## 5 Discussion
### Research Implications
#### 5.3.3 Implementation Costs and Return of Investment (ROI)
Firstly, although minimal privacy concerns and biases were found on average, there is potential for these issues to arise. Recommendation systems may inadvertently favor demographic groups with better access to advanced devices, leading to unequal benefits. Future research should consider implementing safeguards and equitable algorithms to mitigate these biases. Secondly, the detailed implementation costs and return on investment (ROI) associated with integrating AI and AR technologies were not explored. While the potential benefits are significant, the initial and ongoing financial investments are substantial and warrant further investigation. Comprehensive cost-benefit analyses in future studies could guide e-commerce platforms in making informed decisions. The study employed purposive sampling method, which, while ensuring representation of specific user groups, may limit the generalizability of the findings to the broader population. Future research should consider a more diverse and larger sample to enhance the generalizability of the results. Additionally, the rapid pace of technological advancements in AI and AR means that the findings of this study may quickly become outdated. Continuous updates and follow-up studies are necessary to keep the research relevant and reflective of the current state of technology. The focus on users who have engaged with the AR try-on feature for cosmetics on Shopee may overlook the experiences of those who have not used these features. Including these users in future research could provide a more comprehensive understanding of the barriers to adoption and broader user perspectives. Data collection in this study relied on self-reported measures, which can introduce response biases. The use of quantitative methods provided a broad overview but may lack the depth of qualitative insights. Incorporating qualitative approaches, such as interviews or focus groups, in future research could offer deeper insights into user experiences and perceptions. By acknowledging these limitations, this study provides a transparent view of its scope and boundaries, offering a solid foundation for future research to build upon. Conclusions  
The results of this study provide robust support for the theoretical models applied, specifically, the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). The findings demonstrate that perceived ease of use and perceived usefulness are critical determinants of user attitudes towards AI-AR technologies in e-commerce. Furthermore, perceived security and trust are essential in fostering user acceptance and intention to use these technologies. These results are consistent with prior research, reinforcing the importance of these constructs in technology adoption models. The study's contributions lie in extending the application of these models to the context of AI and AR in the cosmetics industry on Shopee, providing both theoretical and practical insights. Overall, this study reveals that the integration of AI and AR technologies in personalized recommendations, particularly on the Shopee e-commerce platform, has a significant positive impact on customer usage intention. Key factors such as perceived ease of use, perceived usefulness, user trust, and innovativeness play important roles in shaping user attitudes and intentions towards the adoption of AI-AR technology. The findings contribute to a better understanding of the interaction dynamics between customers and AI-AR technologies in the context of online shopping. The practical implications of this research can help e-commerce companies improve AI-AR technology implementation strategies and provide a better shopping experience for users.