Addressing the Low Usage of Online Banking Platform by the Corporate Clients in Zambia’s Commercial Banks Based on the TPR Model ()
1. Introduction
In recent years many changes have taken place in the payment system and of particular interest are the Online Banking platforms (Arakpogun et al., 2017). According to Bradley and Stewart (2003), online banking is an electronic consumer interface and an alternative channel of distributions. The Bank of Zambia has continued to intervene via policy implementations relating to payments and amount threshold, but Commercial banks still face challenges with Low utilisation of the Online banking platforms. The Corporate Client Usage of the Online Banking is a key step for Commercial Banks to recoup from the huge investments on Digital Channels.
In 2007 the government of the Republic of Zambia acted the National Payments System Act. This Act gave powers to Bank of Zambia to provide for the management, ad-ministration, operation, supervision and regulation of payments, clearing and Settlement systems policy so as to promote efficiency, stability and safety of the Zambian financial systems. In the same period and among other things the Bank of Zambia was introduced.
· E-money and mobile banking initiatives and money transmission guidelines.
· The Item value limit on the payment streams cleared through the Zambia Electronic Clearing House by the Commercial Banks.
The introduction of Item Value limit of 2007 has brought about investment in the electronic payments channels which include Card, Online Banking, E-Wallet etc. taken by Commercial Banks. This transformation towards electronic payments has greater benefits as it empowers the country’s economy. According to Tan and Teo (2000), Online Banking has recently become the way for the development of banking system, and the role of Online Banking is increasing in many countries. It offers opportunities to create services processes that demand few internal resources, and therefore, lower cost provides wider availability and possibility to reach more customers. From the customers’ point of view, Online Banking allows customers easier access to financial services and time saving in managing their finance. The other perceived positive attribute of E-banking includes the 24 hours and 7 days availability. The new technology however is not without challenges. According to Lee (2009), the perceived risks, in terms of security or privacy risk, are the greatest obstacle to Online Banking adoption. The other additional cons of e-banking include risks and complexity. In 2016 the Bank of Zambia issued circular No. 06/2016 dated 14th October, 2016, revising the Item Value Limit and Electronic funds transfer. The decision was aimed at promoting the use of electronic payment methods.
2. Key Literature Review
2.1. Conceptual Framework (Modified TPR Model) Adapted
A conceptual model or framework describes either graphically or in a narrative form, the main things to be studied which include the key factors and the presumed relationships among them (Miles & Huber, 1994). It is also used to comprehend the place and clarify the direction of a research project. It usually makes use of past research to conclude a theory and methodology for a current research study (Magher, 2018).
In this study the researcher used the theory of perceived risk and technology (TPR) model to address the low usage of Online Banking platforms by the corporate clients in Zambia’s Commercial Banks. (Figure 1)
Theory Perceived risk theory has been used to explain consumers’ behaviour. Other considerable research has also examined the impact of risk on traditional consumer decision making. However, the dimensions of perceived risk may vary according to the product class or service (Featherman & Pavlou, 2003). Featherman and Pavlou (2003) defined perceived risk as the possible loss when pursuing a desired result. Peter and Ryan (1976) also defined perceived risk as a kind of subjective expected loss.
The Six components or types of perceived risk include: financial, performance, social, physical, security and time-loss (Jacoby & Kaplan, 1972; Kaplan & Szybille, 1974; Roselius, 1971).
2.2. Hypotheses
The following section looks at the conceptual framework that was used to formulate the hypothesis. The descriptions outlined establish the background for the independent variables.
H1: Time Risk can positively lead to Corporate Client’s Low usage of the Online Banking Platform.
H2: Social Risk can positively lead to Corporate Client’s Low usage of the Online Banking Platform.
![]()
Figure 1. The framework and development of hypothesis.
H3: Financial Risk can positively lead to Corporate Client’s Low usage of the Online Banking Platform.
H4: Security Risk can positively lead to Corporate Client’s Low usage of the Online Banking Platform.
H5: Performance Risk can positively lead to Corporate Client’s Low usage of the Online Banking Platform.
H6: Technology Risk can positively lead to Corporate Client’s Low usage of the Online Banking Platform.
3. Research Methodology
This study employed a Mixed Method study design. The study was carried at Stanbic Bank Head Office in Lusaka. The study targeted customers with business accounts.
Sampling technique used was Purposive sampling and the Sample size is 303 calculated from a Targeted Population of 1247 Active Commercial client users who are on boarded to Online Banking and Dormant clients on the Online Banking platform. This, however, excluded Non active clients whose accounts have been closed. A Simple Random sampling criteria was later used to pick the 303 participants with the confidence interval of 5%.
The Sample Size has been stimated using the mathematical equation developed by (Yamane, 1967).
Formula:
Formula:
Data Collection done using structured questionnaire via a Kobo Toolbox link sent to the respondents via Email and WhatsApp. The source of the questionnaire was from the conceptual model of the Theory of Perceived Risk and Technology. The questions in the questionnaire came from the Risk factors as indicated from Figure 2 “The Framework and Development of Hypothesis” above.
Data Analysis was done using Statistical Package for the Social Sciences (SPSS) Software by doing multiple regression analysis and correlation analysis to determine the significant difference between the variables.
4. Results and Discussion
From the administered questionnaires only 287 responded out of the sample size of 303; representing 95% of respondents.
4.1. Demographic Characteristics of Participants
The presentation of the data from the questionnaire administered to respondents began with the identification of the respondents in terms of their Gender, Age and the type of the Company (i.e. Corporate, SME and Sole proprietors) they working for.
Figure 2 below which relate to the distribution of the participants by Gender show that 58% of the respondents are male and 42% female. This means that there were a lot of males who participated in this study compared to the female counterparts.
Figure 3 below relate to the distribution of the participants by Age. The majority of the participants were from the Age group 31 - 40 (45%), followed by the Age group 41 - 50 (24%), with the age group 60+ recording the lowest number of participants being 1 percent.
From Figure 4 below, the Company distribution shows that most participants were from the Corporate. The Other having the least of ALL in terms of the participants. The Corporate companies were the majority with 46%, Small-Medium Enterprises at 30% and Sole Proprietors at 17%.
4.2. Factors Affecting the Usage of Online Banking Services
Table 1 below indicates that 46.43% and 48.28% of Females and Males respectively were successfully using Online banking more than 3 times a week.
![]()
Figure 2. Distribution of participants by gender.
![]()
Figure 3. Distribution of participants by Age.
![]()
Figure 4. Types of companies represented in the study.
![]()
Table 1. Cross tabulations of usage of Online banking systems against Gender.
This percentages when added to the results under 2 - 3 times a week means that overally more than half of the respondents are using Online banking at least 2 times in a week. The calculated p-Value for Gender is 0.223 which mean that there’s a weak relationship between gender and the use of the Online banking services
Table 2 below indicates that respondents between the ages 41 - 50 years have a highest percentage of people who are using online banking 3 times a week followed by the age range of 31 - 40 years at 44.44%. This percentages when added to the results under 2 - 3 times a week means that overally more than three quarters of the respondents are comfortably using Online banking. Age showed a strong relationship with the use of Online banking services with the computed p-Value of 0.001.
Table 3 below indicates that respondents from the Corporate companies represent 70% and a highest percentage of people who are using online banking 3 times a week. The Sole proprietors (44.12%) and SMEs (40.68%) show that the respondents under this category Never to once a week do the use the Online banking. The results show that the type of company is statistically significant to usage of the Online banking with a p-Value of 0.000.
Table 4 below indicates that respondents’ knowledge of computers has an impact on the Online banking. Respondents with Very good and Moderate knowledge of computers access online banking more than 3 times a week at 60.94% and 54.44% respectively. This means that the comfortability due to the
![]()
Table 2. Cross tabulations of usage of Online banking systems against Age.
![]()
Table 3. Cross tabulations of usage of Online banking systems against type of company.
![]()
Table 4. Cross tabulations of usage of Online banking systems against Knowledge of computers.
Knowledge of computers helps in using and adopting of the Online banking. The results show that the Knowledge of computers is statistically significant to the usage of the Online banking with a p-Value of 0.000.
Table 5 below indicates that respondents who are from the companies where connectivity is Very poor (87.5) and Poor (83.33%) have low chances of using Online banking, however, respondents with the Good (51.95%) and Very good (70.18) Internet connectivity. This means that Office Internet connectivity affects the using Online banking. The results show that the Office Internet Connectivity is statistically significant to the usage of the Online banking with a p-Value of 0.000.
4.3. Logistic Regression Analysis: Factors Associated with Low Usage of Online Banking Platforms
This section shows the results from a logistic regression analysis where all variables were entered into the model and were tested for significance at P<0.05 in all the categories. This was done first by fitting the background variables in a logistic regression model in-order to check for variables that were significant, and only those that were significant are presented in Table 6.
People in the age group 21 - 30 were 10% less likely to have low usage of electronic banking systems compared to those in the age group below 21 [AOR 0.95, 95% CI 0.15, 5.91]. among the same group of people, those in the age group 41 - 50 were 80% less likely to have low usage of electronic banking systems compared to those in the age group below 21 [AOR 0.25, 95% CI 0.14, 1.59].
By type of company, those in sole proprietorship were 3 times more likely to have low usage of electronic banking systems than those in the corporate companies [AOR 3.44, 95% CI 1.35, 8.80]. By internet connectivity, those with moderate internet connectivity are 10% less likely to have low usage of electronic banking systems than those with very poor internet connectivity [AOR 0.99, 95% CI 0.37, 2.60].
![]()
Table 5. Cross tabulations of usage of Online banking systems against Office Internet connectivity.
![]()
Table 6. Regression Analysis for factors associated with low usage of electronic banking systems.
4.4. Different Uses of Online Banking Platforms
Online banking platforms offer different services and Table 7 depict responses from the respondents on how frequent they use the services on the platform.
Despite most services being offered through electronic banking systems, some clients still prefer going to the bank physically to have their needs attended to. Table 3 shows that; the highest score of 39% and 28.50% of the respondent are Sometimes and Often respectively use Online banking for viewing.
The highest score of 35% and 28.50% of the respondent are Often and Always respectively use Online banking for Balance enquiry.
![]()
Table 7. Different uses of electronic banking systems by clients.
The highest score of 23% and 22% of the respondent are Rarely and Never respectively use Online banking for foreign exchange rate updates.
The highest score of 29% and 26% of the respondent are Rarely and Never respectively use Online banking for Interest Rate updates.
The highest score of 31.50% and 21.50% of the respondent are Always and Never respectively receive Monthly Statements via mail.
The highest score of 33% and 25% of the respondent are Rarely and Never respectively use Online banking for Money transfer.
The highest score of 37% and 24% of the respondent are Rarely and Sometimes respectively use Online banking for Bill payments.
The highest score of 30% and 24% of the respondent are Never and Sometime respectively use Online banking for loading standing order instructions.
The highest score of 48% and 21% of the respondent are Always and Sometimes respectively use Online banking for Interest Rate updates.
4.5. Reasons for Using Manual Instruction Instead of Online Banking Platforms
Figure 5 below shows the reason for using Manual Instructions instead of Online Banking platforms with respondents; Cyber Theft & Fraud (29%), Internet Challenges (25%) and Unstable systems (20%) having the highest scores.
![]()
Figure 5. Reasons for using manual instruction instead of Online banking platforms.
4.6. Hypothesis Results: Perceived Risks vs Usage of Online Banking
The Hypothesis results by using the Correlation Analysis of Perceived Risks of the Independent Variable Vs Usage of the Online Banking as the Dependent Variable. The correlation and regression analysis which utilized Pearson’s correlation coefficient details as per below Tables 8 per Perceived Risk (i.e. Time Risk, Social Risk, Financial Risk, Security Risk, Performance Risk and Technology Risk) types:
**Correlation is significant at the 0.01 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
The above show H5: Performance Risk with a Sig. (2-tailed) of 0.001 which is less than the Correlation Significant value of 0.01. This means that Performance Risk positively lead to Corporate Client’s Low usage of the Online Banking Platform. Also the Pearson correlation coefficient (r) is 0.623 an indication that there’s a positive correlation between the two Variables, Performance Risk and Usage of the Online Banking.
**Correlation is significant at the 0.01 level (2-tailed).
The above show H6: Technology Risk with a Sig. (2-tailed) of 0.001 which is less than the Correlation Significant value of 0.01. This means that Technology Risk positively lead to Corporate Client’s Low usage of the Online Banking Platform. Also the Pearson correlation coefficient is 0.534 an indication that there’s a positive correlation between the two Variables, Technology Risk and Usage of the Online Banking.
**Correlation is significant at the 0.01 level (2-tailed).
The above show H7: Perceived Risk has a Sig. (2-tailed) of 0.001 which is less than the Correlation Significant value of 0.01. This means that Usage of Online Banking by the Corporate Client is usually affected by the Perceived Risk. The Pearson correlation coefficient is 0.566 an indication that there’s a positive correlation between Perceived Risk and Usage of the Online Banking.
Table 9 below shows a Correlation Analysis of the Perceived Risks which is the Independent Variable Vs Usage of the Online Banking as the Dependent Variable. The Significance level is below 0.01. Therefore, the H1, H2, H3, H4, H5 and H6 are significant to the Usage of Online Banking.
![]()
Table 9. Summary of the hypothesis results.
4.7. Perceived Strategies that Commercial Banks Could Use to Promote Online Banking
From Table 10 below, the most prominent strategy which banks could use to promote electronic banking is 1) Advertisements and campaigns (38%). 2) Integrated User-Friendly Apps (14%) was the second most notable strategy which should be used by banks. These are apps used in smartphones that have been made user friendly with a simple interface. 3) Royalty programmes (11.50%) and Training of Clients (11.50%) makes the third strategy. 4) Good Customer Service (10.5%) can make the fourth strategy.
![]()
Table 10. Perceived strategies to be used to promote Online banking platforms.
5. Conclusions and Recommendations
5.1. Conclusion
The study has shown that Perceived risk has a Negative Impact on the Usage of Online Banking platforms in Zambia with respect to the corporate clients. Advertisements & Campaigns, Integrated User Friendly apps, Royalty programmes, Training of Clients and Good Customer Service Centres are some of the factors that can be rigorously used to promote electronic banking.
5.2. Recommendations
The study recommended that;
1) Improve advertisements and sensitisation campaigns on the importance and convenience of using electronic banking systems.
2) The government and other private companies must invest in infrastructure to improve internet connections throughout the country by installing updated systems.
3) Banks must win the trust of the clients by improving on online securities to be able to fight fraud and/or Cyber crime.
4) Enhance client training on Online Banking features and usage.
5) Banks should upgrade their systems to avoid them crashing when there is a lot of traffic.
6) Banks should come up with effective and responsive call centres.
Acknowledgements
The author wishes to acknowledge all participants for their valuable contribution in the study and not forgetting the research supervisor for his relentless dedication.