Predicting the intention and adoption of wearable payment devices using hybrid SEM-neural network analysis

[1]  A. Mamun,et al.  Modelling the intention and adoption of cashless payment methods among the young adults in Malaysia , 2022, Journal of Science and Technology Policy Management.

[2]  M. Ali,et al.  Exploring the smart wearable payment device adoption intention: Using the symmetrical and asymmetrical analysis methods , 2022, Frontiers in Psychology.

[3]  M. E. Hoque,et al.  Predicting the intention to adopt wearable payment devices in China: The use of hybrid SEM-Neural network approach , 2022, PloS one.

[4]  Imdadullah Hidayat-ur-Rehman,et al.  Examining Consumers’ Adoption of Smart Wearable Payments , 2022, SAGE Open.

[5]  Aleksandr Ometov,et al.  Wearables for Industrial Work Safety: A Survey , 2021, Sensors.

[6]  Eugene Abrokwah,et al.  Understanding Factors That Influence Consumer Intention to Use Mobile Money Services: An Application of UTAUT2 With Perceived Risk and Trust , 2021, SAGE Open.

[7]  Nidhi Singh,et al.  Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach , 2021, Information Technology and Management.

[8]  Abdullah Al Mamun,et al.  Cashless Transactions: A Study on Intention and Adoption of e-Wallets , 2021, Sustainability.

[9]  Garry Wei-Han Tan,et al.  Wearable payment: A deep learning-based dual-stage SEM-ANN analysis , 2020, Expert Syst. Appl..

[10]  Wan-Rung Lin,et al.  Factors Affecting the Behavioral Intention to Adopt Mobile Payment: An Empirical Study in Taiwan , 2020, Mathematics.

[11]  Nripendra P. Rana,et al.  Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal , 2020, Int. J. Inf. Manag..

[12]  Dolores M. Frías-Jamilena,et al.  Influence of gamification on perceived self-efficacy: gender and age moderator effect , 2020 .

[13]  Deepak Chawla,et al.  Role of Mediator in Examining the Influence of Antecedents of Mobile Wallet Adoption on Attitude and Intention , 2020, Global Business Review.

[14]  G. Owusu,et al.  Mobile Banking Adoption among the Ghanaian Youth , 2020, Journal of African Business.

[15]  Eunil Park,et al.  User acceptance of smart wearable devices: An expectation-confirmation model approach , 2020, Telematics Informatics.

[16]  Keng-Boon Ooi,et al.  Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach , 2020, Int. J. Inf. Manag..

[17]  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..

[18]  Mehmet Cem Bölen Exploring the determinants of users’ continuance intention in smartwatches , 2020 .

[19]  Reuben F. Burch,et al.  State-of-the-art review of athletic wearable technology: What 113 strength and conditioning coaches and athletic trainers from the USA said about technology in sports , 2020, International Journal of Sports Science & Coaching.

[20]  Alexander Yohan,et al.  BLE-Based Authentication Protocol for Micropayment Using Wearable Device , 2020, Wireless Personal Communications.

[21]  Hendy Mustiko Aji,et al.  COVID-19 and e-wallet usage intention: A multigroup analysis between Indonesia and Malaysia , 2020, Cogent Business & Management.

[22]  N. Arora,et al.  Investigating consumer intention to accept mobile payment systems through unified theory of acceptance model , 2019 .

[23]  M. Leppäniemi,et al.  Examining consumers’ usage intention of contactless payment systems , 2019, International Journal of Bank Marketing.

[24]  Raed A. Said,et al.  Analyzing the Adoption of E-Payment Technologies in UAE Based on Demographic Variables , 2019, 2019 International Conference on Digitization (ICD).

[25]  Neharika Sobti,et al.  Impact of demonetization on diffusion of mobile payment service in India , 2019, Journal of Advances in Management Research.

[26]  Carlos Flavián,et al.  Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers , 2019, Ind. Manag. Data Syst..

[27]  F. Muñoz-Leiva,et al.  The moderating impact of gender on the acceptance of peer-to-peer mobile payment systems , 2019, International Journal of Bank Marketing.

[28]  Haidong Zhao,et al.  Understanding the impact of financial incentives on NFC mobile payment adoption , 2019, International Journal of Bank Marketing.

[29]  Yong-Ming Huang,et al.  Examining students' continued use of desktop services: Perspectives from expectation-confirmation and social influence , 2019, Comput. Hum. Behav..

[30]  Hotna Marina Sitorus,et al.  Examining the Role of Usability, Compatibility and Social Influence in Mobile Banking Adoption in Indonesia , 2019, International Journal of Technology.

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

[32]  Deepak Chawla,et al.  Engaging m-commerce adopters in India , 2019, J. Enterp. Inf. Manag..

[33]  Taejung Kim,et al.  Consumer acceptance of sports wearable technology: the role of technology readiness , 2019, International Journal of Sports Marketing and Sponsorship.

[34]  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..

[35]  Zhiying Liu,et al.  Understanding mobile payment users' continuance intention: a trust transfer perspective , 2018, Internet Res..

[36]  Biplab Datta,et al.  Factors Affecting Mobile Payment Adoption Intention: An Indian Perspective , 2018 .

[37]  Vess Johnson,et al.  Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services , 2018, Comput. Hum. Behav..

[38]  R. Yadav,et al.  Understanding and predicting antecedents of mobile shopping adoption: a developing country perspective , 2018 .

[39]  Sang Cheol Park,et al.  The Effects of Perceived Value, Website Trust and Hotel Trust on Online Hotel Booking Intention , 2017 .

[40]  Zoran Kalinic,et al.  Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach , 2017 .

[41]  Lara Khansa,et al.  Wearable healthcare: Lessons from the past and a peek into the future , 2017, Telematics Informatics.

[42]  Shahriar Mohammadi,et al.  Word of Mouth impact on the adoption of mobile banking in Iran , 2017, Telematics Informatics.

[43]  Beomjin Choi,et al.  Domain-specific innovativeness and new product adoption: A case of wearable devices , 2017, Telematics Informatics.

[44]  Mahbub Hassan,et al.  A Survey of Wearable Devices and Challenges , 2017, IEEE Communications Surveys & Tutorials.

[45]  Edda Tandi Lwoga,et al.  User Acceptance of Mobile Payment: The Effects of User‐Centric Security, System Characteristics and Gender , 2017, Electron. J. Inf. Syst. Dev. Ctries..

[46]  Yogesh Kumar Dwivedi,et al.  Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust , 2017, Int. J. Inf. Manag..

[47]  Anastasios A. Economides,et al.  Mobile-based assessment: Investigating the factors that influence behavioral intention to use , 2017, Comput. Educ..

[48]  Jalayer Khalilzadeh,et al.  Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry , 2017, Comput. Hum. Behav..

[49]  Torsten J. Gerpott,et al.  Who signs up for NFC mobile payment services? Mobile network operator subscribers in Germany , 2017, Electron. Commer. Res. Appl..

[50]  Garry Wei-Han Tan,et al.  Mobile applications in tourism: the future of the tourism industry? , 2017, Ind. Manag. Data Syst..

[51]  Thurasamy Ramayah,et al.  Wearable technologies: The role of usefulness and visibility in smartwatch adoption , 2016, Comput. Hum. Behav..

[52]  Ke-Hai Yuan,et al.  Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation , 2016, Behavior Research Methods.

[53]  Ricardo de Sena Abrahão,et al.  Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT) , 2016 .

[54]  Veera Bhatiasevi An extended UTAUT model to explain the adoption of mobile banking , 2016 .

[55]  H. Ting,et al.  Intention to Use Mobile Payment System: A Case of Developing Market by Ethnicity , 2016 .

[56]  Mehmet Haluk Koksal The intentions of Lebanese consumers to adopt mobile banking , 2016 .

[57]  Zlatko Bezhovski The Future of the Mobile Payment as Electronic Payment System , 2016 .

[58]  Carmine Sellitto,et al.  An investigation of mobile payment (m-payment) services in Thailand , 2016 .

[59]  Agnes L. DeFranco,et al.  It's about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels , 2016 .

[60]  Grey Giddins,et al.  Statistics , 2016, The Journal of hand surgery, European volume.

[61]  Keng-Boon Ooi,et al.  An SEM-artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline , 2015, Expert Syst. Appl..

[62]  Jonathan C. Ho,et al.  The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments , 2015 .

[63]  N. Kock Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach , 2015, Int. J. e Collab..

[64]  Tiago Oliveira,et al.  Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators , 2015, Comput. Hum. Behav..

[65]  Garry Wei-Han Tan,et al.  Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach , 2015, Int. J. Mob. Commun..

[66]  Yogesh Kumar Dwivedi,et al.  Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust , 2015 .

[67]  Shih-Chi Chang,et al.  An Extended TAM to Explore Behavioural Intention of Consumers to Use M-Commerce , 2015, J. Inf. Knowl. Manag..

[68]  A. Palmer,et al.  Enjoyment and social influence: predicting mobile payment adoption , 2015 .

[69]  June Lu,et al.  Are personal innovativeness and social influence critical to continue with mobile commerce? , 2014, Internet Res..

[70]  Andrea Pérez,et al.  Values and Lifestyles in the Adoption of New Technologies Applying VALS Scale , 2014 .

[71]  Juan Sánchez-Fernández,et al.  Antecedents of the adoption of the new mobile payment systems: The moderating effect of age , 2014, Comput. Hum. Behav..

[72]  Garry Wei-Han Tan,et al.  NFC mobile credit card: The next frontier of mobile payment? , 2014, Telematics Informatics.

[73]  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..

[74]  Thomas Lerner,et al.  Mobile Payment , 2013, Springer Fachmedien Wiesbaden.

[75]  Judith C. Forney,et al.  The Moderating Role of Consumer Technology Anxiety in Mobile Shopping Adoption: Differential Effects of Facilitating Conditions and Social Influences , 2013 .

[76]  Garry Wei-Han Tan,et al.  Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach , 2013, Expert Syst. Appl..

[77]  T. Ramayah,et al.  An Empirical Inquiry on Knowledge Sharing Among Academicians in Higher Learning Institutions , 2013 .

[78]  Marko Sarstedt,et al.  Editorial - Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance , 2013 .

[79]  Fujun Lai,et al.  Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research , 2012 .

[80]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[81]  T. Ramayah,et al.  Network collaboration and performance in the tourism sector , 2011 .

[82]  Hsiu-Fen Lin,et al.  An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust , 2011, Int. J. Inf. Manag..

[83]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[84]  Namho Chung,et al.  The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea , 2011 .

[85]  Tao Zhou,et al.  Integrating TTF and UTAUT to explain mobile banking user adoption , 2010, Comput. Hum. Behav..

[86]  A. Chong,et al.  Online banking adoption: an empirical analysis , 2010 .

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

[88]  Pearl Brereton,et al.  Does the technology acceptance model predict actual use? A systematic literature review , 2010, Inf. Softw. Technol..

[89]  Dong-Hee Shin,et al.  Towards an understanding of the consumer acceptance of mobile wallet , 2009, Comput. Hum. Behav..

[90]  A. Kumar,et al.  Age differences in mobile service perceptions: comparison of Generation Y and baby boomers , 2008 .

[91]  Heeseok Lee,et al.  Antecedents of Use-Continuance in Information Systems: Toward an Inegrative View , 2008, J. Comput. Inf. Syst..

[92]  Gill Kirkup,et al.  Gender and cultural differences in Internet use: A study of China and the UK , 2007, Comput. Educ..

[93]  Yan Li,et al.  Senior Citizens' Acceptance of Information Systems: A Study in the Context of e-Government Services , 2006, IEEE Transactions on Engineering Management.

[94]  Heshan Sun,et al.  The role of moderating factors in user technology acceptance , 2006, Int. J. Hum. Comput. Stud..

[95]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

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

[97]  Heikki Karjaluoto,et al.  Internet banking adoption among mature customers: early majority or laggards? , 2003 .

[98]  M. Prensky Do They Really Think Differently , 2001 .

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

[100]  Wynne W. Chin,et al.  Advancing the Theory of Adaptive Structuration: The Development of a Scale to Measure Faithfulness of Appropriation , 1997, Inf. Syst. Res..

[101]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

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

[103]  C. Fornell,et al.  Evaluating Structural Equation Models with Unobservable Variables and Measurement Error , 1981 .

[104]  A. Kahng,et al.  International , 1964, PS: Political Science & Politics.

[105]  Noorshella Binti Che Nawi,et al.  Predictive Accuracy Comparison Between Structural Equation Modelling and Neural Network Approach: A Case of Intention to Adopt Conservative Agriculture Practices , 2021 .

[106]  Ahmad A. Rabaa'i,et al.  Understanding the Determinants of Wearable Payment Adoption: An Empirical Study , 2021, Interdisciplinary Journal of Information, Knowledge, and Management.

[107]  Nizar Souiden,et al.  The interplay of counter-conformity motivation, social influence, and trust in customers' intention to adopt Internet banking services: The case of an emerging country , 2016 .

[108]  Debajyoti Pal,et al.  An Empirical Analysis towards the Adoption of NFC Mobile Payment System by the End User , 2015 .

[109]  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.

[110]  Ying-Feng Kuo,et al.  Towards an understanding of the behavioral intention to use 3G mobile value-added services , 2009, Comput. Hum. Behav..