Developing predictive analytics frameworks to optimize collection development in modern libraries
1 Texas A&M University -Commerce, Texas, USA.
2 Independent Researcher, United Kingdom.
3 Texas A&M University -Commerce, Texas, USA.
Review
International Journal of Scientific Research Updates, 2023, 05(02), 116–128.
Article DOI: 10.53430/ijsru.2023.5.2.0038
Publication history:
Received on 04 March 2023; revised on 28 April 2023; accepted on 02 May 2023
Abstract:
Modern libraries face the challenge of balancing resource constraints with the evolving and diverse needs of their users. Predictive analytics offers a transformative solution by enabling libraries to make data-driven decisions for collection development, optimizing resource allocation, and aligning acquisitions with user demands. This explores the design and implementation of predictive analytics frameworks to enhance library collection strategies. Predictive analytics frameworks leverage historical usage data, user behavior patterns, and external trends in research and publishing to forecast resource demands. By employing machine learning algorithms and statistical models, libraries can identify emerging areas of interest, anticipate future needs, and proactively manage collections. This approach enhances decision-making, minimizes redundancies, and ensures that resources align with institutional goals and user expectations. The framework emphasizes key processes such as data collection, preprocessing, and the integration of predictive insights into procurement strategies. Case studies highlight successful implementations, including demand forecasting for books and journals, optimizing acquisitions for diverse user groups, and managing underutilized resources. These examples demonstrate the potential of predictive analytics to improve user satisfaction while maintaining cost efficiency. However, the study also addresses critical challenges, such as ensuring data quality, mitigating algorithmic biases, and balancing automation with librarian expertise. Ethical considerations, including data privacy and equitable access to analytics technologies, are discussed to promote responsible framework adoption. This research underscores the transformative role of predictive analytics in modern libraries, enabling them to stay agile and responsive in an ever-changing information landscape. It concludes with recommendations for future research, including integrating emerging technologies like AI and IoT to further enhance collection development processes. Libraries adopting predictive analytics frameworks can achieve sustainable, user-centric, and efficient collection management strategies.
Keywords:
Predictive analytics; Modern libraries; Artificial intelligence; Review
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