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Computers in Human Behavior 71 (2017) 196e208
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Computers in Human Behavior
journal homepage: www.elsevier .com/locate/comphumbeh
Understanding factors influencing information communication
technology adoption behavior: The moderators of information literacy
and digital skills
Tai-Kuei Yu a, *, Mei-Lan Lin b, Ying-Kai Liao c
a Department of Business Administration, National Quemoy University, One University Road, Jinning Township, Kinmen Hsien, Taiwan
b Department of Hospitality Management, Southern Taiwan University of Science and Technology, Taiwan
c Bachelor's Program of International Business, Nanhua University, Taiwan
a r t i c l e i n f o
Article history:
Received 17 July 2016
Received in revised form
9 January 2017
Accepted 1 February 2017
Available online 1 February 2017
Keywords:
Media richness theory
Digital divide
Information literacy
Digital skill
Moderating effect
* Corresponding author.
E-mail addresses: yutk2000@gmail.com (T.-K
(M.-L. Lin), yksuper889@nhu.edu.tw (Y.-K. Liao).
http://dx.doi.org/10.1016/j.chb.2017.02.005
0747-5632/© 2017 Elsevier Ltd. All rights reserved.
a b s t r a c t
Digital inequality is one of the most critical issues in the “information age”, few studies have examined
the social inequality in information resources and digital use patterns. In the rural areas, such infor-
mation communication technology (ICT) facilities could not guarantee that users can easily access in-
formation technology and overcome the so-called “digital divide.” This research aims to discover the
psychological factors that influence information and communication technology (ICT) adoption behavior,
as well as confirm whether “information literacy” and “digital skills” have moderator effects in the
research model. Using a survey of 875 participants and a structural equation modeling approach, we find
that task characteristics and social interaction improve media richness, media experience, and media
technostress, which in turn enhance ICT adoption behavior. The proposed theoretical model shows that
the impact of ICT adoption behavior is moderated by information literacy and digital skills. The findings
of this research can offer guidelines for policy makers and educators who evaluate a community's ICT
adoption behavior so as to provide proper access to ICT and promote its visibility by incorporating ICT in
educational activities.
© 2017 Elsevier Ltd. All rights reserved.
1. Introduction
According to Taiwan's internet users behavior investigation
(Taiwan Network Information Center, 2013), the number of regular
Internet users in Taiwan reached 17.98 million in 2013, comprising
77.10% of the entire population. We are in the midst of globalization
and the so-called “Information Age”. Although the cost of infor-
mation and communication technology (ICT) devices has dramat-
ically decreased in recent years, their acquisition and maintenance
still require financial commitment for households in the rural areas.
According to Venkatesh and Sykes (2013), social inequality infor-
mational resourcemaymanifest itself not only in differential digital
access but also in differential digital use patterns. The “digital
divide” generally implies differences in access based on socio-
economic divisions (van Deursen & van Dijk, 2015). In many
. Yu), mllin@stust.edu.tw
countries, residents of urban areas have better information literacy
than those in the rural areas, making the existence of the digital
divide a common rural phenomenon. Compared with the signifi-
cant penetration of information technologies in urban areas, the
application of ICT is less prevalent in rural areas (Gerpott& Ahmadi,
2015a). Hence, individuals who have been reared within more
privileged socio-economic environments acquire more opportu-
nities to access ICT. ICTadoption behavior is a vital topic because ICT
has become ubiquitous, pervading our daily lives in various ways.
This is especially true for the Taiwanese youth, whose exposure to
digital media shapes the way they communicate and interact with
the rest of the world. Some scholars (Friemel, 2016; Ghobadi &
Ghobadi, 2015; van Deursen & van Dijk, 2015; Venkatesh &
Sykes, 2013) have suggested that the high failure rates of projects
meant to reduce the digital divide are due to a lack of under-
standing of different ICT choice behaviors. In fact, the acceptance/
adoption of the ICT approach has been identified as a critical issue
in improving digital divide in its successful usage. Many theoretical
frameworks have been used to measure technology usage and
adoption; however, few have been used in the context of improving
mailto:yutk2000@gmail.com
mailto:mllin@stust.edu.tw
mailto:yksuper889@nhu.edu.tw
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T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208 197
the ICT digital divide.
Recent researches (Brown, Dennis, & Venkatesh, 2010; Weber &
Kauffman, 2011) define ICT as medium that support data and in-
formation processing, storage, transmission, and communication
via the Internet and other means. The flexibility of technology-
enabled work has not only brought many advantages to busi-
nesses and various organizations, but has also become embedded
in the fabric of every activity. The willingness of people to use ICT
needs to be considered together with technology, products or ser-
vices, social interaction, and human factors (Goldhammer,
Naumann, & Kebel, 2013; Zylka, Christoph, Kroehne, Hartig, &
Goldhammer, 2015). ICT conveys forms of knowledge and literacy
as well as integrate places (e.g., home, school, work, and commu-
nity) of learning (Moore, 2013). The primary reason is the greater
expansion of completed Internet infrastructures in urban than in
rural areas. Therefore, in recent years, the utilization of ICT in rural
areas has gradually received more attention because, from both of
economic and social concerns, those ICT drive rural regional
economies and improve the quality of life. Therefore, reducing the
digital divide between urban and rural areas has become main-
stream public policies in many countries (Bruno, Esposito,
Genovese, & Gwebu, 2010; Gerpott & Ahmadi, 2015b; Ghobadi &
Ghobadi, 2015). van Deursen, van Dijk, and Peter (2015) argued
that digital technologies and media have influenced not only the
intensified social connections but also human daily activities as
well. ICT use can reduce the digital divide across the curriculum, as
revealed by examinations of information technology skill and
standardizedmeasures of information literacy. Consequently, while
ICT appears to motivate some people to use new information
technologies, measures have hardly capitalized on the intrinsic
properties of information literacy as well as interactive and online
technology. The digital divide debate has centered on the acquisi-
tion of necessary skills and literacy to use ICT efficiently and
effectively. These changes challenge our understanding of the
digital divide, information literacy, and information technology
skills in rural area communities.
In the contemporary knowledge-based economy and the
Internet era, personal wealth is no longer the sole attribute that
gives a person a rich or poor social status; at present, knowledge
and information have also become important determinants of
wealth (Ayanso, Cho,& Lertwachara, 2014; Van Deursen& Van Dijk,
2011) under the premise that accumulated knowledge is conveyed
worldwide through the Internet-based media. Thus, those who
have no digital skills or no access to computers and the Internet at
home have become the minority groups. Therefore, the digital
divide has lead to a new era of social welfare issue.
In order to understand the usage of technology and ICT adoption
behavior,this study aims to address the following research
questions:
Research question 1. We follow the stream of research that
focuses on individual acceptance of technology by using intention
as the dependent variable. What are the psychological factors and
the characteristics of ICT media impacts of rural residents' ICT
adoption behavior.
Research question 2. How does the information literacy and
digital skill mix moderate the impacts of media experience and
media technostress on ICT adoption behavior.
2. Theoretical foundations
The medium of information technology has rapidly shortened
the communication speed between the communicators and re-
ceivers, thus improving knowledge communication diffusion and
collection. Zhong (2011) and Aesaert, van Braak, Van Nijlen, and
Vanderlinde (2015) argued that the role of information in social
differentiation lies in the information environment based on a
society's resources, relationships, and ICT. Furthermore, ICT itself
plays the role of being the communicative bridge between in-
dividuals and others in a society (De Wit, Heerwegh, & Verhoeven,
2012). The ICT medium could provide service interaction to people
and enable them to access peer opinions; hence, people can see the
potential for additional benefits (e.g., more choices, lower prices,
better quality of goods) through additional effort. Meanwhile,
people all want to eliminate the incompatible barriers within
different media form in ICT, and expect ICT to greatly increase the
collection capability of information and knowledge (King & Xia,
1997; Wu, Chang, & Sha, 2016; Zhang, Li, Ge, & Yen, 2012). In the
process of communication, users have to use their own under-
standing of information structure or content to express knowledge;
the process of information transfer is influenced by individual or
group capabilities, which involve individual factors (e.g., core ca-
pabilities of information and information literacy, social pressure,
common beliefs, and incentives motivation), social network struc-
ture, physical proximity, recipient availability, and media charac-
teristics (presence of symbols and accessibility to media) (Aesaert
et al., 2015; Gerpott & Ahmadi, 2015a; Wang, Guo, & Jou, 2015).
According to Burns, O’Connor, and Stocklmayer (2003), the objec-
tive of effective communication is to achieve the five English
characters of AEIOU: present awareness, enjoyment, interest,
public opinion, and understanding; these comprise the core of a so-
called “net-society.”
2.1. Media richness theory
MRT was first proposed by Daft and Lengel (1986). This theory
posits that different platforms of communication have different
levels of media richness (medium richness/information richness).
Carlson and Zmud (1999) integrated MRT, the symbolic media,
communication patterns, and so on, and proposed channel
expansion theory to clarify the relationship between group
communication and the channel of communication. The term
“media richness” refers to the richness of communication media
that can pass through a number of information channels (Ayanso
et al., 2014; Friemel, 2016; Purdy & Nye, 2000). MRT initially
focused on information in organizations and the process by which
media selection affects organizational structure. MRT posits that
communication consists of a variety of cues, such as verbal tones,
facial expressions, body language, dress, appearance, and settings,
which convey information to receivers, that in turn, creates or
elicits cognitive interpretations and concomitant social, emotional,
and characteristic states (Kahai & Cooper, 2003; Van Deursen, van
Dijk,& Peters, 2011; Kishi, 2008; Purdy&Nye, 2000; Xu, Ma,& See-
To, 2010). Furthermore, the richness of a communication medium
refers to its capacity for immediate feedback, multiple cues, lan-
guage variety, and personalization. On the one hand, the lower the
level of richness, the more difficult it is to quickly communicate,
develop an understanding of each other's viewpoints, and resolve
differences (Bertot, Jaeger,&Hansen, 2012; Liu, Liao,& Pratt, 2009).
On the other hand, the higher the level of richness, the easier it is to
convey information to receivers, thus facilitating better communi-
cation and social interaction because the information receiver
could rapidly and correctly understand the other party (Dennis &
Kinney, 1998; Purdy & Nye, 2000; Liu et al., 2009; Anandarajan,
Zaman, Dai, & Arinze, 2010; Van Deursen et al., 2011). A number
of researches about online classes (e.g. Baehr, 2012; Giesbers,
Rienties, Tempelaar, & Gijselaers, 2013; Lin, Wen, Jou, & Wu,
2014; Liu et al., 2009; Lu, Kim, Dou, & Kumar, 2014; Wang et al.,
2015), workplace (e.g. Badger, Kaminsky, & Behrend, 2014; King
& Xia, 1997; Sheer & Chen, 2004; Wu et al., 2015) and social
networking service (e.g. Lee, Son, & Kim, 2016; Ogara, Koh, &
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208198
Prybutok, 2014) areas were also found positive effects of rich media
on users' evaluations toward ICT platforms.
2.2. Social interactions
Different levels of media richnessmainly depend on four factors,
namely, multiple cues, immediacy of feedback, language diversity,
and participation (Bertot et al., 2012; Click & Petit, 2010; Daft,
Lengel, & Trevino, 1987). According to the previous studies
(Brown et al., 2010; Weber & Kauffman, 2011), a significant rela-
tionship exists between groups of social factors and the technology
adoption intention. This finding suggests that the ICT adoption
behavior of a member within a group is affected by others in the
same group who have already adopted the technology; this phe-
nomenon highlights the importance of “social interaction” (Ayanso
et al., 2014; Hernandez, Montaner, Sese, & Urquizu, 2011).
Furthermore, the Internet-based CMC interaction has a significant
influence on various dimensions of daily life, including work, ed-
ucation, family life, business modes, and so on.
Social interactions are conceptualized based on the affective
social and advice network relationships among individuals. In using
social interaction media platforms (e.g., Facebook, Twitter, Wikis,
YouTube), users' behavior are often affected by their workplace
superiors, colleagues, or friends (i.e., social interaction). The quick
adoption of relevant ICTs increases the media richness perception,
which in turn, promotes communication and the further expansion
of the social network. Social ties influence who can access new
information and the speed by which they receive such information.
When an individual finds that people around him/her use an
interactive tool in communication environments, social influence
may also serve as a strong social motivator to use ICT interactive
tools. In this study, we propose that individuals may want to use an
ICT interactive tool because their friends are using it.
2.3. Media technostress
People prefer user-friendly technologies. Hence, technological
complexity, overload and stress are major barriers that can affect
ICT adoption behavior. Researchers (Ragu-Nathan, Tarafdar, Ragu-
Nathan, & Tu, 2008; Shu, Tu, & Wang, 2011; Tarafdar, Tu, Ragu-
Nathan, & Ragu-Nathan, 2007, 2010, 2015) have started to
explore the various user, task and ICT characteristics that lead to ICT
usage related stress, misuse and information overload. “Computer
technostress” refers to the negative expected or actual process of
individuals interacting with computers or ICT devices (Heinssen,
Glass, & Knight, 1987; Shu et al., 2011). Tarafdar et al. (2007)
developed a measurement scale to measure organizational envi-
ronment and self-efficacy and evaluate the impact of technostress
on the use of ICT media. Meanwhile, RagueNathan et al. (2008)
focused on technostress amongst organizations resulting from the
implementation of successive computer-based business processes
that force employees to constantly adapt to new applications and
workflows. Moreover, Tarafdar et al. (2010) demonstrated the
negative effects of technostress on ICT usage, and established a
negativeassociation between technostress and overall perfor-
mance (Tarafdar et al., 2015). In other words, media technostress is
a form of psychosomatic stress of adaptation caused by one's
inability to cope with the new ICT devices (Ragu-Nathan et al.,
2008). ICT media technostress can be viewed as a result of the
negative consequences on one's attitudes, beliefs, and thoughts by
the direct or indirect use of ICT, also be seen as an antecedent to
one's adoption behavior. In this research, the definition of ICT
media technostress emerges in the negative individual experience
that it imposes on person, who are passive victims, trapped in user
interface or software applications.
2.4. Information literacy and digital skill
Through these theoretical findings on human behavior and the
impact they have on an individual's willingness to adopt ICT, we can
identify the effectiveness of using such technologies. Briefly, such
communication consists of cues that convey information and use of
appropriate skills through appropriate devices that promote ac-
tivities and effective dialogues. Hence, users need to go through the
information medium to obtain the required information (knowl-
edge) and services, thus enabling them to convey messages using
the AEIOU framework. According to the American Library
Association (2000), information literacy, which is the basis of life-
long learning in the Information Age, can be classified into two
kinds: understanding information contents and competency in
using ICT. Information literacy enables people to master informa-
tion content and extend their investigations, recognize when in-
formation is needed, and possess the ability to effectively locate,
evaluate, and use the needed information. Information literacy also
refers to one's capacity to use ICT as means to reach particular
personal and professional goals. ICT use requires a distinct set of
skills that allow one to effectively cope with a medium that is
generally deprived of verbal cues and visual cues. For instance,
before you can evaluate the results of a search query, you must be
able to perform one, or before you can ask a question in an elec-
tronic forum, you need to create a user account and register. Media-
related skills consist of technology literacy, which include a basic
command of ICT operational skills and the ability to manipulate the
complex ICT communication applications.
3. Hypotheses development
Most research on information literacy have not been conducted
in regions that have a digital divide problem. However, these re-
gions play an important role in solving the digital divide issue. Thus,
it is necessary to identify the determinants influencing rural peo-
ple's ICT adoption behavior. Accordingly, the current research at-
tempts to integrate two theories, namely, “media richness theory
(MRT) (Daft & Lengel, 1986)” and “information technology accep-
tance” (e.g., Cognitive theory, Compeau & Higgins, 1995; Task-
technology fit, Goodhue & Thompson, 1995; Technology Accep-
tance Model, Davis, 1998; Theory of Planned Behavior, Hsieh, Rai,&
Keil, 2008) as the theoretical foundation upon which to establish
the research framework for explaining people's ICT adoption
behavior. We also identify information literacy as an important
moderating variable (information and digital skills), because it has
been found to have a significant role in relation to these models.
Many of the characteristics that have been ascribed to ICT are
not innate physical characteristics; rather these are socially derived
characteristics, such as social interaction and immediacy of feed-
back. Task has long been recognized as an important factor influ-
encing adoption behavior (Brown et al., 2010). Many studies in this
field have focused on specific tasks or the complexity level of task
characteristics based on the assumption that task characteristics
play an important role as an antecedent variable of adoption
behavior (Koo, Chung,&Nam, 2015; Zylka et al., 2015). When a task
fits better with the ICT characteristics, such characteristics can have
a stronger influence on a person's adoption behavior (Brown et al.,
2010; Thatcher & Brown, 2010; Han, Hiltz, Fjermestad, & Wang,
2011; Aesaert & van Braak, 2014). When examining user interac-
tion and experiences, the manner by which an individual uses the
media characteristics is just as critical as the ICT medium chosen
(Anandarajan et al., 2010; Giesbers et al., 2013).
Regarding MRT, complex tasks lead to higher ICT adoption
behavior (Daft et al., 1987; Aesaert et al., 2015; Brooks, 2015; Koo
et al., 2015). Individual users determine the richness of ICT media
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208 199
platforms through their experiences and communication trans-
actions using such channels (Rice, 1992; D'ambra, Rice, & O'connor,
1998; Dennis, Fuller, & Valacich, 2008). ICT media can further
enhance the positive effect of media richness on overall conve-
nience by making the information obtained more actionable in
terms of task characteristics. In this situation, individuals are likely
to use rich media for complex tasks and lean media for relatively
simpler topics. Individuals with high willingness to try out a new
ICT would consider the tasks delivered through ICT media as
innovation, and their desire to try the media comes from their
intention to experience the novelty of the ICT. The abovementioned
information leads us to the following hypotheses:
H1. Users' perceived task characteristics of ICT are positively
related to the medium richness of ICT.
H2. Users' perceived task characteristics of ICT are positively
related to their ICT adoption behavior.
Previous studies (Bagchi, Udo, Kirs,& Choden, 2015; Bruno et al.,
2010; Gerpott & Ahmadi, 2015b; Giesbers et al., 2013; Tondeur,
Sinnaeve, Van Houtte, & van Braak, 2010; Weber & Kauffman,
2011) on computer-mediated communication (CMC) have largely
proven that, under the environment of rapidly expanding Internet
infrastructures, information technology has provided a variety of
rich communication channels, enabling people to explore re-
lationships and knowledge through these modern formats at any
time. The ICT media have several unique features for facilitating
interpersonal communications: they provide nearly synchronous
communication, users can simultaneously engage in multiple
conversations on a one-to-one basis through separate windows,
and they allow users to build communities of interest via the tools
of social network (Anandarajan et al., 2010; Giesbers et al., 2013).
Those technologies open the world to many people and facilitate
communication through the exchange of short messages, which are
mostly real-time status updates, as well as facilitate effective divi-
sion and allocation of local and global knowledge and labor through
the coordination of work via ICT.
ICT platforms recognize the growth of many communities and
offer tools that allow users within such communities to manage
membership. Giving messages with personal focus enhances re-
ceivers' involvement and the effectiveness of communication
(Kietzmann, Hermkens, McCarthy, & Silvestre, 2011; Kishi, 2008).
Moreover, the amount of information is another key construct of
efficient communication richness. Generally, the appropriate
amount of information guarantees communication effectiveness
and richer media have better transmission efficiency. However,
information overload can also lead to information inefficiency. For
instance, the receivers may not be able to understand the right
messages from the excessive information (Bons�on & Flores, 2011;
Liu et al., 2009). According to the MRT hypothesis, rational and
effective users choose media of appropriate richness for tasks that
involve communication (D'ambra et al., 1998; Dennis et al., 2008),
and if their choice of communicationmedia is restricted tomedia of
lower richness due to accessibility constraints, social interaction
and media experience may decrease in rural communities. Com-
munitiesperceive a high level of media richness when they are
familiar with either their communication partners or the commu-
nication equipment. For these social interaction purposes, users
often exchange messages and files with subtle meanings and want
to reach mutual understanding (Anandarajan et al., 2010; Walther,
1996). In other words, if a user highly thinks of ICT media as
communication channels, he or she will also consider these as so-
cialization tools, that is, a certain level of ICT medium richness can
facilitate social interaction with others. In this sense, perceived ICT
media richness is an important factor in social interaction, which
leads us to the following hypotheses:
H3. Users' perceived ICT media richness is positively related to
their ICT media experience.
H4. Users' perceived ICTmedia richness is positively related to the
level of their social interaction.
When an individual first begins to use a new ICT medium,
satisfaction often decreases because its use requires new skills and
newmanners of interaction. In this sense, well-designed interactive
ICT applications could generate higher satisfaction by providing
greater control to users who wish to personalize their information
search (Van Deursen et al., 2011). “General media experience” is
frequently operationalized as the number of years an individual has
been using an ICT device or the daily/weekly time spent using an
ICT device. “Media experience,” which refers to the ability to use a
specific type of ICT, can play a role in one's perceptions of ICT. In
some instances, people also need specialized training and opera-
tion experience to acquire skills necessary to use these ICTs. Greater
control over the surfing experience increases the pleasure and
convenience of using ICT, suggesting that people's ICT adoption
behavior is positively related to the degree of characteristics of ICT
media (De Wit et al., 2012). Research on ICT reported that people
are often attracted to the use of ICT to satisfy their curiosity and
imagination and to seek out novel experiences. For individuals who
are more open to experiences, using ICT media platforms for the
purposes of using a new widget and unearthing the world of in-
formation via the Internet have substantial intrinsic interest
(Venkatesh, Sykes, & Venkatraman, 2014). Van Deursen and Van
Dijk (2011) identified frequency of ICT use as a significant predic-
tor of individual digital information processing and usage behavior.
According toMRT research, various task characteristics of ICT, an
experienced user can potentially influence their adoption and use
of ICT (Koo, Wati, & Jung, 2011). For comparable levels of media
richness and use, the lower effort deployed by ICT media is likely to
lead to greater satisfaction. Consequently, an individual's experi-
ence with the specific ICT will improve and the ICT gradually be-
comes easier to use. This is because, as an individual's perceptions
of his/her ability to use ICT media increases, adoption behavior
intention also increases. This leads us to the following hypothesis:
H5. Users' media experience of the ICT is positively related to their
ICT adoption behavior.
Some studies (Bons�on & Flores, 2011; Click & Petit, 2010;
Ghobadi & Ghobadi, 2015; Liu et al., 2009) have reported that so-
cial interaction factors have significant and positive impact on ICT
adoption behavior. Individuals' use of ICT and the development of
their ICT knowledge and skills are socially embedded and depend,
to some context, on feedback and interactionwith other individuals
(e.g., peers and teachers) (Zylka et al., 2015). Further, evidence
suggests that key members from an individual's social network
may have a normative influence upon his/her ICT adoption
behavior (Venkatesh & Brown, 2001; van Deursen, Courtois, & van
Dijk, 2014). This leads us to the following hypothesis:
H6. A users' level of social interaction is positively related to his/
her ICT adoption behavior.
Most of the related studies have demonstrated a relationship
between technostress and ICT behavior (Ayyagari, Grover, & Purvis,
2011; Maier, Laumer, Weinert, & Weitzel, 2015; Ragu-Nathan et al.,
2008; Tarafdar et al., 2015; Tarafdar, Tu, Ragu-Nathan, & Ragu-
Nathan, 2011, 2007). The impact of a strong positive belief system
is likely to transfer to beliefs related to tasks associated with
technology use. Increased technostress commonly leads to lower
ICT adoption intention. In the DOC context, media technostress
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208200
describes the stress phenomenon resulting from the use of ICT for
personal tasks, which can be attributed to characteristics of modern
ICT, such as constant change. Given that ICT use is dependent on
technology, it follows that higher levels of media technostress lead
to lower levels of ICT usage. This leads us to the following
hypothesis:
H7. A medium level of technostress is negatively related to a
user's ICT adoption behavior of ICT.
In this study, we focus on the instrumental “technology” and
“skill” that can be applied in finding answers to the questions at
hand. In recent years, insufficiency of digital skills is a considerable
barrier to one's ability to benefit from the use of ICT media (Bruno
et al., 2010; van Deursen et al., 2014). The ICT digital skills refer to a
set of basic skills in using ICTs. People with insufficient ICT digital
skills might turn to self-directed learning, that is, by learning
through trial and error at home and in the workplace. Other people
can also turn to helpdesks and other sources.
We conceptualized ICT digital skill as self-perceived ability in
handling daily ICT media experiences competently. The current
research focuses on the density of information literacy and digital
skill level, because they have been found to affect users' ICT
adoption behavior (Bertot, Jaeger, & Grimes, 2010; Click & Petit,
2010). Levy (2009) noted that people need to match their infor-
mation technology capabilities with the information processing
capacity of the technology devices they use, including those with
new communication media. Zhong (2011) found that the suffi-
ciency of ICT facilities determines how ICT is implemented into
everyday life and can help people enhance their digital skills. As
such, the relationship between media experience and ICT adoption
behavior may be moderated by information and digital skills. ICT
technostress arises from digital illiteracy and the lack of skills in ICT
use. People in rural communities generally think that ICT media are
only reserved for those with high educational background and
often deny themselves access to computers. Therefore, we present
the following hypotheses:
M1a. The relationship between media experience and ICT adop-
tion behavior is moderated by the level of digital skill, that is, the
relationship is weaker under conditions of low digital skill and
stronger under conditions of high digital skill.
media richness task
characteristics
social interaction
media
experience
H3
H1
H5
H
Fig. 1. Conceptua
M1b. The relationship between media technostress and ICT
adoption behavior is moderated by the level of digital skill, that is,
the relationship is weaker under conditions of low digital skill and
stronger under conditions of high digital skill.
M2a. The relationship between media experience and ICT adop-
tion behavior is moderated by the level of information literacy, that
is, the relationship is weaker under conditions of low information
literacy and stronger under conditions of high information literacy.
M2b. The relationship between media technostress and ICT
adoption behavior is moderated by the level of information literacy,
that is, the relationship is weaker under conditions of low infor-
mation literacy and stronger under conditions of high information
literacy.
A conceptualized structural model, which demonstrates the
moderating role of information and digital skill, is presented in
Fig. 1.
4. Methods and descriptive statisticsThe current study is an attempt to construct a theoretical model
by which to predict and explain community information literacy
and Internet adoption behavior, as well as to test the model
empirically. Specifically, prior studies (Gerpott & Ahmadi, 2015b;
van Deursen et al., 2015) have reported that using scale measure-
ment easily produces unsuitability because extant research have
only been conducted in urban regions and developed countries. In
order to fill this gap, the current study intends to develop an effi-
cient measurement scale for the regions suffering from the digital
divide, that is, domestic rural areas. Adopting the questionnaire
survey method to better understand how information literacy and
digital skills affect rural community residents' willingness to apply
ICT in their daily lives. This study adopts a positivist research
approach, contributing to the methodological pluralism that is
necessary for the complete understanding of a phenomenon.
Rigorous statistical testing was possible as the data were collected
through a structured questionnaire. A pilot test was conducted
before the final questionnaire was distributed to the subjects. To
ensure the appropriateness of the research design, the validity and
reliability of the itemswere also tested. A self-administered, closed-
adoption behavior
media
technostress
information
literacy
digital skill
H2
H6
M2
a
M1
a
4 H7
M2b
M1b
l framework.
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208 201
ended questionnaire with ordered choices was used to survey a
sample of Taiwan digital opportunity center communities.
4.1. Measure development and validation
New ICT adoption behavior is difficult to observe from an
external perspective because of the nature of Internet behavior in
relation to practice and information habitus to use the Internet
(Schradie, 2011; Van Deursen et al., 2011). Furthermore, users are
the best judges on whether behavior intention has changed. In
other words, self-reporting is the best way to measure changes in
actual behavior intention. Here, themeasurement of intentions was
separated from the measurement of actual behavior intention as
we tested the research model. This was done to minimize the
possibility of the participants reconstructing history to present a
consistent and logical picture.
The questionnaire comprised previously published multi-item
scales with favorable psychometric properties. In addition to the
review of preexisting survey instruments from the literature, dur-
ing the construction of the survey questionnaire from the pilot
study, interviews were reviewed for context-specific details that
warranted inclusion. Our scale development followed the recom-
mendations of Straub, Boudreau, and Gefen (2004) as well as the
standard psychometric scale development procedures of DeVellis
(2012). The generation of constructs based on an extensive study
of the prior literature in related fields, such as task characteristics,
richness, experience, and adoption behavior, were adapted from
measurement items validated in previous empirical studies (Brown
et al., 2010; Carlson & Zmud, 1999; Koo et al., 2011; De Wit et al.,
2012; Van Deursen et al., 2011; van Deursen et al., 2014). The
scales for social interaction and media technostress were adapted
from prior research (Anandarajan et al., 2010; Bons�on & Flores,
2011; Ragu-Nathan et al., 2008; Tarafdar et al., 2015) where
possible. The constructs of information literacy and digital skill
were measured using scales adapted from Koltay (2011),
Goldhammer et al. (2013), and Bertot et al. (2010). The existing
literature was first surveyed for items that were previously used for
the constructs employed in the current research.
We developed the first version of the questionnaire in English,
after which the context of the questionnaire items was translated
the measures into Chinese. The double-translation method was
used to ensure conceptual equivalence and increased content val-
idity. Three researchers, who are experts or have great interest in
information technology adoption behavior and information liter-
acy, reviewed the initial instrument. Each expert was provided with
operational definitions for the eight substrata and the 62 initial
items printed on separate index cards. The participants were asked
to note ambiguity in item wording (and recommend changes if
necessary), categorize individual items into eight substrata, and
rank items within each substratum according to their semantic
proximity to that of the underlying substratum. Moreover, the face
and content validities of the instrument were verified based on the
in-depth interviews with these professionals. On the basis of expert
comments, we made minor adjustments to refine the
questionnaire.
Before conducting the final study, the pilot study questionnaire
consisted of 50 questions divided into four major areas: (1) de-
mographic profile of participants; (2) evaluation of ICT task char-
acteristics, richness, experience; and technostress; (3) regarding
their ICT adoption behavior; and (4) personal information literacy
and digital skill. Participants were asked to rate 42 attributes on a 7-
point scale (ranging from “strongly disagree” to “strongly agree”).
To ensure the desired balance and randomness of the items in the
questionnaire, five items were negated, and all items were
randomly arranged to reduce the potential ceiling (or floor) effect
that can induce monotonous responses in response to items
designed to measure the same construct. Considerable effort was
exerted to ensure that each statement in the final instrument
captured the intended meaning of the construct under investiga-
tion. A pilot test was later conducted with 32 adults residing in
Yunlin County, Taiwan, to evaluate whether the revised question-
naire was appropriate in terms of readability, ease of understand-
ing, formatting, and layout. The focus of the pilot study was to
examine the reliability and validity of the scales in the context of
ICT media. Upon response from the pilot study, the internal con-
sistencies of Cronbach's alphas ranging from 0.724 for information
literacy to 0.912 for social interaction imply that the scales used in
this study were satisfactory in terms of measuring the constructs of
interest.
4.2. Sample and descriptive statistics
Since 2005, the National Ministry of Education has invested
resources in digital opportunity centers aimed at upgrading the ICT
infrastructure and establishing life-long learning environments to
narrow the Taiwan's digital divide. This study adopted a quantita-
tive survey and face-to-face interviews to collect data. Participation
in the study was completely voluntary, but was limited to those
subjects who were above the age of 18 and had new ICT adoption
experience via the Internet or mobile applications. After connecting
the staffs of the digital opportunity centers, who helped explain the
content of the questionnaire to the respondents, the duration of
data collection took around 6 months. A total of 1500 question-
naires were sent at the same time. We also designed three distinct
acts that must be completed and two distinct information cues
about the attributes of the task-related stimulus object all re-
spondents have to process when performing a task characteristic.
A total of 1225 questionnaires were returned, of which 350 were
considered problematic because of excessive missing data, “don't
know” or N/A answers, and response biases. These were excluded.
Finally, a total of 875 valid samples, which were collected from a
total of 160 digital opportunity centers of the Taiwan Ministry of
Education, were collected. The valid response rate was 71.43%.
Owing to privacy concerns, the digital opportunity center staffs did
not share information about the participants who chose to com-
plete the study or the date of response. Therefore, we were unable
to calculate coefficient of response bias, using a comparison of early
and late respondents or non-respondents.The sample demographic data in Table 1 indicate a diverse
cross-section of population. Of the respondents, 41.6% were males
and 58.4% were females. The average age of the sample population
was 37.61 years old (standard deviation was 13.6 years old).
Participant status in terms of formal education is varied: less than
grade 11 (19.1%), high school/technical school certificate (41.6%),
faculty degree (16.5%), bachelor's degree (19.4%), master's degree or
above (3.0%), and missing data (0.5%).
4.3. Common methods bias
When the dependent and independent variables are latent and
measured by the same method, common method bias may occur
(Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Owing to the na-
ture of the data collection method, we tested for common method
bias using Harman's one-factor test (Malhotra, Kim, & Patil, 2006;
Podsakoff & Organ, 1986) on eight first-order latent variables in
our research model. Our findings show that no single factor
accounted for the majority of the covariance in the measures,
implying that no common method bias occurred. Therefore, CMV
was not considered a major concern in our data set.
Table 1
Respondent profiles.
Demographics Level Count Percentage
Gender Male 364 41.6
Female 511 58.4
Formal education Less than grades 11 167 19.1
High school/technical school certificate 364 41.6
Faculty degree 144 16.5
Bachelor degree 170 19.4
Master degree or above 26 3.0
Missing value 4 0.5
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208202
5. Results
In the past, solving the moderating effect of research variables
required adopting multiple regression analysis to understand the
relationship of interaction effects between variables. However, this
method can only handle a single dependent variable (Aiken&West,
1991; Baron & Kenny, 1986; Jaccard & Wan, 1995). Partial least
squares (PLS) method is a linear model of exploration or con-
struction of statistical methods in a general linear model, which
includes moderating effects and multiple dependent variables.
With the process of moderating effect test following previous
works (Chin, Marcolin, & Newsted, 2003; Goodhue, Lewis, &
Thompson, 2007), PLS method was used in the modeling and
data analysis. The actual data analysis operations used the
SmartPLS 2.0 software of Ringle, Wende, and Will (2005), which
was developed to process measurement model and structural
model analysis with bootstrapping method sampling analysis to
estimate the parameters in the outer and inner model and t-values
(Chin, 2010, pp. 655e690; Saad�e & Bahli, 2005). With 875 ques-
tionnaires, the research model was over identified; thus, we
applied nonparametric bootstrapping procedure with 2500 repli-
cations to obtain the standard errors of the estimates.
5.1. Measurement model evaluation
By using PLS analysis, the composite reliability (CR) and average
variance extracted volume (AVE) can assess the reliability and
validity of the structural model, respectively. Accordingly, this
study followed Bagozzi and Yi (1988) recommendations and
selected the three most commonly used indicators of future eval-
uation indicators, which reflected themeasurement mode. The first
indicator refers to the reliability of individual items (i.e., factor
loading). This indicator is used to assess the measuring variables'
factor loading of the latent variables and test the statistical signif-
icance of every variable loading. In this study, all factor loadings
Table 2
Construct reliability results.
Construct Cronbach's Alpha
Media richness 0.904
Media experience 0.837
Information technology adoption behavior 0.923
Task characteristics 0.908
Social interaction 0.919
Media technostress 0.805
Information literacy 0.833
Digital skill 0.788
Media experience � information literacy 0.913
Media technostress � information literacy 0.957
Media technostress � digital skill 0.921
Media experience � digital skill 0.947
Discriminant validity ¼ AVE/(Correlation)2
Where (Correlation)2 ¼ highest (Correlation)2 between factors of interest and remaining
were higher than the recommended value of 0.6, which indicated
significance; Furthermore, the sample factor loadings were be-
tween 0.657 and 0.920, consistent with the recommended values of
Hair, Black, Babin, and Anderson (2010). The second indicator refers
to the composition of latent variables of reliability (CR). This value
indicates that the higher the internal consistency reliability of the
potential construct variable (the value of CR), the better it is. Fornell
and Larcker (1981) recommended that the values should be equal
to or greater than 0.6. In the current the CR values ranged from
0.851 to 0.962, thus confirming the reliability of the scales in this
study. The third indicator refers to the AVE, which calculates the
explanatory power of the latent variables to the measured vari-
ables. If the AVE values are higher, then the potential variables have
better discriminant validity and convergent validity. According to
Fornell and Larcker (1981), the standard value must be greater than
0.5, and in the current study, AVE values ranged from 0.486 to
0.805.
Convergent and discriminant validities were evaluated by
calculating the AVE value for each factor within each model.
Discriminant validity is shown if the square root of the AVE of a
measure is larger than its correlation coefficients with other mea-
sures, and the overall discriminant validity coefficient of the factors
achieves satisfactory level discriminant validity. These three in-
dicators suggest that the data support high internal consistency and
discriminant validity. These results, presented in Table 2, confirm
both the convergent and discriminant validities of the proposed
research model (see Table 3).
5.2. Testing the moderating effects
Prior to the analysis of themoderating effects, we first tested the
assumptions of the measurement variable used in this study and
did not find violations of the normality distribution assumption.
We used PLS to test the main effect of media experience and media
technostress and the moderating effects of information literacy and
Composite Reliability AVE Discriminant validity
0.929 0.723 1.322
0.902 0.754 1.819
0.942 0.764 1.342
0.932 0.732 1.749
0.943 0.805 1.472
0.851 0.590 28.547
0.900 0.750 1.318
0.877 0.707 3.105
0.928 0.589 4.091
0.962 0.682 2.413
0.916 0.486 2.382
0.955 0.703 3.665
factors.
Table 3
Baron and Kenny test of moderating effect.
Latent Sample (n ¼ 875)
Model 1 Model 2 Model 3
Task characteristics/ Media richness 0.720** 0.720** 0.720**
Task characteristics/ Adoption behavior 0.149** 0.020 0.021
Media richness/ Media experience 0.693** 0.693** 0.693**
Media experience/ Adoption behavior 0.142** 0.084** �0.005
Media richness/ Social interaction 0.796** 0.796** 0.796**
Social interaction / Adoption behavior 0.143** 0.055* 0.059*
Media technostress/ Adoption behavior 0.139** 0.068** 0.081*
Moderator main effect
Information literacy/ Adoption behavior 0.654** 0.644**
Digital skill/ Adoption behavior 0.079** �0.142
Moderator interaction effect
Media experience � Information literacy/ Adoption behavior �0.054*
Media experience � Digital skill/ Adoption behavior 0.286*
Media technostress � Information literacy/ Adoption behavior �0.031
Media technostress � Digital skill/ Adoption behavior �0.045
Adoption behavior R2 0.179 0.595 0.600
△R2 0.416 0.005
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208 203
digital skills on information technology adoption behavior (Figs. 2
and 3, respectively). For example, to test the moderating effect,
media experience (predictor) and information literacy (moderator)
were multiplied to create an interaction construct (media
experience� information literacy) to predict ICT adoption behavior.
All variables were standardized so that each variable was centered
at zero and had a standardized score, thus avoiding the potential
multi-collinearity problem and making it easier to interpret the
results.
Following Baron and Kenny (1986), the empirical studydeter-
mined the moderating roles of information literacy and digital skill
according to the significance of the interaction terms in Model 3.
Among the four hypothesized moderating effects, M1b and M2b
were not significant, that is, information literacy and digital skill
did not havemediating effects betweenmedia technostress and ICT
adoption behavior. Interestingly, all of the significant interaction
effects were related to media experience. Information literacy
negativelymoderated bothmedia experience effect on ICTadoption
behavior, on the contrary, digital skill was a positive coefficient
(M1a: beta ¼ �0.054, p < 0.05; M2a: beta ¼ 0.286, p < 0.05). These
media
experience
R2=0.481
task 
characteristics
media richness
R2=0.518
social 
interaction
R2=0.633
0.6
93
**
0.796**
0.720**
* p<0.05 ** p<0.01
Fig. 2. Path coefficients for the research mo
additional analyses provide support for the moderation pattern
shown in our model. Meanwhile, Fig. 4 presents the full results of
the moderation analysis, including the structural path estimates
and explained variances. Contrary to Hypothesis M1a, media
experience and information literacy had an opposite direction
interaction effect on ICT adoption behavior. Consistent with Hy-
pothesis M2a, media experience and digital skill had a positive
interaction effect on ICT adoption behavior. Specifically, we found
that the effect of media experience on ICT adoption behavior
increased with a decrease in information literacy. Our results
apparently provide further support for the self-perception experi-
ence, that is, the effect of experience on adoption behavior
increased as information literacy decreased. However, the effects
that media experience had on adoption behavior increased as
digital skill increased.
It is interesting to the analyzed result in model 2; when indi-
vidual ICT media experience is getting richness, the information
literacy is also enhanced. Moreover, some important changes occur
in model 3 shifted from model 2. When the interaction effects
generate by the moderator information literacy, the effect of path
media
technostress
adoption behavior
R2=0.179
0.1
43*
*
0.149**
0.142**
0.1
39
**
del (excluding moderator main effect).
media
technostress
media
experience
R2=0.481
task 
characteristics 
adoption behavior
R2=0.595
media richness
R2=0.518
social 
interaction
R2=0.633
0.0
55*
0.020
0.084**0.6
93
**
0.796**
0.720**
0.0
68
**
digital skill
information 
literacy
0.
456
**
0.
970
**
* p<0.05 ** p<0.01
Fig. 3. Path coefficients for the research model (including moderator main effect).
media
technostress
media
experience
R2=0.481
task 
characteristics
adoption behavior
R2=0.600
media richness
R2=0.518
social 
interaction
R2=0.633
0.0
59*
0.021
-0.0050.6
93
**
0.796**
0.720**
0.0
81
*
digital skill
information 
literacy
0.
64
4*
*
-0
.
241
media technostress
* information literacy 
media technostress
* digital skill
media experience
*information literacy
media experience
*digital skill
-0.045
-0.031
-0.
05
4*
0.2
86*
* p<0.05 ** p<0.01
Fig. 4. Path coefficients for the research model (including interaction effect).
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208204
coefficient on media experience to adoption behavior becomes
insignificant and from positive to negative. The major reason is that
when individual information literacy is higher than the threshold
value of media experience, individual may create and evaluate in-
formation and data misfits in an ethical manner yield IT-included
overload and stress.
In this study, the R2 and Cohen's effect size of the coefficient of
statistical power as indicators of the original research model R2 was
0.600. The sequentially eliminated dependency levels of the media,
task characteristics, social interaction, and media technostress
related to model R2 were 0.594, 0.597, 0.595, and 0.590, respec-
tively. Using the Cohen (1988) effect size criteria (small: 0.02, me-
dium: 0.15 large: 0.35), we found that the factor effect size was
between 0.011 and 0.029, indicating that the moderator effects
were very small.
5.3. Testing the structural model
Recently, Tenenhaus, Vinzi, Chatelin, and Lauro (2005) have
suggested goodness-of-fit (GoF) as a good indicator for measuring
the structural model fitness for PLS pathmodeling. For this research
model, we obtained a GoF value of 0.652, which exceeded the
baseline value of 0.36 for large effect sizes of R2. Thus, with
acceptable model fit, our measures were considered to be appro-
priate for subsequent tests of the causal model and the research
hypotheses. By using the PLS to estimate the path relationship
between every research construct, among total seven path re-
lationships, we found five assumptions reached the significance
level, a ¼ 0.05. The structural model path analysis coefficients for
infusing technology information into community residents'
behavior were as follows: the Task characteristics / Media rich-
ness (0.720); Media richness / Media experience (0.693); Media
richness / Social interaction (0.796); Social interaction/ adop-
tion behavior (0.059); andMedia technostress/ adoption behavior
(0.081). Task characteristics predicted media richness, thus sup-
porting H2. Moreover, media richness had two main effects on
media experience and social interaction, thus supporting H3 and
H4. The results related to the prediction of ICT adoption behavior
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208 205
were consistent with the social interaction and media technostress
hypotheses that were adapted to this context, thus supporting H6
and H7. All estimated standardize path coefficients (significant
paths indicated with an asterisk) are shown in Fig. 4.
The empirical result of interacting effect path relationship be-
tween the moderators showed that information literacy and digital
skill adjusted the level of medium experience to ICT adoption
behavior; as for the variance within variables explanatory power.
Fig. 4 also presents the results of this study with explanatory
powers. The model explains a substantial portion of the variance in
all the endogenous variables: “media richness” for 51.8%, “social
interaction” for 63.3%, “media experience” for 48.1%, and adoption
behavior for 60.0% (see Fig. 4). We also found that the proposed
model explained a significant amount of variation in the endoge-
nous variables (i.e., more than 35% on average). Endogenous vari-
ables showed good explanatory power of variation, which indicated
a construction of model robustness and stability.
6. Discussions
This study showed that ICT media facilitated information ICT
adoption behavior, and that the proposedmodel can lead to a better
theoretical framework by which to understand the intermediation
or disintermediation process taking place among the community
residents (Venkatesh & Sykes, 2013; Venkatesh et al., 2014). The
nature of MRT was theoretically developed and empirically vali-
dated, and this research identified five key constructs of electronic
information intermediation: task characteristics, media richness,
media experience, social interaction, and ICT adoption behavior.
Empirical testing in the rural area community ICT adoption
behavior context showed that these were key dimensions of ICT
media acceptance behavior. On the other hand, the project of
reducing the digital divide in rural villages can be seen as a suc-
cessful project, demonstrating how ICT can be used to support
underprivileged children and the elderly in these areas. Our find-
ings reveal that capturing local knowledge is important but not
sufficient; we extended this work to the context of Taiwan and find
the often studied variables to be fairly predictive even in the new
context.
There are two ways by which interaction can be seen as a very
important characteristic for CMC: ICT serves as a key interactive
medium that supports two-way communication and provides
feedback rapidly and efficientlywithin a global village. Many ICT
devices are designed primarily to facilitate conversations among
individuals and groups, thus ensuring ubiquitous connectivity
among regions through ICT despite variations in political and cul-
tural contexts. Thus, early experiences and social interaction are
driven strongly by medium richness, which is crucial in fostering
the success of digital divide initiatives. Hence, borders for
communication are lessened.
ICT devices give people the ability to access information quickly
and to communicate with others. Consistent with the prior litera-
ture, a higher level of ICT media richness promotes the adoption of
ICT and more frequent use (Brown et al., 2010; Bertot et al., 2010,
2012,; Aesaert et al., 2015), and this variable also leads to higher
social interaction levels (Dennis et al., 2008; Anandarajan et al.,
2010; Van Deursen et al., 2011). In this sense, the important of
ICT richness should be emphasized, because media richness is
positively associated with media experience and social interaction.
In addition, more frequent usage can improve richness of the
communicative medium, because media richness also influences
individuals' media experience, prompting easier integration into
the world of ICT. Consequently, the community behavior of resi-
dents is affected by the medium richness and experience of ICT, but
the nature of task characteristics can only be directly influenced by
media communicative richness.
According to Ghobadi and Ghobadi (2015), ICT can lead to hu-
man interactions that often result in virtual world participation. In
this study, we focus on social interaction because people relate
specifically to sharing information and providing support to other
members of the community to achieve similar goals. The ICT
adoption behavior of peers is influenced by intrinsic motivation; for
instance, others who are already adopting ICT can encourage other
members to further socialize, gain feedback, and initiate discussion,
thus highlighting the importance of “social interaction” in
enhancing ICT adopting behavior (Ayanso et al., 2014; Hernandez
et al., 2011). Additionally, increased civil social interaction has
been identified as a positive determinant of ICT adoption (Brown
et al., 2010; Click & Petit, 2010; van Deursen et al., 2014). This
study goes beyond established notions of social interaction, which
seems highly valuable in the context of ICT-related adoption
behavior research.
In this study, from the media perspective, the task characteris-
tics and ICT media experience have no direct effect on the ICT
adoption behavior of residents in rural communities. Such an
insignificant relationship failed to support MRT (Daft & Lengel,
1986). Contrary to the prior studies based on task characteristics
(Brown et al., 2010; Van Deursen et al., 2011; Purdy&Nye, 2000; Xu
et al., 2010), our results demonstrated that ICT adoption behavior
had no relationship with task characteristics. The users are more
likely to utilize ICT media features when they are better able to do
so for their communicative tasks. With complex tasks, more
frequent communication occurs; furthermore, the more diverse the
ICT media contents, the greater the effect of content on ICT adop-
tion behavior. Using the ICT device to form friendships can be a
positive means of social interaction. Another explanation is that ICT
has been considered to be an efficient communication equipment, a
part of routine life; thus, users do not deliberate on ICT usage to
accomplish certain tasks. People can nowcommunicatewith others
around the world as quickly as possible through ICT media. Such
synchronous communication allows participants to engage in real-
time conversations even when they are in different locations. Sur-
prisingly, this finding indicates that ICT media are chosen by resi-
dents when they face a complexity task.
Meanwhile, we identify media technostress as a means to in-
crease ICT adoption behavior, contrary to the prior literature
(Ayyagari et al., 2011; Tarafdar et al., 2011, 2015, 2010). We further
identify “information literacy” and “digital skills” as potential
means to reduce media technostress. The individual's ability to use
ICT with ease is positively associated with their felicity in accom-
plishing task and usage behavior. Consistent with Shu et al. (2011),
we find that higher dependence on ICT for the completion of
routine tasks and lower levels of digital skill are associated with
higher levels of media technostress. People who hold self-
efficacious digital skills can adapt ICT to challenges more readily
and are more persistent in their pursuit of individual's task.
In addition to extending knowledge in ICT adoption behavior
among rural residents, our study is one of the first to provide a
comprehensivemodel explaining themoderator effect of individual
differences on information literacy and digital skills on ICT use. We
theorized and found support for our model, which demonstrated
significant interaction effects of information literacy and digital
skills on media experience. For the perspective of individual digital
skills, individual media technostress can significantly affect one's
subsequent adoption behavior. Owing to the general lack of levels
of information literacy and digital skills combined with media ex-
periences, people in rural communities often cannot properly
assess the benefits of ICT initiatives.
Moreover, our research model demonstrates the moderating
relationships among media experience, technostress, and ICT
T.-K. Yu et al. / Computers in Human Behavior 71 (2017) 196e208206
adoption behavior, thus providing insights that could drive future
research about the IT artifact and factors that influence user ICT
adoption. However, information literacy and digital skills would
have moderating effects between the degree of media experience
and the community adoption behavior, which also transform the
path of task characteristics and social interaction to community
adoption behavior from a significant effect to an insignificant.
Particularly, when adding interaction effect into the path, the main
effect of individual digital skills to ICT adoption behavior would
become insignificant, indicating that information literacy is the
mainmoderating effect. In order to clarify the moderating effects of
information literacy, we divided the data into three groups: for the
low degree of information literacy, their digital skills moderate the
degree of media experience and community adoption behavior;
individual digital skills can also influence ICT community adoption
behavior; for the medium level of information literacy, digital skills
would moderate the path of media technostress and community
adoption behavior, and moreover, the moderator of information
literacy can affect the path relationship of media experience to
community behavior into a significant relationship.
Our findings revealed that: (1) low information literacy and high
media experience affect ICT adoption behavior negatively, and (2)
low information literacy and highmedia technostress has a positive
influence on ICT adoption behavior. When a digital divide exists, it
is important to keep on investing in information literacy develop-
ment activities for rural communities to help them develop their
ICT competence, although these training initiatives are sensitive to
media experience measures. Relevant in this context is the plea of
van Deursen et al. (2014) who argued that ICT literacy should reflect
the level of ICT competencies of the individuals involved. This
finding not only supports the other studies, which argue that in-
formation literacy is an important factor in new ICT adoption and
increased ICT usage, but also shows that media experiences can be
effective tools in enhancing and reinforcing ICT adoption behavior.
Finally, further research should focus on the two variables
introduced in this study (“information literacy” and “digital skills”),
which are measured as self-reported data. Such anindirect mea-
surement of ICT literacy or digital competency provides relatively
weak self-evaluation of such abilities (Mabe &West, 1982). For this
reason, future research on individual ICT literacy and competency
should try to measure these abilities in a direct and authentic way,
although we attempt to measure individual ICT competences in a
direct way by using complete distinct tasks in our questionnaires.
Future researchers can combine these two direct and indirect
measurement instruments in their new research designs.
7. Conclusion
This study extends the literature by identifying the “MRT,” “in-
formation literacy,” “digital skill” and “ICT adoption behavior”
literature, and proposed a moderating model of the ICT adoption
behavior of residents in a rural village in Taiwan by using structural
equation model to test an empirical model. The present study
provides evidence that adoption of ICT devices behavior and the
relationship between ICT device richness, experience and perceived
ICT media technostress. It provides improvements to the mea-
surement of digital skill and advances understanding of informa-
tion literacy mitigation by considering the role of media experience
and adoption behavior framing. From a practical perspective, un-
derstanding what drives or deters rural community's ICT adoption
behavior can better inform decisions made by information literacy
educators and e-Taiwan policy managers. We hopeful that the
conceptualization model presented in this study serves as an acti-
vator for researches on ICTs and digital divide, and serves a guide
and a call to attract more researches in this area.
Funding
The author thank the Ministry of Science and Technology of
Taiwan for financially supporting this research under contract
MOST: 103-2511-S-507 -001 -MY2.
Conflict of interest
The authors whose names are listed immediately below certify
that they have NO affiliations with or involvement in any organi-
zation or entity with any financial interest (such as honoraria;
educational grants; participation in speakers' bureaus; member-
ship, employment, consultancies, stock ownership, or other equity
interest; and expert testimony or patent-licensing arrangements),
or non-financial interest (such as personal or professional re-
lationships, affiliations, knowledge or beliefs) in the subject matter
or materials discussed in this manuscript.
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