TY - GEN
T1 - Designing representations of behavioral data with blended causality
T2 - 14th International Conference on Persuasive Technology, PERSUASIVE 2019
AU - Chow, Kenny K.N.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019/4/3
Y1 - 2019/4/3
N2 - Many personal informatics systems present users’ behavioral data in numbers or graphs for their reflection, which may not be effective on a daily basis because people do not always act like data scientists. Representation of behavioral data in virtual environments can provide information at a glance. Grounded in conceptual blending theory, insights from social psychology, and existing persuasive design principles, this article is conceptual-theoretical. It argues that representations should be designed like virtual consequences of behavior and related to users’ existing knowledge of comparable cause-effect relationships in order to prompt one’s imaginative beliefs about the behavioral-virtual causality. It proposes a framework that guides designing representations of behavioral data, including (1) identifying scenarios with comparable causality, (2) examining and grounding the mappings in embodied experiences, (3) performing blends between the behavior and the identified scenario, with different virtual consequences corresponding to different user behaviors, and (4) rendering virtual consequences as feedback that dynamically anchors the scenario for similar blends in users. Design cases are presented and analyzed to demonstrate how embodied mappings can be constructed for interventions for lifestyle habits.
AB - Many personal informatics systems present users’ behavioral data in numbers or graphs for their reflection, which may not be effective on a daily basis because people do not always act like data scientists. Representation of behavioral data in virtual environments can provide information at a glance. Grounded in conceptual blending theory, insights from social psychology, and existing persuasive design principles, this article is conceptual-theoretical. It argues that representations should be designed like virtual consequences of behavior and related to users’ existing knowledge of comparable cause-effect relationships in order to prompt one’s imaginative beliefs about the behavioral-virtual causality. It proposes a framework that guides designing representations of behavioral data, including (1) identifying scenarios with comparable causality, (2) examining and grounding the mappings in embodied experiences, (3) performing blends between the behavior and the identified scenario, with different virtual consequences corresponding to different user behaviors, and (4) rendering virtual consequences as feedback that dynamically anchors the scenario for similar blends in users. Design cases are presented and analyzed to demonstrate how embodied mappings can be constructed for interventions for lifestyle habits.
KW - Behavior change
KW - Blending theory
KW - Personal informatics
UR - http://www.scopus.com/inward/record.url?scp=85064551958&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-17287-9_5
DO - 10.1007/978-3-030-17287-9_5
M3 - Conference proceeding
AN - SCOPUS:85064551958
SN - 9783030172862
T3 - Lecture Notes in Computer Science
SP - 52
EP - 64
BT - Persuasive Technology: Development of Persuasive and Behavior Change Support Systems
A2 - Karapanos, Evangelos
A2 - Kyza, Eleni
A2 - Win, Khin Than
A2 - Oinas-Kukkonen, Harri
A2 - Karppinen, Pasi
PB - Springer Cham
Y2 - 9 April 2019 through 11 April 2019
ER -