Investigating the antecedents of investment intention and the mediating effect of investment self-efficacy among young adults in Shandong, China

Lim, Thien Sang; Qi, Peng Chen Article Investigating the antecedents of investment intention and the mediating effect of investment self-efficacy among young adults in Shandong, China Global Business & Finance Review (GBFR) Provided in Cooperation with: People & Global Business Association (P&GBA), Seoul Suggested Citation: Lim, Thien Sang; Qi, Peng Chen (2023) : Investigating the antecedents of investment intention and the mediating effect of investment self-efficacy among young adults in Shandong, China, Global Business & Finance Review (GBFR), ISSN 2384-1648, People & Global Business Association (P&GBA), Seoul, Vol. 28, Iss. 2, pp. 1-16, https://doi.org/10.17549/gbfr.2023.28.2.1 This Version is available at: https://hdl.handle.net/10419/305887 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ I. Introduction Financial investment ideally begins early to allow wealth growth and attain comfortable financial well- Received: Sep. 16, 2022; Revised: Jan. 4, 2023; Accepted: Jan. 10, 2023 † Thien Sang Lim E-mail: [email protected] being in the later stage of life. As signs of financial mismanagement among young adults are progressively rampant (Nielsen, 2019; Gan et al., 2020), young adults suffer more financial distress than older adults (Fenton-O'Creevy & Furnham, 2021), understanding the investment intention of this group is vital. Although normative finance theory is widely accepted to explain investment behaviors, the behavioral finance literature GLOBAL BUSINESS & FINANCE REVIEW, Volume. 28 Issue. 2 (APRIL 2023), 1-16 pISSN 1088-6931 / eISSN 2384-1648∣Https://doi.org/10.17549/gbfr.2023.28.2.1 ⓒ 2023 People and Global Business Association GLOBAL BUSINESS & FINANCE REVIEW www.gbfrjournal.org1) Investigating the Antecedents of Investment Intention and the Mediatin g Effect of Investment Self-efficacy among Young Adults in Shandong, China Thien Sang Lima†, Peng Cheng Qib aSenior Lecturer, Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia bPostgraduate Student, Centre for Postgraduate Studies, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia A B S T R A C T Purpose: This study examines the antecedents and predictors of investment intention and the mediating role of investment self-efficacy. The study empirically investigates the complexity of the decision-making process related to financial investment in Shandong, China. Design/methodology/approach: The Integrated Behavioral Model underpinned the research. The sample was selected using the judgmental-sampling method. A sample of 313 responses from young income earners aged 25 to 39 was analyzed. Twelve hypotheses were tested using the SmartPLS statistical software. Findings: The resultant outcomes contradict the normative theory of finance. The findings revealed that psychological (risk perception and subjective financial knowledge) and sociological (influences of family, friends, and Internet) factors significantly influence the attitude of young income earners toward investment. Investment self-efficacy demonstrates a significant mediating role, as the indirect effect is almost half the total effect. Evidently, subjective financial knowledge positively influences investment self-efficacy, which in turn has a positive influence on investment intent. Research limitations/implications: The result is concerning as young wage earners appear overconfident in their financial skills. Policymakers and relevant market actors should strive to improve real financial knowledge, as real financial knowledge is known to be linked with the effectiveness of financial investments. Future research in this area may adopt a mixed-method approach as it has the potential to uncover new variables and provide a broader spectrum to understand the complexity of people's investment decision-making process. Originality/value: The study highlights the complexity of the decision-making process. It highlights the central role of self-efficacy in explaining investment intention. The empirical evidence from the world's most populous nation, China, expands the relevance of behavioral finance theory in mainstream finance research. Keywords: Behavioral finance, Investment intention, Self-efficacy, Financial knowledge, China ⓒ Copyright: The Author(s). This is an Open Access journal distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution , and reproduction in any medium, provided the original work is properly cited. GLOBAL BUSINESS & FINANCE REVIEW, Volume. 28 Issue. 2 (APRIL 2023), 1-16 2 has been growing steadily since its humble beginning in the 1970s. Founded on three main pillars, namely finance, psychology, and sociology (Ricciardi & Simon, 2000), the integral argument for behavioral finance emphasizes that people are boundedly rational utilitymaximizing actors and assumptions that the Efficient Market Hypothesis (EMH) cannot be realistically applied in the real world. Financial behaviorists recognize that behavior is complex and that the factors motivating behavior can be diverse and circumstantial in relation to the environment. The investment trends and spending habits of the younger generation in China are worrying. The 2021 China Household Finance Survey (CHFS) reported that the proportion of Chinese household allocation for investments was low, and the investment variety was concentrated on only one or two financial products, indicating a lack of investment diversification (Gan et al., 2020). Chinese youth recorded lower savings and investment rates compared to the previous generations. Their debt-management habits were worrying as they recorded a relatively high debt-to-income ratio (Nielsen, 2019). Their habit is a clear sign of poor money management and overspending. Gan et al. (2020) emphasized that much of the spending was on unnecessary items. The lower savings rate and excessive spending call for a study examining the investment intentions of Chinese young adults. If such a trend continues without intervention, their future financial well-being will be threatened. Although previous studies examined investment intent in different countries, such as the United Kingdom (UK) (Collard & Breuer, 2009), Vietnam (Vieider et al., 2019), Malaysia (Lim et al., 2020), and India (Shanmugham et al., 2012), the concepts and perspectives on investing can be different. For example, younger and wealthier citizens in China and those living in prime cities such as Beijing and Shanghai show more positive attitudes toward investments than older people and those living in less developed regions (Gan et al., 2021). This situation points to possible differences in investment attitudes between people in prime cities and those in second-tier cities in China. The study contributes to the body of knowledge by expanding the study of self-efficacy to the realm of investing. It incorporates factors from the three main pillars of behavioral finance by examining investment attitudes and investment self-efficacy factors. In addition, it also analyses whether investment self-efficacy plays a mediating role between financial literacy and investment intention. The study focuses on young adults in secondtier cities as their investment behavior currently lacks clarity. Verick (2009) argued that understanding the financial well-being of this group is extremely complex. The financial knowledge of this group is generally low (Zaiton et al., 2008; Lusardi et al., 2010; Bucher- Koenen et al., 2017), and they are often hardest hit during economic crises (Verick, 2009). The study highlights the complexity of the decisionmaking process by featuring the central role of selfefficacy in explaining investment intention. The empirical evidence from the world's most populous nation, China, expands the relevance of behavioral finance theory in mainstream finance research. Proving new empirical evidence based on young adults in second-tier cities can strengthen the understanding of financial investment intent among young adults. The pieces of evidence are a vital pathway leading to financial well-being improvement. II. Literature Review Behavioral intention is recognized as an immediate determinant of actual behavior (Ajzen, 1991; Ajzen & Fishbein, 2000). The behavioral intention concept is regularly used in social behavior research (Ki & Hon, 2012). Ray (1973) categorized behavior intention and real behavior under the same "co-native" heading. Ki and Hon (2012) mentioned that scholars have been using behavioral intention instead of actual behavior because the intention is the most reliable indicator of people's actual behavior. Consequently, investment intent provides the best predictor of investment behavior when properly measured. The study starts from the premise that people's Thien Sang Lim, Peng Cheng Qi 3 investment intentions are influenced by their investment attitudes and their investing self-efficacy. The anticipated predictive role of attitude and self-efficacy on behavioral intention is explained by the Integrated Behavioral Model, which underpins the study, which posits that cognitive assessment and self-confidence play an important role in the creation of intention (Fishbein & Cappella, 2006). Financial knowledge can be divided into objective (actual) and subjective (perceived) knowledge (Wang, 2009) and serves as a valuable indicator for predicting the quality of people's financial actions. Previously, both actual and perceived knowledge has been studied as a factor associated with antecedent and actual investment decisions. Wang (2016) revealed that actual financial literacy, perceived financial literacy, and financial decisions are linked. On the other hand, Perry and Morris (2005) concluded that financial knowledge drives practical financial decisions. By building on a covariance-based structural equation model, Lim et al. (2020) discovered that objective and subjective knowledge were associated with attitudes toward financial investments. The same study also concluded that only subjective financial knowledge significantly explains investment intent. Interestingly, Xu et al. (2021) proved that both components of financial knowledge significantly explain the perceptions, attitudes, and actual behavior of rural breadwinners regarding financial investments. The cumulation of these findings suggests that the role of financial knowledge remained mixed. Nevertheless, people utilize financial knowledge to build an intrinsic and valuable knowledge trait that leads to the logic and justification of their investment decisions (Alba & Hutchinson, 2000; Grohmann, 2018; Bialowolski et al., 2022). The level of actual financial knowledge is paramount, as people are also surrounded by financial misinformation (Volpe et al., 2002). Financially literate individuals can better assimilate information regarding financial investments, subsequently shaping their perceptions, attitudes, and behaviors toward investing (Litterer, 1965). Hence, people with better financial knowledge generally have a positive attitude toward financial investments and are more likely to invest because they can identify the right information and indicators to develop a constructive stance, which is making better investment decisions (Lusardi & Mitchell, 2014). Individuals with higher levels of financial knowledge can normally employ category-based processing because they are likely to develop a set of expectations regarding financial products over time (Wang, 2006). This study concurs with this idea and posits that financial knowledge leads to positive attitudes toward investing (H4) and investing self-efficacy (H5) and encourages higher intention to invest (H9). People tend to resort to subjective judgment to deal with information when making financial investment decisions, which can lead to inconsistencies in decisions that deviate from normative theory. Frydman and Camerer (2016) highlighted many financial transactions that contradict the logical use of information. Due to their intellectual limitations and minimal financial knowledge, they make financial decisions that defy standard financial principles. In general, people who think they know more are more confident and more comfortable processing information according to what they think they know. Thus, people with high levels of subjective knowledge feel less pressure to act on information and are more likely to make decisions based on their confidence level. Consequently, greater subjective knowledge corresponds to a positive investment self-efficacy (H6) and higher intention to invest (H10). People's dispositions and actions are linked to human interactions that generate consensus and influence decisions (Nofsinger, 2005). Previous research has shown that investment decisions are influenced by binding social norms (norms that dictate what people should think and do). Lim et al. (2020) reported that peer and Internet influence affects people's perceptions in the prime-savings group. Additionally, Xu et al. (2021) found that family influence also plays an important role in shaping the finance perception. Hilgert et al. (2003) examined the predictors of four financial management behavior types: cash flow, debt, investing, and saving. The study found that people do not only learn about financial issues from family and friends. The interactions between the two groups were also related to the improvement in financial GLOBAL BUSINESS & FINANCE REVIEW, Volume. 28 Issue. 2 (APRIL 2023), 1-16 4 behavior. Greenberg and Hershfield (2019) warned that favorable normative values might trigger a backlash. For example, Beshears et al. (2015) revealed that some subgroups chose to avoid saving, probably because they viewed certain social norms as upward social comparison, which was demotivating. As social norms have been shown to be compelling reasons for behavioral alterations, the study suggests that social norms may lead to investment behavior. Therefore, the study expects that the influence of family and friends will affect attitudes toward investing (H2). The Internet is a crucial tool that has transformed how people learn and interact and widened people's social boundaries. Internet influence is far-reaching and has no boundaries. The international Internet community is supported by worldwide digital networks and communicates through them, enabling a sizable and rapid exchange of facts and opinions (Castells, 2014). In different words, the Internet increases sociability and information sharing. Viewed as the technology of freedom, a single person is no longer alone, as individualization no longer means loneliness. As people became familiar with the web, private and public institutions began adopting online platforms, which led to information dissemination (Castells, 2014). In this sense, people may turn to the Internet for financial information (and, unfortunately, misinformation), affecting their investment opinions and decisions (Nofsinger, 2005). Bargh and McKenna (2004) argued that the nature and value of the Internet impact could be a subject of disagreement while its impact on social life is undeniable. As previous evidence on the influence of the Internet on cognition, attitudes, or behavior is mixed (Shanmugham & Ramya, 2012; Lim et al., 2020; Xu et al., 2021), it would be of great interest to examine whether the Internet is causing investment attitudes among young income earners in China. Therefore, the study posits that the Internet influences investment attitude (H3). Financial instruments are mostly intangible in nature, and investment results cannot be confirmed at the time of commitment. Therefore, people will consider investing if they perceive investment risk positively. Individual perception resembles a descriptive norm about whether they should engage in certain behavior. Montana and Kasprzyk (2015) explained that how people perceive something determines their attitude toward it. Thus, a positive perception of the financial investment risk is projected to promote a positive attitude toward investing (Litterer, 1965). Webber et al. (2002) and Trang and Khuong (2017) discovered that people made financial investment decisions when they perceived the investment risk positively. Consequently, the study suggests that risk perception affects attitudes toward investing (H1). Attitudes can be understood as people's general assessment of an issue (Ajzen & Fishbein, 2000) and are crucial to decision-making theory (Newholm & Shaw, 2007). Several classical theories, such as the Perception Formation Model (Litterer, 1965) and the Theory of Planned Behavior (Ajzen & Fishbein, 2000), have proven that attitudes predict behavior. Based on the results of 16 studies, Ajzen (1991) concluded that the role of attitudes deserves attention as they had a notable impact on predicting behavioral intentions. Lee (2009), Shanmugham and Ramya (2012), and Lim et al. (2020) showed that people with positive attitudes toward a certain financial behavior had a positive effect on financial behavior. In terms of attitudes toward financial investments, individuals may have varying degrees of favor or dislike of investing activities. The link between attitude and intention to invest of young adults in second-tier cities in China lack clarity due to limited empirical studies. In order to clarify this notion, the study hypothesized that there would be a correlation between attitudes toward financial investments and investment intent (H7). New investors can feel overwhelmed by the sheer volume of financial information. Intimidated by complexity and information overload, some prefer to avoid investing altogether. Nevertheless, people who believe they are capable and have the skills to make successful investments are more likely to invest (Bandura, 1977). According to Bandura's explanation, investment selfefficacy can be defined as a section of the self-system that encompasses people's investment attitudes, skills, and cognitive abilities. Financial investment management Thien Sang Lim, Peng Cheng Qi 5 requires more than only financial knowledge. People also need an intuition of confidence in their knowledge. Farrell et al. (2016) discovered that financial selfefficacy appears to be one of the strongest predictors of holding financial products. Therefore, people with higher self-efficacy in investing are expected to be more likely to own investment products (H8). Although much emphasis has been placed on improving financial knowledge through financial education programs, the impact of financial knowledge on financial behavioral intentions is mixed. Rothwell et al. (2016) suggested that self-efficacy may mediate the relationship between objective financial literacy and savings outcomes. Nevertheless, no evidence exists on whether the objective and subjective components of financial literacy would influence investment intent through self-efficacy. Therefore, the study examined the mediating role of investment self-efficacy between the association of financial knowledge and investment intention. Consequently, two hypotheses, H11 and H12, are developed, for objective and subjective knowledge, respectively. Figure 1 illustrates the research framework of the study, comprises ten direct hypotheses and two mediation hypotheses. Table 1 describes the 12 hypotheses for this cross-sectional study. III. Research Methods The study focused on investment intent and only targeted respondents who had never invested in any financial instrument. Poterba (2001) categorized the population between the ages of 40 and 69 as the prime savings year and actively participated in investments. Thus, the study participants were under 40 years old, specifically income earners ages 25 to 39. In addition, the respondents must have worked for at least one year to achieve a certain level of income stability for investment. Purposive sampling was employed with the inclusion of several screening questions in the Hypothesis Hypothesis Statement H1 The perception of risk has a positive influence on the attitude toward investing. H2 Family and friends' influence positively affect attitudes toward investing. H3 The Internet has a positive influence on attitudes toward investing. H4 Actual financial knowledge positively influences attitudes toward investing. H5 Actual financial knowledge positively influences the self-efficacy of investments. H6 Subjective financial knowledge positively influences the self-efficacy of investments. H7 Attitude toward investments positively influences investment intention. H8 Investment self-efficacy positively influences investment intention. H9 Actual financial knowledge positively influences investment intention. H10 Subjective financial knowledge positively influences investment intention. Hypothesis Hypothesis Statement H11 Investment self-efficacy mediates the influence of actual financial knowledge on investment intention. H12 Investment self-efficacy mediates the influence of subjective financial knowledge on investment intention. Table 1. Research hypotheses Figure 1. Research Framework GLOBAL BUSINESS & FINANCE REVIEW, Volume. 28 Issue. 2 (APRIL 2023), 1-16 6 survey instrument to ensure data was only collected from those who met the predetermined criteria. The study used 40 items to measure eight constructs listed in Table 2. The items were adapted from previous studies and tailored to fit the research context. This practice is common and has two advantages. First, the validity and reliability of the measurements were assessed. Second, the current results are comparable to previous studies. As the study was conducted in China, the questionnaire was translated into Simplified Chinese using the back-to-back translation method. The questionnaire in English is included in Appendix A. The data was collected in the Shandong Province, which is a second-tier city in China. Data collection took place amid the COVID-19 pandemic. Due to the restrictions on movement imposed in China, the online survey was used as the only practical option. The questionnaire was stored on the Wen Juan Xing platform (the Chinese equivalent of Google Form), and the link and QR code were disseminated using emails and social network platforms. A total of 358 respondents voluntarily completed the survey. According to Yeh's (2009) suggestion, initial screening was performed, resulting in 45 samples being excluded due to issues related to monotone and invariance responses. The final sample size was 313, which exceeded the minimum requirement of 129 samples computed using the G- power calculator. As shown in Table 3, female respondents were more than male respondents. Three-quarters of the respondents were at least 30 years old, while 62% did not have a university degree. IV. Data Screening and Analysis One of the requirements for regression analysis is data normality. The study estimates the univariate and Mardia's multivariate skewness and kurtosis using the Webpower online calculator (https://webpower. psychstat.org/models/kurtosis/). The latent variable scores of the eight constructs were entered for computation. The resultant outcomes based on skewness Demographic Variable Variable sub-groups Percentage (%) Gender Male 47% Female 53% Age 25-29 25% 30-34 36% 35-39 39% Education level High School 33% Diploma 29% Bachelor 22% Master 12% Doctor 4% Table 3. Profile of respondents Construct Number of Items Source Actual Financial Knowledge 6 America Financial Industry Regulatory Authority (FINRA)- Financial Literacy Quiz (2012) Subjective Financial Knowledge 6 Flynn et al. (1999) Risk Perception on Investment 4 Hoffmann et al. (2013) Family and Friends Influence 4 Jorgensen (2010) Internet Influence 5 Jorgensen (2010) Attitude toward Investment 5 Q1 - Q4: Ramayah et al. (2009) Q5 - Mathieson (1991) Investment Self-efficacy 5 Q1 - Q2: Combrink et al. (2020) Q3 - Q5: Dulebohn et al. (2007) Intention to Invest 5 Q1 - Q3: He (2016) [In Chinese] Q4 - Q5: Shiarella et al. (2000) Table 2. Summary of questionnaire construct Thien Sang Lim, Peng Cheng Qi 7 and kurtosis showed that the data were not normally distributed multivariate, Mardia's multivariate skewness ( β = 9.431, p < 0.01) and Mardia's multivariate kurtosis ( β = 108.766, p < 0.01). Thus, to overcome this issue, the testing of the study's hypotheses followed the bootstrapping approach explained by Hair et al. (2019). The data for the independent, mediator and dependent variables were obtained from the same respondents. Therefore, the data must be cleared of the common method or self-informant bias before further analysis. The common method bias (CMB) issue was examined using the full-collinearity test approach, where all the variables were regressed against a common variable. Kock (2015) explained that the full collinearity variance inflation factor (VIF) tend to increase with model complexity in term of latent variables in the model. In total, this study has seven latent variables and classifiable as complex model. Accordingly, the VIF threshold used in CMB tests should be more than 3.3 (Kock, 2015). Kock and Lynn (2012) stated that a VIF threshold of 5.0 could be used. The results for the full-collinearity test are provided in Table 4. As all the VIFs are within the threshold of 5.0 with only two slightly above 3.3, the data do not inherent serious issues related to self-informant bias. Measurement model assessment (MMA) aims to ensure that those measurement items and constructs sufficiently meet internal consistency reliability, convertgence validity, indicator reliability, and discriminant validity (Ramayah et al., 2018). Five items that did not meet the minimum factor loadings of 0.708 (Hair et al., 2017) were removed. As shown in Table 5, all items were adequately loaded on each construct, as proved by factor loadings of at least 0.708, discounting the issue related to unidimensionality. The composite reliability (CR) values of the constructs ranged from 0.825 to 0.947, which is between Hair et al.'s (2019) threshold of 0.60 and 0.95, indicating reliable internal consistency. The lowest convergent validity value (average variance extracted (AVE)) was 0.661, which exceeds the 0.50 threshold. Therefore, convergent validity was achieved. The discriminant validity was examined using the heterotrait-monotrait (HTMT) criterion (Henseler et al., 2015; Franke & Sarstedt, 2019). The threshold for the HTMT values should be less than 0.85 based on the stricter criterion. The more lenient criterion suggests that the value should not exceed 0.90. Table 6 shows that all the HTMT values, except one, are lower than the stricter criterion of ≤ 0.85. The highest HTMT value is 0.894, which is lower than the lenient threshold of 0.90. Thus, it is concluded that the study's respondents understood that the eight constructs are distinct. Based on the results of the validity tests, the measurement items employed in the study are valid and reliable. The inner VIF values were examined. All VIF values in Table 7 are less than 3.3, indicating that the regression model has no lateral collinearity problem (Hair et al., 2017). The coefficients of the independent variables in H1 through H10 are all positive, with eight hypotheses supported (t-value > 1.645, p-value < 0.05, bias-adjusted confidence interval (BCI) lower-level (LL) and BCI upper-level (UL) do not straddle between the value of 0). Interestingly, H4 and H5 involving actual financial knowledge as a factor was unsupported. The R 2 estimates for attitude toward investment, investment self-efficacy, and investment intent were 0.568 (Q 2 = 0.392), 0.380 (Q 2 = 0.274), and 0.592 (Q 2 = 0.502), respectively. The values are classified as moderate (between 0.33 and 0.67) and have a substantial explanatory power (Chin, 2010). Accordingly, the four factors of H1 to H4 can explain 56.8% of the variance in attitude toward investment. Subsequently, the two financial knowledge components (H5 and Constructs VIF Actual Financial Knowledge 1.034 Subjective Financial Knowledge 1.864 Family and Friends Influence 2.162 Internet Influence 2.252 Risk Perception 1.623 Investment Self-Efficacy 3.495 Attitude toward Investment 3.465 Investment Intention 2.297 Measurement Model Assessment Table 4. Full-Collinearity test GLOBAL BUSINESS & FINANCE REVIEW, Volume. 28 Issue. 2 (APRIL 2023), 1-16 8 Constructs Items Loadings CR AVE Actual Financial Knowledge ACT1 1.000 1.000 1.000 Attitude toward Investment ATT1 0.872 0.933 0.737 ATT2 0.829 ATT3 0.846 ATT4 0.875 ATT5 0.868 Family and Friends Influence FFI1 0.857 0.914 0.781FFI2 0.920 FFI3 0.873 Internet Influence INI1 0.743 0.907 0.661 INI2 0.836 INI3 0.804 INI4 0.858 INI5 0.819 Investment Intention INVI1 0.906 0.947 0.856INVI2 0.949 INVI4 0.920 Risk Perception RP2 0.881 0.825 0.703 RP4 0.794 Investment Self-Efficacy SEL1 0.834 0.911 0.719 SEL2 0.902 SEL3 0.848 SEL5 0.805 Subjective Financial Knowledge SUB1 0.827 0.927 0.716 SUB2 0.840 SUB4 0.812 SUB5 0.871 SUB6 0.879 Table 5. Measurement model assessment Constructs 1 2 3 4 5 6 7 8 1. Actual Financial Knowledge 2. Attitude toward Investment 0.074 3. Family and Friend Influence 0.067 0.652 4. Internet Influence 0.075 0.682 0.738 5. Investment Intention 0.154 0.756 0.600 0.682 6. Risk Perception 0.099 0.797 0.581 0.584 0.649 7. Investment Self-Efficacy 0.059 0.894 0.747 0.744 0.752 0.702 8. Subjective Financial Knowledge 0.117 0.574 0.669 0.642 0.532 0.617 0.643 Structural Model Assessment Table 6. Heterotrait-Monotrait ( HTMT ) correlation ratio Thien Sang Lim, Peng Cheng Qi 15 Appendix A: Questionnaire (English Version) Actual Financial Knowledge Suppose you have CNY 100 in a savings account earning 2 percent interest a year. After five years, how much would y ou have? No need to consider inflation. A. More than 110 B. Exactly 110 C. Less than 110 D. Not sure Imagine that the interest rate on your savings account is 1% a year and inflation is 2% a year. After one year, would the money in the account buy more than it does today, exactly the same or less than today? A. More than before B. Same C. Less than before D. Not sure If interest rates rise, what will typically happen to bond prices? A. Rise B. Fall C. Stay the same D. No relationship E. Not sure Buying a single company's stock usually provides a safer return than a stock mutual fund. A. True B. False C. Not sure A 15-year mortgage typically requires higher monthly payments than a 30- year mortgage but the total interest over the life of the loan will be less. A. True B. False C. Not sure Suppose you owe CNY1,000 on a loan and the interest rate y ou are charged is 20% per year compounded annually. If you didn't pay anything off, at this interest rate, how many years would it take for the amount you owe to double? A. Less than 2 years B. 2 to 4 years C. 5 to 9 years D. More than 10 years E. Not sure Subjective Financial knowledge Attitude toward Investment I know pretty much about financial investment. Investing is a wise decision. I know how to judge the different type of investment assets. Investing is a good idea. I am not knowledgeable about financial investment. (Reverse) I like to invest. Among my circle of friends, I am one of the "experts" on financial investment. Investing brings me a pleasant feeling. I think I know enough about financial investment to feel confident when I make an investment. I consider investing is better than saving I can tell if a financial asset is worth to invest or not. Self-efficacy Risk Perception I will generate investment returns above my targets. I consider investing to be very risky. (Reverse) I feel confident in my ability to make investment decisions. I consider investing to be safe. I have enough ability to choosing suitable investment alternatives and invest it. I can lose money easily if I invest. (Reverse) Investing is too complicated for the me to understand. (Reverse) I believe investing to have little risk. I feel capable of investing my income to achieve my financial goals. Family and Friend Influence Investment Intention I learn a lot of knowledge about managing my money from my family and friends. I will invest in the next two years. I discuss financial matters with my family and friends. I have the intention to invest in next two years. I communicate about the financial investment to family and friends. Engaging in investment is my plan in the next two years. I am influenced by the opinion of family and friends on subject relate to financial investment. I will seek investment opportunity in the next two years. GLOBAL BUSINESS & FINANCE REVIEW, Volume. 28 Issue. 2 (APRIL 2023), 1-16 16 Internet Influence The information about financial from Internet is important to me. I discuss about financial using the Internet platform. I pay attention to opinions of Internet community about investment. I get news about financial investment from text-based Internet website. (Such as: East money Information, Weibo, Wind). I get news about financial investment from video-based website. (Such as: YouTube, Bilibili, Tiktok). I get news about financial investment from video-based website. (Such as: YouTube, Bilibili, Tiktok).