Journal of Electronic Resources Librarianship
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/wacq20
Demographic factors influencing the adoption
and use of social media in university libraries in
Ghana: A unified theory of acceptance and use of
technology (UTAUT) approach
Monica Mensah & Omwoyo Bosire Onyancha
To cite this article: Monica Mensah & Omwoyo Bosire Onyancha (2021) Demographic factors
influencing the adoption and use of social media in university libraries in Ghana: A unified theory
of acceptance and use of technology (UTAUT) approach, Journal of Electronic Resources
Librarianship, 33:3, 170-194, DOI: 10.1080/1941126X.2021.1949157
To link to this article:  https://doi.org/10.1080/1941126X.2021.1949157
Published online: 08 Sep 2021.
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JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP
2021, VOL. 33, NO. 3, 170–194
https://doi.org/10.1080/1941126X.2021.1949157
Demographic factors influencing the adoption and use of
social media in university libraries in Ghana: A unified theory
of acceptance and use of technology (UTAUT) approach
Monica Mensaha and Omwoyo Bosire Onyanchab
aUniversity of Ghana; bUniversity of South Africa
ABSTRACT KEYWORDS
The purpose of this study is to examine the influence of demographics as Social media; UTAUT;
moderators of the factors influencing library workers’ and library patrons’ moderators; demographics;
behavioral intention toward the use of social media in university libraries structural equation
in Ghana. The theoretical model for this study was based on the unified modeling; universitylibraries; Ghana
theory of acceptance and use of technology (UTAUT). The study employed
a cross-sectional survey and quantitative data was collected from a total of
six-hundred and five usable responses obtained from the sampled
respondents using a pre-tested questionnaire. Statistical analyses were car-
ried out using the t-test analysis (using Levene’s Test for Equality of
Variances) and the structural equation modeling (SEM) technique. Key find-
ings were that although partial relationships were established, the media-
ting roles of the demographics of library workers and patrons on factors
influencing the intention to use and actual use of social media in the uni-
versity libraries were significant. Establishing the effect of the user demo-
graphics on factors influencing social media use in the context of the
UTAUT is novel within the Ghanaian university library setting and thus con-
tributes new knowledge to methodological discussions to improve practi-
ces and policies on social media adoption and use. However, since this
study was largely quantitative, future research could include both quantita-
tive and qualitative research approaches to explore any other important
constructs that fit into the context and can explain adoption in further
detail. Recommendations based on findings are provided in the article.
Social media is a phenomenon that represents a major shift in the collaboration and communica-
tion among people and professionals around the globe and it has been a globally dominant trend
for communication and information sharing over the last couple of years. Broadly defined, the
term “social media” refers to online platforms that enable the process of creating and sharing
information through the engagement of users by participating in, commenting on and creating
content as a means of communicating with their social group, other users and the public via vir-
tual communities and networks (Harrison et al., 2017). Indeed, the traditional barriers to infor-
mation provision have been enfeebled by the use of social media such that, in less than a decade,
the influence of the traditional media has dwindled, while the adoption and use of social media
have been deployed at an unprecedented rate globally. Today, as pointed out by Raza et al.
(2017), the evolving paradigm of social media is perceived as a breakthrough in the development
CONTACT Monica Mensah momensah@ug.edu.gh, monimamens@gmail.com Balme Library, University of Ghana, P.O
Box LG 24, Accra, Ghana.

 2021 The Author(s). Published with license by Taylor & Francis Group, LLC
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 171
of corporate information sharing because, among other factors, the use of social media applica-
tions increases logarithmically among both casual and academic users.
University libraries and the library profession have not been excluded from the drive to deploy
social media platforms that create the environment for individuals and organizations to exchange
information in real time via chat. Essentially university libraries have increasingly acknowledged
social media as a vital communication space for their survival. For a university library, social
media is about a set of web-based applications that allows patrons to engage with library content
and to assist in building a community with library users that will promote library issues and
events (Smeaton & Davis, 2014).
Research demonstrates that the use of and gratification derived from innovations such as social
media differ according to user characteristics such as age, gender and experience (Khan et al.,
2017). Indeed, literature has considered gender (e.g. Lin & Yeh, 2019), age (e.g. Hwang et al., 2019)
and experience (e.g. Suki & Suki, 2017) as the most commonly tested moderators of factors influ-
encing the behavioral intention to adopt and use a given innovation, and these factors have pro-
duced mixed results. Yet, while mixed results from past research hint that these variables may or
may not moderate the factors influencing the intention to use technology, there seems to be a pau-
city of literature on their relationships with regard to social media use in university libraries.
Accordingly, investigating the moderating effect of the demographics of library workers and library
patrons on factors influencing the adoption and use of social media in university libraries—both for
providing library services and for contacting the library—cannot be underestimated.
Study purpose
This study sought to determine the impact of demographic variables as moderators of factors
influencing the adoption and use of social media in university libraries in Ghana. Specifically, the
study sought to:
i. assess the moderating effect of age on factors influencing the intention to use and actual use
of social media in university libraries in Ghana.
ii. assess the moderating effect of gender on factors influencing the intention to use and actual
use of social media in university libraries in Ghana.
iii. assess the moderating effect of experience on factors influencing the intention to use and
actual use of social media in university libraries in Ghana.
Theoretical framework
The underlining theory of this study is the UTAUT as proposed by Venkatesh et al. (2003). The
UTAUT has been identified as the most popular and current referenced framework in studying the
acceptance and use of new technologies (Bawack & Kamdjoug, 2018) and it has been described as a
new but promising model, partly because of its capacity to explain seventy per cent (70%) of inten-
tion to use technology. After consolidating shared perceptions of theorists on the adoption of inno-
vations, UTAUT included four moderating variables to expound their association with its four key
predicting variables (performance expectancy, effort expectancy, social influence and facilitating con-
ditions) with two outcome variables (behavioral intention and use behavior).
Jaradat and Atyeh (2017) admitted that these moderators permit inferences and explanations
regarding behavioral intention and have been noted to play an important role as control variables,
especially for investigating and understanding factors influencing the intention to use technology.
Nevertheless, despite the prevalence of the application and citation of the UTAUT in a large
number of studies within the field of information science, there have been fewer attempts to actu-
ally consider the use of these moderating factors. In Ghana, the influence of the moderators of
172 M. MENSAH AND O. B. ONYANCHA
Figure 1. Original UTAUT model.
Source: Venkatesh et al. (2003).
the UTAUT on its key constructs relative to social media adoption and use in university libraries
has not received any attention in prior studies.
In terms of moderators, the UTAUT theory assumes that the path from its key predicting con-
structs to the outcome variables are moderated by personal characteristics such as age, gender,
experience and voluntariness of use. From the UTAUT, gender is hypothesized to moderate per-
formance expectancy, effort expectancy and social influence such that the effect is stronger for
females than for males in relation to effort expectancy and social influence. Conversely, for per-
formance expectancy the effect on males is stronger than for females (Venkatesh et al., 2003).
Age is theorized to moderate effort expectancy, performance expectancy, social influence and
facilitating conditions such that the effect on younger workers is stronger for performance expect-
ancy and stronger for older workers in relation to effort expectancy, social influence and facilitat-
ing conditions.
Experience is postulated to moderate effort expectancy, social influence and facilitating conditions
such that the impact on workers in their early stages of experience is high for effort expectancy and
social influence, and a higher influence on workers with increasing experience with respect to facili-
tating conditions. Voluntariness of use is however anticipated to moderate only social influence,
such that the impact on workers particularly in compulsory settings will be stronger.
Figure 1 provides an illustration of the original UTAUT model showing its constructs, moder-
ators and interrelations. Nevertheless, the voluntariness of use is not included in this study, as the
use of social media is not mandatory for providing and accessing library services and resources in
university libraries in Ghana. In other words, although university libraries in Ghana are expected
to use social media for the provision library services there are no prescribed actions to be taken
against library workers or patrons who resist using the system. As such, to increase the model’s
projected validity, and given that the use of social media is voluntary among users of university
libraries in Ghana, gender, age and level of experience were incorporated as moderating variables
in the research framework (Venkatesh et al., 2003). Furthermore, in their original model,
Venkatesh et al. (2003) did not project the influence of the moderators such as age, gender and
experience on the path from behavioral intentions to use behavior.
This study however, attempts to expand the current literature by providing empirical evidence
of the role of these moderators on the behavioral intention to use social media in the context of
academic library services delivery in Ghana. Hence, it is postulated that gender, age and different
levels of experience with social media will result in different adoption and use behavior relative
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 173
Figure 2. Proposed framework. Source: Venkatesh et al., 2003. Adapted from Venkatesh et al. (2003) UTAUT model.
to performance expectancy, effort expectancy, social influence and facilitating conditions in
Ghanaian university libraries (see Figure 2).
Review of pertinent literature
Given that quite a number of studies have employed the UTAUT to explain the use of technology
in general research, employing the UTAUT to examine the influence of its moderators on the use
of social media in academic libraries is scanty and even more limited in academic libraries in
Ghana. Generally, studies deploying the UTAUT to a framework do not examine the relationship
between the theory’s key construct(s) and moderators in relation to social media acceptance in
this context (e.g. Mhina et al., 2019; Salloum et al., 2018). To the best of the researchers’ know-
ledge, this is an early attempt toward a holistic approach to consider the moderators of the
UTAUT in the assessment of the factors influencing the adoption and use of social media in aca-
demic libraries in Ghana. Consequently, this study largely reviews existing literature on the influ-
ence of the moderators of the UTAUT on the use of technology in general.
Moderating influence of gender
“Gender” is defined as a set of characteristics that distinguish between males and females (Faqih
& Jaradat, 2015). Social Psychology literature (e.g., Bandura, 1986) acknowledges noteworthy
behavioral differences between female and male groups in various decision-making situations.
Gender differences have been observed to lead to different decision-making and information
processing (Lim et al., 2017), and gender continues to be a key moderator of technology behav-
ioral intention, with varying effects in different environments (Venkatesh & Davis, 2000).
Generally, gender issues in technology adoption and usage decisions have been recognized as sig-
nificant concerns in the technology acceptance literature, with a number of empirical reports on
the varying levels of acceptance of technology between males and females.
Although the literature offers some understanding of gender variances in technology accept-
ance and use behavior (e.g. Lin & Yeh, 2019), little attention has been given to the understanding
of these factors across genders in the use of social media for library services. Essentially, the lit-
erature regarding gender differences and preferences in social media adoption and use is still in
174 M. MENSAH AND O. B. ONYANCHA
its infancy and there are very few studies clearly addressing gender differences in the acceptance
of social media in university libraries.
According to the gender model, males are supposedly encouraged to be brave and independ-
ent, while females are inclined to be more social, emotional and caring of others (Tana & Ooi,
2018). Males have been noted to have more favorable attitudes toward the acceptance and use of
technology than females, as females tend to exhibit greater risk aversion and less trust in the use
of technology than men (Lim et al., 2017). In support of this viewpoint, Venkatesh et al. (2012)
demonstrated that pragmatic and task-oriented traits are more prominent in male groups, which
make them easier to be influenced by the utility and expected performance of technology than
females. Thus, compared with males, females tend to have greater anxiety when using technology
and lower levels of computer aptitude (Venkatesh & Davis, 2000). In harmony with this view-
point, the UTAUT hypothesized that the path from performance expectancy, effort expectancy
and social influence to behavioral intention is moderated by gender, with a stronger influence for
women than for men (Venkatesh et al., 2016). This norm could perhaps be attributed to the
long-standing recognition of technology as a “boy toy” or a “male-dominant ritual,” developed
exclusively by men for men (Weiser, 2000).
However, the phenomenon of the use of technology as a male-dominated syndrome is rapidly
diminishing as more and more females are using technology. Today, notwithstanding the differ-
ences in the purpose of use, both females and males appear to make equal use of technology.
A host of previous studies have tried to explore gender differences in adopting new technology
in order to manage the development and utilization of new technology in diverse fields.
However, results of the gender effect on technology perception and acceptance in these studies
are inconsistent. In some studies, little or no gender difference was found, whereas other studies
suggested the existence of gender differences. For example, based on the UTAUT model, Khan
et al. (2017) recorded no significant influence of gender on the adoption of digital reference serv-
ices among the university librarians in Pakistan.
Equally, in examining the acceptance of electronic voting machines in India, Chauhan et al.
(2018) found no gender differences in terms of how performance expectancy, effort expectancy and
social influence affect voters’ intention to use technology. Similarly, Arif et al. (2018) found little or
no gender differences in factors affecting the use of web-based services among students in Pakistan.
Likewise, Tana and Ooi (2018) reported an insignificant relationship between the path from per-
formance expectancy, effort expectancy, facilitating conditions and social influence to behavioral
intention and use behavior of Malaysians’ mobile tourism shopping via mobile devices.
In contrast, the results of other previous studies suggested the important moderating role of
gender in the adoption and use of technology (e.g. Heinrichs et al., 2016; Hwang et al., 2019; Lim
et al., 2017; Lin & Yeh, 2019; Ohannessian, 2018). For instance, in an attempt to explain the role
of gender as a moderator of the factors influencing the use of electronic retail websites among
two culturally diverse countries, namely the United States of America and Saudi Arabia,
Heinrichs et al. (2016) found gender to be a significant moderator of the relationships proposed
in their theoretical model and hence highlighted the importance of developing and designing dif-
ferent marketing strategies for males and females. Equally, in the study of Lim et al. (2017), find-
ings based on a structural equation analysis confirmed gender as a significant moderator of the
factors affecting the users’ assessment and use of social media sites, such that females showed sig-
nificantly stronger relationships than did males.
In the mid-Atlantic United States, Ohannessian (2018) collected data from 441 11th and 12th
grade students in order to examine the moderating role of gender in the relationship between
video games and anxiety. They found the moderating role of gender to play video games was
stronger for girls than for boys, such that boys who played video games had lower levels of anx-
iety, while girls playing video games as much had higher levels of anxiety. Similarly, Hwang et al.
(2019) study of 324 restaurant patrons (out of a total of 2,794) in Korea reported gender as an
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 175
important moderating variable of the factors influencing the attitude and behavioral intention to
use drone food delivery services.
Shao et al. (2019) analyzed 740 valid questionnaires from Alipay and Wechat pay users in
China to determine whether there is a significant difference between female and male consumers
regarding continuous intention to use mobile payment platforms. The authors showed that gen-
der moderated the four constructs of e-payment adopted, such that the influence was stronger for
females than for males.
In China, Lin and Yeh (2019) investigated the moderating role of gender in the relationship
between perceived usefulness (performance expectancy) and ease of use (effort expectancy) in using
virtual-reality-supported technology for mental rotation learning among college and undergraduate
students, comprising 36 men and 35 women. They found that gender plays a significant moderating
role in the relationship between the two constructs. In other words, while perceived usefulness was
stronger for men than for women, women tended to regard the technology as more playful and eas-
ier to use (ease of use). Park et al. (2019) examined gender differences in the adoption of multi-
media technology for learning, using data collected from web-based questionnaires sent out to
students. They found that gender significantly moderated the relationship between perceived useful-
ness, perceived ease of use and behavioral intention with regard to intention to use and actual use.
Regardless of the fact that the influence of gender has not yet been proven conclusively, the
UTAUT suggests that gender is an important contextual factor from a theoretical standpoint. It is
clear, therefore, that the effect of gender on technological acceptance needs to be explored further
(Lin & Yeh, 2019). Extrapolating from the theoretical and empirical backgrounds, gender differ-
ences are expected to be observed in university libraries’ decisions about the adoption and use of
social media, such that females may be more strongly influenced by their perceived interest to
use social media for provision and access to library services and resources. Consequently, the
researchers formulated the following hypotheses in this study:
H1: The path from performance expectancy, effort expectancy, social influence and facilitating
conditions to behavioral intention and use behavior is moderated by gender such that the influ-
ence is stronger for females than for males.
H1a: The path from performance expectancy to behavioral intention is moderated by gen-
der, such that the influence is stronger for females than for males.
H1b: The path from effort expectancy to behavioral intention is moderated by gender, such
that the influence is stronger for females than for males.
H1c: The path from social influence to behavioral intention is moderated by gender, such
that the influence is stronger for females than for males.
H1d: The path from facilitating conditions to behavioral intention is moderated by gender,
such that the influence is stronger for females than for males.
H1e: The path from facilitating conditions to use behavior is moderated by gender, such
that the influence is stronger for females than for males.
H1f: The path from behavioral intention to use behavior is moderated by gender, such that
the influence is stronger for females than for males.
Moderating influence of age
Age is another critical demographic factor noted to play an important role in explaining con-
sumer behavior. It influences the acceptance of technology and it is associated with usefulness,
usability, and ease of innovations (Aharony, 2012). Age, as a variable, has been emphasized by
the traditional literature as one of the most important personal traits that moderates the decision
either to adopt or reject a new technology.
176 M. MENSAH AND O. B. ONYANCHA
Literature has proven the influence of age differences in adopting new technology and has
reported a negative relationship between increasing age and intention to adopt a new technology
(Hwang et al., 2019). In other words, because younger individuals are fairly competent in using
technological devices, it has been suggested that they are more receptive and have greater experi-
ence regarding the intention to use and the subsequent use of technology, as opposed to older
individuals. Hardy and Castonguay (2018) emphasized that lack of experience with technology
use among older users usually demotivates them from evaluating the advantages that a given
technology offers.
In terms of the UTAUT, the path from performance expectancy, effort expectancy, social influ-
ence and facilitating conditions to behavioral intention and use behavior is moderated by age,
such that the influence is stronger for older people than for younger people (Venkatesh et al.,
2003). Nevertheless, although age has been observed to influence the initial decision regarding
whether to consider or accept a particular technology or not—and, as such, it is considered a
relevant variable in influencing the behavioral intention to use technology—literature on the
moderating effect of age has not been able to define a common line of reasoning in defence of
the moderating effect of the construct, as hypothesized in the UTAUT model.
While some studies identify a positive relationship (e.g. Isaias et al., 2017; Sobti, 2019) between
the key constructs of the UTAUT and the age of the user—and the probability of their adopting
and using varied technologies—others have obtained mixed results (e.g. Bawack & Kamdjoug,
2018; Chauhan et al., 2018) and even an inverse correlation (e.g. Arif et al., 2018; Palau-Saumell
et al., 2019). In the same way, some studies have included age as a relevant variable in the explan-
ation of social media adoption behavior (e.g. Yuvaraj, 2016; Hoffmann, Suphan, & Meckel, 2016).
Therefore, it might be inferred that age is a relevant moderating factor when observing the adop-
tion and use of social media in university libraries in Ghana.
In Thailand, a study of 600 university students from three different universities in Bangkok
was conducted to explain the use of social media among Thai students. The researchers reported
age as an important moderator of the behavioral intention to use social media, since younger
users were the least affected (Suksa-Ngiam & Chaiyasoonthorn, 2015). Likewise, on the basis of a
review of three classical models (TAM, TRA and UTAUT), Li
ebana-Cabanillas et al. (2014) inves-
tigated the moderating effect of age on the impact of the Zong mobile payment system among a
national panel of 2,012 internet users with profiles on social media networks.
Using a questionnaire survey with items measured on a 7-point Likert scale, the results of the
Structural Equation Modeling (SEM) analysis showed that the age of social network users has an
important effect on their behavior in terms of social media acceptance. Grouping the age of
respondents into two categories, namely younger users (i.e. 35 years and below, totaling 835
respondents and older users (>¼ 35 years, totaling 1,177 respondents), the study generally estab-
lished younger users as being more predisposed to accepting and using new technologies, includ-
ing social media, than were older users.
Employing the UTAUT as a theoretical framework, Yueh et al. (2015) claimed that although
age moderates or influences the factors contributing to the intention to use social media such as
Facebook, younger adults are more likely to spend more time on social media than older ones
because as users age, they become increasingly selective about their social media patterns.
Hoffman, Suphan and Meckel Venkatesh et al. (2016) also collected data from 492 politicians and
revealed a significant difference between social media behavioral intention and use behavior, sug-
gesting that performance expectancy, effort expectancy, social influence and facilitating conditions
are influenced by the age of politicians in Switzerland, such that the influence is stronger for
older politicians than for the younger ones.
In a more recent study, Hardy and Castonguay (2018) investigated the moderating effect of age
from an analysis of the Venkatesh et al. (2016) general social media survey at the University of
Chicago and reported that while the relationship was positive for respondents who were 30 years
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 177
and older, it was negative for those who were in the 18–29-year age group. The relationship between
social media use and the younger group produced a negative coefficient (b ¼ 0.55, p< 0.01), while
the intersection between older respondents produced a positive one (b¼ 0.37, p< 0.01). Therefore,
age had a positive influence on behavioral intention to use social media, with younger individuals
being more likely to adopt and use social media than older individuals.
In contrast, some studies employing the UTAUT seem to report no significant difference
between younger and older individuals in relation to their social media use. For instance, apply-
ing the UTAUT to social media adoption, Kaba and Toure (2014) found that age was not a sig-
nificant moderating factor in terms of performance expectancy, effort expectancy, social influence
and facilitating conditions on the behavioral intention and use behavior of 1,030 students in
selected African countries. Similarly, Yuvaraj (2016) and El Ouirdi, El Ouirdi, Segers and Pais
(2016) have reported that, apart from performance expectancy, age as a moderating variable has
no significant influence on the key constructs of the UTAUT relative to the acceptance of social
media applications in recruiting and selecting processes in India and Europe respectively.
Consequently, the researchers in this study hypothesize as follows:
H2: The path from performance expectancy, effort expectancy, social influence and facilitating
conditions to behavioral intention and use behavior is moderated by age, such that the influence
is stronger for older people than for younger people.
H2a: The path from performance expectancy to behavioral intention is moderated by age,
such that the influence is stronger for older people than for younger people.
H2b: The path from effort expectancy to behavioral intention is moderated by age, such
that the influence is stronger for older people than for younger people.
H2c: The path from social influence to behavioral intention is moderated by age, such that
the influence is stronger for older people than for younger people.
H2d: The path from facilitating conditions to behavioral intention is moderated by age,
such that the influence is stronger for older people than for younger people.
H2e: The path from facilitating conditions to use behavior is moderated by age, such that
the influence is stronger for older people than for younger people.
H2f: The path from behavioral intentions use behavior is moderated by age, such that the
influence is stronger for older people than for younger people.
Moderating influence of prior experience
Experience signifies a continuous opportunity to use a given innovation over time from the first
time of use (Jones & Harvey, 2019). As individuals, the level of experience gained in using tech-
nology might influence the level of effect regarding its use (Jaradat & Atyeh, 2017). However,
Jaradat and Atyeh (2017) submit that increased experience in the use of technology such as social
media would result in a stronger effect on intention to use and actual use over time, because as
users gain more and more experience in the use of technology, the effect on behavioral intention
and subsequent use behavior will attenuate over time.
Essentially, individuals with low levels of experience prefer innovations necessitating insignifi-
cant effort (Venkatesh et al., 2003). As such, individuals with less experience in using technology
would be more concerned about how easy the technology is to operate (Chua, Rezaei, Gu, Oh, &
Jambulingam, 2018).
It is theorized that different levels of experience lead to varied opinions about performance
expectancy, effort expectancy, social influence and facilitating conditions regarding behavioral
intention to use technology (Venkatesh et al., 2003). Nevertheless, studies employing the UTAUT
as a theoretical framework have either supported (e.g. Gan et al., 2017; Ukut & Krairit, 2019) or
refuted (e.g. Arif et al., 2018; Humaid & Ibrahim, 2019) the hypothesis that users’ level of
178 M. MENSAH AND O. B. ONYANCHA
experience has a significant influence on the relationship between technology acceptance and the
UTAUT determining factors of behavioral intention and use behavior.
For instance, as part of the literature reviewed by Ukut and Krairit (2019) on using the
UTAUT, level of experience served as a moderator between performance expectancy and behav-
ioral intention. Furthermore, Gan et al. (2017) highlighted that, although the level of experience
of students from Finland moderated the effect of performance expectancy, social influence and
facilitating conditions on behavioral intentions and use behavior of mobile learning technologies,
the effect was stronger for individuals who adopted mobile learning early on.
Awwad and Al-Majali (2015) collected data from 575 students from public universities in
Jordan on the application of the UTAUT in the context of electronic library (e-library) services,
and they reported mixed results regarding the moderating effect of experience on the construct of
the UTAUT. According to this study, the path from performance expectancy to behavioral inten-
tion was significantly moderated by experience, with the influence of experience being more sali-
ent in the early stages of e-library adoption. The study did not, however, find any significant
relationship between the moderating effect of experience on social influence and behavioral inten-
tion, as well as on the path from facilitating conditions to use behavior.
Likewise, Suki and Suki (2017) claimed that the user’s experience with a given technology
exerts a significant amount of effect on the path from performance expectancy and effort expect-
ancy to behavioral intention, but not from social influence to behavioral intention. In contrast,
Khechine et al. (2014), Isaias et al. (2017), Bawack and Kamdjoug (2018) reported that the path
from performance expectancy, social influence and facilitating conditions to behavioral intention
and use behavior was not significantly moderated by the level of experience in the use
of technology.
Similarly, Humaid and Ibrahim (2019) reported that the moderating effect of experience on
performance expectancy, effort expectancy, social influence and facilitating conditions was not
salient among Saudi business entrepreneurs. The researchers in this study therefore hypothesize
the following:
H3: The path from performance expectancy, effort expectancy, social influence and facilitating
conditions to behavioral intention and use behavior is moderated by experience, such that the
influence is stronger for persons with increased experience.
H3a: The path from performance expectancy to behavioral intention is moderated by
experience, such that the influence is stronger for persons with increased experience.
H3b: The path from effort expectancy to behavioral intention is moderated by experience,
such that the influence is stronger for persons with increased experience.
H3c: The path from social influence to behavioral intention is moderated by experience,
such that the influence is stronger for persons with increased experience.
H3d: The path from facilitating conditions to behavioral intention is moderated by experi-
ence, such that the influence is stronger for persons with increased experience.
H3e: The path from facilitating conditions to use behavior is moderated by experience,
such that the influence is stronger for persons with increased experience.
H3f: The path from behavioral intention to use behavior is moderated by experience, such
that the influence is stronger for persons with increased experience.
Method
A quantitative approach was considered suitable for the study. A total of 31,157 participants com-
prising of 110 library workers and 31,047 library patrons from 4 accredited university libraries in
Ghana were targeted as the study population.
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 179
Library workers included professionals and paraprofessionals whose core duties were directly
related to library services provision and library social media activities.
The library patrons consisted of the primary users of the libraries, namely: teaching staff total-
ing 1,041 and students totaling 30,006. Third and fourth-year students were selected based on the
assumption that they had spent more than a year in the universities and would be more familiar
with social media tools used by their university libraries for the provision of library resources and
services. Teaching staff also comprised of only academic teaching staff who have full-time con-
tract with their respective universities. This was done to avoid a teaching staff member answering
more than one questionnaire since a teaching staff member can have only one full-time teaching
status but several part-time appointments in different universities.
Although the study population was 31,157, its constituents were very homogeneous since they
included students and members of comparable occupations (teaching staff and librarians) and
therefore a smaller sample size was required to make it effective (Neuman, 2014, p. 270; Ngulube,
2015). Indeed, larger samples do not guarantee a representative sample especially when the popu-
lation is homogenous (Bryman, 2012, p. 200) and could result in a waste of resources (Ngulube,
2015), because as the size of the population grows, the returns in precision for sample size
decreases (Neuman, 2014, p. 270). So, Neuman (2014, p. 270) opined that an increase in sample
size for small samples produces a bigger gain in accuracy than for large populations.
Consequently, although there are several methods to determine the samples for a given popu-
lation, this study triangulated the census and published tables’ approaches as the methods for
selecting the sample size for this study, which stood at a total of seven hundred and sixty-seven
(767). Due to the size of the population of the library staff category (110), the census approach is
applied. This implies the enumeration of the entire population of library staff as sample size to
achieve a desirable level of precision and closer representation of the population category. On the
other hand, for the library patrons totaling thirty-one thousand and forty-seven (31,047), includ-
ing thirty thousand and six (30,006) students, and one thousand and forty-one teaching staff
(1041), the Krejcie and Morgan’s published table for determining sample sizes of a given popula-
tion was applied to select the sample sizes. Based on Krejcie and Morgan (1970) table, for a given
population of 30,006 and 1,041, a sample size of 379 and 278 is adequate to provide enough
accuracy for students and teaching staff respectively (see Appendix A).
Measurement items for the questionnaires administered to the study participants were derived
from information obtained from Venkatesh et al. (2003). These were, however, modified to suit
the context of the study. To ensure the survey’s validity, questionnaires designed were pilot tested
on 36 participants conveniently selected from the target population.
The questionnaire had 2 main parts. The first part was designed to obtain respondents’ demograph-
ics such as gender, age, and level of experience in the use of social media. The second part consisted
of statements used to measure key constructs of the UTAUT model deemed to be moderated by indi-
viduals’ demographics, and were self rated on a five-point Likert Scale ranging from 1 (strongly dis-
agree) to 5 (strongly agree). A total of 94 from the library workers representing a response rate of
85.5% (n¼ 110) and 511 from the library patrons representing a response rate of 77.8% (n¼ 657)
completed the questionnaires administered. Hence a usable questionnaire data of 605, representing a
total response rate of 78.6% (n¼ 767), were analyzed using descriptive statistics and the SEM analysis.
Researchers (e.g. Hoffmann et al., 2016; Bawack & Kamdjoug, 2018) have relied on and used
the SEM to estimate the relationships and correlations between theoretical constructs from the
UTAUT to understand behavioral intentions and use behavior. In particular, the use of the SEM
is predominantly acceptable in Social Science research (Khan et al 2019). The SEM normally
comprises of two forms of model analysis, namely the measurement model signifying the theory
that identifies how measured constructs come together to denote the theory and the structural
model signifying the theory that demonstrates how variables are associated to other variables
(Hox & Bechger, 1998), thus rendering the SEM a suitable statistical tool for testing the
study’s hypotheses.
180 M. MENSAH AND O. B. ONYANCHA
Ethical considerations
Ethical clearance was obtained from the universities where the study was conducted. Being a part
of a doctoral research conducted in fulfillment for the award of a Doctor of Philosophy Degree in
Information Science at the Department of Information Science in University of South Africa
(UNISA), permission was sought and granted by the Research Ethics Committee, at UNISA.
Respondents were informed and briefed about the purpose of the study, not forced to partake
in the study, and were given permission to withdraw from the study at any point they felt like
doing so, but were assured of the confidentiality and anonymity of their responses.
Results
The findings are presented according to the hypotheses formulated, and response supplied to
each of the hypothesis. The study does not seek to compare the responses from the various
respondents (i.e. library staff and library patrons), and hence presents the findings from the
respondents as one homogenous group.
Besides, given that the questions for the various categories of respondents were same, presenta-
tion of findings on a particular set of questions addressing a particular research hypothesis are
presented together. This is done for easy collation of the research findings, and to avoid repeti-
tion of the same questions and responses on a particular hypothesis.
Of the 605 usable responses, 347 (57.4%) were males and 258 (42.6%) were females. Analysis
of the ages of the respondents indicated that, 325 (53.7%) were between the ages of 18–35, whilst
those above 35 years were 280 (46.3%).Two hundred and eighty (34.4%) seven of the respondents
had at least used social media for less than five years whilst the remaining 318 (52.6%) had used
such platforms for more than five years.
To evaluate the differential effects of the respondents’ demographics (age, gender and level of
experience), the t-test analysis and the Levene’s Test for Equality of Variances were assessed in
terms of the difference in degrees of freedom (df).
Gender as moderator of factors influencing the adoption and use of social media
Participants were divided into two groups according to their gender (i.e. male and female).
Results of analysis as presented in Table 1 and Appendix C reveal that apart from effort
expectancy and use behavior, there was no statistical significant difference between female and
male respondents on all variables relative to performance expectancy, social influence, facilitating
conditions and behavioral intentions.
For performance expectancy, males recorded a high mean (x̄) of 21.50, with standard deviation (sd)
of 4.64, like that of females who also recorded (x̄ ¼ 21.88, sd ¼ 4.60), t (603) ¼ 995, p ¼ .320.
Likewise, significant differences were also not found between males and females relative to
behavioral intention, since variables relative to this concept were similar. Males recorded (x̄
¼12.51, sd ¼ 2.96), which was not significantly different from females who recorded (x̄ ¼12.72,
sd ¼ 2.82), t (603) ¼ 912, p ¼ .362. For social influence, males (x̄ ¼14.86, sd ¼ 6.08) recorded
a higher mean than females (x̄ ¼ 14.29, sd ¼ 5.94) although no significant difference was
recorded between the two: t (603) ¼ 1.132, p ¼ .258.
Mean scores for facilitating conditions (x̄ ¼ 10.96, sd ¼ 5.24) were equally higher than that of
females (x̄ ¼ 10.45, sd ¼ 5.12), although the difference was not statistically significant. On the
other hand, significant differences were found between males and females relatively on effort
expectancy (p ¼ .000) and use behavior (p ¼ .038).
However, while effort expectancy recorded significantly higher mean for females (x̄ ¼ 16.42,
sd ¼ 4.12) than males (x̄ ¼ 17.67, sd ¼ 3.21), t (601), p< 0.05, use behavior recorded a higher
mean for males (x̄ ¼ 18.12, sd ¼ 5.21) than for females (x̄ ¼ 17.25, sd ¼ 4.87), t (605), p< 0.05.
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 181
Table 1. Influence of gender on factors influencing the adoption and use of social media.
Gender groups
Male (n¼ 347) Female (n¼ 258) N¼ 605
Factors x̄ sd (x̄) sd df T p-value
Performance expectancy 21.50 4.64 21.88 4.60 603 995 0.320
Effort expectancy 16.42 4.12 17.67 3.21 601 4.190 0.000
Social influence 14.86 6.08 14.29 5.94 603 1.132 0.258
Facilitating conditions 10.96 5.24 10.45 5.12 603 1.198 0.231
Behavioral intention 12.51 2.96 12.72 2.82 603 912 0.362
Use behavior 18.12 5.21 17.25 4.87 603 2.078 0.038
Note: Significant level is at p< or ¼0.05. x̄ ¼ mean; sd¼ standard deviation.
Table 2. Influence of age on factors influencing the adoption and use of social media.
Age groups
Young (18–35yrs) (n¼ 325) Old (35yrsþ) (n¼ 280) N¼ 605
Factors x̄ sd x̄ sd df t p-value
Performance expectancy 21.20 4.42 22.20 4.81 603 2.66 0.008
Effort expectancy 16.59 3.67 17.37 3.93 603 2.54 0.011
Social influence 15.18 6.55 13.97 5.28 600 2.51 0.012
Facilitating conditions 12.27 4.92 8.96 4.94 603 8.24 0.000
Behavioral intention 12.29 2.83 12.96 2.96 603 2.85 0.004
Use behavior 20.28 4.37 14.81 4.19 603 15.65 .000
Note: Significant level is at p< or ¼ 0.05. x̄ m¼mean; sd¼ standard deviation.
.
Age as moderator of factors influencing the adoption and use of social media
For easy collation of research findings, participants were divided into two main groups according
to their age. Group one comprised of respondents designated as ‘young’, and these were between
ages ‘18–35’. Group two comprised of those referred to as ‘old’, and these were respondents who
were ‘more than 35 years’.
As displayed in Table 2 and Appendix D, there were no statistically significant differences between
the ‘young’ respondents and ‘old’ respondents relative to their views on: performance expectancy
(‘young’: x̄ ¼ 21.20, sd ¼ 4.42; ‘old’; x̄ ¼ 22.20, sd, t (605) ¼ 2.66, p ¼ .008), effort expectancy
(‘young’: x̄ ¼ 16.59, sd ¼ 3.67, ‘old’; x̄ ¼ 17.37, sd ¼ 3.93, t (605) ¼ 2.54, p ¼ .011) and social
influence (‘young’: x̄ ¼ 15.18; sd ¼ 6.55, ‘old’; x̄ ¼ 13.97, sd ¼ 5.28, t(605) ¼ 2.51, p ¼ .012.
Significant differences were, however, found between ‘young’ and ‘old’ respondents’ relatively
on facilitating conditions, behavioral intentions and use behavior.
Facilitating conditions for ‘young’ respondents’ recorded a mean value of 12.27, which was sig-
nificantly different from ‘old’ respondents recording a mean value of 8.96, where t (605) ¼ 8.24,
with a p-value <0.05 showing a significant difference between younger and older respondents on
facilitating conditions as a factor influencing the use of social media for library services.
Similarly, the mean and standard deviation values for ‘young’ respondents (x̄ ¼ 20.28, sd ¼
4.37) were higher than that of older respondents (x̄ ¼14.81, sd ¼ 4.19) and show a significant
difference t(605) ¼ 15.65, p ¼ .000) between ‘young’ and ‘old’ respondents on use behavior.
For behavioral intention, although significant difference existed between younger and older
respondents at a p-value of .004, the differences in the mean score and standard deviation val-
ues for young (x̄ ¼12.29, sd ¼ 2.83) and older respondents (x̄ ¼12.96, sd ¼ 2.96) were
very small.
182 M. MENSAH AND O. B. ONYANCHA
Table 3. Influence of experience on factors influencing the adoption and use of social media.
User experience levels
No/or < 5yrs (n¼ 287) 5yrs and more (n¼ 318) N¼ 605
Factors x̄ sd x̄ sd df t p-value
Performance expectancy 21.29 4.71 22.07 4.50 603 2.060 0.280
Effort expectancy 16.68 3.88 17.25 3.71 603 1.870 0.655
Social influence 14.19 6.02 15.09 6.02 603 1.857 0.412
Facilitating conditions 10.18 5.14 11.24 5.19 603 25.5 0.036
Behavioral intention 12.19 2.92 13.05 2.81 603 3.649 0.001
Use behavior 13.81 3.15 15.95 3.69 603 7.678 0.000
Note: Significant level is at p< or ¼0.05. x̄ ¼ mean; sd¼ standard deviation.
Experience as moderator of factors influencing the adoption and use of social media
Respondents were divided into two main groups according to their prior experiences in the use
of social media, namely: ‘Group 1: No/or <5 years; ‘Group 2: 5 years and above’. From the ana-
lysis of responses summarized and presented in Table 3 and Appendix E, there was no statistic-
ally significant difference at p< 0.05 for the two experience level groups on performance
expectancy: t(605)¼ 2.060, p¼ 0.280, effort expectancy: t(605)¼1.870, p¼ 0.655, and social influ-
ence: t(605)¼1.857, p¼ 0.412, indicating that respondents with varied experience with social
media had similar views on performance expectancy, effort expectancy and social influence.
Conversely, there was statistically significant difference at p< 0.05 level for the two experience
level groups on facilitating conditions: t(605) ¼ 2.505, p¼ 0.036, behavioral intention t(605)
¼3.649, p¼ 0.001, and use behavior: t(605)¼7.678, p¼ 0.000.
Further, the mean values (x̄) of the two groups on facilitating conditions, behavioral intention
and use behavior indicated significant differences between them. Indeed, as presented in Table 5,
all the actual mean scores for ‘Group 1: No/or <5years’, were lower than those of ‘Group 2: 6yrs
and above’.
Measurement model fits
Confirmatory Factor Analysis (CFA) is carried out to examine the overall fit of the proposed
model. The values obtained, as against the recommended value for each of the model fits index
showing an overall acceptable fit of the study’s measurement model.
Testing study hypotheses
A SEM analysis was conducted to assess the proposed hypotheses. In these analyses, the predictor
variables corresponded to performance expectancy (PE), effort expectancy (EE), social influence
(SI) and facilitating conditions (FC).
The moderating variables corresponded to gender, age and experience. The dependent varia-
bles corresponded to behavioral intention (BI) and use behavior (UB). A summary of the results
from the SEM analysis presented, in Table 5, shows that overall H1, H2 and H3 were par-
tially supported.
Hypothesis 1 (H1): The path from PE, EE, SI and FC on BI and UB is moderated by gender, such that the
influence is stronger for females than for males
Findings from the SEM analysis shows that the path from performance expectancy (H1a)
(b¼ 0.416, p< 0.005), effort expectancy (H1b) (p< 0.005; b¼ 0.263) and facilitating conditions
(H1d) (p< 0.005; b ¼ 0.181) to behavioral intention and use behavior (H1e) (p< 0.005;
b¼ 0.196) are all significantly moderated by gender.
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 183
Table 4. AMOS output for fit statistics of the measurement model.
Values obtained
Model fit index Recommended values Gender Age Experience
GFI >0.90 0.998 0.996 0.998
CMIN <5 1.008 1.895 1.201
NFI >0.90 0.996 0.992 0.995
CFI >0.90 1.000 0.996 0.999
RFI >0.90 0.977 0.957 0.972
TLI >0.90 1.000 0.979 0.995
RMSEA <0.05 0.004 0.039 0.018
RMR <0.02 0.010 0.016 0.012
GFI¼Goodness of fit index, NFI¼Normal fit index, RFI¼ Relative fit index, CFI¼ Comparative fit index, TLI¼ Tucker Lewis
index, RMSEA¼ Root mean square error of approximation, RMR¼ Root mean square residual.
Table 5. Summary of SEM analysis on influence of UTAUT moderators on factors influencing behavioral intention and use
behavior of social media in University Libraries in Ghana.
Hypotheses/Moderator Hypothesized paths Path coefficient (b value) p-value Outcome Remarks
H1-gender H1a: PE!Gen BI 0.416 0.000 Supported Partially
H1b: EE!Gen BI 0.263 0.000 Supported Supported
H1c: SI!Gen BI 0.124 0.397 Not Supported
H1d: FC!Gen BI 0.181 0.000 Supported
H1e: FC!Gen UB 0.196 0.000 Supported
H1f: BI!Gen UB 0.024 0.596 Not Supported
H2-age H2a: PE!Age BI 0.408 0.000 Supported Partially
H2b: EE!Age BI 0.261 0.000 Supported Supported
H2c: SI! Age BI 0.124 0.000 Supported
H2d: FC!Age BI 0.187 0.000 Supported
H2e: FC!Age UB 0.196 0.000 Supported
H2f: BI!Age UB 0.024 0.576 Not supported
H3-experience H3a: PE!Exp BI 0.410 0.000 Supported Partially
H3b: EE!Exp BI 0.265 0.000 Supported Supported
H3c: SI!Exp BI 0.128 0.248 Not Supported
H3d: FC!Exp BI 0.182 0.000 Supported
H3e: FC!Exp UB 0.196 0.000 Supported
H3f: BI!Exp UB 0.024 0.436 Not supported
Note: Significant level is at p < .005.
The path from social influence to behavioral intentions (H1c) (p> 0.005; b¼ 0.124) and from
behavioral intentions to use behavior (H1f) (p> 0.005; b¼ 0.024) were, however, not statistically
significant (see Figure 3)
Hypothesis 2 (H2): The path from PE, EE, SI and FC on BI and UB is moderated by age such that the
influence is stronger for older people than for younger people.
From the results of the SEM analysis, the moderating effect of age on the path from behavioral
intentions and use behavior (H2f) (p> 0.005; b¼ 0.024) was not statistically significant.
The path from performance expectancy (H2a) (p< 0.005; b¼ 0.408), effort expectancy (H2b)
(p< 0.005; b¼ 0.261), social influence (H2c) (p< 0.005; b¼ 0.124) and facilitating conditions
(H2d) (p< 0.005; b ¼ 0.187) on behavioral intention, as well as from facilitating conditions
and use behavior (H2e) (p< 0.005; b¼ 0.196) were all statistically significant (see Figure 4)
Hypothesis 3 (H3): The path from PE, EE, SI and FC on BI and UB is moderated by prior user experience
such that the influence is stronger for persons with increased experience.
The outcome of the SEM analysis on experience as a moderating variable showed that the
path from performance expectancy (H3a) (p< 0.005; b¼ 0.410), effort expectancy (H3b)
(p< 0.005; b¼ 0.265) and facilitating conditions (H3d) (p< 0.005; b ¼ 0.182) on behavioral
intention and use behavior (H3e) (p< 0.005; b¼ 0.196) were all statistically significant.
184 M. MENSAH AND O. B. ONYANCHA
Figure 3. Results for SEM analysis with gender as moderator.
However, the p-values obtained for the path from social influence (H3c) (p> 0.005; b¼ 0.128)
to behavioral intention, and from behavioral intention to use behavior (H3f) (p¼ p> 0.005;
b¼ 0.024) were not statistically significant (see Figure 5).
Discussion of findings
The findings of the study are discussed based on the demographics that informed this study.
Gender as moderator of the factors influencing the adoption and use of social media
Following the moderating effect of gender, the results of the SEM analysis showed partial consist-
ency with the study’s 1st hypothesis (H1), since gender moderated the path from performance
expectancy, effort expectancy, and facilitating conditions to behavioral intention as well as from
facilitating conditions to use behavior of social media in the university libraries surveyed. This
was, however, opposite in terms of the influence of gender on the path from social influence to
behavioral intention and from behavioral intention to use behavior.
These findings are contrary to Hamid and Ibrahim (2019) but consistent with the results of
several studies which demonstrate the influence of gender on technology acceptance using the
UTAUT (e.g. Hwang et al., 2019; Lin & Yeh, 2019; Shao et al., 2019).
Further, the question of whether males and females differ on their acceptances of technology has
received research attention. As shown in the literature reviewed, the argument on the influence of
gender behavior largely demonstrates that the adoption and use of technology is more likely to be
salient to males than females (Lim et al., 2017; Park et al., 2019; Venkatesh et al., 2012).
In a related study, for example, Odewumi, Yusuf, and Oputa (2018) from Nigeria reported sig-
nificant difference between female and male postgraduate students’ intention to use social media
in learning, such that the male students’ perception toward the intention to utilize social media
in learning was more than their female counterparts.
Likewise, Raja-Yusof, Qazi, and Inayat (2017) reported gender as an important moderator but
disclosed that the level of influence of effort expectancy and facilitating conditions on behavioral
intention was more evident in females compared to their male participants.
Nevertheless, in the university libraries surveyed, no significant correlations were established
between female and male respondents on all variables relative to performance expectancy, social influ-
ence and facilitating conditions. This indicates that males and females did not differ on performance
expectancy, social influence and facilitating conditions. With regards to effort expectancy and use
behavior, while the influence on the former was stronger for females (m¼ 16.42, sd ¼ 4.12) than
males (m¼ 17.67, sd ¼ 3.21), t (601) ¼ 4.190, that of the latter was stronger for males (m¼ 18.12,
sd ¼ 5.21) than for females (m¼ 17.25, sd ¼ 4.87), t(605) ¼ 912. This implies that, whilst males
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 185
Figure 4. Results for SEM analysis with age as moderator.
Figure 5. Results for SEM analysis with experience as moderator.
were more likely to perceive the use of social media as an easy to use platform for library services
than females, females may use the library social media more often than males.
Age as moderator of the factors influencing the adoption and use of social media
In view of the SEM analysis, age moderated the path from performance expectancy, effort expect-
ancy, social influence and facilitating conditions to behavioral intention as well as from facilitat-
ing conditions to use behavior of social media in the university libraries surveyed, but refuted the
effect on the path from behavioral intention to use behavior. However, the t-test analysis showed
no statistical difference between the age of the respondents relative to performance expectancy,
effort expectancy and social influence. In other words, although age had a significant effect on
the path from PE-BI, EE-BI, and SI-BI, the influence was not stronger for either younger or older
respondents. This implies that the intention to use the library social media based on the belief
that it will be easy to use, or will improve library services provision and access, or will be used
based on recommendations from important others was not influenced by how old or how young
a library worker or library patron is.
On the other hand, relative to facilitating conditions and use behavior, the effect of age was
stronger among older respondents than for the younger respondents. Thus, unlike older individu-
als (35 years plus), younger individuals (18–35 years) in the university libraries surveyed will actu-
ally use the library social media for providing services and contacting the library when the
needed support for the use of such platforms is provided. Generally, the findings of this study on
the moderating effect of age partially supports the research findings of Hardy and Castonguay
(2018), and Ameen et al. (2018) but are contrary to findings of other studies (e.g. Chatterjee
et al., 2019; Humaid & Ibrahim, 2019; Palau-Saumell et al., 2019; Salahshour Rad et al., 2019).
186 M. MENSAH AND O. B. ONYANCHA
Experience as moderator of the factors influencing the adoption and use of social media
The 3rd hypothesis of this study postulates that the path from performance expectancy, effort
expectancy, social influence and facilitating conditions on behavioral intention and use behavior
is moderated by experience, such that the influence is stronger for persons with increased experi-
ence. Overall, this hypothesis was partially supported, since results from the SEM analysis show
that, although experience moderated the relationship between performance expectancy, effort
expectancy, and facilitating conditions on behavioral intention and use behavior, its influence on
the path from social influence to behavioral intentions and from behavioral intention to use
behavior was statistically insignificant.
In addition, the results of the t-test analysis revealed that the influence of experience was only
stronger for persons with increased experience in the use of social media relative to facilitating
conditions, behavioral intention and use behavior. Thus, by implication, library workers and
library patrons with increased experience in the use of social media platforms will tend to use,
and subsequently use such platforms if they are exposed to the necessary skills, support, guide-
lines, resources, and assistance required in the use of such platforms for accessing and providing
library services. Quite a number of studies are in support of this study’s findings (e.g. McKeown
& Anderson 2016; Palau-Saumell et al., 2019; Suki & Suki, 2017) although others (e.g. Arif et al.,
2018; Hamaid & Ibrahim, 2019; Salahshour Rad et al., 2019) found the reverse.
Conclusion
With the progressing millennia, the adoption and use of social media are no doubt a modern-day
phenomenon for a university library facilitated by advancement in technology.
Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT) theory,
this research assessed the moderating effect of personal characteristics such as gender, age and
user experience on the factors influencing the adoption and use of social media in Ghanaian uni-
versity libraries. Overall, from the SEM analyses, the study’s hypotheses (i.e., H1, H2, and H3),
were partially significant.
Practical implications of the study
Findings of this present study make practical contributions to university libraries that use social
media for delivering services to patrons, and may serve as a guide for future researchers in their
understanding of the acceptance and use of social media as a service provision platform among
university libraries in Ghana. Moreover, the development and use of information technology such
as social media require high budget and investment. As such, understanding the factors that
influence the behavioral intention to use and use behavior of social media is especially critical for
university libraries with often insufficient budget.
Besides, this study suggests that when considering the factors influencing the intention to
use social media for library-related activities, university libraries should regard the personal
demographic profiles such as age, gender and user experience. This indicates that different
cohorts of the library workers and library patrons may attach different weights to various fac-
tors that influence their use of the library social media. As such university libraries in Ghana
would be able to understand the differences among the moderating variable groups and thus,
target the most appropriate groups within the moderating variables. For example, university
libraries are recommended to engage younger library workers and patrons with increased
experience in the use of social media tools as targets to promote social media for library serv-
ices delivery and access.
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 187
Theoretical implications of the study
Based on empirical evidence, this study provides clues to the moderating effect of the library
workers and library patrons profile characteristics such as age, gender, and prior experience in
the use of social media. These factors offer guidance in recognizing the determinants that may
stimulate the adoption and use of social media by university libraries in Ghana.
Generally, this study is novel, and adds to the few studies that have considered the effect of
individual differences (i.e., gender, age, and experience) on the path between the UTAUT model
main constructs, while at the same time reporting significant differences. In particular, previous
research has not provided evidence of the influence of UTAUT theory in the context of social
media acceptance in university libraries in Ghana.
Recommendations for further research
This research was largely quantitative. Future research can be improved by including both quanti-
tative and qualitative research approaches to explore any other important constructs that fit in
the context and can explain adoption in further detail.
Data availability statement
The data that supports the findings of this study are available from the corresponding author (M.M), upon reason-
able request.
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190 M. MENSAH AND O. B. ONYANCHA
Appendix A: Krejcie and Morgan’s published table
N S N S N S N S N S
10 10 100 80 280 162 800 260 2800 338
15 14 110 86 290 165 850 265 3000 341
20 19 120 92 300 169 900 269 3500 246
25 24 130 97 320 175 950 274 4000 351
30 28 140 103 340 181 1000 278 4500 351
35 32 150 108 360 186 1100 285 5000 357
40 36 160 113 380 181 1200 291 6000 361
45 40 180 118 400 196 1300 297 7000 364
50 44 190 123 420 201 1400 302 8000 367
55 48 200 127 440 205 1500 306 9000 368
60 52 210 132 460 210 1600 310 10,000 373
65 56 220 136 480 214 1700 313 15,000 375
70 59 230 140 500 217 1800 317 20,000 377
75 63 240 144 550 225 1900 320 30,000 379
80 66 250 148 600 234 2000 322 40,000 380
85 70 260 152 650 242 2200 327 50,000 381
90 73 270 155 700 248 2400 331 75,000 382
95 76 270 159 750 256 2600 335 100,000 384
Note: “N” is population size; “S” is sample size. Source: Krejcie and Morgan (1970).
Appendix B
Questionnaire survey
1. Please tick as appropriate regarding your gender
Male [ ]
Female [ ]
2. In which age category do you belong?
Below 18 years [ ]
18–30 [ ]
31–35 [ ]
36–40 [ ]
41–45 [ ]
46–50 [ ]
51–55 [ ]
66–60 [ ]
51–55 [ ]
56–60 [ ]
60þ [ ]
3. How many years have you used social media for personal purposes?
Less than 1 year [ ]
1–2years [ ]
3–4 years [ ]
5–10 years [ ]
10 yearsþ [ ]
I do not use social media [ ]
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 191
Factors influencing acceptance and use of social media
On a scale of 1–5 where 1¼ Strongly Disagree, 2¼Moderately Disagree, 3¼Moderately Agree, 4¼Agree, and
5¼ Strongly Agree, please answer questions 4 to 8 by indicating the appropriate response that reflects your level of
agreement with the statements provided in relation to the adoption and use of social media for the provision of
library services and resources, as well as for contacting the library.
Statements Scale
4. Performance expectancy 1 2 3 4 5
Social media is/will be useful for providing and accessing library services and resources
Social media will enable/enables faster communication within the library
Social media will improve/improves communication with the library/patrons
Social media will enhance/enhances communication with the library/patrons
If I use social media, communication with the library/patrons is/will be easier
5. Effort expectancy
I will use/use the library social media because it is easy to learn how to operate
I will use/use the library social media because I find it easy to use
I will use/use the library social media because interacting with it is clear and understanding
I will use/use the library social media because it is easy to develop the skills required to use such tools
6. Social influence
I use/will use the library social media because my colleagues think I should use it
My colleagues and friends think I should the library social media
I use/will use the library social media because people who are important to me think I should use them
I use/will use the library social media because people whose opinions I value think I should use them
Using the library social media is considered a status symbol among my colleagues.
I use/will use the library social media because my colleagues think I should use it
7. Facilitating conditions
I have the knowledge necessary to use the library social media platforms
I have the resources to use the library social media platforms
Someone is available for assistance if I have difficulty with the library social media
There is available guidance on how to use the library social media the platforms
8. behavioral intention
I desire to use the library social media platforms
I expect to use the library social media platforms
I predict to use the library social media platforms
8. Use behavior
It is worthwhile to use the library social media
I actually use the library social media platforms
I will continue using the library social media platforms
I consistently use the library social media platforms
I regularly use the library social media platforms
192 M. MENSAH AND O. B. ONYANCHA
Appendix C: T-test analysis on gender
Levene's test for t-test for equality of 95% confidence
equality of means (x̄) interval
variances of the difference
Independent Sig. Mean(x̄) Std.error
samples test F Sig. t df (2tailed) difference difference Lower Upper
Performance Equal variances 1.053 .305 .995 603 .320 .37840 .38034 1.12536 .36855
expectancy assumed
Equal variances .996 556.38 2 .320 .37840 .37986 1.12454 .36773
not assumed
Effort Equal variances 27.492 .000 4.043 603 .000 1.24880 .30889 1.85543 .64217
expectancy assumed
Equal variances 4.190 601.58 1 .000 1.24880 .29802 1.83408 .66352
not assumed
Social Equal variances .868 .352 1.132 603 .258 .56034 .49512 .41203 1.5327 1
influence assumed
Equal variances 1.136 560.92 8 .257 .56034 .49335 .40870 1.5293 8
not assumed
Facilitating Equal variances 1.089 .297 1.198 603 .231 .51104 .42664 .32685 1.3489 2
condition assumed
Equal variances 1.202 560.94 7 .230 .51104 .42512 .32398 1.3460 5
not assumed
Behavioral Equal variances 1.134 .287 .912 603 .362 .21760 .23870 .68639 .25119
intention assumed
Equal variances .918 567.59 0 .359 .21760 .23701 .68312 .24791
not assumed
Use behavior Equal variances 2.467 .117 2.078 603 .038 .86622 .41679 .04767 1.6847 6
assumed
Equal variances 2.099 572.33 8 .036 .86622 .41272 .05558 1.6768 6
not assumed
JOURNAL OF ELECTRONIC RESOURCES LIBRARIANSHIP 193
Appendix D: T-test analysis on age
Levene's test for t-test for equality of 95% confidence
equality of means (x̄) interval
variances of the difference
Independent Sig. Mean(x̄) Std.error
samples test F Sig. t df (2tailed) difference difference Lower Upper
Performance Equal variances .260 .610 2.664 603 .008 1.00000 .37536 1.73716 .26284
expectancy assumed
Equal variances 2.647 571.988 .008 1.00000 .37772 1.74188 .25812
not assumed
Effort Equal variances .221 .638 2.538 603 .011 .78374 .30886 1.39031 .17717
expectancy assumed
Equal variances 2.525 575.792 .012 .78374 .31044 1.39347 .17401
not assumed
Social Equal variances 29.051 .000 2.468 603 .014 1.20703 .48915 .24639 2.16768
influence assumed
Equal variances 2.507 600.454 .012 1.20703 .48149 .26142 2.15265
not assumed
Facilitating Equal variances 1.139 .286 8.241 603 .000 3.31005 .40166 2.52123 4.09888
condition assumed
Equal variances 8.238 589.011 .000 3.31005 .40180 2.52091 4.09920
not assumed
Behavioral Equal variances .329 .566 2.853 603 .004 .67148 .23534 1.13367 .20930
intention assumed
Equal variances 2.844 581.135 .005 .67148 .23613 1.13525 .20772
not assumed
Use behavior Equal variances .519 .472 15.652 603 .000 5.47593 .34986 4.78885 6.16302
assumed
Equal variances 15.702 596.211 .000 5.47593 .34875 4.79102 6.16085
not assumed
194 M. MENSAH AND O. B. ONYANCHA
Appendix E: T-test analysis on experience
User Std. Std. error
Experience N x̄ deviation mean
Performance No/ Less than 287 21.2956 4.71464 0.26438
expectancy 5 years &
5 years & above 318 22.0697 4.50043 0.26565
Effort No/ Less than 287 16.6761 3.87813 0.21747
expectancy 5 years &
5 years & above 318 17.2544 3.70524 0.21871
Social No/ Less than 287 14.1887 6.00070 0.33650
influence 5 years &
5 years & above 318 15.0976 6.02479 0.35563
Facilitate No/ Less than 287 10.1847 5.14653 0.30379
conditions 5 years &
5 years & above 318 11.2390 5.18972 0.29103
Behavioral No/ Less than 287 12.1950 2.92704 0.16414
intention 5 years &
5 years & above 318 13.0488 2.81437 0.16613
Use behavior No/ Less than 287 13.8113 3.15763 0.17707
5 years &
5 years & above 318 15.9512 3.69526 0.21812
Levene's test for t-test for
equality of equality of 95% confidence interval
variances means (x̄) Std. of the difference
Independent Mean (x̄) error
samples test F Sig. t df Sig. (2tailed) difference difference Lower Upper
Performance Equal variances 1.172 0.280 2.060 603 0.040 0.77409 0.37569 0.03627 1.51190
expectancy assumed
Equal variances 2.065 601.097 0.039 0.77409 0.37479 0.03803 1.51015
not assumed
Effort Equal variances 0.199 0.655 1.870 603 0.062 0.57825 0.30915 −0.02890 1.18541
expectancy assumed
Equal variances 1.875 601.036 0.061 0.57825 0.30843 −0.02748 1.18399
not assumed
Social Equal variances 0.674 0.412 1.857 603 0.064 0.90888 0.48950 −0.05245 1.87021
assumed
Influence Equal variances 1.856 596.201 0.064 0.90888 0.48960 −0.05267 1.87043
not assumed
Facilitate Equal variances 0.317 0.036 −2.505 603 0.013 −1.05432 0.42088 −1.88088 −0.22777
conditions assumed
Equal variances −2.506 597.670 0.012 −1.05432 0.42069 −1.88054 −0.22811
not assumed
Behavioral Equal variances 2.931 0.001 3.649 603 0.000 0.85381 0.23401 0.39424 1.31338
intention assumed
Equal variances 3.656 600.576 0.000 0.85381 0.23354 0.39516 1.31246
not assumed
Use behavior Equal variances 18.715 0.000 7.678 603 0.000 2.13990 0.27871 1.59254 2.68726
assumed
Equal variances 7.617 565.560 0 0.00 2.13990 0.28095 1.58807 2.69173
not assumed