High Versus Low Consumer Attitude and Intention Towards Adoption of Mobile Banking in India: An Empirical Study

Abstract

Although factors influencing adoption of technology-enabled banking has been studied by many researchers, the construct perceived usefulness had been analysed as a single construct. Moreover, the measurement of perceived usefulness by researchers does not discuss sub-dimensions or sub-factors, which could contribute to perceived usefulness associated with a particular technological innovation. This article empirically examines the influence of various factors on user attitude and intention towards adopting mobile banking. Based on focus-group discussion and personal interview with bankers, technology acceptance model (TAM) and studies on technology adoption, we propose a mobile banking adoption model in Indian context. We also examine the impact of demographics on user attitude and intention. Exploratory factor analysis (EFA) on a sample of 367 respondents resulted in seven factors which were found to be reliable. Confirmatory factor analysis demonstrated significant convergent and discriminant validity. Logistic regression equation was used to estimate the degree of correct prediction of user attitude and intention towards mobile banking adoption. Perceived trust, perceived ease of use (PEOU), perceived lifestyle compatibility, perceived efficiency and perceived convenience significantly impacted user attitude. However, user attitude was not found to differ significantly between demographic variables. Similarly, perceived trust, PEOU, perceived lifestyle compatibility and perceived efficiency were found to positively and significantly affect user intention. User intention was found to significantly vary across demographic groups based on gender and household income.

Keywords

Technology adoption logit regression attitude intention antecedents India

Introduction

The growth of technology-enabled banking in India dates back to the opening up of markets in early 1990s. Post liberalization, Indian economy witnessed many reforms, including greater emphasis being placed on information technology and innovation. What started with mere computerization of bank branches slowly extended to inter-connectedness between branches using leased telephone lines and very small aperture terminals (VSATs). Further, the arrival of foreign banks and the setting up of a number of private-sector banks resulted in an altogether different level of competition between banks. IT-driven banking emerged as a source of service differentiation between banks, which found winning new customers and retaining existing ones a key challenge. For the next 15 years, terms like ‘anywhere anytime banking’, ‘tele-banking’, ‘short messaging services (SMS) banking’, ‘ATM banking’, ‘web banking’, ‘Internet banking’, ‘online banking’, ‘electronic banking’, and so on defined the landscape around which multiple channels of banking evolved. As per a recent KPMG report (2016) on digital banking, between 2000 and 2015, the global Internet penetration grew sevenfold from 6.5 to 43 per cent, while in India Internet penetration for individuals grew exponentially from less than a per cent to 30 per cent. Further, the regulatory push for financial inclusion heightened the need for banks to move into the next orbit of digital era by creating an ecosystem wherein all bank service offerings would be made available through all available electronic channels. The latest addition to this evolution has been mobile banking. Currently, three forms of mobile banking services (SMS, client application software or app, and mobile website) are being offered to Indian customers (Dasgupta, Paul & Fuloria, 2011).

The organization of this article is as follows. The first section presents a review of literature on mobile banking adoption and models on technology acceptance and adoption. This section also discusses the factors influencing adoption of mobile banking, which is conceptualized in the form of a causal research model. Formulation of hypotheses is done based upon the review of literature. Research gaps are identified, which is purported to be fulfilled by objectives of the study. The second section discusses the research methodology to achieve the objectives of the study. The third section consists of analysis of data and findings from the study. The final section presents the limitation to the study and its implications for global managers.

Review of Literature

According to Brown, Cajee, Davies and Stroebel (2003), mobile banking can be seen as an extension of Internet banking through a mobile device with its own unique characteristics. Mobile banking is defined as a channel whereby the customer interacts with a bank via a mobile device such as a smart phone or a personal digital assistant (Laukkanen & Pasanen, 2008). Deb and David (2014) view it as ‘an emerging facet of electronic banking that, unlike traditional banking services, which offer very limited functions, is a rich platform for automated banking and other financial services’. For this article, we define mobile banking as using a mobile phone for performing financial transactions such as getting account information, transferring funds, request for a cheque book, opening or renewing fixed deposits, opening a dematerialized account, opening a loan account, making utility payments, and so on.

One of the most discussed models for predicting the degree of acceptance of a new technological innovation is TAM, which provides an explanation of user behaviour towards adopting the same. So much is the popularity of the model that extensions of the original TAM have been developed and tested for various technologies and innovations.

The original TAM model as proposed by Davis (1989) had 14 items each for measuring PEOU and perceived usefulness. Davis, Bagozzi and Warshaw (1989) compared TAM with Theory of Reasoned Action (TRA) which is another popular model to predict human behaviour. According to TRA, a person’s performance of a specified behaviour is determined by his or her behavioural intention (BI) to perform the behaviour and BI is jointly determined by the person’s attitude and subjective norm concerning the behaviour. Similar to TRA, TAM postulates that computer usage is determined by behavioural intention, but differs in that behavioural intention is viewed as being jointly determined by the person’s attitude towards using the system and perceived usefulness. TAM does not include subjective norm as a determinant of BI. Davis (1993) later extended this model to introduce system design features to explain attitude and actual usage behaviour. He found that TAM accounted for 36 per cent of the variance in usage and that perceived usefulness was 50 per cent more influential than ease of use in determining usage. Venkatesh and Davis (1996) used the TAM to study the antecedents and determinants of PEOU and found that the perception of a particular system’s ease of use is determined by user’s computer self-efficacy at all times.

TAM has also been compared with other competing models of acceptance of technology. In one such study by Mathieson (1991), a comparison between TAM and Theory of Planned Behaviour (TPB) was done, and it was found that although both models predicted user attitude and intention to use an information system quite well, TAM was better than TPB empirically.

As evident from the above discussion, TAM is widely accepted as a reliable and valid model to explain user attitude and intention towards acceptance and adoption of various technologies like electronic mail system (Davis, 1993; Gefen & Straub, 1997), text editor (Davis, 1993), World Wide Web—WWW (Lederer, Maupin, Sena & Zhuang, 2000), intranet (Horton, Buck, Waterson & Clegg, 2001), e-commerce and online shopping (Gefen, 2003; Heijden, Verhagen & Creemers, 2003; Henderson & Divett, 2003; Pavlou, 2003), ATM banking (Wan, Luk & Cheris, 2005), phone banking (Wan et al., 2005), SMS or text banking (Hanudin, 2007), internet and online banking (Al-Somali, Gholami & Clegg, 2009; Agarwal, Rastogi & Mehrotra, 2009; Bashir & Madhavaiah, 2014; Chau & Lai, 2003; Chong, Ooi, Lin & Tan, 2010; George & Kumar, 2015; Khare, Khare & Singh, 2010; Pikkarainen, Pikkarainen, Karjaluoto & Pahnila, 2004; Suh & Han, 2003; Tan & Teo, 2000; Wan et al., 2005), m-service (Thakur & Srivastava, 2014; Wang, Lin & Luarn, 2006), mobile banking (Deb & David, 2014; Gu, Lee & Suh, 2009; Khasawneh, 2015; Lewis, Palmer & Moll, 2010; Lin, 2011; Riquelme & Rios, 2010).

Research Framework and Development of Hypotheses

In this section, an attempt is made to review literature on antecedents to mobile banking adoption and propose a conceptual framework which could explain the relationship between these factors and user attitudes and BI.

Perceived Trust

The level of trust associated with various technological innovations may differ among users as the perceived risk associated with technologies varies. For example, online fund transfer may be perceived as riskier as compared to physical branch transfer. In the literature, privacy, security and trust have been mixed together in a large body of research (Nasri & Charfeddine, 2012). Majority of the researchers agree that as compared to offline banking, trust is most important in online banking as the nature of transactions are personal, sensitive and confidential (Heijden et al., 2003). Agarwal et al. (2009) observed that security and trust associated with e-banking had the highest impact on the satisfaction level of customers. Suh and Han (2003) in a study on Korean Internet users found trust to positively and significantly impact the customer’s attitude and intention towards Internet banking. Hernandez and Mazzon (2007) in a study on Internet banking adoption in Brazil found perceived security and privacy to influence Internet banking adoption. Gu et al. (2009) found the path from trust to BI in their integrated model to be positively and significantly associated. Bashir and Madhavaiah (2014) found trust to positively and significantly influence intention to use internet banking among young Indian consumers.

Perceived risk and trust are interlinked concepts and have been identified as key barriers to adopting mobile services (Lewis et al., 2010). The risk or trust factor may be perceived as more in case of mobile devices because mobility increases the threat of security violations in a wireless environment (Riquelme & Rios, 2010). According to Lin (2011) in the context of mobile banking, users develop knowledge-based trust based upon the ability of the stakeholders (banks, telecom companies and financial institutions) to provide competent service ensuring integrity of user data and transactions. This in turn influences attitude and BI. Thus, the above discussion leads to the following hypotheses:

Hypothesis 1 (H1): Perceived trust has a positive effect on user attitude towards adopting mobile banking.

Hypothesis 1a (H1a): Perceived trust has a positive effect on user intention towards adopting mobile banking.

Perceived Ease of Use

Perceived ease of use (PEOU) is defined as the degree to which a person believes that using a particular technology would be free from effort (Davis et al., 1989) and is easy to use and non-complex (Lin, 2011). In the current study, technology acceptance model as proposed by Venkatesh, Morris, Davis and Davis (2003) has been used to study acceptance of mobile banking technology. Gu et al. (2009) proposed an integrated model wherein PEOU was found to be a significant determinant of BI to mobile banking. Suh and Han (2003) found PEOU to positively and significantly influence attitude towards Internet banking. Similarly, Lin (2011) posited that customers do not need to spend significant effort on using mobile banking as the phones and mobile banking applications have user-friendly interfaces. He found that PEOU has a significant effect on attitude and moreover, if customers find mobile banking easy to use, they develop a positive attitude towards adopting it. Nasri and Charfeddine (2012) in the context of Tunisian bank, and Bashir and Madhavaiah (2014) in context of Indian customers, found that PEOU has a significant effect on intention to use Internet banking. George and Kumar (2013) found, among a sample of Indian Internet-banking users, PEOU positively impacts customer satisfaction. Deb and David (2014) in their study on Indian mobile banking users found PEOU to positively and significantly influence attitude towards mobile banking. Thus, PEOU has been as a key determinant in adoption of various information technologies for banking such as the Internet and mobile phone. Based on the above justifications, we postulate the following two hypotheses as depicted in Figure 1.

Hypothesis 2 (H2): PEOU positively and significantly affects user attitude to use mobile banking.

Hypothesis 2a (H2a): PEOU positively and significantly affects user intention to use mobile banking.

Perceived Lifestyle Compatibility

A review of literature concerning an online-banking user reveals that a typical user is viewed as someone who is educated, young and wealthy person with a good knowledge of computers and Internet (Karjaluoto, Mattila & Pento, 2002). Social norms explained in terms of external (friends, peer group, superior) and internal (family and relatives) influence lifestyle which in turn determines the adoption behaviour (Riquelme & Rios, 2010). Deb and David (2014) found the relationship between social influence (approval from friends and family) and attitude towards mobile banking to be positive. Social influence was also found to significantly and positively influence intention to use internet banking (Bashir & Madvaiah, 2014). Therefore, as an extension of Tan and Teo’s (2000) hypothesis of mobile banking, it is expected that more the individual uses a mobile, and the more he or she perceives the mobile banking as compatible with lifestyle, the more likely the individual will adopt mobile banking. Further, Lin (2011) found that perceived compatibility could be explained in terms of the degree to which mobile banking is aligned to their values, experiences, lifestyle and preferences. Singh and Srivastava (2014) found that compatibility is a significant determinant in explaining the intention to adopt mobile banking among Indian customers. In another study by Mohammadi (2015) in Iran, the results revealed that compatibility was found to be the main factor affecting user attitude towards use of mobile banking. All the above lead us to hypothesize that:

Hypothesis 3 (H3): Perceived lifestyle compatibility has a significant positive influence on user attitude to use mobile banking.

Hypothesis 3a (H3a): Perceived lifestyle compatibility has a significant positive influence on user intention to use mobile banking.

Perceived Efficiency

Past studies on technology adoption have consistently shown that perceived usefulness has a strong influence on attitude and intention to adopt online banking (Chong et al., 2010) and mobile banking (Mohammadi, 2015). Although perceived efficiency is not cited directly by many research studies, it manifests itself in the form of the innovation-based attributes discussed in innovation diffusion theory. The innovation-based attributes are ease of use, relative advantage, compatibility, observability and trialability (Rogers, 1995). Perceived relative advantage refers to the degree to which an innovation provides more benefit than its precursors (Lin, 2011) which could be explained in terms of efficiency, economic benefits and enhanced status. Due to the very nature of mobile system, wherein, users can conduct banking transaction while on the move and anytime, customers perceive them as an efficient system which not only saves time and money but also helps them conduct more banking transactions with minimum effort and cost. When customers perceive clear advantages offered by mobile banking, they are more likely to have a positive attitude and BI towards adopting mobile banking (Lin, 2011). The relationship between perceived usefulness and attitude towards mobile banking was found positive (Deb & David, 2014). Hence, we propose the following hypotheses:

Hypothesis 4 (H4): Perceived efficiency has a positive effect on user attitude to use of mobile banking.

Hypothesis 4a (H4a): Perceived efficiency has a positive effect on user intention to use mobile banking.

Perceived Convenience

Mobile phones eliminate the need to queue up for public phones, to purchase tickets (Riquelme & Rios, 2010), as it offers anytime anywhere connectivity. Convenience has been discussed within the literature as an important factor which influences a user decision to adopt a technology innovation be it ATM banking, SMS banking, telephone banking or Internet banking. Thus perceived convenience from the perspective of mobile banking may be defined as the ability to receive 24 by 7 banking services in a manner, which is perceived superior as compared to the alternate banking channels. Suh and Han (2003) examined the impact of perceived usefulness on BI to use Internet banking and found this relationship to be positive and significant.

In more recent studies, Liao and Wong (2008) found that convenience and responsiveness to service requests significantly influence customer interactions with the Internet-enabled e-banking services. Agarwal et al. (2009) found convenience to significantly influence customer’s overall satisfaction with e-banking in India. Numerous studies have shown that perceived usefulness positively affects not only attitude towards a system but also BI to use and actual system usage (Adams et al., 1992; Davis et al., 1989; Jackson et al., 1997). Thus, perceived convenience is posited to result in a positive attitude and intention to adopt mobile banking, leading to the following hypotheses:

Hypothesis 5 (H5): Perceived convenience has a positive effect on user attitude to use of mobile banking.

Hypothesis 5a (H5a): Perceived convenience has a positive effect on user intention to use mobile banking.

Mobile Banking Adoption and Demographic Attributes

Demographic factors like gender, age, income, marital status, occupation, education, experience, and so on have been researched as factors influencing adoption of technology innovations. Researchers (Wan et al., 2005) found that males are more inclined to adopt mobile banking technology thus supporting previous findings (Akinci, Aksoy & Atilgan, 2004) carried out in the context of Internet banking. Laukkanen and Pasanen (2008), in the context of Finland, found age and gender as differentiators between users of mobile banking and other online banking services. However, education, income, occupation and size of the household were found to be insignificant in differentiating the groups.

Past research studies (Deb & David, 2014; Mann & Sahni, 2012) show that user segments can be identified based upon the technology adoption factors which have variations in terms of demographic variables. It is logical to perceive that each cluster profile may vary with respect to demographics and therefore, the user attitude and intention towards technology adoption may vary across demographic groups. Flavián, Guinaliu and Torres (2006) found income, age and gender as factors influencing online banking adoption. Quazi and Talukder (2011) while studying the impact of demographics on individual’s perception and adoption of technological innovation in Australian workplaces found that individuals having higher level of education tend to have a more favourable attitude towards innovation, which is translated into its adoption in a workplace. Correa, Hinsley and De Zuniga (2010), in a sample of US adults, found age and gender groups to differ significantly in usage of social media tools. Young, educated and wealthy customers have been cited as more likely to use Internet banking (Karjaluoto et al., 2002; Mattila, Karjaluoto & Pento 2003; Samudra & Phadtare, 2012; Sathye, 1999). Results from previous studies show that younger individuals are more inclined to use technological advancements compared to older people (Akinci et al., 2004; Kolodinsky, Hogarth & Hilgert, 2004). Similarly, income and occupation have been found by researchers (Jayawardhena & Foley, 2000; Karjaluoto et al., 2002) to influence Internet banking adoption. Szopiński (2016) found age, income and level of education to significantly influence the use of online banking. Jha and Ye (2016) in their study on the usage of Facebook by adults in United States found that perceptions and continued usage to vary across demographic groups based on gender, age, education and income. Another key demographic variable, the marital status of individual, has also been found to influence technology adoption. Previous research studies have shown that married consumers prefer electronic banking transactions (Katz & Aspden, 1997; Stavins, 2001). However, another study by Gan, Clemes, Limsombunchai and Weng (2006) revealed that marital status has no impact on the adoption of electronic banking. Hence, based on the above evidence, the following hypotheses may be derived:

Hypothesis 6 (H6): User attitude towards mobile banking significantly differs across gender groups.

Hypothesis 7 (H7): Age of the respondent negatively influences user attitude towards mobile banking.

Hypothesis 8 (H8): Income of the respondent has a positive influence on user attitude towards mobile banking.

Hypothesis 9 (H9): User attitude towards mobile banking differs across marital status.

Similarly, the hypothesis for the influence of demographic variables on intention can be written as:

Hypothesis 6a (H6a): User intention towards mobile banking adoption significantly differs across gender groups.

Hypothesis 7a (H7a): Age of the respondent negatively influences user intention to use mobile banking.

Hypothesis 8a (H8a): Income of the respondent has a positive effect on user intention to use mobile banking.

Hypothesis 9a (H9a): User intention towards mobile banking differs across marital status.

User Attitude and Intention to Adopt Mobile Banking

Kassim and Ramayah (2015) define BI as a measure of one’s willingness to exert effort while performing certain behaviours. The extent of effectiveness of the system depends on how the users feel for the system; if the users do not rely upon the system and its information, their behaviour towards the system could be negative (Pikkarainen et al., 2004). According to the TRA developed by Fishbein and Ajzen (1975), the BI can be explained by the attitude towards behaviour which is defined as individual positive and negative feelings about behaving in a particular way. Further in TAM proposed by Davis (1989) BI can be explained by attitude towards a system. Similarly, as proposed by Ajzen (1985), behaviour is explained as a function of behavioural intention and behavioural control; wherein behavioural intention is proposed to be influenced by the attitude towards behaviour. Consistent with intention-based models, Lin (2011) found a significant and positive linkage from attitude to BI to explain adoption or continued usage of mobile banking. Deb and David (2014) empirically established the positive influence of attitude on BI. From the mobile banking perspective, greater is the positivity towards mobile platform, more favourable would be the attitude towards using mobile banking, which would result in a positive BI to use mobile banking. This study expects this causal relationship to hold true in the context of mobile banking, which is written as:

Hypothesis 10 (H10): User attitude towards mobile banking adoption has a significant positive influence on user intention to use mobile banking.

Thus, all the above-postulated hypotheses are depicted in Figure 1.

Research Gaps and Objectives

Majority of the past research work have focused on individual models of technology adoption in isolation and very few have discussed the complementary factors influencing adoption. In the present study, we have used the constructs as discussed by two models, that is, innovation diffusion theory and the technology acceptance model. While TAM looks at PEOU and perceived usefulness as factors influencing attitude, the innovation diffusion theory uses perceived relative advantage, ease of use, compatibility, observability and trialability to explain attitude and intention. Both perceived usefulness and perceived relative advantage focus on the advantages mobile banking offers like time and place independence, effort-saving qualities (Mallat et al., 2004), ubiquity, flexibility and mobility (Sulaiman et al., 2007) as compared to other banking channels. In a majority of the research studies using TAM model, perceived usefulness has been taken as a single construct. However, as discussed by Davis (1989), the items used to measure perceived usefulness can be categorized into three substrata. In this research, we focus on understanding the substrata’s of perceived usefulness along with other factors influencing mobile banking adoption. Based on the above discussion, it is evident that there are complementary attributes which TAM and innovation diffusion theory share which need to be empirically examined as a unified model for the adoption of mobile banking. Further, with respect to demographic variables, explorations of demographic correlations pertaining to acceptance of technology have resulted in contradictory viewpoints with respect to adoption (Mann & Sahni, 2012).

This study gains significance by incorporating the attributes of TAM and innovation diffusion theory and proposes a model to investigate the role of those attributes on the attitude and intention of user’s to adopt mobile banking in India. The study has the following broad objectives:

To determine the factors which influence mobile banking adoption.

To study the impact of functional and personality attribute on attitude and intention to adopt mobile banking.

To examine whether demographic variables influence user attitude and BI to adopt mobile banking.

Research Methodology

A review of literature on mobile banking adoption, a focus group discussion with five managers of public sector banks and informal personal interviews with mobile banking users resulted in a pool of 59 statements. These statements were shown to subject matter experts of mobile banking, and they were of the opinion that 17 statements were redundant and could be deleted from the pool. Further, based upon the feedback from the experts, five items were rephrased to ensure uniform understanding of the statements among the respondent group. The study addresses the concerns of content validity as the selected statements were subject to content expert validation which is a method of ensuring content validity (Grant & Davis, 1997). This resulted into the development of a pilot instrument with 42 statements. Respondents were asked to indicate their level of agreement based on a seven-point Likert scale. All the items except attitude were measured on a seven-point Likert scale ranging from ‘1’ (strongly disagree) to ‘7’ (strongly agree). Attitude was measured using bi-polar semantic differential scale.

An online survey was rendered, and a total of 283 complete responses were obtained. Among these, 74.2 per cent (210) are males and 25.8 per cent (73) females. Around 90 per cent of the respondents were 45 years or less in age, around 89 per cent were postgraduates and above, around 57 per cent had engineering as educational background and around 82 per cent were salaried professionals.

These 42 statements were subject to EFA to explore the possible underlying factor structure among a set of observed variables. Principal component analysis with varimax rotation was conducted to identify constructs. Factor analysis resulted in seven factors, which explained 76 per cent of the total variance. These were labelled as PEOU, perceived trust (TR), perceived lifestyle (LS), perceived efficiency (EFF), perceived convenience (CON), attitude (ATT) and BI (INT).

A fresh round of data collection was carried out to confirm if the statements and their identified constructs hold true on another sample. This resulted in a sample of 367 respondents who were found to have an almost similar demographic profile as the pilot survey. Out of the 367 respondents, approximately 75 per cent of them were males and the remaining 25 per cent females. Majority of the respondents were young engineering graduates that have acquired postgraduate degree as their highest qualification. Further, 50 per cent of the respondents were less than or equal to 30 years and 83.4 per cent of them were salaried professionals. Around 53 per cent of the respondents were married while the remaining 47 per cent were single.

Next, to check for internal consistency of the seven factors obtained earlier, a reliability analysis is carried out. The Cronbach’s α of the seven factors and overall is reported in Table 1. Among the factors, BI has the highest Cronbach’s α of 0.976 while Perceived Efficiency has the lowest value of 0.810. Since the value of Cronbach’s α for each of the factors is greater than 0.7, it indicates a very high reliability (Hair, Black, Babin, Anderson & Tatham, 2006).

Table 1.

Internal Consistency of Constructs Measured using Cronbach’s α

S. No.	Construct (No. of Statements)	Cronbach’s α (N = 283)	Cronbach’s α (N = 367)
1	Perceived Ease of Use (PEOU)—(7)	0.910	0.886
2	Perceived Convenience (CON)—(2)	0.815	0.820
3	Perceived Efficiency (EFF)—(4)	0.810	0.795
4	Perceived Trust (TR)—(8)	0.966	0.969
5	Perceived Lifestyle (LS)—(5)	0.926	0.915
6	Attitude (ATT)—(7)	0.897	0.897
7	Behavioural Intention (INT)—(9)	0.976	0.973
8	Overall (42)	0.971	0.969

Source: Prepared by the authors’.

The fitness of the measurement model was estimated through confirmatory factor analysis conducted using IBM SPSS AMOS 20 software. The results of the confirmatory factor analysis (Normed χ² = 2.047; CFI = 0.944; NFI = 0.896, TLI = 0.939 and RMSEA = 0.053) satisfied the required conditions for a good model fit. Values of RMSEA of 0.08 or less, CFI of at least 0.90 and TLI of at least 0.90 indicate a very good model fit (Hu & Bentler, 1998). Further, the model also satisfied the conditions for convergent and discriminant validity.

Convergent validity refers to the extent to which multiple measures of a construct agree with one another (Campbell & Fiske, 1959). There are three conditions which must be fulfilled to ensure convergent validity Firstly, the average variance extracted (AVE) should be more than 0.50 for each construct. Secondly, all items of each construct in the model should have standardized path loadings greater than 0.5 (Gefen, Straub & Boudreau, 2000). Lastly, to ensure convergent validity, composite reliability or internal consistency reliability should be equal to or greater than 0.7 (Hair et al., 2006). Since AVE for all constructs is greater than 0.5, the standardized path loadings are greater than 0.5 and composite reliability values are greater than 0.7, the model satisfies all the conditions for convergent validity.

Discriminant validity refers to the extent to which measures of various constructs differ from each other or the extent to which measures of different constructs are distinct (Campbell & Fiske, 1959). In fact, it tests that the constructs should have no relationship between them. This is achieved by computing the square root of variance extracted for each construct and comparing it with the inter-construct correlation. It is found that the standardized inter-construct correlation for all constructs is less than the corresponding square root of the variance explained. Thus, construct validity measured by convergent and discriminant validity is found to be satisfactory.

To examine the impact of constructs influencing user attitude and intention towards mobile adoption, the average scores of the seven constructs were obtained. As we wanted to predict the Intention and Attitude of the respondents towards adoption of mobile banking, the scores on Intention and Adoption were divided into two groups as High Intention and Low Intention, High Attitude and Low Attitude. The median scores of the Intention and Attitude variable were obtained. The median score for intention was 5.67 and there were 12 respondents with this score. These 12 responses were omitted from the analysis and anyone having a score greater than 5.67 were taken as a respondent with high intention to adopt mobile banking and the one with the score less than 5.67 was considered as one with low intention. There were 180 respondents with high intention and 175 with low intention. Similarly, in case of attitude, the median score was 6.0 and there were 151 respondents having high attitude and 181 having low attitude towards mobile banking adoption.

The logistic regression model was used to predict attitude and intention towards mobile banking adoption. The procedure for developing a model for predicting user attitude, which could be replicated for intention variable is explained below:

Let p = probability of a respondent having a high attitude to adopt mobile banking.

1 − p = probability of a respondent having a low attitude to adopt mobile banking.

$\frac{p}{1 - p} =$ odd ratio of a respondent having a high attitude towards adopting mobile banking.

For high attitude, the value is taken as 1, whereas for the low attitude it is 0.

As we want to study the impact of trust, PEOU, lifestyle compatibility, perceived efficiency and perceived convenience, they were treated as independent variables. Besides these five variables, four demographic variables, namely, gender, age, income and marital status, were also used as independent variables to predict attitude for mobile banking adoption. The demographic variables measured as categorized variables were defined as follows:

Gender (G)

Male = 0; Female = 1

Age (A)

A respondent having age less than equal to 30 years was coded as 0; whereas

A respondent having age greater than 30 years was coded as 1

Income (I)

A respondent having income less than or equal to ₹50000 was coded as 0; whereas

The one having income greater than ₹50000 was coded as 1

Marital Status (MS)

A respondent who is single was coded as 0; whereas A married respondent was coded as 1

Now the logit regression model for attitude towards adopt mobile banking is written as:

\begin{array}{l} z = \ln [p_{i} / (1 - p_{i})] = α + β_{1} * X_{1} + β_{2} * X_{2} + β_{3} * X_{3} + β_{4} * X_{4} + β_{5} * X_{5} + β_{6} * X_{6} + + β_{7} * X_{7} + + β_{8} * X_{8} + β_{9} * X_{9} \end{array}

(1)

where

X1 = Perceived Trust

X2 = PEOU

X3 = Perceived Lifestyle Compatibility

X4 = Perceived Efficiency

X5 = Perceived Convenience

X6 = Gender

X7 = Age

X8 = Monthly Household Income

X9 = Marital Status

To calculate Pi, the probability of ith respondent having a higher attitude for mobile banking adoption, the exponential of the equation (1) can be used as Pi = [Zi/(1 + Zi)]. If the probability Pi of ith respondent is estimated to be greater than 0.5, it is predicted to have a high attitude otherwise low attitude for mobile banking adoption. A positive coefficient corresponding to an independent variable in logit regression indicates that a unit increase in the value of that variable would increase the odd ratio in favour of attitude towards mobile banking more than one time.

The logit regression is estimated using the Maximum Likelihood method. In order to examine the goodness of fit of the logit regression, the value of −2 log likelihood (−2LL) is used. This expression follows a χ² distribution. For a perfect fit, the value of −2LL is zero. Closer the value of −2LL to zero, better is the fit of the logit regression. However, the maximum value of −2LL is not known. For the purpose of model building, one starts with an initial value of −2LL₀ when there are no independent variables (baseline model). This is compared with the 2LL_m when m independent variables are used. The reduction to the value of −2Loglikelihood is examined. This follows a χ² distribution with degrees of freedom equal to number of independent variables. This is also called Likelihood Ratio (LR) statistic and is given by the expression:

x²

The significance of LR statistic would imply that the logit regression equation has a good fit. Another way of measuring overall goodness of fit is Hosmer and Lemeshow classification method. In this method, all the cases are, firstly, divided into approximately 10 equal classes. The actual and predicted events are compared in each class. This is a comprehensive measure of predictive accuracy and is tested with the help of χ² statistics. An insignificant χ² indicates a good fit.

There is no concept of R² in logit regression, rather a concept called count R² is used, which is defined as the percentage of correct prediction. There are pseudo measures of R² like Cox & Snell R² and Nagelkerke R².

Similar procedure is followed for intention.

Estimation and Interpretation of Logit Regression for Attitude and Intention

The logit regression of attitude to adopt mobile banking was estimated using nine independent variables as listed before and the results are presented in Model 1 of Table 2. The results show that the coefficients of the variables X1 to X5 are positive, thereby confirming hypotheses H1–H5. However, the coefficients of X1 and X3 are significant at 1 per cent level, X4 significant at 5 per cent level and X2 and X5 significant at 10 per cent level. Among the demographic variables, it is found that none of the coefficients of these variables are significant. Therefore, hypotheses H6–H9 are not supported. Furthermore, the logit regression has a good fit as given by a significant LR statistic. This is confirmed by Hosmer and Lemeshow test with a χ²- value of 12.637, which corresponds to a p-value of 0.125. This is insignificant thereby confirming that logit regression has a good fit. The count R² value is 81.3 per cent. The values of pseudo R², namely Cox & Snell R² and Nagelkerke R², are 0.459 and 0.613, respectively. These values are satisfactory. In order to improve the predictive power of the model, it was decided to drop all the insignificant variables as obtained in Model 1.

Table 2.

Estimated Logit Regression Equation for Attitude to Adopt Mobile Banking

	Model 1			Model 2
	Coefficient Estimate B	p-value	Exp (B)	Coefficient Estimate B	p-value	Exp (B)
X1 (Perceived trust)	0.469	0.001*	1.598	0.444	0.002*	1.559
X2 (Perceived ease of use)	0.488	0.080***	1.629	0.457	0.092***	1.580
X3 (Perceived lifestyle compatibility)	1.430	0.000*	4.178	1.423	0.000*	4.150
X4 (Perceived efficiency)	0.624	0.015**	1.866	0.686	0.006*	1.986
X5 (Perceived convenience)	0.474	0.062***	1.606	0.439	0.076***	1.552
X6 (Gender)	0.609	0.114	1.838	—	—	—
X7 (Age)	–0.289	0.537	0.749	—	—	—
X8 (Monthly household income)	–0.596	0.201	0.551	—	—	—
X9 (Marital status)	0.133	0.766	1.142	—	—	—
Constant	–20.042	0.000*	0	–19.549	0.000	0
LR (χ²)	203.869			198.325
Hosmer and Lemeshow test	χ² = 12.63	0.125		χ² = 7.69	0.464
Count R²	81.3%			83.4%

Source: Prepared by the authors’.

Notes: *Significance at 1 per cent level.

**Significance at 5 per cent level.

***Significance at 10 per cent level.

Model 2 of Table 2 is estimated using the significant variables, that is, X1–X5 as independent variables. The results show that all the coefficients for the five independent variables have positive signs thereby indicating that hypotheses H1–H5 are satisfied. Further, the coefficients of X1, X3 and X4 are found to be significant at 1 per cent level whereas, the coefficients of X2 and X5 are significant at 10 per cent level. In this case, also the perceived lifestyle compatibility turns out to be the most important variable as a unit increase in it would lead to the odd ratio increasing by 4.15 times in favour of high attitude towards mobile banking. The second important variable is perceived efficiency where the impact of its unit increase results in increasing the odd ratio by 1.986 times. Similarly, the other variables could be interpreted.

Table 3.

Estimated Logit Regression Equation for Intention to Adopt Mobile Banking

	Model 1			Model 2
	Coefficient Estimate B	p-value	Exp (B)	Coefficient Estimate B	p-value	Exp (B)
X1 (Perceived trust)	0.495	0.000*	1.640	0.485	0.000*	1.624
X2 (Perceived ease of use)	0.553	0.021**	1.738	0.598	0.010*	1.890
X3 (Perceived lifestyle compatibility)	1.448	0.000*	4.253	1.482	0.000*	4.400
X4 (Perceived efficiency)	0.414	0.069***	1.512	0.469	0.037**	1.599
X5 (Perceived convenience)	0.282	0.226	1.325	—	—	—
X6 (Gender)	–0.988	0.007*	0.374	–0.881	0.012**	0.414
X7 (Age)	–0.406	0.337	0.666	—	—	—
X8 (Monthly household income)	0.786	0.067***	2.194	0.749	0.075***	2.115
X9 (Marital status)	0.187	0.648	1.206	—	—	—
Constant	–16.889	0.000*	0	–16.045	0.000*	0
LR (χ²)	199.506			197.2
Hosmer and Lemeshow test	χ² = 7.09	0.527		χ² = 8.63	0.370
Count R²	78.6%			79.2%

Source: Prepared by the authors’.

Notes: *Significance at 1 per cent level.

**Significance at 5 per cent level.

***Significance at 10 per cent level.

The LR statistic is significant indicating that the logit regression has a good fit. This is further corroborated by Hosmer and Lemeshow test, which has a χ²- value of 7.693 corresponding to a p-value of 0.464. This is insignificant, which means the logit regression has a good fit. The predictive ability of the model as given by count R² square is 83.4 per cent. Out of the 181 respondents having low attitude towards mobile banking, 82.9 per cent are correctly classified, whereas out of 151 respondents with high attitude 84.1 per cent are correctly classified. The overall correct prediction is 83.4 per cent, which is quite satisfactory. The values of pseudo R² as given by Cox & Snell R² and Nagelkerke R² are 0.450 and 0.601, respectively.

Next, logit regression analysis was carried out to explain the intention towards mobile banking using all the nine independent variables. The result is reported in Model 1 of Table 3. The results indicate that perceived trust, PEOU, perceived lifestyle compatibility, perceived efficiency and perceived convenience have a positive impact on intention to adopt mobile banking. This means that the first five hypotheses (H1a–H5a) are satisfied. However, if we examine the coefficients corresponding to these variables, it is found that perceived convenience is insignificant, although its coefficient is positive and perceived efficiency is significant only at 10 per cent level of significance. The perceived trust and perceived lifestyle compatibility are significant at 1 per cent whereas PEOU is significant at 5 per cent level. From the demographic variables, it is seen that the coefficient of gender is significant at 1 per cent level of significance whereas income is significant at 10 per cent level. Therefore, hypotheses H6a and H8a are supported. The coefficients of other demographic variables like age and marital status are insignificant. Therefore, hypotheses H7a and H9a are not supported. The goodness of fit of the logit regression equation as given by LR statistic is significant with a χ²- value of 199.506 corresponding to a p-value of 0.000. This means that the regression has a good fit. This is confirmed by using Hosmer and Lemeshow test, which follows a χ² distribution. The value of χ² equals 7.093 with a p-value of 0.527 indicating that it is insignificant. This shows that the actual and predicted events do not deviate significantly. The count R² which indicates the percentage of correct prediction of the total number of respondents is 78.6 per cent. The values of pseudo R² as given by Cox & Snell R² and Nagelkerke R² are 0.430 and 0.573, respectively, which are quite satisfactory.

In Model 1, the insignificant variables were perceived convenience, age and marital status. In Model 2, these insignificant variables were removed, and the logit regression was re-estimated with the remaining six independent variables. The results of Model 2 are reported in Table 3. The results indicate that all the variables are significant either at 1 or 5 per cent level except for income, which is significant at 10 per cent level. Further, hypotheses H1a–H5a are satisfied as all these regression coefficients have a positive sign. Among the two demographic variables, gender and income, the coefficient of gender has a negative sign whereas the coefficient of income has a positive sign. The Model 2 also has a good fit as the LR statistic which follows a χ² distribution is significant. This is confirmed by Hosmer and Lemeshow test, which follows a χ² distribution. Its value is 8.678 with a corresponding p-value of 0.370, which is insignificant. This shows that the actual and predicted events do not deviate significantly, and the overall fit of the logit regression is good. Further, the count R²- value as obtained in Model 2 increases to 79.2 per cent from 78.6 per cent as reported in case of Model 1. The values of pseudo R² as given by Cox & Snell R² and Nagelkerke R² are 0.426 and 0.568, respectively, which is satisfactory.

For interpretation, the results of Model 2 would be used. The first four hypotheses (H1a–H4a) are satisfied as they have a positive relationship with the dependent variable and are statistically significant. The results indicate that if perceived trust goes up by one unit, the odd in favour of a respondent having a high intention for mobile banking adoption goes up by 1.624 times. Similarly, if PEOU goes up by one unit, the odd in favour of a respondent having a high intention to adopt mobile banking increases by 1.890 times. The highest impact is observed in case of perceived lifestyle compatibility whereas it is seen that with one-unit increase in lifestyle compatibility, the odd in favour of a respondent having a high intention to adopt mobile banking goes up by 4.40 times. Similar are the results in case of perceived efficiency.

It is seen that in case of a female, the odd ratio are 0.414 times that of a male respondent. This shows that the male respondents are more inclined to having a high intention to adopt mobile banking as compared to their female counterparts. In case of income, it is found that the higher income group has 2.115 times higher odd ratio as compared to the low-income group. Therefore, we can say that in case of H6a, male and respondents with a high-income group have higher intention to adopt mobile banking. Regarding the predictive ability of the model, it is seen that out of the 175 respondents with low intention for mobile banking adoption, 74.9 per cent are correctly classified and out of the 180 respondents with high intention towards mobile banking, 83.3 per cent are correctly classified. Overall, the model can predict 79.2 per cent of the respondents correctly.

The correlation between attitude and intention was computed and found to be 0.607 which is statistically significant at 1 per cent level. The positive correlation supports H10.

Discussion of Results and Implications for Academia and Practitioners

The results of Model 2 of Table 2 pertaining to attitude and Model 2 of Table 3 pertaining to intention to adopt mobile banking are used for discussion and implications for academia and practitioners.

Implications for Academia

With respect to attitude, perceived lifestyle compatibility turns out to be to the most important factor followed by perceived efficiency, PEOU, perceived trust and perceived convenience. This finding is consistent with Heijden et al. (2003), who found perceived risk and PEOU as antecedents of attitude towards online shopping. Regarding PEOU, perceived usefulness (convenience and efficiency) and trust, the empirical evidence in the current study is consistent with earlier studies, including Akturan and Tezcan (2012) which reported that these variables had a direct effect on user attitude. In our study, perceived convenience works out to be a significant factor at 10 per cent level whereas it did not appear in the case of intention to adopt mobile banking. The order of importance of other factors except perceived lifestyle compatibility undergoes a change. It is therefore seen that the factors influencing attitude and intention are more or less same. Our findings support prior research studies: Heijden et al. (2003), Al-Somali et al. (2009), Kaushik and Rahman (2015) and Mohammadi (2015) who found factors of mobile banking adoption to influence attitude and intention.

The demographic variables like gender and income also play an important role in determining intention whereas their role in influencing attitude was insignificant. Our findings support Amin, Hamid, Tanakinjal and Lada’s (2006) work that the effect of gender on attitude towards mobile banking is insignificant. However, in Amin et al.’s (2006) study, males were found to be slightly more inclined to use mobile phone for banking as compared to females, whereas in our study, there is no difference. Further, their study found the effect of age on adoption of mobile banking to be significant, which is contrary to our findings. Our findings are supported by Szopiński (2016) who found that the income of the respondent has a positive influence on propensity (intention) to use online banking.

In case of intention, the results indicate that perceived lifestyle compatibility is the most important variable in discriminating between high intention and low intention to adopt mobile banking. This is followed by PEOU, perceived trust and perceived efficiency. Among the demographic variables, the respondents in the high-income group have 2.115 times more probability of having an intention to adopt mobile banking than the low-income respondents. Similarly, in the role of gender, females have 0.414 times the probability of having an intention to adopt mobile banking as compared to the male counterparts. This means male respondents with high household income are more receptive in their intention to accept mobile banking as compared to female respondents having low income.

The results indicate that there are common factors, which influence both attitude and intention. Further, attitude in turn also influences intention as seen in our study. Therefore, it is seen that the intention to adopt mobile banking would be influenced by not only the technology adoption factors but also by the attitude towards mobile banking. Kassim and Ramayah (2015) in their study on Internet banking usage in Malaysia found attitude to be a significant factor influencing the intention to continue using Internet banking. This confirms findings from earlier studies (Akturan & Tezcan, 2012; Al-Somali et al., 2009; Heijden et al., 2003; Khasawneh, 2015; Montazemi & Saremi, 2015; Nasri & Charfeddine, 2012; Suh & Han, 2003) that have shown a direct influence of technology adoption factors and user attitude on BI. Suh and Han (2003) found trust, perceived usefulness and attitude to explain 75 per cent of the variance in BI to use internet banking. Dasgupta et al. (2011) found perceived usefulness, perceived image, PEOU, perceived value, self-efficacy, perceived credibility and tradition to explain almost 50 per cent variance in BI. Akturan and Tezcan (2012) in their study found perceived usefulness, perceived risk and perceived benefits to explain 68 per cent of the variability in attitude. Further, attitude towards mobile banking explained 53 per cent of the variability in intention to use mobile banking. Montazemi and Saremi (2015) found 10 factors, which affect consumer’s adoption of online banking and developed a model and found that these factors (trust, ease of use, usefulness, etc.) explain 68 per cent of the variation in intention to adopt online banking.

With it, the results not only indicate the important drivers of mobile banking adoption but also validate the TAM (Davis, 1989, 1993; Gefen & Straub, 1997; Venkatesh & Davis, 1996) and other studies on technology adoption in the Indian context (Dasgupta et al., 2011; Deb & David, 2014; George & Kumar, 2015; Kesharwani & Bisht, 2012; Thakur & Srivastava, 2014). In general, most of the findings of this study are found to be similar to the earlier studies which have used TAMs for studying mobile banking. The discussion also emphasizes on the heightened relevance of mobile banking in today’s context and provides researchers with future research areas around mobile banking.

Implications for Practitioners

Since lifestyle compatibility plays the most important role in boosting high intention to adopt mobile banking, therefore, these people are able to relate their intention of mobile banking adoption in a way they manage their finances in terms of real-time mobility to handle banking transactions. Further, they believe that this medium of banking fits into their self-image and style of working. In addition to that, these respondents are part of a community which uses this medium extensively for handling their personal and professional communications. Therefore, a respondent who wishes to be part of this community needs to be connected all the time, as it is treated as a symbol of prosperity and status. Similar findings are reported by earlier research studies in India (Khare et al., 2010; Singh & Srivastava, 2014). Taking a cue from above, banks should provide solutions which can enable the user to have a 360-degree view of his/her finances at any time. Core banking solutions can be a starting point to achieve this.

Further, PEOU is the next important and significant factor which determines the high intention to adopt mobile banking. This result contradicts the prior studies (Chong et al., 2010; Kesharwani & Bisht, 2012; Pikkarainen et al., 2004). However, the findings are consistent with studies (Dasgupta et al., 2011; Riquelme & Rios, 2010) which suggest that PEOU significantly impacts intention. On further investigation, it is found that the degree of easiness with which the respondents are able to interact with the mobile banking website or applications, determines the extent of their intention. It is important for mobile companies as well as mobile software providers to create handsets and apps, which are easy to use and, which does not involve a lot of mental effort. Further, the size of the mobile screen and its brightness and contrast also adds to the ease of use. Since, these days mobile phones have become one-stop storage for personal and professional documents, the mobile app should have an intuitive engine to facilitate banking transactions search. PEOU does not encompass only ease-of-use associated with the banking technology but also ease-of-use with service support. George and Kumar (2015) identified problems faced by internet banking customers like customer support, service problem, web-based problem and password problem which hindered customer satisfaction. These findings may have implications on the way companies and banks design and develop their mobile solution offerings.

The third important factor in influencing the intention to adopt mobile banking is the trust which is defined as the faith users have with this medium. The banks need to advertise on the security of the system and ensure that they are foolproof. In order to instil confidence and trust among users to adopt mobile banking, banks need to offer mobile banking solutions, which have the latest security updates so as to ensure privacy and confidentiality of user data. It is not only the security solution but a security policy which would ensure that the user data remains private and secure. Banks should also share the dos and don’ts of mobile banking regularly with their customers to ensure that the users are aware of the online threats, and they do not fall prey to online frauds. Our study corroborates the findings from earlier studies. Suh and Han (2003) found that trust is a key determinant in explaining the attitude and intention for adoption of mobile banking. Pavlou (2003) found perceived risk, perceived, trust, PEOU and perceived usefulness as a significant predictor of transaction intentions as 56 per cent of the variation in intention to transact was explained by these significant antecedents. Riquelme and Rios (2010) found that perception of risk is negatively associated with intention to adopt mobile phones for banking purpose. Similar results were found by earlier researchers in Indian context; Kesharwani and Bisht (2012) found that perceived risk has a negative and significant impact on BI towards use of Internet banking technologies; and Thakur and Srivastava (2014) reported that security risk and privacy risk are significant sub-dimensions of perceived risk which in turn negatively influence intention for mobile payment services in India. Thus, lower is the perception of risk, higher is the trust and intention towards using adopting mobile banking. Singh and Srivastava (2014) investigated the factors influencing the intention of Indian customers to use mobile banking and found that security significantly affect the customers’ decision to use mobile banking.

The fourth important factor influencing mobile banking intention is efficiency, which is defined as the ability of the medium to save user effort, time and money for performing the same degree of banking transactions. Bankers should emphasize these efficiency parameters while advocating the case for mobile banking. Some of the ways this could be achieved could be in terms of quickly processing user requests and complaints, providing them features, which could help them improve the degree of utilization of banking services in comparatively less time and effort. Banks are suggested to make more use of social media to promote the benefits of mobile banking. The study finds support in the findings of Kesharwani and Bisht (2012) which indicate that intention is affected by perceived usefulness.

Limitation of the Study and Scope for Future Study

In the current study, TAM and previous studies on technology adoption have been used to explain user attitude and intention towards mobile banking adoption. However, there are other alternate models of technology adoption, which can be tested and a comparison can be made on the explanatory power of the model.

The focus of this study is India but a large part of Indian population lives in small towns and villages. The Government of India has launched digital India campaign with an objective of achieving seamless connectivity throughout the country. This includes broadband services in 250,000 villages, 400,000 public internet access points, Wi-Fi services in 250,000 schools, universities and public WiFi hotspots for citizens, and so on. There is no denying the fact a large part of connectivity would be through mobile phones. However, because of socio-economic, cultural and demographic differences, the perception of people towards mobile banking adoption varies. Although it is difficult to generalize the perception of users in India, subsequent research studies need to be carried out to compare perceptions of users in villages, towns and cities towards mobile banking. Further, there are many developing nations, which are at different levels of technology adoption and usage. Particularly in the context of mobile banking these differences could be large. Thus, a comparison of user attitude and intention across developing nations can be carried out to identify the factors for differentiation.

The current study used cross-sectional survey wherein the data was collected at one specific point in time. This could be treated as a limitation considering the pace at which user taste and preferences change pertaining to technology use and adoption. This trend has been reported in a study by KPMG US in 2014, where changing customer preferences and technology figures are among the top drivers of transformation programmes for banks (KPMG, 2016). Therefore, a longitudinal study is needed to verify if the user attitude and intention towards mobile banking adoption or the factors influencing the same remains same or change with passage of time.

Although the findings can be considered as significant in most ways, this research has some limitations, which could be dealt with in future works. The study has an inherent limitation as our sample was skewed towards users who were highly educated (graduate and above). The results could vary if we had a mixed sample. As a matter of fact, it would be interesting to carry out a comparative study with less-educated people. Further, the data was collected from respondents from metros and large cities, and results may not be generalized to smaller cities where the use of smartphones and mobile Internet may not be that high. This comparison could be an area for future study.

Lastly, the actual usage behaviour was not included in the proposed model. The existing model could be modified and the extent to which attitude and intention influence actual usage behaviour could be analysed. This would not only enhance the validity of the model but also identify areas where banks need to focus on to improve actual usage among consumers.

References

Adams

D. A.

Nelson

R. R.

& Todd

P. A.

(1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly, 16(2), 227–247.

Agarwal

Rastogi

& Mehrotra

(2009). Customers’ perspectives regarding e-banking in an emerging economy. Journal of Retailing and Consumer Services, 16(5), 340–351.

Ajzen

(1985). From Intentions to Actions: A Theory of Planned Behavior. In Kuhl

& Beckmann

(Eds), Action Control. SSSP Springer Series in Social Psychology. Berlin, Heidelberg: Springer.

Akinci

Akoy

& Atilgan

(2004). Adoption of internet banking among sophisticated consumer segments in an advanced developing country. International Journal of Bank Marketing, 22(3), 212–232.

Akturan

& Tezcan

(2012). Mobile banking adoption of the youth market. Marketing Intelligence & Planning, 30(4), 444–459.

Amin

Hamid

M. R.

Tanakinjal

& Lada

(2006). Undergraduate attitudes and expectations for mobile banking. Journal of Internet Banking and Commerce, 11(3). Retrieved 18 May 2016, from http://www.arraydev.com/commerce/JIBC/2006-12/JIBC2.htm

Akinci

Aksoy

& Atilgan

(2004). Adoption of internet banking among sophisticated consumer segments in an advanced developing country. International Journal of Bank Marketing, 22(3), 212–232.

Al-Somali

S. A.

Gholami

& Clegg

(2009). An investigation into the online banking acceptance in Saudi Arabia. Technovation, 29(2), 130–141.

Bashir

& Madhavaiah

(2014). Determinants of young consumers’ intention to use internet banking services in India. Vision, 18(3), 153–163.

10.

Brown

Cajee

Davies

& Stroebel

2003, Cell phone banking: Predictors of adoption in South Africa—an exploratory study. International Journal of Information Management, 23(5), 381–394.

11.

Campbell

D. T.

& Fiske

D. W.

(1959). Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix. Psychological Bulletin, 56(2), 81–105.

12.

Chau

P. Y. K.

& Lai

V. S. K.

(2003). An empirical investigation of the determinants of user acceptance of internet banking. Journal of Organizational Computing and Electronic Commerce, 13(2), 123–146.

13.

Chong

A. Y.-L.

Ooi

K.-B.

Lin

& Tan

B.-I.

(2010). Online banking adoption: An empirical analysis. International Journal of Bank Marketing, 28(4), 267–287.

14.

Correa

Hinsley

A. W.

& De Zuniga

H. G.

(2010). Who interacts on the web? The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253.

15.

Dasgupta

Paul

& Fuloria

(2011). Factors affecting behavioral intentions towards mobile banking usage: Empirical evidence from India. Romanian Journal of Marketing, 6(1), 6–28.

16.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

17.

Davis

F. D.

(1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies 38(3), 475–487

18.

Davis

F. D.

Bagozzi

R. P.

& Warshaw

P. R.

(1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

19.

Deb

& David

E. L.

(2014). An empirical examination of customers’ adoption of m-banking in India. Marketing Intelligence & Planning, 32(4), 475–494.

20.

Fishbein

& Ajzen

(1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

21.

Flavián

Guinaliu

& Torres

(2006). How bricks-and-mortar attributes affect online banking adoption. International Journal of Bank Marketing, 24(6), 406–423.

22.

Gan

Clemes

Limsombunchai

& Weng

(2006). A logit analysis of electronic banking in New Zealand. International Journal of Bank Marketing, 24(6), 360–383.

23.

Gefen

& Straub

D. W.

(1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389–400.

24.

Gefen

Straub

D. W.

& Boudreau

M. C.

(2000). Structural equation modelling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–70.

25.

Gefen

(2003). TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1–13.

26.

George

& Kumar

G. S. G.

(2013). Antecedents of customer satisfaction in internet banking: Technology acceptance model (TAM) redefined. Global Business Review, 14(4), 627–638.

27.

George

& Kumar

G. S. G.

(2015). Validation of a scale for measuring problems in internet banking and their effect on customer satisfaction. Vision, 19(4), 312–323.

28.

Grant

J. S.

& Davis

L. L.

(1997). Selection and use of content experts for instrument development. Research in Nursing & Health, 20(3), 269–274.

29.

J.-C.

Lee

S.-C.

& Suh

Y.-H.

(2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605–11616.

30.

Hair

J. F.

Jr, Black

C. W.

Babin

J. B.

Anderson

R. E.

& Tatham

L.R.

(2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Prentice-Hall.

31.

Hanudin

(2007). Extending the technology acceptance model for SMS banking: Analyzing the gender gap among students. International Journal of Business and Society, 8(1), 15–27.

32.

Heijden

H. V.

Verhagen

& Creemers

(2003). Understanding online purchase intentions: Contribution from technology and trust perspectives. European Journal of Information Systems, 12(1), 41–48.

33.

Henderson

& Divett

M. J.

(2003). Perceived usefulness, ease of use and electronic supermarket use. International Journal of Human-Computer Studies, 59(3), 383–395.

34.

Hernandez

J. M. C.

& Mazzon

J. A.

(2007). Adoption of internet banking: Proposition and implementation of an integrated methodology approach. International Journal of Bank Marketing, 25(2), 72–88.

35.

Horton

R. P.

Buck

Waterson

P. E.

& Clegg

W (2001). Explaining intranet use with the technology acceptance model. Journal of Information Technology, 16(4), 237–249.

36.

& Bentler

P. M.

(1998). Fit indices in covariance structure modeling: Sensitivity to under parameterized model misspecification. Psychological Methods, 3(4), 424–453.

37.

Jackson

C. M.

Chow

& Leitch

R. A.

(1997). Toward an Understanding of the Behavioral Intention to Use an Information System. Decision Sciences, 28(2), 357–389.

38.

Jayawardhena

& Foley

(2000). Changes in the banking sector—the case of internet banking in the UK. Internet Research: Electronic Networking Applications and Policy, 10(1), 19–30.

39.

Jha

& Ye

(2016). The Impact of Demographic Variables on Perception of Importance and Continued Usage of Facebook in the US. Global Business Review, 17(1), 1–15

40.

Karjaluoto

Mattila

& Pento

(2002). Factors underlying attitude formation towards online banking in Finland. International Journal of Bank Marketing, 20(6), 261–272.

41.

Kassim

M. N.

& Ramayah

(2015). Perceived risk factors influence on intention to continue using Internet banking among Malaysians. Global Business Review, 16(3), 393–414.

42.

Katz

& Aspden

(1997). Motivations for and barriers to internet usage: Results of a national public opinion survey. Internet Research, 7(3), 170–188.

43.

Kaushik

A. K.

& Rahman

(2015). Innovation adoption across self-service banking technologies in India. International Journal of Bank Marketing, 33(2), 96–121.

44.

Kesharwani

& Bisht

S. S.

(2012). The impact of trust and perceived risk on internet banking adoption in India. International Journal of Bank Marketing, 30(4), 303–322.

45.

Khare

& Singh

(2010). Role of consumer personality in determining preference for online banking in India. Journal of Database Marketing & Customer Strategy Management, 17(3/4), 174–187.

46.

Khasawneh

M. H. A.

(2015). A mobile banking adoption model in the Jordanian market: An integration of TAM with perceived risks and perceived benefits. The Journal of Internet Banking and Commerce, 20(3), 1–13.

47.

Kolodinsky

J. M.

Hogarth

J. M.

& Hilgert

M. A.

(2004). The adoption of electronic banking technologies by US consumers. International Journal of Bank Marketing, 22(4), 238–259.

48.

KPMG (2016). Digital banking: Banking on the go-financial services, February 2016. Retrieved 15 May 2016, from https://www.kpmg.com/IN/en/IssuesAndInsights/ArticlesPublications/Documents/DigitalBanking.pdf

49.

Laukkanen

& Pasanen

(2008). Mobile banking innovators and early adopters: How they differ from other online users? Journal of Financial Services Marketing, 13(2), 86–94.

50.

Lederer

A. L.

Maupin

D. J.

Sena

M. P.

& Zhuang

(2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29(3), 269–282.

51.

Lewis

N. K.

Palmer

& Moll

(2010). Predicting young consumers’ take up of mobile banking services. International Journal of Bank Marketing, 28(5), 410–432.

52.

Liao

& Wong

W. K.

(2008). The determinants of customer interactions with internet enabled e-banking services. Journal of the Operational Research Society, 59(9), 1201–1210.

53.

Lin

H.-F.

(2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252–260.

54.

Mallat

Rossi

& Tuunainen

V. K.

(2004). Mobile banking services. Communications of the ACM, 47(5), 42–46.

55.

Mann

B. J. S.

& Sahni

S. K.

(2012). Profiling adopter categories of internet banking in India: An empirical study. Vision, 16(4), 283–295.

56.

Mathieson

(1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.

57.

Mattila

Karjaluoto

& Pento

(2003). Internet banking adoption among mature customers: Early majority or laggards? Journal of Services Marketing, 17(5), 514–528.

58.

Mohammadi

(2015). A study of mobile banking usage in Iran. International Journal of Bank Marketing, 33(6), 733–759.

59.

Montazemi

A. R.

& Saremi

H. Q.

(2015). Factors affecting adoption of online banking: A meta-analytic structural equation modelling study. Information and Management, 52(2), 210–226.

60.

Nasri

& Charfeddine

(2012). Factors affecting the adoption of internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. Journal of High Technology Management Research, 23(1), 1–14.

61.

Pavlou

P. A.

(2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 69–103.

62.

Pikkarainen

Karjaluoto

& Pahnila

(2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224–235.

63.

Quazi

& Talukder

(2011). Demographic determinants of adoption of technological innovation. Journal of Computer Information Systems, 52(1), 34–47.

64.

Riquelme

H. E.

& Rios

R. E.

(2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328–341.

65.

Rogers

E. M.

(1995). Diffusion of innovations (4th ed.). New York, NY: Free Press.

66.

Samudra

M. S.

& Phadtare

(2012). Factors influencing the adoption of mobile banking with special reference to Pune city. ASCI Journal of Management, 42(1), 51–65.

67.

Sathye

(1999). Adoption of internet banking by Australian consumers: An empirical investigation. International Journal of Bank Marketing, 17(7), 324–334.

68.

Singh

& Srivastava

R. K.

(2014). Factors influencing the adoption of mobile banking in India. International Journal of E-Services and Mobile Applications, 6(4), 1–15.

69.

Stavins

(2001). Effect of consumer characteristics on the use of payment instruments. New England Economic Review, 3, 19–31.

70.

Suh

& Han

(2003). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications, 1(3), 247–263.

71.

Sulaiman

Jaafar

N. I.

& Mohezar

(2007). An overview of mobile banking adoption among the urban community. International Journal of Mobile Communications, 5(2), 157–168.

72.

Szopiński

T. S.

(2016). Factors affecting the adoption of online banking in Poland. Journal of Business Research, 69(11), 4763–4768.

73.

Tan

& Teo

T. S. H.

(2000). Factors influencing the adoption of internet banking. Journal of the Association for Information Systems, 1(1), 1–42.

74.

Thakur

& Srivastava

(2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369–392.

75.

Venkatesh

& Davis

F. D.

(1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 17(3), 451–481.

76.

Venkatesh

Morris

M. G.

Davis

G. B.

& Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

77.

Wan

W. W. N.

Luk

C.-L.

& Cheris

C. W. C.

(2005). Customers’ adoption of banking channels in Hong Kong. International Journal of Bank Marketing, 23(3), 255–272.

78.

Wang

Y. S.

Lin

H. H.

& Luarn

(2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16(2), 157–179.