Using a hybrid methodology for literature review: a case study in depression research
| Date | 03 November 2023 |
| Pages | 305-323 |
| DOI | https://doi.org/10.1108/IDD-03-2022-0020 |
| Published date | 03 November 2023 |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
| Author | Salam Abdallah,Ashraf Khalil |
Using a hybrid methodology for literature
review: a case study in depression research
Salam Abdallah
College of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates, and
Ashraf Khalil
College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
Abstract
Purpose –This study aims to understand and a lay a foundation of how analytics has been used in depression management, this st udy conducts a
systematic literature review using two techniques –text mining and manual review. The proposed methodology would aid researchers in identifying
key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical r esearch objective.
Design/methodology/approach –This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic
manual review.
Findings –The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging
concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research
knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.
Originality/value –This study presents a new methodology for conducting a literature review for empirical research articles. This study has
explored an “HMLR”that combines text mining and manual systematic literature review. Depending on the purpose of the researc h, these two
techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and
revealing areas where research might be lacking.
Keywords Text mining, Literature review, Big data analytics, Semantic analysis, Depression care, Major depressive disorder
Paper type Research paper
1. Introduction
Research is an integral part of the advancement of science and
technology, and a literature review is an important component of
research conducted in any field of science. Systematic literature
review follows a number of well-designed methodological steps as
part of its process, reducing bias to obtain more credible findings
(Pulsiri and Vatananan-hesenvitz, 2018). In any given research
area, it provides an overview of extent research, research trends
and research gaps to identify and develop relevant directions for
subsequent work (Pulsiri and Vatananan-Thesenvitz, 2018;
Jonnalagadda et al.,2015).
As manual data extraction is a very time-consuming process,
automation can help in reducing this time, making the systematic
literature review more efficient time-wise (Jonnalagadda et al.,
2015) while also reducing human effort and workload (Pulsiri
and Vatananan-Thesenvitz, 2018;O’Connor et al.,2019). In
saving time and other costs, automation provides researchers an
opportunity to spend more focus and time on tasks where human
judgment is needed more (Pulsiri and Vatananan-Thesenvitz,
2018;O’Connor et al.,2019). Automated text analysis has the
potential to collect and thoroughly examine huge collections of
documents, providing conceptual insight and a unique “bird’s
eye”view of a research field with great quantitative rigor and high
speed (Luiz et al.,2019).
To the best of our knowledge, there is no framework or
systematic methodology established specifically to guide the
writing of the literature review section in empirical research
articles. Nakano and Muniz Jr. (2018) have proposed practical
guidance for writing a literature review for empirical papers.
Yet, their recommended protocol does not show how scholars
can benefitfrom text mining and data analytictechniques.
Based on the above discussion, we have the following research
objectives:
RO1. Exploring the working of hybrid methodology for
literaturereview (HMLR) using automated textmining
with manualliterature review in a recursiveprocess.
RO2. Validating the working of HMLR by applying it on a
test case of data analyticsused in depression research to
reveal the research gaps.
The rest of the paper is organized as follows. In Section 2, the
background and relevant research on literature review
methodologies and techniques is presented. In Section 3, our
proposed methodology (“HMLR”) is illustrated. Section 4 outlines
the experimental results. The discussion is presented in Section 5,
The current issue and full text archiveof this journal is available on Emerald
Insight at: https://www.emerald.com/insight/2398-6247.htm
Information Discovery and Delivery
52/3 (2024) 305–323
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-03-2022-0020]
Received 9 March 2022
Revised 14 July 2022
12 September 2022
24 November 2022
16 July 2023
Accepted 24 September 2023
305
and limitations are presented. Section 6 concludes the paper and
discusses possible future research directions in Section 7.
2. Background
Systematic literature reviews are methodological studies based
on systematic methods to retrieve and combine the findings
from different studies with the goal of providing a
comprehensive theoretical discussion on a specific topic or
theme (Wohlin, 2014). Systematicmethodologies were used in
reviews to minimize bias and errorin the selection and analysis
of studies (O’Mara-Eves et al., 2015). Table 1 presents a
summary of all the research reviewed in addressingthe topic of
“systematicliterature reviews.”
2.1 Traditional methodologies for literature review
writing (Mono-method)
One of the popular methodologies for reporting in systematic
reviews and meta-analyses is PRISMA (Liberati et al.,2009).
The PRISMA statement consists of a 27-item checklist and a
4-step flow diagram. The four steps are identification,
screening, eligibility and inclusion (Liberati et al.,2009). Yet,
PRISMA does not provide a specific guide on how each step
should be conducted. On the other hand, methods such as the
Snowballing technique describespecifically how articles can be
identified (Wohlin, 2014). Specifically, in Snowballing, new
papers are identified either by examining the reference list of
the currently selected paper (backward snowballing) or by
considering papers which cite the currently selected paper
Table 1 Summary of surveyed literature about systematic literature reviews
Authors, Year Title Methodology
Results and differences from previous
research
Liberati, A., Altman, D. G.,
Tetzlaff, J., Mulrow, C.,
Gøtzsche, P. C., Ioannidis, J. P.
A., ...Moher, D. 2009
The PRISMA statement for reporting
systematic reviews and meta-analyses of
studies that evaluate health care
interventions: explanation and elaboration
Traditional mono-method (Systematic
Reviews and Meta-Analyses [PRISMA])
Presents four steps for reporting in systematic
reviews and meta-analyses
Wohlin, C. 2014 Guidelines for snowballing in systematic
literature studies and a replication in
software engineering
Traditional mono-method (Snowballing) Identifies new papers either by examining the
reference list of the currently selected paper
(backward snowballing) or by considering
papers citing the currently selected paper
(forward snowballing) or both
Fan, W., Wallace, L., Rich, S.,
and Zhang, Z. 2006
Tapping the power of text mining Mixed methods Presents text-mining tools that can
be applied in medicine, business, government,
and education
Feldman, R., and Sanger, J.
2007
The text mining handbook: advanced
approaches in analyzing unstructured data
Mixed methods Provides comprehensive algorithms about text
mining and link detection
Indurkhya, N. 2015 Emerging directions in predictive text mining Mixed methods Identifies six main directions where research in
text mining is heading: Deep Learning, Topic
Models, Graphical Modeling, Summarization,
Sentiment Analysis, Learning from Unlabeled Text
Halevy, A., Norvig, P., and
Pereira, F. 2009
The unreasonable effectiveness of data Mixed methods Argues that choosing unsupervised learning
on huge unlabeled data will yield better results
Frijters et al. (2010) Literature mining for the discovery of hidden
connections between drugs, genes, and
diseases
Mixed methods Presentsa tool that mines the literaturefor new
relationshipsbetween biomedicalconcepts
Korhonen, A., S
eaghdha, D. O.,
Silins, I., Sun, L., Högberg, J.,
and Stenius, U. 2012
Text mining for literature review and
knowledge discovery in cancer risk
assessment and research
Mixed methods Presents a fully integrated text mining tool
designed to support chemical health risk
assessment
Wang, H., Ding, Y., Tang, J.,
Dong, X., He, B., Qiu, J., and
Wild, D. J. 2011
Finding complex biological relationships in
recent PubMed articles using Bio-LDA
Mixed methods Presents an algorithm called Bio-LDA that uses
extracted biological terminology to
automatically identify latent topics and
provides a variety of measures to uncover
putative relations among topics and bio-terms
Biesenthal, C., and Wilden, R.
2014
Multi-level project governance: Trends and
opportunities
Mixed methods Uses the textual data mining software
Leximancer to identify dominant concepts and
themes underlying project governance
research
O’Mara-Eves et al. (2015) Using text mining for study identification in
systematic reviews: A systematic review of
current approaches
Mixed methods Discusses automating through text mining the
process of screening and identifying relevant
studies in an unbiased way for inclusion in
systematic reviews
Indulska et al. (2012) Quantitative approaches to content analysis:
identifying conceptual drift across publication
outlets
Mixed methods Demonstrates how Latent Semantic Analysis
and data mining, can aid researchers in
revealing core content topic areas in large (or
small) data sets, and in visualizing how these
concepts evolve, migrate, converge or diverge
over time
Harden and Thomas (2010) Mixed methods and systematic reviews:
Examples and emerging issues
Mixed methods Distinguishesbetween mono-method and
mixed methodssystematic reviews; andcoined
the term mixedmethods systematic reviews
Oraee et al. (2017) Collaboration in BIM-based construction
networks: A bibliometric-qualitative
literature review
Mixed methods Uses bibliometric analyses to automate the
papers screening process
Source: Table by authors
Hybrid methodology for literature review
Salam Abdallah and Ashraf Khalil
Information Discovery and Delivery
Volume 52 · Number 3 · 2024 · 305–323
306
Get this document and AI-powered insights with a free trial of vLex and Vincent AI
Get Started for FreeStart Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
Start Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
Start Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
Start Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
Start Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
