Impact of big data and data analytics on the provision of data services in academic libraries
| Date | 21 October 2024 |
| Pages | 1039-1054 |
| DOI | https://doi.org/10.1108/EL-02-2024-0045 |
| Published date | 21 October 2024 |
| Author | Naimat Ullah Shah,Salman Bin Naeem,Rubina Bhatti,Amjid Khan,Xia Wang |
Impact of big data and data analytics
on the provision of data services in
academic libraries
Naimat Ullah Shah and Salman Bin Naeem
Department of Information Management, The Islamia University of Bahawalpur,
Bahawalpur,Pakistan
Rubina Bhatti
Department of Library and Information Science,
The Islamia University of Bahawalpur, Bahawalpur, Pakistan
Amjid Khan
Department of Library and Information Sciences,
Allama Iqbal Open University, Islamabad, Pakistan, and
Xia Wang
Inspur Communication Information System Co., LTD, Shandong Province,
Jinan, China
Abstract
Purpose –The purpose of this study is to determine the level of awarenessamong library and information
science (LIS) professionals regarding the perceived utility of big data (BD) and data analytics(DA) in
academic libraries,as well as their influence on the provision of dataservices (DSs).
Design/methodology/approach –A cross-sectionalsurvey was carried out to collect the data for thisstudy.
The populationof this study comprised LIS professionals working in public sectoruniversity libraries. A four-
factor measurement model estimating the influence of BD and DA on the provision of DSs in academic
librarieswas tested using the structural equation modelling.
Findings –The findings revealed that awareness (AW) (β= 0.141, CR = 2.534, p= 0.011) demonstrated a
significant positiveinfluence on the provision of DSs. The perceived utility of BD (β= 0.058,CR = 0.582, p=
0.561), and perceivedutility of DA (β= 0.141, CR = 2.534, p= 0.905) exhibits a positivebut statistically non-
significant impact on the provision of DSs (β= 0.010, CR = 0.120, p= 0.905). The goodness of fit indices
suggest a favourable fit for the model,as evidenced by the following values: χ
2
= 1.400, DF = 164; p= 0.001;
IFI = 0.954;TLI = 0.946; CFI = 0.953; GFI = 0.906; and RMSEA = 0.043.
Originality/value –A new perspective on the use of BD and DA in academic libraries is presentedin this
study.It presents a four-factor measurement model on the influence of BD and DA on the provision of DSs in
universitylibraries.
Keywords Awareness, Utility,Big data, Data analytics, Data se rvices, LIS professionals, Academic libraries
Paper type Research paper
The authors are grateful to Xia Wang, Dr Salman Bin Naeem and Dr Amjad Khan for their assistance
in gathering data, analysing it and reviewing this paper.
Conflicts of interest: The authors declare no conflict of interest is associated with this study.
Data availability: Upon a reasonable request, the corresponding author will provide data
supporting the findings of this study.
The Electronic
Library
1039
Received16 February 2024
Revised15 August 2024
Accepted19September 2024
TheElectronic Library
Vol.42 No. 6, 2024
pp. 1039-1054
© Emerald Publishing Limited
0264-0473
DOI 10.1108/EL-02-2024-0045
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0264-0473.htm
1. Introduction
A rapid technological advancement and exponential growth in data volume, velocity and
variety present unprecedentedchallenges and opportunities for academic libraries (Chen and
Zhang, 2014). The current era is heavily focused on data, and, as a result, we have seen
advancements in scientific data management and data analytics (DA)-related tools and
practices. The dynamic movement of data analysts plays an important role in improving
systems and enhancing data acquisition, management and analytical practices. This has also
led to new developments in dataacquisition, management and analytics (Dong et al.,2018).
The academic library traditionally serves as a repository of knowledge for their parent
institutions, providing access to a variety of resources and supporting their research and
educational missions. Digital technologies are fundamentally changing the nature of library
services and user expectations because of their proliferation and digitization (Bawden and
Robinson, 2016). Data analytics (DA), big data (BD) and artificial intelligence (AI) are all
emerging and have led to a shift in how we live, work and run businesses. It is evident that
these technologieshave also impacted the way we manage resources and services.This is not
unexpected given the impactthey have had on several different areas (Karaboğaet al., 2022).
For example, education sectors aroundthe world are increasingly relying on data mining and
advanced predictive DA to perform tasks,such as recruiting and enrolling students,
predicting their behaviour and developing individualized learning plans (Alhazmi, 2022).
However, there is a need for effective methods for storing, managing and using a huge
amount of data. Research studieson BD are expected to reveal development and changes (Ur
Rehman et al., 2019).
In general, BD refers to enormous amounts of data. However, simply having a large
amount of data is not necessarily valuable on its own. In recent years, people have been
collecting data and have become more adept at performing extensive and sophisticated
analyses (Marr, 2015). A broader and conceptual definition of BD is to “apply alternative
processing methods to data that cannotbe processed by conventional database systems.
Because of the data may be too large, move too quickly,or do not fit into the requirements of
conventional database systems”(Dumbill et al., 2013). Currently, BD and DA are widely
discussed topics in the literature. It has evolvedinto an interdisciplinary field, and the sheer
volume of data today necessitates innovative techniques for data analysis and management
(Bello-Orgaz et al.,2016). The integration of AI into BD can enhance decision-making and
provide more accurate predictionsof future events (Hamad et al., 2020).
1.1Historical development of big data terminology
The literature indicates that duringthe early 1900s, Frederick Winslow Taylor pioneered
scientific management techniquesthat led to BD, “The principles of scientific management”
(Ahmed and Ameen, 2017;Bumblauskas et al., 2017). The studies show that Laney first
introduced the concept of BD in 2001 in a researcharticle, stating that a significant attribute
of BD is that conventional data management approaches cannot handle it effectively (Li
et al., 2019). The term BD is very different for Amazon or Google than for a medium-sized
insurance company, but it is still big when it comes to the perception of those who contend
with it (Urbanic, 2018). The term BD represents these exceptionally vast data sets, and big
data analytics (BDA) indicates looking for experiences by consolidating and inspecting
them. In today’s society, BD can be described in several ways, such as structured,
unstructured, and semi-structured data (Islam and Hu, 2023;Oussous et al., 2018). In
scholarly communication, the use of the term BD has increased significantly during the last
decade. Since 2018, the internet has been used by more than 3.7 billion people, and almost
2.5 exabytes of data are generated every day by them. This explosive growth in data
EL
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