Toma Marian-Vladuț, 2024. "Enhancing Business Operations Through Microlearning, BPM and RPA," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 1831-1847.

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