Kemajuan teknologi dalam industri keuangan telah merevolusi industri yang disebut fintech, yang mengarah pada munculnya financial robo-advisor. Financial robo-advisor ini menarik calon investor dengan menggunakan machine learning algorithm untuk memberikan panduan investasi otomatis dengan pengawasan manusia yang minimal, berdasarkan tujuan keuangan dan toleransi risiko pengguna. Beberapa penelitian telah mengeksplorasi niat untuk mengadopsi financial robo-advisor. Penelitian ini menyelidiki niat pengguna untuk berinvestasi dengan financial robo-advisor, dengan fokus pada beberapa faktor, seperti algorithm interpretability, structural assurance, interactivity, government regulation, perceived financial dan privacy risks, perceived financial benefit dan trust. Penelitian ini mengumpulkan data dari 357 responden dan dianalisis menggunakan Partial Least Square Structural Equation Modelling (PLS-SEM). Temuan menunjukkan bahwa algorithm interpretability dan structural assurance secara negatif dan signifikan mempengaruhi perceived financial dan privacy risks. Sebaliknya, interactivity dan government regulation secara positif dan signifikan mempengaruhi perceived financial benefit. Interactivity juga secara positif dan signifikan mempengaruhi perceived trust, sedangkan government regulation tidak. Selain itu, perceived financial dan privacy risks tidak mempengaruhi investment intention, sementara perceived financial benefit dan trust secara positif dan signifikan mempengaruhi investment intention.

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Technological advancements in the financial industry have revolutionised the industry called fintech, leading to the emergence of financial robo-advisors. These financial robo- advisors attract the potential investors using machine learning algorithms to provide automated investment guidance with minimal human oversight, based on users’ financial goals and risk tolerance. Several researches have explored the intention to adopt financial robo-advisors. This research investigates users’ intentions to invest with financial robo- advisors, focusing on several factors, such as algorithm interpretability, structural assurance, interactivity, government regulation, perceived financial and privacy risks, perceived financial benefit and trust. This research collected data from 357 respondents and analysed using Partial Least Square Structural Equation Modelling (PLS-SEM). The findings indicate that algorithm interpretability and structural assurance negatively and significantly influence perceived financial and privacy risks. Conversely, interactivity and government regulation positively and significantly influence perceived financial benefit. Interactivity also positively and significantly influences perceived trust, whereas government regulation does not. Additionally, perceived financial and privacy risks do not influence investment intention, while perceived financial benefit and trust positively and significantly influence investment intention.