How AI Literacy Affects the Intention to Use AIGC: An Empirical TAM-Based Study_Vol. 2 No. 3 (JBDC 2024)_Journal of Big Data and Computing (ISSN: 2959-0590)

Home > Journal of Big Data and Computing (ISSN: 2959-0590) > Vol. 2 No. 3 (JBDC 2024) >

How AI Literacy Affects the Intention to Use AIGC: An Empirical TAM-Based Study

DOI: https://doi.org/10.62517/jbdc.202401326

Author(s)

Sichen Zhang*

Affiliation(s)

Xianda College of Economics and Humanities, Shanghai International Studies University, Shanghai, China *Corresponding author

Abstract

In the contemporary digital and intelligent society, the accelerated evolution of Artificial Intelligence-Generated Content (AIGC) technology is profoundly transforming a multitude of industries. Nevertheless, there is a notable discrepancy between the public's awareness and acceptance of AIGC, which is largely contingent upon individual AI literacy. This study, which is based on the Technology Acceptance Model (TAM), explores the impact of AI literacy on the intention to use AIGC, with a particular focus on the mediating roles of Perceived Usefulness and Perceived Ease of Use. The results of the regression analysis and structural equation modelling (SEM), based on 630 valid questionnaires, demonstrate that AI literacy has a significant positive influence on Perceived Usefulness and Perceived Ease of Use, which in turn have a positive impact on wanting to use AIGC. Perceived Usefulness and Perceived Ease of Use act as partial mediators between AI literacy and intention to use AIGC. This work provides theoretical support for the widespread adoption of AIGC and offers practical guidance for businesses and policymakers to improve public AI literacy.

Keywords

AI Literacy; Artificial Intelligence Generated Content; AIGC; TAM

References

[1] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [2] Gao, B., Xie, H., Yu, S., Wang, Y., Zuo, W., & Zeng, W. (2024). Exploring user acceptance of AI image generator: Unveiling influential factors in embracing an artistic AIGC software. In F. Zhao & D. Miao (Eds.), AI-generated content. AIGC 2023. Communications in computer and information science (Vol. 1946). Springer, Singapore. https://doi.org/10.1007/978-981-99-7587-7_17 [3] Gelovani, S., Theocharis, Y., Koc-Michalska, K., & Bimber, B. (2024). Intergroup ethnocentrism and social media: Evidence from three Western democracies. Information, Communication & Society, 1–19. https://doi.org/10.1080/1369118X.2024.2375259 [4] Huang, M. H., & Rust, R. T. (2022). A framework for collaborative artificial intelligence in marketing. Journal of Retailing, 98(2), 209-223. https://doi.org/10.1016/j.jretai.2021.03.001.[5] Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727 [6] Liu, Z., & Chen, Z. (2024). Cross-cultural perspectives on artificial intelligence generated content (AIGC): A comparative study of attitudes and acceptance among global products. In P. L. P. Rau (Ed.), Cross-cultural design. HCII 2024. Lecture notes in computer science (Vol. 14702). Springer. https://doi.org/10.1007/978-3-031-60913-8_20 [7] Mortensen, M. (2024). Sneaking AI through the back door: Constructing the identity of Capitol Hill rioters through social media images and facial recognition technologies. Information, Communication & Society. https://doi.org/10.1080/1369118X.2024.2358164. [8] Vallès-Peris, N., & Pareto, J. (2024). Artificial intelligence as a mode of ordering: Automated decision-making in primary care. Information, Communication & Society. https://doi.org/10.1080/1369118X.2024.2406802. [9] Zhou, Q., Xu, Y., & Cai, Y. (2024). An examination of AI literacy among university students: Current status and multifaceted influencing factors. Documentation, Information & Knowledge, 41(3), 38–48. https://doi.org/10.13366/j.dik.2024.03.038. [10] Zhang, M., Xu, Y., & Wei, X. (2024). AI-centered vs. human-centered: Exploring users’ attitude toward AIGC in varying forms of human-AI collaboration. In PACIS 2024 Proceedings (Paper 13). https://aisel.aisnet.org/pacis2024/track01_aibussoc/track01_aibussoc/13.