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Mobile Banking Adoption Among Rural Consumers: Evidence from India

Recently, Financial Inclusion has received greater attention, as it is a key to economic growth and equality, growth of the business, and stability of financial and banking systems. In the process of understanding how technology is useful for financial inclusion, the study tries to understand the elements that prompt the perceived acceptability of mobile banking among the low income and low literate stratum. To explore these elements, the current piece of work utilized the Unified theory of acceptance and use of technology and with the addition of component of perceived risk. The research utilized convenience sampling to collect data from the respondents having Jan Dhan account. The results show that performance expectancy, effort expectancy, social influence, and perceived risk have a significant influence on intention to use mobile banking among financial inclusion stratum. Surprisingly, the facilitating conditions were found insignificant. The outcomes of the research can guide banks and technology houses to develop a more user-acceptable mobile banking system. Also, the outcome will help policymakers to enhance mobile banking system adoption for financial inclusion by considering influencing elements.


INTRODUCTION
The participation of every citizen in a formal financial system is one of the factors for determining economic growth of a country, as financial inclusion provides a lot of benefits and opportunities for individual citizens. Hence, governments and policymakers tend to push for maximum financial inclusion through various programs and methods of technology. However, there are challenges, both from demand and supply side. For instance, financial institutions like banks find it very difficult to serve unprofitable segments in unreachable locations. While people who have a low income and/or low literate feel meaningless to access the 'formal banking' system. Thus, in order to match both demand and supply, technology has a major role to play; for instance, in the form of branchless banking, mini-ATMs, e-cards, business correspondents' support, internet, and mobile banking. Out of these, mobile banking has been gaining traction among the population at large and has become quite easily accessible. Thus, augmenting mobile banking with other technology and IT systems possibly may be a more feasible method for greater financial inclusion. Herein, it may be noted that in developing countries, the financial inclusion rate is low, as these countries are densely populated. Interestingly, these countries have more people who possess mobile phones than those who have proper bank accounts or any financial accessibility (Porteous, 2006). Nevertheless, the possible success of financial _ 1 Jan Dhan users are those people who have opened a bank account under the Pradhan Mantri Jan Dhan Yojana (PMJDY). 301 inclusion through mobile banking would depend on its acceptance among this section of the population. For example, India has an unbanked population of 190 million as per a report published by the World Bank (2018); many of them have mobile phones but are not included with any banking system. Thus, deeper penetration of mobile banking (m-banking), may facilitate banking services, and in the process develop financial inclusion.
Irrespective of various steps taken by Reserve Bank of India (RBI)and other financial institutions, financial inclusions among the rural and underprivileged sections remain unsatisfactory. Importantly, financial inclusion would surely instill the habit of 'savings' among the rural community, which in turn strengthens the economic resources of a country, especially like India. Numerous major steps have been taken by the government of India (GOI) such as bank-led initiatives, a product-based method, government initiative, knowledge-based strategies, technology-based approaches, and regulatorbased approaches (Garg and Agarwal, 2014). Importantly, the Indian government launched several schemes, such as no-frills accounts, kisan credit cards, savings account with an overdraft facility, business correspondents, mobile banking, branchless banking, and financial literacy programs. However, despite these efforts, the rural population has still not whole-heartedly been able to adopt/abide by these schemes and have therefore not been able to be added onto the 'financial inclusiveness' scheme.
As the mobile phone is possibly the largest accessible tool today, the present study explores factors that affect potential users to accept and adopt mobile banking. The objective of this study is to examine the determinants of adoption of mobile banking among the low literate and low-income masses, especially Jan Dhan 1 users in India. The paper is structured as follows: starting with a literature review, it is followed by the methodology, empirical analysis, findings, conclusions, implications, and finally limitations of the work.

FINANCIAL INCLUSION
The economic growth of a region is associated with various factors like input, resources, and productive participation of all stakeholders. Notably, financial inclusion is also one of the important factors that boosts the economic growth of a region by bringing about financial stability and participation. In the Indian context, it refers to "the process of ensuring access to financial services and timely and adequate credit to weaker sections and low-income groups at an affordable cost" (Rangarajan Committee, 2008).Further, it enables access to universal financial products and services by the whole population of the region, notably the weaker sections of the society (Bijoy, 2018).
The number of poor, illiterate, and 'unbanked' population is higher in India as compared to other countries, triggering a financial system imbalance there of; hence, financial inclusion is essential. In the past, scholars have stated that the financial inclusion process is a necessary pillar of financial stability (Hanning and Jansen, 2010;Kumar, 2015). Despite significant growth in business profitability and competitiveness, financial and banking services do not seem to have enough penetration to the low income and low literate population. Thus, in order to reach this stratum of community, the Indian government _ 2 Pradhan Mantri Jan Dhan Yojana (PMJDY) under the National Mission for Financial Inclusion was launched initially for a period of 4 years (in two phases) on 28th August 2014. It envisages universal access to banking facilities with at least one basic banking account for every household, financial literacy, access to credit, insurance, and pension. 302 government introduced the 'Pradhan Mantri Jan-Dhan Yojana' 2 in 2014. The objective of this scheme was to provide the 'lower strata' of society access to financial products and services (e.g. zero balance saving accounts, debit cards, remittance, credit/borrowing, insurance, pension schemes etc.) with a minimum cost. Irrespective of the development in information and communication technologies (ICT) and the increase in the number of bank accounts, India's mobile banking adoption is very low. According to World Bank (2018) report, almost 1.2 billion adults (15-64 years) have opened bank accounts since 2011 till 2017, totaling 3.4 billion accounts, out of 4.9 billion adults, representing 69% penetration.
As on 2017, the number of global mobile cellular subscribers has been estimated to be at 7.7 billion, as reported by the International Telecommunication Union (ITU) (2017). Interestingly, 80% of mobile penetration has been observed in developing countries; this shows that more people have cellular connections than bank accounts or financial accessibility (Porteous, 2006).

MOBILE BANKING ADOPTION MODEL
In the past, numerous adoption models have been incorporated in the context of mobile banking. The studies have utilized well-established theories like Technology Acceptance model (TAM) (Davis, 1989), Theory of Planned Behavior (TPB) (Ajzen, 1991), Innovation Diffusion Theory (IDT) (Rogers, 1995), Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), Theory of Perceived Risk (TPR) (Featherman & Pavlou, 2003) Task Technology Fit (TTF) (Goodhue & Thompson, 1995) and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). In the context of mobile banking, the UTAUT model seems to be the most apt (Carlsson et al., 2006;Min et al., 2008;Park et al., 2007). It comprises of four elements, including performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) (Venkatesh et al., 2003). However, it fails to capture the 'perceived risk' of mobile banking usage among the population at large. We thereby propose to use the UTAUT model and extend it with the element of 'perceived risk' in order to examine mobile banking adoption.

HYPOTHESIS DEVELOPMENT
The current study investigates the adoption of mobile banking across the low income and low literate people having Jan Dhan bank accounts. Notably, all the variables used in this study and the proposed hypotheses relate to behavioral intentions to adopt mobile banking among Jan Dhan users, have been discussed in the following sections.

PERFORMANCE EXPECTANCY
Performance expectancy (PE) is "the degree to which an individual believes that using the system will help him or her to attain gains in job performance" (Venkatesh et al., 2003). The uniqueness of this study lies in the fact that it focuses exclusively on the intention to adopt mobile banking for the people belong to low income and low literate group, while most earlier studies seem to have ignored this population. Various past studies have also confirmed the positive relationship between performance expectancy and intention across different contexts such as in the adoption of remote mobile payment systems (Slade et al., 2015), the mobile banking system in Pakistan (Farah et al., 2018), and virtual reality in learning (Shen et al. 2019). Furthermore, several researchers found that performance expectancy directly influences behavioral intention to use mobile banking (Yu, 2012;Oliveira et al., 2014;Sarfaraz, 2017). The gains in performance include convenience, satisfaction, economic benefits, and personal image (Rogers, 1995;Taylor & Tood, 1995). In this case, performance expectancy captures an individual's opinion of improvement in performance (such as convenient payment service effectiveness and fast response) while using mobile banking (Zhou et al., 2010). Hence, we propose: H1: Performance expectancy has a positive influence on behavioral intention to use mobile banking among Jan Dhan users.

EFFORT EXPECTANCY
Effort expectancy is "the degree of ease associated with the use of the system" (Venkatesh et al., 2003). It is similar to the perceived ease of use element of TAM (Luarn & Lin 2005;Wang, Lin, & Luarn, 2006;Kuo & Yen, 2009;Miltgen et al., 2013). In the case of mobile banking adoption, it measures user perception of the difficulty level, while using mobile banking (Zhou et al., 2010). It is very clear from past studies that effort expectancy affects the behavioral intention across various contexts (Farah et al., 2018;Talukder et al., 2019;Shen et al., 2019). User interfaces, content design, and functional ability (Venkatesh et al., 2003;Kim et al., 2009) of m-banking can influence its adoption (Yu, 2012). In the case of the BoP population, this element is very crucial. Further in this regard, we believe that when people would think that mobile banking is easy to use and clear, then they will accept it. Corroborating from the past studies, we propose the following: H2: Effort expectancy does have a positive influence on the intention to use mobile banking among Jan Dhan users.

SOCIAL INFLUENCE
Social influence is "the degree to which an individual perceives that important others believe he or she should use the new system" (Venkatesh et al. 2003). Social influence is an individual's belief that s/he is following peers or family members who value the use of technology. Social influence measures aspects of subjective norms of TRA (Venkatesh et al., 2003) and echoes the surrounding effects like opinions of friends, relatives, and superiors on users' behavior (López-Nicolás et al., 2008). Past studies have verified the positive relationship of social influence and the intention to adopt mobile banking (Yu, 2012;Ahmed et al., 2017), mobile payment (Abrahão et al., 2016), and mobile learning (Thomas et al., 2013). In the current context, users would accept and use mobile banking as a result of societal influences like friends, family, and opinions of superiors (Zhou et al., 2010). Hence, we hypothesize the following: H3: Social influence has a positive impact on the intention to use mobile banking among Jan Dhan users.

FACILITATING CONDITIONS
Facilitating Condition is "the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system" (Venkatesh et al., 2003). For instance, for introducing mobile banking, various promotional activities, and support from multiple stakeholders do remove barriers to its usage and influence its adoption thereby. If users do not have these necessary financial resources and operational skills, they would not adopt or use mobile banking (Zhou et al., 2010). Oliveira et al. (2014) stated that facilitating conditions does have a significant effect on behavioral intention among Portuguese mobile banking users. Numerous studies in the past have noted the positive influence of facilitating conditions on adoption intention, for instance, to adopt telehealth services among older adults (Cimperman et al., 2016), internet banking (Tarhini et al., 2016), and cloud classroom (Yang et al., 2019). In this context, if Jan Dhan users feel that they have enough resources and knowledge to use this, they will start using it. So, we propose: H4: Facilitating conditions will have a positive impact on the intention to use mobile banking among Jan Dhan users.

PRECEIVED RISK (PR)
Perceived risk refers to "certain types of financial, product performance, social, psychological, physical, or time loss when consumers use technology" (Forsythe & Shi, 2003). It relates to perceiving a loss, while trying to achieve the job completed (Pavlou, 2001). Another study considers perceived risk as an essential determinant of acceptance of online transactions or mobile banking (Cho, 2004). As the use of mobile banking involves a possible degree of uncertainty for the BoP populous, perceived risk is regarded as one of the important antecedents of behavioral intention to use it. Therefore, perceived risk is one of the estimated variables for the intentional behavior of rural customers towards the use of mobile banking services (Bhuvana & Vasantha, 2017). In fact, many people fear that use of m-banking would leak their personal information; this apart, wrong transfer of money through mbanking would lead to a lack of perceived credibility (Akhlaq & Shah, 1970;Luarn & Lin, 2005;Ahmed et al., 2014). By and large, people are uncomfortable with change, and are thereby resistant to the acceptance of technology. For instance, the apprehension about the loss of personal information or lack of security inhibits them from accepting innovation (Pederson & Ling, 2002). Previous studies on information systems have also found perceived risk as a barrier to acceptance (Brown et al., 2003;Laforet & Li, 2005;Laukkanen et al., 2007;Luo et al., 2010;Cruz et al., 2010;Saleem & Rashid, 2011). Trust and privacy are known to be stimulants of risk, and are also associated with behavioral intention (Min et al., 2008). Importantly, perceived risk was observed to be affecting the adoption of mobile banking negatively (Yu, 2012;Yun et al., 2013). In this context, when Jan Dhan users feel that the use of mobile banking is a risky task for them, they will not use it. Thus, we form our next hypothesis.

H5: Perceived risk will have a negative impact on the intention to use mobile banking among Jan Dhan users.
The proposed framework has shown in figure 1. The proposed model has incorporated perceived risk as an additional factor along with the UTAUT.

SAMPLE AND DATA COLLECTION
We used convenience sampling for collecting information from the respondents. The questionnairebased survey was administered among Pradhan Mantri Jan Dhan Yojana (PMJDY) account holders, which is a recent and successful financial inclusion programme. The respondents belonged to surrounding places of Kharagpur, West Bengal; these include Gopali, Amrakola, Chhota Tengra, and Puratan Bazar. The questionnaire was initially framed in English, but it was translated to Bengali vernacular so that the respondents understood the questions better. The Bengali translation was verified by an expert in the Bengali language. While the respondents were looking into the questionnaire, we ensured that we assisted them in making the questions understandable as and when required. We distributed 500 questionnaires to the respondents. After removing incomplete responses, only 311 were used for final data analysis. Table 1 shows the sample characteristics of the respondents.

MEASURES
The questionnaire carried 20 items, which were measured on a five-point Likert scale ranging from 1 as 'strongly disagree' to 5 as 'strongly agree'. Initially, the questionnaire was tested on 50 Jan Dhan respondents to check if the questions were amply clear; notably, all the questions were found to be comprehensible. The elements to measure the constructs have been drawn from existing literature. The specific items used has been documented under appendix A.

RESULTS
The study used a two-step approach; in the first, we analyzed the measurement model to confirm the reliability and validity of construct items. This was followed by the second step, which involved the analysis of the structural model for testing the conceptualized hypotheses (Anderson & Gerbing, 1988).

MEASUREMENT MODEL
As a first step, we conducted the Confirmatory Factor Analysis (CFA) to test for reliability and validity, including both convergent and discriminant validity. The results indicate that the data fits the conceptualized _ 307 conceptualized model well with 2 = 244.269, 2 / = 1.259, = 0.985, = 0.982, = 0.029 (Hair et al. 2010). Further, Cronbach's alpha and composite reliability were tested to check for scale reliability. The value of Cronbach's alpha and composite reliability were found to be above the cut-off value of 0.7 (Bagozzi & Yi, 1988;Gefen et al., 2000;Nunnally & Bernstein, 1994); hence, construct reliability was also achieved. Additionally, the average variance extracted (AVE) and composite reliability were found to be above the cut-off value of 0.5 and 0.7, respectively, establishing thereby convergent validity (Table 2). Furthermore, the square root of AVE was also found to be greater than the inter-correlation coefficient, establishing thereby discriminant validity (Fornell & Lacker, 1981), as shown in Table 3.

STRUCTURAL MODEL AND HYPOTHESIS TESTING
The results of the structural model reveal that the data fitted the conceptualized framework well, having 3 = 0.935; 4 = 0.916; 5 = 0.932; 6 = 0.985; 7 = 0.029 (Byrne, 2013). Performance expectancy ( = 0.599, < 0.001), Effort expectancy ( = 0.101, < 0.01), Social influence ( = 0.048, < 0.1) did seem to have a significant positive influence on the behavior intention to adopt mobile banking, while Perceived risk (β= -0.254, p < 0.001) seemed to have a negative impact on behavioral intention. Interestingly, facilitating conditions were found to be insignificant influencers on behavioral intention. Hence, hypotheses H1, H2 H3, and H5 are supported. However, H4 is found to be insignificant, and hence rejected. The results of hypothesis testing have been shown in Table 4.

DISCUSSION
The results show that their perceived performance expectancy influences the Jan Dhan users positively in adopting mobile banking. The result is thereby in line with other UTAUT studies on mobile banking acceptance (Zhou et al., 2010;Oliveira et al., 2014). Similarly, Effort Expectancy does seem to have a positive influence on behavioral intention to accept mobile banking Jan Dhan account holders, indicating thereby some sort of a 'financial inclusion', especially once they realize the ease and the effort needed to learn and use it. Interestingly, this finding seems to be consistent with existing technology adoption studies (Luarn & Lin, 2005;Miltgen et al., 2013;Martins et al., 2014;Shankar & Datta, 2018), supporting thereby the perceived ease of use or understandability and straightforward functions that would make potential users incline towards accepting mobile banking. Notably, for inexperienced users of new technology like computers, ease of use, and straightforwardness of functions were found to be very significant (Davis et al., 1989;Szajna, 1996). The aspect of 'perceived risk' seems to have a negative influence on the behavioral intention to adopt mobile banking, which shows people perceive higher risk in the form of monetary or nonmonetary loss, theft of data including personal details and uncertainties, leading to adverse effect in using mobile banking which is consistent with past studies based on UTAUT (Luarn & Lin, 2005;Yu, 2012;Yun et al., 2013;Boonsiritomachai, 2017). Additionally, social influence is a big trigger for opinion formation and spreading awareness and has been found to be statistically significant. The result implies that social influence does seem to push individuals to learn and use mobile banking. The finding is thereby consistent with past UTAUT studies in the context of mobile banking acceptance and technology adoption (Zhou et al., 2010;Yu, 2012;Shen et al., 2019).
Notably, we found an insignificant relationship between facilitating conditions and behavioral intention, possibly due to the population in this low income, and low literate stratum is inexperienced and relatively new to mobile banking. The result is supported by Venkatesh et al. (2003), who stated that facilitating conditions could have a strong impact on behavioral intention when users are more experienced. The finding of our study is also found consistent with the other empirical studies (Birch & Irvine, 2009;Wong et al., 2015;Sultana, 2019).

IMPLICATIONS
This research highlights that performance expectancy (PE), effort expectancy (EE), social influence (SI), and perceived risk (PR) are significant reasons of concern, as regards the use of mobile banking among Jan Dhan customers. However, the facilitating conditions (FC) fails to explain behavioral intention to use mobile banking.
This study enriches existing literature through theoretical and practical implications. From a theoretical perspective, the current piece of work has extended the UTAUT model by integrating perceived risk, which is a prominent factor for behavioral intention among low income and low literate strata. This research can provide important insights to the IT department, government, banks, financial institutions, researchers, corporate houses, and other stakeholders, who could utilize the research findings for promoting financial inclusion among the low income and low literate groups. The direct positive effect of performance expectancy on behavioral intention indicates that this section of people would use mobile banking when they realize its value. In this context, policymakers, banks, financial institutions, and corporate houses should join hands to promote the usefulness of mobile banking, so that it triggers the interest among the low income and low literate section. As our study establishes the negative relationship between perceived risk and behavioral intention, the mobile banking provider and other relevant parties should place additional concerns for 'perceived risk'. Banks and mobile banking service providers should build a strong cybersecurity network, giving confidence and assurance to users about the safety of their information and transaction.
Similarly, banks, financial institutions, and corporate houses should introduce more user-friendly mobile banking features and services to improve the ease of use and learning. As social influence is a definite determiner for behavioral intention for using mobile banking among this population, we should provide more helping hands and organize campaigns about financial inclusion. Furthermore, there should be a mass awareness about the safety and credibility of the banking transaction through mass-medias, word-of-mouth (WOM), or e-WOM. 'Facilitating condition' was found to be insignificant in our study, possibly due to the inefficiency of banking infrastructure in rural India (The Economic Times, 2018). The current work believes that the improvement in banking infrastructure and other facilitating factors in a rural area can motivate the people belonging to low income and low literate stratum, to adopt mobile banking.

LIMITATION AND FUTURE SCOPE
There is some limitation of the study too. The study has been confined to a location around Kharagpur, West Bengal. Future research may be done on a large scale by including a bigger sample size, possibly across different parts of India. Moreover, the current study only attempted to know the essential elements that influence mobile banking adoption intention among Jan Dhan account holders. Future studies could incorporate both triggers and barriers specific to Jan Dhan users. Additionally, this study has been conducted on the customer's end; further research could possibly view it from the supply side, e.g. financial institutions, in addition to perception-based research. Further studies could also look to incorporate control variables like age, gender, educational background, etc. to check the predictability of the model.