As early as the 1980s, the paucity of empirical examination of practical economic behavior was identified. Earlier research focused on the financial behavior of specific groups like stock market investors or families. However, there was a drastic change, and the discipline of Behaviour Finance (BF) originated in the 1990s. BF explains investors’ cognitive patterns, including the emotive processes involved and the degree to which they influence decision-making (Sulphey, 2014). It explains the what, why, and how of investment. It also helps explain, identify, and warn about market incongruities like bubbles and crashes. In addition, it studies the psychological and sociological factors that could potentially impact financial or investment decision-making. BF is so versatile that it can help individuals, groups, and entities tame investment decisions. For example, BF studies behavioral inconsistencies and irrationalities of investors and has identified that they result from cognitive and psychological biases (Costa et al., 2019).
In the rapidly changing economic landscape, people are increasingly responsible for personal financial planning, investing, and spending prudently from their resources. As a result, multiple financial instruments have recently gained widespread acceptance. These instruments include various alternative financial services which charge high interest and service fees. The available literature amply supports the influence of FI on individual decisions and financial behavior. Further, FI impacts borrowings, debt management, and saving and investing behavior (Lusardi, 2019). Orientation towards finance (ORTOFIN) is a behavioral pattern characterized by interests and effective money management skills (Loix et al., 2005). Financial literacy (FL) is significant, as educated investors have a culture of savings and investment. This research focuses on identifying whether there exists any relationship between money-related behavior and savings habits among Saudis. It also identifies how they generally handle their income and financial situations and the significant elements of that behavior. Thus, the objective of the study is to identify the level of ORTOFIN and its impact on specific BF variables like FL and Intention towards investment (ITI). The following section reviews the available literature on the variables identified for the study
2. Literature Review
According to the Social Cognitive Theory (Bandura, 1986), people are primarily motivated by external influences rather than always by internal forces. According to this hypothesis, human conduct interacts in a triadic fashion. The approach lists three components: environment, personal factors, and conduct. “Reciprocal determinism” is the term Perry et al. (1990) use to describe this. The different environmental impacts could include the settings in which the person engages in specific behavior and multiple other situational influences. Bandura (1986) enumerates instincts, traits, urges, and other intrinsically personal motivators as personal variables. Self-efficacy, self-control, outcome expectancies, emotional coping, and learning also impact behaviors. According to Bandura’s (1986) Social learning theory, people adapt their behavior to social cues and inputs. The study utilizes inputs from this theory. Based on the social learning theory (Bandura, 1986) and the triadic interaction of behavior (Perry et al., 1990), individuals are expected to look for additional information to get through the insecure and unpredictable position of the current uncertain world. The current study has moorings in the Social Cognitive and Social learning theories. Based on the theoretical background and the existing literature that financial knowledge affects personal financial decisions (Belás et al., 2016; Kıyılar & Acar, 2009; Sulphey & Faisal, 2017, 2020; Sulphey & Nisa, 2014; Widagdo & Roz, 2022), the present study examines its relationship with ITI. The following sections discuss the literature on the variables.
2.1. Orientation Towards Finances (ORTOFIN)
Orientation Towards Finances (ORTOFIN) is “an individual behavioral disposition characterized by personal interests and skills related to managing one’s own finances effectively.” It examines how individuals manage their incomes and financial situations. According to Loix et al. (2005), ORTOFIN involves financial information and personal financial planning. The first dimension indicates that an orientation toward finances is an interest factor involving seeking active financial and economic knowledge, confirmed by Tigges and Jonitz (2000). The next dimension involves the standard logic toward financial management, which focuses on planning-related behavioral competencies. A positive attitude toward money contributes to a better orientation toward money and more effective competencies since it may encourage one to actively focus on personal financial management.
High scoring in ORTOFIN denotes a better financial orientation, which indicates a fair awareness of financial matters and avenues for savings and investments. In addition, ORTOFIN helps measure power and success in life against monetary terms (Loix et al., 2005). They further opine that ORTOFIN includes a keen desire to plan costs and an active interest in financial facts. A higher ORTOFIN value denotes more significant usage of financial tools and instruments, more savings accounts, and a more comprehensive range of investments (Loix et al., 2005). Additionally, they are likelier to be fascinated with monetary aspects and measure success and authority in terms of money. Consequently, they better understand financial means and finer details about savings.
2.2. Financial Literacy (FL.)
Understanding, assessing, and sharing information about money and financial services is FL. It entails making wise financial decisions, making long-term plans, and dealing with life’s ups and downs and how they affect one’s finances. FL is “a set of skills and abilities to navigate the world of money” (Vidovićová, 2021). The OECD (2017) defined FI as:
“the knowledge and understanding of financial concepts and risks but also the skills, motivation, and confidence to apply such knowledge and understanding to make effective decisions across a range of financial contexts, to improve the financial well-being of individuals and society, and to enable participation in economic life.”
Widagdo and Roz (2022) identified it as “a person’s ability to understand finances in general, including savings, investments, debt, and insurance.” According to Atkinson and Messy (2013), FL has three dimensions (financial knowledge, financial behavior, and financial attitude), which contribute to an individual’s decision-making ability to pursue financial well-being. FL has extensive social and socioeconomic roots (Lusardi et al. 2017; Lusardi & Mitchell 2007) and involves knowledge, financial behavior, and financial capability (Amonhaemanon & Vora-Sitta, 2020). FI is associated with higher returns on investments (ROI), as it helps to invest in complex assets, like stocks. In addition, FI has a significant positive correlation with coping with the need for emergency expenses and overcoming income shocks (Hasler et al., 2018). According to Lusardi and Tufano (2015) and Moore (2003), those with low levels of FL could have excessive debts, incurring higher transaction costs, service charges, and high-cost borrowings and mortgages. Mottola (2013) found that those with low FL were likelier to engage in costly credit behaviors. In addition, Utkus and Young (2011) concluded that individuals with FL are unlikely to make higher borrowings.
FL directly impacts financial behavior, including savings and investment behaviors, and are likely to involve in wealth accumulation (Lusardi & Mitchell, 2014; Lusardi, 2019). They are likely to plan their retirement lives. Further, financially literate people are more likely to amass riches (Lusardi & Mitchell, 2014). Oehler et al. (2017) found FL to be low for youth. Moreover, as they are at the breaking point in their lives, they are inexperienced in the markets. Hence, they lack adequate financial knowledge, which could lead to expensive and lasting impacts on markets and economies. FL increases with age until a certain point, which remains constant and could decrease at higher ages (Méndez-Prado et al., 2022). A study by Nanziri and Leibbrandt (2018) found FL to accumulate over time. However, it becomes optimum around 30 years. Ample empirical evidence exists that highly educated individuals have higher FL levels (Alexandra et al., 2020; Liaqat et al., 2020; Nanziri & Leibbrandt, 2018). Bongini and Cucinelli (2019) and Liaqat et al. (2020) identified that higher levels of FL influence the propensity to invest in a pension fund and plan for retirement. Aren and Aydemir (2014) found that FL positively influences multiple financial behaviors. Further, those with high levels of FL effectively manage financial stress (Heckman et al., 2014) and financial well-being (Rahman et al., 2021).
2.3. Intention Towards Investment (ITI)
Any intention is the most proximal determinant of behavior, determined by individuals’ attitudes (Norman et al., 2019). Attitudes toward future behavior may be negative and positive (Schmidt, 2010). Substantial empirical evidence exists on the impact of attitude on behavioral intentions toward financial decisions (Ali, 2011; Phan & Zhou, 2014; Raut, 2020; Raut & Das, 2017). Raut (2020) found a significant relationship between past behavior and investor intention. Moreover, the psychology of individual investors may be negative or positive during the crisis and pandemic period (Phuong, 2021; Shaik, 2021).
Evidence suggests that FL negatively impacts risky investment intentions. Also, higher FL can make the decision-making process more complicated, facilitating investment decisions with lower risks (Abdillah et al., 2019). According to Samek et al. (2021), higher FL could result in better claims and the utilization of social security benefits financial knowledge and literacy influence intention toward investments and various financial behaviors (Aren & Aydemir, 2014; Vlaev et al., 2009). Further, Raut (2020) found FL to be a decisive factor in helping them to make investment decisions. Thus, a robust positive relationship exists between financial literacy and investment decisions.
FL combines the capacity and dependability of investors to weigh financial risks and opportunities and make wise decisions to improve their financial well-being with their comprehension of financial products and principles (Ibrahim & Alqaydi, 2013). It enables investors to have a good understanding of financial products and enhances their ability to consider taking appropriate financial risks and utilizing opportunities to make informed choices toward financial well-being (Ibrahim & Alqaydi, 2013). Recent studies by Abdillah et al. (2019) and Widagdo and Roz (2022), and Yang et al. (2021) also reported that good financial behavior positively influences investment intentions. In addition, Widagdo and Roz (2022) found that students taking financial management courses have better FL and the consequent financial behavior leading to investment intentions.
This section presents the method that tests the proposed hypotheses formulated for the study. Data for the study was collected online using three standardized and validated questionnaires, the details of which are presented below:
1. ORTOFIN: Orientation toward finance was measured using the eight-item ORTOFIN questionnaire developed and standardized by Liox et al. (2005). The questionnaire has two factors – Financial information (with five items) and Personal financial planning (with three items). Sample items include “I try to keep track of general economic trends” and “I systematically keep track of my expenses,” respectively. A few studies that used the ORTOFIN scale include (Sulphey & Faisal, 2017; Sulphey & Nisa, 2014). In addition, the scale has real-world uses and could be helpful when providing financial counseling and training (Loix et al., 2005).
2. Financial literacy: The questionnaire developed by Van Rooij et al. (2011) measured financial literacy. The questionnaire had four items on a five-point scale. A sample item included “The stock market helps to predict stock prices and earnings.” The questionnaire reported a robust Alpa value of 0.86 in earlier studies.
3. Intention towards investment: This variable was measured with the three-item questionnaire developed by Chen (2007). A sample item included “I will encourage my friends and family to invest in the stock market.” Earlier studies have reported relatively good Alpha validity of 0.74 for the questionnaire (Raut, 2020).
All the questionnaires are on a five-point scale – strongly agree, agree, neutral, disagree, and strongly disagree. The control variables include gender, age, qualification, years of experience, and type of industry working.
Data was collected online from gainfully employed samples. The link to the questionnaire was posted on social media groups with the help of group administrations. A short appeal was also provided to facilitate informed consent from the respondents. According to Buhrmester et al. (2011) and Goodman et al. (2013), recruiting respondents online is as effective as other traditional data collection methods. As such, data quality can be assumed.
Further, the author has taken multiple proactive steps to screen the data during data collection, including eliciting responses from Linkedin and other social media groups. This screening process has facilitated high-quality data and consequent generalizability. A total of 210 responses were obtained over four weeks. The respondents belonged to diverse demographics, the details of which are presented in Table 1. The age of the respondents ranged between 21 and 60 years. The average age was 41.48 years. The respondents’ total experience ranged from less than a year to 30 years. The average experience was 14.58 years. Therefore, a fair amount of diversity can be observed from the demographics, from which generalization can be assumed.
Table 1: Demographics of the Participants
The descriptive statistics like mean median, standard deviation (SD), and minimum and maximum values of the sample are presented in Table 2. All the variables had good reliability, as the Cronbach’s alpha of the variables was over 0.5 (Hinton et al., 2014), hence acceptable. The alpha of ORTOFIN was 0.741, 0.592 for FL, and 0.904 for ITI.
Table 2: Descriptive Statistics
Correlation, followed by regression analyses, was done on the data to know the relationship and the contribution between the identified constructs like ORTOFIN, Financial literacy, and Intention towards investment. The Pearson correlation analysis revealed a significant positive correlation at 0.01 level between ORTOFIN and the other two variables – FL and ITI. On the other hand, no significant correlation was observed between ORTFIN and demographic factors like age and experience. Similarly, the two variables, FL and ITI, did not significantly correlate with age and experience. The correlation results are presented in Table 3.
Table 3: Results of Correlation Analysis
Significant at 0.01 level.
Since a significant correlation was observed between the variables, a complete model regression was run by considering ORTOFIN and FL as independent variables and ITI as the dependent variables. Tables 4 and 5 tabulate the results of the regression analysis.
Table 4: ANOVA Results
aDependent Variable: Inerntion towards investment; bPredictors: (Constant), Financial literacy, ORTOFIN.
Table 5: Regression Analysis
Dependent Variable: Inerntion towards investment; R2 = 0.208; F = 27.132; Sig = 0.00.
According to the findings, ORTOFIN and FL, which are independent variables, account for 21% of the variation in Investment intention. The F statistic value was calculated using the ANOVA technique to determine its significance, and it was found to be significant at 27.132. This suggests that the regression model is adequate. In addition, the importance of the independent factors was examined. Both ORTOFIN and FL were revealed to be significant variables. Thus, all the hypotheses formulated for the study are accepted, signifying that ORTOFIN and FL predict ITI.
Furthermore, the results point out that for a unit increase in ORTOFIN, the ITI increases by 0.186 units. ITI will increase by 0.252 units for a unit increase in FL when the control variables are kept constant. Thus, all three hypotheses formulated for the study are accepted.
There exists enough empirical evidence about the antecedents of investment intentions. However, what induces investment behavior is still a matter of deep empirical interest. Therefore, the present study examined the impact of financial behaviors like ORTOFIN and FL on ITI. The findings suggest a significant relationship between the three variables. In addition, it as has been found that ORTOFIN and FL positively influence investment intention. Few studies have been conducted to find the relationship between ORTOFIN and investment intention. The first hypothesis was that a significant positive relationship between ORTOFIN and ITI is accepted at 0.01 level. FL enables appropriate investment decisions due to the proper understanding of probable risks and returns. The second hypothesis that FL is related to ITI is also accepted at the 0.01 level. An earlier study by Sulphey and Shaha (2020) found ORTOFIN to vary based on generation. The finding that FL is related to investment intention substantiates the findings of Bongini and Cucinelli (2019) and Liaqat et al. (2020). This is consistent with Aren and Aydemir’s (2014) findings that FL positively influences multiple financial behaviors.
The finding that ORTOFIN and literacy are related to investment intention is novel, as no earlier studies have examined this aspect, which is unique. However, the result of the present study is different from Baronchelli et al. (2016), which stated that family involvement positively influences financial decision-making. It is also worth noting that the present study did not examine the impact of family involvement. The results of this study thus indicate a positive effect of the variables studied on investment intentions. Thus, financial knowledge generally affects investment intentions, and financial behavior also positively affects investment intentions.
6. Conclusion and Limitations
Although considerable research has been conducted to determine overall financial behavior, scant evidence exists about the relationships examined by the present study. The study determined how financial behaviors like ORTOFIN and literacy impact investments. The information gathered and the results made it possible to examine the intricate connection between the identified financial behaviors. A more distinct and thorough pattern of financial behavior would emerge if a cross-sectional study is conducted and the individuals’ saving behavior is considered. Further, a study with a bigger sample size would aid in resolving a number of the unexpected findings this study produced. This work is anticipated to contribute to the body of knowledge and inspire additional research in this understudied area.
No study could account for every scenario and variable. The current study has its own limitations, as is also the case. Although the respondents were assured anonymity and promised that the responses would be used for research purposes only, at least some respondents would likely have had biased responses due to social desirability factors. Given the size of the study and the time constraints, a longitudinal approach to identify ITI was also impractical. Further, the use of predictive analysis techniques was also not included in this investigation. However, all reasonable measures were taken to ensure that the restrictions did not affect the study’s findings. Future studies could consider these limitations while examining financial and investment behaviors. Further, the study was conducted with samples chosen from Saudi Arabia. Researchers could consider replicating the study in other parts of the world to generalize the findings and examine whether cultural factors have impacted the findings.
The author extends their appreciation to Prince Sattam Bin Abdulaziz University for funding this research work through project no (PSAU/2022/02/21662).
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