=Paper=
{{Paper
|id=Vol-1822/BCSS_2017_paper_2
|storemode=property
|title=Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
|pdfUrl=https://ceur-ws.org/Vol-1822/BCSS_2017_paper_2.pdf
|volume=Vol-1822
|authors=Piiastiina Tikka,Harri Oinas-Kukkonen
|dblpUrl=https://dblp.org/rec/conf/persuasive/TikkaO17
}}
==Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters==
https://ceur-ws.org/Vol-1822/BCSS_2017_paper_2.pdf
Persuading Peers in the Web: Social Influence and
Tweeters vs. non-Tweeters
Piiastiina Tikka, Harri Oinas-Kukkonen
University of Oulu, Finland
piiastiina.tikka@oulu.fi
Abstract. The present paper describes an experiment into the effects of sharing
vs. receiving roles in a behavior change intervention over a social platform,
Twitter. The purpose of the experiment was to explore the possible difference in
how participation styles (tweeting vs. non-tweeting) affected the participants’
perceived health behavior. Participants (N=30) in two groups were either to
encourage healthier eating by tweeting, or to read these messages in a Twitter
feed. The findings from PLS-SEM analysis suggest that the different roles lead
to different perceived health behavior outcomes. Social influence factors
appeared to boost the tweeting group’s efficacy appraisals, but that efficacy was
not seen to influence the perceived health behavior. For the non-tweeting group,
efficacy appraisal influenced perceived health behavior. These observations led
to the conclusion that the tweeting role may affect one’s perception of one’s
actual health behavior, and that for non-tweeters receiving peer support over
social media supports health behavior.
Keywords: Social media; information systems; health behavior; behavior
change; experiment; PLS-SEM.
1 Introduction
We have grown used to sharing parts of our lives online with other people. Some of
them we know well, others less so; in any event the ether has filled with information,
pictures, videos, opinions and all manner of content that could be regarded as personal
immaterial property about ourselves and about others. In the context of the social
Web, sharing is interaction, a series of “mutual or reciprocal actions or influence”
(Collins English Dictionary). Interaction mean communication of thoughts, opinions,
moods and other information, and as such it ranges from general interest to the very
personal in the degree to which the content being shared relates to the person sharing
it.
Interaction contains an element of influence. In social interaction people influence
each other whether in real life or online. Bandura’s Social Learning Theory [1] and
Social Cognitive Theory [2] as well as Zajonc’s work on social facilitation [3] are
some of the central works that have also extended to research on social influence in
the context of (persuasive) technology and information systems. In the area of tech-
nology the concepts of social influence have been considered in [4,5].
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 15
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
When Web users take part in a discourse by voicing opinions, endorsing views, or
even just by passing on some content, there is necessarily an expectation of impact on
other participants in that discourse. The content that is put forward will also reveal
something of the individual sharing it, since other participants can draw conclusions
about the speaker’s values, political views, socio-economic status and various other
characteristics. How true these characteristics are of the individual is another matter,
for people are free to project many different personae in a still fairly anonymous
online environment. Nevertheless, the question remains: what is the impact of the act
of sharing self-generated or curated content with other online users?
The present paper describes an experiment that builds on a number of core theories
of social influence and persuasion and studies the difference between active participa-
tion and passive recipient role in an online interaction situation over a social network-
ing platform. In the present paper we are interested in seeing if sharing-based influ-
ence over a social Web platform produces different efficacy appraisals between two
types of interaction styles. Based on the theories of social learning, social facilitation
and cognitive dissonance we can expect there to be a difference in how the influence
of interaction manifests itself in the experiment participants, depending on whether
their task is to actively contribute and try and influence others or whether their task is
instead simply to receive the messages. Based on the Protection Motivation Theory
[6], response efficacy and self-efficacy measures are feasible indicators of potential
for behavior change.
2 Background: Social influence in the Web
A system like Twitter has not been built for persuasion as such, and therefore the
system features have not been design with the intention to persuade [4]. It does, how-
ever, have is the ability to leverage social influence through its own core features of
message delivery, network forming, comparison, cooperation, competition and even
recognition when other users re-tweet messages or “like” them. Nevertheless, how
users employ all these features is up to them – just as it is equally up to them whether
they use these features at all or whether they take part in anything in any way. Such is
the freedom of the social Web. What, then, follows is that when people do take part,
they have chosen to do so.
Using social media as a delivery channel for supporting health behavior change has
been studied for example in a Twitter based experiment on smoking cessation [7],
finding that such an approach was twice as effective as other methods in helping
smokers to quit. Systems can employ self-referential persuasion in order for their
users to stay with the system, and by creating value and content and by involving
others the users remain active and more loyal to a system [8]. Social media and other
systems that users are already committed to and familiar with would, thus, seem natu-
ral platforms for promoting behavior change.
Bandura’s theories on social learning [1,9] are realized in a social media setting
like Twitter when users see not only that other people contribute but also what they
contribute. Users know that their followers can see any content they share, which can
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 16
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
affect the selection and style of sharing [3]. It is enough to be aware of the presence of
others even if there is no certainty of their paying attention to an individual’s activity
for social facilitation to take place [10]. At the same time, knowing that others are
sharing similar content can act as a further encouragement to share more.
Social influence features of a system are the mechanisms for how people can influ-
ence each other, but the other aspect to consider is the effect of the influencing act
upon an individual performing that act. Based on Festinger’s Cognitive Dissonance
Theory [11], an experiment by Festinger and Carlsmith [12] showed how a conflict
between cognition and behavior was adjusted internally when no obvious external
justification for an anti-cognition behavior was available. In the experiment disso-
nance was reduced by changing a personal opinion so that it would be more in line
with the requested behavior [12].
When receiving a recommendation to alter behavior, the recipient can (naturally)
either accept the message or reject it. Protection Motivation Theory (PMT) explains
the process of accepting or rejecting a behavior recommendation via a threat message
in terms of protection motivation [13]. According to PMT, individuals engage in be-
haviors that aim at reducing a given risk when they have high protection motivation
[14]. In essence, PMT posits that upon receiving a message that presents a threat, for
example negative health consequences of a bad diet, individuals respond to the threat
by considering the source, the severity and the cost of the threat and the behavior
required to avoid it, reaching eventually a high or low protection motivation state
[14]. Adopting behaviors that reduce the threat requires an adequate efficacy appraisal
[13].
On the Web, people take on the roles of ‘trailblazer’ and ‘tracer’ in online interac-
tions, and the social web both needs and thrives on trailblazers [15]. Such trailblazers
make wading in the information flow of the Web easier for others and enable social
navigation [15]. Customizing the media content sources in this way affects how for
example persuasive information is processed [16]. The tracers (also referred to as
lurkers) tend to form a majority of users [17], but the roles are not fixed: a tracer in
one context may well be a participating contributor in another, and the two roles need
each other in the ecosystem of the social Web [15]. It is, then, this continuous dynam-
ic of interaction that creates a persuasive system in which one user can achieve a mas-
tery experience by sharing the right things and another can assess information validity
by selecting the right people to follow.
3 Method
A between-subjects (N=30) experiment was devised for the purpose of studying
the difference in health message acceptance and subsequent perceived health behavior
change in an online social platform context. The effects were observed of interaction
style (active contribution vs. non-contribution styles), pre-test fruit and vegetable
consumption, and social influence on efficacy factors and subsequently on health
behavior. The experiment employed Twitter (twitter.com) as the social networking
platform, owing to the systems’ general availability.
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 17
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
3.1 Model and hypotheses
Figure 1 illustrates the research model developed for the present study. The model
includes the constructs of social influence (SI), efficacy (EFF), pre-test fruit and vege-
table intake (PreTI), preventative style response efficacy (REprev), and perceived
health behavior change (PHBC). Overall, we hypothesized that factors contributing to
higher efficacy appraisal will lead in turn to higher subsequent perceived health be-
havior change. In addition we wanted to determine the role of pre-test fruit and vege-
table consumption in itself in the PHBC construct.
Fig. 1. Research model.
Social influence (SI). This construct is a composite of a selection of observation-
based social influence factors as listed in the Persuasive Systems Design (PSD) model
[4]. The full set of social influence features includes social learning, normative influ-
ence, social comparison, cooperation, competition, recognition, and social facilitation,
as presented by Bandura [1,2] and Zajonc [3].
In the presented model we build upon a relationship between social influence and
efficacy. Self-efficacy beliefs stem from the mastery experience [2] which is the in-
terpreted result of previous performance: success in an activity or behavior feeds a
positive self-efficacy belief for that activity. Self-efficacy can also form from observ-
ing others at an activity [2]. Seeing others complete tasks, perform activities or illus-
trate the common norms for behavior will motivate and encourage a system user to
adopt the target behavior. Also, system users who receive praise or recognition for
their actions from their peers receive confirmation and encouragement, which in turn
can enhance experience of mastery. The construct of social influence (SI) comprises
social comparison, social facilitation and recognition features.
It is on these grounds that we propose two hypotheses regarding the relationship
between social influence and the efficacy constructs in our model:
H1: Social influence has a positive effect on efficacy.
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 18
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
Social influence’s impact on PHBC is not included in the model as part of our
parsing the constructs. Socially influencing features have been studied for example in
[5].
Category variable: Active contributor (AC) and non-contributor (NC) inter-
action style. One group of participants were given the task of contributing infor-
mation, knowledge and experiences over Twitter, thus forming the contributor group
(Tweeters). Meanwhile, another group were instructed to simply observe the commu-
nication that takes place on Twitter, thereby forming the non-contributor group (Non-
tweeters). Both groups were subjected to the exact same content, but participants in
one group generated some of that content themselves. The two groups are a category
variable in the present experiment and used as the basis for subgroup analysis.
Cognitive Dissonance Theory [11] posits that individuals strive towards consisten-
cy. If actions and cognition are not in line with each other, an individual will experi-
ence dissonance and will resort to a dissonance reduction process. In the case of en-
dorsing healthy eating the test participants may not be experiencing a strong conflict
in terms of their beliefs; they may well already be in agreement with the health mes-
sage in question. However, by endorsing the message they will need some internal
justification for their behavior. By justifying their behavior (“I am endorsing this mes-
sage because I believe in it”) the Tweeters should be more susceptible to being per-
suaded by the health message. On the same grounds we expected the response effica-
cy appraisal to benefit from the act of endorsing the message.
H2a: Active contribution style has a more positive effect on efficacy than a non-
contribution style.
H2b: Active contribution style, through increased efficacy, has a stronger positive
impact on perceived health behavior change than the non-contribution style.
Efficacy (EFF): Self-efficacy and Response efficacy. According to the Protection
Motivation Theory (PMT), both efficacy appraisals need to be judged sufficiently
high in order for an individual to be motivated to reduce a perceived (health) threat by
adopting message recommendations [13]. When an individual has sufficient belief in
his or her ability to carry out a behavior and likewise a sufficient belief in the efficacy
of the message (that is, following the advice will reduce the threat of negative health
consequences in the future), there is a higher likelihood of health message acceptance
and the subsequent health behavior to reflect that acceptance. As these two efficacy
types are both needed for a positive effect on behavior, our model has combined them
into one construct.
H3a: Preventative response efficacy has a positive effect on the overall efficacy
appraisal.
H3b: Efficacy has a positive effect on perceived health behavior change.
Pre-test fruit and vegetable intake (PreTI). A single-measure construct, PreTI
offers an indication of the participants’ fruit and vegetable consumption habits before
the experiment. The participants’ existing healthy eating habit may dictate the impact
on perceived health behavior change in the sense that if participants are already eating
plenty of fruit and vegetables before the experiment they are not likely to stop doing
so after the experiment. However, receiving confirmation for the correct behavior that
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 19
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
one is already engaging in could well increase one’s sense of ability and encourage
carrying on or even further improving the target behavior. Therefore we hypothesize:
H4a: Pre-test fruit and vegetable intake has a positive influence on efficacy.
H4b: Pre-test fruit and vegetable intake has a positive influence on perceived
health behavior change.
Perceived health behavior change (PHBC). The interest in the subsequent health
behavior in the present study focuses on short-term (immediate) response to the health
message, measuring the perceived health behavior change. This single variable con-
struct is made up of open-ended responses by the participants on the perceived effect
of the health message. Instead of using another food diary that measures one day very
close to the experiment, the selected approach offered the participants a broader free-
dom to reflect on how they made their food choices after the experiment.
3.2 Measurement instruments
The present study employs a combination of previously developed and validated
instruments as well as ones that have been adapted from their original form to better
match the research and problem domains. Previously validated instruments are effi-
cient, but do not necessarily produce the best match in another context [18]. All
measurement instruments are available from the authors upon request.
Health behavior: Fruit and vegetable consumption (baseline and post test).
The participants were asked to list everything they had eaten the day before and to try
and estimate specifically the consumption of fruit and vegetables as portions. Estimat-
ing portion sizes was an element of the health message provided in the test situation.
The open-ended form approach was adapted from Epton and Harris [19] to suit a
group situation. On the whole, Cox et al. [20] have found that, after guidance, the
portion-based estimations of fruit and vegetable consumption are realistic and relia-
ble.
The post-test questionnaire concluded with an open-ended question “Have you no-
ticed any difference in your food choices after taking part in the experiment? Please
describe.” A verbal, direct, question was selected for this item in order to probe the
participants’ cognition: how they observed their own behavior and thoughts after the
experiment. The responses were then analyzed and broken down into single state-
ments that were further categorized thus forming four ratings ranging from no effect
to perceived effect.
Efficacy measures. Self-efficacy was measured using four items adapted by Epton
and Harris [19] for the purpose of fruit and vegetable consumption from Fuchs, Lep-
pin, Schwarzer, and Wegner [21]. Response efficacy was also measured using a set of
items devised by Epton and Harris [19]. The same efficacy measures have also been
used in a study of fruit and vegetable intake in an online self-affirmation context [22].
As both self-efficacy and response efficacy are required for potential behavior
change, we combined these measures into a single efficacy construct in the present
model and study. However, our explorative analysis revealed that the preventative
response efficacy items differed from promoting ones and the self-efficacy items in
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 20
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
such a way that for the internal consistency of the efficacy construct it was more fea-
sible to handle preventative efficacy items as a construct of their own.
Social influence measures. Social influence was measured using instruments
adapted from earlier studies on socially influencing systems [5]. These were adapted
to match the problem domain and system relevant to the present study.
3.3 Material
Health message. The health message in the experiment focused on the role of fruit
and vegetables as part of a healthy, balanced diet. Specifically, the message promoted
the National Nutrition Council recommendation of eating at least 500g of fruit and
vegetables per day [23]. The material also provided information about a healthier diet
being a factor in the avoidance of various illnesses and health risks. A focused health
message in a form of a short presentation and leaflet was created on the basis of the
2014 guidance material [23].
Sample selection. The sample in the experiment was one of opportunity made up
of volunteers recruited from all parts of the research. The recruitment was done via
blanket e-mail and also by handing out flyers. Total of 37 people approached the re-
searchers and volunteered for the experiment, finally 30 people participated in the test
sessions.
Procedure. The experiment was conducted in six sessions. In the sessions partici-
pants were divided randomly (an A-B-A-B style division) into two groups. The pro-
cedure was explained, the participants filled pre-tweeting part of a questionnaire,
received a health message presentation and materials, and after the tweeting period of
the test, they filled in the post-tweeting part of the questionnaire. Approximately 24
hours later the participants were sent an e-mail link to a post-test food intake ques-
tionnaire that included the open-ended question about their perceived behavior
change. Pre-made Twitter accounts were provided for participants who did not have
one or did not wish to tweet on this topic to their real followers. Presence of followers
was not relevant in the experiment. The participants were explained that they should
consider everything from their own, personal, perspective.
Group A was instructed to be the tweeting group, group B was assigned to the
more passive non-tweeting role. The tweeting groups mission was to promote the
“half a kg a day” health message using any Twitter features and resources they want-
ed. The non-tweeting group was to receive the messages with the premise that they
had a reason (personal/friend/family needing guidance) why they were interested in
the topic. A # (hashtag) topic was provided for the tweeting and the following. Tweet-
ing period was between 15 and 20 minutes. The variance in the tweeting time depend-
ed largely on the group dynamic and the pace of messaging.
In the present experiment the participants were not assessed directly on their per-
ceived health expertise. Such perceived expertise could, potentially, play a role in the
participants’ responses, particularly in the Tweeter group. Some participants may
have had high confidence in their health expertise to begin with, making them com-
fortable with offering advice to others (and vice versa). However, the participants
were always instructed to act from their own perspective, using their own knowledge
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 21
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
or draw from the material provided in the test session. Random division into the ex-
periment groups should also ensure a reasonable distribution of participant character-
istics.
4 Data analysis and results
The research model was analyzed using Partial Least Squares Structural Equation
Modeling (PLS-SEM). The analysis tool was SmartPLS v.3.2.1. [24]. The nature of
the presented research was theory developing, and therefore PLS-SEM was consid-
ered the most suitable method of analysis for explaining the variance between the
constructs in the model. Typical recommendation for sample sizes with PLS-SEM
analysis is 10 times the largest number of paths directed at a particular construct [25],
which the sample size in the present study achieves.
4.1 Sample characteristics
There were 30 participants (14 females, 16 males) in total. Gender division between
groups was near equal: 8 females and 8 males in group A, and 6 females and 8 males
in group B. Age distribution was between 21 and 48 (31 years on average). The ma-
jority of participants were students from various fields of science at a university. Two
thirds of the participants had used Twitter before, and of those the majority had used
the service for a year or more.
4.2 Measurement model
The purpose of PLS-SEM is to predict theories [25]. PLS-SEM analysis demonstrates
explained variance (R2 values) in the constructs (latent variables), indicates the
strength (β-values) of the connections (relationships) between the constructs in the
model, and also tests the significance (p-values) of these relationships [26, 27]. Table
1 presents the descriptive statistics for the study (internal consistency and indicator
reliability).
The overall aim was to achieve a good validity of the measures so that each item
of a construct performed consistently (composite reliability and Cronbach’s Alpha),
and that indicator reliability (convergent validity) showed that the indicators meas-
ured the same phenomenon and shared high proportion of variance (AVE). In order to
ensure such measurement model validity some indicators were omitted when they
reduced the quality of the constructs. On the whole, indicator loadings and internal
consistency above 0.708 were considered acceptable, though for explorative studies
values above .6 are acceptable [25, 28]. In the present study composite reliability is
between .852 and .888 for all items and Cronbach’s alpha is above .6 for REprev and
above .7 for other items. The AVE values were above the minimum suggested value
of 0.50 [29]. Single item constructs do pose a potential risk of reduced validity with
PLS with items that are not observable [27]. However, in the present case the single
item constructs are analyzed composites from open-ended responses by the partici-
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 22
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
pants. Missing values (one item for one participants) were handled with mean value
replacement.
Table 1. Internal consistency and indicator reliability assessment (CA= Cronbach’s Alpha,
CR= composite reliability). Convergent reliability is indicated using AVE and Fornell-Larcker
analysis. Square roots of AVE and inter-construct correlations are shown as a bolded value.
CA CR AVE 1 2 3 4 5
1. EFF .769 .853 .594 .785
2. PHBC .568 1.000
3. PreTI .285 -.090 1.000
4. SI .855 .888 .503 .668 .514 -.046 .655
5. REprev .653 .852 .742 .450 -.099 .041 .323 .817
4.3 Structural model
The research model (Figure 1), was tested and the path coefficients and explained
variances for the model were obtained using bootstrapping (Figure 2). The modeled
constructs were reflective. Bootstrapping was done using 5000 samples and parallel
processing with no sign changes.
Fig. 2. PLS analysis results. ***p<.01, **p<.05, *p<.10.
In the complete model 48% of the variance in efficacy (EFF) was explained by the
preventative response efficacy and social influence (SI) constructs. 28% of the vari-
ance in the perceived health behavior change (PHBC) was explained by efficacy and
pre-test fruit and vegetable intake (PreTI).
4.4 Total effects and effect sizes, and predictive validity of the
model
For determining practical relevance of the model, total effects and effect sizes (f2)
were assessed (table 2). With guideline values of .02, .15, and .315 to indicate small,
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 23
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
medium and large effect, respectively [30], we can assess a latent construct’s contri-
bution to another [25]. In the presented model all effects were valid with two small,
two medium and one large f2. Based on the effects and effect sizes we conclude that
the model has some practical relevance.
EFF PHBC PreTI SI REprev
EFF .297 (med)
PHBC
PreTI .081 (small) .172 (med)
SI .585 (large)
REprev .088 (small)
Table 2. Total effects and effect sizes (f2).
We used the blindfolding procedure to assess predictive validity of the model. The
Stone-Geisser cross-validated redundancy value (Q2) is considered to suggest predic-
tive validity of endogenous constructs when the value is >0. Both efficacy and per-
ceived health behavior change demonstrate Q2>0, thus indicating predictive rele-
vance. For Efficacy, Q2=0.228, and for PHBC Q2=0.182.
4.5 Sub-group analysis
In order to examine the differences in the coefficients and the explained variances
between the two groups (tweeters and non-tweeters, A and B respectively) by means
of PLS analysis with bootstrapping for both groups, using 5000 samples. Tables 3 and
4 illustrate the significant paths and expected variances for the groups.
The observed difference is an interesting one, considering the two distinctly dif-
ferent roles the two groups had in the experiment. Group A was put in a more expert
and pro-active role as regards health behavior and trying to promote it, and the results
seem to suggest that existing eating habits (pre-test fruit and vegetable intake) is sig-
nificant in terms of post-test perception of healthy eating. For this group efficacy and
pre-test eating only contribute some 25% of the post-test perceived health behavior
change. A significant path coefficient from social influence to efficacy for group A
suggests that social influence does indeed boost efficacy. Some 85% of the variance
in the efficacy construct for group A is explained by pre-test fruit and vegetable con-
sumption, social influence and preventative response efficacy measure. The curious
result, then, is that this sense of efficacy does not seem to carry over to the post-test
perceived behavior change (PHBC). One reasonable conclusion is that the confidence
boost that comes from being in the more “knowing” position, where the task is to help
others and to share your own experience and perhaps even expertise with others, does
indeed boost confidence and sense of ability more, and there is no perceived need to
act on advice that you yourself have just offered to others – you already know this! A
study into identity-related behavioral intentions [31] may offer some support for this
conclusion. The study found that when an individual’s identity-related behavioral
intention became known socially, it tended to result in a premature sense of achieving
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 24
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
that aspired-to identity and consequently the individual would not enact the specific
behavior.
In group B it is precisely in the path from efficacy to post-test perceived health
behavior change where most significance can be observed. Social influence appears to
contribute to efficacy (p<.10). As regards efficacy, 60% of the variance is explained
by pre-test fruit and vegetable intake, social influence, and preventative response
efficacy measure together. In this group, 39% of the variance in the PHBC construct
is explained by efficacy and pre-test fruit and vegetable intake together – a notably
higher percentage than with group A. Such observations suggest that the recipient role
in the experiment has led to group B participants accepting the health advice as some-
thing they are to follow.
We hypothesized that SI would have a positive influence on EFF (H1), and that
the influence would be greater for group A than for group B (H2a). In view of the
path coefficients, both of these hypotheses are supported. We also hypothesized that
EFF would have a positive effect on PHBC (H3b), and more so with group A than
with group B (H2b). In this instance, overall the path coefficient from EFF to PHBC
is significant (p<.01), but groupwise we can see that the result was the opposite of the
hypothesis. The relationship between REprev and EFF (H3a) was found to be non-
significant. PreTI was hypothesized to have a positive impact on both EFF and on
PHBC, but this turned out not to be the case.
B B β
Subgroup A (N=16) Subgroup B (=14) Full (N=30)
EFF àPHBC .688n.s. 2.461** 2.600***
PreTIàEFF 1.014n.s. 1.107n.s. 1.381n.s.
PreTIàPHBC 2.939*** 1.165n.s. 3.139***
SIàEFF 6.065*** 1.951* 4.395***
REprevàEFF 1.559n.s. .912n.s. 1.816*
n.s. non-significant, *p<.1, **p<.05, ***p<.01
Table 3. Path coefficients (β) for the sub-group samples and the full sample. Subgroup A=
Tweeters, subgroup B= Non-tweeters.
R2 R2 R2
Subgroup A Subgroup B (N=14) Full (N=30)
(N=16)
EFF .853 (85%) .598 (60%) .475 (48%)
PHBC .247 (25%) .392 (39%) .276 (28%)
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 25
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
Table 4. Variances explained (R2) for sub-group samples and the full sample. Supbgroup A=
Tweeters, subgroup B= Non-tweeters.
5 Discussion
Peer support has been an established form in helping forming and maintaining be-
havior change for various health and lifestyle domains from weight loss to alcohol use
cessation. Such peer support is already available online, and it is only natural to pro-
gress towards more synchronous forms of social interaction that is characteristic of
social networking platforms.
In the present study we found that, as hypothesized, social influence affected effi-
cacy (positively) and that this effect was stronger in the actively contributing group
compared to the group who merely observed the discourse that was taking place on
Twitter. We also confirmed that on the whole, efficacy had a positive impact on per-
ceived health behavior change, but in this case our hypothesis that the effect would be
stronger with the Tweeters turned out to be the reverse: the path was significant for
the Non-tweeters.
The differences in the paths and the variance explained in the constructs led to two
main conclusions. For the non-tweeting group the contributing constructs of efficacy
and existing fruit and vegetable consumption style (pre-test intake) explained more of
the perceived health behavior change than for the actively contributing group. For this
latter group, however, social influence boosted their sense of efficacy more notably
than for the other group. From this we are led to conclude that the Tweeters may have
reached a premature assessment of ability and performance as regards the health be-
havior they were asked to promote. For the Non-tweeters efficacy did impact per-
ceived health behavior change: they more readily accepted the health message and
recognized that they made different food choices after the test.
The limitations of the present study are predominantly in the sample size (N=30).
In addition, this sample was collected by recruiting from a university mailing; such a
sample of opportunity naturally is weighted more heavily towards the higher-educated
population. Furthermore, the sample were allowed to use a made-up Twitter account;
some participants, however, were happy to use their own accounts. Regardless of
these limitations, we maintain that the results from the present study give cause for
designing and conducting further behavior change research in an actual social net-
working platform setting and real system users on the issues presented in this paper.
Having better insights into factors that affect acceptance of a behavior change mes-
sage is of great importance when looking for maximum impact. On the one hand there
is the great mass of social platform users, and on the other there is the potential of
peer support in behavior change. Supporting that peer support in the social network
environment requires understanding the mechanisms of social influence not only in
general terms but also in terms of interaction styles and other behavioral traits that can
be observed online. The present study focused on two interaction styles and by doing
so has shared helpful knowledge about the differences between these styles. Another
area of further research that touches on the present topic is the effect of synchronous
communication in persuasive interactions. The role of unobtrusiveness in interactions
Fifth International Workshop on Behavior Change Support Systems (BCSS’17): 26
Persuading Peers in the Web: Social Influence and Tweeters vs. non-Tweeters
between a system and its user has been touched upon for example in [32], but how
does the synchronicity of computer-mediated persuasion affect the perception of per-
suasiveness or the sustained use of a system?
Our research in the present paper offers theoretical contributions to the existing
body of knowledge by demonstrating the difference in information processing and
impact of social influence between participation styles. From the results we can see
that generating and contributing content in a social context leads to a different out-
come from receiving that content.
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