Boston U. Questrom School of Business, United States
Archival data are linguistic texts, visuals, and artifacts that actors create in and around organization. Archival data have historically been used to supplement other types of qualitative data. However, the use of archival data in qualitative analysis has dramatically increased due to digitalization. Archival data raises new methodological opportunities and challenges that qualitative field methods do not address; in particular, the abundance and heterogeneity of archival data. These features prompt us to rethink how we both sample and theorize using this data. This paper proposes a framework we call “dynamic theorizing,” which emphasizes the need to iterate between sampling and theoretical simplification. Our framework brings iterative sampling front and center as part of generating and simplifying theoretical insights. Our framework enables novel research which expands beyond qualitative research’s traditional boundaries
