An automated quantitative content analysis process for humanitarian logistics research
Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Originality/value: 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.
Journal of Humanitarian Logistics and Supply Chain Management
Digital Object Identifier (DOI)
Kunz, N. (2019). An automated quantitative content analysis process for humanitarian logistics research. Journal of Humanitarian Logistics and Supply Chain Management, Vol. 9 No. 3, pp. 475-491. https://doi.org/10.1108/JHLSCM-06-2018-0051