Understanding the determinants of mobile banking continuance usage intention

The quality of people life and efficiency of banks can be improved by mobile banking (m-banking). The long-term success of m-banking depends on its constant use. The purpose of this paper is to investigate the determinants of m-banking continuance intention to use, using the technology continuance theory (TCT) by including the self-efficacy and channel preference.,Empirical data from 369 Malaysian users who had prior experience with mobile banking were analysed, using partial least squares technique.,The results confirmed that the TCT model had a high exploratory power in explaining users’ perceived usefulness (PU), satisfaction, attitude and intentions to continue to use m-banking. Furthermore, self-efficacy and channel importance were important drivers of continuance intention in the context of m-banking. According to the results, perceived ease of use has no effect on PU and attitude in the post-adoption stage.,The findings help bank managers to understand the importance of meeting customers’ needs and expectations as a prerequisite in enhancing their satisfaction and favourable attitude towards m-banking and consequently their continuance intention.,Based on the TCT model, this study contributes to the limited body of research on continuance intention to use m-banking. Furthermore, self-efficacy and channel preferences were added to the TCT model and the results confirmed the importance of enriching the TCT model to explain continuance intention to use information systems by adding contextual factors.

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

[2]  J. Richard,et al.  Rethinking Catalogue and Online B2B Buyer Channel Preferences in the Education Supplies Market , 2017 .

[3]  Manisha Sharma,et al.  Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation , 2019, Int. J. Inf. Manag..

[4]  Heikki Karjaluoto,et al.  Mobile banking adoption: A literature review , 2015, Telematics Informatics.

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

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

[7]  Gökhan Daghan,et al.  Modeling the continuance usage intention of online learning environments , 2016, Comput. Hum. Behav..

[8]  Chia-Lin Hsu,et al.  Exploring the continuance intention of social networking websites: an empirical research , 2014, Inf. Syst. E Bus. Manag..

[9]  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..

[10]  Charles Jebarajakirthy,et al.  The influence of e-banking service quality on customer loyalty , 2019, International Journal of Bank Marketing.

[11]  Hangjung Zo,et al.  Understanding the MOOCs continuance: The role of openness and reputation , 2015, Comput. Educ..

[12]  Yung-Ming Cheng,et al.  A hybrid model for exploring the antecedents of cloud ERP continuance , 2019, Int. J. Web Inf. Syst..

[13]  Fred D. Davis,et al.  A Model of the Antecedents of Perceived Ease of Use: Development and Test† , 1996 .

[14]  S. Zailani,et al.  EMR continuance usage intention of healthcare professionals , 2017, Informatics for health & social care.

[15]  R. Oliver.  A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

[16]  Beverley Sparks,et al.  Travel app users’ continued use intentions: it’s a matter of value and trust , 2018, Journal of Travel & Tourism Marketing.

[17]  E. Zavadskas,et al.  Sustainable Business Models: A Review , 2019, SSRN Electronic Journal.

[18]  Victor A. Barger,et al.  Mobile banking and AI-enabled mobile banking , 2018, Journal of Research in Interactive Marketing.

[19]  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..

[20]  Igor A. Ambalov,et al.  A meta-analysis of IT continuance: An evaluation of the expectation-confirmation model , 2018, Telematics Informatics.

[21]  Asli Yagmur Akbulut,et al.  Understanding the Determinants of Service Channel Preference in the Early Stages of Adoption: A Social Cognitive Perspective on Online Brokerage Services , 2008, Decis. Sci..

[22]  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..

[23]  Norman Shaw,et al.  The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value , 2019, Int. J. Inf. Manag..

[24]  Jen-Her Wu,et al.  What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model , 2005, Inf. Manag..

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

[26]  Guy Merlin Ngounou,et al.  Optimization of Noise in Non-integrated Instrumentation Amplifier for the Amplification of Very Low Electrophisiological Signals. Case of Electro Cardio Graphic Signals (ECG). , 2014, Journal of Medical Systems.

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

[28]  Wen-Yin Chen,et al.  The impact of inertia and user satisfaction on the continuance intentions to use mobile communication applications: A mobile service quality perspective , 2019, Int. J. Inf. Manag..

[29]  N. Subramanian,et al.  Product delivery service provider selection and customer satisfaction in the era of internet of things: A Chinese e-retailers’ perspective , 2015 .

[30]  Jian Mou,et al.  Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation , 2017, Behav. Inf. Technol..

[31]  Jinsheng Roan,et al.  Barriers to Physicians’ Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals , 2012, Journal of Medical Systems.

[32]  E. Sivadas,et al.  The Effect of Dissolution Intention on Buyer–Seller Relationships , 2012 .

[33]  S. Zailani,et al.  Do lean manufacturing practices have negative impact on job satisfaction? , 2019, International Journal of Lean Six Sigma.

[34]  Chin-Lung Hsu,et al.  Examining Social Networking O2O Apps User Loyalty , 2018, J. Comput. Inf. Syst..

[35]  K. Hedhli,et al.  Toward a contagion-based model of mobile banking adoption , 2019, International Journal of Bank Marketing.

[36]  Yujong Hwang,et al.  Mobile banking use: A comparative study with Brazilian and U.S. participants , 2019, Int. J. Inf. Manag..

[37]  Azadeh Rezvani,et al.  Motivating users toward continued usage of information systems: Self-determination theory perspective , 2017, Comput. Hum. Behav..

[38]  Min-Jae Lee,et al.  Are You Still with Us? A Study of the Post-Adoption Determinants of Sustained Use of Mobile-Banking Services , 2012, J. Organ. Comput. Electron. Commer..

[39]  Noor Fareen Abdul Rahim,et al.  The role of the safety climate in the successful implementation of safety management systems , 2019, Safety Science.

[40]  Yen-Ting Lin,et al.  A ubiquitous English vocabulary learning system: Evidence of active/passive attitudes vs. usefulness/ease-of-use , 2012, Comput. Educ..

[41]  Tao Zhou,et al.  Examining mobile banking user adoption from the perspectives of trust and flow experience , 2012, Inf. Technol. Manag..

[42]  L. T. Le,et al.  E-satisfaction and continuance intention: The moderator role of online ratings , 2019, International Journal of Hospitality Management.

[43]  David M. Szymanski,et al.  Customer satisfaction: A meta-analysis of the empirical evidence , 2001 .

[44]  Yulia Vakulenko,et al.  Customer value in self-service kiosks: a systematic literature review , 2018 .

[45]  Felix T.S. Chan,et al.  Determinants of loyalty to public transit: A model integrating Satisfaction-Loyalty Theory and Expectation-Confirmation Theory , 2018, Transportation Research Part A: Policy and Practice.

[46]  Yujong Hwang,et al.  An empirical study on trust in mobile banking: A developing country perspective , 2016, Comput. Hum. Behav..

[47]  Julían Andrés,et al.  La gestión del conocimiento y su influencia en las capacidades dinámicas: Contrastación empírica en Empresas Colombianas Intensivas en uso de conocimiento , 2020 .

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

[49]  Mathupayas Thongmak,et al.  M-Banking in Metropolitan Bangkok and a Comparison with other Countries , 2011, J. Comput. Inf. Syst..

[50]  Gaby Odekerken-Schröder,et al.  Using PLS path modeling for assessing hierarchial construct models: guidelines and impirical illustration , 2009 .

[51]  B. Foroughi,et al.  Effects of perceived justice for coaches on athletes' satisfaction, commitment, effort, and team unity. , 2014 .

[52]  Rakhi Thakur,et al.  The role of self-efficacy and customer satisfaction in driving loyalty to the mobile shopping application , 2018 .

[53]  Yogesh Kumar Dwivedi,et al.  Examining the impact of gamification on intention of engagement and brand attitude in the marketing context , 2017, Comput. Hum. Behav..

[54]  T. C. Edwin Cheng,et al.  Adoption of internet banking: An empirical study in Hong Kong , 2006, Decis. Support Syst..

[55]  Rupak Rauniar,et al.  Technology acceptance model (TAM) and social media usage: an empirical study on Facebook , 2014, J. Enterp. Inf. Manag..

[56]  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..

[57]  Rathindra Sarathy,et al.  An experimental investigation of the influence of website emotional design features on trust in unfamiliar online vendors , 2017, Comput. Hum. Behav..

[58]  Suhaiza Hanim Binti Dato Mohamad Zailani,et al.  Mobile taxi booking application service’s continuance usage intention by users , 2017 .

[59]  Jonathan E. Jackson,et al.  Examining customer channel selection intention in the omni-channel retail environment , 2019, International Journal of Production Economics.

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

[61]  Heesup Han,et al.  An investigation of the formation of rapport between players and dealers in the casino industry , 2016 .

[62]  Juho Hamari,et al.  International Journal of Information Management Why Do People Use Gamification Services? , 2022 .

[63]  I. Ajzen.  Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. , 2002 .

[64]  Huei-Ting Tsai,et al.  The influences of system usability and user satisfaction on continued Internet banking services usage intention: empirical evidence from Taiwan , 2014, Electronic Commerce Research.

[65]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[66]  Markus Blut,et al.  Acceptance of Smartphone-Based Mobile Shopping: Mobile Benefits, Customer Characteristics, Perceived Risks, and the Impact of Application Context , 2017 .

[67]  Weiwei Wu,et al.  Understanding mobile shopping consumers' continuance intention , 2017, Ind. Manag. Data Syst..

[68]  Chechen Liao,et al.  Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT) , 2009, Int. J. Inf. Manag..

[69]  Nripendra P. Rana,et al.  Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model , 2019, Int. J. Inf. Manag..

[70]  Wantao Yu,et al.  The effects of supply chain integration on customer satisfaction and financial performance: An organizational learning perspective , 2013 .

[71]  Manon Arcand,et al.  Mobile banking service quality and customer relationships , 2017 .

[72]  Z. Ouyang,et al.  Understanding bike sharing use over time by employing extended technology continuance theory , 2019, Transportation Research Part A: Policy and Practice.

[73]  Xiaohui Chen,et al.  Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model , 2017, Comput. Hum. Behav..

[74]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

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

[76]  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.

[77]  H. Mohammadi.  A study of mobile banking usage in Iran , 2015 .

[78]  Thamaraiselvan Natarajan,et al.  The moderating role of device type and age of users on the intention to use mobile shopping applications , 2018 .

[79]  S. Zailani,et al.  RFID Continuance Usage Intention in Health Care Industry , 2017, Quality management in health care.

[80]  T. Oliveira,et al.  Wearable technology: What explains continuance intention in smartwatches? , 2018, Journal of Retailing and Consumer Services.

[81]  Jing Liu,et al.  An investigation of users’ continuance intention towards mobile banking in China , 2016 .