Purchase and use of monographs originally requested on interlibrary loan in a medical school library

Pritchard, Sibille

doi:10.1016/0364-6408(80)90038-1

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…A quick glance at the library literature suggests that publications that explicitly identify or from which one may infer circulation correlates are fairly common. As has the PDA literature, many of the studies on circulation and use-both classic and current-have found relationships between circulation/use and books' selectors (Davidson 1943;Ellis et al 2010;Evans 1970;Hardesty 1981;Pritchard 1980;Tucker 2009). A sizeable amount of research has also concluded or implied that relationships exist between circulation/ use and books' subjects or Library of Congress (LC) classifications (Adams and Noel 2008;Aguilar 1986;Blecic 2000;Britten 1990;Bulick, Sabor, and Flynn 1979;Davidson 1943;Ellis et al 2010;Fenske 1994;Fussler and Simon 1969;Grigg et al 2010;Knievel, Wicht, and Connaway 2006;Lancaster 1982;Lotlikar 1997;Mills 1982;Mouyal 2005;Ochola 2002;Peasgood 1986;Tucker 2009).…”

Section: Review Of Literaturementioning

confidence: 99%

“…Unfortunately, the answer would appear to be that the PDA literature has failed to address this complexity. In reporting their results, many PDA studies have merely offered the reader the number or percentage of PDA books that have circulated, sometimes with figures for traditionally acquired books included for purposes of comparison and sometimes not (Allen et al 2003;Anderson et al 2002;Brug and MacWaters 2004;Campbell 2006;Crane 2011;Houle 2004;Perdue and Van Fleet 1999;Pritchard 1980;Reynolds et al 2010;Teaff 2011;Ward 2002;Ward et al 2003;Way 2009;Zopfi-Jordan 2008). Other studies have additionally offered the numbers or percentages of PDA books that have experienced multiple circulations (Allen et al 2003;Anderson et al 2002;Bombeld and Hanerfeld 2004;Brug and MacWaters 2004;Chan 2004;Houle 2004;Ward et al 2003) or numbers of circulations subsequent to the books' having been returned to the library shelves by their initial ILL requestors (Comer and Lorenzen 2005;Nixon and Saunders 2010;Way 2009).…”

Section: Review Of Literaturementioning

confidence: 99%

“…Librarians have been touting, or at least toying with the idea of, some form of demand-or patron-driven acquisition (PDA) program for at least a century (Chadwell 2009;Dana 1896;Holley and Ankem 2005;Hostetler 2010;Hulsey 2003;Jackson 1989;Peasgood 1986;Pritchard 1980;Rathbone 1909;Rawlinson 1981). However, it would appear that it was not until the past decade or two, perhaps when PDA was tied to interlibrary loan (ILL), that interest in PDA truly proliferated, producing a flurry of books, articles, reports, and presentations in the library literature (Hostetler 2010;Nixon, Freeman, and Ward 2010;Tyler 2011;Way and Garrison 2011) and becoming "one of the most discussed ideas in the world of library collections" (Lugg 2011, 7).…”

Section: Introductionmentioning

confidence: 99%

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Patron-Driven Acquisition and Circulation at an Academic Library: Interaction Effects and Circulation Performance of Print Books Acquired via Librarians’ Orders, Approval Plans, and Patrons’ Interlibrary Loan Requests

Tyler

Falci

Melvin

et al. 2013

Collection Management

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Numerous publications on patron-driven acquisition (PDA) for print books and similar materials have reported that patronrequested materials circulate more. Tying circulation to selector may be failing to address the complex of factors that contributes to items' circulation. In the present study, the authors revisit a PDA program's data and to determine whether PDA print books' circulation advantage persists when the potential interactions of several additional variables are taken into account. As with prior studies, library patrons were significantly better predictors of circulation than were librarians or approval plans. However, librarians proved to be significantly better predictors than were approval plans.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Patron-Driven Acquisition and Circulation at an Academic Library: Interaction Effects and Circulation Performance of Print Books Acquired via Librarians’ Orders, Approval Plans, and Patrons’ Interlibrary Loan Requests

Tyler

Falci

Melvin

et al. 2013

Collection Management

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“…G. E. Evans et al [4] also proposed a model to investigate the use of library books based on the personal history of selected books in several libraries. S.Pritchard et al [5] also investigated the future use of the library books based on the manner of selection. All of the previous work is based on the classification book-loan records according to various attributes.…”

Section: Introductionmentioning

confidence: 99%

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

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Large scale data and predictive analytics are the most challenging tasks in the field of academic data mining. Academic libraries are a great source of information and knowledge to provide a wide range of services to meet end-user requirements. Due to the rapid changes in the educational environment and availability of huge library rental book data, it is required to utilize data mining and machine learning techniques in the context of the academic library to extract and analyze underlying knowledge from rental book data, which is important to facilitate library administration to drive better future decisions to improve and manage library resources effectively. These are the following resources, such as managing future demands of the library books, selection and arrangement of the books, operational efficiency, and also improve the quality of interaction between the library and end-users, etc. This work uses and analyzes a real dataset collected from the library of Jeju National University, the Republic of Korea. The dataset contains 2,211,413 rental book records including 173671 unique book records, 57203 unique number of the rental user, and 78 data parameters. In this paper, we propose a novel model to analyze and predict library rental book data to facilitates library administration in order to plan and manage library resources effectively and provide better services to end-users. The proposed model consists of two different modules; library data analysis and prediction modules. Firstly, we use data mining techniques to analyze and extract useful underlying patterns from library rental book data, which can lead to plan and manage library resources effectively. Secondly, a novel prediction model is proposed based on Deep Neural Network (DNN), Support Vector Regressor (SVR), and Random Forest (RF) to predict future usage of the academic libraries rental books. The performance results of the implemented regression models are evaluated in terms of MAE, MSE, and RMSE. In this paper, it is found that the DNN model performs significantly better than SVR and RF. The experimentation results show that the proposed model improves the future usage of library books to facilitate library administration to plan and manage library resources effectively. Based on the proposed model results, the academic library administration can easily plan and manage resources effectively to provide quality services to end-users. INDEX TERMS Data mining, academic libraries, big data, machine learning, predictive analysis.

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“…Evans [3] investigated the numbers of loans for books classified by the person who had selected the book, academic staff, library staff, and book jobbers, in some academic libraries. Pritchard [4] also investigated the frequencies of loans for books selected on the basis of four criteria, new editions of already held books, requests on interlibrary loan, recommendations by library staff, and recommendations by academic staff. As for Japanese university libraries, Matsui and Isono [5] investigated the turnover rate of books in the library of Nara University, and found the typical change over the years (obsolescence) on the frequency of loans.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Book Selection in a University Library by Loan Record Analysis

Kitajima¹,

Baba²,

Minami³

2015

IJIET

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Abstract-Book selection is an important operation in a library and should be conducted on the basis of reasonable data. This paper evaluates the process of book selection in a university library by analyzing the loan records. The authors classified the books in the library according to Nippon Decimal Classification (NDC), and investigated the turnover rate for each category. The results show that the book selection process in the library was appropriate in terms of the turnover rate although the process was not conducted with considering the turnover rate. Additionally, the authors considered the rate of the books that bought by researchers in the university and accessioned to the library. The results indicate some viewpoints for selecting books in university libraries.Index Terms-Data mining, library, book selection, loan record.

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Purchase and use of monographs originally requested on interlibrary loan in a medical school library

Cited by 9 publications

References 3 publications

Patron-Driven Acquisition and Circulation at an Academic Library: Interaction Effects and Circulation Performance of Print Books Acquired via Librarians’ Orders, Approval Plans, and Patrons’ Interlibrary Loan Requests

Patron-Driven Acquisition and Circulation at an Academic Library: Interaction Effects and Circulation Performance of Print Books Acquired via Librarians’ Orders, Approval Plans, and Patrons’ Interlibrary Loan Requests

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

An Evaluation of Book Selection in a University Library by Loan Record Analysis

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