Advertisement

Social Behaviors: A Social Topology and Interaction Pattern Affect the Properties of a Changed Behavior

  • Tatsuya KonishiEmail author
  • Masatoshi Nagata
  • Masaru Honjo
  • Akio Yoneyama
  • Masayuki Kurokawa
  • Koji Mishima
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11433)

Abstract

The current study is based on the assumption that social topology and its interaction pattern affect users’ behavioral changes, especially continuity. To verify the hypothesis, several metrics have been introduced, and experiments have been conducted, resulting in interesting and quantitative findings. In the experiments, two conditional differences lead to statistic significance in continuity and other metrics; the first difference is the existence of feedback implementation, another one is information visibility. It has been experimentally confirmed that users who received more feedback from system bots (i.e., they did not know that they were controlled until the experiment ended) tend to also send more feedback themselves. Moreover, it has been found that only the fact that the others (i.e., bots), except the participant, sent feedback to each other made the person feel isolated, and the participant sent feedback him/herself to avoid being depressed with no interaction. On the other hand, information visibility had little effect on their continuity and no effect on their consciousness.

Keywords

Social behavior Behavior change Healthcare Social network Continuity 

References

  1. 1.
    Alluhaidan, A., Chatterjee, S., Drew, D., Stibe, A.: Sustaining health behaviors through empowerment: a deductive theoretical model of behavior change based on information and communication technology (ICT). In: Proceedings of Persuasive Technology, pp. 28–41 (2018)Google Scholar
  2. 2.
    Alrobai, A., Dogan, H., Phalp, K., Ali, R.: Building online platforms for peer support groups as a persuasive behavior change technique. In: Proceedings of Persuasive Technology, pp. 70–83 (2018)Google Scholar
  3. 3.
    Casperson, S.L., Sieling, J., Moon, J., Johnson, L., Roemmich, J.N., Whigham, L.: A mobile phone food record app to digitally capture dietary intake for adolescents in a free-living environment: usability study. JMIR mHealth and uHealth, p. e30 (2015)Google Scholar
  4. 4.
    Ellison, N.B., Vitak, J., Gray, R., Lampe, C.: Cultivating social resources on social network sites: Facebook relationship maintenance behaviors and their role in social capital processes. J. Comput.-Mediat. Commun. 855–870 (2014)Google Scholar
  5. 5.
    Fanning, J., Mullen, S.P., McAuley, E.: Increasing physical activity with mobile devices: a meta-analysis. J. Med. Internet Res. e161 (2012)Google Scholar
  6. 6.
    Gamberini, L., et al.: Designing and testing credibility: the case of a serious game on nightlife risks. In: Proceedings of Persuasive Technology, pp. 213–226 (2018)Google Scholar
  7. 7.
    Gamberini, L., et al.: A gamified solution to brief interventions for nightlife well-being. In: Proceedings of Persuasive Technology, pp. 230–241 (2016)Google Scholar
  8. 8.
    Hamari, J.: Do badges increase user activity? A field experiment on the effects of gamification. J. Comput. Hum. Behav. 71, 469–478 (2017)CrossRefGoogle Scholar
  9. 9.
    Hamari, J., Koivisto, J., Pakkanen, T.: Do persuasive technologies persuade? - A review of empirical studies. In: Proceedings of Persuasive Technology, pp. 118–136 (2014)Google Scholar
  10. 10.
    Kasmel, A., Andersen, P.T.: Measurement of community empowerment in three community programs in Rapla (Estonia). J. Environ. Res. Publ. Health 8, 799–817 (2011)CrossRefGoogle Scholar
  11. 11.
    Martire, L.M., Franks, M.M.: The role of social networks in adult health: introduction to the special issue. J. Health Psychol. 33, 501–504 (2014)CrossRefGoogle Scholar
  12. 12.
    Oinas-Kukkonen, H.: Behavior change support systems: a research model and agenda. In: Ploug, T., Hasle, P., Oinas-Kukkonen, H. (eds.) PERSUASIVE 2010. LNCS, vol. 6137, pp. 4–14. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13226-1_3CrossRefGoogle Scholar
  13. 13.
    Putnam, R.D.: Bowling Alone: The Collapse and Revival of American Community. Simon and Schuster, New York (2001)Google Scholar
  14. 14.
    Räisänen, T., Oinas-Kukkonen, H., Pahnila, S.: Finding kairos in quitting smoking: smokers’ perceptions of warning pictures. In: Oinas-Kukkonen, H., Hasle, P., Harjumaa, M., Segerståhl, K., Øhrstrøm, P. (eds.) PERSUASIVE 2008. LNCS, vol. 5033, pp. 254–257. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-68504-3_25CrossRefGoogle Scholar
  15. 15.
    Rollo, M.E., Ash, S., Lyons-Wall, P., Russell, A.: Trial of a mobile phone method for recording dietary intake in adults with type 2 diabetes: evaluation and implications for future applications. J. Telemed. Telecare 17, 318–323 (2011)CrossRefGoogle Scholar
  16. 16.
    Ruijten, P.A.M., Ham, J., Midden, C.J.H.: Investigating the influence of social exclusion on persuasion by a virtual agent. In: Spagnolli, A., Chittaro, L., Gamberini, L. (eds.) PERSUASIVE 2014. LNCS, vol. 8462, pp. 191–200. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07127-5_17CrossRefGoogle Scholar
  17. 17.
    Schoeppe, S., et al.: Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. J. Behav. Nutr. Phys. Act. 13, 127 (2016)CrossRefGoogle Scholar
  18. 18.
    Sherman, C.B., Sherman, C., Price, G., et al.: The Invisible Web: Uncovering Information Sources Search Engines Can’t See. Information Today Inc., New York (2001)Google Scholar
  19. 19.
    Torning, K., Oinas-Kukkonen, H.: Persuasive system design: state of the art and future directions. In: Proceedings of Persuasive Technology, pp. 30:1–30:8 (2009)Google Scholar
  20. 20.
    Verduyn, P., Ybarra, O., Résibois, M., Jonides, J., Kross, E.: Do social network sites enhance or undermine subjective well-being? A critical review. J. Soc. Issues Policy Rev. 11, 274–302 (2017)CrossRefGoogle Scholar
  21. 21.
    Williams, D.: On and off the ’Net: scales for social capital in an online era. J. Comput.-Med. Commun. 593–628 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.KDDI Research, Inc.FujiminoJapan
  2. 2.Aichi University of EducationKariyaJapan
  3. 3.Chubu UniversityKasugaiJapan

Personalised recommendations