Financial Literacy in the Digital era: The role of Technology and Innovation in Transforming Investment Decisions

INTRODUCTION

The financial sector has transformed significantly due to the new paradigm of Industry 4.0, which is based on emerging technologies such as Artificial Intelligence (AI), cyber-physical systems, and Internet of Things (IoT). Gunasekaran et al. (2024), Bai et al. (2022) and subsequently has created the phenomenon of financial technologies (FinTech), which provide new tools and innovations to enable individuals in managing their personal finances and facilitate investment decisions Islam and Khan (2024). Robo-advisors and digital wallets are lowering the cost and complexity of financial services as these technologies enable people to manage their investments. However, the eventual success of those services will depend mainly on the users’ FL and the education that surrounds digital financial competences Ariwangsa et al. (2024). Additionally, Lal et al. (2025) identifies three drivers of DFL being economic resources, educational background, and digital involvement, and also argues that inequality in access to digital resources may magnify distinctions in levels of financial decision-making. These findings support the need to investigate FL not as discrete and isolated rather as a function of digital competencies that influence the investment behavior in the digital age.

The rapid growth in digital technology is changing the financial landscape and allowing individuals to access information and financial services Darwish et al. (2025). Hassan et al. (2024) demonstrate that Fintech self-efficacy moderates the FL investment behavior, implying that literacy and digital confidence together shape the technology based investment choices. Bai et al. (2023) reveals that FL contributes to investment decision making and thus enhances wellbeing, while Luo et al. (2023) draw on micro-level evidence from China to show that the FL of households increases the likelihood of profit in equity investments. Mishra et al. (2024) demonstrate that women who possess a high level of DFL will be more likely to adopt Fintech services and make better investment decisions, illustrating how DFL can lessen gender disparities in financial inclusion.

Xie and Chen (2024) also indicated, households with a high level of DFL are more likely to be entrepreneurs and investors, underscoring the larger societal economic impacts of being digitally competent. Similarly, Olajide et al. (2024) examined multiple generational differences on usage of social media for investment advice, and found that younger generations are more influenced by digital content for investment decisions, which impacts their financial satisfaction. These studies demonstrate the influence of both formal and informal digital networks in shaping investment choices. Along with these transformations, individuals must learn to comprehend and effectively manage financial products and platforms. Although technology is widely available, an inadequate FL and behavioral biases can still lead to adverse financial outcomes Aftab et al. (2025). Therefore, FL is needed to minimize risks associated with digital tools, leading to more prudent financial decisions. Individuals who possess greater FK are better able to analyze risks, diversification opportunities, and evade fraudulent schemes.

The study is focused on examining the impact of technology and innovation in enhancing FL in the digital era and transforming investment decisions among private higher educational teachers in Bengaluru urban. As the academic space continues to change, the role of the teacher is not just delivering static knowledge, rather developing the digital and financial skills to navigate complex information system. Therefore, studying their perception becomes essential.

Review of Literature

Technology and Innovation in Enhancing FL

Technology and innovation have emerged as fundamental drivers to enhance FL in this digital era. For example, Digital Financial Literacy (DFL), extends existing knowledge and skills of FL to locate and tailor digital financial services, and is increasingly referred to as a necessity for effective financial decision-making Khatri et al. (2025). In addition, an empirical study supported the mediating effects of DFL on financial decision making. Kumar et al. (2022), describes DFL as a component of individual financial wellness, and suggests that collaborative educational and policy responses are necessary. Technological advances, such as fintech, have been demonstrated to improve accessibility to budgeting tools, expense tracking, and savings habits Cahyono and Susbiyani (2025). The following discussions indicate the effects of fintech and digital tools on personal finance management behavior. Waliszewski and Warchlewska (2020) concluded that social-economic characteristics were significantly impacted the satisfaction level in AI-based financial planning, while Gautam et al. (2022) reported the fintech adoption through credit card schemes that were positively correlated to digital literacy, especially in contexts with unskilled populations. In accordance with this, Zhang and Fan (2024) reported that mobile FinTech usage can enhance financial well-being when paired with adequate literacy levels. Researchers have also shown that simulation-based learning methods sustain student interaction and retention of the information around financial concepts more effectively than traditional instruction Belgacem et al. (2024). Using the Technology Acceptance Model (TAM), Jariyapan et al. (2022) found that perceived usefulness is the most significant predictor of behavioral intention to adopt crypto currencies, with FL and perceived risk being significant explanatory factors. Despite the growing body of literature, there is still inadequate study on the combined effects of technology and innovations on FL within the digital age, particularly in the context of private higher education teachers in Bengaluru City. To address this gap, the hypothesis considered in this study is:

H1: Technology and innovation is positively associated in enhancing FL in the digital era.

Impact of FL on Investment decisions

The advancements of digital technologies, along with behavioral finance, have created decision-making determinants beyond the FL components, such as attitudes, risk tolerance, and technology-based platforms. Seraj, Alzain, and Alshebami (2022) found that FL significantly improved investor decisions, particularly when moderated by overconfidence of investors. Andersson et al. (2023) revealed that FK, when mediated by behavioral finance variables, helps capital market investors make informed decisions. Rahmiyati and Somodiharjo (2025) expanded on this study and confirmed evidence of a relationship between FL, behavioral biases, and individual investment performance in Indonesia. Innovative methods were also used to further support the relationship. Jariyapan et al. (2022) emphasized the need of FL in the high-risk, technology-driven investment space. Kumar et al. (2023) explored the behavioral, psychological, and demographic-based factors of household investment decisions, offering evidence that digital financial literacy (DFL) serves as a partial moderator between financial capability and decision-making and included psychological and behavioral aspects of investment decisions for a sustained financial future. Likewise, Aisa (2021) also investigated FL and the use of technology-based investment platforms on students’ intention to invest in the capital market. Both FL and the usage of the investment platform were determined to have a significant relation to investment intentions, and it was concluded that early use of financial investment tools would prompt students to actively engage with financial decision-making. Likewise, Zaimovic et al. (2023) systematically reviewed the relationship between FL and FB by highlighting DFL as a growing determinant of improved decision-making. Stressing these digital investment preparedness gaps, Yeo et al. (2023) extended the literature by introducing behavioral finance concepts to the Theory of Financial Planning Behavior (TFPB) within a descriptive contextual model that provides a more accurate account of investment decision-making across diverse contexts. The existing work has explored the impact of FL on investment decisions; limited studies have investigated this relationship in the context of technology and innovation-driven improvements in FL. There is a need to examine how technology-enabled advancements in FL lead to informed investment decisions. Thus, the study postulates the following hypothesis:

H2: FL significantly influences investment decisions.

Conceptual Framework

Research Methodology

The present study adopts the framework of descriptive and explanatory research design. The descriptive design was employed to analyze the demographic characteristics and the explanatory design was used to examine the effect of Technology and Innovation on FL and its impact on ID. The population of the study comprised teachers working in private higher educational institutions in Bangalore Urban. Using non- probability convenience sampling, a total of 128 respondents were selected to represent the study sample. Data was collected with a structured questionnaire that included three items measuring Technology and Innovation, Financial Literacy, and Investment Decisions. Data was analyzed using SPSS Version 26: The reliability of the questionnaire was measured using “Cronbach’s alpha”, descriptive statistics to estimate mean and variability for the demographic data and Regression Analysis to validate the association among variables.

Results

Reliability Results

Table 1

Table 1 Reliability Statistics for the Overall measure
Reliability Statistics
Cronbach's Alpha	N
0.93	29
Source: SPSS 26

Table 1 presents the internal reliability of the scale. The overall reliability of the 29 items indicates Cronbach’s Alpha of .930, validating strong internal consistency. In general, Cronbach’s Alpha above .70 demonstrates the stated variables are good to use. Nunnally (1975)

Table 2

Table 2 Reliability Statistics for Individual Constructs
Constructs	Reliability Statistics
	Cronbach's Alpha	N
Financial Literacy	.826	5
Technology and Innovation	.914	10
Investment Decisions	.893	14
Source: SPSS 26

Table 2 outlines the reliability scale per construct. The Cronbach’s Alpha for Financial Literacy (5 items) = .826; Technology and Innovation (10 items) = .914; and Investment Decisions (14 items) = .893 results show the items in each construct are highly consistent and reliable.

Table 3

Table 3 Demographic Profile
Demographic Profile		Frequency	Percent
	< 30 years	22	17.2
	31-40 years	64	50
	41-50 years	30	23.4
Age	> 50 years	12	9.4
	Male	44	34.4
Gender	Female	84	65.6
	Post Graduate	96	75
Educational Qualification	Doctorate	30	23.4
	Post-Doctoral	2	1.6
Years of Work Experience	< 5 years	14	10.9
	6-10 years	44	34.4
	11-15 years	32	25
	16-20 years	20	15.6
	>20 years	18	14.1
	Unmarried	22	17.2
Marital Status	Married	102	79.7
	Divorced	4	3.1
	Nuclear	72	56.3
Type of family	Joint	46	35.9
	Single Parent	10	7.8
	< 5lakh	42	32.8
Annual income	5 to 10 lakh	64	50
	10 Lakh to 15 lakh	12	9.4
	>15 lakh	10	7.8
Note: N = 128

Table 3 summarizes the demographic profile of the respondents.

A total of 128 respondents represented demographic characteristics. The sample shows that the majority (50%) was aged 31-40 years, 23.4% were 41-50 and 9.4% are above 50 years. The gender distribution includes 65.6% female and 34.4% male. Regarding education, 75% hold post graduate, 23.4% are with Doctorate and 1.6% post-doctoral. In terms of work experience, 34.4% have 6-10 years, 25% with 11-15 years and 10.9 less than 5 years. 79.7% of the respondents are married, and 56.3% were in a Nuclear Family. Income level indicates 50% of them earn between 5 to 10 lakhs, 32.8% below 5 lakhs, and 7.8% above 15 lakhs.

Table 4

Table 4 Descriptive Statistics
Constructs	Descriptive Statistics
	No of items	Mean	Std. Deviation
		Statistic	Statistic
Financial Literacy	5	3.625	.81289
Technology and Innovation	10	3.1578	.83446
Investment Decisions	14	3.7857	.61908
Output: SPSS 26, Note* N=128

The descriptive statistics of the constructs are presented in Table 4 FL, measured with the scale of 5 items, shows a mean of 3.63 (SD = 0.81), suggesting moderate financial knowledge among the respondents. Technology and Innovation, based on the set of 10 constructs has a mean of 3.16 (SD = 0.83), indicating moderate average with more spread in responses compared to Financial Literacy. Investment Decisions measured with the set of 14 items, had the highest mean of (M = 3.79, SD = 0.62) with lower variability.

Regression Analysis

Table 5

Table 5 Technology and Innovation in Enhancing FL in the Digital Era
Coefficients^a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	2.049	0.243		8.419	0
	Technology and Innovation	0.499	0.075	0.512	6.696	0
Dependent Variable: Financial literacy

Table 5 demonstrates the regression results. The regression analysis studied the effect of Technology and Innovation on Financial Literacy. In which financial literacy is taken as the dependent variable, technology and innovation as the independent variable. The results reveals that Technology and innovation is significantly associated with Financial Literacy (B = 0.499, t = 6.696, p < .001.The standardized coefficient (β = .512) indicates the increase in Technology and Innovation will likely improve Financial Literacy levels.

Table 6

Table 6 Impact of FL on Individuals ID
Coefficients^a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	2.253	0.21		10.749	0
	Financial Literacy	0.423	0.056	0.555	7.494	0
Dependent Variable: Investment Decisions

The model shows Investment decision as a dependent variable and financial literacy as a predictor. The results from regression analysis indicates a strong positive influence of FL on Investments decisions (B = .423, t = 7.494, p < .001). The standardized coefficient (β = .555) reveals that respondents with higher level of financial literacy leads to a sound investment decisions.

Overall, the regression results provide evidence for the proposed hypotheses, showing the important role of Technology and Innovation on improving Financial Literacy, thereby exerting a direct effect on an individual's investment decision.

Table 7

Table 7 Hypotheses Testing Results
Hypotheses	Relationship Tested	Standardized Beta (β)	t-value	Sig. (p-value)	Conclusion
H1	Technology and Innovation ➝ Financial Literacy	0.512	6.696	0	Accepted
H2	Financial Literacy ➝ Investment Decisions	0.555	7.494	0	Accepted
Source: Authors Computation

The hypotheses results are presented in Table 7 Technology and Innovation have a significant impact in increasing financial literacy in the digital age (β: .512, t-value: 6.696, p-value: .000). Hence H1 is supported. Similarly Financial Literacy significantly influences Investment decisions (β: .555, t-value: 7.794, p-value: .000) supporting H2.

Discussion

The study examined financial literacy in the digital era and investigated the role of technology and innovation in shaping investment decisions among private higher educational institutions teachers in Bengaluru urban. The findings obtained from a regression analysis indicates the significance of Technology and Innovation in enhancing FL, suggesting that digital tools support individuals in comprehending and managing financial ideas. This aligns with the work of Ferilli et al. (2024) where DFL is claimed to be a key factor in enhancing financial well-being and promoting effective decision making. Moreover, Furinto et al. (2023) states that the development of FL was successful and the fintech aspects were a major element in contributing to the improvements. Moreover, the results suggest that Technology and innovation is significantly related to FL, and these findings are consistent with recent works, namely, Ariwangsa et al. (2024) found FL as a main influencer of investment decisions made by small and medium businesses, with technology representing a moderating aspect to strengthen the relationship between FL and ID making. Futhermore, Raut and Kumar (2023) explains that an individuals who has a high FL, can be characterized as a rational investor, as they evaluates risk and return before making any investment decision. Overall, the discussion emphasizes that increasing financial knowledge using technology can support better financial conduct and sustainable investments.

Conclusion

In the present study, the emphasis was made on three important variables- Technology and Innovation, FL and ID. The evidence suggests that teachers in private higher educational institutions with increased access to digital equipment and technological advances can enhance their financial literacy and make better informed investment decisions. The adoption of financial tools and applications is essential as teacher serves as a learner and also a facilitator in enhancing the next generation to be more rational and sustainable in their financial choices. The study highlights the need for educational institutions to conduct practical- oriented sessions for teachers on the integration of technology and innovation in the area of financial literacy. The government should initiate subsidized access to digital tools, e-learning platforms, and financial planning software for teachers to support continuous learning.

The study is limited to the teachers of private higher educational institutions in Bengaluru urban. The relationship between the study’s variables was tested using regression analysis. Future studies can explore the mediating role of FL between technology and ID across different regions. This can provide a better understanding of the relationship between digital innovations and financial knowledge in affecting investment behavior.

ACKNOWLEDGMENTS

None.

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