Consumer Adoption of Digital Technologies for Lifestyle Monitoring

Despite their potential, the adoption of wearable devices has been relatively slow when compared to other digital technologies. This paper investigates, grounding on the Theory of Planned Behavior, the adoption by end users of digital technologies for lifestyle monitoring. Data on consumers' perception and usage of wearable devices have been collected through a survey administered to 1,000 Italian citizens and further analyzed through a Structural Equation Model approach. Results show that, above the functional value of the device, external influence, particularly doctor opinion, exerts an essential role in adoption. Online health literacy proves to be a relevant factor as well, showing the importance of cultural patterns in wearables diffusion. Implications for academicians, practitioners and policy-makers are provided.

[1]  Scott L Delp,et al.  Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. , 2014, Gait & posture.

[2]  Debora Bettiga,et al.  Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach , 2019, Health Care Management Science.

[3]  I. Ajzen,et al.  A Comparison of the Theory of Planned Behavior and the Theory of Reasoned Action , 1992 .

[4]  I. Ajzen,et al.  How Effective are Behavior Change Interventions Based on the Theory of Planned Behavior?: A Three-Level Meta-Analysis , 2016 .

[5]  Marc A. Tomiuk,et al.  A Comparative Study on Parameter Recovery of Three Approaches to Structural Equation Modeling , 2010 .

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

[7]  Jörg Henseler,et al.  Testing Moderating Effects in PLS Path Models. An Illustration of Available Procedures , 2005 .

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

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

[10]  S. Geisser A predictive approach to the random effect model , 1974 .

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

[12]  Wynne W. Chin The partial least squares approach for structural equation modeling. , 1998 .

[13]  Fred D. Davis User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts , 1993, Int. J. Man Mach. Stud..

[14]  Luis Fernández-Luque,et al.  Health and Social Media: Perfect Storm of Information , 2015, Healthcare informatics research.

[15]  Ken Kwong-Kay Wong,et al.  Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS , 2013 .

[16]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

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

[18]  Marko Sarstedt,et al.  An assessment of the use of partial least squares structural equation modeling in marketing research , 2012 .

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

[20]  Yu-Hui Wang,et al.  Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology , 2018 .

[21]  I. Ajzen Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions , 2015 .

[22]  P. Estabrooks,et al.  A systematic literature review and meta-analysis: The Theory of Planned Behavior's application to understand and predict nutrition-related behaviors in youth. , 2015, Eating behaviors.

[23]  K. Jöreskog,et al.  Intraclass Reliability Estimates: Testing Structural Assumptions , 1974 .

[24]  Darren A. DeWalt,et al.  Literacy and health outcomes , 2006, Journal of General Internal Medicine.

[25]  G. Kok,et al.  The Theory of Planned Behavior: A Review of its Applications to Health-Related Behaviors , 1996, American journal of health promotion : AJHP.

[26]  Marko Sarstedt,et al.  Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results , 2011 .

[27]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[28]  Mahdokht Kalantari,et al.  Consumers' adoption of wearable technologies: literature review, synthesis, and future research agenda , 2017 .

[29]  N. Avkiran An in-depth discussion and illustration of partial least squares structural equation modeling in health care , 2018, Health care management science.

[30]  I. Ajzen,et al.  Prediction of goal directed behaviour: Attitudes, intentions and perceived behavioural control , 1986 .

[31]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[32]  J. de Haes,et al.  Doctor-patient communication: a review of the literature. , 1995, Social science & medicine.

[33]  Stacey L. Sheridan,et al.  Low Health Literacy and Health Outcomes: An Updated Systematic Review , 2011, Annals of Internal Medicine.

[34]  Barbara L. Gross,et al.  Why we buy what we buy: A theory of consumption values , 1991 .

[35]  M. Hagger,et al.  Using meta-analytic path analysis to test theoretical predictions in health behavior: An illustration based on meta-analyses of the theory of planned behavior. , 2016, Preventive medicine.

[36]  Debora Bettiga,et al.  Do mind and body agree? Unconscious versus conscious arousal in product attitude formation , 2017 .

[37]  Debora Bettiga,et al.  Exploring the adoption process of personal technologies: A cognitive-affective approach , 2017 .

[38]  Gerardine Doyle,et al.  Health literacy and public health: A systematic review and integration of definitions and models , 2012, BMC Public Health.

[39]  Debora Bettiga,et al.  Exploring the role of anticipated emotions in product adoption and usage , 2018 .

[40]  A. Paswan,et al.  Consumer innovativeness and perceived risk: implications for high technology product adoption , 2006 .

[41]  R. Bagozzi,et al.  On the evaluation of structural equation models , 1988 .

[42]  R. Cooke,et al.  How well does the theory of planned behaviour predict alcohol consumption? A systematic review and meta-analysis , 2014, Health psychology review.

[43]  Robin Wright,et al.  Wearable Technology: If the Tech Fits, Wear It , 2014 .

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

[45]  Richard L Kravitz,et al.  The Theory of Planned Behavior as it predicts potential intention to seek mental health services for depression among college students , 2016, Journal of American college health : J of ACH.

[46]  I. Ajzen The theory of planned behavior , 1991 .

[47]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[48]  Debora Bettiga,et al.  Exploring Media Convergence: Evidence from Italy , 2013 .

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

[50]  R. Rudd The evolving concept of Health literacy: New directions for health literacy studies , 2015 .