In this project, some of the respondents for the pilot test have been chosen from the researchers' friends and acquaintances. On December 06, 2022, the survey was made available online through the Google Form link. As a result, during the period, many responders offered their thoughts and recommendations to enhance the questions. Both regression analysis and correlation analysis were used to examine the data obtained from the questionnaire. Descriptive analysis was used to determine the respondents' demographic characteristics prior to correlating the data that had been obtained.
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66 4.3 Demographic Profile of Respondents
Table 4.1 Demographic profile of respondents Respondent profile Classification Frequency
(N=202)
Percentage (%)
Gender Male
Female
69 133
34.2 65.8
Age
21 -30 years old 31-40 years old 41-50 years old 51-60 years old
87 50 40 25
43.1 24.8 19.8 12.4
Income Less than RM2500
RM2501- Rm3500 RM3501- RM4500 RM4501- RM5500 RM5501 and above
63 23 36 31 48
31.2 11.4 17.8 15.3 23.8
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Level Education SPM
Diploma Degree Master PHD
19 32 75 41 35
9.4 15.8 37.1 20.3
\17.3
Have you use cashless payment
Yes No
196 6
97 3
Which app used (can choose more than one)
Credit card Debit card DuitNow Touch and Go
E-wallet Online transfer
Ali pay Apple pay Shopee pay
Other
76 147 136 116 165 4 33 101
53
38 73.5
68 58 82.5
2 16.5 50.5 26.5
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The contextual of 202 respondents has been collected in this research. the table of 4.1 consists of gender, age, income, level education, experience of using cashless payment and which app had been used. There were 34.2% from male respondents with 69 staff and 65.8%
of them were 133 female respondents involved in this questionnaire. Majority percentage of respondents is 21-30 years old with 43.1% (N=87) where 24.8% (N=50) are from 31-40 years old, 19.8% (N=40) are from 41-50 years old and the fewest is 51-60 years old which is 12.4%
(N=25). Majority of respondents income in this questionnaire are less than RM2500 with 31.2% (N=63), 11.4% (N=23) from RM2501- RM3500, 17.8% (N=36) from RM 3501 – RM4500, 15.3% (N=31) are from RM 4501-RM5500 and 23.8% (N=48) from RM5501 and above. Most of the respondent’s education level is degree with 37.1% (N=75), 9.4% (N=19) from SPM, 15.8% (N=32) from Diploma, 20.3% (N=41) from Master and 17.3% (N=35) were from PHD. There are 196 (97%) respondents had used cashless payment and 6 respondents did not used cashless payment before. Therefore, 6 of them will not count in the next procedure.
Most of the respondents had used online transfer which is 82.5% (N=165) and the fewest used is Ali pay with 2% (N=4).
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69 4.4 Descriptive Analysis
There are five independent variables and one dependent variable in this study. The mean and standard deviation were computed for each variable in this study utilising description analysis to locate the mean. The factors in this study were measured using a 5-point Likert scale, with 1 being strongly disagree and 5 being strongly agreeing.
4.4.1 Descriptive Analysis for Independent Variables
Table 4.2 shows the descriptive analysis for performance expectancy consisting of four questions in total. According to the outcome, the average mean for performance expectancy is 4.39. The question with the highest mean is 4.49 where most respondents agree that cashless payment is very useful for daily transactions. Meanwhile, the lowest mean score is 4.23.
Table 4.2 Descriptive analysis of performance expectancy Performance Expectancy
Mean Std. Deviation N
Cashless payment can save my time 4.4745 .71207 195
Cashless payment is very useful for daily transaction
4.4949 .80700 195
Cashless payment has increase my productivity
4.3316 .79564 195
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4.2347 .86292 195
Table 4.3 shows the descriptive analysis for facilitating conditions consisting of three questions in total. According to the outcome, the average mean for facilitating conditions is 4.39. The question with the highest mean is 4.45 where most of the respondents agree that they can use cashless payment without help from others. Meanwhile, the lowest mean score is 4.32.
Table 4.3 Descriptive analysis of facilitating condition Facilitating Condition
Mean Std. Deviation N
I have resources on using the cashless payment
4.3214 .85560 195
I can use cashless payment without any help from others
4.4541 .74623 195
I have device to use cashless payment 4.4184 .78346 195
Table 4.4 shows the descriptive analysis for social influence consisting of three questions in total. According to the outcome, the average mean for social influence is 4.04. The question with the highest mean is 4.12 where most respondents agree that the family members
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will influence their behaviour in using cashless payment. Meanwhile, the lowest mean score is 3.89.
Table 4.4 Descriptive analysis of social influence Social Influence
Mean Std. Deviation N
Social media can influence my behaviour in using cashless payment
4.1071 .87925 195
My family members can influence my behavior in using cashless payment
3.8878 .99623 195
My friends can influence my behavior by using cashless payment
4.1224 1.00016 195
Table 4.5 shows the descriptive analysis for innovativeness consisting of three questions in total. According to the outcome, the average mean for innovativeness is 4.29. The question with the highest mean is 4.38 where most of the respondents agree that technology can make my daily routine more peaceful. Meanwhile, the lowest mean score is 4.13.
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Table 4.5 Descriptive analysis of innovativeness Innovativeness
Mean
Std.
Deviation N
I like to use new things 4.3724 .82216 195
I have no doubt on using cashless payment 4.1327 .91861 195
Technology can make my daily routine more peaceful
4.3776 .80403 195
Table 4.6 shows the descriptive analysis for perceived technology security consisting of three questions in total. According to the outcome, the average mean for perceived technology security is 4.12. The question with the highest mean is 4.38 where most of the respondents agree that technology can make my daily routine more peaceful. Meanwhile, the lowest mean score is 3.98.
Table 4.6 Descriptive analysis of perceived technology security Perceived Technology Security
Mean
Std.
Deviation N
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4.3776 .80403 195
I feel secure in using cashless payment 3.9796 .94440 195
It is more secure using cashless payment than using cash
3.9949 .98968 195
Table 4.7 shows the descriptive analysis for hedonic motivation consisting of three questions in total. According to the outcome, the average mean for hedonic motivation is 4.18.
The question with the highest mean is 4.27 where most of the respondents agree that they have fun using cashless payment. Meanwhile, the lowest mean score is 4.11.
Table 4.7 Descriptive analysis of hedonic motivation Hedonic Motivation
Mean
Std.
Deviation N
I have fun using cashless payment 4.2704 .85536 195
After using cashless payment, I do not feel stressed about not having cash on me
4.1378 .96418 195
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Cashless payment makes me feel good 4.1173 .92349 195
4.4.2 Descriptive Analysis for Dependent Variable
Table 4.8 shows the descriptive analysis for consumer acceptance of cashless payment consisted of three questions in total. According to the outcome, the average mean for consumer acceptance of cashless payment is 4.29. The question with the highest mean is 4.38 where most respondents agree that they will use cashless payment in the future. Meanwhile, the lowest mean score is 4.12.
Table 4.8 Descriptive analysis of Consumer Acceptance of cashless payment Consumer Acceptance of Cashless Payment
Mean Std. Deviation N
I always use cashless payment in my daily use
4.1173 .97218 195
I will use cashless payment in the future 4.3776 .73051 195
I always recommend other to people about cashless payment
4.3571 .76795 195
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75 4.5 Validity and Reliability Test
4.5.1 Validity Test
The reliability of a research study's conclusions as compared to similar individuals outside the study is referred to as the validity test. Validity is described by Chua Yan Piaw (2014) as "the capacity of a measurement to measure the genuine value of the idea in hypothesis." In general, research with a high value for validity shows that the conclusions reached are supported by facts or other types of evidence and are able to provide accurate justification for further study. The correlation for each variable is displayed in figure 4.1 below.
If the significance value is more than 0.05, the result is valid; otherwise, the result is invalid.
Pearson Each variable's correlation data demonstrates its validity. The table below shows that each variable of data Pearson Correlation; performance expectancy (.724), facilitating condition (.693), social influence (.571), innovativeness (.781), perceived technology security (.750) and hedonic motivation (.767).
Figure 4.1 Pearson’s Correlation
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According to Drost's (2011) research, "reliability is the degree to which measures are repeatable when different people perform the measurement on other occasions, under different conditions, allegedly with alternative equipment to measure the construct or skill." It can also be described as the consistency or dependability of a construct's measure. Cronbach's alpha coefficient was employed as an indicator in the study to gauge the degree of consistency.
According to a previous study by Hair et al. (2010), values as low as 0.60 may be appropriate for exploratory research, while a value of 0.70 is widely seen as an acceptable value.
Additionally, George and Mallery (2003) suggest a tiered approach consisting of the following:
“≥ .9 – Excellent, ≥ .8 – Good, ≥ .7 – Acceptable, ≥ .6 – Questionable, ≥ .5 – Poor, and ≤ .5 – Unacceptable”. We can conclude that all the items in this study are consistent and reliable.
Table 4.9 shows the Cronbach’s Alpha of each scale.
Table 4.9 The Cronbach’s Alpha of each scale.
NO VARIABLE ITEMS CRONBACH’S ALPHA
1 Performance expectancy 4 .792
2 Facilitating condition 3 .741
3 Social influence 3 .805
4 Innovativeness 3 .769
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5. Perceived technology security 3 .748
6. Hedonic motivation 3 .854
7. Cashless payment 3 .787
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To ascertain if sample data was taken from a regularly distributed population, a normality test is utilised. It is typically carried out to see if the research's data have a normal distribution. The mean, standard deviation, skewness, and kurtosis are displayed in table 4.10 below.
Table 4.10 The Mean, Standard Deviation, Skewness and Kurtosis of each item.
Variables Items Mean Std. deviation Skewness Kurtosis
Performance expectancy 4 4.38 .625 -1.318 1.705
Facilitating condition 3 4.39 .647 -1.741 4.879
Social influence 3 4.04 .810 -1.053 1.069
Innovativeness 3 4.29 .703 -1.313 1.941
Perceived technology security
3 4.10 .758 -.800 .620
Hedonic motivation 3 4.17 .805 -1.035 1.045
Consumer acceptance of cashless payment
3 4.28 .696 .949 .686
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The facilitating circumstance has a mean value of 4.39, whereas social influence has a mean value of 4.04, making it the variable with the highest mean. Performance expectations, social influence, inventiveness, perceived technological security, hedonic incentive, and cashless payment all have kurtosis values between -2 and 2, indicating that these variables are typical. However, the enabling condition's kurtosis value of 4.879 suggests that this variable is not normally distributed. Nevertheless, further analysis, such as Cronbach's alpha, will be performed to assess this variable's reliability. When the skewness value is close to 0, measurements of the score distribution can be regarded as normal. If you look at the table above, the skewness ranges from -.949 to 1.741.
The normal level of a data can also be tested through the Tested of Normality. In research from Coakes & Steed (2007), if the level is significant more than 0.5, then the data meets the assumption of normality. According to the table 4.11 below, for dependent variables of consumer acceptance of cashless payment, the significant level is (.156) which meets the assumption of normality. Meanwhile, for all independent variables also meet the assumption of normality with the significance levels are as follow; performance expectancy (.250), facilitating condition (.181), social influence (.196), innovativeness (.201), perceived technology security (.156) and hedonic motivation (.152).
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Table 4.11 Tests of Normality
Kolmogorov- Smirnov Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Performance expectancy .250 195 .000 .847 195 .000
Facilitating condition .181 195 .000 .823 195 .000
Social influence .196 195 .000 .891 195 .000
Innovativeness .201 195 .000 .858 195 .000
Perceived technology security .156 195 .000 .915 195 .000
Hedonic motivation .152 195 .000 .882 195 .000
Consumer acceptance of cashless payment .156 195 .000 .882 195 .000
4.6.1 Pearson’s Correlation Analysis
The parametric statistics technique is utilised because the samples that were obtained are normally distributed. We assessed the correlation between the variables using Pearson's product-moment correlation coefficient approach. A number of presumptions must be met, including the one that samples are drawn at random from groups with a similar probability of selection and coordination into a sample. The samples are trustworthy and legitimate according
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to the earlier tests. Using Pearson, seven correlation coefficients were evaluated. All correlation coefficients' significance levels were set at the 0.05 level (2-tailed).
The Pearson correlation can be used to evaluate how strong the link is (r). No association exists between the two variables if the r value is 0, and a perfect positive correlation exists if the r value is 1. In the meantime, a r value of -1 indicates a negative correlation. On the basis of table 4.12 below, a general rule for the relationship's strength is provided.
Indicating a positive or negative relationship is the sign + or -.
Table 4.12 Guideline for The Strength of Correlation r value Strength of correlation
r = 0.10 to 0.29 or r = -0.10 to - 0.29
Small
r = 0.30 to 0.49 or r = -0.30 to -0.49 Medium
r = 0.50 to 1.00 or r = -0.50 to -1.00 Large
Referring to table 4.13 below, all the variables have a correlation r value more than 0.50. Firstly, correlation between dependent variable constructs and independent variable constructs is highly correlated around the coefficient above 0.50 (Tabachnick & Fidell, 2001;
Hairet al., 1998). The correlation between each independent variable construct is below 0.90 (Tabachnick & Fidell, 2001; Hair et al., 1998). Through Table 4.13, the correlation between dependent variables (Cashless Payment) with the independent variable has the following
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readings: performance expectancy (.724), facilitating condition (.693), social influence (.571), innovativeness (.781) perceived technology security (.750) and hedonic motivation (.767).
Correlations between dependent variable constructs with independent variables is multicollinearity that involves performance expectancy, innovativeness, perceived technology security and hedonic motivation which exceeds 0.70, that is .724, .781, .750, .767 while the correlation between the independent variables ranged from .571 to .781.
Table 4.13 Pearson’s Product - Moment of Correlation Matrix
No. Variables PE FC SI I PTS HM CACP
1 Performance expectancy (PE)
1
2 Facilitating condition (FC)
.740 1
3 Social influence (SI) .441 .400 1
4 Innovativeness (I) .738 .728 .518 1
5 Perceived technology security (PTS)
.638 .594 .635 .753 1
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(HM)
.675 .607 .568 .793 .774 1
7 Consumer acceptance of cashless
payment (CACP)
.724 .693 .571 .781 .750 .767 1
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In statistics, examining a population parameter assumption is recognised as hypothesis testing. It is used to utilize information to produce a well-informed prediction related to an assumption. There are six hypotheses that have been created in chapter 2
4.7.1 Hypothesis 1
H1: Performance expectancy has a positive effect on consumer acceptance of cashless payment among staff of Universiti Malaysia Kelantan.
The table 4.13 has shown the results of the relationship between performance expectancy and consumer acceptance of cashless payment among UMK’s staff. They have a large strength of correlation between three variables, r = 0.724, n = 195, p < 0.01. The correlation analysis supports that performance expectancy has a positive effect on consumer acceptance of cashless payment among UMK’s staff. The conclusion is that H1 is supported.
4.7.2 Hypothesis 2
H2: Facilitating conditions have a positive effect on consumer acceptance of cashless payment among staff of Universiti Malaysia Kelantan.
The table 4.13 has shown the results of the relationship between facilitating conditions and consumer acceptance of cashless payment among UMK’s staff. They have a large strength of correlation between three variables, r = 0.693, n = 195, p < 0.01. The correlation analysis supports that facilitating conditions have a positive effect on consumer acceptance of cashless payment among UMK’s staff. The conclusion is that H2 is supported.
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H3: Social Influence has a positive effect on consumer acceptance of cashless payment among staff of Universiti Malaysia Kelantan.
The table 4.13 has shown the results of the relationship between social influence and consumer acceptance of cashless payment among UMK’s staff. They have a large strength of correlation between three variables, r = 0.571, n = 195, p < 0.01. The correlation analysis supports that social influence has a positive effect on consumer acceptance of cashless payment among UMK’s staff. The conclusion is that H3 is supported.
4.7.4 Hypothesis 4
H4: Innovativeness has a positive effect on consumer acceptance of cashless payment among staff of Universiti Malaysia Kelantan.
The table 4.13 has shown the results of the relationship between innovativeness and consumer acceptance of cashless payment among UMK’s staff. They have a large strength of correlation between three variables, r = 0.781, n = 195, p < 0.01. The correlation analysis supports that innovativeness has a positive effect on consumer acceptance of cashless payment among UMK’s staff. The conclusion is that H4 is supported.
4.7.5 Hypothesis 5
H5: Perceived technology security has a positive effect on consumer acceptance of cashless payment among staff of Universiti Malaysia Kelantan.
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The table 4.13 has shown the results of the relationship between perceived technology security and consumer acceptance of cashless payment among UMK’s staff. They have a large strength of correlation between three variables, r = 0.750, n = 195, p < 0.01. The correlation analysis supports that perceived technology security has a positive effect on consumer acceptance of cashless payment among UMK’s staff. The conclusion is that H5 is supported.
4.7.6 Hypothesis 6
H6: Hedonic Motivation has a positive effect on consumer acceptance of cashless payment among staff of Universiti Malaysia Kelantan.
The table 4.13 has shown the results of the relationship between hedonic motivation and consumer acceptance of cashless payment among UMK’s staff. They have a large strength of correlation between three variables, r = 0.767, n = 195, p < 0.01. The correlation analysis supports that hedonic motivation has a positive effect on consumer acceptance of cashless payment among UMK’s staff. The conclusion is that H6 is supported.
4.7.7 Regression Analysis
Table 4.14 Regression Analysis
VARIABLES STD BETA
Independent variable
● Performance expectancy .167**
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● Facilitating condition
● Social influence
● Innovativeness
● Perceived technology security
● Hedonic Motivation
.156**
.103**
.186**
.181**
.214**
R R2
R2 Change Sig F
.856 .732 .724 .000
Note: *p<0.10 **P<0.05 ***p<0.01
The results of the regression analysis can be seen in Table 4.14. Regression models that show the relationship between the factors that influence customer acceptance of the cashless payment is significant (F value=85.610, p<0.01). In the model, the R² value is 0.732. It explains that 73 percent of consumer acceptance of cashless payment is explained by the factors and significance. This model shows the existence of meaningful relationships and the conclusions which can be made with 99% confidence that there is a direct relationship between six factors that influence consumer acceptance of cashless payment.
Three variables show a significant relationship which is first, hedonic motivation (β=
0.214, p<.01) which is positively related to consumer acceptance of cashless payment. This means that hedonic motivation influences the consumer acceptance of cashless payment among staff UMK. Second, innovativeness (β= 0.186, p<.01) as well positively related to consumer
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acceptance of cashless payment. This shows that innovativeness also supports the consumer acceptance of cashless payment. Third, the perceived technology security (β= 0.181, p<.05) is related positively with consumer acceptance of cashless payment. Fourth, performance expectancy (β= 0.167, p<.05) also has a positive relationship with consumer acceptance of cashless payment. This means performance expectancy also positively supports the consumer acceptance of cashless payment. Then, facilitating conditions (β= 0.156, p<.05) showed a positive relationship with the consumer acceptance of the cashless payment. Lastly, social influence (β= 0.103, p<.05) also showed a positive relationship with consumer acceptance of cashless payment. Hence, the consumer acceptance of cashless payment among UMK’s staff has been supported by six factors which are hedonic motivation, perceived technology security, innovativeness, facilitating conditions, performance expectancy and social influence.
Therefore, hypotheses H1, H2, H3, H4, H5 and H6 were supported which shows a significant relationship with consumer acceptance of cashless payment.
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In this chapter, we have discussed the data evaluation and findings. The chapter starts with the introduction of the chapter and preliminary analysis. Followed via the demographics profile of respondent and descriptive analysis where we discuss all the 202 respondents' answers. Next, we continue with the validity and reliability test and the normality check to make positive the records that we have amassed have been valid. Then, we discussed hypothesis testing and regression analysis which resulted from the earlier test. Next chapter will be about the discussion and conclusion.
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DISCUSSION AND CONCLUSION 5.1 Introduction
This chapter discusses and explains the research's findings in relation to the research's explanation in chapter 4. Based on the problem and prior research in chapter 2, a summary of the findings was created. Researchers have also spoken about their presumptions regarding the hypothesis testing and whether the study hypothesis was accepted or rejected. This chapter also covered the outcome objectives conclusion in light of the chapter 1 research objectives. In this chapter, researchers will discuss key findings, discussion, implications of the study, limitations of the study, recommendations / suggestions for future research, and overall conclusion of the study. The key findings will explain the summary of the finding from chapter 4 of this research. Along with the independent variables that influence whether or not this research is efficient, researchers will also talk about the research objective and research question. Researchers will explain the struggle or problem that faced during doing this research in the limitation of the study. Researchers also will discuss the recommendation for future research in this chapter.