Can mobile and biometric payments replace cards in the Korean offline payments market? Consumer preference analysis for payment systems using a discrete choice model

Kim, Minji; Kim, Se‐Ho; Kim, Jung-Hun

doi:10.1016/j.tele.2019.02.003

Cited by 52 publications

(44 citation statements)

References 46 publications

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“…Openness was first proposed by Costa and McCrae [69] and defined as curiosity, and an open mind about experiences and values. Those with high openness prefer to experience new imagination and are more receptive to new information Ziegler et al [59]. Openness refers to the tendency of individuals to try new things and experiences.…”

Section: E Openness (Openness To Experience)mentioning

confidence: 99%

Factors Affecting the Use of Facial-Recognition Payment: An Example of Chinese Consumers

Zhang

Kang

2019

IEEE Access

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The emergence and use of facial-recognition payment technology has brought new challenges. Although credit-card payment is quick and easy, it is easy to lose a card or forget the password. Because people use simple passwords and reuse them on different accounts and services, passwords can be shared and cracked. QR payment is inseparable from smart phones, smart phones may be lost, signals may be unstable, and batteries may be exhausted. However, facial-recognition technology, which detects and describes feature vectors without physical contact, directly contributes to overall efficiency, performance, and accuracy. Currently, studies of technical issues of facial-recognition technology and facial-recognition payment systems are very popular. There are many studies that emphasize the working principle of the facial-recognition system, the system's reliability, and the future development trend. However, for non-technical issues, such as from the perspective of consumers, research on the characteristics of facial-recognition payment and the factors affecting consumer's intent to use is rare. Therefore, the purpose of this study is to explore the factors influencing consumers' willingness to use facial-recognition payment systems. This study has selected security, visibility, and expected effort and social image as the feature variables of the facial-recognition payment system. Results in this paper shows that the safety, security, visibility and social image will affect consumers' intent to use the system. It can also influence consumers' intent to use through perceived usefulness. The amount of effort expected not only has direct influence on intent to use but also influences the intent to use by the mediating factor of perceived usefulness. In this article, Openness characteristic (consumer's personality) has a moderating effect on the relationship between security, expected effort and usage intention. INDEX TERMS Facial-recognition payment system, perceived usefulness, openness, intent to use.

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Section: E Openness (Openness To Experience)mentioning

confidence: 99%

Factors Affecting the Use of Facial-Recognition Payment: An Example of Chinese Consumers

Zhang

Kang

2019

IEEE Access

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“…Since the late 1990s, commercial transactions have experienced a rapid digital transformation, driven by consumers' access to new technologies (Mossberger, 2007) and by corporations' efforts to become more customer-centric (Berman, 2012). Digital innovations have changed corporations' strategies (Kane et al, 2015), business to consumer exchanges (Xu, 2014), consumption patterns (Wilska, 2003) and interactions among consumers (Kim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Explanatory and predictive model of the adoption of P2P payment systems

Lara‐Rubio

Ramos

Liébana‐Cabanillas

2020

Behaviour & Information Technology

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The purpose of this paper is to identify the factors affecting to the intention to use of peer-to-peer (P2P) mobile payment. Although mobile technology has become part of everyday life, certain actions and services, such as mobile payments, are still used relatively infrequently. In this paper, we analyse consumers' adoption of P2P mobile payment services. Following a review of previous literature in this field, we identify the main factors that determine the adoption of mobile payments, and then perform a logistic regression (LR) analysis and propose a neural network to predict this adoption. From the logistic regression results obtained we conclude that six variables significantly influence intentions to use P2P payment: ease of use, perceived risk, personal innovativeness, perceived usefulness, subjective norms and perceived enjoyment. With respect to the nonparametric technique, we find that the multilayer perceptrons (MLP) prediction model for the use of P2P payment obtains higher AUC values, and thus is more accurate, than the LR model. This paper is a pioneer study of intention to use with mobile payment using these methodologies. The outcome of this research has important implications for the theory and practice of the adoption of P2P mobile payment services.

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“…Personal information leakage includes name, phone number, home address, and resident registration number. And it also involves detailed information, including income, payment account, car ownership, and financial information (e.g., payment data, credit rating) [65]. Both financial and privacy risks can occur either by payment POS or outsourcing companies, hacking, and internal employees [65].…”

Section: Perceived Technical Compatibilitymentioning

confidence: 99%

“…And it also involves detailed information, including income, payment account, car ownership, and financial information (e.g., payment data, credit rating) [65]. Both financial and privacy risks can occur either by payment POS or outsourcing companies, hacking, and internal employees [65]. Privacy issues are significant barriers for Korean users to accept m-payment services [65].…”

Section: Perceived Technical Compatibilitymentioning

confidence: 99%

“…Both financial and privacy risks can occur either by payment POS or outsourcing companies, hacking, and internal employees [65]. Privacy issues are significant barriers for Korean users to accept m-payment services [65]. If the service providers cannot operate a secure system and protect users' financial and private information from leaking, users' intention to switch to PMP will decrease.…”

Section: Perceived Technical Compatibilitymentioning

confidence: 99%

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Switching to Proximity Mobile Payment: Empirical Evidence from Korean Users

H¹,

Lee²

2021

Preprint

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Individual users' payment behaviors have changed. The diffusion of mobile devices makes people suitable for proximity mobile payment (PMP) services without the need for traditional payment. Existing mobile payment literature mainly focuses on users' adoption and continuous usage behavior, nevertheless, switching behavior on payment received little attention, especially focusing on why users switch from traditional payment to PMP. Thus, the objective of the current research is to investigate factors influencing users’ traditional payment - PMP switching to comprehend how these factors shape users’ switching intentions. We developed a traditional payment - PMP transition model based on the push-pull-mooring framework derived from migration theory. This research adopted a structural equation modeling analysis on 311 valid data. The findings indicate that a push factor drives users away from traditional payment in terms of dissatisfaction. The pull factors, including perceived substitutability and perceived usefulness, attract users to PMP. Furthermore, in terms of perceived technical compatibility, a positive mooring factor, facilitates users' switching to PMP. Negative mooring factor, in terms of perceived risk, hinder users' switching intention. However, another pull factor - perceived ease of use, failed to influence switching intention significantly. This study found some distinctions between mobile payment switching and mobile payment adoption. These findings provide pivotal insights for the mobile payment service providers.

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Can mobile and biometric payments replace cards in the Korean offline payments market? Consumer preference analysis for payment systems using a discrete choice model

Cited by 52 publications

References 46 publications

Factors Affecting the Use of Facial-Recognition Payment: An Example of Chinese Consumers

Factors Affecting the Use of Facial-Recognition Payment: An Example of Chinese Consumers

Explanatory and predictive model of the adoption of P2P payment systems

Switching to Proximity Mobile Payment: Empirical Evidence from Korean Users

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