Does SME’s financing decisions follow pecking order pattern? The role of financial literacy, risk preference, and home bias in SME financing decisions

Sulistianingsih, Henny; Santi, Fitri Article Does SME’s financing decisions follow pecking order pattern? The role of financial literacy, risk preference, and home bias in SME financing decisions Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Sulistianingsih, Henny; Santi, Fitri (2023) : Does SME’s financing decisions follow pecking order pattern? The role of financial literacy, risk preference, and home bias in SME financing decisions, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-17, https://doi.org/10.1080/23311975.2023.2174477 This Version is available at: https://hdl.handle.net/10419/294271 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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The role of financial literacy, risk preference, and home bias in SME financing decisions, Cogent Business & Management, 10:1, 2174477, DOI: 10.1080/23311975.2023.2174477 To link to this article: https://doi.org/10.1080/23311975.2023.2174477 © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 13 Feb 2023. Submit your article to this journal Article views: 2928 View related articles View Crossmark data Citing articles: 3 View citing articles ACCOUNTING, CORPORATE GOVERNANCE & BUSINESS ETHICS | RESEARCH ARTICLE Does SME’s financing decisions follow pecking order pattern? The role of financial literacy, risk preference, and home bias in SME financing decisions Henny Sulistianingsih 1 and Fitri Santi 2 * Abstract: This paper aims to explore the role of financial literacy, risk preference, and home bias in Minangkabau ethnic SME financing decisions, and whether their financing pattern follow the pecking order theory. The theoretical framework model was developed to determine the impact of financial literacy, risk preference, and home bias on the financing decisions of SMEs. The study applied the SEM-PLS method in testing the model. Data collection was carried out through online and offline survey techniques with a sample of 623 SMEs. The results reveal that home bias and risk preference have a negative direct effect on financing decision. Home bias also has a direct positive influence on risk preference. Financial literacy can minimize home bias from negative to positive toward financing decision. However, financial literacy does not have a direct effect on the financing decisions and does not have a significant direct influence on risk preference. Most SMEs have a low level of financial literacy, a high tendency for home bias and a high-risk preference. Financial literacy has strong positive moderation effect on the influence of home bias toward financing decision. Home bias make SME financing decision does not follow pecking order pattern. As SMEs financial literacy level is still very low, thus it is important for financial institutions to educate SMEs by providing several trainings about financial matters. The findings suggest to improve the financial literacy of SMEs informal association groups in order to help them reducing their home bias tendency. This study seeks to explore the pecking order pattern in financing decisions of SMEs. Subjects: Environment & Economics; South East Asian Economics; Economics and Development; Corporate Governance Keywords: financial literacy; home bias; risk preferences; financing decision; SMEs 1. Introduction The financing needs of each type of company will be different, it is easier for companies that go public to get sources of financing, while micro and small enterprises (hereinafter referred to as SMEs) must rely on their own sources of funds, especially when opening a business. Several studies of Small and medium enterprises (SMEs) and financing related to the theory of capital structure, that SMEs follow the pecking order theory, financing needs will be met from internal financing first, if financing is still needed then it will be met from external financing. Delic et al. (2016) found the Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 1 of 17 Received: 06 October 2022 Accepted: 09 January 2023 *Corresponding author: Fitri Santi, Faculty of Economics and Business, University of Bengkulu, Indonesia E-mail: [email protected] Reviewing editor: Collins G. Ntim, Accounting, University of Southampton, Southampton, UK Additional information is available at the end of the article © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. dominance of SMEs using their own funds and bank loans when starting a business or when financing company growth and development. Based on the age level found in research by Nguyen and Luu (2013) which determines access to informal, formal, or both sources of funds, young managers’ access to sources of funds from informal sources such as family and friends. The older they are, the more likely they will be able to access banks or equity. The findings of Sanyal and Mann (2010), Fourati and Affes (2013) show that startup entrepreneurs are more likely to have foreign debt in their capital structure. For entrepreneurs who have a lot of human capital, are less likely to have external debt and are more likely to be financed internally. Home-based activities tend to be financed by internal equity contributions. Highly educated entrepreneurs have more external debt and attract more investors. Financial literacy is an important thing for SMEs managers. Chen and Volpe (1998), Mouna and Anis (2013) explained that individuals with low education and income make the wrong decisions in their finances. Financial literate individuals are more likely to plan for debt provision and retirement (Lusardi et al., 2010. Eniola and Entebang (2017) in their findings stated that financial awareness is important for financial performance, this implies that company owners and managers are aware of the differences in several financial products that are profitable for them, external financing will not be sought if it is difficult to access. The individual decision-making process is influenced by the risk preferences of individual investors. In financial behavior, it is assumed that individuals can become irrational in decision making. This is due to many influencing factors such as psychological, sociocultural, and environmental factors.; Aren and Zengin (2016) state that investors who have high financial literacy tend to take risks to invest in equities and portfolios. Zwaan et al. (2020) stated that women tend to have low levels of self-confidence and financial knowledge and are unable to take risks. Kumar et al. (2017) show that there is a significant relationship between financial literacy and financial behavior and financial decisions. Several empirical financial literacy and risk tolerance has been studied, especially about investment decisions or retirement plans. Financial decisions made by individual investors are not only based on company values but driven by their emotions such as home bias. This home bias is the tendency of investors to choose investment in their home country compared to a combination of stocks from foreign companies. Vries et al. (2017) find that investors show a familiarity bias when choosing between different companies to invest in. Several previous studies have found reasons for controlling for home bias such as inflation hedging and overconfidence (Musa & Simonov, 2004; Ulwan, 2021). As far as what has been studied, financial literacy affects risk preferences, and home bias towards SMEs funding decisions has not been found. The research gap in this study will reveal whether financial literacy variables directly affect risk preference and house bias or moderate the effect of risk preference and house bias variables on Minangkabau ethnic SME financing decisions. The Minangkabau ethnic community is known as one of the communities that carried out the tradition of migrating. Migrating can increase knowledge, broaden horizons, and motivate oneself to seek a better life throughout the country. The entrepreneurial spirit for the Minangkabau community is also very thick with promoting honesty in trading. Hastuti et al. (2015), Sutanto and Nurrachman (2018) stated that the success of the Minangkabau ethnic entrepreneurship in the migration location is inseparable from their character as hardworking, confident, independent, consistency, ingenuity, flexible, economical, and brave enough to face challenges and struggling to rely on oneself in new places. Based on the above empirical study on the characteristics of the Minangkabau ethnicity, this study reveals whether financial literacy, risk preferences, and home bias influence Minangkabau ethnic SME financing decisions. The empirical results of financial literacy have an indirect effect. The effect of financial literacy is mediated by the variables of home bias and risk preference in financing decisions. The data show that the average level of financial literacy is low with a high home bias leading to bias in assessing risk. Home bias increases resulting in the misperception of risk, so that there is an error in judgment in taking more risks than they can bear. Consequently, financing decisions use external financing which has a higher risk. Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 2 of 17 2. Literature review 2.1. Pecking order theory of financing decisions Traditional financing decisions are based on two theoretical frameworks, namely trade off and pecking order theory. Theoretical trade-off model, capital structure is an assumption of a trade-off between the tax advantages of using debt and agency costs. The use of debt will increase the value of the company only to a certain extent. Jensen and Meckling (1976) and Myers (1984) found a negative relationship between capital structure and firm value, meaning that the addition of debt is a sign that the company’s financial performance is not good. By considering bankruptcy costs and agency costs, companies should use debt funds to an optimal level or often called the trade-off theory. This trade off theory implies that managers will think in terms of tradeoffs between tax savings and bankruptcy costs in determining their capital structure. Luigi (1958) pecking order theory directs the decision to choose company financing according to the level of financing, this concept minimizes the need for external financing. The company’s financing will be met first by internal financing sources, and if this is not sufficient, it will be met by external financing sources. And if additional external financing is needed then they choose the source of financing that can minimize the additional costs of asymmetric information. Small and medium enterprises (SMEs) sources of financing are limited to banking and nonbanking financing. If it is related to the capital structure theory that SMEs adhere to the pecking order theory, the financing needs will be met from internal financing first, and if this is insufficient it will be met from external financing. Delic et al. (2016) SMEs are most dominant in their financing using their equity funds and bank loans when starting a business or financing company growth and development. Funds from family and friends (FFF), as well as factoring, are among the least used sources of financing. In developed financial markets, funds from family and friends are one of the most accessible sources of financing for startup companies, while bank loans are not accessible to companies at this stage of their life cycle. Loans from suppliers are also an important source of financing for the growth and development of companies, but this source of financing is insufficient legal protection for investors. In Entrepreneurial start-up activities are more likely to have some external debt in their capital structure if they have more tangible assets that function as collateral and also have a legal form in the amalgamation (Fourati & Affes, 2013; Karyadi & Rizki, 2018; Sanyal & Mann, 2010). For entrepreneurs who have more human capital in entrepreneurial activities, they are less likely to have external debt and are more likely to be financed internally. Home-based activities are more likely to be financed by internal equity contributions. More educated entrepreneurs have more external debt and attract more investors. 2.2. Planned Behavior Theory (TPB) of Financial Behavior Based on Planned Behavior theory (TPB), a person’s intention toward a behavior is formed by three factors, namely attitude, subjective norm, and perceived behavior control which have an impact on a person’s intentions or actions (Ajzen, 1991). Financial decisions related to the phenomenon of financial behavior in line with the demands of business and academic world developments, began to be addressed by the presence of behavioral elements in the decision-making process. Irrational investor decision making can be caused by cognitive and psychological biases. Financial behavior began to be recognized by various parties, especially academics after Solvic (1969) explained the psychological aspects of investment and stockbroker activities, Tversky and Kahneman (1974) stated that assessments under conditions of uncertainty can produce heuristics or bias Investors in investing do not only use the estimated prospects of investment instruments. Investor psychology factors also have an important role in investing. Behavioral finance tries to explain and improve understanding of investors’ reasoning patterns, including the emotional processes involved and the extent to which they affect the decision-making process. Behavioral finance seeks to explain what, why, and how finance and investment, from a human perspective (Ricciardi & Simon, 2000). Behavioral finance complements traditional financial theory by providing behavioral explanations for the irrational behavior of investors. Shefrin (2000) defines behavioral Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 3 of 17 finance as how psychological phenomena influence financial behavior. Nofsinger (2001) defines behavioral finance as studying how humans actually behave in a financial environment. In particular, study how psychology influences financial decisions, companies and financial markets. Based on the description above, it is clearly stated that behavioral finance is an approach that explains how humans invest or handle finances that are influenced by psychological factors. 2.3. Financial literacy and financing decisions Lusardi et al. (2010) defined financial literacy as financial knowledge and the ability to apply it (knowledge and ability). The OECD INFE defines financial literacy as “the combination of awareness, knowledge, skills, attitudes, and behaviors necessary to make financial decisions and ultimately achieve individual financial well-being.” Measuring levels of financial literacy is a growing research issue around the world, with levels and reach. Financial transactions on electronic media are changing so rapidly, so is our understanding of competencies in financial literacy. Financial literacy research has been widely conducted with individual characteristics and financial decision making, such as Chen and Volpe (1998), finding that individuals with little knowledge tend to have wrong opinions and decisions in the areas of general knowledge, saving, borrowing, and investing. Asaad (2015) found that individuals with good knowledge and high self-confidence are more likely to make “good financial decisions.” When self-confidence is high and actual knowledge is low, individuals are more likely to behave in making high-risk financial decisions. 2.4. Home bias and financing decisions Financial decisions made by individual investors are not only based on information but also driven by individual emotions. Financial behavior tends to experience emotional bias. Emotional bias research has been carried out, one of which is home bias. Home bias in financial decisions occurs where investors tend to choose investments in their home country compared to a combination of stocks from foreign companies. Home bias and familiarity bias occur when investors prefer to invest in assets, they are familiar with. Some investors tend to be more comfortable buying company shares in their country rather than diversifying risk through investing in various countries (Dervishaj, 2018; Ullah, 2018). Vries et al. (2017) found that investors show familiarity bias when choosing between different companies to invest in. Several previous studies have found reasons for maintaining home bias such as hedging inflation and overconfidence (Musa & Simonov, 2004). Empirical evidence reveals that individual characteristics have a significant relationship with home bias, in fact men tend to have a greater home bias than women (Karlsson & Nordén, 2007). From a sociological perspective, culture is an important part of understanding individual behavior. Cultural factors also play a role in investment decisions, as investors personally and collectively adhere to maintaining personal relationships within the organizations or communities of which they are members (Ellison & Fudenberg, 1993). Some evidence from behavioral finance research and other sociology suggests that investor behavior is linked to an individual’s cultural origins. This shows that individual investment behavior can be predicted based on cultural characteristics. 2.5. Risk preference and financing decisions Investors in investing certainly hope to benefit from the investment. But in investing there must be a risk, in the form of capital gains or capital losses. The level of risk borne by investors differs from one another, depending on the degree to which investors prefer risk. However, various factors can cause individuals to have different judgments about taking risks. On the other hand, risk attitudes in a more specific context provide a more robust measure of the appropriate context. Gender differences in willingness to take risks may be part of the explanation for differences in behavior. Dohmen et al. (2011)) found that women choose low risk in making decisions. Weber and Hsee, (1998) define risk preference as an individual’s tendency to choose risky options. Bodie et al. (2014) classify the willingness of investors to bear investment risk into 3 types: 1) types of investors who dare to take risks called risk takers or risk lovers or risk seekers. 2) the type of investor who is Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 4 of 17 willing to bear a risk that is proportional to the return they will get or what is called moderate risk. 3) the type of investor who is afraid or reluctant to assume risk is called a risk averter. Aren and Zengin (2016) state that risk perceptions and literacy levels affect individual investment preferences, investors who have high financial literacy tend to take risks to invest in equities and portfolios. Chen and Volpe (2002) stated that women tend to have low levels of selfconfidence and cannot take risks. Barber and Odean (2001) identified single men as more risktaking than women, whereas the findings of Zwaan et al. (2020) that women have lower selfassessment and financial knowledge. These findings underscore the importance of financial literacy for self-assessment planning. Based on the literature the following research hypotheses and framework (Figure 1) can be made: H1. Financial literacy has significant effect on financing decisions H2. Home bias has significant effect on financing decisions H3. There is a significant effect of risk preference on financing decisions H4. Financial literacy has significant effect on home bias H5. There is a significant effect of financial literacy on risk preference H6. Financial literacy moderates the effect of risk preference on financing decisions H7. Financial literacy moderates the effect of home bias on financing decisions H8. There is a significant effect of home bias on risk preference H9. Home bias moderates the effect of risk preference on financing decisions 3. Research method This research is a survey research with quantitative analysis. This research was conducted in several districts and cities in West Sumatra Province. The research subjects are entrepreneurs Home bias (HB) Financial Literacy (FL) Decision Financing (DF) H1 H4 H3 H2 H6 H7 H5 Risk preference (RP) H8 H9 Figure 1. Theoretical framework. Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 5 of 17 from the Minangkabau tribe, who are widely known for their entrepreneurial orientation and migration to various regions in Indonesia and Southeast Asian countries. This study distributed questionnaires directly to respondents out through online and offline survey techniques as a data collection technique. A total of 1000 questionnaires were distributed to SME owners who were used as samples. The sampling technique used is using simple random sampling. Of these, 642 questionnaires were returned. In other words, the response rate obtained is 64.2 percent. Of these, there were several questionnaires with incomplete answers. The final calculation shows that a total of 623 questionnaires are feasible for further analysis. In this study, there are several variables studied. The estimated dependent variable is Financing Decisions (FD), while the independent variables in this study are Financial Literacy (FL), Risk Preference (RP), and Home Bias (HB). The variable of Financial Literacy (FL) is defined as a combination of cognitive ability and investment in human capital related to understanding financial decisions (Lusardi and Mitchell, 2014). The variable was measured by four indicators including identifying financial information, analyzing information in financial context, evaluating financial issues and applying financial knowledge. The variable of Risk Preference (RP) is defined as the attitude of Minangkabau ethnic SME owners in holding towards risks on their decision-making behavior (Wen et al., 2014). The variable was measured by using 3 items adopted from Zyphur et al. (2009). Moreover, the variable of Home Bias (HB) is operationally defined as the behavior of Minangkabau ethnic SME owners who tend to only choose to invest in the area where they live with reasons that are less rational. The variable was measured by using three items adopted from including Lütje and Menkhoff (2007) including institutional characteristics, informational characteristics, behavioral characteristics. The variable of FD is defined as the decisions of Minangkabau ethnic SME owners in determining the proportion of equity and debt capital that must be owned in their company’s capital structure. This variable of financing decisions is estimated with three measurements adopted from Nguyen et al. (2022) including (1) investment decision, (2) working capital decision, and (3) funding decision. The questionnaire used 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). To analyze the hypothesis, this study employed the SEM-PLS method in testing the model. 4. Results The test results to determine outer loading show that the values obtained are acceptable for items measuring the variables financial decision, home bias, risk preference and financial literacy (Table 1). The results also show a Cronbach’s Alpha value of more than 0.7. This is supported by the Composite Reliability value which is higher than 0.7. Thus, all variables in this study are declared reliable. The Average Variance Extracted (AVE) value is also declared acceptable according to the standard value used. Furthermore, validity testing through Discriminant Validity (Fornell-Larcker Criterion) shows the values accepted for all variables in this study. In general, the Fornell-Larcker criterion is to assess discriminant validity by comparing the square root of the AVE value with the correlation of latent variables. In particular, the square root of each AVE construct must be greater than the highest correlation with the other constructs. The results show that the values resulting from the discriminant validity analysis are accepted (Table 2). This means that all variables in this study are valid and can be continued in the next analysis. Heterotrait-Monotrait Ratio (HTMT) test shows the value of validity testing. HTMT is the recommended alternative method for assessing discriminant validity. This method uses a multitraitmultimethod matrix as the basis for measurement. The recommended measurement value of the Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 6 of 17 Heterotrait-Monotrait Ratio (HTMT) in PLS is less than 0.85. The test results show that all variables have an accepted value <0.85 (Table 3). Furthermore, to test the assumption that there is a relationship between independent variables, multicollinearity testing is carried out. The statistical results show the value received (Table 4). More specifically, the recommended value for testing the absence of multicollinearity is VIF < 10. The results show the VIF value for all variables is < 10. Thus, there is no multicollinearity in the model in this study. Table 2. Discriminant validity (Fornell-Larcker Criterion) HB HB*RP FD FL FL*HB FL*RP RP HB 0.751 HB*RP −0.056 1.000 FD −0.126 0.074 1.000 FL 0.276 0.051 0.030 1.000 FL*HB 0.148 0.141 0.111 0.327 1.000 FL*RP 0.071 0.225 0.072 0.132 0.425 1.000 RP 0.414 −0.056 −0.136 0.053 0.070 0.070 0.781 Table 3. Heterotrait-Monotrait Ratio (HTMT) HB HB*RP FD FL FL*HB FL*RP RP HB HB*RP 0.074 FD 0.169 0.074 FL 0.343 0.051 0.030 FL*HB 0.182 0.141 0.111 0.327 FL*RP 0.091 0.225 0.072 0.132 0.425 RP 0.745 0.121 0.164 0.085 0.139 0.119 Table 1. Outer loading, construct reliability and Average Variance Extracted (AVE) Construct Item Outer Loading Cronbach’s Alpha rho_A Composite Reliability Average Variance Extracted (AVE) Financial Decision FD 1.000 1.000 1.000 1.000 1.000 Home Bias HB1 0.768 0.617 0.646 0.794 0.565 HB4 0.648 HB5 0.827 HB*FL 0.827 1.000 1.000 1.000 1.000 Risk Preference RP1 0.598 0.412 0.568 0.750 0.610 RP3 0.929 RP*FL 1.059 1.000 1.000 1.000 1.000 RP*HB 1.465 1.000 1.000 1.000 1.000 Financial Literacy FL 1.000 1.000 1.000 1.000 1.000 Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 7 of 17 SMEs are mostly with low financial literacy, home bias tends to be high and the choice of sources of business funds is external, both formal and non-formal. Home bias can directly affect the risk preferences of SMEs. The results confirmed home bias as a moderating variable does not provide empirical evidence to accept hypothesis. High home bias does not strengthen or weaken the level of risk preference in external financing decision making. These results reveal that SMEs in meeting their financing needs do not follow the pecking order theory pattern, this is possible because of the role of financial literacy, risk preference, and home bias in SMEs. Financial literacy does not have a direct influence on financing decisions. The results of the study on the direct effect of financial literacy with a significant positive home bias. Risk preference influences financing decisions significantly negatively. The effect of financial literacy through the variables of home bias and risk preference on financing decisions, with a negative path coefficient. The data show that the average level of financial literacy is low, resulting in a tendency for increased home bias to lead to bias in assessing risk, as a result of which risk preferences are higher. The tendency for home bias to increase leads to misperceptions of risk so that there is a misjudgment of taking more risks than they can bear. The consequence is that the financing decision to use external financing is a higher risk. The results of this study provide evidence that SMEs in meeting their financing needs do not follow the pecking order theory pattern. As limitation, there are the need for empirical examination regarding controlling for endogeneity. This is important as endogeneity problems most often arise from omitted variables that correlate with one or more independent variables and the dependent variables in the regression model as stated by previous research (e.g., Bijmolt et al., 2005; Rossi, 2014). Omitting such variables induces a correlation between the corresponding independent variables and the dependent variables’ error term (Wooldridge, 2010). That is, the independent variables then not only explain the dependent variable, but also the error in the model. Moreover, even though there have been several previous studies related to gender issues in financing decision, the research model did not involve gender issues. In the future, this research could also be extended by investigating familiar company characteristics and to link familiarity with financial performance to identify potential arbitration opportunities. Researchers can also try to identify the main factors that encourage investors to feel familiar with the company. In this study, the respondents were only in one ethnicity, for future research to compare with other ethnicities in Indonesia and other countries. The results of this study will assist financial advisors or financial institutions in providing training for SMEs that they can easily absorb and practice directly. One way to reduce home bias, because it is related to emotions and different cultures require a persuasive approach in providing education and training that is easy for them to understand. Funding The authors received no direct funding for this research. Author details Henny Sulistianingsih 1 Fitri Santi 2 E-mail: [email protected] 1 Faculty of Economics and Business, Universitas Dharma Andalas, Padang, Indonesia. 2 Faculty of Economics and Business, University of Bengkulu, Bengkulu, Indonesia. Disclosure statement No potential conflict of interest was reported by the authors. Citation information Cite this article as: Does SME’s financing decisions follow pecking order pattern? The role of financial literacy, risk preference, and home bias in SME financing decisions, Henny Sulistianingsih & Fitri Santi, Cogent Business & Management (2023), 10: 2174477. References Agarwal, N., Raman, T., Sharma, V. A., & Jain, V. (2016). Behaviour biases affecting investor attitude. The Social Sciences, 11(14), 3643–3647. https://medwell journals.com/abstract/?doi=sscience.2016.3643.3647 Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179–211. https://doi.org/10.1016/ 0749-5978(91)90020-T Aren, S., & Zengin, A. N. (2016, October). Influence of financial literacy and risk perception on choice of investment. Procedia - Social and Behavioral Sciences, 235, 656–663. https://doi.org/10.1016/j.sbspro.2016.11.047 Asaad, C. T. (2015). Financial literacy and financial behavior: assessing knowledge and confidence. Financial Services Review, 24(2), 101-118. https://scholar.goo Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 14 of 17 gle.com/scholar?cluster= 4634360060103136030&hl=en&as_sdt=0,5 Barber, B. M., & Odean, T. (2001). Boy will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261–292. https://doi.org/10.1162/003355301556400 Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies, 21(2), 785–818. https://doi.org/ 10.1002/9781118467411.ch7 Barber, B. M., & Odean, T. (2011). The behavior of individual investors. In G. M. Constantinides, M. Harris, & R. M. Stulz (eds.), Handbook of Economics of Finance, Vol. 2B (pp. 1533–1570). Asset Pricing, Oxford, UK: North-Holland. https://doi.org/10.1016/B978-0-44459406-8.00022-6 Barbosa, S. D., Gerhardt, M. W., & Kickul, J. R. (2007). The role of cognitive style and risk preference on entrepreneurial self-efficacy and entrepreneurial intentions. Journal of Leadership & Organizational Studies, 13(4), 86–104. https://doi.org/10.1177/ 10717919070130041001 Bijmolt, T. H., Van Heerde, H. J., & Pieters, R. G. (2005). New empirical generalizations on the determinants of price elasticity. Journal of marketing research, 42(2), 141156. https://doi.org/10.1509/jmkr.42.2.141.62296 Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments. McGraw-Hill Education. Bransch, F. (2020). Financial literacy and home bias in household investments. SSRN Electronic Journal, 1–34. https://doi.org/10.2139/ssrn.3596746 Chen, H., & Volpe, R. P. (1998). An analysis of personal financial literacy among college students. Financial Services Review, 7(2), 107–128. https://doi.org/10. 1016/S1057-0810(99)80006-7 Chen, H., & Volpe, R. P. (2002). Gender difference in personal financial literacy among college students. Financial Services Review, 11, 289–307. https://doi. org/10.5897/AJBM10.1267 Coval, J. D., & Moskowitz, T. J. (1999). Home bias at home: local equity preference in domestic portfolios. The Journal of Finance, LIV(6), 54(6), 2045–2073. https:// doi.org/10.1111/0022-1082.00181 Delic, A., Peterka, S. O., & Kurtovic, I. (2016). Is there a relationship between financial literacy, capital structure and competitiveness of SMEs? Ekonomski Vjesnik. 1, 37–50. Dervishaj, B. (2018). Psychological biases, main factors of financial behaviour - A literature review. European Journal of Natural Sciences and Medicine, 1(2), 25. https://doi.org/10.26417/ejnm.v1i2.p25-35 Dlugosch, D., Horn, K., & Wang, M. (2014). Behavioral determinants of home bias - theory and experiment. Working Papers in Economics and Statistics No. 2014-11. University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon), Innsbruck. https://www2.uibk.ac.at/downloads/ c4041030/wpaper/2014-11.pdf Dohmen, T., Falk, A., Huffman, D., Wagner, G. G., Schupp, J., & Wagner, G. G. (2011, June). Individual risk attitudes: measurement, determinants, and behavioral consequences. Journal of the European Economic Association, 9(3), 522–550. https://doi.org/ 10.1111/j.1542-4774.2011.01015.x Ellison, G., & Fudenberg, D. (1993). Rules of thumb for social learning. Journal of Political Economy, 101(4), 612–643. https://doi.org/10.1086/261890 Eniola, A. A., & Entebang, H. (2017). SME managers and financial literacy. Global Business Review, 18(3), 559–576. https://doi.org/10.1177/ 0972150917692063 Fourati, H., & Affes, H. (2013 November). The capital structure of business Start-Up: Is there a pecking order theory or a reversed pecking order? Technology and investment. 4, 244–254. http://dx.doi.org/10. 4236/ti.2013.44029 Gaudecker, H. M. V. (2015). How does household portfolio diversification vary with financial literacy and financial advice?. The Journal of Finance., 70(2), 489-507. https://doi.org/10.1111/jofi.12231 Guiso, L., & Jappelli, T. (2006). Information acquisition and portfolio performance. CeRP Working Paper 52/ 06. Center For Research on Pensions and Welfare Policies. https://www.cerp.carloalberto.org/wp-con tent/uploads/2008/12/wp_52.pdf. Gustafsson, C., & Omark, L. (2015). Financial literacy’s effect on financial risk tolerance -A quantitative study on whether financial literacy has an increasing or decreasing impact on financial risk tolerance. http:// www.diva-portal.org/smash/get/diva2:826787/ FULLTEXT01.pdf Hastuti, P. C., Thoyib, A., Troena, E. A., & Setiawan, M. (2015, September). The minang entrepreneur characteristic. Procedia - Social and Behavioral Sciences, 211, 819–826. https://doi.org/10.1016/j. sbspro.2015.11.108 Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior agency cost and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76) Karlsson, A., & Nordén, L. (2007). Home sweet home: Home bias and international diversification among individual investors. Journal of Banking and Finance, 31(2), 317–333. https://doi.org/10.1016/j.jbankfin. 2006.04.005 Karyadi, U., & Rizki, L. T. (2018). Reconceptualization of empowerment principles by banking in supporting small and medium enterprises according to law No. 20 of 2008 concerning SMEs. Lex Publica, 5(1), 1–7. https:// journal.appthi.org/index.php/lexpublica/article/view/89 Koenen, T. B., Lusardi, A., Alessie, R., & Maarten, V. R. (2017). How financially literate are women? An overview and new insights. The Journal of Consumer Affairs, 51(2), 255–282. https://doi.org/10.1111/joca.12121 Kumar, S., Watung, C., Eunike, N. J., & Luinata, L. (2017). The influence of financial literacy towards financial behavior and its implication on financial decisions: A survey of President University students in Cikarang - Bekasi. Firm Journal of Management Studies, 2(1), 169–179. http://e-journal.president.ac.id/presunivojs/ index.php/FIRM-JOURNAL/article/view/158/87 Luigi, P., & Sorin, V. (2009). A review of the capital structure theories. Annals of Faculty of Economics, 3(1), 315-320. https://scholar.google.com/scholar?cluster= 18161674235666143914&hl=en&as_sdt=0,5 Lusardi, A., Mitchell, O. S., & Curto, V. (2010). Financial literacy among the young. Journal of Consumer Affairs, 44(2), 358–380. https://doi.org/10.1111/j. 1745-6606.2010.01173.x Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. American Economic Journal: Journal of Economic Literature, 52(1), 5-44. http://dx.doi.org/10.1257/jel.52.1.5 Lütje, T., & Menkhoff, L. (2007). What drives home bias? Evidence from fund managers’ views. International Journal of Finance & Economics, 12(1), 21–35. https:// doi.org/10.1002/ijfe.309 Marchand, M. (2012). Behavioral biases in financial decision making. Bachelor Theses Finance, Vol. 8200, pp. Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 15 of 17 1-28. Tilburg University. http://arno.uvt.nl/show.cgi? fid=129741. Mouna, Amari, and Jarboui Anis. “Financial literacy and decision making: An overview from Tunisia.” International Journal of Information, Business and Management 5.4 (2013). 62-73. https://scholar.google. com/scholar?cluster=8668928663898714246&hl= en&as_sdt=0,5 Mudzingiri, C., Mwamba, J. W. M., Keyser, J. N., & Poon, W. C. (2018). Financial behavior, confidence, risk preferences and financial literacy of university students. Cogent Economics & Finance, 6(1), 1–25. https://doi.org/10.1080/23322039.2018.1512366 Muradoglu, Y. G., Levis, M., & Vasileva, K. (2009). “NearHome Bias” in foreign direct investments. Ssrn, 1–33. https://doi.org/10.2139/ssrn.1359168 Musa, M., & Simonov, A. (2006). Hedging, familiarity and portfolio choice. The Review of Financial Studies, 19 (2), 633-685. https://doi.org/10.1093/rfs/hhj013 Myers, S. C. (1984). The Capital Structure Puzzle. The Journal of Finance, 39(3), 575. https://doi.org/10. 2307/2327916 Nguyen, N., & Luu, N. T. H. (2013). Determinants of financing pattern and access to formal -informal credit: The case of small and medium sized enterprises in Viet Nam. Journal of Management Research, 5(2), 240–259. https://doi.org/10.5296/jmr.v5i2.3266 Nguyen, A. T. L., Van Nguyen, D., & Nguyen, N. H. (2022). The relationship between financial decisions and equity risk. Heliyon, 8(8), e10036. https://doi.org/10. 1016/j.heliyon.2022.e10036 Nofsinger, J. R. (2001). The impact of public information on investors. Journal of Banking & Finance, 25(7), 1339–1366. https://doi.org/10.1016/S0378-4266(00) 00133-3 Olakitan, O. O., & Ayobami, A. P. (2011). An Investigation of Personality on Entrepreneurial Success. Journal of Emerging Trend in Economics and Management Sciences, 2(2), 95–103. https://hdl.handle.net/10520/EJC133893 Pompian, M. M. (2006). Behavioral finance and wealth management – How to build optimal portfolios that account for investor biases. Financial Markets and Portfolio Management, 21. https://doi.org/10.1007/s11408-0070065-3 Qureshi, S. A., Rehaman, K. U., & Hunjra, A. I. (2012). Factors affecting investment decision making of equity fund manager. Wulfenia Journal, 19(10), 1–13. https://doi.org/10.17512/pjms.2016.14.2.05 Raut, R. K., Das, N., & Kumar, R. (2018). Extending the theory of planned behaviour: impact of past behavioural biases on the investment decision of Indian investors. Asian Journal of Business and Accounting, 11(1), 265–292. https://doi.org/10.22452/ajba.vol11no1.9 Ricciardi, V., & Simon, H. K. (2000). What is behavioral finance?. Business, Education & Technology Journal, 2(2), 1-9. https://ssrn.com/abstract=256754. Rossi, P. E. (2014). Even the rich can make themselves poor: A critical examination of IV methods in marketing applications. Marketing Science, 33(5), 655672. https://doi.org/10.1287/mksc.2014.0860 Sanyal, P., & Mann, C. L. (2010). The financial structure of startup firms: The role of assets, Information, and Entrepreneur Characteristics. Shefrin, H. (2000). Recent developments in behavioral finance. The Journal of Wealth Management, 3(1), 25–37. https://doi.org/10.3905/jwm.2000.320376 Shusha, A. A. (2017). Does financial literacy moderate the relationship among demographic characteristics and financial risk tolerance? Evidence from Egypt. Australasian Accounting, Business and Finance Journal, 11(3), 67–86. https://doi.org/10.14453/aabfj.v11i3.6 Solvic, P. (1969). Analyzing the expert judge: A study of a stockbroker’s decision process. Journal of Applied Psychology, 27(4), 255–263. https://doi.org/10.1037/ h0027773 Sutanto, O., & Nurrachman, N. (2018). Makna Kewirausahaan Pada Etnis Jawa, Minang, Dan Tionghoa: Sebuah studi representasi sosial. Jurnal Psikologi Ulayat, 5(1), 86. https://doi.org/10.24854/jpu73 Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty. science, 185(4157), 1124-1131. https:// doi.org/10.1126/science.185.4157.1124 Ullah, S. (2018). Corporate environmental disclosure and earnings quality: Evidence from Malaysian firms. Arthatama, 2(2), 64–84. https://arthatamajournal.co. id/index.php/home/article/view/24 Ulwan, A. (2021). The role of young entrepreneurs in labor absorptions and its implications to family economic resilience. Research Horizon, 1(1), 16–27. https://doi. org/10.54518/rh.1.1.2021.16-2 Vries, A. D., Erasmus, P. D., & Gerber, C. (2017). The familiar versus the unfamiliar: Familiarity bias amongst individual investors. Independent Research Journal in the Management Sciences, 171 , 1–10. https://hdl.handle.net/10520/EJC-5526a7898 Weber, E. U., & Hsee, C. (1998). Cross-cultural differences in risk perception, but cross-cultural similarities in attitudes towards perceived risk. Management Science, 44(9), 1205–1217. https://doi.org/10.1287/ mnsc.44.9.1205 Wen, F., He, Z., & Chen, X. (2014). Investors’ risk preference characteristics and conditional skewness. In Mathematical Problems in Engineering (pp. 2014). West, T., de Zwaan, L., & Johnson, D. (2020). Boosting Women: Why They Decline to Provide Responses to Financial Literacy Questions. https://dx.doi.org/10. 2139/ssrn.3668705 Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT Press. Xiao, L. (2011, April). financing high-tech SMEs in China: A three-stage model of business development. Entrepreneurship and Regional Development, 23(3–4), 217–234. https://doi.org/10.1080/08985620903233937 Yao, R., Sharpe, D. L., & Wang, F. (2011). Decomposing the age effect on risk tolerance. Journal of SocioEconomics, 40(6), 879–887. https://doi.org/10.1016/j. socec.2011.08.023 Zhu, M. (2017). A Study on the Factors Affecting the financing of High-Tech SMEs. Asian Social Science, 13(8), 121–128. https://doi.org/10.5539/ass.v13n8p121 Zyphur, M. J., Narayanan, J., Arvey, R. D., & Alexander, G. J. (2009). The genetics of economic risk preferences. Journal of Behavioral Decision Making, 22(4), 367–377. https://doi.org/10.1002/bdm.643 Sulistianingsih & Santi, Cogent Business & Management (2023), 10: 2174477 https://doi.org/10.1080/23311975.2023.2174477 Page 16 of 17 © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. 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