ARTICLE

https://doi.org/10.1057/s41599-023-02483-3 OPEN

Investigating customers’ continuous trust towards

mobile banking apps
Maohao Che 1,2, Sze Yee Ashley Say1, Han Yu 3, Qingji Zhou4, Jared Shu5, Wen Sun5, Xi Luo5 & Hong Xu6 ✉

Gaining continuous trust from mobile banking customers is a critical step in retaining cus-
tomers for their usage of the provided services. The current study aims to investigate how
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customers’ continuous trust is formed at the continuous-use stage. Online survey responses
from 450 frequent mobile banking users are collected. The data were analysed using
structural equation modelling (SEM) based on a proposed model that predicts trust. The
findings successfully validated the model and its reduced form. Based on the model, custo-
mers’ continuous trust can be predicted by mobile banking apps’ perceived ease of use,
privacy assurance and security features, organisation reputation, customer support, and
customers’ previous experience. Furthermore, the interactive relationships among these
proposed factors are proposed and validated in the model. By studying trust in mobile
banking past the initial adoption stage, we provide evidence to support the theoretical fra-
mework of investigating mobile banking continuous trust from the three constructs—mobile
app (perceived ease of use, privacy assurance, security features), organisation (reputation,
customer support), and customer (prior experience).

1 Joint NTU-WeBank Research Centre on FinTech, Nanyang Technological University, Singapore, Singapore. 2 Core Learning, Singapore University of Social

Sciences, Singapore, Singapore. 3 School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore. 4 School of Marina
Science and Technology, Tianjin University, Tianjin, China. 5 WeResearch, WeBank, Shenzhen, China. 6 School of Social Sciences, Nanyang Technological
University, SHHK-04-06, 50 Nanyang Avenue, Singapore 639798, Singapore. ✉email: xuhong@ntu.edu.sg

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T
Introduction
oday, over 6.3 billion smartphone users have been reported trust as an important factor (Jamshidi et al., 2018). Under-
worldwide (Statista, 2021), with a projected growth of standing how existing users perceive various features and
almost hundreds of millions more per year. With the understanding their preferences may provide insights for opti-
increased utilisation and development of mobile communication mising user experience, maintaining mass usage and informing
technology (Ahmad et al., 2020), wireless communication is brand loyalty (Cuesta-Valiño et al., 2023; Zhou et al., 2021).
seeing similarly rapid growth in its speed and reliability (Painuly Despite this, such understandings are not systematically discussed
et al., 2020; Srivastava and Fernandes, 2022). Jointly, these recent in existing studies. How to retain user trust remains unclear and
technological advances have effectuated the shift of consumer lacks a theoretical foundation. Previous research (Mittal and
demand across various sectors towards online transactions, Lassar, 1998) has reported “that the cost of attracting new cus-
greatly enhancing user mobility and access (Cassioli and Balconi, tomers is five times higher than the cost of retaining existing
2022; Souiden et al., 2019). ones” (Petrović et al., 2022, p. 1613). The theoretical and statis-
Banks have been among the first in the financial service sector tical models directly examining continuous trust, especially in
to rapidly transform and adapt interpersonal services to mobile- mobile banking, are not fully understood despite being an
based channels (Alsmadi et al., 2022; Souiden et al., 2021). This increasingly key area of interest. It leads to the following research
advancement has revolutionised the industry both for operating questions:
firms and their general user base. On the one hand, this has
driven firms to optimise financial and customer services to retain RQ1: What are the contributing factors effecting mobile
users and remain competitive within the industry in times of banking customers’ continuous trust;
rapid technological innovation. To maintain their competitive
edge, banks may strive for efficient development and effective RQ2: And to what extent are these effects being manifested?
designs—ideally informed by the consumer’s perspectives. The In the present study, we propose a model of features informing
need for in-depth analysis of the drivers of consumer behaviours continuous trust in mobile banking. We aim to identify critical
has directed studies of mobile banking towards themes such as antecedents for continuous trust and examine how they inform
building long-term relationships, brand loyalty, and, more continuous trust. Potential constructs include perceived ease of
recently, continuous trust (Hoehle et al., 2012b; Kosiba et al., use, privacy assurance and security features, information quality,
2018). organisation reputation, customer support, propensity to trust,
For this technology to thrive in the long term, continuous trust is and previous experience. The validation of the proposed model is
crucial. However, despite the added utility noted above, potential achieved by analysing survey data from a sample of existing
risks, including private information leaks, hacking, lack of control, mobile banking users through structural equation modelling
and poor experiences, may equally deter users from continued use of (SEM). The common features and constructs in mobile banking
the application (Wessels and Drennan, 2010; Zhou, 2012b). Being research have been included in the research frame for continuous
physically unable to directly interact with the service provider trust. Our research contributes to the theoretical framework of
beyond a phone screen, mobile banking interactions may be per- studying mobile banking continuous trust by mobile app, orga-
ceived as limited and insecure. This is a challenge in fully retaining nisation, and customer constructs (Beldad et al., 2010) and pro-
high-level trust to effectively mitigate anxiety and perceived threats vides insights aiding banks to develop informed, realistic
associated with these risks (Hernández-Ortega, 2011). Insights to strategies for strengthening customer interaction and experience.
optimise trust may enhance design for user experience, maintain The rest of the article is organised into four sections: Con-
mass usage, and inform brand loyalty (Jamshidi et al., 2018; Kaur ceptual framework, Methods, Results, and Discussion and con-
et al., 2023; Kosiba et al., 2018). clusion. The “Conceptual framework” section reports the
Continuous trust refers to trust developed past the initial antecedents we have drawn from past research and how they are
adoption stage into the continuous-use stage (Siau and Shen, integrated into a unified model. The “Methods” section presents
2003). At the continuous-use stage, relative amounts of exposure the questionnaire design and survey administration. The
and experience with the technology can be expected (Hoehle “Results” section reports the analysis results. The last section
et al., 2012a). It is noted to be one of the key factors contributing discusses the findings and implications and then conclusions.
to retention and forming continuous-use intentions (Li and Xue,
2021). Mobile banking research on trust is typically focused on
Conceptual framework
the initial adoption stage (Chong et al., 2012) or on investigating
Past research on mobile banking focuses on the adoption models
behavioural and, more recently, continuance usage intentions of
such as the Technology Acceptance Model (Davis, 1989), Unified
the application (Ghobakhloo and Fathi, 2019; Kala Kamdjoug
Theory of Acceptance and Use of Technology (Venkatesh et al.,
et al., 2021; Lin et al., 2023).
2003), Stimuli-Organism-Response (S-O-R) framework (Mehra-
Trust is a dynamic relationship and can change throughout the
bian and Russell, 1974; Shahid et al., 2022). The extant mobile
duration of the interaction (Li and Xue, 2021). The needs of the
banking literature has generally identified three distinct clusters of
customers may change over time after initial adoption. Clearly, at
latent constructs that predict trust—mobile application-related,
a later stage of market penetration, maintaining continuous, long-
organisation-related, and customer-related antecedents (Beldad
term trust proves increasingly relevant. Additionally, as the user is
et al., 2010). This section briefly refers to trust in the continuous-
physically unable to interact directly with mobile banking services
use stage with reference to these perceptions. Guided by the
beyond a phone screen, individuals may further perceive the
following hypotheses, we further relate constructs to form two
interaction as limited and insecure, proving challenging to fully
models proposed for validation.
retain trust in these intangible interactions. Simply offering
mobile banking services may begin to be insufficient in remaining
competitive, making it imperative for banks nowadays to actively Customers’ continuous trust in continuous-use stage. Trust-
seek early strategies for retaining their existing customer base worthiness is defined in retailing literature as “the subjective belief
after initial adoption (Al-Ghazali et al., 2015). High levels of trust with which organisational members collectively assess that a
have been argued to effectively mitigate the anxiety and perceived population of organisations will perform potential transactions
threat associated with these risks, further confirming long-term according to their confident expectations, irrespective of their

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ability to fully monitor them” (Pavlou, 2002, p. 218). Due to H2: The privacy assurance and security features positively
temporal and spatial gaps in the mobile network, mobile banking affect customers’ continuous trust in mobile banking.
generally involves greater risks as compared to offline banking
settings (Zhou, 2012a). Prior to initiating a transaction, customers Information quality (IQ). According to Beldad et al. (2010),
may first need to build sufficient levels of trust towards the information quality refers to the usefulness, completeness, and
platform and its services. While online trust has been studied accuracy of the information provided to customers. It is argued
extensively (Beldad et al., 2010; Kim and Peterson, 2017; Koufaris that if irrelevant or misguided information were to be provided,
and Hampton-Sosa, 2004), research specifically on trust in mobile customers’ trust towards mobile banking service providers might
banking is still argued to be lacking (Kaushik et al., 2020). be negatively affected (Zhou, 2013). This argument has been
Trust is differentiated between initial and continuous trust supported by findings from some past research (Berraies et al.,
(trust at a continuous-use stage) (Lin et al., 2014; Zhou, 2012a), 2015; Geebren et al., 2021; Zhou, 2013, Shareef et al., 2018),
modulated by differing antecedents in the existing literature. though there have also been instances where no significant rela-
While both types of trust studies have identified common tionship between information quality and trust was observed
antecedents such as perceived security and privacy, information (Trabelsi-Zoghlami et al., 2020). As such, the following hypoth-
quality, organisation reputation (Kaushik et al., 2020; Olaleye esis was included for testing:
et al., 2019; Sun et al., 2017), these findings are often inconsistent,
with some studies also having highlighted the significant H3: Higher information quality positively affects customers’
influence of a customer’s previous experience leading to continuous trust in mobile banking.
continuous trust (Jamshidi et al., 2018; Rajaobelina et al., 2021).
The key difference between these two threads of investigation is
the stage of the interaction at the time. These factors are Company-related antecedents
categorised and discussed in the following sections. Organisation reputation (OR). Higher-rated or more reputable
organisations generally garner a more positive or trustworthy
relationship with customers (Beldad et al., 2010; Özkan et al.,
Mobile banking app-related antecedents 2020). In online banking settings, an organisation’s reputation
Perceived ease of use (PEU). This concept is derived from the can be assessed as the company’s credibility in delivering services
technology acceptance model (TAM), defined as “freedom from and reliably catering to its customers’ interests (Kaushik et al.,
effort” spent while using or learning to use a new technology (Davis, 2020; Lee and Chung, 2009). Maintaining a positive organisa-
1989). Ease of use in mobile banking services might be influenced by tional reputation requires large amounts of resources and con-
the user-friendliness of interface design, information presentation sistent reinforcement, and this image rapidly declines with just a
style, and the general ease of interaction with the application few mishaps. Particularly as banking is such a high-risk activity, a
(Jebarajakirthy and Shankar, 2021; Lee and Chung, 2009). Com- bad reputation could be detrimental to the future dealings of a
pared to online banking on computers or tablet devices, mobile company. As such, customers might understandably tend to trust
phone screens are significantly smaller, greatly limiting the amount companies with better reputations where it can be assumed that
of information to be displayed. As such, users may at times find the risking reputational damage outweighs the potential gains of
application challenging to use or be lacking in some way due to sacrificing their customers’ interests (Koufaris and Hampton-
inadequate design. We can expect easier-to-use systems to reduce Sosa, 2004). The following hypothesis is hence proposed:
transaction anxiety and contribute towards greater trust building
(Chien et al., 2012; Sharma and Kakkar, 2022). In mobile banking H4: Positive organisation reputation leads to higher trust
trust studies, the findings on the relationship between perceived ease from customers in mobile banking.
of use and trust have not yet reached a consensus. While studies on
both continuous trust (Petrović et al., 2022; Zhou, 2012a) and initial Customer support (CS). In online banking, customer support is
trust (Kaushik et al., 2020) generally support a positive association generally delivered through technology-based channels such as
with increased ease of use, some studies in South Africa (Van call centres (including phone and email), chat bots, and inte-
Deventer, 2019) and Korea (Gu et al., 2009) have found this rela- grated feedback functions within the app itself (Ganguli and Roy,
tionship to be insignificant. Thus, we seek to test the following 2011). A key difference in the customer support experience in
hypothesis: mobile banking as compared to traditional banking services is the
absence of any face-to-face interactions with the service provider
H1: Customers’ perceived ease of use positively influences (Thakur, 2014). This lack of social presence may add to the
their continuous trust in mobile banking. perceived structural insecurity and lack of quality customer
support. Especially as customers typically only seek customer
Privacy assurance and security features (PASF). Privacy and support when faced with urgent, problematic circumstances, the
security assurances have been considered vital in a customer’s expectations associated with each interaction are likely to be high.
evaluation of trustworthiness (Beldad et al., 2010; Sharma et al., On the other hand, critical and timely support reflects good
2018). It specifically refers to any protective measures such as reliability (Trabelsi-Zoghlami et al., 2020) and thus facilitates
guarantees, contracts, regulations, or transaction procedures that positive experiences (Shahid et al., 2022). This may strengthen a
might effectively assure the customer of their expected outcomes customer’s trust, enabling continuous interaction with the mobile
(Chien et al., 2012). Mobile banking typically involves high-risk banking application based on this assurance (Johannes et al.,
financial transactions, private credit cards or bank account 2018). As such, the following hypothesis is included:
information. Users may choose to revert or disengage from the
service due to a fear of perceived risks and harm. As such, these H5: The perceived level of customer support positively
assurances become even more critical in gaining customers’ trust influences continuous trust in mobile banking.
in the absence of any physical services. Studies have demonstrated To date, past studies in mobile banking trust mostly focused on
this importance and strong link to trust (Kumar et al., 2017; Singh the direct effects of the above variables. However, it is reasonable
and Srivastava, 2018; Zhou, 2012a, Akturan and Tezcan, 2012). to argue that in the absence of professional knowledge of the
Herein, the following hypothesis is examined in the study: mobile banking app, a customer’s perception of the app should

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also be influenced by how reputable these organisations have to mobile banking services. Their perception towards these apps
proven to be and the degree of support they are offered. A recent and providers has likely been heavily influenced by past
mobile banking trust study (Garrouch, 2021) explored the individual experiences. This has implications for our model
relationship between organisation reputation and the perceived predicting continuous trust. In addition to a direct antecedent to
security level of an app. The current study attempts to extend this trust, we therefore further expect previous experience to
by testing how both organisation reputation and customer significantly influence other latent constructs, including perceived
support could influence each of the stated app-related variables: ease of use (H7a), privacy assurances and security features (H7b),
organisation reputation on perceived ease of use (H4a), privacy information quality (H7c), organisation reputation (H7d), and
assurance and security features (H4b), and information quality customer support (H7e). However, as in the literature, we
(H4c); customer support on perceived ease of use (H5a), privacy similarly maintain conceptualising propensity to trust as a stable
assurance and security features (H5b), and information quality personality trait directly related to continuous trust instead of a
(H5c). Besides these organisational supports for mobile banking mediating factor.
customers, it is also important to understand their customers,
such as previous experience with mobile banking and propensity Integrated model. The above-stated hypotheses postulate the
to trust, as in the following section. seven identified latent constructs (perceived ease of use, privacy
assurance and security features, information quality, organisation
reputation, customer support, propensity to trust, and previous
Customers-related antecedents experience) as direct and indirect antecedents to trust. The full
Propensity to trust (PTT). Refers to a customer’s inherent dis- model is shown in Fig. 1.
position to trust others prior to any knowledge or experience to
inform judgement (Gill et al., 2005). It is rooted in an individual’s
Methods
personality in terms of traits of openness to experience and risk-
Questionnaire design. Based on the constructed model, we
taking (Freitag and Bauer, 2016). This has particular implications
designed a questionnaire rated on a 7-point Likert scale
for the initial adoption of mobile banking as customers attempt to
(1-Strongly disagree, 3-Neutral, 7-Strongly agree) with well-validated
accept new and innovative wireless transaction services with
items adapted from previous questionnaires (see Appendix 1).
unknown risks (Beldad et al., 2010). However, studies have
Appendix 1 details the items for each of the latent constructs. In
revealed conflicting findings. Some have shown a propensity to
addition, we further incorporated items regarding respondents’ age
trust to be a significant antecedent to customers’ trust (Kim et al.,
and gender to account for the effects of user demographics.
2009; Zhou, 2011), while other studies have not shown support
for the relationship (Kaushik et al., 2020; Zhou, 2012a). Fur-
thermore, at a continuous-use stage, the effects of propensity to Survey administration and respondents’ demographics. The
trust have not yet been commonly explored. To examine the survey study was approved by the Nanyang Technological Uni-
potential influence of propensity to trust on continuous trust after versity (NTU) Institutional Review Board (IRB reference number:
the initial adoption stage, the following hypothesis is tested in the IRB-2021-305) and carried out in accordance with relevant guide-
current study: lines and regulations. The survey was administered online, and
existing mobile banking users in China were invited to participate.
H6: Customers’ propensity to trust positively influence Respondents were recruited from higher education institution
their continuous trust in mobile banking. communities and public residents in over ten major cities in China.
The questionnaire was first designed in English. The English version
Previous experience (PE) with mobile banking. A customer’s was initially translated into Chinese by an author who spoke native
experience can be defined as their cognitive and affective response Chinese. Subsequently, the translated rendition underwent exam-
before, during, and after interacting with the provided service ination and refinement during a focus group discussion involving six
(Grewal et al., 2009). In studies of initial trust, experience is authors who are proficient in Chinese, thereby ensuring its linguistic
typically inferred as exposure to other related technology (Beldad and cultural validity. To avoid any bias introduced by the instru-
et al., 2010). In mobile banking settings, for example, it refers to a ment, items for each construct were presented randomly. As the
customer’s general familiarity and use of mobile banking appli- present study was concerned with continuous trust at a continuous-
cations (Kaushik et al., 2020). However, at this continuous-use use stage, only respondents who used mobile banking on a regular
stage, customers have already passed the initial adoption hurdle. basis were surveyed. This was achieved by adding a filter question at
Hence, we further developed the concept of previous experience the beginning of the survey. We collected a total of 557 responses.
to sufficiently capture subjective perceptions of mobile banking Responses from duplicated IP addresses were removed to preserve
apps. We expect positive past experiences to contribute towards validity. Forms with incomplete information were also excluded
trust building and negative experiences to be associated with from the dataset. Furthermore, flat-lined responses (identical ratings
lower trust (Wessels and Drennan, 2010). for series of questionnaire items) were also deleted, as they indicate a
In some continuous trust studies, previous experience can also lack of attention. After cleaning, a final sample of 450 responses was
be interpreted as perceived flow of service (Rajaobelina et al., entered for further analysis.
2021), previous knowledge (Yu, 2015), satisfaction (Inan et al., Table 1 presents the respondents’ demographic characteristics
2023; Kazakov et al., 2021), and mobile experience (Rajaobelina and their most frequently used banks. Slightly more male
et al., 2018). In each case, a positive relationship between previous respondents (53.8%) were surveyed. The respondents’ ages
experience and trust was established (Sun et al., 2017). Hence, to ranged from 19 to 50 years, with the age group of 19–25 years
examine the effect of previous experience on continuous trust, we accounting for 37.6% of the respondents. The age and gender
proposed the following: distributions generally fit with and are representative of the
mobile banking user demographics in China (Wang and
H7: Customers’ previous experience positively influences Petrounias, 2017).
their continuous trust in mobile banking.
Again, at a continuous-use stage, customers have since passed Common method bias. As all constructs were collected via a
the initial adoption hurdle and demonstrated frequent exposure single survey instrument using similar rating scales, it was

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Fig. 1 Full model. The figure shows the full theoretical model derived from established hypotheses.

Table 1 Respondents’ demographic characteristics (n = 450).

Characteristics Attributes Frequency Percentage (%) Frequently used banks Frequency Percentage (%)
Gender Female 208 46.2 CCB 92 20.4
Male 242 53.8 ICBC 80 17.8
Age 19–25 years 169 37.6 BOC 76 16.9
26–30 years 71 15.8 CMB 67 14.9
31–35 years 93 20.7 ABOC 47 10.4
36–40 years 65 14.4 BOCM 28 6.2
41–50 years 52 11.6 PSBC 20 4.4
Other banks 40 8.9

necessary to consider the presence of common method bias. The Comparisons between AVE values and squared correlations are
Harman’s single factor test was performed to assess the potential presented in Table 3. As AVE values were higher than the
influence of common method bias (Podsakoff, 2003). The result squared correlation values, discriminant validity was supported in
showed that the total variance extracted by one factor was the current dataset (Hair et al., 2013).
43.65%, which was below the recommended threshold of 50%.
Therefore, the common methods bias was not an issue for the
data collected in the current study. Hypothesis testing. Structural path analysis was then conducted
to test the effectiveness of the proposed full model. As shown in
Table 4, the model fit indices indicate a good fit for the proposed
Results full model, fulfilling criteria including CFI > 0.90, TLI > 0.90, and
First, to evaluate the validity of the measurement items and RMSEA < 0.07 (Hu and Bentler, 1999).
goodness of fit, confirmatory factor analysis (CFA) was performed. Among the hypotheses that directly test continuous trust (H1-7),
After this, the structural models formed in the current study were H3 (information quality—continuous trust) was rejected; direct
tested and compared from the statistics derived. The above- influences from customer-related antecedents (H6: previous experi-
mentioned analyses were performed using IBM SPSS Amos 26. ence—continuous trust; and H7: propensity to trust—continuous
trust) to continuous trust were not supported. However, H4
Measurement model validation. Confirmatory analysis was (organisation reputation—continuous trust) and H5 (customer
conducted to evaluate model fits for each of the measurement support—continuous trust) were supported, indicating that effects
items tested (see Table 2). The presented model indices in the from organisation reputation and customer support could also be
following table indicated a good fit (Hu and Bentler, 1999). mediated by privacy assurance and security features, as well as
Furthermore, the computed composite reliability (CR) values information quality. It should also be noted that though previous
were above 0.7, demonstrating the measurements to be reliable experience was not a direct predictor of continuous trust, the
and appropriate (Hair et al., 2013). The average variance supported hypotheses H7a and H7c-e indicated the influence of
extracted (AVE) values were above the stipulated threshold of 0.5 previous experience on continuous trust to be moderated by several
(Hair et al., 1998), indicating good convergent validity. other factors of interest (e.g., perceived ease of use, organisation

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Table 2 Confirmatory factor analysis results (N = 450). Table 4 Path analysis of full model.

Mean Standard Standardised t-value CR AVE Full model

deviation factor loading
Hypothesis Path Std Coeff P Results
PE1 3.90 1.09 0.82 *** 0.89 0.72
PE2 3.98 0.85 0.87 14.16 H1 PEU -> CT 0.203 0.042 Supported
PE3 4.03 0.82 0.86 14.14 H2 PASF -> 0.173 0.017 Supported
PTT1 3.54 1.04 0.89 *** 0.85 0.73 CT
PTT2 2.84 1.10 0.82 19.68 H3 IQ -> CT −0.006 0.951 Rejected*
PEU1 3.83 0.87 0.83 *** 0.89 0.66 H4 OR -> CT 0.578 <0.001 Supported
PEU2 3.95 0.82 0.82 20.73 H5 CS -> CT 0.595 <0.001 Supported
PEU3 4.02 0.85 0.82 20.54 H6 PTT -> CT −0.070 0.099 Rejected*
PEU4 3.88 0.84 0.79 19.44 H7 PE -> CT −0.085 0.121 Rejected*
PASF1 4.10 0.78 0.88 *** 0.90 0.74 H4a OR -> 0.100 0.209 Rejected*
PASF2 4.06 0.85 0.83 22.24 PEU
PASF3 4.15 0.77 0.87 24.25 H4b OR -> 0.579 <0.001 Supported
IQ1 4.09 0.81 0.78 *** 0.90 0.64 PASF
IQ2 3.97 0.82 0.78 17.76 H4c OR -> IQ 0.307 <0.001 Supported
IQ3 3.98 0.81 0.81 18.60 H5a CS -> PEU 0.023 0.240 Rejected*
IQ4 3.99 0.79 0.82 18.91 H5b CS -> 0.259 0.001 Supported
IQ5 3.92 0.85 0.80 18.39 PASF
OR1 3.92 0.87 0.76 *** 0.80 0.57 H5c CS -> IQ 0.283 <0.001 Supported
OR2 4.23 0.76 0.78 16.62 H7a PE ->PEU 0.918 <0.001 Supported
OR3 3.92 0.93 0.73 15.55 H7b PE -> 0.049 0.681 Rejected*
CS1 3.95 0.84 0.78 *** 0.87 0.62 PASF
CS2 3.96 0.85 0.74 16.61 H7c PE -> IQ 0.380 <0.001 Supported
CS3 4.10 0.80 0.83 19.06 H7d PE -> OR 0.824 <0.001 Supported
CS4 4.12 0.78 0.79 18.08 H7e PE -> CS 0.828 <0.001 Supported
CT1 4.21 0.74 0.90 *** 0.91 0.71 Explanatory power: R2CT = 0.823.
CT2 4.19 0.77 0.88 27.07 Model fit indices: χ2 = 847.666; df = 332; χ2/df = 2.55; IFI = 0.943; TLI = 0.934; CFI = 0.942;
CT3 4.08 0.86 0.70 17.96 RMSEA = 0.059.
PE previous experience, PEU perceived ease of use, PASF privacy assurances and security
CT4 4.16 0.76 0.87 26.34 features, IQ information quality, OR organisation reputation, CS customer support, CT
continuous trust.
Model fit indices: χ2 = 666.77; df = 322; χ2/df = 2.07; IFI = 0.96; TLI = 0.96; CFI = 0.96; *Hypothesis rejected on the confidence interval: α = 0.05.
RMSEA = 0.05.
Degree of freedom (df) = (i2 + i)/2 − j, for i equals the number of manifest variables and j
equals the number of free parameters.
PE previous experience, PEU perceived ease of use, PASF privacy assurances and security reduced model. The reduced model was then compared with the
features, IQ information quality, OR organisation reputation, CS customer support, CT
continuous trust. full model in their goodness of fit and explanatory power (R2)
***Donates a constrained relationship to 1 for identification. regarding continuous trust and parsimony fit indices.
The comparison of two models typically involves the following
three steps: (1) The model fit indices of each model are compared
against the acceptance criteria; (2) If the survey data adequately
Table 3 AVE, construct correlation and squared correlation. fits both models, the explanatory power (in terms of R2) of each
model is then compared, with that of the greater value accepted
Factor PE PTT PEU PASF IQ OR CS CT (Huh et al., 2009; Rust et al., 1995); and (3) If both models are
PE 0.72a 0.03c 0.18 0.10 0.10 0.11 0.11 0.08
equivalent in goodness of fit and explanatory power, the more
PTT 0.17b 0.72 0.04 0.03 0.02 0.03 0.03 0.02 parsimonious model would be adopted.
PEU 0.43 0.21 0.66 0.11 0.15 0.11 0.13 0.10 The comparison results are listed in Table 5 below. In terms of
PASF 0.31 0.17 0.33 0.74 0.11 0.14 0.12 0.13 model fit indices, both models demonstrated acceptable levels of fit
IQ 0.32 0.15 0.39 0.33 0.64 0.12 0.12 0.10 to the collected survey data (Hu and Bentler, 1999). As the
OR 0.33 0.17 0.33 0.37 0.35 0.57 0.14 0.14 differences in model fit indices between the two models are
CS 0.33 0.16 0.36 0.34 0.35 0.37 0.62 0.14 negligible, their explanatory power (R2) regarding continuous trust is
CT 0.28 0.14 0.32 0.36 0.32 0.38 0.37 0.71 compared. It is noted that with reduced variables and links, 81.8% of
PE previous experience, PEU perceived ease of use, PASF privacy assurances and security
the variation in continuous trust was explained by the model, with a
features, IQ information quality, OR organisation reputation, CS customer support, CT reduction of only 0.5% as compared to the full model. The major
continuous trust.
aAverage variance extracted (AVE) values are along the main diagonal.
difference between the two models is in their parsimony fit indices
bCorrelations between constructs are below the main diagonal. (Akaike Information Criterion, AIC). A considerable amount of
cSquared correlations between constructs are above the main diagonal.
reduction in the AIC value of the reduced model (597.254 as
compared to 995.666 in the full model) indicates that the reduced
reputation, and customer support). In addition, the influence of model is more parsimonious. The reduced model was hence selected
respondents’ gender and age was also tested against the construct of as the more suitable model.
continuous trust. The results did not reveal any significant Results of the reduced model from path analysis are presented
relationships. in Fig. 2. All hypotheses were supported at the significance level
of α = 0.05. These observations were similar to the full model.
The personality trait of propensity to trust did not reflect
Reduced model. Considering not all hypotheses were supported customers’ continuous trust. Similarly, information quality was
in the full model, variables and links that did not yield sig- not a strong predictor of trust in the continuous-use stage.
nificance in predicting continuous trust were removed to obtain a Instead, other app-related variables (perceived ease of use, and

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Table 5 Model comparison.

χ2 df χ2/df IFI TLI CFI RMSEA AIC R2CT

Full model 847.666 332 2.553 0.943 0.934 0.942 0.059 995.666 0.823
Reduced model 493.254 179 2.756 0.955 0.947 0.954 0.063 597.254 0.818

Bold values indicates the major difference between the full model and the reduced model.

Fig. 2 Path analysis of reduced model for continuous trust of mobile banking. This figure shows the path analysis results from the reduced model.

privacy assurance and security features) and company-related by integrating the potential influences of customers’ perception of
variables (organisation reputation and customer support) were the company with their continuous trust of the app in our model.
validated as direct antecedents to continuous trust. Some indirect Furthermore, considering the potential influences of a customer’s
effects were also observed in the model. Organisation reputation previous experience on the perception of mobile banking appli-
and customer support indirectly influence continuous trust as cations and associated organisations, effects from previous
mediated by privacy assurance and security features. Further, experience as mediated by other factors were also incorporated in
previous experience did not directly inform continuous trust here. this model. We found the overall indices of good fit for the full
Its effects were mediated by perceived ease of use, organisation model and its reduced form to be generally robust. In both
reputation, and customer support. models, the majority of the variation in continuous trust could be
well explained. As the reduced model yields similar performance
Discussion and conclusion as the full model, based on the parsimonious law, the reduced
Continuous trust in mobile banking is predicted by three main model is accepted for predicting customers’ continuous trust by
sources—product or mobile app (perceived ease of use, privacy the constructs tested in the present study. This implies that while
assurance and security features), organisation (reputation and propensity to trust may not influence continuous trust in the
customer support), and customers (previous experience). Per- continuous-use stage, an individual’s previous experience shaped
ceived ease of use, organisational reputation and customer sup- factors such as perceived ease of use, information quality, orga-
port are associated with previous experience. Privacy assurance nisation reputation, and customer support—which then informed
and security features are predicted by organisational reputation users’ continuous trust levels. This is in line with findings from
and customer support. other studies on trust in mobile retailing apps and online banking
(Kaushik et al., 2020; Zhou, 2012a). Furthermore, organisation
Discussion on supported hypotheses reputation and customer support were found to have the stron-
A model to predict customers’ continuous trust after initial gest effects on strengthening customers’ continuous trust. The
adoption of mobile banking. The current study shows its novelty finding is consistent with the study on corporation reputation by

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Özkan et al. (2020). The two company-related factors could also different from features influencing initial trust formation, high-
influence how customers view the app’s privacy assurances and lighting the necessity of extensive future research on the currently
security features. This is supported by another study on mobile scarce continuous trust studies. On the other hand, information
wallet adoption, where Garrouch (2021) found that company quality was found to have no significant influence on customers’
reputation would impact the perceived security level of the app. trust here. A recent study conducted in Tunisia found similar
The current study did not find any significant age effect on results, arguing customers tended to prioritise and were primarily
continuous trust. This finding is in line with our previous study concerned with security and privacy features over information
(Zhou et al., 2021), where no significant difference was observed quality and aesthetics (Trabelsi-Zoghlami et al., 2020). This might
in satisfaction levels for customers under the age of 50. However, indicate that information quality is not a major contributing
it was noted that customers over 50 years old had significantly factor in retaining users’ trust over time, prompting a re-
higher satisfaction levels as compared to younger age groups. As evaluation of features informing continuous trust.
the current study did not test participants over the age of 50, we
could not conclude if there would be a significant transition in Theoretical contribution. Our research contributes to the theo-
trust levels passing the age of 50. This age effect for older age retical framework of studying mobile banking continuous trust by
groups can be further examined in future research. mobile app, organisation, and customer constructs (Beldad et al.,
2010). The present study outlined factors influencing continuous
Company-related perception influencing app-related perception. trust in mobile banking, having highlighted constructs most relevant
The current trust study adds novelty in understanding the rela- in predicting this relationship in the resulting reduced model. This
tionship between organisation reputation and customer support on indicates a few contributions to the existing literature. First, we have
customers’ perception of the mobile banking app. The reduced contributed towards the scant literature explicitly integrating con-
model revealed both organisation reputation and customer support cepts of continuous trust in mobile banking, particularly after initial
to significantly influence customers’ evaluations of both privacy adoption. Second, by adapting similar constructs of interest in trust
assurance and security features, and information quality in mobile of mobile banking for initial adoption, we invite comparisons
banking apps. This implies that high reputation and strong support between these two stages of consumer trust—before and after initial
from the banks are not only direct signals of trustworthiness but also adoption. Third, the current model serves as a point of theoretical
indirect indicators of their apps’ reliability and information quality explanation for the complex, interrelated relationships of various
as perceived by customers. This set of comprehension offers a new facets of consumer trust and perception of mobile banking appli-
perspective on trust formation studies on mobile banking users. In cations. Future research may consider developments such as inves-
future research that assesses continuous trust in relation to mobile tigating continuous trust across various time points of use and
banking app features, users’ perceptions towards the bank should perhaps the inclusion of other constructs of interest such as temporal
also be taken into consideration. or environmental changes (e.g., before, during and post pandemic).
Customers’ previous experience affecting their perception towards
mobile banking apps and service-providing companies. Con- Practical implications. Findings from the current study may be
tributing new knowledge to the existing mobile banking trust informative to banks with regard to developing effective strategies
studies, the current study proposed that in modelling customers’ for the long-term maintenance of existing customers’ trust in mobile
trust at a continuous-use stage, their perception towards the apps banking apps and thus customer loyalty. We further add to the
(perceived ease of use, privacy assurance and security features, and discussion of designing user-friendly interfaces that might support
information quality) and service providing companies (organisa- functionality and user retention. The most important aspects (per-
tion reputation, and customer support) would be readily influenced ceived ease of use, privacy assurance and security features, organi-
by their previous exposure to these services. Five out of the six sation reputation, and customer support) influencing customers’
proposed hypotheses (with the exception of previous experience— trust were successfully identified. In practice, customers generally
privacy assurance and security features) were supported through appear to show more concern about the security features of the app
the validation of the reduced model with empirical data. This to protect their personal information. This might only be expected to
suggests past experiences with mobile banking significantly mod- be more critical in mobile banking apps, which are directly linked to
erate present perceptions of mobile banking features, alluding to a sensitive financial information. Enhancing this protection of custo-
reciprocal relationship. It is suggested that banks thus pay close mers’ information and developing secure transaction processes are
attention to and even consider strengthening communicative likely to reinforce trust and loyalty in mobile banking (Zhou et al.,
relationships with their customers. This simultaneously allows for 2021). Perhaps equally important, the enhancements in customer
gaining first-hand, reliable insight into their individual experiences protection and security need to be communicated to customers
whilst providing enhanced quality service. through push notifications or advertisement campaigns to
strengthen their trust. Maintaining corporate image is also critical in
The absence of influences from propensity to trust, and informa- retaining this trust, as highly regarded banks are perceived as more
tion quality. The influence of customers’ propensity to trust on likely to treat their customers fairly and cater to their interests. It
continuous trust was not observed, though this has been sup- expects to build confidence in the use of these services. Furthermore,
ported in some studies of initial mobile banking trust (Kaushik ensuring timely and reliable customer support is crucial. Responsive,
et al., 2020; Zhou, 2012a). Propensity to trust indicates the degree timely, and helpful support from banks is critical for customers
to which trust might be assigned to a technology by customers facing issues in the absence of physical interactions. Their experience
given little prior experience or knowledge. This highlights the two with such online customer support might either greatly enhance or
distinct time points and stages of consumer trust, with a potential completely damage their trust in mobile banking, with effects
explanation for this difference being that at a continuous-use potentially extending to the reputation of the banks themselves.
stage, customers have become relatively familiar with mobile
banking through continuous usage, essentially nullifying the Limitations and future research. Varying cultural norms and
effects of an individual’s propensity to trust, which may play a values may differentially inform consumers’ behaviour and percep-
greater role at the initial adoption stage. In theory development, it tions across different markets. Hence the factors that are most
indicates that the set of features influencing continuous trust is influential in retaining Chinese customers’ continuous trust in this

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