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Indian Journal of Finance and Banking; Vol. 4, No. 2; 2020 
                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

79 

 

Antecedents of Financial Inclusion: Evidence from Tripura, India 
 

 
Ranjit Singh PhD 

Associate Professor 
Department of Management Studies 

Indian Institute of Information Technology Allahabad 
Prayagraj-211-15, India 

E-mail: ranjitsingh13@gmail.com 
 

Sankharaj Roy PhD 
Assistant Professor  

ICFAI University Tripura, India 
E-mail: sankharajroy@iutripura.edu.in  

 
Bhartrihari Pandiya PhD 

Assistant Professor  
Asia Pacific Institute of Management, New Delhi, India 

E-mail: bhartrihari.nits@gmail.com  
 
 
Received: August 06, 2020     Accepted: August 19, 2020      Online Published: September 03, 2020 
 
doi: 10.46281/ijfb.v4i2.745    URL: https://doi.org/10.46281/ijfb.v4i2.745 
 
 
Abstract 
The present research assesses the determinants of financial inclusion of members of Self Help Groups (SHGs). The research 
work is done with primary data collection from around 380 members who are beneficiaries of 95 SHGs in Tripura, India. 
After performing factor analysis, six major factors named Physical Infrastructure, Financial Awareness, IT Infrastructure, 
Suitability of Financial Products, Ease of Banking and Economic Status of members of SHGs were identified as major 
factors that play a pivotal role in bringing financial inclusion of the members of SHG. In these 6 factors, some other factors 
were also added such as age, income, education, landholding, the regularity of income, earning members count, gender, caste, 
location, religion, etc. Fitting ordinal logistic regression model, it was found that, the important factors that have an impact 
on financial inclusion on the members of the SHGs are financial awareness, ease of banking and economic status of the 
members, the relevance of financial products, physical infrastructure, monthly income, landholding, education, age and their 
status with respect to the BPL category. The decision-makers in power should collaborate with financial institutions should 
consider and apply this finding during the disbursement of loans.  
 
Keywords: Financial Inclusion, Credit, Financial Institutions, Self Help Group (SHG).        
 
JEL Classification Code: G2, G28, I3.                      
 
1. Introduction  
Exclusive financing concern is a grave issue that has a repercussion on a huge segment of the world populace. Financial 
inclusion deals with providing necessary ingress to relevant products and services relating to finance which is required by 
almost every section in the populace. But the more needy and vulnerable groups are the weaker section of the society whose 
income is low. They are the recipients who should be targeted by financial institutions and get the loans at a rate which they 
can afford (Nair & Tankha, 2015). Financial inclusion is also meant by increasing the reach of these financial services by 
mitigating the barriers faced by them resulting in their inability to have access to formal financial arrangements (Camara, 
Pena, & Tuesta, 2014). It is even discussed to include financial inclusion in the list of next Millennium Development 
Goals (MDGs) (Sinclair & Gamser, 2013) and in the 2030 agenda for sustainable development (Fu, Queralt, & Romano, 
2017). In the Indian subcontinent, there is a system of low intensity of banking accounts because only 35% have accounts 
as per the 2011 census but the household savings add to the national savings in a significant manner (Kant, 2014). The 
families who don‟t have access to the conventional insurance and credit system can be beneficiaries as the formal savings as it 
“enables the building of safety nets to smooth shocks” (Karlan & Morduch, 2009).  After demonetization in India in 2016, 
there is a tremendous push for digital payments but there needs to be a readiness for digital transactions and requisite 
infrastructure for adapting it (Sinha, Pandey, & Madan, 2018).  

mailto:ranjitsingh13@gmail.com
mailto:sankharajroy@iutripura.edu.in
mailto:bhartrihari.nits@gmail.com
https://doi.org/10.46281/ijfb.v4i2.745


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 The institutions having administrative roles and regulatory authorities with the support of financial institutions 
and Non-Government Organizations (NGOs), Self-help Groups along with organizations of microfinance nature are in 
tandem in strategizing objectives for the underprivileged lot. To attain inclusive financing, certain programs by banks like 
linkage programs were initiated to give financial services targeting the poor to alleviate their economic situation rather than 
their dependence on informal systems of credit (Abiad, Cuevas, & Graham, 1988). The scenario in Bangladesh regarding 
financial inclusion is that the extreme underprivileged populaces have the least reach to services related to finances. Few 
financial institutions in the African continent are present to serve the poorest by the program named Village Savings and 
Loans Associations (VSLAs) which focuses on reducing debt and increasing savings (Hendricks & Chidiac, 2011). So, the 
organizations have to accordingly understand the factors which have a role in the inclusive financing for the members of 
SHGs. The better individuals and groups in society have the better fortune to make use of the financial services instruments 
like insurance, online banking and credit system, etc. It helps to improve the chances of usage of products and services by 
reducing the risk of immediate emergency financial (en, Demirguc-Kunt, Klapper, & Peria, 2012). In the Indian scenario, 
the financial institutions are willing to help these SHGs in mitigating their poor economic condition and improve their 
livelihood (Harper, 1996) and with the advent of SHG Bank Linkage Programme as retail marketing channel development 
programs, financial inclusion can be attained (Harper, 2002). The current study explores and identifies the antecedents that 
impact the financial inclusiveness of SHG members in the formal banking sector. Therefore, the objectives of the present 
study are given as follows: 

 To assess the level of inclusive financing of SHG members; 
 To categorize the antecedents of inclusive financing having an impact on the usage of financial products and 

services by SHG members And thus, this research work tries to answer the below research questions: 
 What is the present condition of inclusive financing for SHG members? 

 What are the antecedents of inclusive financing having an impact on the usage of financial products 
and services by SHG members   

 
The present study is significant in exploring the antecedents of the financial inclusion of the members of the 

SHGs. These SHGs are created to empower women especially in the economically backward class which who don‟t have the 
reach to conventional financial products and services and remain in destitution (Raheem, 2012). The study of antecedents 
affecting access to inclusive financing has attained a momentum across the globe as the worldwide regulatory bodies are 
aiming and struggling to include more and more populace under the ambit of formal financial structure. So, the people 
working in various SHGs, considered to be coming from poor households, are mostly in need of banking and financial 
services but dependent on informal sources (Kumar & Mishra, 2011). The study becomes significant because it is based on 
empirical evidence.  

 
2. Literature Review 
A thorough and nearly comprehensive literature review in the field of inclusive financing was done to understand the 
underlying antecedents which have a role in its impact on the populace. The various underlying antecedents were identified 
and studied to have an understanding of the scenario in this sector. Nation Sample Survey Organization (NSSO) in its 
report reported that around 76% of the rural populace has a credit requirement which is satiated by moneylenders. The 
process of financial exclusion can take place only when individuals or groups don‟t use or have a meager use of financial 
services (Ford & Rowlingson, 1966; Kempson & Whyley, 1998). The very perception of financial exclusion is the inability 
to have an access to required financial products and services the reason for which is price, marketing, non-access, various 
conditions or self-exclusion (Beck, Asli, & Soledad, 2008).  

The usage of conventional financial services and products such as insurance, online banking, savings schemes help 
to improve the chances of consumption and reduce the risk of emergency requirements (en et al., 2012; Choudhury & 
Singh, 2015). The underlying socio-economic circumstances of the rural populace are huge restrictions and blockage for 
them is motivating them to seek out the formal financial services (Demirgüc-Kunt & Klapper, 2013). But, there is a huge 
scope for using Information and Communication Technology (ICT) to bring the banking services to the house of the 
consumers that too at a lesser cost (Gupta, 2011; Brynjolfsson & Hitt, 2000; Das, 2010; Gangopadhayay, 2009).   

Moreover, the probability of inclusion of financial needs in the official banking system is high and it leads to 
saving capacity (Beck, Demirgüç-Kunt, & Martines Peria, 2006). However, the economic status of the people such as 
possession of landed property or possession of a house increases the chances of usage of the available products and services 
in banking (Collins & Daryl, 2009; Bhattacharyay, 2016; Sahoo, Pradhan, & Sahu, 2017). Asset endowment is also one of 
the essential factors of reducing poverty (Donovan & Poole, 2013).  

Despite the above factors, it is seen that many people are still not using banking services due to the reason that 
they do not consider is convenient to use and afraid of going to any bank and therefore, some farmers in the rural areas are 
using mobile devices to do book-keeping, making payment as well as receiving money (Rogers, 2003). However, Nwuke 
(1997) found that there is a positive effect of bank density and urbanization on the mobilization of savings. Thus, banking 
services can be provided to the poor‟s using ICT and other latest technology (Hishigsuren, 2006) with proposed IT 
governance framework to improve service quality (Singh, Pandiya, Upadhyay, & Singh, 2020). It needs to be monitored as 
the ability to access and usage sufficiency is not always the signs of success (Porteous & Zollmann, 2016). 



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Fernandes, Lynch Jr., and Netemeyer (2014) and Clarke, Xu, and Zou (2006) have identified that being 
financially educated is a good sign of inclusive financing. Those people who are financially literate are in a better position to 
manage their personal budgeting and finance and making financial decisions (Moore, 2003; Campbell, 2006; Perry & 
Morris, 2005; Lusardi & Olivia, 2011). In addition to the above, physical infrastructure comprising road conditions to 
bank branches, distance of individuals‟ house from the branches of banks/ATMs, or the present post offices. Linking 
market to SHG products, and accessing information about various financial products has a noteworthy role in enabling an 

individual to access various banking and financial products (Kumar, 2013; Fungáčová & Weill, 2013; Tuesta, Sorensen, 
Haring, & Camara, 2015; Kedir, 2003).  

Various variables of demography and also socio-economic nature also affect the level of inclusion financing. The 
traditional microfinance schemes are of advantage to „the economically active poor individuals‟ and they are not the „poorest 
of the poor‟ if the basic definition is considered (Premchander & Harper, 2018). These are age and education (Johnson & 
Nino-Zarazua, 2007; Sahoo, Pradhan, & Sahu, 2017), income (Aslan, Delchet, & Monique, 2012; Kumar, 2013), number 
of adult family members (Roy, Singh, & Singh, 2017a; Sinclair, 2013), economic status (Wangwe, 2004; Harris, Loundes, 
& Webster, 1999), location, gender and religion (Akpandjar, Quartey, & Abor, 2013; Yadav & Sharma, 2016; Demirgüç-
Kunt, Klapper, & Randall, 2013; Bhattacharyay, 2016). Regarding age, it is the aim to develop the generation which is 
upcoming having economic knack and educating the youth regarding its usefulness (Billimoria, Penner, & Knoote, 2013). 

From the literature discussed above, it is observed that most of the empirical research works relevant to the topic 
are done in the various parts of India and other countries. Though many studies have been done in respect of microfinance, 
limited studies are conducted to understand and categorize the antecedents of financial inclusion of the members of SHGs 
in a state like Tripura of India which is one of the smallest states of Indian Union. It is a landlocked state having very 
limited connectivity from the rest of the India and world. Hence, to bridge the above research gaps, it is therefore needed to 
conduct a study identifying the antecedents of inclusive financing for SHG members. 

 
3. Methodology of the Study 
The present research work is confined to the boundary of Tripura state of India. One member form one household who is 
also a member of any SHG operating in Tripura consisted of the sampling unit. The responses were conducted from 
October 2018 to December 2018. There are more than 150 numbers of SHGs in the state of Tripura. From all these 
SHGs, there are approximately 37123 members in all these SHGs which constitute the universe of the study. Using simple 
random sampling, at a 5% level of significance and 5% confidence interval sample size is determined to be 384. The data 
was collected using a structured interview schedule administered to the members of SHGs. A structured interview schedule 
was prepared to measure the level of financial inclusion of the members of SHG. Based on study made by Sahoo, Pradhan, 
and Sahu (2017); Zins and Weill (2016) and Roy, Singh, and Singh (2017b), a pilot study done by the researcher, opinion 
of the experts and observation during the study, 25 items, as given in table 2 of the paper, were identified. The respondents 
were asked to provide their responses by way of rating on a five-point scale where 5 indicates the highest order of agreement 
with the stated item and 1 indicates the least order of agreement with the stated item. Besides, information about gender, 
age, education, caste, family income, land-holding, status about BPL category, religion, etc. was also sought. Statistical tools 
such as mean, standard deviation, principal component analysis, and ordinal logistic regression were used. The factors are 
reduced using the principal component analysis (Tabachnik & Fidell, 2007; Harman, 1976). The reliability of the items in 
the interview schedule was assessed by computing Cronbach‟s Alpha.  Regression analysis was also done to fit in the 
regression model. The profile of the respondents is given in table 1. 
 
Table 1. Demographic profiling of the respondents 
 

Gender of the respondents 

Gender Frequency Percentage 

Male 50 13.0 

Female 334 87.0 

Total 384 100.0 

Age of the respondents  

Age  Frequency Percentage 

15 to 20 years 002 00.50 

21 to 30 years 051 13.30 

31 to 40 years 158 41.10 

41to 50 years 159 41.40 

51 to 60 years 014 03.60 

Total 384 100.0 

Education of the respondents  

Non-Matriculate 179 46.6 

Matriculate 065 16.9 



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12th 092 24.0 

Graduate 048 12.5 

Total 384 100.0 

Category of the respondents  

ST 055 14.3 

SC 075 19.5 

OBC 073 19.0 

General 181 47.1 

Total 384 100.0 

Monthly income of the respondents (In USD)   

< USD 60 037 9.6 

USD 60 to USD 100 164 42.7 

USD 100 to USD 150 087 22.7 

USD 150 to USD 200 061 15.9 

USD 200 to USD 300 024 6.3 

More than USD 300 011 2.9 

Total 384 100.0 

Monthly expenditure of the respondents (In USD) 

< USD 30 041 10.7 

USD 30 to USD 60 196 51.0 

USD 60 to USD 90 100 26.0 

USD 90 to USD 115 033 8.6 

USD 115 to USD 150 010 2.6 

More than USD 150 004 1.0 

Total 384 100.0 

Source: Compiled from the questionnaire 
 

4. Analysis and Findings 
The further analysis of the underlying factors which have a role in inclusive financing of SHG members is discussed in this 
section. For the measurement of the level of the inclusive financing level, a scale was developed. There were 25 items on the 
scale. The reliability of the scale was done by using Cronbachs Alpha. Cronbach‟s Alpha has a value of 0.797. In the existing  
norms of reliability, if a Cronbach‟s Alpha has a value of more than 0.70 then it is considered as an acceptable measure of 
reliability (Nunnaly, 1978; George & Mallery, 2003). This is proof of reliability for the used scale with the underlying fact 
that the items in it are highly correlated and aiming towards the measurement of the latent variable.  
 
4.1 Measuring Inclusive Financing of SHG Members 
For the measurement of inclusive financing of the SHG members, mean value and standard deviation of the 25 items chosen 
for constructing the scale in the measurement of inclusive financing as portrayed in Table 2:  
 
Table 2. Item Statistics 
 

Items Mean 
Value 

Standard 
Deviation 

Awareness about banking and other financial products 2.1667 1.45904 

Satisfied with the features of insurance product 2.2734 1.39215 

Condition of the road reaching to a bank branch 2.3646 1.50799 

Terms and conditions attached to loan products are stiff 2.4115 1.37584 

SHG having linkage to market 2.4349 1.4988 

Monthly household expenditure 2.5312 1.59767 

Financial counseling by banks 2.638 1.58706 

Access to information through newspaper 2.6589 1.57519 

Monthly household income 2.6875 1.49717 

The distance of Branch/ATMs from their house 2.776 1.61159 

Insufficient collateral security for availing bank loan 2.849 1.64089 



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Non-availability of business correspondents 2.9219 1.34383 

Satisfied with the features of credit products 2.974 1.48424 

The documentation process for availing banking services are lengthy 3.0729 1.33829 

The distance of post-office from their house 3.125 1.75079 

Time taken in getting a service is lengthy 3.1432 1.36835 

Fear of rejection by banks/financial institutions 3.2812 1.30836 

Non availability of the internet for online banking 3.3177 1.04374 

Satisfied with the features of savings bank product 3.3307 1.12066 

Prefer taking a loan from informal sources than banks 3.4531 1.5858 

Non-availability of smartphone for mobile banking 3.5234 1.49306 

Land-holding of the members of the SHGs 3.9089 0.82679 

The economic status of the members of the SHGs 3.9792 0.83624 

Regularity of income for paying EMI 4.1589 0.97663 

Satisfied with the services rendered by bank staffs 4.4375 0.86791 

Total of mean values of all the considered items  76.4193 34.08808 

Source: Compiled from the questionnaire 
 

         As shown in above Table 2, it is evident that awareness about banking and other financial products features of 
insurance products and conditions of the road reaching the bank branches are the least contributing factors in overall 
inclusive financing of the SHG members. On the other hand, the economic status of the SHG members, the regularity of 
income for paying EMI, satisfied with the services rendered by bank staffs are the highest contributing factors in bringing 
inclusive financing among the SHG members. 
 There were 25 antecedents in the scale considered for the study. The questionnaire was distributed to the 
respondents asking them to rate the statements in the Likert scale having 5 points. The scale had the scores of 5,4,3,2 and 1 
which commensurate the response of the individuals as strongly agree, agree, neutral, disagree and strongly disagree 
respectively. Further, this total score for inclusive financing would be attained by adding the overall scores of the 
antecedents. The maximum possible score of financial inclusion was 125 (25x5) and the minimum possible score was 25 
(25x1). Logically, the differentiation between the maximum and minimum potential score was 100 (125-25). For 
ascertaining the level of inclusive financing at three levels, the discussed range was divided by 3. It was established to be 
33.33. So, adding 33.33 to 25 (lowest probable score), the score range for the low level of inclusive financing was obtained 
which was 25 – 58.33. Similarly, adding 33.33 with succeeding values, the upcoming higher range could be calculated Singh 
and Bhowal (2011); Singh (2012) Singh and Bhattacharjee (2019) also used similar kinds of interpretation tables to 
interpret their result. The Table 3 below shows the interpretation for the inclusive financing score: 
 
Table 3. Interpretation of financial inclusion score 
 

Scale value Interpretation of scale value 

25 – 58.33 Low level of financial inclusion  

58.33 – 91.66 Moderate level of financial inclusion 

91.66 - 125 High level of financial inclusion 

Source: Compiled from the questionnaire 
 

On the basis of interpretation table given in Table 3, level of financial inclusion is calculated and presented in Table 4. 
 
Table 4. Layers of Financial Inclusion based on the survey 
 

Source: Compiled from the questionnaire 
 

Levels Number of Members Percent 

Low Level of Financial Inclusion 250 65.1 

Moderate Level of Financial Inclusion 84 21.9 

High Level of Financial Inclusion 50 13 

Total 384 100 



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As per the analysis, the majority of respondents that is, 65.1% are in the low layer of inclusion.   
 
The overall mean for all the 25 items considered in the scale is 76.4193 which fall in the range of moderate layer of 
inclusive financing according to Table 4. Thus, the overall level of inclusive financing of the SHG members falls in 
moderate level. 
 
4.2 Identifying Factors Affecting Financial Inclusion through Factor Analysis    
The prerequisite for performing factor analysis is the sample size adequacy. For testing the sample size adequacy, the KMO 
measurement of sample adequacy and Bartlett‟s test had to be performed.  The value of KMO was 0.848. As per the norms, 
a KMO value which falls between 0.7 and 1 signifies sample adequacy (Cerny & Kaiser, 1977) and is sufficient for 

performing Exploratory Factor Analysis (Child, 2006). The Bartlett‟s test of Sphericity 𝑥2 300 = 3341.062 , ρ<.001 
indicated that there existed a relationship pattern among the items.  

The Table 5 below shows that the 6 factors, whose Eigenvalue is more than 1, extracted amidst the mentioned 25 
variables explaining 63.94% of the possible variance in the dependent variable. 
 
Table 5.Total variance explained 
 

 
 

Component 

Initial Eigenvalues Extraction Sums of Squared 
Loadings Squared Loadings 

Rotation Sums of Squared Loadings 

Total %  
of Variance 

Cumulative 
% 

Total % of 
Variance 

Cumulative 
% 

Total % of 
Variance 

Cumulative 
% 

1 4.909 19.636 19.637 4.909 19.636 19.637 3.897 15.588 15.588 

2 3.561 14.244 33.881 3.561 14.244 33.881 3.771 15.084 30.672 

3 3.129 12.516 46.397 3.129 12.516 46.397 2.469 9.876 40.548 

4 1.953 7.812 54.209 1.953 7.812 54.209 2.049 8.196 48.744 

5 1.727 6.908 61.117 1.727 6.908 61.117 1.957 7.828 56.572 

6 0.707 2.828 63.945 0.707 2.828 63.945 1.843 7.372 63.945 

7 0.705 2.820 66.765  

8 0.701 2.804 69.569 

9 0.696 2.784 72.353 

10 0.685 2.740 75.093 

11 0.665 2.66 77.753 

12 0.587 2.348 80.101 

13 0.584 2.336 82.437 

14 0.576 2.304 84.741 

15 0.574 2.296 87.037 

16 0.561 2.244 89.281 

17 0.492 1.968 91.249 

18 0.452 1.808 93.057 

19 0.444 1.776 94.833 

20 0.435 1.740 96.573 

21 0.346 1.384 97.957 

22 0.187 0.748 98.705 

23 0.165 0.660 99.365 

24 0.097 0.388 99.753 

     25 0.062 0.247 100.00 

Extraction Method: Principal Component Analysis, Source: Compiled from the questionnaire 
 
The factors can explain the observed common covariance matrix between the 25 factors for dimensionality 

reduction is attained by applying the principal component analysis (Bartholomew & Child, 1980). It is shown in Table 6.  
 
 
 
 



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Table 6.  Rotated Component Matrix 
 

Factors Component 

1 2 3 4 5 6 

Satisfied with the features of insurance product .791 -
.129 

.184 -.209 .216 .218 

Satisfied with the features of credit products .682 -
.180 

.330 -.184 .194 .122 

Satisfied with the features of savings bank product 
 

.642 .157 .132 .309 .142 .163 

Terms and conditions attached to loan products are stiff .660 -
.198 

.293 -.150 -.207 .094 

The documentation process for availing banking services are lengthy -
.209 

.798 -
.183 

.444 .137 .044 

Time taken in getting a service is lengthy -
.184 

.745 .401 .209 .108 -.152 

Prefer taking a loan from informal sources than banks .309 .718 .136 -.184 .141 .211 

Fear of rejection by banks/financial institutions -
.150 

.610 -
.211 

.309 -.116 -.019 

Non-availability of business correspondents -
.117 

.654 -
.220 

.445 .061 .106 

Insufficient collateral security for availing bank loan .444 .704 .250 .178 .001 .084 

Satisfied with the services rendered by bank staffs .368 .636 -
.151 

.078 -.251 .218 

Condition of the road reaching to the bank branch .117 .444 .728 .058 .279 .122 

The distance of Branch/ATMs from their house .370 .368 .926 .347 .225 .432 

SHG having linkage to market -
.301 

.117 .914 .398 -.128 .079 

The distance of post-office from their house .120 .370 .892 .349 -.045 -.336 

Access to information through newspaper .115 .530 .865 .187 .271 .077 

Monthly household expenditure -
.285 

-
.116 

.309 .642 .208 -.138 

Monthly household income .375 .194 -
.184 

.673 -.357 .323 

The economic status of the members of the SHGs -
.301 

.342 .209 .684 .075 .004 

Landholding of the members of the SHGs .211 -
.207 

-
.150 

.555 -.093 .446 

The regularity of income for paying EMI -
.220 

-
.151 

.217 .658 .256 .070 

Non-availability of the internet for online banking .250 .137 -
.209 

.359 .687 .432 

Non-availability of smartphone for mobile banking -
.151 

.208 .285 .261 .669 .079 

Level of awareness about banking and other financial products .211 .141 .129 .321 .223 .519 

Financial counseling by banks .312 .241 .169 .235 .066 .612 

Extraction Method: Principal Component Analysis 
 Rotation Method: Varimax with Kaiser Normalization. Source: Extraction Method: Principal Component Analysis, Note: Factor loadings over .50 appear in bold 
 
Rotation Method: Varimax with Kaiser Normalization 
 
The process of assigning names to the components was based on the literature review and expert feedback. It is presented in 
Table 7 below.  
 
Table 7. Name of the Component 
 

Sr. No Variables/Factors Mean of  
individual 

items 

Factors obtained 
 after PCA 

Mean of the 
PCA factors 

1 Features of insurance product 2.27 Suitability of  
Financial 
Products 

2.7474 

2 Features of credit products 2.97 

3 Features of savings bank product 3.3307 

4 Terms and conditions attached to loan products 2.4115 

5 The documentation process for availing banking services are 
lengthy 

3.0729 Ease of  
Banking 

3.3084 

6 Time taken in getting a service is lengthy 3.1432 

7 Prefer taking a loan from informal sources than banks 3.4531 



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8 Fear of rejection by banks/financial institutions 3.2812 

9 Non-availability of business correspondents 2.9219 

10 Insufficient collateral security for availing bank loan 2.849 

11 Satisfied with the services rendered by the bank staffs 4.4375 

12 Condition of the road reaching to a bank branch 2.3646 Physical  
Infrastructure 

2.67188 

13 The distance of Bank/ATMs 2.776 

14 SHG having linkage to market 2.4349 

15 The distance of post-office 3.125 

16 Access to information through newspaper 2.6589 

17 Monthly household expenditure 2.5312 Economic status 
of  

members of 
SHGs 

3.45314 

18 Monthly household income 2.6875 

19 The economic status of the members of the SHGs 3.9792 

20 Landholding of the members of the SHGs 3.9089 

21 The regularity of income for paying EMI 4.1589 

22 Availability of internet for online banking 3.3177 I.T.  
Infrastructure 

3.42055 

23 Availability of smartphone for mobile banking 3.5234 

24 Awareness about banking and other financial products 2.1667 Financial 
Awareness 

2.40235 

25 Enhancement of financial counseling of issues that span 
regulators  

2.638 

 Total of the mean value 76.4193   18.00372 

Source: Renaming factors on the based on factor loading 
 

 The mean of the factors has been shown in Table 7 above. The factor „Economic status of the members of SHGs‟ 
has the highest mean which truly depicts how the economic condition of the individual and family matters the most for 
bringing financial inclusion of people. The factor „I.T. Infrastructure‟ ranks 2nd as it is the medium of transaction. With the 
growing usage of digital media such as mobile phones and the internet it is very obvious that more and more individuals will 
use it gradually. A user-friendly interface is very important to target and attract more rural customers. Ease of banking has 
the 3rd rank. The formalities required by the banks such as documentation process and the overall time taken for a 
transaction matters as should be quick and safe as much as possible. The services rendered by the bank should be congenial 
such that the customers don‟t face problems. Suitability of financial products ranks 4th as the features and characteristics of 
the financial product like loan etc also catches the eye of the consumer. Physical infrastructure matters in the least 
magnitude to the consumers. Financial awareness stands last with a mean score of 2.4.  
 
4.3 Impact of These Factors on Financial Inclusion 
After identification of the six factors which affect inclusive financing of SHG members, the next objective of the research 
was to find the impact of demographic and socio-economic factors along with the six factors identified after data reduction 
on the overall inclusive financing of SHG members. The demographic variables identified in this study are gender, age, 
education, caste and religion. The socio-economic variables considered in the study are income, landholding, family 
members, caste and their status regarding the BPL category. Deb and Singh (2018) adopted a similar way of identifying the 
antecedent factors. 

The measure of Multicollinearity affects the parameter of the model of regression. The biasness in logistic 
regression is somewhat prone to collinearity hence it is mandatory to do collinearity test before performing a logistic 
regression analysis (Field, 2005). This condition can be crosschecked by Variance Inflation Factor (VIF) along with the 
Tolerance level using the outcome and predictor variables. The tolerance value if less than 0.1 points toward a severe 
collinearity problem with the multiple predictor variables (Menard, 1995). If the VIF value is greater than 10, then it is a 
matter of apprehension that resembles collinearity (Myers, 1990). The same acceptable level is recommended by other 
authors such as Hair, Anderson, Tatham, and Black (1995) and Kennedy (1992).  

 
Table 8. Coefficient of VIF and Tolerance factor 

 

 
Exploratory Variables 

Collinearity Statistics 

Tolerance VIF 

Suitability of financial products .872 1.147 



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Ease of banking .685 1.461 

Physical infrastructure .949 1.054 

Economic status of the members .721 1.386 

IT infrastructure .927 1.078 

Financial awareness .958 1.044 

Monthly Income .401 1.495 

Land Holding .786 1.273 

Education .588 1.701 

Age .828 1.207 

Regularity of receiving income .685 1.461 

Earning family members count .835 1.198 

Gender .884 1.132 

Caste .842 1.187 

Location .748 1.337 

Religion .869 1.151 

a. Dependent Variable: Level of Financial Inclusion 
 

The above table showing the multicollinearity statistics shows the VIF of each independent variable. As per the 
standard, the acceptable VIF is less than 5. In this analysis, all the VIF is near to one that suggests the absence of 
collinearity between the exploratory variables. Therefore, the assumption for performing logistic regression is met and the 
same can be used for the model.  

To find out the impact, the model has used the financial inclusion index constructed using the interpretation table 
given in table 3 as an ordinal dependent variable. Ordinal logistic regression is used to predict the output with one or more 
independent variables (predictor variable) when the dependent variable is in the ordinal scale. Singh and Bhattacharjee 
(2010a) and Singh and Bhattacharjee (2010b) have sued similar analysis to draw the analogy.  

 
The model of the study is  
 

 
Where, 

Ease of banking 

Financial awareness 

Economic status of the members 

Suitability of financial products 

Physical infrastructure 

  = IT infrastructure 

= Monthly Income 

= Land Holding 

= Education 

= Age 

= Regularity of receiving income 

= Adult family member count 

= Gender 

= BPL Category  

= Location 

= Religion 
 

Before proceeding with the ordinal logistic regression model to discuss the impact of every explanatory antecedent 
existing in the model, it‟s suggested to check if the model adds improvement regarding the capacity to predict the result. A 
comparison between the final model (having explanatory variable) and the intercept only or the baseline model (without any 
explanatory variable) is done to check if whether the model improves the fit to the data in a significant manner.  

 
 



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  Table 9. Model Fitting Information 
 

Model Fitting Information 

Model -2 Log Likelihood Chi-Square df Sig. 

Intercept Only 373.783    

Final 163.668 310.116 16 .000 

Link function: Logit. 

Source: Compiled from data analysis 
 

Table 9 shows the information regarding the model fitting gives the -2 log-likelihood value regarding the 
intercept only or the baseline model and the final model. Log of likelihood is a measure of error or variation. This 
likelihood ratio test follows the Chi-square distribution (Hosmer & Lemeshow, 1989). Further, the Chi-square value of 
310.116 is statistically significant (p-value<.05) and it indicates that the nature of the final model. It shows that the final 
model provides a significant enhancement over the intercept only model. The significance indicates that the model provides 
a better prediction.  

 The logistic regression model shows the strength of association and is measured by Pseudo-R-square which is 
given in Table 10. 

Table 10. Pseudo R-Square 
 

Cox and Snell .540 

Nagelkerke .670 

McFadden .346 

Source: Compiled from Questionnaire 
 

Here the pseudo-R-square, as given shown in table 10, (e.g. Nagelkerke = 67%) shows that the 16 variables 
explain 67 percent of the variation in the level of financial inclusion. 

 
  Table 11. Ordinal Logistic Regression result of financial inclusion 

 

Explanatory Variables Estimated 
Coefficients 

Sig. Value 

Ease of banking 4.090 .000* 

Financial awareness -4.031 .000* 

Economic status of the members 2.874 .000* 

Suitability of financial products 2.031 .000* 

Physical infrastructure 1.653 .000* 

  = IT infrastructure 1.125 .398 

= Monthly Income 0.747 .000* 

= Land Holding 0.553 .003* 

= Education -0.469 .002* 

= Age -0.429 .001* 

= Regularity of receiving income 0.242 .467 

= Adult family member count 0.131 .495 

= Gender 0.123 .735 

= BPL Category  0.112 .004* 



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= Location -0.077 .579 

= Religion 0.039 .860 

                             Dependent Variable: Level of financial inclusion, Link function: Logit.  
                          *significant at 5% level of significance 
  
 Table 11 shows that ease of banking, financial awareness, economic status of the members, suitability of financial 
products, physical infrastructure, monthly income, landholding, education, age and their BPL status is affecting the degree 
of inclusive financing. Thus, these are the factors that should be finally studied deeper for inferences.  
 
5. Conclusion and Policy Implications 
The conclusion of the research work starts from an observation that the overall level of inclusive financing of the SHG 
members is of moderate level. The important issue for all the policy-makers starts from planning to provide the available 
banking services to the poor populace for their upliftment. The financial services need to be within reach of the 
underprivileged for their economic betterment. In the case of the Indian state of Tripura, it is even more important because 
the banking penetration is quite low. The research work has enumerated six factors found after factor analysis that have an 
effect on inclusive financing for SHG members. The factors found are “suitability of financial products, ease of banking, 
physical infrastructure, and economic status of the SHG members, I.T. infrastructure and financial awareness”. The findings 
are quite similar in nature with the conclusions reached by Allen, Demirgüç-Kunt, Klapper, Soledad, and Peria, (2012); 
Kumar (2013). Leyshon and Thrift (1995); Kempson and Whyley (1999); Clamara, Peña, and Tuesta (2014) also ended 
up with related findings in their research work. The identification of the factors has the potential to help the financial 
institutions and regulators to comprehend the rationale behind individual‟s likeness towards financial services which are 
informal in nature instead of conventional ones (Buera & Shin, 2013).  

It was also seen in the research work that the ease of banking and financial awareness/education has the highest 
impact on overall inclusive financing for members of SHG. Thus, it can be inferred that if banking rules are simplified for 
the common and poor people, it will help them to avail the banking facilities to the best of their needs and benefits. The 
conventional financial services and products for example banking transactions, overdrafts, savings, insurance, etc should be 
user friendly. There is also a need for financial counseling for the SHG members which will help them to recognize and rate 
the formal financial services. The banking sector under the aegis of government should work in tandem to set their priorities 
in removing the barriers of communications and usage as discussed above. These efforts will make the financial services and 
products more accessible to the weaker section of the society. The scenario of success in the case of Pradhan Mantri Jan 
Dhan Scheme in India is a testimony in this regard. Similarly, spreading awareness about various financial products and 
services is expected to increase the level of inclusive financing (Feldstein & Horioka, 1980; Bhattacharjee & Singh, 2017; 
Singh & Bhowal, 2010; Bordoloi, Singh, Bhattacharjee, & Bezborah, 2020). It has been also reiterated that the present time 
is feasible for the government to leverage and protect the poor rural individuals socially by enabling them to save more and 
get financially included along with the rest (Zimmerman & Holmes, 2012). This can better be done by giving them the 
experience of banking and modern financial instrument so that they can feel the benefit of these products (Choudhury, 
Singh & Saikia, 2016). The use of these financial products will expose them to the current market scenario and 
consequently, they will learn the science and art of managing the modern financial products (Singh, 2011).  

Age and education are seen as significant factors and are consistent with other studies where similar findings were 
derived (Singh & Bhattacharjee, 2010a). Young and educated people are less likely to be excluded than older and less 
educated members (Johnson & Nino-Zarazua, 2007). 

Ordinal logistic regression shows that IT infrastructure is not a significant factor for bringing financial inclusion 
among the members of the SHGs. Tiwari and Singh (2018) have got similar findings. It also reveals that regularity of 
receiving income, number of adult family members; gender, location and religion have no noteworthy impact on inclusive 
financing.  

As discussed above, the collaboration and joint efforts of government and financial institutions must process this 
information and understand the patterns in it to simplify the process of banking and disbursement of credit to the 
underprivileged populace. The principles of cooperative development can also be thought of in this direction (Donovan, 
Blare, & Poole, 2017). The process of disbursing credits to the underprivileged section in the society should be in 
synchronization with the government policies.  Governments and NGOs can think of the development of a value chain for 
improving economic growth leading to reduced poverty in rural areas (Donovan, Franzel, Cunha, Gyau, & Mithöfer, 2015). 
This will help in bringing inclusive financing to the section of that society which is excluded from it.   

 
6. Scope of Future Research  
The present research work has been conducted in the Indian state of Tripura; hence for a broader generalization of the 
findings to have an impact in the entire country, a comprehensive study should be done on a broader geographical area. 
Moreover, the impact of these determinants with their relative weight on financial inclusion can also be undertaken.  More 
components can be extracted from the literature which might have a comparatively lesser impact on financial inclusion.  A 
thorough ground survey and Focus Group Discussion (FGD) can be conducted to understand the problems and the current 



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situation in the areas. The FGD will provide the scope and atmosphere for unabated and open-ended answers which can 
depict the future areas of action.  

 
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