Journal of International Trade, Logistics and Law, Vol. 6, Num. 2, 2020, 1-12 1 DIGITAL FINANCIAL PRODUCTS AND SERVICES IN ETHIOPIAN BANKING INDUSTRY: AN ENQUIRY ON ITS HINDRANCES OF ADOPTION AND USAGE Debebe Alemu KEBEDE Jimma University, Ethiopia Received: 25 May, 2020 Accepted: July 27, 2020 Published: 10 Dec, 2020 Abstract: This study is undertaken to identify the hindrances of Digital financial products and services adoption and usage in Ethiopia n banking industry with specific refence to some selected commercial banks in Ethiopia, Jimma town. To achieve the aim of the study the primary data was collected from managers of some selected commercial banks through un-structured interview as well as from customers by using convenience method through standard questionnaires. While, secondary data was collected from documents of banks and Journals to triangulate with response obtained from primary data sources. The collected data was analyzed in descriptive and inferential analysis. The findings depicted as the Economic factors like unemployment, inflation, source of customers income and saving habits affecting the adoption of Digital financial products and services by banks. Further, the obsoletion of technological environment, Inflexibility of government policies, Educational background of the customers and diverse cultures of the societies are the other factors that affecting adoption of Financial products. In addition, the digital financial products and services are not properly used by the customers as the result of their low level of awareness, the perceived relative advantage, perceived compatibility and perceived complexity of the products and services. Based on the result it is recommended as the banks should properly adopt the digital financial products and services with taking into account of the external factors. Further, the banks should participate on aggressive promotion to aware the customers about their products and services, the government should formulate policies and regulations that minimizing bureaucracy of adopting technology by banks. Keywords: Digital, Financial products and Services, Adoption, Use, Ethiopia 1. Introduction Banking system is one of the most important economic sectors and strongest financial intermediaries in the economy that plays a key role in economic development in societies through receiving the deposits of depositors and instead pay loans and facilities to applicants and give interest (IRAVANI, GHAZALI and GHAZALI, 2012). So, it is difficult to conceptualize how an economy would operate and survive without the crucial services offered by banks . Business organizations and especially the banking sector are operating in an environment characterized by a complex and competitive climate (Agbolade, 2011). In today’s competitive world, banks are starving to endure survival, in spite of their vital role playing in the economy. Therefore, the banks need to consider several criteria such as bank’s image and performance, speed of transaction, channel of delivery system, banking convenience and product diversity to attract customers to continuously do banking business with them and also changing banking product and service they provided. In a study in Germany, Singh (2006) and Im, Bayus and Mason (2003) posit that innovation has become one of the most attention-grabbing subjects, drawing the interest of business and economic researchers due to its ability to give firms a competitive advantage. Innovation is important for the survival of every business sector, and financial services are no different. Indeed, research confirms that innovation affects a firm’s performance positively (Damanpour, Walker and Avellaneda, 2009). The extent to which the financial sector can make contributions to the economy depends, to a large extent, on the quality and quantity of the products and services it offers customers. Business entities wishing to restore customer confidence need to focus on innovative products that meet their customers’ needs (Reinartz et al., 2011). In addition, to achieve their mission and objectives, they will have to understand their customers’ perceptions (Dusuki and Abdullah, 2007). Such scenario had also led to the changes in the customer’s taste and demand for Debebe Alemu KEBEDE 2 better- and high-quality banking services. So, banks are indebted to apply the financial innovation to respond to such customers’ needs because Innovation is very important to allocate scarce resources for sustainable growth of any industry. To collect significant deposits banks, have to attract customers. But to attract customers it is crucial to know what selection criteria customers are adopting in selecting the banks product and services. Young African are more likely to have a mobile phone, try out new things and be aware of digital channels. However, gaps persist in youth accessing financial services. Digital Financial Services offers great potential for youth, as they are generally more inclined towards digital trends and are starting to use mobile money services for basic transactions (International Finance Corporation,2017). Moreover, banks in Ethiopia launched new products and services for the customers which is new that requires a lot of effort and resources to be easily adopted by customers. As the document of some commercial banks in Ethiopia indicated there are diverse digital financial products and services outdated without even compensated the costs of its adoption. Hence, in order to help banks, improve financial product and services adoption by their customers, it is necessary to identify factors that influence customers’ perceptions toward product and service they launched. Because understanding and adapting to customer motivation and behavior is not an option but an absolute necessity for competitive survival (Owusu‐Frimpong, 1999). Accordingly, this study aims to identify the hindrances of Digital financial products and services adoption and usage in Ethiopian banking industry with specific refence to some selected commercial banks in Ethiopia, Jimma town. The specific objectives of the study: To identify the current status of digital financial products and services in Ethiopian banking industry To identify the factors that affecting the commercial banks in Ethiopia to adopt digital financial products and services To identify the factors that affecting the customers of commercial banks in Ethiopia to use digital financial products and services 2. Research Hypotheses Hypotheses related to Adoption digital financial products and services: H1: The existing economic situation of the country has significant influence on adoption of digital financial products and services in Ethiopian banking industry H2: Existing Technological change has significant influence on adoption of digital financial products and services in Ethiopian banking industry H3: The country government policies have significant influence on adoption of digital financial products and services in Ethiopian banking industry H4: The societal cultures have significant influence on adoption of digital financial products and services in Ethiopian banking industry Hypotheses related to Usage of digital financial products and services: H5: The position of the banks on customers awareness have significant influence on using of digital financial products and services in Ethiopian banking industry H6: The customers’ perceived relative advantage has significant influence on using of digital financial products and services in Ethiopian banking industry H7: The customers’ perceived compatibility has significant influence on using of digital financial products and services in Ethiopian banking industry H8: The customers’ perceived complexity has significant influence on using of digital financial products and services in Ethiopian banking industry H9: The customers perceived trust towards banks has significant influence on using of digital financial products and services in Ethiopian banking industry Digital Financial Products and Services in Ethiopian Banking Industry: An Enquiry on Its Hindrances of Adoption and Usage 3 3. Research Methods 3.1. Research Design The purpose of this study is to identify the factors that hinders the use and adoption of digital Financial products and services in Ethiopian banking industry through illustrating it from some selected commercial banks in Jimma town, and mixed research approach is well suited. Case studies are in which the researcher explores in depth a program, and event, and activity, a process, or one or more individuals(Creswell, 2013). Hence, to achieve the objective of the study, the case study method was applied with explanatory research design as the result of explaining the state of affairs. 3.2. Target Population, Sample Size and Sampling Techniques The target population consists of the managers and customers of Awash Bank, Dashen Bank, Oromia International Banks, Commercial Banks of Ethiopia and United Banks. All of managers of the selected banks branches that consists 42 are considered as a sample. There are large number of customers consisted by those banks hence, according to Hair et al. (1998) a sample size between 200 and 400 is usually acceptable as critical sample size. 270 customers from each selected commercial bank are considered as sample size by using convenience sampling techniques. 3.2.1. Data Collection Instruments The study primary data was collected through un- structured interview and close-ended structured questionnaire items with managers and customers of the selected commercial banks. The questionnaires are developed through the adaptation and modification of instruments from previous studies in the area of adoption studies and usage of innovations with customers of the banks. For measuring this information, the Likert scale method was used to range of responses: Strongly Disagree, Disagree, Neutral, Agree, and Strongly Agree, with a numeric value of 1-5, respectively. As Neuman and Kreuger (2003) explained, Likert-scale is used to ask many people the same questions and examining their answers research questions. So that the researcher would use cross sectional survey in which independent and dependent variables are measured at the same point in time using a single questionnaire (Anol, 2012). The secondary data was collected by extracting relevant and supportive data and information from the secondary data sources. 3.2.2. Data Presentation and Analysis The collected data was presented in tabulation and both descriptive and inferential analysis was applied. The descriptive analysis was used to achieve the first specific objectives, primary data that were collected through interviews, demographic characterized of respondents and mean of variables. The inferential analysis was used to determine the relationship of variables by using Correlation analysis and used to determine the potential connection between independent variables of this study and the dependent variable of study. Regression analysis was used to identify impacts of variables. Accordingly, these multiple tests would be able to identify potential factors that have a significant impact on adoption and use of digital financial product and services. 3.2.3. Factor Analysis Factor analysis is a statistical tool/technique which is used to verify the factor structure of a set of observed variables/constructs. It is also used to tests whether a specified set of constructs is influencing responses in a predicted way (Brown, 2015). Factor analysis will allow us to test that there exists a good relationship between observed variables and their underlying latent constructs. So, to evaluate the construct validity of the factors, factor analysis has been performed. The first pre-test has been done by filling & checking the questionnaire by five commercial bank of Ethiopia managers, to improve the questions and replace any confusing & difficult terms. The purpose of first pretest was also to see, if we have overlooked some important dimensions/elements. Factor analysis is used as a pre-test after collecting empirical data through pre-test questionnaires (full version), to test whether there is significant relationship between the factors to measure and choosing the right variable/questions for measuring an underlying factor. A large sample size has been recommended by different researchers (DeCoster, 1998) to perform Factor Analysis, where the minimum sample size required is 150 (Hair et al., 1998). Therefore, a total of 150 responses have been collected for performing the Factor analysis and it done Debebe Alemu KEBEDE 4 with the help of SPSS 20.0 software, as a second pre-test to verify the conceptualization of the selected constructs/indicators for each factor. After performing Factor Analysis, unimportant and irrelevant questions have been excluded from the full version questionnaire to get a final version of questionnaire. Results from factor analysis have provided factor loadings for each variable (question) where factor loading above 0.70 and KMO above 0.50 is termed as acceptable so that each factor is explained more by its constructed variable (question) than by, (Voorhees et al., 2016). Several variables have factor loading above than 0.70 and KMO above 0.50 that prove as best measure of the corresponding factor. Following this, variables/questions with factor loading above 0.70 are kept for final questionnaire. 3.2.4. Validity and Reliability Validity is the degree to which a measure accurately represents what it is supposed to. According to George and Malley (2003), “Cronbach’s alpha is used as only one criterion for judging instruments or scales. As the current study uses multiple items in all variables, internal consistency analysis was carried out through Cronbach’s alpha reliability tests. Based on the results of the reliability analysis, one can conclude that the items are internal consistent. Validity also refers to the degree to which an instrument measures what it purports to measure (Bryman, 2012). Internal validity refers to correct mapping of the phenomenon with findings. This is through triangulation which is make use of multiple and different sources, methods, investigators, and theories to provide corroborating evidence. Here the result of factor loading of all variables are above significant value of 0.3. External validity refers to generalizability of findings. To assure the generalizability of the study the researcher was collect the information from target population which is interdependent group systematically as it describes the detail of the participants or setting under study using strong action verbs, and quotes. Therefore, the groups will be satisfied to the improvement that will occurred as the result of the studies finding and recommendation. Further, the proposal was demonstrated to university research committees and community to question the researcher critically about his method, practices and processes. Dröst (2011) states that reliability is the extent to which a given measuring instrument produces the same result each time it is used. The researcher used the internal consistency method to determine the reliability of the questionnaires. Reliability was ensured by use of Cronbach Alpha Test of being not less than 0.7 and hence all Variables were retained in the study except perceived trust. 4. Results and Discussions This section presents the analysis, discussion and inferences made on the basis of the responses obtained. All the data were coded and entered in to SPSS version 20.0 and inferences were made based on the statistical results. The research instrument used in the study was survey questionnaire. The location of the study was Addis Ababa city. The study population comprised the commercial banks’ managers, employees and customers. Data was collected from all selected commercial banks managers and from 223 customers which comprises 83% of target population. Table 3 Respondents’ Demographic Variables Background Distribution Frequency Percentage Membership years Less than 5 year 105 47 5-10 years 78 35 Above 10 years 40 18 Types of account holders Saving 134 60 Investment 16 7 Borrowing & sending 28 13 Digital Financial Products and Services in Ethiopian Banking Industry: An Enquiry on Its Hindrances of Adoption and Usage 5 All 45 20 Educational level Illiterate 22 10 Grade 10 complete 38 17 Diploma 45 20 Bachelor degree 85 38 Masters and Above 33 15 Nature of income Family income 38 17 Self-employed/merchant 76 34 Government/NGO 87 39 Irregular income 22 10 Source: From survey 2020 As the table 3 indicates that Majority of respondents (47%) has been found less than 5 years customer of the bank. Which also supported by interview undertaken with respective managers of the banks. Further, the results indicated as most of the respondents are associated with saving account indicating 60%. The managers of commercial banks response indicated as majorly served customers are those associated with saving account. Since there are few customers with investment accounts the deposits accumulation is high that makes money circulation low. The majority of respondents (38%) are first degree holders as well as the majority customers source of income is Government/NGO. The interview results indicated as our country majority of population is farmers and low saving habits in the country the account holders are civil servants. The study further explored if the respondents’ different background had an impact on the end user behavioral intention. So, respondents were also asked to indicate for how long they been the customer of bank and the study further explored if respondents different Affiliation years had an impact on their behavioral intention about the use of digital financial products and services and the result by ANOVA test shows, F (3,220) = 1.178, P<.05, it is observed that there is statistically significant differences in behavioral intention score between respondents due to different years of relationship. This show as period of relationship has impact on customers’ behavioral intention to use digital financial products and services. The study also investigated Reason of association of respondent with bank to determine the extent to which their Reason of association has an impact on their behavioral intention score regarding their understanding about the nature and operations of financial products and services. The result of ANOVA test indicates as the behavioral intention mean score is varies with reason of association. This show as reason of customers’ association with bank has influence on customers’ behavioral intention toward use of digital financial products and services. The study also investigated with educational levels of the respondents associated to determine the extent to which their educational levels has influence on their behavioral intention score regarding their understanding about the nature and operations of digital financial products and services. The results of ANOVA test show as there is statistically significant difference impact among customers with educational level on behavioral intention to use digital financial products and services with the value of F (3,220) = 3.618, P<.001. Respondents also asked a question regarding their source of income to determine whether possibility of needing for different digital financial products and services are based on source of income. The results of ANOVA show that as there is a variation among Account holders to use digital financial products and services as the result of their source of income with the value of F (3,220) = 16.402, P<.001. Debebe Alemu KEBEDE 6 4.1. Correlation Analysis Table 4 Correlation matrix between Adoption and its predictors ADP ECO TECH GOV CUL ADP Pearson Correlation 1 Sig. (2-tailed) ECO Pearson Correlation .659** 1 Sig. (2-tailed) .000 TECH Pearson Correlation .567** .109* 1 Sig. (2-tailed) .000 .032 GOV Pearson Correlation .521** .013 .703** 1 Sig. (2-tailed) .000 .416 .000 CUL Pearson Correlation .454* .429* .711** .712** 1 Sig. (2-tailed) .012 .014 .000 .000 N 42 42 42 42 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Table 4 shows the correlation results which indicate that there was a positive and significant relationship between independent variables and digital financial products and services adoption by commercial banks in Ethiopia. This was evidenced by the p value of 0.012 obtained which is less than that of critical value of 0.05. The positive association between independent variables and digital financial products and services adoption complexity implies that as the variables can affect positive adoption. Table 5 Correlation Matrix Between Intention and Its Predictors INT AW RELAD COMP CLEX INT Pearson Correlation 1 Sig. (2-tailed) AW Pearson Correlation .509** 1 Sig. (2-tailed) .000 RELAD Pearson Correlation .476** .109* 1 Sig. (2-tailed) .000 .032 COMP Pearson Correlation .642** .013 .703** 1 Digital Financial Products and Services in Ethiopian Banking Industry: An Enquiry on Its Hindrances of Adoption and Usage 7 Sig. (2-tailed) .000 .416 .000 CLEX Pearson Correlation -.354* -.129* -.711** -.712** 1 Sig. (2-tailed) .023 .014 .000 .000 N 223 223 223 223 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Table 5 shows the correlation results which indicate that there was a positive and significant relationship between independent variables and Intention to use digital financial products and services except perceived complexity. This was evidenced by the p value of 0.000 obtained which is less than that of critical value of 0.05. The positive association between independent variables and Intention to use digital financial products and services except perceived complexity implies that as the variables can affect positive intention to use. Binary logistic regression estimates the probability that a characteristic is present given the values of explanatory variables. Binary logistic regression was used to model relationship between Independent variables and Intention to use. Table 5: Logistic Regression for Independent Variables Table 5 shows that AW, RELAD, COMP and CLEX are statistically associated with customers intention to use (p < 0.02). The result shows that an improvement in customers awareness creation activity increases the probability of having high customers intention to use by 6.476 times, an increase of the digital financial products and services relative advantage increases the probability of having high customers intention to use by 9.409 times, an increase of the digital financial products and services compatibility with customers need increases the probability of having high customers intention to use by 3.496 times and an increase of the digital financial products and services complexity of operation for the customers increases the probability of having high customers intention to use by 3.061 times 4.2. Regression Analysis The regression analysis was conducted to know by how much the independent variable explains the dependent variable. Before we go to in detail of multiple regression assumption of multivariate normal distribution, independence of errors, and equality of variance were first tested. This study involves a relatively large sample of 270 Variable Beta S.E Wald Df Sig. Exp (B) 95% C.I for EXP (B) Lower Upper AW 1.868 0.607 9.478 1 0.002 6.476 1.971 21.27 Constant -6.414 2.273 7.962 1 0.005 0.002 RELAD 2.242 0.731 9.399 1 0.002 9.409 2.245 39.435 Constant -7.419 2.573 8.312 1 0.004 0.001 COMP 1.252 0.527 5.645 1 0.018 3.496 1.245 9.817 Constant -3.771 1.804 4.368 1 0.037 0.023 CLEX 1.119 0.48 5.423 1 0.02 3.061 1.194 7.846 Constant -3.331 1.652 4.066 1 0.044 0.036 Debebe Alemu KEBEDE 8 customers and all selected commercial banks managers 42) and therefore, the Central Limit Theorem could be applied and hence there is no question on normality of the data. Two major methods were utilized in order to determine the presence of multicollinearity among independent variables in this study. These methodologies involved calculation of both a Tolerance test and Variance Inflation Factor (VIF) (Kleinbaum & Klein, 2002). The results of these analyzes are shows as all predictors VIF is below 10 and none of the Tolerance levels is < or equal to .01. Multicollinearity was not a concern with this data set as confirmed by the main effect regression models. According to Field (2009) the acceptable Durbin – Watson range is between 1.5 and 2.5. In this analysis Durbin – Watson values are ranges from 1.823 to 2.133, which are between the acceptable ranges, show that there was no auto correlation problems in the data used in this research. Thus, the measures selected for assessing independent variables in this study do not reach levels indicate of multicollinearity. Therefore, regression analysis of Predictors and Dependent variables was conducted and the results of the regression analysis are presented as following section. 4.2.1. Regression Analysis of All Predictors and Intention to Use Here it regressed to know the impact of economic situation, technological change, government policies and cultures have on adoption of digital financial products and services. It presented as below: Table 6: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.898a 0.807 0.802 0.317 a. Predictors:(Constant), ECO, TECH, GOV, CUL b. Dependent Variable: ADP Table 7: ANOVA Model Sum of Squares Df Mean Square F Sig. Regression 56.448 3 18.816 186.297 0.000 Residual 3.838 38 0.101 Total 60.286 41 a. Dependent Variable: ADP b. Predictors: (Constant), ECO, TECH, GOV, CUL Table 8: Regression Model Result for Beta Coefficients Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta 1 (Constant) -0.624 0.110 -5.671 0.000 ECO 0.678 0.106 0.477 6.368 0.000 Digital Financial Products and Services in Ethiopian Banking Industry: An Enquiry on Its Hindrances of Adoption and Usage 9 TECH 0.217 0.073 0.189 2.974 0.003 GOV 0.192 0.058 0.181 3.323 0.001 CUL 0.179 0.068 0.140 2.643 0.009 a. Dependent Variable: ADP As the Model summary result of regression analysis on table 6 shows the economic situation of the country, the change of technological environment, the existing government policies and societal culture explain adoption of digital financial products and services in commercial banks in Ethiopia by the value of R2 is .807 and the remining .193 by others variables. 4.2.2. Regression Analysis of All predictors and Intention to Use Here it regressed to know the variation of Awareness, Relative advantage; Compatibility and Complexity have on Intention to use. It presented as below: Table 9 Model Summary Mod el R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Durbin- Watson F Change df1 df2 Sig. F Change 1 .738a 0.544 .540 .74993 13.436 4 218 .000 1.66 a. Predictors:(Constant), AW, RELAD, COMP, CLEX, b. Dependent Variable: INT Table 10 ANOVA Model Sum of Squares Df Mean Square F Sig. 1 Regression 43.5 4 10.875 14.179 .000b Residual 167.206 218 .767 Total 210.706 222 a. Dependent Variable: INT b. Predictors: (Constant), AW, RELAD, COMP, CLEX, Table 11 Coefficients Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta 1 (Constant) 2.388 .585 2.187 4.083 .000 AW .511 .155 .498 3.294 .001 Debebe Alemu KEBEDE 10 RELAD .608 .176 .591 3.465 .000 COMP .429 .014 .399 3.052 .004 CLEX -.424 .154 -.419 -2.688 .011 a. Dependent Variable: INT As the Model summary result of regression analysis on table 9 shows of customers Awareness, Perceived Advantage, perceived Compatibility and perceived complexity on intention to use digital financial products and services indicates as the value of R2 is .544, which means that All predictors accounts for 54.4% of the variation in Intention to use digital financial products and services. 5. Summary of Hypotheses Testing In this study Linear Regression was used to test the research hypotheses. The table below shows the summarized results of the hypotheses tested. 5.1. Test Hypothesis Regarding Adoption of Financial Products Here it regressed to know the impact of economic situation, technological change, government policies and cultures dimensions hypothesis test is done to know which hypothesis is accepted and rejected. Table 12 Regression Coefficients Result from SPSS (Adoption is the dependent variable) Factors Hypothesis Beta T-Values p-values Mean Std.dev ECO H1 0.678 6.368 0.000* 4.292 .62882 TECH H2 0.217 2.974 0.003* 2.287 1.4613 GOV H3 0.192 3.323 0.001 3.285 1.1800 CUL H4 0.179 2.643 0.009* 3.864 .91762 Constant 0.624 3.351 1.72842 *. Statistically significant at the 0.01 level Number of observations = 42, P=0.0000, R-Squared= 0.807 The above table presents the results from the multiple regressions carried out using the three variables: Technological advancement, legal and regulatory barriers, Economic factors and societal cultures as the independent variables and Adoption as the dependent variable. Based on the table above, we can know the goodness-of-fit of the model in general. The p-value of the model is 0.000. This means the probability of variation in the dependent variable to occur by chance (not to be affected by the explained independent variables) is 0.000. This will result in the following null hypotheses. H0: β1>0, β2> 0, β3> 0, β4> 0 Ha: β1<0, β2<0, β3< 0, β4< 0 Therefore, the null hypothesis of 1,2,3 and 4 are accepted. 5.2. Test Hypothesis Regarding Intention Dimension Here the intention dimensions hypothesis test is done to know which hypothesis is accepted and rejected. Digital Financial Products and Services in Ethiopian Banking Industry: An Enquiry on Its Hindrances of Adoption and Usage 11 Table 13: Regression coefficients result from SPSS (Intention is the dependent variable) Factors Hypothesis Beta T-Values Sig. Mean Std. dev AW H5 .511 3.294 .001** 2.323 .94819 RELAD H6 .608 3.465 .000** 4.821 .34093 COMP H7 .429 3.052 .004** 4.799 .42746 CLEX H8 -.424 -2.688 .011** 4.821 .34093 Constant 2.388 4.0596 .98819 *. Statistically significant at the 0.01 level Number of observations = 223, P=0.0000, R-Squared= 0. 544 The above table presents the results from the multiple regressions carried out using the five variables: Awareness, Perceived relative advantage, Perceived compatibility; Perceived Complexity; and Perceived Trust as the independent variables and intention as the dependent variable. Based on the table above, we can know the goodness-of-fit of the model in general. The p-value of the model is 0.000. This means the probability of variation in the dependent variable to occur by chance (not to be affected by the explained independent variables) is 0.000. This will result in the following null hypotheses. H0: β1>0, β2>0, β3>0, β4<0, β5>0 Ha: β1<0, β2<0, β3<0, β4>0, β5<0 This shows that the beta coefficients of the independent variables are greater than zero. Hence, the model can be used to predict attitude based on the explanatory variables. 6. Conclusions There are diverse digital financial products and services provided and launched by commercial banks in Ethiopia. Based on the study results those products are not properly used by the customers as the result of less perceived relative advantage of owing it by the customers, low level of customers awareness towards the existence of the those digital financial products and services, the complexity looking of digital financial products and services to the customers and considering as using different digital financial products and services is not compatible for them. In addition to this the study results from banks adoption of digital financial products and services perspectives demonstrated as dynamics technology in the business environment affects the banks to adopt digital financial products and services, the existing legal and regulatory barriers also prevents banks to adopt, the economic factors like lack of income, irregularity of income, unemployment affect the activity rate of society engaging in any type of financial service that prevents banks to adopt. Furthermore, majority of people keeping their money at home as the result of lack of education, trust and religions that signifying low saving cultures in the country. 7. Implications of the Study 7.1. Managerial implication of the study This study gives hindsight for promoters to aware as the banks should properly adopt the digital financial products and services with taking into account of the external factors. Further, the banks should participate on aggressive promotion to aware the customers about their products and services, the government should formulate policies and regulations that minimizing bureaucracy of adopting technology by banks. Furthermore, the promoters should identify the prospective hindrance on the existing and newly launched digital financial products and services; customer’s level of perception of the product, which should subsequently allow them to formulate and develop policies and regulations, to make an adjustment to the existing banking industry in Ethiopia and to establish the necessary strategies to attract individual customers and retain the existing, technology needed must be available and government support on financing structure. Debebe Alemu KEBEDE 12 7.2. Theoretical Implications The findings of the current study have significant contributions to the body of knowledge in that the study add value to the banking industry on testing the digital financial products and services in banking industry in context of Ethiopia that is lack in the literatures. Furthermore, the study extends and tests the decomposed theory of planned behavior in another context and another area i.e. Digital financial products and services adoption and usage. References Agbolade, O. K. (2011) ‘INFORMATION AND COMMUNICATION TECHNOLOGY AND BANKS PROFITABILITY IN NIGERIA.’, Australian Journal of Business and Management Research. Australian Journal of Business and Management Research (AJBMR), 1(4), p. 102. Anol, B. (2012) ‘Social science research: Principles, methods, and practices’, Scholar Commons, University of Florida. Brown, T. A. (2015) Confirmatory factor analysis for applied research. Guilford publications. Creswell, J. 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