## 2 Literature Review
### Adopting Automation and Artificial Intelligence in Banking  
In recent decades, automation has significantly enhanced banking services, with a positive impact on many users [56,57]. Although automation in banking brings multiple benefits to consumers, empirical studies show that consumers still prefer human interactions and perceive AI performance as inferior to that of human employees [58]. As a result, consumer acceptance of automation is generally low [58, 59, 60, 61].  
Over time, various theories have been developed to identify factors that influence technology adoption. A significant example is the Unified Theory of Acceptance and Use of Technology (UTAUT), which is considered the most comprehensive model for predicting consumer behavior, including in the banking sector [62,63], with the ability to explain about 70% of the variation in behavioral intentions. This theory places particular emphasis on the role of performance expectation as the main determinant of technology adoption [64]. Performance expectancy refers to the degree to which individuals believe that automation can help improve the performance of jobs or other activities [65,66]. It is considered the strongest predictor of behavioral intentions, which in turn significantly influence technology use [65,66].  
In the banking and finance sector, performance expectancy plays an important role in the intention to use and use of automation [67,68]. Following a meta-analysis of 57 studies and 58 datasets, [PERSON] and [PERSON] [69] identified a positive and significant relationship between performance expectancy and the intention to use mobile banking. This intention is closely related to the actual use of these services. Also, [PERSON] and [PERSON] [70] and [PERSON] et al. [67] reported that performance expectancy significantly influences E-banking acceptance. During the pandemic, [PERSON] et al. [71] observed that performance expectation plays a determining role in consumers' intention to adopt online financial robo-advisors. The study on a large sample of consumers in China by [PERSON] et al. [64] emphasizes the importance of performance expectation in the acceptance of robo-advisory services, while [PERSON] et al. [72] demonstrated that this factor has a significant positive impact on the intention to use an investment management system. In their study of young investors in Malaysia and Sweden, [PERSON] [73] emphasized that trust in financial robo-advisors and intentions to use this technology are closely related to performance expectation.  
Thus, the following research hypothesis can be postulated:  
**Hypothesis 1 (HI).**_Higher profitability in banking institutions, as measured by return on equity, enhances more efficient utilization of robotic automation._  
High profitability provides banking institutions with the financial capacity to invest in advanced technologies such as mechatronic systems (e.g., smart automated teller machines) and artificial intelligence-based solutions (e.g., chatbots, robot advisors, predictive analytics). These technologies entail significant upfront costs for acquisition, deployment, and integration into existing banking infrastructure, as well as the need for resources for ongoing maintenance and adaptation.  
Therefore, banks with a higher return on equity (ROE) are better positioned to sustain these investments, leading to more effective adoption of automation technologies and increased operational efficiency. In this context, ROE is not only an outcome of digitalization, but also an enabler of the transition to a banking model based on automation and artificial intelligence.