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Over the years, Content Analysis has been applied in a variety of topical contexts. Quantitative Content Analysis has experienced a renewed attractiveness in recent years because of technological advances and prolific application in mass communication and personal communication research. New media, such as social media and mobile devices, has attracted more and more users. Content Analysis of textual big data faces new challenges.
Having conducted Content Analysis, we went through a systematic literature search by checking a few related databases and indexes, such as Web of Science, PubMed, Nexis Uni, Academic Search Premier, Project Muse, JSTOR, Google Scholar, etc. In line with Duriau, Reger and Pfarrer (2007), “Content Analysis is a class of research methods at the intersection of the qualitative and quantitative traditions. It is promising for rigorous exploration of many important but difficult-to-study issues of interest to organizational researchers in areas as diverse as business policy and strategy, managerial and organizational cognition, organizational behavior, human resources, social-issues management, technology and innovation management, international management, and organizational theory.”
As Lombard, Snyder‐Duch and Bracken indicate (2002), “As a method specifically intended for the study of messages, content analysis is fundamental to mass communication research… Based on the review and these results, concrete guidelines are offered regarding procedures for assessment and reporting of this important aspect of content analysis.”
After first searching based on keywords and phrases, we used the authors’ names from the relevant studies identified in the initial searches and searched the databases again. Furthermore, the reference sections of the identified studies from the first run were then examined as well as existing reviews of the literature.
Affinity Diagramming has been conducted. Consistent with affinity diagramming, related facts need to be organized into distinct clusters. There a few synonyms, such as collaborative sorting, mapping, snowballing, etc. Affinity Diagramming is a very simple but powerful technique for grouping and understanding information. Affinity Diagramming provides a good way to identify and analyze issues. There are several variations of the technique.