How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model
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[1] R. Gurrea,et al. The role played by perceived usability, satisfaction and consumer trust on website loyalty , 2006, Inf. Manag..
[2] Chang Liu,et al. How do post-usage factors and espoused cultural values impact mobile payment continuation? , 2017, Behav. Inf. Technol..
[3] Narasimhaiah Gorla,et al. The impact of IT outsourcing on information systems success , 2014, Inf. Manag..
[4] James C. Anderson,et al. STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .
[5] Nidhi Phutela,et al. Mobile Wallets in India: A Framework for Consumer Adoption , 2019, Int. J. Online Mark..
[6] Hsin Hsin Chang,et al. The impact of customer interface quality, satisfaction and switching costs on e-loyalty: Internet experience as a moderator , 2008, Comput. Hum. Behav..
[7] Debmallya Chatterjee,et al. Determinants of Mobile Wallet Intentions to Use: The Mental Cost Perspective , 2018, Int. J. Hum. Comput. Interact..
[8] Kampan Mukherjee,et al. The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country , 2018, International Journal of Bank Marketing.
[9] Saonee Sarker,et al. Understanding factors affecting users' social networking site continuance: A gender difference perspective , 2017, Inf. Manag..
[10] Younghoon Chang,et al. Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model , 2016, Ind. Manag. Data Syst..
[11] Joonghwa Lee,et al. Why People Pass Along Online Video Advertising: From the Perspectives of the Interpersonal Communication Motives Scale and the Theory of Reasoned Action , 2013 .
[12] Jaehee Cho,et al. The impact of post-adoption beliefs on the continued use of health apps , 2016, Int. J. Medical Informatics.
[13] Yogesh Kumar Dwivedi,et al. What determines success of an e-government service? Validation of an integrative model of e-filing continuance usage , 2018, Gov. Inf. Q..
[14] Tiago Oliveira,et al. Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model , 2018, Information Systems Frontiers.
[15] Anol Bhattacherjee,et al. Information Technology Continuance: A Theoretic Extension and Empirical Test , 2008, J. Comput. Inf. Syst..
[16] Michael Humbani,et al. An integrated framework for the adoption and continuance intention to use mobile payment apps , 2019, International Journal of Bank Marketing.
[17] Gordon B. Davis,et al. User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..
[18] Tao Zhou,et al. An empirical examination of continuance intention of mobile payment services , 2013, Decis. Support Syst..
[19] Ali Nabavi,et al. Information Technology Continuance Intention: A Systematic Literature Review , 2016, Int. J. E Bus. Res..
[20] A. Bandura. The Explanatory and Predictive Scope of Self-Efficacy Theory , 1986 .
[21] Carlos Flavián,et al. The role of security, privacy, usability and reputation in the development of online banking , 2007, Online Inf. Rev..
[22] Zheng Lu,et al. Examining the effects of social influence in pre-adoption phase and initial post-adoption phase in the healthcare context , 2020, Inf. Manag..
[23] S. Mangla,et al. Mobile wallet inhibitors: Developing a comprehensive theory using an integrated model , 2018, Journal of Retailing and Consumer Services.
[24] Anol Bhattacherjee,et al. A unified model of IT continuance: three complementary perspectives and crossover effects , 2015, Eur. J. Inf. Syst..
[25] Yogesh Kumar Dwivedi,et al. Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust , 2015, Journal of Financial Services Marketing.
[26] Jing Liu,et al. An investigation of users’ continuance intention towards mobile banking in China , 2016 .
[27] Dan J. Kim,et al. An Empirical Study of the Impacts of Perceived Security and Knowledge on Continuous Intention to Use Mobile Fintech Payment Services , 2018, Int. J. Hum. Comput. Interact..
[28] Myeong-Cheol Park,et al. Understanding antecedents to perceived information risks , 2016 .
[29] Marjan Mernik,et al. A Systematic Mapping Study driven by the margin of error , 2018, J. Syst. Softw..
[30] Juan Sánchez-Fernández,et al. Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied , 2019, Technological Forecasting and Social Change.
[31] Yogesh Kumar Dwivedi,et al. Exploring consumer adoption of proximity mobile payments , 2014, Journal of Strategic Marketing.
[32] Jon-Chao Hong,et al. Internet cognitive failure relevant to users' satisfaction with content and interface design to reflect continuance intention to use a government e-learning system , 2017, Comput. Hum. Behav..
[33] Fred D. Davis,et al. A Model of the Antecedents of Perceived Ease of Use: Development and Test† , 1996 .
[34] R. Bagozzi,et al. On the evaluation of structural equation models , 1988 .
[35] Neena Sinha,et al. Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence , 2020, Int. J. Inf. Manag..
[36] Yogesh Kumar Dwivedi,et al. Digital Payments Adoption: An Analysis of Literature , 2017, I3E.
[37] Marko Sarstedt,et al. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .
[38] A. Mishra. Attribute-based design perceptions and consumer-brand relationship: Role of user expertise , 2016 .
[39] Sunghyup Sean Hyun,et al. Understanding the determinants of mobile banking continuance usage intention , 2019, J. Enterp. Inf. Manag..
[40] Yee Man Margaret Ng. Re-examining the innovation post-adoption process: The case of Twitter discontinuance , 2020, Comput. Hum. Behav..
[41] Yogesh Kumar Dwivedi,et al. Consumer adoption of mobile banking services: An empirical examination of factors according to adoption stages , 2018, Journal of Retailing and Consumer Services.
[42] R. Oliver,et al. The Dimensionality of Consumption Emotion Patterns and Consumer Satisfaction , 1991 .
[43] Margaret Meiling Luo,et al. Cognitive appraisal of incident handling, affects, and post-adoption behaviors: A test of affective events theory , 2018, Int. J. Inf. Manag..
[44] Barbara H Wixom,et al. A Theoretical Integration of User Satisfaction and Technology Acceptance , 2005, Inf. Syst. Res..
[45] Yujong Hwang,et al. An empirical study on trust in mobile banking: A developing country perspective , 2016, Comput. Hum. Behav..
[46] Hsiu-Lan Ma,et al. Use of a Modified UTAUT Model to Investigate the Perspectives of Internet Access Device Users , 2017, Int. J. Hum. Comput. Interact..
[47] Chun-Der Chen,et al. Cultivating travellers' revisit intention to e-tourism service: the moderating effect of website interactivity , 2015, Behav. Inf. Technol..
[48] Igor A. Ambalov,et al. A meta-analysis of IT continuance: An evaluation of the expectation-confirmation model , 2018, Telematics Informatics.
[49] Timon C. Du,et al. A study of the service quality of general portals , 2009, Inf. Manag..
[50] Tao Zhou,et al. Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow , 2011 .
[51] Byoungsoo Kim,et al. An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation-confirmation model , 2010, Expert Syst. Appl..
[52] Min Zhang,et al. Central or peripheral? Cognition elaboration cues' effect on users' continuance intention of mobile health applications in the developing markets , 2018, Int. J. Medical Informatics.
[53] Hyo-Jeong So,et al. Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs , 2018, Comput. Educ..
[54] Paul Jen-Hwa Hu,et al. Extending the two‐stage information systems continuance model: incorporating UTAUT predictors and the role of context , 2011, Inf. Syst. J..
[55] Anol Bhattacherjee,et al. An empirical analysis of the antecedents of electronic commerce service continuance , 2001, Decis. Support Syst..
[56] Sheng Wu,et al. Integrating perceived playfulness into expectation-confirmation model for web portal context , 2005, Inf. Manag..
[57] Moez Limayem,et al. Understanding information systems continuance: The case of Internet-based learning technologies , 2008, Inf. Manag..
[58] Po-Yin Yen,et al. Exploring nurses' confirmed expectations regarding health IT: A phenomenological study , 2014, Int. J. Medical Informatics.
[59] David C. Yen,et al. Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model , 2007, Comput. Hum. Behav..
[60] Viswanath Venkatesh,et al. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..
[61] M. Lindell,et al. Accounting for common method variance in cross-sectional research designs. , 2001, The Journal of applied psychology.
[62] Rex B. Kline,et al. Principles and Practice of Structural Equation Modeling , 1998 .
[63] Arvid O. I. Hoffmann,et al. The impact of fraud prevention on bank-customer relationships: an empirical investigation in retail banking , 2012 .
[64] Dong-Hee Shin,et al. Towards an understanding of the consumer acceptance of mobile wallet , 2009, Comput. Hum. Behav..
[65] Jalayer Khalilzadeh,et al. Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry , 2017, Comput. Hum. Behav..
[66] Manoj A. Thomas,et al. Mobile Payment , 2013, Springer Fachmedien Wiesbaden.
[67] A. Chin,et al. Mobile Payment Adoption: An Empirical Review and Opportunities for Future Research , 2019 .
[68] Shenglin Ben,et al. Factors affecting consumers’ mobile payment behavior: a meta-analysis , 2019, Electronic Commerce Research.
[69] Brijesh Sivathanu. Adoption of digital payment systems in the era of demonetization in India , 2018, Journal of Science and Technology Policy Management.
[70] Chao-Min Chiu,et al. Internet self-efficacy and electronic service acceptance , 2004, Decis. Support Syst..
[71] Yogesh Kumar Dwivedi,et al. Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service , 2014, Information Systems Frontiers.
[72] Jahyun Goo,et al. Determinants of writing positive and negative electronic word-of-mouth: Empirical evidence for two types of expectation confirmation , 2020, Decis. Support Syst..
[73] Slade Emma. Mobile payment adoption: Classification and review of the extant literature , 2013 .
[74] D. Belanche,et al. Website usability, consumer satisfaction and the intention to use a website: The moderating effect of perceived risk , 2012 .
[75] Niklas Arvidsson,et al. Stakeholders' expectations of mobile payment in retail: lessons from Sweden , 2016 .
[76] Michael Obal,et al. What drives post-adoption usage? Investigating the negative and positive antecedents of disruptive technology continuous adoption intentions , 2017 .
[77] Hsin Hsin Chang,et al. Consumer perception of interface quality, security, and loyalty in electronic commerce , 2009, Inf. Manag..
[78] H. Raghav Rao,et al. A review of contextual factors affecting mobile payment adoption and use , 2019, Journal of Banking and Financial Technology.
[79] Wynne W. Chin. The partial least squares approach for structural equation modeling. , 1998 .
[80] Fred D. Davis,et al. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .
[81] Yogesh Kumar Dwivedi,et al. Devising a research model to examine adoption of mobile payments: An extension of UTAUT2 , 2014 .
[82] Viswanath Venkatesh,et al. Model of Adoption and Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle , 2005, MIS Q..
[83] Dong-Hee Shin,et al. How do credibility and utility play in the user experience of health informatics services? , 2017, Comput. Hum. Behav..
[84] Anil K. Gupta,et al. Consumer adoption of m-banking: a behavioral reasoning theory perspective , 2017 .
[85] Tao Zhou,et al. Integrating TTF and UTAUT to explain mobile banking user adoption , 2010, Comput. Hum. Behav..
[86] Shaorui Li,et al. Understanding Continuance Intention of Mobile Payment Services: An Empirical Study , 2017, J. Comput. Inf. Syst..
[87] R. Oliver. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .
[88] Anol Bhattacherjee,et al. Understanding Changes in Belief and Attitude Toward Information Technology Usage: A Theoretical Model and Longitudinal Test , 2004, MIS Q..
[89] 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..
[90] Tomi Dahlberg,et al. Past, present and future of mobile payments research: A literature review , 2008, Electron. Commer. Res. Appl..
[91] Detmar W. Straub,et al. The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption , 2000, J. Assoc. Inf. Syst..
[92] Yogesh Kumar Dwivedi,et al. Is UTAUT really used or just cited for the sake of it? a systematic review of citations of UTAUT's originating article , 2011, ECIS.
[93] Yogesh Kumar Dwivedi,et al. The Role of Trust and Risk in Mobile Payments Adoption: A Meta-Analytic Review , 2018, PACIS.
[94] Anol Bhattacherjee,et al. Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..
[95] C. Fornell,et al. Evaluating structural equation models with unobservable variables and measurement error. , 1981 .
[96] Bassam Hasan,et al. Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance , 2006, Inf. Manag..
[97] Deborah Compeau,et al. Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..
[98] Christian Fernando Libaque Saenz,et al. An expectation-confirmation model of continuance intention to use mobile instant messaging , 2016, Telematics Informatics.
[99] Kai-Yu Tang,et al. Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives , 2016, Telematics Informatics.
[100] Michel Tenenhaus,et al. PLS path modeling , 2005, Comput. Stat. Data Anal..
[101] Weiwei Wu,et al. Understanding mobile shopping consumers' continuance intention , 2017, Ind. Manag. Data Syst..
[102] Christian Fernando Libaque Saenz,et al. Use and gratifications of mobile SNSs: Facebook and KakaoTalk in Korea , 2015, Telematics Informatics.
[103] Chechen Liao,et al. Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT) , 2009, Int. J. Inf. Manag..
[104] Tomi Dahlberg,et al. A critical review of mobile payment research , 2015, Electron. Commer. Res. Appl..
[105] Sebastian Molinillo,et al. A multi-analytical approach to peer-to-peer mobile payment acceptance prediction , 2019, Journal of Retailing and Consumer Services.
[106] Nidhi Singh,et al. Consumer preference and satisfaction of M-wallets: a study on North Indian consumers , 2017 .
[107] Tiago Oliveira,et al. Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application , 2014, Int. J. Inf. Manag..
[108] Deepak Chawla,et al. Consumer attitude and intention to adopt mobile wallet in India – An empirical study , 2019, International Journal of Bank Marketing.
[109] Jesús M. Alvarado,et al. Developing Multidimensional Likert Scales Using Item Factor Analysis , 2016 .
[110] Seymour Sudman,et al. Effects of Time and Memory Factors on Response in Surveys , 1973 .
[111] Niina Mallat,et al. Exploring consumer adoption of mobile payments - A qualitative study , 2007, J. Strateg. Inf. Syst..
[112] Yongqiang Sun,et al. Wearable health information systems intermittent discontinuance: A revised expectation-disconfirmation model , 2018, Ind. Manag. Data Syst..
[113] Young Ju Joo,et al. Students' expectation, satisfaction, and continuance intention to use digital textbooks , 2017, Comput. Hum. Behav..
[114] Ali Abdallah Alalwan,et al. Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse , 2020, Int. J. Inf. Manag..
[115] Ming-Chi Lee,et al. Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model , 2010, Comput. Educ..
[116] Hangjung Zo,et al. Understanding users’ continuance intention toward smartphone augmented reality applications , 2016 .
[117] Chun-Hua Hsiao,et al. The effects of post-adoption beliefs on the expectation–confirmation model in an electronics retail setting , 2018 .
[118] Biplab Datta,et al. Factors Affecting Mobile Payment Adoption Intention: An Indian Perspective , 2018 .
[119] Garry Wei-Han Tan,et al. Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card , 2016, Expert Syst. Appl..
[120] Ankit Kesharwani,et al. Moderating Effect of Smartphone Addiction on Mobile Wallet Payment Adoption , 2019, Journal of Internet Commerce.
[121] Yong-Ming Huang,et al. Examining students' continued use of desktop services: Perspectives from expectation-confirmation and social influence , 2019, Comput. Hum. Behav..
[122] Himanshu Sharma,et al. A Hybrid SEM-Neural Network Model for Predicting Determinants of Mobile Payment Services , 2019, Inf. Syst. Manag..
[123] Chao-Min Chiu,et al. Understanding e-learning continuance intention: An extension of the Technology Acceptance Model , 2006, Int. J. Hum. Comput. Stud..
[124] Ann Majchrzak,et al. Radical Innovation Without Collocation: A Case Study at Boeing-Rocketdyne , 2001, MIS Q..
[125] Øystein Sørebø,et al. The role of self-determination theory in explaining teachers' motivation to continue to use e-learning technology , 2009, Comput. Educ..
[126] Rakhi Thakur,et al. Customer Adoption of Mobile Payment Services by Professionals across two Cities in India: An Empirical Study Using Modified Technology Acceptance Model , 2013 .
[127] Sanjit Kumar Roy,et al. Consumers’ post-adoption behaviour towards Internet banking: empirical evidence from Australia , 2017, Behav. Inf. Technol..
[128] J. Hair. Multivariate data analysis , 1972 .
[129] Heeseok Lee,et al. Exploring continued online service usage behavior: The roles of self-image congruity and regret , 2009, Comput. Hum. Behav..
[130] Heikki Karjaluoto,et al. How perceived value drives the use of mobile financial services apps , 2019, Int. J. Inf. Manag..
[131] Mas Bambang Baroto,et al. Modelling continuance intention of citizens in government Facebook page: A complementary PLS approach , 2017, Comput. Hum. Behav..
[132] Lingling Xu,et al. Understanding the continuance use of social network sites: a computer self-efficacy perspective , 2015, Behav. Inf. Technol..
[133] Terry Ryan,et al. The Role of Social Presence and Moderating Role of Computer Self-Efficacy in Predicting the Continuance Usage of E-Learning Systems , 2004, J. Inf. Syst. Educ..
[134] S. Thomas,et al. Continuance Intention to Use Facebook: A Study of Perceived Enjoyment and TAM , 2014 .
[135] Yogesh Kumar Dwivedi,et al. Digital Payments Adoption Research: A Meta-Analysis for Generalising the Effects of Attitude, Cost, Innovativeness, Mobility and Price Value on Behavioural Intention , 2018, TDIT.
[136] Jon-Chao Hong,et al. The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch , 2017, Comput. Hum. Behav..
[137] Zhiying Liu,et al. Understanding mobile payment users' continuance intention: a trust transfer perspective , 2018, Internet Res..