Accessibility Barriers Across Swedish Public-Sector Websites : A Taxonomy-Driven Analysis

Sammanfattning

This study examines accessibility barriers in Swedish public-sector websites using ataxonomy-driven, cross-domain analytical approach. As digital public servicesincreasingly function as primary access points for citizens, equitable and inclusive accesshas emerged as a critical societal and institutional concern. Although recognizedstandards such as the Web Content Accessibility Guidelines (WCAG) and regulatoryframeworks exist in the European Union and Sweden, previous research has identifiedongoing accessibility issues across public-sector platforms. To address these limitations in existing research, this study employs a structuredmethodology that combines automated accessibility assessment with taxonomy-basednormalization and persona-informed interpretation. Thirty-six websites across threedomains—universities, municipalities, and healthcare organizations—were analyzedusing automated evaluation tools. The resulting data were transformed into standardizedissue definitions, categorized by barrier type, and mapped to user-impact groups andinterpretive personas. The findings indicate that accessibility barriers are common but unevenly distributedacross domains. A total of 4,469 issue instances were identified, with universities andmunicipalities reporting more accessibility issues compared to healthcare websites.Accessibility issues are concentrated in structural and visual categories, particularlystructure and semantics, visual design and contrast, and compatibility-related issues.These barriers primarily affect users’ ability to navigate content and understandinformation, as reflected in the significant impact on assistive-technology navigation andreading clarity personas. The study further demonstrates that accessibility barrier profiles differ systematicallyacross domains, underscoring the importance of context-sensitive analysis. By integratingtechnical detection with structured interpretation, this research provides a scalable andinterpretable framework for analyzing accessibility barriers beyond compliance metrics.The findings support a transition toward data-driven, user-oriented accessibility practicesin public-sector digital services.