RES EAR C H A R T I C L E Open Access
Analysis of the use of social media in
Higher Education Institutions (HEIs) using
the Technology Acceptance Model
Duvince Zhalimar Dumpit
1*
and Cheryl Joy Fernandez
2
* Correspondence:
djdumpit@up.edu.ph
1
Department of Accounting,
College of Management University
of the Philippines Visayas, Iloilo City,
Philippines 5000
Full list of author information is
available at the end of the article
Abstract
The purpose of this paper is to extend the understanding of the drivers of social media in
higher education institutions (HEIs) in an emerging economy. This research adopts the
Technology Acceptance Model but included subjective norm, perceived playfulness,
Internet reliability and speed as additional constructs. With these inclusions, the model is
appropriate and relevant in explaining users’ adoption and usage behavior of social
media. Data from 500 students from public and private HEIs in the Philippines were
collected and analyzed. We used a combination of statistical analyses such as the Principal
Component Analysis (PCA) and Structural Equation Modeling (SEM) in analysing the
complex relationships between determinants of these technologies. The research
demonstrated that perceived usefulness, perceived ease of use, subjective norm, and
perceived playfulness (happiness) are robust predictors of usage behavior of students.
However, Internet reliability and speed were only significant in (some) public HEIs. This
evidence may be explained by the fact that information and communications technology
(ICT) infrastructure in public HEIs is not a priority or underinvested in developing countries.
On the other hand, the analysis between public and private HEIs undertaken here extends
our understanding towards the different behaviors of users. The findings, though
preliminary, suggest that private HEIs should initiate or continue the use of social media in
classrooms, because intention to use translate to actual use of these tools. Public
institutions, however, should improve Internet reliability and speed and should reassess
their use of social media in order to fully take advantage of the benefits of ICT.
Keywords: Technology Acceptance Model, TAM, Social media, YouTube, Higher
education institution/university, Philippines, Internet speed, Internet reliability
Introduction
Information and communications technology (ICT) and education
In today’s digital economy, success in businesses is attributed to the effective use of in-
formation and communications technology (ICT). Higher Education Institutions
(HEIs) are not exempted from these rapidly changing technological advancements and
hence, cannot afford to lag behind these developments as these can provide valuable
insights to the academic community. For instance, students of today have become
technologically savvy and pro-active users of ICTs. They are seen as ‘active producers
of knowledge’ as they become responsible for their learning (McLoughlin & Lee, 2008).
Thus, HEIs, especially educators, need to plan to address uncertainties by discovering/
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
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indicate if changes were made.
Dumpit and Fernandez International Journal of Educational Technology
in Higher Education (2017) 14:5
DOI 10.1186/s41239-017-0045-2
adapting new ways and processes to enhance student learning, performance, and satis-
faction through the use of ICT.
The image of the future that Teilhard De Chardin (1955), a French philosopher, envis-
aged, which he called ‘Future Earth’, included the overwhelming importance of ICTs in
transforming communities. De Chardin described the Future Earth as a ‘noosphere’ (Greek
word ‘nous’ =mind and ‘sphaira’ = sphere), which encompasses interrelated technologies
and consciousness (Levinson, 2011; Peters & Heraud, 2015). He envisioned the role of
technology in engendering a global consciousness, manifested for example, in social
media in today’s businesses. The Internet and social media also facilitate social production
(Peters & Reveley, 2015), transform users as consumers and/or producers, and bridge the
real and virtual worlds (Levinson, 2011). Together, these arguments strengthen the signifi-
cance of social media in today's contemporary world.
Social media is considered as a one of the game-changers in learning and teaching
(Healy, 2015). It is defined by Kietzmann , Hermkens, McCarthy, and Silvestre (2011,
p.241) as one that ‘employ mobile and web-based technologies to create highly inter-
active platforms via which individuals and communities share, co-create, discuss, and
modify user-generated content.’ Generally, it follows the concept of Web 2.0 and con-
sists two key elements : (1) media research (social presence, media richness) and (2) so-
cial processes (self-presentation, self-dis closure) (Kaplan & Haenlein, 2010). Examples
of social media are blogs (e.g. WordPress), content communitie s (e.g. YouTube), social
networking sites (e.g. Facebook, LinkedIn), collaborative project s (e.g. Wikipedia),
and virtual social worlds (e.g. Second Life) (B alakrishnan & Gan, 2016; Kaplan &
Haenlein, 2010).
The overwhelming popularity of social media has led to a proliferation of studies that
examined its role in higher education. These include analysis of social media usage for
learning in relation to students’ learning styles (Balakrishnan & Gan, 2016); relationship
between personal, teaching, and professional purposes of use of social media by higher
education scholars (Manca & Ranieri, 2016); learner-generated content and its effects
on learning outcomes and satisfaction (Orús et al., 2016); impact of online social net-
works on academic performance (Paul, Baker, & Cochran, 2012); and success factors of
social networking sites (Schlenkrich & Sewry, 2012). In his essay ‘Social Media in
Higher Education,’ Selwyn, (2012) discussed the educational imp lications of social
media in terms of new types of learners, learning, and higher education provision. He
argued that although there are debates on the actual use of social media for learning
and knowledge generation, educators are challenged continually to find ways on how to
effectively utilize social media in higher education settings.
Findings of previous studies (Balakrishnan & Gan, 2016; Schlenkrich & Sewry, 2012;
Sobaih, Moustafa, Ghandforous h, & Khan, 2016) revealed that social media has a great
potential for improving learning experience through active interaction and collabor-
ation. However, there are two major gaps that need to be further investigated. First,
users’ (e.g. students) behavioral intention to use social media is unclear. Second, to the
best of our knowledge, few studies have been conducted on social media and its accept-
ance/rejection in emerging countries such as the Philippines. The issue has grown im-
portance in the light of the recent changes in the business environment (e.g.
competitiveness) and advanc ement in technology in these emerging economies. For ex-
ample, the Philippines has 48 million active social media users with a social media
Dumpit and Fernandez International Journal of Educational Technology in Higher Education (2017) 14:5 Page 2 of 16
penetration of 47% in 2016 (Kemp, 2016). Therefore, to ensure successful adoption of
social media, it can be argued that there is a need to investigate what drives users to
accept or reject the use of social media particularly in these economies.
The central focus of this study, therefore, is to develop an understanding of the fac-
tors and causal relationships that influence the acceptance and behavioral intention to
use social media. It aims to contribute useful insights on how HEIs can fully maximize
the use of social media. Since social media is Internet-based, this study proposed the
Technology Acceptance Model (TAM) as a theoretical framework. We discuss this
framework below.
Technology Acceptance Model (TAM)
A considerable amount of literature has been published on user technology acceptance
and TAM is one of the most frameworks adopted because of its robustness, simplicity,
and applicability in explaining and predicting the attributes that affect user’s adoption
behavior towards new technologies (Lu, Yu, Liu, & Yao, 2003; Marangunić & Granić,
2015; Rauniar, Rawski, Yang, & Johnson, 2014; Venkatesh & Davis, 2000).
Davis (1986) developed the TAM (Fig. 1), which is based on the Theory of Reasoned
Action (TRA), to understand the causal relationships among users’ internal beliefs, atti-
tudes, and intentions as well as to predict and explain acceptance of computer technol-
ogy (Davis et al., 1989). This model posits that the user’s actual usage behavior (actual
use or AU) is directly affected by behavioral intention (intention to use or IU). In turn,
behavioral intention is determined by both the user’s attitude and its perception of use-
fulness. The user’s attitude is considered to be significantly influenced by two key be-
liefs, perceived usefulness (PU) and perceived ease of use (PEOU), and that these
beliefs act as mediators between external varia bles (e.g. design features, prior usage and
experience, computer self-efficacy, and confidence in technology) and intention to use.
Furthermore, TAM theorizes that PEOU indirectly affects IU through PU (Davis et al.,
1989; Venkatesh & Davis, 2000).
The application of TAM is diverse: from wireless Internet (Lu et al., 2003) and
multimedia-on-dem and (Liao, Tsou, & Shu, 2008) to collaborative technologies
(Cheung & Vogel, 2013). Large volumes of these studies modified Davis’ TAM (1986)
to improve its (predictive) validity and applicability to various te chnologies. For in-
stance, Davis et al. (1989) showed tha t the attitude construct does not significantly me-
diate in the belief-intention relationships. In 2000, Venkatesh and Davis (2000)
Fig. 1 Technology Acceptance Model (TAM), redrawn from Davis, Bagozzi, and Warshaw (1989, p. 985)
Dumpit and Fernandez International Journal of Educational Technology in Higher Education (2017) 14:5 Page 3 of 16
proposed an extension for TAM (called TAM2), which incl udes the the oretical con-
structs of social influence and cognitive instrumental processes. They found that these
additional constructs directly affect adoption and usage of "information technology"
(IT) in the workplace. Meanwhile, Marangunić and Granić (2015) an alyzed 85 scientific
publications on TAM from 1986 to 2013 and concluded that studies have continually
identified new constructs that play major roles in influencing the core variables (PU
and PEOU) of TAM.
Since TAM was originally created to explain computer usage behavior, some re-
searchers argue that factors such as perceived playfulness, perceived critical mass, and
social trust should be included to effectively explain the unique characteristics of new
technologies such as social networking sites (SNS) (Ernst, Pfeiffer, & RothLauf, 2013;
Oum & Han, 2011; Rauniar et al., 2014; Sledgianowski & Kulviwat, 2009). This study
recognizes recent developments and therefore, together with the constructs perceived
usefulness (PU) and perceived ease of use (PEOU), we added constructs: subjective
norm (SN), perceived playfulness (PP), and quality of Internet connection which is
comprised of Internet reliability and speed. This is to improve the ability of the model
to predict student’s adoption and usage behavior of social media.
The remainin g part of the paper proceeds a s follows: relevant literatures on the re-
search model are described in “The inter-relationships of determinants of TAM” sec-
tion, then followed by the research methodology in section “Methodology”. The results
of the analyses are presented in section “Results”, and conclusions are describe d in the
final section.
The inter-relationships of determinants of TAM
Using insights from related studies, we conceptualized a modified framework of TAM
for social media (Fig. 2). The resea rch model used original constructs of TAM: per-
ceived usefulness, perceived ease of use, intention to use, and actual use. Additional
constructs were included to the model: subjective norm, perceived playfulness, and
quality of Internet connection, which is comprised of Internet reliability and speed. A
Fig. 2 Hypothesized TAM
Dumpit and Fernandez International Journal of Educational Technology in Higher Education (2017) 14:5 Page 4 of 16
detailed discussion of the underlying hypotheses and the correspondin g literature sup-
porting the model is specified below.
Perceived usefulness (PU) and perceived ease of use (PEOU) are fundamental predictors
of the adoption and use of technology (Davis, 1989). Davis defined PU as ‘thedegreeto
which a person believes that using a particular system would enhance his or her job perform-
ance’ (1989, p. 320). Whereas, PEOU means ‘the degree to which a person believes that using
a particular system would be free of effort’ (1989, p. 320). These relationships are robust
across various types of technologies: m-learning (Althunibat, 2015), Internet-based learning
systems (Saadé & Bahli, 2005), and healthcare information systems (Pai & Huang, 2011).
In-depth and comprehensive studies of Davis (1989) and Davis et al. (1989) revealed
that PU is a stronger driver of usage intention compared to PEOU. A system has favor-
able PU when it improves the performance of the user. While PEOU becomes less sig-
nificant as the user become s more adept at using the system. Interestingly, in social
media applications, PU is seen as an inconsistent determinant of intention to use. This
may be attributed to the nature/type of the information system (IS) being studied, that
is, either hedonic or utilitarian (Ernst et al., 2013; Moqbel, 2012; Sledgianowski &
Kulviwat, 2009). Hedonic IS (such as social media) promotes communication and en-
tertainment to users while, users adopt utilitarian IS (such as online banking) for more
efficient processes and other practical applicatio n.
TAM also hypothesized that there is a positive correlation between PEOU and PU
(Venkatesh & Davis, 2000). In other words, the less complicated a user performs social
media-related activities, the more likely he/she will consider social media sites to be use-
ful. In studies relating to SNS, several authors (Alarcón-Del-Amo, Lorenzo-Romero, &
Gómez-Borja, 2012; Pinho & Soares, 2011; Rauniar et al., 2014) found that PEOU signifi-
cantly determine PU, however, this view was not supported in a study of hedonic and
utilitarian motivations of SNS adoption in Germany (Ernst et al., 2013). Overall, there
seems to be some evidences that PU and PEOU are important in explaining adoption and
usage behavior (Alarcón-Del-Amo et al., 2012; Choi & Chung, 2012; Rauniar et al., 2014;
Sledgianowski & Kulviwat, 2009). Therefore, we hypothesize the following:
Hypothesis 1: High rating of perceived usefulness leads to high intention to use.
Hypothesis 2: High rating of perceived ease of use leads to high intention to use.
Hypothesis 3: High rating of perceived ease of use leads to high perceived usefulness.
Although, not originally a component of Davis (1989) TAM, subjective norm (SN) is
seen a s a major factor of behavioral intention (Choi & Chung, 2012; Venkatesh &
Davis, 2000). Sledgianowski and Kulviwat (2009) point out that SN explains the influ-
ence of society (e.g. peers, significant others) on the way an individual behaves. Inclu-
sion of SN, according to Arpaci (2016), ‘may capture unique variance in attitudes and
intentions (p.155).’ Analyzing 51 studies, Schepers and Wetzels (2007) explained the
critical role of SN to IU and found out that SN has more influence on IU in studies in
Western culture. The positive relationship between SN and IU was also evident in the
acceptance of airline business-to-customer (B2C) eCommerce websites (AB2CEWS)
(Kim, Kim, & Shin, 2009). The authors proposed that airline companies should devel-
oped strategies focused on referents (e.g., family and friends) because a customer’s pur-
chasing behavior is influenced by the opinions of those people. Together, these studies
Dumpit and Fernandez International Journal of Educational Technology in Higher Education (2017) 14:5 Page 5 of 16