Exploring behavioural intentions toward smart healthcare services among medical practitioners: a technology transfer perspective

Recently, a variety of artificial intelligence (AI)-driven smart healthcare services are rapidly emerging in the medical market, such as intelligent image analysis, surgical navigation systems, and aided diagnosis. However, one of the major challenges is practitioners’ hesitation and unwillingness to employ these new technologies in medical practice. This study focuses on identifying the influential factors of adoption intention of smart healthcare services for both clinicians and non-clinicians from the perspective of technology transfer. Through collecting 484 questionnaire data from doctors in Anhui, China, we find support to show that perceived usefulness (PU), attitude, and the experience of using mHealth are key factors that influence both clinicians and non-clinician’s adoption intention. Meanwhile, it is confirmed that subjective norm has a positive effect on only clinicians’ behavioural intention (BI) while perceived risk (PR) has a negative impact on only non-clinicians’ attitude. Among all the constructs, the experience of using mHealth has the strongest positive effect on doctors’ adoption intention on smart healthcare services, a positive impact on the PU and perceived ease of use, and a negative impact on the PR. This study provides an improved understanding of doctors’ BI of smart healthcare services, and practice guidance for product development and marketing strategies.

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

[2]  Randolph Sloof,et al.  Risk, Uncertainty and Entrepreneurship: Evidence from a Lab-in-The-Field Experiment , 2016, Manag. Sci..

[3]  Shuiqing Yang,et al.  Why do consumers adopt online channel? An empirical investigation of two channel extension mechanisms , 2013, Decis. Support Syst..

[4]  Aleksandar Milenkovic,et al.  From Telemedicine to Ubiquitous M-Health , 2008 .

[5]  Rui Zhang,et al.  Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits , 2012, Comput. Hum. Behav..

[6]  A. N. Sah,et al.  Understanding Online Shopping Adoption in India: Unified Theory of Acceptance and Use of Technology 2 UTAUT2 With Perceived Risk Application , 2016 .

[7]  Qiyun Wang,et al.  Rewarded and unrewarded competition in a CSCL environment: A coopetition design with a social cognitive perspective using PLS-SEM analyses , 2017, Comput. Hum. Behav..

[8]  Réjean Plamondon,et al.  Personal digital bodyguards for e-security, e-learning and e-health: A prospective survey , 2018, Pattern Recognit..

[9]  Vassilis Moustakis,et al.  Modeling the acceptance of clinical information systems among hospital medical staff: An extended TAM model , 2011, J. Biomed. Informatics.

[10]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..

[11]  Qi Wu,et al.  Factors that influence users’ adoption intention of mobile health: a structural equation modeling approach , 2017, Int. J. Prod. Res..

[12]  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.

[13]  Jonathan R. Clark,et al.  Broadening Focus: Spillovers, Complementarities and Specialization in the Hospital Industry , 2011, Manag. Sci..

[14]  Paul A. Pavlou,et al.  Predicting E-Services Adoption: A Perceived Risk Facets Perspective , 2002, Int. J. Hum. Comput. Stud..

[15]  Jacob Cohen,et al.  CHAPTER 7 – Chi-Square Tests for Goodness of Fit and Contingency Tables , 1977 .

[16]  Viswanath Venkatesh,et al.  Model of Migration and Use of Platforms: Role of Hierarchy, Current Generation, and Complementarities in Consumer Settings , 2010, Manag. Sci..

[17]  Mher Beglaryan,et al.  Development of a tripolar model of technology acceptance: Hospital-based physicians' perspective on EHR , 2017, Int. J. Medical Informatics.

[18]  Sharon Swee-Lin Tan,et al.  Electronic Health Records: How Can IS Researchers Contribute to Transforming Healthcare? , 2016, MIS Q..

[19]  Byungtae Lee,et al.  Rival precedence and open platform adoption: An empirical analysis , 2018, Int. J. Inf. Manag..

[20]  E. Finkelstein,et al.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes , 2017, JAMA.

[21]  Woojin Lee,et al.  The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model , 2012 .

[22]  I-Chiu Chang,et al.  Physicians' perspectives of adopting computer-assisted navigation in orthopedic surgery , 2016, Int. J. Medical Informatics.

[23]  Bruno M. C. Silva,et al.  Towards a cooperative security system for mobile-health applications , 2014 .

[24]  Bruno S. Frey,et al.  In a field experiment , 2004 .

[25]  C. Stein,et al.  Structural equation modeling. , 2012, Methods in molecular biology.

[26]  Zhenbin Yang,et al.  Analyzing the enabling factors for the organizational decision to adopt healthcare information systems , 2013, Decis. Support Syst..

[27]  Richard J. Holden,et al.  The Technology Acceptance Model: Its past and its future in health care , 2010, J. Biomed. Informatics.

[28]  Mosa Ali Abu-Rgheff,et al.  3G wireless communications for mobile robotic tele-ultrasonography systems , 2006, IEEE Communications Magazine.

[29]  Mingyue Ding,et al.  Non-rigid multi-modal medical image registration by combining L-BFGS-B with cat swarm optimization , 2015, Inf. Sci..

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

[31]  Raymond Y. K. Lau,et al.  Smart health: Big data enabled health paradigm within smart cities , 2017, Expert Syst. Appl..

[32]  Andrew Burton-Jones,et al.  How Can We Develop Contextualized Theories of Effective Use? A Demonstration in the Context of Community-Care Electronic Health Records , 2017, Inf. Syst. Res..

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

[34]  Diane M. Strong,et al.  Extending the technology acceptance model with task-technology fit constructs , 1999, Inf. Manag..

[35]  Anol Bhattacherjee,et al.  Acceptance of e-commerce services: the case of electronic brokerages , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[36]  Pi-Jung Hsieh,et al.  Physicians' acceptance of electronic medical records exchange: An extension of the decomposed TPB model with institutional trust and perceived risk , 2015, Int. J. Medical Informatics.

[37]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[38]  Lihua Huang,et al.  Understanding Usage-Transfer Behavior Between Nonsubstitutable Technologies: Evidence From Instant Messenger and Portal , 2009, IEEE Transactions on Engineering Management.

[39]  Gita Venkataramani Johar,et al.  Egocentric Categorization and Product Judgment: Seeing Your Traits in What You Own (and Their Opposite in What You Don’t) , 2013 .

[40]  Ritu Agarwal,et al.  Electronic Health Records Assimilation and Physician Identity Evolution: An Identity Theory Perspective , 2012, Inf. Syst. Res..

[41]  Marko Sarstedt,et al.  Testing measurement invariance of composites using partial least squares , 2016 .

[42]  Marko Sarstedt,et al.  PLS path modeling and evolutionary segmentation , 2013 .

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

[44]  Kim Huat Goh,et al.  Work Harder or Work Smarter? Information Technology and Resource Allocation in Healthcare Processes , 2015, MIS Q..

[45]  Polychronis Koutsakis,et al.  Adaptive Bandwidth Reservation and Scheduling for Efficient Wireless Telemedicine Traffic Transmission , 2011, IEEE Transactions on Vehicular Technology.

[46]  Martina Ziefle,et al.  From Computer Innovation to Human Integration: Current Trends and Challenges for Pervasive HealthTechnologies , 2014 .

[47]  Gurpreet Dhillon,et al.  A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research , 2014, Inf. Syst. Res..

[48]  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..

[49]  Viswanath Venkatesh,et al.  Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior , 2000, MIS Q..

[50]  M. Jenamani,et al.  Organizational Buyers' Acceptance of Electronic Procurement Services-An Empirical Investigation in Indian Firms , 2015 .

[51]  Trevor T. Moores,et al.  Towards an integrated model of IT acceptance in healthcare , 2012, Decis. Support Syst..

[52]  Viswanath Venkatesh,et al.  Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model , 2000, Inf. Syst. Res..

[53]  Venkateshviswanath,et al.  A Theoretical Extension of the Technology Acceptance Model , 2000 .

[54]  Gilmer Valdes,et al.  Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[55]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[56]  Sophia Ananiadou,et al.  Smart Health and Wellbeing , 2013, TMIS.

[57]  Naresh K. Malhotra,et al.  Research Note - Two Competing Perspectives on Automatic Use: A Theoretical and Empirical Comparison , 2005, Inf. Syst. Res..

[58]  Michael M. Abecassis,et al.  The use of technology for urgent clinician to clinician communications: A systematic review of the literature , 2015, Int. J. Medical Informatics.

[59]  Maryam Ahmadi,et al.  The effects of organizational contextual factors on physicians' attitude toward adoption of Electronic Medical Records , 2015, J. Biomed. Informatics.

[60]  Ing-Long Wu,et al.  The adoption of mobile healthcare by hospital's professionals: An integrative perspective , 2011, Decis. Support Syst..

[61]  Cynthia K. Riemenschneider,et al.  Understanding it adoption decisions in small business: integrating current theories , 2003, Inf. Manag..

[62]  Sue M. Evans,et al.  Attitudes of doctors and nurses towards incident reporting: a qualitative analysis , 2004, The Medical journal of Australia.

[63]  Ying Wang,et al.  Platform adoption by mobile application developers: A multimethodological approach , 2018, Decis. Support Syst..

[64]  Mehrbakhsh Nilashi,et al.  Hospital Information System adoption: Expert perspectives on an adoption framework for Malaysian public hospitals , 2017, Comput. Hum. Behav..

[65]  Mika Immonen,et al.  Telecare services for aging people: Assessment of critical factors influencing the adoption intention , 2013, Comput. Hum. Behav..

[66]  Jie Gu,et al.  Privacy concerns for mobile app download: An elaboration likelihood model perspective , 2017, Decis. Support Syst..

[67]  Linda G. Wallace,et al.  The adoption of software measures: A technology acceptance model (TAM) perspective , 2014, Inf. Manag..

[68]  Robert S H Istepanian,et al.  m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics. , 2018, Methods.

[69]  Michael P Recht,et al.  Technology-Assisted Virtual Consultation for Medical Imaging. , 2016, Journal of the American College of Radiology : JACR.

[70]  William Brown,et al.  Assessment of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating mobile health (mHealth) technology , 2013, J. Biomed. Informatics.

[71]  Rajiv Kohli,et al.  Does Information Technology Investment Influence a Firm's Market Value? A Case of Non-Publicly Traded Healthcare Firms , 2012, MIS Q..

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

[73]  Eric J Topol,et al.  Can mobile health technologies transform health care? , 2013, JAMA.

[74]  Pouyan Esmaeilzadeh,et al.  Adoption of clinical decision support systems in a developing country: Antecedents and outcomes of physician's threat to perceived professional autonomy , 2015, Int. J. Medical Informatics.

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

[76]  Benjamin T. Hazen,et al.  Remanufactured products purchase intentions and behaviour: Evidence from Malaysia , 2017, Int. J. Prod. Res..

[77]  Kamil J. Mizgier,et al.  Integrating the customers’ perceived risks and benefits into the triple-channel retailing , 2017, Int. J. Prod. Res..

[78]  Joel J. P. C. Rodrigues,et al.  Mobile-health: A review of current state in 2015 , 2015, J. Biomed. Informatics.

[79]  Martin Koudstaal,et al.  Risk , Uncertainty and Entrepreneurship : Evidence From a Large Lab-inthe-Field Experiment , 2014 .

[80]  Michel Rousseau,et al.  Electronic health record acceptance by physicians: Testing an integrated theoretical model , 2014, J. Biomed. Informatics.

[81]  I-Chiu Chang,et al.  Predicting medical staff intention to use an online reporting system with modified unified theory of acceptance and use of technology. , 2012, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[82]  Cheolho Yoon,et al.  Extending the TAM for Green IT: A normative perspective , 2018, Comput. Hum. Behav..

[83]  Glyn Lawson,et al.  3D printing system: an innovation for small-scale manufacturing in home settings? – early adopters of 3D printing systems in China , 2016 .

[84]  Daniel Baier,et al.  Enhancing the online decision-making process by using augmented reality: A two country comparison of youth markets , 2017 .

[85]  Jung-Kuei Hsieh,et al.  Post-adoption switching behavior for online service substitutes: A perspective of the push-pull-mooring framework , 2012, Comput. Hum. Behav..

[86]  P. Hackl,et al.  Robustness of partial least-squares method for estimating latent variable quality structures , 1999 .