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TRANSFORMING
WELLBEING
THEORY
transforms.me
https://scottbreslin.org/2016/08/transformation-vs-change/
DIET 1
DIET 2
DIET 3
175 lbs
190 lbs
180 lbs
200 lbs
185 lbs
???
0.99365 = 0.03
1.01365 = 37.78
AC
ACCEPTANCE
KN
KNOWLEDGE
US
USE
IN
INTENTION
AGE
AGE
GEN
GEN
ONL ONL TV
TV
HAP
FULF MIND MIND
MIND
MIND
DIST
DIST DIST
STRFEA FEA FEA
FEA
https://www.engageandprosper.com/approaches/the-carrothttps://www.engageandprosper.com/approaches/the-stick
TODAY
ONLY
%
TRANSACTION TRANSITION TRANSFORMATION
DOT DURATION DIRECTION
1 PERIOD ENDLESS
SOURCE: ONLINE
SOCIAL
INFLUENCE
2019
Empowering Sustainable Change: Emergence of
Transforming Wellbeing Theory
Agnis Stibe1
, Kathrin Röderer2
, Michaela Reisinger2
, and Tobias Nyström3
1
ESLSCA Business School Paris, France
agnis@transforms.me
2
Austrian Institute of Technology, Austria
{kathrin.roederer, michaela.reisinger}@ait.ac.at
3
Uppsala University, Sweden
tobias.nystrom@im.uu.se
Abstract. Majority of global problems and business challenges are byproducts
SELF-
CONTAINED
SELF-
DRIVEN
JANUARY
1st
MEDIATED
ALGORITHMIC
MODERATED
USER
BEHAVIOR
USER
CONTENT
DYNAMIC
DESIGN
DYNAMIC
CONTENT
PERSUASIVE
DESIGN
TRIANGLE
CURVE
METRIC
CIRCLES
ARCHITECTURE
SOCIUM
MODERATION
ETHICS
CT
COMPETITION
SL
LEARNING
SC
COMPARISON
CR
COOPERATION
NI
NORMATIVE
SF
FACILITATION
RE
RECOGNITION
INVOLVEMENT
ENGAGEMENT
PARTICIPATION
POSITIVENEGATIVE
INTENDED
UNINTENDED
MAJOR MINOR
HIGH
LOW
BACKFIRING
TARGET
SURPRISE
DARK GREY
INVISIBLE
VISIBLE
DARK
1
2 3
4
5
6
7
8
TRANSFORMING
CHANGE CARE SOCIOTECH DESIGN
INTELLIGENCE
CLASSIFY UNDERSTAND ANALYTICS
DATA
READ FEEL SENSORS
TRIANGLE
1
ATTITUDE
BEHAVIOR
ENVIRONMENT
1
CURVE
2
ATTITUDE
BEHAVIOR
ENVIRONMENT
-
+
+
2
https://echristensen42.com/2016/10/07/creating-your-own-pathways-through-the-cloud/usesidewalks/
10 0001
METRIC
3
PEOPLE METRIC
BEHAVIOR TODAY
TRANSFORMATION FUTURE
3
CIRCLES
4
SELF-
CONTAINED
SELF-
DRIVEN
JANUARY
1st
SELF-
DRIVEN
SELF-
CONTAINED
JANUARY
1st
4
ARCHITECTURE
5
TRANSFORMING
CHANGE CARE SOCIOTECH DESIGN
INTELLIGENCE
CLASSIFY UNDERSTAND AI / BIG DATA
DATA
READ FEEL SENSORS 5
Persuasive Cities for Sustainable Wellbeing:
Quantified Communities
Agnis Stibe(&)
and Kent Larson
MIT Media Lab, Cambridge, USA
{agnis,kll}@mit.edu
Abstract. Can you imagine a city that feels, understands, and cares about your
wellbeing? Future cities will reshape human behavior in countless ways. New
strategies and models are required for future urban spaces to properly respond to2016
SOCIUM
6
CT
COMPETITION
SL
LEARNING
SC
COMPARISON
CR
COOPERATION
SN
NORMS
SF
FACILITATION
RE
RECOGNITION
INVOLVEMENT
ENGAGEMENT
PARTICIPATION
6
CT
COMPETITION
SL
LEARNING
SC
COMPARISON
CR
COOPERATION
SN
NORMS
SF
FACILITATION
RE
RECOGNITION
SOCIAL INFLUENCE
Downward Upward
Injunctive Descriptive
Goal Gain
Goal Together
Participants Onlookers
INVOLVEMENT PARTICIPATION ENGAGEMENT
6
Towards a Framework for Socially Influencing Systems:
Meta-analysis of Four PLS-SEM Based Studies
Agnis Stibe( )
MIT Media Lab, Cambridge, MA, USA
agnis@mit.edu
Abstract. People continuously experience various types of engagement through
social media, mobile interaction, location-based applications, and other tech-
nologically advanced environments. Often, integral parts of such socio- 2015
CT
COMPETITION
SL
LEARNING
CP
COMPARISON
CR
COOPERATION
SN
NORMS
SF
FACILITATION
RE
RECOGNITION
2019
Unfolding Social Influence in Interactive Persuasive
Technology: Scale Development and Research Model
Abstract. Contrary to the popular perspective held by many scholars and de-
signers, social influence encompasses a much broader and deeper area of be-
havioral science than the commonly used principles of social norms, peer pres-
sure, or social proof. To help scholars develop a deeper understanding of social
influence, this study presents a measurement instrument for evaluating suscep-
tibility to seven social influence principles. The tool was developed over three
iterative rounds of instrument design, and validated through an online survey
MODERATION
7
1274 03
INCREASE
OTHER
PEOPLE
12
0
MODERATE
74
DECREASE
1274 03
7
Advancing Typology of Computer-Supported Influence:
Moderation Effects in Socially Influencing Systems
Agnis Stibe( )
MIT Media Lab, Cambridge, MA, USA
agnis@mit.edu
Abstract. Persuasive technologies are commonly engineered to change beha-
vior and attitudes of users through persuasion and social influence without us-
ing coercion and deception. While earlier research has been extensively focused 2015
ETHICS
8
POSITIVENEGATIVE
INTENDED
UNINTENDED
MAJOR MINOR
HIGH
LOW
BACKFIRING
TARGET
SURPRISE
DARK GREY
INVISIBLE
VISIBLE
DARK
8
Persuasive Backfiring: When Behavior Change
Interventions Trigger Unintended
Negative Outcomes
Agnis Stibe1(&)
and Brian Cugelman2,3
1
MIT Media Lab, Cambridge, MA, USA
agnis@mit.edu
2
Statistical Cybermetrics Research Group,
University of Wolverhampton, Wolverhampton, UK
brian@alterspark.com
3
AlterSpark, Toronto, ON, Canada
2016
MIT
SOURCE: ONLINE
SOURCE: HIGI
0
0,5
1
1,5
2
2,5
TOTAL WEIGHT
+20%
+32%
SELF-
CONTAINED
SELF-
DRIVEN
JANUARY
1st
MEDIATED
ALGORITHMIC
MODERATED
USER
BEHAVIOR
USER
CONTENT
DYNAMIC
DESIGN
DYNAMIC
CONTENT
PERSUASIVE
DESIGN
TRIANGLE
CURVE
METRIC
CIRCLES
ARCHITECTURE
SOCIUM
MODERATION
ETHICS
CT
COMPETITION
SL
LEARNING
SC
COMPARISON
CR
COOPERATION
NI
NORMATIVE
SF
FACILITATION
RE
RECOGNITION
INVOLVEMENT
ENGAGEMENT
PARTICIPATION
POSITIVENEGATIVE
INTENDED
UNINTENDED
MAJOR MINOR
HIGH
LOW
BACKFIRING
TARGET
SURPRISE
DARK GREY
INVISIBLE
VISIBLE
DARK
1
2 3
4
5
6
7
8
TRANSFORMING
CHANGE CARE SOCIOTECH DESIGN
INTELLIGENCE
CLASSIFY UNDERSTAND ANALYTICS
DATA
READ FEEL SENSORS
Revealing the Supremacy of Social Influence

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