Financial Literacy and Financial Well-Being in Rural Households in Ghana: The Role of Financial Information Consumption

Abstract

While financial literacy is crucial in improving the population’s financial well-being, its effectiveness can be enhanced by exposure to financial information. This paper investigates the nexus between financial literacy, financial information consumption, and financial well-being in rural Ghana, framed within the perspectives of prospect theory and resource dependency theory. The study employed cross-sectional data from a survey of 663 rural households using simple random and cluster sampling with reflective-reflective constructs. The data were analysed using partial least squares structural equation modelling. The findings reveal that financial literacy and financial information consumption significantly enhance financial well-being among rural households in Ghana. Financial literacy also promotes financial information consumption. Notably, financial literacy’s impact on financial well-being is stronger when mediated by the consumption of financial information. These findings underscore the importance of improving financial literacy and information access to uplift financial well-being in rural areas. Moreover, the study highlights that financial literacy education is crucial as it plays a mediating role; recipients of financial education experience a more substantial impact. Such findings emphasise the importance of acquiring financial knowledge and effectively processing financial information to achieve financial prosperity, particularly in rural areas. These findings should motivate individuals, especially those in rural areas, to process financial information successfully rather than merely acquiring financial knowledge to attain financial prosperity.

1. Introduction

Over the years, Ghana’s economy has grown significantly, with GDP per capita rising from $253.4 in 2000 to $2238.2 in 2023 [1]. This increase is indicative of the nation’s economic health. However, this progress has not been dispersed equally; in terms of financial well-being, rural populations are trailing behind urban ones. The bulk of the workforce works in the fragile service sector, raising concerns about the distribution of employment. A surge in youth unemployment has resulted from the agricultural sector’s employment share declining from 44% in 2012 to 40% in 2022 [2]. This downturn, which was once the backbone of Ghana’s economy, has affected the rural sector more than the urban sector. Approximately 50% of employment in Ghana’s rural areas is in the primary sector, such as agriculture. The tertiary sector employs around 41% across periods, while the secondary sector accounted for 19% and 14% in 2022 and 2012, respectively. In rural locations, the secondary sector makes up a comparatively small portion of the workforce. Even though primary sector activities predominate in Ghana’s rural areas, the sector’s GDP share has been steadily dropping, going from 22% in 2012 to 20% in 2022 [3]. This implies that the economic progress of the nation is not benefiting rural populations proportionately.

Additionally, in recent times, Ghana has faced severe inflation, which grew from about 32% in 2022 to 40% in 2023 due mostly to rising food costs and currency devaluation. The country’s macroeconomic challenges are having an immediate negative impact on the country’s poverty and living standards. According to estimates, the “international poverty” rate will drop by 4 percentage points to 31% from 2022 in 2023 [4]. Additionally, 44% of people in rural areas and 24% of those in urban areas, respectively, live below the poverty line, indicating that the rural population is disproportionately afflicted by poverty.

It is crucial to have policies and programs in place that support inclusive economic growth and specifically address inequities. This could involve projects like the availability of financial services, information, and training courses. Ghana must prioritise encouraging economic diversity, which, among other methods, includes developing policies to lower inflation and exchange rate volatility to lessen economic inequality and enhance financial well-being (FWB) for all of its residents. One way to accomplish FWB is through financial literacy (FL).

FL involves having a comprehensive understanding of essential financial matters [5]. However, low levels of FL remain a global challenge [6,7]. FL is crucial for enhancing FWB and has become a major policy priority for developing countries [8,9]. Similarly, Sustainable Development Goals (SDGs) 3 and 4 can be advanced with FL. Goal 3 focusses on ensuring health and well-being for individuals of all ages, while Goal 4 emphasises the importance of inclusive and equitable education and promotes lifelong learning opportunities for all. In the past decade, governments, especially in developing countries, have crafted policies and made significant investments to create a financially inclusive and educated society. Despite these efforts, two out of three individuals in the developing world remain financially illiterate [10].

Recently, individuals have been increasingly mandated to manage their retirement funds carefully to ensure their financial well-being both during their working years and after retirement [11,12]. However, the average citizen may lack the necessary knowledge of the financial market’s complexities for effective calculations and planning [13], which might affect people’s FWB [11]. This issue was exemplified during COVID-19, when most people’s FWB was reduced due to loss of employment [14].

Previous studies have documented the link between FL and FWB [14,15,16,17,18,19]. The evidence from these studies suggests a bidirectional relationship between FL and FWB, yet some findings are contradictory [17,20,21,22,23,24,25]. For example, [22] found that financial knowledge did not correlate with projected financial security but did impact current stress levels related to money management. Richards et al. demonstrated that the relationship between financial knowledge and FWB is more indirect than direct [23]. Utkarsh et al. examined FL and FWB in India and found no significant relationship [24]. Riitsalu and Murakas showed that prudent financial behaviour and subjective financial knowledge predict FWB [26]. Lone and Bhat also found a direct, significant impact of FL on FWB, with financial self-efficacy partially mediating the relationship [25]. This literature suggests that neither subjective nor objective FL alone can fully explain a secure financial future. In light of this, Nanda and Banerjee concluded that there is much more to learn about the relationship between FL and FWB [27]. One factor that has not been extensively studied is financial information (FI).

FI is crucial for achieving and maintaining FL to reach desired financial goals [28]. Prior studies have often conflated FL with FI, yet these concepts are distinct [29]. FI pertains to current news about financial market developments, whereas FL encompasses the knowledge one already possesses to leverage these developments. FI is an enabler of FL, which alone might not suffice to ensure sound financial decisions [30]. Conversely, FL enables individuals to unlock doors for individuals to evaluate FI, which is essential for sound decision-making that enhances FWB.

Although past studies have shown that FWB depends on the level of FL [31,32], financial decisions are typically based on both FL and FI. From this perspective, the literature suggests that the informational dimension is critical in determining how FL influences FWB [33]. It further emphasises that lifetime experience in FI consumption is highly relevant to FWB expectations [34], arguing that the more FI an individual receives, the more likely it will influence beneficial financial decision-making [35,36,37]. However, to make good use of FI, one must be financially literate to convert FI into decisions that positively affect FWB [38].

Conrad et al. observe that information exerts a more substantial influence on the financial decisions of economic agents [39], as long as the information consumed is relevant to financial decisions that improve FWB [40]. Tchamyou argues that inadequate FI consumption among the financially literate may lead to inappropriate financial decisions, thereby failing to influence FWB [41]. Therefore, the quality of financial knowledge shapes people’s ability to manage finances [42], which ultimately impacts FWB.

This study employs a multi-theoretical lens to examine the relationship between financial literacy (FL) and financial well-being (FWB). By integrating prospect theory with resource dependency theory, the study aims to understand the mediating role of financial information in the relationship. Prospect theory suggests that FL influences decision-making under uncertainty, thereby impacting FWB [43]. Meanwhile, resource dependency theory emphasises the critical role of FI as a resource for people to rely on when making financial decisions. FL enhances financial decision-making, increases access to financial resources, and ultimately improves FWB by enhancing one’s capacity to consume FI [44]. Thus, FI mediates the relationship between FL and FWB by serving as a crucial resource for risk management, access to financial opportunities, and well-informed decision-making.

The literature discussed above suggests that FI may act as a mechanism through which FL leads to FWB, as has been noted in previous studies, whereby FL might not lead to FWB unless it passes through a mechanism [17,45]. Consequently, several studies have examined mediating variables to explain the mixed evidence in the FL–FWB relationship. Such variables included financial inclusion [8,46,47,48,49,50], financial behaviour [16,51,52], and consumption needs [17].

None of these studies explored the role of FIC. This study seeks to bridge this gap in the literature. In pursuing this analysis, the study seeks to make several other contributions to the current body of knowledge in the area. First, the study focusses on rural areas where financial information has traditionally been low compared to urban settings. The information flow to rural settings has improved with the advent and widespread usage of mobile phones and increased access to the internet. These changes make rural settings crucial for research to advise on inclusive development policies because rural populations are the most financially vulnerable concerning FL [15]. The dynamics of rural and urban contexts differ regarding information flow, level of education, and type of financial products available. The rural context has been less researched, and previous studies have recommended further investigations in these areas [16,53]. Exploring FIC in rural households would be an effective way to understand and improve household living standards.

1.1. Literature Review and Hypothesis Development

Research on FWB spans multiple disciplines, yet it remains broader than it is deep [54]. This highlights the need for further investigation in this area [24]. FWB encompasses an individual’s ability to effectively manage their financial resources, attain financial stability, and achieve financial success [14,54]. According to Brüggen et al., FWB is influenced by various factors, including economic, legal, socio-cultural, political, technological, and marketing elements [54]. Effective personal finance management and the ability to handle and navigate financial challenges are crucial not only for the financial health of individuals and households but also for the overall economy [16]. In response to the growing emphasis on FWB, stakeholders have focused on different aspects such as financial inclusion [55], financial literacy [24], and financial behaviour [16]. The central aim of these studies is to improve FWB [56]. Sehrawat et al. highlight that comprehensive models of FWB have received limited research attention [16]. Most existing models have been developed for high-income countries, such as Norway [57] and Australia [58]. However, only a few models have recently emerged from developing countries such as India [16]. It is important to recognise that theories and evidence derived from industrialised countries may not hold true in developing economies. Therefore, research in developing economies is crucial to enhancing understanding of FWB across socioeconomic contexts. In Ghana, studies have primarily focused on financial inclusion and development, with limited attention given to FWB, particularly in rural settings.

1.2. Link between FL and FIC

Several research studies have investigated the connection between FIC and FL [59,60], but findings remain mixed. Information plays a crucial role in shaping behaviour change [61], and FL is one such type of information. Research has shown that actively seeking information can be a powerful way of driving behaviour change [62]. In this context, information-seeking behaviour influences information consumption, whether actively or not [63,64]. Individuals possess unique resources and capabilities to source information, which helps them gain a competitive advantage. This ability to seek and consume information significantly impacts financial decision-making [65]. Additionally, knowledge can lead to changes in information consumption behaviour, which in turn influences the financial choices consumers make in the future. Information literacy, or knowledge, is a key driver of information consumption for financial decision-making [66,67]. This behaviour is influenced by the perceived marginal benefit of consuming or not consuming information [68]. Financial education has been shown to directly affect financial behaviour [15] and promote positive information consumption patterns [69]. Therefore, FL can have a positive effect on FIC behaviour.

H₁. 

FL has a significantly positive relationship with rural households’ FIC.

1.3. Link between FIC and FWB

The impact of FIC on FWB varies depending on the source, with evidence pointing to both negative and positive influences [70,71,72]. However, the findings remain mixed, largely depending on the context. Lusardi et al. argue that failure to consume necessary FI can lead to depletion of financial wealth, resulting in financial struggles later in life [73]. Lusardi also notes that a lack of access to FI hinders individuals’ and households’ ability to save, borrow, and secure a better financial future [31]. Indeed, growing ignorance of FI can have detrimental effects on FWB. FIC plays a significant role in driving behavioural change [61,74]. Exposure to relevant information helps transfer knowledge needed for effective personal management and enhances financial satisfaction [42,61]. Transparent FI reduces anxiety, increases financial security, and improves individual FWB [20]. Moreover, transparent information is strongly linked to FWB [75]. Therefore, in rural settings, FIC is likely to have a positive association with FWB.

H₂. 

FIC has a significantly positive relationship with FWB.

1.4. Link between FL and FWB

Prior studies have generally highlighted a positive relationship between FL and FWB [15,17], although some findings have been mixed and contradictory [76]. Higher levels of FL are typically associated with better FWB [45,77,78,79]. Chu et al. argue that households with advanced financial knowledge and skills are most likely to choose appropriate financial products and services that meet their financial goals [80], as well as select profitable investment strategies that enhance current and future FWB. These actions are expected to increase financial returns and improve FWB [17]. Furthermore, individuals with greater financial literacy tend to have more financial confidence and a stronger foundation of objective financial knowledge, leading to better financial decision-making. This, in turn, positively impacts their FWB. Thus, we hypothesize that FL is positively correlated with FWB.

H₃. 

FL is positively related to FWB.

1.5. FIC as a Mediator in the Link between FL and FWB

It is worth noting that FL may not directly lead to FWB without passing through mechanisms. Consequently, several studies have introduced mediating variables to explain their relationships. These mediators include financial inclusion [46,47,48,81], financial behaviour [16,48], socio-economic environment [26,47,82], financial education [26,83,84], consumption and consumption needs [17], propensity to plan [85], time perspective [22], and financial decision-making [46,52]. Although empirical studies have shown that intervening variables influence the FL–FWB link, the role of FIC as a mediator remains unexplored. Given the significance of information and the established connection between knowledge and consumption, we hypothesise that FIC mediates the relationship between FL and FWB.

H₄. 

FIC mediates the relationship between FL and FWB.

H₅. 

FIC mediation is stronger amongst the financially literate than amongst the financially illiterate.

1.6. Analytical Framework

Following the literature above, Figure 1 depicts the core of the investigation, i.e., the role of FIC in the relationship between FL and FWB. According to the analytical framework below, achieving FWB requires a high level of FL [44]. Individuals with high FL are competent to make effective financial decisions, avoid debt traps, and effectively manage their finances. The ultimate goal of FL is to achieve FWB [15].

However, FL alone may not be sufficient to guarantee FWB. Additional interventions are often necessary [49], with one such intervention being the provision of financial information [24,54]. Information plays a crucial role in effective financial decision-making [65,86]. In this context, FIC emerges as a key mediating factor. By accessing and utilising financial information, individuals can enhance their decision-making processes, improve their FL, and ultimately achieve better FWB [87].

FL, FIC, and FWB have dynamic and intricate interactions. People are more likely to participate in FIC when their FL rises, and this improves their FWB. On the other hand, reduced FL can result in decreased FWB and financial hardship. Effective financial literacy initiatives are, therefore, crucial to fostering FIC and ultimately enhancing FWB.

Following the analytical model above, the current study uses structural equation modelling to this effect. The structural equation modelling analysis also makes it possible to delve deeper into the analysis by jointly exploring hypotheses depicting other relationships.

The rest of the paper is structured as follows: Section 2 discusses the methodology for the study; Section 3 presents study results; Section 4 discusses the findings; and conclusions, implications, limitations, and future research directions are presented in Section 5.

2. Research Method

2.1. Study Context

This study was conducted in Ghana’s Upper West Region (UWR). At around 18,478 square kilometres, or 12.7% of the country’s total land area, this region is the seventh largest area in the country. It has 11 political districts, with Wa as a regional political and administrative capital. Situated in Ghana’s northwest corner, it borders Burkina Faso to the north, Ivory Coast to the west, the Savanah Region to the south, and the Upper East Region of Ghana to the east. The main economic activity is agriculture, in which 72% of the population is engaged, and it has a long dry season from October to May every year. Because the UWR is classified as the poorest of the poor regions in Ghana [88], there have been many financial education programmes since the 2015 Ghana Statistical Service (GSS) Report [77]. It is a rural region, with 73.6% of its population living in rural areas and a literacy rate of 46% [89]. While there is no reported evidence of the level of financial literacy in the UWR of Ghana, a related study on the level of financial inclusion is reported to be 20% household access to formal financial services [90].

The characteristics of the region and its policy-maker interests make it relevant for a dedicated study to understand the interplay between FL, FIC, and FWB in the region’s rural context. A map of the region is presented in Figure 2 below.

2.2. Study Design

A cross-sectional research design was employed, with a well-structured questionnaire to gather data for hypothesis testing. The study focused on rural households in Ghana with specific reference to the Upper West Region. According to the Housing and Population Census 2021, the region had 134,487 households. Heads of households in the region participated in the study.

Using an online sample calculator (www.surveysystem.com), the minimum sample size for this population was 598 households, but 663 households were analysed. The study used cluster sampling to cover the entire population, just like prior studies [91,92]. Within each cluster, simple random sampling was used to select communities and households where participants reside. The study used a random sampling method to sample clusters, communities within a cluster, and households within a community. A community’s sample size target was based on its total population. A simple random sampling technique gave everybody an equal chance to participate in the study. Heads of the households selected who agreed to participate were enrolled in the study. Otherwise, the next random household was approached until the community’s target sample size was achieved. Unlike another study, which employed cluster and stratified sampling [80], we adopted cluster and simple random sampling.

A repeated two-stage reflective–reflective higher-order construct (HOC) approach was used to test hypotheses and conduct one multigroup analysis to establish significant differences in the mediating role. This method’s implementation necessitates developing and estimating the model indicators that link each low-order construct (LOC). The output score of the LOC became the input for modelling the HOC and assessing the hypotheses under investigation.

The partial least squares approach to structural equation modelling (SEM-PLSs) is a statistical technique that combines structural equation modelling with partial least squares regression. It is particularly useful for modelling complex relationships between variables in a dataset. In SEM-PLSs, a structural model is specified, which represents the theoretical relationships between latent variables (constructs) and observed variables (indicators). The PLSs algorithm is then used to estimate the model parameters, which include the loadings of the indicators onto the latent variables and the path coefficients between the latent variables. SEM-PLSs is a versatile tool capable of analysing both reflective and formative measurement models, as well as capturing mediating and moderating effects. This makes it especially powerful for exploring intricate relationships and testing hypotheses in empirical research.

SEM-PLSs with percentile bootstrapping was the method used for the analysis. According to Hair et al., SEM-PLSs is the appropriate analysis tool because it can estimate complex relational models with multiple constructs, indicator variables, and structural routes without requiring distributional assumptions on the data [93]. This study also followed a standard diagnostic approach to validate the measurement model for the structural analysis, as suggested [93]. The statistical output of the measurement and structural models was estimated using SmartPLS version 4.

The study pilot-tested all primary data collection instruments to ensure they met minimum conditions for robust results. Wilkinson and Birmingham emphasise the importance of conducting a pilot study prior to the main study to confirm the functionality and appropriateness of the methodology, instrument, sampling, and analysis [94]. According to [95], a pilot study sample size of 25–100 subjects is recommended for producing reliable results. For this study, 64 respondents participated in the pilot test to evaluate the credibility of the research instrument. During the pilot, it was observed that some indicators were not loading properly in the rural context, providing the opportunity for improvement by simplifying those questions for greater clarity.

2.3. FL Measurement

The construct is extracted from literature related to FL. To measure FL objectively, this study follows [96,97] with seven financial knowledge and skill questions. Similarly, to measure FL subjectively, the approach of [97] was followed by eight statements/questions. While the objective measure is a multiple-choice answer, the subjective measure is a Likert scale. The measurements adopted were modified to fit the context of the study. The questions/statements are below.

Objective FL (Multiple choice)

• Imagine five of your friends receive a donation of GH¢ 1000.00 and must equally divide the money between them. How much will each of them get? (a) GH¢100; (b) GH¢200; (c) GH¢1000; (d) Don’t know.
• Assume you saw the same television at two different stores for the initial price of GH¢ 1000.00. Shop A offers a discount of GH¢ 150.00, while Shop B offers a discount of 10%. Which of the stores will you buy from? (a) Buying in shop A (discount of GH¢150); (b) Buying in shop B (discount of 10%); (c) The value is the same; (d) Don’t know.
• When the inflation rate or price of goods and services increases, the cost of living rises. This statement is: (a) True; (b) False; (c) Don’t know.
• When you distribute your investments among two or more different business activities, the risk of losing money: (a) increases; (b) decreases; (c) remains unchanged; (d) don’t know.
• Assume Kwame inherits GH¢ 10,000.00 today and Peter inherits GH¢ 10,000.00 in about 3 years. Because of inheritance, who will get richer? (a) Kwame; (b) Peter; (c) They are equally rich; (d) don’t know.
• Suppose you had GH¢100 in a savings account and the interest rate is 2% per year. After 5 years, how much do you think you would have in your account if you left the money to grow? (a) More than 102. (b) Exactly 102. (c) Less than 102. (d) Don’t know.
• Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After one year, how much would you be able to buy with the money in this account? (a) More than today. (b) Exactly the same as today;.(c) Less than today. (d) Don’t know.

Subjective FL (Likert scale level of agreement on a scale of 1–7)

• I compare prices when making a purchase.
• I usually reach the goals I set when managing my money.
• Before buying anything, I carefully check whether I am able to pay for it.
• I save regularly to achieve long-term financial goals such as my children’s education, purchasing a home, and retirement.
• I have a financial reserve equal to or greater than 3 times my monthly expenses, and it can be quickly accessed.
• When deciding on which financial products and loans I will use, I consider the options from various institutions.
• I am able to identify the costs I pay to buy a product on credit.
• I buy on credit when the facility is available rather than paying cash immediately.

2.4. FWB Measurement

To measure FWB objectively, this study follows [98] with modification while employing the measurement scale by [99] for subjective measurement. Eight questions are on subjective measures, and four are on objective measures. Respondents score from 1–10 depending on the answer selected for the subjective measure, while multiple choice is provided for the objective measure. Some indicators were modified to fit the context of the study. The questions/statements are below.

Subjective FWB (Likert scale level of agreement on a scale of 1–10).

• What do you feel is the level of your financial stress?
• How satisfied are you with your present financial situation?
• How do you feel about your current financial situation?
• How often do you worry about being able to meet normal monthly living expenses?
• How confident are you that you could find the money to pay for a financial emergency that costs about GH¢ 2000.00?
• How often does this happen to you? You want to go out to eat, go to a movie. or do something else and do not go because you can’t afford to?
• How frequently do you find yourself just getting by financially and living pay cheque to pay cheque?
• How stressed do you feel about your personal finances in general?

Objective FWB (multiple choice)

• Payment problems encountered in the past year relative to utilities (e.g., light bill and medical bill). Options: in arrears for more than 7 months, in arrears for 7 months, in arrears for 6 months, in arrears for 5 months, in arrears for 4 months, in arrears for 3 months, in arrears for 2 months, in arrears for 1 month, slightly less than 1 month, had no payment problem.
• Days in the past year where you did not have enough funds or money to spend. Options: above 24 weeks, 21–24 weeks, 17–20 weeks, 13–16 weeks, 9–12 weeks, 5–8 weeks, 2–4 weeks, less than 2 weeks but not more than a week, less than 1 week, never had problem of not having enough money to spend.
• Months in the past year when spending exceeded income. Options: 9 months or more, 8 months, 7 months, 6 months, 5 months, 4 months, 3 months, 2 months, 1 month, or never experienced spending exceeding income.
• My household has savings that can cover for… Options: no savings to meet expenses, savings can cover 1 month’s expenses, savings can cover 2 months’ expenses, savings can cover 3 months’ expenses, savings can cover 4 months’ expenses, savings can cover 5 months’ expenses, savings can cover 6 months’ expenses, savings can cover 7 months’ expenses, savings can cover 8 months’ expenses, savings can cover more than 8 months’ expenses.

2.5. FIC Measurement

This study follows the approach of [17,29,100] to measuring financial information consumption with modification. Various questions/statements are adopted and modified to measure financial information consumption with a 7-point Likert scale. Respondents rate how satisfied they are with the sufficiency of financial information consumption. Below are the statements.

Subjective FIC (Likert scale level of information sufficiency on a scale of 1−7).

• My household has sufficient financial information to honour and deal with financial matters.
• My household has information to anticipate and plan for financial income and expenses.
• My household has the information to make rational financial decisions that are in line with financial possibilities.
• My household thinks and looks for relevant information and the financial consequences of purchasing decisions.
• My household tries to be informed and educated about personal financial management.
• My household follows financial information that affects our financial situation on a daily basis.
• My household finds the financial information available to us easy to understand.

3. Results and Data Analysis

Table 1. Descriptive statistics of respondents.

Category		Frequency	Percent (%)
Gender	Female	29	4.4
	Male	634	95.6
Age bracket	18–19	26	3.9
	20–29	152	22.9
	30–39	168	25.3
	40–49	115	17.3
	50–59	81	12.2
	60+	121	18.3
Retiree	No	534	80.5
	Yes	129	19.5
Marital Status	Single	64	9.7
	Married	467	70.4
	Divorced	41	6.2
	Separated	40	6.0
	Widow/widower	51	7.7
Working Sector	Agric–self-employed	489	73.8
	Trading–self-employed	79	11.9
	Formal sector–public	33	5.0
	Formal sector–private	9	1.4
	Unemployed	53	8.0
Accommodation Status	A rented residence	110	16.6
	My own residence	302	45.6
	My parents’ residence	222	33.5
	Institution-provided residence	29	4.4
Receive financial literacy education	No	356	53.7
	Yes	307	46.3

below shows that most head of household respondents were male, accounting for 95.6%, while the remaining 4.4% were females. The lower representation of women in the sample stems from the fact that, in these areas and many developing countries, few women head households or actively participate in financial decision-making [101,102]. This highlights a significant gender imbalance in household leadership roles in rural settings. However, due to the use of robust cluster sampling methods, the sample is still considered representative of the target population. That said, the results should be interpreted with caution, as women’s voices remain underrepresented. The result indicates that the most extensive age distribution falls within the age bracket of 30–39, followed by 60+, accounting for 25.3% and 18.3%, respectively. It needs to be noted that 81.7% are within the active labour force age bracket. This information could help target specific age brackets for policies and services offered to people in household leadership roles. More than four-fifths of the respondents were not retirees, indicating an active labour force among heads of household.

Additionally, 70.4% of the heads of household were married, with the remaining 29.6% representing divorced, separated, single, and widowed. Information from this demographic can be valuable for tailoring services to marital status in rural settings. Additionally, the data indicates that a significant portion of the rural household heads are self-employed, representing 85.7%. This gives us an understanding of the distribution of different working sectors in rural settings, which can help design targeted financial services and products. Almost half of the respondents had received FL education once before. This finding suggests an awakening of interest among the heads of households in rural settings about obtaining financial knowledge that could affect financial decision-making and changing financial behaviour.

3.1. Measurement Model Assessment—Lower-Order Construct (LOC)

In this study, in order to test the hypotheses, the LOCs were assessed; these included financial literacy (subjective), financial well-being (both subjective and objective), and financial information consumption. This was intended to confirm validity and reliability of the contructs. The structural path analsysis is demonstrated in Appendix A.

3.2. Indicator Loadings

The first step for SEM-PLS analysis in the measurement model is to evaluate the indicator reliability. For an indicator of a construct to be reliable, it is suggested to have a factor loading of 0.7 or more [93,103]. However, for an indicator to be deleted from a construct, such an indicator should significantly impact the reliability and validity of the construct [93] from

Table 2. Reliability and validity of indicators and constructs.

Indicators	Loadings	Alpha	CR	AVE
Compare price of goods ⟵ FLS	0.894
Money management expectation ⟵ SFLS	0.935	0.934	0.940	0.735
Check ability to pay ⟵ FLS	0.912
Save to reach financial goal ⟵ FLS	0.916
Have enough reserve for monthly expenses ⟵ FLS	0.902
Consider options for financial decisions ⟵ FLS	0.850
Ability to identify the cost for taking credit ⟵ FLS	0.512
Encountered payment problems monthly ⟵ FWBO	0.692	0.880	0.885	0.745
Lack of money to spend ⟵ FWBO	0.969
Excess spending over income ⟵ FWBO	0.881
Sufficient savings for period ⟵ FWBO	0.887
Level of financial distress ⟵ SFWB	0.847	0.937	0.945	0.697
Satisfaction with present financial situation ⟵ FWBS	0.881
Feeling about current financial situation ⟵ FWBS	0.846
Ability to meet monthly expense ⟵ FWBS	0.879
Confident to meet financial situation ⟵ FWBS	0.867
Ability to afford socialization cost ⟵ FWBS	0.813
Living paycheck to paycheck ⟵ FWBS	0.690
Stress on personal finances ⟵ FWBS	0.842
Have sufficient financial information ⟵ FIC	0.855	0.944	0.947	0.750
Have information to plan income and expenses ⟵ FIC	0.879
Have information for financial possibilities ⟵ FIC	0.888
Look for relevant information on buying decisions ⟵ FIC	0.842
Seek person financial management information ⟵ FIC	0.872
Follow financial information that affects daily life ⟵ FIC	0.861
Availability of financial information is easy to understand ⟵ FIC	0.863

FLS = subjective financial literacy, FWBO = objective financial well-being, FWBS = subjective financial well-being, and FIC = financial information consumption.

. Even though the ability to identify the cost of taking credit (subjective financial literacy indicator), encountering payment problems monthly (objective financial well-being indicator) and living pay cheque to pay cheque (subjective financial well-being indicator), showed loading below 0.70, these indicators were maintained in the constructs as they did not significantly influence the construct’s reliability and validity (see details above). Also, retaining factor loadings below 0.7 but above 0.5 was consistent with the prior recommendation of factor loading to be 0.50 or higher [104,105].

3.3. Construct Reliability

According to [106], the Cronbach alpha test was employed to establish the reliability of a construct in most prior studies. Table 2 displays the results of the alpha reliability. The present study’s Cronbach alpha construct ranges from 0.880 to 0.944, indicating that the construct reliability is well above the threshold of 0.6, as recommended [107,108]. The composite reliability (CR) outcome, as indicated in Table 2 above, is indifferent from the alpha value, confirming the construct reliability of the measurement model.

3.4. Convergent Validity

Aside from the construct reliability, convergent validity is an essential element in the measurement model. This validity is established when the average variance extracted (AVE) value is 0.5 or greater [109]. This confirms that the concepts in use should be related to each other. From Table 2 above, all the constructs have AVE values above 0.50, which range between 0.697 and 0.750, indicating that all the constructs carry convergent validity.

3.5. Discriminant Validity

Hair, Risher [93] noted that one way to assess measurement model discriminant validity was heterotrait–monotrait (HTMT), aside from Fornell and Larker’s criterion and cross-loading [93]. Discriminant construct validity indicates the extent to which a construct is genuinely different from the other constructs in a model [109,110]. Even though there are many means of testing, HTMT is suggested in empirical studies to have superior outcomes over others [111]; hence, it is dominant in contemporary studies. To establish discriminant validity, all pair constructs should be below 0.90 [93].

Table 3. Heterotrait–monotrait (HTMT) for discriminant validity.

Constructs	FIC	FLS	FWBO	FWBS
FIC
FLS	0.362
FWBO	0.597	0.426
FWBS	0.596	0.231	0.723

below shows that all pair values are below the recommended value of less than 0.90; hence, discriminant validity was maintained.

The study also used Fornell and Larker’s criterion to complement HTMT. This approach compares AVE’s square root with the correlation between the latent variables. According to [109], the construct should be able to explain the variance of its indicator better than it does with the other constructs. Therefore, for discriminant validity to be confirmed, the square root of AVE should produce a more excellent value than the correlation with other constructs.

Table 4. Fornell and Larker’s criterion (FLC) for discriminant validity.

Construct	FIC	FLS	FWBO	FWBS
FIC	0.866
FLS	0.351	0.857
FWBO	0.554	0.392	0.863
FWBS	0.569	0.226	0.679	0.835

below demonstrates Fornell and Larker’s criterion, which confirmed the discriminant validity established in Table 3 of HTMT.

3.6. Assessment of Measurement Model–Higher-Order Construct (HOC)

FL and FWB are both higher-order reflective–reflective constructs derived from subjective and objective measurements of LOC, as shown in Appendix B. To validate these HOCs, the same procedure used for evaluating LOC’s was applied. This involved assessing the constructs’ reliability (both indicator reliability and internal consistency) as well as their validity (convergent and discriminant validity).

Table 5. Reliability and construct validity.

Indicator	Loadings	Alpha	CR	AVE
Objective Financial Literacy (FLO) ⟵ FL	0.850	0.664	0.668	0.748
Subjective Financial Literacy (FLS) ⟵ FL	0.879
Objective Financial Well-Being (FWBO) ⟵ FWB	0.921	0.808	0.810	0.839
Subjective Financial Well-Being (FWBS) ⟵ FWB	0.911
Have sufficient financial information ⟵ FIC	0.855	0.944	0.947	0.750
Have information to plan income and expenses ⟵ FIC	0.879
Have information for financial possibilities ⟵ FIC	0.889
Look for relevant information on buying decisions ⟵ FIC	0.841
Seek person financial management information ⟵ FIC	0.872
Follow financial information that affects daily life ⟵ FIC	0.860
Availability of financial information is easy to understand ⟵ FIC	0.862

FL = financial literacy, FWB = financial well-being, and FIC = financial information consumption.

below presents the results of the measurement assessment for these higher-order constructs.

Table 5 above confirms the reliability and convergent validity of the HOCs. The measurement models demonstrate satisfactory reliability and validity, with the indicators’ reliability values exceeding the recommended threshold of 0.70 and Cronbach’s alpha and composite reliability both surpassing the 0.60 benchmarks. In

Table 6. Discriminant validity.

HTMT				FLC
Indicator	FIC	FL	FWB	FIC	FL	FWB
FIC				0.866
FL	0.484			0.387	0.865
FWB	0.698	0.507		0.613	0.375	0.916

, the discriminant validity is adequately supported by both the heterotrait–monotrait ratio (HTMT) and Fornell and Larker’s criterion (FLC). HTMT values fall below the recommended cut-off of 0.90, while the FLC approach, which compares AVE’s square root with the correlation between the latent variables, yields values greater than the correlations with other constructs, as shown in Table 6 below.

The HOC results provide a solid foundation for testing the study’s structural model. Consequently, the items used to measure the constructs in this study are validated and appropriate for assessing and estimating structural model parameters.

3.7. Assessment of Structural Model and Hypotheses Testing

The structural model examines the inter-relationship between FL, FIC, and FWB.

Table 7. Direct relationship.

Hypotheses	Decision	Coef	T Stat	p Values	Percentile Bootstrap 95% Confidence Interval
Complete					Lower	Upper
H1: FL ⟶ FIC	Accepted	0.387	10.980	0.000	0.330	0.446
H2: FIC ⟶ FWB	Accepted	0.550	18.573	0.000	0.501	0.599
H3: FL ⟶ FWB	Accepted	0.162	5.011	0.000	0.110	0.216
Ever Received FL Education
H1: FL ⟶ FIC	Accepted	0.480	10.385	0.000	0.403	0.555
H2: FIC ⟶ FWB	Accepted	0.519	12.238	0.000	0.448	0.588
H3: FL ⟶ FWB	Accepted	0.286	6.011	0.000	0.208	0.364
Never Received FL Education
H1: FL ⟶ FIC	Accepted	0.596	16.385	0.000	0.536	0.656
H2: FIC ⟶ FWB	Accepted	0.207	3.645	0.000	0.119	0.303
H3: FL ⟶ FWB	Accepted	0.105	2.397	0.008	0.035	0.177

below shows the results of the direct relationships.

Table 7 presents the result of the model’s direct effects. In line with hypothesis H₁, the relationship between FL and FIC was examined using path analysis. The result indicates a significant positive relationship between FL and FIC (β = 0.387; p < 0.001; t = 10.980), confirming the hypothesis both in terms of direction and significance. Another hypothesis, H₂, explored the direct impacts of FIC on FWB. The analysis reveals that FIC significantly and positively influences FWB (β = 0.550; p < 0.001; t = 18.573), consistent with the proposed hypothesis H₂. Additionally, the study examined whether the relationship between FL and FWB remains significant when FIC is included in the model. The results show a significant relationship (β = 0.162; p < 0.001; t = 5.011), aligning with hypothesis H₃.

In addition to the complete data analysis, a multigroup analysis was conducted to explore the relationships between heads of households receiving education and those not receiving it. Table 7 shows that both groups exhibited a significant positive relationship between FL and FIC (β = 0.480; p < 0.001; t = 10.385) for those who received FL education and (β = 0.596; p < 0.001; t = 16.385) for those who did not. Similarly, the relationship between FIC and FWB was found to be positive and significant for both those who received FL education (β = 0.519; p < 0.001; t = 12.238) and those who did not (β = 0.207; p < 0.001; t = 3.645). Additionally, the direct relationship between FL and FWB for the FL-educated group (β = 0.286; p < 0.001; t = 6.011) and for the FL-non-educated group (β = 0.105; p < 0.05; t = 2.397) was significant for both groups. The multigroup analysis, as shown in Table 7, indicates that the findings from both groups are largely consistent with the results from the complete data analysis. However, the impact of FL on FIC is stronger among those who never received FL education than those who have. Conversely, for the other relationships, the results favour those who have received FL education, underscoring the significance of FL education in influencing these inter-relationships.

In SEM-PLS output, the significance of the specific indirect effect is crucial for establishing mediation. Mediation cannot be claimed if the specific indirect effects are not significant. Additionally, further examination of the direct effect in the presence of the mediator(s) is necessary to determine the type of mediation when the indirect effect is significant. If the direct effect remains significant alongside the mediator, partial mediation is established; otherwise, full mediation is indicated. Another method of determining mediation is using variance accounted for (VAF). Full mediation is confirmed if the calculated VAF is 80% or greater; partial mediation is present if the indirect effect is significant but the VAF is less than 80%. Partial mediation can be complementary or competitive: a positive effect indicates complementary mediation, while a negative relationship suggests competitive mediation.

Given the critical role of FIC, it is conceptualised as a mediator between FL and FWB. Therefore, a mediation analysis was conducted to test the role of FIC in the relationship. As shown in

Table 8. Mediating role of financial information consumption.

	Complete			Ever Received FL Education			Never Received FL Education
Hypotheses	Coef	T Stat	p Values	Coef	T Stat	p Values	Coef	T Stat	p Values
Specific Indirect Effect
H4: FL ⟶ FIC ⟶ FWB	0.213	9.726	0.000	0.249	8.262	0.000	0.123	3.564	0.000
Direct Effect
FL ⟶ FWB	0.162	5.011	0.000	0.286	6.011	0.000	0.105	2.397	0.008
Total Effect
FL ⟶ FWB	0.375	10.772	0.000	0.535	12.468	0.000	0.228	4.286	0.000

, the study reveals a significant indirect positive relationship between FL and FWB through FIC (β = 0.213; p < 0.001; t = 9.726) based on the specific indirect effect from the complete data. Additionally, multigroup analysis indicates that both the ever-received and never-received FL education groups show significant mediation effects, with the impact of FIC being stronger for those who have received FL education (β = 0.249; p < 0.001; t = 8.262) compared to those who have not (β = 0.123; p < 0.001; t = 3.564). The findings are consistent across complete data and group analysis.

Comparing the specific indirect effects of FIC on the FL–FWB relationship against the direct effects of FL and FWB, Table 8, it is evident that for the complete data and the never-received FL education group, the impact on FWB is strengthened when mediated by FIC. The complete data analysis suggests that FL education enhances FWB, with the impact being almost double for those who have received FL education compared to those who have not. Notably, while FL directly influences FWB (β = 0.162), this effect is amplified when mediated by FIC (β = 0.213), indicating that FIC enhances the influence of FL on FWB better. However, for those who have received FL education, the direct effect is stronger than the mediated effects, while for those who have never received FL education, the opposite is true. This could imply that consuming financial information (FI) beyond an optimal level may impair effective decision-making.

An analysis of Table 8 was conducted to determine the type of mediation. The results show that both direct and specific indirect effects are significantly positive across all cases. Additionally, variance accounted for (VAF) was computed as the ratio of indirect effect to total effect. The VAF for FIC is 56.8% for the complete data (0.213/0.375 = 0.568), 46.5% for the ever-received FL education group (0.249/0.535 = 0.465), and 53.9% for the never-received FL education group (0.123/0.228 = 0.539).

Given that both direct and indirect effects are significantly positive and the VAF value is below 80% across all cases, the mediation role is determined to be partial, with the mediation role being complementary due to positive relationships. Therefore, FL and FIC jointly and positively influence FWB, and hypothesis H₄ is accepted.

3.8. Multigroup Analysis

One of the study hypotheses tested was to examine the relationships in multigroups to see if there was a significant difference between the groups. This was conducted between those who ever received and never received FL education. Bootstrapping multigroup analysis was therefore conducted to see if the differences are significant.

Table 9. Multigroup Analysis.

Hypothesis	Decision	Difference	p-Value
		(Ever Received–Never Received)
H5: FL ⟶ FIC ⟶ FWB	Accepted	0.126	0.003 *
FIC ⟶ FWB		−0.116	0.024 *
FL ⟶ FIC		0.312	0.000 *
FL ⟶ FWB		0.181	0.003 *

Note: * The differences are significant in the relationship between the two groups (p < 0.05).

below shows the outcome of the multigroup analysis.

These findings indicate that the outcome significantly differs between ever-received and never-received FL education in all analysis fronts. This implies that ever-received and never-received FL education significantly differed in how FL by itself and via FIC affect FWB. The above analysis showed significant differences and positive coefficients, indicating that the pathway appears firmer for those who received FL education, except for the FIC effect on FWB. The magnitude favours those who never received an FL education. Additionally, there is a statistically significant difference (p < 0.05) in the indirect effect of FL through FIC to FWB, with a positive difference of 0.126. Hence, H₅ is accordingly supported. The negative statistical difference does exist in favour of those who never had an FL education. This suggests the effect is more pronounced in favour of those who had never had an FL education than those who had ever received an FL education.

Wong [112] argued that the coefficient of determination (R²) assessment is significant in structural model evaluation. Thus, the structural model’s explanatory power was evaluated by assessing the R². The R² value indicates the degree of variance in the endogenous construct(s) explained by the exogenous construct(s) [93]. Based on the acceptable fit recommended by Chin [113], R² values of 0.19, 0.33, and 0.67 are considered weak, moderate, and strong, respectively. From

Table 10. Coefficient of determination (R²), predictive relevance (Q²), and effect size (f²).

	R2	R2 Adjusted	Q2	f2
				FIC	FWB
FIC	0.150	0.148	0.145		0.427
FWB	0.398	0.396	0.136
FL				0.176	0.037

above, the result indicates that FL can explain 15% of the variance in FIC, while FL and FIC jointly explain 39.8% of the variance in FWB.

Similarly, the Q² values indicate how well the path model can predict the original observed data values [114]. Q² > 0 is needed to confirm predictive relevance [93,115]. Table 10 provides the Q² value of the endogenous variables. Following Table 10, Q² values were more significant than zero; thus, the predictive relevance of the model was confirmed. Finally, the effect size (f²) suggests that the effect size of FL on FWB and FIC is smaller than the effect emanating from FIC to FWB.

4. Discussion

Financial literacy (FL) policy remains central in enhancing inclusive and sustainable development because it improves individuals’ FWB. While research has informed policymakers, the factors influencing FL’s impact and the interplay between these factors and FWB are poorly understood. This paper focused on understanding FI consumption and its linkage with FL and FWB, specifically in rural Ghana, a developing country. The emphasis on rural areas was motivated by the fact that most studies have overlooked these settings. With the advent and widespread use of mobile phones and the Internet, rural areas now have more access to financial news. It is important to explore how this affects finances and the interplay between FI consumption, FL, and FWB.

The study aimed to answer whether FL influences FWB, whether FIC influences FWB, and whether FIC mediates the effect of FL on FWB. Empirical analysis showed that FL influenced FIC, supporting the hypothesis that increasing FL enhances FI consumption in rural settings (H₁). Second, the results of this study revealed that the relationship between FIC and FWB is positive and statistically significant in the rural setting. This was expected since adequate consumption of financial information empowers and enriches individual financial decision-making, and it implied an effect on FWB (H₂). Additionally, FL was found to be significantly and positively related to FWB, demonstrating that FL is a key determinant of FWB in rural settings. Achieving FL allows individuals to pursue long-term objectives, maintain financial flexibility, and experience financial satisfaction, supporting the hypothesis that increased FL leads to increased FWB (H₃).

Furthermore, the study found that FIC mediates the relationship between FL and FWB, demonstrating complementary partial mediation. This suggests that growing FIC is crucial for FL to significantly affect FWB. The findings indicate that obtaining FL is beneficial, but FI consumption is necessary to improve FWB (H₄). Multigroup analysis showed that the impact of FI consumption is greater for those who have received FL education compared to those who have not, highlighting the importance of FL education in enhancing FI consumption and, consequently, FWB (H₅). The

Table 11. Summary of accepted hypothesis.

Hypothesis	Decision
Financial Literacy → Financial Well-Being	Accepted
Financial Literacy → Financial Information Consumption	Accepted
Financial Information Consumption → Financial Well-Being	Accepted
Financial Literacy → Financial Information Consumption → Financial Well-Being	Accepted

below shows the accepted hypotheses of the study.

Prior studies have shown a positive relationship between FL and consumption behaviour [116,117]. For example, [116], analysing the link between FL and consumption behaviour in Indonesia using multiple linear regression, found that FL positively influenced consumptive behaviour, while [117] found similar results assessing FL training programmes on household consumption in Ghana employing ordinary least squares. Since consumption depends on information, the finding supports the notion that FL is responsible for FI-seeking behaviour on financial products [29]. Similarly, our study finds that the relationship between FL and FIC is positively related. Losada-Otalora and Alkire examining information transparency and FWB in Colombia using multiple regression, found that information transparency improves FWB [75]. Like [75], our study found a significant positive relationship between FIC and FWB using SEM-PLS in rural settings. Our findings support the claim that lack of information consumption affects individuals’ and households’ ability to save to secure a better financial life [31]. The present and prior study’s findings underscore the significance of maintaining awareness of FIC in financial decision-making. The empirical results of our study align with findings from other developing and emerging economies, such as [14] in Nigeria, [16] in India, [8] in South Africa, and [47,118] in Ghana. All these studies found significant positive relationships between FL and FWB. Additionally, studies like [17,118] found that the presence of mediating factors did not distort the significant effect of FL on FWB, consistent with our finding that FI consumption mediates the FL-FWB relationship without distorting it.

The call for increasing the intensity of FL in developing countries is justified [119], especially in rural settings. The empirical results demonstrate that while FL alone can improve FWB, FI consumption makes this improvement more significant and relevant. This study highlights the criticality of the indirect effect of FI consumption, which is more potent than the direct effect of FL on FWB. This importance confirms the usefulness of interpreting other mediating variables previously identified in prior studies, such as consumption patterns [17], financial behaviour [16], and access to financial services [47]. Xue et al. employing actual pattern consumption as a mediator in Australia using ordered logistic regression, found a consumption pattern to partially mediate the relationship [17]. Sehrawat et al. using financial behaviour as a mediator in India by employing SEM-PLS, found the partial mediating role of financial behaviour in the relationship [16]. Twumasi, et al. used access to financial services to examine the relationship between FL and household income in Ghana by employing a process macro model, and they also found partial mediation [47]. Our findings are also consistent with prior studies. We found partial mediation using FIC as a mediator in the relationship between FL and FWB. Thus affirming [87] conclusion that the relationship between FL and FWB is better for those with regular access to FI. Just as FL can help smooth consumption patterns to reap the benefit of FWB [17], FL helps smoothen FIC to achieve the desired goal of FWB for rural households. The data disaggregated into ever- and never-received FL education findings further demonstrated the importance of financial education. The magnitude of impact for those who had ever received FL education was more than twice that of those who had never received FL education before. Hence, the recent calls for government and development partners to increase investment in FL education further strengthen the situation [120].

Furthermore, the study concurs with the conclusion by [121] that financially literate people are 2.4% less likely to experience financial distress than the opposite. Rural households are better positioned to meet their FWB desire if the head of household obtains some level of financial literacy. In this way, households can attain desirable livelihoods when financially literate [8]. However, contrary to our findings, [24] found FL not a crucial factor in determining young adult FWB as they found no relationship between FL and FWB.

In contrast, [60] analysis, using information source preference and FL in Malaysia, found that information consumption through media and family & peers showed a negative relationship with FL. This finding deviates from current and prior studies to the extent that they found a significant negative relationship. They argued that family and peers are suboptimal options for financial information, and the ineffective nature of FI transmission in the media would have accounted for this. They concluded that consumers should be careful about their financial information sources. Similarly, [122] analysis in the USA using National Financial Capability Study data by employing objective financial literacy and financial satisfaction evidence showed that objective financial knowledge negatively impacted financial satisfaction. Their measurement lacks multidimensionality as the concepts, which might have occasioned this relationship.

On the contrary, this study accounted for multidimensionality and thus showed a positive relationship supporting theory. Like other direct relationships, the difference in effect in the relationship between FIC and FWB favours the never-received category. From this outcome, it could be deduced that overconfidence in FIC on the part of ever-received FL education might have exceeded the optimal level, and the excessive flow of FI may have affected judgement. Hence, the impact of FL education on the relationship is weakened. This finding agrees with the conclusion by [11] that having some level of financial ignorance is optimal in financial decision-making.

5. Conclusions, Implications, Limitations, and Future Research Directions

5.1. Conclusions

This study explored the role of financial information consumption (FIC) in the relationship between financial literacy (FL) and financial well-being (FWB) in rural Ghana. Using a structural equation model, the study illuminated the importance of FL and FIC as key determinants of FWB in rural contexts. It found that FL enhances FWB both directly and through its interaction with FIC. However, FL alone is insufficient; FIC plays a crucial role, and receiving FL education significantly mediates the link between FL and FWB. The findings imply that rural individuals must combine FL with FIC to improve their FWB effectively. Financially literate individuals benefit from consuming financial information to set realistic financial goals and make informed decisions. FIC supports overall well-being by helping individuals stay on track and make necessary adjustments to achieve their financial objectives. Thus, both FL and FIC are essential for assessing and improving FWB in rural settings. This study, therefore, concludes that financial FIC is an important enabler in the relationship between FL and FWB in the rural context of developing countries.

From the findings and discussions, neglecting literacy interventions for the poor leads to unsustainable and non-inclusive development, as true development is measured by its global impact. This paper addresses sustainability by focussing on marginalised groups in rural areas.

5.2. Study Implications

Practically, the study highlights that receiving FL education is vital for mediating the FL-FWB relationship in rural areas. Rural residents who have received FL education are better at utilising their financial knowledge to consume FI and enhance their FWB. Financial educators and advisors should emphasise the role of FIC in improving FWB and integrate it into financial education programs. Regular seminars, workshops, and training sessions organised by NGOs, governments, and financial institutions can help raise awareness and optimise the use of FI in support of prior studies [65,67,68,74,123,124]. Governments should support these educational efforts to align with the sustainable development goals (SDGs).

The prospect theory, which holds that people make decisions based on perceived gains and losses, and the resource dependency theory, which emphasises the significance of resource access in reaching desired outcomes, are both supported by this research. One major theoretical implication is that the relationship between FL and FWB may involve numerous paths, as suggested by the partial mediation role of FIC.

5.3. Limitations and Further Research Directions

Given the partial mediating role of FIC, there are additional characteristics of financial information that were not considered in this study yet could be very important for a holistic understanding of its role, following the earlier recommendations [68,124]. Factors such as changing patterns in financial information, financial information consumption level, and the quality of financial information might significantly influence the relationship between financial literacy and financial well-being (FWB), warranting further examination. Additionally, both FL and FWB were conceptualised as HOCs incorporating both objective and subjective assessments, while FIC was assessed only subjectively. Future studies should investigate additional characteristics of financial information, such as consumption patterns, sources, and quality, to better understand their impact on FWB. Researchers should also consider modelling multiple dimensions of financial information as higher-order constructs (HOCs) to capture a broader perspective and enhance the overall understanding of these relationships.