personalized motivation-supportive messages for increasing ... · diverse motivations to...
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PersonalizedMotivation-supportive Messages
for Increasing Participationin Crowd-civic Systems
Paul Grau (KAIST, TUB), Babak Naderi (TUB), Juho Kim (KAIST)
CSCW 2018
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Crowd-civic systemssupport citizens who work together to collect local knowledge, discover social issues, or reform official policies.
2(McInnis et. al., CSCW 2017)
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3
Local Problem Reporting
FixMyStreet.comIntroduction
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4
Crowdsourced Policymaking
Off-road traffic law crowdsourcing in Finland [Aitamurto 2016]Introduction
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A Crowd-Civic Challenge: Recruitment and Participation
5Introduction
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A Crowd-Civic Challenge: Recruitment and Participation
6Introduction
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A Crowd-Civic Challenge: Recruitment and Participation
7Introduction
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A Crowd-Civic Challenge: Recruitment and Participation
8Introduction
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A Crowd-Civic Challenge: Recruitment and Participation
9
Democratic Representativeness?
Self-selection bias[Aitamurto 2016]
Introduction
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Diverse Motivations to Participate Voluntarily
10
How to move on from “one size fits all”?
[Aitamurto & Saldivar 2017]Introduction
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Research Question
Canmotivation-supportive design,especially when personalized,increase participationin a crowd-civic system?
11Introduction
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Approach: Theory-based Interface Design
12
Personality-targeted Design
Motivation theory
Study 1 Study 2
Discussion
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Personality-targeted Design
UI personalized to match a user’s personality
13
✔ ✔
ApproachMoon 2002, Nov & Arazy 2013, Jia et al. 2016
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Self-Determination Theory (SDT)
Motivational orientations = lasting aspects of one’s personality
How task, environment, and user factors affect motivation differences
14Approach
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Gradient of Self-Determination and Autonomous Motivation
15Simplified excerpt from Figure “Taxonomy of human motivation” [Ryan 2000]
Amotivation Intrinsic MotivationExtrinsic Motivation
Less self-determined
Less autonomous
More self-determined
More autonomous
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Personality-targeted Design
Motivation theory
Study 1 Study 2
Discussion
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Two-part Investigation
17
Study 1: Online Survey
Self-reported preferences
Amazon Mechanical Turk (N=150)
Paid
Study 2: Field Study
Engagement measures
KAIST members(N=120)
Voluntary
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Two-part Investigation
18
Study 1: Online Survey
Self-reported preferences
Amazon Mechanical Turk (N=150)
Paid
Study 2: Field Study
Engagement measures
KAIST members(N=120)
Voluntary
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19Image for baseline version.
Design
Study 1
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Need for Autonomy
Need for Competence
Need for Relatedness
Autonomous orientation
Impersonal orientation
Controlled orientation
Design Versions6 alternative versions
based on different concepts from SDT
+ BaselineStudy 1
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21
Pairwise Comparison Survey“In which version would you personally be more likely
to contribute an idea?”
Study 1
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22
Pairwise Comparison Survey“In which version would you personally be more likely
to contribute an idea?”
Study 1
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Data Collection (N=150)
23
Why did you choose that?
2. Motivation questionnaires
✔A B
1. Preferences
A B C D E F Study 1
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Participants have diverse preferences
24Bradley-Terry Model worth estimates. ANOVA p<0.05. N=99
Individual preference estimate
Control orientation
Autonomous orientation
Control needRelatedness needAutonomy need
Impersonal orientationBaseline
30%
20%
10%
Study 1
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Preferences correlate with motivation scores
25Bradley-Terry Model worth estimates. Highlighted changes p<0.05. N=99
High Amotivation score
Control
Autonomous
Control needRelatedness need
Autonomy need
Impersonal Baseline
30%
20%
10%
Control
Autonomous
Control needRelatedness needAutonomy need
Impersonal Baseline
Low Amotivation score
Study 1
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Study 1 Limitations
Self-reporting (hypothetical bias)
Paid workers, possibly not representative of the general population
26Study 1
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Two-part Investigation
27
Study 1: Online Survey
Self-reported preferences
Amazon Mechanical Turk (N=150)
Paid
Study 2: Field Study
Engagement measures
KAIST members(N=120)
Voluntary
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Treatment Conditions
Controlsupport
Autonomy supportBaseline
Study 2 29
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Example for Different Motivation-supportive MessagesControl supportAutonomy supportBaseline
3 different versions for “New Idea” screens. Study 2
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Treatment Conditions
Controlsupport
Autonomy supportBaseline
PersonalizationStudy 2
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Method
32
Open-call recruitment
Signup group assignment
Engagement measures
Post-survey
Study 2
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Results
33
120
Users
72
Ideas
357
Likes
62
Comments
Study 2
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No correlation between Treatment and Signup Group
34
Control-oriented Group Autonomy-oriented Group
Interaction count per user (N=114)
Least-squares means, GLM for Poisson distributed count data. Study 2
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Observations on Personalization
Using a limited number of questions to classify turned out to be inaccurate.
35Study 2
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Post-hoc classification→ Re-classify users based on post-surveyfull questionnaires (kmeans clustering).
36Study 2
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Correlation between Treatment and Post-hoc Group
37
Control-oriented Group Autonomy-oriented Group
Interaction count per user (N=30)
ANOVA for number of interactions p<0.01 for treatment, group, and interaction;Pair comparisons, Tukey method: left-hand side all p<0.01, right-hand side n.s. Study 2
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Study 2 Limitations
Small N for post-survey
Homogenous population (mostly Korean students)
38Study 2
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Personality-targeted Design
Motivation theory
Study 1 Study 2
Discussion
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Benefits and Challenges of Theory-based Design
SDT has proven to be a useful perspective for designing applications dealing with voluntary participation.
Translating theory to design is not an exact process.
40Discussion
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Possibility of Personalization
Results show personalization is possible,but need to improve automatic classification.
Trade-offs:explicit and implicit data elicitationpotential adverse effectspersonalization and customization
41Discussion
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Challenges of Field Study about Motivation
Advertising study without influencing motivation
How to track diversified (offline) recruitment?
42Discussion
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Let’s move away from “one size fits all” by designing with diverse populations’ motivations in mind.
43Discussion
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Personalized Motivation-supportive Messages forIncreasing Participation in Crowd-civic Systems1. Survey: motivation orientation differences
can explain individual preferences for different motivation-supportive designs.
2. Field study: some tangible effects on actual participation but surfaced tradeoffs.
3. Combination of studies can give a more complete picture.
44Paul Grau
Open-source app and survey code:http://github.com/graup/manyideas
[email protected]: @graycoding
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References for slides[Aitamurto 2016] Tanja Aitamurto and Helene Landemore. Crowdsourced deliberation: The case of the law on offroad traffic in Finland. Policy & Internet, 8(2):174–196, 2016.
[Aitamurto 2017] Tanja Aitamurto and Jorge Saldivar. Motivating participation in crowdsourced policymaking: The interplay of epistemic and interactive aspects. CSCW ‘17. ACM, 2017.
[Deci 1985] Edward L Deci and Richard M Ryan. The general causality orientations scale: Self-determination in personality. Journal of research in personality, 19(2):109–134, 1985.
[Grano 2008] Caterina Grano, Fabio Lucidi, Arnaldo Zelli, and Cristiano Violani. Motives and determinants of volunteering in older adults: An integrated model. The International Journal of Aging and Human Development, 67(4):305–326, 2008.
[Hsieh 2016] Gary Hsieh and Rafał Kocielnik. You get who you pay for: The impact of incentives on participation bias. CSCW ‘16. ACM, 2016.
[McInnis 2017] Brian McInnis, Alissa Centivany, Juho Kim, Marta Pobet, Karen Levy, and Gilly Leshed. Crowdsourcing law and policy: A design-thinking approach to crowd-civic systems. CSCW ’17. ACM, 2017.
[Ryan 2000] Richard M Ryan and Edward L Deci. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1):54–67, 2000.
[Zinnbauer 2015] Dieter Zinnbauer. Crowdsourced corruption reporting: What petri ed forests, street music, bath towels, and the taxman can tell us about the prospects for its future. Policy & Internet, 7(1):1–24, 2015. 45
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Appendix
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Controlled Orientation Autonomous Orientation Impersonal Orientation Baseline
A gift card is a great incentive for someone to participate.
It looks more friendly. It doesn’t try to make me feel guilty for not sharing an idea.
It’s very simple and it doesn’t insult the user by talking down to them.
Making things better for everyone sounds like the best plan overall.
The chance of winning makes me more compelled to participate and try harder.
It’s honest. Having motivational quotes makes the entire program seem less serious.
Preferred by 62% 14% 3% 7%
Qualitative feedback is aligned with expectation
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Overall participation
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8 days, 120 users, 72 ideas, 62 comments, 357 likes38 post-survey responses (32%)
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Detrimental Effects of Controlled RegulationPost-survey data suggests additional effects.
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