Antecedents of Consumers’ Intention to Adopt Wearable Healthcare Devices

Wearable technologies are considered as the possibility of enhancing healthcare productivity and decrease healthcare charge. Regardless of the significance of this technology, inadequate studies have focused on the antecedents of factors influencing consumers’ intention for the adoption of wearable devices. This study aimed to determine the significant factors which have an influence on consumers’ intention for the adoption of wearable healthcare devices. The current study adopts a Technology Acceptance Model (TAM) to explore an individual’s intention for wearable health technology adoption. Data for this study was obtained from 176 Malaysian researchers. The Structural Equation Model (SEM) was performed for testing the proposed research model. The obtained results from SEM indicated that perceived usefulness, perceived ease of use, initial trust and functionality have a statistically significant influence on consumers ‘intention for adoption of wearable healthcare devices. The results of this study will aid the manufacturers and providers to how increase the use of wearable healthcare devices in the healthcare.

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

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

[3]  Jin-Mie Chae Consumer Acceptance Model of Smart Clothing according to Innovation , 2009 .

[4]  Jin-Bo Sim,et al.  Acceptance‐Diffusion Strategies for Tablet‐PCs: Focused on Acceptance Factors of Non‐Users and Satisfaction Factors of Users , 2012 .

[5]  Wawan Dhewanto,et al.  2 . 2 . 1 Perceived Usefulness , Perceived Ease of Use , Intended Use , 2013 .

[6]  Victoria Magrath,et al.  Marketing design elements of mobile fashion retail apps , 2013 .

[7]  Seyed Ahmad Soleymani,et al.  Development of an instrument for assessing the impact of trust on internet banking adoption , 2013 .

[8]  Garry Wei-Han Tan,et al.  Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach , 2014, Comput. Hum. Behav..

[9]  Elena Marchiori,et al.  Hotel Mobile Apps. The Case of 4 and 5 Star Hotels in European German-Speaking Countries , 2014, ENTER.

[10]  Elaheh Yadegaridehkordi,et al.  Cloud Computing Adoption Behaviour: an Application of the Technology Acceptance Model , 2015 .

[11]  H. M. Dahlan,et al.  Theoretical model for Green Information Technology adoption , 2015 .

[12]  Dietmar Jannach,et al.  Recommendation quality, transparency, and website quality for trust-building in recommendation agents , 2016, Electron. Commer. Res. Appl..

[13]  Yiwen Gao,et al.  International Journal of Medical Informatics , 2016 .

[14]  Fuyuan Xu,et al.  An Empirical Study on Factors Influencing Consumers' Initial Trust in Wearable Commerce , 2016, J. Comput. Inf. Syst..

[15]  Philipp A. Rauschnabel,et al.  Augmented reality smart glasses: an investigation of technology acceptance drivers , 2016 .

[16]  Munkee Choi,et al.  User acceptance of wearable devices: An extended perspective of perceived value , 2016, Telematics Informatics.

[17]  A. Canhoto,et al.  Exploring the factors that support adoption and sustained use of health and fitness wearables , 2017 .

[18]  Mehrbakhsh Nilashi,et al.  Customers perspectives on adoption of cloud computing in banking sector , 2016, Information Technology and Management.

[19]  Zoran Kalinic,et al.  International Journal of Information Management , 2016 .

[20]  Devendra Potnis,et al.  Students' intention to adopt Internet-based personal safety wearable devices: Extending UTAUT with trusting belief , 2017, First Monday.

[21]  Yan Zhang,et al.  Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology , 2017, Int. J. Medical Informatics.

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

[23]  Mehrbakhsh Nilashi,et al.  Forecasting social CRM adoption in SMEs: A combined SEM-neural network method , 2017, Comput. Hum. Behav..

[24]  M. Haghi,et al.  Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices , 2017, Healthcare informatics research.

[25]  Rimantas Gatautis,et al.  Mobile application driven consumer engagement , 2017, Telematics Informatics.

[26]  Tugrul U. Daim,et al.  What will it take to adopt smart glasses: A consumer choice based review? , 2017 .

[27]  Sang Yup Lee,et al.  Factors that influence an individual's intention to adopt a wearable healthcare device: The case of a wearable fitness tracker , 2018 .

[28]  Mehrbakhsh Nilashi,et al.  Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method , 2018, Tourism Management.

[29]  Mehrbakhsh Nilashi,et al.  Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities , 2018, Education and Information Technologies.

[30]  Milad Dehghani,et al.  Exploring the motivational factors on continuous usage intention of smartwatches among actual users , 2018, Behav. Inf. Technol..

[31]  Ab Razak Che Hussin,et al.  Toward Green IT adoption: from managerial perspective , 2018, Int. J. Bus. Inf. Syst..

[32]  Rusli Abdullah,et al.  An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices , 2019, Mob. Inf. Syst..

[33]  Simona Sternad Zabukovsek,et al.  SEM–ANN based research of factors’ impact on extended use of ERP systems , 2018, Central Eur. J. Oper. Res..

[34]  K. Raitz Applying Technology , 2019, Bourbon's Backroads.

[35]  Venkateswarulu Gaddam Wearable Devices , 2021, Encyclopedia of Gerontology and Population Aging.