## 5 Discussion
### Research Implications
#### 5.3.3 Implementation Costs and Return of Investment (ROI)
Based on the research results, significant conclusions can be drawn regarding the two main problem formulations:  
* The impact of AI and AR technology integration in personalized recommendation on customer intention to use: The results show that the integration of AI and AR technologies in personalized recommendations has a significant positive impact on customer usage intentions. Factors such as perceived ease of use, benefits of use, and user trust in these technologies contribute positively to the formation of usage intentions. This shows that the implementation of AI-AR technology in presenting personalized recommendations is able to increase user interest and intention to take advantage of these features in the context of online shopping. * Factors influencing customers' intention to use the integration of AI and AR technology in personalized Recommendation: The key factors that influence usage intention involve perceived ease of use, benefits of use, user trust, and innovativeness. The use of AI-AR technology in presenting personalized recommendations on e-commerce platforms, such as Shopee, requires an easy-to-use interface design, emphasis on the benefits provided to users, and efforts to increase the level of user trust in the technology. In addition, the innovation factor is also proven to play an important role in shaping user attitudes and intentions towards the use of AI-AR technology. The following are some suggestions for further research that can be carried out based on the findings of this study:  
* Expansion to other platforms: Conduct similar research on other e-commerce platforms to understand whether the findings are generally applicable or whether there is variability in the use of AI-AR technology across different e-commerce contexts. * Demographic variability analysis: Conduct deeper analysis into the variability in the impact of AI-AR technologies based on demographic factors such as age, gender, and education level. This can provide deeper insights into how these factors affect user acceptance and intentions. * Qualitative studies: Engage in qualitative studies, such as interviews or focus groups, to understand user perspectives more deeply. This can provide richer context and explanations related to users' motivations and experiences. Conceptualization, S.R.A. and B.P. ; Formal analysis, S.R.A. ; Methodology, S.R.A. and I.E. ; Resources, B.P. and E.H.P. ; Software, S.R.A. ; Supervision, B.P., I.E. and E.H.P. ; Validation, B.P., I.E. and E.H.P. ; Writing--original draft, S.R.A. ; Writing--review & editing, S.R.A., B.P. and I.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding and the APC was funded by Faculty of Computer Science, University of Indonesia. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. **Conflicts of Interest:** The authors declare no conflict of interest.