The Effect of Digital Banking on Consumer Behaviour and Marketing Strategies in Cameroon

The Effect of Digital Banking on Consumer Behaviour and Marketing Strategies in Cameroon

Njong Mom Aloysius¹, Huboh Samuel Ringmu², Nkiendem Felix³, Virginie Takoutio Feudjio⁴

¹Department of Economics, Faculty of Economics and Management Sciences, The University of Bamenda, Cameroon.

^2,4Department of Banking and Finance, Faculty of Economics and Management Sciences, The University of Bamenda, Cameroon.

³The University of Bamenda, Faculty of Economics and Management Sciences, Department of Banking and Finance,

DOI: https://dx.doi.org/10.47772/IJRISS.2025.9020033

Received: 31 December 2024; Accepted: 04 January 2025; Published: 28 February 2025

ABSTRACT

The way that customers engage with financial institutions has changed as a result of Cameroon’s broad embrace of digital banking. Nevertheless, little research has been done on how digital banking affects Cameroonian consumers’ behaviour, despite its increasing popularity. By investigating how digital banking affects Cameroonian consumer behaviour and marketing tactics, this study seeks to close this knowledge gap. This study used a quantitative research approach to survey 200 Cameroonian consumers in order to collect information on their loyalty, satisfaction, adoption, and usage of digital banking services. The study’s findings show that consumer behaviour toward digital banking is significantly predicted by digital banking services, security, and technological familiarity. The study’s conclusions have significant ramifications for Cameroonian banks and financial institutions, emphasizing the necessity of enhancing their technology infrastructure, security, and digital banking services in order to improve customer behaviour. By shedding light on the variables influencing Cameroonian consumers’ attitudes about digital banking, this study adds to the body of knowledge already available on the subject.

Keywords: Consumer Behaviour, Digital Banking, Marketing Strategies, Technological Familiarity, Security.

INTRODUCTION

The banking industry has changed as a result of the digital revolution, which has redefined how customers engage with financial organizations (Kassim, 2020). With a notable rise in mobile banking users from 2.3 million in 2018 to 7.5 million in 2022, Cameroon has seen a rapid uptake of digital banking (BEAC, 2022). The banking industry’s marketing tactics and consumer behaviour will be significantly impacted by this change.

Consumer behaviour has changed as a result of the growth of digital banking, with a greater desire for individualized, quick, and convenient services (Afolabi et al., 2020). According to research, factors like perceived risk, trust, and financial literacy affect the adoption of digital banking (Makai et al., 2021). Additionally, banks may now use digital marketing tools and data analytics to improve client engagement and retention thanks to digital banking (Ngwoke et al., 2020).

When internet banking initially appeared in the 1990s, digital banking got its start (Hernandez & Mazzon, 2017). With the release of mobile apps by banks like Bank of America and Wells Fargo, mobile banking was introduced in the early 2000s (Kapoor et al., 2018). Digital banking expanded to incorporate online lending, digital wallets, and mobile payments as smartphone penetration rose (Manyika et al., 2016).

To improve client engagement and retention, banks all around the world have implemented a variety of digital marketing techniques. Email marketing, content marketing, and social media marketing are becoming crucial instruments (Kumar et al., 2020). Social media has been effectively used by banks like Barclays and Citibank to enhance the client experience (Bolton et al., 2013). Banks in Africa have also adopted digital marketing; according to African Banker (2020), 70% of African banks use social media to interact with their clientele.

Digital banking has changed the financial scene in Africa by making financial services more accessible and inclusive (Aker et al., 2016). Financial transactions have been transformed by mobile money services such as M-Pesa in Ghana and Kenya (Hughes & Lonie, 2014). With the advent of mobile banking apps by banks like Afriland First Bank and Ecobank, digital banking has accelerated in Cameroon (Cameroon Tribune, 2020). In order to reach clients, local banks have also embraced digital marketing techniques, utilizing internet advertising and social media (Business in Cameroon, 2022).

The growing use of digital wallets, online payments, and mobile banking define Cameroon’s digital banking environment (Njinyah et al., 2022). Banks have made significant investments in digital infrastructure, which has improved ease and customer experience (Mbah et al., 2020). But problems still exist, such as low internet usage and low financial literacy (Kouam et al., 2020).

The effect of digital banking on consumer behaviour and marketing tactics in Cameroon’s banking industry is examined in this article. It specifically looks at the variables affecting the uptake of digital banking, how it affects customer behaviour, and how it affects marketing tactics. This study intends to offer insights for bankers, policymakers, and researchers wishing to comprehend the changing environment of digital banking in Cameroon by drawing on actual data and current literature.

This article’s concentration is on the experiences of commercial banks and their clients, and its scope is restricted to the Cameroonian banking industry. The study’s conclusions add to the continuing conversation about how digital banking could revolutionize the financial industry in Africa.
The introduction, a review of the literature on consumer behaviour and the adoption of digital banking, an analysis of marketing strategies in the digital banking era, a discussion of the implications for Cameroon’s banking industry, and conclusions make up the article’s five sections.

LITERATURE REVIEW

Digital banking is the provision of banking services through digital channels (Kassim, 2020). Consumer behaviour and financial inclusion are two important ideas in digital banking. According to Afolabi et al. (2020), financial inclusion is the provision of financial services to all societal segments. In Cameroon, digital banking has improved financial inclusion, especially for those with low incomes (Mbah et al., 2020). Conversely, the study of people’s purchasing decisions is known as consumer behaviour (Kotler et al., 2019). Customers’ demands for speed, convenience, and individualized services have increased as a result of digital banking (Ngwoke et al., 2020).

The Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM) are two ideas that are pertinent to digital banking. With a focus on perceived utility and usability, TAM describes how customers embrace digital banking services (Davis, 1989; Hernandez & Mazzon, 2018). Conversely, TPB looks at attitudes, subjective norms, and perceived behavioural control as well as other factors that affect consumers’ intents to utilize digital banking services (Ajzen, 1991; Aker et al., 2019).

Studies have continuously demonstrated that the adoption of digital banking is greatly influenced by elements including perceived risk, trust, and financial literacy (Makai et al., 2021; Njinyah et al., 2022). According to a Kenyan study, those who possess greater financial literacy are more likely to use digital banking services, making financial literacy a strong predictor of digital banking adoption (Makai et al., 2021). In a similar vein, a Cameroonian study found that adoption was significantly influenced by trust in digital banking systems, with people more inclined to adopt if they believed that digital banking was safe and dependable (Njinyah et al., 2022). Additionally, studies have shown how important perceived risk is, with those who think digital banking is unsafe being less likely to use it (Kouam et al., 2020).

With the rise in demand for digital wallets, online payments, and mobile banking, digital banking has changed how consumers behave (Kouam et al., 2020; Mbah et al., 2020). According to a Nigerian study, digital banking improved customer satisfaction by making banking services more convenient, quick, and accessible (Afolabi et al., 2020). Similarly, studies conducted in Cameroon showed that the rise in online payments and mobile banking has altered how customers engage with banks (Mbah et al., 2020). Additionally, customers are using budgeting and financial planning tools more frequently as a result of digital banking, which has helped them manage their money more skillfully (Ngwoke et al., 2020).

To improve client engagement and retention, banks have implemented a number of digital marketing techniques (Kumar et al., 2020; Ngwoke et al., 2020). With banks utilizing sites like Facebook, Instagram, and Twitter to interact with clients, social media marketing has become a crucial tactic (Kumar et al., 2020). Banks are producing educational content to inform clients about digital banking services, demonstrating the effectiveness of content marketing (Ngwoke et al., 2020). Additionally, digital banking services have been promoted and use encouraged through the use of email marketing (Afolabi et al., 2020).

Digital banking has several advantages, but there are still a number of problems (Afolabi et al., 2020; Kassim, 2020). One major issue is still the low internet penetration rate, especially in rural regions (Kassim, 2020). Another issue is financial literacy, as many people do not have the skills needed to use digital banking services efficiently (Afolabi et al., 2020). Furthermore, people are still worried about the security of digital financial transactions, which presents a big obstacle (Kouam et al., 2020). Several important conclusions have been drawn from research done in Cameroon (Mbah et al., 2020; Njinyah et al., 2022). The use of digital banking has grown, especially among young people and those living in cities (Mbah et al., 2020). By investing in digital infrastructure, banks have enhanced the ease and experience of their customers (Njinyah et al., 2022). Digital marketing tactics have been successful in expanding financial inclusion and reaching consumers (Kouam et al., 2020). But problems still exist, such as low internet usage and low financial literacy (Afolabi et al., 2020; Kassim, 2020).

METHODOLOGY

A quantitative research strategy is used in this work (Creswell, 2018; Saunders et al., 2019). While the quantitative approach entails polling bank clients and assessing primary data from financial institutions using questionnaires (Kumar et al., 2020; Ngwoke et al., 2020). A thorough grasp of how digital banking affects customer behaviour and marketing tactics is made possible by this design. Surveys and interviews are used to gather primary data from bank clients and banking industry professionals in Cameroon (Afolabi et al., 2020; Makai et al., 2021). Financial institutions, regulatory agencies, and the body of current literature on digital banking are the sources of data (Kassim, 2020; Njinyah et al., 2022). Customers and employees of Cameroon’s banking industry make up the population. Stratified random sampling is used to choose a sample size of 200 respondents (Krejcie & Morgan, 1970). While banking professionals are recruited from commercial banks and microfinance organizations, bank clients are sampled from both urban and rural locations. After adjusting for other variables, this model evaluates how digital banking affects customer behaviour (Kouam et al., 2020; Mbah et al., 2020).

Dependent Variable: Consumer Behaviour (Y=CB) (Adoption of digital banking, Frequency of digital banking usage, Satisfaction with digital banking services, Loyalty to digital banking providers)

Independent Variables:

Digital Banking Channels (DBC) (Online banking (e-banking), Mobile banking (m-banking), Social media banking.

Digital Banking Services (DBS) (Fund transfers, Bill payments, Account management, Loan applications, Investment services.

Digital Banking Security (DBSec) (Two-factor authentication, Encryption, Secure sockets layer (SSL) certification, Fraud detection systems.

Control Variables: Technological Familiarity (TF) (Computer literacy, Smartphone ownership, Internet usage frequency, Experience with digital payments). These variables are used in the regression model to examine the relationships between digital banking characteristics and consumer behaviour:

Y = β₀ + β₁DBC + β₂DBS + β₃DBSec + β₄TF + ε                                                           (1)

Where: Y = Consumer Behaviour (CB), β0 = Intercept, β1, β2, β3 = Regression coefficients for DBC, DBS, and DBSec, β5 = Control variables for TF, ε = Error term. A more thorough examination of the ways in which various facets of digital banking affect customer behaviour is made possible by this division.  Quantitative data is evaluated using regression analysis, correlation analysis, and descriptive statistics (Field, 2018). To analyse qualitative data, thematic analysis is employed (Braun & Clarke, 2013). Data validation includes ensuring generalizability, validity, and dependability (Creswell, 2018). Reliability is ensured through pilot testing and Cronbach’s alpha analysis. Validity is ensured by content analysis and expert evaluation. Generalizability is ensured by the use of random sampling. According to Saunders et al. (2019), ethical considerations include informed consent, secrecy, and anonymity. Respondents provide written consent before to participation. Data is safely and anonymously stored.

The variables are obtain by building indices. In order to simplify complicated phenomena and make data analysis easier, indexing—the process of combining many variables into a single composite measure—is a typical approach used in this research (Hair et al., 2017). The equally weighted index, sometimes referred to as the simple average index, is one indexing technique that involves calculating the average of the variables. This paper uses this technique to build indices for the variables.

Presentation of Results

In this study, 200 questionnaires were administered, a total of 200 questionnaires were returned constituting 100% return rate. The study was carried out to examine the effect of digital banking on consumer behaviour and marketing strategies in Cameroon. The results were presented using descriptive statistics and ordinary least square regression.  An overview of the descriptive statistics for the variables utilized in the study is given by the results in Table 1. Consumer Behaviour (CB), the dependent variable, has a mean of 1.79 and a standard deviation of 1.321. This implies that Cameroonian customers use and accept digital banking services at a moderate pace.  Digital Banking Channels (DBC), Digital Banking Services (DBS), and Digital Banking Security (DBSec) are independent variables with respective means of 2.17, 2.33, and 2.45. According to these findings, Cameroonian consumers are moderately aware of and use the various digital banking channels, services, and security aspects.  Technological Familiarity (TF), the control variable, with a mean of 1.81 and a standard deviation of 1.021. This implies a modest level of technology familiarity among Cameroonian customers, which could affect their adoption and use of online banking services.  These findings are in line with earlier research that looked at how digital banking services are adopted and used in underdeveloped nations. For instance, Koenig-Lewis et al. (2015) discovered that Ghanaian consumers were only somewhat aware of and used mobile banking services. According to a survey by Awa et al. (2015), online banking services were moderately adopted and used by Nigerian consumers.  The findings also imply that consumers’ adoption and use of digital banking services are significantly influenced by the security of digital banking. This is in line with other research that looked at how security issues affect customers’ use of online banking services. For instance, a 1999 study by Sathye discovered that a major obstacle to the uptake of online banking services was security concerns.  The findings indicate that Cameroonian customers possess a modest level of technology familiarity, which could impact their adoption and utilization of digital banking services. This is in line with other research that looked at how consumers’ adoption of digital banking services is impacted by their expertise with technology. For instance, a study conducted in 2011 by Lee et al. discovered that consumers’ adoption of mobile banking services was significantly predicted by their familiarity with technology.  All things considered, the findings provide light on the variables influencing Cameroonian customers’ adoption and use of digital banking services. The results imply that consumers’ acceptance and use of digital banking services are significantly influenced by digital banking channels, services, and security aspects. The findings also demonstrate how crucial technological familiarity is in influencing customers’ adoption and use of online banking services.

Source: Authors (2024)

 An evaluation of the data’s normality for each of the study’s variables is given by the findings in Table 2. Two popular tests for normalcy are the Shapiro-Wilk and Kolmogorov-Smirnov tests.

The significant Kolmogorov-Smirnov test statistic (p = 0.060) and the non-significant Shapiro-Wilk test statistic (p = 0.660) show that the data for the dependent variable, Consumer Behaviour (Y=CB), is not regularly distributed. This implies that the Consumer Behaviour data is biased, which could be because the data is derived from self-reported metrics.

Additionally, the non-significant Shapiro-Wilk test statistics (p = 0.550, p = 0.222, and p = 0.312, respectively) and Kolmogorov-Smirnov test statistics (p = 0.140, p = 0.510, and p = 0.400, respectively) show that the data for the independent variables, Digital Banking Channels (DBC), Digital Banking Services (DBS), and Digital Banking Security (DBSec), are normally distributed. This implies that these variables’ data is symmetrical and has a normal distribution.

The significant Kolmogorov-Smirnov test statistic (p = 0.110) and the non-significant Shapiro-Wilk test statistic (p = 0.111) for the control variable, Technological Familiarity (TF), show that the data is not regularly distributed. Given that the data is based on self-reported metrics, it is possible that this indicates that the Technological Familiarity data is skewed.

These findings are in line with earlier research that looked into data normalcy in relation to customer behaviour and digital banking. For instance, a study by Gu et al. (2013) discovered that the data regarding the adoption of digital banking was regularly distributed, whereas a study by Al-Smadi (2012) discovered that the data on customer behaviour toward online banking was not.

Concerning the study’s implications, the non-normality of the Consumer Behaviour and Technological Familiarity data may necessitate the application of non-parametric statistical tests or data transformation methods in order to guarantee that the data satisfies the normality assumptions necessary for parametric statistical tests.

Source: Authors (2024)

The validity and reliability of the scales used to measure the study’s variables are evaluated by the results shown in Table 3. The relationship between each item and the overall score for each scale is revealed by the item-total statistics.

The findings show that the adjusted item-total correlation coefficient (.024) for the Consumer Behaviour (Y) scale is low, indicating that there is little link between the item and the scale’s overall score. Furthermore, the scale’s low Cronbach’s alpha coefficient (.393) indicates that its internal consistency dependability is minimal. This could suggest that the scale is inaccurately measuring the Consumer Behaviour construct.

On the other hand, the scales for Technological Familiarity (TF), Digital Banking Channels (DBC), Digital Banking Services (DBS), and Digital Banking Security (DBSec) have higher corrected item-total correlation coefficients (.098,.322,.119, and.247, respectively), indicating a stronger relationship between the items and the scale’s overall score. Furthermore, these scales have higher Cronbach’s alpha coefficients (.324,.106,.299, and.202, respectively), indicating that they are more reliable in terms of internal consistency.

These findings are in line with other research that looked at the validity and reliability of measures used to gauge customer behaviour and the uptake of digital banking. A study by Lee et al. (2013) discovered that the scale for digital banking adoption had good internal consistency reliability, however a study by Wang et al. (2015) indicated that the scale for consumer behaviour toward online banking had low internal consistency reliability.

The findings also imply that the measures for technological familiarity, digital banking channels, digital banking services, and digital banking security are more valid and dependable than the one for consumer behaviour. This could suggest that these measures measure the constructs they are designed to measure more accurately.

Regarding the study’s ramifications, the Consumer Behaviour scale’s poor validity and reliability may need the usage of substitute measurements or data gathering techniques to guarantee correct and trustworthy data. Furthermore, the scales for technological familiarity, digital banking channels, digital banking services, and digital banking security have high dependability and validity, which may suggest that they are better at assessing the constructs they are designed to evaluate.

Source: Authors (2024)

 The pairwise correlations between the variables employed in the study are evaluated by the results shown in Table 4. The correlations shed light on the connections between the dependent variable (consumer behaviour) and the independent variables (digital banking channels, services, security, and technological familiarity).

The findings show that Digital Banking Services (DBS) and Consumer Behaviour (Y) have a substantial positive connection (r = 0.119, p < 0.05). This implies that customers are more likely to display favorable consumer behaviours including adoption, frequency of use, happiness, and loyalty if they use digital banking services. This result is in line with other research that looked at the connection between customer behaviour and digital banking services. For instance, a 2017 study by Alalwan et al. discovered that customer behaviour toward online banking was significantly predicted by digital banking services.

Additionally, the findings show that Technological Familiarity (TF) and Consumer Behaviour (Y) have a substantial positive connection (r = 0.100, p < 0.05). This implies that customers who are more accustomed to using technology are more likely to behave favorably when using digital banking. This result is in line with earlier research that looked at the connection between customer behaviour toward digital banking and technological familiarity. For instance, Hernandez et al. (2018) discovered that consumer behaviour toward mobile banking was significantly predicted by technological familiarity.

However, the findings show no significant relationship between Digital Banking Channels (DBC) and customer Behaviour (Y) (r = -0.068, p > 0.05), indicating that the type of digital banking channel used has no effect on customer behaviour. This result is in line with earlier research that looked at the connection between customer behaviour and digital banking channels. According to a study by Lee et al. (2015), for instance, customer behaviour regarding online banking was not significantly impacted by the kind of digital banking channel used.

Additionally, the findings show no significant relationship between Consumer Behaviour (Y) and Digital Banking Security (DBSec) (r = -0.085, p > 0.05). This implies that customer behaviour is not much impacted by the degree of security offered by digital banking. This result contradicts earlier research that looked at the connection between consumer behaviour and the security of digital banking. For instance, a 1999 study by Sathye discovered that customer behaviour about online banking was significantly predicted by the security of digital banking.

To sum up, the findings of the pairwise correlations shed light on the connections between the independent and dependent variables. The results indicate that while the type of digital banking channel used and the degree of digital banking security do not significantly affect consumer behaviour, digital banking services and technological familiarity are significant determinants of consumer behaviour toward digital banking.

Table 4: Pairwise correlations

The multicollinearity between the independent variables utilized in the study is evaluated by the results shown in Table 5. The degree to which multicollinearity inflates a regression coefficient’s variance is indicated by the Variance Inflation Factor (VIF).

All of the independent variables’ VIF values are less than 2, according to the results, indicating that there isn’t any significant multicollinearity between them. Technological Familiarity (TF), Digital Banking Security (DBSec), Digital Banking Services (DBS), and Digital Banking Channels (DBC) have VIF scores of 1.099, 1.067, 1.052, and 1.04, respectively. The fact that all of these values are near to one indicates that these factors do not significantly correlate with one another.

Additionally, the mean VIF score of 1.065 indicates that the variables do not significantly exhibit multicollinearity. This is in line with earlier research that looked at the connection between consumer behaviour and digital banking. Akhtar et al. (2017), for instance, discovered no significant multicollinearity among the independent variables they employed.

The absence of multicollinearity among the variables implies that each one is adding distinct information to the model and that correlations between the variables are not driving the outcomes. This implies that the findings are solid and gives trust in the study’s outcomes.

The absence of multicollinearity among the variables implies that the investigation can move forward with confidence in the findings, which has significance for the research. With multicollinearity no longer a concern, the study can now investigate the connections between the independent variables and the dependent variable, consumer behaviour.

Table 5: Variance inflation factor

Source: Authors (2024)

The model summary statistics for the regression analysis are summarized in Table 6’s results. Both the predictive ability of the independent variables and the model’s goodness of fit are revealed by the model summary statistics.

According to the findings, the model’s comparatively low R-value of 0.209 indicates that only a tiny percentage of the variance in the dependent variable, consumer behaviour, can be explained by the independent factors. Nonetheless, the model appears to account for almost 58% of the variation in consumer behaviour, according to the R-squared value of 0.580. This indicates that the independent variables are fairly successful in forecasting consumer behaviour, with a moderate to high level of explanatory power.

The model may be somewhat overfitting the data, as indicated by the adjusted R-squared value of 0.540, which is marginally less than the R-squared value. Nonetheless, there is little variation between the R-squared and adjusted R-squared values, indicating that the model is comparatively resilient.

The estimate’s standard error is 1.305, indicating that the model predicts consumer behaviour with a reasonable degree of accuracy. The estimate’s standard error is not very low, though, which raises the possibility that the model is not accounting for all of the data’s variability.

Although there may be some degree of data fluctuation that the model is unable to account for, the model summary statistics generally indicate that the independent variables are quite effective in forecasting consumer behaviour. These results are in line with other research that looked at the connection between consumer behaviour and digital banking. For instance, a study by Singh et al. (2017) discovered that customer behaviour toward digital banking was significantly predicted by digital banking channels and services.

Regarding the study’s consequences, the model summary statistics indicate that the independent variables predict consumer behaviour with a respectable degree of accuracy. Nevertheless, the findings also imply that the model might not account for all of the data’s variability. Therefore, in order to capture the complexity of the data, the study can benefit from the addition of more independent variables or the application of more sophisticated statistical techniques.

Source: Authors (2024)

An evaluation of the regression model’s overall significance is given by the findings in Table 7. The regression model’s sum of squares, degrees of freedom, mean square, F-statistic, and p-value are all shown in the Analysis of Variance (ANOVA) table.

Based on the results, the p-value of 0.000 and the F-statistic of 2.217 show that the regression model is significant. This implies that a considerable amount of the variance in the dependent variable, consumer behaviour, may be explained by the independent variables—digital banking channels, digital banking services, digital banking security, and technological familiarity—taken together.

Additionally, the R-squared value (not displayed in the table) shows that the regression model accounts for around 4.3% of the variance in consumer behaviour. Given the comparatively low degree of explanatory power, it is possible that the model does not account for all the variables that affect consumer behaviour.

These results are in line with other research that looked at the connection between consumer behaviour and digital banking. Tan et al. (2017), for instance, discovered that while digital banking channels and services were substantial predictors of consumer behaviour toward digital banking, the model only accounted for a comparatively modest percentage of the variation in customer behaviour.

The absence of multicollinearity between the variables (as indicated in Table 5) further supports the findings that the independent variables are not redundant. This implies that the results are not being influenced by correlations between the variables and that each independent variable is adding distinct information to the model.

Regarding the study’s implications, the findings indicate that while the independent factors taken together are a substantial predictor of consumer behaviour, the model might benefit from the addition of more independent variables to boost its explanatory power. The findings also emphasize how crucial it is to take into account the distinct contributions that every independent variable makes to the model rather than depending only on the variables’ combined significance.

Source: Authors (2024)

An evaluation of the connections between the independent variables and the dependent variable, consumer behaviour, is given by the data shown in Table 8. The unstandardized coefficients, standardized coefficients, t-statistics, and p-values for every independent variable are listed in the coefficients table.

The findings show a substantial and positive correlation between consumer behaviour and digital banking services (DBS) (β = 0.138, p = 0.002). This implies that customers are more likely to display favorable consumer behaviours including adoption, frequency of use, happiness, and loyalty if they make use of digital banking services like fund transfers, bill payments, and account management.

Additionally, the findings show a positive and substantial correlation between Digital Banking Security (DBSec) and Consumer Behaviour (β = 0.122, p = 0.001). This implies that customers are more likely to behave favorably when they believe that digital banking is secure due to the use of two-factor authentication, encryption, and secure sockets layer (SSL) certification.

On the other hand, the findings show a substantial and negative correlation between Digital Banking Channels (DBC) and Consumer Behaviour (β = -0.097, p = 0.003). This implies that customers may not always behave in a good manner while using digital banking channels like social media, mobile, and online banking.

Additionally, the findings show a positive and substantial association between Consumer Behaviour and Technological Familiarity (TF) (β = 0.100, p = 0.000). According to this, customers who are more accustomed to using computers, smartphones, and the internet are more likely to behave positively as consumers.

These results are in line with other research that looked at the connections between consumer behaviour and digital banking. For instance, a study by Kassim et al. (2017) discovered that security and digital banking services were important indicators of how customers will behave when using digital banking. In a similar vein, Al-Shammari et al. (2016) discovered that consumer behaviour toward online banking was significantly predicted by technological familiarity.

about the study’s ramifications, the findings indicate that customer behaviour about digital banking is significantly influenced by digital banking services, security, and technological familiarity. In order to improve customer behaviour, banks and other financial institutions may need to concentrate on enhancing their technology infrastructure, security, and digital banking services.

Source: Authors (2024)

 An evaluation of the existence of heteroskedasticity in the data is given by the findings in Table 9. When the variance of the residuals varies across all levels of the independent variables, this is known as heteroskedasticity. One popular test for heteroskedasticity is the Breusch-Pagan/Cook-Weisberg test.

The chi-squared statistic of 4.20 and the p-value of 0.0403 show that the data show that the null hypothesis of constant variance (Ho) is rejected. This implies that the data has a considerable amount of heteroskedasticity.

Because it can impact the precision of the standard errors and the dependability of the hypothesis tests, heteroskedasticity may have an impact on how the results are interpreted. The standard errors may be understated when heteroskedasticity is present, which could result in inflated t-statistics and erroneously low p-values.

These results are in line with other research that looked at the problem of heteroskedasticity in relation to consumer behaviour and digital banking. For instance, a study by Faisal et al. (2018) discovered that their data on the adoption of digital banking had heteroskedasticity and that robust standard errors were required to provide accurate estimates.

Regarding the study’s consequences, the existence of heteroskedasticity implies that in order to achieve accurate estimates, resilient standard errors or other methods for dealing with heteroskedasticity may be required. Furthermore, the study can profit from the application of different estimation techniques, like weighted least squares (WLS) or generalized least squares (GLS), which might yield more accurate estimates when heteroskedasticity is present.

Table 9: Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Source: Authors (2024)

CONCLUSION

 This study looked into how Cameroonian marketing strategies and consumer behaviour were affected by digital banking. The results of the study demonstrate that technological familiarity, security, and digital banking services all significantly predict customer behaviour toward digital banking. The study specifically found a strong and favorable relationship between customer behaviour and digital banking services like account management, bill payment, and fund transfers. Similarly, customer behaviour and digital banking security features like encryption, secure sockets layer (SSL) certification, and two-factor authentication are positively and strongly correlated. Additionally, it was demonstrated that technical familiarity—such as computer literacy, smartphone ownership, and internet usage frequency—and customer behaviour were positively and significantly correlated.

Furthermore, the study found no significant multicollinearity between the independent variables, suggesting that each one contributes unique data to the model. The study found that the residuals’ variance is not constant across all levels of the independent variables, a phenomenon known as heteroskedasticity. The findings of the study have important implications for financial institutions and banks in Cameroon. The report specifically suggests that banks and other financial institutions focus on improving their technology infrastructure, security, and digital banking services in order to improve client behaviour. The report also highlights how important it is to consider the unique needs and preferences of Cameroonian consumers when developing digital banking services and marketing strategies. Overall, this study contributes to the body of knowledge currently available on the topic by illuminating the factors impacting Cameroonian consumers’ opinions regarding digital banking. The study’s findings have important implications for lawmakers, academics who research consumer behaviour and digital banking, banks, and other financial institutions. Future research could build on the findings of this study by examining the impact of digital banking on customer behaviour in other African countries. Future studies should also look at how other factors, such demographics, cultural traits, and psychographics, influence how users behave when utilizing digital banking. Finally, future studies might examine the impact of digital banking on consumer behaviour in other industries, such retail and healthcare. It’s critical to acknowledge this study’s numerous shortcomings. First, due of the small sample size of the study, the conclusions might not be as widely relevant as they could be. Second, the study relied on self-reported data, which is subject to bias and inaccuracy. Finally, the study did not examine the long-term effects of digital banking on consumer behaviour, which may be an important area for future research.

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