Investment Intentions Among Early-Career Professionals in Dakshina Kannada District in India: A Behavioral Perspective

Abstract

This study investigates the key factors influencing investment intentions among early career professionals in Dakshina Kannada, focusing on attitudes, subjective norms, perceived behavioural control (PBC), self-efficacy, and investment intention. A quantitative survey method was adopted involving 220 participants selected through stratified random sampling across five talukas: Mangaluru, Puttur, Moodabidre, Sullia, and Mulki. The data collected via both offline and Google Forms included variables related to investment behaviour and demographics. Descriptive statistics were used to summarise participants’ profiles, and Smart PLS was employed to test the proposed hypotheses and explore variable relationships, including the mediating role of self-efficacy. The findings reveal that attitudes, subjective norms, and PBC significantly influence investment intentions. Moreover, self-efficacy partially mediates the link between PBC and investment intentions, highlighting the importance of confidence in managing financial decisions. This study underscores the value of financial literacy and personal beliefs in investment capabilities as drivers of investment behaviour. The practical implications suggest that financial education programs should aim to build both knowledge and confidence. Additionally, leveraging social influence and improving access to investment resources can enhance investment engagement. This study offers valuable insights for educators, policymakers, and financial institutions aiming to foster investment among young professionals.

Introduction

Early-career investment decisions play a pivotal role in shaping an individual's long-term financial security. Understanding the behavioural factors that influence these decisions is essential, as recent research shows that these choices are shaped by psychological and social elements, in addition to logical models [1]. Key behavioural constructs, such as Attitude Toward Investment (Attitude Towards Investment), Subjective Norms (Subjective Norms), perceived behavioural control (Perceived Behavioural Control), and self-efficacy, have been identified as major determinants of investment intentions. A positive attitude indicates a favourable view of investing [2], whereas subjective norms represent social influences from peers and family [3]. Perceived Behavioural Control refers to confidence in one's ability to make investment decisions, particularly in uncertain financial contexts [4]. Self-efficacy, or belief in one's investment abilities, is also a critical factor that may mediate the relationship between Perceived Behavioural Control and investment intention [5]. Despite these insights, there remain gaps in the understanding of how these factors vary across demographics and interact with one another. This study proposes a framework in which attitude, subjective norms, and Perceived Behavioural Control positively influence investment intention and self-efficacy mediates the effect of Perceived Behavioural Control. The findings aim to inform financial literacy programmes and provide practical guidance for advisors and policymakers working with early career professionals.

Literature Review

As an increasing number of people start using online platforms to invest their money, confidence in using these platforms has become very important. This confidence is referred to as self-efficacy. This indicates how sure a person is about handling their own finances. Individuals who believe in their ability to manage money are more likely to make better investment decisions [6]. Not just money skills but knowing how to use digital tools also matters. This phenomenon is known as 'technological self-efficacy'—how comfortable someone is using apps and websites for investing [7, 8]. Studies have also shown that digital skills and the quality of online services directly affect how interested people, especially millennials, are in investing online. When they trust the platform and know how to use it, they are more likely to invest in it [9]. Therefore, if we want more people to use digital investment platforms confidently, we must help them improve their financial and digital confidence. Such confidence will lead to the better use of these platforms and smarter financial decisions. For early-career professionals, attitudes are shaped by initial exposure to financial education or work-related financial incentives, which foster positive investment attitudes. Subjective Norms play an essential role in investment decisions, especially when influenced by trusted sources such as family, peers, or mentors. In collectivist societies such as Dakshina Kannada, subjective norms have a stronger influence, reinforcing social pressures in shaping investment intentions. Perceived Behavioural Control has been identified as a beneficial predictor of investment intention. Professionals with necessary resources, tools, and knowledge are more likely to invest. However, this control requires self-efficacy, which enhances confidence in navigating the investment decisions. Self-efficacy acts as a mediator between Perceived Behavioural Control and investment intention, influencing whether individuals are capable of making effective investment decisions [8]. Studies highlight that self-efficacy improves investment behaviour by fostering beliefs about one's ability to manage financial complexities. Financial literacy shapes self-efficacy and mitigates risk perception. Early-career professionals' financial knowledge and confidence are pivotal in promoting investment behaviours. As more people start using digital investment platforms, it is important for them to feel confident in using these tools. Confidence, known as self-efficacy, plays a major role in how people make financial decisions and whether they choose to invest [6]. When early-career professionals feel financially and technologically confident, they are more likely to use digital platforms effectively and make smart investment choices. Researchers have also found that beliefs about investment (attitude), what others think (social norms), control over investment decisions, and personal confidence (self-efficacy) all work together to shape investment intentions. To help more people invest wisely, we must improve financial knowledge, build confidence, and encourage social encouragement [7].

Theoretical Foundation

The theory of planned behaviour (TPB) is a popular way to understand why people decide to invest. Many studies have shown that when people have a positive attitude towards investing— such as wanting to secure their future or feeling good about saving money—they are more likely to invest. For example, Hassan et al. [10] found that among Muslim investors in Malaysia, a positive attitude, along with support from others (subjective norms) and confidence in their ability (perceived control), strongly affected their investment choices. Similarly, Khan et al. [11] showed that financial education and confidence in handling money (financial self-efficacy) help people take investment plans more seriously. Another study by Baihui et al. [12] examined students and found that their attitudes, social pressure, and self-belief mattered when deciding to invest in online money market funds. All of these studies indicate that TPB constructs—attitude, social influence, and confidence—work together to shape how people think and act about investing. Encouraging a positive mindset and building a belief in one’s ability to invest can motivate more people to participate in financial activities. Extending TPB, the study also included the mediator self-efficacy (self-efficacy), derived from Bandura’s Social Cognitive Theory. An individual's self-efficacy is their belief in their ability to execute a task or behaviour. This construct is crucial for investment decisions because it accounts for the confidence people have in their capacity for financial decision-making, thus strengthening the TPB [8]. It is important to acknowledge that without confidence in one's abilities, a sense of control may not always translate into action.

Proposed Hypotheses

H₁: Attitude toward investment positively influences investment intention.

H₂: Subjective norms positively influence investment intentions.

H₃: Perceived behavioural control positively influences investment intentions.

H₄: Perceived behavioural control positively influences self-efficacy.

H₅: Self-efficacy positively influences investment intentions.

H₆: Self-efficacy mediates the relationship between perceived behavioural control and investment intention.

Figure 1 presents the proposed conceptual framework model.

Figure 1. Proposed Conceptual framework Model Proposed by Authors

Methodology

In this study, quantitative methodology was applied using a survey instrument. With 44 participants from each of the five talukas: Mangaluru, Puttur, Moodabidre, Sullia, and Mulki. Sample size was 220, chosen using a stratified random sampling method. Data were gathered using a questionnaire survey that was accessible both online and via Google Forms. Along with demographic questions on age, gender, education, and occupation, the survey included questions on AT, Subjective Norms, Perceived Behavioural Control, Self-Efficacy, and investment intentions. The demographic answers were compiled into frequencies to provide a general picture of the features of the sample. The hypotheses were tested, and the relationships between the variables, including the mediating function of self-efficacy, were investigated using Smart PLS. This approach efficiently gathers behavioural elements that influence investment choices for early-career professionals in the research area.

Results

Table 1 presents the key variables used in the study along with their definitions and supporting references.

Table 1. Variables used in the study Developed from literature review

Variable	Definition	Reference(s)
Attitude toward Investment (Attitude Towards Investment)	The extent of someone's holds a favourable or unfavourable evaluation of investing, shaped by perceived benefits and outcomes.	Hassan [10]; Baihui [12]
Subjective Norms (Subjective Norms)	Perception on social pressure from significant referents (e.g., family, friends) to either participate in or avoid investing.	Baihui [12]; Khan [11]
Perceived Behavioural Control (Perceived Behavioural Control)	Reflecting both internal capabilities and external resources, the perceived simplicity or difficulty of engaging in investment-related activity.	Sobaih & Elshaer [17]; Baihui [12]
Self-Efficacy (Self-Efficacy)	An individual's perception in their ability to make sound investment decisions and manage financial risks effectively.	Khan [11]; Hassan [10]
Investment Intention (Investment Intention)	The motivational readiness and willingness of an individual to engage in investment activities in the near future.	Baihui [12]; Raghubansha [2]

Demographic Profile of Participants

Table 2 presents the demographic profile of the 220 participants. This information is essential for understanding the background characteristics of the respondents, which may influence their perceptions, behaviours, and responses in the study.

Table 2. Frequency Table of Participant Demographics Primary data collected by the authors

Category	Subcategory	Frequency	Percentage (%)
Age	18–23 years	68	30.91%
	23–28 years	122	55.45%
	28+ years	30	13.64%
	Total	220	100%
Gender	Male	144	65.45%
	Female	76	34.55%
	Total	220	100%
Education Level	High School/PUC/Diploma	8	3.64%
	Undergraduate	146	66.36%
	Master's Degree	48	21.82%
	PhD	18	8.18%
	Total	220	100%
Occupation	Private Sector	122	55.45%
	Government Employee	53	24.09%
	Entrepreneur	18	8.18%
	Others	27	12.27%
	Total	220	100%

The majority were aged 23–28 years (55.45%), followed by those aged 18–23 years (30.91%). Most participants were male (65.45%), while females comprised 34.55%. In terms of education, the largest group held an undergraduate degree (66.36%), followed by those with a master’s degree (21.82%). Regarding occupation, the private sector had the highest representation (55.45%), followed by government employees (24.09%). Understanding these distributions helps contextualise the study's findings and assess the generalisability of the results.

Descriptive Statistics

Table 3 displays the descriptive and normality test results for the 14 items related to the five key constructs: attitude towards investing, subjective norms, perceived behavioural control, self-efficacy, and investment intention. For each item, the table includes the mean, standard deviation, skewness, and kurtosis values, which help to assess the central tendency, variability, and distribution shape of the responses. These indicators are important for evaluating the data quality and determining the appropriateness of parametric analyses.

Table 3. Descriptive Statistics Primary data analysis using SPSS 26

	Mean	Std. Deviation	Skewness		Kurtosis
	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
ATI1	3.70	0.960	-1.034	0.164	1.265	0.327
ATI2	3.70	0.979	-0.889	0.164	0.911	0.327
ATI3	3.68	0.930	-1.020	0.164	1.364	0.327
SN1	3.69	1.018	-1.029	0.164	1.065	0.327
SN2	3.67	0.985	-1.046	0.164	1.195	0.327
SN3	3.60	1.012	-1.003	0.164	1.034	0.327
PBC1	3.65	1.014	-1.074	0.164	1.161	0.327
PBC2	3.71	1.019	-0.950	0.164	0.886	0.327
PBC3	3.63	1.054	-0.801	0.164	0.520	0.327
SE1	3.63	1.014	-0.966	0.164	0.936	0.327
SE2	3.59	1.067	-0.890	0.164	0.625	0.327
SE3	3.66	1.059	-1.007	0.164	0.842	0.327
II1	3.78	0.837	-0.889	0.164	1.741	0.327
II2	3.89	0.860	-0.994	0.164	1.837	0.327
II3	3.86	0.883	-1.328	0.164	2.670	0.327
Valid N (listwise): 220

Mean scores across all items range from 3.59 to 3.89, indicating that participants generally responded positively across the constructs of Attitude Towards Investment, Subjective Norms, Perceived Behavioural Control, Self-Efficacy, and Investment Intention. Items’ standard deviations between 0.837 and 1.067 reflected slight variability in answers. All negative skewness values, which range from -0.801 to -1.328, suggest a left-skewed distribution, while kurtosis values range from 0.520 to 2.670, indicating acceptable levels of peakedness. Although several items deviate slightly from perfect normality, this does not pose a significant concern since SmartPLS does not require multivariate normality. The data are therefore appropriate for analysis using PLS-SEM, which is robust against non-normal distributions and suitable for exploratory and predictive modelling.

Structural Equation Modelling using SMART PLS

Figure 2 illustrates the PLS structural equation modelling (PLS-SEM) results.

Figure 2. PLS Structural Equation Modelling Author’s own work using SmartPLS 4

The SRMR was 0.070, which was below the recommended threshold of 0.08 [13], suggesting a good fit. The NFI is 0.857, which is slightly below the suggested threshold of 0.90, according to El-Sayed and Aneis [14]. Nonetheless, because the analysis was performed using SMART PLS, where model fit indices are viewed as supplementary rather than crucial, this value is still considered acceptable.

Reliability and Validity Analysis

These were evaluated using established psychometric standards. As shown in Table 4, construct reliability and validity were assessed using different values. Discriminant validity has been assessed using several approaches. The results for the HTMT ratio (Table 5) confirm that the constructs are conceptually separate. Moreover, the Fornell-Larcker criterion (Table 6) supported discriminant validity by indicating that the square root of the AVE for each construct exceeded its correlation with other constructs. Finally, the cross-loading analysis (Table 7) provided additional evidence, showing that each item had its highest loading on its respective construct.

Table 4. Construct Reliability, Validity, and Outer Loadings Computed by the authors using SmartPLS (v4.0)

Construct	Indicator	Outer Loading	Cronbach's Alpha	Composite Reliability (CR)	AVE
Attitude Towards Investment	ATI1	0.893	0.885	0.929	0.813
	ATI2	0.908
	ATI3	0.904
Investment Intention	II1	0.890	0.844	0.906	0.762
	II2	0.874
	II3	0.854
Perceived Behavioural Control	PBC1	0.927	0.902	0.939	0.836
	PBC2	0.908
	PBC3	0.908
Self-Efficacy	SE1	0.922	0.910	0.943	0.848
	SE2	0.919
	SE3	0.921
Subjective Norms	SN1	0.905	0.899	0.937	0.832
	SN2	0.916
	SN3	0.915

Strong indicator validity was shown for all item loadings, exceeding 0.70. For every construct, the values for Cronbach's Alpha and CR surpassed 0.84, above the 0.70 internal consistency criterion. Furthermore, in terms of convergent validity [15], the AVE values were between 0.762 and 0.848.

Table 5. Heterotrait-Monotrait Ratio (HTMT) analysis for Discriminant Validity Computed by the authors using SmartPLS (v4.0)

Construct	Attitude Towards Investment	Investment Intention	Perceived Behavioural Control	Self-Efficacy	Subjective Norms
Attitude Towards Investment
Investment Intention	0.674
Perceived Behavioural Control	0.715	0.680
Self-Efficacy	0.670	0.639	0.697
Subjective Norms	0.669	0.654	0.710	0.745

All construct values were found to be below the 0.85 threshold [16], confirming their distinctiveness. The highest recorded HTMT value was 0.745, which occurred between Self-Efficacy and Subjective Norms and was within acceptable limits.

Table 6. Fornell Laker criteria for Discriminant Validity Computed by the authors using SmartPLS (v4.0)

Construct	Attitude Towards Investment	Investment Intention	Perceived Behavioural Control	Self-Efficacy	Subjective Norms
Attitude Towards Investment	0.902
Investment Intention	0.583	0.873
Perceived Behavioural Control	0.638	0.595	0.914
Self-Efficacy	0.601	0.562	0.631	0.921
Subjective Norms	0.597	0.572	0.639	0.675	0.912

Attitude Towards investment emerges as a significant predictor, while Investment Intention has strong yet distinct links with Perceived Behavioural Control and Self-Efficacy [17]. Subjective Norms also retain their conceptual distinctiveness, supporting the applicability of the Theory in elucidating investment intentions [18].

Table 7. Cross loadings analysis for discriminant validity Computed by the authors using SmartPLS (v4.0)

Items	Attitude Towards Investment	Investment Intention	Perceived Behavioural Control	Self -Efficacy	Subjective Norms
ATI1	0.893	0.517	0.605	0.573	0.541
ATI2	0.908	0.529	0.519	0.510	0.520
ATI3	0.904	0.530	0.604	0.543	0.554
II1	0.510	0.890	0.555	0.545	0.530
II2	0.535	0.874	0.514	0.450	0.510
II3	0.480	0.854	0.486	0.473	0.454
PBC1	0.559	0.562	0.927	0.568	0.602
PBC2	0.579	0.538	0.908	0.591	0.597
PBC3	0.613	0.531	0.908	0.572	0.553
SE1	0.559	0.504	0.584	0.922	0.656
SE2	0.564	0.504	0.592	0.919	0.598
SE3	0.538	0.543	0.567	0.921	0.610
SN1	0.553	0.528	0.581	0.593	0.905
SN2	0.546	0.537	0.563	0.659	0.916
SN3	0.535	0.497	0.606	0.592	0.915

The cross-loading matrix verified discriminant validity, as each item exhibited the highest loading on its designated construct. For example, ATI1–ATI3 show strong loadings on attitude towards investment (≥ 0.893), while II1–II3 load significantly on investment intention (≥ 0.854). The moderate cross-loadings between Subjective Norms and self-efficacy imply that social factors may influence investor confidence [19].

Variance Inflation Factor (VIF) Analysis for Multicollinearity

Table 8 presents the analysis of the VIF. Elevated VIF values suggest a strong correlation between predictor variables, which can skew the regression analysis results. Conducting this analysis ensures the model's reliability by identifying and addressing multicollinearity issues, ensuring valid results in PLS-SEM.

Table 8. Variance Inflation Factor (VIF) analysis for multicollinearity Computed by the authors using SmartPLS (v4.0)

Items	VIF	Items	VIF	Items	VIF
ATI1	2.384	PBC1	3.213	SN1	2.596
ATI2	2.639	PBC2	2.702	SN2	2.834
ATI3	2.549	PBC3	2.763	SN3	2.985
II1	2.147	SE1	3.097
II2	2.025	SE2	3.003
II3	1.912	SE3	3.050

The VIF values, ranging from 1.912 to 3.213, were all below the threshold of 5, suggesting that there were no multicollinearity concerns and affirming the stability of the model.

Hypotheses Test Summary

Table 9 displays the outcomes of the hypothesis testing for the relationships outlined in the model. This analysis examined the significance of both direct and indirect connections between the constructs in this study. The Beta (O) values indicate the strength of these relationships, whereas the t statistics and p-values indicate their significance. The significance value was maintained at 0.05. The table sheds light on the pathways in the model that are validated by the data and reminds us of the direct and indirect effects examined in the research. The results of the hypothesis tests, as shown in Table 9, reveal that all direct relationships tested in the model are statistically significant and supported. Specifically, attitude towards investing has a significant impact on investment intention, with a beta value of 0.235 and a p-value of 0.010. Subjective Norms (Subjective Norms) also significantly influence Investment Intention (beta = 0.182, p = 0.026), as does Perceived Behavioural Control (Perceived Behavioural Control) (beta = 0.224, p = 0.004). Additionally, Perceived Behavioural Control strongly affected Self-Efficacy (self-efficacy) (beta = 0.632, p = 0.000), and self-efficacy significantly impacted Investment Intention (beta = 0.155, p = 0.043). The indirect path, Perceived Behavioural Control → self-efficacy → Investment Intention, shows a marginal effect (beta = 0.095) with a p-value of 0.067, suggesting that mediation is marginally supported. Overall, these findings demonstrate that the hypothesised relationships are largely supported, except for the marginally supported indirect path, which indicates partial mediation.

Table 9. Hypotheses Test Summary Structural model analysis using SmartPLS.

	Beta (O)	Standard Deviation	T statistics	P values	Decision
ATI -> II	0.235	0.091	2.572	0.010	Supported
SN -> II	0.182	0.081	2.223	0.026	Supported
PBC -> II	0.224	0.081	2.901	0.004	Supported
PBC -> SE	0.632	0.073	8.618	0.000	Supported
SE -> II	0.155	0.075	2.023	0.043	Supported
PBC -> SE -> II	0.095	0.052	1.831	0.067	Marginally Supported

The hypothesis test summary indicates that all direct relationships tested in the model are statistically significant and supported, as their p-values are below 0.05. Specifically, attitudes towards investment, subjective norms, and perceived behavioural control significantly influence investment intentions. Additionally, Perceived Behavioural Control strongly influenced self-efficacy, and self-efficacy significantly impacted Investment Intention, supporting the mediating role of self-efficacy. The indirect path of Perceived Behavioural Control → self-efficacy → Investment Intention shows a marginal effect with a p-value of 0.067, indicating partial mediation, and is marginally supported.

Quality Criteria: R-Square and F-Square Values

The R-square values show an average level of strength, with Investment Intention (R² = 0.464) explaining 46.4% of the variance and self-efficacy (R² = 0.398) explaining 39.8%. The corresponding adjusted R² values are 0.454 and 0.396, respectively. With regard to effect sizes, the F-square value for the link between Perceived Behavioural Control and self-efficacy was 0.662, which surpasses the 0.15 threshold and shows a modest to significant effect. By contrast, the effects of attitude towards investment, perceived behavioural control, self-efficacy, and subjective norms on investment intentions fall within the range of 0.019–0.052, reflecting minor effects, as advised.

Discussion

Influential Factors on Investment Intentions

The study found that having a positive attitude toward investing significantly increased a person’s intention to invest (β = 0.235, p = 0.010). This finding suggests that when people view investing as useful or rewarding, they are more willing to plan and take investment actions. This aligns with the findings of Munawaroh and Liviana (2022), who reported that millennials’ interest in digital investments was strongly driven by their attitudes and perceptions of investing as a beneficial activity. Subjective norms also had a notable impact (β = 0.182, p = 0.026), indicating that support or opinions from family, friends, or peers positively influence investment intentions. This influence of social surroundings mirrors the observations of Basu et al. [4], who found that early career clinicians’ prescribing decisions were shaped by social pressure and expectations from colleagues and patients, suggesting that social influence is powerful even in professional settings. Perceived behavioural control has a strong effect (β = 0.224, p = 0.004), indicating that when individuals feel confident in their ability to manage investments, they are more likely to invest. This finding resonates with the study by Bland et al. [18], which highlighted that investors’ beliefs in their ability to handle risk and enjoy the process had a meaningful impact on their behaviour, especially in the context of cryptocurrency investments.

Role of Self-Efficacy

Perceived Behavioural Control was strongly linked to self-efficacy (β = 0.632, p < 0.001), meaning that as people feel more in control of their financial actions, their belief in their ability to invest also increases. According to Iraola-Real et al. [7], self-efficacy is closely tied to how individuals confront anxiety and perform in a digital environment. When individuals feel more capable, their outcomes will improve. In this study, self-efficacy significantly influenced investment intention (β = 0.155, p = 0.043), indicating that people who feel confident about managing investments are more inclined to plan and take action. This result supports the findings of Hassan et al. [10], who find that fintech self-efficacy greatly influences Muslim investors’ decisions in Malaysia. Additionally, self-efficacy played a marginal mediating role between Perceived Behavioural Control and investment intention (β = 0.095, p = 0.067). This partial mediation suggests that control helps build confidence (self-efficacy), but other factors may also be involved in translating confidence into action. Magdalena et al. (2023) confirmed a similar pathway where financial self-efficacy enhanced investment decisions by influencing how financial knowledge was used [20].

Practical Implications

These findings suggest that financial education programs must do more than just teach numbers; they should also build confidence and practical skills. Khan et al. [21] emphasised that financial self-efficacy significantly boosts people’s willingness to invest and helps reduce their fear of taking risks. Additionally, social influence should be used in campaigns and peer-to-peer learning groups, as demonstrated by Basu et al. [4]. Encouraging people to discuss investments in their circles can make them more acceptable and motivating. Finally, building confidence through hands-on learning tools can improve perceived control, as demonstrated by Bland et al. [18] in the context of risk management and technology-based investment behaviours.

Conclusion

The findings of this study offer helpful suggestions for stakeholders aiming to enhance investment participation among early career professionals in Dakshina Kannada. The results affirm that psychological factors, such as attitudes towards investment, subjective norms, and perceived behavioural control, play a pivotal role in shaping investment intentions. Furthermore, self-efficacy has emerged as a critical mediator, reinforcing the importance of building individual confidence in financial decision-making. These insights have profound implications for educators, financial institutions and policymakers. Educational institutions should integrate comprehensive financial literacy programmes into their curricula, with a focus on real-life applications to improve attitudes and confidence toward investment. Financial institutions can offer user-friendly digital tools, personalised advisory services, and targeted awareness campaigns that encourage young professionals to actively participate in investment activities. Policymakers are advised to implement inclusive financial education initiatives and incentive-based schemes that empower youth to develop informed and proactive financial behaviour. Collaborative efforts across these sectors can foster a more financially literate and investment-ready population, ultimately contributing to a region’s economic resilience and individual financial well-being.

Significance of the study

This study elucidates the factors influencing early career professionals in the Dakshina Kannada district regarding their investment decisions. Numerous young professionals may generate a satisfactory income yet remain reluctant to invest, primarily due to insufficient confidence, inadequate knowledge, or external influences. This study employs the Theory of Planned Behaviour (TPB) to demonstrate how an individual's attitude towards investment, social influences from family and friends (subjective norms), and confidence in managing financial actions (perceived behavioural control) impact their intention to invest. The significant contribution of this study is the incorporation of self-efficacy, defined as an individual's confidence in making investment decisions. This research indicates that self-efficacy is essential in motivating individuals to invest and serves as a link between perceived control and actual intention. These findings are pertinent for financial educators, policymakers, and financial institutions, highlighting the necessity for financial education programs that enhance both knowledge and confidence. Social networks and accessible investment platforms enhance young professionals' readiness to invest. This study is particularly pertinent to district-level regions, such as Mangaluru, Puttur, and Sullia, where financial access is on the rise, yet support and awareness require enhancement. This study offers significant insights that assist young professionals in initiating their investment journey with increased confidence and in making informed financial decisions for the future.

Conflict of Interest

The authors declare no conflict of interest associated with this study. The research was conducted independently by the authors without any financial, commercial, or personal relationships that could have influenced the results or interpretations. Data collection, analysis, and reporting were conducted ethically and solely for academic and research purposes.

Acknowledgement

The authors express gratitude to the institutions for their support in the accomplishment of this study.