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How can generative AI help in different parts of research? An experiment study on smart cities’ definitions and characteristics

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  • Dashkevych, Oleg
  • Portnov, Boris A.

Abstract

Artificial intelligence (AI) engines, such as ChatGPT, InferKit, and DeepAI, are very popular today and new AI engines, such as Google Bard, Chinchilla AI DeepMind, and GPT-4, constantly emerge. However, question remains how these new data management tools can assist scholars in improving the research design and implementation. In an attempt to answer this question, we focus on one particular research field – definition and identification of smart cities (SCs), – and compare the answers provided by different AI engines with the answers given in a sequence of research papers, prepared without the use of AI and recently published by these authors. In particular, the following aspects of the original studies were re-analysed here using the AI input: a) problem definition; b) summary of current knowledge; c) identification of unknowns; d) research strategy, and e) recommendations for research and practice. As the study reveals, the recommendations of AI engines are, at times, inconsistent and data sources cited are often inaccurate. However, as such engines scan multiple open sources and retrieve relevant information, they can help to bridge gaps in the summary of background studies and streamline the research design, by supplementing missing or overlooked information.

Suggested Citation

  • Dashkevych, Oleg & Portnov, Boris A., 2024. "How can generative AI help in different parts of research? An experiment study on smart cities’ definitions and characteristics," Technology in Society, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:teinso:v:77:y:2024:i:c:s0160791x24001039
    DOI: 10.1016/j.techsoc.2024.102555
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    References listed on IDEAS

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    1. Amany Elbanna & Yogesh Dwivedi & Deborah Bunker & David Wastell, 2020. "The Search for Smartness in Working, Living and Organising: Beyond the ‘Technomagic’," Information Systems Frontiers, Springer, vol. 22(2), pages 275-280, April.
    2. Nishant, Rohit & Kennedy, Mike & Corbett, Jacqueline, 2020. "Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda," International Journal of Information Management, Elsevier, vol. 53(C).
    3. Dirk Helbing & Farzam Fanitabasi & Fosca Giannotti & Regula Hänggli & Carina I. Hausladen & Jeroen van den Hoven & Sachit Mahajan & Dino Pedreschi & Evangelos Pournaras, 2021. "Ethics of Smart Cities: Towards Value-Sensitive Design and Co-Evolving City Life," Sustainability, MDPI, vol. 13(20), pages 1-25, October.
    4. Dashkevych, Oleg & Portnov, Boris A., 2023. "Human-centric, sustainability-driven approach to ranking smart cities worldwide," Technology in Society, Elsevier, vol. 74(C).
    5. Oleg Dashkevych & Boris A. Portnov, 2022. "Criteria for Smart City Identification: A Systematic Literature Review," Sustainability, MDPI, vol. 14(8), pages 1-34, April.
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    Cited by:

    1. Teng, Qiuling & Bai, Xiaoyu & Apuke, Oberiri Destiny, 2024. "Modelling the factors that affect the intention to adopt emerging digital technologies for a sustainable smart world city," Technology in Society, Elsevier, vol. 78(C).
    2. Thomas, Llewellyn D.W. & Romasanta, Angelo Kenneth G. & Pujol Priego, Laia, 2026. "Jagged competencies: Measuring the reliability of generative AI in academic research," Journal of Business Research, Elsevier, vol. 203(C).
    3. Zhao, Xuefeng & Wu, Weiwei & Wu, Delin, 2024. "How does AI perform in industry chain? A patent claims analysis approach," Technology in Society, Elsevier, vol. 79(C).

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