Heterogeneous effects of Unified Payment Interface readiness on digital payment adoption in India: Evidence from causal forest models

The global adoption of Central Bank Digital Currencies (CBDCs) represents a pivotal shift in monetary systems, driven by technological advancements and economic imperatives. While a small number of official digital currencies are in circulation, many nations are launching pilot programs to address financial inclusion challenges and enhance economic resilience. This study aims to identify the determinants of digital currency adoption across 116 countries, using logistic regression to analyze the effects of economic, technological, institutional, and financial factors.
The results show that higher GDP levels significantly increase the likelihood of active CBDC adoption by 332.1 percent and pilot adoption by 212.6 percent, reflecting the role of economic development. Greater internet usage improves the odds of active adoption by 12.7 percent and pilot adoption by 13.4 percent, while financial inclusion indicators, such as account ownership, increase the likelihood of adoption by 59 percent for active initiatives and 141 percent for pilot projects. Monetary freedom positively influences active adoption by 31.1 percent, and higher interest rates increase the odds by 20.8 percent. Conversely, business freedom negatively affects active adoption by 27.5 percent and pilot adoption by 29.1 percent, suggesting that countries with strong private-sector digital payment solutions may rely less on CBDCs.
These findings represent the transformative potential of digital currencies to improve financial inclusion and economic participation. Policymakers should prioritize investments in digital infrastructure and financial inclusion initiatives to facilitate the integration of digital currencies into national economies and empower underserved populations globally.

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.

Consumer well-being reflects subjective physical, emotional, and psychological satisfaction derived from services. This study investigates how the Technology Acceptance Model (TAM), trust, and intention to use fintech services influence consumer well-being in urban Indonesia, with financial behavior as a moderating variable. The research surveyed 390 active fintech users in the Jabodetabek metropolitan area in Indonesia, which includes Jakarta, Bogor, Depok Tangerang, and Bekasi selected due to their high engagement with digital financial services, with the survey being conducted over a three-month period from May to July 2024. The findings reveal that TAM and trust significantly influence consumers’ intention to use fintech services, which mediates their impact on consumer well-being. Notably, intention to use has the strongest direct effect on well-being (path coefficient = 0.548, p < 0.001). However, financial behavior does not significantly moderate the relationship between intention to use and well-being (p = 0.441). These results highlight the pivotal role of trust and ease of access in enhancing consumer satisfaction. From a practical perspective, the findings suggest that fintech providers and policymakers should focus on financial literacy to mitigate risks associated with unregulated fintech use. This study extends theoretical insights into the intersection of technology acceptance and consumer behavior, emphasizing the importance of user-centered approaches. Future research should explore these dynamics in rural contexts to compare community-specific impacts.

Acknowledgment
This research was funded by Directorate of Research, Technology and Community Service (DRTPM) of the Indonesian Ministry of Education and Culture in 2024 with the National Competitive Basic Research Grant scheme with contract number; 0667/E5/AL.04/2024.