Volume 20, Issue 2
  • ISSN 1572-0373
  • E-ISSN: 1572-0381
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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.


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  1. Bernardo, M., Marimon, F., & Alonso-Almeida, M.
    (2012) Functional quality and hedonic quality: A study of the dimensions of e-service quality in online travel agencies. Information & Management, 49, 342–347. 10.1016/j.im.2012.06.005
    https://doi.org/10.1016/j.im.2012.06.005 [Google Scholar]
  2. Bhattacherjee, A.
    (2001) Understanding information systems continuance. MIS Quarterly, 25(3), 351–370. 10.2307/3250921
    https://doi.org/10.2307/3250921 [Google Scholar]
  3. Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A.
    (2014, April). Understanding quantified-selfers’ practices in collecting and exploring personal data. InProceedings of the 32nd annual ACM Conference on Human Factors in Computing Systems (pp.1143–1152). ACM.
    [Google Scholar]
  4. Chemero, A.
    (2003) Radical empiricism through the ages. Contemporary Psychology, 48, 18–20.
    [Google Scholar]
  5. DeLone, W. H., & McLean, E. R.
    (2004) Measuring e-commerce success. International Journal of Electronic Commerce, 9(1), 31–47. 10.1080/10864415.2004.11044317
    https://doi.org/10.1080/10864415.2004.11044317 [Google Scholar]
  6. DiClemente, C. C., Marinilli, A. S., Singh, M., & Bellino, L. E.
    (2001) The role of feedback in the process of health behavior change. American Journal of Health Behavior, 25(3), 217–227. 10.5993/AJHB.25.3.8
    https://doi.org/10.5993/AJHB.25.3.8 [Google Scholar]
  7. Evans, S., Pearce, K., & Vitak, J.
    (2017) Explicating affordances: A conceptual framework for understanding affordances in communication research. Journal of Computer-Mediated Communication, 22(1) 35–52. 10.1111/jcc4.12180
    https://doi.org/10.1111/jcc4.12180 [Google Scholar]
  8. Ghasemaghaei, M., & Hassanein, K.
    (2016) A macro model of online information quality perceptions: A review and synthesis of the literature. Computers in Human Behavior, 55, 972–991. 10.1016/j.chb.2015.09.027
    https://doi.org/10.1016/j.chb.2015.09.027 [Google Scholar]
  9. Gimpel, H., & Niben, M.
    (2013) Quantifying the quantified self: A study on the motivation of patients to track their own health. Thirty Fourth International Conference on Information Systems, Milan, Italy, Dec. 2013.
    [Google Scholar]
  10. Haddadi, H., Mortier, R., McAuley, D., & Crowcroft, J.
    (2013) Human-data interaction. University of Cambridge, Cambridge, UK.
    [Google Scholar]
  11. Hou, J., Nam, Y., Peng, W., & Lee, K.
    (2012) Effects of screen size, viewing angle, and players’ immersion tendencies on game experience. Computers in Human Behavior, 28, 617–623. 10.1016/j.chb.2011.11.007
    https://doi.org/10.1016/j.chb.2011.11.007 [Google Scholar]
  12. Huang, T. & Liao, S.
    (2015) A model of acceptance of augmented-reality interactive technology: the moderating role of cognitive innovativeness. Electronic Commerce Research, 15(2), 269–295. doi:  10.1007/s10660‑014‑9163‑2
    https://doi.org/10.1007/s10660-014-9163-2 [Google Scholar]
  13. Hsu, P., Yen, H., & Chung, J.
    (2016) Assessing ERP post-implementation success at the individual level. Information & Management, 52(8), 925–942. 10.1016/j.im.2015.06.009
    https://doi.org/10.1016/j.im.2015.06.009 [Google Scholar]
  14. Karanam, Y., Filko, L., Kaser, L., Alotaibi, H., Makhsoom, E., & Voida, S.
    (2014) Motivational affordances and personality types in personal informatics. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, p.79–82. doi: > 10.1145/2638728.2638800
    https://doi.org/10.1145/2638728.2638800 [Google Scholar]
  15. Khorakhun, C., & Bhatti, S.
    (2015, October). mHealth through quantified-self: a user study. HealthCom 2015. 17th IEEE International Conference on eHealth Networking, Applications, and Services. Boston, MA, USA, October 2015 10.1109/HealthCom.2015.7454520
    https://doi.org/10.1109/HealthCom.2015.7454520 [Google Scholar]
  16. Kim, J.
    (2016) Interacting socially with the Internet of Things (IoT): Effects of source attribution and specialization in human–IoT interaction. Journal of Computer- Mediated Communication, 21, 420–435. 10.1111/jcc4.12177
    https://doi.org/10.1111/jcc4.12177 [Google Scholar]
  17. Komiak, S.
    (2010) The effects of perceived information quality and perceived system quality on trust and adoption of online reputation systems. AMCIS 2010 Proceedings. Paper 343. aisel.aisnet.org/amcis2010/343
    [Google Scholar]
  18. Lee, K., Lee, S., & Hwang, Y.
    (2014) The impact of hyperlink affordance, psychological reactance, and perceived business tie on trust transfer. Computers in Human Behavior, 30, 110–120. 10.1016/j.chb.2013.08.003
    https://doi.org/10.1016/j.chb.2013.08.003 [Google Scholar]
  19. Li, I., Medynskjy, Y., Froehlich, J., & Larsen, J.
    (2012) Personal informatics in practice: Improving quality of life through data. InCHI 2012, May5–10 2012, Austin, TX, USA. 10.1145/2212776.2212724
    https://doi.org/10.1145/2212776.2212724 [Google Scholar]
  20. Mondi, M., Woods, P. & A. Rafi
    (2008) A uses and gratification expectancy model to predict students’ perceived e-learning experience. Educational Technology & Society, 11 (2), 241–261.
    [Google Scholar]
  21. Oliver, R. L.
    (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. 10.2307/3150499
    https://doi.org/10.2307/3150499 [Google Scholar]
  22. Park, E., & Sundar, S.
    (2015) Can synchronicity and visual modality enhance social presence in mobile messaging?Computers in Human Behavior, 45,121–128. 10.1016/j.chb.2014.12.001
    https://doi.org/10.1016/j.chb.2014.12.001 [Google Scholar]
  23. Petkov, P., Köbler, F., Foth, M., & Krcmar, H.
    (2011, June). Motivating domestic energy conservation through comparative, community-based feedback in mobile and social media. InProceedings of the 5th International Conference on Communities and Technologies (pp.21–30). ACM. 10.1145/2103354.2103358
    https://doi.org/10.1145/2103354.2103358 [Google Scholar]
  24. Roca, J. C., Chiu, C., & Martinez, F. J.
    (2006) Understanding E-learning continuance intention. Int. J. Human-Computer Studies, 64(8), 683–696. 10.1016/j.ijhcs.2006.01.003
    https://doi.org/10.1016/j.ijhcs.2006.01.003 [Google Scholar]
  25. See-To, E., & Ho, K.
    (2016) A study on the impact of design attributes on E-payment service utility. Information & Management, 53(5), 668–681. 10.1016/j.im.2016.02.004
    https://doi.org/10.1016/j.im.2016.02.004 [Google Scholar]
  26. Shin, D.
    (2009) Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information and Management, 46(1), 16–22. doi:  10.1016/j.im.2008.05.004
    https://doi.org/10.1016/j.im.2008.05.004 [Google Scholar]
  27. (2014) Measuring the quality of smartphones: Development of a customer satisfaction index for smart devices. International Journal of Mobile Communications, 12(4), 311–327. doi:  10.1504/IJMC.2014.063650
    https://doi.org/10.1504/IJMC.2014.063650 [Google Scholar]
  28. Shin, D., Lee, S., & Hwang, Y.
    (2017) How do credibility and utility affect the user experience of health informatics services?Computers in Human Behavior, 67, 292–302. 10.1016/j.chb.2016.11.007
    https://doi.org/10.1016/j.chb.2016.11.007 [Google Scholar]
  29. Shin, D., & Biocca, F.
    (2017) Health experience model of personal informatics: The case of a quantified self. Computers in Human Behavior, 69, 62–74. 10.1016/j.chb.2016.12.019
    https://doi.org/10.1016/j.chb.2016.12.019 [Google Scholar]
  30. Shin, D.
    (2016) Cross-platform users’ experiences toward designing interusable systems. International Journal of Human-Computer Interaction, 32(7), 503–514. 10.1080/10447318.2016.1177277
    https://doi.org/10.1080/10447318.2016.1177277 [Google Scholar]
  31. Shin, D., Choi, M., Kim, J., & Lee, J.
    (2016) Interaction, engagement, and perceived interactivity in single-handed interaction. Internet Research, 26(5), 1134–1157. doi:  10.1108/IntR‑12‑2014‑0312
    https://doi.org/10.1108/IntR-12-2014-0312 [Google Scholar]
  32. Shin, D.
    (2015) Quality of experience: Beyond the user experience of smart services. Total Quality Management, 26 (8), 919–932. 10.1080/14783363.2014.912037
    https://doi.org/10.1080/14783363.2014.912037 [Google Scholar]
  33. (2011) Understanding e-book users: Uses and gratification expectancy model. New Media and Society, 13(2), 260–278. 10.1177/1461444810372163
    https://doi.org/10.1177/1461444810372163 [Google Scholar]
  34. Spreng, R. A., & Chiou, S.
    (2002) A cross-cultural assessment of the satisfaction formation process. European Journal of Marketing, 36(8), 829–840. 10.1108/03090560210430827
    https://doi.org/10.1108/03090560210430827 [Google Scholar]
  35. Thatcher, J., & P. L. Perrewe
    (2002) An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), 381–96. 10.2307/4132314
    https://doi.org/10.2307/4132314 [Google Scholar]
  36. Vishwanath, A.
    (2016) Mobile device affordance: explicating how smartphones influence the outcome of phishing attacks. Computers in Human Behavior, 63, 198–207. 10.1016/j.chb.2016.05.035
    https://doi.org/10.1016/j.chb.2016.05.035 [Google Scholar]
  37. Vugt, H., Hoorn, J., Konjin, E., & Dimitriadou, A.
    (2006) Affective affordances: Improving interface character engagement through interaction. International Journal of Human-Computer Studies, 64(9), 874–888. doi:  10.1016/j.ijhcs.2006.04.008
    https://doi.org/10.1016/j.ijhcs.2006.04.008 [Google Scholar]
  38. Wang, Y., Weber, I., & Mitra, P.
    (2016) Quantified self meets social media: Sharing of weight updates on Twitter. Proceedings of the 6th International Conference on Digital Health Conference, p.93–97. 10.1145/2896338.2896363
    https://doi.org/10.1145/2896338.2896363 [Google Scholar]
  39. Yoon, D., & Youn, S.
    (2016) Brand experience on the website. Journal of Interactive Advertising, 16(1), 1–15. doi:  10.1080/15252019.2015.1136249
    https://doi.org/10.1080/15252019.2015.1136249 [Google Scholar]

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