2.11 THEORETICAL BACKGROUND OF FINTECH CUSTOMER PERCEPTION
2.12.2 TECHNOLOGY ACCEPTANCE MODEL -TAM
theories if it is used in a mobile environment or advanced technology (Lu et al. 2011; Bilgihan &
Bujisic 2015). The sections below demonstrate studies based on TAM theory in Fintech
Attitude Towards Usage User’s attitude towards using a system Behavioural Intention User’s intention towards using a system Actual System Use The actual use of a system
In previous studies, scholars used consumers’ perceptions and points of view to discuss factors that affected their behavioural intention to use or have the potential to use new technology. Since Fintech is a high-tech innovative product and service, scholars used the TAM model to explore drivers and inhibitors of using Fintech. Abramova and Böhme (2016) used the TAM model to investigate the consumer adoption of cryptocurrency “Bitcoin”. Bitcoin is a digital currency that operates without a central bank grounded on peer-to-peer through a bitcoin network without the need for intermediaries. It is an example of Fintech's digital innovation in money transmission.
They also integrated the valence model “benefit-risk framework” to study the perceived benefit and risks as key determinates of using Bitcoin. The methodology is a quantitative study that collected data from 2305 respondents (Male 67% and Female 33%). The study was conducted in six European countries. Concepts of TAM and valence include; three dimensions of perceived benefit (i.e. seamless transaction, security and control, and decentralization), and four dimensions of perceived risk (i.e. financial loss, legal risk, operational risk, and adoption risk). Their results found that users have substantial concerns concerning using cryptocurrency due to value
Figure 2.1: Technology Acceptance Model (David 1989)
fluctuation, and the potential risk of financial losses as well as security breaches. Plus, their research highlights are important on consumer protection and clear policies against certain security threats. This study is considered to be the first attempt in the emerging information-sharing literature to study decentralized currencies. It is suggested to continue exploring the multidimensional factors influencing users’ perception to use fintech products and services.
In this context, Stewart and Jürjens (2018) explored the key factors that influence consumer expectations in adopting fintech innovation in Germany. They developed a model based on TAM as well as benefits and risks factors called “intention to adopt FinTech in Germany”. They included customer trust, data security, value-added, user’s design interface and fintech promotion.
209 was the sample size for the study; the data was collected through a survey. Their results found that only 10% of the respondents recognized Fintech and used Fintech. Perceived usefulness was the main deterrent with respect to fraud protection and privacy. It has an immediate impact on users’ intention to use Fintech. The results show that customers do not consider ease of use as an added value to use Fintech, whereas data security has a strong influence on Fintech trust. Authors suggested that future researchers of Fintech should consider educated consumers the most as they might be aware of Fintech products and services.
Benlian et al. (2011) investigated the IT executive perspective on the major opportunities and risks associated with the adoption of software as a service (SaaS). In the study, they used five measures to explain benefits (i.e. cost advantage, strategic flexibility, focus on core, competencies, access to specialized resources and quality improvement), and five measures of risks (i.e. performance risk, economic risk, strategic risk, security risk and managerial risk). They find that strategic risk, security risk and manager risk are significant factors obstructing the path of SaaS. Also, within information system literature, researchers were keen to understand users’ usage of social
commerce. Farivar and Yuan (2014) developed a theoretical framework to investigate users’ usage of social networks using perceived benefits, perceived risks and trust. This study suggested two types of benefits (i.e. Social benefit, and commerce benefit), and two types of risks (i.e. social risk, and commerce risk). User concerns for commerce risk were found to be a factor to be considered by IT service providers challenging the path of commerce usage.
In this regard, Dootson, Beatson and Drennan (2016) investigated the perceived value of bank customers to use social media to interact with financial institutions. The study was conducted among Australian bank customers. Concepts of TAM include perceived usefulness, perceived monetary value and perceived social value. Their results found that perceived usefulness, economic value and social value significantly influence consumer perceived value of adopting social media to interact with financial institutes. Also, they found that customers were willing to use social media if the banks create clear usage instructions and address technology security perceptions. The main limitation of the research as addressed by the authors is that they ignored hedonic value which is linked with social media usage. Plus, another limitation is that they did not address the user’s characteristics, experience and comfort usage of technology.
A recent study by Belanche, Casaló and Flavián (2019) looks at the impact of artificial intelligence on Fintech. The paper examines customers’ behaviour towards the adoption of financial Robo- advisors. They collected 765 potential user responses from North American, British and Portuguese. They incorporated TAM factors perceived usefulness and perceived ease of use, and subjective norms (interpersonal influence and external influence) as influencers of consumer intention to use Robo-advisors. They found that perceived usefulness, perceived ease of use, interpersonal subjective and mass media have a significantly positive effect on attitudes toward service. Also, they highlighted that banks need to consider customer level of familiarity and
understanding of using robots. They suggested that future researchers focus on the actual use of the service as it might give different results. It is also suggested to consider the consumer experience of using Fintech as it will add value to the research context and to conduct the study in Asia and other continents to obtain a global understanding of Fintech adoption.
Also, engineering studies considered studying reasons for the customer to use Fintech services.
Kim et al. (2015) explored the acceptance of payment-type Fintech service among Korean using the TAM model. They developed the Elaboration Likelihood Model to check users’ utilization of services. They found that usefulness, ease of use and credibility significantly impact users’ intent to use Fintech. Self-efficacy was found to be a moderating variable of the relationship between variables and users’ intention to use Fintech. Information privacy was found to be a critical factor for users to consider using Fintech services. Chuang, Liu and Kao (2016) used the TAM model to explore users’ intention to Fintech services among Taiwanese within the engineering industry.
Data was collected from 440 customers. They have added brand and service trust into the analysis to understand the influence on their behavioural intention. They found that brand, service trust, perceived usefulness, perceived ease, attitudes have significantly positive on consumer behavioural intention to use the technologies.
The development of Fintech after China government approves giving microloans to college students. It was considered by (Leong et al. 2017). They studied the development of Fintech as a startup among Chinese students, and qualitatively through case studies. The study found that the development of Fintech impacted college students’ financial situation positively, and the young generation was found to be a factor of Fintech growth with positive intention to use.
In addition to the above, Davis et al. (1989) argued that the TAM model explains consumer acceptance of technology. However, TAM theory has been used repeatedly. It has been widely
criticized for limited explanatory power, and practical value was lacking (Chuttur 2009). However, the validity of TAM measures has been criticized by other researchers. Straub, Keil and Brenner (1997) and McCoy, Galletta and King (2007) claimed that TAM is not universally applicable to explain the usage of technology in different cultures since the model was developed in the USA.
Venkatesh and Davis (2000) were also considered this limitation. They extended TAM to study users’ information system adoption by incorporating social impact and cognitive instrumental procedures as important elements for information system usage.
Similarly, Luarn and Lin (2005) argued that the TAM ignores the risks or constraints that hide users from the utilization of information systems. Also, they suggested the extension of the TAM model with a benefits-risk framework to consider perceived credibility, perceived self-viability and perceived financial cost. In the study, they highlighted data security risk and data transmission as factors impacting users. They also found that perceived credibility (trust) indirectly affects consumer tendency to adopt mobile banking. Their study suggested that inadequate awareness of data security among potential Fintech users equates to slower utilization of Fintech.
In this context, other researchers have studied Fintech using the TRA model. The section below demonstrates theoretical perspectives of TRA and underlying Fintech studies.