Communication dynamics: Fintech's role in promoting sustainable cashless transactions

The study investigates how Fintech services and communication strategies influence the shift from traditional payments to cashless transactions (CLT). It focuses on the mediating role of individuals’ intentions to use cashless transactions (IUCT) in the link between Fintech use and actual use of cashless transactions (AUCT). It also examines financial literacy (FL) as a moderator of the relationship between Fintech-use factors and IUCT, and personal innovativeness (PIN) as a moderator of the IUCT–AUCT relationship. The research is set in the Punjab province of Pakistan among small business owners and informal sector workers (e.g., vendors, small shop owners, taxi and rickshaw drivers), where CLT adoption is posited to improve financial inclusion and drive socioeconomic development (SED). The study aims to clarify the mechanisms and pathways from Fintech use to CLT adoption, financial inclusion, and SED, addressing gaps regarding how intentions, FL, and PIN shape adoption outcomes in underserved populations.

Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extension UTAUT2, the study draws on constructs including performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), perceived usefulness (PU), and price value (PV) to explain IUCT and AUCT. UTAUT2 adds hedonic motivation, price value, and habit and recognizes moderators such as age, gender, and experience. Extending UTAUT2, this study incorporates FL and PIN as additional moderators and links AUCT to financial inclusion and SED. Hypotheses: H1–H5 posit that PE, EE, FC, PU, and PV positively affect IUCT and AUCT; H6 that IUCT positively influences AUCT; H7 that IUCT mediates the effects of PE, EE, FC, PU, PV on AUCT; H8 that FL positively moderates Fintech-use factors’ effects on IUCT; H9 that PIN positively moderates the IUCT–AUCT relationship; H10 that demographics (gender, region, marital status, education, age, household size, income) influence CLT adoption; and H11 that CLT adoption (financial inclusion) improves sustainable livelihood, living standards, and social development.

Design: Two-stage quantitative approach. Stage I applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the extended UTAUT2 framework with mediation (IUCT) and moderation (FL, PIN). Stage II employs a Probit model to estimate the propensity to adopt CLT (financial inclusion) and Propensity Score Matching (PSM) to estimate the Average Treatment effect on the Treated (ATT) of CLT adoption on SED outcomes. Sampling and participants: Survey of small business owners and informal sector workers in Punjab, Pakistan (fruit/vegetable vendors, small shop owners, taxi/auto rickshaw drivers). Of 537 approached, 448 questionnaires were distributed; after data curation, 394 valid responses remained (final response rate 73.37%). Measures: Constructs adapted primarily from Venkatesh et al. (2012) and related sources; all measured on 5-point Likert scales. Stage I variables: PE, EE, FC, PU, PV (each 4–6 items), IUCT (4–6 items), FL (5 items), PIN (4 items), and AUCT (items as per instrument). Stage II variables: Financial Inclusion (FI: adopter=1, non-adopter=0), demographics (gender, marital status, household size, household income, age, region, education) and SED outcomes: Sustainable Livelihood (SLD), Growth in Living Standard (GLS), and Social Development (SDT). The questionnaire was developed in English and translated into Urdu. Analysis procedures: PLS-SEM (SmartPLS) for measurement model validation (indicator loadings, reliability via Cronbach’s alpha, rho_A, composite reliability; convergent validity via AVE; discriminant validity via Fornell-Larcker). Collinearity and common method bias assessed via VIF (<3.3). Structural model estimated with bootstrapping (5,000 subsamples), reporting path coefficients, t-values, R2, Q2, and effect sizes (f2). Stage II: Probit model for adoption probability; PSM (radius, stratification, kernel matching) to estimate ATT of CLT adoption on SLD, GLS, SDT, using propensity scores to match adopters and non-adopters.

Measurement and model fit: All indicator loadings exceeded recommended thresholds; reliability indices (alpha, CR, rho_A) and AVE met criteria; VIF <3.3 indicated no problematic collinearity or common method bias. Predictive relevance confirmed (Q2 > 0). Explained variance: Baseline UTAUT2 model explained R2=0.571 of AUCT. Extended model with FL and PIN increased AUCT R2 to 0.692 (+12.1%). IUCT R2 increased from 0.463 to 0.570. Direct effects (Table 5): Positive significant paths supporting H1–H6. Selected coefficients (a: p<0.01; b: p<0.05):

• PE→IUCT: 0.145a; PE→AUCT: 0.199b
• EE→IUCT: 0.122a; EE→AUCT: 0.198a
• FC→IUCT: 0.207b; FC→AUCT: 0.221b
• PU→IUCT: 0.298b; PU→AUCT: 0.283a
• PV→IUCT: 0.272a; PV→AUCT: 0.244b
• IUCT→AUCT: 0.356a Mediation (Table 6): IUCT partially mediates effects of PE, EE, FC, PU, PV on AUCT (VAF 28.5%–44.9%), supporting H7. Moderation (Table 7):
• FL positively influences IUCT (β=0.358, p=0.000) and significantly moderates Fintech-use factors→IUCT: FL×PE=0.263; FL×EE=0.215; FL×FC=0.116; FL×PU=0.220; FL×PV=0.344 (all p=0.000), supporting H8.
• PIN positively influences AUCT (β=0.131, p=0.000) and moderates IUCT→AUCT (PIN×IUCT=0.245, p=0.000), supporting H9. Determinants of adoption (Probit, Table 9; N=394, Pseudo R2=0.216, LR χ2=89.542, p<0.01): Education levels (secondary, intermediate, graduation) and urban region significantly increase likelihood of adoption; age shows significant association; gender, marital status, household size, and household income are not significant. H10 is partially supported. Impact on SED (PSM, Table 10): CLT adoption (financial inclusion) significantly improves outcomes for small business owners:
• SLD ATT: +0.397 to +0.521 (p<0.01)
• GLS ATT: +0.593 (p<0.05) to +0.744 (p<0.01)
• SDT ATT: +0.425 to +0.476 (p<0.05 to p<0.01) H11 is supported.

Findings demonstrate that Fintech-use perceptions—performance expectancy, effort expectancy, facilitating conditions, perceived usefulness, and price value—enhance intentions (IUCT) and actual use (AUCT) of cashless transactions. Intentions function as a key psychological mechanism connecting Fintech perceptions to behavior, confirming the intention–behavior pathway in UTAUT2. Financial literacy strengthens how Fintech perceptions translate into intentions, suggesting that knowledge and understanding amplify the benefits users perceive. Personal innovativeness facilitates converting intentions into actual use, highlighting individual openness to new technologies as critical for behavior change. Stage II results show that adopting CLT (financial inclusion) is associated with substantial improvements in sustainable livelihoods, living standards, and social development among small business owners in Punjab. Demographic analysis indicates targeted policy needs: urban settings and higher education correlate with higher adoption, while other sociodemographics show limited effects. Overall, the study clarifies the pathway from Fintech perceptions through intentions and adoption to tangible socioeconomic gains, informing strategies to scale inclusive digital finance.

Fintech services and communication strategies effectively promote sustainable cashless transactions by shaping user intentions and behaviors. IUCT mediates the relationship between Fintech-use factors and AUCT, while FL and PIN respectively moderate the intention formation and intention-to-use links. Extending UTAUT2 with these moderators increases explanatory power. Adoption of CLT enhances financial inclusion and leads to significant socioeconomic benefits—improved sustainable livelihoods, higher living standards, and social development—for small business owners and informal sector workers in Punjab, Pakistan. Future work should extend the framework across regions and sectors and explore additional moderating mechanisms and qualitative insights. The study contributes theoretically by extending UTAUT2 to link Fintech-driven adoption with financial inclusion and SED and offers practical guidance for policies and designs that foster inclusive digital finance.

Generalizability is limited to Punjab, Pakistan; findings may not directly transfer to regions with different socioeconomic contexts. The focus on small business owners and informal sector participants may introduce sampling bias relative to formal sectors. The study relies on survey data from a single source, though steps were taken to mitigate common method bias. Future research should broaden geographic and sectoral coverage, incorporate qualitative methods (e.g., interviews, focus groups) to capture cultural and psychosocial drivers, and test additional moderators such as financial empowerment and perceived Fintech quality.