Digital Communication in Higher Education Settings: A Pilot Study on Students’ Behavioural Trends

Abstract

In the present research paper, we argue that digital transformation and students’ behavioural trends are intertwined through the increasing integration of digital technology tools into both academic and personal communication contexts. We construct our argument through a rigorous methodological approach employing quantitative and qualitative analyses, including ANOVA, Kruskal–Wallis, chi-squared, and multiple regression models, to assess the main predictors of digital communication satisfaction and engagement. Hence, we emphasise distinctly the diverse roles of digital communication platforms as relating to the expression of students’ engagement and adaptability, beyond mere technological adoption. While investigating the role of behavioural economics in modelling students’ engagement with digital technologies, we examine the impact of digitalisation on students’ communication patterns, both in terms of personal and academic purposes. To measure student engagement, we employ a mixed-methods approach by carrying out a pilot study (N = 167). The findings underline the role of digital transformation in enhancing students’ access to learning, communication, and collaboration tools, while they also align with Sustainable Development Goal 4 (Quality Education). Our intention is to develop a more comprehensive model that integrates behavioural insights with technology acceptance theories, while another further direction could be exploring longitudinal data to assess the long-term impacts of digital tools on student engagement and learning outcomes.

1. Introduction

Over time, it has been found that technology has had a significant impact on society because of its catalyst capabilities, making an increasing level of social interaction possible only if individuals possess digital skills. Informal education provided within family-related experiences sometimes acts as a departure point for digital competence development, since increasing numbers of children have access to the digital environment and start using technology from an early age. This is due to the fact that human beings are naturally curious and eager to discover new things and seek to develop on numerous levels. Moreover, digital competences encompass those skills or habits that assist individuals of all ages in using technology both socially and professionally, in order to solve as many problems as possible and adapt to various situations. In order to assess these skills, in 2013, the European Commission launched DigComp 1.0, where skills in areas such as information, communication, and new content creation, as well as in areas such as security and problem solving, can be evaluated. Subsequently, two updates to the initial model were introduced: in 2016, DigComp 2.0 emerged, followed, in 2017, by a further improved version, DigComp 2.1 [1].

On one hand, studies have shown that technological development has led to an increased level of social interaction among young Europeans, whose style of communicating and interacting has also shifted across digital landscapes [2]. The ways in which young individuals communicate depend on the connections and relationships that they build with others. A common point is the use of social networks, because these give them the opportunity to stay connected with people from other cities or countries but also to find other individuals who might share the same passions, hobbies, and values. Recent findings also indicate that WhatsApp, YouTube, and Instagram are among the most used digital communication platforms [3].

At the same time, in the case of students, the use of digital communication platforms and social networks has important effects on academic performance but also from the perspective of mental health. Online education has been developed rapidly, and it is appreciated by both students and teachers because it represents an easy and efficient way to access information. This can be accessed from anywhere as long as there is a device and a stable internet connection. Students’ preferred means of connection to online classes are usually through smartphones, laptops, or PCs. Thus, this leads to a high level of time spent in front of screens, which can ultimately have negative effects on students, such as creating an addiction to being connected to the digital environment [4]. Although this addiction can have a moderate level of influence on academic results, the outcomes are worse from a health perspective, as both mental and physical health are considerably affected [5]. Accordingly, scholars seem to agree that digital overload can negatively impact students’ well-being, with multiple side effects related to mental health issues [6].

On the other hand, a common aspect in physical classes is the fact that teachers have the opportunity to maximise their students’ levels of concentration, because the items that can distract them inside a classroom are reduced compared to those that can negatively influence their attention in a virtual environment. When students use both verbal and visual systems, the information that they learn is assimilated more easily into their long-term memory [7]. Researchers from around the world have emphasised that learning through smart devices such as smartphones or laptops is highly useful, as it represents an easy method of accessing information at any time, regardless of geographical location, as long as the technology is controlled by the student and the student is not being controlled by technology [8].

At the same time, a common requirement among students in terms of digital communication is the ability to retrieve information in a fast, clear, and complete manner, encompassing all aspects of academic activities. According to studies, with technological development, many students prefer to receive personalised information, meeting the needs and requirements of each one, instead of general information [9]. Lastly, feedback is highly appreciated, especially through digital communication, as it gives participants the opportunity to improve specific aspects. Often, the desire to achieve their goals in a better way exists, but lacking a solution to a particular obstacle causes them to abandon their journey. Taking into account that the digital environment is one with multiple valuable components, many students feel more comfortable learning and communicating in this way; however, this environment must be regulated so that the physical and mental health of the participants is not endangered [10].

Therefore, the central aim of the current study was to investigate the role of behavioural economics in modelling students’ engagement with digital technologies, focusing on the impact of digitalisation on university students’ communication patterns, both in terms of personal and academic purposes. To achieve the primary objective, the following core research questions were formulated.

RQ1: Does age influence how frequently students use digital communication platforms?

RQ2: Are there significant gender differences between students in their preferred communication platform?

RQ3: Do students from different faculties prefer different digital communication platforms?

RQ4: Are Master’s students more likely to use digital platforms for academic communication than undergraduates?

RQ5: Are students who report improved collaboration, speed, or feedback also more satisfied with academic technical support?

RQ6: Do students who use digital platforms more frequently perceive fewer barriers in communication?

RQ7: Does the higher daily use of digital platforms correlate with negative mental health perceptions?

RQ8: Do students who perceive many barriers expect a negative future impact of digital transformation on communication?

The next section of this paper discusses the impact of digital transformation on higher education communication between educators and students. In this context, the following research hypotheses were formulated.

H1: 

Students enrolled in higher education institutions possess at least a satisfactory level of knowledge in using digital communication platforms.

H2: 

Students are likely to continue using digital platforms in academic communication once a framework has been established by the higher education institution.

H3: 

Using digital platforms in communication enhances students’ engagement and collaboration in educational and academic activities.

2. Materials and Methods

The main aim of this research paper was to investigate the role of behavioural economics in modelling students’ engagement with digital technologies through an exploratory investigation of digitalisation’s impact on university students’ communication patterns, both in terms of personal and academic purposes. It was decided that the best method to be adopted for this investigation was to combine qualitative and quantitative analyses. For the qualitative analysis, we employed a pilot study using a questionnaire-based survey to examine students’ perceptions (N = 167) of digital communication in terms of habit shifts, increased satisfaction, and issues related to digitalisation.

The questionnaire consisted of 28 questions related to students’ opinions, of which the last one was an open-ended question. Except for the last item, all questions used a Likert scale. The instrument design included six sections and 28 variables, out of which fifteen were dependent variables, and it was developed based on the existing literature on behavioural economics, digital education, and communication studies. The dependent variables measured key effects of digitalisation, while independent variables ensured a context for our analysis (

Table 1. Instrument design and variable selection.

Section of Questionnaire	Variable	Type of Variable
1. Demographic information	-Age range	→ quantitative, continuous, independent
	-Gender	→ qualitative, nominal, independent
	-Level of education	→ qualitative, nominal, independent
	-Faculty	→ qualitative, ordinal, independent
2. Digital communication usage	-Frequency of email access	→ quantitative, ordinal, independent
	-Frequency of social media access to communicate	→ quantitative, ordinal, independent
	-Frequency of DM app access to communicate	→ quantitative, ordinal, independent
	-Frequency of video conference platform access	→ quantitative, ordinal, independent
	-Frequency of blog and forum access for communication purposes	→ quantitative, ordinal, independent
	-Preferred communication platform	→ qualitative, nominal, independent
3. Perceived impact of digital transformation on communication	-Digital transformation’s impact on communication with teachers and peers	→ quantitative, ordinal, dependent
	-Improved communication accessibility (due to digital transformation)	→ quantitative, ordinal, dependent
	-Improved communication speed (due to digital transformation)	→ quantitative, ordinal, dependent
	-Improved communication clarity (due to digital transformation)	→ quantitative, ordinal, dependent
	-Improved collaboration (due to digital transformation)	→ quantitative, ordinal, dependent
	-Improved feedback (due to digital transformation)	→ quantitative, ordinal, dependent
4. Challenges in digital communication	-Technical issues (in digital communication)	→ quantitative, ordinal, dependent
	-Lack of face-to-face interaction (difficulty in digital communication)	→ quantitative, ordinal, dependent
	-Digital overload (difficulty in digital communication)	→ quantitative, ordinal, dependent
	-Confusion and misunderstandings (difficulty in digital communication)	→ quantitative, ordinal, dependent
	-No barrier in digital communication	→ qualitative, nominal, dependent
5. Future perspectives and satisfaction	-Perceived impact of digital transformation on future communication	→ quantitative, ordinal, dependent
	-Satisfaction with academic technical support for digital platforms	→ quantitative, ordinal, dependent
	-Perceived greatest advantage of digital communication in academic context	→ qualitative, nominal, dependent
	-Perceived impact of digital communication on mental health	→ quantitative, ordinal, dependent
6. Time spent on digital platforms	-Average daily time spent on digital platforms for academic purposes	→ quantitative, continuous, independent
	-Average daily time using digital platforms for personal purposes	→ quantitative, continuous, independent

Source: Authors’ research.

).

Moreover, the instrument was further assessed in terms of clarity, relevance, and item coherence using the expertise of three professors within Bucharest University of Economic Studies, Romania, who provided valuable feedback that was incorporated into the final version of our questionnaire. The sample of our study consisted of 167 students enrolled at Bucharest University of Economic Studies, who were recruited using a snowballing approach. Data collection was performed in the last week of May 2024, through online questionnaire dissemination via institutional emails and students’ WhatsApp groups. The choice of this sample was justified by students’ potential to relate their perspectives regarding digital communication technologies’ impacts on their behaviours, the challenges, and the perceived benefits in an academic context. Hence, the rationale for this sample’s selection lay in its relevance to our research questions, as students represent a key demographic group experiencing the transformation of communication in both educational and personal contexts.

Nonetheless, we acknowledge certain weaknesses of this approach, such as non-random selection, potential self-selection bias, and the limited generalisability of the results. Altogether, these limitations might have skewed the results, particularly given that the sample was dominated by female students and students from specific faculties. Despite these limitations, our study provides valuable exploratory insights into students’ digital communication experiences and could act as a basis for future comparative or large-scale investigations.

Data management and analysis were performed using SPSS (Version 20). Thus, it was considered that quantitative measures would usefully supplement and extend our findings through a combination of econometric modelling (multiple regression) and statistical analysis. The first stage of the analysis summarised the key variables using descriptive statistics. Next, the second stage employed inferential statistical tests to examine relationships and differences across the groups (correlation analysis, t-test, ANOVA, chi-squared test). Then, we applied multiple regression analysis to assess the key predictors of digital communication satisfaction and engagement and to determine their significance in modelling students’ experiences with digital communication technologies.

In spite of the current national regulatory context, which does not require formal ethical approval for all research on human subjects, our study strictly followed ethical principles to guarantee informed consent, confidentiality, and voluntary participation. For this reason, participants were fully informed about the purpose of the research, the anonymity of their responses, and also their right to withdraw at any given time, while no personal information was collected. Consequently, our study aligned with international ethical standards for social science research in terms of transparency, informed consent, and data security prioritisation.

Finally, three important steps were taken to ensure the trustworthiness and rigor of our study. First, our questionnaire was pre-tested in May 2024 on a small sample of students to refine the items and improve the clarity. Secondly, the results were cross-validated using both quantitative and qualitative data, which guaranteed the greater credibility and robustness of the findings. Thirdly, we maintained clear documentation of the data processing and statistical methods to provide transparency in data handling.

3. Results

Firstly, caution must be applied while interpreting the results due to the failure to obtain the ideal minimum sample size of at least 370 responses. However, because our research consisted of a pilot study, a sample of 167 responses could still provide important insights. Next, a value of 0.691 for the Cronbach alpha coefficient was obtained, which is generally acceptable, enabling us to assess the internal consistency of the multi-item scale in the instrument as good (via SPSS).

The first stage of analysis summarised the key variables using the frequency distribution of responses and descriptive analysis for the variables.

Table 2. Sociodemographic profile of the respondents (N = 167).

Variable		N	%
Gender	Male	60	35.9
	Female	106	63.5
	Other	1	0.6
Age range	<20	4	2.4
	20–22	142	85
	23–25	12	7.2
	>25	9	5.4
Level of education	1st—Bachelor	18	10.8
	2nd—Bachelor	87	52.1
	3rd—Bachelor	49	29.3
	1st—Master	7	4.2
	2nd—Master	6	3.6
Faculty within Bucharest University of Economic Studies	Theoretical and Applied Economics	19	11.4
	Management	28	16.8
	Agrifood and Environmental Economics	85	50.9
	Cybernetics, Statistics, and Informatics	2	1.2
	Accounting and Management Information Systems	1	0.6
	Finance, Insurance, Banking, and Stock Exchange	2	1.2
	Marketing	1	0.6
	Business and Tourism	2	1.2
	Administration and Public Management	26	15.6
	Business Administration in Foreign Languages	1	0.6

Source: Authors’ research.

shows the sociodemographic profile of the respondents, dominated by female students (63.5%). Moreover, the age range of the participants largely placed them in Gen Z, since most of them (85%) were between 20 and 22 years old. Additionally, the vast majority of the respondents (52.1%) were enrolled in the second year of their Bachelor studies, while almost one third of them (29.3%) were about to finish their undergraduate programs. While there are thirteen faculties composing Bucharest University of Economic Studies, it was found that only ten of them were represented in terms of the students’ faculty affiliations, with most of the participants being enrolled in the Faculty of Agrifood and Environmental Economics (50.9%).

Next, descriptive statistics were analysed according to the variable typology, with

Table 3. Descriptive statistics for scale variables.

	N	Min	Max	Mean	Std. Dev.	Skewness		Kurtosis
	Stat.	Stat.	Stat.	Stat.	Stat.	Stat.	Std. Error	Stat.	Std. Error
Age of respondents	167	1	4	2.16	0.537	2.237	0.188	5.736	0.374
Average daily time spent using digital communication platforms for academic purposes	167	1	5	2.42	1.132	0.581	0.188	−0.327	0.374
Average daily time spent using digital platforms for personal purposes	167	1	5	3.71	1.178	−0.543	0.188	−0.608	0.374

Source: Authors’ research.

offering important data on the time spent by the students on digital platforms for both personal and academic purposes. For instance, the time spent on academic digital platforms was more evenly spread, but with a slight tendency for students to spend a moderate amount of time on the university’s digital platform, while the time spent on personal digital platforms was higher on average and presented significant spread in the responses. This suggests that numerous students likely spend a considerable amount of time on digital platforms for personal purposes. In addition, the age of the respondents was significantly skewed (2.237) toward younger students.

Table 4. Descriptive statistics for ordinal variables.

		Level of Education	Frequency of Email Access	Frequency of Social Media Access	Frequency of DM App Access	Frequency of Video Conference Platform Access	Frequency of Blog and Forum Access
N	Valid	167	167	167	167	167	167
Median		2.00	4.00	5.00	5.00	3.00	2.00
Mode		2	4	5	5	3	1

Source: Authors’ research.

presents the summary statistics for ordinal variables, such as the level of education, frequency of email access, frequency of social media access, frequency of DM app access to communicate, frequency of video conference platform access, and frequency of blog and forum access for communication purposes. Regarding the level of education, the median (2.00) and mode (2) indicate that most respondents were undergraduate students. Moreover, the least frequently used communication tools appeared to be blogs and forums, whereas social media and DM apps were the most accessed tools for communication purposes. Email represents another tool that is used quite often, albeit not as frequently as social media and direct messaging applications. Video conference platforms present moderate access, possibly because students use video meetings for academic or professional purposes.

Moreover, the second stage employed inferential statistical analysis to examine the relationships and differences across the groups (correlation analysis, t-test, ANOVA, chi-squared test). We found a weak negative correlation between age and academic (rho = −0.033, p = 0.671) or personal (rho = −0.062, p = 0.428) digital platform use, indicating that age has no significant impact on students’ habits in terms of digital use (RQ1). Moreover, Fisher’s exact test (p = 0.225) suggests that there is no significant relationship between gender and the preferred digital communication platform (RQ2), while the p-value of 0.999 for the Pearson chi-squared test indicates no statistically significant correlation between the faculty and digital platform preference (RQ3). In addition, we found no significant relationship between the level of education and the average daily time spent on digital platforms for personal or academic purposes, which suggests that Master’s students are not more likely to use digital platforms for academic communication than undergraduates (RQ4). However, we observed a weak positive and statistically significant correlation between the time spent on digital platforms for academic purposes and the time spent on digital platforms for personal purposes, which might indicate that students who spend more time on digital communication platforms for academic purposes also tend to spend more time on these platforms for personal reasons. Furthermore, the significant and positive correlation between the preferred communication platform and improved collaboration (p = 0.024) may suggest that students’ preferences for certain communication platforms positively influence their perceptions regarding improved collaboration with their peers. An improved communication speed is also positively correlated with the preferred communication platform (p = 0.016), indicating that students might perceive the communication speed as a great advantage of digitalisation, provided that they also have a preference in terms of digital communication technologies.

Additionally, the Spearman’s rho correlations between the perceived digital communication benefits, such as feedback, collaboration, clarity, speed, and accessibility improvements, and satisfaction with technical support for digital communication platforms show that these factors do not meaningfully influence or correlate with the perceived satisfaction with technical support. This also means that students who report improved collaboration, speed, or feedback are not necessarily more satisfied with academic technical support (RQ5). The result regarding RQ6 shows no significant correlation between the absence of barriers in digital communication and the frequency of accessing a specific communication platform.

In contrast, we found several significant positive correlations regarding the frequency of accessing certain communication platforms (blogs, forums, and social media), as well as with email and direct messaging applications, with all significant p-values being less than 0.05. Similarly, significant positive correlations were identified between video conference platforms, direct messaging applications, and email usage and the perceived impact of digital communication on mental health, suggesting that these platforms might have a notable influence on how students perceive the impact of digital communication on their mental health (RQ7). Interestingly, a Spearman’s rho value of −0.064 resulted from the correlation between digital overload and the perceived impact of digital communication on mental health, which indicates a very weak correlation between these two variables, while the Sig. (two-tailed) value of 0.413 underlines that this relationship is not statistically significant. We further applied an independent-sample t-test (F = 0.116, Sig. = 0.734, p = 0.890), which showed that the difference between male and female participants in the perceived impact of digital communication on mental health is not statistically significant. Likewise, the one-way ANOVA results (F = 0.217, p = 0.929) indicate that there is no significant difference in the perceived impact of digital communication on mental health across the different education levels (first-year, second-year, and third-year Bachelor versus first-year and second-year Master’s students), confirming the fact that these groups are not significantly different from each other in terms of the perceived impact.

Furthermore, the moderate negative correlation between technical issues and a lack of face-to-face interaction (r= −0.185, p = 0.017) might indicate that students tend to consider that the absence of in-person communication only worsens the ability to tackle technical problems. It seems that students who are greatly affected by the digital challenge of a lack of face-to-face interaction may also develop negative perceptions of the future impact of digital transformation (r= −0.217, p = 0.005) on the communication process (RQ8).

Due to the violation of the normal distribution assumption (male = 60, female = 106), we chose to apply a Mann–Whitney U test (p = 0.187) for the comparison of digital transformation’s impact on communication with teachers and peers between male and female students. This showed that there was no strong evidence to support the idea that gender significantly affects the perceptions of digital transformation’s impact on communication. We conducted another Kruskal–Wallis test (p = 0.228), which revealed that there was no statistically significant difference in digital overload between the two gender groups. Similarly, both the chi-squared (Sig. = 0.573) and Fisher’s exact (Sig. = 0.082) tests indicated that there was no significant association between the gender and faculty of the respondents.

Furthermore, we applied a multiple regression analysis to assess the key predictors of satisfaction with technical support for digital communication platforms and to determine their significance in modelling students’ experiences with digital communication technologies. Our model used the faculty of the respondents as the independent variable and their satisfaction with technical support as the dependent variable, and it indicated the quite low explanatory power (R-squared = 4.3%) of the independent variable, while the model was statistically significant.

4. Discussion

The main purpose of this exploratory study was to investigate the impact of digital transformation on students’ behavioural trends, with a focus on their engagement, satisfaction, and adaptability to digital communication technologies in academic environments. Based on theories of digital learning, the technology acceptance model, and behavioural adaptation, this research set out to identify key predictors of students’ experiences with digital communication platforms for academic purposes. Thus, we employed a mixed-methods approach based on a questionnaire, with the results further analysed using quantitative analysis via SPSS. The results indicate that students’ faculty is a statistically significant predictor of their satisfaction with technical support for digital communication platforms, with modest explanatory power (R-squared = 4.3%). However, age and gender were not identified as having a significant influence on students’ perceived impacts of digital communication transformation. Although the significance of the regression model (p = 0.007) might suggest that the faculty affiliation contributes to differences in satisfaction levels, the model’s limited predictive capacity suggests that other omitted variables influence students’ overall digital experiences, such as institutional digital infrastructure or students’ prior experiences, which might influence their engagement with digital communication technologies.

Our findings align with previous studies that suggest that students’ digital experiences are strongly affected by institutional differences, such as faculty-specific infrastructure, digital resources, and support systems. Likewise, prior research studies have indicated that access to adequate training and user-friendly platforms enhance students’ engagement and satisfaction [11]. Moreover, the results also support the technology acceptance model, which emphasises the crucial role of institutional support in positively influencing students’ perceptions of digital technologies. In addition, the findings of our study seem to support the idea that digital communication tools influence students’ engagement and learning behaviours, whilst ensuring the achievement of larger sustainable development agendas, specifically those regarding SDG 4—Quality Education [12]. After achieving familiarity with digital technology tools, students continue to use them, and this leads to a better academic experience. Consequently, the implications could involve reducing the digital gap by promoting digital literacy, flexibility, and engagement, which serve as prime enablers of inclusive and equitable education, especially for rural and disadvantaged students, who often face various challenges in accessing higher education.

Nonetheless, the lack of significance for age and gender contradicts the previous work of Sayaf et al. [13], which highlighted the generational and gender-based differences in digital adaptability and satisfaction [14]. One possible explanation might be that higher education institutions have already reached a level of maturity in terms of digitalisation, which is why digital communication has now become a natural component of academia; this may also have reduced the variability in adaptation across demographic groups. Conversely, other institutional factors, such as university teachers’ digital training programs and standardised platforms, might have minimised any age and gender disparities.

Despite the valuable contribution of our study, its limitations must also be acknowledged. First, the reduced explanatory power of our model (R-squared = 4.3%) might indicate the influence of other variables omitted from this analysis, such as students’ digital literacy, which could be explored along with additional predictors when conducting future research studies. Second, our research measured students’ perceptions, which may have been influenced by biases and the subjectivity of self-reporting. This limitation of our study could be further explored through other qualitative methods, such as semi-structured interviews or focus groups, which could enable the deeper comprehension of students’ experiences and challenges with digital communication technologies. Lastly, the generalisability of our findings is limited by the fact that this study was conducted within a specific academic institution, and further longitudinal studies could examine how digital transformation influences students’ behavioural trends over time.

5. Conclusions

This research paper investigated the impact of digital communication transformation on students’ behavioural trends, with a focus on their engagement, satisfaction, and adaptability to digital communication technologies in academic environments. The main findings suggest that the faculty affiliation significantly influences students’ satisfaction with technical support for digital communication platforms, whereas gender and age are not significant predictors. Additionally, the Kruskal–Wallis and chi-squared tests indicated no statistically significant differences in digital communication difficulties based on gender, while the ANOVA results showed some variations in the experience of digital overload but without statistical robustness. From the standpoint of the Sustainable Development Goals, our research confirms the idea that digital tools in education support SDG 4 by promoting accessibility, inclusivity, and collaboration in learning environments. Therefore, the implications of this study call for significant investments by universities and policymakers in digital infrastructure, digital literacy programs, and adaptive learning technologies in order to ensure sustainable and future-proof education systems.

This exploratory study adds value to the increasing body of knowledge on digital education and communication through the significance of the findings, which emphasise the necessity of personalised support services based on faculty-specific needs, as well as the importance of the continuous evaluation and improvement of digital communication platforms in higher education settings. These findings suggest that institutional rather than demographic factors might have a more significant role in influencing students’ experiences with digital technologies for academic communication purposes. Furthermore, the results of our study support the validation of the research hypothesis and indicate that students who become familiar with the use of digital technology tools for communication are likely to continue using them in an academic context.

Hence, we can conclude that our study aligns with the scientific literature available in the field. The results allow us to conclude that students enrolled in a higher education institution present a satisfactory level of knowledge in the use of digital communication platforms (H1), since they use them almost daily for personal communication and collaboration. At the same time, students take advantage of digital communication platforms and they include them in their academic activities, provided that the university’s digital infrastructure integrates digital communication features that support peer-to-peer collaboration tasks (H2). Moreover, since digital communication platforms enhance collaboration by default, in the academic environment, there is no significant difference regarding this aspect. Nevertheless, using digital platforms for communication enhances students’ engagement and collaboration in educational and academic activities (H3). Thus, all of the research hypotheses are validated in our study. Universities could benefit from higher-quality education through digital platform integration within their digital infrastructure in order to provide students with a collaborative learning environment.

Taking into consideration the limitations of our study, future research could explore the impacts of other variables on students’ perceived satisfaction with digital communication technologies, such as students’ prior digital proficiency and learning styles, or it could examine students’ evolving perceptions over a greater period of time by carrying out longitudinal studies, enabling the deeper comprehension of the long-term effectiveness of digital education strategies.