This research paper focuses on how the risk perception affects the financial investment decisions of the higher education staff in the National Capital Region (NCR) of Delhi.  It aims at outlining psychological, informational, and institutional factors, including loss aversion, overconfidence, financial literacy, and business relationships at work, as well as explaining the interaction of these different factors to create the asset-allocation behaviors system through an academic-trained but behaviourally heterogenous cohort sample (Lathief et al., 2024; Lewandowski, 2022).  The study proposed is expected to reveal some evidence about whether the risk awareness rate is higher and triggers a careful market withdrawal or a calculated exposure of the educated salaried workers, through a set of structured questionnaire data, thematic qualitative analysis, and a summary statistic prepared with the use of SPSS (Kaiser et al., 2022; Lusardi, Michaud, and Mitchell, 2020).

The empirical location of NCR Delhi is characterized by institutional diversity (public versus private institutions, professor versus administrative staff), which makes it possible to investigate role, tenure, or access to employer-linked benefits in more detail (Hans, Choudhary, and Sudan, 2024; Rath, 2023).  The reviewed literature generally indicates that all the mentioned factors financial literacy, behavioural biases, institutional trust, and socio-cultural influence the decision to invest together, and the educative process can change but do not completely eliminate risk aversion and cognitive distortions among investors.  The following paragraphs critically analyze some of the existing studies that can be considered related to this study.

Aren and Zengin (2016) did a study on the synergistic issue of financial literacy and subjective risk perception in investment choices and found that financial literacy contracts participation, but it does not necessarily reduce risk aversion.  Their evidence using mixed-method proposed that literacy reevaluates threat as manageable to selective investors, but it does not eliminate the existing knowledge-based relationship of distortions; this argument is consistent with the hypothesis of this study, that education lessens rather than removes distortion of behavior.

The study by Buturak, Kalmi, and Alanko (2022) adopted experimental interventions to demonstrate that structured financial education alters future risky decisions, albeit to some persisting extent; but knowledge is acquired sometimes to reduce, though not entirely succeeds in eliminating the heuristics such as loss aversion.  The need to evaluate immediate and long-term effects of literacy is highlighted in the design of their experiment, and thus, proves to be the methodological basis of longitudinal research in terms of workplace cohort.

 Holzmeister et al. (2020) conducted cross-national research involving experts and laypeople to determine the factors that affect risk perception and focus on the aspect of trust in regulation and the understanding of information as the key determinants of adopting assets.  The fact that their implications are directly related to the low use of cryptocurrencies and the choosing of mutual funds in the regulated environments studied in the given paper by Holzmeister et al. (2020) in the conditions of an Indian higher education context. In their work, Kaiser et al. (2022) provide an extensive evaluation that confirms the notion that financial education improves knowledge and has a different impact on the further behavior of various demographic groups.  Their strict identification approach implies that education is necessary and inadequate to change the deep-rooted conservative practices- a conclusion that guides notions linking literacy and experience with the chosen equity exposure in the existing group.

Lewandowski (2022) presents but a theoretic synthesis where the two theories of prospect and expected utility are compared to one another, explaining how loss aversion and reference dependence lead to conservative defaults.  This theoretical framework justifies this interpretation of substantial fixed-deposit and insurance allocation as psychologically motivated safety buffers, but not necessarily rational portfolio allocation in the already academically used cohort. Lusardi, Michaud, and Mitchell (2020) measure the outcomes of financial education programs on the quality of decisions and focus on probabilistic arguments and scenario-based training.  Their quantitative approach implies specific items in questionnaires and cognitive tests to determine whether risk awareness represents subjective fear or probabilistic understanding on a calibration scale and, therefore, inform the instrument development that will be adopted in this study.

Maheshwari et al. (2025) consider attitude as a predictor of investment and overconfidence as a predictor of investment, and both prove to replace each other and complementary in terms of whether financial knowledge is positive or negative depending on context.  This also implies that overconfidence can explain the subset whose direct equity allocation is considerable when among employees with higher education, who often have a better domain knowledge. Mamidala, Kumari, and Singh (2024) discuss the status-quo bias and the social spread of heuristics at work based on qualitative interviews; the authors report that peer recommendations and institutional norms, often, receive a tendency to cluster in the choice of products.  This leads to the expectation that the variable of herding and peer effect will become prominent within the boundaries of university networks in NCR Delhi.

The empirical evidence of Akbar, Ahmad, and Buchdadi (2024) works to suggest that financial experience and overconfidence are linked to more risky allocations with a mediator of locus of control.  Their findings lead to an analysis of whether their results have the expected overconfidence and greater direct equity exposure among the faculty in the study, given their longer investing horizon and more experience. Sagnak et al. (2020) combine the prospect theory with pragmatic risk-assessment tools and show how the loss aversion always distorts the decision-making process when faced with uncertainty.  Their combination of theoretical approaches to practical diagnostics gives working measures (e.g., loss-sensitivity indices) that could be adapted to the questionnaire to measure the aversion in the sample.

Banyen (2022) discusses the behavioural determinants of market participation in growing regions of the world and focuses on the role of the socio-cultural context in determining the prevalence of biases.  The articles of Banyen on trust, social learning, and occupational identity allow us to see how the institutional moderators and the role differences should be expected to interrelate between professors and the administrative staff. Liu et al. (2022) examine the roles of risk forecasting and tolerance in Chinese industry portfolio management and show that forecast precision and tolerance are all factors that predict uptake of equity.  Their study indicates that the denotation between the subjective assessment of risk perception and the objective forecast literacy is required to explain the reason why some risk-sensitive persons still increase their exposure to equity.

Despite the existing substantial literature on behavioural finance with respect to the issue of literacy, biases and market involvement (e.g., Kaiser et al., 2022; Lusardi et al., 2020; Lewandowski, 2022), there is a lack of context-specific evidence concerning higher-education staff in emerging-market megaregions.  Most of the available studies sample retail investors in general or focus on general demographic groups.  We therefore do not have a descriptive account of the interaction between occupational role (faculty, administrative), academic credentials and workplace institutional incentives (salary cycles, provident rules) and psychological factors in predicting portfolio composition among a cognitively advantaged but heterogeneously biased group.

The past of research developed a methodological focus either on experimental interventions (Buturak et al., 2022) or broad-scale surveys (Kaiser et al., 2022; Maheshwari et al., 2025), without the combination of target qualitative inquiry and SPSS-based quantitative modeling within a single sector of the workplace.  The lack is observed in the operationalization of complex constructs; that is, calibrated risk awareness vs affective fear, and analyzing whether awareness positively relates with strategic equity exposure instead of reducing participation (Lusardi et al., 2020; Liu et al., 2022).  This article addresses this gap through the usage of a mixed qualitative survey that was created in a higher education environment and an articulate demonstration of behavioural prejudice, literacy and institutional influences.

The principal objective is to determine the impact of subjective risk perception on making asset-allocation decisions among higher-education professionals in NCR Delhi, as well as examining the influence of financial literacy, experience of investment, job role, and institutional trust on the outcomes.  Secondary objectives include the measurement of the prevalence of the behavioural biases (loss aversion, overconfidence, herding), the evaluation of the correlation of the risk awareness with the enhanced distribution of equity, the provision of the practice-based recommendations concerning the advisory intervention and the formation of the practices based on the assessment of the available financial education (Aren and Zengin, 2016; Akbar et al., 2024; Holzmeister et al., 2020).

Research Methodology in this research analysis is the organized strategy employed in order to achieve the objectives of the study.  It describes research design, sampling plan, data gathering methods, and data analysis tools used to explore the relationship between the perceived risk, behavioural biases and investment choices of higher education employees in NCR Delhi.  In this section, the reason why relying on the mixed-method approach, which incorporates quantitative statistical analysis using SPSS and qualitative analysis through the theme, is justified, is explained to explain quantifiable trends and the determinants of behavior underlying them (Basheer and Siddiqui, 2020).  One more way this research analysis makes the operationalization of variables, validation of the instruments and reliability tests clear, thereby guaranteeing methodological rigor and replicability.  Eventually, this section is the foundation of analyzing the outcome of further analyses.

This research paper works with a structured framework of qualitative research to examine how risk perception affects financial investment choice among higher education staff in the National Capital Region (NCR) of Delhi.  NCR is a suitable geographical area due to its high density of higher education institutions, the diversity of employees, and the growing interest of people in formal and informal financial markets (Hans et al., 2024).  It would be anticipated that individuals with employment in the field of higher education will be able to make rational decisions as they have already been exposed to numerous school tasks. Nevertheless, past studies also indicate that even good money users tend to behave irrationally when they do not know what to do (Buturak et al., 2022; Maheshwari et al., 2025).

2.1 Data Collection

The primary data will be obtained through a self-administered study questionnaire that is specially designed to identify multi-dimensional elements of financial risk perception. The questionnaire has five sections; demographic profile, investment experience, perceived financial risk, behavioural biases (e.g. overconfidence, loss aversion, and optimism bias), and preferences in investment tools. This will be done by drawing up questions with the help of a combination of Lickert-scale questions, open-ended responses, and preference ranking to achieve deeper qualitative analysis (Ahmad et al., 2023; Saivasan and Lokhande, 2022).

The study makes use of qualitative data supported by theme interpretation, which is coherent with other methods of behavioural finance that analyze investor psychology beyond numerical signals (Aren and Zengin, 2016; Holzmeister et al., 2020). It is a qualitative method, which has been selected due to its ability to unveil subjective views on financial uncertainty or emotional stimuli as well as social aspects of financial uncertainty (King and Slovic, 2014; Huang and Xu, 2024).

2.2 Sample Size and Selection

The sample size would consist of 120 respondents who are purposely selected among teaching staff and administrative staff of government and non-government universities in the NCR Delhi area.  The questionnaire will be distributed physically and electronically at the institutional mailing lists and at the faculty social clubs.  The inclusion criteria include the presence of one or multiple financial investing experiences with mutual funds, term deposits, insurance plan schemes, or market-linked securities (Adhikari, 2020; Chan et al., 2020).  Participants will be requested to take part voluntarily, and the identities of the interviewees will remain confidential in order to eliminate bias in the responses (Akbulut, 2025).

2.3 Data Analysis

The SPSS software will be used to input and analyze responses based on descriptive statistics, frequency distribution, and cross- tabulation to identify the dominating trends in perceived risk and actual investment decisions (Lathief et al., 2024; Kaiser et al., 2022). Thematic clustering of the qualitative words expressed in open-ended responses will be achieved in this way, which will allow identifying the most common psychological trigger, such as fear of loss, trust in state-sponsored programs, or peer pressure (Sindhu and Kumar, 2014; Dam and Mate, 2017).

Chi-square and correlation tests will be applied to assess the relationships between demographic variables and perceived risk intensities (Liu et al., 2022; Mahmood et al., 2024) as will be the case when applicable. In order to ensure the methodological rigor, the results will be supported by established theoretical concepts, such as Prospect Theory (Lewandowski, 2022) and Affect Heuristic Models (King and Slovic, 2014).

The paradigm of methodology is used to identify the situational but generalizable understanding of risk perception in educated professionals, a group that is often underrepresented in the area of research on behavioural finance in new markets (Mazhar Farid Chishti et al., 2025; Afzal et al., 2023). The acquired insights will provide grounds of policy-level intervention and specific financial advisory approaches.

The section analyses the data collected on 120 higher education employees in Delhi NCR to investigate how demographic, psychological, and behavioural factors affect investment choices. Through SPSS we were able to identify exploited key relationships between investor characteristics and willingness to take risks via statistical procedures including descriptive summaries, cross tabulation, chi-square association tests, Pearson correlation as well as simple regression.

Table 1 Demographic Profile of Respondents (N = 120)

(Source: Primary survey (Questionnaire-based data collection), 2025)

Table 1 shows that most of the people who answered the survey are mid-career professionals, with 64% of them being between the ages of 35 and 54. The predominance of teaching faculty (66%) and the significant representation of postgraduates (58% Master's and 30% PhDs) indicate an academically oriented sample, aligning with previous behavioural finance research undertaken in educational settings. Investment experience is evenly spread across, so it's easy to compare new and experienced investors. The slight male majority (58%) fits with the trend of more males becoming involved in financial markets, but the fact that 42% of the people are women means that gender-based risk perception analysis can be done.

Table 2 Primary Investment Preferences (Top Two Choices per Respondent, N = 120)

(Source: Primary survey (Questionnaire-based data collection), 2025)

Table 2 indicates that the largest investments of NCR higher-education personnel are mutual funds (48%), and fixed deposits (41%). The behavioural tendency that is characterized by risk-aversion or risk-moderate behaviour, including stocks, is high with direct exposure to stocks exhibited by 32% of interviewees with a generally ordinary tendency to take part in low-risk financial instruments typical of the salaried worker. This is an indication that there is an extensive clientele using a barbell strategy, a mixture of professional and capital security in fund management. The interest in cryptocurrencies is very low as the percentage of people who want to deal with it is only 4%. The recorded distribution depicts that the perceived financial risk causes considerable impact on investment conservativeness but allows specially oriented growth exposure.

Table 3 Risk Perception Index Distribution (Composite Score Range: 0–100, N = 120)

(Source: Primary survey (Questionnaire-based data collection), 2025)

Table 3 indicates that the majority of the respondents (68%) who responded to the question are in the moderate risk perception category and the mean score of this group is 56.3. This implies that human resource in higher education is aware of the risk of investments but they do not consider it as a significant impediment. Even the small percentage of individuals who believe that there is a great threat (14) indicates that there are not that many alarmist leanings. The group with low risks (18%) might consist of the conservative investors who invest in guaranteed securities. The perceived risk can be moderate and linked to financial literacy and job security in institutions that will enhance investor confidence during market volatility.

Table 4 Prevalence of Behavioural Biases

(Top Two Self-Identified Biases per Respondent, N = 120)

(Source: Primary survey (Questionnaire-based data collection), 2025)

The most prevalent behavioural bias is loss aversion, exhibited by 64% of individuals. It is a confirmation of the conventional theory of Prospect Theory, whereby a loss is deemed to be more important than an equal-sized gain (Table 4). The influence of overconfidence (38) is significant but restrained, probably due to financial savvy of the profession of higher education. The impact of influence of coworkers and social networks at work on investing decisions is demonstrated by Herring (36%). Anchoring (30%) represents the reliance on history, whereas optimism bias (22) implies selective expectations. All these biases depict a scenario in which rationality exists with systemic distortions even the academically trained groups.

Table 5 Cross-Tabulation: Job Role vs Primary Investment Preference (N = 120)

(Source: Primary survey (Questionnaire-based data collection), 2025)

Table 5 revealed that teaching faculty are more likely to invest their money in mutual funds (52%) and stocks (36%), but administrative personnel are more likely to invest their money in fixed deposits (50%). The chi-square value (χ² (2) = 6.72, p = 0.035) demonstrates that the relationship between work function and decision to invest is statistically significant. This difference is likely to exist because of differences in financial literacy and economic flexibility. The members of the faculty generally have a greater academic exposure and tend to study independently, and this predisposes them to greater chances of risk-taking. The staff employees in administration may prioritize capital protection as these employees are less risk-takers or they are not free to invest because they do not have sufficient freedom. In that way, the employment conditions influence poets greatly in the context of money management.

Table 6 Correlation and Regression Analysis: Risk Perception vs Equity Allocation (N = 120)

(Source: SPSS Output based on primary survey dataset, 2025)

Table 6 indicates that there is a moderate positive relationship between equity allocation and risk perception (r = 0.41, p < .001). It implies that the financial risk-aware people will invest more in the market-linked assets. This is contrary to the prevailing belief that the higher the risk that an investor perceives, the lower will be the likelihood that he or she will invest in a stock. Rather, it means that risk perception by educated professionals is suggests correlation with risk awareness but not risk aversion. The regression result (R 2 = 0.168) shows that perceived risk is a significant predictor of equity allocation although other aspects including financial literacy, behavioural bias, or income stability are not ignored in investment behaviour.

Figure 1 Histogram — Risk Perception Index distribution

Figure 1 illustrates the distribution of the Risk Perception Index of the respondents. This indicates that it has one large peak at the middle range (50 60). This central tendency indicates that the majority of the individuals participating had the middle degree of risk awareness meaning that, they are neither too risk-aversive nor overenthusiastic about taking risks. The long tails at both ends indicate that the number of individuals who are having a strong opinion on financial risk is low. This pattern resembles previous observations in the case of educated professional worlds, where educated rationales and stability in society prevent risk perception to be volatile. The effects of the institutional factors appear to reduce risk-based emotions; due to a stable employment, some kind of system of organized savings, and peer norms, this makes institutional factors appear to have an effect of reducing risk-related emotions, therefore leading to a narrow distribution, which is centrally located. Such uniformity demonstrates the way professional environments assisting academic employees can regulate their risk and come up with more reasonable thoughts with regard to money.

Figure 2 Pie chart — Asset class share in average respondent portfolio

Figure 2 presents the proportion of each kind of investment in the respondents’ portfolios. The portfolios are composed majorly of mutual funds (37%) and fixed deposits (32%). These two products demonstrate a somewhat financial policy, namely, attempting to earn a living at the expense of conducting investments in professionally regulated equity and avoid any risk, depositing money, which is bound to retain its value. Direct equities constitute 20 per cent of the total indicating that not every investor is investing in the market. Lesser parts consist of insurance and pensions (8%) and other products (3%). This kind of structure is common among the behaviour of individuals in salaried, educated populations: they strike a balance between opportunity of gains and the psychology of having safe hold. The small share of direct ownership demonstrates that individuals would give up risk management to fund managers since they do not have lots of time to trade and they are not quite sure that they would be good analysts of the market. The manner in which the portfolios have been configured therefore is a trade-off between being cautious and desiring to perform well amongst the academic professionals.

Figure 3 Bar chart — Prevalence of behavioural biases

Figure 3 illustrates the prevalence of some behavioural biases of the individuals who completed the poll. These biases are loss aversion, the overconfidence and the herding. The most prevalent cognitive bias is the loss aversion and therefore most individuals choose safe investments and are not keen on moving money around when things go bad in the market. However, the cases of overconfidence, albeit, little, are important in experienced faculty investors that exhibit a greater tendency toward direct stock investment. An example of the effects of workplace debates and social proof processes is a behavior that is called herding and is exhibited by correlated decisions and reliance on peers. All these behavioural traits create a portfolio inertia effect and boost balanced investment preferences the combination of mutual funds and deposits as people attempt to align profit motives with psychological comfort (Mahmood et al., 2024). This interaction provides the behavioural foundation of the financial trends that are identified in the study.

The case of investment behaviour among higher-education staff in NCR Delhi is one of a multifactorial system in which financial literacy, experience, behavioural biases, and institutional context are all simultaneously important determinants of decision-making. Although the majority of the population believed that the risk was average, most of them still invested in market-related instruments such as mutual funds and stocks. This observed paradox is explained in the following by the moderating effect of financial literacy and experience: rather than eliminating the perceived risk, education appears to turn the abstract risk into a manageable, measurable one and is used by some respondents to participate in organised exposure (Kaiser et al., 2022; Lusardi, Michaud, and Mitchell, 2020). Faculty that was more deeply quantitatively exposed and had longer histories of involvement in the market were more actively engaged in the market, which corresponds to the results of Maheshwari et al. (2025) and Sivaramakrishnan, Srivastava, and Rastogi (2017).

Behavioural biases are short term decision rules that tend to override rational calculations. The problem of loss aversion was dominant: some human beings kept large amounts of their investment in the fixed deposit and insurances as psychological safety nets, an intuitive characteristic of the prospect theory (Lewandowski, 2022; Sagnak et al., 2020). Meanwhile, the tendency of a group of respondents to become overconfident and optimistic resulted in actively investing their money into stocks and attempting to time the markets (Akbar et al., 2024; Ahmad et al., 2023). The herding and anchoring were evident where the proposals of peers and reliance on past standards created inertias and path dependency in the portfolios (Banyen, 2022; Ahmad, 2024). Such results confirm the available literature that education reduces, yet not eliminates, heuristics; furthermore, education changes the relationship between heuristics and analytical thinking (Buturak, Kalmi, and Alanko, 2022).

Behavior is also determined by the institutional and contextual factors in NCR Delhi. The cycles of salaries, the needs of provident funds as well as tax saving incentives occasionally make short term investments rather than long term strategic planning. The recent macro shocks (the consequences of the pandemic and inflation processes) predisposed a tendency to resort to defensive investments in the case when the stock market was performing well. Some people did this by even selecting conservative instruments (Rath, 2023; Hans, Choudhary, and Sudan, 2024). The lack of trust is a reason why Americans do not invest unregulated investments such as cryptocurrencies to the extent that they invest in regulated mutual funds (Holzmeister et al., 2020; Liu et al., 2022).

As a pragmatic matter, it is necessary to have policy and advisory interventions that target both cognitive and affective aspects, i.e., financial education ought to be provided adding probabilistic reasoning, loss framing through scenarios, and behavioural nudges, and advisors should be sensitive to emotional obstacles (fear, trust) and social influence rather than delivering merely technical information (Lusardi et al., 2020; Lepore and Cunningham, 2024).

4.1 Justification with Existing Research

This section validates the results of the research by identifying similarity in the same with the basis theories of behavioural finance, where the effects of loss aversion, financial literacy, social influence, trust, and demographic moderators all contribute to the same in explaining investment behaviour patterns.

1. Loss aversion as a core driver: The strong preference for deposits and insurance observed here mirrors prospect-theory predictions and empirical demonstrations of loss aversion in investment contexts (Lewandowski, 2022; Sagnak et al., 2020). The present data show loss aversion operating even within an educated cohort, consistent with broader literature.
2. Financial literacy’s nuanced effect: While financial literacy and experience correlated with greater equity/mutual-fund participation, they did not eliminate conservative defaults; education reframed risk as manageable for some but left others risk-averse — matching the mixed evidence in Kaiser et al. (2022), Lusardi et al. (2020), and Buturak et al. (2022).
3. Social networks and herding: Peer influence within institutional networks explained clustering in certain product choices, aligning with Banyen (2022) and Mamidala et al. (2024) that show workplace ties transmit financial heuristics.
4. Overconfidence among faculty: Faculty respondents exhibited higher overconfidence indicators that associated with greater direct equity allocation, consistent with Akbar et al. (2024) and Ahmad et al. (2023).
5. Regulation and trust shape asset adoption: Low cryptocurrency uptake and strong mutual-fund preference reflect trust-based selection documented in Holzmeister et al. (2020) and Liu et al. (2022).
6. Role and age moderate behaviour: Occupational role (faculty vs admin) and age bands significantly moderated instrument choice, in line with Grable (2000), Aydemir & Aren (2017), and Baker et al. (2019).

The researchers determined that the majority of the individuals that participated in the study had an average opinion of financial risk. Nevertheless, those, who were more financially literate and more experienced in investing, were more apt to be willing to invest in stocks. The primary factor that influenced portfolio selections was the loss aversion, and lesser but not unimportant factors were overconfidence and the ability to follow others. The source of the conservativeness of investment among respondents has been supplemented by the institutional rules and recent macroeconomic turmoil. It is interesting to note that risk perception had a positive correlation with the allocation of stock. This demonstrates that there are educated risk-conscious investors, who do not necessarily remain out of the market. Rather, it appears to make one more cautious and prudent about when investing in higher risk financial instruments, which demonstrates a better comprehension of the process of investments.

Figure 4 Conceptual Model: Determinants of Investment Choice among Higher-Education Employees

Figure 4 indicates that there is an interplay between financial awareness and experience, behavioural biases, and the institutional backdrop in influencing investment decisions. Literacy can change risk perception (by making risk manageable to some people), and have a direct effect on decision-making; behavioural biases can have both a direct effect (such as loss aversion resulting in deposits) and an indirect effect by depending on the modulation of risk appetite by institutional factors (salary timing, provision fund policies, macroeconomic shocks). The model combines both present empirical correlations and regressions into the context of the behavioural finance theory. It provides a parsimonious policy-oriented interventions and advisory methodology directed to enhance effective diversification.

In this research, the researcher analytically evaluated the effect of the risk perception, financial literacy, behavioural biases and institutional environment on the investment behaviour of higher-education workers in the National Capital Region (NCR) of Delhi. Among the findings it is shown that although there was moderate overall risk argument among the respondents, their asset allocation behaviour indicated that loss aversion was the most imperative factor that made them Favour low-risk investments such as fixed deposits and insurance. The motivation to avoid a perceived loss was more than the motivation to hunt the potential gains even in an educated group. This confirms the predictions of prospect theory and it concurs with previous empirical studies.

The statistics showed that both financial literacy and investment experience were positively related with engagement in equities and mutual funds, but education by itself was not sufficient to eliminate conservative defaults. This finding supports the inconclusive nature of the findings presented by Kaiser et al. (2022) and Lusardi et al. (2020) and emphasizes the fact that literacy recontextualizes risk as something that can be tackled and brings about changes in risk-averse behavior but not always. In addition, institutional settings and peer networks had a significant impact on preference in investment, which confirms the results of herding behaviors defined by Banyen (2022) and Mamidala et al. (2024). The indicators of overconfidence in faculty participants were more evident, so these employees were more inclined to invest money directly into stocks, which was an outcome of the research conducted by Akbar et al. (2024) and Ahmad et al. (2023).

Demographic and occupational factors improved the analysis: age, occupation and stable income were meaningful variables that influenced perceived and actual risk-taking behavior. Overall, these results indicate that the way individuals invest is not only premised on rational reasons and rationale; it is also premised on cognitive, emotive, and situational influences. The analysis concludes that the irrational bias can be mitigated by increasing financial education and introducing behavioural data to her advisory service, which will help people make strategic decisions regarding investments. Finally, the study highlights how knowledge on risk would be more effective in informing the investment decision than fear of losses, which would enable savvy investors to be more confident in how they engage in dynamic financial markets.

Author's Declaration

Author1 and Author2 state that the research paper under discussion is their work and does not rely on the work of anyone. The research including the data collection, analysis, and interpretation was conducted in an ethically sound and plagiarism-free way. The text has been read and concurred upon by both authors to submit the final version of the text, and that to the best of their knowledge, it has not been published or forwarded to any third party. (Here, you may include information regarding the membership, details and contribution of the author)

Data Availability

The information that supports the findings of this study was based on the primary surveyed data that was taken through a statistical analysis. All datasets will be made by the associated author upon reasonable academic request in order to verify their validity or conduct additional research.

Fundings

This study has never received funds, grants, or sponsorships with any other organization or institution or funding body. The study was carried out and the authors funded it to retain an autonomy of maximum academic level and to remain unbiased towards the research process.

Conflict of Interest

The authors state that neither financial nor non-financial conflicts of interest are related to this research. The authors confirm that personal, institutional, and professional associations did not influence the approach, analysis, and outcomes of the conducted study.

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