Quantify-me: Consumer Acceptance of Wearable Self-tracking Devices

The usage of wearable self-tracking technology has recently emerged as a new big trend in lifestyle and personal optimization in terms of health, fitness and well-being. Currently, only little is known about why people plan or start using such devices. Thus, in our research project, we aim at answering the question of what drives the usage intention of wearable self-tracking technology. Therefore, based on established technology acceptance theories, we deductively develop an acceptance model for wearable self-tracking technologies which sheds light on the pre-adoption criteria of such devices. We validate our proposed model by means of structural equation modeling using empirical data collected in a survey among 206 potential users. Our study identifies perceived usefulness, perceived enjoyment, social influence, trust, personal innovativeness, and perceived support of well-being as the strongest drivers for the intention to use wearable self-tracking technologies. By accounting for the influence of the demographic factors age and gender, we provide a further refined picture.

[1]  June Lu,et al.  Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology , 2005, J. Strateg. Inf. Syst..

[2]  M. Deutsch,et al.  A study of normative and informational social influences upon individual judgement. , 1955, Journal of abnormal psychology.

[3]  Melanie Swan,et al.  Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 , 2012, J. Sens. Actuator Networks.

[4]  Deborah Lupton,et al.  Self-tracking cultures: towards a sociology of personal informatics , 2014, OZCHI.

[5]  J. Reginster,et al.  Smart wearable body sensors for patient self-assessment and monitoring , 2014, Archives of Public Health.

[6]  D. Lupton.  Quantifying the body: monitoring and measuring health in the age of mHealth technologies , 2013 .

[7]  H. Zandhessami,et al.  Determinants of user acceptance of internet banking: An empirical study , 2014 .

[8]  Robert W. Veryzer.  The Place of Product Design and Aesthetics in Consumer Research , 1995 .

[9]  Marvin Berkowitz,et al.  Product shape as a design innovation strategy , 1987 .

[10]  R. Zmud,et al.  Information technology implementation research: a technological diffusion approach , 1990 .

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

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

[13]  Martin Wetzels,et al.  A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects , 2007, Inf. Manag..

[14]  Brent A. Scott,et al.  Trust, trustworthiness, and trust propensity: a meta-analytic test of their unique relationships with risk taking and job performance. , 2007, The Journal of applied psychology.

[15]  Deborah Lupton,et al.  Self-Tracking Modes: Reflexive Self-Monitoring and Data Practices , 2014 .

[16]  Wanda Pratt,et al.  Understanding quantified-selfers' practices in collecting and exploring personal data , 2014, CHI.

[17]  Kenneth C. Laudon,et al.  Environmental and institutional models of system development: a national criminal history system , 1985, CACM.

[18]  Ingoo Han,et al.  Effect of trust on customer acceptance of Internet banking , 2002, Electron. Commer. Res. Appl..

[19]  Max E. Valentinuzzi,et al.  Understanding the human machine , 2004 .

[20]  Mamta Sareen,et al.  Trust and Technology in B2B E-Commerce: Practices and Strategies for Assurance , 2011 .

[21]  Henner Gimpel,et al.  Quantifying the Quantified Self: A Study on the Motivations of Patients to Track Their Own Health , 2013, ICIS.

[22]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[23]  Nils Urbach,et al.  Structural Equation Modeling in Information Systems Research Using Partial Least Squares , 2010 .

[24]  Gordon C. Bruner,et al.  Explaining consumer acceptance of handheld Internet devices , 2005 .

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

[26]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[27]  E. Hirschman.  Innovativeness, Novelty Seeking, and Consumer Creativity , 1980 .

[28]  Ioanna D. Constantiou,et al.  The Complexities of Self-Tracking - An Inquiry into User Reactions and Goal Attainment , 2015, ECIS.

[29]  Mariëlle E. H. Creusen,et al.  The Different Roles of Product Appearance in Consumer Choice , 2005 .

[30]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..

[31]  Matthew Chalmers,et al.  Personal tracking as lived informatics , 2014, CHI.

[32]  Kar Yan Tam,et al.  Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics , 2002, J. Manag. Inf. Syst..

[33]  M. Swan.  Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen , 2012, Journal of personalized medicine.

[34]  Hsi-Peng Lu,et al.  Exploring the emotional, aesthetic, and ergonomic facets of innovative product on fashion technology acceptance model , 2009, Behav. Inf. Technol..

[35]  Gordon C. Bruner,et al.  Toward a unified theory of consumer acceptance technology , 2007 .

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

[37]  R. Bagozzi,et al.  An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors , 2002 .

[38]  Fred D. Davis.  A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[39]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[40]  Phil Johnson,et al.  Understanding Management Research: An Introduction to Epistemology , 2000 .

[41]  P. J. Sher,et al.  Integrating technology readiness into technology acceptance: The TRAM model , 2007 .

[42]  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..

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

[44]  Robert W. Veryzer.  Aesthetic Response and the Influence of Design Principles on Product Preferences , 1993 .

[45]  M. Swan.  Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking , 2009, International journal of environmental research and public health.

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

[47]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[48]  Knut Schlohmann.  Innovatorenorientierte Akzeptanzforschung bei innovativen Medientechnologien , 2012 .