Enhancing Perceived Transparency and Purchase Decisions through Explanations in Personalized Product Recommendations

The increasing adoption of artificial intelligence(AI) in financial services has raised concerns about the transparency of product recommender systems. This study investigates how explanations in personalized product recommendations influence users’ perceived transparency and purchase decisions to help customers make informed decisions and improve product adoption. This research builds on concepts from Explainable Artificial Intelligence (XAI) and Dual-Process Theory. It draws from these theories to understand how users respond to financial product suggestions and how explanations support decision-making. The methodology adopted a comparative experimental design with a within-subject approach. Participants from different backgrounds interacted with the system versions. Structured questionnaires were administered to gather responses on perceived transparency and purchase decisions using a 5-point Likert scale. Statistical analysis included descriptive statistics and the Mann-Whitney U test. Results revealed that users perceived the Personalized Product Recommender (PPR) system with explanations as significantly more transparent (U1 = 2279.5, U2 = 2837, U3 = 3192, p < 0.05) and were more likely to act on the recommendations (U = 2889, p < 0.05). Explanations improved transparency and trust, supporting informed purchase decisions. This research provides insight for designing transparent, user-aligned financial recommender models, which implies improved customer experience strategies and AI decision support. This study presents a working explanation framework with real user evaluation, revealing its value for advancing transparency in financial services. Keywords: Personalized Product Recommender(PPR) System, Transparent explanations, Decision-making, Artificial Intelligence(AI) in Financial Services, Explainable Artificial Intelligence(XAI). Ene U.M., Bakpo F.S., Ebem D.U., Udanor C.N. & Eneh A.H. (2025): Enhancing Perceived Transparency and Purchase Decisions through Explanations in Personalized Product Recommendations. Journal of Advances in Mathematical & Computational Science. Vol. 13, No. 2. Pp 53-66. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V13N2P5