How do technological properties influence user affordance of wearable technologies

Abstract The Internet of things (IoT) affords people plenty of opportunities and a higher quality of life as well as drives a huge amount of data. By drawing on the concept of affordances, this study examines the user experience of personal informatics focusing on the technological and affective nature of affordance. A multi-mixed approach is used by combining qualitative methods and a quantitative survey. Results of the qualitative methods revealed a series of factors that related to the affordance of personal informatics, whereas results of the user model confirmed a significant role for connectivity, control, and synchronicity affordance regarding their underlying link to other variables, namely, expectation, confirmation, and satisfaction. The experiments showed that users’ affordances are greatly influenced by personal traits with interactivity tendency. The findings imply the embodied cognition process of personal informatics in which technological qualities are shaped by users’ perception, traits, and context. The results establish a foundation for wearable technologies through a heuristic quality assessment tool from a user embodied cognitive process. They confirm the validity and utility of applying affordances to the design of IoT as a useful concept, as well as prove that the optimum mix of affordances is crucial to the success or failure of IoT design.

[1]  C. Diclemente,et al.  The role of feedback in the process of health behavior change. , 2001, American journal of health behavior.

[2]  ChiuChao-Min,et al.  Understanding e-learning continuance intention , 2006 .

[3]  Dong-Hee Shin,et al.  How do credibility and utility play in the user experience of health informatics services? , 2017, Comput. Hum. Behav..

[4]  Dong-Hee Shin,et al.  Cross-Platform Users’ Experiences Toward Designing Interusable Systems , 2016, Int. J. Hum. Comput. Interact..

[5]  Dong-Hee Shin,et al.  Understanding e-book users: Uses and gratification expectancy model , 2011, New Media Soc..

[6]  Tseng-Lung Huang,et al.  A model of acceptance of augmented-reality interactive technology: the moderating role of cognitive innovativeness , 2014, Electronic Commerce Research.

[7]  Arun Vishwanath,et al.  Mobile device affordance: Explicating how smartphones influence the outcome of phishing attacks , 2016, Comput. Hum. Behav..

[8]  Saleem N. Bhatti,et al.  mHealth through quantified-self: A user study , 2015, 2015 17th International Conference on E-health Networking, Application & Services (HealthCom).

[9]  Dong-Hee Shin,et al.  Quality of experience: Beyond the user experience of smart services , 2015 .

[10]  LimayemMoez,et al.  Understanding information systems continuance , 2008 .

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

[12]  Dong Hee Shin,et al.  Determinants of customer acceptance of multi-service network: An implication for IP-based technologies , 2009, Inf. Manag..

[13]  HsiuJu Rebecca Yen,et al.  Assessing ERP post-implementation success at the individual level: Revisiting the role of service quality , 2015, Inf. Manag..

[14]  Jessica Vitak,et al.  Explicating Affordances: A Conceptual Framework for Understanding Affordances in Communication Research , 2017, J. Comput. Mediat. Commun..

[15]  Kevin K. W. Ho,et al.  A study on the impact of design attributes on E-payment service utility , 2016, Inf. Manag..

[16]  Ki Joon Kim,et al.  Interacting Socially with the Internet of Things (IoT): Effects of Source Attribution and Specialization in Human-IoT Interaction , 2016, J. Comput. Mediat. Commun..

[17]  Helmut Krcmar,et al.  Motivating domestic energy conservation through comparative, community-based feedback in mobile and social media , 2011, C&T.

[18]  Wei Peng,et al.  Effects of screen size, viewing angle, and players' immersion tendencies on game experience , 2012, Comput. Hum. Behav..

[19]  Jason Bennett Thatcher,et al.  An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy , 2002, MIS Q..

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

[21]  Radical Empiricism Through the Ages. , 2003 .

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

[23]  Khaled Hassanein,et al.  A macro model of online information quality perceptions: A review and synthesis of the literature , 2016, Comput. Hum. Behav..

[24]  Ingmar Weber,et al.  Quantified Self Meets Social Media: Sharing of Weight Updates on Twitter , 2016, Digital Health.

[25]  Richard A. Spreng,et al.  A cross‐cultural assessment of the satisfaction formation process , 2002 .

[26]  Dong-Hee Shin,et al.  Interaction, engagement, and perceived interactivity in single-handed interaction , 2016, Internet Res..

[27]  Johan F. Hoorn,et al.  Affective affordances: Improving interface character engagement through interaction , 2006, Int. J. Hum. Comput. Stud..

[28]  Ahmad Rafi,et al.  A 'Uses and Gratification Expectancy Model' to Predict Students' 'Perceived e-Learning Experience' , 2008, J. Educ. Technol. Soc..

[29]  Kun Chang Lee,et al.  The impact of hyperlink affordance, psychological reactance, and perceived business tie on trust transfer , 2014, Comput. Hum. Behav..

[30]  Hamed Haddadi,et al.  Human-Data Interaction , 2016 .

[31]  S. Shyam Sundar,et al.  Can synchronicity and visual modality enhance social presence in mobile messaging? , 2015, Comput. Hum. Behav..

[32]  Frederic Marimon Viadiu,et al.  Functional quality and hedonic quality: A study of the dimensions of e-service quality in online travel agencies , 2012, Inf. Manag..

[33]  Frank Biocca,et al.  Health experience model of personal informatics: The case of a quantified self , 2017, Comput. Hum. Behav..

[34]  Dong-Hee Shin,et al.  Measuring the quality of smartphones: development of a customer satisfaction index for smart services , 2014, Int. J. Mob. Commun..

[35]  Jon Froehlich,et al.  Personal informatics in practice: improving quality of life through data , 2012, CHI Extended Abstracts.

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

[37]  Seounmi Youn,et al.  Brand Experience on the Website: Its Mediating Role Between Perceived Interactivity and Relationship Quality , 2016 .

[38]  Sherrie X. Y. Komiak,et al.  The Effects of Perceived Information Quality and Perceived System Quality on Trust and Adoption of Online Reputation Systems , 2010, AMCIS.

[39]  Ephraim R. McLean,et al.  Measuring e-Commerce Success: Applying the DeLone & McLean Information Systems Success Model , 2004, Int. J. Electron. Commer..

[40]  Stephen Voida,et al.  Motivational affordances and personality types in personal informatics , 2014, UbiComp Adjunct.

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