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used interchangeably by various studies. Fang and colleagues (2006) categorize the
type of mobile services based on their objectives into three types:

General tasks: These tasks do not involve transaction and gamming such as
mobile email, mobile SMS, browse website, map and search location


Transaction tasks: These tasks include mobile banking, mobile money and
online purchase via internet-store.


Entertainment tasks: These tasks include gaming and entertainment data
services such as mobile game, vote/contest via value added public number,
polyphonic ring tones, downloading logo, wallpapers, listening music via
mobile network and standby background music.

These three types of tasks differ in their objectives. The objective of general tasks is
to search information or communicate with other parties whereas the goal of
transaction tasks is to commit financial transactions. The purpose of entertainment
tasks is to entertain their performers. All of the three tasks above can be found in
Besides, the International Telecommunication Union (ITU) classifies mobile data
services into four categories: communication services, information content services,
entertainment services and commercial services (ITU, 2002; Sadeh, 2002). Mobile
communication services, which are the most widely used form of mobile content
services, include short message service (SMS), multimedia message service
(MMS), e-mails and mobile chatting (ITU, 2002). Mobile entertainment services
include ring-tones, digital characters, horoscope, mobile gamming, mobile video,
and mobile music. Information content services deliver information contents such as
weather news, maps, sport news, traffic information, location based information and
news headlines. Finally, commercial services enable consumer to purchase financial
transactions, booking online, shopping and payment online. All of four categories
can be found in Vietnam.



Theory of Reasoned Action

Figure 2.1 shows a model of the Theory of Reasoned Action (TRA), which is
proposed by Fishbein and Ajzen (1975).

Figure 2.1. The Theory of Reasoned Action model (Ajzen & Fishbein, 1980)
The ultimate objective of TRA is to predict and discover an individual’s behavior
(Ajzen & Fishbein, 1980). Ajezen and Fishbein recommend that individual’s actual
behavior can be determined by considering his or her prior intention along with the
beliefs that a person have for the given behavior. According to TRA, individual’s
intention consists of two basic determinants: attitude that a person has toward the
actual behavior and subjective norm associates with the behavior in question.
Suggesting that attitude of a person toward behavior (A) can be measured by
calculating the sum of the product of all salient beliefs ( : consequences of
performing that behavior) and an evaluation ( ) of those consequences, we have the
formula as below:

The subjective norm (SN) can be determined by considering the sum of the product
of a person’s normative beliefs (n ) which is the perceived expectation of other
individuals or groups and his or her motivation to comply (

). The formula for

measuring the subjective norm along with an actual behavior:
SN = ∑

Hence, the individual behavior intention (BI) can be determined by one formula as
BI = A + SN


TRA provides a useful model that can explains and predicts the actual behavior of
an individual fairy well.

Theory of Planned Behavior

Ajzen (1985) has extended TRA model by proposing the Theory of Planned
Behavior (TPB). Actually, the TPB model is not different from TRA model. In
addition, it takes into account one new construct: perceived behavioral control
(PBC). PBC refers to the perception of control over performance of a given
behavior. PBC is predicted by the effect of two beliefs: control belief and perceived
facilitation. Control beliefs include perceived availability of skills, resources and
opportunities, whereas perceived facilitation is the personal assessment of available
resources to the achievement of a given set of outcomes (Mathieson, 1991). Figure
2.2 shows the model for the TPB.

Figure 2.2. Theory of Planned Behavioral (Matheison, 1991)

Technology Acceptance Model

The Technology Acceptance Model (TAM) applies Fishbein and Ajen’s Theory of
Reasoned Action (TRA) as theoretical basic to explain causal relationship between
the variables in the model (Davis, Bagozzi & Warshaw, 1989). See Figure 2.3 for
the first modified version of TAM.


Figure 2.3. First modified version of TAM (Davis et al, 1989)
This version posits that technology acceptance can be explicated by two variables:
“perceived usefulness” and “perceived ease of use”. Perceived usefulness is defined
as “the degree to which a person believes that using a particular system would
enhance his or her job performance”. Perceived ease of use is defined as “the degree
to which a person believes that using a particular system would be free of effort”.
Although perceived usefulness and perceived ease of use are not only variables
affecting acceptance, they seem to hold a central role (Davis, 1989). This model
also shows that there is case when given system, perceived usefulness and one
person may have a strong behavior intention to use the system without pass through
any attitude. That means there is a direct link between perceived usefulness and
behavioral intention bypass the attitude variable.
Davis et al. (1989) use above model to deploy a study with 107 users to measure
their intention to use system after one-hour introduction about the system and repeat
14 weeks later. Their results show that both “perceived usefulness” and “perceived
ease of use” influence directly on behavior intention to use, thus they eliminate the
role of attitude construct from the model. See Figure 2.4 for the final version of

Figure 2.4. Final version of TAM (Venkatesh & Davis, 1996)


2.4.1. Revised Original TAM with Separate Affective and Cognitive Attitude
As previous review, attitude is one construct of first version of TAM. It uses
Fishbein and Ajen’s Theory of Reasoned Action (TRA) as theoretical basic to
explain the causal relationship between the variables in model (Davis et al., 1989).
However, in the last version, Davis and his colleagues eliminate attitude construct
out of model (Figure 2.4). Although Davis and his colleagues omit attitude from
TAM, many other studies have used the original TAM (included attitude), for
instance, Agarwal and Prasad (1999); Lu, Yao, and Yu (2005); Curran and Meuter
(2005). Thus, it is extremely difficult to compare these studies with contradictory
findings about attitude since consistent measures of attitudes are not used across
Based on TRA, TAM conceptualizes attitude as an affective unidimensional
construct. Contrary to TAM, Cacioppo, Petty, and Crites (1994) have argued: “the
most common classification for the basis of attitude is affect and cognition”. The
affective dimension of attitude focuses on how much the person likes the object of
thought and measures the degree of emotional attraction toward the object. On the
other hand, the cognitive dimension of attitude refers to an individual’s specific
beliefs related to the object and consists of the evaluation, judgment, reception or
perception of the object of thought based on values.
Yang and Yoo (2004) believe that attitude might have important effects on
information system use so that it needs to be reconsidered in the TAM. They also
propose that instead of eliminating the attitude construct as Davis et al. (1989)
worked, two cognitive and affective attitude dimensions are considered (Figure 2.5).
In there, “the cognitive dimension of attitude directly influences individual
information system use, while the affective dimension needs to be treated as an
overcome variable of its own”. Consistent with this perspective, the cognitive
attitude describes the expected performance of the system and the affective attitude
is closely related to the appeal and usability of the system (Zaad & Allouch, 2008).


Figure 2.5. TAM with Affective and Cognitive Attitude. (Yang & Yoo, 2004)
Based on the TAM from Davis et al. (1989), Yang and You’s (2004) research, with
the addition of behavioral intention back as mediator between attitude and usage,
Wang and Liu (2009) develop a conceptual model of cognitive and affective
attitude toward behavioral intentions to use Railway’s Internet Ticket System in
Taiwan. The results of their case studies show that both affective and cognitive
attitudes positively influence behavioral intention (Figure 2.6).

Figure 2.6. Revised TAM with Behavioral Intention, Affective and Cognitive
Attitude (Wang & Liu, 2009)
According to Yang and Yoo (2004), the affective dimension of attitude is
influenced by beliefs and the beliefs can be evaluative or non-evaluative (true or
false). The cognitive attitude can be assigned as an evaluative belief and developed
from non-evaluative beliefs and values, whereas, evaluative beliefs in turn develop
into affective attitude (like or hate). Therefore, Yang and Yoo (2004) point out that
“there is a hierarchical relationship among these four constructs: affective attitude is
influence by cognitive attitude, which is affected by non-evaluative beliefs, which is


in turn developed by values”. The empirical test of Yang and Yoo (2004) found a
positive influence of cognitive attitude on affective attitude.
Again, “attitude” might also have an effect beyond a direct impact on intention.
Several studies have investigated the positively effect of attitude on behavioral
intention, such as, the original TAM (Davis, 1986), the models of Taylor and Todd
(1995a), Morris and Dillon (1997).
2.4.2. Perceived Convenience – An External Variable of TAM
To consumers who use products or services, convenience depends on effort and
time (Berry, Seiders & Grewal, 2002). Therefore, when a product or service saves
time and effort for a user, it is considered convenient. Some researchers posit that
product or service is convenient when it lowers the emotional, cognitive and
physical burdens for a user (Chang, Yan & Tsen, 2012). Another researcher defines
the convenience of product or service by five elements: time, acquisition, use,
execution and place (Brown, 1990). According to the definition of Brown (1990),
the perceived convenience of the wireless network is measured in set of three
elements: time, place and execution (Yoo & Kim, 2007). Yoo and Kim (2007) have
defined perceived convenience as a level of convenience toward time, place and
execution that user perceives when using the wireless network to complete a task.
Moreover, when examining the extended TAM with perceived convenience, they
found that perceived convenience do not affect intention to use directly while
perceived ease of use positively affects perceived convenience and perceived
convenience positively affects perceived usefulness.
In Cheolho and Sanghoon’s (2007) study, a set of four constructs (perceived
usefulness, perceived ease of use, behavioral intention and perceived convenience)
are used to examine a ubiquitous wireless LAN environment. The results have
showed that perceived ease of use positively affects perceived convenience;
perceived convenience positively affects perceived usefulness.


More recent research on investigating English learning through “personal digital
assistant (PDA)” (Chang et al., 2012), which indicates a significantly positive effect
of perceived ease of use on perceived convenience, perceived convenience on
perceived usefulness and perceived convenience on attitude toward using PDAs.

Perceived Mobility

Since the mobile technology has rapidly grown, mobile content services are unique
because of the mobility. We can access the content services anytime and anywhere
so that we also control our works and entertainments in different ways. Perceived
mobility in this study is the extent to which mobile content services are perceived as
being able to provide pervasive and timely connections. This factor might resist or
facilitate usage of mobile content services. Hong, Thong, Moon, and Tam (2008)
also believe that perceived mobility might have a positive relationship with
consumers’ intention to continue use the mobile content services. Ajzen (1991,
2002) theorizes that mobility such a factor is likely to affect the formulation of
behavior intention. Amberg, Hirschmeier, and Wehrmann (2003) propose that
perceived mobility is a construct specific to mobile services.
Mobility may not be satisfied if there are not enough network signal coverage areas,
the device battery is so weak or there is not enough mobile operators offering the
mobile data services. Hence, Krueger (2001) has predicted a demand for “payment
roaming” and the pressure from users for co-operative solutions. Such payment
roaming includes both the mobile users wanting to process payments while
travelling outside of network coverage or to make payments to other networks.
Buhan, Cheong, and Tan (2002) forecast that the good solutions would be able to
interact with other solutions to create a global payment network.

Research Model and Hypothesis Development

2.6.1. Theoretical Model


Taking into consideration the combined models of revised TAM with separate
cognitive and affective attitudes , perceived convenience and perceived mobility,
the specific relationships among the TAM constructs and identified variables - the
theoretical model for this research are proposed (See Figure 2.7).

Figure 2.7. The proposed theoretical model
TAM explains the relationships between perceived ease of use, perceived
usefulness, attitude toward using technology and behavioral intention as the
followings: (1) perceived ease of use positively affects perceived usefulness; (2)
perceived ease of use and perceived usefulness positively affect attitude toward
using technology; (3) attitude toward using technology positively affects behavioral
intention (Davis, 1986).
Regarding perceived ease of use positively affects perceived usefulness; there are
many empirical tests, such as Davis (1986), Yang and Yoo (2004), Wang and Liu
(2009), which prove that users perform well in tasks when they do not need to pay
much effort. Therefore, hypothesis H1 is proposed as follows:
H1: Perceived ease of use positively affects perceived usefulness.
Back to research of Yang and Yoo (2004), the attitude toward using technology is
developed to affective and cognitive attitude. In there, attitude has both affective
and cognitive components. Zaad and Allouch (2008), Petty et al. (1994) have
argued: “the most common classification for the basic of attitude is affective and
cognitive”. As attitude has already been explained in the literature review of this
chapter, many studies point out the different mediating role of two attitudes between


perceived usefulness, perceived ease of use and behavioral intention, such as Yang
and Yoo (2004), Wang and Liu (2009), Alhabahba and colleagues (2012). As a
result, this study attempts to answer the question “what is the causal relationship
between two belief constructs in TAM (perceived usefulness and perceived ease of
use) and two attitude constructs (affective and cognitive attitude) leading to
behavioral intention?” (Yang & Yoo, 2004). Therefore, based on the changes in the
attitudes and the original TAM framework, these following hypotheses are proposed
to reflect the relationships between the addition of the two construct of attitudes and
other variables:
H2: Perceived usefulness positively influences cognitive attitude.
H3: Perceived usefulness positively influences affective attitude.
H4: Perceived ease of use positively influences cognitive attitude.
H5: Perceived ease of use positively influences affective attitude.
In addition, there are many studies confirmed attitudes have a positively influences
behavioral intention, such as Taylor and Todd (1995a), Morris and Dillon (1997),
and Davis (1986). According to Yang and You (2004), “The dyadic view presumes
the affective and cognitive to be independent variables that affect behavioral
intention”. Other recent research on the Railway’s Internet Ticket System (Wang &
Liu, 2009), which demonstrates that both cognitive and affective attitudes positively
influence behavioral intention. In their research, the beta coefficient from cognitive
attitude to behavioral intention is stronger than the beta coefficient from affective
attitude to behavioral intention. Thus, using cognitive and affective attitude
constructs instead of single attitude leads to the following hypotheses:
H6: Cognitive attitude positively influences behavioral intention to use
mobile content services.
H7: Affective attitude positively influences behavioral intention to use
mobile content services.


Reviewed the literature, cognitive attitude also positively influences affective
attitude (Yang & You, 2004). The empirical study of Wang and Liu (2009) shows a
positive influence of cognitive attitude on affective attitude. Thus, there is likely
such evaluative beliefs (cognitive attitude) in turn develop into clients’ affective
attitude. Hence, it hypothesizes that:
H8: Cognitive attitude positively influences affective attitude.
The literature in this chapter shows that perceived convenience is an external
variable of TAM. Yoon and Kim (2007), Chang et al. (2012) found that the
perceived ease of use positively affects perceived convenience and perceived
convenience positively affects perceived usefulness. Namely, the easier the use of
mobile system, the more convenient a user perceives it and the more convenient the
user feels the mobile system is, the more useful one perceives it to be. Based on
these empirical results, this research defines three dimensions of perceived
conveniences are place, time, execution, and then proposes the hypotheses:
H9: Perceived ease of use positively affects on perceived convenience.
H10: Perceived convenience positively affects on perceived usefulness.
Finally, back to the review of perceived mobility, perceived mobility is an addition
variable to TAM, since the perceived mobility has been assigned as important factor
that makes user accept the system (Hong et al., 2008; Amberg et al., 2003; Ajzen,
2002). Therefore, this study hypothesizes that perceived mobility might have a
positive relationship with user’s behavioral intention to use mobile content services.
H11: Perceived mobility has a positive influence on consumers’ behavioral
intention to use mobile content services.
The summary of supporting works for research proposition and theoretical model
are presented on Table 2.1 and Figure 2.7, respectively.