2.1. Continuance Intention for FinTech Services through the Expectation Confirmation Model Prism
FinTech is a blend of two different words, i.e., “finance” or “financial” and “technology.” There is a common understanding of what the term “FinTech” means; however, there is no universally accepted definition of the term (
Varma et al. 2022). Fintech combines various technologies to solve a problem in the finance sector (
Muganyi et al. 2022). It includes not only the services, products, or applications, but also the various models and processes that are used to deliver end-user solutions (
Dzandu et al. 2022;
Herdinata and Pranatasari 2022). FinTech refers to innovative financial services that use technology to create disruptive new trends in services or rewrite financial services to make them more valuable, reasonable, and secure. It has a positive impact on both the economy and the consumer. For the consumer, it provides an efficient and effective user experience, user-friendly designs, and improves the ability to acquire information in real time and in a simple manner (
Memon et al. 2021). Previous research has found that FinTech has contributed to economic development by creating jobs and increasing GDP (
Herdinata and Pranatasari 2022). In the next three to four years, it is expected that GDP in emerging economies will increase by around 6%, with the creation of more than 90 million jobs through FinTech (
Lyons et al. 2022).
The rapid adoption of FinTech over the last decade demonstrates that it is here to stay and thrive. FinTech’s adoption has piqued the interest of various consulting behemoths, which have conducted research in the field. Ernst and Young discovered that more than 60% of people in 27 different countries have adopted FinTech, and that adoption rates are even higher in societies where there is a high level of awareness (
Ernst & Young 2019). In the MENA region, Deloitte conducted a similar study and discovered that more than 80% are ready or willing to adopt FinTech services for banking purposes (
Deloitte 2020). However, adoption varies by country due to different conceptions, levels of awareness, perceptions, and facilitating conditions. Adoption rates in China and India are the highest, while advanced economies such as Japan and France have very low adoption rates of around 30% (
Ernst & Young 2019). Financial demands, the absence or scarcity of basic banking services and infrastructure, funds remittance services, growth in mobile internet and payment services (
Hasan et al. 2021), high service costs of existing mechanisms, persuading and supportive government regulation (
Li et al. 2022) are associated factors.
According to
Rupeika-Apoga and Wendt (
2021), the general idea of FinTech disrupting the (traditional) finance industry, as purported in the literature and public debate, was not confirmed in their study, as study respondents supported integration and collaboration between traditional banks and FinTech companies. FinTech is seen as a driving force for innovation and modernization in the entire finance sector, rather than as a disruptive force. In order to reduce costs, improve systemic financial stability, and mitigate the negative externalities of disruption and competition, incumbent banks are interested in collaborating with FinTech firms (
Varma et al. 2022). As a result, according to
Varma et al. (
2022), the disruptive innovation theory is not fully applicable to the development of financial markets because incumbents are interested in collaboration.
In 2021, the primary concerns of FinTech companies were the availability of qualified employees and/or experienced managers, as well as international expansion (
Rupeika-Apoga and Wendt 2022). This suggests that the primary challenges for the FinTech industry are similar to those faced by the traditional financial industry (
Kaur et al. 2021). The FinTech industry anticipates improved regulatory support, such as a more realistic sandbox approach and a willingness to learn about new business models from regulators, as well as more flexible and open communication (
Rupeika-Apoga and Wendt 2022).
According to
Bhatnagar et al. (
2022), India is recognized as a strong FinTech hub, and as the Indian entrepreneurial landscape develops further, more businesses will be created and supported by more investors. The findings of their study confirm that, depending on an investor’s investment horizons, the Indian FinTech market can generate high returns for risk-averse individuals.
According to the findings of the
Kaur et al. (
2021) study, customers in India’s northern region are genuinely satisfied with the quality of digital banking services. Overall, the study’s findings show that ‘Reliability’ has a significant impact on customer satisfaction, followed by ‘Tangibility’ and ‘Responsiveness’. While providing digital banking services, all banks should provide accurate, reliable information, timely updates, account maintenance, and error-free transactions, according to the study (
Kaur et al. 2021).
Furthermore, whether or not consumers’ needs are met determines the level of technological adaptation. Are customers willing to continue using current services or purchase new ones? The expectation confirmation theory is widely used to evaluate consumer satisfaction and repurchase behavior (
Dabholkar et al. 2000). The main concept of expectation confirmation theory is that consumers would confirm their pre-purchase expectation with post-purchase perceived performance to determine their level of satisfaction and then influence their repurchase intention (
Tsai et al. 2020).
Bhattacherjee (
2001) created the Expectation Confirmation Model (ECM) by modifying the Expectation Confirmation Theory. The resulting model is cognitive in nature and explains a cognitive process that an individual goes through when deciding whether or not to continue using information technology (
Bhattacherjee 2001). The model investigates long-term factors that support individuals’ decisions to use technology again and again (
Alkhwaldi et al. 2022). It consists of four factors that are directly related to an individual’s attitude, namely, confirmation (comparison of actual and expected performance), perceived usefulness (benefits of technology that a user receives), satisfaction (feelings towards a technology), and finally, continuance intention (users’ intention to continue using technology) (
Bhattacherjee 2001).
Figure 1 depicts the conceptual framework of ECM.
Perceived risk refers to the negative thoughts or volatility that a customer experiences when purchasing an item (
Ammann and Schaub 2021). It arouses negative emotions, which alter behavioral intentions (
Udo et al. 2010). Failure in the system can increase perceived risk in technology-dependent services. A user may perceive various types of risks, such as personal, technical, economic, psychological, and privacy risks (
Liebermann and Stashevsky 2002;
Liu et al. 2020;
Udo et al. 2010), which can reduce satisfaction with service quality (
Zhang and Prybutok 2005). Several studies in the past have shown that perceived risk has a negative effect on an individual’s behavioral intention (
Pavlou 2003;
Slade et al. 2015;
Tran 2020). When people are unable to accommodate any vague and uncertain situation, they are more likely to avoid such situations (
Hofstede 1980). When customers realize that a FinTech service can have negative or unwanted consequences, their satisfaction drops, and they avoid using FinTech services (
Kaur et al. 2021).
2.2. Hypotheses
Confirmation (Co) refers to a user’s perception of the similarity between two performances, i.e., the actual and expected performance of a technology (
Bhattacherjee 2001). According to the ECM, an individual experiences cognitive dissonance or mental tension if his expectations prior to accepting a technology are not met, and if such expectations are met, the level of perceived usefulness and satisfaction increases. According to the study, a FinTech service user will have a higher level of usefulness if the technology can meet his expectations prior to adoption or acceptance of the technology. Previous research has shown that confirmation establishes users’ motives, which in turn informs their intention to continue (
Nasution et al. 2022;
Shiau et al. 2020). As per the previous research and the proposed model, it is expected that perceived usefulness (PU) and satisfaction (S) are increased by confirmation (Co). Considering this, we proposed the following hypotheses:
H1a. Confirmation (Co) positively influences Perceived Usefulness (PU).
H1b. Confirmation (Co) positively influences Satisfaction (S).
Perceived Usefulness (PU) denotes how users perceive the benefits of using technology (
Bhattacherjee 2001). It is extrinsic motivation that creates a user’s or customer’s intention to use technology (
Hasan et al. 2021), in which the user or customer believes that using technology will improve their performance. As a result, this can positively affect or influence an individual’s attitude toward adopting and using technology (
Bhattacherjee 2001). Therefore, it is reasonable to assume that if FinTech users believe the technology is beneficial to them, they will continue to use it. According to previous research, perceived usefulness is a critical precursor for both satisfaction (S) and continuation intention (CI) (
Dehnert and Schumann 2022). Hence, we propose the following hypotheses:
H2a. Perceived Usefulness (PU) positively influences Satisfaction (S).
H2b. Perceived Usefulness (PU) positively influences Continuance Intention (CI).
Satisfaction is defined as an individual’s emotional reaction to their previous experience with technology (
Bhattacherjee 2001). The user evaluates his or her experience based on his or her emotional reaction to the performance of a technology (
Wu et al. 2022). According to ECM, it is a significant component of continuance intentions, and previous research has also suggested that it is a necessary precursor to continuance intention (
Sheng et al. 2021;
Xia et al. 2022). Empirical research on FinTech adoption has shown that satisfaction in the behavior of individuals after adoption or acceptance of FinTech can be predicted (
Abdeldayem and Aldulaimi 2021;
Setiawan et al. 2021). Based on this, the study predicts that a satisfied FinTech customer is more likely to continue using the service than a dissatisfied or unsatisfied customer. Hence, the following hypothesis is formulated:
H3. Satisfaction positively (S) influences Continuance Intention (CI).
When a customer, investor, or user chooses a technology, they experience indecision (
Unsal and Brodmann 2021). Risk is inevitable; however, risk can easily lead to higher levels of problems, affecting user satisfaction, usefulness, and attitude toward a technology (
Wu et al. 2021). Perceived risk has also been used as a moderator of the relationship between expected performance and behavioral intention (
Herrmann and Masawi 2022). According to other research studies, a user’s perception of risk makes them less likely to use and adopt a technology (
Lu et al. 2020;
Ryu 2018). However, it is regarded as an important factor that can influence user intentions regarding technology adoption (
Herrmann and Masawi 2022;
Mangini et al. 2021). There has been no research into whether perceived risk acts as a moderator for any of the ECM variables. The current study tested the ECM in relation to FinTech services, with perceived risk added as a moderating factor to the model. In this regard, the study proposes the following hypotheses:
H4a. Perceived Risk (PR) moderates the relationship between Perceived Usefulness (PU) and Satisfaction (S).
H4b. Perceived Risk (PR) moderates the relationship between Confirmation (Co) and Satisfaction (S).
H4c. Perceived Risk (PR) moderates the relationship between Perceived Usefulness (PU) and Continuance Intention (CI).
H4d. Perceived Risk (PR) moderates the relationship between Satisfaction (S) and Continuance Intention (CI).
The Expectation–Confirmation Model was chosen for this study to investigate the factors influencing the intention to continue to use FinTech services.
Figure 2 depicts the conceptual model created with the hypotheses and variable relationships. The base ECM is kept as is, with the addition of perceived risk as a moderator, moderating the relationship between other ECM constructs. The model depicts the relationship between the constructs used and the proposed hypotheses.