User acceptance of smart wearable devices: An expectation-confirmation model approach

Abstract Smart wearable devices have become one of the most popular interactive items in the “smart” era that prioritizes “mobility.” There is significant scholarly interest in not only promoting the successful diffusion of this technology but also providing improved user experience to users of these devices. In line with this trend, this study explores users’ perceptions of smart wearable devices and introduces a comprehensive research model that employs factors that are primarily extracted using the expectation-confirmation, technology acceptance, and flow models. The results derived using both confirmatory factor analysis and structural equation modeling methods (N = 1,380) indicate that users’ intentions to use smart wearable devices are determined by five influential factors: satisfaction, enjoyment, usefulness, flow state, and cost. Both users’ confirmation and service and system quality play notable determinative roles in the research model. Implications and suggestions are presented considering the results.

[1]  Liang-Hong Wu,et al.  Exploring consumers' intention to accept smartwatch , 1970, Comput. Hum. Behav..

[2]  Savvas Papagiannidis,et al.  User experience on mobile video appreciation: How to engross users and to enhance their enjoyment in watching mobile video clips , 2012 .

[3]  Payam Hanafizadeh,et al.  Mobile-banking adoption by Iranian bank clients , 2014, Telematics Informatics.

[4]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[5]  Christian Fernando Libaque Saenz,et al.  An expectation-confirmation model of continuance intention to use mobile instant messaging , 2016, Telematics Informatics.

[6]  Angel P. del Pobil,et al.  Modeling the user acceptance of long-term evolution (LTE) services , 2013, Ann. des Télécommunications.

[7]  GuoXitong,et al.  Investigating m-Health Acceptance from a Protection Motivation Theory Perspective: Gender and Age Differences , 2015 .

[8]  K. Yuan Fit Indices Versus Test Statistics , 2005, Multivariate behavioral research.

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

[10]  Hwansoo Lee,et al.  Exploring user acceptance of streaming media devices: an extended perspective of flow theory , 2018, Inf. Syst. E Bus. Manag..

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

[12]  Jon-Chao Hong,et al.  The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch , 2017, Comput. Hum. Behav..

[13]  Hsin Hsin Chang,et al.  Modifying UTAUT and innovation diffusion theory to reveal online shopping behavior , 2016 .

[14]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Dependent Variable , 1992, Inf. Syst. Res..

[15]  Jaehee Cho,et al.  The impact of post-adoption beliefs on the continued use of health apps , 2016, Int. J. Medical Informatics.

[16]  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..

[17]  Shih-Chih Chen,et al.  Integrating Technology Readiness into the Expectation-Confirmation Model: An Empirical Study of Mobile Services , 2013, Cyberpsychology Behav. Soc. Netw..

[18]  Wei Wang,et al.  Explaining Instant Messaging Continuance Intention: The Role of Personality , 2012, Int. J. Hum. Comput. Interact..

[19]  Pei-Luen Patrick Rau,et al.  The acceptance of personal health devices among patients with chronic conditions , 2015, Int. J. Medical Informatics.

[20]  Eunil Park,et al.  Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model , 2014, Telematics Informatics.

[21]  Stephen G West,et al.  Evaluating model fit for growth curve models: Integration of fit indices from SEM and MLM frameworks. , 2009, Psychological methods.

[22]  Hans van der Heijden,et al.  User Acceptance of Hedonic Information Systems , 2004, MIS Q..

[23]  J. Hoelter The Analysis of Covariance Structures , 1983 .

[24]  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..

[25]  Eunil Park,et al.  The adoption of tele-presence systems: Factors affecting intention to use tele-presence systems , 2013, Kybernetes.

[26]  Francisco J. García-Peñalvo,et al.  Learning with mobile technologies - Students' behavior , 2017, Comput. Hum. Behav..

[27]  Rosa Maria Dangelico,et al.  Smart wearable technologies: Current status and market orientation through a patent analysis , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[28]  Jack Shih-Chieh Hsu,et al.  Understanding the role of satisfaction in the formation of perceived switching value , 2014, Decis. Support Syst..

[29]  Sang Jib Kwon,et al.  An integrated adoption model of solar energy technologies in South Korea , 2014 .

[30]  P. Barrett Structural equation modelling : Adjudging model fit , 2007 .

[31]  Chia-Chen Chen,et al.  What drives purchase intention on Airbnb? Perspectives of consumer reviews, information quality, and media richness , 2018, Telematics Informatics.

[32]  Ming-Chien Hung,et al.  An Examination of the Determinants of Mobile Shopping Continuance , 2012, Int. J. Electron. Bus. Manag..

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

[34]  Frank Biocca,et al.  The Effect of the Agency and Anthropomorphism on Users' Sense of Telepresence, Copresence, and Social Presence in Virtual Environments , 2003, Presence: Teleoperators & Virtual Environments.

[35]  Keng Siau,et al.  Factors Influencing the Adoption of Smart Wearable Devices , 2018, Int. J. Hum. Comput. Interact..

[36]  Jiming Hu,et al.  Understanding Chinese Undergraduates’ Continuance Intention to Use Mobile Book-Reading Apps: An Integrated Model and Empirical Study , 2016 .

[37]  E. Y. Kim,et al.  Modeling consumer adoption of mobile shopping for fashion products in Korea , 2009 .

[38]  Sari Kujala,et al.  The role of expectations in service evaluation: A longitudinal study of a proximity mobile payment service , 2017, Int. J. Hum. Comput. Stud..

[39]  S. Pandey,et al.  Effect of Sample Size on Goodness-Fit of-Fit Indices in Structural Equation Models , 1995 .

[40]  Xin Tan,et al.  User acceptance of SaaS-based collaboration tools: a case of Google Docs , 2015, J. Enterp. Inf. Manag..

[41]  Marko Sarstedt,et al.  Goodness-of-fit indices for partial least squares path modeling , 2013, Comput. Stat..

[42]  Haneen Ali,et al.  Evaluating a smartwatch notification system in a simulated nursing home. , 2019, International journal of older people nursing.

[43]  Eunil Park,et al.  An Integrated Adoption Model of Mobile Cloud Services: Exploration of Key Determinants and Extension of Technology Acceptance Model , 2014, Telematics Informatics.

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

[45]  Te-Wei Ho,et al.  Using a smartwatch with real-time feedback improves the delivery of high-quality cardiopulmonary resuscitation by healthcare professionals. , 2019, Resuscitation.

[46]  Ki Joon Kim,et al.  What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter , 2014 .

[47]  Edward Shih-Tse Wang,et al.  Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention , 2017, Behav. Inf. Technol..

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

[49]  Zied Mani,et al.  Drivers of consumers’ resistance to smart products , 2017 .

[50]  Anol Bhattacherjee,et al.  Understanding Changes in Belief and Attitude Toward Information Technology Usage: A Theoretical Model and Longitudinal Test , 2004, MIS Q..

[51]  Samar Mouakket,et al.  Factors influencing continuance intention to use social network sites: The Facebook case , 2015, Comput. Hum. Behav..

[52]  Neena Sinha,et al.  Taxonomy of Wearable Devices: A Systematic Review of Literature , 2019, Int. J. Technol. Diffusion.

[53]  Dong Wen,et al.  Consumers' perceived attitudes to wearable devices in health monitoring in China: A survey study , 2017, Comput. Methods Programs Biomed..

[54]  Jinyoung Han,et al.  Comprehensive Approaches to User Acceptance of Internet of Things in a Smart Home Environment , 2017, IEEE Internet of Things Journal.

[55]  Uichin Lee,et al.  Smartwatch Wearing Behavior Analysis , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[56]  Tzung-Ru Tsai,et al.  What Drives People to Continue to Play Online Games? An Extension of Technology Model and Theory of Planned Behavior , 2010, Int. J. Hum. Comput. Interact..

[57]  Fevzi Okumus,et al.  Online experiences: flow theory, measuring online customer experience in e-commerce and managerial implications for the lodging industry , 2013, Information Technology & Tourism.

[58]  Hangjung Zo,et al.  Understanding users’ continuance intention toward smartphone augmented reality applications , 2016 .

[59]  Chin-Lung Hsu,et al.  What drives purchase intention for paid mobile apps? - An expectation confirmation model with perceived value , 2015, Electron. Commer. Res. Appl..

[60]  Eunil Park,et al.  Understanding the emergence of wearable devices as next-generation tools for health communication , 2016, Inf. Technol. People.

[61]  Alexander Serenko,et al.  User acceptance of hedonic digital artifacts: A theory of consumption values perspective , 2010, Inf. Manag..

[62]  Chia-Chen Chen,et al.  What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value , 2018, Telematics Informatics.

[63]  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..

[64]  Patrick Y. K. Chau,et al.  Roles of perceived value and individual differences in the acceptance of mobile coupon applications , 2015, Internet Res..

[65]  Thamaraiselvan Natarajan,et al.  Understanding the intention to use mobile shopping applications and its influence on price sensitivity , 2017 .

[66]  Harishchandra Dubey,et al.  EchoWear: smartwatch technology for voice and speech treatments of patients with Parkinson's disease , 2015, Wireless Health.

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

[68]  Eunil Park,et al.  Factors influencing the public intention to use renewable energy technologies in South Korea: Effects of the Fukushima nuclear accident , 2014 .

[69]  Chao-Min Chiu,et al.  Understanding e-learning continuance intention: An extension of the Technology Acceptance Model , 2006, Int. J. Hum. Comput. Stud..