Influencing Factors Analysis for a Social Network Web Based Payment Service in China

WeChat payment has recently become a popular mobile payment service in China by bundling with WeChat, the most popular social network service in China. It is interesting to investigate the reasons for its popularity. In this research, we applied the technology acceptance model to predict its acceptability and to identify variables attributing to its popularity. In addition to the primary explanatory variables, i.e., Perceived Ease of Use and Perceived Usefulness, the proposed framework is further extended to include the constructs of Social Interaction, Trust, Perceived Enjoyment and Use Context. The results indicated that the proposed model is able to explain the variance in a user’s intention to use WeChat payment service. We hope this study can provide insights into understanding the adoption behaviour of socially aware mobile payment services.

[1]  Marc J. Schniederjans,et al.  e-Commerce operations management , 2002 .

[2]  Zhang Xiong,et al.  Social Aware Mobile Payment Service Popularity Analysis: The Case of WeChat Payment in China , 2015, APSCC.

[3]  Rolph E. Anderson,et al.  Multivariate Data Analysis (7th ed. , 2009 .

[4]  Martin Schrepp,et al.  Research-in-progress: User experience evaluation of Student Centered E-Learning Environment for computer science program , 2014, 2014 3rd International Conference on User Science and Engineering (i-USEr).

[5]  Juan Sánchez-Fernández,et al.  The moderating effect of experience in the adoption of mobile payment tools in Virtual Social Networks: The m-Payment Acceptance Model in Virtual Social Networks (MPAM-VSN) , 2014, Int. J. Inf. Manag..

[6]  Bernd W. Wirtz,et al.  Understanding consumer acceptance of mobile payment services: An empirical analysis , 2010, Electron. Commer. Res. Appl..

[7]  Yinying Zhang,et al.  Envelopment-competition Pattern of E-Business Platform -- Insights from the Competition among Taobao, Baidu and Tencent , 2012, 2012 Fifth International Conference on Business Intelligence and Financial Engineering.

[8]  Rui Zhang,et al.  Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits , 2012, Comput. Hum. Behav..

[9]  Murat Kantarcioglu,et al.  A trust-score-based access control in assured information sharing systems: An application of financial credit risk score models , 2014, Risk Decis. Anal..

[10]  John Ingham,et al.  Why do people use information technology? A critical review of the technology acceptance model , 2003, Inf. Manag..

[11]  Tiago H. Falk,et al.  Latent factor analysis for synthesized speech quality-of-experience assessment , 2017, Quality and User Experience.

[12]  Feng Xia,et al.  Event-Based Mobile Social Networks: Services, Technologies, and Applications , 2014, IEEE Access.

[13]  Tao Zhou,et al.  An Empirical Examination of Initial Trust in Mobile Payment , 2014, Wireless Personal Communications.

[14]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[15]  Hamed Qahri Saremi,et al.  Factors Affecting Internet Banking Pre-usage Expectation Formation , 2013, 2013 46th Hawaii International Conference on System Sciences.

[16]  Moustafa Abu El Fadl,et al.  Effect of IT Trading Platform on Financial Risk-Taking and Portfolio Performance , 2015, 2015 48th Hawaii International Conference on System Sciences.

[17]  S. Conner.  The Effect of Social Distance on Consumer Perceptions of a Business When There is a Breach of Credit/Debit Card Security , 2015 .

[18]  Robert E. Kraut,et al.  Trust Across Borders: Buyer-Supplier Trust in Global Business-to-Business E-Commerce , 2012, J. Assoc. Inf. Syst..

[19]  In Lee,et al.  An empirical examination of factors influencing the intention to use mobile payment , 2010, Comput. Hum. Behav..

[20]  Yong Liu,et al.  Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China , 2011, Comput. Hum. Behav..

[21]  Tiago Oliveira,et al.  Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators , 2015, Comput. Hum. Behav..

[22]  K. S. Jasmine,et al.  A New Architecture of Mobile Payment System through Social Media Network , 2014, J. Electron. Commer. Organ..

[23]  Henryk Krawczyk,et al.  Clustering Context Items into User Trust Levels , 2016, DepCoS-RELCOMEX.

[24]  Christophe G. Giraud-Carrier,et al.  Mining useful association rules from questionnaire data , 2014, Intell. Data Anal..

[25]  D. Gefen,et al.  E-commerce: the role of familiarity and trust , 2000 .

[26]  Florence Martin,et al.  Here and now mobile learning: An experimental study on the use of mobile technology , 2013, Comput. Educ..

[27]  Siyu Zhang,et al.  Research on Undergraduates' Perception of Wechat Acceptance , 2014, 2014 IEEE 11th International Conference on e-Business Engineering.

[28]  Salem Alkhalaf,et al.  Government Initiatives: The Missing Key for E-commerce Growth in KSA , 2012, ArXiv.

[29]  M. Wolgast,et al.  What does the Acceptance and Action Questionnaire (AAQ-II) really measure? , 2014, Behavior therapy.

[30]  Niina Mallat,et al.  Exploring consumer adoption of mobile payments - A qualitative study , 2007, J. Strateg. Inf. Syst..

[31]  Arfan Bakhtiar.  Employee perceptions to standards capability: A cross second order confirmatory factors analysis in manufacturing firms-Indonesia , 2013, 2013 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC).

[32]  B. Everitt,et al.  Exploratory Factor Analysis , 2011 .

[33]  A. Soares,et al.  Advertising in online social networks: the role of perceived enjoyment and social influence , 2014 .

[34]  Andreas Klein,et al.  Social activity and structural centrality in online social networks , 2015, Telematics Informatics.

[35]  Saurabh Gupta,et al.  Choosing Web 2.0 Tools for Instruction: An Extension of Task-Technology Fit , 2014, Int. J. Inf. Commun. Technol. Educ..

[36]  Tiago Oliveira,et al.  Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology , 2013, Comput. Hum. Behav..

[37]  Scott B. MacKenzie,et al.  Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques , 2011, MIS Q..

[38]  Héctor Pomares,et al.  Decision Support System to Determine Intention to Use Mobile Payment Systems on Social Networks: A Methodological Analysis , 2016, Int. J. Intell. Syst..

[39]  Deli Yang,et al.  Mobile Payment Pattern Based on Multiple Trusted Platforms - China Case , 2010, 2010 Ninth International Conference on Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR).

[40]  Leon Staphorst,et al.  Structural Equation Modelling based data fusion for technology forecasting: A National Research and Education Network example , 2014, Proceedings of PICMET '14 Conference: Portland International Center for Management of Engineering and Technology; Infrastructure and Service Integration.

[41]  Chunmei Gan,et al.  The Impact of Use Context on Mobile Payment User Adoption: An Empirical Study in China , 2016, WHICEB.

[42]  Francisco Muñoz-Leiva,et al.  Determinants of Intention to Use the Mobile Banking Apps: An Extension of the Classic TAM Model , 2017 .

[43]  Joanne E. McNeish,et al.  Consumer trust and distrust: retaining paper bills in online banking , 2015 .

[44]  Matti Rossi,et al.  The impact of use context on mobile services acceptance: The case of mobile ticketing , 2009, Inf. Manag..

[45]  Hongjiang Xu,et al.  The Effect of Perceived Security on Consumers' Intent to Use: Satisfaction and Loyalty to M-Commerce in China , 2013, J. Electron. Commer. Organ..

[46]  Xiaohui Liang,et al.  Exploiting multimedia services in mobile social networks from security and privacy perspectives , 2014, IEEE Communications Magazine.

[47]  Ali Kara,et al.  Online word-of-mouth communication on social networking sites: An empirical study of Facebook users , 2015 .

[48]  Yi-Shun Wang,et al.  Investigating the individual difference antecedents of perceived enjoyment in students' use of blogging , 2012, Br. J. Educ. Technol..

[49]  Ian McCulloh,et al.  Social networks and spatial configuration - How office layouts drive social interaction , 2012, Soc. Networks.

[50]  Rajeev Sharma,et al.  Estimating the effect of common method variance: the method-method pair technique with an illustration from TAM research , 2009 .

[51]  Tao Zhou,et al.  An empirical examination of continuance intention of mobile payment services , 2013, Decis. Support Syst..

[52]  Teddy Mantoro,et al.  Online payment procedure involving mobile phone network infrastructure and devices , 2011, 2011 International Conference on Multimedia Computing and Systems.

[53]  Lijun Gao,et al.  Evaluating the Operational Risk of Chinese Personal E-Banking , 2010, 2010 International Conference on E-Business and E-Government.

[54]  이훈,et al.  지각된 유용성(Perceived Usefulness)의 영향분석 , 2004 .

[55]  Zoran Kalinic,et al.  Antecedents of customer satisfaction in mobile commerce: Exploring the moderating effect of customization , 2017, Online Inf. Rev..

[56]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[57]  Mehdi Mohamadi,et al.  Effective factors on the success or failure of the online payment systems, focusing on human factors , 2013, 7th International Conference on e-Commerce in Developing Countries:with focus on e-Security.

[58]  Thomas P. Mackey,et al.  Exploring the relationships between Web usability and students' perceived learning in Web-based multimedia (WBMM) tutorials , 2008, Comput. Educ..

[59]  Yongqiang He,et al.  Research on online payment mode of e-commerce , 2010, 2010 IEEE International Conference on Software Engineering and Service Sciences.

[60]  Shih-Ming Pi,et al.  Factors That Affect Consumers’ Trust and Continuous Adoption of Online Financial Services , 2012 .

[61]  Ejaz Ahmed,et al.  Investigating the influence of the most commonly used external variables of TAM on students' Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios , 2016, Comput. Hum. Behav..

[62]  Rebecca Strachan,et al.  A modified TAM for predicting acceptance of digital educational games by teachers , 2017, 2017 IEEE Global Engineering Education Conference (EDUCON).

[63]  Acar Tamersoy,et al.  Graph-based algorithms and models for security, healthcare, and finance , 2016 .

[64]  David B. Humphrey,et al.  Cost Savings from Check 21 Electronic Payment Legislation , 2013 .

[65]  Wenjing Xie,et al.  Social network site use, mobile personal talk and social capital among teenagers , 2014, Comput. Hum. Behav..

[66]  Liu Liu,et al.  Exploring consumer perceived risk and trust for online payments: An empirical study in China's younger generation , 2015, Comput. Hum. Behav..

[67]  Marios Koufaris,et al.  Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior , 2002, Inf. Syst. Res..