Tải bản đầy đủ
CHAPTER 5. CONCLUSIONS, IMPLICATIONS AND LIMITATIONS

CHAPTER 5. CONCLUSIONS, IMPLICATIONS AND LIMITATIONS

Tải bản đầy đủ

63

model (with attitude split to cognitive and affective), it tends to impact directly on
behavioral intention rather than via other mediator variables. This result was not
consistent with many “single attitude construct” studies (Chang et al., 2012) which
confirmed the heavy indirect effect of perceived convenience through other
mediators.
Regarding the perceived ease of use, it plays the important role to change perceived
convenience (

= .69), perceived usefulness (

and affective attitude (

= .61), cognitive attitude (

= .41)

= .37). Although perceived ease of use do not directly

influence behavioral intention to use, it affects behavioral intention to use indirectly
through other mediators in the model (

= .29).

About attitudes, this research is conducted to empirically test two aspects of
attitudes (cognitive and affective attitude) which can be separated to two sociopsychological constructs. According to Davis et al.’s (1989) finding, attitude adds
slight value in explaining the intention to use and the attitude has often been ignored
(or often treated as only affective construct) in TAM. Back to this research, the
overall effects ( ) of cognitive and affective attitudes on behavioral intention are .4
and .24, respectively, mean that the cognitive attitude is more powerful than
affective attitude in explaining the behavioral intention. Hence, if the researcher just
focuses on only affective attitude, the cognitive effects might be bypassed in the
wrong way. The research’s results also confirmed again the process of attitude
changes. In there, there are positive influence of cognitive attitude on affective
attitude (

= .42) and affective attitude mediates the cognitive attitude and

behavioral intention.
Finally, perceived mobility has a significant effect on behavioral intention.
Therefore, the mobility is the important trait of mobile content services. This result
supports the other researches (Ajzen, 2002; Hong et al., 2008).

64

5.2

Managerial Implications

The purposes of this research are: (1) to empirically examine the factors that affect
consumer’s behavioral intention to use mobile content services and (2) to examine
whether two constructs of attitude (affective and cognitive attitude) can be separated
and the important effects of them on behavioral intention to use mobile content
services.
Firstly, the findings shows that, perceived ease of use does not directly influence
behavioral intention to use; however, it affects behavioral intention to use indirectly
through other mediators in the model. Hence, enhancing the perceived ease of use is
helpful to facilitate perceived usefulness, attitudes and perceived convenience and
therefore behavioral intention to use is enhanced. This implication should be noted
for mobile content services project managers so that they can select a suitable
design for their mobile products such as simplifying user interfaces, making user
interface more friendly, increasing customer support, and integrating more help
wizards into mobile applications.
Secondly, it is important to perceive the process of attitude change. According to
Petty et al. (1994):
Attitude is social function. Attitude serves both private and public identity
concerns. Even though attitude has been treated as a vague and fragile
construct in the IS area, its importance on individual behavior and social
influence has been steadily recognized in psychology. Attitude is contagious
and as people work together, they express their own and listen to each other’s
attitude. Therefore, organizations and managers need to be care about the
positive attitude change (pp. 261-270).
Thus, understanding the construct of attitudes can help managers to control the
attitude change and next, improving the user’s acceptance of technology. Changes
in attitude happen quickly and easier than changes in values (Thompsonc & Hunt,

65

1996), so the manager should develop the positive attitudes change. For instance,
enhancing individual motivations, abilities and moods (direct influence), using the
message memory, message credibility, and two-sided communication (persuasive
message).
Thirdly, regarding the practical aspect of implication, the findings provide mobile
operators and content developers with various strategies to sustain the usage of
mobile content services. First of all, they should focus on strategy to emboss
favorable attitudes among consumers in the both ways: focusing on consumer
emotion about products/services (attracting affective attitude) and creating
products/services values (attracting cognitive attitude).
Finally, developing the technology infrastructure to support the true mobility and as
mentioned above, mobile content services project managers should look into
improving perceived ease of use, convenience, and usability of mobile phones and
their mobile content services offering.
5.3

Limitation and Further Research

These findings provide meaningful implications for mobile content services market;
however, the research has some limitations.
Firstly, due to time constraints, only cross-sectional data were collected and the
survey was performed at a specific time point for empirical analysis.
Secondly, the sampling of all the practical users of mobile content services was not
feasible due to some limitations of the number and kinds of mobile
products/services in Vietnam. Moreover, the data collected from participants can be
different due to the usages and increased experiences (Venkatesh et al, 2003);
hence, the future study should focus on longitudinal research, which is helpful for
predicting user’s long-run belief and behavior, and can improve the comprehension
of causal relationships among variables.

66

Thirdly, when filling out the survey, the respondents may consider not only the
mobile content services they had actually used but also services they could see
themselves in the future. If they had not actually used any mobile content services,
the results might have been based on their thoughts or imagination, so the bias could
have been occurred.
Fourthly, the results of data analysis showed that there was the correlation between
two “unexplained variance of endogenous variable”. This correlation was defined as
the covariance of “



”. Back to the literature review, there is a case when give

system, perceived usefulness and one person may a strong behavior intention to use
the system without pass through any attitudes. Hence, this covariance can be
considered as the limitation of research model and the data market in this research
context. Therefore, the researcher should suggest more addition competitive models
to improve the research model.
Next, Davis et al. (1992) have found intrinsic and extrinsic motivation to be key
drivers of intention to use technology. Following this theory, a variable called
perceived enjoyment is an example of intrinsic motivation and perceived usefulness
is an example of an extrinsic motivation to use technology. Therefore, the further
research should investigate where the anchor factors and where the adjustment
factors, which impact on, perceived ease of use in the current research model.
Similarly, the external variables of TAM (TAM2) (Venkatesh & Davis, 2000) are
created to extend TAM to include addition determinants of perceived usefulness and
intention, so the next research should consider more antecedents of usefulness base
on social influence processes (subjective norm, voluntariness, image) and cognitive
instrumental process (output quality, result demonstrability).
Finally, although this research addressed a variety of factors, it might limit other
factors in connections with the acceptance of new technological services among
Vietnamese consumers. So, new addition research should be motivated to include
the effect of other important variables to current research model. For instance, in

67

addition to perceived usefulness (as recommendations above), perceived ease of use
(as recommendations above), other variables after behavioral intention such as the
actual usage could be investigated.

68

REFERENCES
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of
use, and usage of information technology: A replication. MIS Quarterly, 16(2),
227-247.
Agarwal, R., & Prasad, J. (1999). Are individual differences Germane to the
acceptance of new information technologies ?. Decision Sciences, 30(2), 361391.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social
behavior. New Jersey: Prentice-Hall.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J.
Kuhl & J. Beckman (Ed.), Action-control: From cognition to behavior (pp.
11-39). Heidelberg: Springer.
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and
the theory of planned behavior. Journal of Applied Social Psychology, 32(4),
665-683.
Alhabahba, M. M., Ziden, A. A, Albdour, A. A, & Alsayyed, B. T. (2012). The
horse before the cart! The English language learners experience of using elearning system. International Journal of Emerging Technologies in Learning,
7(2), ISSN: 1863-0383.
Amberg, M., Hirschmeier, M., & Wehrmann, J. (2003). The compass acceptance
model for the analysis and evaluation of mobile services. International
Journal

for

Mobile

Communications

(IJMC).

Retrieved

from

http://www.wi3.uni erlangen.de/forschung/publikation/PDF/IJMC.pdf
Bagozzi, R. P. (1984). A prospectus for theory construction in marketing. Journal of
Marketing, 48(4), 11-29.
Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience.
Journal of Marketing, 66(3), 1-17.
Bitner, M. (2001). Service and technology: opportunities and paradoxes. Managing
Services Quality, 11(6), 375-379.

69

Bollen, K. A., & Long, J. S. (1993). Introduction. In K. A. Bollen & J. S. Long
(Ed.), Testing structural equation models (pp. 1-9). Newbury Park, CA: Sage.
Bollen, K. A. (1989). Structural equations with latent variables. New York, NY:
Wiley.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In
B. Kenneth & J. S. Long (Ed.), Testing structural equation models. Newbury
Park, CA: Sage.
Bouwman, H., López-Nicolás, C., Molina-Castillo, F.J., & Van Hattum, P. (2012).
Consumer lifestyles: alternative adoption patterns for advanced mobile
services. International Journal of Mobile Communications (IJMC), 10(2),
169-189.
Brown, L. G. (1990). Convenience in services marketing. Journal of Services
Marketing, 4(1), 53-59.
Buhan, D., Cheong, Y. C., & Tan, C. (2002). Mobile payments in m-commerce.
Telecom Media Networks. Retrieved from
http://www.ebusinessforum.gr/content/downloads/MobilePaymentsinMComm
rce.pdf
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts,
applications, and programming, 331-332. New York: Routledge.
Campbell, D., T., & Fiske, D., W. (1959). Convergent and discriminant validation
by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 80-81.
Cacioppo, J., Petty, R., & Crites, S. (1994). Attitude change. Encyclopedia of
Human Behavior, 1, 261-270.
Cheolho, Y., & Sanghoon, K. (2007). Convenience and TAM in a ubiquitous
computing environment: The case of wireless LAN. Electronic Commerce
Research and Applications, 6, 102-112.
Churchill, A. J. G. (1979). A paradigm for developing better Measures of marketing
constructs. Journal of Marketing Research, 16(1), 64-73.

70

Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an
extended technology acceptance model: Mobile technology and English
learning

for college

students. Australasian Journal of Educational

Technology, 28(5), 809-826.
Curran, J., & Meuter, M. (2005). Self-service technology adoption: comparing three
technologies. Journal of Services Marketing, 19(2), 103-113.
Cuieford, J.P. (1965). Fundamental statistics in psychology and education (4th ed.).
London: McGraw Hill.
Cheong, J., & Park, M. C. (2005). Mobile internet acceptance in Korea. Internet
Research, 15 (2), 125-140.
CBSNews (2010, February 15). Number of Cell Phones Worldwide Hits 4.6B.
Retrieved from http://www.cbsnews.com/news/number-of-cell-phonesworldwide-hits-46b
Davis, F., Bagozzi, R., & Warshaw, P. (1992). Extrinsic and intrinsic motivation to
use computers in the workplace. Journal of Applied Social Psychology,
22(14), 1111-1132.
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance
of information technology. MIS Quarterly, 13(3), 318-340.
Davis, F., Bagozzi, R., & Warshaw, P. (1992). Extrinsic and intrinsic motivation to
use computers in the workplace. Journal of Applied Social Psychology,
22(14), 1111-1132.
Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer
technology: a comparison of two theoretical models. Management Science,
35(8). 982-1003.
DeCoster, J., & Claypool, H. M. (2004). A meta-analysis of priming effects on
impression formation supporting a general model of informational biases.
Personality and Social Psychology Review, 8, 2-27.

71

Ernst & Young (2013). Mobile Money, an Overview for Global Telecommunication
Operators. Retrieved from
http://www.ey.com/Publication/vwLUAssets/Mobile_Money
Fang, X., Chan, S., Jacek, B., & Xu, S. (2006). Moderating effects of task type on
wireless technology acceptance. Journal of Management Information Systems,
Winter 2005–6, 22(3), 123-157.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An
Introduction to Theory and Research. Reading: Addison- Wesley.
Gartner (2011, February 10). Mobile applications will increasingly define the user
experience on high-end devices. Gartner Identifies 10 Consumer Mobile
Applications to Watch in 2012. Retrieved from
http://www.gartner.com/it/page.jsp?id=1544815
Gupta, S., & Kim, H. W. (2007). The moderating effect of transaction experience
on the decision calculus in on-line repurchase. International Journal of
Electronic Commerce, 12(1), 127-158.
Gupta, S., & Kim, H. W. (2006). The moderating effect of transaction experience
on value-driven internet shopping. Proceeding of European Conference on
Information Systems, 807-818.
General Statistics Office (2013). Transport, postal services and telecommunications.
General Statistics Office of Vietnam 2013 Report. Retrieved from
http://www.gso.gov.vn/default_encaspx?tabid=473&idmid=3
Hair, J., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data
analysis (7th ed.). Upper saddle River, New Jersey: Pearson Education
International.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate
data analysis. New Jersey: Prentice-Hall.
Hendrickson, A. E., & White, P. O. (1964). Promax: A quick method for rotation to
oblique simple structure. British Journal of Statistical Psychology, 17(1), 6570.

72

Heywood, H. B. (1931). On finite sequences of real numbers. Proceedings of the
Royal Society of London, 134, 486-501.
Hong, S. J., Thong, J. Y. L., Moon, J. Y., & Tam, K. Y. (2008). Understanding the
behavior of mobile data services consumers. Info. Syst. Front, 10, 431-445.
Hossain, M. M., & Prybutok, V. R. (2008). Consumer acceptance of RFID
technology: An exploratory study. IEEE Transactions on Engineering
Management, 55(2), 316-328.
International Telecommunication Union (2002). ITU Internet reports: Internet for a
mobile generation.
International Telecommunication Union (2012). Measuring the information society.
International

Telecommunication

Union

Report.

Retrieved

from

http://www.itu.int/en/ITUD/Statistics/Documents/publications
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal
computing acceptance factors in small firms: A structural equation model. MIS
Quarterly, 21(3), 279-302.
Jöreskog, K. G., & Sörbom, D. (1984). LISREL-VI user's guide (3rd ed.).
Mooresville, IN: Scientific Software.
Jöreskog, K., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with
the SIMPLIS command language. Chicago, IL: Scientific Software
International Inc.
Kaasinen, E. (2005). User acceptance of mobile services – value of use, trust and
ease of adoption. VTT Publications 566. Doctoral Dissertation.
Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd
ed.). New York: The Guilford Press.
Krueger, M. (2001). The future of m-payments: Business options and policy issues.
Electronic

Payments

Systems

Observatory

http://epso.jrc.es/Docs/Backgrnd-2.pdf

(ePSO).

Retrieved

from

73

Lu, J., Yao, J., & Yu, C. S. (2005). Personal innovativeness, social influences and
adoption of wireless internet services via mobile technology. Journal of
Strategic Information System, 14(3), 245-268.
Liao, C. H., Tsou, C. W., & Huang, M. F. (2007). Factors influencing the usage of
3G mobile services in Taiwan. Online Information Review, 31, 759-774.
Leech, N. L., Barrett, K. C., & Morgan, G. A. (2005). Spss for intermediate
statistics (3rd ed.). New Jersey: Lawrence Erlbaum Associates, Inc.
Mahabir, N., & Geeta, S. (2013). Impact of information technology (IT) on
consumer purchase behavior. Researchers World, Journal of Arts, Science &
Commerce, E-ISSN 2229-4686, ISSN 2231-4172.
Meuter, M., Bitner, M., Ostrom, M., & Brown, S. (2005). Choosing among
alternative services delivery models: An investigation of customer trial of selfservice technology. Journal of Marketing, 69(2), 61-83.
Mathieson, K. (1991). Predicting user intentions: Comparing the technology
acceptance model with the theory of planned behavior. Information Systems
Research, 2(3), 173-191.
Morris, M. G., & Dillon, A. (1997). How user perceptions influence software use.
Decision Support Systems, 58-65.
Muthen, B, & Kaplan, D. (1985). A comparison of some methodologies for the
factor analysis of non-normal Likert variables. British Journal of
Mathematical and Statistical Psychology, 38, 171-80.
Nguyễn Đình Thọ & Nguyễn Thị Mai Trang (2011a). Giáo trình nghiên cứu thị
trường. Nhà Xuất Bản Lao Động.
Nguyễn Đình Thọ & Nguyễn Thi Mai Trang (2011b). Nghiên cứu khoa học
marketing: Ứng dụng mô hình cấu trúc tuyến tính SEM. Nhà Xuất Bản Lao
Động.
Nunnally, J. C. (1967). Coeficient Alpha and the Reliability of Composite
Measurements. Psychometric theory (1st ed.). New York: McGraw-Hill.