Keywords

Credit Cards, Acceptance, Usage, Banking, Demographics, Socio-Economic.

Introduction

Globally the financial institutions predict strong growth within the financial service market, because of changing the global consumer base and demographic profile. However, the speed at which banks and other financial institutions are exploiting these opportunities varies to a good extent. Each association has its own approach and risk enthusiasm, and this prescribes its attitude to service innovation in 2019 and within the years ahead. As such, there's a keen specialize in core renovation, consumer channel innovation, business process automation and digitization as banks are striving to become digital.

With the most recent technological advancement, the Govt. of India has been working hard to promote digital payment systems. The Govt. has reported at 400–1,000% increase in digital transactions since the demonetization. As per the direction of Hon'ble prime minister of India, Sh. Narendra Modi wants one mission and goal i.e. takes the state forward digitally and economically. To endorse the digital payment in India the amount of incentives has been offered by the Govt. to market digital India became Cashless India. To become the cashless India RBI needs to provide momentum for the recognition of plastic card which is steady with the present policy of economic and monetary liberalization (Gambhir, 1998). Within the present scenario, because of monetary liberalization, innovation in consumer banking is that the introduction of Plastic Money. Here, the word plastic money is the hard plastic cards which are used instead of actual bank notes in the form of credit cards.

Thus, the essence of the credit card is captured within the following definition:

‘A card is a minute plastic card issued to its owner as a system of payment. It permits its owner to shop for goods and services supported owner’s promise to purchase for these goods and services. The card issuer generates an account and grants a credit line to its customers from which the customers can credit money for payment to a merchant’ (Sudhagar, 2012).

The credit card is used as a financing instrument apart from a medium of convenience. In other words, it allows us to obtain goods and services with the concept of buy now and pay later (Lee & Kwon, 2002). Banks, retail stores and other companies usually issue credit cards. The term credit derives from a Latin word meaning trust. It is a credit card that permits the holder to borrow money or to purchase goods without paying for them directly with the help of a smart card. A credit card is more than a simple, piece of the credit card. It is a primary flexible payment system that is assigned to a consumer for the small-term without paying any interest (Soman, 1999; Soman, 2001).

A credit card is an asset to the lifestyle. It provides the customers always pay their balance in free on time. It's more suitable to hold than cash and make a decent credit history score. Aside from providing the much-needed substitute to cash, credit cards also provide you with benefits in the form of rewards, cash backs, interest-free period and much more. The demand for credit cards within the Indian market is rising rapidly and thus, leading banks have launched plenty of credit card options for the batch to decide on from. There are a number of credit cards offered in the market, having unique features and benefits associated with them.

Normally, a credit card comes with the subsequent features.

An alternative to Cash: Having a credit card is a very safe and convenient substitute for carrying a bundle of money. It can make shopping hassle-free and can also make sure the security of cash.

Emergencies: Just in case of a medical emergency or different kinds of urgent cash requirements, credit cards can give help by making instant credit available under such situations.

Making Big Purchases: The concept of buy now pay later allows customers to create some huge purchases and setting up an EMI payback scheme with their banks.

Building Credit Score: One in all of the most advantages of possessing a credit card is that it's easy to make a credit history through one’s credit card transactions. Also, repaying credit card dues in time will help improve one’s credit score which, in turn, can help the cardholder in getting loans within the future. Persistently requests are rejected because the candidate doesn't have a credit history and having a credit card is one in all the best ways to make your credit history.

Secure Transactions: The new credit cards feature is that the chip and pin system, which increases their security, in order they are safer than carrying around large bulk of money and also protect you from credit card fraud. Within the case of online transactions, a two-tier confirmation system is tracked, where except the information on the card like card number, expiry date and CVV, OTP or secret password is additionally compulsory to complete the net transaction which is distributed to your registered mobile number.

Tracking Purchases: When using cash, it becomes problematic to stay track of the purchases. Though, with a credit card, one’s can easily maintain a chronicle of the transactions that one has made with the assistance of the monthly credit card statement.

So, a credit card features shows that it plays a significant role in the consumer culture. It is observed that carrying lots of money are often risky and sometimes, one may short of it just when it's needed most. Credit is that the elegant reply out towards the above mentioned problems. It's a secure and suitable choice for cash. Most of the people associate a credit card with prestige and credit worthiness. Customers who avail the facility of credit cards in India are present in every part of the country, but their concentration is mostly in urban areas. However, this explosion has brought in a lot of obstacles among the cardholders that directly impact the consumers towards credit card acceptance. Similarly, the acceptance of credit cards has become an area of economic concern in J&K.

In J&K the use of credit cards is in its initial stages and is not so widespread and as such economic and social concerns are not significant. The problems are occurring at an individual level because customers are not aware of the hidden charges and other financial charges related to credit cards so the customers are suffering from the credit card-driven debt which is the real cause of the problem for them (Hassan, 2015). So, the study has been conducted in the Jammu region to analyse the influence of demographic and socio-economic factors on users' perceptions towards credit cards.

Based on this back age this study is to understand the dynamics between the demographic and socio-economic variables with the utilization of a credit card. The present study focuses on the profile of credit card users' in the Jammu region, covering data on customer demographics and socio-economic background and its relation with the utilization of credit cards. The paper is designed to gain a better understanding of these factors influencing the usage of credit cards and also suggest strategic policy intervention to the stakeholders in the credit card industry for enhancing the acceptance and the usage of credit cards. The study focuses on various dimensions relating to credit card usage, which, among others, includes whether cardholders think credit cards should be made existent in the market and whether their existence can influence customers to possess the cards which in turn, lead to overspending. The section also investigates whether the respondents have Credit Cards related knowledge.

The study will cover the public and private banking sector of Jammu region. Banks under study are selected based on a review of the top credit card issuers in India. According to this survey, ICICI, HDFC, SBI, PNB are amongst the top credit card issuers in India, hence selected for the study. Apart from these, J&K bank is also included in the above survey. However, it is one of the most vibrant banking institutions and is performing well in Jammu and Kashmir (Bhat & Maurya, 2013). Hence, it is also included for the purpose of the study. Accordingly, the study focuses on the customers of ICICI, HDFC, SBI, PNB and J&K banks of the Jammu region. These banks provide a relatively large number of credit card facilities to their customers.

Literature Review

Additionally, different demographic characteristics will lead different view of financial behavior on the credit card usage. As the demographic factors indicate the population structure of the individual. There are alot of studies show that demographic factors can explain the financial behavior on the credit card usage, such as formal education, family income, age, gender, personal income, and marital status. This study attempted to examine the correlation between demographic factors and financial behavior on the credit card usage. Based on these previous studies, the demographic factors that will be used in this research may consist of age, gender, income, education, and marital status.

Furthermore, age is one of the limitations, when someone wants to have a credit card. Based on this rule, the major credit card is owned by someone who was 21 years old and additional credit card is owned by someone who was 17 years old. The age will show the maturity of credit card holders. Someone in adult age will have more than one credit card and more frequently use the credit card (Kaynak et al., 1995). 18-25 years old, is the age of the most widely used credit cards. It is because at that age, they like shopping and more consumptive. Usually at this age, the payment is under control by their parents. When the holder was 25-40 years old, they tend to be wiser than the younger credit card holder, with their needs and capability to pay the debt. Meanwhile, when the holder was more than 40 years old, they do not tend to use credit card alot. According to the older man, the more risk averse to take financial decision.

Hence, the result of previous studies stated that gender associated with someone’s behavior. Gender will show the differences between female and male in the act. A female exhibited more costly credit card behavior than a male. Female more likely to be charge a late fee than male. Female has positive financial behavior on credit card usage than male. Female is less likely to pay in full and they usually use credit card for shopping. Otherwise, state that male will tend to frequently use a credit card. Instead, a female will have more than one credit card.

Research Gap

Review of related literature in the areas of credit card holders’ usage of cardholders’ satisfaction and perception towards credit cards has been made by the researcher. Various research studies conducted by eminent researchers for a span of the two decades in the areas of usage of credit cards and perception towards credit cards have been reviewed and the researcher has understood the gaps in the earlier studies and hence the present study has been carried out

From the review of studies mentioned above, it can be found that many empirical studies have been conducted in the area of credit cards in India and abroad. The major emphasis of the research has been on various issues like attitude, awareness and acceptance. Most of the studies are related to determining the usage pattern of credit cards, credit cards fraud and their prevention, economics of card usage, attitude towards credit cards, general view of credit cards. However, very few have focused their study on socio-economic and demographic variables influencing usage of credit card among bank customers. Studies pertaining to credit cards on banking customers are very few in number. Limited studies have been done on the demographic and the socio-economic factors affecting the use of credit cards, particularly in the state of Jammu and Kashmir.

So, there is comparatively less evidence of research study regarding the role of demographic and socio-economic factors of credit card usage among customers in Jammu region. Few studies have focused on socio-economic factors, while others have addressed technological factors. However, a comprehensive study on the demographic as well as the socio-economic factors for the usage of credit card has not been undertaken in Jammu region. Thus, a detailed study through primary and secondary information has been taken up to provide a framework for analyzing the impact of demographic and socio-economic factors on credit cards usage among customers of select public and private banks. And hence, the present study focuses on the selected usage pattern of banking customers and the impact of demographic and socio-economic variables.

Apart from providing the missing links in the study of credit cards, the present study will be the pace setter to provide holistic view pertaining to the issuance and usage of credit cards.

Research Methodology

As the present study is an empirical research in nature, a survey instrument was formulated in assessing the acceptance of credit cards usage. The study also identified factors affecting the acceptability of credits cards in Jammu region, therefore, concentrated on the primary data only which was collected via using questionnaire. Questionnaire was distributed amongst the sample of 705, which were from Jammu in J&K state. The sample was chosen by purposive convenience sampling method of sampling which is one of the non- probability techniques. Out of total nos. of 705 distributed questionnaires, 630 filled questionnaires were collected. All items of the questionnaire were tested using standard statistical tools including content and criteria validity, reliability was also calculated to measure the internal consistency amongst the items. Internal consistency reliability is the most commonly used psychometric measured assessing survey instrument and scales (Zhang et al., 2000; Xiao et al., 1995). Cronbach’s alpha is the basic formula for determining the reliability based on internal consistency (Kim & Cha, 2002).

Generation of Scale Items

The items of different dimensions of special benefits, sense of security, convenience, perceived risk, debt owed, perceived usefulness and flexibility, were generated from review of relevant literature. The original scale consisted of 51 items rated on 5-point Likert-type scale with anchors of 1 as strongly disagree and 5 as strongly agree. Out of 51 items 42 items were extracted from the study that include (Ming‐Yen Teoh et al., 2013; Pudaruth et al., 2013).

Data Analysis

Demographic and Socio-Economic Profile of Respondents

This section represents the demographic profile of respondents of the study. Data has been collected by distributing 800 questionnaires to the respondents of select public and private bank of Jammu region viz. HDFC, ICICI, PNB, SBI and JKB. Out of 800 distributed questionnaires, 705 filled questionnaires have been returned. However, after initial analysis of raw data only 630 valid replies have been attained for the purpose of data analysis. This infers that the data analysis has been done on 78% of the total sample. The response rates of the respondents based on the demographic characteristics have been presented in Table 1 below.

Table 1 Demographic Profile
Demographic Variables	Bank	Frequency	Percentage
Respondents Banks	SBI	137	21.7
	ICICI	134	21.3
	PNB	125	19.8
	HDFC	140	22.2
	JKB	94	14.9
	Total	630	100.0
Respondents  District	District	Frequency	Percent
	Jammu	217	34.4
	Kathua	216	34.3
	Udhampur	197	31.3
	Total	630	100.0
Respondent’s Marital Status	Marital Status	Frequency	Percent
	Single	148	23.5
	Married	456	72.4
	Divorcee	18	2.9
	Widowed	8	1.3
	Total	630	100.0
Respondent’s Age	Age	Frequency	Percent
	18-30	177	28.1
	31-45	279	44.3
	46-60	140	22.2
	61 & Above	34	5.4
	Total	630	100.0
Respondents Gender	Male	381	60.5
	Female	249	39.5
	Total	630	100.0

The demographic and socio-economic profile of the respondents has been presented in the Table 1 and 2.

Table 2 Socio Economic Profile
Respondent’s Occupation	Occupation	Frequency	Percent
	Self-Employee	130	20.6
	Pvt Employee	185	29.4
	Govt. Employee	220	34.9
	Other	95	15.1
	Total	630	100.0
Respondent’s Education	Education	Frequency	Percent
	UG	94	14.9
	Graduate	364	57.8
	PG	118	18.7
	Ph. D	20	3.2
	Other	34	5.4
	Total	630	100.0
Respondent’s Income Respondent’s Income	Income	Frequency	Percentage
	Upto 2,00,000	224	35.6
	2,00,001-5,00,000	238	37.8
	5,00,001-10,00,000	152	24.1
	10,00,001 & Above	16	2.5
	Total	630	100.0

The data collected with the help of Credit card Usage questionnaire has been analyzed and interpreted in this section on the basis of different demographic factors like Respondents Banks, Respondents district, age, gender, marital status, Respondent’s Occupation, education and income.

The demographic profile of the respondents is reflected in the Table 1. It is clear from the table that majority of the respondents in the Banking organisations i.e. HDFC, SBI, ICICI, PNB and JKB, understudy. The table 1 shows that maximum of the respondents are from HDFC bank 140 (22.2%) followed SBI 137 (21.7%), ICICI 134 (21.3%), PNB 125 (19.8%) and JKB has the least respondent i.e. 94 (14.9%). Further, the collected data has been analysed on the basis of district namely Jammu, Kathua and Udhampur. The table shows that the maximum respondents are from Jammu district i.e. 217 (34.4%), followed by Kathua district i.e. 216 (34.3%) and least from Udhampur 197 (31.5%). As far as the marital status is concerned most of the respondents are married i.e. 456 constituting 72.4% of the data collected. It is clear from the Table 1 that majority of the respondent in the banking organisation understudy are in the age group of 31-45 years i.e., 279 constituting 44.3% of the whole data. It is also pointed out that most of the customers of the banking organisation are male i.e., 381 constituting 60.5 % of the data collected.

Table 2 Socio-economic profile of the respondents also indicates that most of the respondents fall in the occupation group of govt. employees i.e., 220 comprising 34.9% of the data. As far as qualification is concerned maximum respondents are graduates i.e. 364 comprising 57.8 % of the data. Moreover, the collected data as elicited through questionnaire also been analysed on the income basis indicates that most of the respondents fall in the income group of2,00,001-5,00,000 constituting 37.8% of the data.

Descriptive Statistics of Credit Card Usage Variables

The evaluation of the mean values of Credit Card Usage constructs is important to examine the scenario in terms of customer’s utilization of credit cards. In the study, the responses have been measured with the arithmetic mean on a 5 point Likert scales ranging from strongly disagree to strongly agree Table 3.

Table 3 Descriptive Statistics of  Usage Construct
S.no.	Items	Mean	Std. Deviation
1	Credit Card users get extensive benefits for selective purchases	3.42	1.263
2	I spend using Credit Cards to earn points	3.49	1.304
3	I was attracted by the cash rebate system, thus I always spend using Credit Card	3.42	1.232
4	I apply for Credit Cards to get free gifts	3.21	1.22
5	Buying Airline/Railway tickets by using Credit Card at special counter saves time	3.56	1.256
6	I do not need to provide previous bills of Credit Cards when I am applying for another Credit Card	3.13	1.219
7	Lost or stolen cards may result in some expense of inconvenience	3.41	1.263
8	Complete trust in protecting customer's privacy	3.30	1.186
9	Ensure complete privacy over Customer's Information	3.44	1.298
10	Credit Cards convenient ordering by mail or phone	3.27	1.263
11	Billing through Credit Card is more convenient	3.39	1.304
12	Credit Cards offer greater convenience to effect shopping payment	3.38	1.304
13	Credit Cards are more convenient compared to banks branch location	3.51	1.232
14	Worldwide purchases are more convenient with Credit Cards	3.88	1.254
15	ATM has contributed towards Credit Card Acceptance	3.34	1.27
16	Technology facilities encourage me to use Credit Cards	3.32	1.334
17	Credit Cards help customers to make online shopping	3.40	1.395
18	Credit Cards provide customers the advantage of saving time	3.29	1.252
19	Frauds are more in Credit Cards use	3.41	1.263
20	Credit Cards are complex to use	3.30	1.186
21	Using Credit Cards is unsecured	3.56	1.317
22	It takes time to pay bills by Credit Cards	3.37	1.229
23	Using Credit Cards leads to a loss of privacy	3.84	1.324
24	Financial awareness of Credit Cards is not well communicated to customers.	3.45	1.26
25	Credit Cards are not always reliable due to technical problems	3.61	1.253
26	Sense of anxiety is about Credit Cards	3.43	1.129
27	I know exactly the remaining debt that I owed from previous transaction	3.74	1.215
28	The use of multi Credit Card can get you even further into debt	3.67	1.211
29	I will check on my bills to confirm all the transactions made by me  and the amount is correct	3.47	1.369
30	I know exactly how much I spent through Credit Cards every month	3.25	1.293
31	It is easy to find the statement which was not made by me	3.35	1.253
32	Buy first, Repay later	3.10	1.206
33	Purchase things even they are unaffordable	3.57	1.317
34	Purchase without carrying cash	3.51	1.232
35	Installment purchases is free of interest	4.31	0.772
36	Impulsive buying behaviour	3.50	1.166
37	Interest rate up to 45 days	3.45	1.223
38	Credit cards provide 24 hours services	3.40	1.304
39	I use Credit Cards since withdrawal process is simple	3.1	1.206
40	Credit Cards allow easy transfer of money	3.46	1.299
41	I choose credit cards as a medium for speedy transactions	3.37	1.229
42	Credit Cards transactions are more rapid	3.30	1.163
	Overall Mean	3.45

Factor Analysis

The sample adequacy of factor analysis for the Usage Context scale determined by Kaiser-Meyer-Olkin value and the suitability of the factor analysis determined with the Bartlett’s Test of Sphericity have been presented in Table 4 below.

Table 4 KMO and Bartlett's Test of Sphericity for Usage Context
Kaiser-Meyer-Olkin Measure of Sampling Adequacy		0.854
Bartlett's Test of Sphericity	Approx. Chi-Square	12144.728
	Df	433
	Sig.	0.000

The Table 4 above reveals high KMO value (0.854) greater than 0.6, representing the sampling adequacy of data for factor analysis. Further, a high Chi-Square value of 12144.728 with 433 degree of freedom at significance level of 0.000 confirming that population correlation matrix is not an identity matrix as sufficient non-zero correlations existed among the chosen variables (p ≤ 0.01). Therefore, the summary of the results of factor analysis for Card Usage construct is shown in Table 5 below.

Table 5 Factor Analysis Results
Usage Scale-42 statements, (α=0.784)
Dimension	Items	FL	Cronbach’s Alpha
Special Benefits (SF)	SF1.Credit Card users get extensive benefits for selective purchases.	0.963	0.871
	SF2.I spends using credit cards to earn points.	0.748
	SF3.I was attracted by the cash rebate system, thus I always spend using credit card.	0.675
	SF4.I applies for Credit Cards to get free gifts.	0.600
	SF5.Buying Airline/Railway tickets by using credit card at special counter saves time.	0.589
	SF6.I do not need to provide previous bills of credit cards when I am applying for another credit card.	0.580
Sense of Security (SS)	SS1.Lost or stolen cards may result in some expense of inconvenience.	0.963	0.832
	SS2.Complete trust in protecting customer's privacy.	0.949
	SS3.Ensure complete privacy over Customer's Information.	0.932
	SS4.Credit Cards convenient ordering by mail or phone.	0.624
Convenience (C)	C1.Billing through Credit Card is more convenient.	0.987	0.791
	C2.Credit Cards offer greater convenience to effect shopping payment.	0.986
	C3.Credit Cards are more convenient compared to banks branch location.	0.915
	C4.Worldwide purchases are more convenient with Credit Cards.	0.817
	C5.ATM has contributed towards Credit Card Acceptance.	0.805
	C6.Technology facilities encourage me to use Credit Cards.	0.793
	C7.Credit Cards help customers to make online shopping.	0.764
	C8.Credit Cards provide customers the advantage of saving time.	0.630
Perceived Risk (PR)	PR1.Frauds is more in Credit Cards use.	0.963	0.785
	PR2.Credit Cards are complex to use.	0.949
	PR3.Using Credit Cards is unsecured.	0.932
	PR4.It takes time to pay bills by Credit Cards.	0.929
	PR5.Using Credit Cards leads to a loss of privacy.	0.764
	PR6.Financial awareness of Credit Cards is not well communicated to customers.	0.652
	PR7.Credit Cards are not always reliable due to technical problems.	0.551
	PR8.Sense of anxiety is about Credit Cards.	0.550
Awareness about debt owed (AD)	AD1.I knows exactly the remaining debt that is due from previous transaction.	0.733	0.763
	AD2.The use of multi Credit Card can get you even further into debt.	0.688
	AD3.I will check on my bills to confirm all the transactions made by me are correct.	0.676
	AD4.I knows exactly how much I spent through Credit Cards every month.	0.628
	AD5.It is easy to find the statement which was not made by me.	0.542
Perceived Usefulness (PU)	PU1.Buy first, Repay later.	0.963	0.758
	PU2.Purchase things even they are unaffordable.	0.932
	PU3.Purchase without carrying cash.	0.915
	PU4. Installment purchases are free of interest.	0.660
	PU5.Impulsive buying behaviour.	0.632
	PU6.Interest rate up to 45 days.	0.515
Flexibility (F)	F1.Credit cards provide 24 hours services.	0.987	0.699
	F2.I use Credit Cards since withdrawal process is simple.	0.963
	F3.Credit Cards allow easy transfer of money.	0.932
	F4.I chooses credit cards as a medium for speedy transactions.	0.929
	F5.Credit Cards transactions are more rapid.	0.594

Initially, factor analysis has been conducted on 51 items of the scale and the rotation converged in 7 iterations. 9 items have been eliminated which showed either no factor loadings or cross loadings or factor loadings below 0.5. Finally, the factor analysis results into seven factors viz. Special benefits, sense of security, convenience, perceived risk, awareness about debt owed, perceived usefulness and flexibility comprising of only 42 statements as shown in Table 4 above. The resultant 7 factors having eigen values > 1, factor loadings > 0.5 explained 58.311% of cumulative variance, indicate that the data is valid and used for further analysis.

Scree Plot

In Figure 1, the scree plot of Usage construct clearly shows 7 factors before the beginning of the scree. Therefore, the scree test confirms 7 factors to be taken.

Discussion

The in depth analysis of the above six factors are discussed as:

Factors of the present study have been identified with the help of Exploratory Factor Analysis, conducted separately on main constructs viz. Credit card usage construct under which seven factors have been extracted namely Special Benefits, Sense of Security, Convenienc, Perceived Risk, Awareness about debt, Perceived Usefulness, Flexibility with (KMO=0.854, eigen value >1, factor loadings ≥0.5 and total variance extracted=58.412%)

The explored factors of usage are:

F1 Special Benefits (SB)

Special Benefits is the first driver revealed by the study with the rotation sums of squared loading variance of 22.64%. This driver is composed with six items: i) SB1 (0.963), ii) SB2 (0.742), iii) SB3 (0.675.), iv) SB4 (0.600) v) SB5 (0.589) and vi SB6 (0.580).This factor expresses a Cronbach's alpha reliability of 0.871 (87.1%) which is statistically acceptable.

The item ‘Credit card users get extensive benefits for selective purchases’ has the highest factor loading (0.963) showing high association with factor whereas the item ‘I don’t need to provide previous bills of credit cards when I am applying for another credit card’, having the lowest factor loading 0(.580) therefore showing least association with the factor. The mean values of the items under this factor range from 3.13 to 3.56.

F2 Sense of Security (SS)

Sense of security is the second driver revealed by the study with the rotation sums of squared loading variance of 28.64%. This driver is composed with four items: i) SS1 (0.963), ii) SS2 (0.949), iii) SS3 (0.932) and iv) SS4 (0.624). This factor expresses a Cronbach's alpha reliability of 0.832 (83.2%) which is statistically acceptable.

The item ‘Lost or stolen cards may result in some expenses of inconvenience’ has the highest factor loading (0.963) showing high association with factor whereas the item ‘Credit cards convenient ordering by mail or phone’, having the lowest factor loading (0.624) therefore showing least association with the factor. The mean values of the items under this factor range from 3.27 to 3.41.

F3 Convenience(C)

Convenience is the third driver revealed by the study with the rotation sums of squared loading variance of 24.64%. This driver is composed with eight items: i) C1 (0.987), ii) C2 (0.986), iii) C3 (0.915) iv) C4 (0.817). v) C5 (0.805) vi C6 (0.793) vii C7 (0.764) and viii C8 (0.630). This factor expresses a Cronbach's alpha reliability of 0.791 (79.1%) which is statistically acceptable.The item ‘Billing through credit card is more convenient’ has the highest factor loading (0.963) showing high association with factor whereas the item ’Credit cards provides customers the advantage of saving time’, having the lowest factor loading (0.624) therefore showing least association with the factor. The mean values of the items under this factor range from 3.29 to 3.88.

F4 Perceived Risk (PR)

Perceived Risk is the fourth driver revealed by the study with the rotation sums of squared loading variance of 25.54%. This driver is composed with eight items: i) PR1 (0.963), ii) PR2 (0.949), iii) PR3 (0.932) iv) PR4 (0.929) v) PR5 (0.764) vi PR6 (0.652) vii PR7 (0.551) and viii PR8 (0.550). This factor expresses a Cronbach's alpha reliability of 0.785 (78.5%) which is statistically acceptable. The item ‘Frauds are more in credit cards use’has the highest factor loading (0.963) showing high association with factor whereas the item ‘Sense of anxiety is about credit card’, having the lowest factor loading (0.550) therefore showing least association with the factor. The mean values of the items under this factor range from 3.3 to 3.84.

F5 Awareness about debt (AD)

Awareness about debt is the fifth driver revealed by the study with the rotation sums of squared loading variance of 21.24%. This driver is composed with five items: i) AD1 (0.733), ii) AD2 (0.688), iii) AD3 (0.676) iv) AD4 (0.628) and vi AD5 (0.542). This factor expresses a Cronbach's alpha reliability of 0.763 (76.3%) which is statistically acceptable.

The item ‘I know exactly the remaining debt that is due from previous transaction’ has the highest factor loading (0.7333) showing high association with factor whereas the item ‘It is easy to find the statement which is not made by me’, having the lowest factor loading (0.542) therefore showing least association with the factor. The mean values of the items under this factor range from 3.25 to 3.74.

F6 Perceived Usefulness (PU)

Perceived usefulness is the sixth driver revealed by the study with the rotation sums of squared loading variance of 28.14%. This driver is composed with six items: i) PU1 (0.963), ii) PU2 (0.932), iii) PU3 (0.915) iv) PU4 (0.660), v) PU5 (0.632) and v) PU6 (0.515). This factor expresses a Cronbach's alpha reliability of 0.758 (75.8%) which is statistically acceptable. Theitem‘Buy first, repay later’has the highest factor loading (0.963) showing high association with factor whereas the item ‘Purchase things if they are unaffordable’, having the lowest factor loading (0.515) therefore showing least association with the factor. The mean values of the items under this factor range from 3.1 to 3.56.

F7 Flexibility (F)

Flexibility is the seventh driver revealed by the study with the rotation sums of squared loading variance of 23.45%. This driver is composed with five items: i) F1 (0.987), ii) F2 (0.963), iii) F3 (0.932) iv) F4 (0.0.929) and F5 (0.594). This factor expresses a Cronbach's alpha reliability of 0.689 (68.9%) which is statistically acceptable.

The item ‘Credit card provide 24 hour services’ has the highest factor loading (0.987) showing high association with factor whereas the item ‘Credit cards transactions are more rapid’, having the lowest factor loading (0.594) therefore showing least association with the factor. The mean values of the items under this factor range from 3.1 to 3.45.

Limitations and Future Implications

Like other empirical studies, this study is not without its limitations. Our sample consisted of a very small area which limits the generalizability of the results. The sample size itself is relatively small. The study can be strengthened by increasing the sample size and including participants in other geographical areas. With an increased sample size, a more detailed empirical analysis can be performed. This research can serve as a starting point for the acceptance of credit cards, while encouraging further exploration and integration addition adoption constructs. Future research needs to focus on a larger cross section and more diversified samples to verify the findings of the current study. In this respect, research should extend to non-users, banking executives in order to allow a comparative analysis on the factors impacting on acceptance of credit cards among customers in Jammu region. Likewise, an integrated conceptual model relating to the various factors impacting on credit card adoption among customers can be proposed and tested in order to overcome the conceptual limitations of the present study and the research can be extended to other states of India as well as outside the country.

Conclusion

The purpose of this study is to investigate factors affecting acceptability of credit cards in India; therefore, it was concentrated on the primary data only. The study revealed total seven factors namely special benefits, sense of security, convenience, perceived risk, debt owed, perceived usefulness and flexibility. The present study has emphasized on how customers are involved in the acceptance of credit cards in India. Customers are putting more importance on benefits of credit cards such as speed, convenience, environmental friendly and international presence. Customers are also very much interested about issues such as risks and security issues, new features and innovation when adopting credit cards. Hence, it is highly recommended that the banks develop a deep understanding of the factors influencing the acceptance of credit cards in order to adapt their marketing strategies to the potential customers. In fact, the research results can be useful and form practical tools for the policy makers and financial executives who are responsible for designing and marketing credit cards features and innovation at various point of sale in India.

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Received: 04-Jan-2023, Manuscript No. JIBR-23-13214; Editor assigned: 06-Jan-2023, Pre QC No. JIBR-23-13214(PQ); Reviewed: 19-Jan-2023, QC No. JIBR-23-13214; Revised: 26-Jan-2023, Manuscript No. JIBR-23-13214(R); Published: 31-Jan-2023