Library Transaction Data as a Predictor of User Retention in Academic Libraries: A Logistic Regression Approach

Comfort Oluwapelumi Egunyemicln; Ifeoluwa Adenike Ayoola

doi:10.70112/ajist-2026.16.2.4461

Authors

Comfort Oluwapelumi Egunyemicln University of Ilesa, Ilesa, Osun State, Nigeria
Ifeoluwa Adenike Ayoola Bowen University, Iwo, Osun State, Nigeria

DOI:

https://doi.org/10.70112/ajist-2026.16.2.4461

Keywords:

Academic Libraries, Library Transaction Data, User Retention, Predictive Analytics, Logistic Regression, Library Users, Evidence-Based Library Management

Abstract

Academic libraries generate substantial transaction data through circulation systems, online public access catalogues, electronic resource platforms, library access records, and reference services. Despite the availability of these data, many academic libraries continue to use them mainly for descriptive reporting rather than predictive decision-making. This study examined the extent to which library transaction data predict user retention in an academic library using binary logistic regression. The study adopted a correlational research design and used data from 500 registered users selected from four faculties and sixteen departments of the University of Ilesa. The predictor variables were borrowing frequency, OPAC search frequency, electronic resource access, library visit frequency, reference service usage, traditional library circulation use, and user category. User retention was coded as a binary outcome, with retained users coded 1 and inactive users coded 0. Descriptive statistics were used to examine transaction patterns, while binary logistic regression was used to determine significant predictors and the overall contribution of the predictors to retention. The results showed that electronic resource access and library visit frequency were statistically significant positive predictors of user retention, while borrowing frequency approached significance. The overall model was statistically significant, χ²(7) = 151.26, p < .001, with a McFadden pseudo R² of 0.289. The classification results indicated an overall accuracy of 76.0%, with a sensitivity of 72.2% and specificity of 78.1%. The study concludes that routine library transaction data can provide useful evidence for identifying patterns associated with continued library use and supporting evidence-based user engagement strategies. The study recommends the systematic use of transaction data for user monitoring, service planning, and targeted interventions

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