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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.

The authors conduct a literature review of 13 leading supply chain journals to assess the state of the content analysis-based literature and identify opportunities for future research. Additionally, the authors provide a general schema for and illustration of the use of content analysis.

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.

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.

Effective international marketing requires collection of large amounts of data from diverse sources and sensitive use of such information in marketing strategy. While marketing information systems help managers incorporate some kinds of data into their planning, content analysis offers a different set of insights into cultural concepts, themes and trends not usually captured by traditional data systems. Content analysis has evolved as a research technique since the 1920s, largely in social science applications. Today, aided by new analytical techniques and optical scanners, which can read huge volumes of material inexpensively; and state‐of‐the‐art computer software, which can handle languages such as Chinese, Japanese, and Arabic, content analysis has great promise for international marketing applications.

The purpose of this paper is to respond to the call by Dumay and Cai (2014) for new ideas to enhance intellectual capital (IC) research. One possibility is to draw conclusions on comparability across the results of prior studies. This study investigates whether the results of prior IC content analyses are comparable despite differences in their IC research frameworks.

A content analysis of 428 German management reports is conducted, capturing the IC reporting scores for individual IC items to investigate the role of certain widely used IC items. The relationships of IC scores for different combinations of widely used IC items are further examined in a correlation analysis to indicate comparability of prior results.

The findings show that widely used IC items capture the majority of IC reporting and that the IC scores for different combinations of these IC items are highly correlated. These findings indicate that the results of prior IC content analyses are comparable as long as most of the widely used IC items are included in prior IC research frameworks.

The study contributes to IC reporting research as it shows that conclusions can be drawn across prior studies in meta-analyses because the results of prior studies are comparable in rankings and key findings.

Although content analyses of IC reporting have been previously criticised, this study seminally questions the comparability of the results of prior studies due to differences in the IC research frameworks.

Intellectual capital (IC) is believed to be more important resources to add the value of a company rather than physical assets. This gives rise to the increasing practice of reporting IC information in corporate annual report. Over the past fifteen years, considerable numbers of studies have employed content analysis to examine the extent and nature of IC information in several countries, but they presented different results. These results might partly contribute to different methods in counting information. In fact, the previous studies have been critised for not explicitly clarifying how information was recoded and counted which led to incomparable findings. Therefore, this paper firstly seeks to discuss an illustrative example of ‘sense-making‘ process in identifying, categorizing, and counting of IC information in annual reports of pilot sample company. Secondly, the method refined in the pilot study was applied over the final samples of six large companies in the UK from 1974 to 2008 The contribution of this paper is to primarily refine the previous method in recoding information, to send a message that transparency is crucial in content analysis and to facilitate method replication for future studies. Overall, this study demonstrates a marked increase in IC information disclosure was identified over the 35 years. The relational capital information disclosure was relatively more prominent over time, followed by human capital and structural capital.

The purpose of this paper is to review the use of content analysis in social and environmental reporting (SER) research. It explores how the relevant literature has evolved over time and particularly how recent developments have affected the validity and reliability challenges that researchers face when executing the method.

The paper combines a quasi-systematic review of the literature employing content analysis (examining a sample of 251 studies published over the last 40 years in a wide array of journals with interest in the field), with a largely interpretive meta-analysis, using an index, considering the research questions asked and frameworks used as well as the specific content analysis decisions.

A number of issues of concern in the use of the method are identified, mainly over comparability and reliability of coding schemes. Potential explanations are developed and methodological refinements that could enhance the usefulness of content analysis methods in SER research are subsequently proposed.

It should be acknowledged that, as 251 SER studies have been reviewed, there is always the possibility that some unique studies that could have contributed in the discussion have been ignored.

By reviewing the use of the method in a comprehensive sample of 251 SER studies published over the last 40 years in a wide array of journals with interest in the field, the paper also offers a guide for researchers (particularly in the SER field) wishing to employ content analysis in the future.

The paper contributes to the literature by offering a critical and comprehensive review of the method’s theoretical underpinnings and application in SER research, and by describing changing patterns in content analysis, in order to help build a more secure foundation for future work.

Inconsistent research output makes critical literature reviews crucial tools for assessing and developing the knowledge base within a research field. Literature reviews in the field of supply chain management (SCM) are often considerably less stringently presented than other empirical research. Replicability of the research and traceability of the arguments and conclusions call for more transparent and systematic procedures. The purpose of this paper is to elaborate on the importance of literature reviews in SCM.

Literature reviews are defined as primarily qualitative synthesis. Content analysis is introduced and applied for reviewing 22 literature reviews of seven sub‐fields of SCM, published in English‐speaking peer‐reviewed journals between 2000 and 2009. A descriptive evaluation of the literature body is followed by a content analysis on the basis of a specific pattern of analytic categories derived from a typical research process.

Each paper was assessed for the aim of research, the method of data gathering, the method of data analysis, and quality measures. While some papers provide information on all of these categories, many fail to provide all the information. This questions the quality of the literature review process and the findings presented in respective papers.

While 22 literature reviews are taken into account in this paper as the basis of the empirical analysis, this allows for assessing the range of procedures applied in previous literature reviews and for pointing to their strengths and shortcomings.

The findings and subsequent methodological discussions aim at providing practical guidance for SCM researchers on how to use content analysis for conducting literature reviews.

The aim of this paper is to review the use of content analysis as a research method in understanding social and environmental accounting (SEA) and to examine current contemporary foci of this research tradition. Further, seeks to examine several research method issues relating to the use of content analysis are examined.

Contemporary focus and research issues are analyzed to provide some future directions for scholars in the field of SEA, by categorizing work in the SEA, social environmental reporting (SER) and intellectual capital reporting (ICR) literature, according to the following: normative literature/theory/commentaries; focus of empirical investigation; quality SER research; combined research methodologies; content analysis method issues.

Literature indicates that few attempts have been made to combine other research methodologies with content analysis, although it has proven fruitful with the limited investigation undertaken to examine aspects of SER. Further extending the performance reporting by combining SER with ICR may provide useful information.

Increasingly, researchers in the field of SEA need to be able to justify the specific research methods they use when collecting the empirical data that they examine in order to support and test opinions regarding the merit of different approaches to managing, measuring and reporting of SEA.

Traditionally, the focus of content analysis has been narrow but this paper breaks new ground in proposing to broaden the focus to include SEA and to combine content analysis with other methods of data collection.

The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis.

To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency.

Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency.

This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches.

For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines.

To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

In this paper, the authors analyze the use of content analysis in disclosing voluntarily information on intangible assets, the intangible assets disclosures (IAD). The purpose of this paper is to conduct a structured literature review (SLR) that assesses the possibilities and limitations of content analysis.

To that end, the authors analyze the existing literature on the topic in the main international databases. In all, 74 empirical articles utilizing content analysis as a research methodology for IAD were reviewed. Regarding the selection of sources, the authors should indicate that the SLR performed includes academic studies published in journals or presented at conferences and that are always subject to a double process of anonymous review.

The obtained results indicate that despite the frequent use of content analysis in studies on IAD, its use does not meet all expectations.

The study synthesizes the research on content analysis for the case of information on intangible assets, offering an updated and global framework for future researchers through the SLR.

Among other problems, the authors found its excessive emphasis on the amount disclosed in the annual report, ignoring other reports in which more information regarding intangible assets is available, such as in the case of the sustainability reports. Furthermore, the use of very different coding systems and its exclusive use without being combined with other methodologies are detected. These aspects affect the quality problems of the sources used, which directly results in the utility of the evidenced findings.

These conclusions allow the authors to conclude on the need to open different lines of study that review the use of content analysis in this topic.

The work focuses on the quality of disclosures more so than on the quantity, offering a critical view that summarizes the utility of the employment of content analysis for this type of disclosure and its implications for future research on this topic. Despite previous studies, the authors highlight the new insights revealed from IAD research, especially since the seminal paper of Dumay and Cai (2014).

Access to high-quality data is a challenge for humanitarian logistics researchers. However, humanitarian organizations publish large quantities of documents for various stakeholders. Researchers can use these as secondary data, but interpreting big volumes of text is time consuming. The purpose of this paper is to present an automated quantitative content analysis (AQCA) approach that allows researchers to analyze such documents quickly and reliably.

Content analysis is a method to facilitate a systematic description of documents. This paper builds on an existing content analysis method, to which it adds automated steps for processing large quantities of documents. It also presents different measures for quantifying the content of documents.

The AQCA approach has been applied successfully in four papers. For example, it can identify the main theme in a document, categorize documents along different dimensions, or compare the use of a theme in different documents. This paper also identifies several limitations of content analysis in the field of humanitarian logistics research and suggests ways to mitigate them.

The AQCA approach does not provide an exhaustive qualitative analysis of documents. Instead, it aims to analyze documents quickly and reliably to extract the contents’ quantifiable aspects.

Although content analysis has been used in humanitarian logistics research before, no paper has yet proposed an automated, step-by-step approach that researchers can use. It also is the first study to discuss specific limitations of content analysis in the context of humanitarian logistics.