An Empirical Inquiry on Smart card Adaptability among College students

Sujit Kumar Mahato¹, Kushal De²*

¹Assistant Professor of Commerce, Budge Budge College, Maheshtala, West Bengal, India.

²Assistant Professor of Commerce, Dhruba Chand Halder College,

E1/7A, Ramgarh, PO- Naktala, Kolkata- 700047, West Bengal, India.

*Corresponding Author E-mail: dekushal1979@gmail.com

ABSTRACT:

Digitization of the economy is the need of the hour for every nation including India. Today India leads the global digital payment systems. Being an economy with the maximum number of youth population, it is worthy to note the feelings of the youth towards adoption of digitization in their day to day transactions. The study proposed to explore the impact of gender, family income and other significant factors on the intension to adopt smart cards among select group of college students. The empirical data analyzed through SPSS using tools like Cramer’s V test, Pearson correlation co-efficient and logistic regression show that there is no significant statistical bias among college students on the basis of their gender or family income with regards to adoption of smart cards. The variables like awareness, satisfaction, security, perception and e-governance have statistically significant impact on the intention to adopt smart cards among students. Securities of the system and the perceptions about the service provider have the most significant impact on selection and intention to adopt any smart card technology. By giving attention to raising awareness, improving support systems and through efficient e-governance, it is feasible to enhance the adoption of smart cards among the college students.

KEYWORDS: College Students, Awareness, Efficiency, Smart Card, Security, Satisfaction.

INTRODUCTION:

Technology is changing the traditional methods of work and communication of people. One of its application is in the form of smart cards which are physical cards having an integrated circuit chip that can store and process data. These are key instruments that are one step ahead of the conventional magnetic strip cards, used for improving security and efficiency of transactions. They are widely used in various fields such as education, healthcare, banking, insurance, transportation etc. owing to their ability to store huge amounts of data securely and facilitate cryptographic operations. The technology is becoming popular making lives easier and efficient.

The market for smart cards in India is constantly growing. According to a recent report by Astute Analytic, the smart card market in India is expected to grow at a CAGR of 7.22% from 2024 to 2032. The market is expected to reach a size of USD 166.16 billion by 2032. This expected growth highlights the rising trends of smart card technology in India’s digital ecosystem. It is important to understand the factors that influence the adoption of smart cards contributing to the high growth in digital technology. At the forefront of digital change lies the youth of India, the future workforce of the nation. Youth’s acceptance and adoption of digital technologies including smart cards are crucial for sustainable economic development. The adoption of smart card technology by youth particularly the college students is not only essential for the success of initiatives like ‘Digital India’ but also for taking advantage of India’s demographic dividend. The acceptance and use of smart cards among college students is an important area of interest for researchers. Few important researches in this regard are discussed below.

The study of Taherdoost et. al. (2009) investigated the level of knowledge about smart card technology among students and reported that knowledge and investigation are positively correlated with the effective use of smart card technology in society. The study also highlighted the advantages of smart cards, including improved security, convenience, and financial rewards. Teker (2011) stated that anxiety and technological complexity are the external factors that affect the willingness to use multi-purpose smart cards. If the students face anxiety towards use of multipurpose smart cards, their perception of the ease of the system gets negatively affected. Anxiety directly affects intention and indirectly affects perceived usefulness. Technological complexity directly affects the perception of ease, usefulness, and intention. Taherdoost et. al. (2012) found that education and awareness in digital technology among users are significant factors while adopting smart cards. Education helps and guides end users to understand smart cards and their benefits. Awareness among users improves their judgment and ability to use the system. Taherdoost (2017) observed that usefulness, security, ease of use, awareness, support, visibility, image, social norms, and satisfaction are the major factors that have a significant and positive influence on the adoption of smart card technology among university students. Kumar (2019) found that convenience, time utility, and rewards excite users to use smart cards. Security and additional charges are the prime deterrents that impact usage of smart cards. Male respondents were found to be more comfortable and adaptive than female respondents while making payments digitally. Education was also an important factor in improving confidence. Ho et. al. (2015) studied the attitude of students towards their ‘university smart cards’ and found that the users’ attitudes and subjective norms of smart cards positively and significantly affect their usage intentions. The usefulness and ease of use positively and significantly affected attitudes towards the adoption of university smart cards. Ngozi (2023) highlights the significance of comprehensive awareness via seminars, media campaigns, and the distribution of knowledge. It draws attention to the need for strong security and customer service protocols to guarantee successful use, foster confidence, and resolve security issues, all of which improve smart card acceptability and efficacy among students as a whole.

In light of the above discussion, the present study aims to identify and interpret the major factors that influence the adoption of smart cards among college students to enable appropriate strategy formulations by policymakers, technology providers, and educators.

OBJECTIVES AND METHODOLOGY:

The study proposes to unveil the awareness and intension of college students towards adoption and use of smart cards in select colleges of West Bengal. The following objectives are taken up for study by the researchers:

1. To evaluate if gender and family income of students have any significant influence on the intension to adopt smart cards.

2. To analyse the impact of significant factors on the intension to adopt smart cards.

To fulfill the above objectives, a field study is conducted with a closed-ended questionnaire among a sample residing in West Bengal. A total of 211 responses were collected by the researchers from the districts of Kolkata, South 24 Paraganas and Hooghly. The results were analyzed using statistical tools like Cramer’s V Test, Pearson Correlation Co-Efficient, Logistic Regression etc. The data was tabulated in Excel and analyzed in SPSS27.

FINDINGS AND ANALYSIS:

The demographic profile of the sample is plotted in Table below:

Table 1: Demographic Profile of the Respondent

Demographic Profile	Categorical Variable	Frequency	Percent (%)
Gender	Male	119	56.40
Gender	Female	92	43.60
Family Income	Less than10,000	79	37.44
	10,000- 25,000	77	36.49
	25,000–50,000	27	12.80
	50,000-75,000	16	7.58
	75,000-1,00,000	10	4.74
	1,00,000 and above	2	0.95
Use of smart card	Multiple times daily	4	1.90
	Once Daily	4	1.90
	Weekly	27	12.80
	Monthly	140	66.35
	Yearly	7	3.32
	Never used	29	13.74

			Smart card owned^a					Total
			Bank ATM Card	Driving License	Metro Railway Card	Digital Ration Card	Any other card	Total
Gender of the respondent	Male	Count	68	3	12	61	1	145
	Male	% within Gender	46.9%	2.1%	8.3%	42.1%	0.7%
	Female	Count	28	0	3	65	3	99
	Female	% within Gender	28.3%	0.0%	3.0%	65.7%	3.0%
Total		Count	96	3	15	126	4	244
Percentages and totals are based on responses.

From Table 1, it can be observed that there are 56.40% males and 43.60% females out of a total 211 respondents. Respondents’ income profiles show that about 37.44% of the respondents have an income range below Rs. 10,000, 36.49% of respondents have an income range of Rs. 10,000 – 25,000, and the remaining respondents have an income range of more than Rs. 25,000. Most (66.35%) respondent uses a smart card once a month. 13.74% of respondents never used a smart card in their lifetime. Responses collected about smart card uses are from college students.

In table 2, the frequencies of usage of various smart cards have been calculated. A total of 244 responses (multiple responses received from some respondents) are received from people who have used smart cards, like bank ATM cards, credit cards, driving licenses, metro railway cards, digital ration cards, etc. Out of 99 female responses, 28.3% use the bank ATM card, 3% used a metro railway card, 65.7% used a digital ration card, and the remaining 3% used other cards (there were no responses among females on using a driving license). Out of 145 male responses, 46.9% use the bank ATM card, 2.1% use a driving license, 8.3% use a metro railway card, 42.1% use a digital ration card, and the remaining 0.7% has used other cards.

H₀1: There is no significant association between intention to adopt a smart card and gender of the respondent.

Table 3: Symmetric Measures

		Value	ApproximateSignificance
Nominal by Nominal	Phi	0.109	0.113
Nominal by Nominal	Cramer's V	0.109	0.113
N of Valid Cases		211

In table 3, the phi-coefficient is computed with a 5% significance level. As the p-value of the phi-coefficient is 0.113, which is more than 0.05 at the 5% level of significance, it fails to reject the null hypothesis, which means that there is no significant association between the intention to adopt a smart card and gender of the respondent.

H₀2: There is no significant association between the intention to adopt smart cards and family income of the respondents.

Table 4: Symmetric Measures

		Value	Approximate Significance
Nominal by Nominal	Phi	0.172	0.284
Nominal by Nominal	Cramer's V	0.172	0.284
N of Valid Cases		211

In table 4, based on the statistical analysis using Cramer's V test with a p-value of 0.284 at 5% level of significance, we conclude that there is no significant association between family income of the respondents and their intention to adopt smart cards among college students. Therefore, we fail to reject the null hypothesis, which suggests that family income does not play a significant role in influencing the intention to adopt smart cards among college students.

The study measures smart card perception and awareness using a 5-point Likert Scale, ranging from 1 to 5. Eight grid constructs were identified: awareness, ease of use, satisfaction, support, trustworthiness, security, perception, and e-governance. Each construct was framed with multiple sets of attributes.

The first construct, ‘Awareness’ asks questions about the respondents' knowledge of smart cards. Four statements are presented to determine student’s extent of agreement or disagreement with respect to smart card technology. The purpose of the first grid is to assess respondents' knowledge of smart cards and their characteristics.

The second construct, ‘Ease of Use’ examined how user friendly and convenient respondents found smart cards. This grid consists of four statements analysing respondent’s perceptions regarding the ease and usefulness of smart card technology in assisting daily activities.

‘Satisfaction’ the third construct, analyse how satisfied users are with smart cards. Five statements are included in this grid to evaluate the respondent’s satisfaction with different aspects of utilising smart cards.

‘Support’ the fourth construct, highlights the services available to users so that they can use smart cards in an effective manner. This grid consists of four statements, asked students to rate their opinions regarding support-related matter such as customer services and technical help. The goal is to understand how appropriately and successfully support is being provided to smart card users.

In the fifth construct ‘Trustworthiness’, respondents express their opinion about the integrity and dependability of smart cards. This grid consists of four statements which evaluates concerns regarding smart cards with respect to data protection and dependability.

The sixth construct, ‘Security’ tries to look at how respondents feel about smart card security. The four statement grid assesses respondent’s perception on the importance and credibility of smart card security features. Its purpose is to find out how confident they are in smart card security.

The seventh construct, ‘Perception’ is crucial for understanding respondent’s overall opinions and intentions to use smart cards. This grid comprise of five statements to measure respondent’s beliefs and attitudes towards smart card adoption. It intends to capture their willingness and readiness to accept smart card technology.

‘E-Governance’ is the eight and the largest construct of this study. It includes eight statements which assess how E-Governance could be improved through smart card technology. This grid explored the role of smart cards in enhancing e-governance through transparency, efficiency and accessibility of government services.

In table 5, the Pearson correlation coefficient is computed. The p-value of the Pearson correlation is less than 0.05 at the 5% level of significance, which means that awareness, satisfaction, security, perception, e-governance, and intention to adopt smart cards are correlated with each other, but the correlation between ease of use, support, trustworthiness, and intention to use smart cards is not significant, which means there is no significant correlation between them.

The above result suggests that college students are aware of smart card technology. The adoption of smart cards depends on how well younger generations are satisfied with using the technology. Students accept smart card technology not only for their perception and satisfaction of using smart cards but also because of their belief in the security features of this technology and how it helps in their daily activities.

The table 6 uses logistic regression to predict the intention to adopt a smart card based on eight constructs. The dependent variable is ‘Intention to Adopt’, is assessed with eight independent variables: awareness, ease of use, satisfaction, support, trustworthiness, security, perception, and e-governance. The model is suitable for binary options, where ‘Intention to Adopt’ takes the value '1' if the respondent intends to adopt the smart card, ‘0’ otherwise.

The table shows all regression coefficients are not significant except for awareness (p-value = 0.041), support (p-value = 0.035) and e-governance (p-value = 0.014). It means awareness, support, and e-governance significantly affects the intention to use a smart card. The findings from the logistics regression mostly corroborate the correlation table.

		Awareness	Ease of Use	Satisfaction	Support	Trustworthiness	Security	perception	E-Governance
Intention to adopt smart card	Pearson Correlation	.189^**	.045	.221^**	.013	-.092	.181^**	.270^**	.258^**
	Sig. (2-tailed)	.006	.515	.001	.848	.181	.001	.000	.000
	N	211	211	211	211	211	211	211	211
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)

Table 6: Outcome of Logistic Regression of the Constructs

	B	S.E.	Wald	Sig.	Exp(B)	95% C.I.for EXP(B)
	B	S.E.	Wald	Sig.	Exp(B)	Lower	Upper
Awareness	1.332	.652	4.177	.041	3.787	1.056	13.580
Ease of Use	-.459	.497	.854	.356	.632	.239	1.673
Satisfaction	.558	.660	.715	.398	1.748	.479	6.378
Support	-1.678	.797	4.437	.035	.187	.039	.890
Trustworthiness	-.735	.548	1.798	.180	.479	.164	1.404
Security	-.843	.720	1.370	.242	.431	.105	1.766
perception	1.228	.736	2.783	.095	3.416	.807	14.461
e-Governance	2.273	.930	5.978	.014	9.709	1.570	60.050
Constant	-4.049	2.960	1.872	.171	.017
Variable(s) entered on step 1: Awareness, Ease of Use, Satisfaction, Support, Trustworthiness, Security, perception, e-Governance.

Awareness:

· The P-Value for awareness is 0.041, which is below the significance level of 0.05. It indicates that awareness is a significant factor of intention to adopt smart card.

· Higher level of awareness about smart cards leads to higher intention to adopt them.

Support:

· With a P-Value of 0.035, support is also a significant determinant of the intention to adopt smart cards.

· It suggests that the availability of support systems such as customer service, user assistance and help desks can be positively influence the adoption decision.

E-Governance:

· E-Governance has a P-Value of 0.014, which is below the significance level of 0.05. It is the most significant factor among the variables studied.

· The integration of smart card technology in government services will significantly influence the decisions to adopt smart cards.

CONCLUSION:

There is no bias among college students on the basis of gender with regards to adoption of smart card. It is found in our study that there is no statistically significant correlation between the intention to adopt smart card and gender of the respondents. This findings suggests that other factors rather than gender like age group, educational level, technological background could be more significant in deciding the intention for smart card adoption. The study aimed to find out the potential correlation between the family income of respondents and intention to adopt smart card. It is found that there is no statistically significant association between intention to adopt smart card and family income of the respondents. This finding suggests that family income does not have significant impact on decision of college students to adopt smart card. Further research should be done by taking into consideration other variables to have deeper insights into the topic

From Pearson correlation test, it is revealed that the variables namely Awareness, Satisfaction, Security, Perception and E-Governance have statistical significant relationship with the dependent variable namely Intention to adopt smart card. So college students’ intention to adopt smart card is significantly influenced by level of Awareness, Satisfaction, Security, Perception and efficient E-Governance. Logistic regression further explores the influence of independent variables on intention to adopt smart cards and shows that all regression coefficient are not statistically significant except for Awareness, Support and E-Governance. These findings provide valuable insights to stakeholders and policymakers to promote the use of smart card among college students. By giving attention on raising awareness, improving support system and through efficient e-governance it is feasible to enhance the adoption of smart card among college students.

The study though conducted on a small sample in a limited geographical area is significant as it clearly demonstrates the impact of various independent factors namely awareness, ease of use, satisfaction, support, trustworthiness, security, perception, e-governance on the dependent factor intention to adopt smart cards among the target population namely the youth.

REFERENCES:

1. Ho, C. W., Wang, Y. B., and Yen, N. Y. Does Environmental Sustainability Play a Role in the Adoption of Smart Card Technology at Universities in Taiwan: An Integration of TAM and TRA. MDPI Journal. 2015; 7(8): 10994-11009.

2. Kumar, S. An Empirical Study of Adoption of Digital Payments among Students of Delhi University. Journal of IMS Group. 2019; 16(1).

3. Taherdoost, H. Appraising the Smart Card Technology Adoption; Case of Application in University Environment. Procedia Engineering. 2017; 181: 1049-1057.

4. Taherdoost, H., Sahibuddin, S., Namayandeh, M., Jalaliyoon, N., Kalantari, A., and Chaeikar, S. S. Smart Card Adoption Model: Social and Ethical Perspectives. International Journal of Research and Reviews in Computer Science. 2012; 3(4): 1792-1796.

5. Teker, M. (2011). Identifying Factors that Facilitate the Use of Multi-Purpose Smart Cards by University Students: An Empirical Investigation [Master's thesis, Middle East Technical University].

6. Taherdoost, H., Zamani, M., and Namayandeh, M. Study of smart card technology and probe user awareness about it: A case study of Middle Eastern students. IEEE. 2009;:334-338. doi: 10.1109/ICCSIT.2009.5234410

7. Ngozi, R. Single Smart Card Technology: Challenges, Prospect and Security Among Students in Taraba State Polytechnic Suntai. International Journal of Current Researches in Sciences, Social Sciences and Languages. 2023; 3(4): 41-47.

Received on 27.09.2024 Revised on 14.10.2024

Accepted on 29.10.2024 Published on 05.12.2024

Available online on December 31, 2024

Res. J. of Humanities and Social Sciences. 2024;15(4):263-267.

DOI: 10.52711/2321-5828.2024.00039