4.1 Confirmatory Factor Analysis
Having reviewed number of various articles related to IB adoption, it was clear that number of critical aspects are affecting on IB in Sri Lanka.
In this section, the author tried to prove relationship between dependent variable and independent variables using secondary data. Mostly author reviewed models and theories on adoption of E banking by the banks customers. In this research, after reviewing many researches done by the scholars in Sri Lanka and rest of the world, author made maximum effort to re-verify the factors affecting to Internet Banking. With the previous knowledge gained as a bank employee, author constructed the Hypothesis as discussed in the methodology. Later part of the this chapter, author tried to prove the hypothesis between dependent variable and independent variables in more logical way and finally it correlate with the HNB bank.
Further author tried to identify how HNB bank implemented their digital strategy in align with the customers’ needs.
4.2 Critical analysis of Literature Review to identify factors affect to adoption
The table below shows some of the factors suggested by several authors are arranged into sub- categories so they could be distinguished clearly.
Moreover according to the summery, it was able to scrutinize the positive and negative factors affecting internet adoption. To further analyze the negative factors, author adapted the cognitive map in later part.
Table 4 – Analysis of factors affecting to adoption of E banking in general (Literature Review Summary)
Factor Author/s Evidence from Empirical Studies Effect to IB
Ease to use
Amin (2016 )and pls refer the LR. Higher degree of convenience has (+) effect on IB Positive
Wijayarathna (2015) Usage barrier has (– )effect on IB Negative
Qayyum et al.(2012) Sound web site features have (+) effect on IB Positive
Loonam et al. (2008) and Poon (2008), Poor Navigation ; poor design have (-) effect on IB. Negative
Lederer et al. (2001) Proper layout of website has (+) effect on IB Positive
Sadeghi et al (2010) and refer LR Accessibility has (+) effect on customer engagement and satisfaction will lead to (+) effect on IB. Positive
Pikkarainen et al.( 2004) and refer RL Proper navigational attributes and search facilities lead to customers mind favorably to use the system, will lead to (+) IB adoption. Positive
Gunaratnam (2017) Speed of internet has (+)IB adoption Positive
Premarathne et al. (2016) Availability of access (+) on IB adoption Positive
Perceived Usefulness Premarathne et al. (2016) Perceived ease of use has indirect (+) effect on IB. Positive
Safeena et al. (2011) Perceived usefulness has (+) effect on IB. Positive
Davis et al.( 1989) New technology has favorable effect on IB through cost savings. Positive
Jayasiri et al. (2016) Perceived ease of use affecting (+) perceived usefulness as intervening variable. Positive
Attitude Taylor ; Todd, (1995) Different dimensions of attitudinal belief toward (+/-) on IB adoption Positive or negative
Premarathne et al.(2016) Perceived uncertain online security has (-) on IB adoption. Negative
Laforet et al. (2005) Attitudes are influenced by prior expe. of computer and new technology (+) effect on IB adoption. Positive
Premarathne et al.(2016) Techno-phobia has (-) effect on IB adoption. Negative
and security Katsikas(2005) and
Refer the LR Authorization and confidentiality have (+) effect on IB adoption. Positive
Floh ; Treiblmaier, (2006) P;S motivate the customer engagem. may (+) effect the adoption. Positive
Chang, I.-C., et al. (2007) Security issues (-) effect on adoption. Negative
Kumari(2016) Perceived Risk(-)effect on adoption Negative
Hettiarachchi(2015) Fraud risk (-) effect on adoption Negative
Internet knowledge Hettiarachchi(2015) Internet skills.; prior know. (+) effect Positive
Age Premarathne et al. (2016) Age is affected as a moderating factor
on adoption. Moderating factor
Dpt.of census of SL. 25-29 age group as highest IB usage. -do
Shiraj(2015) 25-45 Age group as highest IB in SL. -do
Source – Created by the Author
4.3 Descriptive and Inferential statistical analysis to find out the relationship
Under this section, Hypothetical relationship between selected factors affecting Internet Banking and Internet Banking usage( Usefulness, ease of use, security, Internet knowledge and Age ) was analyzed , conceptualizing as independent variables while the usage of Internet Banking as the dependent variable by using secondary data.
The descriptive statistics included frequency, percentages, means and standard deviation while the inferential statistics included reliability test, computed means and multiple regressions etc.
4.3.1 Analysis of researches carried out by Local scholars
Table 5 – Analysis of factor affecting to IB
Source – Perera (2013)
Table 6 – Summary of Regression Model
Source – Perera ( 2013)
According to the Table 6 , it is revealed that adjusted R square calculated as 0.760, this figure further pointed out that 76% of the variance (adjusted R Square) in IB usage has been explained by the group of five independent variables. Research findings further characterize, considerable strong joint impact from those five independent variables on Internet Banking usage.( Perera ,2013)
In order to analysis the scenario deeply ; justify the above relationship, Descriptive analysis also can be used.
Table 7– Descriptive Analysis of independent variables
Factor Mean Standard Deviation Dispersion
Internet Banking usefulness 4.227 0.394 3.84 – 4.63
Internet Banking ease of use 4.223 0.389 3.83 – 4.61
Internet Banking security 4.060 0.369 3.69 – 4.42
Internet Banking information quality 4.111 0.384 3.72 – 4.49
Source – Perera (2013)
According to Perera (2013) descriptive research findings revealed higher mean values of
Independent variables suggested that Internet Banking users highly correlated with independent variables. Research findings further revealed that standard deviation of factors indicate slight dispersion with mean values; indicated IB users’ agreed level did not vary significantly. According to the data, author can claimed that descriptive research is further supported the Inferential test.
Table 8– Inferential and Descriptive Statistics and correlation Matrix
Source – Gunarathnam et al. (2017)
According to Gunarathnam et al. ( 2017) as illustrate in above table, it is pertaining to note that these four predictors are also positively correlated with e-banking practices which is also in the significant at 99% level. Speed of delivery has the strongest relationship (r=0.771), followed by content & website layout (r=0.760), privacy & convenience (r=0.728), and accessibility (r=0.587). According to him, speed of delivery has more positive effect on IB rather than the other variables.
Table 9 – Factors affecting to IB and significance values
Source – Shiraj ( 2015 )
The above table shows that over all model is significant with a p-value of zero to three decimal places, statistically significant at approximately 66.9% of the variability can be explained by the variables (Attitude toward Change, Perceived benefits, Perceived risks, Occupation, Users’ IT knowledge, Information on online banking) in the model. The magnitude of the relations is presented by the beta coefficients. Attitude toward change is significant with a beta value of 0.30 (p=0.000), perceived benefits with a beta value of 0.22 (p=0.000), perceived risks with a beta value of 0.17 (p=0.016), occupation with a beta value 0.26(p=0.009) and Users’ IT knowledge with a beta value of 0.21 (p=0.000). Information on online banking is not significantly related with the adoption of Internet Banking.(significance value 0.699) (Shiraj,2015 )
Table 10– Illustration of factors affecting to IB
Source – De Silva et al ( 2012)
According to De Silva et al.(2012) , the implication of research finding shows that present level of adoption among Sri Lankan customers can be considered as having strong relationship with the above independent variables which in turn showing a strong influence on customer perceived ease to use. (According to LR summary, author considered above four independent variables (as in Table 10), as sub components of ease to user factor)
Table 11 – Correlation results of factors (Relative Advantage, Perceived Ease of Use and Perceived Usefulness) and Internet Banking adoption.
Source – Perera (2018)
According to Perera (2018), relative advantage and Internet Banking adoption have positive correlation (r=0.677). Moreover perceived ease of use and Internet Banking adoption showed positive correlation (r=0.669).The perceived usefulness also has positive correlation with IB.(r=0.645). At succinctly, researcher has identifed the relative advantage, perceived ease of use, Perceived usefulness have positive relationship with Internet Banking adoption.
4.3.2 Analysis of researches carried out by International scholars
Table 12 – Factors affecting to IB and significance values
Source – Alwan et al.(2016)
PPS – Perceived privacy and security, PEOU – Perceived ease of use, CT- Customer trust
WSQ – Web service quality, CFB – Customer feedback
According to Alwan et al. (2016) illustrated that the standardized coefficient (Beta) values for all independent variables were positive and significant at the confidence level P ? 0.05. Clearly, there is a significant positive relationship between the independent variables of PPS, PEOU, SQ, and CT and the dependent variable (IB adoption). These results are consonant with the findings of previous research (Abu-Assi et al., 2014; Azad et al., 2013; Chong et al., 2010; Rawashdeh, 2015). The study found that the unique contribution of the independent variables is respectively accounted for the website quality, customer trust, perceived ease of use, and perceived privacy and security.
According Hofstede’s national frame work and GLOBE study, different culture cluster denote different attributes based on their regions represent. According Al-Smadi (2012) claimed that only one cultural dimension (uncertainty avoidance) has a positive and significant impact on perceived usefulness and perceived ease of use. Therefore author can further justify the validity of selection above five studies from Sri Lanka to increase the relevance.
Table – 13 Summary of the strengths of independent variables according to the various Authors
Source – Self creation
According to the above summary, author can validate that PEOU, Usefulness, Attitudes and Security are the main factors which affect to adoption of IB in Sri Lanka. This conclusion further confirmed by Davis (1989); Herna’ndez, Jime’nez and Marti’n (2009) highlighted that technological characteristics such as usefulness, ease of use and security as most important factors which affect Internet Banking usage. At succinctly, Author can partly conclude that behavior of customers showing more or less unique similarities in worldwide once they dealt with IB adoption.
4.4 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.
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 towar