Why do people use mobile payment technologies and why would they continue? An examination and implications from India

Pal, Abhipsa; Herath, Tejaswini; Dé, Rahul; Rao, H. Raghav

doi:10.1016/j.respol.2021.104228

Cited by 75 publications

(39 citation statements)

References 114 publications

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“…Continuance intention (CI) Mean = 5.414; SD = 1.182; Cronbach's a = 0.949; CR = 0.946; AVE = 0.819(Zhao and Bacao, 2021;Yang et al, 2021;Pal et al, 2021) I intend to continue using m-payments more frequently in the future during COVID-19 pandemic 0.916 I am willing to continuously use m-payments in the near future during COVID-19 pandemic 0.937 I intend to increase my use of m-payments in the future 0.942 I intend to increase the frequency of my overall use of the m-payments 0.927 Actual use (AU) Mean = 5.427; SD = 1.207; Cronbach's a = 0.923; CR = 0.922; AVE = 0.744 (Yang et al, 2021; Pal et al, 2021) I often use m-payments to manage my account 0.878 I often use m-payments to transfer and remit money 0.897 I often use m-payments to make payments 0.904 I use m-payment apps for a variety of transactions 0.925…”

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confidence: 99%

“…Perceived security (PS) Mean = 5.413; SD = 0.1.109; Cronbach's a = 0.922; CR = 0.913; AVE = 0.723(Aslam et al, 2017;Zhao and Bacao, 2021;Pal et al., 2021) I would find m-payment services secure when conducting my payment transactions 0.909 I feel m-payments are secure when transmitting sensitive information during COVID-19 pandemic 0.911 The risk of abuse of usage (e.g. names of business partners, payment amount) is low 0.889 The password protection guarantees security 0.894 Social influence (SI) Mean = 5.357; SD = 1.237; Cronbach's a = 0.942; CR = 0.941; AVE = 0.801 (Zhao and Bacao, 2021; Yang et al, 2021; Jouda, 2020) People who are important to me (e.g.…”

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confidence: 99%

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Customers’ continuance usage of mobile payment during the COVID-19 pandemic

Istijanto¹,

Handoko²

2022

SJME

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Purpose The COVID-19 pandemic has significantly affected how consumers make payment choices. This study aims to develop a comprehensive model explaining customers’ continuance usage of mobile payment during the COVID-19 pandemic by investigating both the pull (positive) factors of mobile payment and the push (negative) factors of cash payment. Design/methodology/approach A survey was conducted on 508 mobile payment users. A quota sampling method was applied to collect the data. Then, the data were analyzed using structural equation modeling. This study employed SPSS and LISREL software. Findings This study reveals that four antecedent factors: favorable attitude toward mobile payment, social influence, facilitating conditions and unfavorable attitude toward cash payment, positively affect the continuance intention to use mobile payment during the COVID-19 pandemic. The finding also corroborates the effect of continuance intention on the actual use of mobile payment. Practical implications This research provides valuable insights for formulating business strategies. The results indicate that mobile payment providers should not only consider the positive aspects of mobile payments but also the negative aspects of cash payment when encouraging the continuance usage of mobile payments to customers. Originality/value This study is among the first to empirically test the effect of unfavorable attitudes toward cash payment on the continuing use of mobile payment. Specifically, the research extends the unified theory of acceptance and use of technology by adding the push–pull-mooring model to enhance the explanatory power.

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confidence: 99%

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confidence: 99%

Customers’ continuance usage of mobile payment during the COVID-19 pandemic

Istijanto¹,

Handoko²

2022

SJME

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“…Based on the previous research summary, we put forward the influence of privacy, security, familiarity, system quality, trust, risk, and benefit on consumers' intention to use these systems. Capturing usage intentions helps to understand the reasons for actual use and continued use [53]. Consumers' intention to adopt mobile payment systems is regarded as a significant predictor of their actual adoption of such systems [16].…”

Section: Intentionmentioning

confidence: 99%

Internet of Things Background: An Empirical Study on the Payment Intention of Central Bank Digital Currency Design

Jin

Mei

et al. 2022

Mobile Information Systems

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This article proposes and verifies the consumer acceptance intention model under the background of central bank digital currency payment. It modifies the theory of technology acceptance (TAM), rational action theory (TRA), and extended valence framework (EVF) through the construction of perceived privacy, perceived security, perceived system quality, and perceived benefits. The data collected from 325 questionnaires on consumers’ attitudes toward the central bank’s digital currency were used to apply the structural equation model to evaluate the prediction model. The empirical results indicate that perceived privacy, perceived security, and perceived system quality are important predictors of perceived trust, and perceived trust is an important predictor of perceived interest and intention. Perceived privacy, perceived security, and perceived system quality are key factors influencing consumers’ intentions.

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“…The term "design" refers to the new innovation's system quality (Pal et al, 2021). System quality with design features, according to Zhou (2013), comprises "ease of use, and navigation. "…”

Section: Design Constraints Barriermentioning

confidence: 99%

Identifying Consumer Resistance of Mobile Payment During Covid-19: An Interpretive Structural Modeling (Ism) Approach

Singh

Singh²

2022

Business, Management and Economics Engineering

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Purpose – Due to country-wise lockdown and state-wise curfews in COVID-19, people were not able to make offline payments (i.e. cash payments) during purchases in India. So, people are switching their payment behavior from offline to online mode. But, as per the central bank report, the rate of adoption through mobile payments is still slow. The paper focuses on identifying critical barriers to mobile payment systems (MPSs) adoption in India. Innovation resistance theory (IRT) has been used as a base model for barriers, despite the wide range of choices of barriers available in the MPSs context. Additionally, three external variables which are out of the wider coverage of IRT constructs were incorporated in this paper. The study, on the other hand, adds to innovation resistance theory in the frame of reference of MPSs from a theoretical perspective. Interpretive structural modeling (ISM), together with MICMAC analysis is brought into play to analyse the direct and indirect relationship amongst the barriers. Research methodology – ISM approach has been used to establish the relationship among the eight (08) identified barriers, through literature and expert opinions. The key barriers to high driving power are then identified with the help of MICMAC analysis. Findings – The results reveal that value barrier (b2), image barrier (b5) and visibility barrier (b7) are the most significant variables. Interestingly, IRTs’ risk barrier (b3) and privacy barrier (b6) from the literature fall in the lowest level of the ISM model. The majority of the barriers fall under quadrant III of MICMAC analysis, indicating the high driving and dependence power. Research limitations – The developed ISM model is based on the sentiments of five (05) experts, which could be biased and influence the structural model’s final output. Due to COVID-19, data has been collected through online video conferencing mode, this may vary if data will be collected through an offline or face-to-face interview. The proposed model’s key findings aim to assist in explaining the barriers that exist during MPS adoption. Originality/Value – This study is the first attempt to use the ISM approach in conjunction with IRT to detect barriers within MPSs. The result of this paper will guide and motivate the researcher to analyse more critical barriers with IRT to contribute to the theoretical development.

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Why do people use mobile payment technologies and why would they continue? An examination and implications from India

Cited by 75 publications

References 114 publications

Customers’ continuance usage of mobile payment during the COVID-19 pandemic

Customers’ continuance usage of mobile payment during the COVID-19 pandemic

Internet of Things Background: An Empirical Study on the Payment Intention of Central Bank Digital Currency Design

Identifying Consumer Resistance of Mobile Payment During Covid-19: An Interpretive Structural Modeling (Ism) Approach

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