Moderator affect of Personal innovativeness in technology

In this study, personal innovativeness in technology is proposed as a moderator which is predicted to affect on pair- relationships: PE- mobile banking usage, PU- mobile banking usage, PR- mobile banking usage and FC- mobile banking usage.

Generally, moderator effects are indicated by the interaction of X and M in explaining Y

The following multiple regression equation is estimated:

Y = a + b1X + b2M + b3XM + e

40 Where: a= intercept

b1X= linear effect of X

b2M= linear effect of M

b3XM= moderator effect of M on X

In this study, M is represented by PIT, X is PE, PU, PR and FC in turn.

In general, b1measures the simple effect of X when M equals zero. However in this study, neither X and M has zero as a meaningful value (Likert scale ranges from 1 to 7). To interpret the results and determine simple effects, M at the mean and at plus and minus one standard deviation from the mean will be used. In particular, if PIT goes from mean minus one standard deviation (4.27-1.32) to mean plus one standard deviation (4.27+1.32), b3 will indicate how much X changes as the consequence.

Table 9: Moderating effect of PIT- Summary of regression results Variables Unstandardized coefficients p-value

Hypothesis 12a: PIT moderates the effect of PE on usage

(Constant) 2.036 .001

APE -.291 .018

APIT -.383 .015

PEPIT .086 .003

Hypothesis 12b: PIT moderates the effect of PU on USAGE

(Constant) 1.887 .001

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APU -.296 .022

APIT -.273 .093

PUPIT .071 .017

Hypothesis 12c: PIT moderates the effect of PR on USAGE

(Constant) 1.840 .001

APR -.323 .007

APIT -.126 .374

PRPIT .051 .060

Hypothesis 12d: PIT moderates the effect of FC on USE

(Constant) 2.416 .000

AFC -.393 .001

APIT -.391 .030

FCPIT .092 .003

Hypothesis 12e: PIT moderates the effect of Behavioral Intention on USE

(Constant) 2.033 .000

BI -.316 .001

APIT -.296 .009

BIPIT .075 .000

The result of testing moderating effect of PIT on PE and USE shows significant level lower than 0.05 which means the interaction between PIT and PE on USE does exist. The effect of PE also became significant eventually with p-value of 0.018 which means that if PIT goes lower than its mean minus one standard deviation (4.27-1.32= 2.95), perceived ease will has negative impact on

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mobile banking usage (beta= -0.296). It also means that when people have less willingness to experience new technology, higher perceived ease of use will eventually reduce actual usage of mobile banking. Coefficient of 0.086 indicates that the interaction of PIT with PE has positive effect on mobile banking usage. The increase in interaction between PIT and PE is associated with an increase in actual usage of mobile banking. For more explanation, when PIT and PE move toward the same direction, mobile banking usage will be encouraged.

With beta of 0.071, significant of 0.017< 0.05; coefficient of 0.092 and significant level at 0.030, the result also reveals positive moderating effect of PIT pair relationships between PU- USE and FC- USE. Similar results were found with BI and USE where PIT took role as moderating factor.

Beta of 0.075 with significant level of 0.000 represents positive significant effect of PIT on the relationship of BI and USE. It can be said that PIT has partly role in eliminating the barrier between BI and USE which was discussed earlier.

Contradictory, it is found that PIT does not have significant moderation effect on relationship of PR and USE which means customer will not change their perceived risks of mobile banking regardless how innovative they are in technology. However, it is obvious that when PIT came in to the picture, it turned the impact of PE, PU, PR, FC and BI on mobile banking usage into significance (with p-values < 0.05).

On further step, this study double checked the level of PE, PU, PR and FC for population by performing t-test if mean of population is larger than 6 (in 7 point Likert) which means high level in measurement.

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Table 10: Summary of T-test results (Compare mean to 6)

Variables Mean Std. deviation Significant level

(T-test: mean>6)

PE 4.795 1.3887 0.000

PU 4.745 1.396 0.000

PR 4.759 1.481 0.000

FC 5.352 1.503 0.000

T-test result suggested that although populations understand the advantages and easiness of mobile banking over other banking channels, they also believe in the security and sustainable support from government and other parties, but they still not adopt individually. The changes that moderator factor- PIT makes reveals crucial role of individual pioneering characteristic in transferring technology from idea to actual usage. This finding was strongly supported by Rogers (Rogers E. , 1995) and other researchers (Agarwal & Prasad, 1998); (Yiu, Grant, & Edgar, 2007), saying innovative individuals are active information seekers of new idea, are able to cope with high levels of uncertainty, and develop more positive intentions toward acceptance.

In deeper analysis, we employed cross tabulation and Pearson Chi-Square test (confidence level of 95%) on personal innovativeness in technology (PIT) and other demography factors namely age, education level, job status and income. Mobile using experience and internet banking experience were also included to catch up target group of high PIT. Chi-square test compared mean of PIT among different level of demographic factors. The result showed that there is no difference in PIT level in term of age, education background, job position, mobile using and internet banking experience (Chi-square test significant level were 0.795, 0.927, 0.192, 0.090,

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0.150 respectively). However, expected mean of population in PIT differs by different income level (sig. = 0.005< 0.05). Most of high PIT people have monthly income ranging from 200 to under 1000 USD, which is also consistent with mobile banking users (refer to mobile banking users‟ profile in the beginning of this chapter). It became clear that potential group of mobile banking adopters should be targeted at young, middle income (200-1000 USD/month).

Table 11: Pearson Chi-Square test results

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