




































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. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 
Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                     

 

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