Abstract

This study investigates the relationship between the quality and availability of broadband services and user satisfaction within Public Higher Educational Institutions (PHEIs) in Malaysia. The primary objective is to examine how the quality of internet services influences the satisfaction of students, faculty staff, and institutional stakeholders. Employing a quantitative research approach, data were collected through a structured survey comprising 387 respondents, including students, faculty, and service providers from both urban and suburban universities. Pearson correlation analysis was utilised to determine the strength and significance of the relationship between user satisfaction (SATISFACTION) and the quality of the internet connection (QoE). The analysis revealed a very strong positive correlation (r = 0.855, p < 0.01) between these variables, indicating that improvements in internet quality led to increased user satisfaction. Cross-tabulation analysis further identified significant disparities in satisfaction levels between urban and suburban universities, with suburban respondents reporting higher dissatisfaction. This trend suggests that suburban universities face greater challenges in providing adequate broadband services compared to their urban counterparts. Based on these findings, the study concludes that enhancing broadband quality, particularly in suburban areas, is essential for improving user satisfaction and supporting effective digital learning. Recommendations include investing in upgraded broadband infrastructure and conducting regular assessments to ensure consistent service quality across all PHEIs.

Introduction

The growing integration of e-learning platforms into public higher educational institutions has transformed the academic landscape, emphasising the need for reliable and high-quality broadband services. E-learning, defined as the use of digital technologies to deliver educational content and facilitate student engagement, has become a core component of modern education. The effectiveness of e-learning relies heavily on the quality of internet services, as students and faculty members depend on seamless connectivity for accessing online resources, attending virtual classes, and participating in collaborative learning activities (Shahzad & Khan, 2023). Despite significant investments in digital infrastructure, many public universities continue to face challenges in delivering consistent broadband service quality, hindering the full realisation of e-learning's potential (Salah & Munene, 2024).

User satisfaction is a critical indicator of broadband service quality in the context of digital education. According to Lei and Kamal Bashah (2024), the quality of internet service, including factors such as bandwidth, latency, and reliability, significantly influences user experience and satisfaction in educational settings. Similarly, Chopra, Misra, and Bhaskar (2024) highlight the role of perceived usefulness and overall service quality in driving the continuous use of digital libraries and e-learning platforms. Inadequate broadband service can disrupt students' access to learning materials, negatively impacting their academic performance and reducing the effectiveness of online teaching methods (Mir, Tahir, & Amir, 2023).

In the context of digitalisation in public universities, broadband service quality is a key determinant of the successful implementation of virtual learning environments (Joshua, 2024). The use of multimedia content and real-time video conferencing in e-learning requires stable and high-speed internet connections to ensure uninterrupted learning experiences. However, many public higher educational institutions, particularly in developing regions, struggle with inadequate network infrastructure, leading to frequent connectivity issues (Weigl et al., 2024). This limitation not only affects student engagement but also undermines the broader goals of digital education, such as inclusivity and accessibility (Abreu-Romu, 2023).

While public universities have increasingly adopted e-learning as a strategic approach to enhance educational access and quality, the inconsistent broadband service quality remains a significant barrier to effective online learning. Broadband service disruptions, characterised by slow speeds, high latency, and poor reliability, have been reported to hinder user satisfaction and reduce the overall quality of e-learning experiences (Lin, 2024). Existing literature suggests that despite the advancements in digital infrastructure, many public institutions fail to meet the expected standards of internet service quality, leading to negative perceptions of e-learning among users (Sa'ari, Goulding, & Sahak, 2023).

Moreover, there is a gap in understanding the direct impact of broadband service quality on e-learning effectiveness from a user-centric perspective. While several studies have explored the technical aspects of broadband performance (Upadhyaya & Ahuja, 2019), fewer have focused on how these metrics influence user satisfaction and learning outcomes. As highlighted by Salah and Munene (2024), the disconnect between digitalisation efforts and the quality-of-service delivery in public universities points to a need for more comprehensive research examining the relationship between broadband quality and e-learning experiences.

The primary objective of this study is to evaluate the impact of broadband service quality on the effectiveness of e-learning in Public Higher Educational Institutions (PHEIs). This research aims to understand how variations in broadband performance influence user satisfaction and learning outcomes. Specifically, the study focuses on two secondary objectives to determine the relationship between the quality and availability of broadband services and the satisfaction levels of users within PHEIs and to assess the Quality of Experience (QoE) of PHEI users, including both faculty and students, in utilising fixed and mobile broadband services on campus and in adjacent areas. In summation this study aims to address this gap by evaluating the impact of broadband service quality on the effectiveness of e-learning in public higher educational institutions.

Literature Review

Quality of Broadband Services in Higher Educational Institutions

Broadband service quality is integral to digital service delivery within higher educational institutions (HEIs), especially in the context of e-learning and digital libraries. Salah and Munene (2024) emphasised that digitalisation in public universities enhances the efficiency of service delivery, particularly when quality broadband services are accessible across key locations such as hostels, lecture theatres, and administrative blocks. Lei and Kamal Bashah (2024) contributed to this discussion by examining internet behavioural models that can enhance the Quality of Service (QoS) through user profiling. Their findings underline the importance of reliable internet infrastructure, suggesting that HEIs must focus on optimising broadband coverage to meet increasing demands for online learning and digital library access.

Chansanam et al. (2021) discussed the development of an online learning platform for university students in Thailand, stressing that the quality of broadband services is critical to the platform's success. The authors highlighted that disruptions in internet services can lead to dissatisfaction among users, impacting the overall adoption and usage of digital learning tools. Similarly, Upadhyaya and Ahuja (2019) introduced an innovative quality of service model tailored for cloud computing in libraries, emphasising the role of consistent and high-quality broadband in ensuring seamless access to cloud-based educational resources.

User Satisfaction and Quality of Experience (QoE) in E-Learning

The concept of user satisfaction is closely linked to the QoE provided by broadband services, particularly in the context of e-learning. Chopra, Misra, and Bhaskar (2024) explored this relationship in the context of digital library usage, finding that overall quality, perceived usefulness, and user satisfaction are interdependent factors. The study revealed that when broadband services are perceived as reliable, users are more likely to engage consistently with digital resources. Lin (2024) extended this perspective by developing an integrated framework to analyse student satisfaction during the COVID-19 pandemic. The findings suggested that fluctuating broadband service quality was a major source of dissatisfaction, affecting both faculty and student experiences in virtual classrooms.

Mir, Tahir, and Amir (2023) highlighted the role of user-centred design in improving the QoE for e-learning platforms. Their case study utilising Google Analytics demonstrated that user satisfaction can be enhanced through continuous monitoring and optimisation of internet service quality. Similarly, Shahzad, Khan, and Javeed (2024) conducted a bibliometric study on e-learning technologies, showing that enhancements in broadband services correlate with improved user engagement and satisfaction, particularly in smart library environments.

In the context of public values and user-centric service design, Weigl et al. (2024) discussed the tensions between the objectives of e-government services and the needs of users. They emphasised that prioritising user-centric service quality, particularly broadband availability, is essential to enhance satisfaction and drive positive user experiences. The study suggests that educational institutions must adopt a more user-focused approach to digital service delivery, integrating reliable broadband infrastructure as a key component of their e-learning strategies.

Impact of Broadband Connectivity on E-Learning Engagement and Outcomes

The availability and quality of broadband connectivity are decisive factors affecting e-learning engagement and educational outcomes. Pathak and Kashyap (2023) used an electroencephalogram (EEG) signal analysis to measure user engagement, finding that disruptions in broadband services significantly reduce students' cognitive engagement in virtual learning environments. This insight aligns with the findings of Ganie et al. (2020), who investigated the impact of e-learning on child education in rural India. Their study indicated that poor internet infrastructure not only hampers the adoption of e-learning but also negatively impacts learning outcomes, particularly in underserved regions.

Joshua (2024) developed an optimised multimedia compression model aimed at enhancing the QoS in virtual learning environments. The research highlighted that efficient broadband connectivity is crucial for delivering high-quality multimedia content without interruptions, which is essential for maintaining user engagement. Similarly, Shahzad and Khan (2023) reviewed the effects of e-learning technologies on university librarians, noting that robust broadband infrastructure supports the development of innovative competencies and the efficient operation of smart library services.

Yang et al. (2023) explored the relationship between students' personalities and their engagement with e-learning platforms post-COVID-19, underscoring the importance of perceived broadband quality in influencing satisfaction and engagement. Their study showed that students with positive perceptions of internet service quality were more likely to exhibit higher levels of engagement and 'stickiness' towards e-learning platforms, reinforcing the critical role of broadband services in enhancing educational experiences.

Finally, Mathivanan et al. (2021) examined the adoption of e-learning during the lockdown in India, revealing that the rapid shift to online education was heavily dependent on the quality of broadband services. The authors concluded that internet connectivity issues were a primary barrier to effective learning during this period, highlighting the need for investment in broadband infrastructure to support future digital education initiatives. Similarly, Amigud and Pell (2024) conducted a comparative analysis of dissatisfaction with e-learning, identifying poor internet connectivity as one of the main reasons for student frustration and dropout rates.

In summary, the reviewed literature consistently underscores the importance of reliable broadband services in enhancing user satisfaction, engagement, and learning outcomes within HEIs. The studies suggest that addressing broadband quality issues is essential for the successful implementation of e-learning strategies and for meeting the evolving needs of students and faculty in digital educational environments. As higher education increasingly relies on digital platforms, ensuring robust and high-quality broadband connectivity will be critical to the success of e-learning initiatives.

Methodology

This study employs a quantitative research design to investigate the impact of broadband service quality on e-learning engagement, user satisfaction, and overall learning outcomes within Malaysian higher educational institutions. The quantitative approach was selected due to its ability to provide a comprehensive statistical analysis of data collected from a diverse group of respondents, including students, faculty staff, service providers, and institutional stakeholders. By using structured questionnaires, this study aims to quantify the perceptions, experiences, and expectations regarding broadband quality and its influence on the e-learning environment. The emphasis on a data-driven approach ensures objective and replicable findings that can inform policy recommendations and strategic planning for enhancing digital learning services.

The primary data for this study were collected through a structured questionnaire survey, distributed to a sample of 387 participants across several Malaysian universities. The sample was stratified to include key respondent groups: students (undergraduates and postgraduates), faculty members (lecturers and administrative staff), broadband service providers, and institutional stakeholders involved in e-learning implementation and management. The questionnaire was designed based on validated instruments from previous studies on broadband service quality and user satisfaction in educational contexts. It contained sections on demographic information, perceptions of broadband service quality, frequency and intensity of e-learning usage, levels of user satisfaction, and challenges faced during online learning activities. The responses were collected using a five-point Likert scale to facilitate quantifiable analysis.

Data analysis was conducted using descriptive and inferential statistical techniques, allowing for a detailed examination of the relationships between broadband service quality and key indicators of user satisfaction and e-learning engagement. Descriptive statistics provided insights into the general trends and patterns in the data, while inferential statistics, including regression analysis and ANOVA, were employed to test the hypotheses and determine the strength of associations between broadband quality and the study variables. The use of Statistical Package for the Social Sciences (SPSS) software ensured efficient handling of the dataset and enabled accurate computation of the results. Findings from this analysis are expected to provide actionable insights for stakeholders in Malaysian higher educational institutions, guiding efforts to improve broadband infrastructure and enhance the digital learning experience.

Findings and Analysis

Correlational Statistical Analysis was carried out to determine the relationship between the quality and availability of broadband services and the satisfaction levels of users within PHEIs and to assess the QoE of PHEI users. The result is depicted in the table below:

Table 1: Correlational analysis.

To understand how the quality and availability of broadband services affect user satisfaction in Public Higher Education Institutions (PHEIs), a Pearson correlation analysis was conducted. The analysis focused on two key variables: user satisfaction with the internet connection (SATISFACTION) and the quality of the internet connection (QoE).

The Pearson correlation coefficient for SATISFACTION and QoE was found to be 0.855, indicating a very strong positive correlation. This relationship is statistically significant, with a p-value of 0.000, well below the 0.01 threshold. This result suggests that as the quality of the internet connection improves, user satisfaction significantly increases.

These findings reveal the critical role of high-quality internet connections in influencing user satisfaction within PHEIs. Given the significant correlation, we can confidently assert that this relationship is not due to random chance. Thus, ensuring reliable and high-quality broadband services in educational institutions is vital for enhancing user satisfaction.

Additionally, a cross-tabulation analysis examined the differences in QoE between students from urban and suburban universities. The results highlighted significant distinctions. Among those who were very dissatisfied with the QoE, 96.4 per cent were from suburban universities, while only 3.6 per cent were from urban universities, with an adjusted residual of 3.4. Similarly, 91.0 per cent of respondents who were dissatisfied came from suburban universities, compared to only 7.5 per cent from urban universities, with adjusted residuals of -4.8 for urban and 4.5 for suburban. Respondents who reported being neutral about the QoE were predominantly from suburban universities (81.8 per cent), with 18.2 per cent from urban universities, and an adjusted residual of 3.8 for suburban universities. Interestingly, satisfaction levels were more evenly distributed between the two categories, with 50.0% from urban and 50.0 per cent from suburban universities. However, this balance is deceptive, as the adjusted residuals indicate a positive skew for urban universities (5.4), suggesting that respondents from urban universities are more likely to report satisfaction than their suburban counterparts.

In the very satisfied category, 68.1 per cent of respondents were from urban universities, whereas only 31.9 per cent were from suburban universities, with adjusted residuals of 5.6 for urban and -5.5 for suburban universities. The cross-tabulation reveals that students and faculty staff from suburban universities tend to report higher levels of dissatisfaction and neutrality towards their QoE, while urban university students are more likely to express satisfaction and high satisfaction. This pattern indicates a significant disparity in the perceived quality of broadband services between urban and suburban educational institutions.

The chi-square (χ²) test results, as presented in Table 4, demonstrate a statistically significant difference among the variables analysed. The Pearson chi-square value is 105.079 with 8 degrees of freedom, and the asymptotic significance (2-sided) is 0.000, indicating that the observed differences are highly unlikely to be due to chance. The likelihood ratio test corroborates these findings, with a value of 114.382 and the same level of significance. Additionally, the Linear-by-Linear Association test further supports the results, showing a value of 23.201 with a significance level of 0.000. These findings collectively indicate a strong linear relationship and a significant association between the categories analysed in the data.

Table 2: Significant Differences in QoE Categories by University Type (Chi-Square (χ²) Test).

The chi-square (χ²) test was conducted to assess whether there are significant differences in the Quality of Experience (QoE) of broadband services between urban and suburban universities. The results, presented in Table 2, reveal a highly significant difference, evidenced by a Pearson chi-square value of 90.248 and a p-value of 0.000, which is well below the conventional threshold of 0.05. The Likelihood Ratio test also confirms this significance, with a value of 95.898 and a p-value of 0.000. Furthermore, the Linear-by-Linear Association supports this finding, showing a value of 21.875 and a p-value of 0.000. These results suggest that the QoE of broadband services significantly differs between students at urban and suburban universities. However, it is important to note that 33.3% of the cells have an expected count of less than 5, with the minimum expected count being 0.07. This could potentially affect the reliability of the chi-square results. Despite this limitation, the overall findings strongly indicate a notable difference in the broadband service quality experiences between these two types of institutions.

Table 3: Satisfaction Levels (SQC2) by University Type (Cross Tabulation).

The cross-tabulation analysis compared the satisfaction levels (SQC2) with broadband services between students from urban and suburban universities, as presented in Table 3. The results reveal a significant disparity in satisfaction levels across these categories. Among those who are "Very dissatisfied", all 27 respondents are from suburban universities, representing 10.4 per cent of suburban university respondents and 7.0 per cent of the total respondents. The adjusted residual value of 3.8 indicates a significant association with suburban universities.

For those who are "Dissatisfied", 58 respondents are from suburban universities, and 6 are from urban universities, accounting for 22.3 per cent of suburban university respondents and 4.8 per cent of urban university respondents. This category also has a higher adjusted residual value for suburban universities (4.1), indicating a notable difference. Respondents who are "Neutral" about their satisfaction are predominantly from suburban universities (100 respondents), representing 38.5 per cent of suburban university respondents and 25.9 per cent of the total respondents. The adjusted residual value of 5.2 suggests a significant association with suburban universities in this category.

The "Satisfied" category is predominantly represented by urban university respondents, with 80 out of 142 satisfied respondents being from urban universities, comprising 64.0 per cent of urban university respondents and 20.7 per cent of the total respondents. The adjusted residual value of 7.7 for urban universities highlights a significant association with this group. Among those who are "Very satisfied", the majority are also from urban universities (23 out of 36 respondents), representing 18.4 per cent of urban university respondents and 6.0 per cent of the total respondents. The adjusted residual value of 4.2 underscores a significant positive association with urban universities. The data reveal a substantial difference in satisfaction levels between urban and suburban universities. Urban universities show higher levels of satisfaction and very satisfied respondents, while suburban universities exhibit higher levels of dissatisfaction and neutrality.

Table 4: Significant Differences in Satisfaction Categories (SQC2) by University Type (Chi-squared (χ²) Test).

The chi-square (χ2) test results, as presented in Table 4, demonstrate a statistically significant difference among the variables analysed. The Pearson chi-square value is 105.079 with 8 degrees of freedom, and the asymptotic significance (2-sided) is 0.000, indicating that the observed differences are highly unlikely to be due to chance. The likelihood ratio test corroborates these findings, with a value of 114.382 and the same level of significance. Additionally, the Linear-by-Linear Association test further supports the results, showing a value of 23.201 with a significance level of 0.000. These findings collectively indicate a strong linear relationship and a significant association between the categories analysed in the data.

Table 5: Linear Relationship Between Variables (Symmetric Measures).

Recommendations

Based on these findings, it is recommended that policy-makers and institutional stakeholders prioritise improving broadband infrastructure, particularly in suburban universities, to bridge the quality gap in internet services. Investment in upgraded broadband networks and increased service coverage in suburban areas will likely enhance user satisfaction and facilitate a more equitable digital learning experience across all PHEIs. Additionally, regular assessments of broadband service quality should be conducted to monitor user satisfaction and identify areas for continuous improvement. By addressing the disparities in internet service quality, educational institutions can ensure a consistent and positive online learning experience for all students and staff, regardless of their geographic location.

Conclusion

This study provides a comprehensive analysis of the relationship between the quality and availability of broadband services and user satisfaction in PHEIs in Malaysia. The results of the Pearson correlation analysis indicate a strong positive correlation (r = 0.855, p < 0.01) between the QoE and user satisfaction. This significant relationship underscores the importance of reliable and high-quality broadband services in enhancing user experiences in the digital learning environment of PHEIs. The findings highlight that as the quality of the internet connection improves, users are more likely to express higher levels of satisfaction, reinforcing the critical role of robust broadband infrastructure in supporting effective online learning and academic activities. Further analysis through cross-tabulation reveals a noticeable disparity in the perceived quality of broadband services between students from urban and suburban universities. The data shows that respondents from suburban universities report significantly higher levels of dissatisfaction and neutrality regarding the QoE compared to their urban counterparts. Conversely, students and faculty from urban universities are more likely to express satisfaction and high satisfaction with broadband services. This disparity suggests that suburban institutions face greater challenges in providing adequate internet services, which may hinder the effectiveness of e-learning initiatives and negatively impact the overall user experience. The findings indicate a need for targeted interventions to address the broadband service gaps in suburban educational institutions.

References