AI personalization strongly boosts long-term mobile payment engagement

The study identifies a major gap in the existing literature: while early mobile payment research relied on models that explained initial adoption, such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, these frameworks no longer capture the realities of a mature mobile payment environment. With usage widespread and cash transactions increasingly rare, users are no longer asking whether mobile payment systems are easy or useful; instead, researchers must understand the deeper psychological, experiential, and contextual factors that drive continued use.

A new empirical study has found that personalized AI has become a key driver of mobile payment retention among Chinese consumers, reshaping the factors that influence financial technology behaviors in one of the world’s most digitized payment markets. The findings highlight a major shift in how users relate to payment technologies as mobile transactions become routine and adoption plateaus, making continued use the new frontier of competition for digital payment platforms.

The research, titled “The Influence of Personalized AI on Users’ Intention to Continue Using Mobile Payments: A Contingency Perspective,” published in the Journal of Theoretical and Applied Electronic Commerce Research, states that the era of adoption-focused research has passed, and sustaining user retention in AI-integrated financial ecosystems has become a top priority as mobile payments surpass an 85 percent penetration rate in China.

AI personalization emerges as a key force behind user retention

The study identifies a major gap in the existing literature: while early mobile payment research relied on models that explained initial adoption, such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, these frameworks no longer capture the realities of a mature mobile payment environment. With usage widespread and cash transactions increasingly rare, users are no longer asking whether mobile payment systems are easy or useful; instead, researchers must understand the deeper psychological, experiential, and contextual factors that drive continued use.

To address this gap, the authors employ two theoretical frameworks. The first is the Uses and Gratifications Theory, which views users as active agents who adopt and continue using technologies to satisfy evolving needs. The second is the contingency perspective, which asserts that behaviors are shaped by the interaction of personal characteristics and environmental conditions rather than by any single factor.

Given this scenario, the study investigates how personalized AI, a rapidly expanding feature in mobile payment ecosystems, influences user retention. Personalized AI refers to intelligent, data-driven systems that generate tailored recommendations, adaptive features, and customized experiences based on a user’s preferences, past behaviors, and contextual interactions.

Using data from 515 mobile payment users in China, the researchers find that personalized AI significantly increases users’ intention to continue relying on mobile payment. The result holds even after accounting for variables such as age, gender, income, education, and occupation. Personalized AI enhances cognitive value by providing context-aware suggestions, financial management tools, and individualized service flows. It also provides emotional reinforcement by strengthening trust, convenience, and decision-making confidence. These combined effects deepen satisfaction and lead users to integrate mobile payment systems more firmly into daily life.

The study frames personalized AI as part of a technological evolution shaping the Fourth Industrial Revolution, where advanced computation, data analytics, and machine learning are transforming financial infrastructure. With mobile payment platforms increasingly embedding AI-driven automation, the technology’s role has shifted from a supplementary feature to a structural driver of user retention.

According to the analysis, personalized AI meets users’ expectations for long-term engagement through dynamic updates, instant feedback, adaptive service processes, and the ability to evolve with user preferences. As platforms become more sophisticated, the threshold for sustained engagement depends less on novelty or convenience and more on how effectively the system can deliver tailored experiences that fit diverse user needs.

Age, education, social network, and technology environment shape AI’s effectiveness

The study also identifies four moderating variables that significantly influence how strongly users respond to AI personalization. These variables, age, education level, social network strength, and technological diversity, demonstrate that AI’s effectiveness is not uniform across users but depends on personal capabilities and environmental contexts.

Age emerges as a critical factor weakening the influence of personalized AI. Younger users tend to possess stronger digital literacy, higher adaptability to new technologies, and more comfort with AI-driven personalization. They recognize the value of tailored services and respond positively to advanced features embedded in mobile payment systems. In contrast, older users often face cognitive and perceptual barriers that reduce their ability to interpret and appreciate AI-driven customization. Their concerns about complexity, security, and privacy prevent them from fully leveraging the benefits of personalization, thereby dampening the impact of AI on retention intentions.

Education level shows the opposite effect, significantly strengthening the influence of personalized AI. Users with higher education possess greater information-processing skills and technological confidence, allowing them to appreciate the complexity and benefits of AI-enabled personalization. These users can more easily align AI recommendations with their personal needs, understand the mechanics behind system features, and trust advanced services. Their exposure to technology-driven environments and ability to evaluate digital interactions enhance sustained usage.

The study also finds that social network strength plays a substantial role. Users embedded in strong social communities respond more positively to personalized AI because they receive validation from peers, observe usage patterns, and gain insights from shared experiences. Social influence amplifies the perceived reliability and relevance of AI-driven recommendations, reinforcing positive habits and encouraging continued mobile payment use. In socially dense networks, users are more likely to conform to group norms and maintain behaviors validated by their connections.

Finally, technological diversity significantly enhances AI’s impact. Users operating across multiple devices, operating systems, and payment functions experience a richer technological environment that increases compatibility and service flexibility. This environment makes personalized AI more useful because it can adapt across contexts, deliver recommendations seamlessly, and maintain performance across platforms. Technological diversity reduces friction and strengthens the perceived value of continuity.

Together, these moderating variables reveal that AI personalization is most effective among younger, educated, socially connected, and technologically versatile users. Conversely, older users or those with limited education, weak social networks, or narrow technological exposure may not benefit as much from AI-enhanced mobile payment systems. These differences highlight the need for targeted strategies when deploying AI in financial services.