INDIAN JOURNAL OF FINANCE AND BANKING 11(1) (2022), 29-37 29 FINANCE AND BANKING IJFB VOL 11 NO 1 (2022) P-ISSN 2574-6081 E-ISSN 2574-609X Available online at https://www.cribfb.com Journal homepage: https://www.cribfb.com/journal/index.php/ijfb Published by CRIBFB, USA ASSESSING RELATIVE WEIGHT OF DETERMINANTS OF INVESTMENT IN INDEX SCHEME OF MUTUAL FUNDS Debadrita Dev (a) Sujit Deb (b) Ranjit Singh (c)1 Lokendra Puri (d) (a) Research Scholar, Faculty of Management and Commerce, ICFAI University, Tripura, Kamalghat-799210, Agartala, India; E-mail: debadrita.scholar@iutripura.edu.in (b) Professor, Faculty of Management Studies, ICFAI University, Tripura, Kamalghat-799210 Tripura, India; E-mail: sujitdeb@iutripiura.edu.in (c) Professor, Department of Management Studies, Indian Institute of Information Technology Allahabad-211012 Uttar Pradesh, India; E-mail: ranjitsingh@iiita.ac.in (d) Research Scholar, Department of Management Studies, Indian Institute of Information Technology Allahabad-211012 Uttar Pradesh, India; E-mail: rsm2022504@iiita.ac.in A R T I C L E I N F O Article History: Received: 2nd October 2022 Accepted: 23rd November 2022 Online Publication: 12th December 2022 Keywords: Bank Employees, Behavioural Finance, Mutual Fund, Preference JEL Classification Codes: E22, G11, G21, G41, P4 A B S T R A C T The main aim of this study is to assess the relative weight of determinants of investment in Index schemes of mutual funds. The target population for the study came out to be 880. Using a simple random sampling method, the sample size was determined to be 268. Of these, 262 bank employees responded to the questionnaire, and the rest 6 were reluctant. A 95% confidence interval and ±5% margin of error have been used to estimate the overall sample confidence level. For the practice of data, the collection questionnaire method was used. Ordinal logistic regression and Kendall are used to assess the relative weight of determinants of investment in different index schemes of mutual funds. The study discovered that various psychological characteristics like risk perception and attitude are significant determinants of mutual fund investment in Tripura. Also, the interaction effect with demographic and psychological factors influences the volume of investment in mutual funds in Tripura. The study has good inputs for the fund managers of mutual fund Companies. They can know the determinants of investments in mutual funds and their impact on the volume of investment. This study will guide the policymaker on which determinants should be given more weight. The study will assist in designing a strategy for what level of training is required to improve psychological factors toward investment in the mutual fund. The study is very original. It is first attempted to assess the relative weight of determinants for preferring index schemes of the mutual fund. © 2022 by the authors. Licensee CRIBFB, USA. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). INTRODUCTION Mutual funds are an investment instrument where investors pool their money to gain returns on their money over an amount of time. There is a bunch of different securities to invest such as bonds, gold, and stocks, to seek the potential rate of return. Mutual funds are like a bridge that facilitates the investors to gather their funds with pre-determined investment aims. The fund manager utilizes these funds to invest in various securities. Mutual funds work for the utmost interest of the investors not only by providing them with the liquid fund, balanced funds, growth funds, and index funds as options but also gives them the advantage of a diversified portfolio (Kumar, 2011). There are many mutual fund schemes for investors. At the same time, the investors' investment decisions are affected by several determinants. The investor receives a proportionate share of the fund's loss, income, expense, and gain. The fund's objectives are mentioned in the fund's booklet, a legal file covering all related material about the fund, like history, performance, and officers. The mutual fund is not a substitution for the stock and bonds. It pools the money of numerous investors and invests in bonds, money market instruments, and other types of securities and stocks (Dunna, 2012). The preferences of salaried individuals are mostly depended on demographic and socio-economic variables (Bashir et al., 2013). Demographic and socio-economic variables play an active role in affecting the choices of investors (Shinde & Zanvar, 2015). Each investor's 1Corresponding author: ORCID ID: 0000-0001-9408-9525 © 2022 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA. https://doi.org/10.46281/ijfb.v11i1.1847 To cite this article: Dev, D., Deb, S., Singh, R., & Puri, L. (2022). ASSESSING RELATIVE WEIGHT OF DETERMINANTS OF INVESTMENT IN INDEX SCHEME OF MUTUAL FUNDS. Indian Journal of Finance and Banking, 11(1), 29-37. https://doi.org/10.46281/ijfb.v11i1.1847 http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) https://doi.org/10.46281/ijfb.v11i1.1847 https://orcid.org/0000-0003-2847-768X https://orcid.org/0000-0002-6836-6856 https://orcid.org/0000-0001-9408-9525 https://orcid.org/0000-0002-9774-289X Dev et al., Indian Journal of Finance and Banking 11(1) (2022), 29-37 30 decision varies from one another in various demographic, economic, psychological, and social factors (Deb & Singh, 2017a). Investors differ in their choices and preferences. This difference in preferences is affected by different factors such as the classification of the portfolio, reducing the level of risk, and higher the amount of tax benefits. All mentioned factors are the top factors that influence the investor's liking for investment in mutual funds (Saibaba & Vipparthi, 2012). Well-off and highly educated Indian investors often prefer financial products with risk-free returns (Sultana, 2010). A good understanding from a financial perspective is much needed to take the best possible higher return on the investment. Mutual funds offer the diversified benefits of a highly efficiently managed portfolio at a lower cost to the investor in the various securities depending on the schemes' objectives (Chakraborty & Digal, 2013). Instead of investing directly in equity shares, employees choose to invest more money in mutual funds. The investors' preferences are essential to gain a finer mindset of financial market applicants' preferences and behavior (Heckman, 2001). Singh (2002) revealed that tax exemption plays a vital role in investors' preference for public sector mutual funds. In contrast, investors are obligated to have a desire, patience, positive vision, and prudence because investors' behavior also varies over a period which plays an important role at the time of investment (Ansari et al., 2013). In the current scenario, various schemes are available in the market. It is essential to recognize the preferences and choices of the investors and the factors that affect these preferences and choices (Mehta & Shah, 2012). Investors vary in their choices, and as per their preferences, wide varieties of mutual funds have been launched in the market. Most middle-income investors prefer mutual funds for investment (Kumar & Bansal, 2014). As of now, knowledge of mutual funds has increased among people. While in the context of India, the role of the Securities and Exchange Board of India (SEBI) should be highlighted to gain a better mindfulness of investment among investors. As per the theoretical background, it has been noticed that psychological, demographic, and socio-economic factors play an important role in investing in mutual funds. For new investors with a lack of technical expertise, mutual funds are the popular investment option to invest. By studying the relevant literature, it can be concluded that psychological, demographic, and socio-economic factors play a crucial role in mutual funds' investment. To the best of our knowledge, this work is to investigate the specific proportion of known factors of investment bank employees' preference for index schemes of the mutual fund. The main objective of the present study is to assess the relative weight of determinants of investment in Index schemes of mutual funds. Thus, the current study tries to answer the research questions of the study, which are given below:  RQ.1 what determinants are influencing the investment decision of investors?  RQ.2 what are the bank staff's favorite levels toward the indexing scheme of mutual funds in Tripura? The following hypotheses were tested in this study:  H0: There is no significant linkage between the selected determinants and preference for investments in index schemes of mutual funds.  H1: There is a significant linkage between the selected determinants and preference for investments in index schemes of mutual funds. The rest of the paper consists of various sections such as the literature review and theoretical background of the study; research methodology; result; discussion and policy implications, and finally, the study’s conclusion. LITERATURE REVIEW The attitude of the investor and the magnitude of investment are positively related, which means investors have a favorable attitude towards investment in mutual funds and invest in higher volume than those who are not having a favorable attitude (Singh et al., 2021). It is significant to study the investors' investment behavior based on their demographic profile and understand their requirements (Chakraborty & Digital, 2013). Tax advantages, higher return, capital, and price appreciation are the foremost factors that influence the investment decisions of a retail investor (Roy et al., 2017). Considering the needs of the investors' several schemes are offered by mutual funds (Geetha & Ramesh, 2011). From their study, Geetha and Ramesh (2012) tell that there is indeed a relationship between demographic determinants and various sources of mindfulness obtained by investors. Their study also gives a clear idea regarding the investor's perception of different investment opportunities. Also, they stated that investors in a developing country usually tend to invest more in financial assets than physical ones. It was the opposite initially for Indian investors. Bodla and Sunita (2008) study shows that there are nearly 609 schemes with various features presented by mutual funds. They also found that income schemes are preferred over overgrowth schemes concerning assets under management. Gupta et al. (2011) concluded that investors prefer a balanced fund. Chakraborty and Digital (2013) studied that the need for liquidity is high for an investor. Thus, he is more interested in open-ended funds. Gupta et al. (2011) disclosed that investors prefer balanced funds the most for investment. Mehta and Shah (2012) discovered that in making investment decisions, investors choose equity schemes more. There is a positive mindset of employees toward the selection of mutual funds (Murugan, 2012). Fear psychosis of employees, lack of confidence and awareness, and knowledge of mutual funds are the three major factors affecting risk perception (Deb & Singh, 2018). Age, gender, experience, and family income influence the investors' risk perception (Deb & Singh, 2017a). Risk is a frequent factor in every financial investment and has a meaningful impact on the investor's choice (Yang & Qiu, 2005; Deb & Singh, 2016; Bhattacharjee et al., 2020). To manage a risky situation, a decent idea about risk, whether rational or irrational, plays a vital role (Sindhu & Kumar, 2014). Kaur and Kaushik (2016) have recognized a strong association between investment decisions and socio-economic factors, risk perception, and awareness level of investors. Education, gender, age, and annual income are some of the few socio-economic and demographic elements that influence the investor's investment decision (Shinde & Zanvar, 2015; Deb & Singh, 2016). Many studies suggested that both male and female adopts different investing approach to investing their money (Bajtelsmit & Bernasek, 1996; Dezső & Loewenstein, 2012; Jianakoplos & Bernasek, 1998). Several studies explained that age is also an important factor at the time of the decision, Dev et al., Indian Journal of Finance and Banking 11(1) (2022), 29-37 31 like the higher the age, the higher the experience level (Alexander et al., 1999). The investor's income also changes the investor's decision (Hallahan et al., 2004; Ansari et al., 2013; Walia & Kiran, 2009; Watson & McNaughton, 2007). Several studies found that the marital status of the investor also affects the investor's investment decision (Arano et al., 2010; Grable & Roszkowski, 2007; Lazzarone, 1996). Few studies suggested that the educational level of the investor is also a significant factor at the time of investment in funds (Bellante & Green, 2004; Gilliam & Chatterjee, 2011; Al-Ajmi, 2008; Das, 2011). Moreover, various studies found that experience is also one of the major factors influencing investors' decisions (Corter & Chen, 2006; Deb & Singh, 2017b). After going through the above literature reviews, it has been observed that risk perception, level of awareness, and attitude are the significant elements that impact the choice of investment of the investors. In addition, six socio-economic and demographic components have been identified. Overall, nine elements impact an investor's decision to invest in a mutual fund. MATERIALS AND METHODS The population targeted for the present study includes the employees of banks with their own sponsored mutual funds. The target population is 880 employees (as of 1st April 2021), taken from the banks. These banks have their sponsor mutual funds. A sample of 268 employees has been determined based on the criterion at a 5% confidence interval and 95% confidence level. Two hundred sixty-eight random numbers out of 880 have been generated using a simple random sampling method. A tested questionnaire has been shared with all the selected employees. A psychometric scale was used to test the employees' risk perception, attitude, and awareness. Finally, 262 employees responded, and the rest were reluctant to respond. Questionnaires filled by 262 bank employees were collected by visiting their respective banks. Kendell's tau correlation coefficient is considered for assessing relative weight among the significant factors. Factor analysis was performed by Choudhury et al. (2016). RESULTS Table 1 shows the preference for the indexing scheme. The table indicates that 25.6% of the employees, i.e., 67 being the highest, have very low preference levels. We can also see that 25.2% of the employees, i.e., 66, have a moderate preference level for index schemes, and 23.7%, i.e., 62 employees out of 262 employees, have a high preference level for index schemes of the mutual funds. Table 1. Preference for index scheme Index schemes Level of preference No. of employee Percent Very High preference 15 5.7 High preference 62 23.7 Moderate 66 25.2 Low preference 36 13.7 Very low preference 67 25.6 Not applicable 16 6.1 Total 262 100 Factors Affecting Investment Preference in Index Schemes The literature review identified nine variables as factors for preferring the indexing scheme. These variables were regarded as independent variables. Since there is a multi-collinearity effect between the independent variables; therefore, the regression model is not expected to provide a good result for which factor analysis is done. Two criteria, i.e., Varimax rotation criteria and Eigenvalue criteria greater than one, were used to identify and avoid cross-loading between the factors. The KMO test was applied to check the adequacy of the samples. The result of sample adequacy was 0.661, which is an acceptable result that means that the samples obtained were sufficient for the present study. Bartlett's test of sphericity was important. It generally specifies that the correlations between the variables are sufficient to continue. Table 2 displays the summary of the results of the sample adequacy. Table 2 shows that the value of communalities is less than 0.5 in the case of the factor named education, so it has been ignored from the study. The factors whose commonalities values are more than 0.5 were to be kept in the study (Hair et al., 2009). In the process of factor analysis, commonality indicates how much variance is described by each variable for the derived factors (Mishra, 2015). Table 2. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy .661 Bartlett's Test of Sphericity Approx. Chi-Square 547.048 D.F 21 Significance .000 Table 3. Total variance explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of variance Cumulative % Total %of variance Cumulative % Total % of variance Cumulative % 1 2.406 34.374 34.374 2.406 34.374 34.374 2.209 31.558 31.558 Dev et al., Indian Journal of Finance and Banking 11(1) (2022), 29-37 32 2 1.896 27.079 61.453 1.896 27.079 61.453 2.093 29.895 61.453 3 .944 13.480 74.933 4 .594 8.493 83.426 5 .545 7.780 91.206 6 .401 5.732 96.938 7 .214 3.062 100.000 In the next part, the breakdown of the derived factors and their complete variances are described with the help of all the factors retrieved. After avoiding cross-loading, the factors have been obtained. The variance was 61.453%, which the three loaded factors can explain. A detailed explanation of the variables loaded in various factors is shown in table 4. Table 4. Varimax rotated loading Factors and Variables Factor1 Factor 2 Demographic and socio-economic variables Age .884 Family income .708 Education Experience .907 Psychological factor Risk perception -.826 Attitude .822 Awareness level .745 Table 4 shows the results of the rotated component matrix. The categorizations of the variables are done based on the arrangement, and two factors were found. These factors are termed demographic, psychological, and socio-economic factors. Demographic and socio-economic variables are considered Factor 1, including family income, experience, and age. Since the correlations between the variables were less than 0.05, education was not considered gender, and the nominal scale measured marital status. Therefore, they were not appropriate for factor analysis. Factor 2 was the psychological factor which included variables like risk perception, attitude, and awareness level. The relative weight of chosen determinants on the preference of index schemes The Ordinal logistic regression is applied to determine the impact of the selected factors in the preference for the mutual fund index schemes. Here, the dependent variable is the choice in the indexing scheme, and the independent variables are the selected variables. Here, the Dependent variable is the preference for index schemes which has been coded in table 5. Table 5. Coding of the Likert Scale Used Y Preference 1 VERY HIGH 2 HIGH 3 MODERATE 4 LOW 5 LEAST The independent variables are the chosen factors of bank employees working in banks with sponsor mutual funds. An ordinal model has been used for the indexing scheme. Here, the dependent variable is considered to be the preference of the indexing scheme. The independent variables have been derived using factor analysis, and because of the nominal nature of education level, gender, and marital status, these are considered in factor analysis. In this analysis, the coding shown in table 6 is used. Table 6. Coding for the analysis GENDER 1 MALE 2 FEMALE EDUCATION 1 GRADUATE 2 POSTGRADUATE MARITAL 1 UNMARRIED 2 MARRIED Dev et al., Indian Journal of Finance and Banking 11(1) (2022), 29-37 33 Table 7. Information on Model Fitting Index schemes Model -2 Log Likelihood Chi-Square Df Sig. Intercept Only 840.577 Final 770.152 70.425 10 .000 Table 7 describes the impact of each selected factor in the model for index schemes; it is important to find out that needed to find out that the model has the skill to foresee the result. This is done by comparing the 'Intercept only' model with the 'Final' model. This comparison was used to check if the data fit had improved considerably. For all six models, the statistically important 0.05 is the p-value of the chi-square statistic, which means that the final model excels the intercept- only model. As per the chi-square value, it is clear that the model can give a better prediction. Table 8. Goodness-of-fit Chi-Square Df Sig. Index schemes Pearson 1350.434 1205 .392 Deviance 750.272 1205 1.000 Table 8 shows Pearson's chi-square statistic for the model (along with the deviance-based chi-square statistic). It examines whether the observed data is reliable and with the fitted model. It appeared to form the result that the model fits very well for every model with more than 0.05 p values. Table 9. Pseudo R-Square Index schemes Cox and Snell .536 Table 9 interprets the findings for values of the fitted ordinal logistic regression using Cox and Snell R2, which are satisfactory, i.e., the Higher the value, the better outcomes will be produced by the model. Table 10. Parameter Estimates (Index scheme) Parameter Estimates Estimate Std. Error Wald Df Sig. Preference level in mutual fund (Threshold) Very Highly preferable = 1.00 -3.103 .447 48.098 1 .000 [Highly preferable = 2.00] -1.498 .416 12.962 1 .000 Moderate preferable = 3.00] -.266 .407 .425 1 .514 [Least preferable = 4.00] .510 .409 1.558 1 .212 Determinants [Gender=1.00(Male)] -.315 .333 .894 1 .344 [Gender=2.00(Female)] 0a . . 0 . [Marital Status=1.00(Married)] -.346 .287 1.453 1 .228 [Marital Status=2.00(Unmarried)] 0a . . 0 . [Education=1.00(Graduate)] .383 .266 2.070 1 .041 [Education=2.00(Postgraduate)] 0a . . 0 . Factor1 .076 .144 .276 1 .599 Factor2 2.062 .507 16.536 1 .000 Interaction effect [Education=1.00] * Factor2 1.649 .655 6.326 1 .012 [Education=2.00] * Factor2 0a . . 0 . Factor1* Factor2 -1.168 .332 12.400 1 .000 [Gender=1.00] * [Education=2.00] * Factor2 .515 .479 1.157 1 .282 [Gender=1.00] * [Education=2.00] * Factor2 -1.189 .543 4.795 1 .029 [Gender=2.00] * [Education=1.00] * Factor2 0a . . 0 . [Gender=1.00] * Factor1* Factor2 -.915 .350 6.834 1 .009 [Gender=2.00] * Factor1* Factor2 0a . . 0 . The Beta coefficient for the taken determinants explains that Factor 2, such as the education level and psychological factors, are the sole indicators for preference of index scheme at a 5% significance level. Unlike Factor 2, Factor 1, i.e., Demographic, marital status, and socio-economic factor, along with gender and education level, does not directly affect the preference for the indexing scheme. Therefore, attitude, awareness level, and risk perception are vital indicators for the preference of Index Schemes. Moreover, rather than psychological factors, nine other factors are thought to be vital indicators that affect the preference for the indexing scheme, as already mentioned in table 9. Change in gender affects the fluctuations in the preference level of the indexing scheme considering that Factor 1 and Factor 2 are at the same level. The interrelation of Factor1and Factor2 has a significant effect on the choice of index scheme of the mutual fund. Considering the similar amount of psychological factor, change in gender and level of education also leads to changes in the amount of preference of the indexing scheme. Dev et al., Indian Journal of Finance and Banking 11(1) (2022), 29-37 34 Measuring the Relative Weight of Determinants through Correlation Kendall’s tau correlation has been considered to detect the relative weight of specific determinants of investment in index schemes. Age, attitude, awareness level, education, experience, family income, gender, marital status, and risk perception are the nine identified variables. Out of these nine variables, it is found that attitude, awareness level, education, and risk perception directly influence the preference of the index schemes shown by applying ordinal logistic regression analysis in the tables. Table 11 shows the correlations between the selected factors, the preference level of index schemes, and their significance level. Table 11. Correlation among preference level of index scheme and investment determinants Sl.no Selected determinants on index scheme of Mutual fund Pearson Correlation (Kendall's tau_b) In d e x sc h em e s 1 Education Pearson Correlation (Kendall's tau_b) -.191 Sig. (2-tailed) .000 2 Risk perception Pearson Correlation (Kendall's tau_b) -.265 Sig. (2-tailed) .000 3 Awareness level Pearson Correlation (Kendall's tau_b) .191 Sig. (2-tailed) .000 4 Attitude Pearson Correlation (Kendall's tau_b) .292 Sig. (2-tailed) .000 As per table 11, various psychological factors like attitude, awareness level, and risk perception are vital for index schemes. Aside from the mentioned education, psychological variables are also considered significant factors. Kendall’s tau correlation is conducted to calculate the relative weight of four factors persuading the investors' preference in index schemes of mutual funds. The higher value of Kendall's tau b, the higher the degree of relation between the preference of index schemes and the chosen factors. In table 12, the relative weights of the statistically vital factors are ranked based on their correlation value. Here the highest weight is given as Rank 1, and the lowest is given as Rank 4. Table 12. The relative weight of investment determinants for the Index scheme Sl no Selected determinants on Index scheme of Mutual fund Rank 1 Attitude Rank1 2 Awareness level Rank3 3 Risk perception Rank2 4 Education Rank4 Table 12 shows that the vital role of investment choice towards the index schemes of the mutual fund is influenced by psychological factors, followed by demographic variables. To persuade the investment choice of the bank employees for investment in index schemes, attitude amidst all the determinants got the highest weightage, followed by risk perception being the second vital factor, awareness level being the third, and education being the least. DISCUSSIONS As results indicate that the preference level for index schemes of mutual funds varies. Based on the analysis and result, 67 employees preferred very low while 66 had a moderate preference, and 62 had a high preference for the indexing scheme of the mutual funds, as shown in Table 2. Due to multi-collinearity, a regression cannot give a good fit result. The KMO test is done to know the appropriateness of the test. 0.661 is fully acceptable sample adequacy. Bartlett's test was also done, which shows that the variables' correlations were sufficient to proceed. Further, factors whose commonalities were more than 0.5 were considered otherwise not considered. The Ordinal logistic regression is also employed to determine the effect of the selected factors in the preference for the mutual fund index schemes. For the model, Pearson's chi-square statistic resulted that the model fits very well, as shown in Table 8. Further, it is found that to persuade the investment preference of the employees for investment in index schemes, attitude amidst all the determinants got the highest weightage, followed by risk perception being the second vital factor, awareness level being the third, and education being the least. Academic Implications India is a big developing economy having a middle-class population with huge opportunities. It is the first time to assess the relative weight of determinants for preferring an indexing scheme of the mutual fund. The sample for the study is taken from the state of India, i.e., Tripura. From the lens of academicians, it is a very big opportunity to take this study as a base to go further in more detail and broad. Managerial Implications The study findings are a very good indication for the manager of the mutual fund company. The fund manager can know about the determinants of investments in mutual fund and their effect on the volume of investment. It will be beneficial for Dev et al., Indian Journal of Finance and Banking 11(1) (2022), 29-37 35 them to take appropriate action accordingly so that value of the whole company can be leveled up. Companies should also educate their employees about investment education. Policy Implications The study will provide a better base for the policymakers too. The study will assist the policymakers in knowing the value of determinants and weigh all these determinants as per their weight. The study will also guide the policymakers in designing strategy means what level and what kind of training is required to upgrade the psychological factors towards investment in the mutual fund (Bhattacharjee & Singh, 2017; Bordoloi et al., 2020). Future Scope of the Study The present study is done first to assess the relative weight of determinants for preferring an indexing scheme of the mutual fund. Data is collected within one state of India, i.e., Tripura. Future studies may increase the sample size by covering the whole nation(s) sample. The current study goes with the sample of 262 furnished questionnaires. In contrast, from the angle of future scope, the sample size can be increased to go as accurately as possible because, as we know, the larger the sample size, the higher the accuracy rate. A similar study can also be taken to study the impact of the merger of one bank with another bank; later on, their employees have to work for another bank (Leesa & Singh, 2017). The impact of digital payments on mutual fund investment can also be an important area of study (Kajol et al., 2022; Kajol & Singh, 2022). Social Network Analysis (SNA) can identify the factors affecting the relative weights of investment in mutual funds (Kajol, Nath et al., 2020; Kajol, Biswas, et al., 2020). CONCLUSIONS At the beginning of the current study, the authors targeted the population of 880 employees as of 1st April 2021. The reason behind targeting these employees is that their banks have their own sponsored mutual funds. On fulfilling the criterion of 95% confidence level and 5 % confidence level, a sample of a total of 268 employees has been determined. A pre-tested questionnaire was administered to all 268 selected employees. While only 262 employees out of 268 responded. A total of 9 variables influencing the investors' choices have been observed. It also establishes that all the factors do not have an equal impact on investors' decision-making towards investment in index schemes of mutual funds. The conclusion drawn from the findings observes that psychological components such as risk perception, level of awareness, and attitude are vital determinants associated with the demographic and socio-economic factors for favoring the index schemes of the mutual funds. Not all demographic variables need to influence mutual fund preference directly. Nevertheless, it has an interaction effect with psychological factors as well. Author Contributions: Conceptualization, D.D. and L.P.; Methodology, S.D.; Software, S.D.; Validation, D.D., S.D., R.S. and L.P.; Formal Analysis, R.S. and S.D.; Investigation, R.S.; Resources, S.D.; Data Curation, S.D.; Writing – Original Draft Preparation, D.D. and S.D.; Writing – Review & Editing, R.S. and L.P.; Visualization, R.S. and S.D.; Supervision, R.S.; Project Administration, R.S. and S.D.; Funding Acquisition, D.D., S.D., R.S. and L.P. Authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups or sensitive issues. Funding: The authors received no direct funding for this research. Acknowledgments: Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 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