Persuasive Socio-Technical Systems: Practicing Social Influence Powers to Change People's Behaviors and Attitudes. Twitter Case Studies.

Persuasive Socio-Technical Systems:
Practicing Social Influence Powers to Change People's Behaviors and Attitudes

Twitter Case Studies

Agnis Stibe
Doctoral Candidate and Project Researcher
Department of Information Processing Science

agnis.stibe@oulu.fi
29224488
@agsti

Agnis Stibe “Persuasive Socio-Technical Systems”

Persuasion is: !
!

the inﬂuence !
of beliefs, attitudes, intentions, motivations, or behaviors.!

a process !
aimed at changing peopleʼs attitude or behavior, by using written
or spoken words to convey information, feelings, or reasoning, or a
combination of them.!

Source: http://en.wikipedia.org/wiki/Persuasion

Riga Business School
October 8, 2012 .oulu.fi


Source: http://www.flickr.com/photos/34557143@N07/3283901503/



Source: BJ Fogg



Behavior Change Support Systems!
!
- PSD Model!
- O/C Matrix!

Source: Oinas-Kukkonen H.


Persuasion postulates
IT is never neutral
(P1)

PSD Model Consistency Incrementality Routes
(P2) (P3) (P4)

Usefulness and ease Unobtrusiveness Transparency
of use (P5) (P6) (P7)

Persuasion context

The intent The event The strategy

Intended Use, user, and Message, route
outcome/change technology contexts

Persuasive software features

Primary task support Computer-human Perceived system Social influence
dialogue support credibility
October 8, 2012 .oulu.fi Source: Oinas-Kukkonen H.


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Outcome/Change Matrix
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.oulu.fi

BCSS. The perceived systemStibe “Persuasive Socio-Technical Systems”principles relate to how to design
Agnis
credibility design
system so that it is more believable and thereby more persuasive. The desig
principles in the social influence category describe how to design the system so that
Categoriesusers by leveraging social influence.
motivates of Persuasive Features

Social
influence
User
Primary task
support

Human-computer
!
dialogue
Perceived
system credibility

Other users

Fig. 1. Four categories of design principles for BCSSs

Tørning and Oinas-Kukkonen [25] have analyzed the scientific research publication

, and provides affective feedback for the user to adopt Socio-Technical Systems” results of this study suggest that very few
Agnis Stibe “Persuasive
activity. The
ing habits while working at the computer. Chi et al. [9] studies resulted in achieving the intended goal. Only a fe
d a smart kitchen application for improving home took advantage of any persuasive techniques, and none
by providing calorie awareness regarding the food interventions were conceptually designed through p
s used Categories of Persuasivethe cooking
in the meals prepared during Features design frameworks. The conclusion of this study
This was based on ubiquitous sensors for tracking the designing a new generation of BCSSs should be based
f calories in different ingredients, and then providing frameworks.

Persuasive systems design
techniques

Primary task support Dialogue support System credibility Social support
Tailoring Suggestion Surface credibility Social comparison
Tunneling Praise Authority Normative influence
Reduction Liking Trustworthiness Social learning
Self-monitoring Reminders Expertise Recognition
Simulation Rewards Real-world feel Cooperation
Personalization Similarity 3rd party endorsements Social facilitation
Rehearsal Social role Verifiability Competition

Figure 1. Persuasive systems design techniques.




Expected Contribution

Social Learning Incrementality?!

Social Comparison
Cognitive!
Dissonance?!
Normative Influence Behavior
Change
Social Facilitation

Cooperation

Competition Participation

Recognition

Feedback



Socio-Technical Context

Social Web

Individuals
Persuasion

Social Influence


Table 1 Behavior change related theories
Theory of Reasoned Action Individual behavior is determined by behavioral intentions, i.e., an individual's

Behavior Change
attitude toward the behavior and subjective norms about the behavior [6]
Theory of Planned Behavior Individual's perception of the ease with which the behavior can be performed,
i.e., behavioral control, influences individual’s behaviors [7]
Related Theories Technology Acceptance Model Perceived usefulness and perceived ease of use determine an individual's
intention to use a system, which leads into actual system use; perceived
ease of use impacts perceived usefulness; assumes that actors are free to
act without limitations when they just have an intention to act; based on
Theory of Reasoned Action [16]
Unified Theory of Acceptance Performance expectancy, effort expectancy, social influence, and facilitating
and Use of Technology conditions determine the usage intention and usage behavior, whereas
gender, age, experience, and voluntariness of use moderate this impact;
extended from Technology Acceptance Model [17]
Self-Efficacy Theory Individuals who perceive themselves as capable of taking action also do take
action; strengthening the sense of efficacy happens through vicarious
experiences, social models, social persuasion, and reducing people's stress
reactions and altering their negative emotional proclivities and
misinterpretations of their physical states [8, 21]
Social Cognitive Theory Observing others performing a behavior influences the perceptions of
individual’s own ability to perform the behavior, i.e. self-efficacy, and the
perceived expected outcomes [9]
Elaboration Likelihood Model Central and peripheral routes are key routes for persuasion; central route is
used when information processing is based upon critical thinking; peripheral
route is based on rules of thumb; change via central route is more enduring,
resistant and predictive of behavior [10]
Cognitive Dissonance Theory Individuals seek consistency among their cognitions such as beliefs and
opinions; inconsistency between attitudes or behaviors creates dissonance
that needs to be eliminated [18]
Goal Setting Theory Goals affect performance through directing attention and effort, energizing,
persistence, and by leading to arousal and/or use of task-relevant knowledge
and strategies; the highest goals produce the highest levels of effort and
performance; specific, difficult goals consistently lead to higher performance
than urging people to do their best; when goals are self-set, people with high
self-efficacy set higher goals than people with lower self-efficacy; people with
high self-efficacy are more committed to the assigned goals and and to
responding more positively to negative feedback [19]
Computer Self-Efficacy Computer self-efficacy means individual’s judgment of one’s capabilities to
use computers for both task performance and computer performance;
anxiety, innovativeness, task characteristics, prior performance, and
perceived effort play a role; based on Self-Efficacy Theory [20]




CASE STUDY : 1!
!

Comparative Analysis of Recognition and Competition!
as Features of Social Inﬂuence Using Twitter!
!

Source: Stibe A. and Oinas-Kukkonen H.



Research Context

Social Cognitive Theory : Self-Regulation

PSD model : Social Influence

Recognition Competition




Research Question

How and to what extent social inﬂuence design principles!
can persuade people !
to participate in sharing feedback?!

Recognition Competition




Research Framework
USER FACTORS SOFTWARE FEATURES USER BEHAVIOR
PERSONAL ENVIRONMENTAL BEHAVIORAL

Malone and Lepper, 1987 Oinas-Kukkonen and
Interpersonal Motivators Harjumaa, 2009, PSD

Cooperation CR
Cooperation H1
Bandura, 1991
Social Cognitive Theory Competition
CT
Competition H2
Judgment

Self-Regulation Recognition User Behavior
RE H3
Targeted to
Recognition
Self-Response Feedback Sharing
H4
Observation Vicarious Learning SL
Social Learning
Social Learning Theory Bandura, 1976
H5
Social Facilitation SF
Social Facilitation
Zajonc, 1965
!



Research Setting

•  A system developed on top of Twitter

•  A pilot study conducted in class setting with master students
–  37 participants in two computer rooms
•  18 in recognition room
•  19 in competition room
–  30 minutes hands-on use of the system
–  6 questions in total displayed to the participants
–  Participants responded to questions using Twitter

•  Online questionnaire about perceptions (47 questions, mainly Likert-7)




Persuasive 2012
Linköping, Sweden: June 7, 2012 .oulu.fi


Findings: Recognition vs. Competition

Independent sample t-test

Item Recognition Competition t-value df p
Twitter is a powerful tool to call
for action outside the virtual 5.50 4.32 2.937 35 .006**
world.
I believe that the system would
5.56 4.47 2.775 35 .009**
work well in a real airport.
I think that the system is effective
for encouraging users to 6.11 5.11 2.570 35 .015*
participate.

More encouraging to participate




Findings: Had vs. Had Not (seen themselves on the screen)

Item Yes No t-value df p
Displaying public recognition or All 5.44 3.25 4.512 33 .000**
the top responders helped me to Recognition 5.54 3.50 3.427 15 .004**
monitor my performance. Competition 5.36 3.00 2.977 16 .009**
Tweets provided by others on the All Non-significant difference
big display encouraged me to Recognition 5.69 5.00 3.323 12 .006**
come up with my tweets. Competition Non-significant difference
Displaying public recognition or All 5.00 3.75 2.352 33 .025*
the top responders motivated me Recognition 5.38 3.50 2.409 15 .029*
to produce more tweets. Competition Non-significant difference

More encouraging and motivating to tweet




Conclusions

•  Contributions:
–  Scientific:
An empirical analysis of persuasive software features from the PSD model;
–  For business:
A persuasive and operational system to engage customers in feedback sharing.

•  Limitations:
–  Class setting;
–  Sample: education and age;
–  Missing the control group.

•  Further research:
–  Field-testing - actual use;
–  Other social influence features.




CASE STUDY : 2!
!

Social Inﬂuence on Customer Engagement: !
The Effects of Social Learning, Social Comparison, and Normative Inﬂuence !

Source: Stibe A., Oinas-Kukkonen H., and Lehto T.



Social Cognitive Model

PERSONAL!
!
USER FACTORS:!
-  Vicarious learning!
-  Self-regulation!

ENVIRONMENTAL! BEHAVIORAL!
! !
SOFTWARE FEATURES:! BEHAVIORAL INTENTION:!
-  Social learning! -  To engage in feedback
-  Social comparison! sharing (using
-  Normative inﬂuence! information system)!




Research Model

Persuasive Software Features!
SC!
Social Comparison!
H4d!
H3!

H4c!
SL! NI!
Social Learning! Normative Inﬂuence!

H4b! H2!

PP!
H4a! Perceived Persuasiveness!

H1!

BI!
Behavioral Intention!




Ongoing Studies: Social Comparison



Ongoing Studies: Normative Influence



Results

Persuasive Software Features!
SC!
Social Comparison!
β=0.59**" 34%!

β=0.20*"

β=0.47**" NI!
SL! Normative Inﬂuence!
Social Learning!
36%!

β=0.21*" β=0.53**"

PP!
β=0.28*" Perceived Persuasiveness!
45%!

β=0.28*"

BI!
Behavioral Intention!
24%!




CASE STUDY : 3!
!

Incremental Persuasion through Microblogging:!
A Survey of Twitter Users in Latvia!

Source: Stibe A., Oinas-Kukkonen H., Berzina i., and Pahnila S.



Research question

What kinds of inherent persuasion patterns
do exist in Twitter that can !
change usersʼ behaviors and/or attitudes? !




Research settings July 19-28, 2010
Latvia
Quantitative survey online:
-  37 questions
-  403 valid responses

Invitations for users:
-  7 tweets by authors
-  1 author’s blog entry in
-  http://ilzeberzina.wordpress.com/
-  Several authors’ messages in other social networks

-  37 retweets by other Twitter users
-  1 reference in technology blogger article




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-)(.*%
!"#$% Profile of the respondents
&'()*%

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Number of followees and followers you have in Twitter?

(!!"

'!!"

&!!"

%!!" 516617**+"

516617*3+"
$!!"

#!!"

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)*++",-./"(" ("01/,-+",1" #",1"$"2*.3+" $"2*.3+"./4"
01/,-+" #"2*.3" 013*"




How often do you tweet?

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,!"#
+!"#
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)!"# ;.:.72<#=4./#>.7#?..@#
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-.//#0123# )#45301/# $#05#%# %#6.27/#
)#45301/# 05#$#6.27# 6.27/# 238#457.#

The amount of tweeting
increases over time.
χ2(6)=18.059, p=0.006




Regarding content in Twitter you consider yourself as?

$!!"#
,!"#
+!"#
*!"# 97.2057#
)!"#
:./;538.7#
(!"#
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45301/# 05#$#6.27# 457.#

Experienced users generate more
content than new users.
χ2(9)=29.789, p=0.000




What is the level of credibility in Twitter?

(!!"#

'!"#

&!"# 567-#

%!"# 8*4690#-67-#

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,1#(#2*.3# $#2*.3+#
2*.3+#
./4#013*#

The longer one has used the Twitter χ2(9)=21.130, p=0.012
the higher trust the user has for it.




Are there unwritten behavioral rules in Twitter?

$!!"#
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)!"# 9./#
(!"# :278#05#/26#
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45301/# 05#$#6.27# 457.#

Twitter users learn over time unwritten χ2(6)=19.064, p=0.004
communication and/or behavioral rules in Twitter.




Is Twitter a powerful tool to call to action outside the virtual world?

(!!"#

'!"#

&!"#
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$!"#
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2*.3+#
013*#

Twitter is powerful tool to call for action offline, i.e. outside the
virtual world, and experienced users are more ready to take action
based on their communication via Twitter.
χ2(6)=18.551, p=0.005




Summary of findings
Number of
followers and
Intensity of
followees tweeting

Content Trust
generators information

Powerful tool to
Recognize call to action
unwritten outside the
communication virtual world
rules




4th postulate of Persuasive Systems Design framework

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CHANGE
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I N C R E M E NTAL STE PS




CASE STUDY : 4 (ongoing)!
!

A Longitudinal Study of Behaviors and Attitudes !
of Twitter users in Latvia!


Twitter influences my thoughts.

0 20 40 60 80 100 120 140 160 180 200

Disagree completely
Pilnībā nepiekrītu

Disagree
Nepiekrītu

Somewhat disagree
Daļēji nepiekrītu

Undecided
Neesmu izlēmis

Somewhat agree
Daļēji piekrītu A
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

In Twitter, there are norms that should be followed by users, including me.
(Normative Influence)

0 20 40 60 80 100 120 140 160

Disagree completely

Disagree
Nepiekrītu

Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

Twitter allows me to compare myself with others.
(Social Comparison)

0 20 40 60 80 100 120 140 160 180 200

Disagree completely

Disagree
Nepiekrītu

Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

In Twitter, I can observe the behavior of other users and learn from it.
(Social Learning)

0 20 40 60 80 100 120 140 160 180 200 220

Disagree completely

Disagree
Nepiekrītu

Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

Twitter is an influential tool to call for actions outside the virtual world.

0 20 40 60 80 100 120 140 160 180 200

Disagree completely

Disagree
Nepiekrītu

Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

In Twitter, there is an observable tendency of followers to stratify in the
groups of interests.

0 20 40 60 80 100 120 140 160 180 200 220

Disagree completely

Disagree
Nepiekrītu

Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

Twitter influences my behavior.

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Disagree completely

Disagree
Nepiekrītu B
Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Daļēji piekrītu B
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

In Twitter, I can compete with other users.
(Competition)

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140

Disagree completely

Disagree
Nepiekrītu B
Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

In Twitter, users receive recognition for special merit.
(Recognition)

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140

Disagree completely

Disagree
Nepiekrītu B
Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

There are “unwritten” communication and behavioral rules in Twitter,
which users need to follow.

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140

Disagree completely

Disagree
Nepiekrītu B
Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Agree
Piekrītu

Agree completely
Pilnībā piekrītu

.oulu.fi

In Twitter, I can observe other current active users.
(Social Facilitation)

0 20 40 60 80 100 120 140 160 180 200

Disagree completely

Disagree
Nepiekrītu

Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Daļēji piekrītu

Agree
Piekrītu C
Agree completely
Pilnībā piekrītu

.oulu.fi

In Twitter, I have an opportunity to cooperate with others.
(Cooperation)

0 20 40 60 80 100 120 140 160 180 200 220 240 260

Disagree completely

Disagree
Nepiekrītu

Somewhat disagree

Undecided
Neesmu izlēmis

Somewhat agree
Daļēji piekrītu

Agree
Piekrītu C
Agree completely
Pilnībā piekrītu

.oulu.fi


Summary!



Summary of Current Findings

Behavior
Change
Recognition

Competition

Participation

Social Facilitation Cooperation

Feedback

Social Comparison Normative Influence


Agnis.Stibe@oulu.fi
@agsti
29224488

Thanks to:

the Foundation of Nokia Corporation

the Finnish Funding Agency for Technology and Innovation

the Doctoral Program on Software and Systems Engineering

Persuasive Socio-Technical Systems: Practicing Social Influence Powers to Change People's Behaviors and Attitudes. Twitter Case Studies.

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