Department of Policy and Planning Sciences
Discussion Paper Series

No.1331

Factors towards Intention to Use Mobile Entertainment
Services among Young Users: Comparative
Cross-country and Cross-temporal Studiesby
Qazi M. GHYAS, and Fumiyo N. KONDO

Jul 2015

UNIVERSITY OF TSUKUBA
Tsukuba, Ibaraki 305-8573
JAPANFactors towards Intention to Use Mobile Entertainment Services
among Young Users: Comparative Cross-country andCross-temporal Studies
Qazi Mahdia Ghyas1) and Fumiyo N. Kondo2)
Abstract
This study aims to understand how mobile usage in Japan and Bangladesh access mobile
entertainment services among young users. We examined a cross-national and across-temporal analysis of behavioral intention factors by a combined technology acceptancemodel and the theory of planned behavior model. By using the factors of perceived value,
perceived behavioral control, subjective norm and attitude, we analyzed three sets of data: (1)Bangladesh in 2014; (2) Japan in 2014; and (3) Japan in 2009. Results show that consumer’s
intention to use mobile entertainment services for Bangladesh 2014 was the same as those forJapan 2009: perceived value and attitude were found to have positive significant influence on
the intention to use the services. However, results for Japan 2014 show differently fromthose for Japan 2009: no significant influence on the intention to use the services forperceived value and attitude, but significant for perceived behavioral control.
Keywords: Mobile entertainment services; Cross-country; Cross-temporal; Young users;
TAM-TPB model; Perceived value; Perceived Behavioral Control; Attitude, Behavioral
intention to use
1.

Introduction

Mobile phone services offer a wide range of purposes from business to entertainment anda mobile phone has become a ubiquitous tool in technologically developed or developing
places all around the world. There are nearly 7 billion mobile subscriptions worldwide (TheInternational Telecommunication Union). This is equivalent to 95.5 percent of the world
population. Mobile market growth is being driven by demand from the developing world, led
by rapid mobile adoption in China and India, the world's most populous nations. There are
5.4 billion mobile subscriptions in the developing world – that’s 78 percent of global. Mobile
penetration in the developing world now is 90.2 percent, but there is still potential for growth.
On the other hand, mobile subscriptions in the developed world is rapidly reaching saturation
point. There are 1.5 billion subscriptions in developed nations with 120.8 percent mobile
penetration. There is already more than one mobile subscription per person in developed
nations.
There are significant business opportunities in relation with mobile entertainment service
all over the world. Even there is little room for growth in developed countries; the sales ofmobile entertainment services by global internet companies such as DeNA and GREE are
expanding the market of mobile services, with a focus on social games, e-commerce, and web
services for mobile devices (kondo and Ishida, 2014). It has been reported that by 2015
mobile content could be worth well in excess of $1 trillion, with voice comprising only a
10% share of the market. Especially, the market value of mobile entertainment service isincreasing markedly and the number of users is growing rapidly. For the first time since thelaunch of the iPhone back in 2007, Japan is the top grossing country for iPhone’s mostpopular content: games. Japan that has a history of spending billions on mobile games before
the arrival of smartphones and tablets, is taking to the app stores of Google and Apple big1) Department of Social Systems and Management, Graduate School of Systems & Information
Engineering, University of Tsukuba, Japan
2) Division of Policy and Planning Sciences, Faculty of Engineering, Information and Systems
University of Tsukuba, Japantime. It has estimated that 160 billion apps will be downloaded in 2017, gaming being one of
the fastest growing segment for Japan. For Bangladesh, general entertainment services (news,
religion, games, music, etc.) are the largest used services, followed by social media, ringtone
and the others (services to recharge prepaid, pays for calls/SMS, missed called alerts, etc.) ina sequential order (GSMA Intelligence, 2014). It seems that operators have a large footprintin the mobile entertainment services sector and the market is rapidly expanding in Japan and
Bangladesh. With the growing importance of Mobile entertainment services, it is important to
identify the factors relating to user intention to obtain mobile entertainment services andinvestigate their antecedents for current and future service development.
User’s perception regarding the use of mobile entertainment services have been identified
by exploring their behavioral intention in past researches. Quan et. al., (2010) researched acategory of mobile transaction services in China using a TAM-TPB (explained in section 3)
approach. They found that a combined TAM-TPB model is suitable for evaluating thepotential “intention to use” specific mobile commerce services. Nysveen et al., (2005)
developed and tested a modified TAM-TPB in order to explain consumers’ “intention to use”
four types of mobile services. Even though research in mobile service behavioral intention
has used the TAM-TPB extensively, the literature contains little regarding its applicability tomobile entertainment services market. So, here we apply the novel approach to assess thepotential adoption of mobile entertainment services through a cross-national analysis basedon the technology acceptance model (TAM) and the theory of planned behavior (TPB).
Here we examine whether there are differences within the antecedents of “intention to use”
when the services used differ, focusing on multiple mobile information services. Most paststudies have focused on adoption of task-oriented technology. Adoption ofentertainment-oriented technology such as mobile entertainment service has hardly beenaddressed. As indicated in the study by Hsu et. al, (2004), the factors influencing the adoptionof entertainment-oriented technology are different from the factors influencing the adoptionof task-oriented technology. This provides a need for undertaking this study. We also do across-national analysis between developed and developing countries, so that we are able togeneralize the results and observe any differences. To understand mobile entertainment
service acceptance, the TAM-TPB is suitable as it is suggested by Kondo and Ishida, 2014.
We ground our research framework in TAM-TPB model of Kondo and Ishida (2014) with
their factors of perceived value, perceived behavioral control, attitude and subjective norm on
mobile entertainment service intention behavior of young people. More details of the model
will be provided later.
The rest of the article is organized as follows. The next sections describe thecross-cultural studies of mobile entertainment services, overview of mobile services in Japanand Bangladesh, the TAM, the TPB, the TAM-TPB model and the background for this study
and the hypotheses. These are followed by sections on methodology and statistical results.
The last sections present a summary of the findings, conclusions, and directions for future
research.
2.

Cross-Cultural Studies of Mobile Entertainment Services

As the use of mobile devices and Internet has spread globally, mobile technology andm-commerce usage patterns vary across different cultures. Cross-cultural issues are highly
related to mobile commerce adoption. However, little research has been conducted oncross-cultural issues in the mobile entertainment environment. This might be due to thedifficulties in conducting culture research (Straub et al. 2002). Okazaki et al., (2008)
examined user “intention to use” mobile game application for USA, Spain and Czech
Republic customers by using TAM (explained in section 4) model among young users.

Kondo and Ishida (2014) have examined the “intention to use” most frequently used mobile
entertainment services (game, music, ringtone) for Japan and USA by using TAM-TPB
model.
Very few prior studies focus on the antecedents of consumer acceptance and theirintentions to use mobile entertainment services in a cross-cultural context. In a global
business environment, researchers and practitioners need to develop deeper understanding of
the impact of consumer characteristics and their behavior on the acceptance of mobile
entertainment services in different cultures.
3.

Overview of Mobile Services in Japan and Bangladesh

There are 14 countries in the world with over 100 million mobile subscriptions; among
them Japan is ranging seven and Bangladesh is ranging twelve. In Japan, there were 111
million mobile subscribers, 92.5 million of them use mobile data services – that’s 83 percentof the subscriber base, or 72 percent of the population (Telecommunications Carriers
Association Japan, 2010). There are about 69 million mobile Internet users, which equals 62
percent of mobile subscribers in total. According to ComScore's excellent 2010 Mobile Yearin Review (February 2011), 16.3 percent of Japanese customers are playing games and 12.9
percent are listening music via mobile.
In recent years, the uses of mobile phones have witnessed tremendous growth in
Bangladesh. The total number of Mobile Phone subscriptions has reached 118.49 million at
the end of September 2014. Mobile phone penetration rate was 69.5% at August, 2014
(BTRC, 2014). Mobile service users among adults are actually above 50% in Bangladesh(GSMA intelligence, 2014). Bangladesh is predominantly a prepaid and 2G markets, as 3G
has only recently been deployed following delays in the auction process (3G today makes up
just 2% of connections). However, mobile internet penetration is over 20%, mostly from 2Gfeature phones. In other words, there is a potential desire for internet access that is growing.
Mobile operators in Bangladesh are concentrated in the entertainment category. Mobile
services in Bangladesh have categorized in two categories: Entertainment and development
services. Among them, general entertainment services (news, religion, games, music, etc.) aremostly used services. Table 1 shows the mobile market of Japan and Bangladesh by numberof subscriptions in 2013.
Table 1

Mobile markets by number of subscriptions in 2013
Mobilesubscriptions

Population

3G/4G
subscriptions

Rank

Countryin millions

in millions%ofpopulationin millions% of population

7

Japan

137.9m127.6m

108.00%108.8m85.30%

12

Bangladesh

116.0m154.7m

75.00%

34.6m*

22.3%*

Source: Paul Lambert, Informa (Q2 2013) ; national telecoms regulators* Bangladesh: mobile Web subscribers (not necessarily 3G)

4.

Theoretical Framework

Emerging information technology cannot deliver improved organizational effectiveness if
it is not accepted and used by potential users. Technology Acceptance Model (TAM) andTheory of Planned Behavior (TPB) are the most successful and effective models formeasurements of digital device based usage among practitioners and academics.
4.1
TAM
TAM provides a basis for discovering the impact of external variables on internal
perceptions (beliefs), attitudes, and intentions (Davis et al., 1992). According to TAM, aperson’s performance of a specific behavior is determined by his/her behavioral intention(BI) to perform the behavior and BI is jointly determined by the person’s attitude (A) andsubjective norm (SN) concerning the behavior in question (Ajzen and Fishbein, 1980).
Fishbein and Ajzen (1975) define attitude as “an individual's positive or negative feelings(evaluative affect) about performing the target behavior”. Individuals form attitudes toward a
behavior by evaluating their beliefs through an expectancy-value model. For each attitude
toward a behavior, individuals multiply the belief strength by the outcome evaluation andthen sum the entire set of resulting weights to form the attitude.
4.2

TPB
TPB includes an additional construct, perceived behavioral control (PBC). PBC is definedas one’s perception of the difficulty of performing a behavior (Taylor and Todd, 1995). The
TPB views that the people have control over their own behavior lies on a continuum frombehaviors that are easily performed to those requiring considerable effort and resources.
4.3

The TAM-TPB Model
In order to form a behavioral intention, customers concern to know what kind of benefit
or values them can obtain from the services, this is perceived value. Perceived value defines
as “the consumer’s overall assessment of the usefulness of a product/service based on
perceptions on what is received and what is given (Heinonen, 2004). The consumer’s overall
assessment of the usefulness of a service depends on the quality and price information forusing the services. Perceived value defines the utility derived from the perceived quality andperceived cost when users accept a mobile commerce (Sweeney et al., 1999). The more users
will be beneficial, the more they will intend to use the services. Use intention measures thepossibility that a consumer will use the service. The more the user intention, the more their
willingness will be to use the service (Dodds et al., 1991). The willingness to use is affectedby the perceived value as suggested by Groth (1995). Perceived value have positive
association with decision making which has also indicated by (Zeithaml, 1988). Therefore,
we will observe whether perceived value has any influence on intention to use mobile
entertainment services.
Several researches have done by using different theoretical model for mobile service
market. TAM model was investigated for mobile application use by Seok Kang, 2014. TPB
Model was studied for customers in India on mobile entertainment services (games, music,
videos, camera, and internet access) (Kumar and Janaki, 2011). There are very few studies
which used TAM-TPB model for mobile entertainment service.
The TAM-TPB is an effective predictive model in field that leads to a certain degree of
behavioral change by individuals have summarized by Kondo and Ishida (2014). They
proposed to use the concept of perceived value. Our TAM-TPB model will be constructed byusing the following five concepts to deal with entertainments services in Japan and
Bangladesh mobile markets: Behavioral control, perceived value, attitude towards mobile
services, behavioral intention to use, and subjective norm. Therefore, we examine user
behavioral intentions based on the model shown in Figure 1. Thus, we postulate the following
hypotheses:

H1. Perceived behavioral control positively influences perceived value in Japan 2009, Japan
2014 and Bangladesh 2014.
H2. Perceived value positively influences attitude in Japan 2009, Japan 2014 and Bangladesh
2014.
H3. Perceived behavioral control positively influences attitude in Japan 2009, Japan 2014 andBangladesh 2014.
H4. Attitude positively influences behavioral intention of overall entertainment services inJapan 2009, Japan 2014 and Bangladesh 2014.
H5. Perceived value positively influences behavioral intention of overall entertainment
services in Japan 2009, Japan 2014 and Bangladesh 2014.
H6. Perceived behavioral control positively influences behavioral intention of overall
entertainment services in Japan 2009, Japan 2014 and Bangladesh 2014.
H7. Subjective norm positively influences behavioral intention of overall entertainment
services in Japan 2009, Japan 2014 and Bangladesh 2014.
The authors define mobile entertainment as any type of leisure activity consumed on
mobile devices that utilizes the wireless telecommunication network which incurs a cost upon
usage and interacts with service providers (Wong and Hiew, 2005). They included
downloading ring tone, logo, music and movie; playing games, accessing location-based
entertainment services and etc. as mobile entertainment services. Usage patterns of mobile
entertainment services may have differences or similarities among countries. Four factors ofmobile services have identified by Ghyas et al., (2011): Information intensiveness (Factor 1),
entertainment (Factor 2), low penetration service (Factor 3), and communication service(Factor 4) on Japan and USA data. Factor 2 represents services with entertainment
characteristics. To investigate the intention to use behavior, we focus on Factor 2entertainment services (game, music and ringtones) for our cross-national analysis. Kondo
and Ishida (2014) also researched these services for USA and Japan. Here we consider Game,music and ringtone as mobile entertainment services for the comparison of Bangladesh andJapan.

Figure 1.

The TAM-TPB model of Kondo and Ishida (2014)

5. Methodology

5.1

Data Collection
Two surveys were conducted in Japan of 214 mobile phone users at a university in
Ibaraki and 66 users at a university in Tokyo. An online questionnaire was distributed. The
Japanese surveys were conducted in Ibaraki and Tokyo in November, 2009 and in Ibaraki
from August to October, 2014. Valid responses were received from 173 individuals in 2014
and 242 respondents in Japan in 2009.
Five surveys were conducted in faculty of Business Administration from five universities
in Dhaka, Bangladesh in the period of June-August, 2014. Individual mobile phone users
from three different private universities were invited to participate and 135, 40 and 50
responses were obtained, respectively. In addition, two surveys were conducted in two public
universities, resulting in 68 and 33 responses from mobile phone users, respectively. In total,
326 subjects received the questionnaires, resulting in 276 completed responses.
We used responses from young users who were 18-30 years for this research as it has
been found that the youth is the lead segment in adopting mobile entertainment (Andreou etal. 2002). We also used responses from experienced mobile device users, following theapproach of Kondo and Ishida (2014) that experience is required to form opinions. They
eliminated non-mature cell phone users in order to eliminate the strong influence of IT
dexterity in their research. We also eliminated 2 responses from the data set, which werefrom non-user and non-mature (less than 6 months) cell phone users. Therefore, after theelimination, in table 2 there were 251 respondents for Bangladesh in 2014, 173 for Japan in2014 and 242 for japan in 2009.
Table 2 Demographics of Respondents to Q1: How many years have passed since you
started to use your first cell phone?
Bangladesh 2014
Category

Values

N=251

(%)

Age

18-20
20-25
25-30
No answer
Male
Female6 months - less than 1 year

35
203
13
N/A
163
88
1

13.9
80.9
5.2
N/A
65
35
0.4

1 year - less than 2 years
2 years - less than 3 years

12
20

4.8
7.9

3 years - less than 5 years
5 years - less than 10 years

48
142

19.1
56.6

10 years or more

28

11.2

Gender
Q1

Japan 2009

Japan 2014

N=242

(%)

N=173

(%)

40
160
12
30
162
80
1
4

17
66
5
12
67
33
0
2

80
93
N/A
N/A
143
36
1
7

46
54
N/A
N/A
83
21
1
4

6
57

2
24

5
51

3
29

158
16

65
7

100
9

58
5

Our initial survey items have shown in table 3. These items were adapted from previous
studies, Kondo and Ishida (2014). We used the following sets of 5 and 10 point likert scales:
Strongly disagree/strongly agree; unfair/fair; very low/ very high; definitely won`t/ definitely
will.
Table 3

Constructs name, item, type and source

Construct

Items

Type



SourcesnameBehavioral
intention
touse

Attitudetowards
mobileservices
Subjective
normPerceived
behavioral
control

Perceived
value

Either (1) “Do you have theintention to use the following
mobile information service on acontinuous basis?” or (2) “Do
you have the intention to use thefollowing mobile information
service in the future?” (3)Please rate the intention to use
for the following service bychecking.
(1) Overall, mobile information
services are important to me. (2)Overall, mobile information
services can satisfy different
needs of mine.
(1) People important to me
think I should use mobile
information services (family
members, lovers, friends, yourboss etc.) (2) It is expected thatpeople like me to use mobile
information services. (3) People
I look up to expect me to use
mobile information services.
(1) I feel free to use the kind of
mobile information services I
like to. (2) Using mobile
information services is entirely
within my control. (3) I have thenecessary means and resources
to use mobile information
services.
(1) Please rate the quality ofmobile services given the prices
you pay. (2) Please rate theprices of mobile services given
the quality you receive.

5-point
LikertDefinitely
won’t
definitely will

--

Kondo
and
Ishida (2014)

5-point
LikertStrongly disagreestrongly agree

--

Kondo
and
Ishida (2014),

5-point
Likertstrongly disagreestrongly agree

--

Kondo
and
Ishida (2014)

5-point
Likertstrongly disagreestrongly agree

--

Kondo
and
Ishida (2014)10-point
bipolarUnfair -- fair

5.2

Kondo
and
Ishida (2014),

Measurement Scales
In order to assess the reliability and validity of the constructs, several measures werecomputed. The composite reliability (CR), average variance extracted (AVE) and the squaredinter-correlations (SIC) are reported for Japan 2009, Bangladesh 2014 and Japan 2014 in
table 4, 5 and 6. The coefficients for each factor are shown in table 4, 5 and 6. Since allvalues exceeded 0.7, it was concluded that the items of these constructs had common parts.
The threshold for CR is 0.70 or higher (Bagozzi & Yi, 2011). All AVE values were above 0.5
indicating convergent validity. For discriminant validity, the AVE of each construct should
exceed the SIC of the construct with other constructs. As shown in the table 4, 5 and 6, thisholds true for all constructs.

Table 4

CR, AVE, and SIC for Each Construct for Japan 2009

Constructs

CR

Constracts

AVE

1

2
AVE and SIC

3

4

ATT

0.893

0.807

0.807

BI

0.883

0.716

0.220

0.716

PBC

0.844

0.652

0.374

0.165

0.652

PV

0.862

0.757

0.258

0.226

0.216

0.757

SN

0.902

0.754

0.534

0.216

0.426

0.261

5

0.754

Note: The values of AVE are on the diagonal and those of SIC on the off-diagonal.
Table 5
CR, AVE, and SIC for Each Construct for Bangladesh 2014
Constracts
Constructs

CR

ATT

AVE

1

2
AVE and SIC

3

4

0.900

0.818

0.818

BI

0.889

0.728

0.269

0.728

PBC

0.865

0.681

0.406

0.102

0.681

PV

0.862

0.757

0.287

0.232

0.209

0.757

SN

0.892

0.733

0.528

0.211

0.635

0.215

5

0.733

Note: The values of AVE are on the diagonal and those of SIC on the off-diagonal.
Table 6
CR, AVE, and SIC for Each Construct for Japan 2014
Constracts
Constructs

CR

ATT

AVE

1

2
AVE and SIC

3

4

0.920

0.852

0.852

BI

0.778

0.554

0.180

0.554

PBC

0.838

0.639

0.556

0.269

0.639

PV

0.882

0.790

0.297

0.045

0.134

0.790

SN

0.854

0.663

0.563

0.112

0.516

0.262

5

0.663

Note: The values of AVE are on the diagonal and those of SIC on the off-diagonal.
5.3
Analytical Method
The research models were analyzed for Bangladesh 2014, Japan 2009 and Japan 2014 via
structural equation modeling (SEM) by using the statistical software AMOS version 22.0.
The SEM is a statistical approach to understand social and natural phenomena by identifying
a causal relationship between the observed variables and the latent variables that cannot be
observed directly. A useful and powerful aspect of SEM is the test of hypotheses acrosssamples (Bagozzi & Yi, 2011). In order to compare Japan 2009 and Bangladesh 2014 data,
multi-group analyses were conducted on the models to determine whether there arestatistically significant differences between the parameter estimates for Japan and United
Bangladesh. The research model for Japan 2014 data were analyzed individually.
Based on fit indices, the best was obtained. the classification of these model fit indices asroughly two types, absolute fit indices and comparative fit indices summarized by Hooper et.
al. (2008). The absolute fit indices include goodness-of-fit (GFI), adjusted goodness-of-fit(AGFI) and root mean square error of approximation (RMSEA). The comparative fit indicesinclude Akaike Information Criterion (AIC), Browne-Cudeck Criterion (BCC), Normed Fit
Index (NFI) and Tucker-Lewis Index (TLI). Threshold guidelines exist for these indices. The
overall fit indices of the revised models were examined.

6. Results
We have conducted Three TAM-TPB models in order to establish the best-fit index. The
three TAM-TPB models are as follows: The research model in Figure 1 was Model 1. Model
2 and 3 were created by using a step-by-step approach. Model 2 was created by deleting thenon-significant path from subjective norm to behavioral intention in Model 1. Model 3 wascreated by deleting the non-significant path from PBC to behavioral intention in Model 2.
Table 7 shows the fit measures for the three models for Bangladesh 2014 and Japan 2009 data.
All fit indices are in the acceptable range for model 3. These results indicate that Model 3 has
the smallest AIC value (183.8) and has the information criterion with the most significant
estimated coefficients. Therefore, it was selected as the best model. The values for GFI(0 .963) and AGFI (0.931) exceeded 0.9 and are thus in the acceptable range. The RMSEA is
0.031, which is less than 0.05 and thus considered to be a good fit (Tsang et al. 2004). Table
7 shows that the goodness of fit of generated or re-specified models are better compared tothe hypothesized model. Table 8 shows the combined multi-group used in Model 3. In
summary, generated values for model 3 are superior to those for the other models.
Table 7
Values of Model Selection Criteria on Each Model for Japan 2009 and
Bangladesh 2014 (N=214)
Multi-group analysis
Model 1 (hypothesized model)
Model 2 (without SN->BI)
Model 3 ( without PBC->BI)

Table 8
2014

GFI
0.889
0.963
0.963

AGFI
0.825
0.930
0.931

RMSEA
0.79
0.031
0.031

CFI
0.851
0.981
0.982

AIC
559.6
186.2
183.8

BCC
568.9
191.8
189.2

Comparison of Model Fitting on Model 3 between Japan 2009 and Bangladesh
GFI

Sample size

AGFI

RMSEA

CFI

AIC

BCC

TLI

Japan 2009

0.961

214

0.928

0.048

0.978

94.4

97.1

0.966

Bangladesh 2014

0.964

214

0.935

0.038

0.985

89.4

92.1

0.978

Multi-group

0.963

214

0.931

0.031

0.982

183.8

189.2

0.972

The final model for Japan is shown in Figure 2 and for Bangladesh in Figure 3. The
coefficients of the measurement variables that explain the latent variables are all significant at
the 5% level. Therefore, the measurement variables generally appear to explain the latentvariables well. Table 9 shows the standardized estimates for Japan 2009 and Bangladesh
2014. All of the constructs for both countries are statistically significant at the 10% level orless. These results show that the models for the two countries fit well and that they werestructurally similar. Hypotheses from H1 to H5 in model 3 were supported for Japan 2009
and Bangladesh 2014 in our analysis.
We also observed slightly different levels of standardized estimates for the path from
PBC to attitude and attitude to behavioral intention between two countries. The standardized
values of the estimates for the path from PBC to attitude in the Bangladesh data (0.439) werelarger than those for the Japan (0.392). Similarly, for the path from attitude to behavioral
intention, the estimated value for Bangladesh (0.230) was larger than that for Japan (0.172).
Table 9Standardized Estimates of Model 3 for Japan 2009 and Bangladesh 2014

Hypotheses

Paths

H1

Perceived
behavioral
control!Perceived value
Perceived behavioral control !Attitude
Perceived value !Attitude
Attitude !Behavioral intention
Perceived value ! Behavioral intention

H2
H3
H4
H5

Japan 2009

Bangladesh
2014

z- value

0.283**

0.279**

0.541

0.392***
0.252**
0.172**
0.221**

0.439***
0.265**
0.230**
0.220**-1.10
0.15-0.322
0.316

Note: ***p <0.001; **p <0.05; *p <0.1
These results appear to generally the strength of consumers’ perceptions on the primaryconstructs to behavioral intention could be similar among countries and cultures. In order toconfirm the differences between the estimated values, we conducted Z-tests on the level
differences between the estimates for Japan 2009 and the Bangladesh 2014. The Z value
shows no statistically significant differences exist in the estimated values. The basic causal
structure of TAM-TPB research model could be equivalent across these countries after
adjusting of time differences.

Figure 2.

Path Analysis of Model 4 for Japan 2009 at significance level
Note: ***p <0.001; **p <0.05; *p <0.1.

Figure 3.

Path Analysis of Model 4 for Bangladesh 2014 at significance level
Note: ***p <0.001; **p <0.05; *p <0.1.

The three TAM-TPB models for Japan 2014 data have observed. The research model inFigure 1 was Model 1. Model 2 was created by deleting the non-significant path fromsubjective norm to behavioral intention in Model 1. Model 3 was created by deleting thenon-significant path from PV to behavioral intention in Model 2. Table 10 shows the fit
measures for the three models for Japan 2014 data. All fit indices are in the acceptable range
for model 3. These results indicate that Model 3 has the smallest AIC value (89.47). The
values for GFI (0 .957) and AGFI (0.921) exceeded 0.9 and are thus in the acceptable range.
The RMSEA is 0.043, which is less than 0.05 and thus considered to be a good fit (Tsang etal. 2004). The final selected model 3 with the standardized estimates for Japan 2014 is shownin Figure 4. All of the constructs are statistically significant at the 10% level or less except
the path from perceived value and attitude to behavioral intention. But perceived behavioral
control becomes significant positive relation with intention to use for Japan in 2014. This
makes it convenient for Japanese customers in 2014 to use mobile entertainment services,giving perceived behavioral control a significant effect on intention to use. Hypotheses from
H1 to H3 and H6 in model 3 were supported for Japan 2014 data. These results of japan 2009
and Japan 2014 seem that the relationship of the perceptions of value, control belief andattitude to behavioral intention could vary in the same country (Japan) at different times.
Table 10
Values of Model Selection Criteria on Each Model for Japan 2014
Model 1 (hypothesized model)
Model 2 (without SN->BI)
Model 3 ( without PV->BI)

GFI
0.892
0.957
0.957

AGFI
0.830
0.919
0.921

RMSEA
0.106
0.045
0.043

CFI
0.849
0.980
0.981

AIC
235.698
91.12489.473BCC
241.546
94.67792.889

TLI
0.797
0.969
0.972

Figure 4. Path Analysis of Model 4 for Japan 2014 at significance level
Note: ***p <0.001; **p <0.05; *p <0.1.
Therefore, the supported hypotheses are:
H1: Mobile users’ perceived behavioral control towards mobile entertainment services have a
direct positive impact on their perceived value to use mobile entertainment services for Japan
2009, Japan 2014 and Bangladesh 2014 and the hypothesis is accepted.
H2: Mobile users’ perceived behavior control towards mobile entertainment services highpositive impact on their attitude to use mobile entertainment services for Japan 2009, Japan
2014 and Bangladesh 2014 and the hypothesis is accepted.
H3: Mobile users’ perceived value towards mobile entertainment services has positive impact
on their attitude to use mobile entertainment services for Japan 2009, Japan 2014 andBangladesh 2014 and the hypothesis is accepted.
H4: Mobile users’ attitude towards mobile entertainment services has a direct positive impact

on their intention to use mobile entertainment Japan 2009 and Bangladesh 2014 and the
hypothesis is accepted. But Mobile users’ attitude towards mobile entertainment services has
a non-significant and negative impact on their intention to use mobile entertainment for Japan2014.
H5: Mobile users’ perceived value towards mobile entertainment services has positive impact
on their behavior intention to use mobile entertainment services for Japan 2009 and
Bangladesh 2014 and the hypothesis is accepted.
H6: Mobile users’ perceived behavioral control towards mobile entertainment services has
positive impact on their behavior intention to use mobile entertainment services for Japan
2014 and the hypothesis is accepted.
7. Discussion
Based on various theories in information systems, we empirically tested TAM-TPB
model that explains consumers’ intention to use mobile entertainment services in two distinct
cultural contexts: Japan 2009 and Bangladesh 2014. We also operationalized consumers’
intention to use mobile entertainment services in two different time contexts: Japan 2009 andJapan 2014. The results are discussed below.
7.1.

Comparative examination of Similarities
There have similarities in the path relationship of construct in Japan 2009 and Bangladesh
2014. We could not detect any statistically significant differences between Japanese andBangladeshi consumers along the dimensions of perceived value, subjective norm, behavioral
control, attitude and behavioral intention to use in the mobile entertainment service
environment. In other words, they regard these characteristics of m-entertainment service at
about equal level in Japan 2009 and Bangladesh 2014.
Our analysis found strong impact of attitude to behavioral intention, perceived behavioral
control to attitude, perceived behavioral control to perceived value, perceived value toattitude and perceived value to behavioral intention for mobile entertainment services inJapan 2009 and Bangladesh 2014. This follows same what were found in previous research of
Kondo and Ishida (2014) for 2009 mobile entertainment service data of Japan and USA. They
also similarly suggested that Users’ positive attitude is influenced by the positive value
perception and the perception of value is affected by the user’s positive perception of theirability to using entertainment services.
Our results show that behavioral control concerning mobile entertainment services is avery important driver of attitude comparing other constructs. But perceived behavioral
control is not supportive for intention to use both for Japan 2009 and Bangladesh 2014. Thisis an agreement with Kim et al (2009) for Korea on mobile entertainment services. This
indicates that levels of skillfulness in using mobile services are not related to intention to use
mobile entertainment service when the both countries were at the innovation level ofsmartphone and 3G services.
Young user’s intention to adopt mobile entertainment services appears to be determined
to a greater extent by perceived value that they are receiving. Constantiou et al (2009) for
Denmark and Uk young adults and Wong and Hiew (2005) for Malaysia have also
underlined the importance of perceived value to intention to use for mobile services. It meansthat potential new adopters will only be attracted to mobile entertainment if there is a clear
perceived benefit of the services from their perspective.
As technology grows and develops, the mobile phone appears to be at the forefront ofboth exponential growth and the evolution of culture. In this sense, the Japanese mobile
market is years ahead of the Bangladesh market and is leading the way with respect to themobile phone culture. The smart phone and 3G market in Japan expanded in 2008-2009 with
innovative and diverse formats such as personal/governmental/corporate communications. At
the “innovator” phase, disposable income has been highly correlated with early-stage mobile
phone adoption in developed and emerging markets. Marketing become generally more
important in maturing mobile service market (Kalba 2008). To shine with the 3G growing
world, Bangladesh had started its journey of smartphones and 3G on 2012-2013. Our data
deal with mobile entertainment service in Japan 2009 and Bangladesh 2014 in order to adjust
adoption stage of smartphones and 3G. There were time difference but the results seem
similar because Bangladesh in 2014 is at the same growth stage of provided 3G mobile
services where Japan was in 2009.
On the other hand, after making a cross-country inference from the convergence ofmobile entertainment service adoption, this study summarized that young consumer’ basic
perceptions on behavioral intention and attitude toward mobile entertainment services will besimilar because of the presence of global youth culture. Constaintiou et al (2009) also did not
get significant differences in the mobile services use (such as entertainment services) except
banking services between Denmark and UK. May be at that time, developed countries was insame situation of providing 3G mobile services as now Bangladesh is in 2014.
7.2.
Comparative examination of differences
Our results confirmed the significant paths from perceived behavioral control to attitude,
perceived behavioral control to perceived value, perceived value to attitude and perceived
behavioral control to behavioral intention for mobile entertainment services for Japan 2014.
There are significant differences in consumers’ perceptions of value, behavioral control,
attitude to behavioral intention between Japan 2009 and Japan 2014.
The path from perceived value to intention to use was supportive for Japan 2009. As in
Japan 2009, smartphones and 3G services was just introduced in Japan they were conscious
about their monetary and perceived entertainment benefit. The finding suggests that theJapanese young consider the value of entertainment services and cost before deciding to use
mobile entertainment services in 2009. For perceived value, the condition can be forinnovators.
Perceived behavioral control has become a supportive indicator to intention to use but
perceived value was not for Japan 2014. After being an experienced users of 3G/4G services
and smartphones, now most of the young users might feel that they have skills, ability, money,
time and others resources which form their positive intention to use mobile entertainment
services (game, music, ringtone). The result shows that PBC can be effective when most ofpeople are in later stage of using mobile information services through their smartphone and
3G/4G services.
Attitude does not find significant relationship with behavioral intention to use mobile
entertainment services in Japan 2014. With an agreement, Taylor and Todd (1995) and
Venkatesh et al. (2003) also found no empirical support for this relationship for theexperienced individual oriented and organizational-oriented information technologies. The
path from attitude to BI was not significant but the path from PBC to BI was stronger in theirfindings. They suggested that attitude will not translate completely to behavior intention. As
individuals gain experience over time, their perception and attitude changes. This refers to thedegree that user’s favorable evaluation at early moment of adoption become useless for theintention to use mobile entertainment services at the later moment of adoption stage. The
effect of attitude has been possibly captured by the perceived behavioral control andknowledge gained from experience, so that attitude has no significant effect on users’
behavioral intention.
We also obtained supportive result from Yang (2013) which predicted young American
consumers’ mobile apps intention and use in Nov, 2011. Mobile application attitude was notsupported but PBC was supported for mobile apps intention in this research. A 2011 Nielsen
survey reveals that 44% of U.S. mobile subscribers own a smartphone and 63% of thesesmartphone owners have mobile data services on their devices. In Feb 2010, 72% of total
population used mobile data services in Japan. In Oct 2013, 85.3% of total population in
Japan was 3G/4G subscribers. The global youth in developed spaces have experienced more
about smartphones and data services after 2009-2010 with introduction of iPhone and 3G/4G
services. The developed nation seems almost in the same situation of using mobile
information services. The users who believe that they have necessary means and resources touse mobile entertainment services are more likely to use mobile entertainment services.
Without a data plan and necessary means, young consumers will have no control over using
mobile entertainment services.
We found differences between Japanese 2009 and Japanese 2014 and similarities
between Japanese 2009 and Bangladeshi 2014 mobile entertainment service usage by young
adults through TAM-TPB model. Thus, after having economic status of the two countries andtime differences, these patterns can be considered as universalities or generalities of mobile
service use beyond recognized cultural differences. Service providers in Bangladesh canrevise their business models to reach out to mobile entertainment consumers when they are in
early stage of smartphones and 3G by following the introduction stage of Japan in 2009
which will provide an expectation of further expansion of its future market.
8. Limitations
Our study has some limitations. All of the respondents are young students who have goodeducation and knowledge in technology who had from several universities in Japan and
Bangladesh. Also, they are convenience samples. Thus the results cannot be generalized to
the whole population in Japan and Bangladesh. Although the limitations exist, the usage ofsuch samples provides useful insights on describing an emerging market of mobile
entertainment services among young adults who are innovative users, which are otherwise
not available.
9. Conclusion
This study represents an attempt to find out the factors that influence the intention to use
of mobile entertainment services among Japan 2009, Bangladesh 2014 and Japan 2014. This
study makes an important contribution to the consumer behavior literature, because it is oneof the first attempts to explain mobile entertainment service adoption by both a cross-country
and cross-temporary context.
The findings of our study fully corroborate the previous studies of Constantiou et al(2009) concerning Denmark and UK, Kim et al (2009) for Korea and Kondo and Ishida(2014) concerning USA and Japan customers. At introduction stage of smartphone and 3G
services (2009), this global adoption patterns for mobile entertainment services seemed
accepted by other technologically developed places like Japan USA, Korea, Denmark, UKand etc. So, the globalization has forged a common consumer profile in the mobile
entertainment service marketplace on the basis of market growth. This study finding can beused by service developers to devise appropriate service strategy to strengthen their products
and design recommendations and improve interfaces and technologies for the potential users
in technologically developing spaces as like Bangladesh in 2014.
In this study, we noted that having resources and skill to use smart mobile devices andmobile data services could affect the intention to use mobile entertainment services. At theearly moments of adoption of smartphones and 3G services in Japan 2009 and Bangladesh
2014, we found factors’ concerning perceived value was an effective factor on intention touse. Whereas at the later moment of adoption of smartphones and 3G/4G services in Japan

2014, we found factors’ concerning perceived behavioral control is an effective factor on
intention to use. We strongly recommend an increased focus on the effects of perceived value
and perceived behavioral control in future research.
This suggests that this TAM-TPB is fairly efficient as a model to predict the behavioral
intention to use mobile entertainment services among global young and cross temporal
researches. Concerning the impact of perceived value, perceived behavioral control andattitude on intention to use mobile entertainment services, the interaction between thelater-emerging market and early-emerging market dimensions leads to choices on what
factors need to be customized to a given market. However, the model of intention to use
mobile entertainment service may vary across different life cycle stage of mobile information
services. Therefore, future studies by researchers should classify into more understanding ofuser’s perceived value, perceived behavioral control and attitude towards intention to use
mobile entertainment services in a developed and developing society that highlights theearlier and later moment of mobile information service adoption. Identification of such
structural differences may provide an opportunity for expanding similar markets
internationally after adjusting time differences.
References
The International Telecommunication Union (May 2014)
http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2014-e.pdf
Kondo, N. F. & Ishida, H. (2014). A Cross-National Analysis of Intention to Use Multiple Mobile
Entertainment Services, Journal of Global Information Technology Management, 17: 45–60, 2014.
GSMA intelligence (2014).
https://gsmaintelligence.com/analysis/2014/08/country-overview-bangladesh/440/
Quan, S., Hao, C., & Jianxin, Y. (2010). Factors influencing the adoption of mobile service in China: An
integration of TAM. Journal of Computers, 5, 289–300.
Nysveen, H., Pedersen, P. E., & Thorbjornsen, H. (2005). Intention to use mobile services:
antecedents and cross-service comparisons, Journal of the Academy of Marketing Science, 33(3),330–347.
Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social
influences and flow experience, Information & Management, 41(7), 853–868.
Straub, D.W., Loch, W., Aristo, R., Karahanna, E., and Strite, M. (2002) “Toward a Theory-Based
Measurement of Culture.” Journal of Global Information Management, 10, 1, 13-23.
Okazaki, S., Skapa, R. & Grande, I. (2008), Capturing Global Youth: Mobile Gaming in the U.S., Spain,
and the Czech Republic, Journal of Computer-Mediated Communication 13, 827–855.
Telecommunications Carriers Association Japan, 2010
http://www.tca.or.jp/english/database/2010/02/index.html
ComScore's excellent 2010 Mobile Year in Review (February 2011)
http://www.comscore.com/Insights/Press-Releases/2011/2/comScore-Releases-InauguralReport-The-2010-Mobile-Year-in-Review
Paul Lambert, Informa (Q2 2013) ; national telecoms regulators
http://www.ovum.com/analyst-opinion/
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use
computers in the workplace, Journal of Applied Social Psychology, 22, 1111–1132.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior, Prentice
Hall, Englewood Cliffs, NJ.
Fishbein, M. and Ajzen, I. Belief, Attitude, Intention and Behavior: An Introduction to Theory andResearch, Addison-Wesley, Reading, MA, 1975.
Taylor, S., and Todd, P. A. “Assessing IT Usage: The Role of Prior Experience,” MIS Quarterly (19:2),
1995, pp. 561-570.
Heinonen, K. (2004). Reconceptualizing customer perceived value: the value of time and place.
Managing Service Quality 14(2/3): 205-215
Dodds WB, Monroe KB & Grewal D (1991). The effects of price, brand and store information onbuyers' product evaluations. Journal of Marketing Research, 28(August), 307-319.

Groth, J. C. (1995). Exclusive value and the pricing of services, Manage. Decis., 33: 22-29. DOI:
10.1108/00251749510093905
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end mode andsynthesis of evidence. Journal of Marketing, 52, 2–22.
Seok K. (2014). Factors influencing intention of mobile application use. Int. J. of Mobile
Communications, 2014 Vol.12, No.4, pp.360 - 379
G., N., Satish Kumar and T., V., Janaki (2011). An empirical research on mobile user’s intention andbehavior towards mobile entertainment services in India based on Theory of planned behavior
model, International Journal of research in Commerce, IT and management, Vol. 1, Issue no. 4, 8690.
Ghyas, Q. M., Kondo, N. F., & Kawamoto, T. (2011). Communication needs of Japan and the UnitedStates: A comparative analysis of the use of mobile information services. Proceedings of the FirstInternational Conference on Mobile Services, Resources, and Users, Barcelona, Spain.
A. S. Andreou, et al., "Mobile Commerce Applications and Services: A Design and Development
Approach," 2002.
Bagozzi, R. P., & Yi, Y. (2011). Specification, evaluation, and interpretation of structural equation
models. Journal of the Academy of Marketing Science, 40, 8–34.
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines fordetermining model fit. Electronic Journal of Business Research Methods, 6, 53–60.
Tsang, M. M., Ho, S. C., & Liang, T. P. (2004). Consumer attitudes toward mobile advertising: An
empirical study, International Journal of Electronic Commerce, 8, 65–67.
Kihyun K., Gyeung M. K., and Eun S. K., (2009). Measuring the compatibility factors in mobile
entertainment service adoption, Journal of Computer Information Systems; Fall 2009, Vol. 50 Issue
1, p141.
Constantiou, I, Papazafeiropoulou, A & Vendelø, MT 2009, 'Does Culture Affect the Adoption of
Mobile Services?: A Comparative Study of Young Adults' Perceptions in Denmark and the UK' Data
Base - for Advances in Information Systems, vol 40, no. 4, pp. 132-147.
C. C. Wong and P. L. Hiew, (2005). Diffusion of Mobile Entertainment in Malaysia: Drivers andBarriers, proceedings of world academy of science, engineering and technology, Volume 5 April
2005 ISSN 1307-6884.
Kas Kalba (2008). The Global Adoption and Diffusion of Mobile Phones, Program on Information
Resources Policy, Center for Information Policy Research, Harvard University, ISBN
0-9798243-03 P-08-2.
Venkatesh, V., Morris, M., Davis, G.B., and Davis, F.D. “User Acceptance of Information Technology:
Toward a Unified View,” MIS Quarterly (27:3), 2003, pp. 425-78.
Yang H., C., (2013). Young American Consumers’ Acceptance of Mobile Applications, Journal ofComputer Information Systems, 85-96.