Received: 3 August 2019 Revised: 23 January 2020 Accepted: 8 February 2020
DOI: 10.1111/jcal.12433
A R T I C L E
Persuasive social features that promote knowledge
sharing among tertiary students on social networking sites:
An empirical study
Isaac Wiafe1 | Felix N. Koranteng2 | Ebenezer Owusu1 | Akon O. Ekpezu1 |
Samuel A. Gyamfi3
1Department of Computer Science, University
of Ghana, Legon-Accra, Ghana Abstract
2Department of Information Systems and Persuasive system features have been widely adopted to encourage attitude and
Innovation, Ghana Institute of Management
behaviour change. Recently, most social networking sites (SNS) adopt some form of
and Public Administration, Achimota-Accra,
Ghana persuasive system features that leverage social influence to deliberately induce pre-
3Department of Information Technology scribed behaviours in their users. However, studies on how these features can be
Education, University of Education, Winneba,
Kumasi-Ashanti, Ghana used to promote knowledge sharing are inadequate; particularly, regarding how SNS
that have been developed solely for academic purposes can adopt these features to
Correspondence
Isaac Wiafe, Department of Computer Science, promote knowledge sharing. To address this knowledge gap, this study integrates
University of Ghana, P O Box LG163, Legon- constructs from the social capital theory and persuasive systems design model to
Accra, Ghana.
Email: iwiafe@ug.edu.gh investigate the impact of persuasive social features on knowledge sharing among stu-
dents of tertiary institutions on academic social networking sites. Data are quantita-
Felix N. Koranteng, Department of Information
Systems and Innovation, Ghana Institute of tively gathered from 218 respondents from tertiary institutions and statistically
Management and Public Administration, analyzed. The results suggest that perceived dialogue support and perceived social
P O Box AH50, Achimota-Accra, Ghana.
Email: felixnkoranteng@gmail.com support have strong influences on knowledge sharing behaviour.
Peer Review K E YWORD S
The peer review history for this article is
knowledge sharing, persuasive features, social facilitation, social networking sites, social
available at https://publons.com/publon/10.
1111/jcal.12433. support
1 | INTRODUCTION (ASNS). Examples of such sites are Kudos, ResearchGate and Men-
deley. ASNS connect academics with common interests: they provide
Over the past decade, persuasive technologies have gained a substan- features such as discussion boards and email services that enable col-
tial role in the lives of many individuals (Wiafe & Nakata, 2012). They laboration and knowledge sharing activities. Thus, they support social
have been adopted to induce attitude change and motivate individual relationships and interactions, which are essential factors of knowl-
behaviour (Agnisarman, Madathil, & Stanley, 2018). Persuasive tech- edge sharing (Lim & Richardson, 2016). Individuals may be influenced
nologies are extremely prevalent on social networking sites (SNS) to share knowledge not only by physically interacting with others
(Fogg, 2008). SNS are mediums for social activity that enable users to nearby but also through information systems (IS) that are specifically
create profiles and share content with others in a network (Ellison, designed for such purposes (Stibe, 2015). Consequently, IS can be
Gibbs, & Weber, 2015). engineered with social influence principles through their interfaces
In contrast to generic SNS such as Facebook and Twitter, some and designs to induce a desired behaviour (Oinas-Kukkonen, 2013). In
SNS are specifically designed for academic collaboration to promote essence, ASNS leverages on social influence principles such as recog-
knowledge sharing (Jeng, He, Jiang, & Zhang, 2012; Koranteng & nition and normative influence to facilitate changes in knowledge
Wiafe, 2019). These are called academic social networking sites sharing behaviour of its users.
J Comput Assist Learn. 2020;1–10. wileyonlinelibrary.com/journal/jcal © 2020 John Wiley & Sons Ltd 1
2 WIAFE ET AL.
Besides developing robust systems and software, ASNS devel- with this notion (Bock & Kim, 2002; Lin, 2007; Vuori & Okkonen,
opers are required to build systems that continuously engage users in 2012). In this study, attention is focused on the role of social relation-
knowledge sharing activities. It is therefore imperative that developers ships and interactions in knowledge sharing activities. Particularly, this
understand the social factors that influence users' knowledge sharing study seeks to identify persuasive features that are embedded in ASNS
behaviour on such sites. However, research that focuses on and how they influence knowledge sharing behaviour.
persuasive social features of ASNS is scarce. Although some studies Current studies indicate that the internet has been flooded with
have examined social persuasive principles (Stibe, 2015; Stibe & social technologies (Appleford, Bottum, & Thatcher, 2014). For instance,
Oinas-Kukkonen, 2014), these studies were conducted aside from the social media are rapidly being integrated into business websites and
scope of knowledge sharing and ASNS. For instance, Stibe and Oinas- their processes in order to increase customer interactions and to facili-
kukkonen (2014) explored the influence of social persuasive principles tate product and service improvements. Social media have also been
on publicly displayed systems. successfully integrated into lifestyle applications such as weight loss,
Accordingly, this study examines the persuasive social features healthier lifestyles and security-aware systems (Wiafe & Nakata, 2012).
that promote knowledge sharing on ASNS. It adopts constructs from In such situations, social technologies are harnessed to intentionally
the social capital model (Nahapiet & Ghoshal, 1998) and persuasive change human behaviour and or attitude (Wiafe, Nakata, & Gulliver,
systems continuous use intention model (Lehto & Oinas-kukkonen, 2011). Consequently, social media have gained acceptance for knowl-
2015), which are integrated to develop a research model. It is note- edge sharing activities. Their applications include ASNS, where generic
worthy that Lehto and Oinas-kukkonen (2015)'s model was developed social influence principles have been identified to promote knowledge
based on the persuasive system design model by Oinas-Kukkonen sharing behaviour (Koranteng &Wiafe, 2019).
and Harjumaa (2009). The research model developed in this study is As IS evolve from a static repository of information with fixed
tested on a sample of tertiary students to identify and validate key interfaces to favourable social environments and platforms that per-
features that promote knowledge sharing. The findings from this mit users to actively interact and share information (Bruns, 2008), it is
study indicate that social principles including social support, social possible to design systems with persuasive intent. In other words,
interactions and social identification play a major role in promoting dynamic systems are designed with variable content to alter human
knowledge sharing on ASNS. cognition to a predetermined concept or idea. Although one can argue
In the next section, persuasive social features and their relation to that existing ASNS incorporate persuasive features into their design
knowledge sharing are discussed. This is followed by a discussion on principles, there is a lack of sufficient information as to whether these
the research model and hypothesis formulation. Thereafter, the persuasive features intend to promote knowledge sharing. This is
research methodology, data analysis, findings and research implica- more worrying, particularly, considering the evidence that users on
tions are presented. these sites have been identified as information consumers but not
sharers (Collins, Shiffman, & Rock, 2016; Meishar-Tal & Pieterse,
2017). This observation may be attributed to the lack of a clear under-
2 | PERSUASIVE SOCIAL FEATURES AND standing of the role of these features on knowledge sharing. As men-
KNOWLEDGE SHARING tioned earlier, existing studies mostly focus on how the affordances of
generic SNS impact knowledge sharing (Koranteng, Sarsah, Kuada, &
In today's research environment, collaboration and knowledge sharing Gyamfi, 2019). However, it will be more appropriate if attention is
are a necessity (Garcia-Sánchez, Diaz-Diaz, & De Saá-Pérez, 2019). given to persuasive social networking features since it has been sub-
Although several institutions invest huge sums of money into the stantiated that persuasion features that leverage on social activities
acquisition of software, databases and networking infrastructure, the are effective for behaviour and attitude change (Oyibo, Orji, &
attainment of effective knowledge sharing continues to be a challenge, Vassileva, 2017; Torning, Hall, & Oinas-Kukkonen, 2009).
especially tacit knowledge. As such, socio-cognitive mechanisms are In this paper, we define a persuasive social feature as any feature,
adopted to encourage knowledge-sharing (Zhang, Zhu, & Wang, 2019). principle or factor that exhibits the capability of changing an individual's
Effective knowledge sharing may be achieved through direct contacts behaviour via social networking. The next section discusses some per-
and relationships between individuals (Endres & Chowdhury, 2019). suasive social features and their hypothesized relationships regarding
Consequently, effective knowledge sharing requires communication knowledge sharing on ASNS.
opportunities and good interaction among members within a commu-
nity (Zheng, 2017). Social factors such as trust, interaction, identifica-
tion, and shared objectives have been identified as the foundations for 3 | RESEARCH MODEL AND HYPOTHESIS
relationship building and they also have significant impacts on knowl- FORMULATION
edge sharing among individuals (Chiu, Hsu, & Wang, 2006; Chow &
Chan, 2008; Koranteng, Wiafe, & Kuada, 2019; Tsai & Ghoshal, 1998; According to Markus and Saunders (2007), IS researchers must
Wasko & Faraj, 2005). Also, there are suggestions that factors such as develop fresh theories in an attempt to complement the quick
monetary rewards and incentives influence knowledge sharing behav- changes in IS and their effects on phenomena. It is also essential to
iour (Bartol & Srivastava, 2002). However, some researchers disagree adopt appropriate measurement items that have been tried and tested
WIAFE ET AL. 3
as they unveil existing knowledge gaps and also provide direction for Nakata, & Gulliver, 2012). Shared language facilitates shared under-
research. Consequently, the research model presented in this study standing and effective communication. It promotes shared meaning
(see Figure 1) is an integration of selected constructs from prior stud- among individuals which enhances participation in social interactions
ies by Nahapiet and Ghoshal (1998), and Lehto and Oinas-kukkonen (Davison, Ou, & Martinsons, 2018) and may result in persuasion.
(2015). The selection was based on the definition of persuasive social According to Sin and Kim (2013), the lack of shared language often
features as explained earlier. Shared language, shared vision, social hinders successful interactions among individuals from different disci-
interaction ties and knowledge sharing were adopted from social capi- plines. Thus, for individuals to effectively interact on ASNS, there
tal concepts proposed by Nahapiet and Ghoshal (1998) whereas social must be a shared vocabulary (Koranteng & Wiafe, 2019). Moreover,
identification and dialogue support were adopted from Lehto and because groups formed on ASNS are mostly domain-specific, there is
Oinas-kukkonen's (2015) work. The modified definitions of the a higher probability that members will have a shared interpretation,
selected constructs and their sources are presented in Table 1. especially regarding jargon and keywords. When members understand
each other, they are likely to interact frequently. This frequent com-
munication encourages member closeness to each other. Thus, there
3.1 | Shared language and social interaction ties is a significant relationship between shared language and knowledge
sharing (Koranteng, Sarsah, Kuada, & Gyamfi, 2019).
In any social setting, language is important for social interactions To confirm this relationship on ASNS, it is hypothesized that:
(Omotayo & Babalola, 2016) and it supports persuasion (Dragoni,
Bailoni, Maimone, Marchesoni, & Eccher, 2019; Wiafe, Alhammad, H1a Shared language positively influence social interaction ties within
an academic social networking site.
3.2 | Shared vision and social interaction ties
The more individuals find others with similar characteristics in terms of
vision and values, the higher the frequency of interaction (Al-Daihani,
Al-Qallaf, & AlSaheeb, 2018). Thus, individuals with similar objectives
are expected to have higher levels of interactions than those who do
not. Forslund Frykedal and Hammar Chiriac (2018) confirmed that
members with common goals mostly participate in group activities. As
mentioned earlier, groups on ASNS are formed based on a particular
discipline and thus, users readily find groups in their relevant field and
with similar objectives. While this is true, how such similarities affect
F IGURE 1 Research model showing the relationships between interactions on ASNS is not definite (Koranteng & Wiafe, 2019). There-
persuasive social features of academic social networking sites fore, to explore this relationship further, it is hypothesized that:
TABLE 1 Model constructs, definition and sources
Construct Definition Source
Shared language (SL) It is a common vocabulary that enable actors to Nahapiet and Ghoshal (1998); Tsai and Ghoshal
communicate with common understanding (1998)
Shared vision (SV) It entails common goals and objectives of members within Aslam, Shahzad, Syed, and Ramish (2013); Chiu et al.
a social network (2006)
Social interaction ties (SIT) It represents the strength of the relationships, the Chiu et al. (2006); Tsai and Ghoshal (1998)
frequency, and intensity of interactions among members
of a network
Perceived dialogue support This shows users' perception of how a system provides Adaji and Vassileva (2016); Fogg and Nass (1997)
(PDS) relevant, motivating feedback to its users via words,
images, sounds and other forms of media
Social identification (SI) This involves identifying with other system users, their Aslam et al. (2013); Ma and Agarwal (2007)
shared characteristics, common interests/language
Perceived social support This represent users' perception of how the system Chiu et al. (2006)
(PSS) motivates them through other members
Knowledge sharing (KS) This entails the willingness of individuals in a network to Bock, Zmud, Kim, and Lee (2005); Chiu et al. (2006)
share their acquired knowledge with others
4 WIAFE ET AL.
H1b Shared vision positively influence social interaction ties within an Nakata, & Gulliver, 2014) and expectations in addition to influences
academic social networking site. from others (Salahshour Rad, Nilashi, Mohamed Dahlan, & Ibrahim,
2019). These influences may include support that others provide
them. As explained by Fogg (2009), although an individual may have
3.3 | Social interaction ties, social identification the motivation to perform a behaviour, the lack of ability to perform
and perceived social support the behaviour hinders the possibility of the behaviour performance.
Thus, support from others is essential. Perceived social support has
Based on the definition of social interaction ties in Table 1, it is been demonstrated to be a key factor that influences decisions (Orji,
expected that individuals will have positive affection towards a group 2014). Yet, as mentioned earlier, there is little evidence on how per-
when there is increased communication among members (Dubos, ceived social support influences knowledge sharing, especially within
2017). Social interaction enables frequent communication among mem- online academic groups. Perceived social support is enhanced by a
bers (Lim & Richardson, 2016), and increases group spirit and members' reciprocal exchange of resources between parties. Mostly, within any
affection towards a group (Koranteng, Wiafe, Katsriku, & Apau, 2019). social setting (such as social networks), individuals expect their actions
Since ASNS augment the formation and strengthening of social ties to be reciprocated (Kwahk & Park, 2016). Koranteng, Sarsah, Kuada,
(Curry, Kiddle, & Simmonds, 2009) within academic social groups, social and Gyamfi (2019) argues that individuals will share knowledge when
interaction is expected to promote social identification and support as they presume it to be fair and mutual. This is because, there is a strong
established in other studies that did not consider academic networks. relationship between reciprocal exchanges and intention to participate
However, this assertion has not been empirically evaluated for the spe- in online groups (Moghavvemi, Sharabati, Klobas, & Sulaiman, 2018).
cific case of ASNS. Hence, it is hypothesized that: Since ASNS are formed to promote knowledge sharing, it is expected
that perceived social support would also promote knowledge sharing.
H2a Social interaction ties positively influence social identification Hence, it is hypothesized that:
within an academic social networking site.
H4 Perceived social support positively influence knowledge sharing on
H2b Social interaction ties positively influence perceived social support academic social networking sites.
within an academic social networking site.
3.6 | Perceived dialogue support, social
3.4 | Social identification and perceived social identification and knowledge sharing
support
IS are social actors (Oinas-Kukkonen & Harjumaa, 2009); hence,
As previously defined in Table 1, social identification emphasizes an human interactions with computers are similar in other social settings.
individual's sense of belongingness to a group (Koranteng & Wiafe, Dialogue support defines the principles that support users to reach
2019). According to the social identity theory (Tajfel & Turner, 1986), their intended behaviour by keeping them active and motivated.
individuals classify themselves as group members through participa- ASNS may provide dialogue support in the form of notification, feed-
tion in group activities. Engagement in group actions facilitate the for- back, and prompts. Lehto, Oinas-Kukkonen, and Drozd (2012) and
mation of loyalty, mutual support, and faithfulness among group Lehto and Oinas-kukkonen (2015) explained that the incorporation of
members (Ellemers, Kortekaas, & Jaap, 1999). Jeng et al. (2012) such features influences user's perception of the support they receive
suggested that group members on ASNS must effectively support from group members. This support may lead to a sense of inclusive-
each other. It is therefore expected that as individuals take pride in ness. In other words, the provision of perceived effective dialogue
belonging to a group, they will seek to support each other to demon- support increases interactions among members (Wiafe, 2017). As a
strate cohesion in that group. Hence: consequence, members form stronger bonds with an increase in group
belongingness. However, such a relationship is yet to be confirmed on
H3 Social identification positively influences perceived social support ASNS. It is expected that an increase in interaction among groups on
within an academic social networking site. ASNS will also lead to increased knowledge sharing. As indicated ear-
lier, reciprocal exchanges such as feedback influence users' knowledge
sharing behaviour. Therefore:
3.5 | Perceived social support and knowledge
sharing H5a Perceived dialogue support positively influences social identification
within an academic social networking site.
The relationship between perceived social support and knowledge
sharing has not been adequately studied. However, it is known that an H5b Perceived dialogue support positively influences knowledge sharing
individual's behaviour is largely influenced by their thoughts (Wiafe, within an academic social networking site.
WIAFE ET AL. 5
4 | METHODOLOGY of AVE of a latent variable should be higher than correlations with
other variables. Accordingly, Table 4 supports the validity of the dis-
The hypotheses were analyzed to examine the significance of the rela- criminants (the shaded diagonal figures of Table 4 are square roots
tionships. An English-based survey questionnaire was developed using of the AVEs).
Google Forms. This approach is relatively cheaper and faster as com-
pared to offline or paper base questionnaire administration. The elec-
tronic questionnaire (see Table 3 for question items) was distributed 5.1 | Structural model
through academic social networks to ensure that all responses are
from individuals who use such networks. All question items were The relationships among the constructs were examined using
presented and adopted in English from prior studies (see Table 1). The bootstrapping (100 samples) procedure. Kock (2010) recommends
five-point Likert scale ranging from ‘strongly agree’ (5) to ‘strongly dis- this procedure when the sample size is greater than 100. Using a
agree’ (1) was used to reduce respondent's frustration levels and one-tailed t test, path coefficients were considered significant for p-
therefore increase the response rate (Sachdev & Verma, 2004). Opin- values less than 0.05. Table 5 presents a summary of the results
ions of respondents that were gathered relate to (a) Shared Language from the bootstrap procedure. All the relationships were observed
(b) Shared Vision (c) Social Interaction Ties (d) Social Identification to be significant. The maximum p-value was .015 and it was
(e) Perceived Dialogue Support, (f) Perceived Social Support and recorded for the relationship between shared vision and social
(g) Knowledge Sharing, within the academic SNS that a respondent interaction ties.
belongs to. All the questionnaire items emphasized anonymity and Figure 2 shows the PLS analysis of the hypothesized model.
participation was purely voluntary. Convenience sampling was used to Shared language and shared vision explained 28.6% of the variance in
select participants. The questions were pre-tested to ensure that the social interaction ties. Dialogue support and social interaction ties
modifications made did not affect comprehension. The pre-test results accounted for 39.8% of social identification. However, social identifi-
indicated that none of the 15 initial respondents had issues answering cation and social interaction ties explained 42.4% of social identifica-
them. The pre-test responses were not used for the main analysis. tion, whereas social identification and dialogue support explained
Partial Least Squares Structural Equation Model (PLS-SEM) was 34.2% of the variance in knowledge sharing.
adopted to analyze the research model. This technique was chosen
because it is effective for exploring relationships between constructs
as well as predicting the effects of independent variables (Hair Jr, 6 | DISCUSSION
Hult, Ringle, & Sarstedt, 2016). Hair, Sarstedt, Ringle, and Mena
(2012) argued that PLS-SEM is appropriate for research that extends The research model presented in this article examined persuasive
existing theory. Therefore, SmartPLS 3.0 software was used for the social features that promote knowledge sharing on ASNS. The results
analysis. After about 4 months of data collection, a total of suggest that all proposed relationships were significant. Shared lan-
218 responses were received. About 83% of the respondents were guage and shared vision were observed to be strong antecedents of
male and 17.4% were female. The majority (73.9%) were below social interaction ties. As indicated earlier, social interaction is an ave-
30 years, 22% were between 30 and 40 years and 4.1% were above nue for information exchange (Lim & Richardson, 2016). For individuals
40 years. Additionally, 78% have postgraduate degrees or above and to frequently interact, there must be a common understanding and
22% have undergraduate degrees. Table 2 provides a summary of objective. These characteristics enable members to describe and inter-
respondents' demographic data. pret situations. Common understanding and objective further guides
and promote effective interactions among members (Zheng, 2017).
5 | MEASUREMENT MODEL
TABLE 2 Demographics of respondents (N = 218)
The constructs and their indicators were examined for internal consis- Demographics Value Frequency Percentage
tency, reliability, convergence and discriminant validity as proposed Sex
by Coltman, Devinney, Midgley, and Venaik (2008). They were Male 180 82.6
observed to be reliable since they met Barclay, Higgins, and Thomp- Female 38 17.4
son (1995)'s (i.e., threshold of 0.7) criteria (see Table 3). Internal con- Age
sistency was assessed using Cronbach's alpha and composite Below 30 161 73.9
reliability. Table 4 indicates that all items were above the minimum 30–40 48 22
value of 0.7 recommendation by Bagozzi and Yi (1988).
Above 40 9 4.1
The findings from discriminant validity testing are presented in
Education
Table 4. It was performed by comparing the square root of the aver- level Postgraduate 171 78
age variance extracted (AVE) of latent variables with other latent
Undergraduate 47 22
variables. Fornell and Lacker (1981) proposed that the square root
6 WIAFE ET AL.
TABLE 3 Question items, loadings and sources
Construct Items Question items Load Source
Shared language SL1 The members of my academic social network use 0.766 Koranteng et al. (Koranteng, Sarsah,
common terms or jargons. Kuada, & Gyamfi, 2019)
SL2 Members of my academic social network use 0.891
understandable communication pattern during
the discussion.
SL3 Members of my academic social networks use 0.894
understandable narrative forms.
Shared vision SV1 Members of my academic social network share the 0.909 Koranteng, Sarsah, Kuada, and Gyamfi
vision of helping others solve their professional (2019)
problems.
SV2 Members of my academic social network share the 0.939
same goal of learning from each other.
SV3 Members of my academic social network share the 0.903
same value that helping others is pleasant.
Social interaction SIT1 I maintain close social relationships with some 0.812 Koranteng, Sarsah, Kuada, and Gyamfi
ties members of my academic social network. (2019)
SIT2 I spend a lot of time interacting with some 0.816
members of my academic social network.
SIT3 I have frequent communication with some 0.897
members of my academic social network.
Social identification SOID1 I care about my friends on my academic social 0.742 Koranteng, Sarsah, Kuada, and Gyamfi
networking site. (2019); Lehto and Oinas-kukkonen
SOID2 I have a strong positive feeling toward my 0.768 (2015)
academic social network.
SOID3 I have the feeling of togetherness or closeness in 0.836
my academic social network.
Perceived social SOCS1 Through academic social networking sites, I am 0.795 Lehto and Oinas-kukkonen (2015)
support able to get support when I need it.
SOCS2 I share my experiences with my friends using 0.745
academic social networking sites.
SOCS3 I learn from the experiences of my peers on my 0.796
academic social networking site.
Dialogue support DIAL1 The academic social networking site that I use 0.800 Lehto and Oinas-kukkonen (2015)
motivates me to perform my daily activities.
DIAL2 The academic social networking site that I use 0.770
provides me with the right feedback on the task I
perform.
DIAL3 The academic social networking site that I use 0.806
provides messages of praise when I complete a
task.
Knowledge sharing KS1 I enjoy sharing knowledge sharing with my 0.832 Koranteng, Sarsah, Kuada, and Gyamfi
academic social network friends (2019)
KS2 I feel that my members in my social network enjoy 0.802
sharing their knowledge with each other
KS3 It seems to me that my academic social network 0.802
friends share the best knowledge they have
Indeed, consistent with findings from Chiu et al. (2006) and Abhari, (2001), the findings indicated that ASNS increases interpersonal inter-
Xiao, & Davidson, (2017), communication and interaction among group action and communication. Hence, the use of common language serves
members increase when they share similar traits such as language. This as a motivator that persuade users of ASNS to increase communication
finding suggests that groups on ASNS are formed based on particular and share knowledge. Some studies have explained that communica-
disciplines and fields because, members share similar aspirations and tion norms may evolve with time (Senyo, Liu, Sun, & Effah, 2016), and
use jargon and acronyms that are understandable. Consequently, they thus it is expected that this evolution will further improve knowledge
are able to bond, cooperate and interact closely. Again, contrary to Nie sharing as stronger bonds are formed.
WIAFE ET AL. 7
TABLE 4 Reliabilities, AVEs, and
CA CR AVE DIAL KS SIT SL SOCS SOID SV
inter-constructs correlations (N = 218)
DIAL 0.710 0.853 0.627 0.792
KS 0.745 0.880 0.659 0.426 0.812
SIT 0.797 0.888 0.710 0.245 0.402 0.843
SL 0.813 0.815 0.726 0.240 0.480 0.507 0.852
SOCS 0.765 0.826 0.595 0.298 0.509 0.529 0.443 0.772
SOID 0.784 0.941 0.613 0.385 0.435 0.579 0.459 0.616 0.783
SV 0.906 0.835 0.841 0.396 0.153 0.270 0.201 0.259 0.365 0.917
Note: AVE: average variance extracted; CA, Cronbach's alpha; CR, composite reliability.
TABLE 5 Statistical path coefficients of structural model (N = 218)
Original Sample T statistics Hypothesis
sample (O) mean (M) SD (|O/SD|) p values supported
DIAL ! KS 0.301 0.303 0.088 3.429 0.000 Yes
DIAL ! SOID 0.259 0.265 0.088 2.940 0.002 Yes
SIT ! SOCS 0.259 0.255 0.068 3.833 0.000 Yes
SIT ! SOID 0.516 0.518 0.089 5.765 0.000 Yes
SL ! SIT 0.471 0.475 0.066 7.193 0.000 Yes
SOCS ! KS 0.420 0.426 0.095 4.399 0.000 Yes
SOID ! SOCS 0.466 0.464 0.101 4.613 0.000 Yes
SV ! SIT 0.175 0.184 0.081 2.169 0.015 Yes
Richardson (2016) state, ASNS supports social relationships by provid-
ing functionalities that reduce the proximity of interactions among
members. For example, most ASNS provide emailing and instant chat
services, which enable synchronous interactions among users. These
functionalities also eliminate boundaries that hinder successful inter-
actions such as cost and distance. Again, since members of ASNS
seek to acquire knowledge (Koranteng et. al., 2019) and social net-
works become beneficial when they are used to make meaningful
connections (Clark, Algoe, & Green, 2018), the interaction between
members further promotes knowledge sharing. Positive affection and
fondness are developed as members interact more frequently and
share ideas (Dubos, 2017). The increase in successful interaction
increases members' ‘we’ mentality and their sense of togetherness
F IGURE 2 Structural model (Lin & Lu, 2011). This supports Ellemers et al. 's (1999) argument that
when there is a high perception of belongingness, group members
mutually support each other and develop loyalty for other members.
Social interaction ties had a significant impact on social identifi- Perceived dialogue support was observed to positively influence
cation and perceived social support. It strongly predicted perceived social identification. This validates Oinas-Kukkonen and Harjumaa
social support. This indicates that as the intensity of interactions (2009) and Dabi, Wiafe, Stibe, and Abdulai (2018) claim that IS are
among members increase, they feel a sense of belonging towards social actors, hence, individuals perceive their interactions with ASNS
their academic network and thus support each other. This persua- as similar in other social settings. ASNS provide features such as pro-
sive feature (social interaction) has been identified in several studies mpts and notifications, which can provide members with responses
as a key persuasive feature (Oinas-Kukkonen & Harjumaa, 2009; and feedback on the activities they perform. As mentioned earlier,
Oyibo et al., 2017). This finding disproves arguments by Kraut et al. individuals expect reciprocal exchanges to justify their expense in
(1998) and Tonioni et al. (2012) that states that online social net- terms of time and effort (Thibaut, 2017). When a person's actions are
works reduce social involvement and make individuals depressed, reciprocated, they perceive other members to be fair (Moghavvemi
anxious and lonely. It can therefore be confirmed that within ASNS et al., 2018) and they develop positive affect and likeness towards
social interaction ties do not reduce social involvement. As Lim and other group members (Koranteng, Sarsah, Kuada, & Gyamfi, 2019).
8 WIAFE ET AL.
Perceived dialogue support and perceived social support had sig- Intelligence and Lecture Notes in Bioinformatics), 10293 LNCS(February),
nificant influence on knowledge sharing. It involves the posting and 139–153. https://doi.org/10.1007/978-3-319-58481-2_12
Adaji, I., & Vassileva, J. (2016). Evaluating personalization and persuasion
viewing of information. This indicates that ASNS members presume
in e-commerce. In Persuasive 2016 (Vol. 1582, pp. 107–113).
their interactions with the system and other users to be mutual and Agnisarman, S., Madathil, K. C., & Stanley, L. (2018). A survey of empirical
fair. A member's perception of fairness depends on the comparison of studies on persuasive technologies to promote sustainable living. Sus-
his/her input and corresponding outcome from others. Thus, when tainable Computing: Informatics and Systems, 19, 112–122.
Al-Daihani, S. M., Al-Qallaf, J. S., & AlSaheeb, S. A. (2018). Use of social
this equilibrate, users become satisfied and motivated to give more
media by social science academics for scholarly communication. Global
inputs. Member feedback and prompts ignite willingness to share Knowledge, Memory and Communication, 67(6/7), 412–424.
knowledge. This serves as a persuasive feature. Furthermore, the exis- Appleford, S., Bottum, J. R., & Thatcher, J. B. (2014). Understanding the
tence of mutual support as claimed by Jeng et al. (2012) motivates social web: Towards defining an interdisciplinary research agenda for
information systems. ACM SIGMIS Database: The DATABASE for
users to share knowledge.
Advances in Information Systems, 45(1), 29–37.
Accordingly, designers of ASNS should incorporate these features Aslam, M. M. H., Shahzad, K., Syed, A. R., & Ramish, A. (2013). Social capi-
into their designs to ensure that their sites promote sharing of knowl- tal and knowledge sharing as determinants of academic performance.
edge. More importantly, persuasive features such as perceived dia- Journals of Behavioral and Applied Management, 15(1), 25–42.
logue and social support are direct predictors of knowledge sharing Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation
models. Journal of the Academy of Marketing Science, 16(1), 74–94.
and thus must not be omitted in ASNS. Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares
(PLS) approach to casual Modeling: Personal computer adoption and
use as an illustration. Technology Studies, 2(2), 285–309.
7 | CONCLUSION Bartol, K. M., & Srivastava, A. (2002). Encouraging knowledge sharing: The role
of organizational reward systems. Journal of Leadership & Organizational
Studies, 9(1), 64–76. https://doi.org/10.1177/107179190200900105
This study incorporated social constructs from prior studies to exam- Bock, G. W., & Kim, Y.-G. (2002). Breaking the myths of rewards: An
ine persuasive features that influence knowledge sharing behaviour exploratory study of attitudes about knowledge sharing. Information
on ASNS. The results suggest that dialogue support and perceived Resources Management Journal (IRMJ), 15(2), 14–21.
Bock, G.-W., Zmud, R. W., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral inten-
social support positively influence knowledge sharing activities on
tion formation in knowledge sharing: Examining the roles of extrinsic
ASNS, whereas social interaction ties and social identification are key motivators, social-psychological forces, and organizational climate. MIS
predictors for perceived social support. The findings suggest that Quarterly, 29(1), 87–111.
developers must keenly consider the incorporation of social persua- Bruns, A. (2008). Blogs, Wikipedia, second life, and beyond: From production
to produsage. Peter Lang.
sive principles in designing ASNS.
Chiu, C.-M., Hsu, M.-H., & Wang, E. T. G. (2006). Understanding knowledge
Although findings from this study have established the relevance sharing in virtual communities: An integration of social capital and social
of social persuasive features, the study is limited to ASNS in addition cognitive theories. Decision Support Systems, 42(3), 1872–1888.
to the use of convenience sampling. As such, the findings are limited. Chow, W. S., & Chan, L. S. (2008). Social network, social trust and shared
goals in organizational knowledge sharing. Information & Management,
More so, controlled variables such as gender and age were not
45(7), 458–465.
considered in the analysis and thus future studies must replicate the Clark, J. L., Algoe, S. B., & Green, M. C. (2018). Social network sites and well-
study in other domains such as learning management systems using being: The role of social connection. Current Directions in Psychological
other sampling techniques to validate these claims. Science, 27(1), 32–37. https://doi.org/10.1177/0963721417730833
Collins, K., Shiffman, D., & Rock, J. (2016). How are scientists using social
media in the workplace? PLoS One, 11(10), e0162680. https://doi.org/
CONFLICT OF INTEREST 10.1371/journal.pone.0162680
The authors declare no conflicts of interest. Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. (2008). Formative
versus reflective measurement models: Two applications of formative
DATA AVAILABILITY STATEMENT measurement. Journal of Business Research, 61(12), 1250–1262.
Curry, R., Kiddle, C., & Simmonds, R. (2009). Social networking and scien-
Data sharing is not applicable to this article as no new data were
tific gateways at Supercomputing. In 5th grid computing environments
created or analyzed in this study. workshop (p. 4).
Dabi, J., Wiafe, I., Stibe, A., & Abdulai, J.-D. (2018). Can an enterprise sys-
ORCID tem persuade? The role of perceived effectiveness and social influ-
ence. In International conference on persuasive technology (pp. 45–55).
Isaac Wiafe https://orcid.org/0000-0003-1149-3309
Davison, R. M., Ou, C. X. J., & Martinsons, M. G. (2018). Interpersonal
Felix N. Koranteng https://orcid.org/0000-0001-5917-381X knowledge exchange in China: The impact of guanxi and social media.
Akon O. Ekpezu https://orcid.org/0000-0002-9502-1052 Information & Management, 55(2), 224–234.
Dragoni, M., Bailoni, T., Maimone, R., Marchesoni, M., & Eccher, C. (2019).
A flexible knowledge-based architecture for supporting the adoption
REFERENCES
of healthy lifestyles with persuasive dialogs. In Data science for
Abhari, K., Xiao, B., & Davidson, E. (2017). Communication in co- healthcare (pp. 239–265). Springer.
innovation networks: A moderated mediation model of social Dubos, R. (2017). Social capital: Theory and research. Routledge.
affordances, social experience, and desire for learning. Lecture Notes in Ellemers, N., Kortekaas, P., & Jaap, W. O. (1999). Self-categorization, com-
Computer Science (Including Subseries Lecture Notes in Artificial mitment to the group and social self-esteem as related but distinct
WIAFE ET AL. 9
aspects of social identity. European Journal of Social Psychology, 29 Lehto, T., & Oinas-kukkonen, H. (2015). Explaining and predicting per-
(2–3), 371–389. ceived effectiveness and use continuance intention of a behaviour
Ellison, N. B., Gibbs, J. L., & Weber, M. S. (2015). The use of enterprise change support system for weight loss. Behaviour & Information Tech-
social network sites for knowledge sharing in distributed organiza- nology, 34(2), 176–189.
tions: The role of organizational affordances. American Behavioral Sci- Lehto, T., Oinas-Kukkonen, H., & Drozd, F. (2012). Factors affecting per-
entist, 59(1), 103–123. https://doi.org/10.1177/0002764214540510 ceived persuasiveness of a behavior change support system. In Pro-
Endres, M. L., & Chowdhury, S. (2019). Team and individual interactions ceedings of the international conference on information systems (ICIS)
with reciprocity in individual knowledge sharing. In Effective knowledge (pp. 1–15).
management systems in modern society (pp. 123–145). IGI Global. Lim, J., & Richardson, J. C. (2016). Exploring the effects of students' social
Fogg, B. J. (2008). Mass interpersonal persuasion: An early view of a new networking experience on social presence and perceptions of using
phenomenon 2 Facebook makes a new form of persuasion possible SNSs for educational purposes. The Internet and Higher Education, 29,
3 A Stanford Course Leverages Mass Interpersonal Persuasion. In 31–39.
International conference on persuasive technology (pp. 23–34). Springer, Lin, H. F. (2007). Effects of extrinsic and intrinsic motivation on employee
Berlin, Heidelberg. knowledge sharing intentions. Journal of Information Science, 33(2),
Fogg, B. J. (2009). A behavior model for persuasive design. In Proceedings 135–149. https://doi.org/10.1177/0165551506068174
of the 4th international conference on persuasive technology (p. 40). Lin, K.-Y., & Lu, H.-P. (2011). Intention to continue using Facebook fan
https://doi.org/10.1145/1541948.1541999 pages from the perspective of social capital theory. Cyberpsychology,
Fogg, B. J., & Nass, C. (1997). Silicon sycophants: The effects of computers Behavior and Social Networking, 14(10), 565–570.
that flatter. International Journal of Human Computer Studies, 46(5), Ma, M., & Agarwal, R. (2007). Through a glass darkly: Information technol-
551–561. https://doi.org/10.1006/ijhc.1996.0104 ogy design, identity verification, and knowledge contribution in online
Fornell, C., & Lacker, D. F. (1981). Evaluating structural equation modeling communities. Information Systems Research, 18(1), 42–67. https://doi.
for travel behavior research. Transportation Research Part B, University org/10.2307/23211831
of Michigan, 37, 1–25. Markus, M. L., & Saunders, C. (2007). Editor's comments: Looking for a
Forslund Frykedal, K., & Hammar Chiriac, E. (2018). Student collaboration few good concepts… and theories… for the information systems field.
in group work: Inclusion as participation. International Journal of Dis- MIS Quarterly, 31(1), iii–vi.
ability, Development and Education, 65(2), 183–198. Meishar-Tal, H., & Pieterse, E. (2017). Why do academics use academic
Garcia-Sánchez, P., Diaz-Diaz, N. L., & De Saá-Pérez, P. (2019). Social capi- social networking sites? The International Review of Research in Open
tal and knowledge sharing in academic research teams. International and Distributed Learning, 18(1), 1–22.
Review of Administrative Sciences, 85(1), 191–207. Moghavvemi, S., Sharabati, M., Klobas, J. E., & Sulaiman, A. (2018). Effect
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of of trust and perceived reciprocal benefit on students' knowledge shar-
the use of partial least squares structural equation modeling in marketing ing via Facebook and academic performance. Electronic Journal of
research. Journal of the Academy of Marketing Science, 40(3), 414–433. Knowledge Management, 16(1), 23–35.
Hair, J. F., Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and
partial least squares structural equation modeling (PLS-SEM). Sage. the organizational advantage. The Academy of Management Review, 23
Jeng, W., He, D., Jiang, J., & Zhang, Y. (2012). Groups in Mendeley: (2), 242–266.
Owners' descriptions and group outcomes. In Proceedings of the associ- Nie, N. H. (2001). Sociability, interpersonal relations, and the internet: Rec-
ation for information science and technology (Vol. 49, pp. 1–4). Wiley onciling conflicting findings. American Behavioral Scientist, 45(3),
Online Library. 420–435.
Kock, N. (2010). Using WarpPLS in e-collaboration studies: An overview of Oinas-Kukkonen, H. (2013). A foundation for the study of behavior change
five main analysis steps. International Journal of E-Collaboration, 6 support systems. Personal and Ubiquitous Computing, 17(6), 1223–1235.
(4), 1–11. https://doi.org/10.1007/s00779-012-0591-5
Koranteng, F. N., Sarsah, F. K., Kuada, E., & Gyamfi, S. A. (2019). An empiri- Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive systems design:
cal investigation into the perceived effectiveness of collaborative soft- Key issues, process model, and system features. Communications of the
ware for students' projects. Education and Information Technologies, Association for Information Systems, 24(28), 485–500.
1–24. https://doi.org/10.1007/s10639-019-10011-7 Omotayo, F. O., & Babalola, S. O. (2016). Factors influencing knowledge
Koranteng, F. N., & Wiafe, I. (2019). Factors that promote knowledge shar- sharing among information and communication technology artisans in
ing on academic social networking sites: An empirical study. Education Nigeria. Journal of Systems and Information Technology, 18(2), 148–169.
and Information Technologies, 24(2), 1211–1236. https://doi.org/10. Orji, R. (2014). Exploring the persuasiveness of behavior change support
1007/s10639-018-9825-0 strategies and possible gender differences. In CEUR workshop proceed-
Koranteng, F. N., Wiafe, I., Katsriku, F. A., & Apau, R. (2019). Understand- ings (pp. 41–57).
ing trust on social networking sites among tertiary students: An empir- Oyibo, K., Orji, R., & Vassileva, J. (2017). Investigation of the persuasiveness
ical study in Ghana. Applied Computing and Informatics. https://doi. of social influence in persuasive technology and the effect of age and
org/10.1016/j.aci.2019.07.003 gender. In Personalization in persuasive technology workshop, persuasive
Koranteng, F. N., Wiafe, I., & Kuada, E. (2019). An empirical study of the technology (pp. 32–44). https://doi.org/10.1145/3099023.3099071
relationship between social networking sites and students' engage- Sachdev, S. B., & Verma, H. V. (2004). Relative importance of service qual-
ment in higher education. Journal of Educational Computing Research, ity dimensions: A multisectoral study. Journal of Services Research, 4
57(5), 1131–1159. https://doi.org/https://doi.org/10.1177/ (1), 1–24.
0735633118787528 Salahshour Rad, M., Nilashi, M., Mohamed Dahlan, H., & Ibrahim, O.
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & (2019). Academic researchers' behavioural intention to use academic
Scherlis, W. (1998). Internet paradox: A social technology that reduces social networking sites: A case of Malaysian research universities.
social involvement and psychological well-being? American Psycholo- Information Development, 35(2), 245–261.
gist, 53(9), 1017–1031. Senyo, P. K., Liu, K., Sun, L., & Effah, J. (2016). Evolution of norms in the
Kwahk, K.-Y., & Park, D.-H. (2016). The effects of network sharing on emergence of digital business ecosystems. In International conference
knowledge-sharing activities and job performance in enterprise social on informatics and semiotics in organisation (ICISO) (Vol. 477,
media environments. Computers in Human Behavior, 55, 826–839. pp. 79–84). https://doi.org/10.1007/978-3-319-42102-5
10 WIAFE ET AL.
Sin, S.-C. J., & Kim, K.-S. (2013). International students' everyday life infor- Wiafe, I., & Nakata, K. (2012). Bibliographic analysis of persuasive systems:
mation seeking: The informational value of social networking sites. Techniques, methods and domains of application. In Persuasive tech-
Library & Information Science Research, 35(2), 107–116. nology: Design for health and safety; the 7th international conference on
Stibe, A. (2015). Towards a framework for socially influencing systems: persuasive technology; PERSUASIVE 2012; Linköping; Sweden; June 6-8;
Meta-analysis of four PLS-SEM based studies. In International confer- Adjunct Proceedings (pp. 61–64).
ence on persuasive technology (pp. 172–183). Cham: Springer. https:// Wiafe, I., Nakata, K., & Gulliver, S. (2014). Categorizing users in behavior
doi.org/10.1007/978-3-319-20306-5 change support systems based on cognitive dissonance. Personal and
Stibe, A., & Oinas-kukkonen, H. (2014). Using social influence for motivat- Ubiquitous Computing, 18(7), 1677–1687.
ing customers to generate and share feedback. In International confer- Wiafe, I., Nakata, K., & Gulliver, S. R. (2011). Designing persuasive third
ence on persuasive technology (pp. 224–235). Cham: Springer. party applications for social networking services based on the 3D-RAB
Tajfel, H., & Turner, J. (1986). The social identity theory of intergroup model. In J. Park, L. Yang, & C. Lee (Eds.), Communications in computer
behaviour. In S. Worchel & W. G. Austin (Eds.), Psychology of intergroup and information science (185, pp. 54–61). SpringerBerlin Heidelberg.
relations (pp. 7–24). Chicago, IL: Nelson Hall. Zhang, J., Zhu, Q., & Wang, Y. (2019). Social capital on consumer
Thibaut, J. W. (2017). The social psychology of groups (1st edition). knowledge-sharing in virtual brand communities: The mediating effect
Routledge. https://doi.org/https://doi.org/10.4324/9781315135007 of pan-family consciousness. Sustainability, 11(2), 339.
Tonioni, F., D'Alessandris, L., Lai, C., Martinelli, D., Corvino, S., Vasale, M., … Zheng, T. (2017). A literature review on knowledge sharing. Open Journal of
Bria, P. (2012). Internet addiction: Hours spent online, behaviors and Social Sciences, 5(3), 51–58. https://doi.org/10.4236/jss.2017.53006
psychological symptoms. General Hospital Psychiatry, 34(1), 80–87. Wiafe, I., Alhammad, M. M. M., Nakata, K., & Gulliver, S. R. S. R. (2012).
Torning, K., Hall, C., & Oinas-kukkonen, H. (2009). Persuasive system Analyzing the persuasion context of the persuasive systems design
design: State of the art and future directions. In Proceedings of the 4th model with the 3D-RAB model. In M. Bang & E. Ragnemalm (Eds.),
international conference on persuasive technology (p. 30). ACM. Lecture Notes in Computer Science (Design for Health and Safety)
Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role (Vol. 7284 LNCS, pp. 193–202). Springer, Berlin, Heidelberg. https://
of intrafirm networks. Academy of Management Journal, 41(4), doi.org/10.1007/978-3-642-31037-9_17
464–476.
Vuori, V., & Okkonen, J. (2012). Knowledge sharing motivational factors of
using an intra-organizational social media platform. Journal of Knowl-
edge Management, 16(4), 592–603.
Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social How to cite this article: Wiafe I, Koranteng FN, Owusu E,
capital and knowledge contribution in electronic networks of practice. Ekpezu AO, Gyamfi SA. Persuasive social features that
MIS Quarterly, 29(1), 35–57. promote knowledge sharing among tertiary students on social
Wiafe, I. (2017). The role of U-FADE in selecting persuasive system fea-
networking sites: An empirical study. J Comput Assist Learn.
tures. In Encyclopedia of Information Science and Technology, Fourth Edi-
tion, (pp. 7785–7795). https://doi.org/10.4018/978-1-5225-2255-3. 2020;1–10. https://doi.org/10.1111/jcal.12433
ch677