Influential Factors and User Behavior of Mobile Reading

Abstract With the popularization of mobile Internet and smart terminals, mobile reading with diversity and mobility has become a hot issue in the industry and academia. This article comes up with a hypothetical model of mobile reading user acceptance behavior based on the technology acceptance model and unified theory of acceptance and use of technology and conducts an analysis of the reliability and validity of questionnaire data. Based on this, the model fitness is analyzed as well as the path hypotheses testing. We find that user-perceived ease of use greatly influences perceived usefulness (path coefficient=0.841), and user attitude (path coefficient=0.860) and behavioral intention (path coefficient=0.154) are significantly impacted by perceived usefulness. The impact of social influence on user attitude toward using mobile reading is significant (path coefficient=0.341), but the influence of perceived payment is not obvious. The moderating effect of living habit is not obvious because the absolute value of the critical ratio is under 2.58 with a significance level of 0.01.

[1]  Yi-Shun Wang,et al.  An examination of the determinants of customer loyalty in mobile commerce contexts , 2006, Inf. Manag..

[2]  S. Barnes,et al.  Driving consumer acceptance of mobile marketing: a theoretical framework and empirical study , 2005 .

[3]  Uchenna Cyril Eze,et al.  An extended TAM for analyzing adoption behavior of mobile coupon , 2009, MoMM.

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

[5]  Wendy Wood,et al.  Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. , 1998 .

[6]  Sally Rao Hill,et al.  A Conceptual Framework and Propositions for the Acceptance of Mobile Services , 2007, J. Theor. Appl. Electron. Commer. Res..

[7]  Younghwa Lee,et al.  The Technology Acceptance Model: Past, Present, and Future , 2003, Commun. Assoc. Inf. Syst..

[8]  Ying Li,et al.  Empirical Study and Model of User’s Acceptance for Mobile Commerce in China , 2012 .

[9]  Lynn Jeffrey,et al.  The influence of students' ICT skills and their adoption of mobile learning , 2013 .

[10]  Shin-Yuan Hung,et al.  Determinants of user acceptance of the e-Government services: The case of online tax filing and payment system , 2006, Gov. Inf. Q..

[11]  Dongwon Lee,et al.  On the Explanation of Factors Affecting E-Commerce Adoption , 2001, ICIS.

[12]  Eija Kaasinen,et al.  User Acceptance of Mobile Services , 2009, Int. J. Mob. Hum. Comput. Interact..

[13]  Ahmad Abu-Al-Aish,et al.  International of Research in Open and Distributed Learning Factors Influencing Students’ Acceptance of M-Learning: An Investigation in Higher Education , 2022 .

[14]  Dr. Hsin Kuang Chi,et al.  Applying Theory of Reasoned Action and Technology Acceptance Model to Investigate Purchase Behavior on Smartphone , 2011 .

[15]  Patrick Y. K. Chau,et al.  An Empirical Assessment of a Modified Technology Acceptance Model , 1996, J. Manag. Inf. Syst..

[16]  Anol Bhattacherjee,et al.  Acceptance of e-commerce services: the case of electronic brokerages , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[17]  Jairak Kallaya,et al.  An Acceptance of Mobile Learning for Higher Education Students in Thailand , 2009 .

[18]  Xu Ji-sheng.  Determinants of User Acceptance of the E-Government Services——the Case of Online Tax Filing and Payment System , 2008 .

[19]  Dai-Yon Cho,et al.  Analysis of Trust in Internet and Mobile Commerce Adoption , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[20]  Anol Bhattacherjee,et al.  Information Technology Continuance: A Theoretic Extension and Empirical Test , 2008, J. Comput. Inf. Syst..

[21]  Hsiu-Yuan Wang,et al.  User acceptance of mobile internet based on the Unified Theory of Acceptance and Use of Technology: Investigating the determinants and gender differences , 2010 .

[22]  Kim.  Post-Adoption Behavior of Mobile Internet Users : A Model-Based Comparison between Continuers and Discontinuers Hoyoung , 2003 .

[23]  Quan Sun,et al.  Factors influencing the adoption of mobile service in China: An integration of TAM , 2010, J. Comput..

[24]  Brian L. Quick,et al.  Perceived Behavioral Control of Wildfire Scale , 2014 .

[25]  Charles D. Barrett.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[26]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[27]  G. Zinkhan,et al.  Consumer Conformity: Review and Applications for Marketing Theory and Practice , 1999 .

[28]  Keng Siau,et al.  Mobile Commerce: Promises, Challenges and Research Agenda , 2001, J. Database Manag..

[29]  Tugrul U. Daim,et al.  Exploring technology acceptance for mobile services , 2011 .

[30]  Mark West and Han Ei Chew,et al.  Reading in the mobile era: A study of mobile reading in developing countries , 2014 .

[31]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[32]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.