Dataset on determinants of intention and investment behaviour amongst young Indonesian millennials

The dataset examines three factors (i.e. financial literacy, self-control, peer-influence) that influence investment behaviour amongst young Indonesian millennials. Using a non-probability sampling technique, a total of 265 young millennials attending Investment Gallery seminar and workshop events in Jakarta, Indonesia completed an investment behaviour survey. The reflective measurement was performed using the Partial Least Square Structural Equation Model (PLS-SEM) to the screened data from the survey (N = 213). The measurement includes the evaluation of outer loading, internal reliability, and convergent reliability. The final equation model was evaluated and estimated using SmartPLS v.3.3.2 program. The dataset is beneficial for companies seeking for potential investors from young millennials. The dataset also provides valuable insights for the Indonesian Ministry of Education and Culture (MoEC) and universities, particularly the faculty of economy and business in preparing their students with the financial literacy and investment knowledge.


Subject
Economy and Business Specific subject area Economy and Business Type of data Table  How data were acquired A survey method was carried out to collect the quantitative data Data format Raw Analysed Filtered Parameters for data collection The data on young Indonesian millennials' financial literacy, self-control, peer-influence and investment behaviour were collected using an investment behaviour questionnaire.

Description of data collection
A total of 265 young Indonesian millennials attending Investment Gallery seminar and workshop events completed an investment behaviour survey. After the screening process, 213 data were evaluated using the Partial Least Square Structural Equation Model (PLS-SEM) Data source location Jakarta, Indonesia Data accessibility The data available in Mendeley Data: https://data.mendeley.com/datasets/d5kck749zs/2

Value of the data
• The dataset describes financial literacy, self-control, peer-influence and investment behaviour amongst young Indonesian millennials who attended Investment Gallery seminar and workshop events in a private university in Jakarta, Indonesia. The dataset is useful for companies who are seeking potential investors from the young Indonesian millennials • The dataset is also useful in providing insights for the Indonesian Ministry of Education and Culture (MoEC) and universities, particularly the faculty of economy and business to prepare their students with financial literacy and investment knowledge. More importantly, the university teachers and curriculum designers can use the information from the dataset to develop relevant course syllabus that promotes financial literacy and investment knowledge. • In the field of behavioural economic research, the dataset helps researchers to address the current issues related to the roles of individual financial literacy and financial behaviour (e.g. individuals' making a decision about consumption, saving, debts, investment) in maintaining financial stability in both micro and macro economy settings. • The dataset might also be useful for researchers who are interested in evaluating the impact of young millennials' investment behaviour on the greater national economy in Indonesian settings.

Data description
A survey method was employed to gather the primary data. A five-point Likert scale questionnaire with 30 items was adapted from literature [ 1 , 2 ] and was classified into four variables. The three exogenous variables are financial literacy or FL (6 items), self-control or SC (9 items), peer-influence or PI (6 items), and an endogenous variable of investment behaviour or IB (9 items). In addition to these variables, demographic information was gathered from the participant (see Table 1 ). The adapted questionnaire was translated into the native of Bahasa Indonesia to allow participants to comprehend the information. The questionnaire is provided as a supplementary file of this paper.

Experimental design, materials, and methods
The primary data collection was performed through an online survey. Using a non-probability sampling technique, a total of 265 young Indonesian millennials attending the Investment  Gallery seminar and workshop events in a private university in Jakarta, Indonesia last 11 January 2020 completed an online version of the investment behaviour survey. Prior to the distribution, the participants were given a brief explanation about the survey and the instruction on how to complete it. The collected data then were tabulated using an excel application and were screened for missing values and outliers. The residual values above 1.96 were considered outliers [3] . Literature [ 3 , 4 ] has suggested that outliers can influence the parametric statistics and statistical significance result. Of 265 data, 52 data were found to be outliers and thus were removed. Garson [3] argues that the removal of outliers can improve robustness of PLS-SEM results. The remaining 213 data were analysed and characterised as in Table 1 below. The normality assessment was performed by evaluating Skewness and Kurtosis [5] . As shown in Table 2 , all the data fit the acceptable range of the Skewness and Kurtosis values. The Skew- ness and Kurtosis values were observed to be normal (the Skewness values ranged between −1 and 1, the Kurtosis values were between −2 and 2), indicating the data were normally distributed.
The reflective measurement of the remaining 213 data was performed using the Partial Least Square Structural Equation Model (PL S-SEM) with SmartPL S [6] . The proposed model ( Fig. 1 ) was measured in line with the reflective measurement criteria [4] . First, the composite reliability (CR) and Cronbach's alpha are expected to be higher than 0.70, and the outer loading should be above 0.70. Third, the accepted value of the convergent reliability should outweigh 0.50. Table 3 presents the result of PLS-SEM algorithm analysis.
The result of PLS-SEM algorithm analysis showed that three variables (i.e. FL, SC and IB) met the reflective measurement criteria. The Cronbach's alpha and composite reliability (CR) values of these variables were reported higher than 0.70. The convergent reliability, which was examined through the average variance extracted (AVE), was observed higher than 0.50. However, the other exogenous variable of Peer-influence (PI) failed to meet the criteria and thus was removed from further analysis. Furthermore, Hair, Hult, Ringle, and Sarstedt [4] argue that the item loadings between 0.40 and 0.70 should be considered for deletion if only the deletion can increase the composite reliability. Five items (i.e. FL1, FL5, SC7, SC9 and IB9) were removed due to insufficient outer loading values. Second PLS-SEM algorithm analysis was performed after the item deletion, and the results revealed an increase in CR and AVE value as in Table 4 .
The final model was developed after the second analysis ( Fig. 2 ). The model comprised of three variables (i.e. FL, SC and IB) with 19 items. PLS-SEM analysis using SmartPLS v.3.3.2 program was applied to evaluate and estimate the model. The outer loading for each item is presented in Fig. 2 .  I have the ability to prepare my own budget weekly and monthly Self-Control I don't save, because I think it is too hard. I enjoy spending money on things that aren't practical. When I get money, I always spend it immediately (within 1 or 2 days). 'I see it, I like it, I buy it' describe me. 'Just do it' describes the way I buy things. 'Buy now, think about it later' describe me. I always failed to control myself from spending money. I am more concerned with what happens to me in short run than in long run. When I set having goals for myself, I rarely achieve them.

Peer Influence
As far I know, some of my friends regularly do save with a saving account. I always discuss financial management issue (saving) with my friends. I always discuss financial management issue (investment) with my friends. I always spend my leisure time with my friends. I always involve in money spending activities with friends. I always follow the information about investment growth. Investment behaviour I put money aside on a regular basis for the future. In order to invest, I often compare prices before I make purchase. In order to invest, I often consider whether the stock prices are valuable when I sell it. In order to invest, I often understanding the fundamental analysis. I always have money available in the event of my failed investment. In order to invest, I plan to manage my expenses. I save my money in order to do investment. I invest to achieve certain goals. I have some investment account in money market and also capital market.