Identify the negative factors by using Cognitive map.
Author could able to discuss the most of the positive factors which affect to adoption. In this section, author made more effort to identify negative factors and finally they summarized as mentioned follows by using cognitive map. According to Suraweera, et al. (2011) , customer reluctance model demonstrates that there are number of significant factors for Sri Lankan customers to refrain from using Internet Banking. Some of them can be attributed to customer behavior and perception, on one side, and to the banks as the service provider, on the other. Zarook (2010) and Rajapaksha (2017) tried to identify the barriers that are avoiding customers from using IB and the reasons. There were 11 potential factors identified to be a barrier for Internet Banking adoption. “Security Concern” and “No Human Touch” were identified as the major barriers preventing the respondents from using Internet Banking. Five other factors such as

“No Interest”, “No Necessity”, “Time Consuming”, “No Knowledge” and “Price” were considered as moderate barriers which did not have a strong influence. The rest of the factors, “No Benefit”, “Difficult to Use”, “Computer Fatigue” and “Language” were identified as least barriers for adoption.

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4.5 Cognitive map
Cognitive maps have been used as a tool for data analysis and to explore customer behavior
patterns associated with reluctance to use Internet Banking. The cognitive map points to four major factors, namely, customer perception, customer conduct, technological barriers, poor and poor service quality, that contribute towards customer non-acceptance of ( or reluctance to use) IB (Suraweera et al., 2011).

Figure 10 – Cognitive map of Non adoption of Internet Banking

Source – Author creation
4.6 Overall Discussion – Verification of Hypotheses testing
Hypotheses were tested in logical way mainly using Literature Review summary and secondary data analysis of descriptive and inferential researches carried out by scholars both local and international.
According to the analysis and the theories of TAM and TPR, Author can claim that hypothesis can be proved as follows.
H1: Higher perceived ease to using internet will be positively related to the adoption of Internet Banking.

According to Perera (2013), results from the regression model analysis, the effect of Internet Banking ease of use had positive effect on Internet Banking usage, showing Beta value was stood at .159 and p

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