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Difference between Leisure and Work Contexts: The Roles of Perceived Enjoyment and Perceived Usefulness in Predicting Mobile Video Calling Use Acceptance

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There is a rapidly growing body of literature on mobile video calling, which is a promising communication technology; however, little research has focused on user acceptance of mobile video calling, especially in different use contexts. This study explored factors (especially perceived enjoyment) influencing the intention of users to employ video calling in different contexts (a work and a leisure context) by applying the technology acceptance model (TAM) combined with the theory of planned behavior. The revised research model differentiated external factors (subjective norms and personal innovativeness) from internal factors (perceived usefulness, perceived ease of use (PEU), perceived enjoyment, and intention to use mobile video calling). In addition, the current study investigated predictors of perceived enjoyment across these two contexts. With the use of a structured questionnaire, participants were divided in two groups and completed self-report measures related to one context; a total of 386 student respondents’ responses were analyzed. The results indicated that users’ intentions were directly predicted by their perceived enjoyment of video calling (β ≥ 0.35) and the call’s perceived usefulness (β ≥ 0.27) and PEU (β = 0.13, only for the leisure context), which jointly explained at least 55.6% of the variance in use intention. In addition to the effects of these predictors on mobile video calling use acceptance, an assessment of the moderating effects of different contexts indicated that perceived enjoyment played a more important role in influencing intention for the leisure context, while perceived usefulness appeared to be more important for the work context. This study’s findings are important in that they provide strong support for the necessity of distinguishing among different types of contexts when predicting users’ intentions to use video calling. Furthermore, the results showed that perceived enjoyment was most significantly influenced by perceived usefulness (β ≥ 0.61), followed by PEU (β ≥ 0.13). In summary, the roles of core TAM variables (especially perceived enjoyment and perceived usefulness) and of external factors (subjective norms and personal innovativeness) differed between the leisure and work contexts. The implications of these findings are discussed.
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fpsyg-08-00350 March 6, 2017 Time: 15:38 # 1
ORIGINAL RESEARCH
published: 08 March 2017
doi: 10.3389/fpsyg.2017.00350
Edited by:
Radha R. Sharma,
Management Development Institute
(MDI), India
Reviewed by:
M. Teresa Anguera,
University of Barcelona, Spain
Laurent Sovet,
Paris Descartes University, France
*Correspondence:
Ronggang Zhou
zhrg@buaa.edu.cn
Specialty section:
This article was submitted to
Organizational Psychology,
a section of the journal
Frontiers in Psychology
Received: 28 August 2016
Accepted: 23 February 2017
Published: 08 March 2017
Citation:
Zhou R and Feng C (2017) Difference
between Leisure and Work Contexts:
The Roles of Perceived Enjoyment
and Perceived Usefulness
in Predicting Mobile Video Calling Use
Acceptance. Front. Psychol. 8:350.
doi: 10.3389/fpsyg.2017.00350
Difference between Leisure and
Work Contexts: The Roles of
Perceived Enjoyment and Perceived
Usefulness in Predicting Mobile
Video Calling Use Acceptance
Ronggang Zhou*and Caihong Feng
School of Economics and Management, Beihang University, Beijing, China
There is a rapidly growing body of literature on mobile video calling, which is a
promising communication technology; however, little research has focused on user
acceptance of mobile video calling, especially in different use contexts. This study
explored factors (especially perceived enjoyment) influencing the intention of users to
employ video calling in different contexts (a work and a leisure context) by applying the
technology acceptance model (TAM) combined with the theory of planned behavior. The
revised research model differentiated external factors (subjective norms and personal
innovativeness) from internal factors (perceived usefulness, perceived ease of use
(PEU), perceived enjoyment, and intention to use mobile video calling). In addition, the
current study investigated predictors of perceived enjoyment across these two contexts.
With the use of a structured questionnaire, participants were divided in two groups
and completed self-report measures related to one context; a total of 386 student
respondents’ responses were analyzed. The results indicated that users’ intentions
were directly predicted by their perceived enjoyment of video calling (β0.35) and
the call’s perceived usefulness (β0.27) and PEU (β=0.13, only for the leisure
context), which jointly explained at least 55.6% of the variance in use intention. In
addition to the effects of these predictors on mobile video calling use acceptance,
an assessment of the moderating effects of different contexts indicated that perceived
enjoyment played a more important role in influencing intention for the leisure context,
while perceived usefulness appeared to be more important for the work context. This
study’s findings are important in that they provide strong support for the necessity of
distinguishing among different types of contexts when predicting users’ intentions to
use video calling. Furthermore, the results showed that perceived enjoyment was most
significantly influenced by perceived usefulness (β0.61), followed by PEU (β0.13). In
summary, the roles of core TAM variables (especially perceived enjoyment and perceived
usefulness) and of external factors (subjective norms and personal innovativeness)
differed between the leisure and work contexts. The implications of these findings are
discussed.
Keywords: mobile video calling, technology acceptance model, perceived enjoyment, perceived usefulness, work
context, leisure context
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Zhou and Feng Mobile Video Calling Use Acceptance
INTRODUCTION
Mobile value-added services (e.g., web games, chat rooms, e-mail,
and online payments) have become an important resource that
creates revenue for business providers. One of these companies’
aims is to attract new subscribers and retain old ones. With
the development of telecommunication technologies (such as 4G
and LTE) and smart phones, certain new and important mobile
value-added services continue to attract people who use cell
phones to communicate via video calling. For example, Juniper
Research forecasted that video calling (i.e., Skype) users alone
are expected to increase by over 130 million by 2018. This figure
does not include messaging, push-to-talk or audio calling options
(Nay, 2014). Recently, the Facebook Messenger app became a
competitor in the video calling market. This app can be used for
video calls, even those between an iOS user and an Android user.
Facebook indicated that it had 600 million monthly Messenger
users (Alan, 2015). In China, providers have made many attempts
to encourage the use of mobile video calling, but the effect has
been minor (Yang, 2014). In 2013, the Internet Data Center (IDC)
predicted that the proportion of users who use video calling will
not see rapid growth in China; an increase from 3.1% in 2013
to 4.9% in 2018 is expected. Video calling is not a common
occurrence in the daily lives of most users; however, overall,
Chinese Internet users are familiar with video communication
via instant messaging services, such as QQ (Internet Data Center,
2013).
Although telecommunications firms in China have been
committed to solving technical issues to offer better technical
support for the use of video calling, why has this service
not been more widely accepted? With the growing popularity
of smart phones and the development of wireless network
technology in mobile phones, those support technologies can be
realized. Under these circumstances, video calling has naturally
become a new opportunity for telecom service providers to
generate revenue. Information and communication technology
(ICT) firms are trying to capture a competitive edge and
greater market share in video calling and related markets.
However, it is unclear how they can attract customers. Consumer
acceptance is probably the main factor driving the diffusion
of video calling. Video calling is an attractive technology, as
users can make video calls with friends and family during
leisure time and use it for video conferencing during work
hours. Therefore, reliably identifying and understanding factors
affecting consumers’ behavioral intention to use this service in
different contexts has become a key issue.
From the perspective of consumer demand,
telecommunications providers have a vested interest in
understanding the behavior of video call users. A significant body
of theoretical research has focused on media, technology, and
service acceptance behavior by applying the theory of reasoned
action (TRA) (Ajzen and Fishbein, 1980), the theory of planned
behavior (TPB) (Ajzen, 1991), and the technology acceptance
model (TAM) (Davis, 1989). Although there is also a rich and
rapidly growing body of literature on video calling, surprisingly
little research has been conducted based on the TAM. This model
can be used to investigate how life contexts (such as work or
leisure contexts) affect users’ acceptance of new technologies,
such as video calling services. Empirical studies are needed
to explore the predictive effects of factors that are relevant to
video calling acceptance behavior while considering different
contexts. This study proposes a research model that integrates
components of the TPB with the revised TAM to examine the
effects of factors influencing acceptance behavior outcomes for
video calling. Furthermore, this work compares the predictive
effects of perceived usefulness and perceived enjoyment on the
intention to use video calling services, in particular, including
work contexts (i.e., work-related purposes) and leisure contexts
(i.e., chat-related purposes). The outcomes should also benefit
practitioners in the telecom sector by helping them to identify
appropriate marketing strategies to increase the acceptance of
video calling in the future.
LITERATURE REVIEW AND RESEARCH
HYPOTHESES
In the current study, we used the TAM as a theoretical framework,
adding belief elements from the TPB to examine users’ acceptance
of video calling technology in leisure and work contexts. In the
following sections, we review empirical studies focusing on user
acceptance and then identify research hypotheses.
Theoretical Models to Understanding
Users’ Decision to Use Technology
Theories of Reasoned Action and Planned Behavior
(TRA and TPB)
In terms of understanding users’ decision to use technology,
several theoretical models have been widely applied in previous
studies. The earlier related model of the TRA assumes that
beliefs affect behavior; it is a well-known model for predicting
the intention to perform a behavior based on an individual’s
attitudinal and normative beliefs (Fishbein and Ajzen, 1975;
Ajzen and Fishbein, 1980;Ajzen, 1985;Jin, 2014). Then, the
TPB, an extension of the TRA, was proposed to accommodate
new ways of identifying variables (Schifter and Ajzen, 1985;
Ajzen, 1991). Both the TRA and the TPB have been used in
various topics to investigate the influence of personal variables
(e.g., subjective norms, individual traits, and perceived behavioral
control) on behavioral intentions or decision behaviors, such as
the acceptance of the World Wide Web (Klobas and Clyde, 2000)
and the acceptance of mobile technology (Luarn and Lin, 2005).
The two models, especially the TPB, have been employed in a
wide range of behavioral disciplines and proven to be a successful
model for understanding behavior in a variety of situations
(Kang et al., 2006). Additionally, the TRA and TPB provided
a theoretical foundation for the development of the TAM for
investigating users’ decision to use technology or technology-
related products.
Technology Acceptance Model (TAM)
Based on the TRA, Davis et al. (1989) developed the TAM to
predict information technology acceptance and usage behavior.
A variety of studies have confirmed that the TAM is applicable for
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example, the use of various technologies applications (Agarwal,
2000), such as the World Wide Web (Horst et al., 2007),
electronic commerce (Chung et al., 2010), mobile devices (Lin
and Liu, 2009), and technological implants (Pelegrín-Borondo
et al., 2016). Among several applicable theoretical frameworks,
the TAM may be the most suitable theory for studying users
acceptance of new technology in the following aspects. (1) Its
reliability has been demonstrated in various tests (Venkatesh
and Davis, 2000;Jin, 2014). (2) Compared with other theoretical
models used to explain consumer behavior and information
technology adoption, the high generalizability and interactivity of
the TAM facilitate proposing more valuable practical applications
(Davis et al., 1989;Venkatesh and Davis, 2000;Juaneda-Ayensa
et al., 2016). (3) In accordance with specific research aims, the
TAM allows the inclusion of core variables (perceived usefulness,
perceived ease of use (PEU), attitudes and intention to behave
in a certain way, which is the major determinant of actual usage
behavior) (Davis, 1989;Davis et al., 1989;Yi et al., 2006) and
numerous extended measures (e.g., playfulness, subjective norms,
innovativeness, and self-efficacy) for explaining ICT use (Igbaria
et al., 1995;Karahanna et al., 1999;Agarwal and Karahanna, 2000;
Venkatesh and Davis, 2000).
Having comprehensively considered the various theories,
this study generally used the TAM as its basic theoretical
foundation while including the beliefs of the revised TPB as
external variables. Previous research has successfully integrated
the TPB into the TAM to investigate technology acceptance
behavior (Akman and Mishra, 2015). The current study aimed to
investigate how perceived enjoyment and the two original TAM
variables (i.e., perceived usefulness and PEU) influence users
engagement in video calling in different contexts. Several studies
have explored whether the belief-based variables of the TAM are
mediators of its external variables (Venkatesh and Brown, 2001;
Porter and Donthu, 2006); thus, this study also extended the
above core TAM variable by adding external variables from the
TPB, including subjective norms and personal innovativeness.
The follow sections review these variables and issues.
Effects of Components of TAM on Users’
Decision and Proposed Research Model
According to the TAM and the TPB, several important internal
and external factors affect consumers’ intention to adopt video
calling services: perceived usefulness, PEU, perceived enjoyment,
subjective norms, and personal innovativeness. Furthermore,
different contexts of use should be considered a crucial factor to
be investigated.
Standard Variables: Perceived Usefulness, Perceived
Ease of Use and Perceived Enjoyment
Perceived usefulness and PEU, as the TAM’s primary factors,
determine users’ acceptance or rejection of ICT (Davis, 1989).
Perceived usefulness is defined as “the degree to which a person
believes that using the particular technology would enhance
his/her job performance.” PEU is defined as “the extent to which
a person believes that using a technology is free of effort.”
Perceived usefulness and PEU jointly determine attitudes toward
usage behavior or directly predict behavioral intention (Davis,
1989). Perceived usefulness also mediates the effect of PEU
on behavioral intention. Perceived usefulness can predict the
behavioral intention to use, which directly affects actual usage
behavior. These two attitudinal factors (perceived usefulness and
PEU) are always considered as extrinsic motivation for usage
intention (Davis and Wiedenbeck, 2001). Since the original
model of TAM was proposed, the effects of perceived usefulness
and PEU on users’ acceptance of technology have been supported
by a large body of research. From an instrumental perspective,
performing a behavior is considered a means to achieve other
goals or to gain other valued outcomes (Venkatesh, 2000). In this
respect, the decision to use mobile video calling can be predicted
by perceived usefulness.
However, previous studies have indicated that perceived
usefulness and PEU cannot fully explain consumers’ behavioral
intentions to use a new technology (Venkatesh and Davis,
2000;Chen et al., 2002;Bruner and Kumar, 2005;Liao and
Tsou, 2009;Jin, 2014). Researchers have found that technology
usage is affected by both extrinsic motivation (i.e., usefulness)
and intrinsic motivation (i.e., enjoyment). Perceived enjoyment
has been defined as “the extent to which the activity of
using the computer is perceived to be enjoyable in its own
right, apart from any performance consequences that may
be anticipated” (Davis and Wiedenbeck, 2001). With more
“hedonic” service or technology use acceptance addressed by
TAM (e.g., Van der Heijden, 2004), perceived enjoyment has
been confirmed to have a significant influence on users’ intention
to use technology. A number of studies have indicated that
enjoyment is a particularly powerful predictor of use decision
for technologies such as Facebook (Quan-Haase and Young,
2010), Sina Weibo (Wang et al., 2016), mass media (Nabi and
Krcmar, 2004;Ledbetter et al., 2016), the telephone (O’Keefe
and Sulanowski, 1995), websites (Van der Heijden, 2003), online
shopping (Childers et al., 2001), and social networking sites
(Chuang et al., 2017). Although we found no research on
mobile video calling use acceptance in terms of perceived
enjoyment, some previous studies have addressed the issue
for other interpersonal communication media or technologies,
such as instant messaging (Li et al., 2005;Lu and Xu, 2006).
Several theoretical perspectives assert that enjoyment “might
be the most basic motivation to consume any communication
media” (Sherry, 2004;Griffin et al., 2015). Lu and Xu (2006)
proposed three additional variables based on the TAM, including
an “immersion” experience, perceived enjoyment and privacy.
Then, they conducted a study on instant communication
usage behavior, which examined whether behavioral intention
was a function of perceived usefulness, perceived enjoyment,
“immersion” experience, privacy or attitude. They concluded that
perceived enjoyment has a direct effect on behavioral intention.
All these findings imply that the intrinsic motivational factor,
enjoyment, may also play a significant role in acceptance of
mobile value-added services, such as video calling.
Another important issue is related to the relationship among
perceived enjoyment, perceived usefulness, and PEU. In the
original version of the TAM, generally perceived usefulness has
a more powerful effect than PEU in predicting behavioral use
intention, and perceived usefulness also mediates the effect of
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PEU on user acceptance (Davis, 1989). Some previous studies
intended to focus on the association among perceived enjoyment,
perceived usefulness, and PEU (e.g., Davis et al., 1992;Van der
Heijden, 2004;Sun and Zhang, 2006). However, no consistent
results can be concluded based on previous studies. On the one
hand, perceived enjoyment has been shown to have a significant
influence on predicting the related constructs of perceived
usefulness and PEU (e.g., Venkatesh, 2000;Teo and Noyes, 2011).
On the other hand, some studies have indicated that perceived
usefulness and PEU had a more significant effect on perceived
enjoyment (e.g., Van der Heijden, 2004;Liao et al., 2008). Sun and
Zhang (2006) reviewed the relevant literature and investigated the
causal relationship between perceived enjoyment and PEU. Their
findings indicated that the direction of perceived enjoyment
influenced the PEU, which outweighed the opposing direction
for utilitarian systems. However, when perceived enjoyment was
considered to explain users’ technology use decisions, some
researchers removed the construct of PEU, and their findings
tended to support that the influence of perceived enjoyment on
behavioral intention outweighs the predictive effect of perceived
usefulness on behavioral intention (e.g., Verkasalo et al., 2010;
Lin and Lu, 2011). Although mobile video calling can be
used for both work and leisure goals, we tend to consider
it as an individual hedonic service. Thus, the influences of
perceived usefulness and PEU on perceived enjoyment were
examined.
Collectively, regarding mobile video calling use acceptance,
the above concerns led to the following hypotheses:
H1. Perceived usefulness would have a positive influence on
the perceived enjoyment of video calling usage in addition
to having a direct impact on users’ behavioral intention to
use mobile video calling.
H2. Perceived ease of use would have a positive influence on
the perceived enjoyment of video calling usage.
H3. Perceived enjoyment would have a positive influence on
users’ behavioral intention to adopt mobile video calling.
H4. Perceived usefulness and perceived enjoyment would
jointly determine the behavioral intention to use mobile
video calling.
Extended Variables: Subjective Norms and Perceived
Innovativeness
According to the TRA and TPB, subjective norms reflect how
consumers are affected by their perception that those who
are important to them think that they should (or should
not) perform a given behavior (Warner and DeFleur, 1969;
Fishbein and Ajzen, 1973;Schofield, 1975;Hasan et al., 2016;
Wei et al., 2016). As the common component of the TRA
and the TPB, the positive effect of subjective norms on
users’ behavioral intentions has been validated by numerous
studies. For example, Peace et al. (2003) studied software
piracy technology intentions using a model based on the TRA,
the TPB, expected utility theory and deterrence theory; they
found that subjective norms were a significant predictor of
the intention to illegally copy software technology. Venkatesh
et al. (2003) used a unified view to consider user acceptance
of information technology, and their research suggested that
subjective norms significantly influenced behavioral intentions
in mandatory environments (such as work contexts). Hsu
and Lu’s (2004) research also indicated that subjective norms
and attitudes together explained 80% of variance in network
game technology users’ behavioral intentions. These cumulative
results suggested that users perceiving much greater approval
and support from the people around them can increase user
acceptance decisions.
Another important individual variable is personal
innovativeness, which is defined as an individual’s willingness
to try out any new technology (Agarwal and Prasad, 1998).
Personal innovativeness was found to also positively affect
individuals’ perceptions of new information technologies (Lewis
et al., 2003;López-Nicolás et al., 2008). Some previous studies
indicated that personal innovativeness has an influence on
users’ attitudes toward accepting new technologies. Rogers
(1971) believed that personal innovativeness could predict users’
attitudes toward acceptance of new technology; users with a high
level of personal innovativeness were usually earlier to accept
new technology. Herrero Crespo and Rodríguez del Bosque
(2008) identified effects of personal innovativeness on the
acceptance of e-commerce. Their results indicated that electronic
commerce acceptance is determined by attitudes (or emotional
evaluations) toward the system, subjective norms and personal
innovativeness in the domain of information technology. Yang
et al. (2012) argued that the ability of personal innovativeness
to predict users’ acceptance of new technology is influenced by
the environment. Regarding the association between perceived
enjoyment and personal innovativeness, some people may have
doubts and resistance to using new technology, while others
can perceive enjoyment from new technology. Those with a
high level of personal innovativeness may tend to more easily
derive enjoyment and satisfaction from using new technology.
However, the relationship between personal innovativeness and
perceived enjoyment needs more understanding. Therefore, in
this study, we considered the effects of personal innovativeness
on the acceptance of video calling technology.
Since the role of perceived enjoyment in the acceptance
of video calling was investigated in this study, the predictive
effects of subjective norms and personal innovativeness on
using this mobile service may also be moderated through
perceived enjoyment. Thus, when considered as a whole, TAM-
based studies indicate that the above-mentioned variables from
the TAM and the TPB should significantly affect consumer
acceptance and usage of video calling. That is, the external
variables are strongly interrelated according to the TAM.
The corresponding hypotheses regarding subjective norms and
personal innovativeness that were tested in this study are as
follows:
H5. Subjective norms positively would affect users’ perceived
enjoyment and use decisions regarding mobile video
calling.
H6. Personal innovativeness positively would affect users’
perceived enjoyment and use decisions regarding mobile
video calling.
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Influence of Use Context on Video
Calling Use Decisions
Most previous empirical studies based on the TAM were based
on voluntary contexts, and all the participants using the new
technology were voluntarily participating in leisure contexts
(Davis et al., 1989). However, whether their conclusions were
applicable to the condition of forced work remained to be
explored.
The core variables (e.g., perceived usefulness, PEU, and
perceived enjoyment) in the TAM are consistent with basic
measures of user experience. For example, to consolidate
the definitions in usability community, the International
Organization for Standardization (ISO) defined usability as “the
extent to which a product can be used by specified users to achieve
specified goals with effectiveness, efficiency and satisfaction in a
specified context of use.” That is, user perception and acceptance
of technology can mean different things to different people in
different specific use contexts. In an earlier study, Davis et al.
(1989) also indicated the importance of use contexts with the
use of the TAM framework. They explored users’ acceptance
of WriteOne, which is a type of human-computer interaction
technology, and noted a limitation of their study, i.e., all users
were in a leisure context in which there was no compulsion
to use the technology. They admitted that they ignored the
context variable and emphasized the necessity of discussing the
influence of different contexts when exploring users’ acceptance
of new technologies. After that, many extended TAM researchers
investigating users’ acceptance of new technologies began to take
usage contexts into consideration (Moore and Benbasat, 1991;
Hartwick and Barki, 1994;Van der Heijden, 2004).
As a motivational variable, users’ enjoyment perception
tends to be more dependent on use context. Some previous
studies considered perceived enjoyment to be a focal aspect
of entertainment media usage because individuals consume
these media primarily to seek fun or pleasure (Vorderer et al.,
2004;Pe-Than et al., 2014). However, beyond the entertainment
context, perceived enjoyment was also found to have a substantial
impact on users’ behavioral intentions regarding task-oriented
applications, such as online information sharing (Li et al., 2005;
Kim et al., 2009;Pe-Than et al., 2014). Bruner and Kumar
(2005) focused on Palm Internet equipment in an empirical study
employing the revised TAM model in the consumer context.
The influencing factors were divided into two types: practical
factors and interest factors. The results indicated that in leisure
contexts (differing from the results for work-related contexts),
the effect of perceived enjoyment on behavioral intention was
more significant than that of perceived usefulness, which had no
significance. In fact, in their research on acceptance of the World
Wide Web, Moon and Kim (2001) assert that attitudes can be
influenced by situational factors and the interaction between an
individual and a given situation.
Clearly, the motivations and feelings of users who use these
technologies in different contexts differ. Correspondingly, the
degree to which different factors influence customers’ acceptance
of these technologies will also vary. As a new type of information
communication technology, video calling has characteristics
oriented toward both entertainment and work. Additionally,
potential users can either use cell phones to make video calls
with friends and family during their leisure time or for video
conferencing during work time, implying that the degree to
which the varying factors affect users’ behavioral intentions may
differ in different contexts. However, few previous studies related
to video calling have focused on investigating how different
situations affect users’ video calling acceptance based on the
TAM. Therefore, this study proposed considering the different
contexts (work contexts and leisure contexts) to investigate the
ways in which different contexts influence users’ acceptance
of video calling technology. The corresponding hypothesis was
proposed as follows:
H7. The degree to which perceived enjoyment affects
behavioral intentions to use mobile video calling would
vary in different user contexts, i.e., work contexts and
leisure contexts.
Thus, these proposed hypotheses generated the research
model described in Figure 1.
MATERIALS AND METHODS
Materials
Video Calling Use Context
Following a method used in previous studies adopting the
TPB that encourages respondents to answer questions in a
relatively natural way, two potential use contexts were generated,
both including the same written introduction regarding video
calling services. One context depicted a situation in which the
respondents use video calling to contact a colleague for work
purposes. The second context depicted a situation in which the
respondents used video calling to contact a friend for leisure
purposes.
The written introduction read as follows:
Since the issuing of 4G licenses and the completion of 4G
networks, 4G mobile value-added services have become
increasingly popular, including “video calling,” which is
one of the typical services offered by telecommunications
businesses. As the name suggests, cell phone video calling
services enable both sides of the conversation to see each
other smoothly and clearly in real time. Currently, when
using a mobile phone to make video calls, both sides need
to place a cell phone in front of them so that the phone’s
camera can relay images of both users.
The contexts were as follows:
Work Context
Suppose you currently have a position in a company located
in a city that has established a 4G network and that you
are currently using a 4G mobile phone that is able to
support video calls. It is a weekday morning, and you are
working. You need to use the phone to call a colleague to
address a work-related issue. Your colleague’s cell phone
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FIGURE 1 | Proposed research model.
also supports video calls. When you call, you can choose
conventional talk mode (i.e., non-video call) or video call
mode.
Leisure Context
Suppose you currently have a position in a company located
in a city that has established a 4G network and that you are
currently using a 4G mobile phone that is able to support
video calls. It is a weekend morning, and you are looking
over photos you have previously taken. At this point, you
remember that it is time for you to contact a friend, and
you are ready to use the cell phone to call him or her to
say hello. Your friend’s cell phone also supports video calls.
When you call, you can choose conventional talk mode (i.e.,
non-video call) or video call mode.
Questionnaire
A self-administered questionnaire was developed based on
existing scales, using minor wording modifications to fit items
into the two different contexts. Following each context, and
in line with previous studies on mobile service use decisions,
participants responded to a series of items constructed based
on an extended TAM questionnaire (i.e., behavioral intention,
perceived usefulness, perceived enjoyment, and PEU). All
items designed for the above measures were presented to the
respondents in random order. Following the context-specific
questions, respondents responded to questions about subjective
norms and personal innovativeness (again, items were presented
in random order), and they completed several demographic
measures (e.g., age and gender). Respondents rated each item
measuring the standard and extended components of the TAM
on 7-point unipolar scales ranging from 1 (a negatively scored
statement) to 7 (a positively scored statement).
Behavioral intention
Behavioral intention was assessed by calculating the mean score
of the following four items (Davis, 1989;Venkatesh and Davis,
2000;Chen and Chen, 2009): “In the future, the likelihood that
I will use video calling for this situation is (very unlikely to very
likely)” (BI1); “In this context, I would expect to use video calling”
(very unlikely to very likely) (BI2); “In this context, the likelihood
that I will use video calling is (very unlikely to very likely)” (BI3);
and “In the future, I will use video calling for this situation” (very
unlikely to very likely) (BI4).
Perceived usefulness
Following a method used in previous studies (Davis, 1989;
Venkatesh and Davis, 2000;Chen and Chen, 2009), the mean
score of the following three items was used to measure perceived
usefulness: “In this context, using video calling would help me to
communicate with others” (strongly disagree to strongly agree)
(PU1); “In this context, using video calling would help me
to improve the efficiency of mobile communication” (strongly
disagree to strongly agree) (PU2); and “In this context, using
video calling is useful for me” (strongly disagree to strongly agree)
(PU3).
Perceived enjoyment
Previous studies have also examined the perceived enjoyment of
the use of mobile-data services. In line with these studies (Ragheb
and Beard, 1982;Van der Heijden, 2003, 2004), the mean score
of the following three items was used as a measure of perceived
enjoyment: “In this context, the use of video calling provides
me with enjoyment” (very unlikely to very likely) (PE1); “In this
context, the use of video calling would be a good experience
for me” (strongly disagree to strongly agree) (PE2); and “In this
context, using video calling is pleasurable” (strongly disagree to
strongly agree) (PE3).
Perceived ease of use
In line with previous studies using the TAM framework (Davis,
1989;Venkatesh and Davis, 2000;Chen and Chen, 2009), a direct
aggregate measure was used to assess PEU. The mean score of
the following three items was used as a measure of PEU: “In
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this context, it would be easy for me to become skilled at using
video calling” (strongly disagree to strongly agree) (PEU1); “In
this context, learning to use video calling would be easy for me”
(strongly disagree to strongly agree) (PEU2); and “In this context,
the use of video calling is (very difficult to very easy) for me”
(PEU3).
Subjective norms
Previous studies have also examined subjective norms. In line
with these studies (Taylor and Todd, 1995;Agarwal and Prasad,
1998), the mean score of the following two items was used as
a measure of subjective norms: “In this context, people who
influence my behavior would think that I should use video
calling” (very unlikely to very likely) (SN1) and “In this context,
I am expected to use video calling by the other person on the
phone” (very unlikely to very likely) (SN2).
Personal innovativeness
Following a method used in previous studies (Hurt et al., 1977;
Agarwal and Prasad, 1998), the mean score of the following three
items was used to measure personal innovativeness: “If I heard
about a new information technology, I would look for ways to
experiment with it” (very unlikely to very likely) (PI1); “Among
my peers, I am usually the first to try out new information
technologies” (very unlikely to very likely) (PI2); and “I like to
experiment with new information technologies” (very unlikely to
very likely) (PI3).
Demographic measures
Demographic information, including age, gender, city of
residence, specialized subject of study, service provider, and
expenditure on mobile services per month, was also collected.
Participants and Data Collection
In terms of gender and city of residence, the respondents
were approximately balanced. A total of 386 students who
were potential users of video calling participated in the
study; however, 18 (4.7%) were eliminated due to incomplete
or non-sensical responses. The subjects of the survey were
university students from Beijing and Beihai, China. The two
context-specific questionnaires were randomly distributed to the
participants. Nearly half of the respondents from each city were
asked to complete the questionnaire about the work context,
and the other half from each city were asked to complete
the questionnaire about the leisure context. Among the 368
respondents who provided valid responses, 182 respondents
answered the questions regarding the work context (98 and 84
students were sampled from Beijing and Beihai, respectively), and
the remaining 186 respondents answered the questions regarding
the leisure context (101 and 85 students were sampled from
Beijing and Beihai, respectively). Thus, the responses to each
context-specific questionnaire were nearly equal. For the 368
valid responses, the subjects were between the ages of 17 and
26 years, with an average age of 20.9 years (SD =1.5); 188
respondents were male (51.1%), and 180 were female (48.9%). In
terms of specialized subject of study, 19.6% of the respondents
reported that they specialized in human and social sciences;
those studying some type of art accounted for only 3.3%.
Most of the respondents specialized in management (38.6%) or
science and technology (37.8%). Regarding service providers,
most subjects reported using China Mobile (90.5%), while China
Unicom and China Telecom represented only 4.6 and 3.8%
of reported providers, respectively. In terms of expenditure on
mobile services per month, almost 12.8% of users spent less than
30 Yuan; 34.8% spent between 30 and 50 Yuan; 26.9% spent
between 31 and 70 Yuan; and the remainder (25.5%) spent more
than 70 Yuan.
Each respondent was approached randomly by a trained
interviewer at a campus location, such as a library or classroom.
The study protocol was approved by the Human Research
Ethics Committee in School of Economics and Management
at Beihang University. All the respondents were asked to read
a written introduction to the research and the format of the
questionnaire, and they then completed an informed consent
form, followed by the questionnaires. The respondents were
assured that their participation was voluntary and that their
responses would be anonymous. For those who agreed to
participate, the questionnaire took approximately 10 min to
complete.
RESULTS AND ANALYSIS
Measurement Model
To analyze the measurement validity, we used AMOS 21.0 to
examine the core TAM components in the research model,
i.e., a four-factor structure including behavioral intention,
perceived enjoyment, perceived usefulness, and PEU. Following
similar, previously conducted studies (e.g., Jung et al., 2009),
seven common model-fit measures were used to estimate the
measurement model’s fit. As shown in Table 1, all of the
model-fit index values satisfied or exceeded their respective
common acceptance levels. Therefore, it was concluded that the
measurement model had a good fit with the collected data.
In addition to the model fit, we tested the validity and
reliability of the measurements by verifying the convergent and
discriminant validity of the core scales. As shown in Table 2,
according to Tabachnick and Fidell (2007), the questionnaire
items had high levels of convergent validity because the item
loadings ranged from 0.71 to 0.86 and were all higher than 0.55.
The higher the loading value is, the more accurate the item is as
a measurement of the construct. A loading value above 0.55 is
acceptable for interpreting the construct (Comrey and Lee, 1992).
In addition, the lowest average variance extracted (AVE) among
all the components was 0.588, which exceeded the requirement
of 0.50 and demonstrated that the data had good convergent
validity.
According to Fornell and Larcker (1981), if the AVE value
is above 0.50 and the composite reliability (CR) value is above
0.70, then the reliability is acceptable. Table 2 shows that all the
squared multiple correlations (SMCs) of the measured variables
were higher than the criterion (0.50) and that the CR values for all
the constructs were above the recommended level of 0.70. These
results indicate that the scales had good reliability. Fornell and
Larcker (1981) also suggested that discriminant validity can be
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verified when the square root of the AVE (the diagonal elements
in Table 3) for each construct is higher than its correlations with
the other constructs. Table 3 shows that all of the square roots
of the AVE for each construct were higher than the correlations,
indicating that the discriminant validity was acceptable. Overall,
the validity and reliability of the questionnaire were acceptable.
TABLE 1 | Fit indices for the measurement model.
Fit indices Recommended Result
value
χ2/d. f. (chi-square/degrees of freedom) <3 2.314
GFI (goodness of fit index) >0.9 0.948
RMSEA (root mean square error of approximation) <0.08 0.060
RMR (root mean square residual) <0.08 0.052
NFI (normed fit index) >0.9 0.950
NNFI (non-normed fit index) >0.9 0.961
CFI (comparative fit index) >0.9 0.971
TABLE 2 | Standardized factor loadings, SMC and CR for the TAM core
variable questionnaires.
Construct Item Item loading SMC CR AVE
Behavioral intention BI1 0.728 0.531 0.866 0.618
BI2 0.860 0.740
BI3 0.807 0.651
BI4 0.743 0.552
Perceived enjoyment PE1 0.716 0.512 0.827 0.615
PE2 0.810 0.656
PE3 0.822 0.676
Perceived usefulness PU1 0.730 0.532 0.810 0.588
PU2 0.750 0.563
PU3 0.817 0.667
Perceived ease of use PEU1 0.710 0.504 0.824 0.611
PEU2 0.847 0.717
PEU3 0.781 0.610
Descriptive Statistics and Analysis
The means and zero-order correlation coefficients for the various
measures, organized by context type (work or leisure), are shown
in Table 3. Regarding the different contexts considered overall,
the means of the scales indicate that respondents had a slightly
positive intention to use video calling (M=4.83), perceived the
behavior to be somewhat enjoyable (M=4.89), perceived this
behavior as being of moderate usefulness (M=4.73), perceived
that the service should be easy to use (M=5.10), felt social
approval from important others (M=5.04), and reported a
medium degree of personal innovativeness (M=4.43). All of
the research model variables were found to positively correlate
with each other in both situations. Gender was not associated
with most of study variables, with the exception of negatively
correlating with PEU in the work context.
To validate the study’s hypotheses, one-way analyses of
variance were used to test whether the intention to use, along
with other TAM variables, differed between the contexts (work
and leisure). Compared with the work context, respondents were
more likely to make a video call [F(1,367) =4.51, p<0.05,
η2=0.01], perceived that the technology should be more
enjoyable [F(1,367) =7.56, p<0.05, η2=0.02] and perceived
that video calling should be easier to use [F(1,367) =5.69,
p<0.05, η2=0.02] in the leisure context. There were no
significant differences in perceived usefulness [F(1,367) =2.96,
p>0.05, η2=0.01], subjective norms [F(1,367) =2.78, p>0.05,
η2=0.01], or personal innovativeness [F(1,367) =0.71, p>0.05,
η2=0.002] between the work and leisure contexts.
Predicting Users’ Intention to Use Video
Calling
The results reflecting the relationship between the standard TAM
components and behavioral intention (obtained by regressing
the predictive components on intention to use mobile video
calling) are displayed in Table 4. This table shows the results
of a series of hierarchical multiple linear regression analyses
TABLE 3 | Descriptive statistics and zero-order correlations between the study variables: work and leisure contexts.
Variable 1 2 3 4 5 6 GenderaMean SD
Work context (n=182)
(1) Behavioral intention (0.87) 0.68∗∗∗ 0.70∗∗∗ 0.34∗∗∗ 0.28∗∗∗ 0.23∗∗ 0.01 4.71 1.06
(2) Perceived enjoyment (0.81) 0.73∗∗∗ 0.39∗∗∗ 0.36∗∗∗ 0.22∗∗ 0.04 4.74 1.02
(3) Perceived usefulness (0.80) 0.33∗∗∗ 0.28∗∗∗ 0.19 0.01 4.64 1.06
(4) Perceived ease of use (0.77) 0.35∗∗∗ 0.27∗∗∗ 0.19∗∗ 4.98 0.85
(5) Subjective norms (0.68) 0.22∗∗ 0.02 4.99 0.95
(6) Personal innovativeness (0.70) 0.11 4.52 0.98
Leisure context (n=186)
(1) Behavioral intention (0.86) 0.73∗∗∗ 0.68∗∗∗ 0.49∗∗∗ 0.50∗∗∗ 0.45∗∗∗ 0.09 4.94 1.02
(2) Perceived enjoyment (0.83) 0.77∗∗∗ 0.53∗∗∗ 0.45∗∗∗ 0.42∗∗∗ 0.11 5.03 0.97
(3) Perceived usefulness (0.82) 0.45∗∗∗ 0.38∗∗∗ 0.38∗∗∗ 0.10 4.82 1.02
(4) Perceived ease of use (0.85) 0.46∗∗∗ 0.34∗∗∗ 0.10 5.21 0.97
(5) Subjective norms (0.75) 0.28∗∗∗ 0.09 5.08 1.07
(6) Personal innovativeness (0.77) 0.11 4.34 1.07
Internal consistency estimates (Cronbach’s alpha) are in parentheses on the diagonal. aGender (1 =male, 2 =female). p<0.05, ∗∗p<0.01, ∗∗∗ p<0.001.
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that were used to assess, for each context, the contributions of
standard and extended components of the TAM to the prediction
of behavioral intention. Correlational analyses and analyses of
variance indicated that gender was not a significant variable for
behavioral intention; therefore, it was not included as a predictor.
For each of the two contexts, the key predictors of respondents’
intention to use the service were identified by first regressing the
original TAM components (perceived usefulness and PEU) on
behavioral intention, and then perceived enjoyment was added
to the regression model. In the last step, the extended TAM
variables (subjective norms and personal innovativeness) were
added to the three-step hierarchical regression analysis. In this
way, it was possible to assess the predictive utility of each variable
after controlling for the influence of other variables.
Considering the leisure context, in step 1, the two original
TAM predictors (i.e., perceived usefulness and PEU) were able
to explain 50.20% of the variance in behavioral intentions
[F(2,183) =92.23, p<0.001], with both original predictors
having a significant influence on predicting the intention to use
video calling. In step 3, perceived enjoyment, when added to the
regression analysis, was able to explain an additional 7.5% of
the variance, resulting in a significant increase to 57.7% [Fchange
(1,182) =32.40, p<0.001], and all three TAM variables emerged
as significant predictors (especially perceived enjoyment and
perceived usefulness). In step 3, the extended TAM variables were
able to explain an additional 3.8% of the variance in using video
calling [Fchange (2,180) =8.92, p<0.001], with subjective norms
and personal innovativeness having a moderately limited but
statistically significant independent effect, along with perceived
enjoyment and perceived usefulness. In this step, the influence of
PEU on behavioral intention disappeared.
In the work context, in step 1, the original TAM variables
were able to explain 50.1% of the variance in mobile video calling
[F(2,179) =89.96, p<0.001], with both variables (especially
perceived usefulness) emerging as significant predictors. When
added to the regression analysis in step 2, perceived enjoyment
resulted in a substantial increase to 55.6% in the variance in
using mobile video calling [1R2=5.5%, Fchange (1,178) =21.98,
p<0.001], with perceived enjoyment and perceived usefulness
emerging as very significant predictors. The effect of PEU
weakened to the point of non-significance. In step 3, when the
extended TAM variables were added to the regression equation,
they were able to explain only an additional 0.40% of the variance
[F(2,176) =0.73, p>0.05]. Subjective norms and personal
innovativeness did not emerge as significant predictors along
with PEU.
Overall, in the context of work, perceived usefulness was
a strong determinant of intention to use video calling, and
perceived enjoyment was a significant secondary determinant. By
contrast, for potential leisure usage, perceived enjoyment was the
strongest predictor and perceived usefulness become a secondary
predictor. In addition, the context mediated the predictive power
of PEU and the two extended variables (subjective norms and
personal innovativeness), which tended to have a moderate or
small independent effect on the intention to use video calling in
the leisure situation only.
Predicting Users’ Perceived Enjoyment
of Video Calling
To address the effects of the two original TAM variables and
the two extended TAM variables on perceived enjoyment and
to test the corresponding hypothesis, a two-step hierarchical
regression analysis was conducted to investigate the utility of the
predictors, especially perceived usefulness and PEU, for perceived
enjoyment. For each of the two contexts, perceived usefulness
and PEU were entered in step 1, and the extended variables
of subjective norms and personal innovativeness were added in
step 2. By controlling for the influence of other variables, this
approach allowed us to test the associations between perceived
enjoyment and other study variables. The results are summarized
in Table 5.
As shown in Table 5, in step 1, the two original TAM variables
resulted in substantial and significant counts, representing 63.0%
and 55.4% of the observed variance in the leisure and work
contexts (F110.99, p<0.001), respectively. Perceived
usefulness (β0.66, p<0.001) and PEU (β0.17, p<0.01)
emerged as very significant predictors in both contexts. For both
contexts, as presented in step 2, the extended TAM variables,
when added to the regression equation, only increased the
TABLE 4 | Hierarchical regression analyses: predicting intention to use mobile video calling.
Step and predictor Leisure context (n=186) Work context (n=182)
Step 1 βStep 2 βStep 3 βStep 1 βStep 2 βStep 3 β
(1) Perceived ease of use 0.23∗∗∗ 0.130.06 0.120.06 0.04
Perceived usefulness 0.57∗∗∗ 0.27∗∗∗ 0.25∗∗ 0.66∗∗∗ 0.43∗∗∗ 0.42∗∗∗
(2) Perceived enjoyment 0.45∗∗∗ 0.38∗∗∗ 0.35∗∗∗ 0.34∗∗∗
(3) Subjective norms 0.18∗∗ 0.01
Personal innovativeness 0.130.06
R20.502 0.577 0.615 0.501 0.556 0.560
1R20.502 0.075 0.038 0.501 0.055 0.004
Fchange 92.23∗∗∗ 32.40∗∗∗ 8.92∗∗∗ 89.96∗∗∗ 21.98∗∗∗ 0.73
Degree of change freedom (2,183) (1,182) (2,180) (2,179) (1,178) (2,176)
p<0.05, ∗∗ p<0.01, ∗∗∗ p<0.001.
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TABLE 5 | Hierarchical regression analysis: predicting perceived
enjoyment.
Step and predictor Leisure context
(n=186)
Work context
(n=182)
Step 1 βStep 2 βStep 1 βStep 2 β
(1) Perceived usefulness 0.66∗∗∗ 0.61∗∗∗ 0.67∗∗∗ 0.64∗∗∗
Perceived ease of use 0.23∗∗∗ 0.17∗∗ 0.17∗∗ 0.13
(2) Subjective norms 0.100.13
Personal innovativeness 0.100.03
R20.630 0.647 0.554 0.570
1R20.630 0.017 0.554 0.017
Fchange 155.90∗∗∗ 4.41110.99∗∗∗ 3.42
Degree of change freedom (2,183) (2,181) (2,179) (2,177)
p<0.05, ∗∗ p<0.01, ∗∗∗ p<0.001.
variance by an additional 1.7%, and subjective norms emerged
as a weak significant predictor (β0.10, p<0.05) (F3.42,
p<0.05). However, personal innovativeness tended to be a
weak significant predictor in the leisure context only (β=0.10,
p<0.05). All of these findings indicated that perceived usefulness
had a greater influence on mobile video calling than PEU.
Furthermore, there were limited differences in the degree to
which various factors affected behavioral intentions for different
contexts (the work and leisure contexts), except for the influence
of personal innovativeness.
DISCUSSION
The goals of this research were to examine the factors influencing
users’ intention to use video calling (especially the effect
of perceived enjoyment on users’ intention to adopt mobile
video calling), as well as to distinguish the influence of
different contexts (the work context and the leisure context) on
predicting user acceptance of this service. The data collected
from participants were utilized to test the proposed research
model. The results showed that users’ intentions were directly
determined by perceived usefulness and perceived enjoyment,
and the proposed research model explained 56.0% and 61.5%
of the total variance in users’ intentions in the work and leisure
contexts, respectively. In addition, there were some context-
specific differences in predicting user acceptance of mobile video
calling.
Predicting Effects of Perceived
Usefulness and Perceived Enjoyment on
Video Calling Use Decisions
From the perspective of user acceptance, we examined the effects
of perceived usefulness, PEU, perceived enjoyment, subjective
norms, and personal innovativeness on mobile video calling
via the proposed research model. The results of this research
indicated that perceived enjoyment could be considered a core
predictor along with perceived usefulness in predicting users’
intentions toward mobile video calling, especially for the leisure
context.
In line with our expectations, hypotheses 1 and 3 were
confirmed. Perceived enjoyment and perceived usefulness
emerged as significant predictors of the intention to use mobile
video calling. This finding is consistent with the findings of
prior TAM-based studies (e.g., Lu and Xu, 2006) indicating
that the predictive effect of perceived enjoyment on behavioral
intention is direct. Additionally, our results are in agreement
with those of prior TAM-based studies of ICT, indicating that
perceived usefulness is a strong predictor of acceptance intention
in TAM (Davis, 1989). In general, perceived usefulness is a
cognitive belief, while perceived enjoyment tends to reflect users’
feelings in both pre-acceptance and post-acceptance. Although
the predictive power of these two factors (perceived usefulness
and perceived enjoyment) varies across contexts, the associations
between them and video call users’ acceptance are strong.
Regarding practical implications, perceived enjoyment, as
an added factor in the basic structure, exerted a significant
effect on users’ intention to use mobile video calling in
both contexts. Therefore, to enhance consumers’ behavioral
intention to adopt mobile value-added services, such as video
calling, perceived enjoyment should be the primary focus. This
finding suggests that in a specific context, users’ motivation
to use a mobile value-added service is derived from the
enjoyment of using the service. Mobile video calling services
offer visual and interactive functions, including the convenience
of enabling people on both sides to watch dynamic images on
the screen and experience the good feelings that result from
communicating. Furthermore, these services offer the benefit of
being cost-effective for potential customers. These rich features
of mobile value-added service can provide customers with
a more pleasant experience. Therefore, the entertaining and
enjoyable content provided by video calling services can be
treated as an important factor determining the acceptance of
this service. In summary, to enhance consumers’ behavioral
intention to use video calling, providers do not need to convey
a great deal of technical information to consumers when
promoting value-added services; instead, they should consider
focusing on improving consumers’ perceived enjoyment of this
technology.
Predicting Effects of Related Variables
on Perceived Enjoyment
The results of the current study show that the perceived
enjoyment of using video calling is significantly affected
by perceived usefulness, indicating that users’ perception of
usefulness is a key determinant of perceived enjoyment levels,
which is in line with hypothesis 1. Thus, according to hypothesis
4, perceived usefulness influences intention in two ways: directly
and indirectly via perceived enjoyment. Furthermore, PEU is
positively related to perceived enjoyment. These findings are
consistent with hypothesis 2. In addition, subjective norms
emerged as a weak significant predictor of perceived enjoyment
in both the work context and the leisure context. This
finding supports hypothesis 4 to some extent. Venkatesh et al.
(2003) found that subjective norms significantly influenced the
behavioral intention of accepting information technology in a
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mandatory environment (i.e., the work context). In this study,
subjective norms emerged as a significant predictor of use
intention only in the leisure context. This finding may be related
to the characteristics of video calling. People tend to be more
engaged in video calling in the leisure context than in the work
context. Communicating with friends in a face-to-face way makes
users perceive greater enjoyment. If users realize that many
people are using video calling, this may increase users’ willingness
to use video calling. However, personal innovativeness tended
to be a weak significant predictor of both users’ acceptance
and perceived enjoyment in the leisure context only. Thus,
hypothesis 6 was partially supported. This finding indicated that
people who reported high personal innovativeness tended to
report high use intention and enjoyment in the leisure goal
context.
According to our research, the improvement of perceived
enjoyment depends on increases in perceived usefulness, PEU
and subjective norms in general. Providers attempting to attract
consumers to use this service can increase users’ satisfaction
by improving the usefulness of the technology and by making
the video call interface easier to operate, both of which can
directly affect users’ behavioral intention to adopt video calling.
Additionally, improving public perceptions of the technology and
encouraging more users to adopt video calling services may cause
users to perceive greater social approval from important others.
Thus, consumers will be more willing to use this technology and
obtain more enjoyment when doing so.
Effects of Different Contexts on
Predicting User Acceptance
In the current study, perceived enjoyment was the most
important predictor of users’ intention to use video calling
in the leisure context, while perceived usefulness was the
most important predictor in the work context. Additionally,
perceived enjoyment and perceived usefulness had different roles
regarding the context. Our results revealed that context-specific
differences influenced users’ video calling acceptance, which
supports hypothesis 7. For example, perceived usefulness and
perceived enjoyment jointly determined the behavioral intention
to use video calling in both the work context and the leisure
context. However, in the work context, the role of perceived
usefulness was more important for explaining users’ behavioral
intentions than perceived enjoyment was. By contrast, perceived
enjoyment had a more powerful effect on behavioral intentions in
the leisure context. In addition, the predictive effect of perceived
usefulness on perceived enjoyment was stronger in the work
context than in the leisure context. However, the effect of PEU
on perceived enjoyment was weaker in the work context than
in the leisure context. These important findings indicate the
necessity of distinguishing between different contexts (the work
context and the leisure context) when predicting user acceptance
of video calling. These results are consistent with those of
previous studies (Moon and Kim, 2001;Bruner and Kumar,
2005). Users’ attitudes can be influenced by situational factors
and by the interaction between the individual and his or her
situation.
Thus, to attract more consumers to video calling services
and to reduce the cost of developing video calling technology,
providers in the telecommunications industry should consider
developing two different usage context patterns for video calling.
Technology development could be better targeted to consumers’
different demands in different contexts. For instance, the effect
of perceived enjoyment was greater in the leisure context than
in the work context. Therefore, developers should pay more
attention to the recreational design of video calling, adding more
attractive entertainment elements when developing technology
for the leisure context. In interactive video chat, for example,
they might add the effect of a dynamic figure to express the
behavior of kissing by showing a cartoon kiss in the video call
screen. By contrast, providers should devote more attention to
improving the practicality of video calling technology and help
users improve task performance for the work context, where
perceived usefulness is more important. Additionally, the work
context design may need to be more formal in appearance.
Because the video call interface is easy to operate on users’ mobile
devices, users are not overly concerned about PEU in the work
context. However, in the leisure context, users consider PEU:
the less effort users need to expend in learning to use video
calling, the more willing they will be to use it in the leisure
context.
CONCLUSION AND LIMITATIONS
This study investigated consumers’ behavioral intentions to adopt
a specific mobile value-added service: video calling. The following
conclusions were reached. First, as the results of this study
show, perceived enjoyment is one important factor directly
impacting consumers’ behavioral intention to adopt video calling
technology. The perceived enjoyment of using video calling
is significantly affected by perceived usefulness, followed by
PEU. In particular, the predictive effect of perceived usefulness
on perceived enjoyment is stronger in the work context than
that in the leisure context. However, the effect of PEU on
perceived enjoyment is weaker in the work context than in
the leisure context. Second, the intention to use video calling
services is primarily determined by perceived usefulness and
perceived enjoyment. In the work context, perceived usefulness
has a stronger predictive effect on the intention to use video
calling. By contrast, perceived enjoyment has a more powerful
effect on behavioral intention in the leisure context. Third, as
external variables, subjective norms and personal innovativeness
both have a very limited influence on perceived enjoyment
and on the intention to use video calling. Finally, the degree
of the effect exercised by all of the reliable factors on the
intention to use video calling differs in different contexts.
Users in the leisure context expressed a stronger intention to
use video calling services, in addition to demanding greater
ease of use and a higher level of enjoyment in using value-
added video calling services, compared with users in the work
context.
The limitations of this study are as follows. First, the subjects
were university students from Beijing and Beihai, China, which
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means that the results could not be generalized to all consumers.
Second, unlike in Western countries, the 4G telecom service
market in China is still under development and the costs are
relatively high. Thus, a sampling of all the practical users of
mobile value-added services was not available. Third, as a result
of time constraints, only cross-sectional data were analyzed.
The extent to which current behavioral intention to use video
calling can be used to predict future behavior is unknown.
Because a longitudinal research method could not be adopted,
prudence was required in the discussion of causal relationships
between constructs. In the future, when consumers have a
higher level of involvement in mobile value-added services,
studies in this field could undertake in-depth investigations
to allow more objective conclusions. Additionally, this study
primarily investigated the endogenous variables (and a few
external variables) affecting behavioral intentions. Follow-up
studies could examine more external variables affecting the
adoption of mobile value-added services, such as personality
traits. External variables, especially personal innovativeness,
should be considered along with a combination of personal
traits. For instance, personality traits (such as the Big Five)
could also be relevant important predictors in determining users’
social network sites, which are a typical interpersonal technology
(e.g., Chuang et al., 2017). To obtain more comprehensive data,
the sampled user groups could be enlarged, and differences in
behavioral intentions between different user groups could be
compared.
AUTHOR CONTRIBUTIONS
Conceived and designed the experiments: RZ. Performed the
survey: RZ. Analyzed the data: RZ. Wrote the manuscript: RZ
and CF.
FUNDING
This study was supported by the National Natural Science
Foundation of China (NSFC, 71640034 and 31271100).
ACKNOWLEDGMENT
We thank the reviewers for their very helpful comments and
suggestions regarding an earlier version of this paper.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2017 Zhou and Feng. This is an open-access article distributed under the
terms of the Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) or licensor
are credited and that the original publication in this journal is cited, in accordance
with accepted academic practice. No use, distribution or reproduction is permitted
which does not comply with these terms.
Frontiers in Psychology | www.frontiersin.org 14 March 2017 | Volume 8 | Article 350
... All the previous variables may also be predicted by subjective norms, which are a particular form of social influence related to the opinions of important others about the individual's potential use of a specific technology (Schepers & Wetzels, 2007). Subjective norms were found to be positive predictors of perceived usefulness and intention to use (Schepers & Wetzels, 2007), but also of perceived ease of use (e.g., Al-Rahmi, Alzahrani, Yahaya, Alalwan, & Kamin, 2020) and perceived enjoyment (e.g., Zhou, & Feng, 2017). The relationships between the previous variables of the TAM are presented in Figure 1. ...
... Finally, we expect that subjective norms would be a positive predictor of perceived usefulness (H8), perceived enjoyment (H9), and perceived ease of use (H10). Being encouraged by those around you to use a technological device can lead you to find it (a) more useful, because you trust them, (b) easier to use, because you think that those around you will be able to help, and (c) more pleasant, because you think that you will be able to share this experience with them, all of these reasons ultimately increase intention to use technology (Al-Rahmi et al., 2020;Schepers & Wetzels, 2007;Zhou & Feng, 2017). Figure 1 represents the hypothesized model. ...
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... Venkatesh and Davis (2000) examined an extended version of the technology acceptance model (TAM) and presented SC as "Social Influence" which is a predictor of technology adoption (Venkatesh & Davis, 2000). The authors argued that users' behavioral intention based on perceived value (PU and PE) is affected by the behaviors displayed by others (SC; Venkatesh & Davis, 2000;Zhou & Feng, 2017). A study by Beldad and Hegner (2018) examined German users' willingness to continue using a fitness app based on their PU and revealed that behaviors and opinions of others (SC) about the fitness app affected users' willingness to continue using the app (Beldad & Hegner, 2018). ...
... DI measured users' willingness to donate to the content and likelihood of donating to similar contents in the future based on the credibility of the article and PE and usefulness (Wan et al., 2017;Ye et al., 2015). PE measured the extent to which users find the content entertaining, pleasant, and enjoyable (Zhou & Feng, 2017). Items that measured PE are reformed from Chao (2019) and Zhou and Feng (2017). ...
... PE measured the extent to which users find the content entertaining, pleasant, and enjoyable (Zhou & Feng, 2017). Items that measured PE are reformed from Chao (2019) and Zhou and Feng (2017). PU variable was measured based on the idea sharing, productivity enhancement, and performance enhancement attributes of an article perceived by the user or the reader (Scherer et al., 2015). ...
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... That leads the user to be more motivated and enjoy performing the activity several times (Suki & Suki, 2011). One of the studies shows perceived enjoyment (PE) considered as a key role which affects intention for any daily life context (Zhou & Feng, 2017). ...
... In addition, the results that are related to the impact of the perceived enjoyment are in agreement with the results present in previous studies (Bakhshi et al., 2014;Balog & Pribeanu, 2010;Carless, 2006;C. Davis & Ryder, 2012;Parkin et al., 2012;Suki & Suki, 2011;Zhou & Feng, 2017). It illustrates the significance of perceived enjoyment along with the effect of the perceived usefulness by adopting technologies positively having great effect on improving the ongoing process of learning by utilizing e-feedback. ...
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Consumer's impulsive behavior affects the sales and revenue of the merchants or businesses. This study adopts the Stimulus-Organism-Response (S-OR) Model to examine the impact of users' E-wallet usage behavior on impulse buying. The results obtained from 199 valid online questionnaires show that the perceived enjoyment of using an E-wallet positively affects users' impulse buying behavior. Subjective norms and visual appeal positively influence perceived enjoyment. This study found that consumers' impulsive buying behavior positively impacted satisfaction, indicating that consumers making unplanned purchases using E-wallet would positively influence their satisfaction towards E-wallet. In sum, the findings of this study could provide valuable insights for mobile payment applications designers (e.g., E-wallet) in better understanding users' preferences for E-wallet. Furthermore, the findings presented in this research could also provide practical implications for the merchants or marketers to strengthen their impulse buying strategy.
... (Liu et al. 2019). Research has shown that subjective norms positively influence users' perceived enjoyment of mobile video calling (Zhou and Feng 2017). The study also demonstrated that subjective norms emerged as a weak significant antecedent of perceived enjoyment in adopting mobile video calling. ...
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This study investigates the factors influencing Generation Y and Z’s satisfaction and perceived enjoyment of using E-wallet. This paper further assesses whether consumers perceived enjoyment and satisfaction with using E-wallet would significantly affect their impulsive buying behavior. PLS-SEM was conducted based on 201 valid responses from active E-wallet users collected through an online survey. The results revealed that perceived interactivity and subjective norm positively influenced perceived enjoyment and satisfaction with using E-wallet, respectively. Perceived risk had no significant impact on perceived enjoyment and satisfaction with E-wallet, whereas visual appeal positively influenced perceived enjoyment but not satisfaction. Moreover, this study found that perceived enjoyment of using an E-wallet positively affected impulse buying while satisfaction with E-wallet had no significant relationship with impulse buying. Implications and recommendations for future research are discussed in this paper.
... Therefore, PE relates to the hedonic value and describes how the service gives subjective pleasant experience (Holdack et al., 2020). Zhou and Feng (2017) reported that PU has a greater influence when video calling is a work context, and PE has a greater influence when it is in a leisure context. From this point of view, we can predict Frontiers in Psychology | www.frontiersin.org ...
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Many messengers and social networking services (SNSs) use emojis and stickers as a means of communication. Stickers express individual emotions well, allowing long texts to be replaced with small pictures. As the use of stickers increased, stickers were commercialized on a few platforms and showed remarkable growth as people bought and used stickers with their favorite characters, products, or entertainers online. Depending on their personality, individuals have different motivations for using stickers that determine the usefulness and enjoyment of stickers, affecting their purchase decisions. In the present study, participants ( n = 302) who were randomly recruited from a university completed an online questionnaire assessing the Big Five personality characteristics, motivations for using stickers, and the technology acceptance model (TAM). Results using partial least squares structural equation modeling (PLS-SEM) revealed that each personality trait affected different motivations for using stickers. Moreover, motivations for using stickers also influenced different technology acceptance variables. Finally, perceived usefulness, enjoyment, and ease of use had a positive effect on the intention to purchase stickers. This study has implications in that it is an exploratory approach to the intention to purchase stickers, which has been investigated by few prior studies, and it sheds light on the relationship between personality, motivation, and TAM in purchasing stickers. It also suggests that personality and motivation factors can be considered in personalized recommendation services.
... In general, supervisors are key players in driving the acceptance of digital tools and technologies (Cortellazzo et al., 2019). Obviously, the supervisors at PubConsult have not managed to persuade their subordinates about its usefulness (Zhou and Feng, 2017) as the camera discourse only circled around potential surveillance. Regardless of the initial intention, the organizational actors were aware of the technology's affordances for implicit, hidden control, which supposedly enabled supervisors to counteract the organizational intention of "no individual performance measurement. ...
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This article examines managerial control practices in a public bureaucracy at the moment of introducing remote work as part with a new ways of working (NWW) project. The qualitative study builds on 38 interviews with supervisors and subordinates conducted before the advent of COVID-19. By interpreting interviewees’ conversations about current and anticipated future work practices in the changing work setting, we reveal tacit and hidden practices of managerial control that are currently prevalent in many organizations introducing remote working. Three constitutive moments of the organization’s transformation to NWW are analytically distinguished: (i) how implicit becomes explicit, (ii) how collective becomes self, and (iii) how personal becomes impersonal. Our findings emphasize that the transition to NWW must take into account prevailing institutional logics and must reconnect to a fundamental and often neglected question: What does doing work mean within the particular organization? Negotiating this fundamental question might help to overcome supervisors’ uncertainties about managerial control and provide clarity to subordinates about what is expected from them while working remotely. Finally, we discuss how the transition to NWW may serve as both an opportunity and a potential threat to established organizational practices while highlighting the challenge supervisors face when the institutional logics conflict with remote working.
... Numerous empirical studies confirmed the positive impacts of perceived usefulness and perceived enjoyment on attitude and behavioral intent (e.g. Alsaleh et al., 2019; Zhou and Feng, 2017). Lee et al. (2006) found positive effects of perceived usefulness and perceived enjoyment on attitude and behavioral intent in an online shopping context. ...
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... Development of cognition will be induced based on individual understanding of stimulation, resulting in response for affective reactions [64]. Zhou and Feng [65] proposed that perceived usefulness would have a positive influence on the perceived enjoyment of video calling usage. According to prior research, the following hypothesis: ...
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