The investigation of financial self-efficacy, financial well-being, and saving behaviour is informed by two complementary theoretical paradigms: Social Cognitive Theory (SCT) and Subjective Well-Being Theory (SWBT). Each framework offers distinct yet converging explanations for how psychological confidence, perceived financial security, and behavioural outcomes interact within organizational and emerging economy contexts.

Social Cognitive Theory, pioneered by [6], posits that human behaviour is shaped by reciprocal interactions between personal factors (cognitive appraisals, self-beliefs, emotional regulation), environmental conditions (institutional support, economic volatility, social norms), and behavioural outcomes (actual financial management practices). Within financial contexts, SCT emphasizes that financial self-efficacy is not merely an inherited trait but a malleable psychological construct cultivated through mastery experiences, vicarious learning, verbal persuasion, and physiological feedback ([40]; [21]; [12]). Individuals who successfully navigate budgeting, debt management, or investment decisions develop reinforced financial confidence, which in turn increases the likelihood of future prudent financial behaviours such as consistent saving, emergency fund development, and retirement planning ([49]; [71]). SCT further suggests that self-efficacy influences behaviour both directly and indirectly, often operating through psychological mediators such as perceived control, stress reduction, and enhanced decision-making capacity ([6]; [10]). In volatile economic environments, SCT predicts that individuals with high FSE will exhibit greater financial resilience, maintain disciplined saving habits, and report higher subjective financial well-being, as confidence buffers against uncertainty and promotes proactive financial planning ([36]; [61]).

Subjective Well-Being Theory, rooted in the broader psychological literature on happiness and life satisfaction ([17]; [33]), conceptualizes well-being as a multidimensional construct encompassing cognitive evaluations of life circumstances, emotional experiences, and domain-specific satisfaction. Financial well-being, as a domain-specific manifestation of SWB, reflects an individual’s perceived financial security, satisfaction with current financial status, and confidence in future financial stability ([7]; [9]; [50]). SWBT posits that perceived well-being is not merely a passive outcome of income or wealth but an active psychological state influenced by cognitive appraisals, behavioural habits, and environmental framing ([16]; [52]). Individuals who perceive themselves as financially well are more likely to engage in long-term planning, avoid impulsive financial decisions, and maintain consistent saving behaviours, as perceived security reduces financial anxiety and enhances cognitive bandwidth for rational decision-making ([27]; [64]; [19]). Conversely, financial stress depletes psychological resources, leading to present bias, debt accumulation, and diminished saving capacity ([32]; [41]). SWBT thus positions financial well-being as both an outcome of psychological confidence and a catalyst for sustained financial behaviour.

Behavioural Economics provides additional explanatory depth by highlighting how psychological heuristics, cognitive biases, and environmental defaults shape financial decision-making ([34]; [67]; [19]). Key concepts such as mental accounting, loss aversion, present bias, and default effects demonstrate that financial behaviour is rarely purely rational but heavily influenced by psychological framing and perceived security ([62]; [66]). From this perspective, financial self-efficacy enhances perceived financial well-being by reducing uncertainty anxiety and promoting proactive financial habits, which in turn reinforce saving behaviour through positive feedback loops ([12]; [71]). Collectively, SCT, SWBT, and behavioural economics yield converging predictions: financial self-efficacy directly promotes saving behaviour, but this relationship is partially channeled through enhanced financial well-being, which serves as a psychological bridge between confidence and action.

2.2.1. Financial Self-Efficacy and Financial Well-Being

Social Cognitive Theory posits that self-efficacy beliefs directly influence psychological well-being by enhancing perceived control, reducing anxiety, and fostering positive outcome expectancies ([6]; [40]). In financial contexts, individuals with high FSE report greater confidence in managing monetary resources, which translates into reduced financial stress, enhanced perceived security, and higher subjective financial well-being ([21]; [10]; [48]). Empirical studies consistently demonstrate positive associations between FSE and FWB across diverse populations, with financially confident individuals exhibiting greater satisfaction with their financial status and stronger future financial optimism ([71]; [58]; [39]). In Kenyan university contexts, where institutional financial stressors are prevalent, FSE may serve as a critical psychological buffer, enabling employees to maintain positive financial appraisals despite external economic constraints ([8]; [18]). We therefore hypothesize:

H1:Financialself-efficacywillbepositivelyassociatedwithfinancialwell-beingamonguniversitystaffinKenya.

2.2.2. Financial Well-Being and Saving Behaviour

Subjective Well-Being Theory suggests that perceived financial security enhances cognitive bandwidth, reduces present bias, and promotes long-term planning orientation ([16]; [32]; [64]). Individuals who feel financially well are more likely to engage in disciplined saving habits, maintain emergency funds, and avoid impulsive spending, as perceived security diminishes anxiety-driven financial decisions ([28]; [12]; [57]). Empirical research confirms that FWB positively predicts saving behaviour, budget adherence, and retirement planning across emerging and developed economies ([45]; [72]; [42]). In Kenyan public universities, where salary delays and inflationary pressures threaten financial stability, FWB may serve as a psychological catalyst for consistent saving practices, enabling employees to navigate economic uncertainties with greater discipline ([24]; [36]). We hypothesize:

H2:Financialwell-beingwillbepositivelyassociatedwithsavingbehaviouramonguniversitystaffinKenya.

2.2.3. Financial Self-Efficacy and Saving Behaviour

Social Cognitive Theory predicts that self-efficacy directly influences behavioural outcomes by enhancing motivation, persistence, and goal-directed action ([6]; [21]). Financially confident individuals are more likely to set monetary goals, adhere to budgets, and maintain consistent saving habits, as self-belief translates into behavioural commitment ([40]; [12]; [49]). Empirical studies demonstrate direct positive relationships between FSE and saving behaviour, with high-efficacy individuals exhibiting greater financial discipline and lower debt accumulation ([10]; [58]; [39]). In volatile economic environments, FSE may directly drive saving behaviour by fostering resilience against financial shocks and promoting proactive financial planning ([71]; [61]). We hypothesize:

H3:Financialself-efficacywillbepositivelyassociatedwithsavingbehaviouramonguniversitystaffinKenya.

2.2.4. The Mediating Role of Financial Well-Being

Integrating SCT and SWBT, financial well-being is theorized to partially mediate the relationship between financial self-efficacy and saving behaviour. FSE enhances perceived financial security and reduces anxiety, which in turn promotes cognitive clarity, long-term planning, and disciplined saving habits ([6]; [16]; [64]). Empirical mediation studies confirm that psychological well-being channels the effects of financial confidence into tangible behavioural outcomes, with FWB serving as a critical mechanism linking self-beliefs to financial action ([61]; [12]; [59]). In Kenyan university contexts, where structural financial stressors may attenuate direct efficacy-behaviour linkages, FWB may provide the psychological stability necessary to translate confidence into consistent saving practices ([8]; [18]). We hypothesize:

H4:Financialwell-beingwillpartiallymediatetherelationshipbetweenfinancialself-efficacyandsavingbehaviouramonguniversitystaffinKenya.

Figure 1 presents the conceptual framework guiding this study. Financial self-efficacy (X) is modelled as the independent predictor, financial well-being (M) as the mediator, and saving behaviour (Y) as the dependent outcome. Socio-demographic variables (gender, age, education, job designation, work experience) are included as covariates to control for potential confounding effects. The model assumes direct linear relationships while testing indirect mediation pathways, consistent with [30] PROCESS 4.2 framework and standard mediation assumptions ([44]; [20]) (Table 1).

Figure 1. Conceptual model of financial self-efficacy, financial well-being, and saving behaviour. Source: [29] Model 4.

Table 1. Summary of hypotheses.

This study employs an explanatory, cross-sectional survey design to examine the direct and indirect relationships between financial self-efficacy, financial well-being, and saving behaviour among working adults in Kenyan public universities. The university act of 2012 provides a general framework for chartering universities in Kenya. The universities were selected based on their geographical locality on the whole of Western Kenya, that is west of Nakuru city. The inclusion criteria, a fully chartered publicly-funded university excluding publicly-funded university colleges (Koitaleel Samoei, Alupe and Turkana university colleges) and privately-chartered universities and university colleges. The study employed a proportionate stratified random sampling at university levels and randomization at departmental levels. Cross-sectional designs are appropriate for testing the direct relationships and mediation pathways at a single point in time, particularly when investigating psychological and behavioural antecedents that are relatively stable across short intervals ([60]; [65]). The explanatory nature of the design enables hypothesis testing through multiple regression and bootstrapped mediation analysis, isolating the unique contribution of each psychological predictor while controlling for multicollinearity and confounding variables ([23]; [44]; [30]). Given the study’s focus on psychological financial confidence and behavioural readiness, a survey-based approach ensures measurement of perceptual and action-oriented dimensions of financial management ([71]; [7]).

The target population comprises 9786 academic and administrative staff across eight public universities in Western Kenya. These institutions were selected due to their exposure to systemic financial stressors, including delayed capitation, salary arrears, pension uncertainties, and limited financial wellness programming ([8]; [18]). Using [73] sample size formula at a 95% confidence level and 5% margin of error, a minimum sample of 385 respondents was determined. Stratified random sampling ensured proportional representation across job categories (academic, administrative, support staff), institutional tiers, and demographic strata. Questionnaires were distributed electronically and physically, with follow-up reminders to maximize response rates. Of 385 distributed instruments, 369 were returned, yielding 354 complete responses. Four cases were excluded due to extreme outliers or incomplete data, resulting in a final analyzable sample of 350 participants (91.9% effective response rate). This sample exceeds the minimum requirement for mediation analysis with five covariates ([23]; [65]), providing adequate statistical power (1 − β > 0.80) for detecting medium effect sizes ([14]).

All constructs were measured using previously validated instruments, adapted to the Kenyan university context through pilot testing (n = 30) and expert review (three finance academics, two behavioural economists). Items were anchored on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).

FinancialSelf-Efficacy(FSE). An 8-item scale adapted from validated financial self-efficacy and behavioural intention instruments assessed the frequency, consistency, and confidence of financial management practices. The items included; IwillbeabletoachievemostofthegoalsthatIhavesetformyself, Whenfacingdifficulttasks, IamcertainthatIwillaccomplishthem, Ingeneral, IthinkthatIcanobtainoutcomesthatareimportanttome, IbelieveIcansucceedatmostanyendeavourtowhichIsetmymind, Iwillbeabletosuccessfullyovercomemanychallenges, IamconfidentthatIcanperformeffectivelyonmanydifferenttasks, Comparedtootherpeople, IcandomosttasksverywellandEvenwhenthingsaretough, Icanperformquitewell. The items were contextualized to the study to captured both foundational financial management (e.g., budget adherence, expense tracking) and advanced financial planning (e.g., investment confidence, long-term goal setting). The scale demonstrates excellent internal consistency (Cronbach’s α = 0.907).

FinancialWell-Being(FWB). Measured using the InCharge Financial Distress/Financial Well-Being (IFDFW) scale ([54]), an 8-item instrument capturing perceived financial security, satisfaction, and future financial confidence. The items were contextualized to the study and include the following statements: Iamsatisfiedwithmypersonalfinances, Ifeelhappyaboutmycurrentfinancialsituation, Ifeelsecureaboutmyretirementplan, IamconfidentthatIwillhaveafinanciallycomfortableretirement, Iamconfidentfindingthemoneytopayforafinancialemergencycost, Ifrequentlyfindmyselfeagerlywaitingforthenextpayday, IoftenfindithardtofinanciallyenjoydoingthingsIlovewithmyfriendsandfamilyandIoftenworryaboutbeingabletomeetmymonthlylivingexpenses. The scale exhibited strong reliability (Cronbach’s α = 0.857).

SavingBehaviour(SB). Assessed using a 6-item validated scale ([15]; [3]) measuring habitual saving practices, budget adherence, and financial goal pursuit. The items included: IusuallypayattentionontheamountofmoneyIsetaside, Ialwayssticktomymoney-managementstrategies, WhenIreceivemoney, Ialwayssetasideaportionofit, Isetasidemoneytoaccomplishspecificobjectives, Iusuallysetasidefundsforthefutureonaregularbasis, andInordertosavemoney, Ioftenconsiderwhetherapurchaseisnecessarybeforemakingit. The items were contextualized to the study and the scale demonstrated excellent internal consistency (Cronbach’s α = 0.873).

Socio-DemographicCovariates. Participants self-reported gender (0 = male, 1 = female), age (5-point categorical scale), education level (4-point ordinal scale), job designation (4-point hierarchical scale), and work experience (4-point tenure scale), consistent with standard demographic classification in financial behaviour research ([41]; [25]).

Data were analyzed using IBM SPSS Statistics Version 28 and PROCESS 4.2 ([30]). Prior to hypothesis testing, data quality was assessed through missing value analysis, outlier detection, and assumption checking. Missing data (<2%) were handled via listwise deletion, justified by minimal missingness and random distribution ([65]). Outliers were screened using z-scores (|z| > 3.29) and Mahalanobis distance; four cases were removed due to extreme response patterns. Normality was assessed via skewness/kurtosis (acceptable range: ±2.0) and Shapiro-Wilk tests; all variables fell within acceptable bounds ([37]). Multicollinearity was evaluated using tolerance (>0.20) and variance inflation factor (VIF < 10.0); diagnostics confirmed independence of predictors ([38]; [63]). Homoscedasticity was verified via residual plots, and linearity was confirmed through scatterplots and residual analysis ([23]). Common method variance was assessed using Harman’s single-factor test, yielding a first-factor variance of 32.1%, well below the 50% threshold, indicating minimal CMV bias ([53]).

Hypothesis testing proceeded through [30] PROCESS Model 4, a robust bootstrap-based mediation framework that simultaneously estimates direct, indirect, and total effects while controlling for covariates. The analytical strategy followed four sequential stages:

1) Descriptive Statistics: Means, standard deviations, and frequency distributions characterized the sample and variables.

2) Reliability and Factor Analysis: Cronbach’s alpha and PCA confirmed scale psychometrics.

3) Bivariate Correlations: Pearson’s r examined zero-order relationships among study variables and covariates.

4) Mediation Analysis: PROCESS Model 4 tested the direct effect of FSE on SB (path c’), the indirect effect via FWB (path a × b), and the total effect (path c). Bias-corrected percentile bootstrap confidence intervals (5000 resamples) determined mediation significance at 95% CI. Model fit was assessed via R², F-tests, and ANOVA. Individual predictor significance was evaluated using t-tests, standardized β coefficients, and 95% confidence intervals ([23]; [30]). All tests employed a two-tailed alpha level of 0.05, with results reported in compliance with APA 7th edition standards.

The descriptive statistics characterize the demographic profile of respondents across five key variables: gender, age, educational attainment, job designation, and work experience. The sample exhibited a slight male preponderance, with males constituting 55.1% (n = 193) while female participants accounted for 44.9% (n = 157). Respondents’ ages revealed a workforce predominantly concentrated in early-to-mid career stages. The largest age cohort comprised individuals aged 33 - 43 years (n = 136; 38.9%), followed by those aged 44 - 54 years (n = 83; 23.7%) and 22 - 32 years (n = 80; 22.9%). Older employees aged 55 - 65 years represented 13.4% of the sample (n = 47), while only a minimal proportion (1.1%; n = 4) reported being above 65 years of age. Educational qualifications reflected the credential-intensive nature of the university sector. Bachelor’s degree holders constituted the largest group (n = 140; 40.0%), followed by diploma holders (n = 103; 29.4%). Postgraduate qualifications were well represented, with master’s degree holders accounting for 20.6% (n = 72) and doctoral degree holders comprising 10.0% (n = 35). Tenure within the university system varied considerably. The most experienced cohort, those with more than 15 years of service, represented the largest experience category (n = 119; 34.0%). Mid-tenure staff with 5 - 9 years of experience constituted 32.3% (n = 113), while those with 10 - 14 years of service accounted for 20.6% (n = 72). Early-career employees with less than four years of experience comprised 13.1% (n = 46). Collectively, these demographic characteristics depict a sample of university employees who are predominantly male, mid-career, moderately to highly educated, and experienced in institutional service, providing essential context for interpreting subsequent psychological and behavioural analyses.

Before analysis, items were examined using Harman’s one factor to test for common method variance by entering study variables into factor analysis and the single-factor explained 37.862% way below the 50% threshold thus ruling out common method variance (Table 2).

Table 2. Factors analysis of the instrument.

Prior to factor analysis, data suitability was confirmed. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.870, exceeding the 0.60 minimum ([35]). Bartlett’s Test of Sphericity was highly significant, χ²(561) = 5466.985, p < 0.001, indicating the correlation matrix was factorable ([65]). Principal Component Analysis with Varimax rotation extracted five factors explaining 73.911% of total variance. The Financial Self-Efficacy scale yielded two factors (Eigenvalues = 3.294 and 2.677), accounting for 27.141% of variance, with all items demonstrating strong primary loadings (0.584 - 0.860) and communalities above 0.60. The Financial Well-Being scale produced a two-factor solution (Eigenvalues = 3.989 and 2.435), explaining 29.202% of variance, with loadings ranging from 0.827 to 0.914. The Saving Behaviour scale loaded onto a single component (Eigenvalue = 3.865), explaining 17.569% of variance, with loadings between 0.645 and 0.803. Internal consistency was excellent across all constructs: FSE (α = 0.907), FWB (α = 0.857), and SB (α = 0.873), substantially exceeding the 0.70 threshold for acceptable reliability ([51]). These psychometric results confirm the robustness and dimensionality of the measurement instruments, supporting their suitability for mediation analysis. After the conclusion of the factor analysis, the study generated a single index using the geometric mean of the total number of items per variable to represent the study variable in correlation and regression analysis.

Table 3 presents Pearson bivariate correlations among all study variables. Financial self-efficacy demonstrated strong positive correlations with saving (r = 0.483, p < 0.01) and financial wellbeing (r = 0.256, p < 0.01), consistent with theoretical predictions. Financial well-being also correlated positively with saving behaviour (r = 0.336, p < 0.01), suggesting a meaningful behavioural linkage.

Table 3. Summary statistics.

*significant levels at 0.05.

The mediation model was tested using PROCESS Macro Version 4.2 (Model 4) with 5000 bootstrap resamples. Table 4 presents the regression coefficients, standard errors, t-values, p-values, and confidence intervals for the total, direct, and indirect effects.

Table 4. Results for direct, indirect and total effects.

Path a (FSE → FWB): Financial self-efficacy significantly associated with financial well-being (β = 0.2853, SE = 0.0729, t = 5.0115, p < 0.001). The model explained 9.64% of variance in FWB (R² = 0.0964, F(7, 342) = 5.9587, p < 0.001), indicating a strong psychological linkage between confidence and perceived financial security. None of the covariates reached statistical significance, confirming the robustness of the FSE-FWB relationship.

Path b (FSE → SB): Financial wellbeing significantly correlated with saving behaviour (β = 0.2406, SE = 0.0064, t = 4.5672, p < 0.001).

Total Effect (Path c'): The total effect of FSE on SB was significant (β = 0.4149, SE = 0.0078, t = 8.2709 p < 0.001) indicating a strong overall relationship before accounting for the mediator. The full model, including FSE and covariates, explained 28.97% of variance in SB.

The results in Table 5 show the study’s conditional process analysis using [29] process macro 4.6 (Model 4). The study tested the indirect path between financial knowledge and financial well-being via Saving behaviour (a₁ × b₁). The study then used a bootstrapping approach to test for the mediation process, with confidence intervals resampled 5000 times at 95% CI.

Table 5. Mediation results.

p-value = 0.000, LLCI = Lower-Level Confidence Interval, ULCI = Upper-Level Confidence Interval. Note: All covariates controlled. 5000 bootstrap samples. LLCI/ULCI = Lower/Upper Level Confidence Interval.

Direct Effect (Path c'): The direct effect remained significant (β = 0.4149, SE = 0.0078, 95% CI [0.0493, 0.0801]), confirming that FSE influences SB independently of FWB.

Indirect Effect (Path a × b): The bootstrap analysis yielded a significant indirect effect of 0.0686 (BootSE = 0.0201, 95% BootCI [0.0333, 0.1122]). Since the confidence interval does not contain zero, mediation is statistically supported. The proportion mediated was approximately 14.20% (0.0686/0.4835), indicating that financial well-being accounts for more than 10% of the total effect of FSE on SB.

Hypothesis testing outcomes confirm that H1, H2, H3, and H4 are all supported. Financial self-efficacy positively influences financial well-being (H1), financial well-being positively predicts saving behaviour (H2), financial self-efficacy directly influences saving behaviour (H3), and financial well-being partially mediates the FSE-SB relationship (H4).

This study investigated the direct and indirect relationships between financial self-efficacy, financial well-being, and saving behaviour among 350 working adults across public universities in Western Kenya. The results yielded a clear and consistent pattern: financial self-efficacy significantly influenced both financial well-being and saving behaviour, with financial well-being serving as a partial psychological mediator. The total effect of FSE on SB (β = 0.4835) was substantial, with 14.20% of this effect channeled indirectly through enhanced financial well-being (β = 0.0686). The remaining 85.8% operated directly, confirming that FSE influences saving behaviour through both psychological security and independent behavioural mechanisms. All socio-demographic covariates (gender, age, education, job designation, work experience) failed to reach statistical significance, reinforcing the conclusion that psychological constructs exert stronger influence on financial outcomes than basic demographic profiles. These findings fundamentally challenge conventional assumptions that demographic characteristics reliably forecast financial confidence and behaviour, particularly in emerging economy academic workforces.

H1:FinancialSelf-EfficacyandFinancialWell-Being

Consistent with Social Cognitive Theory, FSE emerged as a powerful influencer of FWB (β = 0.2853, p < 0.001). This finding aligns with empirical research demonstrating that financial confidence reduces anxiety, enhances perceived control, and fosters positive financial appraisals ([6]; [21]; [10]). In Kenyan university contexts, where institutional financial stressors are prevalent, FSE likely serves as a psychological buffer, enabling employees to maintain positive financial outlooks despite external economic constraints ([8]; [18]). The strong association underscores the importance of cultivating financial confidence as a foundational element of workplace financial wellness programs.

H2:FinancialWell-BeingandSavingBehaviour

Financial well-being significantly shaped saving behaviour (β = 0.2406, p < 0.001), supporting Subjective Well-Being Theory’s assertion that perceived financial security enhances cognitive bandwidth and promotes long-term planning ([16]; [32]; [64]). Employees who feel financially secure are more likely to engage in disciplined saving habits, maintain emergency funds, and avoid impulsive spending, as perceived stability diminishes anxiety-driven financial decisions ([28]; [12]; [57]). This finding highlights FWB as a critical psychological catalyst for consistent financial action, particularly in volatile economic environments.

H3:FinancialSelf-EfficacyandSavingBehaviour

FSE demonstrated a significant direct effect on saving behaviour (β = 0.4149, p = 0.005), confirming Social Cognitive Theoretical proposition that self-efficacy directly influences behavioural outcomes through enhanced motivation and goal-directed action ([6]; [40]; [49]). Financially confident individuals are more likely to set monetary goals, adhere to budgets, and maintain consistent saving habits, as self-belief translates into behavioural commitment ([12]; [58]). This direct pathway suggests that FSE operates independently of perceived well-being, likely through habit formation, financial planning routines, and self-regulatory mechanisms.

H4:TheMediatingRoleofFinancialWell-Being

The significant indirect effect (0.2406, 95% CI [0.0333, 0.1122]) confirms partial mediation, aligning with integrated SCT and SWBT frameworks. FSE enhances perceived financial security, which in turn promotes cognitive clarity, reduces present bias, and reinforces disciplined saving practices ([6]; [16]; [64]). The partial nature of mediation indicates that while FWB is a critical psychological bridge, FSE also directly drives saving behaviour through independent mechanisms such as self-control, financial literacy application, and behavioural habituation ([71]; [61]; [12]). This dual-pathway model provides nuanced insights into how psychological confidence translates into tangible financial action.

As a caution to the findings, the authors noted that finding’s is constrained by omission of critical structural variables. Independently, income level fundamentally dictates an employee’s absolute capacity to save, meaning high financial self-efficacy cannot easily overcome severe income constraints or delayed salary disbursements common in Kenyan public universities. Secondly, household debt exerts distinct structural pressure; high debt-to-income ratios force individuals into reactive financial management, potentially neutralizing the positive behavioural effects of financial well-being on consistent saving habits. Finally, familial obligations introduce unique socio-cultural financial demands, such as supporting extended family members, which can rapidly drain disposable income regardless of an individual’s psychological confidence. By omitting these three critical moderators, the study risks overstating its direct psychological pathways, as unmeasured financial realities may independently skew, confound, or suppress the observed relationships between self-efficacy, well-being, and actual saving behaviour among Kenyan university staff, limiting the model’s generalizability.

All socio-demographic covariates (gender, age, education, job designation, work experience) failed to reach statistical significance in both regression models, consistent with contemporary behavioural finance research demonstrating that psychological and structural factors dominate financial confidence and behaviour formation ([32]; [19]; [64]). This null finding challenges demographic determinism embedded in traditional economic models, supporting the proposition that financial self-efficacy and well-being are more function of psychological resilience, institutional support, and financial socialization than of chronological age, occupational rank, or educational attainment ([41]; [27]; [56]). In Kenyan academia, where structural financial stressors and delayed promotions are common, demographic advantages may be neutralized, rendering psychological constructs more predictive of financial outcomes ([8]; [18]).

The pattern of results carries substantial theoretical implications. First, the findings validate integrated Social Cognitive and Subjective Well-Being frameworks, demonstrating that financial self-efficacy operates through both direct behavioural pathways and indirect psychological mechanisms. Second, the partial mediation effect underscores the importance of distinguishing between confidence, perceived security, and actual financial behaviour in theoretical models of financial capability. Third, the negligible explanatory power of socio-demographic covariates challenges traditional demographic determinism, supporting behavioural economics’ assertion that psychological, structural, and contextual factors dominate financial confidence formation ([32]; [67]; [19]). Future theoretical frameworks should integrate psychological mediators (self-control, future time orientation, financial socialization), structural moderators (income volatility, debt burden, institutional benefits), and behavioural nudges (automatic enrollment, default options, commitment devices) to explain financial behaviour variance more accurately ([56]; [13]; [71]). The study also underscores the need for culturally contextualized theories that account for emerging economy labour market dynamics, where traditional demographic assumptions may not hold ([8]; [1]).

The findings carry actionable implications for financial educators, university administrators, and national policymakers. First, universal financial wellness programs should replace demographically targeted interventions, as financial self-efficacy and well-being are not systematically associated with age, gender, rank, or experience. Workplace financial coaching, automated payroll deductions, and default retirement enrollment should be offered to all employees regardless of demographic profile ([66]; [19]). Second, Kenyan universities should institutionalize financial literacy programming that transcends academic qualifications, focusing on behavioural habit formation, mental accounting awareness, and emergency fund development ([41]; [71]). Third, policymakers should prioritize structural reforms that stabilize income flows, enhance pension portability, and expand financial inclusion through digital savings platforms ([24]; [5]). Finally, employer-sponsored financial counseling should be integrated into human resource frameworks, recognizing that financial confidence and well-being directly impact job satisfaction, productivity, and institutional commitment ([4]; [11]).

Several limitations warrant acknowledgment. First, the cross-sectional design precludes causal inference; longitudinal tracking is required to determine whether FSE causally improves FWB and SB, or whether unmeasured variables explain both. Second, demographic variables were measured categorically, potentially reducing sensitivity to detect true effects; continuous measures (e.g., exact income, tenure in months) would enhance statistical power. Third, the study did not assess critical moderators such as income level, household debt, familial obligations, or objective financial literacy, which may confound psychological effects. Fourth, the sample was restricted to Kenyan public universities, limiting generalizability to private institutions, non-academic workforces, or other African contexts.

Future research should: 1) incorporate psychological measures (self-control, delay discounting, financial socialization) alongside demographics to determine relative predictive power; 2) employ longitudinal or experimental designs to establish causal pathways; 3) use continuous financial metrics and objective financial data to complement behavioural self-reports; 4) examine cross-cultural variations in psychological predictive utility; and 5) evaluate the efficacy of behavioural nudges such as automatic savings and goal-setting in university employee populations. By addressing these gaps, researchers can develop more precise theoretical models and evidence-based interventions that enhance financial capability across diverse socioeconomic contexts.