nt. J. Business Information Systems, Vol. X, No. Y, xxxx 1
Copyright © 20XX Inderscience Enterprises Ltd.
Will I use it again? Impact of customer behavioural
intentions on FinTech continuance under expectation
Hafsa Hamid, Kanwal Iqbal Khan* and
Syed Khurram Abbas Sherazia
Institute of Business and Management,
University of Engineering and Technology Lahore,
Lahore, Punjab 39161, Pakistan
Abstract: Financial technology (FinTech) services are becoming more
prevalent during the COVID-19 pandemic, leading individuals to adopt digital
methods for financial transactions worldwide. But its future implications are
still doubtful. The current study analyses the relationship between the
customers’ behavioural intentions and continuance of FinTech services with the
mediating role of confirmation and satisfaction during their perceptions in the
post-usage phase. Data were collected through questionnaires survey from the
targeted respondents after pre-screening questions about the FinTech services.
The findings revealed that perceived usefulness, security, risk, and knowledge
significantly affect FinTech continuance through confirmation and satisfaction.
The data were further divided according to user types of technology adopters.
The results revealed that late adopters require more knowledge about FinTech
services for continuing their usage. These findings will be helpful for FinTech
companies to develop customer retention strategies that will drive customers
towards its continuous usage and help companies yield high returns.
Keywords: behavioural customer intention; financial technology; expectancy
confirmation model; continuance intention; digital methods; financial
transactions; perceived risk.
Reference to this paper should be made as follows: Hamid, H., Khan, K.I. and
Sherazia, S.K.A. (xxxx) ‘Will I use it again? Impact of customer behavioural
intentions on FinTech continuance under expectation confirmation theory’,
Int. J. Business Information Systems, Vol. X, No. Y, pp.xxx–xxx.
Biographical notes: Hafsa Hamid is a research scholar at the Institute of
Business and Management, University of Engineering and Technology, Lahore,
Kanwal Iqbal Khan serves as an Assistant Professor at the Institute of Business
and Management, University of Engineering and Technology, Lahore,
Syed Khurram Abbas Sherazi is a research scholar at the Institute of Business
and Management, University of Engineering and Technology, Lahore,
. Hamid et al.
Financial technology (FinTech) is regarded as one of the most significant financial
industry innovations with rapid advancements in information technology (Najaf et al.,
2022). This emerging technology witnessed phenomenal investment growth as it
reshaped the financial industry and improved financial services (Mathrani 2021;
Shahrokhi, 2008). In simpler terms, a diverse and stable financial landscape emerged
through FinTech (Nasir et al., 2021). A growing trend in FinTech has been observed in
developing countries due to a dramatic rise in mobile broadband penetration. An average
of 15% FinTech adoption rate was observed in 2015, which drastically increased to
nearly 64% in 2019 (Gulamhuseinwala et al., 2015). In Pakistan, a 30% increase in
internet penetration contributed to an increase in the country’s intensely low levels of
financial exclusion. Therefore, traditional financial and non-financial institutions started
thriving to provide diversified and innovative financial services to attract customers (Baxi
and Patel, 2021).
Consequently, it helps to decrease the 93% of the unbanked adult population in the
country (Rizvi et al., 2017). However, despite the increase in efforts to provide
innovative financial services, the continuous usage of FinTech is still doubtful. It may
presumably be due to the underlying risks associated with the financial services and the
lack of education amongst the potential customers (Yuan and Xu, 2020). Prior studies
have observed that perceived risk is the factor that causes the discontinuance of FinTech
usage and influences the behavioural intentions of the customers (Chiang, 2013; Liang
and Yeh, 2011; Zhou, 2013). Cunningham (1967) distinguished perceived risks into six
dimensions related to the performance, time or opportunities, the safety of the services,
social and psychological factors, and the financial considerations.
Limited research has been conducted on the factors influencing the customers’
perceptions and post-usage behaviour that ultimately affect their continuance intention.
Prior literature has extensively used the expectation confirmation theory to determine
consumers’ satisfaction and post-usage behaviour (Aljaafreh et al., 2021). This theory
explains that a user continuance intention depends on three key factors: perceived
usefulness, the extent of consumers’ confirmation of expectations, and their satisfaction
level (Gupta et al., 2021). However, various types of user perceptions also tend to
influence their behaviour in the post-usage stage. It includes perceived security,
perceived risks associated with innovative technology, and consumers’ knowledge of
FinTech services. The diffusion speed of new information technology services also
depends on user characteristics and types of technology (Escobar-Rodríguez and
Romero-Alonso, 2014). The differentiation between the user types is essential concerning
FinTech services provided by the companies because it may help them understand the
characteristics of the various user groups. It can help them effectively deliver their
services and meet consumers’ expectations and demands, thus, eventually enhancing the
continuous usage of the FinTech services (Ryu, 2018).
Therefore, this study aims to understand the user perceptions in the post-adoption
stage and determine how usefulness, security, risks, and knowledge may jointly influence
the continuous usage of FinTech services based on the expectancy confirmation model.
The data were further segregated into two categories of technology adopters (early and
late adopters) to understand user behaviours better. The following research questions
prompted this study
Will I use it again? 3
firstly, how do behavioural intentions amongst technology adopters vary when using
FinTech services? Secondly, what are the impacts of perceived value on FinTech
continuance? Lastly, how do perceived usefulness, security, knowledge and risk impact
the behavioural intentions of customers that lead to their continuance intentions towards
The global pandemic in 2020 was observed to lead to a major change in lifestyles and
the methods of conducting business when strict social restrictions were imposed (Khan
et al., 2021a). Therefore, although COVID-19 triggered the extensive usage of FinTech
services, users’ intentions need to be evaluated, considering that for some adopters of
technology, the adoption of FinTech services was instead forced due to the pandemic.
Furthermore, customers’ perceptions play a significant role in evaluating customers’
behaviour that leads to their continuance intentions of usage towards a particular product
or service. These perceptions are related to the usefulness, security, risk and knowledge
regarding the service. This study intends to contribute to the literature in several ways.
Firstly, it can help practitioners to understand the perceptions of users clearly in the post-
adoption phase. Using the multi-dimensional concepts of perceived usefulness, security,
risk and knowledge, it has analysed the personal effects of the underlying determinants
and assessed the overall impact of these perceptions on the FinTech continuance
intention. Secondly, this research contributes to helping the FinTech companies to
develop retention strategies, especially in the post-COVID times, that will drive
customers towards the continued usage of the services and help the companies to get
long-term success by yielding practical implications.
2 Theories and literature review
The expectancy confirmation model was proposed by Bhattacherjee et al. (2008), which
was derived from the expectation confirmation theory by Oliver (1980). This model is
used in the post-adoption domain, consisting of three key variables: post-adoption
perceived usefulness, confirmation, and satisfaction. The expectancy confirmation model
contends that expectations, whether perceived or not, lead to satisfaction in the post-
purchase phase (Gupta and Bhatt, 2021), which can be measured with the help of
expectations and performance dissonance, which can either be positive or negative
(Oliver, 1980). Bhattacherjee and Lin (2015) proposed user confirmation against the
overall consumer experience. They suggested the impact of those experiences on the
post-adoption perceived usefulness and the continuous intentions of the users. As a result,
the theory of expectation confirmation by Oliver (1980) was used to examine the
repurchase intentions of customers and their patterns of comparison of the ex-post
perceived performance with the ex-ante expected performance. The users then determine
their confirmation of the intended performance of the product or service that eventually
affects their degree of satisfaction (Zhou et al., 2018a).
On the other hand, in the context of customer behavioural intentions regarding
technological innovation, Davis (1989) proposed the technology acceptance model. This
theory was based upon the theory of reasoned action related to the psychological
perspective of the customer (Ajzen and Fishbein, 1977). However, the technology
acceptance model used two predictors: perceived usefulness and perceived ease of use, of
behavioural intention. It is expected under the theory of reasoned action that customers’
actions are closely associated with their actual behaviour. Therefore, their behavioural
. Hamid et al.
intentions help to determine their attitudes and the subjective norms that influence their
behavioural intentions (Mansur et al., 2019). This theory provides strong theoretical bases
for the technology-related models. It supports technology adoption models due to its
distinctive explanations regarding customer willingness during the adoption phase (King
and He, 2006).
2.1 User types
The knowledge of user type is essential for the Fintech service providers because it helps
them to offer and deliver their services and meet the expectations of the customers that
lead to the continuous usage of the Fintech services (Ryu and Lee, 2018). The speed of
diffusion concerning new information technology services depends on it
(Escobar-Rodríguez and Romero-Alonso, 2014). Sahin and Rogers (2006) categorised
user types into six categories based on their nature of technology adoption. These types
include innovators who are always willing to adopt new technology without hesitation
(Ryu, 2018). The early adopters are described as opinion leaders and are respected
amongst their social circles. The early majority adopters are labelled as deliberate. The
late majority adopter stands to be the more hesitant and sceptical towards the
innovation’s value, and lastly, the laggards prefer traditional means over innovative
technology (Ryu and Lee, 2018). Consumers’ variety of needs and expectations may
result from the differences in users concerning the adoption and usage of innovation
(Ryu, 2018). However, early and late adopters represent the majority of types of
technology adopters. They increase the chances of success because the late adopters of a
particular product or service may become an early adopter in introducing the next
generation of services.
Early adopters of technology are consumers or individuals interested in adopting the
latest technologies and services and are not hesitant to take risks (Kim et al., 2010).
Consumers who fall under this category of adopters are often considered opinion leaders
since they possess the potential to encourage and convince others to use or adopt
innovation by providing information that may be evaluative (Sahin and Rogers, 2006).
Adoption is a critical decision even if the benefits or risks of a particular product or
service are clearly stated or defined (Harrison and Waite, 2006). Escobar-Rodríguez and
Romero-Alonso (2014) supported these strands by arguing that early adopters are
consumers who are more encouraged and willing to adopt new information technologies
and use them. They tend to have a more positive attitude towards the introduction of
innovations in information technology. In the context of Fintech services, early adopters
are the consumers who weigh the usefulness or the benefits to be greater than the risks
On the other hand, late adopters comprise consumers or individuals who make up
about one-third of the social system who wait until most of their peers adopt a specific
innovation (Sahin and Rogers, 2006). Consumers in this category of adopters are more
reserved, hesitant and sceptical when adopting innovations or information technology
(Ryu and Lee, 2018). They tend to be more resistant to change and hold suspicions
towards change agents (Escobar-Rodríguez and Romero-Alonso, 2014). Before these
consumers adopt a particular innovation or information technology, they tend to ensure
and be sure whether the innovation or the benefits of the innovative products or services
will not fail (Kim et al., 2010). Similarly, the diffusion of innovation process tends to be
slower than the other sectors, making it difficult for companies to create and introduce
Will I use it again? 5
financial innovations, even though there are several evolutions of transactional processes
involving information technology in the financial sector (Sahin and Rogers, 2006).
Consumers tend to be more conservative in adopting new technologies in the financial
sectors due to trust issues, risk, chances of fraud (Ryu and Lee, 2018). Whereas late
adopters are reluctant to become active users of new information technologies and
possess negative attitudes, unlike early adopters, when adopting new services with
Contrary to early adopters, the perceived risk of Fintech services is more significant
for late adopters than the benefits of Fintech services (Ryu, 2018). As a result, the
differentiation of early adopters and late adopters is more significant in the financial
sector than in the other sectors (Ryu and Lee, 2018). Table 1 summarises the types of
technology adopters that were identified by Sahin and Rogers (2006).
Table 1 Types of technology adopter
User type Description
Innovators Willing to adopt new technology
Show interest in the adoption of new technology
Consider practicality and productivity of technology before adopting rather than its
Focus on convenience and solutions provided by technology
More cautious before adopting technology
Hesitant and sceptical towards the value of innovation in the particular technology
Laggards Slowest in adopting new technology
Prefer traditional means over new innovative technology
Table 1 presents those innovators and early adopter are considered trendsetters who adopt
the technology and drive the other categories to adopt the technology. However, the late
majority and laggards tend to be more hesitant towards adoption instead of traditional
2.2 Perceived usefulness and fintech continuance
As illustrated by extant literature, perceived usefulness is how the users or customers
perceive effectiveness or efficiency, achieved through the performance of various Fintech
services (Saksonova and Kuzmina-Merlino, 2017). The expectancy confirmation model
starts with the key determinants of satisfaction, perceived usefulness and confirmation
after actual use. Prior studies define perceived usefulness as a degree to which users can
achieve their goals through information systems. As a result, users tend to continue their
usage once they determine that the particular information system is useful enough for
them and helps them achieve their specific target (Cao et al., 2020). Previous empirical
studies have proved the relationships between usefulness, confirmation, satisfaction and
Furthermore, prior studies stated that users’ continuance intentions are positively
affected when using online service websites and determining usefulness (Fang et al.,
. Hamid et al.
2014). Perceived usefulness is the users’ perceptions of the benefits they expect from
Fintech usage (Davis, 1989). Due to the uncertainty in the usage of information systems,
the initial use as perceived by the users may be unstable, and the perceived usefulness of
information systems can be adjusted through the confirmation experience (Bhattacherjee,
2001a). As a result, the confirmation of users’ expectations may positively influence
perceived value, which further augments customer satisfaction and eventually drives the
continuance intentions (Bhattacherjee, 2001b).
The technology acceptance model has found perceived usefulness as a significant
belief that may influence the information system acceptance behaviours in various
computing technologies that involve end-users (Taylor and Todd, 1995). Venkatesh et al.
(2003) also supported a positive relationship between perceived usefulness and the
behavioural intention of consumers. However, perceived usefulness accounts for an
essential construct in determining technology adoption (Chen and Li, 2017). However,
the perceived usefulness construct is often misinterpreted with the perceived usefulness
in the pre usage phase described in the technology acceptance model. Bhattacherjee et al.
(2008) suggested that using post usage perceived usefulness would reflect beliefs
accumulated from the past perceptions of usefulness.
Extant literature has proved the influence of perceived usefulness on the consumers’
technological innovation adoption intention, which impacts their actual usage (Gefen and
Straub, 2003). A study conducted by Tan and Lau (2016) concerning mobile banking
services proved a positive relationship between perceived usefulness and the behavioural
intention of users that explains when the perceived usefulness of Fintech services is
higher, the actual usage of the services will also be higher. Prior studies also hypothesised
perceived usefulness concerning the usage of technology of interest as a predictor of
behavioural intention (Kim et al., 2017). It has also been confirmed by Yuan et al. (2019)
when applying the expectancy confirmation theory in information systems to study the
continuance intentions of customers in regard to smartphone banking services, which
illustrated that confirmation has significant influences on perceived usefulness.
Moreover, studies have shown that users’ confirmation in the post-adoption stage using
smartphone banking applications or services significantly influences the perceived
usefulness (Susanto et al., 2016). Therefore, as proved by Lim et al. (2019) that a
relationship existed between usefulness, confirmation and satisfaction, we can state our
H1 Perceived usefulness has a significant impact on FinTech continuance.
H1.1 Confirmation and satisfaction mediate the relationship between perceived
usefulness and Fintech continuance.
2.3 Perceived security and fintech continuance
Perceptions regarding the users’ security protection are described by Bhattacherjee
(2001b) as a cognitive process that tends to impact users’ emotional and behavioural
intentions. It is stated by Gupta et al. (2021) that perceived security is one of the critical
drivers for embracing new information technology. Lim et al. (2019) stated that perceived
security in mobile Fintech services increases the confirmation of Fintech service users
and increases perceived usefulness. The research concluded how perceived security
protection by users is associated positively with confirmation and satisfaction and, thus,
continuance intention. When users recognise high-security protection, various
Will I use it again? 7
mechanisms for security control and procedures for Fintech services, this eventually
confirms their expectations regarding the stability to use that particular service. Thus,
users start considering that service as stable enough to continue their usage. Perceived
security is also one of the significant drivers that help reduce the perceived risk of online
consumers’ perceptions (Al-Debei et al., 2015).
Previous studies showed that it helps to motivate consumers to adopt mobile banking
(Laforet and Li, 2005). Therefore, consumers’ level of trust is also influenced by the
perceived security of consumers when they purchase products and services related to
tourism online (Kim et al., 2015). The study conducted by Gai et al. (2018) categorised
security and privacy issues into three dimensions: business operations, outsourcing and
financial privacy. Baxi and Patel (2021) have also defined perceived security as personal
evaluations done by consumers and their perceptions of the risks in security associated
with electronic payment systems. Moreover, perceived security has been defined by
Yenisey et al. (2005) as the level of security that consumers may feel when they purchase
products or services online/e-commerce websites (Lee and Turban, 2001).
Similarly, when trust is low, it stagnates the growth of e-commerce (Baxi and Patel,
2021). In a similar vein, users tend to use electronic payment systems with a peaceful
state of mind when they are offered trustworthy solutions for security problems that may
occur (Tsiakis and Sthephanides, 2005). However, Schierz et al. (2010) contended that
the perceived security of consumers influences the frequency of mobile payments and is
one of the key reasons consumers may not purchase or transact products and services
online (Ponte et al., 2015). Therefore, particularly in financial transactions, consumers
need to have a sense of security. Empirical evidence shows that users confirm their
expectations regarding service stability when they recognise high-security protection,
procedures or control mechanisms of the Fintech services provided. Therefore, perceived
security is a construct that affects satisfaction and continuance intention (Lim et al.,
2019). Therefore, based on these arguments, we can establish a hypothesis statement:
H2 Perceived security has a significant impact on Fintech Continuance.
H2.1 Confirmation and satisfaction mediate the relationship between perceived security
and Fintech continuance.
2.4 Perceived knowledge and fintech continuance
Knowledge competence refers to the knowledge possessed by individuals regarding the
usage of information technology services (Motta et al., 2019). Prior studies conducted by
Kim et al. (2010) reported a positive influence on the existence of a relationship between
M-payments and the intentions of consumers to use the services. Consumers categorised
as web novices have been observed to rely more on basic yet attractive features that a
website interface provides. However, Web experts tend to use the website interfaces
based on their past experiences and utilise the knowledge to facilitate their information
processing and distinguish between the relevant and irrelevant information (Rieh, 2004).
Knowledge related to information technology is deemed necessary for promoting
information technology services such as Fintech services (Motta et al., 2019). An
example was illustrated in this study was discussed concerning the knowledge about
Fintech services that stated how such knowledge could make the service more reliable.
Extant literature also states the significance of trust and perceived trustworthiness of
consumers concerning the acceptance of Fintech services. These are either knowledge-
. Hamid et al.
based or developed through prior customer experiences (Jünger and Mietzner, 2020). The
research involved a primary collection of data involving questions from consumers
through an online survey related to self-assessed levels of financial knowledge and
knowledge about financial products. The study also proved that private households
possessing high levels of financial knowledge were deemed likely to adopt Fintech
services. This is because households are likely to understand financial products better
when they have a higher affinity for finance and higher levels of financial education than
households possessing a lower level of financial knowledge not distinguishing between
the various offers.
In the study related to the innovation-decision process, Sahin and Rogers (2006)
distinguished knowledge into three types: awareness knowledge, how-to knowledge, and
principles knowledge. The awareness knowledge included consumers’ knowledge related
to a particular innovation that could motivate them to learn more about the innovation
and eventually adopt it (Oliveira et al., 2016). The how-to knowledge involved
information regarding the usage of the innovation and how it can be used correctly.
According to Rogers (2006), this type of knowledge has been described as a critical
variable in the innovation-decision process. It has been observed that consumers need to
possess sufficient knowledge to increase the chances of innovation adoption. Therefore,
this knowledge is also deemed critical for relatively more complex types of innovations
(Aljaafreh et al., 2021). The last type of knowledge is the principles knowledge which
includes questions about why or how a particular innovation works. Although innovation
can be adopted without consumers possessing this type of knowledge, when the
innovation is misused, it may cause a discontinuance of the innovation (Sahin and
Rogers, 2006). As a result, for knowledge creation, education regarding technology and
practice must provide how to experiences and know why experiences (Seemann, 2003). It
is also to be noted that consumers may possess all types of knowledge. Yet, they may not
adopt the innovation due to their attitudes that also mould the adoption or rejection of the
particular innovation (Sahin and Rogers, 2006). Therefore, we can form our hypotheses
H3 Perceived knowledge has a significant impact on Fintech continuance.
H3.1 Confirmation and satisfaction mediate the relationship between perceived
knowledge and Fintech continuance.
2.5 Perceived risk and fintech continuance
Perceived risk is the feeling of uncertainty regarding the eventual negative consequences
of using a service. It is considered a key barrier for consumers when Fintech services
usage is considered (Ryu, 2018). Empirical studies have proven that perceived risks
impact the intentions to adopt a particular service or technology. Therefore, as
Poromatikul et al. (2019) stated, the higher the perceived risk, the lower the technology
adoption and vice versa. Little research has been conducted on perceived risks in the
post-adoption phases, even if they significantly impact users’ continuance intentions.
Empirical evidence suggests that perceived risk comprises three functions: users’ amount
at stake or the amount that could be lost if consequences of the services are unfavourable,
users’ subjective feelings of certainty that consequences will be unfavourable. As a result,
levels of perceived risks vary according to the levels of uncertainty and the amount at
stake (Chen and Li, 2017). Ryu and Lee (2018) defined perceived risk as consumers’
Will I use it again? 9
perception concerning the uncertainty or the negative consequences possibly associated
with the usage of Fintech services.
Prior literature related to the usage of information systems, a negative impact of
perceived risk is observed, affecting consumers’ intentions to use information technology
services (Yuan and Xu, 2020). When considering the negative impacts of Bitcoins usage,
it has been observed that multi-faceted perceived risks can play a significant role (Gupta
et al., 2021). Innovation in Fintech services brings several risks, making Fintech services
for the consumers vulnerable to far-reaching risks due to the emerging and unprecedented
nature of the Fintech services. It also shows that risks associated with the likelihood that
the services may fail or are inadequate tend to become a hurdle in the continuous usage of
the Fintech services (Ryu, 2018). Prior research conducted by Cunningham (1967)
distinguished perceived risks into six dimensions related to the performance, time or
opportunities, the safety of the services, social and psychological factors associated, and
the financial considerations. However, Ryu (2018) developed the framework of
Cunningham (1967) in Fintech services and distinguished four types of risks related to
financial, legal, security, and operational factors.
Financial risks are associated with the financial losses that may occur in transactions
when using Fintech services (Forsythe et al., 2006). This type of risk is also considered
one of the most consistent predictors of observing online and mobile usage behaviours
described in prior literature regarding information systems (Liu et al., 2019). Nasir et al.
(2021) stated that financial risks might occur due to malfunctioning of transaction
systems, frauds, moral hazards, and the like have a negative relationship with the
On the other hand, legal risks are referred to with the vagueness of the legal statuses
of Fintech companies or the overall lack of international regulations for Fintech services
(Ryu and Lee, 2018). Due to the strict access controls enabled within the financial
industry, Fintech services are comparatively difficult to be introduced by the Fintech
companies without attaining corresponding financial business admissions (Zhou et al.,
2018b). An example regarding the legal risks associated with Fintech services was
illustrated by Ryu (2018). Wherein the intervention of the Korean government was
discussed, and the implementation of the financial regulations on financial institutions in
Korea hinders the entry and growth of Fintech businesses in Korea because this
hindrance creates reluctance regarding the usage of services amongst the consumers.
Similarly, the extraordinary nature of Fintech services in the market and the absence of
regulations related to financial losses and various security issues associated with Fintech
services result in consumers distrustful and anxious.
Security risks or issues are referred to the potential loss that a consumer may bear due
to fraud or accounts being hacked that makes the security of the financial transactions be
compromised (Chawla and Joshi, 2019). A major concern for consumers is the invasion
of privacy, often conceptualised when discussing security risks in electronic services
(Lwin et al., 2007). This accounts for a primary concern for online and mobile users
when fraudulent acts or intrusions by hackers can cause consumers to suffer monetary
losses along with having their privacy violated (Lee, 2009). Prior research has stated
when discussing Fintech services and their usage that the potential for financial and data
losses and privacy intrusions is relatively higher and thus increases the overall perceived
risk associated with Fintech services (Schierz et al., 2010).
The operational risks are often described as critical barriers because operational
losses may often lead to financial disturbances and cause large financial institutions to
. Hamid et al.
collapse (Ryu and Lee, 2018). Operational losses may occur due to inadequate internal
processes, employees and the systems that may have failed (Barakat and Hussainey,
2013). Consumers tend to discontinue their Fintech services when the likelihood of
financial systems and operations is potentially high. In services like mobile banking,
users tend to decrease their usage or continuance intention if they recognise that their
personal information is unsafe, decreasing their confirmation and satisfaction levels (Lin
and Bhattacherjee, 2009). As a result, we can hypothesise the following statement:
H4 Perceived risk has a significant impact on Fintech continuance.
H4.1 Confirmation and satisfaction mediate the relationship between perceived risk and
2.6 Satisfaction and fintech continuance
Satisfaction regarding services is described as an ex-post evaluation by consumers after a
company renders its services (Fornell, 1992). It underscores the psychological or the
affective states that may relate to or result from cognitive appraisals of confirmation or
the expectation-performance discrepancies (Bhattacherjee, 2001b). This concept is often
operationalised with the expectancy disconfirmation theory (Oliver and Swan, 1989). The
existing gaps between the perceived performance of a product or service and the expected
performance of the product or service are the main drivers (Poromatikul et al., 2019).
Westbrook and Oliver (1991) described that the consumers’ continuance intentions for
the services like M-wallet are primarily an outcome of satisfaction with its consumption
as the post-choice evaluative judgment of the overall performance. Furthermore,
Bhattacherjee (2001a) stated that users’ satisfaction with information systems use
positively impacts their continuation intentions towards the same information system.
When users confirm their expectations in using smartphone banking services, this, in
turn, will increase user satisfaction and improve their trust level toward the service
(Susanto et al., 2016).
Consumers tend to condense the cognitive efforts by establishing a set of beliefs
based on previous experiences that they can easily deduce to new situations (Kim et al.,
2015). Fang et al. (2014) illustrated that satisfaction with the help of an example stating
how satisfied customers’ happiness reflects the vendor’s equitable outcomes and
consumer welfare based on their previous experience. Therefore, the concept of
satisfaction also highlights the perceived effective performance of a trusted party
concerning reliability and their performance in past transactions, which further confirms
the integrity of the trusted party and is then entrusted with future transactions (Ganesan,
1994). As a result, a prior transaction that may have been successful and satisfactory
helps to enhance a consumer’s confidence and motivates them to conduct future
transactions with the respective purveyors, which also fuels their continuance or
repurchase intentions concerning the products or services offered (Mayer et al., 1995).
In the context of information systems, it is often expected that satisfaction is the key
driver that reinforces the intentions of consumers to continue their usage of the system
(Limayem et al., 2007). Prior studies have also stated that the concept of satisfaction can
also be illustrated as a cumulative feeling that consumers may feel due to the multiple
numbers of positive interactions with the applications (Liu et al., 2013). Users’
satisfaction can also refer to their affective attitude towards applications resulting from
users experiencing direct interactions (Doll et al., 2004). The continuous usage of
Will I use it again? 11
services is often driven when consumers’ expectations regarding the application are met,
and they tend to be satisfied with the respective application (Bhattacherjee, 2001b). When
discussing prior studies concerning M-wallets, consumers possess high perceptions of the
built-in security functions and features. This belief helps to satisfy the payment
experiences and the product (Liu et al., 2019). Hsiao et al. (2016) stated that when a
consumer is satisfied with the M-wallet services, it determines whether they will continue
using it as the critical vehicle for digitally making payments.
From the technology acceptance model perspective, it is to be noted that consumer
satisfaction and its impact on the information system acceptance were not considered in
the standard version (de Luna et al., 2019). Various studies have been previously
conducted using the ECM and validating the relationship between satisfaction and
continuance intention of consumers (Hsiao et al., 2016) and regarding payments or
banking services (Yuan et al., 2019). For instance, Deng et al. (2010) found the positive
impact of consumer satisfaction on consumers’ continuance intention concerning IT
services. Therefore, we can hypothesise the direct relationships between Confirmation,
Satisfaction and Fintech continuance as:
H5 Confirmation has a significant relationship with satisfaction.
H6 Satisfaction has a significant relationship with Fintech continuance.
Based on FinTech usage and the theories mentioned above, the hypotheses specified to
define the research model. Accordingly, Figure 1 presented the conceptual framework of
the study. We added perceived security, perceived knowledge and perceived risk in the
context of FinTech to the ECT-IS as the antecedents of perceived usefulness,
confirmation and satisfaction, which directly and indirectly influence the continuance
intentions of customers.
Figure 1 Conceptual framework
3 Materials and methods
This study integrates perceived usefulness, security, knowledge, and risk using the
expectancy confirmation theory to predict and explain the FinTech continuance intentions
of consumers in Pakistan. Pakistan is considered a cash-based economy, although the low
. Hamid et al.
access to finance has remained a primary problem for several years. Prior studies have
reported the unbanked population as 93% of the total adult population of Pakistan (Rizvi
et al., 2017). Similarly, Pakistan possesses a low level of financial inclusion compared to
the other countries in the region and global standards. It has also been reported that only
12% of men had bank accounts out of the total population, and nearly 6% of women had
bank accounts (Aljaafreh et al., 2021). Due to such elevated levels of financial exclusion
in the country, individuals and businesses become susceptible to income shocks and bear
an increase in operating costs and dampening of future investments (Rizvi et al., 2017).
Therefore, the use of technology can help enlarge the outreach geographically and help
overcome low literacy levels. It has become relatively more manageable with the
introduction of new technological advancements in branchless banking and the
emergence of mobile banking, which has made access to finance more enhanced
Moreover, Pakistan tends to be a potentially attractive market for the growth of
FinTech services because of the significant growth in the youth population, the usage of
disruptive internet, penetration of smartphones, and consumers’ inclined preferences
towards smartphones and social media. Along with the emergence of digital payments
provided by e-commerce websites, the overall financial system indicated the capacity to
absorb innovation (Rizvi et al., 2017). Furthermore, Pakistan has a strict regulatory
framework in financial services with laws such as the payment system operators,
payment service providers, and branchless banking regulations issued by the state bank of
Pakistan. These have helped become a firm platform for the growth of FinTech, which
can be carefully controlled and regulated.
However, the FinTech industry is still at its infancy stage in Pakistan, and such strict
regulations could threaten this emerging industry (Rizvi et al., 2017). Traditional FinTech
companies collaborate with existing financial service providers, pose as technology
providers, and work through traditional pricing models. The emerging FinTech providers
are also known as disruptors collaborating with financial institutions such as banks or
firms offering financial services through new engagement models. They help to provide
the latest technological solutions to facilitate the current needs of consumers.
According to the Pakistan Microfinance Network (2019) reports, the potential of
Pakistan in digital finance is estimated to rise to at least $36 billion by the year 2025. It
shall also contribute to a 7% increase in the GDP of Pakistan and create a substantial
number of jobs that would result in nearly $263 billion new deposits. Therefore, various
FinTech companies in Pakistan have started emerging recently. It is to be noted that the
established financial institutions in Pakistan provide many FinTech services. Similarly,
micro-finance banks owned by telecommunication companies in Pakistan also provide
FinTech services. Therefore, to study the continuance intentions of customers in Pakistan,
empirical data for this study was obtained.
3.1 Data collection and sampling technique
A well-designed structured questionnaire was adapted from prior literature and included
questions related to all the constructs included in the study. Table 1 presents the detailed
variable description and measurements. A five-point Likert scale was used to evaluate all
the measures that ranged from (1) ‘Strongly Disagree’ to (5) ‘Strongly Agree’. A pilot
test was initially conducted involving ten respondents who possessed experience in using
FinTech services. These respondents provided comments on the content of the
Will I use it again? 13
questionnaire and regarding the structure as well. Based on the feedback given by these
respondents, the length of the questionnaire was considered acceptable. However, several
suggestions were given that concerned the wording of a few items. In order to ensure the
validity of the content, ambiguous questions were identified and then modified.
Table 2 Pre-screening questions for user types
User types What did you think about when adopting fintech services? (E.g., digital wealth
management, mobile banking, mobile payment services like EasyPaisa)
I like using new technology
I tend to be the first in using new products and services.
I am willing to take risks
I am hesitant to take risk
I still worry about new technologies.
I tend to continue using existing products and services.
Table 2 illustrates the questions included in the questionnaire to determine the user types.
Early adopters were determined through their motivation to use new products and
services and their willingness to take risks. In contrast, the late adopters were determined
through questions that helped configure the hesitance of users to adopt or use new
technologies and whether these types of users preferred staying within their comfort
zones by using the existing products and services.
Table 3 Variable Descriptions and Measurements
Construct Operational definition Items Reference
Perception of users concerning the expected
benefits of FinTech usage
6 Bhattacherjee (2001b)
A cognitive process tends to impact the
emotional intentions as well as the
behavioural intentions of users.
6 Lim et al. (2019)
Referred to potential financial loss in
6 Featherman and Pavlou
(2003) and Lee (2009)
Customers having knowledge about IT is
significant in the promotion of usage of IT
4 Kim et al. (2010)
The intention of users to continue investing
in the usage of FinTech services
4 Bhattacherjee et al. (2008)
Confirmation Perception of users of the resemblance
between the expectation of FinTech usage
and actual performance
4 Bhattacherjee and Lin
Satisfaction Effect of users and their feelings regarding
FinTech services before their use
3 Bhattacherjee (2001a)
The gender distribution of the respondents was expected to be kept relatively equal.
Online questionnaires were distributed by keeping in mind the restrictions due to the
COVID-19 pandemic, which benefited us due to its rapid response time, cost absence and
the absence of local geographical boundaries. Respondents were approached on social
media platforms specifically, Facebook, Instagram, Twitter and LinkedIn. They were
initially briefed about Fintech services such as digital banking and mobile wallets and
. Hamid et al.
were then asked if they used Fintech services. Respondents who used these services in
their everyday routine were then deemed eligible for responding to questions given in the
questionnaire. Out of 340 respondents, 335 responses were used for this study, indicating
a usable response rate of 98.5%.
SMART PLS 3.0 is a fundamental tool that helps investigate complex research issues that
incorporate unnoticed factors and the connection between various variables. The
measurement model for this study was validated using the partial least squares equation
modelling (PLS-SEM). Out of 335 respondents, 57% were male, and 43% were female.
18% were aged between 20–25, 33% were aged between 26–30, 19% were between
31–35, 12% were between ages 36–40, and 18% were the older respondents aged above
40. Furthermore, the respondents’ incomes were reported, and16.3% of respondents had a
monthly income between Rs. 10,000–30,000, 55% respondents earned between
Rs.31,000–50,000 and 28% respondents had a monthly income that was above
Rs. 50,000. Moreover, the respondents’ occupations were categorised into five different
types wherein 16.3% were students, 14.3% were housewives, 41.7% were corporate
employees, and 24.3% were businessmen/businesswomen currently running their
startups/businesses 3.3% respondents were retired individuals. Lastly, the regularity of
FinTech usage was also determined, and it was found that 16% used FinTech services
daily, 28.3% used services weekly, and about 56% of respondents used FinTech services
monthly. Table 4 describes the descriptive analysis of the study.
When discussing the differences in characteristics between the two user types,
Table 4 illustrates that around 56% of respondents were male and 43.8% were female in
the user type 1 category, while nearly 46% male and 32.7% females were in the user type
2 category. Moreover, the maximum frequency of respondents’ age was between 26 to 30
years for both user types, i.e., 35% and 23.5%, respectively. Furthermore, in both
categories, most individuals were corporate employees (51.8%) and (29.1%). Lastly,
respondents primarily used FinTech services every month in both user categories, i.e.,
61.3% and 39.4%, respectively.
Table 5 describes the values of correlation of the independent variables and mediating
variables with the dependent variable, fintech continuance. The highest correlation is
between satisfaction and Fintech continuance, which is 0.868. Furthermore, the mean and
standard deviation values for each of the two user types have been mentioned. The extent
of the difference relative to the variation in the data sample is measured using the t-
values. The highest t-value is of perceived risk, which is 12.6%. Furthermore, the
Wilcoxon test, also called the rank-sum test, is non-parametric in statistics used to
compare two groups. Therefore, this test calculates and establishes whether the two
groups are significantly different from one another. The highest value of the Wilcoxon
test is for perceived risk, which is –10.542, while the lowest value is for confirmation
which is –3.584.
According to the results of the estimation of the PLS model, the loading values of the
items is above 0.5, which suggests that the model has successfully met the requirements
of convergent validity (Hair et al., 2014). The purpose of the convergent validity is that it
confirms that the items in the study reflect their corresponding factor effectively (Khan
et al., 2021b). It also suggests the extent of a positive correlation between the factors of
Will I use it again? 15
the same construct. Therefore, the average variance extract (AVE) is used to measure the
convergent validity of each construct. In this study, the AVE values of each of the
constructs were reported as perceived security = 0.598, perceived usefulness = 0.632,
perceived risk = 0.731, perceived knowledge = 0.665, confirmation = 0.734,
satisfaction = 0.723 and fintech continuance = 0.776.
Table 4 Descriptive analysis
Characteristics Frequency (%) User type 1 User type 2
Male 171 (51%) 77 (56.2%) 116 (46.2%)
Female 164 (48.9%) 60 (43.8%) 82 (32.7%)
Above 40 55
Single 189 (56.4%) 92 (27.4%) 95 (28.3%)
Married 146 (43.5%) 40 (11.9%) 108 (32.2%)
10,000–30,000 49 (16.3%) 24 (17.5%) 35 (13.9%)
31,000–50,000 165 (55%) 87 (63.5%) 96 (38.2%)
Above 50,000 86 (28.7%) 26 (19%) 67 (26.7%)
Students 49 (16.3%) 21 (15.3%) 33 (13.1%)
Housewives 43 (14.3%) 20 (14.6%) 23 (9.2%)
Corporate employees 125 (41.7%) 71 (51.8%) 73 (29.1%)
Businessmen 73 (24.3%) 20 (14.6%) 63 (25.1%)
Retired 10 (3.3%) 5 (3.6%) 6 (2.4%)
Regularity of fintech usage
Daily 48 (16%) 14 (10.2%) 39 (15.5%)
Weekly 85 (28.3%) 39 (28.5%) 60 (23.9%)
Monthly 167 (55.7%) 84 (61.3%) 99 (39.4%)
Table 5 User types
Variable Correlation User type 1 User type 2 T-value Wilcoxon
Mean S.D Mean S.D
PU 0.662** 3.157 0.861 3.962 0.546 –5.352*** –4.635***
PS 0.612** 3.372 0.819 3.716 0.538 –4.315*** –3.821***
PR –0.493** 3.768 0.908 2.574 0.768 12.584*** –10.542***
PK 0.378** 3.547 0.838 3.880 0.687 –3.837** –3.880***
CON 0.781** 3.392 0.964 3.843 0.567 –4.920*** –3.584***
SAT 0.868** 3.355 1.024 4.072 0.484 –7.626*** –6.481***
Note: **Correlation is significant at the 0.01 level (2-tailed).
. Hamid et al.
Table 6 Reliability constructs
Constructs Cronbach’s alpha CR AVE Mean SD
PU 0.884 0.911 0.632 3.768 0.730
PS 0.865 0.899 0.598 3.476 0.763
PR 0.926 0.942 0.731 3.101 1.018
PK 0.831 0.888 0.665 3.759 0.753
CON 0.879 0.917 0.734 3.651 0.792
SAT 0.809 0.886 0.723 3.789 0.843
FINCONT 0.904 0.933 0.776 3.670 0.918
Table 6 describes the values of reliability, the mean and standard deviation of each of the
variables. The internal consistency for all the constructs was measured through Cronbach
alpha and composite reliability (CR). This internal consistency confirms the
relationship’s intensity, and the threshold value for this is 0.70. The values of composite
reliability for each variable were perceived security = 0.899, perceived usefulness =
0.911, perceived risk = 0.942, perceived knowledge = 0.888, confirmation = 0.917,
satisfaction = 0.886 and lastly, fintech continuance = 0.933. The discriminant validity in
PLS describes how each construct distinguishes itself from the other constructs (Fornell
and Bookstein, 1982). The threshold value for the AVE squared is greater than 0.5, with
the results being suitable if the exogenous variable has its values greater than 0.5
diagonally. On the other hand, if the values are lesser than 0.5, the construct is considered
Table 7 Validity of Constructs
Constructs CON FINCONT PK PR PS PU SAT
FINCONT 0.789 0.881
PK 0.438 0.419 0.816
PR –0.364 –0.424 –0.162 0.855
PS 0.687 0.625 0.416 –0.303 0.773
PU 0.666 0.674 0.432 –0.314 0.727 0.795
SAT 0.832 0.871 0.455 –0.494 0.727 0.788 0.850
Table 7 shows the values of Fornell-Larcker Criterion for each of the variables which
were confirmation = 0.857, fintech continuance = 0.881, perceived knowledge = 0.816,
perceived risk = 0.855, perceived security = 0.773, perceived usefulness = 0.795 and
satisfaction = 0.850. Another observation from the table above is that each of the values
of the variables is highest in their respective columns. The italic values in Table 7
presented the same. The values of R-square illustrate how well the variables explain each
other. It is most often used in regression analysis to evaluate the degree model fit. They
commonly have a range between 0 and 1 and are stated as a percentage.
Figure 2 illustrates the R-square values, path coefficients and the outer loadings of the
variables. The R-square values are shown within the confirmation, satisfaction and
Fintech continuance circles which are 0.562, 0.692 and 0.759, respectively. The path
coefficients are shown between the latent variables. The negative value of the path
Will I use it again? 17
coefficient between perceived risk and confirmation indicates that the value of
confirmation will decrease with an increase in perceived risk. Lastly, the outer loading
values indicate how the respective latent variables are explaining the items. The threshold
value of the outer loadings is > 0.5. When discussing the model fit test values for the
proposed model, the value of SRMR was 0.065, which is lesser than within the threshold
value (0.08). Moreover, the values of chi-square were 2760.86. The value of NFI was
0.715, which represents acceptable goodness of fit level.
Figure 2 Measurement model (see online version for colours)
Table 8 describes the direct and indirect relationships between the latent variables and the
coefficients and values of t-statistics. The p-values in Table 8 for direct relationships
describe the significance of the path and the relationship of the variables. Therefore, it
can be stated that the direct relationships between satisfaction and FinTech continuance,
confirmation and satisfaction, perceived security and confirmation, perceived usefulness
and confirmation, perceived risk and confirmation and perceived knowledge and
confirmation all have a direct and significant relationship with each other. On the other
hand, indirect effects in the mediation analysis are determined through the intervening
variables. Therefore, one specific mediator is analysed with the rest of the model. From
table 8, we can see that the t-statistics and the p-values are significant for all the indirect
relationships between the variables. Moreover, confirmation-satisfaction-fintech
continuance has the highest value of indirect effect as compared to all other relationships.
As a result, it can be concluded that confirmation and Satisfaction mediate the
relationships between perceived usefulness (H1), perceived security (H2), perceived
knowledge (H3), perceived risk (H4) and fintech continuance, respectively, thus
supporting our proposed hypotheses. This is because, for all the variables, the t-statistics
. Hamid et al.
are >1.96, and the p-values are <0.05. This type of mediation is called partial mediation
or quasi-mediation, wherein all three paths are significant for all the variables.
Table 8 Results for structural equation modelling
Hypotheses Coefficients T-statistics P-values
PSCON 0.38 6.51 ***
PUCON 0.29 4.45 ***
PRCON –0.14 3.61 ***
PKCON 0.13 2.87 ***
CONSAT 0.83 40.48 ***
SATFINCONT 0.87 60.42 ***
PRCONSATFINCONT –0.099 3.708 ***
PU CONSAT -> FINCONT 0.211 4.393 ***
PSCONSAT -> FINCONT 0.275 6.536 ***
PKCONSAT -> FINCONT 0.096 2.887 ***
PRCONSAT –0.113 3.722 ***
PSCONSAT 0.315 6.656 ***
PUCONSAT 0.242 4.473 ***
PKCONSAT 0.111 2.897 ***
CONSATFINCONT 0.725 28.591 ***
Note: ***Significant at the 0.001 level (2-tailed).
5 Early adopters vs late adopters: a comparison
This study observed two types of technology adopters: early adopters and late adopters,
based on the respondents’ responses to FinTech services. This was done with the help of
a screening question that determined whether respondents were interested in and liked
using new technology and whether they preferred taking risks when using it. Respondents
who opted for choices related to liking FinTech services and taking risks concerning the
new technology and considered themselves one of the first individuals to adopt the
technology were classified as the early adopters. On the other hand, respondents who
were hesitant to take risks to new technology and were comfortable using traditional
banking methods were instead classified as late adopters. The frequency of early adopters
in this study were 137 respondents, and 198 individuals were late adopters out of a total
sample of 335 respondents. This also justifies that most early adopters were young
individuals who preferred using the latest technologies and found FinTech services more
convenient and hassle-free.
Early adopters in this study were labelled as user type 1 and had a frequency of 137
respondents, of which 77 users were male, and 60 were female. Moreover, the highest
frequency of user type 1 ages ranged between 26-30 years which was 35% of
Will I use it again? 19
respondents, while the lowest frequency of respondents in user type 1 ranged between 36-
40 years which was only 8% of respondents. The incomes of respondents categorised as
user type 1 were that 17.5% had incomes between Rs.10,000–Rs.30,000, around 63.5%
respondents had incomes ranging between Rs.31,000-Rs.50,000 and respondents with
incomes above Rs.50,000 19%. Most of the user type respondents were corporate
employees (51.8%), and users mostly used FinTech services monthly (61.3%). (See
Table 9 Comparison of user type 1 and user type 2
Hypotheses User type 1 User type 2
values Coefficients T-
PRCONSATFINCONT –0.137 3.337 *** –0.104 3.523 ***
PUCONSATFINCONT 0.233 2.791 *** 0.096 2.357 **
PSCONSAT 0.351 4.509 *** 0.221 3.817 ***
PUCONSAT 0.257 2.812 *** 0.145 2.475 **
CONSATFINCONT 0.794 28.005 *** 0.493 12.932 ***
PSCONSATFINCONT 0.319 4.436 *** 0.147 3.699 ***
PKCONSAT 0.162 3.315 *** 0.026 0.545 0.586
PKCONSATFINCONT 0.147 3.335 *** 0.017 0.537 0.592
PRCONSAT –0.151 3.354 *** –0.157 3.649 ***
Note: ***Significant at the 0.001 level (2-tailed), **Significant at the 0.01 level
Late adopters in this study were labelled as user type 2 and had a frequency of 198
respondents, of which 116 users were male, and 82 were female. Moreover, the highest
frequency of user type 2 ages ranged between 26-30 years which was 59 respondents,
while the lowest frequency of respondents in user type 2 ranged between 36–40 years
which was only 26 respondents. The incomes of respondents categorised as user type 2
were such that 14% had incomes between Rs.10,000–Rs.30,000, around 38.2%
respondents had incomes ranging between Rs.31,000–Rs.50,000 and respondents with
incomes above Rs.50,000 26.7%. Most of the user type respondents were corporate
employees (29%), and users mostly used FinTech services monthly (39.4%).
(See Table 4).
The comparison of user types is described in Table 10. It can be observed that for
user type 1, all the paths are significant, while for user type 2, confirmation and
satisfaction do not mediate the relationship between perceived knowledge and FinTech
continuance. Similarly, confirmation also does not mediate the relationship between
perceived knowledge and satisfaction. The findings of this study indicated that the
proposed hypotheses H1-H4 were supported based on the mediation analysis and the
direct relationships of the variables. The analysis proved that confirmation and
satisfaction intervene in the relationship between perceived usefulness and FinTech
continuance, perceived security and FinTech continuance, perceived risk and FinTech
continuance, and the perceived knowledge and FinTech continuance. The values of p <
0.05 suggested that the total, direct and indirect relationships of the variables were
. Hamid et al.
The analysis of this study was then further divided according to the user types
wherein two types of consumers were selected through the screening question given in
the online questionnaire. Respondents were divided into two types of early adopters and
late adopters. Early adopters were labelled as user type 1 interested in using new
technology and services and were not hesitant to use them. These types of users are
considered as the first users in their circle to adopt new technology. The user type 1
respondent findings confirmed that confirmation and satisfaction mediate the relationship
between user perceptions and the intentions regarding FinTech Continuance.
Similarly, the second type of users were the late adopters that were labelled as user
type 2. These respondents worried about new technologies were hesitant to use them and
preferred to use traditional financial transactions. The findings of user type 2 respondents
confirmed that confirmation and satisfaction mediate the relationship between perceived
usefulness, security and risk with FinTech Continuance but not with perceived
knowledge. This indicates that late adopters may be hesitant to use FinTech services
because they lack sufficient knowledge regarding the particular services and do not
understand their usefulness. Therefore, they are unable to utilise FinTech services.
Furthermore, it is also to be noted that the frequency of late adopters is greater than
that of early adopters. Out of the total sample of 335 respondents, 198 respondents were
late adopters, and 137 were early adopters. This indicates that consumers in Pakistan may
lack sufficient knowledge regarding FinTech, which causes them to hesitate in using the
various services provided by banks and other related FinTech companies. As a result,
there is a need to educate consumers regarding the benefits of FinTech, create ease, add
value in their digital applications so that these users gain knowledge, and be assured that
the new technologies being introduced will prove to be useful for them.
The primary purpose of this study was to explain the relationship between perceived
value and FinTech continuance with the mediating role of confirmation and satisfaction,
and in doing so, a theoretical model was also proposed. Customers’ perceived value
comprised of its perceived usefulness, security, risks and knowledge associated with
FinTech services in Pakistan. Using the expectation confirmation theory, the mediating
variables confirmation and satisfaction helped determine and confirm the continuance
intentions of customers regarding their continued usage of FinTech services in Pakistan
in post-COVID times. The main motive of this study is to address the usage of FinTech
services in Pakistan, specifically in the post COVID times when people were forced to
adopt the services due to social distancing restrictions and lockdowns all over the
country. The unit of analysis for this study was FinTech users, who were further divided
into two user types, early adopters and late adopters, through a screening question in the
online questionnaire during data collection. The data was then used for empirical testing.
The overall findings of this research concluded that confirmation and Satisfaction
mediate the relationship between perceived usefulness, security, risk and knowledge with
FinTech knowledge. However, individual analysis of the user types indicated that
perceived knowledge was a major concern for late adopters that caused users to be
hesitant towards adopting and continuing their usage of FinTech services. Therefore, the
lack of knowledge regarding FinTech services can ultimately prove to be a barrier in
easing restrictions regarding FinTech services in Pakistan, such as BitCoin or digital
Will I use it again? 21
wealth management and P2P lending. As people gain knowledge regarding FinTech
services, their overall perceptions regarding the particular technology will change,
influencing their post-adoption behaviour leading to their intentions of continuing their
usage of FinTech.
6.1 Theoretical and practical implications
This study revealed several theoretical implications. For instance, empirical examinations
were conducted to examine the comprehensive set of perceived usefulness, security,
knowledge and risk in the FinTech continuance intention using the ECT model. This is an
area of research that was not addressed in prior studies. Moreover, by using multi-
dimensional concepts of perceived usefulness, security, risk and knowledge, this study
observed the individual effects of the underlying determinants and assessed the overall
impact of these perceptions on FinTech continuance intention.
There were several practical implications in this study as well. Firstly, this research
contributes to helping FinTech companies develop retention strategies, especially in post
COVID times, that will drive customers towards the continued usage of the services and
further help the companies secure long-term success by yielding valuable implications.
Users’ perceptions are highlighted as the main drivers of customer behaviours and
intentions in the post-adoption phase. The findings of this study imply that users are
willing to continuously use FinTech services, although they require knowledge about the
services to utilise them efficiently. Moreover, this study also provides FinTech firms
managers with valuable insight into the factors they need to emphasise when marketing
their FinTech services or devising strategies to retain their existing customers. The
comparatively higher frequency of late adopters in the study thus reveals that although
users may be willing to continue their use of FinTech, streamlined and user-friendly
application interfaces need to be designed for the country’s less-educated population.
This would help FinTech firms gain new customers and help educate them about the
services being provided.
This study also implies that the strict regulatory framework monitoring data security
and avoiding financial losses has resulted in customers trusting the respective banks or
FinTech firms with their data and finances. However, risk-mitigation strategies still need
to be closely monitored. Lastly, distinguishing between early adopters and late adopters
can help FinTech companies comprehend each user type’s various characteristics. This
would help them efficiently deliver FinTech services while meeting their expectations,
needs and demands, ultimately leading to the continuous usage of the services.
6.2 Limitations and future research
It is worth mentioning that there are several limitations in this study. Firstly, this study
focused only on the perceptions of the customers and their impact on the behaviour in the
post-usage phase leading to the continuance of the service. Furthermore, this study was
explicitly based in Pakistan therefore, this study has some geographical limitations, and
the results cannot be applied in other countries. Moreover, Pakistan has not yet been
developed and has several restrictions regarding the various FinTech services such as
BitCoin and P2P lending that are widely used worldwide. Therefore, researchers can
further study the perceptions of customers regarding those services. Another limitation is
that this study only focuses on the perceptions of customers. Future research can be
. Hamid et al.
conducted on the perceptions of bank employees and how FinTech services have
impacted their work. Lastly, only two user types have been discussed in this study out of
five types of technology adopters. Based on the behaviour of the other three types, future
research can yield different results.
Ajzen, I. and Fishbein, M. (1977) ‘Attitude-behavior relations: A theoretical analysis and review of
empirical research’, Psychological Bulletin, Vol. 84, No. 5, pp.888–918.
Al-Debei, M.M., Akroush, M.N. and Ashouri, M.I. (2015) ‘Consumer attitudes towards online
shopping: The effects of trust, perceived benefits, and perceived web quality’, Internet
Research, Vol. 25, No. 5, pp.707–733.
Aljaafreh, A., Al-Hujran, O., Al-Ani, A., Al-Debei, M.M. and Al-Dmour, N. (2021) ‘Investigating
the role of online initial trust in explaining the adoption intention of internet banking services’,
International Journal of Business Information Systems, Vol.36 No.4, pp.474 – 505.
Barakat, A. and Hussainey, K. (2013) ‘Bank governance, regulation, supervision, and risk
reporting: evidence from operational risk disclosures in European banks’, International
Review of Financial Analysis, Vol. 30 No.1, pp.254–273.
Baxi, C. and Patel, J.D. (2021) ‘Use of mobile wallet among consumers: underlining the role of
task-technology fit and network externalities’, International Journal of Business Information
Systems, Vol. 37, No. 4, pp.544–563.
Bhattacherjee, A. (2001a) ‘An empirical analysis of the antecedents of electronic commerce service
continuance’, Decision Support Systems, Vol. 32, No. 2, pp.201–214.
Bhattacherjee, A. (2001b) ‘Understanding information systems continuance: an
expectation-confirmation model’, MIS Quarterly: Management Information Systems, Vol. 25,
No. 3, pp.351–370.
Bhattacherjee, A. and Lin, C.P. (2015) ‘A unified model of IT continuance: three complementary
perspectives and crossover effects’, European Journal of Information Systems, Vol. 24, No. 4,
Bhattacherjee, A., Perols, J. and Sanford, C. (2008) ‘Information technology continuance: a
theoretic extension and empirical test’, Journal of Computer Information Systems,
International Association for Computer Information Systems, Vol. 49, No. 1, pp.17–26.
Cao, S., Lyu, H. and Xu, X. (2020) ‘Insurtech development: evidence from Chinese media reports’,
Technological Forecasting and Social Change, Vol. 161, p. 120277.
Chawla, D. and Joshi, H. (2019) ‘Consumer attitude and intention to adopt mobile wallet in India –
an empirical study’, International Journal of Bank Marketing, Vol. 37, No. 7, pp.1590–1618.
Chen, X. and Li, S. (2017) ‘Understanding continuance intention of mobile payment services: an
empirical study’, Journal of Computer Information Systems, Vol. 57, No. 4, pp.287–298.
Chiang, H.S. (2013) ‘Continuous usage of social networking sites: The effect of innovation and
gratification attributes’, Online Information Review, Vol. 37, No. 6, pp.851–871.
Cunningham, S.M. (1967) ‘The major dimensions of perceived risk’, in Cox, D.F. (Ed.): Risk
Taking and Information Handling in Consumer Behaviour, pp.82–111, Harvard University
Press, Boston, United States.
Davis, F.D. (1989) ‘Perceived usefulness, perceived ease of use, and user acceptance of
information technology’, MIS Quarterly: Management Information Systems, Management
Information Systems Research Center, Vol. 13, No. 3, pp.319–339.
de Luna, I.R., Liébana-Cabanillas, F., Sánchez-Fernández, J. and Muñoz-Leiva, F. (2019) ‘Mobile
payment is not all the same: The adoption of mobile payment systems depending on the
technology applied’, Technological Forecasting and Social Change, August, Vol. 146, No. 8,
Will I use it again? 23
Deng, Z., Lu, Y., Wei, K.K. and Zhang, J. (2010) ‘Understanding customer satisfaction and loyalty:
An empirical study of mobile instant messages in China’, International Journal of Information
Management, Vol. 30, No. 4, pp.289–300.
Doll, W.J., Deng, X., Raghunathan, T.S., Torkzadeh, G. and Xia, W. (2004) ‘The meaning and
measurement of user satisfaction: A multigroup invariance analysis of the end-user computing
satisfaction instrument’, Journal of Management Information Systems, Vol. 21, No. 1,
Escobar-Rodríguez, T. and Romero-Alonso, M. (2014) ‘The acceptance of information technology
innovations in hospitals: Differences between early and late adopters’, Behaviour and
Information Technology, Vol. 33, No. 11, pp.1231–1243.
Fang, Y.H., Chiu, C.M. and Wang, E.T.G. (2014) ‘Understanding customers’ satisfaction and
repurchase intentions: An integration of IS success model, trust, and justice’, Internet
Research, Vol. 21, No. 4, pp.479–503.
Fornell, C. (1992) ‘A national customer satisfaction barometer: the Swedish experience’, Journal of
Marketing, Vol. 56, No. 1, pp.1–6.
Fornell, C. and Bookstein, F.L. (1982) ‘Two structural equation models: LISREL and PLS applied
to consumer exit-voice theory’, Journal of Marketing Research, Vol. 19, No. 4, pp.440–452.
Forsythe, S., Liu, C., Shannon, D. and Gardner, L.C. (2006) ‘Development of a scale to measure
the perceived benefits and risks of online shopping’, Journal of Interactive Marketing,
Vol. 20, No. 2, pp.55–75.
Gai, K., Qiu, M. and Sun, X. (2018) ‘A survey on FinTech’, Journal of Network and Computer
Applications, Vol. 103 No.1, pp.262–273.
Ganesan, S. (1994) ‘Determinants of long-term orientation in buyer-seller relationships’, Journal of
Marketing, Vol. 58, No. 2, pp.1–12.
Gefen and Straub. (2003) ‘Managing user trust in B2C e-services’, E-Service Journal, Vol. 2,
No. 2, pp. 1–7.
Gulamhuseinwala, I., Bull, T. and Lewis, S. (2015) ‘FinTech is gaining traction and young, high-
income users are the early adopters’, Journal of Financial Perspectives, Vol. 3, No. 3,
Gupta, K.P., Manrai, R. and Goel, U. (2021) ‘Analyzing the factors that affect the adoption of
payments bank services in India: an analytic hierarchy process approach’, International
Journal of Business Information Systems, Vol. 37 No.4, pp.522-543.
Gupta, M. and Bhatt, K. (2021) ‘A multidimensional measure of system quality - an empirical
study in context of mobile banking apps in India’, International Journal of Business
Information Systems, Vol. 38 No.1, pp.1-16.
Gupta, P., Prashar, S. and Vijay T.S. (2021) ‘Chandan Parsad Examining the influence of
antecedents of continuous intention to use an informational app: The role of perceived
usefulness and perceived ease of use’, International Journal of Business Information Systems,
Vol. 36, No. 2, pp.270–287.
Hair, J.F., Sarstedt, M., Hopkins, L. and Kuppelwieser, V.G. (2014) ‘Partial least squares structural
equation modeling (PLS-SEM): An emerging tool in business research’, European Business
Review, Vol. 26, No. 2, pp.106–121.
Harrison, T. and Waite, K. (2006) ‘A time-based assessment of the influences, uses and benefits of
intermediary website adoption’, Information and Management, Vol. 43, No. 8, pp.1002–1013.
Hsiao, C.H., Chang, J.J. and Tang, K.Y. (2016) ‘Exploring the influential factors in continuance
usage of mobile social Apps: Satisfaction, habit, and customer value perspectives’, Telematics
and Informatics, Vol. 33, No. 2, pp.342–355.
Jünger, M. and Mietzner, M. (2020) ‘Banking goes digital: the adoption of fintech services by
German households’, Finance Research Letters, Vol. 34, No. 1, p. 101260.
Khan, K.I., Nasir, A. and Saleem, S. (2021). ‘Bibliometric analysis of post Covid-19 management
strategies in hospitality and tourism industry’, Frontiers in Psychology, Vol. 12 No.1,
. Hamid et al.
Khan, K.I., Niazi, A., Nasir, A., Hussain, M. and Khan, M.I. (2021) ‘The effect of COVID-19 on
the hospitality industry: The implication for open innovation’, Journal of Open Innovation:
Technology, Market, and Complexity, Vol. 7, No. 1, pp.30.
Kim, C., Mirusmonov, M. and Lee, I. (2010) ‘An empirical examination of factors influencing the
intention to use mobile payment’, Computers in Human Behavior, Vol. 26, No. 3, pp.310–322.
Kim, H.Y., Lee, J.Y., Mun, J.M. and Johnson, K.K.P. (2017) ‘Consumer adoption of smart in-store
technology: assessing the predictive value of attitude versus beliefs in the technology
acceptance model’, International Journal of Fashion Design, Technology and Education,
Vol. 10, No. 1, pp.26–36.
Kim, Y., Park, Y.-J., Choi, J. and Yeon, J. (2015) ‘An empirical study on the adoption of ‘Fintech’
service: Focused on mobile payment services’, Advanced Science and Technology Letters,
Vol. 114, No. 1, pp.136–140.
King, W.R. and He, J. (2006) ‘A meta-analysis of the technology acceptance model’, Information
and Management, Vol. 43, No. 6, pp.740–755.
Laforet, S. and Li, X. (2005) ‘Consumers’ attitudes towards online and mobile banking in China’,
International Journal of Bank Marketing, Vol. 23, No. 5, pp.362–380.
Lee, M.C. (2009) ‘Factors influencing the adoption of internet banking: an integration of TAM and
TPB with perceived risk and perceived benefit’, Electronic Commerce Research and
Applications, Vol. 8, No. 3, pp.130–141.
Lee, M.K.O. and Turban, E. (2001) ‘A trust model for consumer internet shopping’, International
Journal of Electronic Commerce, Vol. 6, No. 1, pp.75–91.
Liang, T.P. and Yeh, Y.H. (2011) ‘Effect of use contexts on the continuous use of mobile services:
The case of mobile games’, Personal and Ubiquitous Computing, Vol. 15, No. 2, pp.187–196.
Lim, S.H., Kim, D.J., Hur, Y. and Park, K. (2019) ‘An empirical study of the impacts of perceived
security and knowledge on continuous intention to use mobile fintech payment services’,
International Journal of Human-Computer Interaction, Vol. 35, No. 10, pp.886–898.
Limayem, M., Hirt, S.G. and Cheung, C.M.K. (2007) ‘How habit limits the predictive power of
intention: the case of information systems continuance’, MIS Quarterly: Management
Information Systems, Vol. 31, No. 4, pp.705–737.
Lin, C.P. and Bhattacherjee, A. (2009) ‘Understanding online social support and its antecedents: a
socio-cognitive model’, Social Science Journal, Vol. 46, No. 4, pp.724–737.
Liu, M.T., Brock, J.L., Shi, G.C., Chu, R. and Tseng, T.H. (2013) ‘Perceived benefits, perceived
risk, and trust: Influences on consumers’ group buying behaviour’, Asia Pacific Journal of
Marketing and Logistics, Vol. 25, No. 2, pp.225–248.
Liu, Z., Ben, S. and Zhang, R. (2019) ‘Factors affecting consumers’ mobile payment behavior: a
meta-analysis’, Electronic Commerce Research, Vol. 19, No. 3, pp.575–601.
Lwin, M., Wirtz, J. and Williams, J.D. (2007) ‘Consumer online privacy concerns and responses: a
power–responsibility equilibrium perspective’, Journal of the Academy of Marketing Science,
Vol. 35, No. 4, pp.572–585.
Mansur, D.M., Chamidah, N., Oesman, Y.M., Kusuma Putra, A.H.P., Sule, E.T. and Kartini. D.
(2019) ‘Moderating of the role of technology theory to the existence of consumer behavior on
e-commerce’, Journal of Distribution Science, Vol. 17, No. 7, pp.15–25.
Mathrani, S. (2021) ‘Critical business intelligence practices to create meta-knowledge’,
International Journal of Business Information Systems, Vol. 36, No. 1, pp.1–20.
Mayer, R.C., Davis, J.H. and Schoorman, F.D. (1995) ‘An integrative model of organizational
trust’, The Academy of Management Review, The Academy of Management, Vol. 20, No. 3,
Motta, R.C., Silva, V. and Travassos, G.H. (2019) ‘Towards a more in-depth understanding of the
IoT paradigm and its challenges’, Journal of Software Engineering Research and
Development, Vol. 7 No.1, pp.1-18.
Will I use it again? 25
Najaf, K., Subramaniam, R.K. and Atayah, O.F. (2022) ‘Understanding the implications of FinTech
Peer-to-Peer (P2P) lending during the COVID-19 pandemic’, Journal of Sustainable Finance
& Investment, Vol. 12, No. 1, pp.1–16.
Nasir, A., Shaukat, K., Khan, K.I., Hameed, I.A., Alam, T.M. and Luo, S., 2021. ‘Trends and
directions of financial technology (Fintech) in society and environment: A bibliometric study’,
Applied Sciences, Vol. 11, No. 21, p.10353.
Oliveira, T., Thomas, M., Baptista, G. and Campos, F. (2016) ‘Mobile payment: Understanding the
determinants of customer adoption and intention to recommend the technology’, Computers in
Human Behavior, Vol. 61, No. 1, pp.404–414.
Oliver, L.R. (1980) ‘A cognitive model of the antecedents and consequences of satisfaction
decisions’, Journal of Marketing Research, Vol. 17, No. 4, pp.460–469.
Oliver, L.R. and Swan, J.E. (1989) ‘Equity and disconfirmation perceptions as influences on
merchant and product satisfaction’, Journal of Consumer Research, Vol. 16, No. 3,
Ponte, E.B., Carvajal-Trujillo, E. and Escobar-Rodríguez, T. (2015) ‘Influence of trust and
perceived value on the intention to purchase travel online: integrating the effects of assurance
on trust antecedents’, Tourism Management, Vol. 47, No. 1, pp.286–302.
Poromatikul, C., De Maeyer, P., Leelapanyalert, K. and Zaby, S. (2019) ‘Drivers of continuance
intention with mobile banking apps’, International Journal of Bank Marketing, Vol. 38, No. 1,
Rieh, S.Y. (2004) ‘On the Web at home: Information seeking and Web searching in the home
environment’, Journal of the American Society for Information Science and Technology,
Vol. 55, No. 8, pp.743–753.
Rizvi, S.K.A., Naqvi, B. and Tanveer, F. (2017) ‘Mobile banking: A potential catalyst for financial
inclusion and growth in Pakistan’, The Lahore Journal of Economics, Vol. 22, No. 1S,
Ryu, H.-S. (2018) ‘What makes users willing or hesitant to use Fintech?: The moderating effect of
user type’, Industrial Management and Data Systems, Vol. 118, No. 3, pp.541–569.
Ryu, H.S. and Lee, J.N. (2018) ‘Understanding the role of technology in service innovation:
Comparison of three theoretical perspectives’, Information and Management, Vol. 55, No. 3,
Sahin, I. and Rogers, F. (2006) ‘Detailed review of Rogers diffusion of innovations theory and
educational technology-related studies based on Rogers’, The Turkish Online Journal of
Educational Technology, Vol. 5, No. 2, pp.14–23.
Saksonova, S. and Kuzmina-Merlino, I. (2017) ‘Fintech as financial innovation - The possibilities
and problems of implementation’, European Research Studies Journal, Vol. 20, No. 3,
Schierz, P.G., Schilke, O. and Wirtz, B.W. (2010) ‘Understanding consumer acceptance of mobile
payment services: An empirical analysis’, Electronic Commerce Research and Applications,
Vol. 9, No. 3, pp.209–216.
Seemann, K. (2003) ‘Basic principles in holistic technology education’, Journal of Technology
Education, Vol. 14, No. 2, pp.28–39.
Shahrokhi, M. (2008) ‘E-finance: status, innovations, resources and future challenges’, Managerial
Finance, Vol. 34, No. 6, pp.365–398.
Susanto, A., Chang, Y. and Ha, Y. (2016) ‘Determinants of continuance intention to use the
smartphone banking services: An extension to the expectation-confirmation model’, Industrial
Management and Data Systems, Vol. 116, No. 3, pp.508–525.
Tan, E. and Lau, J.L. (2016) ‘Behavioural intention to adopt mobile banking among the millennial
generation’, Young Consumers, Vol. 17, No. 1, pp.18–31.
Taylor, S. and Todd, P.A. (1995) ‘Understanding information technology usage: a test of
competing models’, Information Systems Research, Vol. 6, No. 2, pp.144–176.
. Hamid et al.
Tsiakis, T. and Sthephanides, G. (2005) ‘The concept of security and trust in electronic payments’,
Computers and Security, Vol. 24, No. 1, pp.10–15.
Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003) ‘User acceptance of information
technology: Toward a unified view’, MIS Quarterly, Vol. 27, No. 3, pp.425–478.
Westbrook, R.A. and Oliver, R.L. (1991) ‘The dimensionality of consumption emotion patterns and
consumer satisfaction’, Journal of Consumer Research, Vol. 18, No. 1, pp.84.
Yenisey, M.M., Ozok, A.A. and Salvendy, G. (2005) ‘Perceived security determinants in e-
commerce among Turkish university students’, Behaviour and Information Technology, Vol.
24, No. 4, pp.259–274.
Yuan, K. and Xu, D. (2020) ‘Legal governance on fintech risks: effects and lessons from China’,
Asian Journal of Law and Society, Vol. 7, No. 2, pp.275–304.
Yuan, Y., Lai, F. and Chu, Z. (2019) ‘Continuous usage intention of Internet banking: a
commitment-trust model’, Information Systems and E-Business Management, Vol. 17, No. 1,
Zhou, T. (2013) ‘An empirical examination of continuance intention of mobile payment services’,
Decision Support Systems, Vol. 54, No. 2, pp.1085–1091.
Zhou, W., Tsiga, Z., Li, B., Zheng, S. and Jiang, S. (2018) ‘What influence users’ e-finance
continuance intention? the moderating role of trust’, Industrial Management and Data
Systems, Vol. 118, No. 8, pp.1647–1670.