Extending the TAM Model: Intention of Management Students to Use Mobile Banking: Evidence from India

Abstract

The educated youth in India have adapted well to technology and technology-based solutions. Much of it has been attributed to the increased usage of computers, Internet and the smartphones among the educated youth in India. It becomes a useful and important exercise, therefore, to study their attitude towards new technology-based products and services in view of the fact that this segment has a requirement for utilizing banking services for at least the next 30 years. Banking has been one industry which has been in the forefront in offering technology-based delivery channels like ATM, Internet banking and mobile banking. This article studies the underlying factors influencing management students in their intention to use mobile banking. The study utilizes two constructs of the technology adoption model and then extends it further to include two additional constructs. The results suggest that perceived usefulness and perceived ease of use, social influence and trust propensity are the underlying factors in respect of the behavioural intention to use mobile banking services.

Keywords

Mobile banking TAM management students intention to use mobile banking

Introduction

Today, mobile banking services along with Internet banking services have fundamentally changed the ways and methods of doing banking operations by its customers and banks have also used these technology-based service delivery channels not only as a new way to increase customer satisfaction, but also as a strategy to reduce costs and increase profitability. Banks have laid their emphasis on the implementation on mobile banking services because it has helped them to sharpen their focus on reducing costs, enabled them to sustain their competitive advantage, provide a higher level of convenience to users, and also as a tool to cater to ‘unbanked’ customers. The application of third generation (3G) mobile communication technologies has triggered the rapid development of mobile commerce. A variety of mobile services such as mobile instant messaging, mobile search and mobile music have been very popular among users. These services are mainly related to communication, information and entertainment application. However, mobile transaction services such as mobile banking have been adopted by a minority of users. Many traditional banks are now aggressively promoting their mobile banking services. In order to achieve success, a prerequisite is to facilitate user adoption and usage of mobile banking.

In mobile banking the users adopt mobile terminals to conduct payment such as balance enquiry, transference and bill payment at anytime from anywhere (Dahlberg, Mallat, Ondrus & Zmijewska, 2008).

It is an innovation that could become one of m-commerce’s value-added applications (Lee, McGoldrick, Keeling & Doherty, 2003) and could have a huge economic impact (Varshney, 2004). It is expected to transform and redefine business models associated with financial services (Rask & Dholakia, 2001) and frees users from operational, spatial and temporal limitations, and enables them to conduct their banking operations from anywhere. This provides great convenience to users.

The Government of India has given a push to financial inclusion by announcing the Jan Dhan Yojana—a programme to provide access to banking services to every household. Under the scheme, over 7 crore households are proposed to be covered. It was estimated that only 58.7 per cent of the Indian households had access to banking services and post Jan Dhan, the government’s statement is that over 90 per cent of the households are now covered. Running complementary to this is the fact that as per Telecom Regulatory Authority of India (TRAI) statistics the tele-density of wireless subscribers as of March 2015 is 77.27 per cent and the total number of wireless subscribers stood at 969.89 million. With increased affordability of smartphones, it is only but natural that the tendency to use mobile phones will increase in future and with that the usage of mobile phones for banking services is also expected to increase. The educated youth of India have adapted readily to the technology-based services as is evident from the IT revolution in India. They have been comfortable with using computers, utilizing services through Internet and now have shown their inclination to utilize the smartphone for technology-based services. Management students in India being part of this demographic dividend have also readily adapted to the technology based innovations and using computers and Internet are a part of their curriculum. Most importantly, this segment needs to transact with banks for the next three decades of their working life for their financial needs. It was therefore thought a study on the underlying factors which would make them use mobile banking services would be of immense interest to the banking industry and also the researchers in this field. The rest of this article is organized as follows: the next section provides the related literature followed by the research methodology. The subsequent section presents the empirical findings and the article concludes with managerial implications, limitations and scope for future research.

Literature Review

A large number of research articles are available on Internet and mobile banking focusing on different types of users. This rich literature available across different journals helped in arriving at the possible variables for our study. The primary basis for this study is the Technology Adoption Model (TAM) in its extended form. Technology Adoption Model is well known for its predictive power which makes it easy to apply across various situations (Venkatesh & Morris, 2000). Technology Adoption Model was primarily meant for information systems and therefore for mobile banking, it was sought to be extended. Previous research has used information technology adoption theories such as technology acceptance model (Kim, Mirusmonov & Lee, 2010; Schierz, Schilke & Wirtz, 2010), innovation diffusion theory (IDT) (Mallat, 2007), and the unified theory of acceptance and use of technology (UTAUT) (Luo, Li, Zhang & Shim, 2010) to examine mobile banking user behaviour. The first construct of the TAM model is perceived usefulness (PU). It has been defined as the degree to which an individual believes that using a particular system would enhance his or her performance (Davis, 1989). There have been many previous studies which provide empirical evidence of the significant effect of PU on the intention to use (Kim et al., 2010; Schierz et al., 2010; Venkatesh & Davis, 1996; Venkatesh & Morris, 2000) to name a few. Guriting and Ndubisi (2006) found that PU significantly determines behavioural intention. Ravi Kumar, Bose and Raghavan (2011) also indicated that the intention to use for management students was significantly impacted by PU in respect of Internet banking. The second construct of the TAM model is perceived ease of use (PEOU). Perceived ease of use indicates the degree to which an individual believes that using a particular system would be free of effort (Davis, 1989). The TAM model states that if an application is perceived to be easy to use, it would have a greater level of acceptance. Research done in the past in the context of PEOU provides evidence of the significant effect of PEOU on the intention to use (Davis, 1989; Venkatesh & Morris, 2000). George and Kumar (2013) found out that PEOU has a positive impact on the customer satisfaction of Internet banking users. This study aims to extend the TAM model in the context of mobile banking. The first additional construct proposed is social influence (SI).

Hanudin, Abdul, Lada and Anis (2008) empirically found that individual intention to use mobile banking was significantly affected by people surrounding them. Similarly, Singh, Srivastava and Srivastava (2010) discovered that individual decisions to adopt mobile commerce services were influenced by friends and family members and argued that argued that mobile commerce users are not just technology users, but also part of social network. Laukkanen (2007) and Dasgupta, Paul and Fuloria (2011) observed that perceived image was a significant factor for people willingness to adopt mobile banking. The second construct that this study is proposing to analyze is trust propensity (TP). Trust propensity represents a person’s disposition to rely on others in various situations. When people make a judgement of a service without prior knowledge, those with a higher propensity to trust are more likely to assume that the service is dependable (McKnight, Cummings & Chervany, 1998). A person’s initial trust in mobile banking is therefore expected to be a function of his/her propensity to trust when there are no experiential elements to factor in (Gimun, BongSik & Ho, 2009). Zhou (2011) indicated that users with high TP tend to have positive attitudes towards new technologies. ‘Thus they will more readily build trust in mobile banking. In contrast, those users with low trust propensity may doubt the credibility of mobile banking, which represents an emerging service.’ The purpose of this research is to identify the underlying factors affecting the intention to use mobile banking among management students at the postgraduate level. Mobile banking has been a recent development in India seeing gradual progress. As such studies on mobile banking in the Indian context have been far and few. Also, there is no study devoted to the intention of management students to use mobile banking even as they represent a prime potential user segment for this service delivery channel. This study aims to contribute in this emerging area and fill this research gap with reference to India.

Research Methodology

The study was conducted using a structured questionnaire (see Appendix 1). The first part of the questionnaire elicited personal details of the respondent. The second part had statements using a 5-point Likert scale from strongly disagree to strongly agree to get the response to the questionnaires. All the respondents had a smartphone and had a basic idea of banking services but not yet started using the mobile banking delivery channel. In addition, they were well versed with computer and Internet-based operations. The questionnaire was pilot tested among 20 students to refine the instrument. The questionnaire was then circulated among 190 students studying final year postgraduate courses in management in different management institutes across the eight major cities in India, namely, Delhi, Mumbai, Chennai, Kolkata, Ahmedabad, Bengaluru, Hyderabad and Pune and about to graduate in a few months time. The respondents were students specializing in the areas of marketing, finance, operations and human resources, apart from undergoing core courses in different areas of management. Out of these, 152 students responded and after removing the results of incomplete questionnaires; 144 of them which were usable were taken into consideration for data analysis and interpretation. The results were analyzed using SPSS software package version 19.

Data Presentation and Findings

The sample comprised of 115 males and 29 females. This corresponds to around 80 per cent males and 20 per cent females. The age group of the management students who participated in the study was 21–30 years. The characteristics of the respondents are summarized in Table 1.

Table 1.

Demographic Profile of the Sample

S. No.	Item	No. of Respondents	Percentage
1	Male	115	79.86
2	Female	29	20.14
3	Age group: 21–25 years	98	68.06
4	Age group: 25–30 years	46	31.94
5	Nationality: Indian	144	100

Source: Authors’ own findings/authors’ calculations.

In order to ascertain whether factor analysis could be performed on the data, the test for Sampling Adequacy was performed as per Table 2.

Table 2.

Test for Sampling Adequacy

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.788
Bartlett’s test of sphericity	Approx. chi-square	125.823
	Df	253
	Sig.	0.000

Source: Authors’ own findings/authors’ calculations.

Next, factor analysis of the data was carried out on SPSS software to obtain the rotated component matrix and for obtaining significant factors underlying the intention to use mobile banking.

The Kaiser–Meyer–Olkin test for sampling adequacy basically indicates if the data is suitable for factor analysis. The KMO test in this study revealed a figure of 0.788. As the value is above 0.5, the variables meet the condition for factor analysis (Hair, Anderson, Tatham & Black, 1998). Also the Bartlett’s test of sphericity displayed a significant value (p < 0.05) indicating relationships between variables. The factors were extracted using the principal component analysis method. The rotation method was varimax with Kaiser normalization and the rotation converged in 18 iterations (see Appendix 2). The rotated factor loadings are indicated in the Table 3.

Furthermore, descriptive statistics (mean and standard deviation (SD)) run on the factors extracted confirmed that PU, PEOU, SI and TP were the factors underlying the intention to use mobile banking services. The values of the mean ranged from 3.93 to 4.26 and the SD was in the range between 0.336 and 0.636. The values of the mean and SD obtained are indicated in the Table 3.

Table 3.

Factor Loadings and Descriptive Statistics of the Study Variables Related to the Intention to Use Mobile Banking among Management Students

Variable Name	Factor Loadings	Mean	Standard Deviation
Perceived usefulnessMobile banking would save my travelling expenses to the bank.Mobile banking would be useful as it would save my time.Mobile banking would be useful because of its convenience to use anywhere.	0.8430.7600.693	4.24	0.636
Perceived ease of useLearning to use mobile banking would be easy for me.Mobile banking would provide me with easy user interface.	0.6540.636	3.93	0.478
Social influenceI would show my social group that I use mobile banking.I would use mobile banking if my social group uses it.I would discuss the features of mobile banking with my social group.I would use mobile banking if people who are important to me would try to convince me.	0.7960.7420.7250.687	4.18	0.538
Trust propensityMy privacy related to mobile banking would not be compromised.My mobile banking transactions would be secure.My trust level on mobile banking would be same as banking in person through a branch.While using mobile banking I think my information would be kept confidential.	0.8620.8570.8070.804	4.26	0.336
Behavioural intentionI intend to use mobile banking.I predict that I shall use mobile banking.	0.8120.798	4.11	0.466

Source: Authors’ own findings/authors’ calculations.

Each of the above extracted factors had an eigenvalue of more than 1 and they could explain 69.88 per cent of the total variance.

A reliability analysis was done to check the internal consistency of the factors and the Cronbach’s alpha in each of the cases was found to be above the acceptable level as described in Table 4.

As it can be observed from Table 4, the values of Cronbach’s alpha values are in the range from 0.792 to 0.899. The range exceeds the minimum alpha value of 0.6 (Hair et al., 1998). The results of the factor analysis thus indicate that PU, PEOU, SI and TP are significant factors underlying the intention of management students to use mobile banking.

Table 4.

Reliability Index to Check the Internal Consistency of the Factors

S. No.	Name of the Factor	Cronbach’s Alpha
1.	Perceived usefulness	0.792
2.	Perceived ease of use	0.857
3	Social influence	0.899
4.	Trust propensity	0.839
5.	Behavioural intention	0.802

Source: Authors’ own findings/authors’ calculations.

From the factors that emerged from the factor analysis regarding the intention to use mobile banking services, a correlation analysis was performed to test the inter-correlation. The inter-correlation matrix is given in Table 5.

Table 5.

Inter-correlations among the Study Variables Related to Mobile Banking

Correlations
		PU	PEOU	SI	TP	BI
PU	Pearson correlation	1	0.468**	0.525**	0.546**	0.550**
	Sig. (2-tailed)		0.001	0.000	0.000	0.000
	N	144	144	144	144	144
PEOU	Pearson correlation	0.518**	1	0.545**	0.569**	0.436**
	Sig. (2-tailed)	0.001		0.000	0.000	0.000
	N	144	144	144	144	144
SI	Pearson correlation	0.425**	0.545**	1	0.522**	0.417**
	Sig. (2-tailed)	0.000	0.000		0.000	0.000
	N	144	144	144	144	144
TP	Pearson correlation	0.546**	0.569**	0.522**	1	0.567**
	Sig. (2-tailed)	0.000	0.000	0.000		0.000
	N	144	144	144	144	144
BI	Pearson correlation	0.550**	0.436**	0.477**	0.567**	1
	Sig. (2-tailed)	0.000	0.000	0.000	0.000
	N	144	144	144	144	144

Source: Authors’ own findings/authors’ calculations.

Note: **Correlation is significant at the 0.01 level (two-tailed).

From the table it is clear that all the factors, namely, PU, PEOU, SI, TP and the variable behavioural intention to use mobile banking, were correlated with the correlation being significant at the 1 per cent level. The value of the inter-correlations among the factors ranged from 0.425 to 0.569.

Regression was then performed on the data to find out if the extracted factors significantly affected the dependent variable intention to use mobile banking. The results are summarized in Table 6.

Table 6.

Regression Analysis on the Intention to Use Mobile Banking among Management Students

Coefficients^a
Model	Unstandardized Coefficients		Standardized Coefficients	T	Sig.
	B	Std. Error	Beta
(Constant)	1.112	0.284		3.863	0.000
Perceived usefulness	0.170	0.041	0.266	4.649	0.000
Perceived ease of use	0.203	0.072	0.218	4.103	0.002
Social influence	0.291	0.061	0.273	5.119	0.000
Trust propensity	0.265	0.043	0.301	6.026	0.000

Source: Authors’ own findings/authors’ calculations.

Note: ^aDependent variable: Intention to use mobile banking.

R²R²F

The value of R² (Adj.) = 0.583 indicates that it is a statistically significant model wherein 58.3 per cent of the variance in the dependent variable intention to use mobile banking is explained by the independent variables PU, PEOU, SI and TP. The β values in the regression results ranged from 0.218 to 0.301 and all the independent variables displayed a p value of < 0.05 indicating a strong effect on the dependent variable intention to use mobile banking.

From the table it is observed that PU (β = 0.266, t = 4.649, p < 0.05) is significantly associated with the intention to use mobile banking thereby proving H1. Banks should, therefore, make all efforts to make mobile banking highly useful by providing to the extent possible the entire range of services through the mobile phone.

Perceived ease of use (β = 0.218, t = 4.103, p < 0.05) also has a significant impact on the intention to use mobile banking, thereby proving H2. Banks need to strive in order to make their mobile banking services appear simple and easy to operate.

Social influence (β = 0.273, t = 5.119, p < 0.05) is significantly associated with the intention to use mobile banking, thereby proving H3. Banks would do well to promote mobile banking as a tool for enhancing a person’s social image.

Trust propensity (β = 0.301, t = 6.026, p < 0.05) is significantly associated with the intention to use mobile banking thereby proving H4. Banks should do well to create an atmosphere of secure banking through mobile phones, thereby increasing the levels of TP among prospective mobile banking users.

Conclusion

This study reflects the understanding of mobile banking from the point of view of management students in India. The primary objective of this study was to study the intention to use mobile banking service delivery channel among management student in India. The findings of this study indicate that the intention to use mobile banking among management students is influenced by four factors—PU, PEOU, SI and TP. This article first builds the TAM model in terms of its two constructs, namely, PU and PEOU, in the context of the intention to use mobile banking in India and then extends the TAM model to include two new constructs, namely, SI and TP, which were derived from the literature on mobile banking. The study provides a direction on the determinants that influence a management student’s intention to use mobile banking. This segment of students have been selected as they represent the potential to be active banking customers in the immediate future where they will have a tendency to test the different service delivery channels offered by the banks and adapt those found to be useful. Due to the emergence of smartphones, with their ability to perform multiple tasks for its users and thereby its increasing penetration among the population globally, studies on mobile banking have been a focus area across the world. However, in India there has been a constant gap in literature in this field. The study makes a contribution to the rather scarce literature on mobile banking in the Indian context. It is established by this study that management students in India are likely to find mobile banking useful and easy to use while adding the constructs of SI and TP to the TAM model. The same study can be extrapolated to understand the mediator or moderator variables, if any affecting the usage of mobile banking using structural equation modelling. Banks operating in India would do well to lay emphasis on each of these factors to attract this lucrative and potential segment of management students to utilize their mobile banking services.

Managerial Implications

The implications of this study are various. The TAM model is validated here as management students perceive mobile banking to be useful and also feel that in view of their high comfort level in the using smartphones and its applications, this service is also perceived to be easy. Also another important factor is SI and as shown by the findings since people tend to follow the bandwagon effect. Banks would do well, therefore, to create an atmosphere where using mobile banking may be perceived to be enhancing a person’s social status in his/her circle. This article suggests that if there has to be increased usage of mobile banking among management students, then developers of the mobile applications must give importance to trust. Banks should create an aura of trust around themselves by various tangible actions so that intending mobile banking users feel a high level of TP and could feel safe while using the service. In fact, TP would strongly affect the intention to use mobile banking.

Banks would, therefore, do well to tap this segment of management students who are just waiting to enter the corporate world and are ready to use the latest technology-based service delivery channels like mobile banking in view of their high comfort levels with smartphone usage. Based on the results of this study, this article proposes a model (as shown in the Figure 1) for banks to tap the intention to use mobile banking among management students.

Figure 1.

Source: Authors’ own findings/authors’ calculations.

Limitations and Direction for Future Research

This study was limited to management students at the postgraduate level. A significant population of India completes its education at the undergraduate level and then goes to work. This offers scope to initiate studies focused on undergraduate students who are going to work after their graduation. Perhaps this could throw up new factors on the intention to use mobile banking among undergraduate students.

Footnotes

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Appendix

Appendix 2.

Factor Analysis Table

Factors	PU	PEOU	SI	TP	BI
Mobile banking would save my travelling expenses to the bank.	0.843	0.123	0.237	0.097	0.134
Mobile banking would be useful as it would save my time.	0.760	0.302	0.396	0.166	0.209
Mobile banking would be useful because of its convenience to use anywhere.	0.693	0.219	0.049	0.139	0.199
Using mobile banking would improve my productivity.	0.497	0.365	0.266	0.088	0.138
Using mobile banking would improve my effectiveness in utilizing banking services.	0.412	0.218	0.191	0.319	0.128
Learning to use mobile banking would be easy for me.	0.230	0.654	0.033	0.307	0.116
Mobile banking would provide me with easy user interface.	0.146	0.636	0.079	0.208	0.197
It would be easy for me to become skilful at using mobile banking.	0.219	0.491	0.213	0.059	0.301
Mobile banking would be flexible to interact with.	0.062	0.196	0.045	0.111	0.286
I would show my social group that I use mobile banking.	0.112	0.322	0.796	0.211	0.412
I would use mobile banking if my social group uses it.	0.097	0.087	0.742	0.333	0.203
I would discuss the features of mobile banking with my social group.	0.113	0.218	0.725	0.266	0.126
I would use mobile banking if people who are important to me would try to convince me.	0.292	0.196	0.687	0.307	0.402
My privacy related to mobile banking would not be compromised.	0.256	0.178	0.059	0.862	0.271
My mobile banking transactions would be secure.	0.312	0.218	0.073	0.857	0.220
My trust level on mobile banking would be same as banking in person through a branch.	0.032	0.101	0.234	0.807	0.242
While using mobile banking I think my information would be kept confidential.	0.232	0.343	0.083	0.804	0.083
I intend to use mobile banking.	0.112	0.206	0.029	0.307	0.812
I predict that I shall use mobile banking.	0.286	0.045	0.037	0.312	0.798

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