Identifying the Factors Affecting Users’ Adoption of Social Networking

Duygu Fındık Coşkunçay
International Journal of Human Computer Interaction (IJHCI), Volume (4) : Issue (1) : 2013 1
Identifying the Factors Affecting Users’ Adoption of Social
Networking
Duygu Fındık Coşkunçay fduygu@metu.edu.tr
Informatics Institute/Information Systems
Middle East Technical University
Ankara, 06800, Turkey
Abstract
Through the rapid expansion of information and communication technologies, social networking
sites have received much more attention in the scope of internet communication. Success of a
social web primarily depends on users’ satisfaction. In this context, this study aims to identify the
influencing factors that affect users’ satisfaction towards social networking site use. A
multidimensional model has been proposed based on the Information Quality, System Quality,
Environmental and Affective dimensions to assess the effects of key variables – Semantic
Intention, Usability, Web-Page Aesthetics, Subjective Norm and Trust- on users’ satisfaction.
Facebook was chosen as a focused social networking site, because of its popularity. A
comprehensive survey instrument was applied to 203 Facebook users. Also, Structural Equation
Modeling, particularly Partial Least Square, was conducted to analyze the proposed research
model. As a result, proposed multidimensional research model predicts the factors influencing
users’ satisfaction towards social networking site use and relationships among these factors. The
findings of this research will be valuable for literature by analyzing the influencing factors that
have not been previously researched in the context of social networking satisfaction area.
Keywords: Social Networking Satisfaction, Structural Equation Modeling, Partial Least Squares
1. INTRODUCTION
Social networking concept has emerged out of the growing social needs such as establishing new
social relations, finding friends with similar interests, sharing knowledge and content with other
people [1]. Social networking with the expanding popularity have become among the most
famous sites on the Web. The number of users might be shown as evidence for their popularity:
Linkedln has 41 million users, MySpace has 67 million users, Twitter has 98 million users and
Facebook has 540 million users [2]. Although the number of users is high enough to easily accept
popularity of social networking sites, the reasons behind their success are unclear [3]. According
to researchers [4], users’ satisfaction plays the major role for the success of web sites. In this
regard, the literature needs research to identify the factors influencing users’ satisfaction towards
social networking sites.
In this study, Facebook is chosen as social web to identify users’ satisfaction toward social
networking. The major reason underlying this selection is that Facebook is the most popular
social networking site in TURKEY with 22 million users; also TURKEY is among the first five
countries with Facebook use [5]. Social Networking Satisfaction Model (SNSM) has been
proposed to identify the factors affecting users’ satisfaction towards social networking.
Multidimensional perspective is considered when developing the research model; Information
Quality – Semantic Intention, System Quality – Usability and Web Page Aesthetics,
Environmental Issue - Subjective Norm and Affective Issues – Trust. The present study makes
contribution to the literature for several reasons. Firstly, this study is the first attempt to examine
users’ satisfaction from the constructed multidimensional perspectives in the context of social
networking. Secondly, effects of semantic information have never been examined before by
researchers to evaluate users’ satisfaction. Thirdly, usability and web-page aesthetic as a system

quality have not been employed to measure users’ satisfaction in the context of social networking.
In addition, there has been no other study examining the effects of other people on users’
satisfaction towards social web. On the other hand, SNSM can greatly benefit system developers
to better understand how users’ satisfaction can be increased and how the social networking sites
can be improved.
As a result, in order to increase users’ satisfaction when using social networking sites, it is
essential to understand the reasons behind users’ rejection and identify the critical factors
affecting their satisfaction. Therefore the aims of this study are as follows:
• Identifying the key factors affecting users’ satisfaction of social networking site use.
• Developing a multidimensional model to reveal the main reasons behind the users’
satisfaction of social networking site use.
The research question of this research is as follow;
• To what extend Semantic Intention, Usability, Web-Page Aesthetics, Subjective Norm
and Trust affect Facebook users’ satisfaction towards social web use.
2. LITERATURE REVIEW
2.1 Social Networking
Social networking sites enable individuals to “(1) construct a public or semi-public profile within a
bounded system, (2) articulate a list of other users with whom they share a connection, and (3)
view and traverse their list of connections and those made by others within the system” [6,
pp.211]. Most of these sites share the basic features like allowing individuals to represent
themselves with creation of profiles, meeting with others, finding new jobs, receiving or providing
recommendation or much more [7].
According to the Facebook press room, Facebook is the most popular and fastest growing social
networking site and it has been using since 2004 and today it has more than 500 million active
users. This social networking site initially considered college and universities as a target group;
however, it extended this group with everyone including high schools and other organizations [8].
As a study states that [9], researchers evaluated Facebook in different aspects like trust and
privacy issues [6], [7], [8], [10], [11], motivations to use [9], [12], [13], [14], [15], usage effects
[16], [17], [18] and identity and self-presentation of Facebook [19], [20], [21].
Although researchers were examined Facebook from different angles, little research investigated
the factors that influence social networking adoption [12]. Users’ adoption and decision to
continue social networking use primarily depends on users’ satisfaction [9].
2.2 Satisfaction of Information System
“…satisfaction in a given situation is the sum of one’s feelings or attitudes towards a variety of
factors affecting that situation” [22, p. 531]. Satisfaction is suggested as a success measure in the
information systems area [23], [24]. The studies show that there is a strong relation between
users’ satisfaction and their intention to information system use [25], [26].
Satisfaction has been considered in different theoretical frameworks in the area of information
systems [27]. However, any single study has not considered information quality, system quality,
environmental issues and affective factors as a theoretical framework to evaluate users’
satisfaction towards social networking. Success of information systems depends on quality of
information and systems [24]. Also environmental factors and affective conditions are important
determinants of users’ satisfaction. In the scope of this study, a research model is proposed to
assess users’ satisfaction in the context of social networking as taking information quality, system
quality, environmental and affective issues base.

3. RESEARCH METHOD
3.1 Research Model
In this study, a multidimensional model has been proposed to reveal the critical factors affecting
users’ satisfaction towards use of social networking. The model is named as Social Networking
Satisfaction Model (SNSM) (shown in Figure 1). This simulation increase understandability and
visuality between social networking satisfaction factor and the dimensions and the related
variables examined under this dimensions. When developing SNSM, it is avoided to examine
users’ satisfaction from one perspective. Because it is recommended that a researcher should
avoid using single linear methodology when assessing the attitudes of users towards a
technology [28]. The critical measurement constructs are categorized by taking the IS Success
Model [24], Social Cognitive Theory [29] and affective issue as base. Information Quality, System
Quality, Environmental Issue and Affective Issue dimensions are considered when selecting the
factors influencing users’ satisfaction towards social networking. Initially, the effects of critical
factors on satisfaction and relations between the constructs under the same dimension are
hypothesized. During the structural model evaluation the meaningful relations among the
constructs are tried to be revealed.
Information Quality: This concept is dealt with the relevance, timeliness, reliability,
completeness, precision and accuracy of information produced by an information system [24].
User satisfaction is affected from “Informativeness” including relevance, comprehensiveness,
recentness, accuracy and credibility, “Accessibility” consisting of convenience, timeliness and
interpretability and “Adaptability” [30], [31]. Information quality is critical if it is expected to produce
information for decision-making from an application of information technology [24]. For example,
word-processor does not actually produce information therefore information quality is not a critical
factor such a system. However, it is expected from the social networking sites to produce
meaningful information for users. For this reason, Semantic Intention is explored under the
Information Quality dimension to examine the importance of semantic information in social
networking and how users’ satisfaction is affected with the availability of semantic information.
Semantic Intention (SI): With the emergence of limitations in accessing the vast information on
the Web [32], semantic web concept has become popular as a solution to overcome this
dilemma. Semantic web helps to create an information infrastructure by accessing data from
variety of sources to achieve a task [33]. Semantic web is defined as “... an extension of the
current web in which information is given well-defined meaning, better enabling computers and
people to work in cooperation” [34, p.3].
Support of social networks with semantic web brings innovations to attract the intention of users.
The closer interaction between semantic web and social networking will be beneficial [32]. He
stated that “the creation of mashups or combination of diverse sources of data and services could
greatly benefit from the shared representations and protocols proposed by the Semantic Web
community”. Success of the Web depends on social adaption as much as technological issues
[35]. With the help of semantic web, interoperability and social adoption have become critical
factors for the global scale of the Web [32]. Therefore, with the help of Semantic Intention
construct, it is possible to understand how users’ satisfaction is affected if social networking sites
that are supported with semantic information. The hypothesis related with Semantic Intention is
as below;
H1: Support of social networking with semantic information positively influences users’
satisfaction towards social networking.
System quality is concerned with the consistency of user interface, ease of use, quality of
documentation and system flexibility [24]. A study shows that system quality indirectly influences
users’ perception towards system use [36]. According to [14], system quality is a critical point
should be taken into consideration, because users are reluctant to use a web site if they
experience problems on access, difficulty in navigation, constantly lived delays in response and

disconnection [37]. Usability and Web Page Aesthetics factors are examined under the System
Quality dimension to reveal their effects on satisfaction of the user.
Usability (U): Usability is summarized as “the capability in human functional terms to be used
easily and effectively by the specified range of users, given specified training and user support, to
fulfill the specified range of tasks, within the specified range of environmental scenarios” [38,
p.340]. With the help of Usability construct, it is tried to measure that how users’ success rate
meeting in specified aims, ease of use perception and perceived efficiency of system use affect
their gratification towards system. The hypotheses related with Usability are as follows;
H2: Usability positively influences users’ satisfaction towards social networking.
Web Page Aesthetics (WPA): Aesthetic concept was neglected until the first quarter of 21th
century, because of the different focus points between computer industry and design criteria of
human computer interaction [39]. However, as Norman’s prescience [40], [41], the appropriation
of modern design based on setting aesthetics ahead of usability. As it is seen aesthetics and
usability present two orthogonal dimensions of human computer interaction; however there is a
strong relation between usability and aesthetics as seen in the definition “measurement of
usability defines the success or lack thereof in a GUI design” [42, p.60]. Also, recent research
revealed that there is a strong relation between visual aesthetics of computer interfaces and
users’ satisfaction and pleasure [43]. Therefore, it is aimed to experimentally explore users’
perception of aesthetics on their usability and satisfaction perception.
H3: Web Page Aesthetics positively influence users’ satisfaction towards social networking.
H4: Web Page Aesthetics positively influence users’ usability perception towards social
networking.
Environmental Issues: Social Cognitive Theory (SCT) [29] is widely accepted and empirically
validated model to evaluate an individual’s behavior [44]. The theory examined the reciprocally
determined factors that are environmental influences, cognitive and other personal factors and
behavior. Individuals select the environment in which they exist and influenced by those
environments. In addition behavior is affected by environmental factors or situational
characteristics and cognitive and other personal factors [44]. Therefore, Subjective Norm is
added into the research model under Environmental Issue dimension in order to analyze the
effects of social pressure over users’ satisfaction of social networking.
Subjective Norm (SN): Subjective Norm reflects the effects of social influence to perform or not
to perform a behavior [45]. In other words, subjective norm concerns with normative beliefs about
the expectation from other people [46]. Subjective norm is defined as “person’s perception that
most people who are important to him think he should or should not perform the behavior in
question” [47, p.302]. With the help of Subjective Norm construct it is aimed to measure how the
others’ opinions affect users’ gratification. Thus the following hypotheses are formulated;
H5: Subjective Norm positively influences users’ satisfaction towards social networking.
Affective Issue: Affective concept states that systems have emotion and ethical dilemma, as well
as a number of social and philosophical questions [48]. According to Picard [49], emotion plays a
vital role between users and systems; because it underlines the process of perception, decision-
making, creativity, empathic understanding, memory, as well as in social interaction [50], [51],
[52], [53]. According to Lee and See [54] states that trust which is an effective response and it
affects information selection, interpretation and intention to rely. In the scope of this study, the
relation between trust and satisfaction is examined to reveal how users’ affective basis effects
their system interaction.

Trust (T): Trust is defined as "the extent to which one is willing to ascribe good intentions to and
have confidence in the words and actions of other people" [55, p.39]. Trust is explained as a
belief that affects our attitude towards the system use [56]. Activities performed in social
networking sites like creating profile (name, surname, birth date, ethnicity, etc.), sharing
documents (images, text, audio and video), manifesting the status information, connecting with
friends, and etc. expose privacy concerns. In this point, it can be said that trust is an important
factors for successful online interaction for sharing information and building new relationships
[57]. In this sense, some questions come into prominence. What role does trust play in users’
satisfaction from using the system and how does it play role when users intent to use social
networking sites? In order to answer these questions, the following hypothesis is presented
below;
H6: Trust perception positively influences users’ gratification towards social networking.
In addition to the direct relation between the proposed critical factors and satisfaction, the
relations among the influencing factors will be examined during the structural model evaluation.
FIGURE 1: Social Networking Satisfaction Model (SNSM).
3.2 Instrument Development
After a detailed literature review, a comprehensive survey instrument was developed to
empirically validate the research model. The survey instrument consisted of two parts. The first
part included demographic questions and the second part included measurement items to assess
Semantic Intention, Usability, Web-Page Aesthetics, Trust, Subjective Norm and Satisfaction
factors. The references of the measurement items are as follows; Semantic Intention was self-
developed, Usability was adopted from Nielsen’s Ten Usability Heuristics [58], [59], Web-Page
Aesthetics was adopted from Mullet & Sano [60], Trust was adopted from the researches Pan &
Zinkhan [61] and Dwyer, Hiltz &Passerini [8], Satisfaction was adopted from Bhattacherjee [62],

[63]. Five point likert scale type was used for measurement items in which 1 stated strongly
disagreement and 5 stated strongly agreement.
Content validity helps to assess whether the measurement items represent the constructs tried to
measured, and cover full range of the constructs [64]. Before survey instrument applied in pilot
study, content validity was checked with group including ten PhD students and expert judgments.
According to their feedbacks some re-wording was done to make measurement items more
understandable and comprehensive. After content validity was checked, reliability of the survey
instrument assessed with pilot study, 47 measurement items were used in main research. The
results of the pilot study are shown below.
3.3 Pilot Study Data Collection and Participants
The aim of the pilot sampling was to find people who use Facebook social networking site most
frequently. In the pilot study, convenience sampling method was used. The sample size of the
pilot study was 31 (52% female and 48% male). The participants were in 20 to 50 age range and
the average age was 28.5. Most of the participants were graduate and undergraduate students.
65% percent of the participants reported more than once Facebook usage in a day. This result
shows that the aimed target sample was reached. Also the actual survey instrument was
prepared according to the results of reliability statistics (based on inter item consistency -
Cronbach’s Alpha was 0.890) and feedbacks of the participants.
3.4 Main Study Data Collection and Participants
The final survey instrument was distributed 500 Facebook users in Turkey. Snowball sampling
technique was considered in data collection. A total of 269 responses were obtained in a month.
The total response rate was 53.8%. The demographic profile of the participants was presented
with frequency of Facebook use, gender, age, education level, occupation and used social
networking sites. 67% of the participants were using Facebook more than once in a day. 49% of
the participants were female and 51% of them were male. This demographic result shows that
there was no big diversity in gender in terms of sample sizes. Participants were in 18 to 53 age
range and the average age was 26.7. 81% of the participants were including graduate and
undergraduate students, 15% of them had a PhD level and the remaining had high school
graduate. 46% of the participants were working in public sectors and then private sector and
students followed with 26% and 21% respectively. Also, 25%, 13%, 6%, 5% of the participants
were using Twitter, Linkedln, Xing and MySpace social networking sites respectively in addition to
Facebook.
4. DATA ANALYSES AND RESULTS
4.1 Preliminary Analysis
In order to prepare the data set for further analyses, a set of preliminary data analyses that are
missing value analysis, outlier detection and normality need to be conducted [65]. Firstly, missing
values need to be handled by considering the missing value analyses. The cases should be
removed from the data set, if the extent of missing values is greater upper than 50% [65].
Therefore the sample size dropped from 269 to 203 after the cases including missing values
higher than 50%. If the missing data level is under 10% any imputation method can be used to
solve the missing value problems [65]. In the data set, the missing value level was under 10% for
both in all the cases and variables. Therefore, mean substitution was used for missing values.
Secondly, outliers that are the cases with values well above or well below the majority of other
cases should be detected and handled [66]. In order to detect problematic values as an outlier in
the data set the comparison between mean and trimmed mean values were considered [67].
However there were not huge differences were detected between these values to consider outlier
problem. Lastly, normality assumption is evaluated to determine the required type of structural
equation modeling. If the data set is normally distributed covariance based structural equation will
be performed and if the data set is not normally distributed component based structural equation
modeling will be performed to determine the relation between constructs of the proposed
research model [68]. Normality assumption was checked with skewness and kurtosis values and

Kolmogorov-Simirnow test [64] and it was assumed that the distribution of the data set was not
normal.
4.2 Identifying the Factor Structure
The aim of exploratory factor analysis is to identify the factor structure of data set for a set of
variables by determining how many factors exist [69]. A maximum likelihood was conducted on
the 47 items with direct oblimin rotation because the factors were related with each other [64].
The Kaiser-Meyer-Olkin (KMO) measure equaled to .85 confirmed the sampling adequacy for the
explanatory factor analysis [64]. Bartlett’s test of sphericity x2 (1081) = 5866.45, p < .001,
indicated that correlations between items were sufficiently large for maximum likelihood. Seven
components had eigenvalues over Kaiser’s criterion of 1 and combination explained 52.70% of
the variance. According to Hair and his colleagues [65] factor loadings should be 0.3-0.4 to meet
the minimal level for explanation of structure. Table 1 shows the factor loadings after rotation. The
items had a factor loading value above 0.40 cluster on the same factor suggest that factor 1
represents Usability, factor 2 represents Aesthetics, factor 3 represents Semantic Intention, factor
4 represents Subjective Norm, factor 5 represents Trust, factor 6 represents Satisfaction. The
problems with measurement items (written italic in Table 1) were as follows;
The eighth and ninth items of Usability did not cluster under the first factor as expected. The sixth
item of Semantic Intention did not cluster under the Semantic Intention factor. The third, fourth
and ninth items of Satisfaction did not strongly load under the same factor.
4.3 Reliability Assessment
Reliability analysis is required to measure the consistency of the survey instrument [64].
Reliability of the survey instrument can be evaluated with the inter-item consistency and that is
assessed with Cronbach’s Alpha [70]. Cronbach’s Alpha value around 0.8 indicates a good
reliability [64]. Total scale Cronbach’s Alpha value was .920 and this result showed that the total
scale had a high reliability. Also, reliability analyses were performed for all sub-scales and the
results showed that (in Table 1) all had high reliabilities.
Construct / Item
Factor
Loading
Cronbach’s
Alpha
Coefficient
Reliability
Result
Usability (U)
U1: While navigating the Facebook, I know exactly where I am.
U2: After visiting a page on Facebook, I generally know where I go
next.
U3: The words, phrases, and concepts presented on the Facebook
are familiar. In other words, the Facebook speak my language.
U4 : I am able to easily recover when I went to go wrong page
U5: When encountering errors, Facebook provides good error
messages.
U6: Facebook does not require an extensive use of memory. I am
able to recognize and do not need recall.
U7: Facebook is flexible to use.
U8: Any extraneous information is not given on Facebook
U9: Facebook is an efficient social network to use.
.543
.599
.443
.705
.523
.530
.464
-
-
.784 Good
Web Page Aesthetic (WPA)
WPA1: The same visual language elements are used throughout
the Facebook.
WPA2: There are not any extraneous details either functionally or
aesthetically on Facebook.
WPA3: Design elements are related with main purpose of the
Facebook and the expected visitors.
WPA4: Interactive elements are clearly distinguishable in both form
and function.
WPA5: I am able to see clearly how individual parts of the
.552
.442
.573
.686
.443
.826 Good

Facebook relate to each other.
WPA6: Contrasting colors are used to make it easy for users to
distinguish between different areas of focus on Facebook.
WPA7: Similar elements are grouped together contextually on
Facebook.
WPA8: Elements are ordered in a hierarchy on Facebook.
WPA9: Design elements are balanced in either a symmetrical or
asymmetrical layout, throughout the Facebook.
.465
.600
.634
.642
Semantic Intention (SI)
SI1: I would be glad if Facebook had an option to give me current
information about my occupation
SI2: I would be glad if Facebook provides personalized
advertisements through taking account of my previous online
shopping history
SI3: I would be glad if Facebook had an option to provide me
newsbreaks from my daily followed newspapers.
SI4: I would be glad if Facebook had provided special offer
information from my favorite shopping websites.
SI5: I would be glad if Facebook had brought me information from
other social networking sites about my friends of whom I am
searching about.
SI6: I would be glad if Facebook had an option to remove the
photos in my profile tagged to friends who are deleted from my
friend list.
SI7: I would be glad if Facebook informs me of social activities in
the city I live (i.e cinema, theatre, concert )by considering my areas
of interest.
SI8: I would be glad if Facebook became a much more
semantically (personalized) social network.
.632
.696
.732
.742
.502
-
.556
.630
.832 Good
Subjective Norm (SN)
SN1: My friends think that I should use Facebook
SN2: My family think that I should use Facebook
SN3: People important to me support my use of Facebook
SN4: People who influence me think that I should use Facebook
SN5: People whose opinions I value prefer that I should use
Facebook
-.620
-.459
-.771
-.979
-.886
.885 Good
Trust (T)
T1: Facebook is a trustworthy social network
T2: I can count on Facebook to protect my privacy
T3: Facebook can be relied on to keep its promises
T4: I can count on Facebook to protect customers’ personal
information from unauthorized use
T5: I feel that the privacy of my personal information is protected by
Facebook
T6: I trust that Facebook will not use my personal information for
any other purposes
-.607
-.897
-.739
-.889
-.938
-.947
.922 Good
Satisfaction (STS)
STS1: I am satisfied with the performance of Facebook.
STS2: I am pleased with the experience of using Facebook.
STS3: My decision to use Facebook was a wise one
STS4: Overall, the information I get from Facebook is very
satisfying.
STS5: All things considered, I am very satisfied with Facebook as a
social network.
STS 6: Using Facebook is pleasant.
STS7: I have fun using Facebook.
STS8: I find using Facebook to be enjoyable.
STS9: I find using Facebook to be interesting.
STS10 - STS6: Overall, my interaction with Facebook is very
satisfying
.482
.833
-
-
.467
.865
.729
.868
-
.476
.900 Good
TABLE 1: Factor Structure and Reliability of Data Set.

4.4 Assessment of the Proposed Research Model
The validity of the proposed research model was evaluated with component based structural
equation modeling because of the non-normal data structure. SMART PLS was used to assess
structural model of proposed research model. Before analyzing the structural validity of the
model, the measurement model was assessed.
4.4.1 Assessment of the measurement model
Confirmatory factor analysis was used to evaluate measurement model. This analysis helped to
examine which variable was load on which factor as well as the correlation among the factors.
Measurement model evaluation was performed with convergent validity and discriminant validity
that are the most important components of construct validity.
Convergent validity is defined as the degree to which two variables share variance due to a given
concept and correlation [71]. Convergent validity is evaluated with Factor Loadings (FL),
Composite Reliability (CR), and Avarage Variance Extracted (AVE) [65].
Factor loadings validate the correlation between each measurement items and their constructs.
Factor loadings ideally should be 0.7 or higher, however 0.5 or higher is acceptable. As shown in
Table 2 factor loadings of all measurement items were change between 0.420 and 0.927. U1, U2,
WPA2, WPA3, SI5 measurement items had factor loadings below 0.6 and they were removed
from the data set.
Composite reliability represents internal consistency that means all measurement items represent
its latent constructs and this value should be minimum 0.7. In this study, composite reliability
values were between 0.845 and 0.956.
Average variance extracted (AVE) value should be calculated for each latent constructs of the
model. AVE value should be over 0.5 for adequate convergent validity. The AVE values of the
factors in the proposed research model ranged between 0.501 and 0.788.
These results show that the measurement model provided an adequate convergent validity.
Item Factor Loadings
Composite Reliability
(CR)
Average Variance
Extracted (AVE)
U1
U2
U3
U4
U5
U6
U7
0.494
0.590
0.716
0.767
0.672
0.692
0.691
0.845 52%
WPA1
WPA2
WPA3
WPA4
WPA5
WPA6
WPA7
WPA8
WPA9
0.665
0.420
0.603
0.744
0.661
0.640
0.761
0.680
0.649
0.868 50%
SI1
SI2
SI3
SI4
SI5
SI7
SI8
0.663
0.750
0.815
0.785
0.549
0.723
0.624
0.874 54%

SN1
SN2
SN3
SN4
SN5
0.701
0.720
0.892
0.912
0.916
0.918 69%
T1
T2
T3
T4
T5
T6
0.781
0.922
0.855
0.913
0.919
0.927
0.956 78%
STS1
STS2
STS5
STS6
STS7
STS8
STS10
0.691
0.877
0.773
0.814
0.868
0.867
0.802
0.845 66%
TABLE 2: Convergent Validity for Measurement Model.
Discriminant Validity was proved to show each constructs of the model was adequately different
from each other. In order to show differences the square root of the average variance calculated
for each constructs should be greater than the correlation between a given constructs and all
other constructs [72]. As shown in Table 3, the diagonal shows the square root of average
variance calculated for each constructs and these values are greater than the other correlation
values. Therefore, discriminant validity was also met the construct validity.
Construct WPA SI SN STS T U
WPA 0.697
SI 0.095 0.735
SN 0.196 0.237 0.833
STS 0.360 0.252 0.528 0.815
T 0.227 0.219 0.481 0.433 0.887
U 0.506 0.116 0.116 0.421 0.119 0.722
TABLE 3: Discriminant Validity for Measurement Model.
4.4.2 Assessment of Structural model
Statistical significance of hypotheses was assessed by considering the path coefficient values
(standardized betas). A bootstrapping procedure was applied on the data set including 203
samples to evaluate significance level of the relations between constructs. The result of analyses
showed that the SNSM accounted for 45% variances of Satisfaction. Figure 2 shows the
estimated path coefficients between constructs of the structural model.

FIGURE 2: Structural Model of SNSM.
* Blue arrows show significant hypotheses, black arrows show additional relations, red arrows show rejected
hypotheses
The structural evaluation of SNSM showed that Semantic Intention and Web Page Aesthetics
factors did not significantly affect Satisfaction, so H1 and H3 were rejected. On the contrary, three
strong positive relations were found between Satisfaction and Usability, Trust and Subjective
Norm at the p<0.001 level. Therefore H2, H5 and H6 were accepted. Also, Web Page Aesthetics
positively affected Usability at p<0.001 level, so H4 was accepted. In addition to initially
hypothesized relations, additional significant relations were found during the structural model
evaluation. For example, there was a strong positive relation between Semantic Intention and
Trust at p<0.001 level. Moreover, SN had a positive and significant relations with Trust at p<0.001
level. The significance value and standardized path coefficients of the SNSM are shown in Table
4.
Relationships Hi T-Values Β Decision
SI->STS
U->STS
WPA->STS
WPA->U
T->STS
SN->STS
SI->T
SN->T
H1
H2
H3
H4
H5
H6
Additional Relation
Additional Relation
1.235
5.407
1.240
9.641
3.132
6.286
6.723
6.723
0.066
0.308***
0.082
0.503***
0.183**
0.372***
0.202***
0.431***
Rejected
Accepted
Rejected
Accepted
Accepted
Accepted
Accepted
Accepted
**p < 0.01; ***p<0.001
TABLE 4: Summary of Hypotheses Tests.
5. DISCUSSION
In this empirical research, a number of relations were examined to reveal the factors affecting
users’ satisfaction towards social networking. SNSM showed the relations among the factors
examined under the Information Quality, System Quality, Environmental and Affective Issues
dimensions.
The first dimension examined the effects of information quality on users’ satisfaction towards
social networking use. With this dimension it was expected to measure effects of information
content on users’ satisfaction. Diversification and usefulness of the information was examined in

the scope of semantic concept. In the proposed research model the relation between Semantic
Intention and Satisfaction was analyzed. The main aim of the semantic information in social
networking was to give beneficial information from multiple resources. However, statistical result
showed that there was not a significant relation between Semantic Intention and Satisfaction.
This result can be interpreted as semantic information gathered from different source of the web
does not as effective as expected on users’ satisfaction of social networking use. The reason of
this insignificant relation may be reliability of data obtained from the web [32]. Also there are most
prominent positive and negative aspects of social networking sites [3]. As positive experiences,
enjoyment, fun/playfulness, excitement, self-expression and curiosity come into prominence. This
means that social networking navigation is strongly related with his or her enjoyment. Also, as a
curiosity consideration of social networking, users only wonder information about their friends [3].
Also, this insignificant relation is parallel with the study of [14]. The researcher found insignificant
relation between information quality and users satisfaction. He interpreted this insignificant
relation as the users do not care good information, the social web mainly focus on interaction and
communication among the members. In addition, vast amount of data available in web platform
like, text, audio-video, image and etc. Trust and privacy issues come into prominence with the
facing this huge amount of data. While the availability of this vast amount of data helps to
complement social networking with semantic information, there is privacy and trust consideration
appears. With the help of SNSM, the relation between Semantic Intention and Trust was
examined. This relation was an additional relation was not previously hypothesized. The
statistical result showed that semantic information provided by social networking significantly
related with trust. This means that people want to trust the information gathered from different
sources in social web.
The second dimension tried to identify the effects of system quality on users’ satisfaction towards
semantic web use. Under the system quality dimension the effects of usability and web-page
aesthetics on users’ satisfaction were examined. The statistical analysis showed that Usability
had a strong effect on users’ satisfaction. This means that users found the site effective and easy
to learn to accomplish tasks, adapted easily to variation in tasks and satisfied with the system use
[38]. On the contrary, there was a non-significant relation was found between web page
aesthetics and satisfaction of users. This result shows that users did not care the aesthetics of
the social networking site. Dramatically, the users’ expectation of interface quality towards
Facebook fairly poor and satisfaction of users can be achieved without good interface quality
because their main expectations are interaction and communication [14]. According to the study
of Butler [42], “aesthetics is referred as non-quantifiable, subjective, and affect based experience
of system use; however usability is commonly measured by relatively objective means and sets
efficiency as its foremost criterion” [39]. One may well assert that the different effects that
usability and Web-Page aesthetics have on satisfaction may be explained by the fact that
aesthetic is a subjective concept, while usability is objective. In addition there was a significant
relation was found between web page aesthetics and usability factors. The finding refers that the
users who interacted with an aesthetic social networking site perceived the system as more
usable than users who interacted with less aesthetic social networking site. This result is parallel
with the other studies existing in the literature [39], [73], [74]. The previous researchers
considered different applications while assessing the relation between aesthetics and usability.
For example, the effects of aesthetics on usefulness by considering ATM machines were
examined [39], [73] and DeAngeli, Sutcliffe & Hartmann used web-sites to evaluate the relation
between these two factors and the researcher found a strong relation between aesthetics and
usability perception of users [75].
The third dimension identified the effect of environmental issue examining effects of subjective
norm on users’ satisfaction. There was a positive and significant relation was observed between
subjective norm and satisfaction of users. This significant relation refers that users’ gratification
increase when the other people around the users give concrete support towards their use of
social networking site. In other words, people pleased with other people’s encouragement of
using information systems. According to the information obtained so far, several studies prove the
effects of subjective norm on users’ continuance intention and system use in information system

contexts [46], [76], [77]; however any study has not examined the relation between subjective
norm and satisfaction. Moreover, a significant relation was found between subjective norm and
trust during the analyses. This relation shows that the trust perception of users strongly affected
from opinion of other peoples. The strong relation between subjective norm and trust is supported
with the statement of Wu and Chen [78]. They stress that “whatever types of trust are with direct
and indirect influences on subjective norm, they are all the important antecedents of subjective
norm in on-line service”.
The last dimension affective issue identified the effects of trust over satisfaction. The structural
model showed that there was a positive and direct relation between trust and satisfaction. This
relation emphasize that the users who feel more confidence towards social networking site have
more pleasure with social web interaction.
6. CONCLUSION
This study was conducted to propose social networking satisfaction model titled as SNSM from
the perspective of Facebook users. The concept of satisfaction is not adequate to consider the
human factors for usability [79]. Therefore the model composed of four dimensions – Information
Quality, System Quality, Environmental and Affective Issues. The proposed multidimensional
model was evaluated structurally to examine the effects of following variables of corresponding
dimensions on satisfaction of social networking site users; (1) Information Quality – Semantic
Intention, (2) System Quality – Usability and Web-Page Aesthetics, (3) Environmental Issue –
Subjective Norm, (4) Affective Issue – Trust. The structural model explained a significant amount
of variance of satisfaction towards social networking site use. In addition, the findings contribute
the literature by revealing the factors that influence users’ satisfaction of social web. The findings
guide the system developers for continuous improvement of the system.
When we look at the literature, it is seen that the number of studies examining the user
satisfaction on Facebook usage is not satisfactory. In the literature, the researchers examined the
relation between Facebook use and students’ life satisfaction, social trust, civic engagement, and
political participation [80]. However, any single study in the literature has not considered effects of
variables examined in the scope of this research on users’ satisfaction.
As a guide for further researchers, this multidimensional model can be extended with external
dimensions in order to provide different perspectives. There may be different influencing factors
affecting users’ satisfaction like privacy, perceived ease of use, perceived usefulness,
organizational support and so on. Web-applications are different from traditional software
systems and maintenance process becomes a cumbersome process [81]. How the maintenance
of the systems will affect the users’ satisfaction can also be examined by the future researchers.
Another future research would be performed to validate SNSM on different social networking
sites. In addition, the effects of Semantic Intention and Web-Page Aesthetics should be examined
in new research in the scope of social networking site satisfaction.
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