Abstract
Background: One of the most widely used technologies in the present era is the artificial intelligence-based ChatGPT technology, which was developed by OpenAI in 2022. However, there is a paucity of academic studies examining the utilisation of this technology in the context of logistics management, which plays a pivotal role in the global trade ecosystem. Moreover, no empirical study has been identified that examines the factors influencing user intentions in the context of ChatGPT usage in logistics management. The objectives of this research are twofold: firstly, to ascertain the factors influencing Turkish users' intentions to use this technology, and secondly, to address the aforementioned information gap. Methods: To this end, online and face-to-face surveys were conducted among 547 respondents, comprising university students in logistics departments (either currently employed in the sector or not), logistics professionals and academics. The data were analysed using PLS-SEM with the technology acceptance model (TAM) and the Theory of Planned Behavior (TPB) models. Results: The results indicated that the explained variance was 0.542. It was observed that users’ trust had a direct impact on perceived usefulness and perceived ease of use. Furthermore, intention was positively affected by attitude and perceived usefulness. Conclusions: ChatGPT, an artificial intelligence-based technology with the potential to transform business models across all sectors in the near future, also offers numerous opportunities in the context of logistics management. In this context, given that the present study is the first to examine users' intentions for use in the context of logistics management, it is hoped that it will contribute to the existing literature and provide both academics and industry stakeholders with a conceptual framework and future projections.
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Alnıpak, S., & Toraman, Y. (2025). ‘“I AM CHATGPT. WOULD YOU ACCEPT ME TO ASSIST YOU IN LOGISTICS MANAGEMENT?”’ AN EMPIRICAL STUDY FROM TÜRKIYE. Logforum, 21(3), 473–491. https://doi.org/10.17270/J.LOG.001251
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