A Study on Behavioral Patterns of Using Mobile Wallets

This report investigates the behavioral patterns influencing the adoption and usage of mobile wallets. It examines factors such as perceived usefulness, ease of use, perceived risk, self-efficacy, attitude, and anxiety to determine their impact on users' intention to use e-wallets. The study uses regression analysis and ANOVA tests to analyze collected data, revealing that attitude, subjective norms, and perceived usefulness significantly affect the intention to use e-wallets, while perceived ease of use, perceived risk, self-efficacy, and anxiety show insignificance due to co-linearity. Furthermore, the research finds a significant difference in the average age of users across different levels of intention to use e-wallets, supporting the idea that technology adoption varies with age. The report concludes by highlighting the importance of understanding these behavioral factors for promoting the effective adoption of mobile wallet technology.

Running head: BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Behavioral Pattern of Using Mobile Wallets
Name of the Student:
Name of the University:
Author Note:

1BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Table of Contents
Introduction....................................................................................................................2
Literature Review...........................................................................................................2
Result and Analysis........................................................................................................4
Discussion and Conclusion............................................................................................6
Reference........................................................................................................................7
Appendices.....................................................................................................................8
Appendix 1: Regression Analysis..............................................................................8
Appendix 2: One-Way ANOVA Test......................................................................12

2BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Introduction
The new generation of banking system is getting digitalised and in the growth
of this modern era digital wallet is a significant invention. The objective of the report
is to find out the major factors influencing use of e-wallet and at what extent.
Moreover, it is found that young people are more attached to the digital world and
they find the e-wallet more convenient. So, the second objective is to find out the
difference in the average age of the user and the amount of use of e-wallet (Biucky
and Harandi 2017). Hence, the following research questions are developed:
RQ1: Is there any impact of usefulness, ease of use, perceived risk, self-efficacy,
attitude and anxiety on the intention to use the e-wallet?
RQ2: Is there any difference in the age of the users across different level of intention
to use the e-wallet?
Literature Review
There are studies that has found perceived usefulness and perceived ease-of-
use are the two major factors in the field of technological integration. The perceived
usefulness indicates a person’s extent of belief that use of mobile money can improve
the performance of that person. The features and services provided by the mobile
money can endorse the attitude. The higher intention of using mobile money can be
raised by the positive endorsement of attitude.
Mobile money, e-wallet and online transfer and payments has raised the
velocity of money which has significant economic impact. However, there are few
draw backs in this system due to difficulty in use and the lack of knowledge about the
cyber security (Biucky and Harandi 2017). So, this paper wants to investigate the
factors that has impact on using the e-wallet with all the convenience and
inconveniences from the consumers end. The self-efficacy represents the self-

3BEHAVIORAL PATTERN OF USING MOBILE WALLETS
confidence regarding the possession of the essential skills in order to finish a task.
This indicates that using an e-wallet to making payments consumers intentionally
follow activity within the set of perceived abilities or skills (Zhang et al., 2017). This
is a significant factor that has contribution in understanding the behaviour of the users
towards digitalisation and using e-wallets.
The anxiety towards new technology or in particular to the digitalisation of
payment methods shows the state of mind of the users who are concerned about the
ability and the willingness for adaptation of technology (Okcu at al, 2019). The
adaptation of new technology is negatively influenced by the anxiety.
Research hypothesis is developed under the light of the above theories and
paper works on the technology acceptance model. The e-wallet self-efficacy, e-wallet
anxiety, perceived usefulness and perceived ease-of-use are discussed in this section
and on the basis of this the below research hypothesis is developed:
Null Hypothesis, H0: Self-efficacy, e-wallet anxiety, perceived usefulness and
perceived ease-of-use has no influence on the intention of using e-wallet.
Alternative Hypothesis, HA: Self-efficacy, e-wallet anxiety, perceived usefulness and
perceived ease-of-use has a significant influence on the intention of using e-wallet.
The age of the users plays a significant influence on the behaviour. Due to this
fact, this report has tried to check whether there is a significant difference in age
across the different level of intention to use the e-wallet. The following hypothesis is
developed for this concern:
Null Hypothesis, H0: In the different level of intention to use e-wallet, average age of
user is same.
Alternative Hypothesis, HA: In the different level of intention to use e-wallet, average
age of user is different.

4BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Result and Analysis
The regression analysis is conducted on the collected sample data. The
stepwise regression analysis created 3 models and the final model has attitude,
subjective norm and perceived usefulness as significant variables. The adjusted R2 of
the model is found to be 0.80 which implies the model is explained by 80% of
accuracy with the help of the explanatory variables: attitude, subjective norm and
perceived usefulness. The standard error of the estimate is 0.767 which is very low.
The ANOVA test has found that the F-stat is 508.003 with corresponding p-
value 0.000 which indicates that the explanatory variables are better than the
coefficient model.
Table 1: Model Summary of regression
Model 3
R 0.895
R Square 0.801
Adjusted R Square 0.800
Std. Error of the Estimate 0.767
Change Statistics R Square Change 0.004
F Change 7.394
df1 1
df2 378
Sig. F Change 0.007
Model Summary
Table 2: ANOVA table of regression
Model Sum of Squares df Mean Square F Sig.
Regression 896.961 3 298.987 508.003 0
Residual 222.473 378 0.589
Total 1119.435 381
ANOVA
The below table presents the significance and the coefficients of the variables.
A constant is found to be significant at 5% significance level with the t-stat value of -

5BEHAVIORAL PATTERN OF USING MOBILE WALLETS
2.516. The p-value of the attitude, subjective norm and PUsefulness are 0.00, 0.00 and
0.007.
Table 3: Result of regression
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
Lower
Bound
Upper
Bound
(Constant) -0.353 0.140 -2.516 0.012 -0.630 -0.077
Attitude 0.751 0.045 0.694 16.753 0.000 0.663 0.839
SubjectiveNorm 0.183 0.032 0.166 5.725 0.000 0.120 0.246
PUsefulness 0.125 0.046 0.107 2.719 0.007 0.035 0.215
Model
Coefficients
95.0%
Confidence
The below table presents the residuals statistics which indicates the residuals
follows standard normal distribution with the mean value of 0.00 and the standard
deviation value of 1.
Table 4: Residuals statistics of regression
Minimum Maximum Mean Std. Deviation N
Predicted Value 0.70 7.05 4.51 1.53 382
Residual -5.05 3.02 0.00 0.76 382
Std. Predicted Value -2.48 1.66 0.00 1.00 382
Std. Residual -6.59 3.94 0.00 1.00 382
Residuals Statisticsa
A one-way ANOVA test is performed in order to find out the difference in the
age of the users across different levels of intention to use the e-wallet. The test
statistic which is F-stat is 10.10 with the p-value of 0.00. This implies that there is a
difference in average wage at least in one level of intention to user to use the e-wallet.
Table 5: One-Way ANOVA test result
Sum of Squares df Mean Square F Sig.
Between Groups 11393.05 6 1898.84 10.10 0.00
Within Groups 70536.89 375 188.10
Total 81929.94 381
ANOVA

6BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Discussion and Conclusion
The above analysis has found a significant impact of the attitude, subjective
norm and PUsefulness which supports the literature discussed in the second section of
the report. However, perceived ease of use, perceived risk, self-efficacy and anxiety is
found to be insignificant. The insignificance is aroused due to the co-linearity.
Moreover, the correlation is very week between this variables and the intention to use
the e-wallet. This simply contradicts the literature review. Now, the one-way ANOVA
test has found that the average age is different for different level of intention to use
the e-wallet. As it is discussed in the literature, the use of modern technology and
ability to use those technology for example use of mobile money or e-wallet differs
across the age. In this sense, the result supports the literature (Alnawafleh et al.,
2018). The draw backs of the research is that it considers a small sample and there are
few statistical consequences which can be avoided by using more variables and using
advance models.

7BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Reference
Alalwan, A.A., Dwivedi, Y.K., Rana, N.P. and Williams, M.D., 2016. Consumer
adoption of mobile banking in Jordan: examining the role of usefulness, ease of use,
perceived risk and self-efficacy. Journal of Enterprise Information Management,
29(1), pp.118-139.
Alnawafleh, E.A.T., Tambi, A.M.A., Abdullah, A.A., Alsheikh, G.A.A. and Ghazali,
P.L., 2018. The Impact of Service Quality, Subjective Norms, and Voluntariness on
Acceptance of Provider’s Mobile Telecommunication Service in Jordan.
International Journal of Engineering & Technology, 7(4.34), pp.149-152.
Biucky, S.T. and Harandi, S.R., 2017. The effects of perceived risk on social
commerce adoption based on tam model. " International Journal of Electronic
Commerce Studies", 8(2), pp.173-196.
Okcu, S., Koksalmis, G.H., Basak, E. and Calisir, F., 2019. Factors Affecting
Intention to Use Big Data Tools: An Extended Technology Acceptance Model. In
Industrial Engineering in the Big Data Era (pp. 401-416). Springer, Cham.
Rahman, M.M., Lesch, M.F., Horrey, W.J. and Strawderman, L., 2017. Assessing the
utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accident
Analysis & Prevention, 108, pp.361-373.
Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X. and Lin, J., 2017. User acceptance
of mobile health services from users’ perspectives: The role of self-efficacy and
response-efficacy in technology acceptance. Informatics for Health and Social Care,
42(2), pp.194-206.
Zhao, Y., Ni, Q. and Zhou, R., 2018. What factors influence the mobile health service
adoption? A meta-analysis and the moderating role of age. International Journal of
Information Management, 43, pp.342-350.

8BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Appendices
Appendix 1: Regression Analysis
Descriptive Statistics
Mean Std. Deviation N
UsageIntention 4.5131 1.71410 382
PUsefulness 4.9005 1.47082 382
PEaseOfUse 5.2565 1.31103 382
PerceivedRisk 4.4372 1.44541 382
SelfEfficacy 4.9843 1.29800 382
Attitude 4.7238 1.58538 382
SubjectiveNorm 3.8783 1.55991 382
Anxiety 4.0380 1.62267 382
Correlations
UsageInte
ntion
PUsefuln
ess
PEaseOf
Use
Perceived
Risk
SelfEffic
acy
Attitud
e
Subjective
Norm
Anxiet
y
Pearson
Correlation
UsageIntent
ion 1.000 .760 .661 -.324 .473 .882 .646 -.334
PUsefulnes
s .760 1.000 .703 -.276 .479 .810 .548 -.320
PEaseOfUs
e .661 .703 1.000 -.249 .555 .698 .482 -.445
PerceivedRi
sk -.324 -.276 -.249 1.000 -.052 -.378 -.152 .557
SelfEfficacy .473 .479 .555 -.052 1.000 .510 .379 -.227
Attitude .882 .810 .698 -.378 .510 1.000 .607 -.411
SubjectiveN
orm .646 .548 .482 -.152 .379 .607 1.000 -.037
Anxiety -.334 -.320 -.445 .557 -.227 -.411 -.037 1.000
Sig. (1-tailed)
UsageIntent
ion . .000 .000 .000 .000 .000 .000 .000
PUsefulnes
s .000 . .000 .000 .000 .000 .000 .000
PEaseOfUs
e .000 .000 . .000 .000 .000 .000 .000
PerceivedRi
sk .000 .000 .000 . .154 .000 .001 .000
SelfEfficacy .000 .000 .000 .154 . .000 .000 .000

9BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Attitude .000 .000 .000 .000 .000 . .000 .000
SubjectiveN
orm .000 .000 .000 .001 .000 .000 . .234
Anxiety .000 .000 .000 .000 .000 .000 .234 .
N
UsageIntent
ion 382 382 382 382 382 382 382 382
PUsefulnes
s 382 382 382 382 382 382 382 382
PEaseOfUs
e 382 382 382 382 382 382 382 382
PerceivedRi
sk 382 382 382 382 382 382 382 382
SelfEfficacy 382 382 382 382 382 382 382 382
Attitude 382 382 382 382 382 382 382 382
SubjectiveN
orm 382 382 382 382 382 382 382 382
Anxiety 382 382 382 382 382 382 382 382
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 Attitude .
Stepwise (Criteria:
Probability-of-F-to-
enter <= .050,
Probability-of-F-to-
remove >= .100).
2 SubjectiveNorm .
Stepwise (Criteria:
Probability-of-F-to-
enter <= .050,
Probability-of-F-to-
remove >= .100).
3 PUsefulness .
Stepwise (Criteria:
Probability-of-F-to-
enter <= .050,
Probability-of-F-to-
remove >= .100).
a. Dependent Variable: UsageIntention

10BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Model Summaryd
Mode
l
R R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .882a .778 .777 .80903 .778 1330.29
9 1 380 .000
2 .893b .797 .796 .77362 .020 36.584 1 379 .000
3 .895c .801 .800 .76717 .004 7.394 1 378 .007
a. Predictors: (Constant), Attitude
b. Predictors: (Constant), Attitude, SubjectiveNorm
c. Predictors: (Constant), Attitude, SubjectiveNorm, PUsefulness
d. Dependent Variable: UsageIntention
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 870.715 1 870.715 1330.299 .000b
Residual 248.720 380 .655
Total 1119.435 381
2
Regression 892.610 2 446.305 745.728 .000c
Residual 226.825 379 .598
Total 1119.435 381
3
Regression 896.961 3 298.987 508.003 .000d
Residual 222.473 378 .589
Total 1119.435 381
a. Dependent Variable: UsageIntention
b. Predictors: (Constant), Attitude
c. Predictors: (Constant), Attitude, SubjectiveNorm
d. Predictors: (Constant), Attitude, SubjectiveNorm, PUsefulness
Coefficientsa
Model Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig. 95.0% Confidence Interval
for B
B Std. Error Beta Lower
Bound
Upper
Bound
1
(Constant) .009 .130 .067 .947 -.247 .265
Attitude .954 .026 .882 36.473 .000 .902 1.005

11BEHAVIORAL PATTERN OF USING MOBILE WALLETS
2
(Constant) -.196 .129 -1.518 .130 -.450 .058
Attitude .838 .031 .775 26.651 .000 .776 .900
SubjectiveNor
m .193 .032 .176 6.048 .000 .130 .256
3
(Constant) -.353 .140 -2.516 .012 -.630 -.077
Attitude .751 .045 .694 16.753 .000 .663 .839
SubjectiveNor
m .183 .032 .166 5.725 .000 .120 .246
PUsefulness .125 .046 .107 2.719 .007 .035 .215
a. Dependent Variable: UsageIntention
Excluded Variablesa
Model Beta In t Sig. Partial Correlation Collinearity
Statistics
Tolerance
1
PUsefulness .134b 3.302 .001 .167 .344
PEaseOfUse .087b 2.607 .009 .133 .512
PerceivedRisk .012b .442 .659 .023 .857
SelfEfficacy .032b 1.124 .262 .058 .740
SubjectiveNorm .176b 6.048 .000 .297 .632
Anxiety .035b 1.311 .191 .067 .831
2
PUsefulness .107c 2.719 .007 .139 .339
PEaseOfUse .068c 2.102 .036 .107 .507
PerceivedRisk -.004c -.175 .861 -.009 .847
SelfEfficacy .015c .563 .574 .029 .732
Anxiety -.011c -.417 .677 -.021 .760
3
PEaseOfUse .045d 1.311 .191 .067 .455
PerceivedRisk -.007d -.297 .767 -.015 .846
SelfEfficacy .007d .244 .808 .013 .722
Anxiety -.010d -.387 .699 -.020 .760
a. Dependent Variable: UsageIntention
b. Predictors in the Model: (Constant), Attitude
c. Predictors in the Model: (Constant), Attitude, SubjectiveNorm
d. Predictors in the Model: (Constant), Attitude, SubjectiveNorm, PUsefulness
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value .7049 7.0544 4.5131 1.53435 382

12BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Residual -5.05439 3.02093 .00000 .76415 382
Std. Predicted Value -2.482 1.656 .000 1.000 382
Std. Residual -6.588 3.938 .000 .996 382
a. Dependent Variable: UsageIntention
Appendix 2: One-Way ANOVA Test
Test of Homogeneity of Variances
What is your age?
Levene Statistic df1 df2 Sig.
2.169 6 375 .045
ANOVA
What is your age?
Sum of Squares df Mean Square F Sig.
Between Groups 11393.049 6 1898.841 10.095 .000
Within Groups 70536.891 375 188.098

13BEHAVIORAL PATTERN OF USING MOBILE WALLETS
Total 81929.940 381
Multiple Comparisons
Dependent Variable: What is your age?
Tukey HSD
(I) UsageIntention (J) UsageIntention Mean
Difference (I-J)
Std. Error Sig. 95% Confidence Interval
Lower Bound Upper Bound
1.00
2.00 4.388 3.636 .891 -6.39 15.17
3.00 7.521 3.460 .312 -2.73 17.78
4.00 9.032* 2.808 .024 .71 17.36
5.00 14.438* 2.822 .000 6.07 22.81
6.00 18.017* 2.822 .000 9.65 26.38
7.00 15.050* 3.190 .000 5.59 24.51
2.00
1.00 -4.388 3.636 .891 -15.17 6.39
3.00 3.133 3.714 .980 -7.88 14.14
4.00 4.644 3.116 .751 -4.59 13.88
5.00 10.051* 3.129 .024 .78 19.33
6.00 13.629* 3.129 .000 4.35 22.90
7.00 10.662* 3.464 .036 .39 20.93
3.00
1.00 -7.521 3.460 .312 -17.78 2.73
2.00 -3.133 3.714 .980 -14.14 7.88
4.00 1.511 2.908 .999 -7.11 10.13
5.00 6.917 2.922 .215 -1.74 15.58
6.00 10.496* 2.922 .007 1.83 19.16
7.00 7.529 3.278 .248 -2.19 17.25
4.00
1.00 -9.032* 2.808 .024 -17.36 -.71
2.00 -4.644 3.116 .751 -13.88 4.59
3.00 -1.511 2.908 .999 -10.13 7.11
5.00 5.406 2.110 .141 -.85 11.66
6.00 8.985* 2.110 .001 2.73 15.24
7.00 6.018 2.582 .232 -1.64 13.67
5.00
1.00 -14.438* 2.822 .000 -22.81 -6.07
2.00 -10.051* 3.129 .024 -19.33 -.78
3.00 -6.917 2.922 .215 -15.58 1.74
4.00 -5.406 2.110 .141 -11.66 .85
6.00 3.578 2.129 .629 -2.73 9.89
7.00 .611 2.597 1.000 -7.09 8.31
6.00
1.00 -18.017* 2.822 .000 -26.38 -9.65
2.00 -13.629* 3.129 .000 -22.90 -4.35
3.00 -10.496* 2.922 .007 -19.16 -1.83

14BEHAVIORAL PATTERN OF USING MOBILE WALLETS
4.00 -8.985* 2.110 .001 -15.24 -2.73
5.00 -3.578 2.129 .629 -9.89 2.73
7.00 -2.967 2.597 .914 -10.67 4.73
7.00
1.00 -15.050* 3.190 .000 -24.51 -5.59
2.00 -10.662* 3.464 .036 -20.93 -.39
3.00 -7.529 3.278 .248 -17.25 2.19
4.00 -6.018 2.582 .232 -13.67 1.64
5.00 -.611 2.597 1.000 -8.31 7.09
6.00 2.967 2.597 .914 -4.73 10.67
*. The mean difference is significant at the 0.05 level.
What is your age?
Tukey HSD
UsageIntention N Subset for alpha = 0.05
1 2 3 4
6.00 83 36.77
7.00 42 39.74 39.74
5.00 83 40.35 40.35
4.00 86 45.76 45.76
3.00 30 47.27 47.27 47.27
2.00 25 50.40 50.40
1.00 33 54.79
Sig. .891 .147 .703 .148
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 42.900.
b. The group sizes are unequal. The harmonic mean of the group sizes is used.
Type I error levels are not guaranteed.