Deeve Technologies
Peer-reviewedJournal articleQuantitative

A data-driven analytical evaluation of ICT portal features and their influence on faculty user satisfaction in higher education

Dyna D., John Albert M., John Augustus D., Dinnes D., Crimary M., Jhyclyff J., Serafin P., Kristine S.

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JournalIJCSMC, ISSN 2320-088X
VolumeVol. 15, Issue 5
Pages64-71
PublishedMay 2026
DOI10.47760/ijcsmc.2026.v15i05.007
Headline finding
76%of the variance in faculty satisfaction is explained by the portal's quality features (R² = 0.76).
88faculty respondents, stratified random sampling from a population of 113
r 0.73-0.79strong, significant correlation for every quality dimension (p < .001)
Reliabilitythe strongest single predictor of satisfaction
0.92 / 0.89content validity index and Cronbach's alpha of the instrument
Abstract

This study applies predictive analytics to examine how ICT portal quality influences faculty user satisfaction using the ISO/IEC 25010 framework. A quantitative research design employing descriptive, correlational, and predictive analyses was utilized. Data were collected from 88 faculty members through stratified random sampling using a structured questionnaire. Pearson correlation and multiple regression analyses were conducted.

Results revealed that ICT portal quality and faculty user satisfaction were rated at very high levels. All system quality dimensions showed strong and significant relationships with satisfaction (r = 0.73 to 0.79, p < 0.001). Regression analysis identified reliability, performance efficiency, usability, and security as significant predictors, while functional suitability was not. The model demonstrated strong explanatory power (R² = 0.76). The findings indicate that faculty user satisfaction is primarily driven by system performance and user experience. This study contributes a predictive analytics-based evaluation model that enables institutions to prioritize ICT system improvements based on measurable impact.

Keywords
ICT Portal EvaluationFaculty User SatisfactionSystem QualityPredictive AnalyticsRegression Analysis
My role

Co-author, one of six MIT student researchers on the paper, alongside faculty members Serafin P. and Kristine S.

Institution

State University of Northern Negros, Sagay City, Negros Occidental

Framework

Input, process, output, outcome

01 · InputICT portal featuresUsability, functional suitability, reliability, performance efficiency, security
02 · ProcessAnalysisDescriptive, correlation and regression analysis
03 · OutputFindingsLevel of quality, significant relationships, key predictors
04 · OutcomeDecisionsData-driven insights and improvement priorities
Methodology

How the study was done

DesignQuantitative descriptive design with correlational and predictive analysis.
Respondents88 of 113 College of Maritime Studies faculty, stratified across Deck, Engine and General Education (Slovin's formula, 5% margin of error).
InstrumentISO/IEC 25010-based questionnaire on a 5-point Likert scale. Content validity index 0.92, Cronbach's alpha 0.89.
AnalysisWeighted mean, Pearson correlation, and multiple linear regression.
Results

What the data showed

Mean ratingsTable I · 5-point scale · all rated Very High
Security
4.60
Faculty user satisfaction
4.54
Functional suitability
4.44
Usability
4.39
Reliability
4.34
Performance efficiency
4.32
Correlation with satisfactionTable II
Reliabilityr = 0.79
Securityr = 0.77
Performance efficiencyr = 0.76
Functional suitabilityr = 0.74
Usabilityr = 0.73
All strong and significant, p < .001
Which qualities predict satisfactionTable III · multiple regression
Quality dimensionB (coefficient)t-valuep-valueResult
Reliability
0.311
2.4230.018Significant
Performance efficiency
0.261
2.8830.005Significant
Usability
0.232
2.8130.006Significant
Security
0.211
2.1990.031Significant
Functional suitability
0.036
0.3060.760Not significant
R0.87
R²0.76
Adjusted R²0.75
Standard error0.37
Model fitVery strong
Discussion

What it means

Stability winsReliability was the strongest predictor. Users judge a system first by whether it works consistently.
Features are the baselineFunctional suitability was not significant. Once the core features exist, performance and experience matter more than adding more.
Prioritise by impactRegression shows relative influence, so institutions can fund the improvements that move satisfaction, not the ones that look important.
Conclusion

Reliability, performance efficiency, usability and security significantly influence faculty user satisfaction, while functional suitability does not. System performance and user experience matter more than the mere availability of features.

Limitations

Single institution, self-reported data and a cross-sectional design. User training, technical support and institutional policy were not modelled. The paper proposes multi-institution and longitudinal samples as future work.

Cite this paper

Dyna D., John Albert M., John Augustus D., Dinnes D., Crimary M., Jhyclyff J., Serafin P., & Kristine S. (2026). A data-driven analytical evaluation of ICT portal features and their influence on faculty user satisfaction in higher education. International Journal of Computer Science and Mobile Computing, 15(5), 64-71. https://doi.org/10.47760/ijcsmc.2026.v15i05.007

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Why it matters for your project

I build software, and I measure whether it works.

The same questions I research are the ones I ask on client builds: is it reliable, is it fast, is it easy to use, and is it secure. Those four drove satisfaction in my ICT portal study, and they shape how I scope and test every project.

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