Understanding continuance usage of mobile services

Users' continuance usage is crucial to mobile service providers' success. Integrating both perspectives of flow and switching costs, this research identified the factors affecting continuance usage of mobile services. The results indicate that effort expectancy and ubiquitous connection affect flow, which in turn affects performance expectancy. Switching costs affect switching barrier. Flow, performance expectancy and switching barrier determine continuance usage. The results imply that service providers need to concern both user experience and switching costs in order to retain users and facilitate their continuance usage.

[1]  Imsook Ha,et al.  Determinants of adoption of mobile games under mobile broadband wireless access environment , 2007, Inf. Manag..

[2]  Zhaohua Deng,et al.  Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China , 2010, Int. J. Inf. Manag..

[3]  Ping Zhang,et al.  Flow in Computer-Mediated Environments: Promises and Challenges , 2005, Commun. Assoc. Inf. Syst..

[4]  Hsin-Hui Lin,et al.  The relationship of service failure severity, service recovery justice and perceived switching costs with customer loyalty in the context of e-tailing , 2011, Int. J. Inf. Manag..

[5]  Barbara D. Klein,et al.  Beyond the Test of the Four Channel Model of Flow in the Context of Online Shopping , 2009, Commun. Assoc. Inf. Syst..

[6]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[7]  Sonja Wiley-Patton,et al.  Consumer adoption of mobile TV: Examining psychological flow and media content , 2009, Comput. Hum. Behav..

[8]  Il Im,et al.  An international comparison of technology adoption: Testing the UTAUT model , 2011, Inf. Manag..

[9]  F. Reichheld,et al.  E-LOYALTY: YOUR SECRET WEAPON ON THE WEB , 2003 .

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

[11]  J. Nunnally.  Psychometric Theory (2nd ed), New York: McGraw-Hill. , 1978 .

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

[13]  Heng-Chiang Huang,et al.  Determinants of e-repurchase intentions: An integrative model of quadruple retention drivers , 2007, Inf. Manag..

[14]  Viswanath Venkatesh,et al.  Consumer Acceptance of Personal Information and Communication Technology Services , 2011, IEEE Transactions on Engineering Management.

[15]  Hsien-Tung Tsai,et al.  Why on-line customers remain with a particular e-retailer: An integrative model and empirical evidence , 2006 .

[16]  Bongsik Shin,et al.  Erratum: Examining influencing factors of post-adoption usage of mobile internet: Focus on the user perception of supplier-side attributes (Information Systems Frontiers DOI: 10.1007/s10796-009-9184-x) , 2011 .

[17]  Donna L. Hoffman,et al.  Flow Online: Lessons Learned and Future Prospects , 2009 .

[18]  M. Csíkszentmihályi,et al.  Optimal experience: Psychological studies of flow in consciousness. , 1988 .

[19]  Dennis L. Hoffman,et al.  Marketing in Hypermedia Computer-Mediated Environments : Conceptual Foundations 1 ) , 1998 .

[20]  Kun Chang Lee,et al.  Transfer From Offline Trust to Key Online Perceptions: An Empirical Study , 2007, IEEE Transactions on Engineering Management.

[21]  Kar Yan Tam,et al.  Understanding the behavior of mobile data services consumers , 2008, Inf. Syst. Frontiers.

[22]  ThaeMin Lee,et al.  THE IMPACT OF PERCEPTIONS OF INTERACTIVITY ON CUSTOMER TRUST AND TRANSACTION INTENTIONS IN MOBILE COMMERCE , 2005 .

[23]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[24]  Marshall Scott Poole,et al.  Antecedents of flow in online shopping: a test of alternative models , 2009, Inf. Syst. J..

[25]  A. Hausman,et al.  The effect of web interface features on consumer online purchase intentions , 2009 .

[26]  Hee-Woong Kim,et al.  The Effects of Switching Costs on User Resistance to Enterprise Systems Implementation , 2011, IEEE Transactions on Engineering Management.

[27]  Hsin Hsin Chang,et al.  An investigation of user communication behavior in computer mediated environments , 2008, Comput. Hum. Behav..

[28]  Kar Yan Tam,et al.  The Effects of Post-Adoption Beliefs on the Expectation-Confirmation Model for Information Technology Continuance , 2006, Int. J. Hum. Comput. Stud..

[29]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[30]  James G. Morris,et al.  Research Note - Online Users' Switching Costs: Their Nature and Formation , 2012, Inf. Syst. Res..

[31]  Jamie Carlson,et al.  Examining the effects of website-induced flow in professional sporting team websites , 2010, Internet Res..

[32]  Tsung-Hsien Kuo,et al.  How can one amplify the effect of e-learning? An examination of high-tech employees' computer attitude and flow experience , 2010, Comput. Hum. Behav..

[33]  Sung-Byung Yang,et al.  An Odyssey into Virtual Worlds: Exploring the Impacts of Technological and Spatial Environments , 2011, MIS Q..

[34]  Detmar W. Straub,et al.  Validation Guidelines for IS Positivist Research , 2004, Commun. Assoc. Inf. Syst..

[35]  Tao Zhou,et al.  Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience , 2011, Comput. Hum. Behav..

[36]  Ting-Peng Liang,et al.  Effect of use contexts on the continuous use of mobile services: the case of mobile games , 2011, Personal and Ubiquitous Computing.

[37]  Wei-Jaw Deng,et al.  The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services , 2009, Comput. Hum. Behav..

[38]  Kuo-Wei Lee,et al.  From marketplace to marketspace: Investigating the consumer switch to online banking , 2011, Electron. Commer. Res. Appl..

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

[40]  P. M. Podsakoff,et al.  Self-Reports in Organizational Research: Problems and Prospects , 1986 .

[41]  Bongsik Shin,et al.  Understanding Post-adoption Usage of Mobile Data Services: The Role of Supplier-side Variables , 2009, J. Assoc. Inf. Syst..

[42]  Elena Karahanna,et al.  Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..

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

[44]  Confirmation Model.  UNDERSTANDING INFORMATION SYSTEMS CONTINUANCE: AN EXPECTATION- , 2001 .

[45]  Lorin M. Hitt,et al.  Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry , 2002, Inf. Syst. Res..

[46]  Naresh K. Malhotra,et al.  Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research , 2006, Manag. Sci..

[47]  Chung-Tzer Liu,et al.  he effects of relationship quality and switching barriers on customer loyalty , 2010 .

[48]  Bongsik Shin,et al.  Examining influencing factors of post-adoption usage of mobile internet: Focus on the user perception of supplier-side attributes , 2010, Inf. Syst. Frontiers.