@misc{10481/107767,
year = {2025},
month = {10},
url = {https://hdl.handle.net/10481/107767},
abstract = {Website performance directly impacts user experience, trust, and competitiveness. While
numerous studies have proposed evaluation methods, there is still no comprehensive synthesis that integrates performance metrics with predictive models. This study conducts a
systematic literature review (SLR) following the PRISMA framework across seven academic
databases (2010–2024). From 6657 initial records, 30 high-quality studies were included
after rigorous screening and quality assessment. In addition, 59 website performance metrics were identified and validated through an expert survey, resulting in 16 core indicators.
The review highlights a dominant reliance on traditional evaluation metrics (e.g., Load
Time, Page Size, Response Time) and reveals limited adoption of machine learning and
deep learning approaches. Most existing studies focus on e-government and educational
websites, with little attention to e-commerce, healthcare, and industry domains. Furthermore, the geographic distribution of research remains uneven, with a concentration in
Asia and limited contributions from North America and Africa. This study contributes by
(i) consolidating and validating a set of 16 critical performance metrics, (ii) critically analyzing current methodologies, and (iii) identifying gaps in domain coverage and intelligent prediction models. Future research should prioritize cross-domain benchmarks,
integrate machine learning for scalable predictions, and address the lack of standardized
evaluation protocols.},
organization = {MICIU/AEI/10.13039/501100011033 - ERDF, EU (PID2023-147409NB-C21)},
organization = {European Union – NextGenerationEU/PRTR (TED2021-131699B-I00; TED2021-129938B-I00)},
publisher = {MDPI},
keywords = {website performance},
keywords = {Systematic Literature Review},
keywords = {PRISMA},
title = {Predicting Website Performance: A Systematic Review of Metrics, Methods, and Research Gaps (2010–2024)},
doi = {10.3390/computers14100446},
author = {Ghattas, Mohammad and Odeh, Suhail Musa Issa and Mora García, Antonio Miguel},
}