A Study on the Adoption Factors of Cell Phone Video Call Technology
Bo Zhang, Hao Jin
School of Management, Hebei University of Technology, Tianjin, China
(zhbo@hebut.edu.cn, jbs@hebut.edu.cn )
Abstract - Recent years, mobile technology has become
increasingly common in today’s everyday life. However, the
data shows that the usage rate of cell phone video call in
China is still very low. The conceptual model extend the
original TAM by including subjective norm, perceived risk,
perceived compatibility and consumer innovativeness, to
make it more effective in predicting users’ acceptance of
phone video call. The empirical result analyzes the key
driver of acceptance of phone video call and provides a
theoretic way to raise the usage rate and efficiency of it. This
research defined the barriers that obstruct phone video call
diffusion in China and then it end up with recommendations
for firm’ decision makers, to improve phone video call in
their firms, as well as recommendations for future
researches with in the same scope of this research. We also
hope to do some contributions to the current theoretic frame
of consumer innovative actions.
Keywords - phone video call, diffusion of innovation,
Technology Acceptance Model, intention to use
I. INTRODUCTION
Driven by the increasing mobility of today’s modern
society, the number of mobile phone users has
significantly increased in recent years. In particular, as a
key increment business of mobile operators, the countries
all over the world has a positive forecast for cell video
call. Cell phone video call technology has huge
development potential. Globally, not only Europe and the
United States but also South Korea, cell video call has
become increasing popularity. In contrast, since 3G
network construction of China starts relatively late, the
usage rate of cell phone video call is still in a very low
level at present. Just a few number of China mobile
consumers are using this service, while huge range of
China consumers don’t using it, and others not even heard
of it. Thus, the actual market penetration of phone video
call services deviates from previous predictions seriously.
Along with the demand for promotion and
popularization of 3G technology in China, the issues that
consumers how to accept or reject this business has
become increasing focused and concerned by academic
circles. It is quite deficiency and exigent to use systematic
analysis to cell phone video calls for the country's
consumer behavior and characteristics of technology
adoption. In view of this, using theory and method of
innovation diffusion and technology acceptance, this
thesis will analyze the question of why consumers have
not accept phone video call, and will study the key factors
in consumers’ acceptance of phone video call services,
offer theory according and meaningful guide for managers
how can enhance the number of customers who choose
this way of communication as an alternative to more
traditional communication mode effectively. For this
purpose, the research extended the original TAM by
including new constructs and collected 422 usable
samples from a representative sample of 500 respondents.
Then we used the structural equation modeling software
Lisrel8.70 to test a theory-based research model of phone
video call acceptance.
The outline of the current article is as follows: First,
we extend the original technology acceptance model
TAM, to make it more effective in predicting users’
acceptance of technologies, related to mobile services in
general, the acceptance of cell video call in particular in
China. Based on the literature review and the preliminary
study, the researcher extended the original TAM by
adding some key constructs (perceived risk, perceived
compatibility and consumer innovativeness). Then we
present our hypotheses to find the barriers that obstruct
the diffusion of phone video call in China and make
Chinese customers unwilling to adopt this service, And
also; to give imaginations and solutions for solving this
problem.
II. LITERATURE REVIEW
In the IT/IS literature, many models have been
advanced to predict users’ new technology usage [1].
Among them, the technology acceptance model (TAM),
proposed by Davis [2], and has evolved as the most
popular. TAM has seen many applications and extensions
In the IT/IS field since its development. The benefits of
TAM include reliable instruments with empirical
soundness, conciseness and excellent measurement
properties. TAM is also applied to a wide range of
research questions, including attitude toward self-service
solutions [3], wireless LAN usage [4], and adoption of
internet banking [5].
TAM is predictive model, but in fact it usually does
not provide sufficient understanding with the necessary
information to create user acceptance for new technology
and services, thus; many researchers have extended the
original model to make it more affective, such as
perceived enjoyment in the internet, perceived critical
mass in groupware usage, perceived user resources in a
bulletin board system, perceived playfulness in the web
context, and compatibility in a virtual store. Moreover,
many extended perception variables have been added to
TAM in previous studies in specific contexts.
III. HYPOTHESES
The original technology acceptance model (TAM)
separated between attitude and intention to use.
According to the TAM, attitude towards using a
technology has a positive direct effect on intention to use
[6] [7]
. The implied relationship is reflected by our first
hypothesis:
H1: Attitude towards using phone video call has a positive
direct effect on the intention to use phone video call.
According to diffusion theory, users are only willing
to accept innovations which provide a unique advantage
compared to existing solutions [8]. This research adopts
the definition perceived usefulness that was given by
Davis; “subjective probability that using a specific
application or technology will increase his or her job
performance within an organization context” [2]. The
original technology acceptance model (TAM), suggested
that Perceived usefulness can affect users intention to use
technology indirectly through its influence on attitude
towards use. Hence:
H2: Perceived usefulness of phone video call has a
positive direct effect on the attitude towards using phone
video call.
IN the original technology acceptance model (TAM),
perceived ease of use is considered as a major influence
on attitude towards a technology. This influence appears
clearly from the individual’s evaluation of the mental
effort involved in using the technology. Consequently, we
incorporate perceived ease of use of phone video call in
our research model. Also it has been found that perceived
ease of use can influence users’ perceived usefulness of
the technology [6]. Hence:
H3: Perceived ease of use of phone video call has a
positive direct effect on the attitude towards using phone
video call.
H4: Perceived ease of use of phone video call has a
positive direct effect on perceived usefulness of phone
video call.
The original technology acceptance model (TAM),
doesn’t include the impact of social influence on the
decision of adopting technology. Social information can
influence individual innovation decisions over and above
the traditional sources of influence such as individual use
of the system. Thus, social pressure is also was one of the
motivations which can affect technology acceptance.
Venkatesh and Davis extend the original TAM by
including subjective norm as an additional factor, and the
empirical study found that subjective norm not only
directly influence consumers’ intention to use, also to
consumers’ assessment to it usefulness[6]. Accordingly,
we present hypothesis:
H5: Subjective norm has a positive direct effect on the
intention to use phone video call.
H6: Subjective norm has a positive direct effect on
perceived usefulness of phone video call.
The external variables, which were added to the
research model, can also have an impact on consumer’s
decision of using phone video call; perceived
compatibility was found to have an impact on consumer’s
decision to adopt new technology. Tornatzky and Klein
find perceived compatibility to be an important innovation
characteristic driving consumer acceptance [9]. Extant
research shows perceived compatibility both has positive
effects on the attitude toward using a technology and
perceived usefulness. In view of these findings, we
hypothesize the following:
H7: Perceived compatibility of phone video call has a
positive direct effect on perceived usefulness of phone
video call.
H8: Perceived compatibility of phone video call has a
positive direct effect on the attitude towards using phone
video call.
We further extend the original technology acceptance
model by including the perceived risk of phone video call
as an additional factor. Innovations usually come with
varieties of risks (such as technical complexity, high
prices, and the novelty). Huang and Chuang, based on the
theories of planned behavior (TPB), make perceived risk
as a new variables influencing innovation adoption, and
the results show that the effect is remarkable [10]. Lwin[11],
Chen[12], Kim[13] also found the perceived risk of
consumers and the attitude towards using unknown
technical products has an obvious negative correlation,
and proved by empirical evidences. Hence:
H9: Perceived risk of phone video call has a negative
direct effect on the attitude towards using phone video
call.
Innovation diffusion theory suggests that the early
adopters and later adopters are different in personality
traits and character. Some studies hold it as a general
individual personality, and define this potential tendency
to accept new products as consumer innovativeness.
Therefore consumer innovativeness will have a strong
effect on their adopting to any new technology.
Steenkamp[14], Zhang[15] and Chen[16]’s empirical studies
found that consumers’ consumer innovativeness have a
certain promotion effects on their attitude towards using
innovation. Thus, we present hypothesis:
H10: Consumer innovativeness has a positive direct effect
on the attitude towards using phone video call.
The consumer acceptance model appears in Fig. 1.
Fig. 1. Conceptual model.
IV. SURVEY AND DATA ANALYSIS
A. Survey
The survey was conducted using a standardized
questionnaire, and there were a total of 28 measurement
items in the questionnaire of the survey. For the research
questionnaire, the researcher choose a five-point Likert
scale and the codes from 1 (“quite disagree”) to 5 (“quite
agree”). Table I provides a list of all measurement items
and their sources. To collect the quantitative data for this
research, 500 persons were randomly chosen to fill the
questionnaire survey. At the end of the data collection
period, 422 usable samples were received, with 225
female and 197 male. Of these samples, 43.4% were in
their late twenties, 31.8% were 30–39 years old, 16.4%
were 40–49, and 8.4% were over 50.
B. Reliability and validity analysis
After survey we used Crobach alpha with SPSS18.0
software to make reliability analysis. In this research the
reliability test was used to examine the consistency with
which individuals respond to the test in diverse occasions.
We first conducted analyses separately for each factor and
calculated coefficient alphas, composite reliabilities, and
corrected Item—total correlation (CITC). Table II
provides the detailed description of the scales used to
measure each of the variables. Crobach alpha for all
research variables are greater than 0.6; composite
reliabilities are exceed the recommended threshold of 0.7;
and corrected Item—total correlation values ranged from
0.464 to 0.823. From these results it is conclude that the
scales have high levels of internal consistency, and are
considered to be suitably reliable,
In addition, we assess measurement validity through
the content validity and structural validity. The whole
scale is developed based on the mature scale of prior
studies, so it has already had good content validity itself.
We conducted a confirmatory factor analysis (CFA) using
Lisrel8.70 to assess structural validity of the measurement.
All the constructs were measured with multiple indicators.
If a factor’s loading is lower than 0.4 or its T value is
lower than 1.95, the factor will be eliminated. The results
showed that the size of each factor loading ranged from
0.52 to 0.91 and the T values for those indicators ranged
from 7.58 to 19.15. Table II shows the results of the test
and factor loading items of all the variables. Overall, we
conclude that all the items of the questionnaire are
accepted in the final study.
TABLE I
MEASUREMENT ITEMS
Construct
References
Intention to use
Davis (1989)
Venkatesh and Davis (2000)
Attitude towards
use
Van der Heijden (2003)
Yang and Yoo (2004)
Perceived
usefulness
Van der Heijden (2003) [17]
Perceived ease
of use
Venkatesh and Davis (2000)
Subjective norm
Venkatesh and Davis (2000)
Perceived
compatibility
Plouffe et al. (2001) [18]
Perceived risk
Luarn and Lin (2005) [19]
Consumer
innovativeness
Dr Niki Hynes（2006）[20]
Items
I'm using or planning to use phone video call recently.
Given the opportunity, I will use phone video call.
In the near future, I am willing to more use phone video call.
Using phone video call is a good idea.
Using phone video call is a wise choice.
In general, using phone video call is beneficial.
Using phone video call can shorten the distance between I and others.
Using phone video call makes my communication become easier.
Using phone video call makes my communication become more fun.
In general, phone video call is a useful communication technology.
It is easy to become skillful at using phone video call.
The interaction with phone video call is clear and understandable.
It is easy to perform the steps required to use phone video call.
In general, phone video call is a technology that easy to master.
People who are important to me would recommend using phone video call.
People who are important to me would find using phone video call beneficial.
People who are important to me would find using phone video call a good idea.
Using phone video call fits well with my lifestyle.
Using phone video call fits well with my past experience.
In general, phone video call fits well with my current need of communication.
I worry about that using phone video call may leak my privacy.
I worry about that using phone video call may lead to high costs.
I worry about that the network is instability, may be disrupted when communication.
Using phone video call leads me to some degree of tension and worry.
I am willing to try new things in life.
I like to accept challenges from new things; even it would worth my time and effort.
I think new lifestyle and ways of consumption is a progress to the past.
When the new product or technology appearance, I usually to be the earlier adopter.
TABLE II
RELIABILITY AND VALIDITY ANALYSIS
Construct
Intention
to use
Attitude
towards use
Perceived
usefulness
Perceived
ease of use
Subjective
norm
Perceived
compatibility
Perceived
risk
Consumer
innovativene
ss
Items
IN1
IN2
IN3
AT1
AT2
AT3
PU1
PU2
PU3
PU4
PE1
PE2
PE3
PE4
SN1
SN2
SN3
PC1
PC2
PC3
PR1
PR2
PR3
PR4
CI1
CI2
CI3
CI4
Cronbach
alpha
0.784
0.825
0.839
0.888
0.689
0.797
0.864
0.839
Loadings
T-value
0.74
0.83
0.66
0.80
0.74
0.81
0.68
0.67
0.76
0.91
0.78
0.78
0.79
0.91
0.54
0.52
0.65
0.57
0.71
0.68
0.79
0.77
0.81
0.77
0.76
0.62
0.89
0.75
13.65
16.10
11.81
15.35
13.91
15.74
12.47
12.08
14.44
18.62
15.26
15.23
15.45
19.15
7.85
7.58
9.34
8.92
11.08
10.57
15.05
14.69
15.61
14.65
14.27
10.89
17.77
14.08
C. Structural equation model analysis
Fig. 2. Results of model estimation.
Previous table illustrates that the tolerance values for
all dimensions were accepted, the next step was to
examine the structural model. The overall fit indices of
the structural model were within the range that scholars
generally recommend. The ratio of chi-square to degrees
of freedom was 1.632, which met the recommended
criteria of less than 3. The goodness of fit index (GFI) and
adjusted goodness of fit index (AGFI) were 0.90 and 0.88,
respectively. The normed fit index (NFI) and comparative
fit index (CFI) were 0.93 and 0.98, respectively. The
standard root mean square residual (SRMR) and root
mean square error of approximation (RMSEA) was 0.43
and 0.36. A comparison of these values against those
recommended in the literature suggests that the model
estimation result is quite satisfactory. Fig. 2 summarizes
the estimation results and shows the relationships among
constructs that are statistically significant.
V. RESULTS
We found support for H1, attitude has a significant
and positive relationship with the intention to use phone
video call (b = 0.71; p≤0.001). Moreover, the path
coefficient of 0.14, significant at a 1% level, points to
perceived usefulness has a strong positive relationship
with the attitude towards using phone video call, as
proposed in H2. Further, structural links from perceived
ease of use to the attitude (b = 0.03; p >0 .05) and from
perceived ease of use to the perceived usefulness (b =
0.10; p>0.05) are not significant. Thus, H3 and H4 were
rejected. In addition, the relationship proposed in H5 and
H6 are confirmed; that is, subjective norm has positive
effects to the intention to use phone video call (b = 0.21;
p≤0.001) and the perceived usefulness (b = 0.16; p≤0.01).
The results also provide strong evidence for the effects of
perceived compatibility on the attitude (b =0.26; p≤0.001)
and the perceived usefulness (b = 0.20; p≤0.001), in
support of H7 and H8. Regarding the perceived risk factor,
we found a significant link with the attitude (b = -0.32;
p≤0.001), implying that the perceived risk has a strong
negative effect on attitude towards using phone video call.
Therefore, H9 is confirmed. In the end, the construct of
consumer innovativeness has a significant relationship
with the constructs of attitude (b = 0.12; p≤0.05). Thus,
H10 is supported. In the entire model for all predictors,
R2 explains 68.2% of the variance related to intention to
use phone video call, indicating that the model highlights
a comprehensive set of important factors that are
associated with consumer acceptance.
In order to examine the total effects and establish a
ranking among the drivers of phone video call acceptance,
we multiply the coefficients along the paths. (Table IV)
[21]
. For example, the total effect of compatibility on
intention to use equals the indirect effect via perceived
usefulness plus the indirect effect via attitude towards use
(0.20*0.14*0.71 + 0.26*0.71 = 0.20).
TABLE III
TOTAL EFFECT ON INTENTION TO USE
Factor
Total effect son intention to use.
Subjective norm
0.23
Perceived
-0.23
risk
Perceived compatibility
Perceived usefulness
Consumer innovativeness
Perceived ease of use
0.20
0.10
0.08
0
VI. DISCUSSION
The research presents a powerful model to predict
technology acceptance and recognizes that there are many
factors that could affect the success and effectiveness of
phone video call in developing countries. The empirical
study found that subjective norm and perceived risk have
the greatest impact on the intention to use phone video call.
It is important since both perceived risk and subjective
norm are not part of the original technology acceptance
model and thus are often not considered by researchers.
The same is true for the factor perceived compatibility.
The six factors summarized in Table IV can serve as a
guideline for increasing further market penetration of
phone video call services. The impact of subjective norm
was found to be very important in the research model. It
can be implied that firms need to identify early adopters
and stimulate their usage of phone video call services, so
that they can serve as a reference facilitating broad
diffusion in the future. Perceived risk is another key factor
of the consumer acceptance of phone video call. Thus,
firms need to increase the security of using phone video
call by using the most developed technology in this
field ， besides improving this services focused on
charges, communication performance and privacy
protection when promoting it. The research which was
conducted by supports in explaining a part of the rest
whereas it explains 20% of the variance of consumers’
perceived compatibility of using phone video call in
developing countries. Thus, firms should develop phone
video call technology and solutions that make consumers
regard it as well-suited to their individual behavioral
patterns. Similarly, perceived usefulness and consumer
innovativeness also of “some concern” for affects effects
on access of using phone video call, so firms can use the
way of advertise to make users know more about the
benefits and joy of this service, thereby increasing
consumers’ cognitive of usefulness and Cause interests of
potential consumers.
REFERENCES
[1] Venkatesh, V., Davis, F.D, “A theoretical extension of the
technology acceptance model: Four longitudinal field
study,” Management Science, vol. 46, no. 2, pp. 186-204,
2000.
[2] Davis, “I Perceived usefulness, Perceived ease of use, and
user acceptance of information technology,” MIS Quarterly,
vol. 13, no. 3, pp. 319-340, 1989.
[3] Cheng-de CEN, Xiao-tian GAN, “A Study of Customer
Participation and Its Antecedents in Self-service
Technologies,” (in Chinese), Forecasting, vol. 30, no. 2, pp.
21-27, 2011.
[4] Yoon, C., and Kim, S, “Convenience and TAM in a
ubiquitous computing environment: the case of wireless
LAN,” Electronic Commerce Research and Applications,
vol.1, pp. 102-112, 2007.
[5] Lee, M.-C, “Factors influencing the adoption of internet
banking: an integration of TAM and TPB with perceived
risk and perceived benefit,” Electronic Commerce Research
and Applications, vol. 8, pp. 130-141, 2009.
[6] Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F.
D, “User acceptance of information technology: toward a
unified view,” MIS Quarterly, vol. 27, pp. 425–478, 2003.
[7] Yang, H, D., and Yoo, Y, “It’s all about attitude: revisiting
the technology acceptance model: four longitudinal field
studies,” Decision Support Systems, vol.38, no. 1, pp. 1931, 2004.
[8] Rogers, E.M, Diffusion of Innovations (4thedtion), New
York: Free Press, 1995. pp. 16–25.
[9] Tornatzky, L. G., and Klein, K. J, “Innovation
characteristics and innovation adoption implementation: a
meta-analysis of findings,” IEEE Transactions on
Engineering Management, vol. 29, no. 1, pp. 28-45, 1982.
[10] Huang E, Chuang MH, “Extending the Theory of Planned
Behavior as a Model to Explain Post-merger Employee
Behavior of IS Use,” Computers in Human Behavior, vol.
23, pp. 240-252, 2007.
[11] Lwin, M., Wirtz, J., and Williams, J. D, “Consumer online
privacy concerns and responses: a power-responsibility
equilibrium perspective,” Journal of the Academy of
Marketing Science, vol. 35, pp. 572-585, 2007.
[12] Chen, L.D., “A model of consumer acceptance of mobile
payment,”
International
Journal
of
Mobile
Communications, vol. 6, no. 1, pp. 32–52, 2008.
[13] Changsu Kim, Wang Tao, Namchul Shin, Ki-Soo Kim, “An
empirical study of customers' perceptions of security and
trust in e-payment systems,” Electronic Commerce
Research and Applications, vol. 9, pp.84-95, 2010.
[14] Steenkamp,J E M,and Gielens,K, “Consumer and market
drivers of the trial probability of new consumer packaged
goods,” Journal of Marketing, vol. 30, no. 4, pp. 368-384,
2003.
[15] Hong-hong ZHANG, Mei-hua ZHOU, Lu JIANG “An
empirical study of consumer characteristics and Intention to
adopt innovation,” (in Chinese), Consumer Economics,
vol.25, no. 6, pp. 68-70, 2009.
[16] Wen-pei CHEN, Wei LIU and Yi Li, “An empirical study
of innate consumer innovativeness, personal characteristics
and new-product adoption behavior,” (in Chinese),
Management review, vol.22, no. 5, pp. 35-40, 2010.
[17] Van der heijden, H, “Factors influencing the usage of
Websites: the case of generic portal in The Netherlands,”
Information and Management, vol.40, no. 6, pp.541-549,
2003.
[18] Plouffe, C. R., Hulland, J. S., and Vandenbosch, M,
“Richness versus parsimony in modeling technology
adoption decisions - understanding merchant adoption of a
smart card-based payment system,” Information Systems
Research, vol.12, no. 2, pp.208-222, 2001.
[19] Luarn, P., and Lin, H.-H, “Toward an understanding of the
behavioural intention to use mobile banking,” Computers in
Human Behavior, vol.21, no. 6, pp. 738-891, 2005.
[20] Dr Niki Hynes, Stanley Lo, “Innovativeness and consumer
involvement in the Chinese market,” Singapore Managem
ent Review, vol. 28, no. 2, pp. 31-46, 2006.
[21] Bollen, K. A, “Total, direct and indirect effects in structural
equation models,” Sociological Methodology, vol. 17, pp.
37–69, 1987.