AGE DOES NOT AFFECT THE ACCEPTANCE AND USE OF MOBILE PAYMENT IN INDONESIA

Kania Kismadi, Rini Inthalasari and Minsani Mariani. Binus Business School, Jakarta. ...................................................................................................................... Manuscript Info Abstract ......................... ........................................................................ Manuscript History Received: 14 September 2019 Final Accepted: 16 October 2019 Published: November 2019


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Age as a moderating factor Kotler and Keller stated that the desires and abilities of consumers changed with the changing people (2016). Consumer behavior is also influenced by three factors, namely cultural, social, and personal factors. One of the personal factors is human. Research on the factors of influencing the behavior of these consumers can also provide information on how to reach out and serve consumers more effectively (Kotler & Keller, 2016).
Age was chosen to be a moderating variable because different age groups have different behaviors. So, when age is used as a moderating variable, each variable will have different effect strength on the acceptance and use of mobile payment.
There is research which suggests that more consumers have a tendency to be more difficult in the promotion of new technology, so as to influence the learning process of their new technology (Morris et al. 2005; Plude and Hoyer 1985, as included in Venkatesh, 2012). There is also research that shows that the younger generation is more able to accept new technology, while the older generation will need more assistance in implementing its services (Cabanillas, Fernández, & Leiva, 2015).
However, there is a research that also states that it is not the fact of moderation that also influences PE, EE, SI, FC, HM, PV, and HB towards the intention of using internet marketing in Malaysia and Taiwan (Isa & Wong, 2015). Other research also states that we do not have a significant relationship to e-payment adoption in Indonesia, except for the Perceived Ease of Use and Perceived Usefulness factors, with relatively small relationships (Riskinanto, Kelana, & Hilmawan, 2017).
From some of the studies above, it can be seen that there are still differences in the effect of age on the factor of acceptance and use of mobile payment. Some say that age has an effect, some don't. In addition, studies have not yet been found in detail in detail about how weak are the relationships between the factors that have influenced the acceptance and use of mobile payments, in different segments of people. It is important to know that mobile payment service providers understand the needs of each group of users. By understanding the character and behavior of the generators, it will be easier for businesses to embrace and build good relationships with their consumers, so that the needs of all consumers can be fulfilled, which would result in the increased adoption of mobile payment, and leading it to the improvement of the performance in the company. In addition, age is also a moderator in the relationship between Effort Expectancy and Behavioral Intention, where people with older age will find it more difficult to process more complex stimuli (Plude & Hoyer, 1985, as quoted in Isa & Wong, 2015).

Social Influence
Social Influence (SI) is defined as the level at which people around users use the same system or technology, so the users feel that they have to use it as well.
Kim, Park, Choi, & Yeon, 2016 states that social influence has a positive relationship with the intention to use, meaning that, social influence can affect the acceptance and use of mobile payment. With the increasing use of social media, consumers are more likely to transfer knowledge. However, this may not be obtained by older consumers, because they are less active in social media, so social influence and affiliation are more needed in order to receive new technology (Isa & Wong, 2015).

Facilitating Conditions
Facilitating Conditions (FC) are defined as supporting infrastructure that increases user intentions to use mobile payments, such as networks, the number of merchants that exist, and the completeness of other machines that support the payment.
The results of Dahlberg et.al's research in 2006 stated that merchants also had an important role in implementing mobile payments. The more merchants, the easier will be to find them, the more people who use mobile payment services, the viral effect in the social environment (Manaf & Ariyanti, 2017). Age also plays a role of moderation, where older people are more in need of Facilitating Conditions than those who are younger (Isa & Wong, 2015). A study also states that Hedonic Motivation is very strong in influencing the intention to use ABC easy tap (Manaf & Ariyanti, 2017), where the results of this study also support the UTAUT 2 theory that Hedonic Motivation is one of the very strong determinants in consumer products and that Hedonic Motivation has a strong influence on Behavioral Intention in a group of younger men (Venkatesh, Thong, & Xu, 2012).

Price Value
Price Value (PV) is defined as the value or value between the benefits obtained and the costs that must be spent to use a particular system or technology. PV is positive when the benefits gained are greater than the costs incurred, and this supports the positive impact on the intended use. When using the sense of value, the value obtained is greater than the costs incurred (PV is positive and significant), so the use of certain positive technologies (mobile payment) increases.
The importance of Price Value among young consumers and consumers has also been theorized by Deaux & Lewis in 1984, where young consumers are believed to be too sensitive to the prices of older consumers (Isa & Wong, 2015).

H6: The influence of Price Value on behavioral intention is stronger in the older age groups
Habit Habit (HB) is defined as the extent to which users use technology automatically based on learning in the past. HB is also indicated as one of the biggest factors that is able to explain the use of technology ( The population of mobile payment users is calculated based on calculation of penetration data mobile phones reduced by users who have not been touched by FinTech. Percentage of cell phone penetration reaches 67% of the population or as many as 177.9 million users (We Are Social, 2018), and that 69% of cell phone users have not been touched by fintech (CNN Indonesia, 2018), then fintech users amounted to 31% or as many as 55,149. 000 users.
The researcher then uses the Slovin formula to determine the number of samples. This formula was first introduced by Slovin in 1960. The Slovin formula is a formula used when the population is very large. This formula allows researchers to get a small sample, but can represent the entire population. This formula calculates the minimum number of samples if the behavior of a population is not known with certainty (Hidayat, 2017). The formula is as follows: n = N / (1+ (N x e²)), where n is the number of samples, N is the number of populations, and e is a standard. This study will use a standard of 5% or a degree of trust of 95%.
In order to get the population as follows: n = 55,149,000 /(1+(55.149,000 x 0.05 ²)) n = 55,149,000 / (1+ (137,872.5)) n = 55,149,000 / 137,873.5 n = 399.99 Based on the formula above the number of n = 399.99 is generated, so the researchers set the number of samples to be 400.
This research is analyzed by using statistical Least Square (PLS) tests using SmartPLS software. This technique was chosen because this study contains enough relations between variables that are more complex. This technique allows researchers to separate relationships between multiple variables that are more complex because there are many indicators involved. This technique is also more robust or double balance multiple regression analysis (Geladi & Kowalski, 1986), so that research results can be more consistent when implemented in other types of research. From this analysis, the researcher wants to look at any variables that affect the acceptance and use of mobile payments in Indonesia, as well as see the strengths of their influence in different groups of people.

Analysis and Results:-
This research resulted in 400 respondents in the survey, with 15.3% respondents aged 18-24 years, 42.5% aged 25-34 years, 19.8% aged 35-44 years, 10.3% aged 45-54 years, and 12.3% aged more than 55 years. If there is a group in the youth and age groups, then the human group has a percentage of 60% and one group of 40%. These results are sufficiently balanced to obtain representative results between human groups who act as moderating variables in this study.

Usage Behavior
The results of the study to answer the dependent variable Usage Behavior can be seen from the frequency of use, in which the researcher divides the answers into five groups, ie always (every day), often (3-4 times a week), sometimes (1-2 times a week), rarely (3-4 times a month), and rarely once. (1-2 times a month).
The results showed that 35.8% of respondents used mobile payment 3-4 times a week, 25.5% 1-2 times a week, and 23.8% used it every day. While 7% use it rarely or only 1-2 times in a period of months.
This shows that the respondent is an active mobile payment user, seen from the frequency of its use which is quite frequent in their daily activities.

Reliability and Validity Testing
Before carrying out further analysis of the structural model, measurement analysis was carried out to test the validity and reliability of the study. This test was conducted using smartPLS software.
To test it, researchers conducted PLS Path Modeling calculations to see the value of Average Variance Extracted (AVE). From the first calculation, six indicators were found that have AVE values below 0.5, namely BI3, FC1, FC3, FC5, FC7, and SI3. After that, a second calculation is performed and all constructs have a AVE value of more than 0.5 and a Composite Reliability value of more than 0.7, which means that it is stated to be valid and reliable.
In addition, the researcher also looked at the Discriminant Validity, and found that all diagonal figures in the tabs were more thick than the numbers below, which indicated that all variables were valid and further analysis of the Structural Model could be carried out.

Hypothesis Testing
Structural Model Analysis is conducted to test the research hypothesis. Both use the Bootstrapping method by multiplying the sample into 5000 sub-samples.
Testing is done by separating between groups of young people and old people, to see the significance and strength of the influence of variables in different groups of people.
In the results of the calculation of young people, where then it was found, that there were three variables that significantly affected the Behavior Intention to Use, namely Facilitating Condition, Habit, and Performance

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If it is compared, then we can also see that the Path Coefficient value for the group of people is one or even different, and 6) Social Influence, which is the same as the Impact.
While eight hypotheses were proposed, only four were accepted, namely H5, H6, H7a, and H8, in which Hedonic Motivation affected Behavior Intention to Use and its influence was stronger in the older human group (H5), Price Value influenced Behavior Intention to Use and its influence was stronger in the older group (H5). (H6), and Habit influences Behavior Intention to Use and its influence is stronger in the older group of people (H7a), and Behavior Intention which influences Usage Behavior (H8).
No effect on Effort Expectancy or increased ease of use of a technology has been caused because the technology is developing with very fast in the last year. The use of technology in an older group of people who were previously thought to be less tech-savvy has actually been more easy to adapt to the development of the times. This also makes the difference between not being a body and a factor that influences the acceptance of a person against new technology (in this case mobile payment), which results in the use of the mobile payment itself.
This result is also supported by the fact that the level of strength of each variable has less than the same value among groups of people, showing that there are no other variables that affect the acceptance and use of a very new technology.
While, if, this is more in the past, we have seen that the Facilitating Condition which is a supporting infrastructure that increases the user intention to use mobile payment is actually stronger in the human group than in the elderly, because consumers are more likely to be chosen. In this sense, when the camera is in a way that a provider does not have a good infrastructure, then it is easy to move to another provider that can provide better infrastructure. Another 934 thing is with the older group of people who are more likely to look for safety by using applications that they have already known before. This is also supported by results that show that Performance Expectancy has a stronger influence on older groups, because they have a tendency to use more familiar applications, so that the performance of it, is an important factor. When the application cannot be used, then it is assumed that they will find more meaningful obstacles for young people who are more easily moved to other applications.
Finally, social influence or the influence of the environment around the user in determining the Behavior Intention to Use appears to have a very small coefficient in both groups of people, which means that the effect is very small. This could have been due to the fact that users of this period have been able to increase their own needs, so that they would not be influenced by others to use a new technology.

Conclusions And Suggestions:-
From the results of calculations using the MSSPL above, it can be concluded that humanity does not have the influence on the acceptance and use of mobile payment in Indonesia.
The results of the calculation of the influence on the two groups of people also showed that they were not different, where Performance Expectancy, Facilitating Condition, and Habits were the same variables that gave a significant influence on both groups of people.
This shows that the company as a provider of mobile payment services must maintain its application, increase network and improve its infrastructure, as well as provide a promotion that makes people become used using mobile payment applications in day-to-day activities, which then, it leads to "dependency" on users of these mobile payment services.
Companies are also advised to continue to maintain the ease of use of the application as well as the promos provided, although the Variable Effort Expectancy and Price Value do not show a significant effect. This is because the two variables are judged to be correlated with both Performance Expectancy and Hedonic Motivation, which are variables that have a significant effect.
However, seeing that the value of Social Influence is so small and has no effect, the company is advised for not to influence too much on focus or promotion budget for the programs that involve people, such as influencer marketing, brand ambassadorship, member-get-member programs, referral programs, or other related similar programs. Researchers suggest that companies focus the budget of these programs so that they are able to be diverted to other variables that, indeed, show a significant influence on the acceptance and use of mobile payment, so the application of increases and the performance of the business can continue to grow.
936 benefits or advantages that are gained and making him/her be more likely to receive and use mobile payment.
transactions possible to be easier and faster (PE3) Using mobile payment is increasing my productivity Describes the pleasure obtained from using mobile payment. As long as someone is happy in using certain technologies, their use will also increase.
(HM1) I use mobile payment because it gives me pleasure (HM2) I use mobile payment because I can collect points and it makes me happy (HM3) I use mobile payment because I can get a lot of promos and it makes me happy Price Value (PV) Describe the value or value between the benefits obtained, compared with the costs that must be spent to use mobile payment (value for money). When users feel the value obtained is greater than the costs incurred, then the intention to use mobile payment is increasing.
(PV1) I use mobile payment because many promos are offered (PV2) I use a mobile payment because many discounts are given (BI1) The benefits I get from mobile payments (performance expectancy) make me want to use mobile payments (BI2) The ease of use of mobile payments (effort expectancy) makes me want to use mobile payments (BI3) Increasingly friends / family / spouse / public figures who use mobile payment (social influence) make me want to use mobile payment (BI4) More and more infrastructure that accepts mobile payment payments (facilitating conditions) makes me want to use mobile payments more (BI5) The pleasure that can be obtained from using mobile payment (hedonic motivation) will make me more willing to use mobile payment. (BI6) When the value obtained is higher than the cost incurred (price value) will make me more willing to use mobile payment (BI7) As long as you get used to the new technology (habit) will make you want to use mobile payment