基于NFC技术的校园支付应用创新研究

首先讨论了移动支付的概念、发展历史以及应用形式,并对移动支付体系进行深度研究;其次,讲述了NFC技术的概念、发展现状和应用场景;最后,将NFC融入移动电子商务中的移动支付体系中,分析了NFC技术在校园支付中的应用,针对校园的特殊场景进行应用分析,以便为未来NFC技术在移动电子商务近距离支付的相关研究提供帮助与推进作用。     

移动支付安全性问题研究

移动支付是允许移动用户利用无线通信技术,使用其移动终端对所消费的商品或服务进行账务支付的一种服务方式。在移动电子商务迅速抢占市场的同时,作为移动电子商务重要组成部分的移动支付近年来发展如火如荼。本文通过对移动支付相关概念的介绍,分析了目前移动支付存在的安全问题,提出了一些解决办法。     

移动支付安全性问题研究

移动支付是允许移动用户利用无线通信技术,使用其移动终端对所消费的商品或服务进行账务支付的一种服务方式。在移动电子商务迅速抢占市场的同时,作为移动电子商务重要组成部分的移动支付近年来发展如火如荼。本文通过对移动支付相关概念的介绍,分析了目前移动支付存在的安全问题,提出了一些解决办法。     

基于WPKI的移动电子商务研究

移动电子商务是移动信息服务和电子商务融合的产物,构建安全灵活的移动支付机制是完善商业模式的关键环节。从移动电子商务应用的角度,分析无线公开密钥基础设施的结构和组成。对移动电子商务的支付形式和支付过程进行阐述,对移动电子商务交易模式进行划分,提出基于WPKI的移动电子商务安全框架。     

浅谈移动支付的安全性

随着科学技术的不断发展,计算机技术与网络技术得到了广泛的应用,并且产生了电子商务,在电子商务发展过程中,为了满足其支付的需求,移动支付随之产生,移动支付作为电子商务最为重要的环节,其安全性直接关系着电子商务的发展,因此,需要重视移动支付的安全性。本文将分析移动支付的概况,包括移动支付的涵义、类别、特点及发展现状等,并将根据移动支付存在的安全问题,将提出相应的解决措施,旨在提升移动支付的安全性,促进电子商务的稳步发展。     

移动支付产业发展现状及趋势

随着移动互联网产业的兴起,用户在互联网上的使用习惯快速迁移到移动互联网上,应用的发展撬动移动电子商务需求,移动支付也成为快速发展的新兴支付手段,未来移动支付将成为主要的支付方式。通过对中国移动支付产业发展历程的梳理,展望移动支付产业的未来。     

移动Agent与电子支付研究

移动Agent具有很多传统网络计算模型不具备的优点,很有可能成为一种新的计算方式。特别是随着Internet的迅速发展,许多学者和工业界人士对移动Agent在电子商务中应用的前景非常看好。就移动Agent在电子支付系统中的应用做了初步研究,在Agent系统中增加了有关电子支付的模块,将对其在电子商务中的应用大有益处。     

基于IC卡的电子支付模型研究

电子支付技术是电子商务中的核心技术,也是目前制约电子商务发展的一个根本性问题。文章就电子商务中的电子支付技术进行了探讨,以SET协议为基础,提出了三种参考模型,并对这三种电子支付模型进行了对比分析,给出了这三种电子支付模型的优缺点和共同点。     

易观智库:2011年国内移动支付市场规模将达52.4亿元

易观智库发布的《国内移动支付市场年度综合报告2011》显示,随着移动互联网业务的普及和移动电子商务的发展,远程支付市场规模将迅速发展,同时运营商和银联对近距支付推广力度也将不断增强,成为移动支付市场发展的重要驱动力量。据易观     

面向移动电子商务的手机客户端应用研究

随着移动用户的迅速增加,以及移动通讯的应用向纵深发展,移动电子商务已成为潮流和趋势,手机支付是移动电子商务的基石,对推动移动电子商务的全面发展具有重要意义,而手机客户端是手机支付业务的重要环节,相比网站、短信、wap等,它要求充分发挥手机终端便携性的强势,要求具有更好的用户体验及方便性。文章提出了一种面向移动电子商务的...     

User acceptance of mobile commerce: an empirical study in Macau

This study aims to examine the positive and negative factors that can significantly explain user acceptance of mobile commerce (m-commerce) in Macau. A technology acceptance model for m-commerce with five factors is constructed. The proposed model is tested using data collected from 219 respondents. Confirmatory factor analysis is performed to examine the reliability and validity of the model, and structural equation modelling is performed to access the relationship between behaviour intention and each factor. The acceptance of m-commerce is influenced by factors including performance expectancy, social influence, facilitating conditions and privacy concern; while effort expectancy is insignificant in this case. The results of the study are useful for m-commerce service providers to adjust their strategies for promoting m-commerce services. This study contributes to the practice by providing a user technology acceptance model for m-commerce that can be used as a foundation for future research.     

基于可用性的移动商务系统采纳研究框架*

从系统可用性的角度来探究移动商务的采纳问题,探讨了对移动商务系统可用性的定义和测量,分析了可用性对采纳的影响机制问题,提出移动商务系统可用性受到移动设备特性、WAP网站特性、移动通信网络特性以及情境因素等四个方面因素的影响,并对这四个方面进行了深入的分析和探讨。提出了一种基于可用性的移动商务采纳研究框架,希望引起学术界对移动商务系统可用性的更多关注,并更好理解影响移动商务采纳的因素。     

Predicting m-commerce adoption determinants: A neural network approach

M-commerce has continued to grow at an explosive rate. This purpose of this paper is to examine the predictors of m-commerce adoption by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The extended model incorporates additional constructs such as perceived value, trust, perceived enjoyment and personal innovativeness. A non-linear, non-compensatory model is developed to understand the predictors of m-commerce adoptions. Online survey was used to collect data from 140 Chinese users. Neural network analysis was used to predict m-commerce adoption, and the model was compared with the results from regression analysis. The neural network model outperformed the regression model in adoption prediction, and captured the non-linear relationships between predictors such as perceived value, trust, perceived enjoyment, personal innovativeness, users demographic profiles (e.g. age, gender and educational level), effort expectancy, performance expectancy, social influence and facilitating conditions with m-commerce adoption. This study applied neural network to provide further understanding of m-commerce adoption decisions based on a non-linear, non-compensatory model. The UTAUT model was also extended to examine consumer information systems such as m-commerce. The m-commerce study conducted in this research is in China, one of the fastest growing m-commerce markets in the world.     

Service and P2P based secure media sharing in mobile commerce environments

Service technology is slowly evolving to be a promising technology for developing applications in open, loosely coupled and distributed computing environments, e.g., in mobile commerce (m-commerce). Services technology can shield the heterogeneous platforms and is suitable for m-commerce applications. Peer-to-Peer (P2P) technology becomes more and more popular for mobile commerce applications. For secure media distribution in m-commerce applications, the security and P2P rights management become more and more urgent. New schemas and architectures for secure P2P based m-commerce applications, which are expected to function automatically or semi-automatically, are expected. In this paper, a secure media service system is presented, which can trace illegal distributors in m-commerce applications. In this scheme, the decryption operation and fingerprint embedding operation are combined together, which avoids the leakage of clear media content in mobile transfer. Additionally, these operations are implemented by the peer, which makes the scheme compliant with existing Peer-to-Peer Digital Rights Management (DRM) systems and very proper for secure media distribution in m-commerce applications. The architectures and modes of secure media distribution in m-commerce environment are presented and discussed.     

Determining the mobile commerce user requirements using an analytic approach

Independent studies have shown that mobile commerce (m-commerce) can have an important influence on business and society in the future. Hence, network designers, service providers, vendors and application developers must carefully take the needs and considerations of various users into account to provide better services and attract them to m-commerce. Consequently, identifying the m-commerce user requirements and their significance becomes an essential and crucial process for the standardization and improvement of associated systems. On this line, the objective of this paper is to propose an analytic framework to provide practitioners a more effective and efficient model for prioritizing m-commerce requirements.     

A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption

The advancement in wireless and mobile technologies has presented tremendous business opportunity for mobile-commerce (m-commerce). This research aims to examine the factors that influence consumers’ m-commerce adoption intention. Variables such as perceived usefulness, perceived ease of use, perceived enjoyment, trust, cost, network influence, and variety of services were used to examine the adoption intentions of consumers. Data was collected from 376 m-commerce users. A multi-analytic approach was proposed whereby the research model was tested using structural equation modeling (SEM), and the results from SEM were used as inputs for a neural network model to predict m-commerce adoption. The result showed that perceived usefulness, perceived enjoyment, trust, cost, network influence, and trust have significant influence on consumers’ m-commerce adoption intentions. However, the neural network model developed in this research showed that the best predictors of m-commerce adoption are network influence, trust, perceived usefulness, variety of service, and perceived enjoyment. This research proposed an innovative new approach to understand m-commerce adoption, and the result for this study will be useful for telecommunication and m-commerce companies in formulating strategies to attract more consumers.     

A confidence-based framework for business to consumer (B2C) mobile commerce adoption

The Technology Acceptance Model (TAM) has been considered to be fundamental in determining the acceptance of new technology in the past decades. The two beliefs, ease of use and usefulness, in the model may not, however, fully explain the consumers’ behavior in an emerging environment, such as mobile commerce (m-commerce). This paper aims to develop a framework for m-commerce adoption in consumer decision-making processes. In this paper TAM has been adopted and extended to analyze successful m-commerce adoption. The key elements of the proposed confidence-based framework for B2C m-commerce adoption include psychological and behavioral factors. Psychological factors include history-based confidence, institution-based confidence and personality-based confidence. Behavioral factors include perceived ease of use and perceived usefulness of the mobile application and technology.     

The conceptualization and measurement of m-commerce user satisfaction

Many of the instruments for measuring user-perceived system quality and user information satisfaction were developed in the context of mainframe, PC and wire-based technologies of a bygone era. With the proliferation of wireless technologies, users are increasingly interfacing and interacting with mobile commerce (m-commerce) systems. It is therefore important to develop new instruments and scales, which are directly targeted for these new interfaces and applications. User satisfaction is commonly acknowledged as one of the useful proxy measures of system success. This study addresses the concern for an effective m-commerce system design by means of the conceptualization and measurement of an m-commerce user satisfaction (MCUS) construct. In this article, we introduce and define this construct of MCUS, provide empirical validation of the construct and its underlying dimensionality, develop a standardized instrument with desirable psychometric properties for measuring MCUS, and explore the measure’s theoretical and practical application. This empirically validated instrument will be useful to researchers in developing and testing m-commerce systems theories, as well as to organizations in designing better-accepted m-commerce systems.     

The emergence of m-commerce

Driven by a widespread understanding of the Internet's capabilities, the power of electronic commerce, and advances in wireless technologies and devices, mobile commerce (m-commerce) is rapidly approaching the business forefront. According to independent research findings, m-commerce, the conducting of business and services over portable, wireless devices, will soon be a dominant force in business and society. The viability of these projections depends on the power of the underlying technology drivers and the attractiveness of m-commerce applications. To compete in a marketplace dominated by wireless devices, businesses must devise effective m-commerce strategies. Building successful strategies begins by recognizing the forces driving m-commerce's emergence.     

Enhancement of e-commerce via mobile accesses to the Internet

The potential of the Internet has been expanded substantially by a new generation of mobile devices, opening the door for rapid growth of m-commerce. While the traditional PC access to the Internet continues to be vital for exploiting the advantages of the Internet, the mobile access appears to attract more people because of flexible accesses to the Internet in a ubiquitous manner. Accordingly, e-commerce is now in the process of being converted into m-commerce. The purpose of this paper is to develop and analyze a mathematical model for comparing e-commerce via the traditional PC access only with m-commerce which accommodates both the traditional PC access and the mobile access. The distribution of the number of products purchased by time t and the distribution of the time required for selling K products are derived explicitly, enabling one to assess the impact of mobile devices on e-businesses. Numerical examples are given for illustrating behavioral differences between m-commerce consumers and traditional e-commerce consumers.