Content analysis in SCM research: past uses and future research opportunities

DOIhttps://doi.org/10.1108/IJLM-09-2016-0200
Pages152-190
Published date12 February 2018
Date12 February 2018
AuthorSaif Mir,Shih-Hao Lu,David Cantor,Christian Hofer
Subject MatterManagement science & operations,Logistics
Content analysis in SCM research:
past uses and future research
opportunities
Saif Mir
Department of Supply Chain and Information Management, College of Charleston,
Charleston, South Carolina, USA
Shih-Hao Lu
Department of Business Administration,
National Taiwan University of Science and Technology, Taipei, Taiwan
David Cantor
Department of Supply Chain and Information Systems, Iowa State University,
Ames, Iowa, USA, and
Christian Hofer
Department of Supply Chain Management, Sam M. Walton College of Business,
University of Arkansas, Fayetteville, Arkansas, USA
Abstract
Purpose Content analysis is a methodology that has been used in many academic disciplines as a means to
extract quantitative measures from textual information. The purpose of this paper is to document the use of
content analysis in the supply chain literature. The authors also discuss opportunities for future research.
Design/methodology/approach The authors conduct a literature review of 13 leading supply chain
journalsto assess the state of thecontent analysis-basedliterature and identifyopportunities for futureresearch.
Additionally, theauthors provide a general schema for andillustration of the use of content analysis.
Findings The findings suggest that content analysis for quantitative studies and hypothesis testing
purposes has rarely been used in the supply chain discipline. The research also suggests that in order to fully
realize the potential of content analysis, future content analysis research should conduct more hypothesis
testing, employ diverse data sets, utilize state-of-the-art content analysis software programs, and leverage
multi-method research designs.
Originality/value The current research synthesizes the use of content analysis methods in the supply
chain domain and promotes the need to capitalize on the advantages offered by this research methodology.
The paper also presents several topics for future research that can benefit from the content analysis method.
Keywords North America, Supply chain management, Content analysis, Literature review,
Research methodology
Paper type General review
1. Introduction
Content analysis is a scientific method that summarizes and quantitatively analyzes archival
documents (Neuendorf, 2002). Scholarly interest in content analysis methods has increased
markedly in recent years in a variety of disciplines including strategic management,
psychology, communication, and other social science disciplines (Duriau et al., 2007).
Its increase in popularity can, in part, be attributed to advances in software technology and
the proliferation of secondary data sources (Waller and Fawcett, 2013). Indeed, content
analysis techniques provide a means for supply chain scholars to efficiently construct and
analyze rich dataacross multiple firms and industriesas well as across time. In this study, we
review the use of the content analysis research method in the supply chain literature and
outline how supply chain scholars can leverage content analysis of archival data to advance
supply chain management (SCM) research.
The International Journal of
Logistics Management
Vol. 29 No. 1, 2018
pp. 152-190
© Emerald PublishingLimited
0957-4093
DOI 10.1108/IJLM-09-2016-0200
Received 3 September 2016
Revised 17 February 2017
23 May 2017
Accepted 29 May 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0957-4093.htm
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29,1
It is critically important for the supply chain discipline to remain current on emerging
methodological developments because research methods that were deemed appropriate ten
or more years ago may no longer be acceptable (Hazen, 2016; Goldsby and Zinn, 2015;
Guide and Ketokivi, 2015). For example, many supply chain scholars have utilized
cross-sectional surveys in order to examine supply chain research questions (Larson and
Poist, 2004). Yet, low survey response rates, concerns of potential respondent biases, and
associated measurement errors pose daunting challenges to supply chain researchers who
are interested in examining firm behavior via survey methods (Boyer and Swink, 2008).
Content analysis is one methodology that could, in some instances, offer a solution to the
problems listed above. Content analysis is the systematic, objective, quantitative analysis
of message characteristics(Neuendorf, 2002, p. 1). Compared to other research methods
such as laboratory experiments, content analysis involves measuring variables in their
natural state without any manipulation (Neuendorf, 2002). Scholars can content analyze
data that is in a variety of formats such as interviews, videos, official company reports,
press releases, etc. (Weber, 1990). There are several advantages associated with using
content analysis in the supply chain and logistics disciplines. First, content analysis allows
for an unobtrusive investigation of a wide variety of research issues using secondary data
(Krippendorff, 2012). While some supply chain scholars have utilized secondary data
sources such as company websites, annual reports, and trade magazines (Dos Santos et al.,
2013; Haughton and Desmeules, 2001; Pooley and Dunn, 1994), content analysis studies in
the supply chain discipline have not fully leveraged archival data. Secondary data sources
are of particular value in situations where data collection methods such as surveys might be
difficult to implement due to, for example, low response rates (Griffis et al., 2003). Second,
with the increasing availability of various archival data sources to supply chain scholars,
content analysis provides the opportunity to analyze longitudinal or panel data which then
enables scholars to examine phenomena over time using time series or panel regression
techniques (see e.g. Hofer et al., 2012). Third, through the use of computer software, supply
chain scholars can find value from the content analysis of archival data to speed up the data
collection and analysis process (Tangpong, 2011). Fourth, content analysis can facilitate the
exploitation and analysis of innovative supply chain data sources such as company reports,
news articles, or transcripts. Fifth, content analysis methods can be used to conduct both
qualitative and quantitative research, thus making it a valuable tool in the triangulation of
research questions (Boyer and Swink, 2008).
As is the case with anymethodology, there are potential challenges associated with using
content analysis in the supply chain discipline. These challenges might include determining
appropriatemeasures of the construct of interest,creating keyword dictionariesor codebooks,
converting documents video and/or audio data from a non-standard format into a content
analyzable format, demonstrating reliability and validity, and conducting automated or
semi-automatedcontent analysis (Tangpong, 2011;Neuendorf, 2002). Many of these concerns
are particularly salient when content analysis methods are used as a basis for statistical
analysis. As such, we summarize and outline avenues for the use of content analysis as a
powerful tool for quantitative SCM research.
This study, thus, makes original and interesting contributions to the SCM literature.
First, we contribute to the methods-focused SCM literature (e.g. Tangpong, 2011; Larson and
Poist 2004; Griffis et al., 2003) by analyzing and describing previous use cases of content
analysis methods in supply chain and logistics research. In particular, our work builds on
earlier studies by Tangpong (2011) who examined the use of content analysis methods in the
supply chain and operations literature between 2002 and 2007 and Seuring and Gold (2012)
who provided guidelines for the use of content analysis to conduct literature reviews.
Our research differs from the above in that it covers all publications in leading SCM and
operations management journals up until and including 2016 and provides a more extensive
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analysis in
SCM research
summary of how content analysis has been incorporated and implemented in prior research.
In so doing, we seek to generate increased interest in the content analysis methodology by:
presenting a framework for the application of the content analysis method in supply chain
research; reviewing, analyzing, and summarizing the current state of content analysis in the
SCM literature; and outlining several ideas for future content analysis-based research.
Our study also contributes to the methods-focused SCM literature by presenting an
example of how content analysis can be effectively used in future research for quantitative
construct measurement and hypothesis testing purposes. In particular, we illustrate the
potential of computer-assisted text analysis (CATA) in facilitating SCM research. This is
important since we find that prior SCM literature has rarely used content analysis for
construct measurement and hypothesis testing purposes. We hope to increase the
popularity of the content analysis method, especially as a quantitative tool, in the supply
chain discipline and highlight opportunities for future research.
2. A brief overview of the content analysis method
In this section, we briefly outline the key steps involved in the implementation of the
content analysis method. This method can be used to analyze textual or with some
modifications video and audio data. The guidelines presented below, adapted from
Neuendorf (2002), summarize the steps of how to conduct a content analysis. We apply
these guidelines to analyze the content analysis-based SCM literature in Section 3 and,
in Section 4, offer an illustration of how content analysis can be applied in a SCM
research context.
2.1 Variable conceptualization and operationalization
An important first step in content analysis is the determination of the variables that will be
operationalized. Based on the research question, there is flexibility on how to use content
analysis to operationalize the variables of interest (Neuendorf, 2002). Relatedly, decisions
about the unit of analysis need to be made. When variable conceptualization and
operationalization is complete, the researcher can proceed to develop a coding schema.
Further, Neuendorf (2002) mentions that researchers should focus on content validity at this
stage. If the variable operationalizations are not consistent with the variable definitions, the
data coding in the later stages will be erroneous and will subsequently lead to false claims.
2.2 Coding schema
In this step, scholars need to develop a codebook which provides a detailed definition of each
latent construct and its associated codes or themes (Neuendorf, 2002). After the text mining
(or structured content analysis) is complete, the codes or themes identified in the text are
mapped back to the latent construct for subsequent analysis. The coding process requires
the creation of a coding template that contains a detailed definition of the variables. In this
phase, the researcher creates a dictionary or lists of keywords or keyword combinations that
represent each construct. The text mining process can be completed using either human
coders or computer-assisted text analysis (CATA) software. The human coding process
should involve at least two coders for inter-coder reliability reasons.
2.3 Training and coding
Training the coders is an important step because the coding process needs to be
implemented consistently by each coder individually and across coders (Neuendorf, 2002).
The initial step involves pilot testing the training instructions to ensure that the coders
share a common understanding of the variables specified in the codebook. After the training
is conducted, the coders independently code a sample of documents. Upon the conclusion of
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