Applied Computing and Informatics xxx (xxxx) xxxContents lists available at ScienceDirect
Applied Computing and Informatics
journal homepage: www.sciencedirect .comUnderstanding trust on social networking sites among tertiary students:
An empirical study in Ghana⇑ Corresponding author.
E-mail addresses: iwiafe@ug.edu.gh (I. Wiafe), fkatsriku@ug.edu.gh (F.A.
Katsriku).
Peer review under responsibility of King Saud University.
Production and hosting by Elsevier
https://doi.org/10.1016/j.aci.2019.07.003
2210-8327/
 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: F. N. Koranteng, I. Wiafe, F. A. Katsriku et al., , Applied Computing and Informatics, https://doi.org/10.1016/j.aci.2019.0Felix Nti Koranteng a, Isaac Wiafe b, Ferdinand Apietu Katsriku b,⇑, Richard Apau c
aDepartment of Information Systems and Innovation, Ghana Institute of Management and Public Administration, P O Box AH50, Achimota-Accra, Ghana
bDepartment of Computer Science, University of Ghana, P O Box LG163, Legon-Accra, Ghana
cDepartment of Computer Science, Kwame Nkrumah University of Science and Technology (KNUST), Ghana
a r t i c l e i n f o a b s t r a c tArticle history:
Received 14 March 2019
Revised 24 July 2019
Accepted 30 July 2019
Available online xxxx
Keywords:
Social networking sites
Trust
Social Network Trust
Shared language
Social capitalUser trust in social networking sites (SNS) has become an important issue in SNS discussions. This is
because of its impact on knowledge sharing, social commerce, social interaction, among many others.
However, information systems researchers have primarily explored the benefits of trust with little atten-
tion to its antecedents. In an attempt to address this knowledge gap, this study proposed a model that
investigated the factors that promote trust among SNS users. Data was gathered from voluntary respon-
dents using a questionnaire. A PLS-SEM analysis of 912 valid responses suggested that Norm of
Reciprocity, Social Interaction Ties and Identification are significant factors that encourage Trust among
SNS users. Shared Language was also identified to have impact on Norm of Reciprocity, Social Interaction
Ties and Identification. The results of the study provide significant theoretical and practical contributions.
They bridge the knowledge gap regarding the formation of Trust on SNS. The model evaluated explains
49.6% of the variance in Trust and thus suitable for analyzing the antecedents of Trust on SNS.
Furthermore, with the significance of Identification, Social Interaction Ties and Norm of Reciprocity on
Trust, SNS developers are tasked to offer SNS features that proliferate the formation of these factors as
well as shared interpretations.

 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an
open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).1. Introduction
The rapid evolution of the Internet over the last decades has
transformed the world into a global village. Recent technologies
such as social networking sites (SNS) have provided the necessary
infrastructure and platform to support the development of new
social structures [51]. Currently, social networking sites enable
effective ways for knowledge sharing, collaboration and
professional networking [24,52]. Due to the popularity of these
innovations among students, higher educational institutions are
increasingly adopting it to improve students’ experience [20].
College students have particularly, been identified as the mostdominant user group of SNS [38]. They frequently interact, collab-
orate and share knowledge using these media [2,75]. This makes
SNS an innovative alternative in enhancing students’ engagement
and learning outcomes [38,72]. Nonetheless, they have also created
new challenges for peer-to-peer communication [40]. Specifically,
interactions on these platforms are mostly technology-mediated
and thus limit interpersonal relationships based on face-to-face
communication. Consequently, the absence of face-to-face interac-
tion affects the level of trust among users [23].
Trust is one’s belief in the capabilities, honesty and reliability of
others [34]. It is a peer’s confidence that others will not knowingly
cause harm to them [14]. Similar to face-to-face interaction, trust is
a major factor that affect online behavior [13]. Chiu et al. [14] indi-
cated that trust is actually one of the main barriers of online com-
munication. Other studies have also demonstrated the importance
of trust in related activities such as online knowledge sharing
behavior among tertiary students [47]. Trust has the ability to
reduce uncertainties and associated risk [50]. Despite the extant
literature on online trust, many of these studies sought to investi-
gate the consequences of trust rather than its antecedents [40].
Thus, studies that examine the factors that enable the formation
of trust on social networking sites are inadequate [68].7.003
2 F.N. Koranteng et al. / Applied Computing and Informatics xxx (xxxx) xxxGiven that college students prefer interactions on SNS than off-
line environments [70], contributions to theories that promote
trust on SNS platforms will impact its usage. More importantly,
as higher educational institutions continue to adopt SNS to engage
students in learning activities (and the increasing prominence of
trust) it has become imperative to understand the factors that
informs trust among students on these platforms. In doing so, insti-
tutions will be better equipped in facilitating teaching and learning
activities. This paper therefore seeks to expand the current litera-
ture on SNS and Trust. It adopts concepts from the social capital
theory to investigate the factors that promote the formation of
trust among users of SNS in higher education. The next section dis-
cusses related literature on SNS and Trust. This is followed by a
review of the fundamental theory for the study. Furthermore, the
data analysis, results and discussions are also presented. This paper
ends with drawn up theoretical and practical contributions. Finally,
conclusions and suggestions for future studies are drawn.2. Literature review
Trust has been studied among scholars from diverse disciplines.
This has resulted in varied definitions for the concept [46]. Sociol-
ogists define trust as a set of expectations held by individuals
involved in an exchange [93]. From a social learning perspective,
it is the expectations shared by individuals that a promise or word
of other individuals could be relied upon [73]. It can also be
regarded as a psychological state comprising the intention to
accept vulnerability based upon positive expectations of the
behavior of another [10]. In a nutshell, trust may be bottled up
as the willingness of a person to be defenseless to others based
on the assumption that they will conform to expected behavior
irrespective of the ability to monitor or control the other party
[76]. Researchers have defined trust in the context of person to
person relations as applied in traditional offline environment.
Nonetheless, these definitions may be applicable in online environ-
ments since the social principles of interaction in both offline and
online settings are similar [87]. Thus, studies have successfully
adopted and applied these definitions in online trust studies
[3,14,53,52]. Following a similar approach, this study defines trust
as the believe that network members will not knowingly cause
harm to others and will adhere to the acceptable norms [52].
Although offline trust definitions are applicable in online set-
tings, the situational elements that influence the formation of trust
differs. For instance, exchange appears to be a common factor in
both settings [7], however, exchanges in offline environments are
different from online settings. Issues regarding physical distance
is also different in these environments. One of the benefits of
online communities is increased access to informational resources
[39]. In offline environments, students for example are limited to
forming groups with a few colleagues within a specific geographic
boundary. This means that students are limited and hugely depen-
dent on their circle. Meanwhile, SNS eliminate these geographical
boundaries. They provide a vast array of competitive alternatives
as well as new information sources [1]. They also increase stu-
dents’ sovereignty and this affects the nature of trust formation
within SNS [64]. In addition, human network attributes such as
non-verbal language on which trust is built in the traditional envi-
ronment are absent on SNS. Thus it reduces the richness of com-
munication among members [31]. These differences emphasize
the need to investigate trust on SNS.
Yet, prior studies have mostly explored online trust as a conse-
quence often within the context of e-commerce [28,65,80], e-
government [60,74,86] and knowledge sharing [14,53,52] with
fewer studies on antecedents of trust among online social network
members. Hsu et al., [46] explored the antecedents of trust amongPlease cite this article as: F. N. Koranteng, I. Wiafe, F. A. Katsriku et al., , Applimembers of a virtual community. The researchers observed that
knowledge growth, perceived responsiveness and shared values
affect trust positively. Benefit attraction and shared values have
also been found to impact trust significantly [90]. According to
Wang [87], trust among SNS members is influenced by information
quality, reciprocity, shared value, reputation, satisfaction and SNS
interaction with familiarity as a moderating construct.
Gefen, Benbasat, & Pavlou [36] identified how differences in cul-
ture affects trust and therefore call for researchers to explore the
concept across different geographic domains. In other words, the
factors that promote interpersonal trust in society differ. This is
because, the formation of trust is informed by individual’s beliefs
and values which are often guided by culture. As such, individuals
from cultures that are threatened by uncertainties are less trusting
than the others [78]. Hence, there is the need to test if the con-
firmed relationships in previous studies hold in countries with
higher uncertainty avoidance. In addition, the study will also
uncover the factors that promote trust with samples from higher
uncertainty avoidance countries.
2.1. Theoretical framework
A number of theoretical models have been developed to explain
trust formation. In Moorman, Deshpande, & Zaltman [61]’s frame-
work, trust building is considered to consist of three stages: ante-
cedents, process and outcomes. The antecedents refer to the
determinants of trust. According to Moorman et al., [61], trust is
mainly determined by individual characteristics (e.g. job experi-
ence) and organizational characteristics (e.g. organizational struc-
ture). Relatedly, McKnight, Choudhury, & Kacmar [58] also
proposed the Trust Building Model (TBM). The model explains that
reputation, information quality, organizational factors and per-
ceived risks informs trust. Similarly, other studies have adopted
the Commitment-Trust Theory (CTT) [62] to explain the antece-
dents of trust. In the CTT, trust is dependent on shared values, com-
munication and opportunistic behavior. Although these models
have been widely used in virtual community trust research [87],
their applicability are mainly focused on person-to-organization
or person-to-technology [57]. This study, however, focuses on the
relationships between users of SNS and influential factors for the
formation of trust. Therefore, the aforementioned theories are less
applicable.
The Social Capital Theory (SCT) suggests that resources are
gained through social relationships [69]. It argues that individuals
derive benefits from associating with others [17]. It is a multidi-
mensional construct represented as having structural, relational
and cognitive dimensions. The structural dimensions describe the
form of relationships, pattern of linkages and how connections
among individuals are configured [8]. Its relational dimension
deals with the nature of the connections among network members.
The major resources of this dimension are trust, norms of reciproc-
ity and identification. The cognitive dimension deals with similar-
ities in members’ beliefs and understanding. The major features of
this dimension are shared language and codes. Following Chang
and Chuang [12] and Koranteng & Wiafe [52], this study adopts
Social Interaction Ties as a variable of the structural dimension;
Trust, Identification and Norm of Reciprocity as variables for the
relational dimension and finally Shared Language as a manifesta-
tion of the cognitive dimension. This approach reduces measure-
ment errors and enables the researchers to compare the validity
of similar variables across different contexts.
In recent times, the SCT has become popular in discussing issues
relating to relationships among users on online networks. This is
because, online social networks possess features that facilitate
the accumulation of benefits such as employment, reputation,
information, power, influence, etc. [27]. Accordingly, researchersed Computing and Informatics, https://doi.org/10.1016/j.aci.2019.07.003
F.N. Koranteng et al. / Applied Computing and Informatics xxx (xxxx) xxx 3have adopted the theory to explain information exchange, knowl-
edge sharing and civic engagement, [3,14,53,52,85,88], mostly as a
derived benefit. SCT is suitable for investigating issues regarding
social relationships hence consistent with the objective of this
study. Hämäläinen [43] argues that there is a positive relationship
between the social capital constructs. In other words, trust can be
measured as a product of other constructs [87]. Hence, this study
investigates the interrelationships between Social Interaction Ties,
Identification, Norm of Reciprocity and Shared Language and their
influence on Trust. Table 1 summarizes the construct definitions
and literature sources whereas Fig. 1 presents the hypothesized
model.
2.2. Hypothesis formulation
2.2.1. Shared Language, Norm of Reciprocity, social Interaction Ties
and Identification
Shared language refers to a common vocabulary that permits a
shared understanding among group members [3] and creates a
pedestal for people to significantly interact and return actions
[66]. It supports effective communication and verbal exchanges
Furthermore, shared language facilitates the development of com-
mon behavioral principles and homogenous symbol systems that
enhances affiliation and attachment towards group members
[33]. Sin & Kim [79] identified language as a major barrier for suc-
cessful interaction among networked members. Undeniably, it is
easier for people to build stronger bonds and reciprocate kindness
when they share common attributes [87]. Empirical evidence from
Koranteng et al., [53] indicates that shared language is a pre-
requisite for knowledge exchange. Thus, shared language encour-
ages member involvement in group discussions Aslam et al. [3]
which provoke stronger ties and a sense of belonging [33]. The for-
mation of shared interpretation on SNS seems to be problematic
because it connects users from different geographical location
and cultural disciplines. However, SNS link individuals with com-
mon beliefs and norms, and provide an enabling environment for
common understanding [77]. Therefore, it is hypothesized that;
H1a: Shared Language positively affect Norm of Reciprocity on
social networking sites
H1b: Shared Language positively affect Social Interaction Ties on
social networking sites
H1c: Shared Language positively affect Identification on social
networking sites
2.2.2. Norm of reciprocity, social interaction ties and trust
Norm of Reciprocity is defined by members perception that
interactions among themselves is mutual and fair [14]. It is the
expectation that other group members will return favors. The prin-
ciples of the Social Exchange Theory propose that there is higher
resource exchange when group members observe a strong norm
of reciprocity [21]. Similarly, mutual and fair exchanges build
strong ties among members which promotes trust [87]. That is,
members’ responsiveness increases the intensity of communica-
tion among themselves and thus their integrity and benevolence
[71]. Also, people are more likely to trust others who reciprocateTable 1
Definition of Constructs and Sources.
Construct Definition
Shared Language (SL) It is a common vocabulary that enable network mem
Norm of Reciprocity (NR) It is the tendency of people to feel compelled to retu
Social Interaction Ties (SIT) It represents the frequency of interactions and the s
Identification (ID) It refers to an individual’s perception of inclusivenes
Trust (TR) It is the believe that network members will not kno
Please cite this article as: F. N. Koranteng, I. Wiafe, F. A. Katsriku et al., , Applisupport and help [30]. Though it is widely accepted that SNS facil-
itate information exchange [54], scholars have indicated that
reciprocity is nonexistent on these platforms. For example, Collins
et al. [18] and Meishar-Tal & Pieterse [59] assert that SNS users are
consumers of information but do not actively engage in creation
and sharing. To test this claim, we hypothesized that:
H2a: Norm of Reciprocity positively affect Social Interaction Ties
on social networking sites.
H2b: Norm of Reciprocity positively affect Trust on social net-
working sites.
2.2.3. Social Interaction Ties, Identification and trust
Social Interaction Ties denotes the intensity of interactions
between group members [14]. It outlines the strength of relation-
ship and communication frequency among members [84]. Higher
interaction among members leads to higher self-identification
and trust among members [71]. Consequently, the more people
are familiar with members within their network, the more they
feel attached and trust them [84]. Social networking sites eliminate
communication barriers such as distance and cost [22] by provid-
ing users with effective tools for synchronous communication. Lim
& Richardson [54] opine that, social networking sites augment fre-
quent information exchange. Therefore promoting closeness
among network members will lead to an enhanced sense of
belonging and trust Dubos [25]. According to Koranteng et al.,
[53], frequent communication is a precursor of trust. Similarly,
Mu, Peng, & Love [63] confirm that, close interactions among net-
work members increases their inclusiveness and positive feeling
towards each other. However, in contrast, Tonioni et al., [83] argue
that social networking sites use reduces social involvement and
make users lonely and depressed. To validate the above claims,
the following hypotheses were tested:
H3a: Social Interaction Ties positively affect Identification on
social networking sites.
H3b: Social Interaction Ties positively affect Trust on social net-
working sites.
2.2.4. Identification and trust
As defined earlier, Identification embodies a person’s percep-
tions of his/her inclusiveness in a societyChiu et al. [14]. It mea-
sures an individual’s positive feeling and sense of attachment
within a community. The development of such attachments has
been argued to be doubtful on social networking sites given con-
cerns such as cyber-bullying [35]. Conversely, findings from Mala-
tesh & Dhanasree [55] refute such claims. Social networking sites
connect like-minded users to exchange affective and emotional
support [48]. This facilitates the development of mutual commit-
ment, attachment and loyalty [49] and therefore trust among net-
work members [82]. According to the social identity theory [81]
people perceive their network members in more desirable ways
(i.e. trustworthy) than others outside the network. Similarly,
Dumitru and Schoop [26] found a significant relationship between
Identification and Trust among group members. Hence it is
hypothesized that;Source
bers to communicate with a common understanding. [3,29,53]
rn actions when they believe others will do same for them. [3,14]
trength of the relationship among network members. [14,52]
s in a community. [3,14]
wingly cause harm to others and adhere to the acceptable norms. [52]
ed Computing and Informatics, https://doi.org/10.1016/j.aci.2019.07.003
4 F.N. Koranteng et al. / Applied Computing and Informatics xxx (xxxx) xxx
Fig. 1. Proposed Research Model.
Table 2
Demographics of Respondents (N = 921).
Demographics Value Frequency Percentage
Sex
Male 663 72%
Female 258 28%
Age
Below 30 442 48%
30–40 359 39%
Above 40 120 13%
Highest Education Level
Postgraduate 635 69%
Undergraduate 268 31%H4: Identification positively influence Trust on social network-
ing sites.
3. Research methodology
A quantitative survey approach was adopted for this study.
Google Form was used to design an English-based questionnaire
which was distributed via social networking platforms such as
Facebook, WhatsApp, Twitter, etc. This approach ensured that all
participants are familiar with online social networks. The approach
was also meant to explore responses from a variety of SNS users
unlike Wang [87] who sampled only users of Wei Bo (an SNS plat-
form in China). Although participation was solely voluntary, the
questionnaire was also designed to ensure anonymity and confi-
dentiality. A brief statement that explained the purpose of study
was attached to the questionnaire. Relevant respondents’ demo-
graphics and their perceptions on key variables that promote trust
were collected. Thus, the five-point Likert scale questions ranging
from ‘‘strongly agree” (5) to ‘‘strongly disagree” (1) were used
to measure respondent’s perception relating to (i) Shared Language
(ii) Social Interaction Ties (iii) Identification (iv) Norm of Reciproc-
ity and (v) Trust were measured. All constructs and question items
were adopted and modified from prior studies (see Table 1).
Respondents were recruited using convenience sampling
technique.
3.1. Data collection
Convenience sampling was used to select 2000 respondents
from 3 main public universities in Ghana and questionnaire was
sent to them via various SNS platforms. This sampling approach
was used because none of the universities agreed to provide a full
list of enrolled students, thus a true random sampling could not be
performed. Nine hundred and twenty-one (921) responses were
received, indicating a response rate of 46%. All respondents were
active SNS users who spent at least 30 min a day on SNS platforms.
Since all fields in the questionnaire were mandatory, there was no
missing data in the 921 responses received. Out of this sample, 72%
were male and the rest (28%) were female. Majority representing
48% were below 30 years whereas 39% were between 30 and 40Please cite this article as: F. N. Koranteng, I. Wiafe, F. A. Katsriku et al., , Appliand 13% above 40. Most of the responses came from undergraduate
students (69%) and 31% were postgraduate students. A summary of
the respondents’ demographics is shown in Table 2.4. Analysis and findings
The hypothesized model was validated using Partial Least
Square Structural Equation Modelling (PLS-SEM). This technique
was adopted not only because of its suitability for exploratory
studies [41] and predicting the relationships between latent vari-
ables [42] but also its robustness to errors from multivariate distri-
butions [37]. Furthermore, since the sample size is larger than ten
times the number of structural paths directed at a construct, PLS-
SEM is considered suitable for this study. The SmartPLS 3.0 was
used to analyze the structural model.4.1. Measurement
In Structural Equation Modelling (SEM), Coltman et al. [19]
argued that analysis of the measurement model must focus on
item reliability, internal consistency, convergent validity and dis-
criminant validity. Therefore, this approach was adopted for ana-
lyzing the measurement model. All item loadings (see appendix)
were above Barclay et al. [6]’s recommended threshold of 0.7 hence
they were considered valid. Internal consistency was measureded Computing and Informatics, https://doi.org/10.1016/j.aci.2019.07.003
F.N. Koranteng et al. / Applied Computing and Informatics xxx (xxxx) xxx 5
Table 3
Construct Validity and Reliability.
CA CR AVE ID NR SIT SL TR
ID 0.781 0.859 0.604 0.777
NR 0.754 0.858 0.670 0.695 0.818
SIT 0.798 0.817 0.534 0.642 0.592 0.731
SL 0.786 0.827 0.615 0.728 0.671 0.579 0.784
TR 0.787 0.863 0.613 0.586 0.578 0.653 0.513 0.783
Note: CA; Cronbach’s Alpha, CR; Composite Reliability, AVE; Average Variance Extracted.using Cronbach’s Alpha and composite reliability. Table 3 indicates
that all constructs were above 0.7 as suggested by Bagozzi and Yi
[4]. The Average Variance Extracted (AVE) was used to test the con-
vergent validity. All AVEs greater than 0.5 are considered to be
valid [89]. Discriminant validity was evaluated with both the
Fornell-Lacker criterion [32] and Heterotrait-Monotrait Ratio
(HTMT) [45]. Using the Fornell-Lacker criterion, discriminant valid-
ity was tested by finding the square root of the AVEs of the latent
variable and compared to the correlations between the corre-
sponding latent variable and other latent variables. Fornell &
Lacker [32] recommended that the square root of the AVE of the
latent variable should be greater than the correlations of all other
latent variables. The highlighted diagonal entries shown in Table 3
represent the results for discriminant validity. Similarly, Table 4
presents the result for the discriminant validity assessment using
HTMT as recommended by Henseler, Ringle, & Sarstedt [45]. The
diagonal values in Table 4 indicate that all entries were below
Clark and Watson [15]’s maximum requirement of 0.85. Moreover,
Variance Inflation Factor (VIF) was used to evaluate the possibility
of multicollinearity. All VIF values must be <3 [42]. Table 5 shows
collinearity did not disturb the findings of the study. Finally, SRMR,
NFI and RMS_theta was used to assess model fit. According to Hen-
seler, Hubona, & Ray [44], SRMR < 0.08, NFI greater than 0.90 and
RMS_theta closer to zero is preferred. The results confirmed the
validity of the model since all the tolerance level were met (see
appendix two).4.2. Structural model
The bootstrap technique was adopted to examine the signifi-
cance and strength of the predicted relationships. Specifically, we
examined the effect of Shared Language on Norm of Reciprocity,
Social Interaction Ties and Identification and how these constructs
also affected Trust. All predicted relationships were significant. In
particular, Shared Language positively influenced Norm of
Reciprocity (b = 0.671, p < 0.001), Social Interaction TiesTable 4
Discriminant Validity Test with Heterotrait-Monotrait Ratio (HTMT).
ID NR SIT SL
NR 0.788
SIT 0.772 0.788
SL 0.690 0.621 0.730
TR 0.740 0.729 0.774 0.690
Table 5
Multicollinearity Testing with Variance Inflation Factor.
ID NR SIT TR
ID 2.297
NR 1.820 2.082
SIT 1.504 1.828
SL 1.504 1.000 1.820
Please cite this article as: F. N. Koranteng, I. Wiafe, F. A. Katsriku et al., , Appli(b = 0.330, p < 0.001) and Identification (b = 0.537, p < 0.001) thus
validating H1a, H1b and H1c.
Norm of Reciprocity (b = 0.371, p < 0.001) positively influenced
Social Interaction Ties. Meanwhile, Social Interaction Ties
(b = 0.331, p < 0.001) also positively affected Identification. From
Fig. 2, it is also shown that Norm of Reciprocity (b = 0.211,
p < 0.005), Social Interaction Ties (b = 0.419, p < 0.001) and Identi-
fication (b = 0.171, p < 0.01) all positively influenced Trust. This
indicates that all the other five hypotheses (H2a, H2b, H3a, H3b and
H4) were supported. Table 6 presents a summary of the signifi-
cance of path coefficients. The effect sizes (f 2) between constructs
were also studied using Cohen [16]’s criteria. Cohen [16] asserted
that the effect of a construct on the others can be irrelevant (i.e. f
2 < 0.02), small (
0.02), medium (
0.15) or large (
0.35). From
Table 6, Shared Language has a larger effect on norm of reciprocity
(0.820) and Identification (0.484) but smaller effect on Social Inter-
action Ties (0.102). Similarly, while Identification (0.025) and
Norm of Reciprocity (0.042) had small effects on Trust, Social Inter-
action Ties (0.190) had a medium effect.
The SmartPLS software was also used to assess the mechanisms
through which all the independent variables affect the dependent
variable using mediation analysis procedure. A partial mediation
was observed among all the variables. This indicates that, the
direct and indirect effects among all the variables were significant.
This confirms Hämäläinen [43]’s position that, the constructs in the
social capital theory significantly influence each other. Table 7
shows the summary of the mediation effects.
5. Discussion
Trust is one of the most important issues in online activities.
However, relevant researches have predominantly focused on its
consequences with little attention to its antecedents. Therefore,
this study investigated the factors that promote Trust among SNS
users. As stated earlier, the research model analyzed in this paper
was tested with 921 Ghanaian university students who are users of
various SNSs. In the next sections we discuss the theoretical and
practical implications of our findings.
5.1. Theoretical contribution
The results of this study provide relevant theoretical contribu-
tions. Specifically, statistical tests on measurement model demon-
strate acceptable convergent and discriminant validity whereas the
structural model explained 49.6% of the variance in Trust among
SNS users. This indicates that the proposed model is appropriate
for examining the antecedents of Trust. In addition, it augments
existing literature on SNS and Trust, especially when culture influ-
ences people’s concept of Trust.
Previous studies had indicated that members of SNS platforms
rarely interact [18,59] and thus making them lonely and depressed
[83]. The results from this study refute such claims. Shared Lan-
guage significantly affected Norm of Reciprocity, Social Interaction
Ties and Identification. This suggests that, students with common
perspectives are connected via SNS platforms. Therefore, thised Computing and Informatics, https://doi.org/10.1016/j.aci.2019.07.003
6 F.N. Koranteng et al. / Applied Computing and Informatics xxx (xxxx) xxx
Fig. 2. PLS Analysis of Structural Model.
Table 6
Significance of Path Coefficients.
Original Sample (O) Sample Mean (M) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Values Effect Size Supported?
ID -> TR 0.171 0.176 0.069 2.485 0.007 0.025 Yes
NR -> SIT 0.371 0.377 0.056 6.615 0.000 0.128 Yes
NR -> TR 0.211 0.210 0.074 2.845 0.002 0.042 Yes
SIT -> ID 0.331 0.330 0.046 7.239 0.000 0.183 Yes
SIT -> TR 0.419 0.418 0.057 7.303 0.000 0.190 Yes
SL -> ID 0.537 0.541 0.042 12.795 0.000 0.484 Yes
SL -> NR 0.671 0.677 0.034 19.702 0.000 0.820 Yes
SL -> SIT 0.330 0.325 0.060 5.505 0.000 0.102 Yes
Table 7
Specific Indirect Effects.
Original Sample (O) Sample Mean (M) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Values
NR -> SIT -> ID 0.123 0.126 0.030 4.036 0.000
SL -> NR -> SIT -> ID 0.082 0.085 0.021 3.913 0.000
SL -> SIT -> ID 0.109 0.109 0.024 4.601 0.000
SL -> NR -> SIT 0.249 0.253 0.043 5.836 0.000
NR -> SIT -> ID -> TR 0.021 0.022 0.011 1.933 0.027
SL -> NR -> SIT -> ID -> TR 0.014 0.015 0.007 1.894 0.029
SIT -> ID -> TR 0.057 0.058 0.026 2.213 0.014
SL -> SIT -> ID -> TR 0.019 0.019 0.009 2.101 0.018
SL -> ID -> TR 0.092 0.092 0.037 2.467 0.007
SL -> NR -> TR 0.142 0.141 0.049 2.870 0.002
NR -> SIT -> TR 0.155 0.158 0.035 4.495 0.000
SL -> NR -> SIT -> TR 0.104 0.106 0.025 4.172 0.000
SL -> SIT -> TR 0.138 0.138 0.033 4.191 0.000commonality enables seamless interactions among themselves
thus permitting the formation of tighter bonds and also increases
their participation in mutual exchanges. This outcome is consistent
with findings from existing studies Aslam et al. [3,52,87]. For
instance, many students on SNS prefer interactions on SNS and
recognize the need to provide feedback [56] hence they easily
identified with each other.
The results also indicated that Norm of Reciprocity, Social
Interaction Ties and Identification were significant factors thatPlease cite this article as: F. N. Koranteng, I. Wiafe, F. A. Katsriku et al., , Applipromoted Trust. This means that, the integrity and honesty of col-
leagues are defined by their reciprocated responses. Students are
more inclined to trust others who they expect to return support
and favors. This finding is similar to previous studies conducted
by Bouchillon [9] and Zhang, Li, Wu, & Li, [92]. However it contra-
dicts claims made by Bapna et al. [5]. Researches have shown that
students spend a lot of time interacting with their friends on SNS
platforms [67], Yeboah and Ewur [91]. Therefore, they are familiar
with and purview to their principles and values. This increasesed Computing and Informatics, https://doi.org/10.1016/j.aci.2019.07.003
F.N. Koranteng et al. / Applied Computing and Informatics xxx (xxxx) xxx 7their trust in colleagues and are able to identify with friends with
similar beliefs and perceptions.
5.2. Practical contributions
The results from this study provide many insightful contribu-
tions for SNS developers who seek to promote Trust. First of all,
SNS developers must design tools that augment shared interpreta-
tion on SNS. Specifically, they must develop algorithms that con-
nect users with common beliefs. For instance, based on a
person’s profile and characteristics, the system could suggest
others with similar characteristics to follow. This will enable peo-
ple to easily find others with similar traits and values.
Also, in response to the significant impacts of social interactions
and exchanges on SNS users’ Trust, SNS developers must offer
functionalities that support frequent communications and
exchanges among users. Though most SNS offer synchronous peer
to peer communications platforms such as Chats, it is imperative to
introduce design features that persuade, encourage and motivate
users to frequently interact with others via such media. Perhaps,
reward systems and packages could be given to users who give
prompt feedback to others.
Furthermore, designers should develop SNS systems that
encourage the ‘‘We” feeling among SNS users. For example, the
Twitter Feed feature increases the feeling of social presence. Such
platforms are important enablers for users to witness the activities
of their friends. Because, network members can easily relate to thePlease cite this article as: F. N. Koranteng, I. Wiafe, F. A. Katsriku et al., , Appliactivities of their friends, they get involve by liking or commenting.
This increases members’ perception of being part of a community
and therefore develop strong attachment to their friends [11].
6. Conclusion
In an attempt to augment existing literature on SNS which had
predominantly measured the effects of Trust, this study proposed a
model to investigate the antecedents of Trust among SNS users.
Particularly, it theorized that Shared Language predicted Norm of
Reciprocity, Social Interaction Ties and Identification while these
variables also predicted Trust. The analysis of data from 921
respondents using PLS-SEM validated these relationships. The
study adopted the convenience sampling technique for data collec-
tion, though the random sampling approach would have been
more suitable for this study. Hence, our findings cannot be
generalized.
Further studies should be conducted in other jurisdictions to
assess the applicability of the model in those settings. Also, the
proposed relationships of the model were tested using cross-
sectional data. Given that peoples’ ideologies and behavioral pat-
terns changes with time, a longitudinal approach would have bet-
ter explained the findings in the long term.
Declaration of Competing Interest
None.Appendix. One: Constructs, question items and loadingsShared Language SL1 The members in my social network use common terms or jargons.ed Computing and Informatics, https://doi.org/10.1016/j.aci.2019.0.848
SL2 Members in my social network use understandable communication pattern during the
discussion.
0.854SL3 Members in my social networks use understandable narrative forms. 0.867
Norm of Reciprocity NR1 I know that other members in my social network will help me, so it’s only fair to help other
members.
0.897NR2 I believe that members in my social network would support me if I need it. 0.888
NR3 I share information with members in my social network as they always do with me 0.885Social Interaction
TiesSIT1 I maintain close social relationships with some members in my social network. 0.898
SIT2 I spend a lot of time interacting with some members in my social network. 0.857
SIT3 I have frequent communication with some members in my social network. 0.948
SIT4 I establish contact with some members in my social network. 0.890Identification ID1 I feel a sense of belonging towards my social network members. 0.854
ID2 I have the feeling of togetherness or closeness in my social network members. 0.868
ID3 I have a strong positive feeling toward my social network members. 0.885
ID4 I am proud to be a member of my social network members. 0.814Trust TR1 Members in my social network will not take advantage of others even when the opportunity
arises.0.862TR2 Members in my social network will always keep the promises they make to one another. 0.867
TR3 Members in my social network would not knowingly do anything to disrupt the conversation. 0.869
TR4 Members in my social network are truthful in dealing with one another. 0.973Appendix Two: Model Fit SummaryMeasure ValueSRMR 0.068
NFI 0.940
RMS_theta 0.00207.003
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