E-Wallets : Diffusion and Adoption in Indian Economy

Mobile wallets can be viewed as the digital version of a physical wallet someone would carry. The mobile wallet revolution is well underway, but the winning providers are far from decided. The purpose of this paper is to understand the factors affecting the increase in user proportion and its significance in adoption of e-wallet and also the disparity in user proportion in metro and tier-2 cities. A total 285 valid responses as a part of pilot test are analyzed to establish the outline of the study. In November 2016 aftermath of demonetization affected the user base and increased the number of e-wallets adoption by small vendors in Mumbai area. Looking at the user perspective, the majority of the respondents uses e-wallets; the proportion of users in the metropolitan cities is more as compared to the tier-2 cities. In addition, the only significant variable for e-wallet adoption that was indicated was ‘Simplicity’, which implies the ease of use of the wallet payment system. Looking at the vendor perspective, the e-wallet adoption is much less than what had anticipated. One of the unexpected factors was the fact that the vendors are approached by the e-wallet representatives to adopt it. Hence e-wallets have been adopted by the user population and are satisfied with it. The vendor market hasn’t been diffused into yet, and seeing the difficulties and problem they face, it seems difficult that they will be able to penetrate it in the future.


Introduction:
Mobile phones are rapidly shaping how consumers search, purchase and pay for goods and services.A lot of innovations have been focused on mobile as a channel, resulting in disruptive business models across industries (Jacques Bughin, 2013).Interoperability and ubiquity of mobile devices, fall in prices of data, the emergence of mobile-based business models, coupled with lower cost of investment in payment hardware for merchants, have set the stage of rapid adoption of mobile wallets across the globe.Mobile payments are defined as the use of a mobile device to conduct a payment transaction in which money or funds are transferred from a payer to a receiver via an intermediary or directly without an intermediary.Mobile wallets can be viewed as the digital version of a physical wallet someone would carry.It"s a mobile platform where people can store their money just like in a bank account.(Shukla, 2016).Money can then be loaded into the wallet using a debit or credit card, online banking, retail outlet or via cash (a rechargeable kiosk).A wallet doesn"t require twofactor authentication, unlike card based payments which make wallet payment fast and have a higher success rate.The digital transformation phase that India is entering helps in bringing multiple alternatives to card/cash payment methods.Mobile  (Shukla, 2016), we realized the perspective adopted for the research is that of retailers and market players.Also, there is an absence of deductive reasoning to link the conclusion provided to the data gathered.Furthermore, the detailed research reports on Ewallets by consultancy firms (Ernst & Young LLP, 2016) (The Boston Consulting Group, Inc. and Google India Private Limited, 2016) does provide us with relevant data and inferences about the benefits, challenges and characteristics of E-wallets along with the factors responsible for their adoption and growth in Indian Economy.However, they have failed to discuss the retention rate and providing a perspective of the users of E-wallets as a payment method.In addition, there are no suggestions for the e-wallet industry to grow and the changes required to do the same.To understand the current standing of E-wallets in the Indian economy, it is crucial to study the impact of Demonetization on the payment method trends of the citizens and how it has benefitted the E-wallet industry.Along with this, there were various government policies that were changed and implemented to promote digital payments and the impact of the same has not been taken into consideration.The current scenario of the Indian economy is at a crucial turning point with the digital Volume IX Issue 2, May 2018 11 www.scholarshub.net India initiative, demonetization and the government support that is emphasizing on educating and creating awareness about payment alternatives available to all the citizens.India is on the starting line to go cashless.Since E-wallets are one of the major methods that may be adopted, it becomes crucial to understand their current standing and provide future projections of the possibilities.

Research Methodology:
To study the extent of adoption of e-wallets and the factors affecting the same "Technology Acceptance Model" (TAM) is used.
To analyze the diffusion rate of e-wallets in metropolitan and tier-2 cities if there is a change in the mode of payments offered by small vendors following objectives and hypothesis are made.When one is thinking about using an e-wallet, the perceived usefulness (expectation of result) also take into consideration the cost of this payment instrument, which will determine its relative perceived advantage.We thusly wind up with an expanded TAM that can be utilized to comprehend consumer acknowledgment of an innovation and not just by users inside an association Perceived Usefulness of Mobile Wallet: Perceived usefulness is defined as "the degree to which an individual believes that using a particular system would enhance his or her performance".Attitude toward Using Mobile Wallet: Attitude toward using is defined as an individual"s positive or negative feeling about performing the target behavior.Fishbein and Ajzen have persuasively argued that, in the context of the theory of reasoned action, an individual"s actual behavior hinged on that individual"s attitude toward that particular behavior.

Hypothesis:
1. Null Hypothysis: Proportion of users of e-wallets was not impacted by demonetization.Alternate Hypothesis: Proportion of users of e-wallets increased after demonetization.2. Null Hypothysis: Proportion of users of e-wallets is equal in metropolitan and tier-2 cities. Alternate Hypothesis: Proportion of users of e-wallets is more in metropolitan cities. 3. Null Hypothysis: Proportion of vendors using ewallets was not impacted by demonetization.Alternate Hypothesis: Proportion of vendors using ewallets increased after demonetization.Ho: p2 0.658; H1: p2 < 0.658 p1: Proportion of users using e-wallets before demonetization (0.658) p2: Proportion of users using e-wallets after demonetization Z Cal = -8.876;Z Tab = ±1.65As Z Cal < Z Tab , We will reject the null Hypothesis Therefore, p2 > p1 i.e.Proportion of Users after demonetization is lesser than proportion of Users before demonetization.Population variation of e-wallet users in metropolitan and tier-2 cities: Ho: p1 = p2; H1: p1 > p2 p1: Proportion of e-wallet users in metropolitan cities p2: Proportion of e-wallet users in tier-2 cities Z Cal = 1.711;Z Tab = 1.65As Z Cal < Z Tab , We will reject the null Hypothesis Therefore, P1 >P2 i.e.Proportion of users in metropolitan cities is greater than proportion of users in tier-2 cities. Population variation of vendors using e-wallets before and after demonetization: Ho: p2 ≤ 0.179 H1: p2 > 0.179 p1: Proportion of vendors using e-wallets before demonetization p1 = 0.179 p2: Proportion of vendors using e-wallets after demonetization Z Cal = 8.18; Z Tab = 1.65As Z Cal > Z Tab , We will reject the null Hypothesis Therefore, p2 > p1 i.e.Proportion of Vendors after demonetization is greater than proportion of Vendors before demonetization.Data Analysis (User Adoption) 285 people took part in the survey out of which 183 people use E-wallets and 102 do not.

Impact of demonetization:
Figure 6: Impact of Demonetization on user adoption from the graph, we see that there is a little impact.

Findings and Discussions (User Adoption):
Binary Logistic Regression: Since the correlation matrix"s determinant (Factor analysis) is close to zero, the independent variables do not have multi-collinearity present.This ensures that the assumptions required for performing the binary logistic regression hold true.
The binary logistic regression: Comparing our model with the null model: With every 1 unit change in simplicity, there is a 0.573% chance of the user adopting e-wallets.

Finding and Discussion (Vendor Adoption):
We took data from 50 different vendors selling fruits, vegetable or small makeshift shops.Out of which 23 vendors use, e-wallets and 27 do not.
Analysing the impact of demonetization

Data Analysis using tools (Vendor Adoption) Binary logistic regression
Since the correlation matrix"s determinant (Factor analysis) is close to zero, the independent variables do not have multi-collinearity present.This ensures that the assumptions required for performing the binary logistic regression hold true.The binary logistic regression: Comparing our model with the null model: Objectives:  To understand the factors affecting the increase in user proportion and the significance of them. To understand the factors responsible for the variation in user proportion in metro and tier-2 cities.  To gauge the impact of demonetization. To understand the current adoption of e-wallets by small vendors in Mumbai area. To study the trends that has increased the e-wallet usage. To study what may supplant e-wallets.Theoretical Framework: "Technology Acceptance Model" (TAM) is used as the standard reference model for explaining the diffusion of new Information and Communication Technology.This model is an adaptation of the theory of reasoned action proposed by Ajzen and Fishbein to explain and predict the behavior of people in a specific situation.

Figure 1
Figure 1 Modified TAM: Sampling: Sampling based on small, homogeneous and readily available population. Cluster Sampling: Cluster Sampling based on the location i.e.Metropolitan and Tier-2 cities  Convenience Sampling: Convenience Sampling involves subjects with convenient accessibility and proximity.To measure the internal consistency of the variables: Cronbach"s Alpha To measure the correlation between the factors: Factor Analysis; Binomial Logistic Regression Software Tools: Excel; SPSS; Google Forms Hypothesis Testing Number of users before and after demonetization: www.scholarshub.net

Figure
Figure 2: Age

Figure 7 :
Figure 7: Impact of Demonetization on vendor adoption It is clear that demonetization has an effect on e-wallet adoption.Data Analysis using tools (Vendor Adoption) Binary logistic regressionSince the correlation matrix"s determinant (Factor analysis) is close to zero, the independent variables do not have multi-collinearity present.This ensures that the assumptions required for performing the binary logistic regression hold true.The binary logistic regression:

ISSN: 2249-0310 EISSN: 2229-5674 Volume IX Issue 2, May 2018 10 www.scholarshub.net payments
have been suggested as a solution to facilitate micropayments in electronic and mobile commerce transactions and to encourage reduced use of cash at point-of-sales terminals (SHARMA, 2011).
(TNN, 2016)elated Literature:The mobile wallet market in India is expected to grow at over 190 per cent to reach `1,512 billion by the financial year 2022 from the current level of about `1.5 billion, says a study conducted jointly by trade body Assocham and business consulting firm RNCOS.(IANS,2016)Ernst & Young LLP, 2016 stated that the number of mobiles across the world far exceeds that of any other device and its adoption is increasing rapidly.There are 997 million mobile phone subscribers and 239 million smartphone users in India.The major factors that support the growth in mobile payments include: ban on bank notes was made public.The Paytm app also got a big push with a 200% increase in downloads in the past that day(TNN, 2016).The major gap noticed based on the various research papers and articles that were studied was: the main subject of study was digital payments and mobile payments.We referred to (Denis Dennehy D. S., 2015) for understanding the drivers, challenges and willingness to use mobile payments.The research papers have a generalized focus and there is a lack of emphasis on E-wallets.E-wallets is a new phenomenon which is growing since 2010-11 in India.Based on the literature review performed by us It is quite clear that Paytm takes the lead in all the categories.

Table 1 : Omnibus Test (User)
Since the significance level is less than 0.05, the null hypothesis, i.e. the prediction model is insignificant is rejected.Hence, it can be interpreted that the prediction model is more significant than the null model.The Nagelkerke R Square value is analogous to the R square value in linear/ multiple regression.Hence, it tells us the percentage of variance that can be explained by the analysed model.Hence, in this case, our model explains 92% variance.

Interpreting all the variables: Table 4: Variable Significance Test (User) B S.E. Wald Df Sig. Exp(B) Step 1
The null hypothesis is that the variables are insignificant i.e. the B value should be 0. If the value under the Sig.column is less than 0.05, the null hypothesis is rejected and it can be interpreted that

Table 5 : Omnibus Test (Vendor) Omnibus Tests of Model Coefficients
The Nagelkerke R Square value is analogous to the R square value in linear/ multiple regression.Hence, it tells us the percentage of variance that can be explained by the analysed model.Hence, in this case, our model explains 80.3% variance.
Interpretation: Since the significance level is less than 0.05, the null hypothesis, i.e. the prediction model is insignificant is rejected.Hence, it can be interpreted that the prediction model is more significant than the null model.

Table 7 : Accuracy Test (Vendor) Classification Table a
The Classification table summarizes the hits and misses in the classification process that was done based on our model.Hence, it can be seen that our model is 89.6% accurate.

Interpreting all the variables: Table 8: Variable Significance Test (Vendor) Variables in the Equation
The null hypothesis is that the variables are insignificant i.e. the B value should be 0. If the value under the Sig.column is less than 0.05, the null hypothesis is rejected and it can be interpreted that b. Variable(s) entered on step 2: Awareness.Interpretation: