Mobile payment-banking efficiency nexus—A concise review of the evolution and empirical exploration of the Taiwan banking industry

Abstract

This paper utilizes an advanced Network Data Envelopment Analysis (DEA) model to examine the impact of mobile payment on the efficiency of Taiwan banking industry. Inheriting the literature, we separate the banking operation process into two stages, namely profitability and marketability. Mobile payment is then considered as the core factor in the second stage. Our paper discovers network DEA model can effectively enhance the analysis of banking industry’s efficiency, and mobile payment has a notable impact on Taiwan banking industry. Regarding the profitability stage, there is only one efficient bank in 2019 and 2022, respectively. These banks also perform better in terms of “mobile payment production”. In the marketability stage, there is also only one bank in 2021 and one bank in 2022, that can reach to unique efficiency score. This indicates many banks attempt to increase earnings per share through investing in mobile payment services. However, the achievement still needs more wait. This leads to the fact that no bank can reach the ultimate overall efficiency. Within our sample, we also find that regarding promoting mobile payment services, Private Banks outperform Government Banks.

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Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
https://doi.org/10.24294/jipd.v8i6.6057
1
Article
Mobile payment-banking efficiency nexus—A concise review of the
evolution and empirical exploration of the Taiwan banking industry
Manh-Trung Phung1, Chen-Yu Kao2,*, Cheng-Ping Cheng3, Yi-Jyun Liu3, Lien-Wen Liang4
1 Financial Management Faculty, Vietnam Maritime University, Haiphong 182582, Vietnam
2 Shaoguan University, Shaoguan, Guangdong 512005, China
3 Department of Finance, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan
4 Department of Finance, Chinese Culture University, Taipei 11114, Taiwan
* Corresponding author: Chen-Yu Kao, littsefish@gmail.com
Abstract: This paper utilizes an advanced Network Data Envelopment Analysis (DEA) model
to examine the impact of mobile payment on the efficiency of Taiwan banking industry.
Inheriting the literature, we separate the banking operation process into two stages, namely
profitability and marketability. Mobile payment is then considered as the core factor in the
second stage. Our paper discovers network DEA model can effectively enhance the analysis of
banking industry’s efficiency, and mobile payment has a notable impact on Taiwan banking
industry. Regarding the profitability stage, there is only one efficient bank in 2019 and 2022,
respectively. These banks also perform better in terms of “mobile payment production”. In the
marketability stage, there is also only one bank in 2021 and one bank in 2022, that can reach
to unique efficiency score. This indicates many banks attempt to increase earnings per share
through investing in mobile payment services. However, the achievement still needs more wait.
This leads to the fact that no bank can reach the ultimate overall efficiency. Within our sample,
we also find that regarding promoting mobile payment services, Private Banks outperform
Government Banks.
Keywords: mobile payment; profitability efficiency; marketability efficiency; network data
envelopment analysis
1. Introduction
In recent years, the relentless introduction of novel financial technologies has
engendered substantial qualitative transformations across various facets of the
traditional financial industry, encompassing products, services, payments, transactions,
credits, and operational processes (Niankara and Traoret, 2023). This transformation
has been particularly propelled by remarkable advancements in communication
technology and the widespread proliferation of mobile devices, leading to the
emergence of mobile payment as a prevailing trend in transactional models (Le et al.,
2022). Additionally, governments worldwide have been actively engaged in the
process of opening up relevant laws and regulations to facilitate the transition towards
a cashless society (Rahman et al., 2022). A pertinent exemplification of this trend is
evident in China, where mobile payment services have become widely accessible,
extending their reach from high-end fashion boutiques to local community newsstands.
Utilizing the convenience of QR Code scanning, merchants can effortlessly conduct
cashless transactions via smartphones, thereby challenging the established business
models of global banks (Sleiman et al., 2023).
CITATION
Phung MT, Kao CY, Cheng CP, et al.
(2024). Mobile payment-banking
efficiency nexus—A concise review
of the evolution and empirical
exploration of the Taiwan banking
industry. Journal of Infrastructure,
Policy and Development. 8(6): 6057.
https://doi.org/10.24294/jipd.v8i6.6057
ARTICLE INFO
Received: 26 April 2024
Accepted: 11 May 2024
Available online: 12 June 2024
COPYRIGHT
Copyright © 2024 by author(s).
Journal of Infrastructure, Policy and
Development is published by EnPress
Publisher, LLC. This work is licensed
under the Creative Commons
Attribution (CC BY) license.
https://creativecommons.org/licenses/
by/4.0/

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
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To allure the masses and enhance profitability, worldwide financial institutions
have exerted considerable endeavors in the realm of mobile payment technology
(Albashrawi and Motiwalla, 2019; Khan et al., 2016; Shareef et al., 2018).
Nevertheless, it is noteworthy that the developmental emphasis and the extent of
impact of mobile payment technology tend to vary across different periods and
countries (Aloulou et al., 2023).
Hedman and Henningsson (2015) emphasized that the development of mobile
payments constitutes a significant financial innovation that has reshaped the payment
market within the mobile payments ecosystem. This innovation has attracted new
payment service providers leveraging novel technologies to carve out their niches,
while established traditional banking institutions seek to safeguard their oligopoly. In
early 2010, the European Union introduced the mobile payment market cooperation
(MPMC) framework, which engenders a dynamic interplay of mutual competition and
collaboration among mobile phone manufacturers, telecommunications companies,
traditional banks, and third-party payment entities within the mobile payment
ecosystem (Bianchi et al., 2023). For instance, in the Netherlands, prominent banks
and telecom operators have collaboratively undertaken a trusted service manager
(TSM) project for mobile payment systems (Hasan et al., 2021). However, the success
or failure of mobile payment platforms necessitates examination through the lens of
collective action theory and platform theory to discern the intricacies of competition
and cooperation between banks and telecom operators. Hedman and Henningsson
(2015), therefore, highlights that divergent strategic goals, conflicting interests, and
governance challenges can lead to the fragmentation of mobile payment platforms.
Reuver et al. (2015) further underscored that the aforementioned issues within the
mobile payment ecosystem can be partially attributed to the platform’s openness to
third-party payment operators and corporate governance considerations. Given the
dominant presence of traditional large-scale banks in the consumer payments market
in the Netherlands, competition predominantly unfolds between banks rather than
between banks and telecom operators. This observation underscores the need for a
comprehensive understanding of the interactions and dynamics between key
stakeholders to comprehend the evolving landscape of mobile payment platforms.
In the Asian context, there is a growing focus on investigating the implications
of mobile payments on both information security and consumer behavior,
encompassing aspects related to user interfaces and mobile payment platforms.
Scholars have increasingly directed their attention towards this domain. For instance,
Lee and Chung (2009) employed a structural equation model (SEM) to examine South
Korean users’ trust and satisfaction with mobile banking. Their study incorporated
influential factors, including system quality, user interface, and information quality.
The findings revealed that the mobile payment platform’s information security
environment and the provision of fast and accurate information were pivotal factors
significantly impacting user perceptions. Similarly, Zhou et al. (2021) conducted an
online survey involving 224 customers of a large-scale Chinese bank. Their research
demonstrated that the service quality of mobile banking has a direct and substantial
influence on bank customer loyalty. Additionally, they observed that the user interface
design of mobile banking wielded the most significant indirect effect in attracting
consumers. The study also identified several crucial factors directly or indirectly

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
3
shaping consumers’ preferences, among which user interface design, system quality,
information security environment, and service quality played key roles. Al-Okaily
(2023) undertook a comprehensive investigation into the determinants impacting users’
e-loyalty within the domain of mobile payment technologies, specifically focusing on
e-wallet payment apps. The empirical research employed a survey methodology
administered to a cohort of 251 individuals utilizing e-wallet apps. The findings of this
study significantly advanced the understanding of pivotal factors influencing e-wallet
adoption, thereby proffering actionable recommendations aimed at augmenting the
broader dissemination of financial technology. Indeed, the growing body of research
signifies the escalating attention and interest in comprehending the multifaceted
dimensions of mobile payment platforms and their implications for information
security and consumer behavior in the Asian context.
The advent of mobile payment has brought about significant transformations in
Taiwan’s banking industry’s financial service model (Lian and Li, 2021). In tandem
with the introduction of internet banking, enabling customers to engage in financial
transactions and wealth management through smartphones, banks are actively
capitalizing on big data analysis to capitalize on the burgeoning mobile payment
market. Despite these progressive initiatives, statistics from the Financial Supervisory
Commission indicate that the proportion of electronic payment in the country still lags
behind major countries in East Asia in recent years (Shang and Chiu, 2023). As a
response to this situation, there is an imperative to intensify efforts in promoting the
“Five-Year Doubling Electronic Payment Usage Rate” plan. Initially launched to
double the original non-cash payment rate from 26% in 2015 to 52% in 2020, the plan
has encountered challenges, primarily exacerbated by the disruptive impact of the
COVID-19 pandemic. The inclusion of the “ATM transfer” project in 2020 only
enabled a non-cash payment rate of 51.7%, falling short of the targeted standard.
Therefore, there exists a pressing need to redouble initiatives and strategies to achieve
higher rates of electronic payment adoption and bridge the gap with East Asian
counterparts.
To comprehensively augment mobile payment adoption, the Taiwan Financial
Supervisory Commission unveiled a three-year plan for non-cash payment in March
2021 (Fu et al., 2022). The overarching objective of this plan is to achieve substantial
growth in the “non-cash payment transaction amount” by 8% annually, ultimately
culminating in a total transaction value of 6 trillion NTD by the year 2023. Moreover,
the plan aims to propel the “non-cash payment transaction number” by 50%, reflecting
an impressive annual growth rate of 15%, thus attaining 7.832 billion transactions.
Despite the challenges posed by the COVID-19 epidemic in Taiwan during 2021, the
momentum towards increased non-cash payments remained robust, resulting in a
notable yearly surge of 9.4%, with the amount of non-cash payments reaching 5.44
trillion NTD. This achievement signifies significant progress towards the Financial
Supervisory Commission’s target, effectively bringing the amount of non-cash
payment transactions closer to the desired milestone by the conclusion of 2022.
The investigation regarding the influence of mobile payment activities on
banking sector’s performance has attracted significant scholarly attention. Despite a
notable increase in research within this field in recent years, several gaps in the
literature remain unaddressed. Firstly, the prevalent use of primary data, typically

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
4
derived from surveys, to represent the mobile payment variable, presents constraints
for financial analysis. Secondly, the predominant reliance on regression models to
assess the impact of mobile payments on performance raises concerns regarding the
exogeneity of the mobile payment variable in relation to bank efficiency. Lastly,
previous research often oversimplifies banking efficiency by primarily concentrating
on financial metrics like Return on Assets (ROA) and/or Tobin’s Q, rather than
considering the multidimensional aggregate operational efficiency intrinsic to this
complex industry.
This paper employs a two-stage Network DEA model to investigate the impact
of mobile payment on the operating performance of Taiwan’s banking industry. The
additive efficiency approach proposed by Chen et al. (2009) is utilized, assuming both
Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS), to decompose
total efficiency into individual efficiency scores. Subsequently, following the two-
stage structure introduced by Seiford and Zhu (1999), the bank’s profitability
efficiency is examined in the first stage, while the bank’s marketability efficiency is
analyzed in the second stage. Through a detailed exploration of the internal operational
processes, this study analyzes the source of inefficiency in actual business
performance by scrutinizing efficiency values and weight values at individual stages.
During the transition from the first stage to the second stage, the impact of mobile
payment (as an intermediate measurement) is assessed using two key variables:
“number of users” and “mobile payment electronic transaction volume”. These
variables serve as the focal points for analyzing the efficiency performance of
individual stages. The quality of stage efficiency values and the magnitude of weight
values significantly influence the overall operating performance of the bank,
representing the central focus of this empirical research. Additionally, this article will
compare the difference in the operating efficiency of public and private banks in
Taiwan under the context of integrating mobile payment activities by employing the
non-parametric Mann-Whitney U Test.
Our research contributes significantly in two key aspects. Firstly, it stands out as
one of the few studies that systematically consolidates the literature on the
development of electronic payments, with a specific emphasis on Taiwan market. It is
noteworthy that Taiwan and China represent two markets characterized by substantial
differences in the electronic payments ecosystem. While China, being a large market,
has experienced early and extensive development of electronic payments (as
extensively documented in the existing literature), the adoption of electronic payments
in Taiwan is relatively in its early stages. Secondly, from an empirical perspective, our
study utilizes datasets that directly capture mobile payment activities within
Taiwanese banks, which are officially provided by the Taiwan Financial Supervisory
Commission. Different from Tong et al. (2023), we incorporate the demand deposit
variable as a crucial resource for mobile payment activities. Our model enables the
efficiency decomposition capability for the network operation and elucidates the
role/impact of mobile payment on the operational process in banking industry.
Furthermore, the research’s primary focus lies in assessing marketability rather than
delving into the internal operational efficiency of banks.
The rest of this paper is structured as follows. In the second section, we introduce
the operational aspects of mobile payment services and present an overview of

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5
Taiwan’s current payment market. The third section elucidates the efficiency
decomposition method of the two-stage Network DEA model and outlines the
analytical framework established for this study. Subsequently, the fourth section
presents the empirical analysis of results, followed by the conclusion and
recommendations in the final section.
2. Literature review
2.1. Mobile payment—Definition and some features
Mobile payment, in its comprehensive scope, constitutes a digital financial
service that empowers users to conduct, authorize, and successfully complete financial
transactions, while also facilitating seamless fund transfers through the utilization of
mobile devices interconnected with the Internet or wireless communication
technology (Slade et al., 2015). Commonly referred to as Mobile Payment Services
(MPSs), these services encompass various forms such as mobile wallets, mobile
remittances, contactless payments, or proximity payments, and have emerged as a
rapidly expanding segment within the domain of mobile banking (Jung et al., 2020).
By encompassing mobile wallets and mobile remittances, this technology enables
secure and legitimate transactions via users’ mobile devices, effectively obviating the
necessity for physical cash, checks, or credit cards during payment processes, and
effectively transitioning to digital payment methods (Alsmadi et al., 2022). Mobile
payment applications can operate in both a “peer-to-peer” (P2P) environment, where
users execute electronic transfers through banking channels, as well as in physical
entities providing financial services. In P2P mobile payments, individuals can easily
conduct electronic transfers through their banks, such as splitting restaurant bills or
collectively purchasing event tickets via mobile devices. On the other hand, mobile
payment at brick-and-mortar outlets involves users making payments for specific
goods or services at the checkout counter, leveraging a dedicated mobile app instead
of cash or credit cards. Businesses offering this payment method require specific point-
of-sale (POS) equipment to process transactions efficiently.
Research findings suggest that among MPSs, those leveraging personal social
networks are notably more prevalent among young adults in the United States
compared to other types of MPSs. Moreover, the adoption of MPSs by users is
influenced by several key factors, including expected performance, specialization,
trust, compatibility, and community influence. The implications of these studies hold
substantive significance for the development of the financial industry (Jung et al.,
2020). Additionally, other scholarly investigations have underscored the paramount
importance of expected efficacy in determining users’ inclination to adopt MPSs
(Koenig-Lewis et al., 2015; Musa et al., 2015; Slade et al., 2015; Tan et al., 2014; Teo
et al., 2015). Furthermore, the surge in mobile payment apps in recent years is
unsurprising, given users’ increasing reliance on mobile devices for various daily
activities, including messaging, public transportation, and health data monitoring. This
growing reliance on mobile devices has fostered a receptive environment for the
proliferation of mobile payment applications.

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
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2.2. Current situation of mobile payment in Taiwan
In 2017, the Financial Supervisory Commission (FSC) and the Ministry of
Finance jointly introduced Taiwan Mobile Payment (Taiwan Pay), a collaborative
effort involving numerous domestic financial institutions, with the primary objective
of penetrating the financial card payment market. Taiwan Pay integrated the
functionality of financial card payments into mobile phones, offering a common
platform based on the “QR Code Common Payment Standard” application
development. The service targeted users without credit cards, categorized as an
electronic payment service. It prioritized the introduction of Taiwan Pay services for
various livelihood-related expenses, including water, electricity, parking fees, and
taxes, in addition to general consumption. The initiative aimed to leverage this
advantage and establish itself as a national payment brand, encouraging wider
adoption of mobile payment services among the population. However, despite such
advantages, the usage rate of mobile payment in Taiwan has not seen a notable
increase. The main impediment lies in the fact that Taiwan Pay operates as an open
payment system, connecting to various banks, resulting in difficulties in seamless
integration. Furthermore, the imperfect user interface hinders the overall user
experience, leading to inconvenience and restrictions in its use.
In recent years, Taiwanese banks have actively sought to enhance their presence
in the mobile payment market by pursuing various strategies. Besides collaborating
with payment companies, some banks have also ventured into developing their
independent mobile payment services. In alignment with the “Regulations on the
Administration of Electronic Payment Institutions”, companies are permitted to apply
for licenses to provide electronic payment services if they facilitate users in registering
and opening electronic payment accounts as intermediaries for fund transfers, value
storage, and transmitting receipt and payment messages through electronic devices.
These services include operations such as “receipt and payment of actual transaction
funds”, “receipt of stored value funds”, and “transfer between electronic payment
accounts”, which facilitate seamless transactions between payers and payees. These
electronic payment services can be offered through cross-industry alliances or by the
banks themselves. However, technical challenges have prompted most banks to form
collaborations with electronic payment operators to facilitate the implementation of
their mobile payment initiatives.
From an industry-wide perspective, the implementation of the new “Regulations
on the Administration of Electronic Payment Institutions” on 1 July 2021, marks a
significant milestone in Taiwan’s electronic payment development (Lian and Li, 2021).
With the successful integration of the new electronic checks and electronic check
systems, a crucial phase has been initiated. A financial company has been entrusted
with building an inter-agency sharing platform for electronic payments, with Taiwan
Cooperative Bank designated as the responsible entity for account settlement. This
integrated platform encompasses various functionalities, including the national fee
payment platform, fund transfer platform (enabling inter-bank account information
inspection, agreed links, and deductions), QR Code common payment (facilitating
cross-border remittances and transactions), and online shopping capabilities.
Furthermore, this development facilitates seamless transfers between different

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
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electronic payment platforms, alongside the introduction of foreign currency trading,
domestic and foreign small-amount exchanges, and the integration and discounting of
bonus points. These advancements contribute significantly to the Financial
Supervisory Commission’s objective of effectively managing and controlling risks
associated with physical and virtual stored value tools. Consequently, the expansion
of the electronic payment and electronic ticket payment ecosystem is poised to be
achieved successfully.
The prevalence of mobile payment usage in Taiwan is progressively increasing,
creating novel prospects for the electronic payment industry. According to the 2021
mobile payment consumer survey conducted by the Institute for Information Industry,
the percentage of Taiwanese individuals favoring card payments declined from 35%
in 2020 to 26% in 2021. This observation indicates that mobile payment has evolved
from being merely a technological trend to an integral part of daily life. Several factors
contribute to this transformation: firstly, the contactless transaction model, driven by
the pandemic, has played a significant role. Secondly, major retail outlets and
prominent e-commerce players like PX Mart, Family Mart, 7-11, Shin Kong
Mitsukoshi, Carrefour, Shopee, etc., have all ventured into self-operated payment
channels over the past couple of years, which, in conjunction with the growth of
delivery platforms, has expanded the application scenarios for mobile payment (Fu et
al., 2022). Additionally, this momentum has hastened the Financial Supervisory
Commission’s approval for the establishment of two exclusive electronic payment
institutions, namely PXPay Plus and QuanYing+Pay (two of the earliest electronic
payment solutions providers founded in Taiwan). This shift in consumer behavior and
the industry’s response have solidified the integration of mobile payment into
everyday life in Taiwan.
The implementation of the new “Regulations on the Administration of Electronic
Payment Institutions” is expected to bring about three significant changes in the
application scenarios of electronic payment. These changes include cross-institutional
cash flow, such as transfers between JKOPAY accounts and Easy Card accounts, value
storage, and transfers in foreign currency, such as exchanging US dollars for New
Taiwan dollars, and trading of financial products.
Currently, the Financial Supervisory Commission has granted electronic payment
licenses to 28 institutions including 19 banks forming the sample for this research. The
exclusion of other institutions from the sample is primarily due to the unrelated nature
of their business and limited data availability. Most banks are actively engaged in the
realm of mobile payment. Although the banking industry has yet to lead in mobile
payment branding, it holds the potential to diversify its reliance on mainstream mobile
payment companies by venturing into the sales of derivative financial products and
establishing its member services in the long term. Consequently, launching self-
operated mobile payment services enables banks to broaden their collaborative
partnerships across various industries. As a result of such cooperation, consumers are
presented with a wider array of choices within different sectors, enhancing the overall
versatility of the mobile payment ecosystem.

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
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2.3. The network DEA and performance of banking industry
DEA is a robust approach used for evaluating the relative performance of
individual Decision-Making Units (DMUs) concerning multiple input and output
variables. In recent years, DEA has gained widespread popularity in management and
financial research. However, traditional DEA models, such as the CCR model
(Charnes et al., 1978) and BCC model (Banker et al., 1984), solely focus on the
conversion process of input and output for each DMU, thereby overlooking the
internal operational processes from input to output. This limitation may result in
potential errors in efficiency estimation outcomes. Consequently, scholars such as
Färe and Grosskopf (1996) and Tone and Tsutsui (2007) have ventured into exploring
the internal structure of DMUs across various industries, giving rise to the
development of the Network DEA models. These models aim to capture the intricacies
of the internal operations within DMUs and offer a more comprehensive and accurate
assessment of their efficiency scores. By adopting the Network DEA, this study,
therefore, endeavors to provide a thorough evaluation of how mobile payment impacts
the operating efficiency of Taiwan’s banking industry.
The internal structure proposed in the literature of Network DEA exhibits a
noteworthy complexity, encompassing a range of structured networks, including series
and parallel configurations, as well as unstructured arrangements. In the context of
sequential or vertical networks, different structures like two-stage, multi-stage, or
hybrid frameworks have been explored in prior research (Tone and Tsutsui, 2007). For
the purpose of this paper, the model design adopts a serial two-stage structure as its
basis.
Regarding the two-stage Network DEA model, Ruggiero (1998) elucidated that
the internal structure typically aims to explore DMUs in specific circumstances or
contexts, with a particular emphasis on the second stage. This aspect allows for an
analysis of the influence of environmental variables, external variables, discretionary
variables, and classification variables on the stage efficiency value. An illustrative
application of this approach is demonstrated by Kao and Hwang (2008) in their
investigation of the efficiency of 24 insurance companies in Taiwan. They formulated
a network structure comprising two stages, where the first stage assessed the efficiency
of market capacity, while the second stage examined the efficiency of profitability.
Within the present literature concerning banking performance assessment using
the DEA, two primary research directions emerge. The first direction employs
conventional DEA models in its first stage to estimate operational efficiency, followed
by the application of a regression model, such as Tobit, to explore the determinants
impacting those efficiency scores. Abidin et al. (2021) differentiates the efficiency
between Conventional Banks and Regional Development Banks in Indonesia. Their
investigation underscored the significant influence of Return on Assets (ROA) solely
on Conventional Banks, whereas Regional Development Banks were found to be
affected by both ROA and non-performing loans. In a similar vein, Endri et al. (2022)
conducted an evaluation and analysis of the factors influencing the efficiency of
Islamic Rural Banks in Indonesia. Conversely, the alternative approach delves into an
exploration of the factors shaping bank efficiency by dissecting their contributions
within a framework, conceptualizing banking operation as a network. Seiford and Zhu

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
9
(1999) employed a two-stage Network DEA to investigate the profitability and
marketability performance of large commercial banks in the US. It utilized a sample
of the top 55 banks in 1995. Empirical findings revealed that nearly 90% of the large
commercial banks exhibited inefficiencies in both the profitability stage and the
marketability stage. Moreover, a considerable proportion of these banks demonstrated
diminishing scale efficiency in the marketability stage, while some showcased
increasing scale efficiency in the profitability stage. Consequently, the study inferred
that bank size might hurt the marketability stage. In a similar vein, Luo (2003) also
employed the two-stage Network DEA model to scrutinize the operating performance
of 245 large banks in the US. The research findings indicated relatively poor
performance among the current large banks concerning the second stage of
marketability efficiency. Furthermore, the study identified that 34 banks
(approximately 14% of the sample) exhibited relatively high profitability efficiency in
the first stage; however, their market performance in the second stage did not align
with expectations, thus falling short of being satisfactory.
3. Network DEA and research model
The implementation of DEA has long been recognized as one of the most
effective methods for evaluating the operational performance of individual DMUs. In
recent years, researchers in the field of Network DEA have dedicated considerable
effort to exploring the intricacies of DMUs in various specific contexts. In this pursuit,
mathematical models have been devised, leading to different solutions and
decomposition pathways. For instance, in the case of two-stage networks, two primary
decomposition methods have been developed. The first method defines the overall
efficiency as the multiplicative combination of efficiency values for the two stages.
This approach was initially employed by the renowned pioneers Kao and Hwang
(2008) in their mathematical model for efficiency calculations, yet it is limited to
scenarios with constant returns to scale.
The other approach is the linear additive method, where the total efficiency is
expressed as the weighted average of the efficiency of each stage. Chen et al. (2009)
pioneered this method, expanding upon Kao and Hwang (2008)’s work. This approach
allows for the consideration of both constant return to scale and variable return to scale
simultaneously and can be widely applied to network DEA involving more than two
stages (Cook et al., 2010), thereby offering distinct advantages in empirical
applications. In this paper, the mathematical model adopted follows the summation
method proposed by Chen et al. (2009).
3.1. Solution for the two-stage network DEA structure
Since its introduction by Charnes et al. (1978). In 1978, DEA has been emerged
as a widely employed method for assessing the relative efficiency of the DMUs.
Concurrently, researchers have devoted considerable efforts to investigating the
underlying factors influencing relative inefficiency in operational processes.
Particularly, there has been a growing interest among scholars in unveiling the “black
box” of DMUs, aiming to elucidate the sources of inefficiency by dissecting the
components of total efficiency. Within the existing literature, the study of overall

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
10
efficiency solution and disassembly constitutes the two principal categories of
research in this domain.
Initially, Banker et al. (1984) undertook an analysis of the internal structure of
the DEA model, wherein they deconstructed the overall efficiency of DMUs into the
product of scale efficiency and technical efficiency. Subsequently, this line of inquiry
is further extended by decomposing the total efficiency into the weighted arithmetic
mean of the efficiency values associated with individual output items. These
investigations collectively contribute to a comprehensive understanding of the diverse
methods utilized in disentangling the components of total efficiency within the DEA
framework.
Another strand of research places emphasis on considering the production
process as a composite of multiple stages. Consequently, the intricate overall
production process can be dissected into individual sub-processes for detailed analysis.
Within this significant line of inquiry, certain intermediate measures are designated
both as the output items of the preceding stage and as the input items of the subsequent
stage. Notably, pioneering works, such as Färe and Grosskopf (1996) or Seiford and
Zhu (1999), have delved into this approach, shedding light on the benefits of
scrutinizing sub-processes to gain deeper insights into the complexities of the overall
production process.
When delving into the complexities of the overall production process, one of the
simplest cases involves a tandem (serial) system, as depicted in Figure 1. This system
comprises two distinct sub-processes that are not operated in isolation but are
interconnected. Seiford and Zhu (1999) adopted this system as a basis to explore the
overall production process of the top 55 large commercial banks in the United States,
deconstructing it into two stages: the profitability stage and the marketability stage for
in-depth analysis. Notably, whether conducting an efficiency analysis for the
profitability stage, the marketability stage, or the overall production process’s total
efficiency, all three investigations were treated as individual independent DEA models.
Zhu (2000) similarly employed this methodology to examine the financial efficiency
of the top 500 companies featured in Fortune Magazine.
Figure 1. General two-stage network DEA model.
Indeed, the application of the independent two-stage DEA model has extended to
various domains, including the analysis of Major League Baseball teams (Sexton and
Lewis, 2003), information technology (Chen and Zhu, 2004; Chen et al., 2006), and
property insurance (Kao and Hwang, 2008). Building upon this foundation, Liang et
al. (2008) further explored the mathematical decomposition method of total efficiency,

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
11
introducing Game theory concepts to devise two DEA models and efficiently
decompose efficiency.
This paper adopts an approach proposed by Chen et al. (2009), as a subsequent
advancement, for determining the overall efficiency value of DMUs by calculating the
weighted sum of efficiency values for each stage, as opposed to using a simple product
of these values. This novel method offers additional benefits, as the analysis of the
weights assigned to each sub-stage allows for the identification of the relative
“importance” of these sub-stages. Such insights are valuable for understanding
resource allocation across stages and assessing the potential causes of operational
inefficiency. By employing a weighted approach rather than a simple arithmetic mean
to combine the sub-stage efficiencies, this method takes into account the significance
of each sub-stage and contributes to a more nuanced and insightful assessment of
overall efficiency.
Let’s denote 
, 
 as the efficiency of the first stage and the second stage of
DMU0. They are, therefore, written as follows,

 








 



  


 

 
(1)
and

  













   



 


 
(2)
wherein, n represents the number of DMUs selected for evaluation (with j = 1, 2, ...,
n), the final constraints ensure that all weighted variables must be positive (where ε is
a constant greater than zero).
The denominator of Equation (1) represents the inputs (X) of the first stage, while
the numerator corresponds to the outputs (Z). These outputs, then, are absorbed to
generate the final products of the system, namely, Y. The relationship between the first
and second stages is represented by their corresponding weights.







 












 




(3)
It is essential to emphasize that the weight assigned to each stage is determined
by dividing the virtual resources of that stage by the total resources of the two-stages
system, and their summation is unique, that is  .
If we denote 
 as the system’s overall efficiency, this score is decomposed as
the combination of stage A and stage B’s efficiencies and their weights.

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
12

  



 








 





(4)
Building upon this idea, the overall efficiency of a DMU can be solved through
the following mathematical programming formulation.





 






 

 



 



 


  




  


 




   



 




  



   

 

 


 


  
(5)
3.2. Framework design and variables selection
This study adopts the decomposition approach proposed by Chen et al. (2009)
and applies the two-stage process setting model employed by Seiford and Zhu (1999),
as illustrated in Figure 1. The selection of variables pertinent to electronic payments,
with a specific emphasis on mobile payments, by financial institutions has constituted
a widely debated subject within the scholarly discourse. Stoica et al. (2015) undertook
an investigation examining the ramifications of internet banking on the operational
efficiency of the banking sector in Romania. In this inquiry, the authors utilized a
model in which the average daily “reach” rate for internet banking websites functioned
as the principal output variable, serving as a gauge for the effectiveness of internet
banking operations. However, the use of this variable as a proxy poses challenges in
accurately gauging the efficiency of non-cash payment activities, given the inherent
difficulty in ascertaining the precise motivations underlying access to a bank’s website.
Le and Ngo (2020) asserted that variables representing non-cash transaction tools,
including the number of issued cards, quantity of ATMs, and POS machines, exert a
substantial positive influence on a bank’s profitability. These variables, viewed from
a production-oriented perspective, signify investments in payment channels rather
than the intrinsic efficiency of cashless payment operations. In a complementary vein,
Tong et al. (2023) employed variables that offer a more precise reflection of mobile
payment activities, namely the number of mobile payment users and electronic
financial transaction volume. This study adopts the intermediation approach, where
variables related to mobile payments and deposits are harnessed to generate traditional
bank outputs, encompassing lending, investment, and non-interest income.
The banking performance evaluation framework employed in this research
focuses on scrutinizing the relationship between two main stages, namely Profitability
and Marketability. This conceptual framework draws upon the perspectives delineated
by Seiford and Zhu (1999). According to this framework, banks initially strive to

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
13
optimize their profit and promote their comparative advantage products, following
which their outputs are assessed by the financial market through market-oriented
indicators. The originality of this study lies in the incorporation of variables
delineating mobile payment activities, serving as a bridge that links the Profitability
and Marketability stages. The ideology behind this notion is that the financial market
accords significance to mobile payment activities as a pivotal determinant of banks’
essential progression. Specifically, in the initial stage, except for the two well-accepted
inputs—equity (X1) and total employee expenses (X2), we employ demand deposits
(X3) as the the primary source of mobile payments. These inputs are combined to
produce traditional outputs, namely total revenues (Z1), as well as mobile payment-
specific outputs, that are the number of mobile payment users (Z2), and the volume of
electronic financial transactions (Z3). Our key concerned variable—mobile
payment—is treated as both output in the first stage and input for the second stage. As
a result, Z2 and Z3 signify the outcomes of banks’ endeavors in promoting and
utilizing mobile payment. Importantly, these outcomes exert a direct influence on the
market efficiency in the subsequent stage of each bank. In line with the current
regulations in Taiwan, electronic payment enables fund transfers and value storage
between different accounts. Prior to utilizing electronic payment methods for
transactions, customers are required to bind their payment accounts (account link) and
complete verification using payment tools such as bank accounts or credit cards. As a
result, a higher number of mobile payment users or a greater electronic financial
transaction volume signifies an association with enhanced profitability and heightened
market development capabilities. The model’s variables definition for the inputs X,
intermediate measures Z, and outputs Y is provided in Table 1.
Table 1. Definition of variables.
Variable
Unit
Definition and Source of Data
Inputs
Equity (X1)
106 NTD
The owner’s equity – total assets minus total liabilities (TEJ).
Total employee expenses (X2)
106 NTD
The total amount of employee expense of the company for the year (TEJ).
Demand deposit (X3)
106 NTD
The total amount of check deposit and demand deposit (TEJ)
Inter-mediate
Total Revenues (Z1)
106 NTD
Total revenues from all bank’s operations (TEJ)
No of Users (Z2)
Thousand
people
The number of users who have registered and opened an electronic
payment account and have not yet terminated the contract – montly average
(Taiwan FSC).
Electronic transaction volume
(Z3)
103 NTD
The total amount of money that the electronic payment institution provides
for the service of receiving and paying transaction funds on behalf of the
user during a year (Taiwan FSC).
Outputs
Net Income (Y1)
106 NTD
Total of net interest income plus net non-interest income (TEJ)
EPS (Y2)
NTD
Earning per Share – Preferred Stock Dividend/weighted average number of
issued stocks (TEJ)
Note: TEJ stands for Taiwan Economic Journal—one of the most comprehensive financial database of
Taiwanese firms.
At this juncture, the first stage pertains to the bank’s profitability analysis. The
outputs encompass total revenues, along with the number of mobile payment users and
electronic financial transaction volume. These three outputs are referred to as
intermediate measures and serve as the inputs for the second stage’s marketability

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
14
efficiency analysis. In this stage, banks focus on establishing reputation in the financial
market. A thriving financial business, characterized by an increased number of bank
accounts linked to opening accounts and higher financial transaction volumes, such as
online deposits and securities accounts, not only enables undertaking larger corporate
loans but also augments asset management profits, thereby generating heightened
market efficiency.
Recently, banks have shown a dedicated commitment to enhancing the flexibility
of digital finance and virtual channel services, encompassing online platforms, digital
accounts, and mobile payment facilities. The advancement of financial technology has
enabled a more precise and real-time response to financial consumers’ needs.
Additionally, there has been a notable increase in the proportion of business conducted
by banks through virtual channels. As a result, the integration of virtual and physical
aspects, alongside the analysis of operational performance and resource allocation
within physical branches, necessitates more efficient assessments. Furthermore,
various stakeholders, including regulators, policymakers, bank managers, and
investors, have come to recognize the significance of appropriate measures and
technology utilization in bolstering banks’ financial stability and long-term
performance (López-Penabad et al., 2022).
4. Empirical results analysis
The data for this study were collected from the annual reports of various banks
for the years from 2019 until 2022, along with information sourced from the Financial
Supervisory Commission’s official website and the Taiwan Economic Journal (TEJ)
database. The assessment sample comprises 19 banks that have been licensed by the
Financial Supervisory Commission to operate electronic payment institutions.
Descriptive statistics of the variables used in the model is shown in Table 2.
Table 2. Variables’ descriptive statistics.
Variables
Mean
Median
St.D.
Min
Max
Inputs
Equity (X1)
188.316
189.831
91.246
32.788
402.191
Total employee expenses (X2)
12.116
12.234
6.432
2.281
35.254
Demand deposit (X3)
1117.9
1159.9
518.7
229.4
2335.5
Inter-mediate
Total Revenues (Z1)
52.174
53.122
26.721
10.769
144.778
No of Users (Z2)
108.615
2.226
306.663
0.038
1607.916
Electronic transaction volume (Z3)
100.327
7.698
354.979
0.143
1718.270
Outputs
Net Income (Y1)
40.100
38.638
21.967
7.484
118.767
EPS (Y2)
1.596
1.430
0.636
0.630
3.500
This study employs Lingo 18 to develop a dedicated program for the
comprehensive assessment of 19 banks operating in Taiwan during the years 2019 and
2022. The evaluation primarily focuses on three critical dimensions: profitability
efficiency, marketability efficiency, and overall efficiency. To enhance the clarity of
our analysis, we include the bank code in the initial column of the table, while the
efficiency values are indicated within the right square brackets to signify their
respective rankings.

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
15
4.1. Profitability efficiency analysis
Based on the model presented in Figure 1 and the data provided in Table 2, three
primary input factors influencing profitability are equity (X1), total employee
expenses (X2), and demand deposit (X3). On the other hand, the output metrics under
consideration consist of total revenues (Z1), the number of mobile payment users (Z2),
and electronic financial transaction volume (Z3). Consequently, the crux of
profitability efficiency hinges on the ability of each bank to optimize its inputs
efficiently, yielding maximum revenues and transactional outcomes. To gain deeper
insights into the determinants of profitability efficiency for each bank, we draw upon
empirical findings from the tables and scrutinize the operational and financial data
disclosed in the annual reports published by Taiwan’s banking industry on an annual
basis. This approach allows us to unravel the underlying factors shaping the
profitability efficiency of these financial institutions.
Table 3 presents a comprehensive overview of the profitability efficiency scores
for the 19 banks during the years 2019 to 2022. Notably, our empirical findings reveal
that in 2019, four banks achieved a stage efficiency value of 1. Similarly, the number
of “efficient” banks in 2022 is five. Among these, CTBC Bank and Taipei Fubon Bank
are the only two banks that maintained unique efficiency scores in these two years.
Towards the lower end of the efficiency ranking, we observe the inclusion of several
sizable financial institutions, namely Hua Nan Bank, Chang Hwa Bank, Yuanta Bank,
and Mega Bank. Notably, these banks exhibit an efficiency score range typically
hovering between 0.5 and 0.6, indicating a relatively lower level of efficiency when
compared to their counterparts in the study.
An examination of the five banks that exhibited perfect profitability efficiency in
2022, reveals noteworthy trends. During this period, there was a noticeable uptick in
electronic financial transaction volumes related to mobile payments, accompanied by
a substantial surge in the number of mobile payment users. Notably, except Cathay
Bank, all of these others demonstrated an increase in their mobile payment user base
which displayed a relatively stable performance in this regard.
Furthermore, we observed significant developments in the profitability
performance of the 19 banks within the initial stage of assessment, particularly in 2022.
Notably, the number of banks achieving a stage efficiency value of 1 increased during
this period. Furthermore, our findings highlight marked improvements in the
profitability performance of Taishin Bank. Not only did the bank attain a stage
efficiency value of 1, but it also ascended to a shared first-place ranking. Noteworthy
progress was evident in several key metrics for Taishin Bank in the profitability stage
of 2022. Specifically, its revenues, number of mobile payment users, and electronic
financial transaction volume all experienced substantial growth. Notably, the number
of mobile payment users doubled, while the electronic financial transaction volume
increased by a noteworthy 50%.
Within the computation of a bank’s overall efficiency, the stage weight value
assumes significance as it reflects the relative contribution of each stage’s efficiency
performance. This value holds implications for the allocation of resources within the
bank’s overarching business strategy (Chen et al., 2009). A noteworthy observation
emerges from this analysis: the emphasis placed on profitability by the 19 banks

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
16
exhibited a relative increase. However, paradoxically, the average profitability
efficiency declined during this period. This divergence suggests that the allocation of
resources may not have yielded the optimal benefits, underscoring the need for a more
effective resource utilization strategy.
Table 3. Profitability efficiency scores and their weights of the 19 Taiwan banks.
Banks
2019
2020
2021
2022
Mean
Eff.
Weight
Eff.
Weight
Eff.
Weight
Eff.
Weight
Eff.
5858 Bank of Taiwan 臺銀
0.535
0.868
(14)
0.588
0.699
(13)
0.620
0.614
(14)
0.540
0.851
(12)
0.758
5857 Land Bank of Taiwan 土銀
0.512
0.953
(7)
0.563
0.776
(8)
0.573
0.745
(8)
0.506
0.977
(7)
0.863
5854 Taiwan Cooperative Bank 合庫
0.562
0.778
(17)
0.606
0.650
(16)
0.628
0.592
(15)
0.563
0.776
(16)
0.699
5844 First Bank 一銀
0.556
0.799
(15)
0.601
0.665
(15)
0.613
0.631
(13)
0.544
0.839
(13)
0.733
5838 Hua Nan Bank 華銀
0.576
0.737
(19)
0.628
0.592
(18)
0.639
0.565
(16)
0.557
0.797
(15)
0.673
2801 Chang Hwa Bank 彰銀
0.564
0.773
(18)
0.629
0.589
(19)
0.658
0.519
(18)
0.579
0.729
(18)
0.652
5876 Shang Hai Bank 上海商銀
0.500
1.000
(1)
0.533
0.877
(3)
0.577
0.734
(9)
0.538
0.858
(11)
0.867
5836 Taipei Fubon Bank 台北富邦銀
0.500
1.000
(1)
0.525
0.905
(2)
0.549
0.820
(2)
0.500
1.000
(1)
0.931
5835 Cathay United Bank 國泰世華
0.506
0.977
(5)
0.557
0.796
(6)
0.561
0.783
(4)
0.500
1.000
(1)
0.889
5843 Mega Bank 兆豐商銀
0.532
0.880
(12)
0.626
0.599
(17)
0.667
0.499
(19)
0.555
0.800
(14)
0.694
2834 Taiwan Business Bank 臺企銀
0.534
0.871
(13)
0.581
0.722
(12)
0.595
0.681
(12)
0.527
0.898
(9)
0.793
2893 Shin Kong Bank 新光銀行
0.521
0.920
(8)
0.565
0.769
(10)
0.567
0.762
(5)
0.500
1.000
(1)
0.863
2895 Sunny Bank 陽信商銀
0.528
0.893
(9)
0.564
0.774
(9)
0.569
0.758
(6)
0.525
0.904
(8)
0.832
2845 Far Eastern Bank 遠東銀
0.530
0.888
(10)
0.562
0.779
(7)
0.591
0.693
(11)
0.566
0.766
(17)
0.782
5852 Yuanta Bank 元大銀
0.559
0.790
(16)
0.594
0.682
(14)
0.642
0.558
(17)
0.594
0.685
(19)
0.679
5849 Bank SinoPac 永豐銀行
0.532
0.880
(11)
0.572
0.749
(11)
0.587
0.702
(10)
0.504
0.984
(6)
0.829
5847 E.Sun Bank 玉山銀
0.506
0.976
(6)
0.545
0.834
(4)
0.560
0.784
(3)
0.500
1.000
(1)
0.899
5848 Taishin Bank 台新銀
0.500
1.000
(1)
0.546
0.830
(5)
0.572
0.749
(7)
0.531
0.883
(10)
0.866
5841 CTBT Bank 中信銀
0.500
1.000
(1)
0.524
0.908
(1)
0.545
0.834
(1)
0.500
1.000
(1)
0.935
Mean
0.529
0.894
0.574
0.747
0.595
0.685
0.533
0.881
Examining the profitability weight values for two successive years in Table 3, it
becomes evident that approximately 53% to 54% of the resource allocation within
Taiwan’s banking industry is directed toward the profitability stage. This allocation
signifies that, over the long term, banks place greater reliance on achieving
profitability in their operations. However, it is essential to note that this heavy
emphasis on profitability does not necessarily guarantee the attainment of superior
overall operating efficiency.
During the consecutive years of 2020 and 2021, it is noteworthy that none of the
banks reached the maximum efficiency score. The ability to discriminate efficiency is

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
17
most evident during this period. Specifically, CTBC Bank retained its status as the
leader with the highest efficiency scores, registering values of 0.908 in 2020 and 0.843
in 2021. Conversely, on the other end of the spectrum, Changhwa Bank and Mega
Bank held the lowest positions in the rankings, recording efficiency scores of merely
0.589 and 0.499, respectively.
4.2. Marketability efficiency analysis
Table 4 presents the empirical findings pertaining to the marketability efficiency
of the 19 banks in the period of 2019 and 2022. In 2019, two banks, namely Sunny
Bank and E.Sun Bank, occupied the top positions in the ranking, registered scores of
0.937 and 0.898, respectively. However, shifting our focus to the stage efficiency in
2022, it becomes apparent that while Sunny Bank retains as the first, E.Sun Bank falls
to the third quarter (ranked 14) in the ranking board. The second position is replaced
by Shanghai Bank with the efficiency score equals to 0.869.
Table 4. Marketability efficiency scores and their weights of the 19 Taiwan banks.
Banks
2019
2020
2021
2022
Mean
Eff.
Weight
Eff.
Weight
Eff.
Weight
Eff.
Weight
Eff.
5858 Bank of Taiwan 臺銀
0.465
0.621
(18)
0.412
0.606
(19)
0.380
0.771
(17
0.460
0.604
(18)
0.651
5857 Land Bank of Taiwan 土銀
0.488
0.601
(19)
0.437
0.674
(18)
0.427
0.753
(19)
0.494
0.604
(19)
0.658
5854 Taiwan Cooperative Bank 合庫
0.438
0.744
(14)
0.394
0.860
(9)
0.372
0.921
(11)
0.437
0.735
(15)
0.815
5844 First Bank 一銀
0.444
0.817
(7)
0.399
0.882
(8)
0.387
0.947
(8)
0.456
0.787
(8)
0.858
5838 Hua Nan Bank 華銀
0.424
0.809
(9)
0.372
0.896
(7)
0.361
0.965
(6)
0.443
0.784
(9)
0.863
2801 Chang Hwa Bank 彰銀
0.436
0.755
(13)
0.371
0.816
(15)
0.342
0.917
(13)
0.421
0.768
(11)
0.814
5876 Shang Hai Bank 上海商銀
0.500
0.798
(10)
0.467
0.853
(12)
0.423
1.000
(1)
0.462
0.869
(2)
0.880
5836 Taipei Fubon Bank 台北富邦銀
0.500
0.709
(16)
0.475
0.719
(17)
0.451
0.757
(18)
0.500
0.670
(17)
0.714
5835 Cathay United Bank 國泰世華
0.494
0.838
(5)
0.443
0.930
(4)
0.439
0.937
(10)
0.500
0.836
(5)
0.885
5843 Mega Bank 兆豐商銀
0.468
0.769
(11)
0.374
0.951
(3)
0.333
1.000
(1)
0.445
0.764
(12)
0.871
2834 Taiwan Business Bank 臺企銀
0.466
0.737
(15)
0.419
0.821
(14)
0.405
0.917
(14)
0.473
0.803
(7)
0.820
2893 Shin Kong Bank 新光銀行
0.479
0.811
(8)
0.435
0.912
(6)
0.433
0.978
(4)
0.500
0.818
(6)
0.880
2895 Sunny Bank 陽信商銀
0.472
0.937
(1)
0.436
0.964
(2)
0.431
0.961
(7)
0.475
1.000
(1)
0.966
2845 Far Eastern Bank 遠東銀
0.470
0.859
(4)
0.438
0.828
(13)
0.409
0.898
(15)
0.434
0.850
(4)
0.859
5852 Yuanta Bank 元大銀
0.441
0.832
(6)
0.406
0.853
(11)
0.358
0.941
(9)
0.406
0.781
(10)
0.852
5849 Bank SinoPac 永豐銀行
0.468
0.706
(17)
0.428
0.776
(16)
0.413
0.868
(16)
0.496
0.733
(16)
0.771
5847 E.Sun Bank 玉山銀
0.494
0.898
(2)
0.455
0.977
(1)
0.440
0.978
(3)
0.500
0.742
(14)
0.899
5848 Taishin Bank 台新銀
0.500
0.767
(12)
0.454
0.854
(10)
0.428
0.919
(12)
0.469
0.755
(13)
0.824
5841 CTBT Bank 中信銀
0.500
0.885
(3)
0.476
0.921
(5)
0.455
0.977
(5)
0.500
0.869
(3)
0.913
Mean
0.471
0.784
0.426
0.847
0.405
0.916
0.467
0.777

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
18
Among these 19 banks, Far Eastern International Bank is the focus of our
attention. Worth noting is its foray into digital innovation, exemplified by its
pioneering initiative, the Bankee Social, slated to launch Taiwan’s inaugural
Metaverse branch in 2022. This groundbreaking endeavor empowers members to
curate their unique branches in the Metaverse, offering an immersive experience of
being a branch manager and community owner. Bankee’s Play-to-earn (P2E) strategy
caters to the immersive experience and targets a younger demographic, aligning with
the themes discussed in the literature (Jung et al., 2020).
Our analysis further reveals fluctuations in the performance of the 19 banks in
the second stage of marketability, with an increased trend of efficiency in 2020 and
2021, followed by a decrease in 2022. Shanghai Bank and Taiwan Business Bank
notably improved their relative positions in the marketability efficiency rankings in
2022. Notably, more than half of the lower-efficient banks, such as the Bank of Taiwan,
Taiwan Cooperative Bank, Hua Nan Bank, and Mega Bank, belong to the public sector.
This underscores the public banks’ strategic disregard for market-oriented business.
First Bank, as a public institution, emerges as a standout with the highest number
of mobile payment users and electronic financial transaction volume among its peers
in the public banking sector. While its efficiency and quality improvements have been
modest, it has been diligently executing a series of digital transformation initiatives
since 2017. To foster consensus and drive innovation, it established a “Digital Strategy
Development Group” in 2021, extending its digital transformation efforts toward a
more inclusive and sustainable business model, indicative of its adeptness in market
business operations.
Analysis of Figure 1 and Table 2 reveals that the three primary input variables
in the marketability stage encompass revenues (Z1), the number of mobile payment
users (Z2), and the volume of electronic financial transactions (Z3), while the outputs
include net income (Y1) and earnings per share (Y2). Hence, mobile payment usage
emerges as a pivotal factor in the marketability stage of banking. It is anticipated that
a higher number of mobile payment users or increased electronic financial transaction
volume will lead to higher market efficiency values for competitive banks.
Delving into the banks that exhibited relative improvements in marketability
efficiency rankings in 2022, we observe a relative uptick in the user base related to
mobile payment variables. Moreover, these banks maintained their performance levels
in terms of mobile payment electronic transaction volume, witnessing notable growth
in electronic financial transaction volumes. Notably, when examining the net income
and earnings per share data, they all posted higher figures in 2022 compared to 20119.
This analysis underscores that these banks have effectively harnessed returns by
bolstering their market-oriented business activities, as reflected in their improved
performance and rankings in marketabilities.
It is discernible that the marketability weights within Taiwan’s banking industry
range between 45% and 46%. This observation implies that the industry tends to
allocate relatively fewer resources to business activities that have the potential to
generate higher market value for banks. However, it is imperative to recognize that
agile management, grounded in customer-centricity and strategic resource allocation,
along with the implementation of an enhanced customer experience service model,

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
19
represents specialized paradigms capable of significantly augmenting customer
loyalty and engagement.
4.3. Overall efficiency analysis
The overall efficiency measure employed in this study is a composite of weighted
individual-stage efficiencies. Additionally, as we transition from the first stage to the
second stage, the analysis incorporates intermediate outputs, specifically the number
of mobile payment users and the volume of mobile payment electronic financial
transactions, to assess the efficiency performance of each stage. The pivotal variables,
coupled with the quality of stage efficiency values and the magnitude of weight values,
wield a substantial influence on the overall operational performance of banks.
In this part, we conduct an overall efficiency analysis for the period 2019 and
2022. To streamline the presentation, we present the estimated results, as delineated
in Table 5. The average overall efficiency for the year 2019 stands at 0.840, followed
by a decreasing trend in 2020 (0.786) and 2021 (0.775), and finally boosted up again
in 2022 (0.830). Notably, the group of underperformed banks, falling below this
average threshold, include Bank of Taiwan, Taiwan Land Bank, Taiwan Cooperative
Bank, First Bank, Huanan Bank, Changhwa Bank, and Taiwan Business Bank. It’s
noteworthy that not only are these banks publicly owned, but their relative
performance in total efficiency during 2018 was also suboptimal. Upon scrutinizing
the individual stage efficiencies and weightings of these banks, it becomes evident that
there has been an insufficient adjustment in resource allocation and strategic focus.
CTBC Bank is the only one that achieved a unique overall efficiency score of 1
in the period 2019–2021, before slightly decreasing in 2022 (0.934). It stands as the
sole bank among the 19 banks analyzed to attain a total efficiency value of 1 across
three consecutive years. Another interesting story is derived from Far Eastern Bank
which, in 2016, established a digital financial business group and launched the digital
sub-brand, Bankee Community Banking, in 2019. This initiative introduced an
innovative business model rooted in the sharing economy, thereby reshaping the
traditional one-way relationships between banks and customers and fostering
increased competition. It has had three major transformative effects, specifically in
terms of data autonomy.
Furthermore, regarding overall operational efficiency, Sunny Bank and Shinkong
Bank demonstrated an improved relative performance and ranking of efficiency values
in 2022 compared to 2019. In the first stage, profitability efficiency, Sunny Bank
consistently maintained modest efficiency scores and ranked in the middle while
Shinkong Bank exhibited a great improvement to jump from the middle group to the
first tier in 2022. Furthermore, their performance in the second stage, related to
marketability also exhibited enhanced relative performance and ranking in 2022
compared to the previous year. Additionally, the two banks posted an average earnings
per share of 2.86 yuan over the four years, second only to Shanghai Bank at 3.44 yuan.
These banks continue to fortify their market activities, elevate the proficiency of
financial professionals, and enhance customer satisfaction. These efforts are palpably
reflected in the amplified numbers of mobile payment users and the volume of
electronic financial transactions.

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
20
Table 5. Overall efficiency scores of the 19 Taiwan banks.
Banks
2019
2020
2021
2022
Mean
Eff.
Eff.
Rank
Eff.
Rank
Eff.
Rank
Eff.
Rank
5858 Bank of Taiwan 臺銀
0.753
19
0.661
19
0.674
17
0.738
18
0.706
5857 Land Bank of Taiwan 土銀
0.781
15
0.732
15
0.748
13
0.793
13
0.763
5854 Taiwan Cooperative Bank 合庫
0.763
18
0.732
14
0.714
14
0.758
16
0.742
5844 First Bank 一銀
0.807
13
0.751
13
0.753
12
0.815
11
0.782
5838 Hua Nan Bank 華銀
0.768
16
0.705
17
0.710
15
0.791
14
0.743
2801 Chang Hwa Bank 彰銀
0.765
17
0.674
18
0.655
19
0.745
17
0.710
5876 Shang Hai Bank 上海商銀
0.899
5
0.866
3
0.846
5
0.863
6
0.869
5836 Taipei Fubon Bank 台北富邦銀
0.854
9
0.816
8
0.792
8
0.835
9
0.824
5835 Cathay United Bank 國泰世華
0.908
4
0.855
5
0.851
4
0.918
3
0.883
5843 Mega Bank 兆豐商銀
0.828
10
0.731
16
0.666
18
0.784
15
0.752
2834 Taiwan Business Bank 臺企銀
0.809
11
0.764
10
0.776
10
0.853
8
0.800
2893 Shin Kong Bank 新光銀行
0.868
8
0.831
7
0.856
3
0.909
4
0.866
2895 Sunny Bank 陽信商銀
0.914
3
0.857
4
0.845
6
0.950
1
0.891
2845 Far Eastern Bank 遠東銀
0.875
7
0.801
9
0.777
9
0.802
12
0.814
5852 Yuanta Bank 元大銀
0.808
12
0.752
12
0.695
16
0.724
19
0.745
5849 Bank SinoPac 永豐銀行
0.799
14
0.760
11
0.771
11
0.860
7
0.797
5847 E.Sun Bank 玉山銀
0.937
2
0.899
2
0.869
2
0.871
5
0.894
5848 Taishin Bank 台新銀
0.883
6
0.841
6
0.822
7
0.823
10
0.842
5841 CTBT Bank 中信銀
0.943
1
0.914
1
0.899
1
0.934
2
0.923
Mean
0.840
0.786
0.775
0.830
4.4. Some discussions
The empirical evidence reveals a discernible pattern in the banking sector of
Taiwan, where the average profitability stage’s weights outweigh the ones of
marketability, registering at approximately 0.54 as opposed to 0.42. This observation
signifies that Taiwan’s banking industry places greater reliance on profitability
performance as a key driver of its operational strategies. Concurrently, resource
allocation and investment decisions exhibit a conspicuous inclination toward
emphasizing performance metrics. The bank’s profit-generating activities
predominantly encompass operational efficiency and profitability, with relatively less
emphasis directed towards activities aimed at enhancing market value, commonly
referred to as market capability efficiency. This empirical outcome aligns with the
findings reported by Luo (2003), whose research on 245 major banks in the United
States yielded congruent results.

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
21
As delineated in extant scholarly investigations such as those by Kumbhakar and
Wang (2007) and Bardhan (2013), discernible disparities frequently manifest in the
operational performance of public banks vis-à-vis private banks. To ascertain and
compare the divergence in the operating efficiency of Taiwan’s public and private
banks under the mobile payment efficiency framework, this study employs the non-
parametric Mann-Whitney U-Test. The null hypothesis (H0) posited herein asserts
there is no difference between the operating efficiency of Taiwan’s public sector banks
when compared to private banks when mobile payment activities are considered. To
scrutinize this proposition, separate tests are conducted with a focus on overall
efficiency, profitability efficiency, and marketability efficiency.
The resulting z-values derived from these tests yield −4.642, −4.116, and −3.341,
respectively, all surpassing the critical threshold of 1.96 in absolute magnitude. This
compellingly leads to the rejection of the null hypothesis across all three dimensions,
signifying that the mobile payment efficiency model exerts a significant divergence
on the operational efficiency of public banks at both the individual stage and the
aggregate level. Furthermore, the empirical evidence highlights a noteworthy disparity
in the operating performance between private and public banks, with private banks
exhibiting markedly superior operational efficiency.
Last but not least, our key concern revolves with the contribution of variables
representing mobile payment activities. In the research model, these variables serve as
both outputs for the profitability and inputs for the marketability stages. By employing
the formula 





 , we can compare the relative contributions of
these variables with Total revenue (traditional profitability) to the overall efficiency
of the system. The result indicates that the contributions of Total revenue, Electronic
transaction volume, and No of Users base are 62%, 35%, and 3%, respectively. This
outcome aligns with the analysis of the current situation of the non-cash payment
sector in Taiwan. Concurrently, it suggests that mobile payment activities have not
garnered adequate attention from investors.
5. Conclusions and managerial suggestions
In the preceding era, the burgeoning dependence of contemporary banking on
digital technology was an unforeseen trajectory. Institutions lacking robust digital
capabilities have gradually witnessed a decline in customer support within the
intensely competitive landscape of financial innovation. This shifting paradigm in
customer preferences has instigated a direct transformation in banks’ operational
paradigms (Al-Okaily et al., 2023). Notably, in European and American markets, the
ascendancy of “challenger banks” has gained significant traction, compelling
traditional banks to intensify their investments in digital infrastructure and services.
Conversely, customers increasingly exhibit a pragmatic indifference toward whether
their service provider is a conventional bank or a financial technology company; their
paramount concern lies in the expeditious accessibility of requisite services via their
mobile devices. Consequently, the financial sector has undergone a notable evolution
towards enhanced adaptability and sophistication, with mobile technology emerging
as an indispensable competence for every financial operator. In this dynamic milieu,

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
22
seizing clientele from diverse channels becomes imperative, allowing for the
conversion of crises into opportunities and the cultivation of a new industry landscape.
Drawing upon the most recent statistical data from the “Financial Technology
Investment and Application in the Financial Industry” survey, released by the
Financial Supervisory Commission in August 2022, it is discerned that the total
investment by the domestic financial industry in the development of financial
technology exhibited a year-on-year decrease of 2.351 billion NTD in 2021. This
decline can be primarily attributed to the high base established in 2020, particularly in
the realm of pure online banking, leading to a notable contraction in growth,
approximating nearly 13%. Nonetheless, the projected investment amount for 2022
has surged to 31.215 billion NTD, signaling a noteworthy estimated annual growth
rate of 96.8%. This upward trajectory underscores the substantial emphasis that
domestic financial institutions have placed on the realm of financial technology,
accentuating its strategic significance within the sector.
Furthermore, it is noteworthy that the collaboration between the domestic
financial industry and the financial technology sector exhibited a notable upsurge, with
a year-on-year increase of approximately 16% in 2021 as compared to 2020. This
collaboration encompasses a spectrum of domains, encompassing information security,
big data utilization, artificial intelligence applications, anti-money laundering (AML)
measures, Know Your Customer (KYC) protocols, and payment systems, among
others.
Despite the widespread application of the DEA approach for evaluating
efficiency within the financial sector, it is notable that there remains a scarcity of
studies employing this approach to assess the performance of banks in Taiwan. This
study, therefore, contributes to the literature of this field by utilizing the Network DEA
to explore the practical performance of Taiwan’s banking industry in the context of
mobile payment integration. Important findings of the research include: (1) there exists
a predominant allocation of resources, within Taiwan’s banking industry, that is
directed towards the profit-generation stage. However, it is discerned that there exists
a shortfall in the allocation of resources towards the market-oriented phase,
specifically, those activities aimed at augmenting the market value of banks; (2) when
mobile payment is incorporated as an intermediary variable within the second stage, it
unequivocally enhances the discernment of factors that contribute to inefficiencies
within Taiwan’s banking industry. This underscores the imperative for Taiwan’s
financial sector, which may be perceived as somewhat trailing in the global drive for
financial innovation and mobile payment services, to redouble its efforts. To rectify
the operational inefficiencies plaguing the industry, it is incumbent upon Taiwan’s
financial sector to bolster its technical proficiency and enhance the penetration rate of
mobile payment services, aligning itself more effectively with the evolving dynamics
of the global financial landscape; (3) within the cohort of 19 sampled banks, a
noteworthy observation emerges, wherein the operational performance of private
banks conspicuously surpasses that of public banks across each stage of performance
assessment. This discernible divergence underscores a pressing imperative in the
context of the ongoing wave of financial reform—the eight prominent banks affiliated
with the public sector must intensify their efforts to bridge the performance gap and
strive for competitiveness parity with their private sector counterparts.

Journal of Infrastructure, Policy and Development 2024, 8(6), 6057.
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It is imperative for the Taiwan government to proactively foster a more
financially inclusive and amicable milieu, and take concerted action to champion
measures aimed at catalyzing the advancement of financial technology. Moreover, the
government should wholeheartedly endorse and incentivize collaborative initiatives
between the financial and technology sectors. In recent years, Taiwan’s banking
industry has steadfastly adhered to a corporate ethos rooted in sustainable development
and collaborative partnerships, thereby contributing to both societal and
environmental well-being. By actively integrating green energy and responsible
lending practices with philanthropic endeavors and shareholder engagement, the
industry has wielded a constructive influence on the financial landscape. The
enhancement of mobile payment services serves as a pivotal conduit towards aligning
with the United Nations Sustainable Development Goals (SDGs), implementing
Environmental, Social, and Governance (ESG) principles, and catalyzing sustainable
consumption patterns.
In addition to the aforementioned contributions, this study has certain limitations.
Firstly, concerning the marketability stage, numerous investors are not solely
concerned with the bank’s growth but also with the attractiveness of its stocks, as
indicated by variables such as stock turnover. Incorporating these variables would
provide a more precise depiction of the performance of marketability. Furthermore,
exploring operational risks as an undesirable output variable of mobile payment
activities, such as losses stemming from transaction system errors or resultant legal
actions, holds considerable potential for future research endeavors.
Author contributions: Conceptualization, MTP and CYK; methodology, MTP and
CYK; software, MTP and CYK; validation, CPC and LWL; formal analysis, MTP and
CYK; investigation, MTP, CYK, and YJL; resources, MTP, CYK, and YJL; data
curation, MTP, CYK and YJL; writing—original draft preparation, MTP; writing—
review and editing, CYK; visualization, MTP and CYK; supervision, CPC and LWL;
project administration, CPC and LWL; funding acquisition, CYK, CPC and LWL. All
authors have read and agreed to the published version of the manuscript.
Conflict of interest: The authors declare no conflict of interest.
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