An analysis of the use of service banking-based mobile banking solutions

Abstract

Digitalization has transformed how industries operate, with the banking industry significantly affected. Services once delivered solely through physical bank branches have increasingly shifted toward digital platforms, enabling customers to access financial products regardless of time and location. Consequently, customer expectations have evolved, with a growing demand for speed, accessibility and personalization in banking services. In response to these shifting demands and the opportunities arising, financial institutions are gradually transitioning from conventional frameworks to next-generation digital banking models. One of the emerging models in the financial technologies industry is service banking. Service banking, i.e., Banking-as-a-Service (BaaS), is a business model that enables banks to offer their products and services through the platforms of non-banking companies. Through this collaborative model, banks serve as service providers, while non-banking companies serve as interface providers, creating an integrated financial ecosystem. From the end user perspective, various factors influence customers' intention to use a new financial technology. Consequently, this study mainly aims to investigate the factors influencing consumers' intention to use service banking-based mobile banking solutions. This study proposes a research model on Behavioral Intention (BI) grounded in the Unified Theory of Acceptance and Use of Technology-3 (UTAUT-3) framework. Several modifications are applied to the existing framework to fit the specific context of this study better. Firstly, given that service banking-based mobile banking solutions are relatively new to Turkish consumers, and insufficient time has elapsed for them to develop habitual use, the habit construct is excluded from the framework. Also, the price value construct is not included in the model, since consumers in Türkiye are not subject to any monetary cost for using service banking-based mobile banking solutions. Lastly, two additional constructs are considered relevant to the context and included in the model: brand trust and perceived risk. Consequently, in the research model, the hypotheses are constructed as performance expectancy, effort expectancy, social influence, hedonic motivation, personal innovativeness and brand trust having a significant and positive effect on behavioral intention, whereas perceived risk having a significant and negative effect. To test the hypotheses, structural equation modeling (SEM) is used. This study focuses on getirfinans, the first service banking–based mobile platform in Türkiye. The data are collected through an online survey. The research sample consists of mobile banking users. The survey items are measured on a 5-point Likert scale with an additional "No Opinion" option. Responses marked as "No Opinion" are treated as missing values and questionnaires with more than 30% missing values are eliminated from the dataset. After eliminating invalid responses, the sample consists of 237 respondents. The collected data are analyzed using Microsoft Excel, SPSS 27.0 and LISREL 8.80. Prior to analysis based on SEM, a normality test is conducted to examine whether the sample is normally distributed. The results indicate that all items have skewness and kurtosis values within the range of -1.5 and +1.5, suggesting that the factors performance expectancy, effort expectancy, social influence, hedonic motivation, facilitating conditions, personal innovativeness and behavioral intention are relatively normally distributed. To assess the internal consistency and convergent validity of the measurement model, composite reliability (CR), Cronbach's alpha and average variance extracted (AVE) values are analyzed. The results indicate high internal consistency and convergence validity for each construct. Also, discriminant validity is assessed as part of the measurement model analysis to ensure that each construct in the model represents a unique phenomenon. The results of the structural model analysis posit that, performance expectancy, hedonic motivation, personal innovativeness and brand trust have a statistically significant and positive effect on behavioral intention, and perceived risk has a statistically significant and negative effect on behavioral intention. Among these factors, brand trust and perceived risk demonstrate the strongest influence on consumers' behavioral intention. No significant effects of effort expectancy, social influence and facilitating conditions on behavioral intention are observed. The findings of the study highlight the crucial role of trust and perceived risk within the context of BaaS. In the BaaS model, the provision of financial services through non-bank platforms may increase perceived uncertainty compared to traditional banking, thereby further strengthening the importance of brand trust. The lack of a significant effect of effort expectancy and facilitating conditions on behavioral intention indicates that technical infrastructure and supportive conditions alone are not sufficient to determine users' intention to use BaaS–based mobile banking solutions. These findings suggest that, particularly in the case of new and relatively less familiar financial technologies, psychological and cognitive factors may play a more influential role than purely functional factors. The findings of this study are expected to contribute to the existing body of knowledge on mobile banking solutions and BaaS in the context of consumer acceptance. The study further extends the literature by integrating perceived risk and brand trust variables into a UTAUT-3-based research model. In addition, the findings provide valuable insights for both potential and existing service banks and interface providers operating within the BaaS model. There are several limitations that should be considered when interpreting the findings of this study. Since the research was conducted in specific cities in Türkiye, and the sample does not represent the entire country, the generalizability of the results may be limited. Some model fit indices fall outside commonly accepted threshold values, which may be related to the sample size; therefore, testing the model with larger samples in future studies is recommended. Moreover, moderating effects were not incorporated into the model; particularly, the moderating roles of perceived risk and brand trust on the relationships between other constructs could be examined in future research. Additionally, this study examines the BaaS model solely from a business-to-consumer (B2C) perspective, while the business-to-business (B2B) relationships between the service bank and the interface provider are beyond its scope. Future research investigating the B2B dimension of the model may provide valuable contributions to the literature.