Query Topic, Technology Acceptance, and Session Drop-Off: A Socio-Technical Analysis of a Conversational Virtual Reference Assistant in a Nigerian University Library

Academic libraries increasingly deploy conversational artificial intelligence (AI) as natural language virtual reference assistants, yet the behavioural traces these systems generate are rarely mined to anticipate when and why student research effort breaks down. Anchored in socio-technical systems (STS) theory and the Unified Theory of Acceptance and Use of Technology (UTAUT), this study examined student research engagement using the linked conversational logs of a virtual reference assistant and a structured technology acceptance survey administered in a Nigerian university library. A concurrent mixed-source design combined descriptive analysis of six pre-classified query topics, Kaplan–Meier and Cox proportional hazards survival analysis of session drop-off (N = 3,612 sessions), binary logistic regression of retrieval success, and hierarchical multiple regression of behavioural engagement on UTAUT constructs with a total-enumeration census (N = 147) of registered student users. Technical-friction topics, particularly remote-access troubleshooting, predicted the fastest research drop-off (Cox HR = 3.99, p < .001) and the lowest probability of retrieval success, but neither facilitating conditions nor research self-efficacy showed a statistically significant protective association with the drop-off hazard once topic was accounted for. Logistic regression classified retrieval success with 72.4% accuracy (AUC = .74, Nagelkerke R² = .21), and UTAUT constructs explained 48.2% of the variance in behavioural engagement, with performance expectancy, facilitating conditions, and research self-efficacy emerging as significant predictors; effort expectancy and social influence were not significant. The findings indicate that query-topic membership, rather than the social subsystem variables measured here, is the dominant driver of when a session is abandoned, while the social subsystem instead shapes the perceptual outcomes of engagement and the session-level probability of success. The study offers a portable analytic pipeline grounded in transparent, re-runnable statistics for converting reference assistant logs into an early warning instrument for academic libraries, and it discusses why the technical and social signals diverge across outcomes.

conversational AI, virtual reference services, query topic analysis, survival analysis

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