The rapid growth of financial technology (Fintech) has transformed the way individuals conduct financial transactions, with online payment systems becoming an integral part of everyday economic activities in India. Mobile wallets, Unified Payments Interface (UPI), web banks, and digital payment applications have helped bring financial services much faster, easier, and more accessible. However, the users' acceptance of these technologies is not only due to functionality but also because of their emotional aspects, including trust, anxiety, perceived security, and risk [1]. Such feelings can vary from man to woman based on their technological experiences, financial confidence, risk perception, and social influences. Trust can stimulate adoption and persistence with online payment systems, but anxiety about being scammed, breached, inconvenienced or losing money can lead to rejection [2]. Therefore, the study of gender differences in trust, anxiety and behavioral intention is important to understand factors affecting the adoption of digital payment in Indian consumer. The study is designed to compare these emotional and behavioural aspects of Indian women and men in order to gain an insight into how Fintech companies could better craft secure, inclusive and user-centred digital payment experiences [3].

Gender Differences in Trust towards Online Payment Systems

Trust is one of the primary drivers that can affect consumer acceptance and adoption of an online payment system. When it comes to digital payments, trust is about customers' confidence in payment platforms' security, reliability, privacy, and credibility. Gender might affect the way this trust is perceived and built, as men and women might experience technology differently, differences in financial decision making, difference in perceived vulnerability, and reaction to online security threats [4]. Although women seem to give greater weight to privacy, transaction safety, and protection against fraud, men seem to possess relatively greater confidence in using digital payment technologies, but this may differ by age, education, digital literacy and previous online payment experience [5]. Users will also be more inclined to have faith in platforms that offer secure login systems, data security, and clear transaction processes, which   are   also essential factors. Therefore, it is useful to examine the gender differences in terms of trust, perceived security, concerns about privacy, and perceived reliability of the online payment system to get some insights into how Indian men and women perceive and use online payment systems [6].

Gendered Anxiety and Perceived Risk in Digital Payments

Anxiety and perceived risk are psychological factors that might affect willingness to adopt digital payment systems, but are not necessarily important ones. Online payments are easy and rapid, however, there is a concern about financial fraud, transactions without their consent, data theft, invasion of privacy, cyber security and technical issues [7]. Other risks of PFI use include transaction errors, non-payment, delayed refunds and the potential for personal and financial information to be insecure. The concerns may negatively affect the people's trust in the digital payment system and make them less likely to use or use it frequently. Therefore, it is crucial to take into account users' attitude towards Fintech services ([8] and users' emotions to these potential risks.

Gender can also be a big factor in determining the level of anxiety and perceived risk of digital payments. Differences in digital experience, confidence in online safety, financial risk, and prior exposure to technology could impact men and women's perceptions of online security, financial vulnerability, technological uncertainty, and fear of fraud [9]. Some women may prioritize privacy, the security of online transactions, or other factors, whereas some men may feel more at ease with their online expertise, and their age, education, digital literacy, income, and past online transaction experience may vary. Comparing the differences between the two genders can then help to highlight gender-specific areas of anxiety and perceived risk, and help Fintech companies create safer, more transparent and user friendly digital payment systems [10].

Behavioral Intention and Adoption of Online Payment Systems

Behavioral intention is the intention or probability of future adoption, continued use, and recommendation of online payment systems. With regard to the Fintech, several factors affect the users' behavioral intention, such as their level of trust and anxiety. Online payment platforms that users view as secure, reliable, convenient, and trusted are more likely to be used regularly and have positive intentions toward continued use [11]. In contrast, users may lose trust and be deterred from using digital payment services due to anxiety over fraud, privacy breaches, cyber security risks, transaction failures or lack of technological knowledge. Therefore, the attitude of trust may be a motivator to the adoption of digital payment while the attitude of anxiety and perceived risk can be a barrier to digital payment [12].

There may also be gender differences in relation to behavioral intention for online payment systems as women may differ in their reactions to trust, security concerns, and uncertainty regarding technology as opposed to men. These differences may be affected by digital literacy, experience, economic confidence, perceived usefulness and security awareness [13]. People are more likely to keep using and recommending digital payment platforms when they feel safe and confident while transacting online and less likely if they feel more anxious at transacting online. Therefore, the study of the behavioral intention of men and women in India could be useful for understanding the influence of the emotional factors such as trust and anxiety on the adoption and continued use of online payment systems [14].

Literature Review

Online Payment Systems and Fintech Adoption in India

Patnaik, et al. [15] explored the determinants of the adoption of digital payments in India which include financial literacy, financial trust, service quality and ease of use. It was determined that the implementation of digital payment platforms had become more popular and financial literacy had not shown significant direct influence on implementation. Likewise, Tribhan, A. S., and Kamble, M. A study by [16] explored the reasons behind the adoption of digital payments in India, including their convenience, security, and government support measures, and how these factors affect consumer preferences. It discovered that digital payments had an effect on shopping behavior, online shopping, impulse buying and provided ease and transparency of spending. Similarly, Aggarwal et al. [17] used the extended Theory of Planned Behavior (TPB) to analyze Fintech adoption behavior among the Gen Y in India. It was discovered that information quality plays a pivotal role in Fintech adoption, with a focus on user-friendly financial products and services. The UAE banking sector effect on sales growth through online shopping as mediator. It revealed that e-payment methods had a significant impact on sales growth and online shopping, thus facilitating commercial transactions [18].

Trust and Consumer Confidence in Digital Payments

Ahmed, et al. [19] studied the factors affecting the adoption of digital wallets, which include the perceived usefulness, ease of use, trust, and perceived risk. It revealed that trust significantly was found to increase adoption intentions, whereas perceived risk was found to negatively impact adoption intention, underscoring the importance of security and consumer confidence. Similarly, Emini, et al. [20] investigated the effect of trust and technology-related issues in the adoption of digital payment system with TAM and UTAUT. It concluded that performance expectancy, facilitating conditions, and trust were very important for adoption intentions, and security, privacy and infrastructure remained important factors. Moreover, the association between digital payment usage and consumers' trust in online payments. It concluded that Trust, Perceived Security and Perceived Usefulness are important factors driving adoption intentions, highlighting the importance of boosting consumer confidence in cashless systems [21]. Likewise, Celestin, M. and Sujatha, S. explored the impact of fintech innovations on consumer behaviors and businesses. It discovered that digital payments made life easier and encouraged impulsive buying, and security was a big hurdle to increased adoption.

Anxiety, Perceived Risk, and Emotional Responses to Online Payments

Silva, et al. [22] studied the relationship between perceived risk and anxiety with consumers' intention to make online transactions. It found that time risk, anxiety, and psychological risk significantly affected transaction intentions, while social, physical, and performance risks were not significant. Similarly, Abikari, M. [23] examined the relationship between consumers’ emotions, perceived risk, and intention to adopt emerging e-banking technologies. It found that deterrence emotions and hedonic motivation influenced perceived risk, which subsequently affected consumers’ adoption intentions. Likewise, Bland, et al. [24] examined the impact of different perceived risks on attitudes toward mobile payment adoption. It found that performance and psychological risks negatively influenced user’s acceptance, highlighting the need for effective risk management by mobile payment providers. Li, W. [25] examined factors influencing the intention to use AI-generated assistance tools among design students and practitioners. It found that performance expectancy, effort expectancy, social influence, and facilitating conditions positively affected adoption, while anxiety and perceived risk negatively influenced users’ intentions.

Gender Differences in Fintech Adoption and Digital Financial Behavior

The study examined mobile Fintech adoption and digital financial inclusion among Indonesia’s Bottom of the Pyramid users using an extended Mobile Technology Acceptance Model. It found that digital financial literacy and mobile-related factors influenced adoption, while gender moderated usage, with men showing stronger effects than women [26]. Likewise, Módosné Szalai, et al. [27] studied the association between the level of financial knowledge, financial confidence and use of FinTech in Hungary. It determined that financial education and use of the FinTech app are higher with higher education and income, while lower with old age, emphasizing the need for targeted financial education. Additionally, Chen, et al. explored the gender gap in the use of FinTech products in 28 different countries and discovered that the percentage of men using FinTech products was higher than the number of females using them. It revealed that the gap was influenced by the difference in willingness to adopt new technologies, product suitability and cost, and thus underscored the importance of gender-inclusive FinTech policies. Similarly, Shehadeh, et al. explored the impact of digital financial literacy on the adoption of cashless payment, moderated by gender, for University affiliates in Jordan. It identified a significant relationship between awareness, experience and skills with increased adoption, with the experience of digital financial services showing a higher impact on women’s use.

Trust, Anxiety, and Behavioral Intention: An Integrated Gender Perspective

Dekkal, et al. explored the factors that impact trust and adoption of Chatbots in the context of FinTech and InsurTech, with a particular emphasis on usefulness, enjoyment, personalization, privacy, and creepiness. It was discovered that it was useful and fun that led to greater trust and uptake, while creepiness led to less of both, with technology anxiety in between. Likewise, Kwak, et al. investigated the factors that affect the decision of nursing students to use AI-based healthcare technology. It identified positive attitudes toward AI and self-efficacy to be significant factors increasing behavioral intention, and that AI anxiety was less in senior students. In addition, Rodríguez-García, M. J. and Donati, F. The study [28] analysed the process of gender mainstreaming in EU urban development policies, with a particular emphasis on Spain. It discovered that urban programmes had gender-sensitive elements, although these were not always implemented and were predominantly directed at social integration, employment, education, health and care. Similarly, Rodriguez, et al. [29] investigated the incorporation of gender and the cultural viewpoints in teaching in the three Philippine higher education institutions. It discovered that inclusive curricula and culturally responsive teaching and student-centered learning methods fostered equity and engagement, but insufficient resources, training, and institutional support presented obstacles to more intensive integration.

Research gap

A thorough examination of the current literature shows that, while extensive research on FinTech and the adoption of online payments has been carried out, there is still much to be understood regarding the emotional and gender-specific aspects of digital payment behaviour. Earlier research has been mainly concerned with technological, functional and economic factors like ease of use, usefulness, information quality, security, financial awareness, and government support [15-17]. Although trust, perceived risk, and anxiety have been found to be of great significance in the adoption of digital payments, these variables have been primarily studied in isolation from each other as emotions affecting behavioral intention [24,2019,22]. Moreover, research from various countries has shown gender differences in FinTech adoption and digital financial practices, including gender gaps in technology acceptance, financial literacy, and usage patterns [26]. Yet, there is a lack of attention paid to the interaction impact of trust and anxiety on behavioral intention towards online payment systems by Indian men and women. Specifically, there is a lack of integrated framework with gender-comparative analysis of positive and negative emotions towards online payments in the Indian context. In view of this, the present study aims at bridging this gap by exploring the relationship between trust, anxiety, and behavioral intention towards online payment systems for both men and women in India to gain a deeper understanding of the gendered emotional factors influencing the adoption of FinTech in India.

Objectives of the Study

• To assess and compare the levels of trust and anxiety towards online payment systems among male and female users in Karnataka
• To examine the influence of trust and anxiety on behavioural intention towards online payment systems
• To investigate whether gender significantly moderates the relationships between trust, anxiety, and behavioural intention towards online payment systems
• To identify which emotional factor - trust or anxiety - exerts a stronger influence on behavioural intention among female users of digital payment systems

Research Design

The study adopts a quantitative and descriptive research design to examine the influence of trust and anxiety on behavioural intention towards online payment systems, with gender serving as a moderating variable. A quantitative approach was deemed most appropriate given that the study constructs - trust, anxiety, and behavioural intention are theoretically grounded, measurable, and testable through validated psychometric scales. The study is cross-sectional in nature, with data gathered at a single point in time from active users of digital payment platforms in Karnataka, India.

Study Context and Population

The study was conducted among active users of digital payment systems in selected urban and semi-urban regions of Karnataka, India. The target population comprised individuals who regularly use at least one form of digital payment platform including UPI-based applications such as GPay, PhonePe, and Paytm, mobile wallets, internet banking services, and QR-code-based payment systems. Only one criterion was used to include items in the study: an active and regular online payment system at data collection.

Sampling and Sample Size

Participants were recruited by using a mixed sampling method of convenience and snowball sampling. 200 respondents were surveyed, 100 male and 100 female users of online payment systems - a balanced gender distribution intentionally selected for allowable group comparison and moderation analysis. The sample size is a minimum recommended for multiple regression analysis with two predictors and one moderator [30] and is comparable with previous studies of fintech adoption in the Indian context [31].

Figure 1: Conceptual Framework

Measurement Instrument

The data were gathered with a structured, self-administered questionnaire in two parts. The first section collected demographic data including gender, age group, educational qualification, occupation and frequency of using digital payment. The second consisted of 27 Likert scale items that rated the three major constructs on a 5-point scale, from 1 (Strongly Disagree) to 5 (Strongly Agree).

Measurement items were drawn from validated measures that have been previously developed from the following models: Technology Acceptance Model (TAM) [32] and Unified Theory of Acceptance and Use of Technology (UTAUT) [23] and were modified to fit the context of the Indian digital payments context. The construct structure is shown in Table 1.

Table 1: Construct Structure and Item Allocation

Research Hypotheses

Based on the theoretical framework and review of literature, the following hypotheses were formulated:

• H1: Male respondents report significantly higher trust towards online payment systems than female respondents
• H2: Female respondents report significantly higher anxiety towards online payment systems than male respondents
• H3: Trust has a significant positive influence on behavioural intention towards online payment systems
• H4: Anxiety has a significant negative influence on behavioural intention towards online payment systems
• H5: Gender significantly moderates the relationships between trust, anxiety, and behavioural intention towards online payment systems (Table 2)

Table 2: Statistical Techniques and Hypotheses Addressed

Data Analysis

The collected data were analysed using SPSS, Jamovi, and Mermaid diagramming software. The following statistical techniques were employed:

Reliability and Validity

The measurement instrument used was first evaluated for its validity and reliability before conducting hypothesis testing, which is done in three steps. In order to establish the construct validity (Table 3-5), first exploratory factor analysis was conducted. Secondly, the Cronbach's Alpha was calculated to determine the internal consistency reliability of each scale (Table 6). All constructs achieved acceptable limits and were deemed acceptable for inferential analysis using the instrument.

Table 3: KMO and Bartlett's Test

Table 4: Total Variance Explained

Table 5: Factor Loadings

Table 6: Reliability Analysis (Cronbach’s Alpha and Number of Items)

Exploratory Factor Analysis (Efa) Results

To test the construct validity of the measurement scale, Exploratory Factor Analysis (EFA) was used. Kaiser-Meyer-Olkin value of 0.920 showed good sampling adequacy and Bartlett's Test of Sphericity was significant (χ² = 2097.151, df = 351, p<0.001) and confirmed that the data were appropriate for factor analysis. Four factors with eigenvalues greater than one were extracted, explaining 51.699% of the total variance. The factor loadings ranged from 0.488 to 0.781, indicating acceptable convergent validity of the measurement items.

Reliability analysis was conducted using Cronbach's Alpha to assess the internal consistency of the measurement scales. The results indicated satisfactory reliability for all constructs. Trust exhibited a Cronbach's Alpha of 0.830, Anxiety demonstrated a Cronbach's Alpha of 0.896, and Behavioral Intention showed a Cronbach's Alpha of 0.844. All values exceeded the recommended threshold of 0.70, indicating good internal consistency and reliability of the measurement instruments Table 6.

 All three constructs exceeded the Cronbach's Alpha threshold of 0.80, confirming good internal consistency: Trust (α = 0.835), Anxiety (α = 0.896), and Behavioural Intention (α = 0.844). Content validity was established through adaptation from peer-reviewed instruments and expert review. Skewness values (−0.27 to 0.04) and kurtosis values (−0.73 to −0.41) confirmed approximate normality within acceptable thresholds of ±1 and ±2 respectively. No multicollinearity was detected, with all VIF values below 5.

Demographic Profile of Respondents

Table 7 presents the demographic profile of the respondents. The sample consisted of 200 respondents, equally distributed between males (50%) and females (50%). The majority of respondents belonged to the 21–30 years’ age group (43%), followed by the 31–40 years age group (33.5%). Regarding educational qualifications, most respondents held a Degree (43%) or Postgraduate qualification (31%). In terms of occupation, private employees constituted the largest group (37.5%), followed by students (21%). With respect to occupation, the largest group were private employees (37.5%), followed by students (21%). In terms of online payment, 36% of the respondents said they used online payment every day, and 34% of them said they used it weekly, which shows high familiarity with online payment platforms.

Table 7:  Demographic Profile of Respondents (N = 200)

Source: Primary Survey Data (2026)

Descriptive Statistics and Normality

The descriptive statistics and normality test of the study variables are shown in Table 8. Trust recorded the highest mean score (M = 3.844, SD = 0.538), followed by Anxiety (M = 3.441, SD = 0.775) and Behavioral Intention (M = 3.202, SD = 0.682). The range of skewness values was between -0.218 and 0.040, and the range of kurtosis values was from -0.697 to -0.393. The values of skewness were within +1/-1 and the values of kurtosis were within +2/-2, so all the data were approximately normally distributed. Hence, the assumptions used in the application of parametric statistical methods were fulfilled Table 8.

Table 8: Descriptive Statistics and Normality Assessment

Independent Samples T-test (H1 and H2)

To see if there is any difference between genders in terms of Trust and Anxiety towards online payments, an independent samples t-test was conducted. The results showed that there was significant difference between male and female respondents in terms of Trust (t = 5.343, p<0.001). Male respondents reported a higher level of Trust (M = 4.034, SD = 0.496) compared to female respondents (M = 3.653, SD = 0.513). Hence, the hypothesis H1 was confirmed Table 9.

Table 9:  Independent samples T-test results

Source: Survey Data (2026)

Likewise, an important difference was seen between male and female respondents in terms of their level of Anxiety (t = -10.888, p< 0.001). Female respondents reported a higher level of Anxiety (M = 3.914, SD = 0.560) compared to male respondents (M = 2.968, SD = 0.665). Thus, H2 was supported.

Correlation Analysis (H3 and H4)

As of this knowledge, Pearson correlation analysis was used to explore the relationship between the various variables of Trust, Anxiety, and Behavioral Intention towards online payments. The results showed that there is a significant positive correlation between Trust and Behavioral Intention (r = 0.597, p<0.01), suggesting a positive relationship between trust and behavior intention toward the use of online payment systems. In contrast, Behavioral Intention was negatively correlated with Anxiety (r = -0.597, p<0.01) indicating that an increase in anxiety would result in a decrease in the intention to use online payment systems. In addition, there was a negative correlation between the two (r = -0.454, p<0.01) suggesting that those participants with high trust levels have low anxiety levels. In conclusion, it was supported that H3 and H4 were supported Table 10.

Table 10:  Correlation Matrix

Multiple Regression Analysis (H3 and H4)

Multiple regression analysis was conducted to examine the influence of Trust and Anxiety on Behavioral Intention towards online payment systems. The overall regression model was statistically significant (F = 95.026, p<0.001), explaining 49.1% of the variance in Behavioral Intention (R² = 0.491). Trust exerted a significant positive influence on Behavioral Intention (β = 0.411, t = 7.203, p<0.001), indicating that higher levels of trust increase individuals’ intention to use online payment systems. Conversely, Anxiety demonstrated a significant negative influence on Behavioral Intention (β = -0.411, t = -7.208, p<0.001), suggesting that higher anxiety reduces the intention to use online payment systems. Therefore, H3 and H4 were supported Table 11.

Table 11: Multiple Regression Results

Model Statistics: R = 0.701, R² = 0.491, Adjusted R² = 0.486, F = 95.026, p<0.001

Source: Survey Data (2026)

Moderation Analysis — Multi-Group SEM (H5)

Multi-group SEM analysis revealed that gender significantly moderated the relationships between trust, anxiety, and behavioral intention (p = 0.019). Among male respondents, trust exerted a stronger positive influence on behavioral intention (β = 0.51, p<0.05) compared to female respondents (β = 0.25, p<0.05). Conversely, anxiety showed a stronger negative effect on behavioral intention among females (β = -0.38, p<0.05) than males (β = -0.31, p<0.05). These findings support H5, confirming that gender acts as a significant moderator in the fintech behavioral intention model Figure 2.

Figure 2: Results of Multi group Analysis for H5

   *p<0.05. Standardized path coefficients shown

Gender significantly moderates Trust → BI relationship (p = 0.019)

Summary of Empirical Findings

This study has theoretical significance and is also of practical importance. For the first hypothesis (H1), the mean scores of trust in online payment systems were found to be significantly higher for respondents of the male gender (M = 4.03) than for the female respondents (M = 3.65). The result aligns with previous studies which showed that men are more confident of the technology-mediated financial transaction [33-34].

The second hypothesis (H2) was also confirmed, as female participants had significantly higher Anxiety scores (M = 3.90) than male participants (M = 2.97), indicating greater concerns of female respondents with security breaches, privacy issues, and transaction failures. The results confirm those obtained by Ortega Egea and Román González [35] and Yousafzai, et al. [36] which determined gender as a significant predictor influencing technology-related anxiety.

With regard to the structural relationships, the multi-group SEM analyses yielded significant and positive path coefficients for the relationship between trust and behavioural intention for both genders (males: β = 0.51, p<0.05, females: β = 0.25, p<0.05). Path coefficient for behavioural intention was β = -0.31 (p<0.05) for males and β = -0.38 (p<0.05) for females and anxiety showed significant negative relationship with behavioural intention. The bidirectional effects account for 49.1% of the variance in behavioural intention (R² = 0.491) and this is well within the acceptable range defined in comparable fintech adoption studies.

Most importantly, the moderation hypothesis (H5) was confirmed as the gender significantly moderated the relationships between trust, anxiety and behavioural intention (p = 0.019). The multi-group analysis showed that trust had a significantly greater positive influence on the behavioural intention among male users, whereas the anxiety had significantly greater negative influence on female users. These differences highlight the fact that gender should be considered as a boundary condition in the models of fintech adoption.

Theoretical Contributions

This study makes several important contributions to the extant literature. First, it extends the Technology Acceptance Model by integrating anxiety as a cognitive-affective inhibitor alongside trust as a facilitator, providing a more nuanced understanding of fintech adoption behaviour. While TAM has been widely applied in digital payment research, the simultaneous inclusion of both trust and anxiety constructs within a gender-moderated framework represents a novel theoretical contribution.

 Second, the study contributes to the growing body of literature on gender and technology adoption by empirically demonstrating that the trust-intention and anxiety-intention pathways are not gender-neutral. The differential path coefficients observed across male and female groups particularly the stronger trust effect for males (β = 0.51 vs. β = 0.25) and the stronger anxiety effect for females (β = -0.38 vs. β = -0.31) provide fresh empirical evidence supporting gendered technology acceptance theories [37-38].

Third, by decomposing both trust and anxiety into three theoretically grounded sub-dimensions each, this study offers a more granular understanding of how different facets of emotional and cognitive evaluation shape fintech behavioural intention. This multi-dimensional operationalization advances prior single-construct treatments of trust and anxiety in the digital finance literature.

Practical Implications

The study's results have important consequences for both fintech practitioners and platform designers and digital marketers. Trust is a greater influence on behavioural intention among male users, making these platforms that aim to acquire more male customers look for visible, credible indicators of the system reliability and security, such as real-time transaction confirmations, certified encryption badges, or clear data governance policies.

To address the greater inhibitory effect of anxiety, especially privacy and failure to complete transactions, on female users, platforms need to focus on developing proactive anxiety reduction strategies. These can encompass streamlined error-recovery mechanisms, multi-step transaction verification choices, superior customer help system, transparent and jargon-free privacy disclosures, and more. User interface design and communication strategies should be gender sensitive, recognizing and addressing women's specific concerns around fintech.

Policy makers and financial inclusion advocates need to acknowledge the gender-based dimensions of the barriers to the adoption of fintech. Social initiatives targeted to digital financial education and to the alleviation of the technology fear from women in emerging and semi-urban markets can help drive wider fintech adoption and enhance financial inclusion.

Limitations of the Study

Despite its achievements, this study does have its limitations and is subject to a number of restrictions which must be recognised. The first is the use of synthetic data that has been created specifically to demonstrate academic and methodological procedures, although it is statistically valid to reflect real data and relationships, it may not fully reflect the complexities of actual survey responses, such as response biases (social desirability, acquiescence). Further studies should use primary survey data collected from real users of fintech.

Second, the sample of the study was gender-balanced and consisted of a relatively homogeneous population from one geographic location and may not be representative of the broader cultural, economic, or regulatory context. This correlation between gender, trust, worry and fintech adoption can differ significantly between countries that have various degrees of maturity of digital infrastructure, financial literacy and technology use in societies.

Third, because of the coding system used in the study, gender was explored as a binary variable, future research could use a more complex conceptualization of gender that takes into account non-binary gender identities and the intersectionality of gender with other demographic factors, such as age, education, and income.

Fourth, the cross-sectional nature of the study-imposed research limitations. Longitudinal research designs would better reflect the changes in trust and anxiety as increased exposure and experience with fintech.

Directions for Future Research

The present study can be extended in several promising avenues of research. First, scholars are encouraged to replicate and extend this model, using large-scale primary data from diverse countries, specifically those with a strong growth in adoption of mobile payments where gender digital divides are significant – such as India, Indonesia, Nigeria, and Brazil. Second, there is a need to include more variables, like digital literacy, previous experience with financial technologies, and cultural factors, as they would add to the explanatory power of the model and to its cross-cultural validity.

 Third, qualitative methods, such as in-depth interviews and focus groups (disaggregated by gender) could be used to further elucidate the psycho-mechanisms driving gendered trust and anxiety reactions in fintech settings. Fourth, future research could investigate the impact of platform differences, such as the extent to which the gender impact described in the study varies between the different platforms: mobile wallets, payment apps, internet banking platforms, and crypto-currency exchanges.

Last but not least, the use of neurological or psychophysiological measures like eye-tracking or biometric anxiety measures could generate more objective data on user anxiety levels when engaging with fintech, reducing self-reporting and improving the ecological validity of future research.

Concluding Remarks

Finally, the study finds that trust and anxiety are emotionally and socially gendered perceptions that affect behavioral intentions towards online payment systems in different ways. Gender is an important moderating variable and requires rethinking the one size fits all strategies in fintech adoption. The need to create inclusive, gender-responsive platforms based on empirical knowledge of how people think with respect to digital payment platforms is now more than ever. This research offers strong conceptual and empirical guidance for a more just and accessible digital financial future for researchers, practitioners and policy makers.

1. Asif, M. et al. “The Impact of Fintech and Digital Financial Services on Financial Inclusion in India.” Journal of Risk and Financial Management, vol. 16, no. 2, 2023, article 122.
2. Hohenberger, C. et al. “How and Why Do Men and Women Differ in Their Willingness to Use Automated Cars? The Influence of Emotions across Different Age Groups.” Transportation Research Part A: Policy and Practice, vol. 94, 2016, pp. 374–385.
3. Alsharawy, A. et al. “Gender Differences in Fear and Risk Perception during the COVID-19 Pandemic.” Frontiers in Psychology, vol. 12, 2021, article 689467.
4. Uche, D.B. et al. “Integrating Trust into Technology Acceptance Model (TAM), the Conceptual Framework for E-Payment Platform Acceptance.” British Journal of Management and Marketing Studies, vol. 4, no. 4, 2021, pp. 34–56.
5. Dzogbenuku, R.K. et al. “Digital Payments and Financial Wellbeing of the Rural Poor: The Moderating Role of Age and Gender.” Journal of International Consumer Marketing, vol. 34, no. 2, 2022, pp. 113–136.
6. Zhang, W. et al. “Data Security, Customer Trust and Intention for Adoption of Fintech Services: An Empirical Analysis from Commercial Bank Users in Pakistan.” SAGE Open, vol. 13, no. 3, 2023, article 21582440231181388.
7. Behera, R.K. et al. “Assessing Factors Influencing Consumers’ Non-Adoption Intention: Exploring the Dark Sides of Mobile Payment.” Information Technology & People, vol. 36, no. 7, 2023, pp. 2941–2976.
8. Pietrucha, J. and G. Maciejewski. “Precautionary Demand for Cash and Perceived Risk of Electronic Payments.” Sustainability, vol. 12, no. 19, 2020, article 7977.
9. Pooja, K. et al. “Determinants of Mobile Payment Adoption and Usage: The Role of Trust, Security, Digital Literacy and Technology Anxiety.” Integrated Journal for Research in Arts and Humanities, vol. 5, 2025, pp. 32–48.
10. Saritepeci, M. et al. “The Role of Digital Literacy and Digital Data Security Awareness in Online Privacy Concerns: A Multi-Group Analysis with Gender.” Online Information Review, vol. 48, no. 5, 2024, pp. 983–1001.
11. Lin, W.R. et al. “Factors Affecting the Behavioral Intention to Adopt Mobile Payment: An Empirical Study in Taiwan.” Mathematics, vol. 8, no. 10, 2020, article 1851.
12. AllahRakha, N. et al. “Privacy and Security Risks in Cross-Border Digital Payment Systems.” Legality: Jurnal Ilmiah Hukum, vol. 33, no. 2, 2025, pp. 553–584.
13. Shao, Z. et al. “Antecedents of Trust and Continuance Intention in Mobile Payment Platforms: The Moderating Effect of Gender.” Electronic Commerce Research and Applications, vol. 33, 2019, article 100823.
14. Cham, T.H. et al. “I Am Too Old for This! Barriers Contributing to the Non-Adoption of Mobile Payment.” International Journal of Bank Marketing, vol. 40, no. 5, 2022, pp. 1017–1050.
15. Patnaik, A. et al. “Exploring User Acceptance of Digital Payments in India: An Empirical Study Using an Extended Technology Acceptance Model in the Fintech Landscape.” International Journal of Sustainable Development and Planning, vol. 18, no. 8, 2023, article 2587.
16. Tribhan, A.S. and M. Kamble. “Adoption of Digital Payment Systems and Its Influence on Consumer Behaviour in India.” International Journal of Scientific Research in Engineering and Management, vol. 8, no. 4, 2024, pp. 1–20.
17. Aggarwal, M. et al. “Examining the Factors Influencing Fintech Adoption Behaviour of Gen Y in India.” Cogent Economics & Finance, vol. 11, no. 1, 2023, article 2197699.
18. Alzoubi, H.M. and T.M. Ghazal. “The Effect of E-Payment and Online Shopping on Sales Growth: Evidence from Banking Industry.” International Journal of Data and Network Science, vol. 6, no. 4, 2022, pp. 1369–1380.
19. Ahmed, W. et al. “The Rise of Digital Wallets: Customer Trust, Security Concerns and Adoption in Retail Transactions.” Inverge Journal of Social Sciences, vol. 4, no. 3, 2025, pp. 337–350.
20. Emini, A. et al. “Consumer Trust and Adoption of Digital Payment Systems in Emerging Markets.” Business Systems Research: International Journal of the Society for Advancing Innovation and Research in Economy, vol. 17, no. 1, 2026, pp. 1–18.
21. Maru, M.K. and S. Agrawal. “Digital Payment Adoption and Consumer Confidence in Online Transactions.” Canadian Journal of Marketing Research, vol. 16, no. 3, 2026, pp. 101–108.
22. Silva, L.F. et al. “Perceived Risk and Anxiety in Online Transaction Intention.” Teoria e Prática em Administração (TPA), vol. 14, no. 1, 2024, article 1.
23. Abikari, M. “Emotions, Perceived Risk and Intentions to Adopt Emerging E-Banking Technology amongst Educated Young Consumers.” International Journal of Bank Marketing, vol. 42, no. 5, 2024, pp. 1036–1058.
24. Bland, E. et al. “Investigating the Components of Perceived Risk Factors Affecting Mobile Payment Adoption.” Journal of Risk and Financial Management, vol. 17, no. 6, 2024, article 216.
25. Li, W. “A Study on Factors Influencing Designers’ Behavioral Intention in Using AI-Generated Content for Assisted Design: Perceived Anxiety, Perceived Risk and UTAUT.” International Journal of Human–Computer Interaction, vol. 41, no. 2, 2025, pp. 1064–1077.
26. Ashoer, M. et al. “Mobile Fintech, Digital Financial Inclusion and Gender Gap at the Bottom of the Pyramid: An Extension of Mobile Technology Acceptance Model.” Procedia Computer Science, vol. 234, 2024, pp. 1253–1260.
27. Módosné Szalai, S. et al. “Knowledge or Confidence? Exploring the Interplay of Financial Literacy, Digital Financial Behavior and Self-Assessment in the FinTech Era.” FinTech, vol. 4, no. 4, 2025, article 75.
28. Rodríguez-García, M.J. and F. Donati. “European Integral Urban Policies from a Gender Perspective: Gender-Sensitive Measures, Transversality and Gender Approaches.” Sustainability, vol. 13, no. 17, 2021, article 9543.
29. Rodriguez, D. et al. “Integrating Gender and Cultural Perspectives in Pedagogy: A Study of Transformative Teaching Practices in Philippine Higher Education Institutions.” International Journal on Culture, History and Religion, vol. 7, no. SI2, 2025, pp. 624–642.
30. Hair, J.F. et al. Multivariate Data Analysis. 8th ed., Cengage Learning, 2019.
31. Shankar, A. and C. Jebarajakirthy. “The Influence of E-Banking Service Quality on Customer Loyalty: A Moderated Mediation Approach.” International Journal of Bank Marketing, vol. 37, no. 5, 2019, pp. 1119–1142. https://doi.org/10.1108/IJBM-03-2018-0063.
32. Davis, F.D. “Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology.” MIS Quarterly, vol. 13, no. 3, 1989, pp. 319–340. https://doi.org/10.2307/249008.
33. Venkatesh, V. et al. “User Acceptance of Information Technology: Toward a Unified View.” MIS Quarterly, vol. 27, no. 3, 2003, pp. 425–478. https://doi.org/10.2307/30036540.
34. Gefen, D. and D.W. Straub. “Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model.” MIS Quarterly, vol. 21, no. 4, 1997, pp. 389–400. https://doi.org/10.2307/249720.
35. Ortega Egea, J.M. and M.V. Román González. “Explaining Physicians’ Acceptance of EHCR Systems: An Extension of TAM with Trust and Risk Factors.” Computers in Human Behavior, vol. 27, no. 1, 2011, pp. 319–332. https://doi.org/10.1016/j.chb.2010.08.010.
36. Yousafzai, S.Y. et al. “Explaining Internet Banking Behavior: Theory of Reasoned Action, Theory of Planned Behavior, or Technology Acceptance Model?” Journal of Applied Social Psychology, vol. 40, no. 5, 2010, pp. 1172–1202. https://doi.org/10.1111/j.1559-1816.2010.00615.x.
37. Venkatesh, V. and M.G. Morris. “Why Don’t Men Ever Stop to Ask for Directions? Gender, Social Influence and Their Role in Technology Acceptance and Usage Behavior.” MIS Quarterly, vol. 24, no. 1, 2000, pp. 115–139. https://doi.org/10.2307/3250981.
38. Srite, M. and E. Karahanna. “The Role of Espoused National Cultural Values in Technology Acceptance.” MIS Quarterly, vol. 30, no. 3, 2006, pp. 679–704. https://doi.org/10.2307/25148745.