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Watch Your Mobile Payment: An Empirical Study of Privacy Disclosure

机译:观看您的手机支付:对隐私披露的实证研究

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Using a smartphone as payment device has become a highly attractive feature that is increasingly influencing user acceptance. Electronic wallets, near field communication, and mobile shopping applications, are all incentives that push users to adopt m-payment. Hence, this makes the sensitive data that already exists on everyone's smartphone easily collated to their financial transaction details. In fact, misusing m-payment can be a real privacy threat. The existing privacy issues regarding m-payment are already numerous, and can be caused by different factors. We investigate, through an empirical survey-based study, the different factors and their potential correlations and regression values. We identify three factors that influence directly privacy disclosure: the user's privacy concerns, his risk perception, and the protection measure appropriateness. These factors are impacted by indirect ones, which are linked to the users' and the technology's characteristics, and the behaviour of institutions and companies. In order to analyse the impact of each factor, we define a new research model for privacy disclosure based on several hypotheses. The study is mainly based on a five-item scale survey, and on the modelling of structural equations. In addition to the impact estimations for each factor, our study results indicate that the privacy disclosure in m-payment is primarily caused by the "protection measure appropriateness"; which, in its turn, impacted by "the m-payment convenience". We discuss in this paper the research model, the methodology, the findings and their significance.
机译:使用智能手机作为支付设备已成为一个非常有吸引力的特征,越来越多地影响用户验收。电子钱包,近场通信和移动购物应用,都是推动用户采用M-Payment的激励措施。因此,这使得在每个人的智能手机上都存在已存在的敏感数据以容易地整理到其财务交易细节。事实上,滥用M-Payment可以是真正的隐私威胁。关于M-Payal的现有隐私问题已经很多,并且可能是由不同因素引起的。我们通过基于经验的调查的研究,不同的因素及其潜在的相关性和回归值来调查。我们确定直接隐私披露的三个因素:用户的隐私问题,他的风险感知和保护措施适当性。这些因素受到间接的因素,与用户相关,技术的特征以及机构和公司的行为。为了分析每个因素的影响,我们基于几个假设来定义一个新的隐私披露研究模型。该研究主要基于五项规模调查,以及结构方程的建模。除了每个因素的影响估计外,我们的研究结果表明,M-PEPATION的隐私披露主要由“保护措施适当性”引起;转弯时,它受到“M-Paypoveience”的影响。我们在本文中讨论了研究模型,方法,研究结果及其意义。

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