Asian Journal of Management Sciences & Education Vol. 11(1) January-April 2022__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

INFLUENCING FACTORS ON ADOPTION OF TELKOMSELMAXSTREAM VIDEO STREAMING APPLICATION USING MODIFIED
THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY 2 (UTAUT 2)
Rustama Lumbantoruan1, Maya Ariyanti2
1˒2School of Economics & Business, Telkom University,
INDONESIA.

1

rustama.lumbantoruan@gmail.com, 2ariyanti@telkomuniversity.ac.id
ABSTRACTPT Telkomsel as the leading Mobile Operator in Indonesia with the mission of"delivering mobile digital services and solutions, Maxstream is one of the digital video
as Video Market Place (VMP) launched in June 2018. With the support of
telecommunication service coverage that has reached 99% of the regions and customer
based reaching 178 million must be able to become market leader for digital services
which align with its mission. In this research will be measured perception of Maxstream
users by using modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT
2) which consists of Performance Expectancy (PE), Effort Expectancy (EE), Social
Influence (SI), Content (CO), Habit (HT), Price Value (PV), Behavior Intention (BI) andUsage Behavior Intention (UB) and moderation of age and gender variables. In addition,
this study evaluates whether the variables are positively significant to Behavior Intention(BI) and Usage Behavior Intention (UB) with moderate variable age and gendervariables in Maxstream application users.
Data collection methodology is done through spreading of questionnaires by using
Google docs to 405 Maxstream users with Purposive Sampling technique. Based on the
results of data processing, it can be seen that the user perceptions of these variables
Performance Expectancy (PE), Effort Expectancy (EE), Content (CO), Habit (HT), Price
Value (PV) showed high value, means showing a good perception on variable while theSocial Influence (SI) showed a low value that shows a less good perception because thisapplication is still relatively new to the market launched. The analytical technique used
to interpret and analyze data in research is by Partial Least Square (PLS) technique Structural Equation Model (SEM). The result of the data analysis will explain thecausality relationship between the variables in the modified UTAUT2 model modified by
the Content (CO) variable. The model used can be accepted after assumptions have been
fulfilled that is endogenous construct variant (R²) with NFI value = 0.70 (high category).
Exogenous and endogenous measurement models have been tested using outer model(outer model) and structural (inner model) models. Furthermore, the model is analyzed
for model of causality relationship between variables that influence and influenced by
the variables in UTAUT2 model that meet the criteria of original sample (O)> 0.7,
Composite Reliability (CR)> 0.7, Average Variance Extracted (AVE)> 0.7, R² = 0.91
(solid category) and Goodness of Fit (GoF) consisting of Normed Fit Index NFI = 0.71
(large category) and chi-square = 0 <0.05. Based on the results of data analysis can beconcluded that the model is acceptable.
Based on the results of the research, to increase the adoption of video streaming users
Maxstream Telkomsel must consider the dominant component of Performance
Expectancy (PE), Effort Expectancy (EE), Content (CO), Habit (HT), Price Value (PV),
Facilitating Condition (FC)
Keywords: Performance Expectancy (PE), Effort Expectancy (EE), Content (CO),Social Influence (SI), Habit (HT), Facilitating Condition (PV), Price Value (PV),Behavior Intention (BI) Behavior (UB), UTAUT2, Video Market Place (VMP)
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Asian Journal of Management Sciences & Education Vol. 11(1) January-April 2022__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________INTRODUCTION
Recently, as the information era, humans place great emphasis on information. Humans
always feel thirsty for information because it has become a necessity. This need indirectly
triggered the development of communication technology. With the internet, and thepenetration of smart phones (smartphones) makes people easily and freely choose the desired
information, one of them is to enjoy entertainment content wherever and whenever with thedevice they have.
According to Marshall Mc. Luhan (1964), with the internet, the world like Global Village (aglobal village) with a characteristic where what happens in a country, then in a short time canbe known by other countries. According to the results of research conducted by Ericsson in2017 predicted global television viewers in 2020, 50% watch via their mobile devices. Apartfrom what was found from their research, it shows that currently consumers have more accessto TV and video services than ever before and 1 in 8 consumers believe they will get lost in
the large amount of video content available in the future. From the increasingly fragmented
user experience, 6 out of 10 consumers now rate content discovery as "very important" whensubscribing to a new video on demand service (https://www.ericsson.com/en/pressreleases/2017/10/ tv-in-2020-50-percent-of-viewing-will-be-mobile) and through a global
survey in the previous year by Nielsen Company as a market leader engaged in global
information and media and focusing on research and conducting research in providing
information about marketing and consumers, television, and conducting research on othermedia, it was found that video-on-demand audiences would be dominated by the younger
generation.
PT Telkomsel as the leading Cellular Operator in Indonesia with the mission of "providing
mobile digital services and solutions" which has 181 million subscribers in the month of June2018 consisting of 98 million internet users with 60 million video streaming users. Telkomsel
has released a one stop video portal application that allows customers to access all premium
content from various local and international video on demand and TV channels, through theirmobile phones anywhere and anytime that can be downloaded by users from Google Play or
the App Store. (Source: https://www.telkomsel.com/maxstream). This research aims to
explore important values that are expected by Telkomsel data service customers, especially
users of Video Streaming Services (VideoMax) to adopt and use Maxstream services as thelatest service from Telkomsel. In addition, it also aims to find out how to maintain the growthof this application user by gaining a better understanding so that it can provide practical
implications for service providers in this business sector in the future because based on
previous experience of some existing applications such as MyTelkomsel Apps (release 2015)
and Super Soccer TV (collaboration since 2017 and ending in 2018) shows a decrease in user
growth when compared to the initial period the application was released to the market. The
method that will be used in analyzing the behavior of customers in adopting a new service or
technology is to use the modified Unified Theory of Acceptance and Use of Technology 2(UTAUT 2).
The further variable to be considered in this study is the influence of gender (gender), age(age) and experience in using smartphone applications (experience) so that this research is
expected to provide more comprehensive results.
Based on the above phenomenon it can be concluded that some research problems are as
follows:

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Asian Journal of Management Sciences & Education Vol. 11(1) January-April 2022__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

1. How much is the respondent's assessment of the modified UTAUT2 model variables inthis research Performance Expectancy (PE), Effort Expectancy (EE), Social Influence(SI), Content (CO), Habit (HT), Price Value (PV), Behavior Intention (BI), Usage
Behavior Intention (UB), age and gender moderation?
2. What are the significancies of the following variables Performance Expectancy (PE),
Effort Expectancy (EE), Social Influence (SI), Content (CO), Habit (HT), Hedonic
Motivation (HM), and Price Value (PV) have a significant positive effect on Behavior
Intention (BI) and Usage Behavior Intention (UB)?
3. Are variables of Age (A) and Gender (G) moderating in the relationship of Performance
Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Content (CO), Habit(HT), Price Value (PV) towards Behavior Intention (BI) and Usage Behavior Intention(UB)?
Research Framework & Hypotheses

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H1: Performance expectancy (PE) positive effect on behavioral intention (BI).
H2: Effort expectancy (EE) has a positive effect on behavioral intention (BI).
H3: Social Influence (SI) positive effect on behavioral intention (BI)H4: Content (CO) positive effect on behavioral Intention (BI)H5: Facilitating condition (FC) has positive effect on Usage Behavior (UB)
H6: Hedonic Motivation (HM) has positive effect on Behavioral Intention (BI)H7: Price Value (PV) positive ( )H8: Habit (HT) has positive effect on behavioral intention (BI)H9: Behavioral Intention BI) has positive effect on Usage Behavior (UB)

Experience variable is eliminated because data retrieval conducted only once (cross sectional)
so that if a new Telkomsel customer uses the Maxstream Telkomsel application for the firsttime, then they do not have yet the experiences.
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Asian Journal of Management Sciences & Education Vol. 11(1) January-April 2022__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________RESEARCH METHODOLOGY
1. Literature methodology consists of reading journals and theses to get additional variableinsight into the content of this research
2. Descriptive and causal through surveys because it will explain customer perceptions andbehavior of the variables.
3. In this research conducted on Maxstream application users to fill in the table variables
and the matrix in this study
4. The test uses the SEM - PLS (Partial Least Square) method with Smart PLS 3
programming with a total of 33 indicators
5. The results of smart PSL are analyzed, conclusions, and suggestions for companies andfuture researchers
MATERIALS AND METHODS
ParticipantsThis research respondent targeted to 405 customers of PT. Telkomsel whose preference to
use the Telkomsel Maxstream video marketplace application, the data collection techniqueused to obtain data is through questionnaires distributed to 705 customers with 405
respondents watching video on demand through the Telkomsel Maxstream application and
300 respondents who do not watch video on demand through the Maxstream application.
Instruments
Data collection methodology was to distribute questionnaire with 33 questions divided into 8
variables: 3 Performance Expectancy (PE), 4 Effort Expectancy (EE), 4 Social Influence(SI), 5 Content (CO), 3 Habit (HT), 3 Hedonic Motivation (HM), 3 Facilitating Conditions(FC), 3 Price Value (PV), 4 Behavioral Intention (BI), 4 Usage Behavior - UB
Data Analytics
The chosen data analytics method of the data should be in accordance with the researchpattern and the variables to be studied. The analytical method is used in this research using
PLS-SEM with two evaluation steps: 1) evaluation of measurement (outer model) to assess
the validity and reliability of the model through the validity of convergent and discriminant,and 2) evaluation of structural (inner model) that aims to predict the relationship between
latent variables 3) descriptive statistical analysis to describe the respondent's response index
and various constructs developed and differential statistics for testing hypotheses.
RESULTS
More comprehensive picture of the description of the respondents consisted of gender andage. The subjects consisted of 228 men (56,29%) and 177 women (43,71%). In term of age,693 respondens (63,57%) were 26 – 40 years old, 224 respondens (20.55%) were 17 – 25
years old, 132 respondens (12.11%) were 41 – 55 years old, and 15 respondens (1.37%)
above 55 years old.
The descriptive analysis shows that all UTAUT-2 variables are at a high level of perception
with the Social Influence variable having the highest score with an average value of 80.77%,
followed by Price Value (78.39%), Habit (77.2%), Content (77.12%), Effort Expectacy(76.94%), Behavior Intention (76.4), Performance Expectacy (76.46%), Facilitating
Condition (75.47%) and Usage Behavior (74.48%).
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Asian Journal of Management Sciences & Education Vol. 11(1) January-April 2022__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________The measurement model (Outer Model) involves the relationship of each criterion
representing a latent variable with each indicator. The focus of the measurement model
assessment is on the validity of the component used to represent each construct as suggested
by some researchers (eg, Chin, 2010; Hair et al., 2011; Henseler et al., 2009). The evaluationof measurement model (Outer Model) consists of construct validity, convergent validity, anddiscriminant validity.
The constract validity refers to the extent to which measuring devices can measure theoreticalaspects. Loading factor and cross loading value which are higher than 0.70 are consideredsignificant (Hair et al., 2011), based on the results, all indicators of each construct variable
have a loading factor above 0.7 so that it can be concluded that all indicators of the constructvariable are valid.
The next step of convergent validity is the evaluation of the value of Average Variance
Extracted (AVE). The construct is said to have good convergent validity if the AVE value isabove 0.5, based on AVE testing results, all construct variables are ≥ 0.5 so that it can beconcluded that all construct variables have good convergent validity.
To find out Discriminant Validity is done by testing with the Cross Loading method using the
SmartPLS 3.0 application. The test resulted that all indicators used in the researchquestionnaire have valid and reliable characteristics.
Analysis of SEM-PLS – OUTER MODEL

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Asian Journal of Management Sciences & Education Vol. 11(1) January-April 2022__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________RESULTS
1. Construct Validity
All loading factor indicators and constructs > 0,50
2. Composite Reliability (CR)All CR of variables > 0,70
3. AVEAll AVE of variables > 0,50
4. Discriminant Validity
Correlation (loading) indicators in the construct > to other construct
Based on the results of the outer model testing which has fulfilled all the parameters(construct validity, CR, AVE, and discriminant validity, thus, it can be said that the model
has met the validity requirements so that no indicators of each latent variable areremoved/discarded.
Changes in R2 values can be used to explain the effect of certain exogenous latent variables
on endogenous latent variables whether they have substantive influence or not. The R-Squarevalue will measure the level of variation in changes in independent variables to dependent sothat the higher the value of R-Square, the better the predictive results of the model understudy.
R-Square testing is used to measure the level of change in the dependent variable on the
independent variable. The higher the R-Square value the better the model can predict fromthe research object, based on the results the value of R-Square on the construct variable
Behavior Intention is 0.9127 meaning that the percentage of Intention Behavior can be
explained by Performance Expectancy, Effort Expectancy, Social Influence, Content, Habit,
Price Value, and Facilitating Conditions by 91.27% while for Usage Behavior can be
explained by Intention Behavior by 70.99%.
Assessment using the Goodness of Fit (GoF) parameter is one way to verify SEM-PLS(Tenenhaus et al., 2004).
T-value testing is intended to test whether the independent variable partially has a significant
effect on the dependent variable. If the probability (prob value)> 0.05 or -t table <t count <ttable then H0 is accepted. Whereas if the probability (prob value) <0.05 or t count ≤ -t Tableor t count> t table then H0 is rejected. From the statistical table data known t Table for alpha= 0.05 is 1.96 and t table for alpha = 0.10 is 1.65
CONCLUSIONS
Based on the results of research on Telkomsel customers who use Maxstream video
streaming applications can be summarized as follows:
1. The level of user assessment of the modification factors of the UTAUT2 model in thisresearch are at a high level of above 77%, thus all the following varibles are high user
rating: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI),
Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), Habit (HT),Hedonic Motivation, Content (CO), Behavior Intention (BI), and Behavior to Use (BU).

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Asian Journal of Management Sciences & Education Vol. 11(1) January-April 2022__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

2. Based on the research result, there are 6 independent variables in this study that have asignificant positive effect on Behavior Intention (BI) and one variable that has no
significant effect, while the details are as follows:a) Performance Expectancy (PE) has a significant positive effect on Behavior Intention(BI) with 0.076 and t count 2.4707 path coefficients.
b) Effort Expectancy (EE) has positive but insignificant effect on Behavior Intention (BI)with path coefficients 0.0268 and t count 2.0023
c) Social Influence (SI) did not have a significant positive effect on Behavior Intention(BI) with path coefficients 0.026 and t count 1.0028.
d) Price Value (PV) has a significant positive effect on Behavior Intention (BI) with pathcoefficients 0.171 and t count 3.2589e) Content Factor (CO) has a significant positive effect on Behavior Intention (BI) withpath coefficients 0.294 and t count 1.9916
f) Hedonic Motivation (HM) has a significant positive effect on Behavior Intention (BI)with path coefficients 0.290 and t count 1.9907g) Habit factor (HT) has a significant positive effect on Behavior Intention (BI) with pathcoefficients 0.201 and t count 2.3779
3. Based on the research results on the dependent variable Behavior Intention (BI) has asignificant positive effect on Behavior to Use (BU) with the path coefficients 0.745 and t
count 27.5300
4. From the test results on the dependent variable Facilitating Conditions (FC) have asignificant positive effect on Behavior to Use (BU) with 0.154 and t count pathcoefficients 4.9372
5. Based on the research results of the moderator varibale of sex and age, there were no
significant effect on Behavior Intention (BI) with a value of 0.844 t count (below 1.96)
RECOMMENDATIONS
Based on the result of data processing, analyzing the results of the research, and draws
conclusions, there are several things that can be taken into consideration and suggestions forthe company or Maxstream application services and also for further research as follows:
Suggestions for the company or recommendations from the results of this study are:a. Aspects of Price Value are the factors that have the most influence on users in adopting
Maxstream video streaming applications and the results of descriptive analysis note thatthe weakest assessment is the factor of subscribing to video-on-demand packages
Maxstream Telkomsel applications are cheaper than other similar applications because ofthat Telkomsel must pay attention in implementing the price scheme.
b. Content aspect is a factor that has the smallest significant effect on influencing customers
to adopt the Maxstream video streaming application and from the analysis it is known thatthe weakest assessment is the weakest of respondents' assessment of video-on-demandcontent is always updated regularly. Therefore, it is recommended that Telkomsel update
content regularly, enriching national and international content such as soccer services
other than the international league needs to be enriched with national football content
according to customer preferences because it is not yet dominant in this study so that thelevel of customer acceptance can be improved.
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c. Facilitating Conditions aspect is a factor that has a customer influence to increase the useof Maxstream from the results of valuation analysis The most important item that must be
considered by Telkomsel is the quality of streaming services on the application(unbroken) and information available in the FAQ or through Telkomsel Custumer Care
(Call Center, Email, FB and Twitter) because it has the lowest score.
d. Aspects of Social Influence being a variable did not have a significant effect on this study,
consumers claimed that they would use Maxstream if their family and friends had alreadyused a lot, therefore Telkomsel had to be more aggressive in promoting the benefits ofusing this application so that many consumers were interested. Become a leader, word of
mouth in various groups through social media or in the community as its endorser
SUGGESTIONS FOR FURTHER RESEARCH
In this research, there are seven main variables that have a significant positive impact on the
adoption of Maxstream applications by Telkomsel customers, namely Performance
Expectancy, Expentacy Effort, Facilitating Condition, Hedonic Motivation, Content, Habit,
and Price Value. It is necessary to do further research on factors that are not significant,namely Social Influence on the Intention Behavior variables that throughout the results of thestudy literature conducted by the authors have only found one research result so that not
many have discussed it.

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