Analysis of Factors Affecting Ai-Powered Design Tool Adoption in Ux Design in Sri Lanka
Authors
Kumara PDKA, Amitha
Department of Information Technology, University of Sri Jayewardenepura, Sri Lanka (LK)
AKILA, G A G
Department of Information Technology, University of Sri Jayewardenepura, Sri Lanka (LK)
Article Information
Publication Timeline
Submitted: 2025-05-18
Published: 2025-05-18
Abstract
Abstract: The integration of Artificial Intelligence (AI) into User Experience (UX) design has introduced significant transformations by enhancing efficiency, creativity, and decision-making. This study examines the critical factors influencing the adoption of AI-powered design tools among UX professionals in Sri Lanka. Employing a quantitative research approach, data were collected via a structured survey and analyzed using Structural Equation Modeling (SEM) through SmartPLS 4. Results reveal that positive attitudes toward technology, effort expectancy, performance expectancy, and facilitating conditions significantly predict adoption intention, while perceived risks and social influence exhibit limited influence. This research advances theoretical understanding by validating the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) within the UX context. Practical implications are drawn for encouraging AI adoption by creating supportive environments and enhancing perceptions of ease and usefulness. Limitations and directions for future cross-cultural and longitudinal research are discussed.
Keywords
UX Design, AI-Powered Tools, Technology Adoption, Sri Lanka, Structural Equation Modeling
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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References
1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T [Google Scholar] [Crossref]
2. Amarasinghe, A., & Wickramasinghe, S. (2020). A comprehensive review of cutaneous leishmaniasis in Sri Lanka and identification of existing knowledge gaps. Acta Parasitologica, 65(2), 300–309. https://doi.org/10.2478/s11686-019-00175-6 [Google Scholar] [Crossref]
3. Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430. https://doi.org/10.1016/j.chb.2015.04.024 [Google Scholar] [Crossref]
4. Chen, L. S. L., Jiang, M., Jia, F., & Liu, G. (2021). Artificial intelligence adoption in business-to-business marketing: Toward a conceptual framework. Journal of Business & Industrial Marketing, 37(5), 1025–1044. https://doi.org/10.1108/JBIM-03-2021-0145 [Google Scholar] [Crossref]
5. 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 [Google Scholar] [Crossref]
6. Dora, M., Kumar, A., Mangla, S. K., Pant, A., & Kamal, M. M. (2021). Critical success factors influencing artificial intelligence adoption in food supply chains. International Journal of Production Research, 60(14), 4621–4640. https://doi.org/10.1080/00207543.2021.1927991 [Google Scholar] [Crossref]
7. Dwivedi, Y. K., et al. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 [Google Scholar] [Crossref]
8. Fan, M., Li, Y., & Truong, K. (2020). Automatic detection of usability problem encounters in think-aloud sessions. ACM Transactions on Interactive Intelligent Systems, 10(2), 1–24. https://doi.org/10.1145/3375478 [Google Scholar] [Crossref]
9. Fan, M., Yang, X., Yu, T., Liao, V., & Zhao, J. (2022). Human-AI collaboration for UX evaluation: Effects of explanation and synchronization. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1–32. https://doi.org/10.1145/3512905 [Google Scholar] [Crossref]
10. Ferreira, J., & Monteiro, M. P. (2020). What are people doing about XAI user experience? A survey on AI explainability research and practice. In Design, User Experience, and Usability. Interaction Design (pp. 19–32). Springer. https://doi.org/10.1007/978-3-030-49760-6_2 [Google Scholar] [Crossref]
11. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley. [Google Scholar] [Crossref]
12. Fonseka, K., Jaharadak, A., & Raman, M. (2022). Impact of e-commerce adoption on business performance of SMEs in Sri Lanka: Moderating role of artificial intelligence. International Journal of Social Economics, 49(10), 1518–1531. https://doi.org/10.1108/IJSE-01-2021-0040 [Google Scholar] [Crossref]
13. Gmeiner, F., Yang, H., Yao, L., Holstein, K., & Martelaro, N. (2023). Exploring challenges and opportunities to support designers in learning to co-create with AI-based manufacturing design tools. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3580707 [Google Scholar] [Crossref]
14. Gu, B., & Sung, Y. (2023). UX framework including imbalanced UX dataset reduction method for analyzing interaction trends of agent systems. Sensors, 23(3), 1651. https://doi.org/10.3390/s23031651 [Google Scholar] [Crossref]
15. Hong Chuyen, N. T., & Vinh, N. T. (2023). An empirical analysis of predictors of AI-powered design tool adoption. TEM Journal, 12(4), 1482–1489. https://doi.org/10.18421/TEM124-28 [Google Scholar] [Crossref]
16. Khanolkar, P. M., Vrolijk, A., & Olechowski, A. (2023). A case study of the decision-making behind the automation of a composites-based design process. Proceedings of the Design Society, 3, 49–58. https://doi.org/10.1017/pds.2023.6 [Google Scholar] [Crossref]
17. Kalantari, M., & Lethbridge, C. (2022). Human-AI collaboration challenges in design: A case study and literature review. International Journal of Human–Computer Studies, 162, 102772. https://doi.org/10.1016/j.ijhcs.2022.102772 [Google Scholar] [Crossref]
18. Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the technology acceptance model (TAM) in combination with the technology–organisation–environment (TOE) framework. Buildings, 12(2), 90. https://doi.org/10.3390/buildings12020090 [Google Scholar] [Crossref]
19. Nikolopoulou, K. (2023). Partial least squares structural equation modeling (PLS-SEM) in educational research: An updated guideline. Education and Information Technologies, 28(1), 129–148. https://doi.org/10.1007/s10639-021-10807-8 [Google Scholar] [Crossref]
20. Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689–703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004 [Google Scholar] [Crossref]
21. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275 [Google Scholar] [Crossref]
22. Rai, A., Constantinides, P., & Sarker, S. (2021). Next-generation digital platforms: Toward human-AI hybrids. MIS Quarterly, 45(3), 1433–1450. https://doi.org/10.25300/MISQ/2021/15753 [Google Scholar] [Crossref]
23. Rauschnabel, P. A., He, J., & Ro, Y. K. (2018). Antecedents to the adoption of augmented reality smart glasses: A closer look at privacy risks. Journal of Business Research, 92, 374–384. https://doi.org/10.1016/j.jbusres.2018.08.008 [Google Scholar] [Crossref]
24. Vall, R. R. F. de la, & Araya, F. G. (2023). Exploring the benefits and challenges of AI-language learning tools. International Journal of Social Sciences and Humanities Invention, 10(1), 7569–7576. https://doi.org/10.18535/ijsshi/v10i01.05 [Google Scholar] [Crossref]
25. 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 [Google Scholar] [Crossref]
26. Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212–227. https://doi.org/10.1111/jpim.12522 [Google Scholar] [Crossref]
27. Wang, Q., Sun, X., & Yang, J. (2020). The influence of perceived ease of use on user adoption of online services: A longitudinal study. Journal of Organizational Computing and Electronic Commerce, 30(3), 210–230. https://doi.org/10.1080/10919392.2020.1778476 [Google Scholar] [Crossref]
28. Yang, Q., Steinfeld, A., Rosé, C. P., & Zimmerman, J. (2024). Re-examining human-AI collaboration design challenges: Insights for future UX. International Journal of Human-Computer Interaction, 40(1), 56–74. https://doi.org/10.1080/10447318.2023.2221484 [Google Scholar] [Crossref]
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