How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model

Abstract Extant research examines the factors that cause the initial adoption of digital technologies, like mobile wallets, with limited focus on post-adoption behaviours. This work proposes a novel extended expectation–confirmation model which explores the impact of pre-adoption expectancies and confirmation on post-adoption satisfaction and continuance intentions. The model also explores the roles played by the post-adoption factors like perceived user interface quality, perceived security and self-efficacy. The findings indicate that pre-adoption performance/effort expectancies impact consumption-driven confirmation, which in turn affects the post-adoption perceived usefulness, post-adoption perceived security, and user satisfaction. Further, satisfaction, post-adoption self-efficacy and post-adoption perceived usefulness are found to be strong antecedents of the user’s continuance intention. The framework contributes to the extant research by integrating both pre- and post-adoption constructs that determine post-adoption continuance intentions. The framework also guides the M-wallet application developers to enhance user satisfaction and continuance intentions by meeting their pre-adoption expectations through consumption-driven confirmation, in order to stay relevant in an extremely competitive m-payments business.

[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..