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From obsolescence to optimization: a predictive framework for managing print collection aging in academic libraries.

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Title: From obsolescence to optimization: a predictive framework for managing print collection aging in academic libraries.
Authors: Ma, Shuang1 (AUTHOR) 18101@tongji.edu.cn, He, Qingfang1 (AUTHOR) qfhe@tongji.edu.cn
Source: Journal of Documentation. 2026, Vol. 82 Issue 4, p989-1002. 14p.
Subject Terms: *Library circulation & loans, *Library administration, *Academic libraries, *Collection development in libraries, *Bibliometrics, *Machine learning, Collection management (Libraries), Shelving (Furniture)
Abstract: Purpose: This study addresses the challenge of managing aging print collections in academic libraries by developing a data-driven framework that quantifies discipline-specific aging patterns and predicts future trends to inform dynamic shelving strategies. Design/methodology/approach: We extend Bernal's document aging model to library circulation data. Utilizing loan records (2001–2023) from 22 disciplines at a major university library, we fit a negative exponential decay model to quantify aging half-lives. A Gradient Boosting Regression Trees (GBRT) algorithm is then employed to forecast future aging parameters. These predictions inform a novel four-quadrant classification system and a weighted hierarchical prioritization model for shelving. Findings: The negative exponential model demonstrated robust goodness-of-fit (mean R2 = 0.75) across disciplines, validating its applicability to loan data. Significant cross-disciplinary heterogeneity was observed (mean half-life: 4.9 ± 0.7 years). The GBRT model achieved good prediction accuracy for initial loan rates (overall MAPE = 11.9%). The resulting framework categorizes disciplines into three actionable preservation tiers (Tier-1: open shelves; Tier-2: thematic displays; Tier-3: dense storage). Practical implications: The proposed framework offers library managers a replicable, data-driven tool to optimize physical space allocation, balance collection utility with preservation needs, and justify resource reconfiguration. Originality/value: This research makes three key contributions: (1) it theoretically extends document obsolescence analysis from citations to circulation data; (2) it methodologically innovates by integrating bibliometric modeling with machine learning for prediction; (3) it provides practitioners with a novel, predictive decision-making framework for evidence-based space management. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Documentation is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Text:
  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 194733894
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: From obsolescence to optimization: a predictive framework for managing print collection aging in academic libraries.
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  Data: <searchLink fieldCode="AR" term="%22Ma%2C+Shuang%22">Ma, Shuang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 18101@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22He%2C+Qingfang%22">He, Qingfang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qfhe@tongji.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Documentation%22">Journal of Documentation</searchLink>. 2026, Vol. 82 Issue 4, p989-1002. 14p.
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  Data: *<searchLink fieldCode="DE" term="%22Library+circulation+%26+loans%22">Library circulation & loans</searchLink><br />*<searchLink fieldCode="DE" term="%22Library+administration%22">Library administration</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+libraries%22">Academic libraries</searchLink><br />*<searchLink fieldCode="DE" term="%22Collection+development+in+libraries%22">Collection development in libraries</searchLink><br />*<searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Collection+management+%28Libraries%29%22">Collection management (Libraries)</searchLink><br /><searchLink fieldCode="DE" term="%22Shelving+%28Furniture%29%22">Shelving (Furniture)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: This study addresses the challenge of managing aging print collections in academic libraries by developing a data-driven framework that quantifies discipline-specific aging patterns and predicts future trends to inform dynamic shelving strategies. Design/methodology/approach: We extend Bernal's document aging model to library circulation data. Utilizing loan records (2001–2023) from 22 disciplines at a major university library, we fit a negative exponential decay model to quantify aging half-lives. A Gradient Boosting Regression Trees (GBRT) algorithm is then employed to forecast future aging parameters. These predictions inform a novel four-quadrant classification system and a weighted hierarchical prioritization model for shelving. Findings: The negative exponential model demonstrated robust goodness-of-fit (mean R2 = 0.75) across disciplines, validating its applicability to loan data. Significant cross-disciplinary heterogeneity was observed (mean half-life: 4.9 ± 0.7 years). The GBRT model achieved good prediction accuracy for initial loan rates (overall MAPE = 11.9%). The resulting framework categorizes disciplines into three actionable preservation tiers (Tier-1: open shelves; Tier-2: thematic displays; Tier-3: dense storage). Practical implications: The proposed framework offers library managers a replicable, data-driven tool to optimize physical space allocation, balance collection utility with preservation needs, and justify resource reconfiguration. Originality/value: This research makes three key contributions: (1) it theoretically extends document obsolescence analysis from citations to circulation data; (2) it methodologically innovates by integrating bibliometric modeling with machine learning for prediction; (3) it provides practitioners with a novel, predictive decision-making framework for evidence-based space management. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Documentation is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 14
        StartPage: 989
    Subjects:
      – SubjectFull: Library circulation & loans
        Type: general
      – SubjectFull: Library administration
        Type: general
      – SubjectFull: Academic libraries
        Type: general
      – SubjectFull: Collection development in libraries
        Type: general
      – SubjectFull: Bibliometrics
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Collection management (Libraries)
        Type: general
      – SubjectFull: Shelving (Furniture)
        Type: general
    Titles:
      – TitleFull: From obsolescence to optimization: a predictive framework for managing print collection aging in academic libraries.
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            NameFull: Ma, Shuang
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          Name:
            NameFull: He, Qingfang
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            – D: 01
              M: 07
              Text: 2026
              Type: published
              Y: 2026
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