A Study on Development of Dual Phase Mobile Banking Adoption Model

The paper focused on development of mobile banking adoption model depicting two phases of mobile banking adoption vis-à-vis reducing the resistance to adopt mobile banking and inducing the adoption of mobile banking. The paper has used integrated Technology Acceptance Model (TAM) as proposed by Gu et al. (2009), along with two other factors namely Trust and Relative Advantage to study mobile banking adoption behaviour and Resistance Model as proposed by Laukkanen & Kiviniemi (2010) adding relative disadvantage (negative relative advantage) as one more factor, to study the mobile banking resistance behaviour. The data has been collected us-ing online as well as offline questionnaire from 633 respondents in India. The model of dual phase mobile banking adoption will raise an opportunity for increased use of mobile banking in India.


INTRODUCTION
The diversity of schemes introduced by Government of India, viz., Jan Dhan Accounts, Adhaar seeding and linking Mobile number with the bank account, have opened the doors for banks to get maximum advantage by introducing alternate delivery channels for banking services and mobile banking is the first priority of the banks among available alternative channels. Table 1 and Figure  1 depicted below have indicated the trends of wireless connections in India since 2012. As mentioned in Table 1 and Figure 1, there is 30% increase in subscribers from 864.72 million in 2012 to 1127.37 million in 2016. The above depicted increasing trend of mobile or cell phone usage in India since 2012 clearly indicates the opportunity for promoting mobile banking, but before talking about promotion we must have a look at the trends of Mobile Banking in India in the next section.

MOBILE BANKING TRENDS IN INDIA
As discussed above, the increasing number of wireless connections and relative low score of financial inclusion in India attracting the attention towards that segment of potential Mobile Banking users who are having mobiles but not mobile banking. As mobile banking can also cover those nooks of the country as were not covered by the manual or branch banking. Even the exhaustive range of financial services available through Mobile banking will also convert major portion of partially included population to financially include. RBI data reveals the tremendous increase in value and volume of mobile banking transactions during the last half-decade in India. The table and corresponding figure below has been depicting the trends of Mobile Banking transactions in India since 2012.

IDENTIFICATION OF RESEARCH PROBLEM
With 30% increase in wireless/ Mobile subscribers since 2012, (TRAI Press release No. 08/2013;09/2014;11/2015;15/2016;12/2017), India have clear opportunity of promoting Mobile Banking. But India is still not able to promote mobile banking as compared to other developing countries. Hence the need of the hour is to understand the Mobile Banking Adoption and Resistance study the resistance behaviour in relation to Mobile Banking (Laukkanen et al., 2007, Laukkanen & Kiviniemi, 2010, Yu et al., 2015. Some of seminal studies integrated the factors relating to both resistance and adoption and developed integrated model studying resistance towards and adoption of new technology (Chemingui & lallouna, 2013, Mohammadi, 2015, Yuan et al., 2016. The introduction of new technologies like Internet Banking and Mobile Banking, in banking sector, as a solution to reach the unbanked to provide basic banking facilities has also been highlighted in the previous research work (Rani, 2006, Donovan, 2012, Siddik, 2014, Mutsune, 2015. In India research work on Mobile banking has picked up during last decade. Studies either discussed Adoption behaviour for adoption of Mobile Banking (Laukkanen, 2007, Zhou, 2012, Coster & McEwen, 2013, Chaouali et al., 2017 or the Resistance behaviour towards Mobile Banking (Laukkanen & Kiviniemi, 2010, Yu et al., 2015. The new model integrating both Adoption and Resistance behaviour has been used very rear (Chemingui & lallouna, 2013, Mohammadi, 2015.
Going more deep in literature, various factors comes out from the seminal research work on Mobile Banking related behaviour and new technology adoption and resistance behaviour. The list of factors has been large enough including (DOI) relative advantages, compatibility, observability, perceived risk, trialability, perceived complexity (Sulaiman et al., 2006, Shambare, guidance, andCruz et al., 2010, verified Cost, Unsuitable Device, Perceived Risk and Complexity as barriers to resist adoption of Mobile Banking in India. Some previous studies on adoption behaviour of Mobile Banking employed the Diffusion of Innovation (DOI) theory (Dash & Tech, 2014, Kharim et al., 2011, Mattila, 2003and Al-Jabri & Sohail, 2012 to find out the factors influencing adoption behaviour. Accordingly the relative advantages, compatibility and observability, perceived risk (Mattila, 2003and Al-Jabri & Sohail, 2012), Self Efficacy (Brown et al., 2003and Khraim et al., 2011 and mimetic forces (Dash & Tech, 2014)  Some researchers employed unified theory of acceptance and use of technology (UTAUT) (Venkatesh &Yu, 2012)  Further prior research used Technology Acceptance Model (TAM) of Davis, 1989 stressing upon two key determinants, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) influencing adoption of Mobile Banking. Ravindran, 2012, integrated MIAC (model of innovation adoption and continuance) with TAM to study the service quality perceptions and continuance innovation in Mobile Banking in Indian context. Gu et al., 2009 andLee et al., 2007, conducted research in Korea applying Trust extended TAM. Gu et al., 2009, has studied Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) with trust whereas Lee et al., 2007 considered only Perceived Usefulness (PU) along with trust and Perceived Risk (PR). Both the studies emphasized the significance of Perceived Usefulness and trust. Lee et al., 2007, concluded that the Perceived Risk indirectly influences the adoption behaviour by affecting trust. Gu et al., 2009, concluded that Self Efficacy has been strong antecedent of Perceived Ease of Use and Structural Assurance has been strong antecedent of Trust but Perceived Ease of Use indirectly influences the behaviour through Perceived Usefulness. Same way Bhuvana & Vasantha, 2017, also studied Trust and Perceived Risk adding to base TAM model (Perceived Ease of Use and Perceived Usefulness) to inculcate adoption of Mobile Banking. Dahlberg et al., 2003, also conducted research using Trust enhanced TAM and found that trust has been the most significant construct of the model in explaining the adoption behaviour. Phan & Daim, 2011, prioritize technology, social factors and habit along with original TAM factors to explore the technology acceptance for Mobile Banking services and found that the Perceived Ease of Use and Perceived Usefulness has been top two factors that influence the adoption of mobile services. The various integrated models in combination with original TAM also add many other factors too like perception Trust as a significant factor was also studied in past research (Mukherjee &Nath, 2003 andLuo et al., 2010). Mukherjee & Nath, 2003, observed that shared value has been most critical to developing trust as well as relationship commitment. Communication has a moderate influence on trust, while opportunistic behaviour has significant negative effect. Also finds higher perceived trust to enhance significantly customers' commitment in online banking transaction. Vyas, 2012, defined trustworthiness of an innovation as degree to which the consumers have confidence in its marketer's reliability and integrity. Trust in one's bank depends on the reliability in correcting erroneous transaction, to compensate for losses due to security infringements and response to different queries in context of e banking study. The role of trust encompasses the exchange and interactions of a retail bank with its customers on various dimensions of online banking (Sohail & Shanmugham, 2003 Gu et al., 2009, hypotheses that PEOU develop trust and trust influence the degree of PU to influence behaviour intention and further stated that Trust develops in Mobile Banking due to one's familiarity with bank situational normality and structural assurance (most important) along with PEOU. Luo et al., 2010, conjointly examine multidimensional trust and multi-faceted risk perceptions in the initial adoption stage of the wireless platform and concluded that structural assurance significantly influences behaviour intention through adjusting people's risk perception. Kim et al., 2009, reveals that structural assurance personal propensity to trust and relative benefits significantly in the same order of significance help in developing initial trust in Mobile Banking. Dahlberg et al., 2003, included  Emphasizing the significance of Trust Dehlberg et al., 2003, Lee et al., 2007and Gu et al., 2009 apply Trust extended Technology Acceptance Model in their study. Gu et al., 2009, studied Perceived Ease of Use and Perceived Usefulness with trust whereas Lee et al., 2007, considered only PU along with trust and Perceived Risk (PR). Both the studies reveal the significance of PU and trust. Lee et al., 2007, conclude that the PR indirectly influences the adoption behaviour by affecting trust. Gu et al., 2009, concluded that Self Efficacy is strong antecedent of Perceived Ease of Use and Structural Assurance is strong antecedent of Trust but Perceived Ease of Use indirectly influences the behaviour through Perceived Usefulness. Dahlberg et al., 2003, also conducted research using Trust enhanced TAM and found that trust is most significant construct of the model in explaining the adoption behaviour. Phan & Daim (2011) prioritize technology, social factors and habit along with original TAM factors to explore the technology acceptance for Mobile Banking services and found that the Perceived Ease of Use and Perceived Usefulness are top two factors that influence the adoption of mobile services.
In addition to above literatures review the recent research related to Mobile Banking by Ragaventhar (2017) described that the fear factor in the mind of users of Mobile banking resists them from using Mobile Banking and same can be reduced by increasing knowledge. Tseng et al. (2017) says that there is always a trade-off between the use of mobile banking for convince and security in terms of privacy of information in today's mobile age and the privacy plays a significant role in influencing the adoption of Mobile Banking. Mehta, (2017) talks of challenges and operational risk faced by payment banks introduced by RBI in August 2015 and concluded that there is no 'Standard' or 'one size fit all' approach to tackle the challenges for Payment Banks and these banks should come out with their own operation risk management systems. Shaikh, Hanafizadeh & Karjaluoto,(2017), conceptualized the Mobile Banking and payment Systems (MBPS), and suggested new collaboration of banking, fintech and telecoms to provide value added services under MBPS. Bataev, (2017), study the comparative analysis of the virtual banks with classical financial institutions is carried out to assess the virtual financial institutions development in the Russian banking system and found that the use of the modern achievements of science and technology led to the creation of knowledge based economy.
As the literature review of the study pointed out that there were very few studies conducted in the past on both the dimension of behaviour. But the truth can't be ignored that before creating adoption of any technology innovation it must be first tried by the user and that first trial has always been affected by the resistance among the users. That's why various past studies included both resistance factors and adoption factors (Bharati & Mohammadi, 2009, Medhi et al. 2009, Yang, 2009, Chemingui & Lallouna, 2013, Thakur & Srivastva, 2013and Mohammadi, 2015

THE PROPOSED MODEL
Most of researchers have used DOI, UTAUT, TAM and Trust extended TAM to found the factors significantly influencing the adoption behaviour for Mobile Banking, whereas some researchers developed their own models integrating the significant factors from prior studies and introducing some new factors in context of the attributes of their samples or culture or mind set of people belonging to the country in which they are conducting the study. The present study has concentrate on Technology Acceptance Model integrating the two important factors of Trust and Relative Advantage in context of India.
For Adoption side model the study used the existing model of Trust enhanced TAM used by Gu, Lee and Suh, 2009, who discussed about three basic factor influencing Adoption of Mobile Banking those has been Perceived Ease of Use, Perceived Usefulness and Trust. The author added one factor Relative Advantage to their existing model and introduced integrated Trust enhanced TAM model as above. The explanation of the factors has been discussed below.
PEOU refers to ease of performing banking transactions and navigation of bank's website in context of E-Banking in India (Sohail & Shanmugham, 2003). Perceived Usefulness (PU) refers to the degree to which a person believes that using a particular system would enhance his or her job performance (Davis, 1989). Relative Advantage (RA) refers to the degree to which an innovation is seen as being superior to its predecessor (Vyas, 2012). The relative advantage of Mobile Banking over other methods of banking like e-banking and branch banking would really effect the adoption of Mobile Banking. Being location free (perceived convenience) and system free is very important relative advantage of Mobile Banking as other features of anywhere, anytime, one touch, convenience and time saving etc. (Püschel et al., 2010). The major trigger of Mobile Banking services are accessibility, anytime and anywhere availability, saving time and efforts (Mattila, 2003). An innovation is relatively advantageous if providing more benefits than its predecessors (Moore & Benbasat, 1991). Relative advantage of Mobile Banking significantly affects the Mobile Banking adoption (Moore & Benbasat, 1991, Püschel et al., 2010and Kharim, 2011. About TRUST, Vyas, 2012, defined trustworthiness of an innovation as degree to which the consumers have confidence in its marketer's reliability and integrity. Trust in one's bank depends on the reliability in correcting erroneous transaction, to compensate for losses due to security infringements and response to different queries in context of e banking study. The role of trust encompasses the exchange and interactions of a retail bank with its customers on various dimensions of online banking (Sohail & Shanmugham, 2003). The shared value is most critical in developing trust while opportunistic behaviour has significant negative impact. The higher perceived trust to enhance customer's commitment significantly affect in online banking transactions (Mukherjee & Nath, 2003). In previous studies on trust based TAM, different views came out regarding the relationship of Trust, PEOU and PU. Gu et al., 2009, hypotheses that PEOU develop trust and trust influence the degree of PU to influence behaviour intention and further stated that Trust develops in Mobile Banking due to one's familiarity with bank situational normality and structural assurance (most important) along with PEOU. Same way Luo et al., 2010, concluded that structural assurance significantly influences behaviour intention through adjusting people's risk perception. Kim et al., 2009, reveals that structural assurance personal propensity to trust and relative benefits significantly in the same order of significance help in developing initial trust in Mobile Banking. Dahlberg et al., 2003, included in trust extended TAM, perceived trust and disposition of trust as trust better explain the customer adoption of mobile payment solution. Relative advantage as a factor influencing Adoption of Mobile Banking defined by Vyas, 2012, is comparative advantage of new innovation or technology as against its predecessor. The predecessor of Mobile Banking has been Internet Banking which offered anytime anywhere banking using personal computers with a lease line internet connection. The cost was high with limited mobility, as to carry Personal computer or laptop like a mobile phone mark the difference. In context of Mobile Banking in India the author verified the relative advantage based on three pillars-Telecommunication and or Internet Network, Availability of Internet connection on phone, Compatibility of cell phone device for Mobile Banking in terms of operating system, old device/smart phones which in real terms presents the competitive advantage of Mobile Banking over Internet Banking and/or Manual Banking. The (CRT) consumer resistance theory (Ram & Sheth, 1989) in context of Mobile Banking highlighted five resistance barriers namely, Value Barriers, Usage Barriers, Risk Barriers, Tradition Barriers and Image barriers (Yu et al., 2015, Laukkanen & Kiviniemi, 2010, Barati & Mohammadi, 2009). Whereas adopting new integrated Resistance Models, Bamoriya & Singh, 2013, discuss about security concerns, network problems and insufficient operating guidance, and Cruz et al., 2010, verified Cost, Unsuitable Device, Perceived Risk and Complexity as barriers to resist adoption of Mobile Banking in India. The proposed study has also adopted the Consumer Resistance Theory as adopted by Laukkanen & Kiviniemi, 2010, while studying the role of information in Mobile Banking Resistance, but the present study has integrated one additional factor named Relative Disadvantage (The negative side of Relative Advantage).
First talking about functional barriers, When an innovation doesn't perform as per its monetary value, say cost-benefit analysis force the users to remain stick to existing substitutes of technology (Ram and Sheth, 1989). They resist to switching over to new innovation due to Value Barrier. In Mobile Banking the monetary values has involved, loss/theft of pin and mobile device (Al-Jabri & Sohail, 2012), security fraud, theft of Trojan causing their bank accounts leaked, easy attack on phone security (Huili & Zhong, 2011), unauthorized use, transaction errors, lack of transaction records and documentation, vagueness of transaction privacy, device and mobile network reliability (Dahlberg et al., 2003) has been the different facets of risks verified by seminal studies. Chung, 2009 andLaukkanen &Kiviniemi, 2010, explained the difficulty in using Mobile Banking in terms of visibility and demonstration due small screen size and tiny keys compared to personal computer's screen and keyboard. The way they perform their Banking transactions has been totally transformed by Mobile Banking. Limited timings shifted to anytime banking, personal visits shifted to anywhere banking, paper forms shifted to soft forms, shifting to totally new digital banking as compared to old manual banking has raised the Usage Barrier resisting adoption of Mobile Banking. In country like India, visiting the bank, maintaining personal relationships with bank staff and talk while having a cup of tea or coffee, amuse the banking customer and helps a lot in creating trust between the two. This tradition works a long way in maintaining long lasting relationship with the bank and resists the adoption of Mobile Banking. The pre existing negative image in the mind of users adversely affects the image for new innovation and users resists adopting the new innovation. Particularly for Mobile Banking, adverse image of Mobile telecommunication technology, telecommunication network, cell phones, bank etc. resists the users from adopting Mobile Banking. Addition of Rela-tive Disadvantage by the author particularly in context of Mobile Banking in India indicating the non availability of Mobile Banking on the Mobile device, owned by customer, due incompatible operating system and/or on old keyboard mobiles, network failure for telecommunication and/or internet network and cell phones without internet connection can perform limited USSD code related Mobile Banking.
Hence the model for proposed study has to be two phases as given below:

RESEARCH METHODOLOGY
The current research is based on primary data collected using both online as well as offline methods. The questionnaire is designed using the constructs used in prior Trust extended TAM by Gu et al. (2009) and consumer resistance model by Laukkanen & Kiviniemi (2010). Divided into four sections the questionnaire focused on subject's Demographics in first section, Mobile Banking knowledge and usage, in second section, Mobile Banking Adoption Factors in third and Mobile Banking Resistance Factors in last section. The third and forth section is about how the respondent perceive each variable in proposed model and through a five point Likert scale the respondent is asked to mark his or her level of agreement or disagreement to each item.

SAMPLING DESIGN AND SAMPLE SIZE
The data used is collected from 633 potential and existing customers of banks in India using both online and offline questionnaires by personally contacting the visitors in the banks in three states or UTs of Punjab, Haryana and Chandigarh.

DATA ANALYSIS TOOLS
The statistical tool of Cronbach's Alpha to check the content reliability and validity and Factor Analysis for dimension reduction has been used by the study under consideration.

Section 1 : Demographic profile
The data about respondents demographics was collected under five parameters namely gender, age, occupation, education and area. The summary of demographic profile is given in the table below.

Mobile Connection status
The table below evident that 94% respondents holds and uses Mobile Devices. Again, indicating the need to promote the mobile banking adoption.

MOBILE BANKING STATUS
The table below presents the number of mobile banking users and non users. Out of total sample of 633, only 372 respondents have been using mobile banking. The ratio of mobile banking users (59%), compared to the ratio of mobile users (94%), raise the importance of mobile banking promotion.

Awareness about various Mobile Banking Services
The various Mobile Banking Services as listed by author has been given below.

Usage of various Mobile Banking Services for the Mobile Banking Users
This question particularly asked the Mobile Banking users, which of Mobile banking services they has been using given the same list as has been used for knowledge about Mobile Banking service. Accordingly the table below presents the information about the percentage of users out of total respondents, using different Mobile Banking Services.

Section 3: Factors affecting Mobile Banking Adoption Behaviour
The TAM model adding two factors of Trust and Relative Advantage is used under the research to study the mobile banking adoption behaviour, further divided into variables, presented by number of statements. PU and PEOU is determined by self-efficacy, system quality, social influence and facilitating conditions, likewise trust is determined by familiarity with bank, calculativebased trust, structural assurances and situational normality. Finally there are 28 statements representing 12 variables of mobile banking adoption behaviour.

Section 4: Factors affecting Mobile banking Resistance Behaviour
The resistance model of five barriers namely Usage, Value, Risk, Tradition and Image integrated with relative disadvantage represented by 22 items was taken to study the mobile banking resistance behaviour.

Reliability and Validity Testing
To remove the errors at early stage of research and to increase the efficiency of the research by ensuring that the questionnaire will collect the data relevant to the research objective, the validity and reliability testing of data collection instrument is vital for any research based of Primary Data Collection (Bolarinwa, 2015).

Reliability Testing
The reliability test measures the consistency in findings, based on data collected from the same respondents, over different time zones (Hair et al., 2006). Reliability ensure that the items of the measurement scale used by the study are error free and accurate (Zikmund, 2000). To study applied Cronbach's Alpha for testing the consistency between the items within the independent variables. Statistically the value of Cronbach's Alpha above 0.7 indicates that the variables of measurement scale are reliable (Nunnally, 1978). The author use SPSS version 23.0 to calculate the reliability statistics of mobile banking adoption scale and mobile banking resistance scale. The table below depicts the reliability statistics summary for two scales used in the study namely, 'Mobile Banking Adoption' and 'Mobile Banking Resistance'.

Source: Calculations done by author
The Cronbach's Alpha reliability statistics in above table for mobile banking adoption scale has been .877 for total 28 items divided into 4 constructs and for mobile banking resistance scale has been .853 for total 22 items divided into 6 constructs. The sample size for both the scales has been different, as in the survey instrument the option has been given to mobile banking non users to skip the mobile banking adoption scale and directly switch to mobile banking resistance scale. Therefore there are 372 mobile banking users out of 633 total sample size of survey 1. The statistics indicate that both scales are reliable as the value of Cronbach's Alpha has been greater than 0.7 for both.
Journal of Technology Management for Growing Economies, Volume 9, Number 2, October 2018

Validity Testing
The validity means to ensure that the study finds what it has been intending to find (Zikmund, 2003). The validity is of three types, content validity, face validity and construct validity. In the present study the content validity of mobile banking adoption scale and mobile banking resistance scale pre exits as the well established questionnaires on mobile banking adoption and mobile banking resistance has been used. The questionnaire used for primary data collection for the study has been designed by merging two well established questionnaires of prior studies. The Section 3, Adoption Side Factors, the questionnaire has been taken from established research conducted by Gu, Le and Suh in 2009 on "Determinants of behavioral intention to mobile banking", cited 446 times and published by Elsevier Limited. Same way The Section 3, Resistance side Factors, the questionnaire has been taken from established research conducted by Laukkanen and Kiviniemi in 2010 on "The role of information in mobile banking resistance", cited 157 times and published by Emrald Group Publishing Limited. Therefore the Face Validity has been ensured by adapting the variables from two well established Models as above. As regards to the Content Validity, the views of ten experts have been considered to make further corrections and modifications in the questionnaire as a whole, like sequencing of questions in Banking Profile, Awareness and Usage of Different Services. These experts include two Research Professors, two Bank Managers, two IT experts of Mobile Banking, two Bank Customers using Mobile Banking, two Bank Customers not using Mobile Banking. The result of factor analysis has been thus discussed next.

Factor Analysis
Also known as factor reduction technique, used to reduce the large number of variables into small number of factors based on set of common score (variance). So it assesses the structure of the relationships between the large number of variables through a common variance, known as factors. Factor analysis, help the researcher by reducing the number large number of variables into small number of factors, thus form the new factors and define the new interrelationships between the items and forms the new constructs accordingly.
The exploratory factor analysis first test the adequacy of data and need of factor analysis through statistics of Kaiser-Meyer-Olkin test and Bartlett's test of sphericity, followed by calculation of the Communalities for each item in the scale, finally the factor loadings are calculated to define the new latent factors. According the author has explained the factor analysis in the same sequence.

Sample Size
The total 633 responses has been usable for exploratory factor analysis. But as explained above the number of respondents for mobile banking adoption scale has been different. Therefore total 633 responses has been available for mobile banking resistance scale and out of these only 372 responses has been usable for mobile banking adoption scale.

Kaiser-Meyer-Olkin (KMO) and Bartlett's test of sphericity
For applying factor analysis, the adequacy of sample is measured by KMO (Kaiser-Meyer-Olkin). Its value ranges from 0 to 1, where the value close to 1 indicates high variations in the observed variables. So the factor analysis would worth applying, for extracting new reshuffled factors (Field, 2009). The KMO above 0.5 is acceptable, but variations are analysed differently as KMO more than 0.9 is considered superb, between 0.8-0.9 as great and between 0.7-0.8 as good (Field, 2009). Likewise, significant (Sig. <0.05) Bartlett's test of sphericity amply that correlation matrix is not identical matrix so it's worth applying factor analysis. The KMO and Barlett's test of sphericity of mobile banking adoption scale and mobile banking resistance scale is presented below. .000 .000

Source: Calculations done by author
For both scales, the value of KMO has been approximately 0.8, which has been indicating great opportunity to apply factor analysis, according to Field, . Even significant Barlett's test of Sphericity i.e. Sig. <0.05, indicates that present correlation matrix is not identical matrix so there has been great scope for application of factor analysis.

COMMUNALITIES
The communalities indicate that how well the factorization has been performed for different variables. The values close to one depicts that the new extracted factors has explained most of the variations. The table below presents the value of communalities for 28 items under 12 dependent and independent variables under mobile banking adoption scale.

FACTOR LOADINGS
As the main objective of the EFA is to reduce the predicted variables and devise the new model with latent variables using the rule of highly correlated items within the factors and less or no correlation between the factors. According to the factor loadings calculated for each item of mobile banking adoption scale the new mobile banking adoption scale with 4 components or variables has been calculated by the author with the help of SPSS version 23. Evident by the factors loadings the new factors under mobile banking adoption scale has been formed by merging the existing items in new arrangement as below. Finally the four factors namely PEOU (Perceived Ease of Use), PU (Perceived Usefulness), T (Trust) and RA (Related Advantage), has been extracted by exploratory factor analysis for mobile banking adoption scale.

Source: Calculation done by author
Same way the factor loadings of 22 items contained in mobile banking resistance scale divided into 6 factors and not any item has been rearranged. No cross loadings in factorization tables of mobile banking adoption and resistance scales, has been good sign of construct validity. Even the high factor loadings (mostly above 0.7) also validate the constructs formed using EFA.

CONCLUSION
The research has the main objective to develop an integrated model to study two phases of behaviour towards Mobile Banking, first the resistance to use Mobile Banking and second the adoption of Mobile Banking, in order to help government and other stake holders in designing policies for increasing financial inclusion in India by promoting Mobile Banking. The research resulted into six important resistance factors namely value barrier, risk barrier, image barrier, tradition barrier, usage barrier and relative disadvantage and four adoption factors namely perceived usefulness, perceived ease of use, trust and relative advantage, influencing the adoption of Mobile Banking, moreover the model is developed using prior models used in seminal studies keeping in mind the culture and values of India. The paper presents the New Integrated Model for reducing customers' resistance and increasing adoption of Mobile Banking. More research in this field is still needed as Mobile Banking is in its naïve stage, the present research provide the newly developed model, which can be further evaluated by the researchers in future.

LIMITATIONS
Any research that is concerned with people's behavior and attitudes is bound to have some limitations. Those limitations need to be taken into consideration while interpretation and application of results. As the study has modeled around some prior studies in other countries, it has to be adapted according to Indian Values, Culture and believes. Such issues have been kept in consideration. The research targets an all India audience, as India is a country with such a big, diverse and unique Diaspora, no sample can be termed as perfect to represent whole India. The subject being more on the technical usage front, a pure random sampling wasn't possible and results need to be considered while generalizing.

IMPLICATIONS AND SIGNIFICANCE
The study will help all the stakeholder of the Banking industry in India, say Banks, Regulators, all existing and potential bank customers, Telecom. The study will help the regulators (RBI and GOI) and Banks to formulate the strategy to use mobile technology for increasing the Mobile Banking of households in the fold of formal banking services by introducing Mobile Banking after thoroughly understanding the factors influencing and resisting the usage of Mobile Banking. Such strategies will further help the existing and potential bank customers to use the Mobile Banking for their basic banking needs.