Information overload: a concept analysis
| Date | 26 May 2022 |
| Pages | 144-159 |
| DOI | https://doi.org/10.1108/JD-06-2021-0118 |
| Published date | 26 May 2022 |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
| Author | Mohamed Amine Belabbes,Ian Ruthven,Yashar Moshfeghi,Diane Rasmussen Pennington |
Information overload:
a concept analysis
Mohamed Amine Belabbes, Ian Ruthven, Yashar Moshfeghi and
Diane Rasmussen Pennington
Department of Computer and Information Sciences, University of Strathclyde,
Glasgow, UK
Abstract
Purpose –With the shift to an information-based society and to the de-centralisation of information,
information overload has attracted a growing interest in the computer and information science research
communities. However, there is no clear understanding of the meaning of the term, and while there have been
many proposed definitions, there is no consensus. The goal of this work was to define the concept of
“information overload”. In order to do so, a concept analysis using Rodgers’approach was performed.
Design/methodology/approach –A concept analysis using Rodgers’approach based on a corpus of
documents published between 2010 and September 2020 was conducted. One surrogate for “information
overload”, which is “cognitive overload”was identified. The corpus of documents consisted of 151 documents
for information overloadand ten for cognitive overload.All documents were from the fieldsof computer science
and information science, and were retrieved from three databases: Association for Computing Machinery
(ACM) Digital Library, SCOPUS and Library and Information Science Abstracts (LISA).
Findings –The themes identified from the authors’concept analysis allowed us to extract the triggers,
manifestations and consequences of information overload. They found triggers related to information
characteristics, information need, the working environment, the cognitive abilities of individuals and the
information environment. In terms of manifestations, they found that information overload manifests itself
both emotionally and cognitively. The consequences of information overload were both internal and external.
These findings allowed them to provide a definition of information overload.
Originality/value –Through the authors’concept analysis, they were able to clarify the components of
information overload and provide a definition of the concept.
Keywords Information overload, Concept analysis, Cognitive overload, Computer science, Information
science, Information retrieval, Information technology
Paper type Research paper
1. Introduction
With the rise in the amount of information available and ease of access, one phenomenon has
taken an important place in users’information interaction experiences: information overload
(IO). While researchers have extensively explored the concept, there is no universally agreed
upon definition.We found one popular definition for IO by Bawdenet al. (1999), which defines
IO as the result of so muchuseful and relevant information that ithinders rather than helps.
In addition to not having a single definition, the term “cognitive overload”is often
interchangeably used to denote “information overload”in computer science and information
science.“Cognitive overload”(CO)comes from cognitive psychologyand stems from Cognitive
Load Theory(CLT) (Sweller, 1988). Swellerexplains in CLT that in order to maximisecognitive
performance, we should not overload ourworking memory with information.
The concept of IO has seen a growing interest in recent years, and it has moved to the
forefront of computer science (CS) and information science (IS) research. For example, in the
ACM Digital Library, the keywords “information overload”and “cognitive overload”have
seen a linear growth with 53 papers mentioning the word in 2003 against 254 in 2020 [1].
A few phenomena can explain this growth: the shift from an industrial to an information-
based economy (MacDonald et al., 2011), and the advent of social media (Koroleva et al., 2010).
Social media created a fertile environment for the development of IO, whether through the
JD
79,1
144
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0022-0418.htm
Received 17 June 2021
Revised 31 March 2022
Accepted 3 April 2022
Journal of Documentation
Vol. 79 No. 1, 2023
pp. 144-159
© Emerald Publishing Limited
0022-0418
DOI 10.1108/JD-06-2021-0118
diversity of content presented (Koltay, 2017), the increasing number of sources, or the push of
irrelevant information and unwanted advertisements (Fu et al., 2020). In their conceptual
model of IO in social networks, Koroleva et al. (2010) highlighted a positive correlation
between the growing amount of connections and IO. The paper also noted an increase in users
discontinuing their use of a service (Fu et al., 2020;Sasaki et al., 2015) as a coping mechanism.
In the earlydays of informationretrieval (IR), a perceivedcause of IO was the poorrecall and
precision of search engines as there was a lack of effective algorithms to access the growing
body of digitalinformation (Montebello,1998). Nowadays, the problemof IO is still present, but
the challengesidentified are more diverse,including a lack of information literacy, an increase
in duplicate information, increase in redundant information, increase in the quantity of
information,individual limitations, lackof prior knowledge, task complexity,lack of language
proficiency and timeconstraints (Bawden and Robinson, 2021;Hiltz and Plotnick,2013).
To mitigate some of these challenges, advanced filtering and summarisation algorithms
have been developed (Kaufhold et al., 2020). The aim of these algorithms is to reduce the
amount of information individuals are facing online. In addition, thanks to the development in
artificial intelligence and the improvements in recommender systems (Batmaz et al., 2019),
there are new approaches to tackling the problem of IO, including an interest in developing
emotion-based recommender systems which take into account the emotions that information
elicits in individuals (Costa and Macedo, 2013;Rasmussen Pennington, 2016).
In the past, research in the field of IR considered IO as a problem (Montebello, 1998). IO
was not researched as much in IR compared to other fields such as organisation science and
marketing, where extensive studies have been conducted (Eppler and Mengis, 2008).
Researchers in these fields have offered several definitions of, and antecedents to, IO.
There is no consensus regarding the definition of IO, and the majority of work tackling IO
in CS and IS lack rigour in defining the concept. With the growing amount and sources of
information, IO became a crucial problem for our society. It favours the growth of
misinformation and disinformation such as fake news (Gunaratne et al., 2020). It is also an
obstacle for emergency services to filter out illegitimate information (Kaufhold et al., 2020).
Furthermore, it impacts the mental health of the broader population (Matthes et al., 2020),
leading people to drop out of their tasks and make bad decisions (Phillips-Wren and
Adya, 2020).
Therefore, it is essential to establish a definition that highlights critical attributes of the
concept, because a clear operationalisation will allow researchers to study it with greater
rigour. In this paper, we applied a concept analysis (Foley and Davis, 2017) using Rodgers’
approach (Rodgers, 1989), and we aimed to offer a clear definition of IO. Concept analysis is a
method we borrowed from nursing science that provides a straightforward and reproducible
clarification, as well as an explanation of, concepts (Tofthagen and Fagerstrøm, 2010).
The paper is organised as follows: we first introduce previous research around IO and why
a concept analysis is necessary. Then, we present the methodology used and how we
surveyed the papers. Subsequently, we present the findings through the different themes we
extracted. Finally, we define IO and conclude our work.
2. Background
Various types of overloads have been presented in the literature and are closely associated
with IO. Alongside IO, there is information anxiety, infobesity and cognitive overload
(Bawden and Robinson, 2009;Lauri and Virkus, 2018;Sabeeh and Ismail, 2013). Marques and
Batista (2017) argued that the most recent research in IO should be classed as communication
overload (Marques and Batista, 2017;Virkus et al., 2017), as it deals with the exchange of
information and communication across the web among individuals and groups. These
different terms which are interchangeably used with IO (Jackson and Farzaneh, 2012) highlight
a lack of consensus on what is the concept of IO.
Information
overload:
a concept
analysis
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