Selection of modeling for quality assurance of commercial banking services in Vietnam

This study synthesizes models of assessing the quality of banking services. The study also identifies

the advantages of using assessment models while also limiting the use of these models in Vietnam.

Based on that assessment, this study uses the BANKSERV model (Avkiran, 1994) and presents five

groups of factors that influence the quality of banking services and using this model in the case of

Bank of Investment and Development of Vietnam. These include: staff, utility, reliability,

information, counter services. After applying the analytical model, the research results showed that

the "staff" component had the greatest impact on the quality of BIDV's e-banking services, followed

by "utility", " information "," trust "in the end is the" service counters "component.

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quality variable The results of the analysis show that the Eigenvalue indicator is formed so that the Service quality factor reaches 2,645, the Total variance explained reaches 82.157% (over 50%), the KMO and Barlett test reaches 0.747 with the implication level reaches sig = 0. The factor loading reaches a minimum of 0.906. All mentioned indicators satisfy the conditions so that the factor discovering analysis meets the statistical meaning and high practical applicability during the analysing process. Table 7. EFA analysis results for the variable Quality of service Total Variance Explained Comp onent Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 2.465 82.157 82.157 2.465 82.157 82.157 2 .294 9.810 91.967 3 .241 8.033 100.000 Source: Author’s research results Pearson correlation results Pearson correlation analysis is one of the steps to analyse quantitative SPSS. Usually, this step will be carried out before the regression analysis. While conducting the Pearson correlation analysis, we have table: 799 Table 8. Pearson correlation results Correlations STF IF SC UTT RBT QBS STF Pearson Correlation 1 .232** .245** .174** .392** .592** Sig. (2-tailed) .000 .000 .002 .000 .000 N 312 312 312 312 312 312 IF Pearson Correlation .232** 1 .261** .290** .329** .597** Sig. (2-tailed) .000 .000 .000 .000 .000 N 312 312 312 312 312 312 SC Pearson Correlation .245** .261** 1 .325** .231** .408** Sig. (2-tailed) .000 .000 .000 .000 .000 N 312 312 312 312 312 312 UTT Pearson Correlation .174** .290** .325** 1 .317** .603** Sig. (2-tailed) .002 .000 .000 .000 .000 N 312 312 312 312 312 312 RBT Pearson Correlation .392** .329** .231** .317** 1 .496** Sig. (2-tailed) .000 .000 .000 .000 .000 N 312 312 312 312 312 312 QBS Pearson Correlation .592** .597** .408** .603** .496** 1 Sig. (2-tailed) .000 .000 .000 .000 .000 N 312 312 312 312 312 312 Source: Author’s research results As seen from the table, the sig between independent variable and assisting variable is smaller the 0.05, therefore no variables are removed from the model. Multivariate regression results - R square is 0.74 = 74%. Hence the variables Employee, Utility, Reliability, Information and Service counter are put into running the regression affecting 74% of the change of BIDV Bank service quality 800 - Testing sig F = 0.00 < 0.05, therefore the regression model has a wider meaning - Regression without any factors removed because the testing sig t of each independent variable is smaller than 0.05. - VIF coefficient of independent variables is smaller than 10, therefore no multicollinearity occurs. - Constant in the regression equation represents the slope, it does not go with the variable so it doesn’t affect to equation. Especially the models using Likert scale, this constant does not have the comment meaning, so the sig of the Constant, bigger or smaller than 0.05, positive or negative, all unimportant. - Non-standardized regression equation: QBS = -0.149 + 0.308STF + 0.273IF + 0.052SC + 0.288UTT + 0.069RBT - Standardized regression equation: CLDV = 0.391STF + 0.345IF + 0.078SC + 0.381UTT + 0.09RBT Looking at the standardized regression equation, we can see that the Employee factor has the most significant impact on the BIDV Bank service quality. The second most influential factor is the Utility factor, followed by Information factor, Reliability factor and lastly, Service counter factor. In recent years, BIDV is developing its retail system, retail customers and small and medium business which is the main target of BIDV in recent years as well as in the future. Therefore, the improvement of the banking service quality to satisfy these retail customers is crucial. From 2015 to 2017, there have been positive changes in the BIDV’s results of operation. Table 9. Operational results of BIDV 2015-2017 Unit: millions VND Year 2015 2016 2017 2016 compa red to 2015 2017 compar ed to 2016 Net income 19.314.969 23.434.595 30.955.331 21,33% 32,09% Net profit from business activities 13.624.988 16.907.435 23.512.483 24,09% 39,07% Profit after corporate income tax 6.376.756 6.228.856 6.945.586 11,51% -2,32% Source: author’s analysis The revenue increase significantly through the years, 2016 net income increased to 21,33% compared to 2015, 2017 was also a huge leap for BIDV when the net income reached 30.995.331 million VND, increased by 32,09% compared to 201. Thanks to that, the profit of BIDV also increased. 801 As for the accounting balance of BIDV 2015-2017, the author has analyzed and generalized as follow: Table 10. Accounting balance of BIDV 2015-2017 Unit: millions VND Year 2015 2016 2017 2016 compared to 2015 (%) 2017 compared to 2016 (%) Short term assets 695.491.203 829.401.979 1.021.414.355 19,25% 23,15% Long term assets 155.178.446 177.002.171 180.869.488 14,06% 2,18% Total assets 850.669.649 1.006.404.150 1.202.283.843 18,31% 19,46% Total debt 808.334.189 962.259.901 1.153.449.833 19,04% 19,87% Equity 40.949.722 42.540.497 45.961.294 3,88% 8,04% Interests of minority shareholders 1.385.738 1.603.752 2.872.716 15,73% 79,12% Total resources 850.669.649 1.006.404.150 1.202.283.843 18,31% 19,46% Source: author’s analysis Overall, the assets and liabilities of BIDV increased in 2015-2017. Total assets in 2017 reaches around 1.202.284 billion VND, increased by 19,25% compared to 2016, continues to remain the largest bank on the market. The increase in the scale shows that BIDV is developing. Additionally, based on the press information no. 11/2018, business cards also gain achievements: net income in card activities increased by 37%, credit card sale hitting 47%, total sale growth increased by 25%. Notably, the increase in net domestic card is 1,37 times higher than 2016. Total outstanding credit and investment reaches 1.154.154 billion VND, including the TCKT credit debt, individuals reach 862,604 billion VND, increased by 17% compared to 2016, accounting for 13.7% market share of the whole industry. The total of capital reached 1,124,961 billion VND, in which the organization resource and the population reached 933.834 billion VND, increased by 17.4% compared to 2016, accounting for 12,8% of the entire banking industry. The retail activities with retail debt raised by 35%, accounting for 30% of the total debt, retail capital increased by 19%, accounting for 55% of the total, the total retail net income accounted for 31% of total net income. With this tremendous growth, the expansion must go with quality assurance, improve financial capability, diversify ownership, focusing on strategic sale and complete capital increase from released share for foreign investors. BIDV’s strategic goal in 2018 802 is to strive to reconstruct income, continue to diversify clients background, continue to implement organizational conversion that goes along with improving quality of staff, reduce branch model and focus the resource for business activities. In order to accomplish these goals, BIDV needs to take measures to improve the quality of service, to meet the needs and satisfaction of customers when using BIDV’s services. Not only will that help the bank keep its customers but can also expand the customer network as one of the strategies of BIDV in 2018. 4. Conclusion The study has shown how the Bankserv model is used to evaluate the quality of service of BIDV with 5 factors that has massive impact on the quality of e-bank service, which are: Staff, Utility, Reliability, Information, Service counter. Results show that the ‘’Staff’’ factor has the strongest impact on the quality of BIDV’s e-bank service, followed by ‘’Utility”, “Information”, “Reliability” and finally ‘’Service counter’’. After identifying the level of impact of each factors, the author has recommended some measures to improve the quality of bank service. At the same time, it is hoped that the results can be a useful reference for other banks to improve their service quality, thereby promoting no-cash payment, bringing benefits to the company. The study is based on the application of factor analysis techniques (EFA), which is a technique used widely to evaluate the quality of service in general and more specifically, the quality of e-bank service, in order to identify factors that customers really care about when they evaluate the quality of service. Our study group hopes to bring clear evaluation on the quality of BIDV’s e-bank service. References Aldlaigan, A. H. and Francis A. Buttle (2002), “SYSTRA-SQ: a new measure of bank service quality”, International Journal of Service Industry Management, Vol.13,No.4, pp. 362 - 381. Arun Kumar G., Manjunath S. J., Naveen Kumar H., “A study of retail service quality in organized retailing”, International Journal of Engineering and Management Sciences, 3 (3) (2012), 370-372. Avkiran, N.K. (1994), “Developing an instrument to measure customer service quality in branch banking”, International Journal of Bank Marketing, Vol. 12, No. 6, pp. 10 – 42. 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