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A model of consumer acceptance of mobile payment

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TLDR
This study proposes a research model that examines the factors which determine consumer acceptance of m-payment, expanding the Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT).
Abstract
One promising area of mobile commerce (m-commerce) that is receiving growing attention globally is mobile payment (m-payment). m-payment refers to making payments using mobile devices. Understanding the determinants of consumer acceptance of m-payment will provide important theoretical contributions to the field and lead to the development of more effective m-payment devices and systems. By expanding the Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT), this study proposes a research model that examines the factors which determine consumer acceptance of m-payment. Significant support for the model was found in the data collected from a survey of 299 potential m-payment users. Implications for practice and suggestions for future research are provided.

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Full length articleMobile payment: Understanding the determinants of customer adoption and intention to recommend the technology

TL;DR: Compatibility, perceived technology security, performance expectations, innovativeness, and social influence are found to have significant direct and indirect effects over the adoption of mobile payment and the intention to recommend this technology.
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An empirical examination of continuance intention of mobile payment services

TL;DR: Drawing on the information systems success model and flow theory, this research identified the factors affecting continuance intention of mobile payment and indicated that servicequality is the main factor affecting trust, whereas system quality is themain factor affecting satisfaction.
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Towards an understanding of the consumer acceptance of mobile wallet

TL;DR: The proposed model brings together extant research on mobile payment and provides an important cluster of antecedents to eventual technology acceptance via constructs of behavioral intention to use and actual system usage.
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Predicting young consumers' take up of mobile banking services

TL;DR: In this paper, the authors investigated the barriers for adopting mobile banking services and found that compatibility, trust, credibility, perceived risk and cost were significant indicators for the adoption of m-banking services.
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Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits

TL;DR: The findings show that behavioral beliefs in combination with social influences and personal traits are all important determinants for mobile payment services adoption and use, but their impacts on behavioral intention do vary across in different stages.
References
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Perceived Usefulness, Perceived Ease of Use, and User

TL;DR: Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage.
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Perceived usefulness, perceived ease of use, and user acceptance of information technology

TL;DR: In this article, the authors developed and validated new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance.
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Structural equation modeling in practice: a review and recommended two-step approach

TL;DR: In this paper, the authors provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development, and present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests.
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User acceptance of information technology: toward a unified view

TL;DR: The Unified Theory of Acceptance and Use of Technology (UTAUT) as mentioned in this paper is a unified model that integrates elements across the eight models, and empirically validate the unified model.
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Alternative Ways of Assessing Model Fit

TL;DR: In this paper, two types of error involved in fitting a model are considered, error of approximation and error of fit, where the first involves the fit of the model, and the second involves the model's shape.
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