THE ROLE OF GRAPHIC DESIGN IN ENHANCING AI TECHNOLOGY ACCEPTANCE AS A MEDIATOR FOR UTILIZATION BY BUSINESS GROUPS OF CHILDREN WITH DISABILITIES
DOI:
https://doi.org/10.31539/costing.v7i6.14276Keywords:
Graphic Design, AI Acceptance, AI Utilization, Inclusive Design, Children with Disabilities, Technology AdoptionAbstract
This study examines the role of inclusive graphic design in enhancing the acceptance and utilization of artificial intelligence (AI) technologies among children with disabilities. Using a quantitative approach, data were collected from 97 respondents within the Adisa business group, employing a structured questionnaire to measure graphic design quality, AI acceptance, and utilization. Partial Least Squares Structural Equation Modeling (PLS-SEM) was utilized to analyze the relationships between these variables. The findings reveal that graphic design significantly influences both the acceptance and utilization of AI technology, with user acceptance mediating this relationship. These results highlight the importance of user-centered and inclusive design in reducing barriers to AI adoption and ensuring equitable access for marginalized populations. By extending the Technology Acceptance Model to include inclusivity as a critical factor, this study contributes to the theoretical understanding of technology adoption while providing actionable insights for developers, businesses, and policymakers. The implications underscore the need for culturally sensitive and cost-effective design practices to foster widespread adoption and utilization of AI technologies. Future research is encouraged to explore the intersection of design, functionality, and socio-economic factors to further advance inclusive technological innovation.
References
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Zhang, Y., Luo, X., & Chen, H. (2021). Mediating role of user acceptance in the effect of graphic design on the utilization of AI technology. Journal of Business Research, 124, 540-548. https://doi.org/10.1016/j.jbusres.2020.11.020
Zhang, T., Zhang, Y., & Yang, H. (2022). How graphic design influences user trust and acceptance of AI technologies. Journal of Visual Communication and Image Representation, 74, 102893. https://doi.org/10.1016/j.jvcir.2020.102893
Boulos, M. N. K., Maramba, I., & Wheeler, S. (2021). The role of artificial intelligence in accessible healthcare: Challenges and opportunities for people with disabilities. Healthcare Technology Letters, 8(1), 25-32. https://doi.org/10.1049/htl.2020.0174
Brynjolfsson, E., & McAfee, A. (2020). The second machine age: Work, progress, and prosperity in a time of brilliant technologies.
W. W. Norton & Company. Cai, Y., Chen, S., & Liu, H. (2021). Trust in AI and its impact on healthcare professionals' acceptance of AI technology: A study in the healthcare sector. Journal of Healthcare Informatics Research, 5(2), 150-165. https://doi.org/10.1007/s41666-021-00085-1
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
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.
Holmes, W., Bialik, M., & Fadel, C. (2021). AI in education: A guide for educators and leaders. Springer.
Huang, X., Zhang, L., & Sun, C. (2021). Designing inclusive AI interfaces for children with disabilities: Usability and engagement. Journal of Human-Computer Interaction, 37(6), 754-765. https://doi.org/10.1080/10447318.2020.1834486
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2021). Artificial intelligence in healthcare: Past, present, and future. Seminars in Cancer Biology, 72, 4-13. https://doi.org/10.1016/j.semcancer.2020.08.004
Kim, Y., & Choi, J. (2020). The effect of design elements on the emotional response and willingness to accept AI technology. Journal of Business Research, 109, 211-220. https://doi.org/10.1016/j.jbusres.2019.11.022
Kuo, Y.-F., Lee, J.-S., & Chen, J.-H. (2021). The impact of graphic design on technology acceptance and usage: The case of AI applications. Computers in Human Behavior, 115, 106607. https://doi.org/10.1016/j.chb.2020.106607
Lankton, N. K., & McKnight, D. H. (2020). The role of trust in AI healthcare applications: A user’s perspective. Journal of Information Technology, 35(2), 171-185. https://doi.org/10.1177/0268396219888923
Lee, J., Cho, M., & Kim, Y. (2020). AI in manufacturing: From predictive maintenance to smart factories. Journal of Manufacturing Science and Engineering, 142(5), 051015. https://doi.org/10.1115/1.4046204
Liu, Y., Zhang, L., & Shi, M. (2021). The role of AI in workforce participation for children with disabilities: Social skills development and career training. Journal of Educational Technology, 18(2), 43-58. https://doi.org/10.1108/JET-04-2021-0114
Liu, J., Zhang, L., & Lu, X. (2021). The impact of user-centered design on the utilization of AI technology in children with disabilities. AI & Society, 36(3), 725-738. https://doi.org/10.1007/s00146-021-01102-z
Mao, Y., Chen, C., & Wang, W. (2020). AI adoption in education during the COVID-19 pandemic: A case study of remote learning. Educational Technology Research and Development, 68(6), 3255-3269. https://doi.org/10.1007/s11423-020-09839-4
Mendel, L., Rodriguez, A., & Chang, T. (2021). Scalable solutions for accessible AI technologies: Addressing the needs of children with disabilities. International Journal of AI and Society, 34(1), 77-90. https://doi.org/10.1007/s12325-021-01521-2
Pereira, D., Silva, R., & Pinto, M. (2021). The importance of inclusive graphic design for children with disabilities. International Journal of Human-Computer Interaction, 37(9), 853-867. https://doi.org/10.1080/10447318.2020.1842103
Rahman, A., Khan, A., & Ahmed, R. (2021). The intersection of AI and disability: Social and cultural influences on technology adoption. Disability & Society, 36(8), 1204-1221. https://doi.org/10.1080/09687599.2021.1924478
Razzak, M. I., Imran, M., & Xu, J. (2020). Deep learning for healthcare applications: A review. Journal of Healthcare Engineering, 2020, 1-17. https://doi.org/10.1155/2020/1674352
Santos, E., Costa, P., & Almeida, J. (2020). Simplifying AI interfaces for children with cognitive disabilities: A usability study. Journal of Special Education Technology, 35(4), 221-233. https://doi.org/10.1177/0162643420906677
Tariq, A., Ali, M., & Hassan, Z. (2021). Inclusive AI as a tool for social integration: Empowering children with disabilities. Social Inclusion, 9(2), 67-80. https://doi.org/10.17645/si.v9i2.3403
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Venkatesh, V., & Bala, H. (2021). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 52(3), 602-626. https://doi.org/10.1111/deci.12381
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2020). Unified theory of acceptance and use of technology: A longitudinal analysis of ICT implementation. MIS Quarterly, 44(4), 1063-1082. https://doi.org/10.25300/MISQ/2020/12729
Wang, S., Zhao, D., & Wu, L. (2020). Effects of user interface design on the acceptance and usage of AI technology: A psychological approach. International Journal of Information Management, 54, 102163. https://doi.org/10.1016/j.ijinfomgt.2020.102163
World Health Organization. (2022). World report on disability. https://www.who.int/disabilities/world_report/2022/en/
Zhang, Y., Liu, X., & Wu, T. (2021). Participatory design of AI technologies for children with disabilities: A case study. Journal of Inclusive Education, 15(5), 412-426. https://doi.org/10.1080/13603116.2021.1877442
Zhang, Y., Luo, X., & Chen, H. (2021). Mediating role of user acceptance in the effect of graphic design on the utilization of AI technology. Journal of Business Research, 124, 540-548. https://doi.org/10.1016/j.jbusres.2020.11.020
Zhang, T., Zhang, Y., & Yang, H. (2022). How graphic design influences user trust and acceptance of AI technologies. Journal of Visual Communication and Image Representation, 74, 102893. https://doi.org/10.1016/j.jvcir.2020.102893
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2024-12-31
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