The first set of responses relates to demographic data—gender, age, education, computer literacy, and income. While gender is irrelevant to the research objectives, other demographics are key to analyzing digital solution usage.

As observed in Table 1 , 56.6 percent of participants were female, while the remaining 43.3 percent were male—in absolute numbers out of 145 participants 82 were female and 63 were male.

Table 1. Gender distribution.

Source: Authors’ computation, 2024.

Based on the information included in Table 2 , participants were divided into four age groups which showed that almost 52.4 % of all participants were between 25 and 44 years old. The least number of participants are older than 65, only 15 participants. Also, there was a low number of the youngest gender group between 18 and 24 years old.

Table 2. Age distribution.

Source: Authors’ computation, 2024.

The conclusion drawn from Tables 3 and Tables 4 , is that education and computer literacy complement each other, since these categories seem to be interconnected and mutually supportive.

According to Table 5 , the youngest and the oldest participants are within the lowest categories of income per month. Those that belong to the group of youngest participants just started working and probably have just a few years of experience, while on the other side, the 65+ category is close to or in retirement and therefore their income is lower, compared to the other two categories.

Table 3. Age distribution and level of education.

Source: Authors’ computation, 2024.

Table 4. Age distribution and computer literacy level.

Source: Authors’ computation, 2024.

Table 5. Age and monthly income.

Source: Authors’ computation, 2024.

Tables 6 and Tables 7 present the sample’s educational level distribution and computer literacy distribution, respectively. Most of the participants have a graduate degree a total of 93 or 64.1% of the sample, followed by participants with an undergraduate degree (24 participants) and those with a secondary education degree (22 participants).

Table 6. Education distribution.

Source: Authors’ computation, 2024.

Table 7. Education and computer literacy distribution.

Source: Authors’ computation, 2024.

Education and computer literacy go hand in hand, so there is no graduate degree without computer skills, while on the other hand, persons with only primary education, have no computer skills at all. Results indicate that persons with graduate degrees are more familiar with computers/technology/digitalization and probably will be more oriented toward new trends and have higher acceptance levels, compared to the group without education and computer skills.

Table 8 shows that monthly income is directly related with education, especially in the higher categories, meaning that higher salaries, above EUR 1000, are reserved for participants with graduate degrees, while there is no person with secondary or lower education that has a salary above EUR 1000.

Most of the participants are computer literate, a total of 106 or 80% of the sample, followed by participants with partial computer skills, a total of 29 participants, while 10 participants are computer illiterate as can be seen in Table 9 .

Table 8. Education and monthly income distribution.

Source: Authors’ computation, 2024.

Table 9. Computer literacy distribution.

Source: Authors’ computation, 2024.

Similar results with the education hold for computer literacy, as displayed in Table 10 ; i.e., someone who is computer illiterate cannot obtain a salary above EUR 1000, and with partial computer skills only 3 out of 29 can have a monthly income above EUR 1000.

Within the sample of 145 participants, the majority earns between EUR 1000 - EUR 2000, which is a total of 53 participants or 36.6%, followed by the lower category, that of EUR 500 - EUR 1000, or 37 participants. The third category, the one above EUR 2000, accounts for 33 participants or 22.8%, as displayed in Table 11 .

The second group of results refers to the level and frequency of usage of digital solutions. In Table 12 most of the sample, or 58.6%, use digital services above 5 years.

Table 10. Computer literacy and monthly income distribution.

Source: Authors’ computation, 2024.

Table 11. Monthly income distribution.

Source: Authors’ computation, 2024.

Table 12. Level of usage of digital services.

Source: Authors’ computation, 2024.

Looking closer at the age categories in Table 13 , it is evident that the majority of the sample, or 61 participants, have been using digital services for more than 5 years, and they belong to the 25 - 44 group, followed by 22 participants from the 45 - 64 group. All of those who belong to the youngest age group use digital services, while only one who never used any digital services is from the “silent generation” of 64+.

Table 13. Level of usage of digital services within the different age groups.

Source: Authors’ computation, 2024.

There is no person with higher or graduate degree in the sample that does not use digital services, while those that do not use any of the digital services are those having a lower level of education ( Table 14 ).

Table 14. Level of usage of digital services and level of education.

Source: Authors’ computation, 2024.

Those that do not use digital services belong to the computer illiterate group, which is similar to what we observed for the education level. On the other hand, all of those with a good level of computer skills have been using digital services, and most of them for more than 5 years. All of the above are evident in Table 15 above.

Table 15. Level of usage of digital services within the different age groups.

Source: Authors’ computation, 2024.

Similar to education, computer literacy and monthly income are also interconnected, where the higher the education level is, the higher their computer literacy, and the higher the level of usage of digital services as well, as described in Table 16 .

Table 16. Level of usage of digital services within the different age groups.

Source: Authors’ computation, 2024.

The observed frequency of usage of digital services is rather higher, with 73.8% using digital services for all of their transactions ( Table 17 ).

Table 17. Level of usage of digital services within the different age groups.

Source: Authors’ computation, 2024.

Showing alignment with the level of usage, the highest frequency is within the age group of 25 - 44 (68) and 45 - 64 (29) of those using digital services for all their transactions; within the group of those above 64+ only 2 are using digital services for all their transactions, while we find the same number of people not using digital services at all ( Table 18 ).

Table 18. Frequency of usage of digital services within the different age groups.

Source: Authors’ computation, 2024.

As can be seen in Table 19 , the majority of graduate degree holders is using digital services for all their transactions, while people with an educational level of primary education are using digital services only 1 to 3 times per month or not at all.

Table 19. Level of usage of digital services within the different age groups.

Source: Authors’ computation, 2024.

The majority of computer literate persons are using digital services for all transactions, while computer illiterate ones rarely use a computer, approximately 1 to 3 times per month or not at all ( Table 20 ).

Table 20. Level of usage of digital services within the different age groups.

Source: Authors’ computation, 2024.

With regards to the two highest paid groups in terms of monthly income (EUR 1000 - 2000 and above EUR 2000), 79 out of 86 are using digital services for all transactions. The second main finding emanating form Table 21 is that the lower the income, the lower the frequency of the usage of digital banking services.

Table 21. Level of usage of digital services within the different age groups.

Source: Authors’ computation, 2024.

Figure 1 presents the use of the most common digital services. What is clear is that out of the 6 digital services that are offered, the majority uses M-banking and E-Banking, which are evidently the two dominant services, followed by the smart ATM. All other services are used by a small number of people, around 20 for the E-wallet and Online loans/account opening, and less than 10 for the Digital Assistant. These constitute less than 15% of the sample.

The third group of results covers customer satisfaction, divided into four subcategories: Efficiency, System Availability and Reliability, Fulfillment, and Security and Privacy. To assess perceived service quality differences, these results are analysed across social factors such as age, education, computer literacy, and income. The Means tables, displaying the central tendency for each group, highlight differences in dependent variables. These comparisons, analysed using multivariate techniques, are vital for identifying patterns and informing decision-making, with social factors as the dependent variable across all subgroups.

The analysis of age as a social factor reveals high satisfaction scores in Efficiency, System, and Fulfillment categories, while Privacy scores lower. Data security and privacy remain concerns across all age groups.

Table 22 shows that the 25 - 64 age group scored higher, while older customers displayed the largest deviation in perceiving digital service efficiency.

Table 22. Efficiency by age in years.

Source: Authors’ computation, 2024.

System availability and reliability scores range from 4.37 to 4.67, with the youngest group (under 24) scoring the highest (4.76 - 4.81). The oldest group exhibits the lowest result (4.0), reflecting lower trust in new technology. Within the questions referred to system availability and reliability the mean score of the sample is between 4.37 - 4.67, while the highest scores within the youngest age group up to 24 years are 4.76 - 4.81. Younger generations are raised with different types of digital content delivered through mobile phones and tablets, which can be an indication of their faster acceptance of new products and services. On the other side, the oldest group again exhibits the lowest score (4.0), which signifies less trust in availability and reliability, thus, even less trust in new technology ( Table 23 ).

Table 23. System availability by age in years.

Source: Authors’ computation, 2024.

Table 24 shows that fulfillment scores are high (above 4.6), with younger age groups scoring higher. The 45 - 64 group scores slightly lower, and the oldest group shows the lowest fulfillment. Then, Table 25 reports privacy scores signifying that the 45 - 64 age group reports the highest mean scores.

Table 24. Fulfillment by age in years.

Source: Authors’ computation, 2024.

Table 25. Privacy by age in years.

Source: Authors’ computation, 2024.

The second social category that can play a significant role in the level of acceptance and usage of digital services is education. The initial premise of this paper is that the lowest the education level is, the lowest the acceptance and usage level of digital services are. Analysis of the results shows that the greatest visible difference between classified social groups based on education are recorded in this subcategory.

Our results in Table 26 , show explicitly the lowest level of results within the group of primary education participants, where mean results for the positive formulated questions is within the range of 3.33 - 3.83, while the sample mean is 4.34 - 4.70.

Table 26. Efficiency by level of education.

Source: Authors’ computation, 2024.

Results of means record the gradual growth of the graduate education population, which is the only group above the sample mean for all the questions.

The mean scores of system availability and reliability of the sample listed in Table 27 , are between 4.37 - 4.68, while the highest scores within the two most educated groups are between 4.38 - 4.84. There is also a pattern with the primary education group, which is visible in this subcategory as well, where the results for the means vary and are between 3.17 - 3.50; a finding that is significantly lower compared to the result for the sample mean.

Table 27. System availability by level of education.

Source: Authors’ computation, 2024.

A very high score on fulfillment is observed. It is above the sample mean of 4.6, for the Graduate degree subgroup, while the primary education group posted scores between 3.17 - 3.83, indicating neutral responses on average ( Table 28 ).

Table 28. Fulfillment by level of education.

Source: Authors’ computation, 2024.

At the sample level, privacy has recorded the lowest scores, indicating it as the main concern of users of digital services, as can be seen in Table 29 . Within the primary education group, a score of ≤ 3, exhibits significant doubt in terms of the protection of personal information, transactions details, and financial information.

Table 29. Privacy by level of education.

Source: Authors’ computation, 2024.

Along with education, computer literacy plays a significant role in the degree of acceptance and usage of digital services. The positive effect of the increase in computer literacy, reported in the last 10 years according to the data from the census, is visible in this study, where only 10 participants self-declared as being computer illiterate.

Results for the evaluated efficiency of digital services, reported in Table 30 , are the lowest for the illiterate population (3.50 - 3.80) and the highest for the computer literate (4.45 - 4.87), while the sample mean is at a range of 4.36 - 4.70.

Table 30. Efficiency by computer literacy level.

Source: Authors’ computation, 2024.

Again, strong difference in the results from illiterate group compared to other two groups is observed in terms of system availability in Table 31 , where both the partial and literate population recorded means are above the sample mean of ≥ 4.37 - 4.67, while the illiterate group results are between 3.40 - 3.50.

When all individuals are considered (regardless of the category in which they belong), the mean scores for each computer literacy feature fall between the means of the computer illiterate and the computer literate categories. Standard deviations for the overall data are moderate, indicating moderate variability in responses across the entire sample.

Table 31. System availability by computer literacy level.

Source: Authors’ computation, 2024.

Computer literate population, being the dominant population within the sample, has recorded very high scores on the fulfillment questions within a range of 4.58 - 4.81 (for positively formulated questions). Population with partial computer skills is of a rather similar level with the entire sample, while computer illiteracy is still present. Table 32 reports the above findings.

Table 32. Fulfillment by computer literacy level.

Source: Authors’ computation, 2024.

All questions about privacy (and security) exhibit a clear trend where computer literate individuals have the highest mean score, followed by individuals with partial computer skills and then the computer illiterate individuals ( Table 33 ). The overall mean score falls between the means of computer literate and partial computer skills categories.

Table 33. Privacy by computer literacy level.

Source: Authors’ computation, 2024.

Monthly income is the last social category that has been used for the evaluation of the acceptance of, utilization of, and satisfaction with digital services. The main result in the overall distribution is a very small deviation between the different social groups; an indication that monthly income does not represent a social category that plays a significant role in the usage of digital services. One of the reasons for this finding is the observation of the structure of the monthly income by age, where the youngest people are usually the ones with the lowest revenues, but in this case they are also very well digitally oriented with a high level of acceptance of new technologies.

Individuals with monthly incomes up to EUR 500 have efficiency scores from 4.05 to 4.36. Those earning EUR 500 - 1000 score between 4.38 and 4.54; the EUR 1000 - 2000 group shows scores from 4.57 to 4.92, while the highest income group scores are between 4.30 and 4.94. What we can infer is that higher incomes correlate with higher efficiency scores ( Table 34 ).

Table 34. Efficiency by monthly income.

Source: Authors’ computation, 2024.

Again, in Table 35 , we can observe that there is a very small difference between the results obtained among different social groups that are divided according to the monthly income. At the sample level, mean results for all questions vary between 4.37 - 4.68 which is below the mean results for efficiency. There is a visible pattern though, that the higher the income, the higher the mean scores across all system availability categories.

Table 35. System availability by monthly income.

Source: Authors’ computation, 2024.

The overall mean ratings for all income groups and systems range from 4.46 to 4.66, with the standard deviations for the total mean ranging from 0.676 to 0.850. The data presented in Table 36 is an indication of moderate variability or dispersion in the ratings within each income group and system.

Table 36. Efficiency by monthly income.

Source: Authors’ computation, 2024.

With regards to privacy, in Table 37 , we can see that the group with an income of EUR 1000 - 2000 records the highest scores, but the trend that the higher the income is, the higher the scores found in previous categories does not hold here, since the highest paid individuals recorded lower scores on privacy and security concerns.

Table 37. Privacy by monthly income.

Source: Authors’ computation, 2024.

Next, we explored the level of satisfaction per age group by addressing whether there is satisfaction with the E and/or M banking applications’ overall performance, whether participants would prefer customized digital solutions, and whether they prefer the use of digital services instead of visiting the bank ( Table 38 ).

Table 38. Level of satisfaction by age in years.

Source: Authors’ computation, 2024.

The overall satisfaction has a mean of 2.74, with the oldest population exhibiting the lowest and the youngest the highest scores. The mean preference for customized digital solutions is 1.99, indicating that older users might benefit from simpler options. The preference for digital services over visiting banks is 2.86, reflecting their primary usage.

Like previous results, what Table 39 signifies is that the lowest the level of education, the lower the scores, and vice versa; graduate degree category exhibits higher results compared to the sample mean, and other categories are below the sample mean; while the primary education group shows the highest score on customized solutions.

Computers literacy exhibits similar results with the education in general; illiterate persons demonstrate lower scores on the e-services usage satisfaction, thus they prefer to go to the bank, while the highest scores hold for the customized solutions ( Table 40 ).

The smallest difference between the different social categories, with a standard deviation in the range of 0.486 - 0.764, suggests a moderate level of variability in satisfaction across all respondents. This is evident by the data reported in Table 41 .

Table 39. Level of satisfaction by level of education.

Source: Authors’ computation, 2024.

Table 40. Level of satisfaction by computer literacy level.

Source: Authors’ computation, 2024.

Table 41. Level of satisfaction by monthly income.

Source: Authors’ computation, 2024.

In summary, the analysis of survey results reveals crucial insights into the digitalization of banking services in Serbia, aligning with the study’s initial research objectives. The findings highlight a transformative shift in the banking landscape, where the volume and monetary value of transactions via mobile and electronic channels have significantly increased. This evolution suggests that traditional teller roles may become obsolete as banks explore cashless and peopleless branch models. However, challenges remain, particularly concerning security concerns, which emerged as the foremost issue for many respondents. This underscores the need for enhanced customer trust to facilitate the broader acceptance and utilization of digital banking solutions.

Moreover, the impact of sociodemographic factors such as age, education, computer literacy, and income on digital service usage and perceived quality is pronounced. The results indicate that older individuals exhibit lower levels of engagement with digital banking, while educational background and computer literacy are directly correlated with service adoption. Respondents expressed a clear preference for customized digital solutions, particularly those who are older or have a lower educational level, suggesting that a tiered approach to M-banking could effectively cater to diverse user needs. These findings resonate with existing literature, emphasizing the critical role of demographic factors in shaping digital banking experiences. As the discussion unfolds, it will explore these themes further, considering the implications of these findings for the future of digital banking in Serbia.

The research affirms that the digitalization of banking services is an ongoing, evolving process, evidenced by the increasing volume and value of mobile and electronic banking transactions. The trend towards peopleless and cashless branches suggests that traditional teller positions may become obsolete, as most transactions can now be executed through electronic channels and intelligent ATMs.

A PEST analysis revealed that sociological and technological factors are paramount in the digitalization of banking services. Our results highlight security concerns as the most significant challenge, surpassing issues related to effectiveness, fulfillment, and system functionality. Addressing the lack of trust in security and privacy is crucial for enhancing acceptance, utilization, and customer satisfaction with digital services ( Mehta & Manhas, 2005 ; Özlen & Djedovic, 2017 ).

The study confirms the pivotal role of social factors in influencing the usage and acceptance of digital solutions, which ultimately impacts the perceived quality of digital services. Ali (2023) corroborates this finding, noting significant associations between demographic variables (gender, age, education, working status, and household income) and digital banking adoption.

The key findings of the research can be summarized as follows. With regards to Age, the oldest age category shows the lowest levels of utilization and satisfaction with digital services; in terms of Education and Computer Literacy, lower levels correspond to diminished acceptance and utilization of digital services. Dimitrova (2022) further emphasizes the direct relationship between financial literacy and the digitalization of banking services; Finally, when analyzing the effect of Income, less disparity was observed between income groups, likely due to younger individuals being both in lower income brackets and exhibiting a more tech-savvy behavior. Given Serbia’s aging population and predominance of secondary education levels among its population, the overall acceptance and utilization of digital services may be lower than the survey results suggest.

The survey revealed a preference for customized digital solutions among older individuals (64+), those with primary education, and the computer illiterate. This aligns with Ali’s (2023) finding that higher adoption rates can be achieved through systems designed to satisfy different target groups, with simplicity and ease of use playing the most important role.

A strategic proposal emerging from these findings is the introduction of tiered M-banking solutions: A basic package prioritizing user-friendliness with fundamental functionalities; an advanced package offering a broader spectrum of features. This approach could optimize user experience and accessibility across diverse population segments.

Jiang & Taşkin (2023) emphasize the importance of considering demographic characteristics and understanding customer demands when developing digital banking strategies. Conversely, Agarwal et al. (2023) highlight that customers engage with digital banking services due to perceived value, such as reduced stress, lower costs, and time savings. To sustain customer trust, financial institutions must proactively address security challenges, prevent fraud, and safeguard the confidentiality of customer transactions and financial information.

In conclusion, this research underscores the complex interplay of social, technological, and security factors in the adoption and satisfaction of digital banking services. Future strategies should focus on tailored solutions, enhanced security measures, and targeted education to bridge the digital divide across diverse demographic groups.

Digital banking services represent a significant technological advancement in the banking industry. Previous research has largely focused on the impact of digitalization on banking, examining factors influencing the adoption of digital banking services and exploring customer satisfaction and perceptions in markets such as India, New Zealand, and Vietnam. However, there is a notable lack of research on the Serbian banking sector. Since each market has unique characteristics, this paper aimed to fill this gap by providing insights specific to Serbia. The findings do not only contribute to this underexplored area but also offer broader implications for other fields. This study has attempted to bridge the literature gap and explore the relationship between demographic characteristics, social variables, service quality, and customer satisfaction across various markets.

The introductory section highlighted a prevalent industry trend where many market participants follow a one-size-fits-all strategy. This study’s unique contribution lies in its potential to help industry managers make more informed decisions regarding customized and implemented tailored banking solutions. By offering additional insights that might initially be overlooked, this research encourages a more refined approach to decision-making. With the support of these insights, the decision-making process for developing digital services is expected to become more precise, ultimately benefiting the banking industry and its institutions. The anticipated outcomes include higher utilization rates and increased customer satisfaction, positioning banks to extract greater value from their operations.