Developing a Readability-Impact Framework for Public Library Digital Services

Thesis Abstract

Public library digital services increasingly deliver information, learning resources, and community programming through online catalogs, e-books, mobile apps, and social platforms, yet user engagement often hinges on readability and perceived impact, particularly among diverse urban populations with varying literacy levels. This study addresses a gap in library and information science by developing a Readability-Impact Framework (RIF) that integrates text readability metrics with user-perceived impact on digital service usage, comprehension, and information seeking outcomes. The central aim is to articulate a theoretical model that links readability dimensions—lexical simplicity, syntactic complexity, visual layout, and multimedia integration—to measurable service impact indicators such as search success rate, time on task, user satisfaction, repeat usage, and information literacy gains. Specific objectives are (1) to identify readability dimensions salient to public library digital interfaces and content, (2) to examine the relationship between readability measures and user engagement metrics across age, education, and digital literacy strata, (3) to develop a parsimonious yet robust Readability-Impact Framework validated through empirical data, (4) to test the framework using a mixed-methods dataset comprising quantitative usage analytics and qualitative user feedback, and (5) to generate actionable guidelines for designing more inclusive and effective digital library services. The study adopts a pragmatist paradigm to accommodate both measurable outcomes and user experiences, drawing on information behavior theory and cognitive load theory to frame the interpretation of readability effects on search strategies and learning outcomes. A mixed-methods research design is employed. Quantitative data are drawn from a sample of 1,200 users across five public libraries in a metropolitan region, including system usage logs, task-based usability tests with 400 participants stratified by age (18–24, 25–44, 45–64, 65+), and pre-post assessments of information literacy. Readability metrics are computed at the interface and content levels using established scales (Flesch Reading Ease, SMOG, Gunning-Fog, and a bespoke visual-readability composite). Multivariate regression analyses assess associations between readability scores and dependent variables such as completion rate, time to complete tasks, search success, and self-reported satisfaction, while controlling for demographic covariates and prior digital literacy. Structural equation modeling (SEM) evaluates the proposed pathways in the Readability-Impact Framework, testing direct and indirect effects of readability on engagement, comprehension, and learning outcomes. Qualitative data comprise 40 in-depth interviews and 20 focus groups (6–8 participants each) to elicit nuanced perceptions of readability, interface design, and perceived impact on information-seeking efficacy. Thematic analysis identifies recurring patterns, while triangulation with usage metrics enhances validity. Data collection instruments include standardized usability tasks, validated information-literacy instruments (e.g., ILAT scales), an adapted User Readability Perception Inventory, system analytics extracts, and interview/focus group guides aligned with the framework’s constructs. Instrument validity is established through expert review and pilot testing (n=50). Reliability is assessed via Cronbach’s alpha and test-retest procedures for survey-based measures. Data analysis proceeds in three stages (i) descriptive statistics and correlation analyses to characterize readability levels and engagement indicators; (ii) regression and SEM to test hypothesized relationships within the RIF; and (iii) thematic coding of qualitative data to explain observed quantitative patterns and to refine the framework’s theoretical propositions. Sensitivity analyses address potential biases due to self-selection and digital divide effects. Expected findings indicate that improved readability—across lexical, syntactic, and visual dimensions—positively influences task success, reduces cognitive load, and enhances perceived usefulness and learning gains, with stronger effects for users with lower formal education and lower digital literacy. The framework is anticipated to reveal mediating roles for perceived usability and cognitive load, and Moderation by demographic variables such as age and language proficiency. The study contributes to knowledge by operationalizing a novel Readability-Impact Framework that integrates literacy science with information behavior in a public library context, offering validated measures and design guidelines for inclusive digital services. Practical implications include evidence-based design recommendations for interface typography, content simplification, navigational structure, and multimedia integration to optimize accessibility and impact. The main conclusion posits that readability enhancements are a core lever for maximizing public library digital service impact, particularly for underrepresented user groups, with policymakers and library practitioners advised to adopt instrumented readability audits and ongoing user-centered evaluation. Recommendations emphasize iterative design cycles, targeted interventions for digital novices, and the incorporation of readability metrics into service-level agreements and digital content governance.