Journal of Internet Banking and Commerce ISSN: 1204-5357
ISSN: 1204-5357
+44-175-271-2024BEZA MUCHE TEKA
Ph.D. scholar at Punjabi University, School of Management Studies, Patiala, India
DHIRAJ SHARMA
Assistant professor at Punjabi university school of management studies, Patiala, India
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Purpose: Regardless of the fact that the influence of demographic factors on users' adoption or usage behavior of e-banking channels for banking transactions have been intensively examined by studies carried out mostly in the developed countries, this area is not well studied in the developing countries especially in Ethiopia. Therefore, the main objective of this study is to investigate the influence of demographic factors on users' adoption of e-banking systems in Ethiopia from the current users' perspective. Research Methodology: Descriptive type of research was applied for this study. A total of 600 users' of e-banking services were used as a sample survey from those commercial banks that are using e-banking systems as a means of banking service provision in Addis Ababa, Ethiopia. A well-structured and randomly administered questionnaire was used to collect the relevant information from those customers who are using at least one form of e-banking systems. Interview was also used to collect supporting data from e-banking department managers of each respective bank. Data gathered from customers were analyzed using independent sample T-test and one way analysis of variance (ANOVA). The entire statistical tests were conducted using SPSS version 21. Findings: The findings of this study imply that except for gender, the remaining demographic variables such as age, income, educational level and occupational status have no significant influence on users' e-banking usage behavior which implies that those users' who are in different age, income, educational status and occupation category have similar e-banking adoption or usage behavior. Implication: The findings from this study (particularly from gender perspective) suggest that commercial banks in Ethiopia should create more awareness to their e-banking users' (especially to females) in order to develop better e-banking usage practice.
Adoption; E-Banking Users'; Independent Sample T-Test; ANOVA
The increasingly competitive environment in the financial services market together with globalization, financial liberalization and technology revolution have opened the door of new and more efficient delivery and processing channels as well as more innovative products and services in the banking industry [1]. The most recent delivery channel introduced is electronic banking. Know a days, the adoption of e-banking in the banking industry began to occur quite extensively as a channel of distribution for financial services [2,3]. E-banking is an umbrella term for the process by which customers can conduct various banking transactions 24 hours a day and 7 days a week electronically without the need to visit physical branch. It is changing the way banking customers conduct their banking transaction.
E-banking is defined as the automated delivery of new and traditional banking products and services directly to customers through electronic, interactive communication channels [4]. It should be noted that, even though the definition of electronic banking varies among researchers because of its ability to provide several types of services through which bank customers can request information and carry out most retail banking services through different electronic devices, in the context of this paper, e-banking is defined as a variety of self-service platforms such as internet (online) banking, telephone banking, mobile phone banking, and PC banking where by customers access these services using electronic devices like personal computer, Automated teller machine (ATM), Point of sale terminals and mobile phones without their physical presence in the bank [5].
The banking industry of most developed and developing countries have started to offer electronic banking services to improve the effectiveness of distribution channels through reducing the transaction cost and increasing the speed of services. Know a days, electronic banking has become the way for the advance of banking system, and its role is increasing in many parts of the world. Its appearance has provoked many banks to develop marketing and information technology strategies in order to stay competitive. It offers opportunities to create services processes that demand few bank personnel and other internal resources, and thereby reduces the cost of providing banking services to their clients [6]. It also provides wider availability and possibility to cover more geographical area and hence to reach more customers. From the customers' angle, among other benefits, electronic banking allows customers easier access to financial services, convenience and time saving in managing their finance [4,7,8]. Aladwani [9] also stated that electronic banking provide faster, easier and more reliable services to customers. However, customers’ can get the above services from banks if and only if they adopt or use the system. According to Rogers et al. [10] and in view of this study, adoption is defined as the acceptance and continued use of a product, service or idea (like e-banking for the current study). Regardless of the fact that the literature on e-banking is ample with studies carried out mostly in the developed countries, this area is not well studied in the developing countries especially those of the Sub Saharan Africa region, where commercial banks are demanding to introduce e-banking systems to improve their operations, reduce costs, increase productivity and also to fulfill the rising demand of modern banking services from their customers side. This therefore means that e-banking is becoming a strategic weapon used in the distribution channel for their services in the face of intense competition from both home and abroad. However, the efforts aimed at developing better and easier electronic banking systems seem to have remained largely overlooked by the customers of most sub Saharan Africa region [11] where by Ethiopia is not exceptional. The adoption of electronic banking services from Ethiopian bank customers’ perspective is still low; most bank customers continue to conduct most of their banking transactions using traditional methods [12].
Studies related to the factors that affect customers’ e-banking usage behavior have been at the forefront of several research works in the developed and developing countries. For example, previous studies conducted by Donnelie et al. [13]; Izogo et al. [14] and Margaret et al. [15] investigated that demographic factors such as gender, age, income and occupation are among the important factors that influence users’ e-banking adoption behavior. Nevertheless, this area is not well studied from the viewpoint of Ethiopian banking customers. Therefore, there is a need to understand the relevance of e-banking in developing countries such as Ethiopia and more empirical studies should be conducted to address the impact of demographic factors on users’ e-banking adoption or usage behavior. Thus, the main objective of this paper is to examine the impact of demographic factors on users’ e-banking adoption behavior in Ethiopia.
The influence of demographic factors on users’ adoption of E-banking systems-empirical evidence
Introduction
It has been widely recognized that demographic factors have a great impact on consumer attitudes and behavior towards new technology acceptance such as e-banking. Age, gender, educational level, income and occupation are among the most influential demographic variables affecting e-banking usage. The empirical studies related to these important demographic factors from the perspective of e-banking usage are discussed in the following sections.
Gender
In the context of this paper, gender refers to the difference in the adoption and usage of new technology such as e-banking between male and female [11]. The impact of gender on customers’ e-banking usage behavior has been validated by a number of scholars as explained below.
A study conducted by Alagheband [16] to identify factors affecting the adoption of e-banking services indicated that men represent the segment with the highest use of e-banking. Similarly, Alafeef et al. [17] on their study regarding the influence of demographic factors on e-banking adoption discovered that gender has strong effects on the adoption level of e-banking applications in which males have greater e-banking usage experience as compared to females. Azouzi [18] also discovered that gender is a crucial variable impacting the customers, attitude towards the adoption of e-banking. Similarly, Muzividzi et al. [19] on their study shown that e-banking is popular with men than women. This may be because men have the courage to take up new technology even with little information about it. Men usually are keen to experiment than women. However, Ismail et al. [20] and Izogo [14] found that there is no significant association between e-banking usages with gender. Besides, Sheshadri et al. [21] found that gender does not have an effect on the customer adoption of electronic banking. Both genders have equivalent level of adoption of these services as now a day’s both genders are employed and so they have their individual bank accounts and have their own practice of these technological services. Both genders have a diverse knowledge on these services presented by their banks. Therefore, they conclude that gender does not play a role in link with the technology adoption as both males and females are qualified in today’s situation.
Age
The impact of age on consumers’ e-banking usage practice is investigated by various previous scholars and thus the empirical findings of these researchers are discussed here under.
A study conducted by Abenet [22] concerning the determinants of e-banking adoption in Ethiopia revealed that the young age group is more computer literate and finds it easy to accept and use new technologies. Poon [23] and Azouzi [18] on their study also supports that young and computer literate respondents are using or are willing to use electronic banking. The hypothesis tested to diagnose the relationship between age and e-banking preference by Yitbarek et al. [24] shows a gradual but steady decline in the percentage preference of e-banking as the age group increases. This means that the percentage preference for e-banking for the 18 to 25 years age group is greater than the percentage preference for e-banking for the above 60 years age group. This makes it quite clear that the younger the age group, the greater their preference for electronic banking. In line with these findings, a study conducted by Izogo [14] and Alafeef et al. [17] concerning the impact of demographic factors on e-banking adoption among bank customers found that age has significant effect on customers’ adoption and usage of e-banking. It implies that young and more educated peoples are better in their adoption of e-banking as compared to their counter parts. In addition, a thorough study conducted by Margaret et al. [15] shows that the young generation is more familiar with computer and internet, so they are more interested in using the e-banking system particularly ATM and online transaction rather than old and traditional banking services. However, in contrary to the above findings Annin et al. [25] investigated that age has no significance impact on e-banking adoption. A study conducted by Sheshadri et al. [21] in order to identify the influence of demographic variables on customer adoption of e-banking services using ANOVA also revealed that there is no significant difference in the customer adoption of electronic banking among the respondents based on age. This study infers that age does not significantly influences the customer adoption of electronic banking. The perception towards customer adoption does not vary with age. Customers belonging to different age group have the same perception in adopting these electronic banking services. Further, Alhinai et al. [26] also found that age has no significant impact on consumers’ willingness to use e-banking services.
Educational Level
The impact of education on bank customers’ e-banking usage practice is discussed below by reviewing various previous studies. For example; a study conducted by Abenet [22] in Ethiopia found that e-banking usage practice is greater among those peoples who are in a better educational level as compared to others, so educational level has positive impact on e-banking adoption. This finding is in line with Edwin et al. [26] who found that consumers' level of education and ICT knowledge impacts their acceptance of e-banking services. A number of the respondents were ICT literate and used it in their everyday transactions, which shows a fair amount of ICT knowledge. Further a study conducted by Izogo [14], Alafeef et al. [17] and Margaret et al. [15] concerning the impact of demographic factors on e-banking adoption among bank customers using Chi-Square Test found that educational status has significant effect on customers’ adoption and usage of e-banking. They discovered that the education level is the strongest positive factor that influences the adoption level of e-banking whereby the younger generations are highly educated. In line with this Tater et al. [27] on their study identified that customers with post-graduate and graduate qualifications are mostly adaptors of IT banking services such as e-banking. This implies that higher qualification is associated with bringing attention towards new technology banking services and qualification is a factor found to be relevant.
However, in contrary to the above finding, Lee et al. [28] found that education has no significant impact on customers’ internet banking adoption behavior. Alhinai et al. [25] and Alagheband [16] also found that educational level has no significant impact on consumers’ willingness to use e-banking services
Income
It refers to the extent to which the level of income users’ have will influence their e-banking usage practice [11]. With regard to the impact of income on consumers’ e-banking adoption or usage practice, Ismail et al. [20] on their study investigated that e-banking usage is associated with clients’ income, account type, and computer and internet literacy. High income clients and those who have current account and computer and internet literate are more likely to use e-banking services [23]. Similarly, Annin et al. [25] clearly indicate that monthly income level is among the socio-economic factors that significantly influence bank customers’ decision to use e-banking. However, contradictory results were found by Munusamy, De Run, Chelliah and Annamalah [29]; Alagheband [16] and Annin et al. [25] who stated that income have no significant impact on e-banking adoption. Further, Izogo [14] found that income do not have significant effect on customers’ adoption and usage of e-banking. This implies that there is no significance difference in their e-banking adoption behavior between consumers who are in different income groups.
Occupation
A person’s occupation also influences his or her consumption pattern [22]. Previous empirical studies related to the impact of occupation on e-banking adoption or usage is discussed below. For example; Annin et al. [25] on their study found a positive and significant relationship between occupation and e-banking adoption. With regard to occupation type, Alagheband [16] on his study found that higher users’ of e-banking has been evident for government employees rather than other types of employments. Further, Mohammed [29] also investigated that graduated and employed male customers who belong from higher income category and having a bank account preferably in government banks are greatly emphasized to the use of IT based banking services. However, in contrary to these findings Ismail et al. [20] and Munusamy et al. found that occupation has no significant impact on e-banking adoption. Sheshadri and Rani [21] also infer that there is no significant difference in the customer adoption of electronic banking based on occupation. This implies that occupation has no role to play in the customer adoption of electronic banking or e-banking users’ who are in different occupation have similar e-banking adoption or usage practice.
To sum up, with regard to the impact of demographic factors discussed above on users’ e-banking adoption behavior, most of the previous work results imply that age, educational status and occupation have significant impact on consumers’ e-banking usage behavior. However, the findings related to the impact of gender and income is contradictory and difficult to conclude.
Research Hypotheses
From the above review of literature, the following hypotheses were formulated with regard to the impact of demographic factors on users’ e-banking adoption or usage behavior:
H1: There is significant e-banking usage behavior difference between males and females.
H2: There is significant e-banking usage behavior difference between customers’ who are in different age categories.
H3: There is significant e-banking usage behavior difference between customers’ who are in different income categories.
H4: There is significant e-banking usage behavior difference between customers’ who are in different educational level.
H5: There is significant e-banking usage behavior difference between customers’ who are in different occupational status.
The sampling population was defined as customers’ of Ethiopian commercial banks who are using at least one form of e-banking channels. Both private and public commercial bank customers located in Addis Ababa, Ethiopia were included as a target population. The research was carried out in Addis Ababa as a representative geographical area of the population of Ethiopia. Purposive sampling technique was used to select the target population for this study (i.e., only the customers of those banks that are providing e-banking services by using at least two forms of e-banking channels as well as those banks who started offering the service before a year at the time of data collection were included in the sample). When this research was conducted, there are 16 commercial banks in Ethiopia. However, based on the above criteria samples were taken from seven banks such as commercial bank of Ethiopia, Dashen Bank S.C, Wogagen bank S.C, United Bank S.C, Abyssinia Bank S.C, Abay Bank S.C and Zemen Bank S.C. Of which one is government bank (commercial bank of Ethiopia) and the remaining six are private banks. Then, random sampling was used to select sample respondents. With regard to the instrument used to collect primary data for this study, a self-administered questionnaire was developed and distributed to the target respondents. The items used to measure e-banking adoption or usage behavior were adapted from past study [30] having significant modification. To ensure the content and face validity of the instrument, five volunteer bank personnel’s and five staff members (lecturers) who have good understanding in the area of interest examined issues related to the relevance and wordings of the questionnaire items, length of survey and time taken to complete the questions. Feedbacks received from these experts were taken into account to improve the final questionnaire. The internal consistency reliability test of the items for the dependent variable (usage behavior) was checked by using Cronbach’s Alpha and the result is 0.889 which is above the recommended cut off value of 0.7 and above by Hair et al. [31]. The required sample size was estimated based on the number of variables included in the study. In this regard Hair et al. [31] recommended that the sample size should be 15-20 observations per variable for generalization purposes. Krejcie et al. [32] also suggested that for a population having more than 1,000,000 target groups a sample size of 384 is acceptable. Hence, based on these justifications, and by giving allowance for errors and non-response rates, a total of 600 respondents were considered as acceptable sample size for the current study. However, among these much of questionnaires distributed only 495 were returned which gives a response rate of 82.5% but after removing those incomplete questionnaires, the actual sample size used for analysis in this study was 420 respondents (70%). In addition, secondary data obtained from related published journals, online articles, books and international conference papers were also used.
Method of Data Analysis
Once the data is collected, coded, entered and cleaned; it goes through descriptive data analysis techniques. Descriptive techniques involved the use of descriptive statistics such as Independent Sample T-test and Analysis of Variance (ANOVA). Independent sample t-test was used to compare e- banking adoption behavior between male and female bank customers. In addition, one way analysis of variance (ANOVA) was used to compare the difference in e-banking adoption behavior between respondents who are in different age, educational level, income and occupational status categories. However, the results from the application of independent sample t-test and one way analysis of variance (ANOVA) tells us only whether the e-banking usage behavior between users’ who are different in gender, occupational status, income, educational level and age groups are significantly different or not. But, the probability value does not tell the degree to which the two variables are associated with one another. Therefore, this gap was filled by calculating the effect size [33]. Effect size statistics provide an indication of the magnitude of the differences between groups or the amount of the total variance in the dependent variable that is predictable or explained from the independent variable [34]. There are a number of different effect size statistics, the most commonly used being eta squared and Cohen’s d. Eta squared can range from 0 to 1 and represents the proportion of variance in the dependent variable that is explained by the independent (group) variable [33]. In this study eta squared is used to calculate effect size. The formula used to calculate eta squared for both independent sample t-test and one way analysis of variance is presented below.
Eta squared for independent sample t-test=t2/t2 + (N1 + N2 – 2), where, t represents t-value from independent sample t-test, N1 and N2 represents sample size for the two groups (number of males and females in this study).
Eta squared for one way ANOVA=Sum of Squares between Groups/Total Sum of Squares
Based on the above formula, the guidelines proposed by Cohen [33] for interpreting this value are: 0.01=small effect, 0.06=moderate effect and 0.14=large effect.
In addition, in order to test whether the variance for different groups is the same or not, Levene’s test for equality of variance was used and the implication is that if the significance level of Levene’s test p-value is 0.05 or less, it indicates that the variances for the different groups are not the same. Therefore, the data violate the assumption of equal variance, if on the other hand the significance value for Levene’s test is larger than 0.05; the data satisfies the assumption of equality of variance [33]. The entire test is performed using SPSS version 21.
As indicated in the Table 1 the significance value for levene’s test is 0.322 which is above 0.05 and it proves that the variance for the two groups (males and females) is the same and thereby it shows that the assumption of equality of variance is satisfied.
Table 1: Independent Sample T-test (Gender Vs. E-Banking Adoption or Usage behavior). Source: SPSS output, 2016.
| Group Statistics | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gender | N | Mean | Std. Deviation | Std. Error Mean | |||||||||
| Mean AU | MALE | 243 | 3.1800 | 0.96490 | 0.06190 | ||||||||
| FEMALE | 177 | 2.8305 | 1.02293 | 0.07689 | |||||||||
| Independent Samples Test | |||||||||||||
| Levene's Test for Equality of Variances | t-test for Equality of Means | ||||||||||||
| F | Sig. | T | Df | Sig. (2-tailed) |
Mean Difference | Std. Error | 95% Confidence Interval of the Difference | ||||||
| Lower | Upper | ||||||||||||
| Mean: Usage Behavior |
Equal variances assumed | 0.984 | 0.322 | 3.57 | 418 | 0.000 | 0.34953 | 0.0978 | 0.15728 | 0.54178 | |||
| Equal variances not assumed | 3.54 | 366.2 | 0.000 | 0.34953 | 0.0987 | 0.15543 | 0.54364 | ||||||
The independent sample T-test output (P-value less than 0.05) shows that there is statistically significant difference between males and females in their e-banking adoption or usage behavior which means that hypothesis H1 is accepted. The mean value for males is 3.18 whereas the mean value for females is 2.83 which indicate the existence of practical significance difference between males and females with regard to their e-banking usage or adoption behavior [33,35]. That means males have better e-banking usage practice as compared to females. However, the effect size value of 0.0296 indicates that only 2.96% of e-banking adoption is explained by gender which is too small as per the general guideline given by Cohen.
In line with the above finding, a study conducted by Alagheband [16] to identify factors affecting the adoption of e-banking services indicated that men represent the segment with the highest use of e-banking. Similarly, a study conducted by Milion [36] in Gondar-Ethiopia with regard to the usage of e-banking by customers found that the majority of current e-banking users’ were males as compared to females. Azouzi [18] also discovered that gender is a crucial variable impacting the customers, attitude towards the adoption of e-banking. Moreover, Muzividzi et al. [19] on their study shown that e-banking is popular with men than women (Table 2). This may be because men have the courage to take up new technology even with little information about it.
Table 2: One Way Analysis of Variance (Age vs. E-Banking Usage Behavior) ANOVA. Source: SPSS output, 2016.
| Mean: E-Banking Usage Behavior | |||||
|---|---|---|---|---|---|
| Sum of Squares | df | Mean Square | F | Sig. | |
| Between Groups | 4.203 | 3 | 1.401 | 1.395 | .244 |
| Within Groups | 417.784 | 416 | 1.004 | ||
| Total | 421.987 | 419 | |||
| Test of Homogeneity of Variances | |||||
| Mean: E-Banking Usage Behavior | |||||
| Levene Statistic | df1 | df2 | Sig. | ||
| 0.625 | 3 | 416 | .599 | ||
The test of homogeneity of variance in the above Table 2 indicated that the significance value for levene’s test is 0.599 which is above 0.05 and it proves that the variance across the different age groups is the same and thereby it shows that the assumption of equality of variance is satisfied. The one way analysis of variance (ANOVA) output across the four age groups (Group 1: 18-25 years; Group 2: 26-32 years; Group 3: 33-40 years and Group 4: above 40 years) shows that there is no statistically significant difference at 0.05 level of significance in e-banking adoption or usage behavior scores for the four age categories or groups with F (3, 416)=1.395, p=0.244 which means that hypothesis H2 is not accepted. This implies that age has no significant effect on consumers’ e-banking adoption or usage behavior. This may be due to the fact that since almost all of the e-banking users’ are young with the age range of 18-40, it is difficult to get significant e-banking usage difference between users’ who are in the same age status (young) or in other words it is difficult to compare young with young and thereby to get significant e-banking usage behavior. In addition, the effect size value of 0.009 also indicates that only 0.9% of e-banking adoption is explained by age which is too small as per the general guideline given by Cohen. Therefore, it implies that e-banking usage behavior is not predicted or explained by age which is in line with the ANOVA test result.
In consistent with the current study finding, Annin et al. [25] investigated that age has no significance impact on e-banking adoption. A study conducted by Sheshadri et al. [21] in order to identify the influence of demographic variables on customer adoption of e-banking services using ANOVA also revealed that there is no significant difference in the customers’ adoption of electronic banking among the respondents based on age. It implies that the perception towards customer adoption does not vary with Age. Customers belonging to different age group have the same e-banking usage practice. Further, Annin et al. [25] and Sheshadri et al. [21] also found that age has no significant impact on consumers’ willingness to use e-banking services (Table 3).
Table 3: One Way Analysis of Variance (Educational level Vs. E-Banking Usage Behavior) ANOVA. Source: SPSS output, 2016.
| ANOVA | |||||
|---|---|---|---|---|---|
| Mean: E-Banking Usage Behavior | |||||
| Sum of Squares | Df | Mean Square | F | Sig. | |
| Between Groups | 2.784 | 4 | 0.696 | 0.689 | 0.600 |
| Within Groups | 419.203 | 415 | 1.010 | ||
| Total | 421.987 | 419 | |||
| Test of Homogeneity of Variances | |||||
| Mean: E-Banking Usage Behavior | |||||
| Levene Statistic | df1 | df2 | Sig. | ||
| 0.494 | 4 | 415 | 0.740 | ||
The test of equality of variance in the above Table 3 indicated that the significance value for levene’s test is 0.740 which is above 0.05 and it proves that the variance across the five groups of educational level (Group 1: Elementary School Complete, Group 2: High School Complete, Group 3: College Diploma, Group 4: First Degree, and Group 5: Second Degree and above) is the same and thereby it shows that the assumption of equality of variance is satisfied. The one way analysis of variance (ANOVA) output across the groups indicated that there is no statistically significant difference at 0.05 level of significance in e-banking adoption or usage behavior scores for the five educational level categories or groups with F (4, 415)=0.689, p=0.600 which means that hypothesis H3 is not accepted. This implies that educational level has no significant effect on consumers’ e-banking adoption or usage behavior. In addition, the effect size value of 0.006 indicates that only 0.6% of e-banking adoption is explained by educational level which is too small as per the general guideline given by Cohen. Therefore, it implies that e-banking usage behavior is not predicted or explained by educational level which is in line with the ANOVA test result. In consistent with the above finding, Ismail et al. [20] and Lee et al. [28] on their study found that education has no significant impact on customers’ internet banking adoption behavior. Annin et al. [25] and Alagheband [16] also found that educational level has no significant impact on consumers’ willingness to use e-banking services.
The test of equality of variance in the above table indicated that the significance value for levene’s test is 0.959 which is above 0.05 and it proves that the variance across the four groups of occupational status (Group 1: Government employee, Group 2: Private employee, Group 3: Self-employed and Group 4: Pensioner) is the same and thereby it shows that the assumption of equality of variance is satisfied. The one way analysis of variance (ANOVA) output across the groups indicated that there is no statistically significant difference at 0.05 level of significance in e-banking adoption or usage behavior scores for the four occupational status categories with F (2, 417)=1.459, p=0.234 which means that hypothesis H4 is not accepted. This implies that occupational status has no significant effect on consumers’ e-banking adoption or usage behavior. In addition, the effect size value of 0.007 indicates that only 0.7% of e-banking adoption is explained by occupational status which is too small as per the general guideline given by Cohen (Table 4). Therefore, it implies that e-banking usage behavior is not predicted or explained by occupational status which is in line with the ANOVA test result.
Table 4: One Way Analysis of Variance (Occupational Status vs. E-banking Usage Behavior). Source: SPSS output, 2016.
| Test of Homogeneity of Variances | |||||
|---|---|---|---|---|---|
| Mean: E-Banking Usage Behavior | |||||
| Levene Statistic | df1 | df2 | Sig. | ||
| .042 | 2 | 417 | .959 | ||
| ANOVA | |||||
| Mean: E-Banking Usage Behavior | |||||
| Between Groups | 2.932 | 2 | 1.466 | 1.459 | .234 |
| Within Groups | 419.055 | 417 | 1.005 | ||
| Total | 421.987 | 419 | |||
Similar to the above finding, Ismail et al. [20] and Munusamy found that occupation has no significant impact on e-banking adoption. Sheshadri et al. [21] also infer that there is no significant difference in the customer adoption of electronic banking based on occupation (Table 5). This implies that occupation has no role to play in the customer adoption of electronic banking.
Table 5: One Way Analysis of Variance (Income vs. E-Banking Usage Behavior). Source: SPSS output, 2016.
| Test of Homogeneity of Variances | ||||||||
|---|---|---|---|---|---|---|---|---|
| Mean: E-Banking Usage Behavior | ||||||||
| Levene Statistic | df1 | df2 | Sig. | |||||
| .764 | 4 | 415 | 0.549 | |||||
| ANOVA | ||||||||
| Mean: E-Banking Usage Behavior | ||||||||
| Sum of Squares | Df | Mean Square | F | Sig. | ||||
| Between Groups | 5.543 | 4 | 1.386 | 1.381 | 0.240 | |||
| Within Groups | 416.444 | 415 | 1.003 | |||||
| Total | 421.987 | 419 | ||||||
The test of equality of variance in the above Table 5 indicated that the significance value for levene’s test is 0.549 which is above 0.05 and it proves that the variance across the five groups of income level (Group 1: less than 2000, Group 2: 2000-3999, Group 3:4000-5999, Group 4: 6000-9999 and Group 5: greater than 10,000) is the same and thereby it shows that the assumption of equality of variance is satisfied. The one way analysis of variance (ANOVA) output across the groups indicated that there is no statistically significant difference at 0.05 level of significance in e-banking adoption or usage behavior scores for the five income level categories with F (4, 415)=1.381, p=0.240 which means that hypothesis H5 is not accepted. This implies that income has no significant effect on consumers’ e-banking adoption or usage behavior. In addition, the effect size value of 0.013 indicates that only 1.3% of e-banking adoption is explained by income which is too small as per the general guideline given by Cohen (1988). Therefore, it implies that e-banking usage behavior is not predicted or explained by income which is in line with the ANOVA test result. In line with the above, Munusamy et al., Alagheband [16] and Annin et al. [25] on their study investigated that income have no significant impact on e-banking adoption (Table 6). Further, Izogo et al. [14] found that income do not have significant effect on customers’ adoption and usage of e-banking. This implies that there is no significance difference in their e-banking adoption behavior between consumers who are in different income groups.
Table 6: Summary of Hypothesis Testing Results.
| Research Hypothesis | Hypotheses | Result/Decision |
|---|---|---|
| There is significant e-banking usage behavior difference between males and females. | H1 | Accepted |
| There is significant e-banking usage behavior difference between customers’ who are in different age categories. | H2 | Not Accepted |
| There is significant e-banking usage behavior difference between customers’ who are in different income categories. | H3 | Not Accepted |
| There is significant e-banking usage behavior difference between customers’ who are in different educational level. | H4 | Not Accepted |
| There is significant e-banking usage behavior difference between customers’ who are in different occupational status. | H5 | Not Accepted |
As proved above in the results and discussion part, except for gender, all the remaining demographic variables such as age, income, educational level and occupational status have no significant influence on users’ e-banking usage behavior which implies that those users’ who are in different age, income, educational status and occupation category have similar e-banking adoption or usage behavior. This may be due to the fact that electronic banking is in an infant or introduction stage in the Ethiopian banking industry.
Even though this study has achieved its objective, it has its own limitations and potential future research directions. First: this study excludes the views of non-adopters or non-users’ of e-banking systems and corporate customers. Therefore, future researchers in the area of e-banking in developing countries especially in Ethiopia should consider the views of non-users’ as well as corporate customers because inclusion of these groups of bank customers will broaden the study and helps to capture their views and also it enables to do comparison between adopters and non-adopters intention towards e-banking adoption or usage behavior. Second: this study was based on cross-sectional data; therefore, one possible direction for future studies is to conduct a longitudinal study to see whether or not the variables and their relationships are consistent with time.
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