TY - GEN
T1 - Well-being's predictive value a gamified approach to managing smart communities
AU - Hall, Margeret
AU - Caton, Simon
AU - Weinhardt, Christof
N1 - Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Well-being is a multifaceted concept, having intellectual origins in philosophy, psychology, economics, political science, and other disciplines. Its presence is correlated with a variety of institutional and business critical indicators. To date, methods to assess well-being are performed infrequently and superficially; resulting in highly aggregated observations. In this paper, we present well-being as a predictive entity for the management of a smart community. Our vision is a low latency method for the observation and measurement of well-being within a community or institution that enables different resolutions of data, e.g. at the level of an individual, a social or demographic group, or an institution. Using well-being in this manner enables realistic, faster and less expensive data collection in a smart system. However, as the data needed for assessing well-being is highly sensitive personal information, constituents require incentives and familiar settings to reveal this information, which we establish with Facebook and gamification. To evaluate the predictive value of well-being, we conducted a series of surveys to observe different self-reported psychological aspects of participants. Our key findings were that neuroticism and extroversion seem to have the highest predictive value of self-reported well-being levels. This information can be used to create expected trends of well-being for smart community management.
AB - Well-being is a multifaceted concept, having intellectual origins in philosophy, psychology, economics, political science, and other disciplines. Its presence is correlated with a variety of institutional and business critical indicators. To date, methods to assess well-being are performed infrequently and superficially; resulting in highly aggregated observations. In this paper, we present well-being as a predictive entity for the management of a smart community. Our vision is a low latency method for the observation and measurement of well-being within a community or institution that enables different resolutions of data, e.g. at the level of an individual, a social or demographic group, or an institution. Using well-being in this manner enables realistic, faster and less expensive data collection in a smart system. However, as the data needed for assessing well-being is highly sensitive personal information, constituents require incentives and familiar settings to reveal this information, which we establish with Facebook and gamification. To evaluate the predictive value of well-being, we conducted a series of surveys to observe different self-reported psychological aspects of participants. Our key findings were that neuroticism and extroversion seem to have the highest predictive value of self-reported well-being levels. This information can be used to create expected trends of well-being for smart community management.
KW - Smart community management
KW - gamification
KW - human flourishing
KW - social computing
KW - well-being
UR - http://www.scopus.com/inward/record.url?scp=84880864705&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880864705&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39371-6_2
DO - 10.1007/978-3-642-39371-6_2
M3 - Conference contribution
AN - SCOPUS:84880864705
SN - 9783642393709
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 22
BT - Online Communities and Social Computing - 5th International Conference, OCSC 2013, Held as Part of HCI International 2013, Proceedings
PB - Springer Verlag
T2 - 5th International Conference on Online Communities and Social Computing, OCSC 2013, Held as Part of 15th International Conference on Human-Computer Interaction, HCI International 2013
Y2 - 21 July 2013 through 26 July 2013
ER -