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Record W2116305820 · doi:10.18438/b8h89k

Library Fines Make a Difference in Academic Library Book Return Behaviour

2015· article· en· W2116305820 on OpenAlexvenueno aff
Sue F. Phelps

Bibliographic record

VenueEvidence Based Library and Information Practice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary scienceAcademic libraryPopulationSignificant differencePsychologySociologyMathematicsDemographyComputer scienceStatistics

Abstract

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A Review of: Sung, J. S., & Tolppanen, B. P. (2013). Do library fines work?: Analysis of the effectiveness of fines on patron’s return behavior at two mid-sized academic libraries. Journal of Academic Librarianship, 39(6), 506-511. http://dx.doi.org/10.1016/j.acalib.2013.08.011 Abstract Objectives – To quantify library fines and their impact on patron return behaviour. Design – Hypothesis testing of data extracted from integrated library systems. Setting – Two midsize academic libraries, including one from the Pacific, University of Hawaii at Manoa (UHM), and one from the Midwest, Eastern Illinois University (EIU). Subjects – Undergraduates, graduate students, and faculty. Methods – The authors collected data from two midsized universities. The universities have identical integrated library systems, which allowed for uniform data extraction. The authors counted book returns in each population group (undergraduates, graduate students, and faculty) for those books that were returned before and after the due dates with a focus on late fees as the primary variable. The authors tested the following five hypotheses: • Hypothesis 1: “There is no difference in return rates before due dates among the UHM patron groups because the fine policy is the same for all patron groups” (p. 507). • Hypothesis 2: “Before 2006, the EIU undergraduate students’ return rates before due dates should be the highest among the three EIU groups because this was the only group which had overdue fines. . . . There should be no difference in the return rates before due dates between EIU faculty and EIU graduate students (both groups had no overdue fines)” (p. 507). • Hypothesis 3: “EIU graduate students’ return rates before due dates was lower for 2002–2006 than 2007–2011” (p. 507). This hypothesis tests the impact of a change in fine policy that the library implemented in the fall of 2006. • Hypothesis 4: “UHM undergraduate students’ return rates before due dates is higher than that of EIU undergraduate students” because there is no grace period for UHM undergraduates (p. 507). EIU undergraduate students have a 10-day grace period. • Hypothesis 5: “UHM faculty’s return rates before due dates is higher than that of EIU faculty” (p. 507). UHM faculty incur overdue fines, but EIU faculty encounter no penalty for overdue materials. From the integrated library systems, the authors extracted data for the number of books returned before due dates and after overdue notices and for the number of books borrowed by the different populations for the time period starting with Fall 2002 and ending with Spring 2011. The authors analyzed the data using Statistical Package for Social Science (SPSS) and made comparisons using analysis of variance (ANOVA) expressed with an F-ratio and p-value < 0.01 as the level of significance. Main Results – The findings did not support hypotheses 1 or 2. For hypothesis 1, in which fines were the same for undergraduates, graduate students, and faculty, return rates increased with academic level and faculty groups. The rates were 90.4%, 93.9%, and 95.7%, respectively (F = 112.1, p < 0.001). For hypothesis 2, the return rate was 88.8% for undergraduates, 92.6% for graduate students, and 80.1% for faculty. The group difference was small but still statistically significant (F = 171.4, p < 0.001). The graduate students, who were not fined, had significantly higher return rates before due dates than undergraduates who incurred fines. Graduate students had higher return rates than faculty, though both groups had no fines. The data did not support hypotheses 3 and 4. For hypothesis 3, no significant change occurred in return rates before and after imposing fines (F = 5.75, p = .031). For hypothesis 4, the return rates of undergraduates at the university with a grace period showed no statistically significant difference in return rates from those undergraduates with no grace period (F = 4.355, p = .044). The findings supported hypothesis 5. The return rates indicated a statistically significant difference between faculty with fines for overdue books and those with no fines (F = 1701, p < 0.001). For those hypotheses for which the differences were not significant, the authors cite other variables, including reminders, grace periods, maturity of the borrower, withholding of privileges, fees, and lost book charges, that may contribute to return rates. Conclusions – In answer to the main research question, the authors conclude that “fines indeed make a difference” (p. 511) in patron book return conduct. However, they also note that fines can mar the reputation of the library creating a barrier to access and that courtesy notices and overdue notices are also effective ways to ensure timely return of materials.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.534
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.317
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2015
Admission routes1
Has abstractyes

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