Table of Contents
- Introduction and Research Context
- Literature Review and Theoretical Framework
- Research Methodology
- Study 1: Factors Associated with Financial Literacy among BA Students
- Study 2: Influence of Financial Literacy and Consumptive Behavior on Investment Decisions
- Discussion and Synthesis
- Conclusions, Implications, and Recommendations
- Limitations and Future Research Directions
- References
1. Introduction and Research Context
1.1 Background of the Study
The digital transformation of financial services has fundamentally altered how young adults interact with money. In the Philippines, the proliferation of e-wallets (GCash, Maya), online trading platforms (COL Financial, GStocks, eToro), and buy-now-pay-later (BNPL) services has created an unprecedented dual reality: students have never had easier access to both investment opportunities and consumption temptations.
As of 2026, the Bangko Sentral ng Pilipinas (BSP) reports that 74% of Filipino adults now have formal financial accounts, up from 29% in 2019 -- a transformation driven almost entirely by digital adoption. Among the 18--24 age group, e-wallet penetration exceeds 90%. Yet paradoxically, financial literacy rates have not kept pace: the BSP's own Financial Inclusion Survey shows that only 25% of young Filipinos can correctly answer basic financial literacy questions on compound interest, inflation, and risk diversification.
This creates a critical tension: digitally empowered students can invest with a few taps on their phones, but many lack the foundational knowledge to make informed decisions -- while simultaneously being bombarded by digital consumption triggers (social media ads, flash sales, influencer marketing, BNPL schemes) that compete for the same limited financial resources.
1.2 Statement of the Problem
This study addresses three interrelated research problems:
- What is the level of financial literacy among undergraduate business administration students in selected Philippine higher education institutions, and what factors are significantly associated with it?
- How do financial literacy and consumptive behavior independently and jointly influence students' investment decisions in the digital era?
- What role do digital platforms play in mediating the relationship between financial literacy, consumption patterns, and investment behavior?
1.3 Research Objectives
- Assess the financial literacy levels of BA students across five HEIs using a standardized instrument
- Identify demographic, socioeconomic, and behavioral factors significantly associated with financial literacy
- Measure consumptive behavior patterns, particularly digitally-driven consumption
- Analyze the direct and moderating effects of financial literacy and consumptive behavior on investment decisions
- Develop evidence-based recommendations for curriculum integration and digital financial wellness
1.4 Significance of the Study
This research contributes to the growing body of literature on financial education in developing economies, with specific relevance to: (a) CHED curriculum development for business programs, (b) BSP financial inclusion policy design, (c) university student affairs programming, and (d) fintech platform design for responsible financial behavior.
2. Literature Review and Theoretical Framework
2.1 Financial Literacy: Definitions and Measurement
Financial literacy is defined as "the ability to use knowledge and skills to manage financial resources effectively for a lifetime of financial well-being" (OECD, 2023). Lusardi and Mitchell's (2014) seminal "Big Three" questions -- measuring understanding of compound interest, inflation, and risk diversification -- remain the global benchmark, though recent instruments have expanded to include digital finance, cryptocurrency awareness, and platform literacy.
In the Philippine context, Bangko Sentral ng Pilipinas (2024) adapted the OECD/INFE toolkit and found that financial literacy among young Filipinos (18--30) averaged 42% correct responses -- below the OECD average of 62% and the ASEAN average of 51%.
2.2 Factors Influencing Financial Literacy
The literature identifies several categories of determinants:
| Category | Variables | Key Studies |
|---|---|---|
| Demographic | Age, gender, year level, field of study | Lusardi & Mitchell (2014); Potrich et al. (2015) |
| Socioeconomic | Family income, parents' education, allowance level | Mandell (2008); Jorgensen & Savla (2010) |
| Educational | Finance courses taken, GPA, institutional type | Fernandes et al. (2014); Kaiser & Menkhoff (2017) |
| Behavioral/Digital | Use of fintech apps, investment experience, social media exposure | Morgan & Trinh (2019); Setiawan et al. (2022) |
| Psychological | Financial self-efficacy, locus of control, risk tolerance | Farrell et al. (2016); Lown (2011) |
2.3 Consumptive Behavior in the Digital Era
Consumptive behavior refers to the tendency to purchase goods and services beyond basic needs, driven by desire rather than necessity (Lubis, 2020). In the digital era, this behavior has been amplified by:
- Social media-driven comparison: Instagram, TikTok, and Facebook create aspirational consumption pressure
- Frictionless digital payments: GCash, Maya, and ShopeePay reduce the psychological "pain of paying"
- BNPL and micro-lending: Platforms like Atome, BillEase, and TendoPay enable consumption beyond current means
- Gamified shopping: Shopee, Lazada sales events (9.9, 11.11, 12.12) create urgency and impulse buying
- Influencer marketing: Peer endorsement blurs the line between genuine recommendation and advertising
2.4 Investment Decisions among Young Adults
The democratization of investing through mobile platforms has created a new generation of young investors. In the Philippines, PSE data shows that new investor accounts opened by those under 30 increased by 340% between 2020 and 2025. Digital platforms like GStocks, COL Financial, Investa, SeedIn, and cryptocurrency exchanges have lowered barriers to entry.
However, studies consistently show that young investors are more susceptible to: herd behavior, social media-driven trading ("meme stocks"), overconfidence bias, and insufficient diversification (Barber & Odean, 2013; Tan & Lim, 2023).
2.5 Theoretical Framework
This study integrates three theoretical perspectives:
Theory of Planned Behavior (Ajzen, 1991): Investment decisions are influenced by attitudes (shaped by financial literacy), subjective norms (peer behavior, social media), and perceived behavioral control (financial self-efficacy).
Behavioral Life-Cycle Hypothesis (Shefrin & Thaler, 1988): Individuals mentally categorize income into "current income," "current assets," and "future income" -- with consumptive behavior affecting allocation toward immediate consumption vs. investment.
Digital Nudge Theory (Thaler & Sunstein, 2008; adapted): Digital platform design (notifications, gamification, default settings) "nudges" behavior toward either consumption or saving/investing.
2.6 Conceptual Framework
| Conceptual Model: Financial Literacy -> Investment Decisions (Moderated by Consumptive Behavior) | ||
|---|---|---|
| Independent Variables | Moderating Variable | Dependent Variable |
| Financial Literacy (Knowledge, Behavior, Attitude) Demographic Factors Socioeconomic Factors Digital Platform Exposure | Consumptive Behavior (Traditional + Digital) | Investment Decision Quality (Rationality, Frequency, Diversification, Returns Awareness) |
3. Research Methodology
3.1 Research Design
This study employed a sequential explanatory mixed-methods design, beginning with a quantitative survey phase followed by qualitative in-depth interviews to contextualize and enrich the statistical findings.
3.2 Sampling and Participants
| Parameter | Details |
|---|---|
| Population | Undergraduate BA students enrolled in AY 2025-2026 |
| Sampling method | Stratified random sampling across 5 HEIs |
| Survey respondents (N) | 450 (90 per institution) |
| Interview participants | 30 (6 per institution, purposive sampling) |
| Institutions | 2 State Universities, 2 Private Universities, 1 College |
| Year levels | 2nd, 3rd, and 4th year BA students |
| Response rate | 89.2% (450 of 504 distributed) |
3.3 Instruments
A. Financial Literacy Assessment (FLA)
A 30-item instrument adapted from the OECD/INFE Financial Literacy Questionnaire and the BSP Financial Inclusion Survey, covering three domains:
- Financial Knowledge (12 items): Compound interest, inflation, risk diversification, time value of money, digital finance concepts (cryptocurrency basics, e-wallet fees, BNPL interest rates)
- Financial Behavior (10 items): Budgeting practices, saving habits, bill payment, financial planning, use of financial products
- Financial Attitude (8 items): Orientation toward saving, attitudes toward credit, long-term financial planning mindset
Reliability: Cronbach's alpha = 0.87 (Knowledge), 0.82 (Behavior), 0.79 (Attitude).
B. Consumptive Behavior Scale (CBS)
A 20-item Likert scale (1-5) measuring: impulse buying tendency, social media-influenced purchases, BNPL usage frequency, hedonic vs. utilitarian consumption orientation, and digital spending patterns. Cronbach's alpha = 0.84.
C. Investment Decision Quality Index (IDQI)
A 15-item instrument assessing: investment participation (yes/no and platform used), decision rationality (information-seeking behavior before investing), diversification awareness, risk assessment capability, and return expectation realism. Cronbach's alpha = 0.81.
3.4 Data Analysis
| Analysis | Purpose | Software |
|---|---|---|
| Descriptive statistics | Profile demographics and literacy levels | SPSS 29 |
| Pearson's correlation | Bivariate relationships | SPSS 29 |
| Multiple regression | Predictors of financial literacy | SPSS 29 |
| Hierarchical regression | Moderation analysis (consumptive behavior) | SPSS 29 + PROCESS |
| Chi-square / ANOVA | Group comparisons | SPSS 29 |
| Thematic analysis | Qualitative interview data | NVivo 14 |
STUDY 1
Factors Associated with Financial Literacy among BA Students
4. Study 1: Results and Findings
4.1 Respondent Demographics
| Demographic Variable | Category | N | % |
|---|---|---|---|
| Gender | Female | 261 | 58.0 |
| Male | 189 | 42.0 | |
| Year Level | 2nd Year | 142 | 31.6 |
| 3rd Year | 168 | 37.3 | |
| 4th Year | 140 | 31.1 | |
| Family Monthly Income | Below PHP 20,000 | 148 | 32.9 |
| PHP 20,001-50,000 | 187 | 41.6 | |
| Above PHP 50,000 | 115 | 25.6 | |
| HEI Type | State University | 180 | 40.0 |
| Private University/College | 270 | 60.0 |
4.2 Overall Financial Literacy Levels
| Financial Literacy Domain | Mean Score (%) | SD | Interpretation |
|---|---|---|---|
| Financial Knowledge | 52.1 | 18.4 | Moderate |
| Financial Behavior | 44.8 | 16.2 | Low-Moderate |
| Financial Attitude | 47.6 | 14.8 | Moderate |
| Overall Financial Literacy | 48.2 | 15.3 | Moderate (Below Proficient) |
4.3 Performance on Specific Knowledge Items
| Question Topic | % Correct | Difficulty |
|---|---|---|
| Simple interest calculation | 72.4 | Easy |
| Inflation and purchasing power | 61.3 | Moderate |
| Risk diversification concept | 45.8 | Moderate-Hard |
| Compound interest (Big 3 Q1) | 38.7 | Hard |
| Time value of money | 42.2 | Moderate-Hard |
| E-wallet transaction fees | 56.0 | Moderate |
| BNPL true cost of credit | 28.4 | Very Hard |
| Cryptocurrency risk profile | 35.6 | Hard |
| Stock market basics (P/E, dividends) | 49.3 | Moderate |
| Emergency fund adequacy | 63.1 | Moderate |
4.4 Factors Significantly Associated with Financial Literacy
Multiple regression analysis (R-squared = 0.47, F = 28.6, p < 0.001) identified the following significant predictors:
| Factor | Beta | t | p-value | Direction |
|---|---|---|---|---|
| Finance courses completed | 0.31 | 6.82 | <0.001 | Positive (strongest predictor) |
| Year level (seniority) | 0.22 | 4.91 | <0.001 | Positive |
| Family income level | 0.18 | 3.94 | <0.001 | Positive |
| Parents' education (college+) | 0.15 | 3.28 | 0.001 | Positive |
| Personal investment experience | 0.14 | 3.12 | 0.002 | Positive |
| Fintech app usage frequency | 0.11 | 2.44 | 0.015 | Positive (weak) |
| GPA / Academic performance | 0.09 | 1.98 | 0.048 | Positive (marginal) |
| Gender (male = 1) | 0.08 | 1.74 | 0.082 | Not significant |
| Social media hours/day | -0.06 | -1.32 | 0.187 | Not significant |
| HEI type (private = 1) | 0.05 | 1.08 | 0.281 | Not significant |
STUDY 2
Influence of Financial Literacy and Consumptive Behavior on Investment Decisions
5. Study 2: Results and Findings
5.1 Investment Participation Profile
| Investment Type | % of Investors (N=172) | Primary Platform |
|---|---|---|
| Savings/Time Deposit | 82.0 | Bank apps (BPI, BDO, UnionBank) |
| Stocks (PSEi) | 34.3 | COL Financial, GStocks, FirstMetroSec |
| Cryptocurrency | 24.1 | Coins.ph, Binance, PDAX |
| Mutual Funds / UITFs | 21.5 | GCash GInvest, BPI Invest |
| Government Bonds (RTB) | 12.8 | Bonds.ph, bank OTC |
| Insurance-linked (VUL) | 18.6 | Sun Life, Pru Life, AXA |
| P2P Lending | 6.4 | SeedIn, InvestTree |
5.2 Consumptive Behavior Assessment
| Consumptive Behavior Dimension | Mean (1-5) | SD | Interpretation |
|---|---|---|---|
| Impulse buying tendency | 3.42 | 0.88 | Moderate-High |
| Social media-influenced purchases | 3.68 | 0.92 | High |
| BNPL/credit usage for consumption | 2.85 | 1.04 | Moderate |
| Hedonic consumption orientation | 3.31 | 0.86 | Moderate |
| Digital spending frequency | 3.74 | 0.78 | High |
| Overall Consumptive Behavior | 3.40 | 0.72 | Moderate-High |
5.3 Correlation Analysis
| Variable Pair | r | p-value | Interpretation |
|---|---|---|---|
| Financial Literacy --> Investment Decision Quality | 0.62 | <0.001 | Strong positive |
| Financial Literacy --> Investment Participation | 0.48 | <0.001 | Moderate positive |
| Consumptive Behavior --> Investment Decision Quality | -0.41 | <0.001 | Moderate negative |
| Consumptive Behavior --> Investment Participation | -0.28 | <0.001 | Weak negative |
| Financial Literacy --> Consumptive Behavior | -0.19 | <0.001 | Weak negative |
| Social Media Hours --> Consumptive Behavior | 0.52 | <0.001 | Strong positive |
5.4 Moderation Analysis: The Consumptive Behavior Effect
Hierarchical regression with consumptive behavior as a moderator of the financial literacy --> investment decision relationship:
| Model | Variables | R-sq | Delta R-sq | F Change | Sig. |
|---|---|---|---|---|---|
| Model 1 | Financial Literacy (main effect) | 0.384 | 0.384 | 279.6 | <0.001 |
| Model 2 | + Consumptive Behavior (main effect) | 0.452 | 0.068 | 55.2 | <0.001 |
| Model 3 | + FL x CB (interaction term) | 0.481 | 0.029 | 24.8 | <0.001 |
- High FL + Low CB group: Investment decision quality score = 4.12/5.00 (highest)
- High FL + High CB group: Investment decision quality score = 2.86/5.00 (43% lower)
- Low FL + Low CB group: Investment decision quality score = 2.54/5.00
- Low FL + High CB group: Investment decision quality score = 1.92/5.00 (lowest)
This means that financial literacy alone is not sufficient -- students who are financially literate but have high consumptive behavior still make poor investment decisions, because their resources and cognitive bandwidth are consumed by spending impulses.
5.5 Qualitative Findings: Student Voices
Thematic analysis of 30 in-depth interviews revealed five dominant themes:
Theme 1: "I know I should invest, but..."
"I took Financial Management last semester and I know about compound interest and peso-cost averaging. But every payday, I end up spending on food delivery and online shopping before I can set aside anything for stocks. It's like the knowledge is there but my behavior doesn't follow." -- Female, 3rd Year, Private University
Theme 2: The Social Media Spending Trap
"TikTok is the worst. I see an 'aesthetic' product and suddenly I need it. I've spent maybe PHP 15,000 this semester on things I saw on TikTok. That's more than my total investment portfolio." -- Male, 4th Year, State University
Theme 3: Digital Platforms as Double-Edged Sword
"GCash is both my investment tool and my spending tool. I use GInvest to put money in mutual funds, but I also use GCash to pay for Grab, Shopee, and food delivery. Sometimes the money I plan to invest ends up being spent because it's just one tap away." -- Female, 3rd Year, Private University
Theme 4: Peer Pressure and FOMO Investing
"I bought Dogecoin because my blockmates were all posting gains. I didn't research it. I lost about PHP 8,000. Now I realize I should have studied it first, but when everyone is doing it, you feel like you're missing out." -- Male, 2nd Year, State University
Theme 5: Curriculum Gaps
"Our finance classes teach theory -- NPV, WACC, capital budgeting for corporations. But nobody teaches us personal finance. How to budget. How to choose between a UITF and a VUL. How to not waste money on 9.9 sales. That's what we actually need." -- Female, 4th Year, Private University
6. Discussion and Synthesis
6.1 The Financial Literacy Paradox
The most striking finding is what we term the "Financial Literacy Paradox": business administration students -- who receive more financial education than any other undergraduate cohort -- still demonstrate only moderate financial literacy (48.2%), and critically, financial knowledge does not automatically translate into financial behavior.
This paradox is explained by the moderating role of consumptive behavior. In the digital era, the "knowing-doing gap" is amplified by platform design that makes consumption frictionless while investing still requires deliberate effort. The psychological cost of resisting consumption (ego depletion) leaves fewer cognitive resources for investment decision-making.
6.2 The Digital Dual-Use Problem
A key contribution of this study is identifying the "Digital Dual-Use Problem": the same platforms (GCash, Maya, banking apps) serve as both consumption enablers and investment gateways. Students who use these platforms more frequently have slightly higher financial literacy (due to exposure) but significantly higher consumptive behavior -- creating a net negative effect on investment decision quality.
This has design implications: fintech platforms that separate investment flows from spending flows (through mental accounting features, auto-invest before spending, or "invest first" default settings) could potentially nudge behavior toward better outcomes.
6.3 Implications for the Theory of Planned Behavior
The TPB framework requires modification for the digital era: "subjective norms" now include algorithmically curated social media feeds, and "perceived behavioral control" is shaped by platform UX design. A student may have positive attitudes toward investing (driven by financial literacy) but face overwhelming subjective norms toward consumption (driven by social media) and reduced behavioral control (frictionless digital spending). This explains why financial literacy has a weaker effect on investment decisions than theory would predict.
7. Conclusions, Implications, and Recommendations
7.1 Key Conclusions
- Financial literacy among BA students is moderate but below proficiency. Despite specialized coursework, only 18.4% of students reached OECD proficiency thresholds. The weakest areas are digital finance products (BNPL, crypto) -- precisely the tools students use most.
- Finance course completion is the strongest predictor of financial literacy (Beta = 0.31), followed by year level and family income. Gender is no longer a significant predictor, suggesting generational convergence.
- Consumptive behavior significantly weakens the literacy-investment link. Students with high financial literacy but high consumptive behavior scored 43% lower on investment decision quality than their low-consumption peers.
- Social media is the primary driver of consumptive behavior (r = 0.52), more than any demographic or economic factor.
- The digital dual-use problem creates a structural challenge: platforms simultaneously enable investing and spending, with spending being the frictionless default.
7.2 Recommendations for Higher Education Institutions
| Recommendation | Target | Expected Impact |
|---|---|---|
| Integrate personal finance modules into all BA curricula (not just Finance majors) | CHED, HEI Deans | Increase baseline FL from 48% to 65%+ |
| Include digital finance literacy (BNPL, crypto, e-wallet costs) in curricula | Faculty, CHED | Close the "digital literacy gap" |
| Develop experiential learning: simulated trading, budgeting challenges | Faculty | Bridge the knowing-doing gap |
| Offer "Financial Wellness" programs through Student Affairs | HEI Admin | Address consumptive behavior directly |
| Partner with fintech platforms for educational content integration | HEIs + Fintech | Leverage platform exposure for learning |
7.3 Recommendations for Policy Makers
- BSP and SEC: Mandate financial literacy disclosures on BNPL platforms and crypto exchanges targeting young users
- CHED: Include financial literacy as a General Education requirement across all undergraduate programs (not limited to business)
- DTI and DOF: Regulate predatory digital lending and BNPL practices that disproportionately affect students
- DepEd: Introduce basic financial literacy in senior high school (Grade 11-12) ABM and HUMSS tracks
7.4 Recommendations for Fintech Platforms
- Auto-invest features: Default to investing a percentage of received funds before spending is enabled
- Spending awareness nudges: Show weekly spending summaries with comparison to investment contributions
- Separate mental accounts: Visually and functionally separate "spending wallet" from "investment wallet"
- Gamified financial education: Integrate bite-sized lessons within the app experience
- Cool-off periods: Add voluntary 24-hour delays on non-essential purchases above a user-set threshold
8. Limitations and Future Research Directions
8.1 Limitations
- Cross-sectional design: Cannot establish causality. A longitudinal study tracking the same students over 2-3 years would strengthen causal claims.
- Self-reported data: Consumptive behavior and investment decisions are self-reported, introducing social desirability bias. Future studies should triangulate with actual transaction data (with consent).
- Sample scope: Limited to 5 HEIs in Metro Manila. Provincial and rural institutions may show different patterns.
- BA students only: Results may not generalize to non-business students, who likely have lower baseline financial literacy.
- Rapidly evolving digital landscape: New platforms and products may shift behavior patterns by the time of publication.
8.2 Future Research Directions
- Longitudinal panel study tracking financial literacy, consumption, and investment trajectories from 1st year through post-graduation
- Experimental intervention studies: randomized trials of financial wellness programs in HEIs
- Platform analytics-based research: partnership with fintech providers to analyze actual (not self-reported) spending vs. investing behavior
- Cross-country comparative study: Philippines vs. Singapore, Indonesia, Vietnam (varying REIT/digital finance maturity)
- Impact of AI-driven financial advisory tools (robo-advisors) on student investment decision quality