Effect of Fintech on the Productivity in the Taiwan Banking Industry

Yang, Yung-Lieh; Hsueh, Hsiu-Wen; Huang, Ming-Hsiang; Cho, Tsui‐Yueh; Kishi, Yausuyuki

doi:10.17706/ijeeee.2017.7.4.255-263

Cited by 10 publications

(12 citation statements)

References 4 publications

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“…Researchers have explored a wide range of topics, but there are rarely any studies about the effect of fintech and other high-tech services on the operation of the banking industry. One of the few papers that addressed this issue is by Yang et al (2017) [45], who used the cost Malmquist productivity index to estimate the effect of fintech on the banking sector.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Network DEA to Explore the Effect of Mobile Payment on Taiwanese Bank Efficiency

et al. 2023

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In order to explore the potential influence of new mobile payment services on the Ephesus model developed by Kao and Hwang (2008), we investigated the relative performance of 19 Taiwanese banks from 2018 to 2021. The network efficiency was divided into two parts: management efficiency and profitability efficiency. Only one bank achieved total efficiency from 2019 to 2021. The stage efficiencies showed increments during the first three years (2018–2020), but they declined in 2021. Most banks had low efficiency in the management stage and high efficiency in the profitability stage, suggesting that there are weaknesses in the management of physical resources but high awareness that mobile payment services can be exploited to achieve high profitability. Our results prove that mobile payment is a potential and profitable new service that Taiwanese banks should take advantage of. Comparing the results between the CCR model and the NDEA model, we observed that the NDEA model has more explanatory power, as it provides insight into the internal structure of the working process of Taiwanese banks.

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Section: Literature Reviewmentioning

confidence: 99%

Using Network DEA to Explore the Effect of Mobile Payment on Taiwanese Bank Efficiency

et al. 2023

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“…Furthermore, fintech is outlined as a set of activities that have the capacity to run and deliver the business in a revolutionary way. It has influenced and reshaped the business models of traditional banks (Yang et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Fintech on Financial Institutions: The Case of Georgia

Lashkhi¹,

Чарая

Boyarchuk

et al. 2022

TalTech Journal of European Studies

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Using Georgia as an example, this article analyses the fintech ecosystem and the impact of fintech companies on traditional financial institutions. Both qualitative and qualitative research has been carried out for this research. Based on the analysis of Georgian and international financial markets, as well as the qualitative and regressive analysis used in the study, assumptions were made about the impact of fintech companies on traditional financial institutions. Moreover, the study identifies the challenges that hinder the development of the fintech ecosystem in Georgia regarding infrastructure, finance, regulation, and education. This article also reveals several aspects that positively affect the outcome of fintech and the diversification of the Georgian financial market. At the end of the article, the relevant conclusions and applicable recommendations are given.

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“…Recent research has focused extensively on the efficiency analysis of financial institutions. Omrani et al (2023) as demonstrated by Yang et al (2017), Ortaköy and Özsürünç (2019), Ky et al (2019), andLiu et al (2019). Some scholars also believe that fintech has had an impact on financial transactions and services, such as Gomber et al (2018).…”

Section: Introductionmentioning

confidence: 98%

“…In dynamic DEA, Klopp (1985) first proposes window analysis, and subsequent developments include the Malmquist index by Färe et al (1994). Scholars using the Malmquist index for analysis are Ramakrishna et al (2016), Shi et al (2017), Yang et al (2017), and Mansour and Moussawi (2019). Färe and Grosskopf (1996) include inter-connecting activities in dynamic analysis, and subsequent developments appear in Park and Park (2009).…”

Section: Introductionmentioning

confidence: 99%

Non‐performance loans, operation, and recycle efficiency analysis—Dynamic Two‐stage Directional Distance Function Recycle with Assurance Regions model

Liao,

Lin,

Chang

et al. 2023

Manage Decis Econ

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Previous studies have failed to analyze a bank's operation performance and non‐performing loans at the same time. Therefore, this research uses the Dynamic Two‐stage directional distance function (DDF) Recycle with Assurance Regions (ARs) model to explore the efficiency of Taiwanese banks in their operation stage and recycling stage. The research results are as follows. (1) The six‐year overall average efficiency is .7966. Taipei Star Bank has the best efficiency, and Fubon, Bank SinoPac, and Union Bank have the worst efficiency rankings. (2) Most banks have good efficiency in the operation stage, while efficiency in the recovery cycle stage fluctuates.

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Effect of Fintech on the Productivity in the Taiwan Banking Industry

Cited by 10 publications

References 4 publications

Using Network DEA to Explore the Effect of Mobile Payment on Taiwanese Bank Efficiency

Using Network DEA to Explore the Effect of Mobile Payment on Taiwanese Bank Efficiency

The Impact of Fintech on Financial Institutions: The Case of Georgia

Non‐performance loans, operation, and recycle efficiency analysis—Dynamic Two‐stage Directional Distance Function Recycle with Assurance Regions model

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