Hartarto Junaedi’s scientific contributions

Background: Mobile banking adoption continues to grow, but user retention remains a challenge. Understanding the factors influencing continuance intention is crucial for improving long-term engagement. Prior research highlights the importance of confirmation, perceived usefulness, security, satisfaction, trust, and convenience, yet their interrelationships require further exploration. Objective: This study examines key determinants of users' intention to continue using mobile banking services, focusing on how confirmation, perceived usefulness, security, satisfaction, trust, and convenience influence this decision. Methods: A quantitative study was conducted using structural equation modeling (SEM) to analyze relationships among these factors. Data were collected from mobile banking users and assessed for statistical significance. Results: Confirmation significantly impacts perceived usefulness (0.576) and satisfaction (0.527). Perceived usefulness influences satisfaction (0.289) and continuance intention (0.396), while satisfaction also affects continuance intention (0.240). Trust plays a role (0.211), and perceived security strongly influences trust (0.651). Perceived convenience also impacts continuance intention (0.304), emphasizing its importance in user experience. Conclusion: Confirmation and security are critical for satisfaction and trust, which drive continued mobile banking use. Strengthening security, improving perceived usefulness, and fostering trust can enhance user retention. Future studies should explore additional variables, test the model across demographics, and assess the impact of emerging technologies like AI and blockchain. Longitudinal and experimental research may offer deeper insights into these evolving relationships.

This study aims to contribute to the proof of factors likely to determine the success of M-learning adoption based on previous research. This is done because there are many different theoretical models proposed. However, there is not yet a model that can be generally accepted as an established theoretical model for the adoption of M-learning in universities. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determinants that influence the adoption of M-learning using the Technology Acceptance Model (TAM). To collect the data, researchers distributed questionnaires to respondents using google forms. Forms are distributed via WhatsApp and Telegram. The data used was 515 M-learning users. Theoretical model research was carried out with Structural Equation Model (SEM) analysis, then SPSS and Amos as support for analysis. There are six factors that determine the results of acceptance of M-leaning adoption in this study, namely Social Influence, Perceived Enjoyment, Facilitating Condition, Self-Efficacy, Perceived Usefulness, and Perceived Ease of Use. The five factors that show positive and significant relationships are Social Influence, Perceived Enjoyment, Self-Efficacy, Perceived Usefulness, and Perceived Ease of Use. Perceived Usefulness has the first strongest positive and significant value, and then Social Influence has the second strongest value. Each factor has a medium influence value on Behavioral Intention. That factor is the most influential in the application of M-learning in universities.