Influence Factors on the Adoption of a Financial Application [Abstract]

Golan Carmi, Nethanel Jay Drezner
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The questionnaire was tested on a focus group and certain adjustments were made based on the feedback. Thereupon, the questionnaire was sent online via social media. A total of 497 questionnaires were received, 402 were filled correctly and found suitable for statistical analysis. The statistical analysis included Alpha Cronbach Test, Pearson correlation test, and linear regression. \n\nContribution: By combining DIT theory (Diffusion of Innovative Technology) suggested by Rogers, and TAM model (Technology Acceptance Model) presented by Davis, we expanding our understanding of the technological and psychological factors affecting financial application validation. Finding the influencing factors can help develop and implement future financial applications. Banks will be able to develop applications that truly meet the needs, desires and concerns of their target customers, thus able to save costs and improve their services to their customers.\n\nFindings: Six factors were tested in this study: relative advantage, complexity, compatibility, observability, experiencing, and perceived risk. The main findings showed significant negative correlation between age and relative advantage, so that as the age of the user increased the relative advantage decreased. In addition, a significant positive correlation was found between age and observability, so that as the age of the user increased the observability decreased. Also was found a significant negative correlation between age and the variable adoption of a financial application, so that as the age of the subject increases the financial application adoption decreases. No significant correlation was found between age and compatibility, complexity, relative risk. In addition, a significant positive correlation was found between the numbers of months of experience that the bank offers to the customers to the positive decision to adopt a financial application. No significant correlation was found between the demographic variables, education and wages, and financial application adoption. The regression analysis led to a significant result, so that the variables together explain 69.3% adoption of financial application. According to the research hypothesis, the relative advantage, compatibility, experiencing, and observability have the most significant positive effect on financial application adoption.\n\nRecommendations for Practitioners: We recommend programmers to focus on meeting the customers’ needs that best match the criteria delineated above. 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Abstract

Aim/Purpose: Today more and more transactions and acquisitions are controlled directly from mobile devices, especially smartphones applications. Previous studies have examined the adoption of financial applications based on a single theory as a theoretical basis. In order to examine the phenomenon in a wider way, we used in this study two theories as a theoretical basis. Background: It is important to define the main technological and psychological factors that affect the choice of potential customers to adopt or prefer financial applications. By combining two theories in the study, we expanded the examination of the phenomenon of adopting financial technology. Methodology: The study questionnaire was based on two questionnaires from previous studies. The questionnaire was tested on a focus group and certain adjustments were made based on the feedback. Thereupon, the questionnaire was sent online via social media. A total of 497 questionnaires were received, 402 were filled correctly and found suitable for statistical analysis. The statistical analysis included Alpha Cronbach Test, Pearson correlation test, and linear regression. Contribution: By combining DIT theory (Diffusion of Innovative Technology) suggested by Rogers, and TAM model (Technology Acceptance Model) presented by Davis, we expanding our understanding of the technological and psychological factors affecting financial application validation. Finding the influencing factors can help develop and implement future financial applications. Banks will be able to develop applications that truly meet the needs, desires and concerns of their target customers, thus able to save costs and improve their services to their customers. Findings: Six factors were tested in this study: relative advantage, complexity, compatibility, observability, experiencing, and perceived risk. The main findings showed significant negative correlation between age and relative advantage, so that as the age of the user increased the relative advantage decreased. In addition, a significant positive correlation was found between age and observability, so that as the age of the user increased the observability decreased. Also was found a significant negative correlation between age and the variable adoption of a financial application, so that as the age of the subject increases the financial application adoption decreases. No significant correlation was found between age and compatibility, complexity, relative risk. In addition, a significant positive correlation was found between the numbers of months of experience that the bank offers to the customers to the positive decision to adopt a financial application. No significant correlation was found between the demographic variables, education and wages, and financial application adoption. The regression analysis led to a significant result, so that the variables together explain 69.3% adoption of financial application. According to the research hypothesis, the relative advantage, compatibility, experiencing, and observability have the most significant positive effect on financial application adoption. Recommendations for Practitioners: We recommend programmers to focus on meeting the customers’ needs that best match the criteria delineated above. In addition, by understanding the influencing factors, marketers should use these criteria to reduce the psychological concerns of customers that delay the adoption of a financial application. Recommendations for Researchers: Since financial applications are becoming more useful as financial transfers, other behavioral aspects that influence the adoption of technology should be examined. We recommend conducting further research based on behavioral, economic, and technological theories. Impact on Society: A better understanding of the influencing factors will derive a better planning and development of financial applications, regarding the three most significant factors: relative advantage, compatibility, and observability. This process will result better and wider adoption of financial applications by customers, and will bring more customers to use financial transfers by smartphones. Future Research: Other studies can be used other theoretical basis for research; to examine specific populations, for example, in terms of older populations; to examine cultural and social influences factors on the adoption of financial applications.
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金融应用程序采用的影响因素[摘要]
目标/目的:如今,越来越多的交易和收购直接通过移动设备进行控制,尤其是智能手机应用程序。以往的研究都是基于单一理论作为理论基础来考察金融应用的采用。为了更广泛地考察这一现象,我们在本研究中采用了两种理论作为理论基础。背景:定义影响潜在客户选择采用或偏爱金融应用程序的主要技术和心理因素是很重要的。通过结合研究中的两种理论,我们扩大了对采用金融科技现象的考察。研究方法:研究问卷是基于先前研究的两份问卷。问卷在焦点小组中进行了测试,并根据反馈进行了一定的调整。于是,问卷通过社交媒体发到网上。共收到问卷497份,正确填写402份,适合进行统计分析。统计分析采用Alpha Cronbach检验、Pearson相关检验和线性回归。贡献:通过结合Rogers提出的创新技术扩散理论(DIT)和Davis提出的技术接受模型(TAM),我们扩展了对影响金融应用验证的技术和心理因素的理解。发现影响因素有助于开发和实施未来的金融应用。银行将能够开发真正满足其目标客户需求、愿望和关注的应用程序,从而能够节省成本并改善对客户的服务。结果:本研究测试了六个因素:相对优势、复杂性、兼容性、可观察性、体验性和感知风险。主要发现年龄与相对优势呈显著负相关,即随着用户年龄的增加,相对优势降低。此外,年龄与可观察性之间存在显著的正相关关系,随着用户年龄的增加,可观察性降低。此外,我们还发现年龄与财务应用程序的变量采用之间存在显著的负相关关系,因此,随着受试者年龄的增加,财务应用程序的采用会减少。年龄与相容性、复杂性、相对危险性无显著相关。此外,发现银行提供给客户的经验月数与采用金融应用程序的积极决策之间存在显著的正相关关系。人口统计变量、教育和工资与金融应用采纳之间没有发现显著的相关性。回归分析得出显著的结果,使得这些变量共同解释了69.3%的财务应用采纳。根据研究假设,相对优势、兼容性、体验性和可观察性对财务应用采纳的正向影响最为显著。对从业者的建议:我们建议程序员专注于满足客户的需求,这些需求与上面描述的标准最匹配。此外,通过了解影响因素,营销人员应该使用这些标准来减少客户延迟采用金融应用程序的心理担忧。给研究人员的建议:由于金融应用作为金融转移正变得越来越有用,因此应该研究影响技术采用的其他行为方面。我们建议在行为、经济和技术理论的基础上进行进一步的研究。对社会的影响:更好地了解影响因素将有助于更好地规划和开发金融应用程序,涉及三个最重要的因素:相对优势、兼容性和可观察性。这一过程将使客户更好、更广泛地采用金融应用程序,并将使更多的客户使用智能手机进行金融转账。未来研究方向:其他研究可以利用其他理论基础进行研究;检查特定的人口,例如老龄人口;研究影响金融应用采纳的文化及社会因素。
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