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The Effects of Media on Financial Literacy in Ilala Municipality, Tanzania: A Comprehensive Analysis of Digital Platforms

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03 September 2025

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09 September 2025

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Abstract
This study investigates how digital finance platforms influence financial literacy in Ilala Municipality, Tanzania, focusing on the effects of media exposure, financial content quality, and user engagement. The specific objectives involved investigating the effects of exposure to media, the effects of the quality of content, and the effects of exposure to digital platforms on financial literacy. A quantitative cross-sectional design was applied, using structured questionnaires to collect data from 201 respondents. Statistical analyses, including regression and mediation tests, were conducted to examine the relationships. Findings reveal that media exposure through digital sources negatively affects financial literacy (B = -3.018, p < 0.001), which means that unfiltered information can limit financial knowledge. Meanwhile, the financial content quality (B = 3.774, p < 0.001) and digital media exposure (B = 7.995, p < 0.001) significantly enhance money literacy. The study provides evidence for policymakers, financial institutions, and educators to leverage digital platforms as tools for financial literacy campaigns. Emphasis should be placed on ensuring high-quality content and promoting interactive engagement. The paper contributes to the limited empirical literature in Sub-Saharan Africa, highlighting the mechanisms by which digital finance platforms affect literacy in an urban Tanzanian setting.
Keywords: 
digital financial platforms
;  
financial literacy
;  
media exposure
;  
user engagement
;  
Tanzania
Subject: 
Business, Economics and Management  -   Finance

1. Introduction

Digital finance has expanded access to financial services globally, with platforms like M-Pesa, Tigo Pesa, and Airtel Money driving inclusion in Tanzania. Despite high adoption, financial literacy covering budgeting, saving, and investing remains low. In Ilala Municipality, Dar es Salaam, where digital activity is high, this study examines whether media exposure, content quality, and user engagement on digital platforms enhance financial literacy.
Globally, access to financial information does not always translate to literacy, as seen in the U.S., Europe, and Asia (FINRA, 2022; Lusardi & Mitchell, 2017; Suri & Jack, 2016; World Bank, 2022). In Sub-Saharan Africa, only 22% of adults are financially literate (AfDB, 2021), with mobile platforms improving awareness but structural and cultural barriers persisting (GSMA, 2023; Napier et al., 2020).
In Tanzania, over 65% of adults use mobile money, yet financial literacy is only 28% (Bank of Tanzania, 2023; FSDT, 2022). Urban areas like Dar es Salaam benefit from high smartphone penetration (78%) and internet access, but challenges remain due to misinformation, language barriers, and gender disparities (GSMA, 2023; Rutashobya et al., 2021). Existing initiatives have not fully bridged the gap (Finscope Tanzania, 2022). This study investigates how digital media factors influence financial literacy in Ilala Municipality.
  • Objectives:
    • To examine the influence of media exposure through digital platforms on financial literacy levels in Ilala Municipality.
    • To assess how the quality of digital financial content influences financial literacy in Ilala Municipality.
    • To analyze the effect of engagement with digital platforms on financial literacy in Ilala Municipality.

2. Literature Review and Hypotheses Development

2.1. Digital Financial Literacy

Digital finance has increased access to financial services worldwide, with platforms like M-Pesa, Tigo Pesa, and Airtel Money expanding inclusion in Tanzania. Despite high adoption, financial literacy covering budgeting, saving, and investing remains low. Globally and in Sub-Saharan Africa, access to financial information does not always translate to literacy, with only 22% of adults in the region considered financially literate. In Tanzania, over 65% of adults use mobile money, yet only 28% are financially literate. Urban areas like Dar es Salaam benefit from high smartphone penetration and internet access, but challenges such as misinformation, language barriers, and gender disparities persist. This study examines how media exposure, content quality, and engagement with digital platforms influence financial literacy in Ilala Municipality.

2.2. Media Exposure

Media exposure is the frequency and extent of engaging with media sources, shaping knowledge and behavior. In financial literacy, it involves accessing personal finance information via traditional and digital media, including social platforms like Facebook, Instagram, and YouTube (OECD, 2021; Lin, 2023; Bonsón et al., 2021). Repeated exposure to diverse financial content can improve understanding and attitudes toward money management. In this study, media exposure is measured as how Ilala residents use online media for financial education and its influence on informed financial decision-making.

2.3. Digital Financial Platforms

Digital platforms such as websites, mobile apps, and social networks facilitate access to financial education and services, enabling users to manage finances, invest, and pay bills (Kiptum, 2018). They are especially valuable in developing countries, offering affordable, accessible, and interactive learning opportunities (Vargo et al., 2022). Mobile technologies and customized apps further support financial literacy through budgeting and investment guidance. In this study, digital platforms are examined as an independent variable to assess their impact on financial knowledge and decision-making among residents of Ilala Municipality.

2.4. Financial Literacy

Financial literacy is the ability to understand and manage personal finances, including budgeting, saving, investing, and debt management, as well as applying concepts like interest rates, inflation, and risk management (OECD, 2022; Lusardi, 2021). It involves both knowledge and practical skills for informed decision-making, such as retirement planning and responsible debt use. With the rise of online financial services, digital literacy is crucial for navigating mobile banking and other digital platforms. In this study, financial literacy is the dependent variable, assessed to examine how media exposure and engagement with digital platforms influence financial knowledge, behavior, and decision-making in Ilala Municipality.

2.5. Theoretical Framework

This study is grounded in the Uses and Gratification Theory (UGT) and Technology Acceptance Model (TAM), which emphasize perceived usefulness and ease of use in technology adoption, and the Financial Literacy Framework, which views literacy as a product of exposure, comprehension, and behavioural change.
i. 
Uses and Gratifications Theory
The Uses and Gratifications Theory (UGT) posits that individuals actively select media to satisfy needs like information, social interaction, and entertainment (Katz, Blumler, & Gurevitch, 1973). Unlike passive audience models, it emphasizes purposeful media use. In the digital era, UGT explains how people turn to mobile apps, social media, and online platforms to enhance financial knowledge (Ruggiero, 2000). For this study, UGT provides a framework showing how Ilala residents’ media exposure, content quality, and engagement with digital platforms drive financial literacy by reflecting their motivations and active learning behaviors (Papathanassopoulos & Negrine, 2021).
ii. 
Technology Acceptance Model (TAM)
The Technology Acceptance Model (TAM) explains technology adoption through perceived usefulness and ease of use (Davis, 1989). In this study, TAM assesses how the relevance, clarity, and accessibility of digital financial content influence financial literacy. Perceived usefulness reflects the value of online resources, while ease of use emphasizes user-friendly design for sustained engagement. When combined with the Uses and Gratifications Theory (UGT), which captures users’ motivational drivers, the integrated framework explains both why residents of Ilala Municipality engage with digital platforms and how they adopt and retain them, enhancing financial literacy outcomes.

2.6. Media Exposure through Digital Platforms

Chaudhary and Luthra (2022) demonstrated that media exposure through digital platforms such as blogs and apps positively influences financial literacy in emerging economies by improving decision-making in budgeting and investment. Their findings underscore the role of digital platforms as effective tools for enhancing financial knowledge. Similarly, Alam and Islam (2021) found that exposure to online platforms, including YouTube tutorials and financial blogs, improved financial literacy among Bangladeshi youth, particularly in debt management and savings. However, they also highlighted gaps in the accessibility and availability of engaging financial content in emerging markets. Both studies affirm that digital platforms can significantly enhance financial literacy, supporting this study’s focus on Ilala Municipality, where media exposure is critical to addressing financial knowledge gaps.

2.7. The Quality of Digital Financial Content

Wang and Zhou (2023) found that the quality of digital financial content strongly influences financial literacy in urban China, with clear, accurate, and user-friendly materials significantly improving knowledge in areas such as retirement planning and investment. Similarly, Müller and Weber (2022) showed in Germany that high-quality, transparent, and tailored financial content enhances user confidence and decision-making. Both studies highlight that the effectiveness of financial education depends on the accuracy and clarity of online content, supporting this study’s objective of examining how the quality of digital financial content shapes financial literacy in Ilala Municipality.

2.8. Engagement with Digital Platforms

Kim and Park (2021) in South Korea found that frequent interactions with digital platforms such as mobile banking apps, online financial courses, and blogs significantly improved financial literacy, particularly in budgeting and investment management. Similarly, Oluwatayo and Oladimeji (2022) in Nigeria showed that active and consistent use of mobile financial platforms enhanced understanding of savings, loans, and investment opportunities, leading to better financial decision-making. Both studies emphasize that sustained engagement with digital platforms is crucial for strengthening financial literacy, directly supporting this study’s objective of assessing the role of user engagement with digital finance platforms in Ilala Municipality.

2.9. Conceptual Framework

The study uses a conceptual model showing how media exposure, digital financial content quality, and user engagement on digital platforms influence financial literacy. Media exposure measures how frequently and diversely individuals access digital financial information and the perceived credibility of sources. Content quality assesses the accuracy, clarity, and relevance of information in relation to users’ financial needs. User engagement captures active participation, including interaction with platforms, use of financial tools, and duration of platform use. Together, these factors explain how digital platforms enhance financial knowledge, skills, and behaviors, ultimately improving overall financial literacy.
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Figure 1. Conceptual Framework Source: Literature review, (2025).
Figure 1. Conceptual Framework Source: Literature review, (2025).
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3. Materials and Methods

3.1. Study Area

The study was conducted in Ilala Municipality, Dar es Salaam, Tanzania, a densely populated and socioeconomically diverse urban area near the central business district. Its strategic location, strong transport links, widespread mobile phone and internet use, and concentration of financial institutions make it an ideal setting to examine how digital platforms influence financial literacy. Ilala’s mix of income levels, education, and digital media exposure provides a representative urban population, while its coordinates (6.7921° S, 39.2035° E) situate it near the coast and central commercial hub. These factors collectively create an optimal environment to study the role of digital media in shaping financial literacy in a digitally active urban context.

3.2. Research Design

A quantitative cross-sectional design was employed, whereby data were collected at a single point in time to examine the relationship between media exposure, digital financial content quality, user engagement, and financial literacy in Ilala Municipality, Dar es Salaam. This design was cost-effective and time-efficient, making it appropriate for resource-constrained projects, while also enabling the use of statistical techniques such as regression analysis for hypothesis testing (Kothari, 2016; Creswell & Creswell, 2018). By sampling Ilala’s socioeconomically diverse population, the design enhanced the potential for generalizing findings to similar urban contexts (Zamzam, 2018; Huyler & McGill, 2019).

3.3. Sampling and Data Collection

The study employed stratified random sampling across Ilala Municipality’s 36 wards, with a sample of 216 respondents determined using Yamane’s formula and proportionally allocated. Data were collected via structured Likert-scale questionnaires, both physical and online, measuring media exposure, digital content quality, platform engagement, and financial literacy. Out of 216, 201 valid responses were obtained (93.05% response rate), mainly from respondents aged 18–34. Reliability tests showed high internal consistency (Cronbach’s alpha = 0.934) and stable responses, confirming that the findings reliably reflect media’s impact on financial literacy in the municipality.

3.4. Measures

Table 1 illustrates the measurement plan of this study for both independent and dependent variables. Each of these indicators is pegged on a Likert scale to quantify subjective perception/behaviors.
Table 1. Measurement of Variables.
Table 1. Measurement of Variables.
S/N Variables Measure/Indicator Description Sources
Independent
1. Media Exposure through Digital Platforms Frequency of Media Use Respondents showed the level of their usage of online platforms. Data was gathered through a Likert scale. Hsieh & Lee, (2021)
Type of Content Consumed Participants identified the proportion of financial and non-financial information that they read on the internet platforms. Data was gathered utilizing the Likert scale. Chatterjee & Soni (2022); Moutinho & Santos, (2023)
Source Credibility Respondents rated how much they think the source is reliable. Sites’ trustworthiness was assessed with Likert scale items. Lee & Choi, (2021); Rho & Yang, (2022)
2. Quality of Digital Financial Content Accuracy of Information Respondents rated whether content consumed in finances matches confirmed sources. This was done through the use of Likert scale measures. Batra, & Sharma, (2021); Taneja & Kumar, (2022)
Clarity and Readability Participants evaluate the readability and comprehensibility of the digital financial information, with a focus on understanding and ease of access. Responses were collected using Likert scale. Kumar, & Mehra, (2021); Sharma, & Gupta, (2022)
Relevance of Topics The importance of the issues to the financial decision-making of the respondent were quantified. Data was collected using a Likert scale approach. Lee & Park, (2022); Hsieh & Lin, (2023)
3. Engagement With Digital Platforms Interaction Level Participants indicated their own degree of participation (comments, shares, discussions) on fiscal content through digital media based on Likert scale questions. Kumar, & Mehra, (2021); Sharma, & Gupta, (2022)
Utilization of Tools Participants assessed their utilization of electronic resources such as calculators, applications, or online guides to financial literacy by using Likert scale items. Kumar, & Mehra, (2021); Sharma, & Gupta, (2022)
Dependent
1. Financial Literacy Knowledge Acquisition Respondents were examined for their understanding of financial concepts by standard financial literacy tests or questionnaires. Kumar, & Mehra, (2021); Sharma, & Gupta, (2022)
Source: Researcher, (2024).

3.5. Validity and Reliability of the Study

i. 
Validity of the Data
Validity ensures research tools measure what they are intended to. This study addressed content validity by aligning questionnaire items with key dimensions—media exposure, digital content quality, digital platform use, and financial literacy—and having experts review them for relevance. Construct validity was confirmed by comparing survey items with established financial literacy theories, ensuring the constructs were measured accurately and theoretically sound.
Table 2. KMO and Bartlett’s Test.
Table 2. KMO and Bartlett’s Test.
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .691
Bartlett’s Test of Sphericity Approx. Chi-Square 432.513
Df 36
Sig. .000
Source: SPSS Output, (2025).
Pilot testing confirmed that the questionnaire effectively measured the intended variables. The KMO value of 0.691 indicated moderate sampling adequacy suitable for factor analysis, while Bartlett’s Test of Sphericity (χ² = 432.513, df = 36, p < .000) confirmed strong item correlations. Together, these results established construct validity, showing that the instrument was appropriate for identifying latent constructs related to financial literacy and media use.
Table 3. Pattern Matrix.
Table 3. Pattern Matrix.
Variables Componenta
1 2 3 4
Frequency of Media Use -.169 -.309 -.804 .089
Type of Content Consumed .191 .750 .171 -.127
Source Credibility .073 -.266 -.004 .839
Accuracy of Information -.102 -.162 .713 .241
Clarity and Readability -.342 .743 .000 -.132
Relevance of Topics -.120 -.504 .312 -.448
Interaction Level -.130 .079 .442 .652
Utilization of Tools .921 -.190 .078 -.102
Usage Duration .742 .075 -.047 .153
Extraction Method: Principal Component Analysis.
Rotation Method: Promax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
Source: SPSS Output, (2025).
The Pattern Matrix identified four distinct components, confirming the multidimensionality of the constructs. Component 1 captured Use of Tools and Length of Use, while Component 2 reflected Nature of Content Used and Clarity and Legibility. Component 3 represented Accuracy of Data, and Component 4 encompassed Credibility of Source and Degree of Interaction. These results demonstrate that the instrument successfully distinguished key factors of online media consumption, reinforcing its construct validity in measuring engagement drivers influencing financial literacy.
ii. 
Reliability of the Data
Reliability refers to the consistency of measurement over time (Chiang et al., 2015). In this study, it ensured dependable measurement of constructs such as media exposure, digital financial content quality, and engagement levels in relation to financial literacy. Reliability was assessed using Cronbach’s alpha, with values of 0.7 or higher considered acceptable. The results showed that all constructs met this threshold, indicating reliable measures. Cronbach’s alpha, a widely used indicator of internal consistency, evaluates how closely related items within a scale are as a group (Surucu & Maslakci, 2020).
Table 4. Reliability Statistics.
Table 4. Reliability Statistics.
Cronbach’s Alpha Cronbach’s Alpha Based on Standardized Items N of Items
.934 .940 52
Source: SPSS Output, (2025).
According to Middleton (2020), a Cronbach’s alpha above 0.7 is considered acceptable for research, indicating adequate internal consistency. In this study, the Cronbach’s alpha was 0.934 across 52 items, exceeding 0.9 and demonstrating very high reliability of the variables. In addition, test-retest reliability was assessed by re-administering the questionnaire to a sub-sample after a short interval, confirming stability of responses over time. These reliability and validity measures ensure that the findings are both robust and representative, supporting the conclusion that media use contributes meaningfully to enhancing financial literacy in Ilala Municipality.

3.6. Ethical Considerations

The study adhered to research ethics by ensuring informed consent, voluntary participation, and the right to withdraw without consequences. Anonymity and confidentiality were maintained, with no personal identifiers collected, secure data storage, and results reported in aggregate to protect participants’ privacy.

3.7. Data Analysis

Data were analyzed using IBM SPSS Statistics v26. Descriptive statistics summarized respondents’ demographics, media usage, and financial literacy levels, while inferential analysis, including nonparametric correlation, examined the strength and direction of relationships between media exposure, content quality, platform engagement, and financial literacy.
Multiple regression analysis further established the extent to which these independent variables explained variation in financial literacy outcomes.
The model used is specified as:
Y= β0+β1X1+β2X2+β3X3+ϵ
where:
  • Y = Financial literacy score (dependent variable)
  • X1 = Media exposure (frequency and type of financial media consumed)
  • X2 = Quality of digital financial content (accuracy, clarity, and relevance)
  • X3 = Engagement with digital platforms (intensity and use of financial tools)
  • β0 = Constant
  • β1, β2, β3 = Regression coefficients
  • ε = Error term
The regression tested the independent effects of each predictor while controlling for the others, enabling insights into their relative contributions to financial literacy.
To ensure reliability and validity, standard regression assumptions were tested:
  • Linearity: verified through scatterplots and residual analysis.
  • Homoscedasticity: checked via residual plots.
  • Normality of residuals: assessed using P-P plots and Shapiro-Wilk tests.
  • No multicollinearity: confirmed by acceptable Variance Inflation Factor (VIF < 10) values.
These steps enhanced the robustness of the findings, ensuring valid inferences about the role of digital media in shaping financial literacy outcomes.

4. Results

Respond Rate of the Respondents

Table 5 indicates that 216 questionnaires were distributed to participants, both physically and online. Of these, 201 were completed and returned, representing a response rate of 93.05%, while 15 questionnaires (6.95%) were either missing or partially completed. A response rate exceeding 75% is considered acceptable for research validity (Kothari, 2016), confirming that the study’s data are suitable for further analysis.
Table 5. Response Rate of the Respondents.
Table 5. Response Rate of the Respondents.
Responses Frequency Percent (%)
Valid Response 201 93.05
Missing Response 15 6.95
Total 216 100
Source: Field study, (2025).
The high response rate strengthens the reliability of the findings, ensuring that the results are representative of the target population.

Demographic Information of the Respondents

Table 6 summarizes the demographic characteristics of respondents. Most respondents (62.2%) were aged 18–34 years, indicating a predominantly youthful sample. Regarding education, the majority (56.2%) held college or undergraduate degrees, while 29.4% had completed secondary education, reflecting a relatively educated sample.
Table 6. Demographic Information (n=201).
Table 6. Demographic Information (n=201).
Age of the Respondents Frequency Percent (%)
18-24 60 29.9
25-34 65 32.3
35-44 57 28.4
45-54 13 6.5
55 and above 6 3.0
Educational Level of the Respondents Frequency Percent (%)
Primary Education Level 19 9.5
Secondary Education Level 59 29.4
College/Undergraduate degree 113 56.2
Postgraduate degree 10 5.0
Frequency of Digital Platform Usage by Respondents Frequency Percent (%)
Daily 51 25.4
Weekly 66 32.8
Monthly 46 22.9
Rarely 22 10.9
Never 16 8.0
Monthly Income Level of the Respondents Frequency Percent (%)
Below TZS 500,000/= 74 36.8
TZS 500,000/= - 1,000,000/= 84 41.8
TZS 1,000,000/= - 1,500,000/= 28 13.9
TZS 1,500,000/= - 2,000,000/= 8 4.0
Above TZS 2,000,000/= 7 3.5
Source: Field study, (2025).
Respondents reported frequent use of digital platforms: 25.4% used them daily, and 32.8% weekly. Most respondents (78.6%) earned less than TZS 1,000,000 per month, indicating a low-income, youthful, and educated population—an ideal sample for studying digital financial literacy.

Findings Based on Specific Objectives

Media Exposure Through Digital Platforms on Financial Literacy Levels

Respondents were asked to report on the frequency of media use, types of content consumed, and perceived credibility of sources. A five-point Likert scale was employed (1 = Strongly Disagree to 5 = Strongly Agree).
i) 
Frequency of Digital Platforms Use
Table 7 indicates that respondents frequently rely on digital platforms for financial information. The highest mean (M = 3.73, SD = 1.09) reflects frequent overall use, with mobile apps and social media commonly used sources (M = 3.57, SD = 1.20).
Table 7. Frequency of Digital Media Platforms Use by the Respondents (n= 201).
Table 7. Frequency of Digital Media Platforms Use by the Respondents (n= 201).
Statements N Minimum Maximum Mean Std. Deviation
I frequently use digital platforms to access financial information. 201 1.00 5.00 3.7264 1.09076
I regularly access digital platforms (e.g., mobile apps, social media) to learn about finances. 201 1.00 5.00 3.5672 1.20280
I rely on digital platforms daily for updates on financial matters. 201 1.00 5.00 3.5721 1.18576
My frequency of using digital platforms for financial purposes has increased over the past year. 201 1.00 5.00 3.5075 1.11857
Digital platforms are my primary source of financial information. 201 1.00 5.00 3.5174 1.20870
Source: Field Study, (2025).
The findings reveal increased reliance on digital platforms for financial information, suggesting their critical role in enhancing financial literacy (Lusardi & Mitchell, 2014).
ii) 
Type of Content Consumed
Table 8 shows that financial content is the most accessed category (M = 3.93), followed by diverse financial learning content (M = 3.69). However, prioritization and personalization scores were lower (M = 3.24–3.43), indicating that users may not fully align content with personal financial goals.
Table 8. Type of Content Consumed on Digital Platforms used by Respondents (n=201).
Table 8. Type of Content Consumed on Digital Platforms used by Respondents (n=201).
Statements N Minimum Maximum Mean Std. Deviation
The content I consume on digital platforms is mostly related to financial topics. 201 1.00 5.00 3.9254 1.02440
I prioritize financial content over non-financial content when using digital platforms. 201 1.00 5.00 3.2388 1.32389
The financial content I find on digital platforms is relevant to my personal financial needs. 201 1.00 5.00 3.4279 1.23936
I often seek educational content on budgeting and savings on digital platforms. 201 1.00 5.00 3.3781 1.34399
Digital platforms provide a variety of content that caters to my financial learning. 201 1.00 5.00 3.6866 1.21913
Source: Field Study, (2025).
iii) 
Source Credibility
  • Source Credibility of Digital Platforms
Respondents expressed high trust in digital platforms, with credibility influencing decision-making (M = 4.05) and high ratings for reliability (M = 4.07). However, lower scores for preferring familiar sources (M = 3.54) indicate that verification practices are inconsistent.
Table 9. Source Credibility of the Digital Platforms used by Respondents.
Table 9. Source Credibility of the Digital Platforms used by Respondents.
Statements N Minimum Maximum Mean Std. Deviation
I trust the financial content provided on the digital platforms I use. 201 1.00 5.00 4.2189 .91205
The financial platforms I access are reliable and well-researched. 201 1.00 5.00 4.0746 1.02927
I prefer digital financial content from recognized and verified sources. 201 1.00 5.00 3.5423 1.37821
Credibility of the source affects my decision to follow the advice given in digital financial content. 201 1.00 5.00 4.0498 1.03320
I verify the information obtained from digital platforms before applying it to my financial decisions. 201 1.00 5.00 3.6418 1.08676
Source: Field Study, (2025).
The results suggest that while credibility is generally high, critical evaluation of content is moderate, emphasizing the need for improved digital literacy skills (OECD, 2020).

The Quality of Digital Financial Content and Financial Literacy

The study assessed how financial content quality—measured by accuracy, clarity/readability, and relevance—affects respondents’ financial literacy. Participants rated these factors on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), with results presented in the subsequent tables.
i) 
Accuracy of Information in Digital Platforms
Table 10 indicates that respondents generally perceive digital financial content as accurate, with mean scores above 3.6. The highest-rated item (M = 3.90) reflects agreement that online financial information is factually correct, supporting its role in improving financial literacy (Huston, 2010).
Table 10. Accuracy of Information in Digital Platforms.
Table 10. Accuracy of Information in Digital Platforms.
Statements N Minimum Maximum Mean Std. Deviation
The financial content that I access from the digital platforms is factually correct. 201 1.00 5.00 3.9005 1.18324
The information provided by digital financial platforms about budgeting and investment is accurate. 201 1.00 5.00 3.7413 1.09669
The content agrees with the finance principles I am used to. 201 1.00 5.00 3.8806 1.12057
I seldom encounter misinformation on the digital platforms that I use to learn about finances. 201 1.00 5.00 3.8159 1.08671
For me, accuracy in financial content is an important condition for using digital platforms for financial purposes. 201 1.00 5.00 3.6915 1.33206
Source: Field study, (2025).
High standard deviations indicate some variation in perception, but overall, accuracy remains a strong determinant of content credibility.
ii) 
Clarity and Readability in Digital Platforms
Table 11 shows respondents perceive digital financial content as clear and easy to understand (M ≥ 3.75). Visual aids such as charts and videos (M = 3.96) enhance comprehension, highlighting the importance of design in digital financial literacy (OECD, 2020).
Table 11. Clarity and Readability in Digital Platforms.
Table 11. Clarity and Readability in Digital Platforms.
Statements N Minimum Maximum Mean Std. Deviation
Financial content on digital platforms is easy to understand. 201 1.00 5.00 3.8856 1.05917
The language used in digital financial content is clear and concise. 201 1.00 5.00 3.7612 1.18012
Visual aids on digital platforms (charts, videos) make financial content easier to understand. 201 1.00 5.00 3.9552 1.03585
I do not understand the financial content on some digital platforms. (Reverse-coded) 201 1.00 5.00 3.7861 1.16576
Digital platforms effectively simplify complex financial topics. 201 1.00 5.00 3.9154 1.16953
Source: Field Study, (2025).
The results suggest that content clarity is satisfactory, and digital media are generally effective in facilitating understanding of financial concepts.
iii) 
Relevance of Topics
Table 12 demonstrates that respondents find digital financial content relevant to personal financial needs and goals (M ≥ 3.60). Items such as personal financial problem-solving scored highest (M = 3.88), indicating strong perceived applicability (Lusardi & Mitchell, 2014).
Table 12. Relevance of Topics in Digital Platform.
Table 12. Relevance of Topics in Digital Platform.
Statements N Minimum Maximum Mean Std. Deviation
Digital platforms provide content that matches my financial goals. 201 3.00 5.00 3.8756 .62406
I find financial content on digital platforms relevant to my current needs. 201 3.00 5.00 3.8308 .64903
The topics covered in digital financial content are practical for everyday financial decisions. 201 2.00 5.00 3.6070 .65555
Financial content on digital platforms is frequently updated to reflect current trends. 201 1.00 5.00 3.7015 .99521
I often find financial content on digital platforms that applies directly to my financial challenges. 201 1.00 5.00 3.8806 .86352
Source: Field Study, (2024).
Users perceive digital content as relevant, though frequency of updates could be enhanced to ensure continuous applicability.

Engagement with Digital Platforms and Financial Literacy

Engagement was assessed through interaction level, use of digital tools, and duration of platform use.
i) 
Interaction Level
Table 13 shows moderate interaction with online financial communities. Highest scores were for contribution to decision-making (M = 3.46), while active participation (sharing content, forum posts) was relatively low, suggesting potential for increased engagement.
Table 13. Interaction Level with Digital Platforms (n=201).
Table 13. Interaction Level with Digital Platforms (n=201).
Statements N Minimum Maximum Mean Std. Deviation
I always take part in online discussion forums related to finance. 201 1.00 5.00 3.0846 .85312
I often comment or share some financial content that I have come across on digital platforms. 201 1.00 5.00 3.0498 .81087
Engaging with other users of finance has improved my knowledge about the subject. 201 1.00 5.00 3.1393 .92763
I ask frequently ask questions or seek advice on financial platforms. 201 1.00 5.00 3.3085 1.03169
Interaction with the digital financial community adds something to my financial decision-making. 201 1.00 5.00 3.4577 .83633
Source: Field study, (2025).
The findings indicate engagement adds value but is underutilized, suggesting a need for interventions to foster active participation.
ii) 
Utilization of Tools
Table 14 presents result on using digital tools to control finances, with overall positive but mixed levels of participation. Respondents tend to agree somewhat use tools like budgetary software or calculators (mean = 3.03) and that respondents enhance their planning ability (mean = 3.19).
Table 14. Utilization of Tools in digital platforms.
Table 14. Utilization of Tools in digital platforms.
Statements N Minimum Maximum Mean Std. Deviation
I often use digital tools, such as budgeting applications or calculators in managing my finances. 201 1.00 5.00 3.0348 1.02654
The digital tools available on platforms have enhanced my financial planning skills. 201 1.00 5.00 3.1891 .84503
I find it easy to use financial tools on digital platforms. 201 2.00 33.00 3.8507 2.35534
Digital tools on the platforms give me an accurate idea about my financial status. 201 1.00 5.00 3.3632 1.42914
I make decisions regarding investment and savings based on digital tools. 201 1.00 5.00 3.3881 1.11295
Source: Field Study, (2025).
The results reflect moderate engagement with digital tools, supporting incremental gains in financial literacy.
iii) 
Usage Duration of digital platforms
Table 15 shows that respondents spend meaningful time learning finance on digital platforms (M = 3.26–3.46). Outlier values (Max = 45) suggest data entry issues, but overall, time investment contributes to enhanced financial literacy (Huston, 2010).
Table 15. Usage Duration of Digital Platforms (n=201).
Table 15. Usage Duration of Digital Platforms (n=201).
Statements N Minimum Maximum Mean Std. Deviation
I spend ample time on digital platforms to learn more about finance. 201 1.00 5.00 3.2637 1.03688
The time I spent on financial platforms contributed a lot to my understanding of finance. 201 1.00 5.00 3.4577 1.01954
I spend a certain number of minutes each day accessing financial content on digital devices. 201 1.00 5.00 3.4179 1.11107
Spending more hours on digital devices has advanced my financial literacy. 201 1.00 5.00 3.4428 1.16532
I hardly know how time flies when learning on digital platforms about finances. 201 1.00 45.00 3.8856 3.10030
Source: Field study, (2024).

Financial Literacy of the Respondents

Table 16 presents self-reported financial knowledge acquisition. Respondents reported increased understanding of budgeting, saving, and investing (M = 3.94), as well as confidence in financial decisions (M = 3.97), demonstrating the positive role of digital platforms (Lusardi & Mitchell, 2014).
Table 16. Knowledge Acquisition in Digital Platforms (n=201).
Table 16. Knowledge Acquisition in Digital Platforms (n=201).
Statement of the Respondents N Minimum Maximum Mean Std. Deviation
I have been taken through the basics, including budgeting, saving, and investment 201 1.00 5.00 3.9353 1.20449
I am confident in making financial decisions based on knowledge gained from the digital platforms. 201 1.00 5.00 3.9701 1.10865
Digital financial content has improved my ability to manage my personal finances. 201 1.00 5.00 3.8109 1.05550
I can understand the risks involved with debt and credit management. 201 1.00 5.00 3.7612 1.21354
I can plan and track my expenses effectively using the knowledge of finance that I have gained. 201 1.00 5.00 3.4975 1.38248
Digital platforms have improved my understanding of financial products, such as loans and insurance. 201 1.00 5.00 3.6119 1.35228
I feel more financially empowered after consuming financial content on digital platforms. 201 1.00 5.00 3.6219 1.29473
Source: Field Study, (2025).
High standard deviations indicate variability among respondents, possibly due to differing literacy levels or content quality.

Inferential Statistic

Normality Distribution of the Variables

Before applying parametric tests, normality of the variables was assessed using Kolmogorov–Smirnov and Shapiro–Wilk tests. Table 17 shows that p-values for all variables were below 0.05, indicating non-normal distributions. Consequently, non-parametric tests such as Spearman’s rank correlation and the Kruskal–Wallis test were employed to examine relationships and differences.
Table 17. Tests of Normality.
Table 17. Tests of Normality.
Variables Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Frequency of Media Use .175 201 .000 .931 201 .000
Type of Content Consumed .194 201 .000 .907 201 .000
Source Credibility .225 201 .000 .890 201 .000
Accuracy of Information .155 201 .000 .895 201 .000
Clarity and Readability .176 201 .000 .866 201 .000
Relevance of Topics .266 201 .000 .893 201 .000
Interaction Level .149 201 .000 .951 201 .000
Utilization of Tools .143 201 .000 .925 201 .000
Usage Duration .170 201 .000 .913 201 .000
Financial Literacy (Dependent Variable) .172 201 .000 .878 201 .000
a. Lilliefors Significance Correction
Source: SPSS Output, (2025).
The non-normal distribution suggests that financial literacy and its predictors vary among respondents, warranting the use of non-parametric methods

Correlation Analysis 

i. 
Kruskal Wallis Test
The Kruskal–Wallis test (Table 18) assessed whether variations in media exposure, quality of online financial content, and engagement with digital platforms significantly affect financial literacy. All three factors were statistically significant, indicating meaningful contributions to financial knowledge.
Table 18. Kruskal Wallis Test Statistics.
Table 18. Kruskal Wallis Test Statistics.
Media Exposure Through Digital Platforms Quality of Digital Financial Content Engagement With Digital Platforms
Kruskal-Wallis Ha,b 37.982 15.224 12.920
Df 4 4 4
Asymp. Sig. .000 .004 .012
a. Kruskal Wallis Test
b. Grouping Variable: Financial Literacy
Source: SPSS Output, (2025).
The results indicate that higher exposure, better-quality content, and active engagement are associated with differences in financial literacy levels among respondents.
ii. 
Spearman’s Rank Correlation
Spearman’s correlation (Table 19) was used to examine relationships between financial literacy and its predictors. Notably, media exposure showed a negative correlation (ρ = -0.264, p < 0.01), while content quality (ρ = 0.150, p < 0.05) and engagement (ρ = 0.144, p < 0.05) were positively correlated, highlighting the importance of content quality and interaction over mere exposure.
Table 19. Spearman’s Rank Correlations.
Table 19. Spearman’s Rank Correlations.
Financial Literacy Media Exposure via Digital Platforms Quality of Digital Financial Content Engagement With Digital Platforms
Spearman’s rho Financial Literacy Correlation Coefficient 1.000 -.264** .150* .144*
Sig. (2-tailed) . .000 .033 .041
N 201 201 201 201
Media Exposure via Digital Platforms Correlation Coefficient -.264** 1.000 -.292** .232**
Sig. (2-tailed) .000 . .000 .001
N 201 201 201 201
Quality of Digital Financial Content Correlation Coefficient .150* -.292** 1.000 -.178*
Sig. (2-tailed) .033 .000 . .011
N 201 201 201 201
Engagement With Digital Platforms Correlation Coefficient .144* .232** -.178* 1.000
Sig. (2-tailed) .041 .001 .011 .
N 201 201 201 201
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Source: SPSS Output, (2025).
Interpretation: Simply accessing content does not guarantee learning; active engagement and high-quality content are critical for improving financial literacy (Huston, 2010).

Regression Analysis

i) 
Linearity Test
Table 20 indicates linear relationships between predictors and financial literacy. Media exposure shows a strong linear relationship, while quality of content and engagement exhibit slight deviations, suggesting more complex effects.
Table 20. Test for Linearity.
Table 20. Test for Linearity.
Sum of Squares df Mean Square F Sig.
Financial Literacy * Media Exposure through Digital Platforms Between Groups (Combined) 43.896 36 1.219 1.672 0.017
Linearity 8.593 1 8.593 11.786 0.001
Deviation from Linearity 35.303 35 1.009 1.384 0.092
Within Groups 119.568 164 0.729
Total 163.464 200
Financial Literacy * Quality of Digital Financial Content Between Groups (Combined) 47.921 25 1.917 2.903 0
Linearity 8.505 1 8.505 12.881 0
Deviation from Linearity 39.416 24 1.642 2.487 0
Within Groups 115.543 175 0.66
Total 163.464 200
Financial Literacy * Engagement with Digital Platforms Between Groups (Combined) 56.633 29 1.953 3.126 0
Linearity 3.338 1 3.338 5.343 0.022
Deviation from Linearity 53.296 28 1.903 3.047 0
Within Groups 106.831 171 0.625
Total 163.464 200
Source: SPSS Output, (2025).
ii) 
Homoscedasticity Test
Residuals were visually assessed (Figure 2) and found to be randomly distributed, indicating constant variance and validating the assumptions of regression analysis.
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Figure 2. the Visual Test for Homoscedasticity.
Figure 2. the Visual Test for Homoscedasticity.
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iii) 
Multicollinearity Test
Table 21 confirms no multicollinearity (VIF < 10, Tolerance > 0.1), meaning each
Table 21. Test for Multicollinearity.
Table 21. Test for Multicollinearity.
Variables Collinearity Statistics
Tolerance VIF
Media Exposure through Digital Platforms .814 1.229
Quality of Digital Financial Content .757 1.322
Engagement with Digital Platforms .915 1.093
a. Dependent Variable: Financial Literacy
Source: SPSS Output, (2025).
iv) 
Model Summary
Table 22 shows the regression model explains 60.3% of the variance in financial literacy (R² = 0.603), indicating a strong predictive relationship between digital platform factors and financial literacy.
Table 22. Model Summary.
Table 22. Model Summary.
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .751a .603 .593 1.0955
a. Predictors: (Constant), Engagement with Digital Platforms, Media Exposure through Digital Platforms, Quality of Digital Financial Content
Source: SPSS Output, (2025)
v)
ANOVA
The regression model is statistically significant (F = 28.613, p < 0.001), confirming that media exposure, content quality, and engagement collectively influence financial literacy (Table 23).
Table 23. ANOVA.
Table 23. ANOVA.
Model Sum of Squares df Mean Square F Sig.
1 Regression 98.193 3 32.731 28.613 .000b
Residual a 225.356 197 1.144
Total 323.549 200
a. Dependent Variable: Financial Literacy
b. Predictors: (Constant), Engagement with Digital Platforms, Media Exposure Through Digital Platforms, Quality of Digital Financial Content
Source: SPSS Output, (2025)
vi) 
Coefficients Analysis
Table 24 presents standardized regression coefficients. Media exposure has a significant negative effect (B = -3.018), while quality of content (B = 3.774) and engagement (B = 7.995) have strong positive effects, highlighting the critical role of interactive and high-quality digital content in improving financial literacy.
Table 24. Coefficients.
Table 24. Coefficients.
Modela Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -14.161 4.029 -3.514 .001
Media Exposure through Digital Platforms -3.018 .711 -.265 -4.243 .000
Quality of Digital Financial Content 3.774 .645 .379 5.852 .000
Engagement with Digital Platforms 7.995 1.109 .425 7.209 .000
a. Dependent Variable: Financial Literacy
Source: SPSS Output, (2025).
Interpretation: Excessive exposure without quality control can negatively affect financial literacy, whereas well-structured content and active engagement significantly enhance knowledge and decision-making capabilities (OECD, 2020; Lusardi & Mitchell, 2014).

5. Discussion

5.1. Influence of Media Exposure Through Digital Platforms on Financial Literacy Levels in Ilala Municipality

The study found a negative and statistically significant relationship between media exposure and financial literacy (coefficient = -3.018, p = 0.000), suggesting that higher exposure to digital media content was linked to lower financial literacy. This may result from information overload, where unstructured, poorly curated, or misleading content confuses users and hinders learning. The finding contrasts with studies on structured educational media (Chaudhary & Luthra, 2022) and aligns with the Uses and Gratifications Theory (UGT), which emphasizes that media promotes learning only when content meets users’ informational needs.

5.2. Effect of Digital Financial Content Quality on Financial Literacy in Ilala Municipality

The study found a positive and statistically significant relationship between the quality of digital financial content and financial literacy (coefficient = 3.774, p = 0.000). Respondents exposed to accurate, clear, and relevant content showed higher financial literacy. These results support Wang and Zhou (2023) and align with the Technology Acceptance Model (TAM), indicating that content perceived as useful and reliable promotes engagement and learning. Unlike contexts with limited digital access (Mensah & Boateng, 2020), Ilala Municipality’s strong digital infrastructure allows quality content to effectively enhance financial knowledge.

5.3. Effect of Engagement with Digital Platforms on Financial Literacy in Ilala Municipality

The study found a strong, statistically significant positive relationship between engagement with digital platforms and financial literacy (coefficient = 7.995, p = 0.000). Active use of interactive tools and budgeting apps was linked to higher financial literacy. These findings align with Kim and Park (2021) but contrast with Gichuki and Mwangi (2019), likely due to Tanzania’s more user-friendly and interactive platforms. The results support the Technology Acceptance Model (TAM), highlighting that perceived usefulness, ease of use, and interactivity drive adoption and improved financial literacy.

6. Conclusions

The findings underscore the multifaceted nature of digital financial literacy. While access to digital platforms is widespread, passive consumption does not guarantee knowledge acquisition. Users benefit most from high-quality content that is accurate, clear, relevant, and interactive. The negative effect of media exposure alone aligns with the argument that excessive or uncurated information can impede comprehension (Huston, 2010). Conversely, engagement through tools, discussion forums, and practical application strengthens financial decision-making and empowerment.
In summary, the study highlights that digital financial literacy is enhanced by a combination of quality content, active engagement, and selective media use. Policymakers and financial educators should prioritize interactive, trustworthy, and personalized digital financial education programs to maximize learning outcomes

7. Limitations

The study’s cross-sectional design limits assessment of long-term effects, and reliance on quantitative data may overlook individual experiences and motivations. Findings from Ilala Municipality may not generalize to other regions with different digital access or socioeconomic conditions. Self-reported data introduces potential response bias, and observed associations cannot establish causality, as prior knowledge or offline learning may also influence financial literacy outcomes.

8. Directions for Further Study

Future research should explore how financial literacy evolves over time through sustained use of digital platforms, addressing the limitations of cross-sectional studies. The influence of social media personalities and finance bloggers, as well as differences between urban and rural populations, warrants investigation. Additionally, studies should compare learning modalities videos, interactive programs, and AI-powered advisors against traditional text-based approaches. Behavioral factors such as motivation, trust, and risk perception should also be examined to understand their impact on engagement with digital financial platforms.

Funding

No funding received to finance this research rather author’s monthly salaries.

Acknowledgments

The author thanks Dr. Deogratias Dafi for guidance, the College of Business Education for resources and support, and family, colleagues, and peers for their encouragement.

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